Next Article in Journal
A Case for Offering HPV Self-Sampling to Well-Screened Women. Comment on Lesack et al. Willingness to Self-Collect a Sample for HPV-Based Cervical Cancer Screening in a Well-Screened Cohort: HPV FOCAL Survey Results. Curr. Oncol. 2022, 29, 3860–3869
Previous Article in Journal
Treatment of Basal Cell Carcinoma with Electrochemotherapy: Insights from the InspECT Registry (2008–2019)
Previous Article in Special Issue
PIK3CA Mutation as Potential Poor Prognostic Marker in Asian Female Breast Cancer Patients Who Received Adjuvant Chemotherapy
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation

1
Disease Pathway Management, Clinical Institutes and Quality Programs, Ontario Health, 525 University Avenue, Toronto, ON M5G 2L3, Canada
2
Department of Surgery, University of Toronto, 149 College Street, Toronto, ON M5T 1P5, Canada
3
Institute of Health Policy, Management, and Evaluation, University of Toronto, 155 College Street 4th Floor, Toronto, ON M5T 3M6, Canada
4
Data and Decision Sciences, Health System Performance and Support, Ontario Health, 525 University Avenue, Toronto, ON M5G 2L3, Canada
5
Quality Measurement and Evaluation, Clinical Institutes and Quality Programs, Ontario Health, 525 University Avenue, Toronto, ON M5G 2L3, Canada
6
Medical Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
7
Department of Oncology, McMaster University, 699 Concession Street Suite 4-204, Hamilton, ON L8V 5C2, Canada
*
Author to whom correspondence should be addressed.
Current Affiliations: Klick Labs, Klick Health, Toronto, ON M4W 3R8, Canada.
These authors contributed equally to this work.
Curr. Oncol. 2022, 29(8), 5338-5367; https://doi.org/10.3390/curroncol29080424
Submission received: 16 May 2022 / Revised: 9 July 2022 / Accepted: 19 July 2022 / Published: 28 July 2022

Abstract

:
Breast cancer recurrence is an important outcome for patients and healthcare systems, but it is not routinely reported in cancer registries. We developed an algorithm to identify patients who experienced recurrence or a second case of primary breast cancer (combined as a “second breast cancer event”) using administrative data from the population of Ontario, Canada. A retrospective cohort study design was used including patients diagnosed with stage 0-III breast cancer in the Ontario Cancer Registry between 1 January 2009 and 31 December 2012 and alive six months post-diagnosis. We applied the algorithm to healthcare utilization data from six months post-diagnosis until death or 31 December 2013, whichever came first. We validated the algorithm’s diagnostic accuracy against a manual patient record review (n = 2245 patients). The algorithm had a sensitivity of 85%, a specificity of 94%, a positive predictive value of 67%, a negative predictive value of 98%, an accuracy of 93%, a kappa value of 71%, and a prevalence-adjusted bias-adjusted kappa value of 85%. The second breast cancer event rate was 16.5% according to the algorithm and 13.0% according to manual review. Our algorithm’s performance was comparable to previously published algorithms and is sufficient for healthcare system monitoring. Administrative data from a population can, therefore, be interpreted using new methods to identify new outcome measures.

1. Introduction

Breast cancer recurrence is an important outcome for patients and healthcare systems, but recurrence is not routinely reported in cancer registries or other administrative datasets [1,2,3,4]. Ontario Health (Cancer Care Ontario) is an agency of the government of Ontario, Canada, that measures cancer system performance, among other functions. Measuring breast cancer recurrence in the population of Ontario could inform healthcare system planning and quality improvement since recurrence has been associated with modifiable factors such as margin positivity after surgery [5,6] and treatment selection [5,7,8], and treating recurrence requires significant healthcare resources [9]. Moreover, many breast cancer survivors worry about recurrence [10,11] and both recurrences and second primary breast cancers have been associated with reduced survival [5,12,13], so recurrence rates could inform discussions of risk.
The gold standard for identifying cancer recurrence is a manual review of patient information, which is not feasible at the population level. Researchers have used other methods to identify breast cancer recurrences, such as surveying patients directly [14], or developing algorithms for identifying breast cancer recurrences [3,15,16,17,18] or second breast cancer events (SBCEs) [1,2,19], which combine local and distant recurrences and second primary breast cancers. However, at the population level, patient surveys are impractical, and some algorithms may not be appropriate: some algorithms have been developed from highly selected breast cancer cohorts (potentially with specific treatment patterns), and some did not identify second primary breast cancers as well as local and distant recurrences. Developing an algorithm that could be applied across a population could support system-level decision making, increase algorithm generalizability, and ensure sufficient numbers of SBCEs to provide precise estimates of algorithm accuracy since breast cancer recurrence rates are generally low. Since algorithms developed in other jurisdictions would need to be validated before they could be applied to the Ontario population, and some existing algorithms incorporate data that are inaccessible in Ontario or Canada, we aimed to:
(1)
Develop a novel algorithm for measuring SBCE rates (recurrences and second primary breast cancers) in a population using routinely collected administrative data;
(2)
Validate the algorithm’s diagnostic accuracy using the results of a manual record review in a large sub-cohort of patients.
For this study, we defined an SBCE as evidence of a local, regional, or distant breast cancer recurrence or a new primary breast cancer observed more than 180 days after the incident breast cancer diagnosis.

2. Materials and Methods

2.1. Patient Selection and Data Sources

This retrospective cohort study included all female patients 18 years old or older diagnosed with stage 0-III breast cancer in the Ontario Cancer Registry [20] between 1 January 2009 and 31 December 2012. Patients with a prior diagnosis of breast or other cancer were included, as prior diagnoses were not expected to change the outcome of interest (detection of recurrence after the incident date). Healthcare utilization data from incident diagnosis until 31 December 2013 or patient death, whichever came first, were retrieved for analysis. Patients were excluded if they were diagnosed with lymphoma in the breast or skin cancer on the breast or died within 180 days (six months) of diagnosis.
Patients’ unique Ontario Health Insurance Plan numbers [21] were used to link data. The Ontario Registrar General provided the cause-of-death data. Stage data, including tumor characteristics, were retrieved from the Ontario Cancer Registry [20]. Inpatient procedure data, including associated diagnosis codes, were retrieved from the Discharge Abstract Database [22]. Emergency department visit data, outpatient procedure data, and associated diagnosis codes were retrieved from the National Ambulatory Care Reporting System [22]. Data about cancer-related consultations, decisions, and treatments, including systemic therapy and radiation therapy, were retrieved from the Activity Level Reporting database [22]. Data about approved funding requests for systemic therapy were retrieved from the New Drug Funding Program database [22]. Additional data about systemic treatment with targeted or endocrine therapy for Ontario residents age 65 and over or on social assistance were retrieved from the Ontario Drug Benefit database [22]. Due to Ontario Health (Cancer Care Ontario)’s designation as a “prescribed entity” for the purposes of Section 45 (1) of the Personal Health Information Protection Act of 2004, an ethics review was not required.

2.2. Index Test: Developing the Algorithm

An expert panel including surgical, medical, and radiation oncologists with expertise in breast cancer management determined algorithm criteria, i.e., types of healthcare events likely to indicate an SBCE. Criteria were based on standard-of-care curative treatments that each breast cancer patient in Ontario should be offered (Figure 1). Time frames for algorithm criteria were based on clinicians’ expertise and their review of study cohort data indicating when healthcare events for each criterion occurred relative to diagnosis. The algorithm was applied to each patient’s data starting at 180 days post-diagnosis through death or the end of the follow-up period in order to distinguish between treatment for the incident breast cancer and treatment for an SBCE. Breast cancer-related healthcare events that occurred within 180 days after the diagnosis date were considered to indicate management of the initial breast cancer, local progression, or distant disease that was occult at diagnosis.
All criteria were applied to the entire patient cohort and could be applied in any order. A patient only had to meet one of the criteria one time to be considered as having an SBCE. For the criteria based on procedures and radiotherapy treatments, probable contralateral second primary breast cancers could be identified among SBCEs in the breast based on the laterality of procedures and diagnoses. See Appendix A for code lists for each criterion.

2.3. Manual Record Review

A manual record review, the reference standard test, was conducted for a sub-cohort of patients seen at the Odette Cancer Center in Toronto, Canada, and the Juravinski Cancer Center in Hamilton, Canada. We calculated, a priori, the number of records required for review to accurately validate the algorithm given the prevalence of recurrence in patients with stages I, II, and III breast cancer. Stages I and II breast cancer are diagnosed much more often than stage III breast cancer, but stage III breast cancer patients are more likely to experience an SBCE [23]. To ensure sufficient statistical power (a sufficient number of patients with SBCEs in the validation sub-cohort), we sampled approximately 1000 patients with stages I, II, and III breast cancer, representing each stage at equal proportions rather than picking a random sample that would reflect the natural incidence of each stage in the population. Stage III breast cancer patients, therefore, represented a larger proportion of the validation sub-cohort than their proportion in the entire cohort. Assuming recurrence rates of 2%, 7.7%, and 20% for stage I, II, and III patients, respectively, we aimed to be able to detect an algorithm sensitivity of 75%, 85%, and 90% for stages I, II and III, and specificity of 99%, 95%, and 90% for stages I, II, and III breast cancer patients, respectively. Sampling 1000 patients of each stage (total n = 3000), we expected to observe sensitivity and specificity in the ranges of 52–91% and 98–100% for stage I; 75–92% and 93–96% for stage II; and 85–94% and 88–92% for stage III breast cancer patients. Approximately equal numbers of stage I, II, and III patients were randomly selected from each cancer center for the validation sub-cohort.
Clinical research professionals unaware of the algorithm’s SBCE classifications manually reviewed sub-cohort records. If patients met manual review criteria for experiencing an SBCE, the evidence (clinical, radiological, or tissue-based), anatomical location, and treatment information were documented. When SBCE status was unclear, the study leader at the center (A.E. or J.S.) would adjudicate. If SBCE status remained indeterminate, patients were excluded from the manual record review.
Manual review results were linked to administrative data and algorithm classifications using patients’ medical record numbers. A member of the study team (C.H.) re-reviewed administrative and manually collected data for all false-positive cases (patients classified as experiencing an SBCE by the algorithm but not reviewers). Administrative documents clearly indicative of an SBCE (e.g., a pathology report showing breast cancer or a record of systemic therapy for metastatic breast cancer) were considered more accurate than the results of a manual record review at a single center, as patients may have been diagnosed and/or treated at different centers.

2.4. Statistical Methods

Patient characteristics were summarized as counts with proportions for categorical data and means with standard deviations for continuous data. For continuous variables with skewed distributions, medians and interquartile ranges were used. Patients excluded during the manual record review were compared with patients who remained in the validation sub-cohort using Pearson’s chi-squared tests and a Cochran–Mantel–Haenszel statistic [24] (Appendix B). Algorithm diagnostic accuracy was assessed by calculating agreement statistics: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, kappa, and prevalence-adjusted bias-adjusted kappa (PABAK), due to criticism of the kappa statistic for its dependence on outcome prevalence [25,26,27,28]. Additional agreement statistics were calculated to verify that including patients with prior cancer diagnoses did not affect algorithm diagnostic accuracy (Appendix C). Analyses were performed using SAS® software version 9.4 for Microsoft Windows. Copyright © 2013 SAS Institute Inc., Cary, NC, USA.

3. Results

3.1. Cohort Characteristics and Algorithm Classifications

The study cohort included 31,782 patients (Figure 2); the median follow-up time was 34 months (approximately 2.8 years; Table 1).
The algorithm classified 3796 patients as experiencing an SBCE based on a maximum of 6109 events (true total unavailable due to small cell suppression of cause-of-death data by stage) for an SBCE rate of 11.9% (Table 2). Procedure and diagnosis data classified the most patients as experiencing an SBCE and events as indicating an SBCE of any criterion, followed by radiation data, systemic treatment data, and cause-of-death data (Figure 3). Notably, for all criteria except the cause of death criterion, more healthcare events indicating an SBCE were identified than patients experiencing the events, suggesting that some patients who met the criterion met it based on multiple events.

3.2. Exclusions during Manual Review and Validation Sub-Cohort Characteristics

Of the 3258 patients selected for the manual record review, 1013 patients were excluded because their records could not be retrieved, they did not have sufficient records for review at a study center, or their SBCE status was indeterminate. The remaining validation sub-cohort was 2245 patients (Table 3).
Pearson’s chi-squared tests indicated a potential relationship between stage at diagnosis and likelihood of exclusion during manual review based on a marginally significant p-value of 0.044 (Table A8). The Cochran–Mantel–Haenszel statistic [24] demonstrated that after controlling for the stage at diagnosis, more excluded patients were classified by the algorithm as having an SBCE (Table A9; p-value < 0.0136).

3.3. Algorithm Diagnostic Accuracy

After a case-by-case review of false-positive results (patients classified as experiencing an SBCE by the algorithm but not by manual review), 16 patients’ manual review SBCE statuses were revised due to definitive evidence of SBCEs in administrative data, making them true positive. Algorithm and manual review SBCE classifications after this revision are compared in Table 4A,B. The algorithm had a sensitivity of 85%, a specificity of 94%, a PPV of 67%, an NPV of 98%, a kappa of 71%, and a PABAK of 85% (Table 4C).
Prior cancer history did not observably affect the algorithm’s diagnostic accuracy, though this may be attributable to the small proportion of patients with prior cancer history (Appendix C).

4. Discussion

Our study demonstrates the feasibility of quantifying SBCE rates in populations by analyzing administrative data using new methods. The sensitivity and specificity of our algorithm were comparable or superior to previously published SBCE [1,2,16,19,29] and recurrence identification [3,15,17] algorithms, though the PPV was slightly lower. Our algorithm may, therefore, be useful in scenarios where the overestimation of the SBCE rate is less important (e.g., system capacity planning). High specificity and NPV make our algorithm useful for identifying patients unlikely to have experienced an SBCE (e.g., for studies about interventions to reduce recurrence rates). The overall accuracy of 92% supports our algorithm’s appropriateness for use in health system monitoring and exceeds the acceptable accuracy threshold chosen by Livaudais-Toman et al. [30].
The sensitivity of the algorithm was limited by the lack of important data in administrative databases. Some patients with SBCEs likely received treatments that were not specific to breast cancer, such as palliative care, or treatments not reported in administrative data, such as endocrine therapy in patients under age 65 and not on social assistance. Since the proportions of such patients are likely to remain constant, it may be possible to apply a correction to, or acknowledge a probable false-negative rate in, estimates of SBCE prevalence.
The relatively low PPV was attributable to false-positive SBCE classifications by the algorithm, i.e., treatments meeting criteria though they were probably not indicated for SBCEs. For example, surgical procedures occurring more than six months following a diagnosis such as a mastectomy with or without reconstruction may have reflected prophylactic treatment, patients’ aesthetic preferences, or potentially primary treatment after neoadjuvant chemotherapy. Other false positives were attributable to the limitations of manual record reviews: Some patients were erroneously determined not to have an SBCE during the manual review because they received care at multiple centers due to treatment availability or personal relocation. This likely also explains the increased rate of SBCEs according to the algorithm among patients whose records were excluded from the manual review.
Each algorithm criterion appears relevant since each criterion identified different patients. Procedure and associated diagnosis data seem especially useful, though further research is required to determine the accuracy of each criterion. Investigating why some patients were only identified posthumously based on the cause-of-death data could elucidate gaps or suggest how many patients do not receive SBCE-specific therapy.
Although we developed our algorithm from a population, a larger and more diverse group than some other authors used to develop algorithms, adjusting individual criteria or the data observation period to align with previously published algorithms could potentially improve performance. Other authors analyzed data starting after a longer time post-diagnosis or after completion of each patient’s primary treatment [1,2,3]; similar changes might reduce our false-positive rate and improve PPV. Other SBCE and breast cancer recurrence identification algorithms have incorporated different types of healthcare events [3,19], numbers [1,3] or rates of occurrence [1,2,19] of events, or intervals between events [1,2]. Promisingly, some SBCE algorithms generated by machine learning used similar criteria to those chosen by clinical experts for our algorithm [1,2].
There are some limitations to our study. Excluding patients from the validation sub-cohort during the manual record review may have led to unmeasured differences between the final sub-cohort and the entire cohort. Reviewing patient records at academic tertiary care centers offering specialized treatments may have increased the inclusion of patients who received care at multiple centers, impeding the review of comprehensive treatment records. Inter-rater reliability was not measured, though chart reviewers and study leaders met regularly to maximize consistency. Finally, we applied our algorithm to data from six months post-breast cancer diagnosis to a maximum of four years post-diagnosis, which does not represent the entire at-risk period for SBCEs. The algorithm’s accuracy may differ depending on the duration of follow-up.

5. Conclusions

Despite these limitations, we calculated an SBCE rate with acceptable accuracy for healthcare system monitoring by applying an algorithm to administrative data. The algorithm may be applicable to other patient populations or other cancer types with similar patterns of treatment since the data types used to identify second cancer events were not specific to breast cancer. Future developments may include adjusting algorithm criteria, incorporating additional administrative datasets, or experimenting with machine learning methods, which could potentially improve algorithm performance and expand algorithm utility.

Author Contributions

Conceptualization, C.M.B.H., K.F., B.G., A.E. and J.S.; methodology, C.M.B.H., O.S., M.E., K.F., P.M., B.G., A.E. and J.S.; validation, C.M.B.H., M.E., A.E. and J.S.; formal analysis, O.S., M.E., P.M. and A.V.E.; investigation, C.M.B.H., A.E. and J.S.; data curation, O.S., M.E., P.M. and A.V.E.; writing—original draft preparation, C.M.B.H. and K.F.; writing—review and editing, C.M.B.H., K.F., M.E., A.E. and J.S.; supervision, C.M.B.H., K.F., A.E. and J.S.; project administration, K.F.; funding acquisition, C.M.B.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Ontario Health (Cancer Care Ontario), specifically the Data and Decision Sciences and Disease Pathway Management groups, through funding provided by the Ontario Ministry of Health. The opinions, results, views, and conclusions reported in this publication are those of the authors and do not necessarily reflect those of Ontario Health (Cancer Care Ontario). No endorsement by Ontario Health (Cancer Care Ontario) is intended or should be inferred. Initial work on this project was supported by a Cancer Care Ontario grant.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the fact that this study exclusively analyzed routinely collected administrative data that Ontario Health (Cancer Care Ontario) is authorized to collect due to its status as a “prescribed entity” for the purposes of Section 45 (1) of the Personal Health Information Protection Act (PHIPA) of 2004. As a prescribed entity, Ontario Health (Cancer Care Ontario) is authorized to collect personal health information from health information custodians without the consent of the patient and to use such personal health information for the purpose of analysis or compiling statistical information with respect to the management, evaluation, or monitoring of the allocation of resources to or planning for all or part of the health system, including the delivery of services.

Informed Consent Statement

Patient consent was waived because Ontario Health (Cancer Care Ontario) is designated a “prescribed entity” for the purposes of Section 45 (1) of the Personal Health Information Protection Act (PHIPA) of 2004. As a prescribed entity, Ontario Health (Cancer Care Ontario) is authorized to collect personal health information from health information custodians without the consent of the patient and to use such personal health information for the purpose of analysis or compiling statistical information with respect to the management, evaluation, or monitoring of the allocation of resources to or planning for all or part of the health system, including the delivery of services.

Data Availability Statement

Data de-identified to a level suitable for public release may be provided upon request to the corresponding author, due to privacy restrictions. Ontario Health is prohibited from making the data used in this research publicly accessible if they include potentially identifiable personal health information and/or personal information as defined in Ontario law, specifically the Personal Health Information Protection Act (PHIPA) and the Freedom of Information and Protection of Privacy Act (FIPPA).

Acknowledgments

Grace Bannerman assisted with the preparation of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Appendix A.1. Algorithm Criteria Codes

Please note that criteria were applied to patient data from six months (180 days) after breast cancer diagnosis through the end of follow-up on 31 December 2013 or patient death, whichever came first. For the radiation therapy criterion, if a patient underwent a single course of radiation therapy, it was only considered to indicate a second breast cancer event (SBCE) if it occurred more than 365 days post-diagnosis because Ontario guidelines recommend primary radiation therapy occur after surgery or after chemotherapy, if applicable. If a patient underwent multiple courses of radiation, the first course was considered treatment for the initial breast cancer regardless of when it occurred. If a second or later course occurred more than 180 days post-diagnosis, it was considered evidence of an SBCE.

Appendix A.2. Death from Breast Cancer Criterion

Patients met the cause of death criterion if their cause of death was coded as breast cancer, as listed below.
  • Data Source(s): Death records from the Ontario Registrar General.
  • Coding system: International Classification of Diseases, version 10 (ICD10).
Table A1. Death record code indicating death from a second breast cancer event.
Table A1. Death record code indicating death from a second breast cancer event.
Code(s)Code Description
C509Malignant neoplasm of breast, unspecified

Appendix A.3. Procedure and Diagnosis Criterion

Patients met the procedure and diagnosis criterion if they underwent one of the procedures listed associated with one of the diagnoses listed.

Appendix A.3.1. Procedures

  • Data Source(s): Discharge Abstract Database, National Ambulatory Care Reporting System.
  • Coding system: Canadian Classification of Health Interventions, versions 2009, 2012, and 2015.
Table A2. Procedure codes for the procedure and associated diagnosis criterion.
Table A2. Procedure codes for the procedure and associated diagnosis criterion.
Canadian Classification of Health Interventions CodeCanadian Classification of Health Interventions Code Description
1AA80SZXXLRepair mening brn cranial flap OA xenogr
1AA87SZExcision partial, meninges and dura mater of brain using apposition technique [e.g., suture]
1AA87SZXXNExcis prt mening brn cranial flap OA synth mat
1AC27JXRadiation, ventricles of brain using focused beam [e.g., gamma knife, cyber knife stereotactic radiosurgery]
1AC52MBSJDrainage, ventricles of brain burr hole technique drainage to skin (of head) catheter or shunt (temporarily) left in situ
1AC52SEDrainage, ventricles of brain burr hole technique drainage without shunt or catheter left in situ
1AF87DAGXExcision partial, pituitary region endoscopic (via sinus) approach with device NEC
1AJ87SZAZExcision partial, cerebellum open [craniotomy flap] approach with ultrasonic aspirator [e.g., CUSA]
1AJ87SZGXExcision partial, cerebellum open [craniotomy flap] approach with device NEC
1AN27JARadiation, brain using external beam [for teletherapy NEC]
1AN27JXRadiation, brain using focused beam [e.g., gamma knife, cyber knife stereotactic radiosurgery]
1AN53SEFTImplantation of internal device, brain burr hole technique for access of [semipermeable] catheter [e.g., for chemical palliative infusion]
1AN53SZFTImplantation of internal device, brain craniotomy [or craniectomy] flap technique for access of [semipermeable] catheter [e.g., for chemical palliative infusion]
1AN87SEAZExcision partial, brain burr hole technique for access with ultrasonic aspirator [e.g., CUSA]
1AN87SZAGExcision partial, brain craniotomy [or craniectomy] flap technique for access with laser
1AN87SZAZExcision partial, brain craniotomy [or craniectomy] flap technique for access with ultrasonic aspirator [e.g., CUSA]
1AN87SZGXExcision partial, brain craniotomy [or craniectomy] flap technique for access with device NEC
1AW27JARadiation, spinal cord using external beam [for teletherapy NEC]
1AX35HAM0Pharmacotherapy (local), spinal canal and meninges Percutaneous (needle) approach using antineoplastic agent NEC
1AX35HAP1Pharmacotherapy (local), spinal canal and meninges percutaneous [needle] approach using anesthetic agent
1AX52MESJDrainage, spinal canal and meninges open approach shunt terminating in abdominal cavity [e.g., lumboperitoneal shunt]
1AX87LAGXExcision partial, spinal canal and meninges using extradural incision technique [e.g., for space occupying lesion of canal] open approach with combined sources of tissue for closure with device NEC
1AX87WKGXExcision partial, spinal canal and meninges using intradural incision technique [e.g., for meningeal mass] open approach with apposition technique [e.g., suturing] with device NEC
1EA27JARadiation, cranium using external beam
1EA87LANWExcision partial, cranium open approach no tissue used [for closure of wound] using plate, screw device (with or without wire or mesh)
1EA87LANWNExcise prt cranium OA &plate/scrw synth mater
1EA92LYXXAExc rad w reconstruct cranium cranial base oth appr autogr
1EQ27JARadiation, soft tissue of head and neck using external beam
1FM87VWExcision partial, parotid gland using open approach with preservation of facial nerve technique
1GM59BAGXDestruction, bronchus NEC using endoscopic per orifice approach and device NEC
1GR87DAExcision partial, lobe of lung using endoscopic approach [VATS]
1GR87QBExcision partial, lobe of lung using open thoracic approach
1GR89DAExcision total, lobe of lung using endoscopic approach [VATS]
1GR89QBExcision total, lobe of lung using open thoracic approach
1GR91QBExcision radical, lobe of lung open thoracic approach with simple closure
1GR91QBXXNExcise rad lobe lung thor OA synth mater
1GT27JARadiation, lung NEC using external beam
1GT80LARepair, lung NEC using open approach
1GT87DAExcision partial, lung NEC using endoscopic approach [VATS]
1GT87QBExcision partial, lung NEC using open thoracic approach
1GT89DAExcise tot lung EA
1GV52DADrainage, pleura using endoscopic approach [VATS]
1GV52DATSDrainage, pleura using endoscopic approach and leaving drainage tube in situ
1GV52HADrainage, pleura using percutaneous (needle) approach
1GV52HAHEDrainage, pleura using percutaneous catheter (intracostal) with underwater seal drainage system
1GV52HATKDrainage, pleura using percutaneous catheter with suction pump, (under water seal or negative pressure)
1GV52LADrainage, pleura using open approach
1GV52LATSDrainage, pleura using open approach and leaving drainage tube in situ
1GV54JATSManagement of internal device, pleura of drainage tube [e.g., thoracotomy or pleural cavity drain] using external approach
1GV59DAGXDestruction, pleura using endoscopic approach [VATS] and device NEC
1GV59DAZ9Destruction, pleura using endoscopic approach and chemical agent NEC
1GV59HAZ9Destruction, pleura using percutaneous instillation of agent NEC (e.g., blood, talc)
1GV87DAExcision partial, pleura using endoscopic approach [VATS]
1GV89DAExcision total, pleura using endoscopic approach [VATS]
1GZ31CANDVentilation, respiratory system NEC invasive per orifice approach by endotracheal intubation and positive pressure
1GZ31CBNDVentilation, respiratory system NEC non-invasive approach and positive pressure ventilation (e.g., CPAP, BIPAP)
1GZ32CAMYOxygenation, respiratory system NEC using bulk storage manifold system
1HA87LAExcision partial, pericardium using open approach
1MC87LAExcision partial, lymph node(s), cervical using open approach with no tissue
1MC87LAXXEExcise prt lymph nd neck OA loc flp
1MC89LAExcision total, lymph node(s), cervical using open approach with no tissue
1MC91LAExcision radical, lymph node(s), cervical without tissue radical neck dissection
1MC91VBExcision radical, lymph node(s), cervical without tissue modified radical neck dissection
1MD27JARadiation, lymph node(s), axillary using external beam
1MD87LAExcision partial, lymph node(s), axillary using open approach
1MD89LAExcision total, lymph node(s), axillary using open approach
1MD89LAXXEExcise tot axil lymph nd OA loc flp
1MD89LAXXGExcise tot axil lymph nd OA ped flp
1ME87DAExcision partial, lymph node(s), mediastinal using endoscopic approach
1ME89DAExcision total, lymph node(s), mediastinal using endoscopic approach
1MF27JARadiation, lymph node(s), intrathoracic NEC using external beam
1MF87LAExcision partial, lymph node(s), intrathoracic NEC using open approach
1MH27JARadiation, lymph node(s), pelvic using external beam
1MZ27JARadiation, lymphatic system NEC using external beam
1NF90LAXXGExc tot w reconstr stom OA w jejnm
1NK87RFExcision partial, small intestine open approach enteroenterostomy anastomosis technique
1NQ57CJExtraction, rectum using per orifice approach and manual technique
1NQ87TFExcision partial, rectum open abdominal [e.g., anterior] approach colostomy (or ileostomy) with closure of rectal stump [e.g., Hartmann technique] or submucous fistula
1OA27JARadiation liver using external beam
1OA59HAAWDestruction, liver percutaneous approach using radiofrequency
1OA87DAExcision partial, liver using endoscopic (laparoscopic)approach
1OA87LAExcision partial, liver using open approach
1OA87LAAZExcision partial, liver using ultrasonic aspirator device (for dissection) and open approach
1OE50BANRDilate bile dct EPO retro &stent
1OE52GPTSDrainage, bile ducts using percutaneous transluminal approach [e.g., transhepatic] leaving catheter (tube) in situ
1OE89UFExcision total, bile ducts using open approach and hepaticojejunostomy technique [for anastomosis]
1OT52HATSDrain abd cav perc app &tube NOS
1PE52HHDrainage, renal pelvis using percutaneous approach with insertion of tube (e.g., nephrostomy, pyelostomy)
1PE59BAAGDestruction, renal pelvis endoscopic per orifice approach Using laser (tissue ablation)
1PM52BATSDrain bladder EPO &tube NOS
1PM87BAExcision partial, bladder using endoscopic per orifice approach
1PV52HADrainage, surgically created urinary tract using percutaneous needle aspiration
1RD89DAExcision total, ovary with fallopian tube using endoscopic [laparoscopic] approach
1RD89LAExcise tot ovary w fallop OA
1RM89AAExcision total, uterus and surrounding structures using combined laparoscopic and vaginal approach
1SC27JARadiation, spinal vertebrae using external beam
1SC74PFNWFixation, spinal vertebrae open posterior approach [Includes: posterolateral approach] using screw, screw with plate or rod
1SC75LLKDNFuse sp vert ant OA &wire/staple synth mater
1SC75PFGXNFuse sp vert post OA &dev NEC synth mater
1SC75PFNWAFuse sp vert post OA &plate/scrw autogr
1SC75PFNWNFuse sp vert post OA &plate/scrw synth mater
1SC75PFNWQFuse sp vert post OA &plate/scrw combo tis
1SC80HABDNRepair sp vert perc app w balloon & synth mat
1SC80HAXXNRepair sp vert perc injct synth mater
1SC80PFRepair, spinal vertebrae using posterior approach
1SC89LLNWAExcise tot sp vert ant OA &plate/scrw autogr
1SC89LLNWKExcise tot sp vert ant OA &plate/scrw homogr
1SC89LLNWNExcise tot sp vert ant OA &plate/scrw synth mat
1SC89LLNWQExcise tot sp vert ant OA &plate/scrw combo tis
1SC89LNNWNExcis tot sp vert ant w post &plate/scrw syn mat
1SC89PFGXExcision total, spinal vertebrae posterior approach [posterolateral approach] no tissue used (device only) using device NEC
1SC89PFNWNExcise tot sp vert post OA &plate/scrw synth mater
1SF74HANWFixation, sacrum and coccyx using percutaneous approach and screw, screw with plate
1SH87LAXXEExcise prt s t back OA loc flp
1SQ27JARadiation, pelvis using external beam
1SQ87LAPMNExcise prt pelvis OA &hip endoprosth synth mat
1SY80LARepair m chest & abd OA apposition
1SY87LAExcision partial, muscles of the chest and abdomen using simple apposition technique [e.g., suture, staple] (for closure of surgical defect)
1SY87LAXXEExcise prt m chest & abd OA loc flp
1SY87LAXXFExcise prt m chest & abd non viable free flp
1SZ27JARadiation, soft tissue of the chest and abdomen using external beam
1SZ87LAExcision partial, soft tissue of the chest and abdomen using open approach and apposition [suture, staple] (to close surgical defect)
1SZ87LAXXAExcise prt s t chest & abd OA autogr
1SZ87LAXXEExcise prt s t chest & abd OA loc flp
1SZ87LAXXGExcise prt s t chest & abd OA ped flp
1TK74HALQFixation, humerus percutaneous approach [e.g., with closed or no reduction] fixation device alone using intramedullary nail
1TK74LALQFixation, humerus open approach fixation device alone using intramedullary nail
1TK74LANWFixation, humerus open approach fixation device alone using plate, screw
1TK80LAXXNRepair humerus OA synth mater
1TK87LANWNExcise prt humerus OA &plate/scrw synth mater
1TV87LAExcision partial, radius and ulna no tissue used (for closure of defect) using no fixative device
1TZ27JARadiation, arm NEC using external beam
1VA74HANVFixation, hip joint percutaneous approach [e.g., with closed reduction or no reduction] fixation device alone using pin, nail
1VA74LALQFixation, hip joint open approach fixation device alone using intramedullary nail
1VA74LALQNFix hip OA & intramed nail synth mater
1VA74LANVFixation, hip joint open approach fixation device alone using pin, nail
1VA74LANWFixation, hip joint open approach fixation device alone using plate, screw
1VC74HALQFixation, femur percutaneous approach [e.g., with closed reduction or no reduction] fixation device alone using intramedullary nail
1VC74LALQFixation, femur open approach fixation device alone using intramedullary nail
1VC74LALQNFix femur OA &intramed nail synth mater
1VC74LANWQFix femur OA &plate/scrw combo tis
1VC80LAKDQRepair femur OA &fix dev NEC combo tis
1VC87LALQExcision partial, femur no tissue used (for closure of defect) using intramedullary nail
1VC87LANVNExcise prt femur OA &pin/nail synth mater
1VC87LANWExcision partial, femur with synthetic tissue [bone cement, paste] using screw, plate and screw
1VC87LAPMNExcise prt femur OA &endoprosth synth mat
1VC91LAPNNExcise rad femur OA &dual comp prosth synth mater
1VD87LAXXAExcise prt m hip & thigh OA autogr
1VQ74LALQFixation, tibia and fibula open approach fixation device alone using intramedullary nail
1VQ87LANWNExcise prt tib & fib OA &plate/scrw synth mater
1VZ27JARadiation, leg NEC using external beam
1YA87LAExcision partial, scalp open [excisional] approach Without tissue repair
1YK84LAXXERe/construct nipple OA loc flp
1YK84LAXXQRe/construct nipple OA combo tis
1YK87LAExcision partial, nipple using open excisional approach
1YK87LAXXEExcise prt nipple OA loc flp
1YK89LAExcision total, nipple using open approach
1YK90LAXXEExc tot w reconstr nipple OA loc flp
1YK90LAXXQExc tot w reconstr nipple OA combo tis
1YL87LAExcision partial, lactiferous duct using open approach
1YL89LAExcision total, lactiferous duct using open approach
1YM27JARadiation, breast using external beam
1YM52HADrainage, breast using needle aspiration
1YM52HAAVDrainage, breast using percutaneous approach with probe
1YM52LADrainage, breast using incisional approach
1YM53HAEMImplantation of internal device, breast of brachytherapy applicator using percutaneous approach
1YM53LAEMImplantation of internal device, breast of brachytherapy applicator using open approach
1YM54HAG2Management of internal device, breast using percutaneous (needle) approach with synthetic agent [e.g., silicone]
1YM54HAW1Management of internal device, breast using percutaneous (needle) approach with augmentation agent [e.g., saline, soya]
1YM55LATPRemoval of device, breast without capsulectomy of tissue expander
1YM55WJPMRemoval of device, breast with capsulectomy (with or without inframammary fold repair) of breast implant [prosthesis]
1YM72LARelease breast OA
1YM74LAFixation, breast using open approach
1YM78LAXXERepair decr sz breast loc flp
1YM78VQRepair by decreasing size, breast using peri areolar round block excisional technique
1YM79LAPMRepair by increasing size, breast open approach without tissue with implantation of prosthesis
1YM79LATPRepair by increasing size, breast open approach without tissue with implantation of tissue expander
1YM79LATPGAugment breast OA w tiss expandr &ped flp
1YM80LARepair, breast open approach without tissue with no implantation of device
1YM80LAPMRepair, breast open approach without tissue with implantation of breast prosthesis
1YM80LAPMARepair breast w prosth autogr
1YM80LAPMFRepair breast OA w prosth free flp
1YM80LAPMG2009: Repair, breast using distant pedicled flap (1) with implantation of breast prosthesis
2012: Repair, breast open approach using distant pedicled flap with implantation of breast prosthesis
1YM80LATPRepair, breast open approach without tissue with implantation of tissue expander
1YM80LATPERepair breast w tiss expandr loc flp
1YM80LATPG2009: Repair, breast using distant pedicled flap (1) with implantation of tissue expander
2012: Repair, breast open approach using distant pedicled flap with implantation of tissue expander
1YM80LATPKRepair breast OA w tiss expandr homogr
1YM80LAXXA2009: Repair, breast using autograft with no implantation of device
2012: Repair, breast open approach using autograft with no implantation of device
1YM80LAXXERepair breast w loc flp
1YM80LAXXF2009: Repair, breast using free flap with no implantation of device
2012: Repair, breast open approach using free flap with no implantation of device
1YM80LAXXG2009: Repair, breast using distant pedicled flap with no implantation of device
2012: Repair, breast open approach using distant pedicled flap with no implantation of device
1YM87DAExcision partial, breast using endoscopic approach with simple apposition
1YM87GBExcision partial, breast using endoscopic guide wire (or needle hook) excision technique with simple apposition of tissue
1YM87LAExcision partial, breast using open approach with simple apposition of tissue (e.g., suturing)
1YM87LAXXAExcise prt breast OA autogr
1YM87LAXXEExcise prt breast OA loc flp
1YM87UTExcision partial, breast using open guide wire (or needle hook) excision technique and simple apposition of tissue
1YM88LAPMExcision partial with reconstruction, breast without tissue with implantation of prosthesis
1YM88LAPMEExc prt breast w prosth loc flp reconst
1YM88LAPMFExc prt breast w prosth free flp reconstr
1YM88LAPMGExc prt breast w prosth ped flp reconstr
1YM88LAQFExc prt breast w prosth/tis expand reconstr
1YM88LAQFEExc prt breast w prosth/tis expand loc flp reconst
1YM88LATPExcision partial with reconstruction, breast without tissue with implantation of tissue expander
1YM88LATPEExc prt breast w tiss expandr &loc flp reconst
1YM88LATPFExc prt breast w tiss expand free flp reconstr
1YM88LATPGExc prt breast w tiss expand ped flp reconstr
1YM88LATPKExc prt breast w tiss expand homogr reconstr
1YM88LAXXEExc prt breast w loc flp reconstr
1YM88LAXXFExc prt breast w free flp reconstr
1YM88LAXXGExc prt breast w ped flp reconstr
1YM89LAExcision total, breast using open approach
1YM89LAXXAExcise tot breast w autogr
1YM89LAXXEExcise tot breast OA loc flp
1YM90LAPMExcision total with reconstruction, breast simple mastectomy with no node dissection without tissue with implantation of breast prosthesis
1YM90LAPMEExc tot breast prosth loc flp reconstr
1YM90LAPMFExc tot breast prosthesis free flp reconstr
1YM90LAPMGExc tot breast prosth ped flp reconstr
1YM90LAQFExc tot breast prosth w tiss expand reconstr
1YM90LAQFEExc tot breast prosth tis expand loc flp reconst
1YM90LAQFGExc tot breast prosth tis expand ped flp reconst
1YM90LATPExcision total with reconstruction, breast simple mastectomy with no node dissection without tissue with implantation of tissue expander
1YM90LATPFExc tot breast tiss expand free flp reconstr
1YM90LATPGExc tot breast tiss expand ped flp reconstr
1YM90LAXXFExc tot breast free flp reconstr
1YM90LAXXGExc tot breast ped flp reconstr
1YM90LAXXQExc tot w reconstr breast OA combo tis
1YM91LAExcision radical, breast without tissue modified or NOS
1YM91LATPExcision radical, breast with implantation of tissue expander modified or NOS
1YM91LAXXA2009: Excision radical (modified), breast using autograft
2012: Excision radical, breast using autograft modified or NOS
1YM91LAXXE2009: Excision (modified) radical, breast using local flap
2012: Excision radical, breast using local flap modified or NOS
1YM91TRExcision radical, breast without tissue extended [Urban]
1YM91TRXXE2009: Excision extended radical, breast using local flap
2012: Excision radical, breast using local flap extended [Urban]
1YM92LAPMEMod rad mastectmy w prosth loc flp reconst
1YM92LAPMFMod rad mastectmy w prosth free flp reconst
1YM92LAPMGMod rad mastectmy w prosth ped flp reconst
1YM92LAQFEMod rad mastectmy w prosth tiss expand loc flp
1YM92LAQFGMod rad mastectmy w prosth tiss expand ped flp
1YM92LATPEMod rad mastectmy w tiss expandr loc flp reconst
1YM92LATPFMod rad mastectmy w tiss expand free flp reconst
1YM92LATPGMod rad mastectmy w tiss expand ped flp reconst
1YM92LAXXFMod rad mastectmy w free flp reconst
1YM92LAXXGMod rad mastectmy w ped flp reconst
1YM92LAXXQ2009: Excision radical with reconstruction, breast modified or NOS with no implanted device using combined sources of tissue (e.g., free and pedicled TRAM flap)
2012: Excision radical with reconstruction, breast modified or NOS using combined sources of tissue (e.g., free and pedicled TRAM flap) with no implanted device
1YM92TRPMEExt rad mastectmy w prosth loc flp reconst
1YM92TRTPEExt rad mastectmy wtiss expand loc flp reconst
1YM92TRXXQExc rad w reconstr breast OA w ext rad excisn combo tis
1YR87LAExcision partial, skin of axillary region open [excisional] approach with apposition technique (e.g., suture, glue) for closure
1YR87LAXXBExcise prt sk axilla &splt gr
1YS87LAExcision partial, skin of abdomen and trunk open [excisional] approach with apposition technique (suture, glue) for closure
1YS87LAXXEExcise prt sk abd & trunk &loc flp
1ZZ35CAM0Pharmacotherapy, total body antineoplastic and immunomodulating agents per orifice (oral) approach antineoplastic agent NOS
1ZZ35CAM2Pharmacotherapy, total body antineoplastic and immunomodulating agents per orifice (oral) approach antimetabolite
1ZZ35CAM4Pharmacotherapy, total body antineoplastic and immunomodulating agents per orifice (oral) approach cytotoxic antibiotic and related substance
1ZZ35CAM5Pharmacotherapy, total body antineoplastic and immunomodulating agents per orifice (oral) approach other antineoplastic
1ZZ35HAK7Pharm tx NEC perc app &macrolide/lincosamide
1ZZ35HAM0Pharmacotherapy, total body antineoplastic and immunomodulating agents percutaneous needle approach [intramuscular, intravenous, subcutaneous, intradermal] antineoplastic agent NOS
1ZZ35HAM3Pharmacotherapy, total body antineoplastic and immunomodulating agents percutaneous approach [intramuscular, intravenous, subcutaneous, intradermal] plant alkaloid and other natural product
1ZZ35HAM4Pharmacotherapy, total body antineoplastic and immunomodulating agents percutaneous approach [intramuscular, intravenous, subcutaneous, intradermal] cytotoxic antibiotic and related substance
1ZZ35HAM5Pharmacotherapy, total body antineoplastic and immunomodulating agents percutaneous approach [intramuscular, intravenous, subcutaneous, intradermal] other antineoplastic
1ZZ35HAM9Pharmacotherapy, total body antineoplastic and immunomodulating agents percutaneous approach [intramuscular, intravenous, subcutaneous, intradermal] Combination [multiple] antineoplastic agents
1ZZ35HAN5Pharmacotherapy, total body musculoskeletal system agents percutaneous approach [intramuscular, intravenous, subcutaneous, intradermal] drug for treatment of bone disease
2AX13HASpecimen collection (diagnostic), spinal canal and meninges using percutaneous (needle) approach
2EQ71HABiopsy s t head & neck perc ndle app
2FU71HABiopsy thyr gl perc ndle app
2GM71BABiopsy, bronchus using endoscopic per orifice approach
2GM71BPBiopsy, bronchus using endoscopic per orifice approach with needle aspiration
2GM71BRBiopsy, bronchus using endoscopic per orifice approach with brushing/washing
2GT71BABiopsy, lung using endoscopic per orifice approach
2GT71BPBiopsy, lung using endoscopic per orifice approach and needle aspiration
2GT71HABiopsy, lung using percutaneous (needle) approach
2GW71DABiopsy mediast endo app
2HZ24JAXJECG NOS (ext applic record electrode)
2ME71BPBiopsy, mediastinal lymph nodes endoscopic per orifice, with needle aspiration
2ME71DABiopsy, mediastinal lymph nodes using endoscopic approach
2ME71LABiopsy, mediastinal lymph nodes using open approach
2MZ71HABiopsy lymph sys perc ndle app
2NF71BABiopsy stomach EPO app
2NK70BABLInspect sm intest EPO app & gastroscope
2OT71DABiopsy, abdominal cavity using endoscopic [laparoscopic] approach
2SZ71HABiopsy s t chest & abd perc ndle app
2WY71HABiopsy bone marrow perc ndle app
2YK71HABiopsy, nipple using percutaneous approach (needle, punch)
2YK71LABiopsy, nipple using open [incisional] approach
2YM70LAInspection, breast NOS using open approach
2YM71HABiopsy, breast NOS using percutaneous (needle) aspiration
2YM71HAGXBiopsy, breast NOS percutaneous approach using device NEC
2YM71LABiopsy, breast NOS incisional biopsy
2ZZ02ZXAssessment (examination), total body for determining candidacy for treatment
2ZZ13RASpecimen collect NEC vn puncture
3AN40WEMRI brain with & without enhancement
3ER20WCCT head with enhancement
3OG10WZXray b dct w pancr w endo retrograde injct contr
3OT30DAU/S abd cav alone
3SC40WEMRI sp vert with & without enhancement
3WZ70CCNuclear study msk sys SPECT tomo
3YM30DAU/S breast u/s only
7SC08PLMinistrate NEC personal care chronic pain

Appendix A.3.2. Diagnoses

  • Data Source(s): Discharge Abstract Database, National Ambulatory Care Reporting System.
  • Coding system: International Classification of Diseases, version 10 (ICD10), 2015.
Table A3. International Classification of Diseases version 10 diagnosis codes associated with procedures that indicated a second breast cancer event.
Table A3. International Classification of Diseases version 10 diagnosis codes associated with procedures that indicated a second breast cancer event.
International Classification of Diseases (Version 10) CodesInternational Classification of Diseases (Version 10) Code Descriptions
C50Malignant neoplasm of breast
C22Malignant neoplasm of liver and intrahepatic bile ducts (excluding biliary tract NOS, secondary malignant neoplasm of liver)
C34Malignant neoplasm of bronchus and lung
C41Malignant neoplasm of bone and articular cartilage of other and unspecified sites
D43Neoplasm of uncertain or unknown behaviour of brain and central nervous system (excluding peripheral nerves and autonomic nervous system)
C71Malignant neoplasm of brain (excluding cranial nerves, retrobulbar tissue)
C77Secondary and unspecified malignant neoplasm of lymph nodes (excluding malignant neoplasm of lymph nodes, specified as primary)
C78Secondary malignant neoplasm of respiratory and digestive organs
C78.0Secondary malignant neoplasm of lung
C78.3Secondary malignant neoplasm of other and unspecified respiratory organs
C78.7Secondary malignant neoplasm of liver and intrahepatic bile duct
D48Neoplasm of uncertain or unknown behaviour of other and unspecified sites (excluding neurofibromatosis (nonmalignant))
D48.0Bone and articular cartilage (excluding articular cartilage and cartilage of the ear, larynx, and nose; the connective tissue of the eyelid; and synovia).
D48.6Breast (including connective tissue of breast, cystosarcoma phyllodes; excluding skin of breast)
D37Neoplasm of uncertain or unknown behaviour of oral cavity and digestive organs
D37.6Liver, gallbladder and bile ducts
D38Neoplasm of uncertain or unknown behaviour of middle ear and respiratory and intrathoracic organs (excluding heart)
D38.1Trachea, bronchus and lung
C79Secondary malignant neoplasm of other and unspecified sites
C79.3Secondary malignant neoplasm of brain and cerebral meninges
C79.4Secondary malignant neoplasm of other and unspecified parts of nervous system
C79.5Secondary malignant neoplasm of bone and bone marrow

Appendix A.4. Systemic Therapy Criterion

Patients met the systemic therapy criterion if they received one of the drugs listed, in some cases, for one of the indications listed.
  • Data Source(s): Activity Level Reporting database.
  • Coding system: Not applicable.
Table A4. Systemic therapy data types and descriptions that indicated a second breast cancer event.
Table A4. Systemic therapy data types and descriptions that indicated a second breast cancer event.
Data Type Analyzed by AlgorithmDescription
Drug descriptionPAMIDRONATE
CLODRONATE
VINORELBINE
PACLITAXEL
ERIBULIN
PERTUZUMAB
TRASTUZUMAB EMTANSINE
  • Data Source(s): New Drug Funding Program database.
  • Coding system: Proprietary to Ontario Health.
Table A5. Disease indications and funding policy name or name of drug received by patient that indicated a second breast cancer event.
Table A5. Disease indications and funding policy name or name of drug received by patient that indicated a second breast cancer event.
Disease IndicationPolicy Name/Drug Name
Metastatic or Incurable Locally Advanced—Breast CancerEribulin
Unresectable Locally Recurrent or Metastatic—Breast CancerPertuzumab with Trastuzumab
Trastuzumab Emtansine
Unresectable Locally Advanced or Metastatic Breast Cancer as Third or Subsequent Line of Treatment (Time-Limited)Trastuzumab Emtansine
Metastatic Breast CancerClodronate (IV)
Docetaxel
Nab-Paclitaxel
Paclitaxel
Pamidronate
Trastuzumab in combination with Docetaxel
Trastuzumab in combination with Paclitaxel
Trastuzumab in combination with Vinorelbine
Trastuzumab with First Line Docetaxel
Trastuzumab—Single Agent
Vinorelbine
Second Line—Metastatic Breast CancerTrastuzumab

Appendix A.5. Radiation Treatment Criterion

Patients met this criterion if they received radiation therapy in one of the anatomical sites listed to treat one of the associated diagnoses listed in the appropriate time period.

Appendix A.6. Body Regions Where Radiation Was Applied

  • Data Source(s): Activity Level Reporting database.
  • Coding system: Proprietary to Ontario Health.
Table A6. Body regions and codes for receiving radiation that indicated a second breast cancer event.
Table A6. Body regions and codes for receiving radiation that indicated a second breast cancer event.
Body Region GroupBody Region CodeBody Region Code Description
ABDOMENABDLLeft abdomen
ABDOMEN (continued)ABDOWhole abdomen
ABDRRight abdomen
ABLBLower abdomen
ABLLLeft lower abdomen
ABLRRight lower abdomen
ABUBUpper abdomen
ABULLeft upper abdomen
ABURRight upper abdomen
ADRLLeft adrenal
ADRRRight adrenal
BILEBile duct
COLNColon
EPIGEpigastrium
GALLGall bladder
INVYInverted ‘y’ (dog-leg, hockey-stick)
KIDLLeft kidney
KIDRRight kidney
LIVRLiver
PANCPancreas
PARAPara-aortic nodes
SPLESpleen
STOMStomach
CHESTAXILLeft axilla
AXIRRight axilla
BREBBilateral breast
BRELLeft breast
BRERRight breast
CHEBBilateral chest lung & area involve
CHELLeft chest
CHERRight chest
CHWBBilateral chest wall (w/o breast)
CHWLLeft chest wall
CHWRRight chest wall
CLABBilateral clavicle
CLALLeft clavicle
CLARRight clavicle
ESOILower esophagus
ESOMMiddle esophagus
ESOSUpper esophagus
ESOWEntire esophagus
HEMLLeft hemimantle
HEMRRight hemimantle
HERTHeart
CHEST
(continued)
IMCBBilateral internal mammary chain
LUNBBilateral lung
LUNLLeft lung
LUNRRight lung
MANTMantle
MEDIMediastinum
PLELLeft pleura (as in mesothelioma)
PLERRight pleura
RIBLLeft ribs
RIBRRight ribs
SCABBilateral scapula
SCALLeft scapula
SCARRight scapula
SCNBBilateral supraclavicular nodes
SCNLLeft supraclavicular nodes
SCNRRight supraclavicular nodes
STERSternum
HEADANTBBilateral antrum (bull’s eye)
ANTLLeft antrum
ANTRRight antrum
BRAIBrain
CHKLLeft cheek
CHKRRight cheek
EARLLeft ear
EARRRight ear
ETHMEthmoid sinus
EYEBBilateral eyes
EYELLeft eye
EYERRight eye
FACBBilateral face
FACLLeft face
FACRRight face
FLOOFloor of mouth (boosts)
FOSSPosterior fossa
GINGGingiva
HEADHead
LACBBilateral lacrimal gland
LACLLeft lacrimal gland
LACRRight lacrimal gland
LIPBBoth lip(s)
HEAD (continued)LIPILower lip
LIPSUpper lip
MANBBilateral mandible
MANLLeft mandible
MANRRight mandible
MAXBBilateral maxilla
MAXLLeft maxilla
MAXRRight maxilla
NASANasal fossa
NASONasopharynx
ORALOral cavity/buccal mucosa
ORBBBilateral orbit
ORBLLeft orbit
ORBRRight orbit
OROPOropharynx
PALHHard palate
PALSSoft palate
PALXPalate unspecified
PARLLeft parotid
PARRRight parotid
PITUPituitary
SALLLeft salivary gland
SALRRight salivary gland
SKULSkull
SPHESphenoid sinus
SUBMSubmandibular glands
TONGTongue
TONSTonsil
UVULUvula
LOWER LIMBANKBBilateral ankle
ANKLLeft ankle
ANKRRight ankle
FEMBBilateral femur
FEMLLeft femur
FEMRRight femur
FIBLLeft fibula
FIBRRight fibula
LOWER LIMB (continued)FOOBBilateral feet
FOOLLeft foot
FOORRight foot
HEEBBilateral heel
HEELLeft heel
HEERRight heel
HIPBBilateral hip
HIPLLeft hip
HIPRRight hip
KNEBBilateral knee
KNELLeft knee
KNERRight knee
LEGBBilateral leg
LEGLLeft leg
LEGRRight leg
LELBLower bilateral leg
LELLLower left leg
LELRLower right leg
LEUBUpper bilateral leg
LEULUpper left leg
LEURUpper right leg
TIBLLeft tibia
TIBRRight tibia
TOELLeft toes
TOERRight toes
NECKHYPOHypopharynx
LARPLarygopharynx
LARYLarynx
NECBBilateral neck includes nodes
NECLLeft neck includes nodes
NECRRight neck includes nodes
PYRIPyriform fossa (sinuses)
THYBThyroid
TRACTrachea
SPINECOCCCoccyx
SACRSacrum
SPCTCervical & thoracic spine
SPICCervical spine
SPILLumbar spine
SPITThoracic spine
SPIWWhole spine
SPLSLumbo-sacral spine
SPTLThoracic & lumbar spine
UPPER LIMBARLLLower left arm
ARLRLower right arm
ARMBBilateral arms
ARMLLeft arm
ARMRRight arm
ARULUpper left arm
ARURUpper right arm
FINGFinger (including thumbs)
HANBBilateral hand
HANLLeft hand
HANRRight hand
HUMLLeft humerus
HUMRRight humerus
RADLLeft radius
RADRRight radius
SHOBBilateral shoulder
SHOLLeft shoulder
SHORRight shoulder
ULNLLeft ulna
ULNRRight ulna

Appendix A.7. Diagnoses Associated with Radiation

  • Data Source(s): Activity Level Reporting database.
  • Coding system: International Classification of Diseases, version 10 (ICD10), 2015.
Table A7. International Classification of Diseases version 10 diagnosis codes that indicated a second breast cancer event.
Table A7. International Classification of Diseases version 10 diagnosis codes that indicated a second breast cancer event.
CodesCode Description (ICD-10 Version 2015)
C50Malignant neoplasm of breast
C34Malignant neoplasm of bronchus and lung
C40Malignant neoplasm of bone and articular cartilage of limbs
C71Malignant neoplasm of brain
C77Secondary and unspecified malignant neoplasm of lymph nodes
C78Secondary malignant neoplasm of respiratory and digestive organs
C79Secondary malignant neoplasm of other and unspecified sites

Appendix B

Exclusions during Manual Record Review and Comparison to Final Validation Sub-Cohort

Of the 3258 patients selected for manual record review, 1013 patients were excluded because their records could not be retrieved, they did not have sufficient records for review at a study center, or their SBCE status was indeterminate. The remaining validation sub-cohort was 2245 patients (main text Table 3). We conducted additional statistical analyses to determine whether patients excluded during manual record review differed from patients who remained in the validation sub-cohort. Pearson’s Chi-squared tests were used to determine whether patients excluded during manual record review differed from the patients remaining in the validation sub-cohort based on stage at diagnosis and algorithm classification as having or not having an SBCE. A Cochran-Mantel-Haenszel statistic [24] was used to test for conditional independence between remaining in the validation sub-cohort and algorithm SBCE classification after controlling for stage at diagnosis.
Pearson’s chi-squared tests indicated a potential relationship between stage at diagnosis and likelihood of exclusion during manual review based on a marginally significant p-value of 0.044 (main text Table 4A). The Cochran-Mantel-Haenszel statistic [24] demonstrated that after controlling for stage at diagnosis, the algorithm classified more excluded patients as having an SBCE (main text Table 4B; p-value < 0.0136).
Table A8. Stage at diagnosis among patients excluded during manual review and patients remaining in the validation sub-cohort.
Table A8. Stage at diagnosis among patients excluded during manual review and patients remaining in the validation sub-cohort.
Patient GroupStage at Diagnosis
Stage 1
N (%)
Stage 2
N (%)
Stage 3
N (%)
Total
N
Remaining validation sub-cohort701 (31.2%)812 (36.2%)732 (32.6%)2245
Excluded during manual review347 (34.3%)322 (31.8%)344 (34.0%)1013
Abbreviations: N, number.
Table A9. Algorithm classification as experiencing a second breast cancer event (SBCE) stratified by stage at diagnosis and exclusion during manual review.
Table A9. Algorithm classification as experiencing a second breast cancer event (SBCE) stratified by stage at diagnosis and exclusion during manual review.
Stage at DiagnosisPatient GroupAlgorithm SBCE Classification
SBCE
N (Row%)
No SBCE
N (Row%)
Stage 1Remaining validation sub-cohort48 (6.8%)653 (93.2%)
Excluded during manual review27 (7.8%)320 (92.2%)
Stage 2Remaining validation sub-cohort107 (13.2%)705 (86.8%)
Excluded during manual review61 (18.9%)261 (81.1%)
Stage 3Remaining validation sub-cohort216 (29.5%)516 (70.5%)
Excluded during manual review114 (33.1%)230 (66.9%)
Abbreviations: N, number; SBCE, second breast cancer event.

Appendix C

Algorithm Diagnostic Accuracy by Prior Cancer History

Algorithm diagnostic accuracy was assessed for patients with a history of cancer prior to the breast cancer diagnosis that qualified them for inclusion in this study. Diagnostic accuracy was similar for the entire cohort, patients with no prior cancer, and patients with no prior breast cancer, though sensitivity decreased for patients with any prior cancer (prior breast or non-breast cancer, or both). Patients with prior breast cancers constituted too small a group to analyze separately. The comparable diagnostic accuracy for patients with no prior cancer and no prior breast cancer suggests that inclusion of patients with prior non-breast cancers did not meaningfully affect algorithm performance.
Table A10. Algorithm diagnostic accuracy at classifying patients as experiencing a second breast cancer event (SBCE), stratified by prior cancer status.
Table A10. Algorithm diagnostic accuracy at classifying patients as experiencing a second breast cancer event (SBCE), stratified by prior cancer status.
Patients’ Cancer Status Prior to Cohort EntryNAgreement Statistic
% (95% Confidence Interval)
SensitivitySpecificityPositive Predictive ValueNegative Predictive ValueAccuracyKappa 1Prevalence-Adjusted Bias-Adjusted Kappa 1
Remaining validation sub-cohort224585.3
(80.7–89.1)
93.8
(92.6–94.8)
67.1
(62.1–71.9)
97.7
(96.9–98.3)
92.7
(91.5–93.7)
70.9
(66.7–75.0)
85.3
(83.0–87.4)
No prior breast cancer (no prior cancer and prior non-breast cancer)218285.9
(81.3–89.8)
93.7
(92.5–94.8)
66.5
(61.3–71.4)
97.9
(97.1–98.5)
92.7
(91.5–93.8)
70.8
(66.5–75.0)
85.4
(83.1–87.5)
Any prior cancer (prior breast cancer, non-breast cancer, or both)16779.3
(60.3–92.0)
93.5
(88.0–97.0)
71.9
(53.3–86.3)
95.6
(90.6–98.4)
91.0
(85.6–94.9)
69.9
(55.7–84.1)
82.0
(71.2–89.8)
No prior cancer207885.9
(81.1–89.9)
93.8
(92.6–94.8)
66.7
(61.4–71.7)
97.9
(97.1–98.5)
92.8
(91.6–93.9)
70.9
(66.6–75.3)
85.6
(83.2–87.7)
Abbreviations: N, number; SBCE, second breast cancer event. 1 The Fleiss method of confidence interval calculation was used to calculate the confidence intervals for the kappa and prevalence-adjusted bias-adjusted kappa statistics [28].

References

  1. Chubak, J.; Yu, O.; Pocobelli, G.; Lamerato, L.; Webster, J.; Prout, M.N.; Ulcickas Yood, M.; Barlow, W.E.; Buist, D.S.M. Administrative data algorithms to identify second breast cancer events following early-stage invasive breast cancer. JNCI J. Natl. Cancer Inst. 2012, 104, 931–940. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Xu, Y.; Kong, S.; Cheung, W.Y.; Bouchard-Fortier, A.; Dort, J.C.; Quan, H.; Buie, E.M.; McKinnon, G.; Quan, M.L. Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data. BMC Cancer 2019, 19, 210. [Google Scholar] [CrossRef]
  3. Ritzwoller, D.P.; Hassett, M.J.; Uno, H.; Cronin, A.M.; Carroll, N.M.; Hornbrook, M.C.; Kushi, L.C. Development, validation, and dissemination of a breast cancer recurrence detection and timing informatics algorithm. JNCI J. Natl. Cancer Inst. 2017, 110, 273–281. [Google Scholar] [CrossRef] [PubMed]
  4. In, H.; Simon, C.A.; Phillips, J.L.; Posner, M.C.; Ko, C.Y.; Winchester, D.P. The quest for population-level cancer recurrence data; current deficiencies and targets for improvement. J. Surg. Oncol. 2015, 111, 657–662. [Google Scholar] [CrossRef]
  5. Maishman, T.; Cutress, R.I.; Hernandez, A.; Gerty, S.; Copson, E.R.; Durcan, L.; Eccles, D.M. Local recurrence and breast oncological surgery in young women with breast cancer: The POSH observational cohort study. Ann. Surg. 2017, 266, 165–172. [Google Scholar] [PubMed] [Green Version]
  6. Pilewskie, M.; Morrow, M. Margins in breast cancer: How much is enough? Cancer 2018, 124, 1335–1341. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Pivot, X.; Asmar, L.; Hortobagyi, G.N.; Theriault, R.; Pastorini, F.; Buzdar, A. A retrospective study of first indicators of breast cancer recurrence. Oncology 2000, 58, 185–190. [Google Scholar] [CrossRef] [PubMed]
  8. Pan, H.; Gray, R.; Braybrooke, J.; Davies, C.; Taylor, C.; McGale, P.; Peto, R.; Pritchard, K.I.; Bergh, J.; Dowsett, M.; et al. 20-year risks of breast-cancer recurrence after stopping endocrine therapy at 5 years. N. Engl. J. Med. 2017, 377, 1836–1846. [Google Scholar] [CrossRef] [Green Version]
  9. Will, B.P.; Berthelot, J.-M.; Le Petit, C.; Tomiak, E.M.; Verma, S.; Evans, W.K. Estimates of the lifetime costs of breast cancer treatment in Canada. Eur. J. Cancer 2000, 36, 724–735. [Google Scholar] [CrossRef]
  10. Hawley, S.T.; Janz, N.K.; Griffith, K.A.; Jagsi, R.; Friese, C.R.; Kurian, A.W.; Hamilton, A.S.; Ward, K.C.; Morrow, M.; Wallner, L.P.; et al. Recurrence risk perception and quality of life following treatment of breast cancer. Breast Cancer Res. Treat. 2017, 161, 557–565. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Tewari, A.; Chagpar, A.B. Worry about breast cancer recurrence: A population-based analysis. Am. Surg. 2014, 80, 640–645. [Google Scholar] [CrossRef] [PubMed]
  12. Geurts, Y.M.; Witteveen, A.; Bretveld, R.; Poortmans, P.M.; Sonke, G.S.; Strobbe, L.J.A.; Siesling, S. Patterns and predictors of first and subsequent recurrence in women with early breast cancer. Breast Cancer Res. Treat. 2017, 165, 709–720. [Google Scholar] [CrossRef]
  13. Soerjomataram, I.; Louwman, M.W.J.; Ribot, J.G.; Roukema, J.A.; Coebergh, J.W.W. An overview of prognostic factors for long-term survivors of breast cancer. Breast Cancer Res. Treat. 2008, 107, 309–330. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Beatty, J.D.; Sun, Q.; Markowitz, D.; Chubak, J.; Huang, B.; Etzioni, R. Identifying breast cancer recurrence histories via patient-reported outcomes. J. Cancer Surviv. 2022, 16, 388–396. [Google Scholar] [CrossRef] [PubMed]
  15. Hassett, M.J.; Ritzwoller, D.P.; Taback, N.; Carroll, N.; Cronin, A.M.; Ting, G.V.; Schrag, D.; Warren, J.L.; Hornbrook, M.C.; Weeks, J.C. Validating billing/encounter codes as indicators of lung, colorectal, breast, and prostate cancer recurrence using 2 large contemporary cohorts. Med. Care 2014, 52, e65–e73. [Google Scholar] [CrossRef] [Green Version]
  16. Whyte, J.L.; Engel-Nitz, N.M.; Teitelbaum, A.; Gomez Rey, G.; Kallich, J.D. An evaluation of algorithms for identifying metastatic breast, lung, or colorectal cancer in administrative claims data. Med. Care 2015, 53, e49–e57. [Google Scholar] [CrossRef] [PubMed]
  17. Cronin-Fenton, D.; Kjærsgaard, A.; Nørgaard, M.; Amelio, J.; Liede, A.; Hernandez, R.K.; Sørensen, H.T. Breast cancer recurrence, bone metastases, and visceral metastases in women with stage II and III breast cancer in Denmark. Breast Cancer Res. Treat. 2018, 167, 517–528. [Google Scholar] [CrossRef]
  18. Henriques Abreu, P.; Santos, M.; Henriques Abreu, M.; Aveleira Andrade, B.; Silva, D. Predicting breast cancer recurrence using machine learning techniques: A systematic review. ACM Comput. Surv. 2016, 49, 1–40. [Google Scholar]
  19. Haque, R.; Shi, J.; Schottinger, J.E.; Ahmed, S.A.; Chung, J.; Avila, C.; Lee, V.S.; Cheetham, T.C.; Habel, L.A.; Fletcher, S.W.; et al. A hybrid approach to identify subsequent breast cancer using pathology and automated health information data. Med. Care 2015, 53, 380–385. [Google Scholar] [PubMed]
  20. How We Collect Cancer Registry Data. Available online: https://www.cancercareontario.ca/en/data-research/accessing-data/technical-information/cancer-registry-data-collection (accessed on 8 July 2022).
  21. Apply for OHIP and Get a Health Card. Available online: https://www.ontario.ca/page/apply-ohip-and-get-health-card#section-0 (accessed on 8 July 2022).
  22. Access Data. Available online: https://www.ccohealth.ca/en/access-data (accessed on 8 July 2022).
  23. Ontario Cancer Statistics 2016. Available online: https://www.cancercareontario.ca/en/statistical-reports/ontario-cancer-statistics-2016 (accessed on 8 July 2022).
  24. Agresti, A. An Introduction to Categorical Data Analysis, 2nd ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2007. [Google Scholar]
  25. Byrt, T.; Bishop, J.; Carlin, J.B. Bias, prevalence and kappa. J. Clin. Epidemiol. 1993, 46, 423–429. [Google Scholar] [CrossRef]
  26. Sim, J.; Wright, C.C. The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Phys. Ther. 2005, 85, 257–268. [Google Scholar] [CrossRef] [Green Version]
  27. Marrie, R.A.; Fisk, J.D.; Yu, B.N.; Leung, S.; Elliott, L.; Caetano, P.; Warren, S.; Evans, C.; Wolfson, C.; Svenson, L.W.; et al. Mental comorbidity and multiple sclerosis: Validating administrative data to support population-based surveillance. BMC Neurol. 2013, 13, 16. [Google Scholar]
  28. Fleiss, J.L.; Cohen, J.; Everitt, B.S. Large sample standard errors of kappa and weighted kappa. Psychol. Bull. 1969, 72, 323–327. [Google Scholar]
  29. Kroenke, C.H.; Chubak, J.; Johnson, L.; Castillo, A.; Weltzien, E.; Caan, B.J. Enhancing breast cancer recurrence algorithms through selective use of medical record data. JNCI J. Natl. Cancer Inst. 2015, 108, djv336. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Livaudais-Toman, J.; Egorova, N.; Franco, R.; Prasad-Hayes, M.; Howell, E.A.; Wisnivesky, J.; Bickell, N.A. A validation study of administrative claims data to measure ovarian cancer recurrence and secondary debulking surgery. EGEMS 2016, 4, 1208. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Algorithm criteria with definitions and rationale. Each criterion was applied to the entire study cohort. Patients could meet a single criterion multiple times or meet multiple criteria. For this study, we considered patients to have experienced a second breast cancer event (SBCE) if they met one criterion one time between 180 days post-diagnosis and their death or the end of follow-up.
Figure 1. Algorithm criteria with definitions and rationale. Each criterion was applied to the entire study cohort. Patients could meet a single criterion multiple times or meet multiple criteria. For this study, we considered patients to have experienced a second breast cancer event (SBCE) if they met one criterion one time between 180 days post-diagnosis and their death or the end of follow-up.
Curroncol 29 00424 g001
Figure 2. Patient inclusion/exclusion criteria.
Figure 2. Patient inclusion/exclusion criteria.
Curroncol 29 00424 g002
Figure 3. Proportions of patients classified by the algorithm as experiencing a second breast cancer event based on a single criterion (lined bars) or combinations of criteria (solid bars). Criterion/criteria groups are mutually exclusive and collectively exhaustive. All criteria were applied to the entire cohort and could be applied in any order: 1—death from breast cancer; 2—procedure and associated diagnosis; 3—systemic treatment; 4—radiotherapy.
Figure 3. Proportions of patients classified by the algorithm as experiencing a second breast cancer event based on a single criterion (lined bars) or combinations of criteria (solid bars). Criterion/criteria groups are mutually exclusive and collectively exhaustive. All criteria were applied to the entire cohort and could be applied in any order: 1—death from breast cancer; 2—procedure and associated diagnosis; 3—systemic treatment; 4—radiotherapy.
Curroncol 29 00424 g003
Table 1. Cohort Description.
Table 1. Cohort Description.
CharacteristicStage at Diagnosis, N (% of Stage Total)
Stage 0
N = 1528
Stage I
N = 13,575
Stage II
N = 12,141
Stage III
N = 4538
Death during follow-up6 (0.4%)271 (2.0%)583 (4.8%)490 (10.8%)
Median follow-up in months (IQR)30.3
(22.4, 40.5)
35.0
(23.4, 46.4)
34.0
(22.9, 46.4)
32.9
(21.5, 45.0)
Median age at diagnosis (IQR)60.0
(52.0, 68.0)
63.0
(54.0, 71.0)
61.0
(50.0, 73.0)
58.0
(48.0, 71.0)
Substage at diagnosis
01528 (100.0%)
I 3552 (26.2%)
IA 9508 (70.0%)
IB 515 (3.8%)
II 277 (2.3%)
IIA 7774 (64.0%)
IIB 4090 (33.7%)
III 275 (6.1%)
IIIA 2538 (55.9%)
IIIB 785 (17.3%)
IIIC 898 (19.8%)
IIINOS 42 (0.9%)
Median tumor size, mm (IQR)15.0
(7.0, 25.0)
12.0
(9.0, 16.0)
26.0
(22.0, 35.0)
45.0
(28.0, 65.0)
Patients missing tumor size data1502 (98.3%)4245 (31.3%)3783 (31.2%)1696 (37.4%)
Year of diagnosis
2009 117 (1.1%)2869 (21.1%)2776 (22.9%)1055 (23.2%)
2010528 (34.6%)3620 (26.7%)3141 (25.9%)1210 (26.7%)
2011506 (33.1%)3612 (26.6%)3117 (25.7%)1153 (25.4%)
2012477 (31.2%)3474 (25.6%)3107 (25.6%)1120 (24.7%)
Laterality of original breast cancer diagnosis
Right715 (46.8%)6925 (51.0%)6141 (50.6%)2292 (50.5%)
Left818 (53.5%)6849 (50.5%)6193 (51.0%)2309 (50.9%)
Tumor morphology
Ductal carcinoma49 (3.2%)8069 (59.4%)6657 (54.8%)2296 (50.6%)
Lobular carcinoma<6549 (4.0%)730 (6.0%)313 (6.9%)
Mixed carcinoma01030 (7.6%)896 (7.4%)315 (6.9%)
Sarcoma0<643 (0.4%)<6
Other<641–4595 (0.8%)19–24
Invasive cancer, missing morphology1477 (96.7%)3881 (28.6%)3720 (30.6%)1589 (35.0%)
Tumor estrogen receptor
Borderline or positive8 (0.5%)8193 (60.4%)6438 (53.0%)2140 (47.2%)
Negative8 (0.5%)1041 (7.7%)1620 (13.3%)706 (15.6%)
Missing 21512 (99.0%)4341 (32.0%)4083 (33.6%)1692 (37.3%)
Tumor progesterone receptor
Borderline or positive<67461 (55.0%)5795 (47.7%)1858 (40.9%)
Negative9–131765 (13.0%)2258 (18.6%)980 (21.6%)
Missing 21514 (99.1%)4349 (32.0%)4088 (33.7%)1700 (37.5%)
Tumor human epidermal growth factor receptor 2 (HER2) status
Negative or equivocal<67266 (53.5%)6066 (50.0%)1944 (42.8%)
Positive<6727 (5.4%)997 (8.2%)543 (12.0%)
Missing 21523 (99.7%)5582 (41.1%)5078 (41.8%)2051 (45.2%)
Abbreviations: IQR, interquartile range; mm, millimeters; N, number; NOS, not otherwise specified; SBCE, second breast cancer event. 1 Fewer patients were diagnosed with breast cancer in 2009 because Ontario changed diagnostic criteria in 2010 to use the Surveillance, Epidemiology, and End Results system. 2 Biomarker status is not routinely tested in patients with ductal carcinoma in situ. Missing biomarker data for this cohort are likely due to methods of biomarker reporting to the Ontario Cancer Registry, rather than biomarker status not being measured.
Table 2. Algorithm classifications of second breast cancer events (SBCEs) in the entire cohort.
Table 2. Algorithm classifications of second breast cancer events (SBCEs) in the entire cohort.
CharacteristicStage at Diagnosis, N (% of Stage Total)
Stage 0
N = 1528
Stage I
N = 13,575
Stage II
N = 12,141
Stage III
N = 4538
Algorithm classifications
Patients with SBCEs62 (4.1%)760 (5.6%)1635 (13.5%)1339 (29.5%)
Patients with probable contralateral
second primary breast cancers 1
24 (1.6%)122 (0.9%)146 (1.2%)86 (1.9%)
Algorithm classifications by data type (criterion)
Cause of death data
Patients with SBCEs<665 (0.5%)301 (2.5%)381 (8.4%)
Procedure and associated diagnosis data
Patients with SBCEs56 (3.7%)625 (4.6%)1158 (9.5%)867 (19.1%)
Events596541238961
Contralateral events231049955
Systemic treatment data
Patients with SBCEs7 (0.5%)82 (0.6%)356 (2.9%)486 (10.7%)
Events792402549
Radiation therapy data
Patients with SBCEs12 (0.8%)188 (1.4%)492 (4.1%)425 (9.4%)
Events15220615545
Contralateral events7506645
Manual record review location
No review1528 (100.0%)12,874 (94.8%)11,329 (93.3%)3806 (83.9%)
Juravinski Cancer Centre0 (0%)433 (3.2%)474 (3.9%)416 (9.2%)
Odette Cancer Centre0 (0%)268 (2.0%)338 (2.8%)316 (7.0%)
Death during follow-up 6 (0.4%)271 (2.0%)583 (4.8%)490 (10.8%)
Median follow-up in months (IQR) 30.3
(22.4, 40.5)
35.0
(23.4, 46.4)
34.0
(22.9, 46.4)
32.9
(21.5, 45.0)
History of primary cancer before cohort entry
Prior breast and non-breast cancer7 (0.5%)66 (0.5%)37 (0.3%)15 (0.3%)
Prior breast cancer only84 (5.5%)623 (4.6%)405 (3.3%)115 (2.5%)
Prior non-breast cancer only76 (5.0%)825 (6.1%)685 (5.6%)227 (5.0%)
No prior cancer1361 (89.1%)12,061 (88.8%)11,014 (90.7%)4181 (92.1%)
Abbreviations: IQR, interquartile range; mm, millimeters; N, number; NOS, not otherwise specified; SBCE, second breast cancer event. 1 Patients classified as having contralateral second primary breast cancers according to the criteria based on procedures and radiotherapy treatments are a subset of patients classified as having an SBCE.
Table 3. Validation sub-cohort characteristics.
Table 3. Validation sub-cohort characteristics.
CharacteristicStage, N (%)
Stage I
N = 701
Stage II
N = 812
Stage III
N = 732
Total
N = 2245
Death during follow-up14 (2.0%)31 (3.8%)73 (10.0%)118 (5.3%)
Median follow-up in months (IQR)34.8
(23.5, 47.5)
36.1
(23.5, 47.8)
31.2
(21.3, 44.4)
34.1
(22.8, 46.6)
Median age at diagnosis (IQR)59.0
(51.0, 68.0)
58.0
(49.0, 68.0)
55.5
(47.0, 66.0)
57.0
(49.0, 67.0)
History of primary cancer before cohort entry
Prior breast cancer (alone or with non-breast cancer)24 (3.4%)25 (3.0%)14 (1.9%)63 (2.8%)
Prior non-breast cancer32 (4.6%)36 (4.4%)36 (4.9%)104 (4.6%)
No prior cancer645 (92.0%)751 (92.5%)682 (93.2%)2078 (92.6%)
Year of diagnosis
2009165 (23.5%)205 (25.2%)167 (22.8%)537 (23.9%)
2010169 (24.1%)216 (26.6%)177 (24.2%)562 (25.0%)
2011187 (26.7%)191 (23.5%)190 (26.0%)568 (25.3%)
2012180 (25.7%)200 (24.6%)198 (27.0%)578 (25.7%)
Substage at diagnosis
I201 (28.7%) 201 (9.0%)
IA472 (67.3%) 472 (21.0%)
IB28 (4.0%) 28 (1.2%)
II 21 (2.6%) 21 (0.9%)
IIA 490 (60.3%) 490 (21.8%)
IIB 301 (37.1%) 301 (13.4%)
III or IIINOS 34 (4.6%)34 (1.5%)
IIIA 436 (59.6%)436 (19.4%)
IIIB 108 (14.8%)108 (4.8%)
IIIC 154 (21.0%)154 (6.9%)
Median tumor size, mm (IQR)13.0
(10.0, 17.0)
28.0
(22.0, 35.0)
52.0
(30.0, 70.0)
25.0
(15.0, 41.0)
Patients missing tumor size data233 (33.2%)282 (34.7%)267 (36.5%)782 (34.8%)
Laterality of original diagnosis
Right363 (51.8%)390 (48.0%)360 (49.2%)1113 (49.6%)
Left337 (48.1%)427 (52.6%)377 (51.5%)1141 (50.8%)
Tumor morphology
Ductal carcinoma418 (59.6%)446 (54.9%)360 (49.2%)1224 (54.5%)
Lobular carcinoma22 (3.1%)45 (5.5%)62 (8.5%)129 (5.7%)
Mixed carcinoma36–4034–3851 (7.0%)127 (5.7%)
Sarcoma00<6<6
Other<6<6<64–8
Invasive cancer, missing morphology220 (31.4%)282 (34.7%)254 (34.7%)756 (33.7%)
Tumor estrogen receptor
Borderline or positive403 (57.5%)405 (49.9%)332 (45.4%)1140 (50.8%)
Negative63 (9.0%)121 (14.9%)132 (18.0%)316 (14.1%)
Missing 1235 (33.5%)286 (35.2%)268 (36.6%)789 (35.1%)
Tumor progesterone receptor
Borderline or positive367 (52.4%)365 (45.0%)286 (39.1%)1018 (45.3%)
Negative99 (14.1%)161 (19.8%)176 (24.0%)436 (19.4%)
Missing 1235 (33.5%)286 (35.2%)270 (36.9%)791 (35.2%)
Tumor human epidermal growth factor receptor 2 (HER2) status
Negative or equivocal379 (54.1%)407 (50.1%)341 (46.6%)1127 (50.2%)
Positive43 (6.1%)77 (9.5%)86 (11.7%)206 (9.2%)
Missing 1279 (39.8%)328 (40.4%)305 (41.7%)912 (40.6%)
Abbreviations: IQR, interquartile range; mm, millimeter; N, number; NOS, not otherwise specified; SBCE, second breast cancer event. 1 Missing biomarker data for this cohort is likely due to methods of biomarker reporting to the Ontario Cancer Registry, rather than biomarker status not being measured.
Table 4. (A) Algorithm and manual review classifications of second breast cancer events (SBCEs) in the validation sub-cohort; (B) comparison of algorithm and manual record review classifications of patients as experiencing a second breast cancer event (SBCE); (C) algorithm diagnostic accuracy at classifying patients as experiencing a second breast cancer event (SBCE).
Table 4. (A) Algorithm and manual review classifications of second breast cancer events (SBCEs) in the validation sub-cohort; (B) comparison of algorithm and manual record review classifications of patients as experiencing a second breast cancer event (SBCE); (C) algorithm diagnostic accuracy at classifying patients as experiencing a second breast cancer event (SBCE).
(A)
CharacteristicStage, N (%)
Stage I
N = 701
Stage II
N = 812
Stage III
N = 732
Total
N = 2245
Manual review classifications 1
Patients with SBCEs27 (3.9%)83 (10.2%)182 (24.9%)292 (13.0%)
Patients with probable contralateral second primary breast cancers 2<65–1011 (1.5%)22 (1.0%)
Algorithm SBCE classifications
Patients with SBCEs48 (6.8%)107 (13.2%)216 (29.5%)371 (16.5%)
Patients with likely contralateral second primary breast cancers 27 (1.0%)11 (1.4%)22 (3.0%)40 (1.8%)
Algorithm classifications by data type (criterion)
Cause of death data
Patients<628–3268 (9.3%)101 (4.5%)
Procedure and diagnosis data
Patients with SBCEs36 (5.1%)71 (8.7%)134 (18.3%)241 (10.7%)
Events3779159275
Contralateral events671326
Systemic treatment data
Patients with SBCEs9 (1.3%)42 (5.2%)88 (12.0%)139 (6.2%)
Events94293144
Radiation therapy data
Patients with SBCEs20 (2.9%)47 (5.8%)89 (12.2%)156 (6.9%)
Events2561112198
Contralateral events<65–91323
Manual record review location
Juravinski Cancer Centre 433 (61.8%)474 (58.4%)416 (56.8%)1323 (58.9%)
Odette Cancer Centre268 (38.2%)338 (41.6%)316 (43.2%)922 (41.1%)
(B)
Algorithm Classifications (N)Manual Record Review (N)Total
No SBCESBCE 1
No SBCE1831431874
SBCE122249371
Total19532922245
(C)
NAgreement Statistic
% (95% Confidence Interval)
SensitivitySpecificityPositive Predictive ValueNegative Predictive ValueAccuracyKappa 3Prevalence-Adjusted Bias-Adjusted Kappa 3
224585.3
(80.7–89.1)
93.8
(92.6–94.8)
67.1
(62.1–71.9)
97.7
(96.9–98.3)
92.7
(91.5–93.7)
70.9
(66.7–75.0)
85.3
(83.0–87.4)
Abbreviations: IQR, interquartile range; mm, millimeter; N, number; NOS, not otherwise specified; SBCE, second breast cancer event. 1 Manual review classifications in this table account for the 16 patients whose manual review SBCE status was updated from “no SBCE” to “SBCE” after case-by-case review based on definitive evidence of SBCE in administrative data. 2 Patients classified as having contralateral second primary breast cancers are a subset of patients classified as having an SBCE. 3 The Fleiss method of confidence interval calculation was used to calculate the confidence intervals for the kappa and prevalence-adjusted bias-adjusted kappa statistics [28].
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Holloway, C.M.B.; Shabestari, O.; Eberg, M.; Forster, K.; Murray, P.; Green, B.; Esensoy, A.V.; Eisen, A.; Sussman, J. Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation. Curr. Oncol. 2022, 29, 5338-5367. https://doi.org/10.3390/curroncol29080424

AMA Style

Holloway CMB, Shabestari O, Eberg M, Forster K, Murray P, Green B, Esensoy AV, Eisen A, Sussman J. Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation. Current Oncology. 2022; 29(8):5338-5367. https://doi.org/10.3390/curroncol29080424

Chicago/Turabian Style

Holloway, Claire M. B., Omid Shabestari, Maria Eberg, Katharina Forster, Paula Murray, Bo Green, Ali Vahit Esensoy, Andrea Eisen, and Jonathan Sussman. 2022. "Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation" Current Oncology 29, no. 8: 5338-5367. https://doi.org/10.3390/curroncol29080424

Article Metrics

Back to TopTop