Cancers 2019, 11(1), 50; https://doi.org/10.3390/cancers11010050
Dynamic Thromboembolic Risk Modelling to Target Appropriate Preventative Strategies for Patients with Non-Small Cell Lung Cancer
1
Department of Epidemiology and Preventive Medicine Monash University, 99 Commercial Road, Melbourne, VIC 3004, Australia
2
Pharmacy Department, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
3
Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC 3010, Australia
4
Department of Radiation Oncology Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
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Department of Medical Oncology, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
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Department of Respiratory and Sleep Disorders Medicine, Royal Melbourne Hospital, Grattan Street, Parkville, VIC 3050, Australia
7
Department of Anaesthesia, Perioperative and Pain Medicine, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
8
Department of Pathology, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
9
Department of Haematology, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia
*
Authors to whom correspondence should be addressed.
Received: 18 December 2018 / Accepted: 29 December 2018 / Published: 8 January 2019
(This article belongs to the Special Issue The Role of Thrombosis and Haemostasis in Cancer)
Abstract
Prevention of cancer-associated thromboembolism (TE) remains a significant clinical challenge and priority world-wide safety initiative. In this prospective non-small cell lung cancer (NSCLC) cohort, longitudinal TE risk profiling (clinical and biomarker) was undertaken to develop risk stratification models for targeted TE prevention. These were compared with published models from Khorana, CATS, PROTECHT, CONKO, and CATS/MICA. The NSCLC cohort of 129 patients, median follow-up 22.0 months (range 5.6—31.3), demonstrated a hypercoagulable profile in >75% patients and TE incidence of 19%. High TE risk patients were those receiving chemotherapy with baseline fibrinogen ≥ 4 g/L and d-dimer ≥ 0.5 mg/L; or baseline d-dimer ≥ 1.5 mg/L; or month 1 d-dimer ≥ 1.5 mg/L. The model predicted TE with 100% sensitivity and 34% specificity (c-index 0.67), with TE incidence 27% vs. 0% for high vs. low-risk. A comparison using the Khorana, PROTECHT, and CONKO methods were not discriminatory; TE incidence 17–25% vs. 14–19% for high vs. low-risk (c-index 0.51–0.59). Continuous d-dimer (CATS/MICA model) was also not predictive of TE. Independent of tumour stage, high TE risk was associated with cancer progression (HR 1.9, p = 0.01) and mortality (HR 2.2, p = 0.02). The model was tested for scalability in a prospective gastrointestinal cancer cohort with equipotency demonstrated; 80% sensitivity and 39% specificity. This proposed TE risk prediction model is simple, practical, potent and can be used in the clinic for real-time, decision-making for targeted thromboprophylaxis. Validation in a multicentre randomised interventional study is underway (ACTRN12618000811202). View Full-TextKeywords:
thromboembolism; non-small cell lung cancer; risk prediction; pulmonary embolism; deep vein thrombosis
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MDPI and ACS Style
Alexander, M.; Ball, D.; Solomon, B.; MacManus, M.; Manser, R.; Riedel, B.; Westerman, D.; Evans, S.M.; Wolfe, R.; Burbury, K. Dynamic Thromboembolic Risk Modelling to Target Appropriate Preventative Strategies for Patients with Non-Small Cell Lung Cancer. Cancers 2019, 11, 50.
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