Next Article in Journal
Epidemiology of Fall Injury in Rural Bangladesh
Previous Article in Journal
The Relationship between Obesity and Urban Environment in Seoul
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 14
Issue 8
10.3390/ijerph14080899
ijerph-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Is Preoperative Patient-Reported Health Status Associated with Mortality after Total Hip Replacement?

by
Peter Cnudde
1,2,3,*,
Szilard Nemes
1,2,
Maziar Mohaddes
1,2,
John Timperley
4,
Göran Garellick
1,2,
Kristina Burström
5,6,7 and
Ola Rolfson
1,2,5
1
Swedish Hip Arthroplasty Register, Centre of Registers Västra Götaland, Medicinargatan 18G, SE 413 45 Gothenburg, Sweden
2
Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, SE 413 45 Gothenburg, Sweden
3
Department of Orthopaedics, Hywel Dda University Healthboard, Prince Philip Hospital, Bryngwynmawr, Llanelli SA14 8ED, UK
4
Hip Unit, Princess Elizabeth Orthopaedic Centre, Royal Devon & Exeter Hospital Barrack Road, Exeter EX2 5DW, UK
5
Department of Learning, Informatics, Management and Ethics (LIME), Health Outcomes and Economic Evaluation Research Group, Karolinska Institutet, Tomtebodavägen 18 a, SE 171 77 Stockholm, Sweden
6
Department of Public Health, Equity and Health Policy Research Group, Karolinska Institutet, Tomtebodavägen 18 a, SE 171 77 Stockholm, Sweden
7
Health Care Services, Stockholm County Council, Tomtebodavägen 18 a, SE 171 77 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(8), 899; https://doi.org/10.3390/ijerph14080899
Received: 27 June 2017 / Revised: 3 August 2017 / Accepted: 7 August 2017 / Published: 10 August 2017
(This article belongs to the Section Global Health)
Browse Figures
Versions Notes

Abstract

:
The influence of comorbidities and worse physical status on mortality following total hip replacement (THR) leads to the idea that patient-reported health status may also be a predictor of mortality. The aim of this study was to investigate the relationship between patient-reported health status before THR and the risk of dying up to 5 years post-operatively. For these analyses, we used register data on 42,862 THR patients with primary hip osteoarthritis operated between 2008 and 2012. The relative survival ratio was calculated by dividing the observed survival in the patient group by age- and sex-adjusted expected survival of the general population. Pre-operative responses to the five EQ-5D-3L (EuroQol Group) dimensions along with age, sex, education status, year of surgery, and hospital type were used as independent variables. Results shown that, as a group, THR patients had a better survival than the general population. Broken down by the five EQ-5D-3L dimensions we observed differentiated survival patters. For all dimensions, those reporting extreme problems had higher mortality than those reporting moderate or no problems. In conclusion, worse health status according to the EQ-5-3L before THR is associated with higher mortality up to five years after surgery. EQ-5D-3L responses may be useful in a multifactorial individualized risk assessment before THR.

1. Introduction

The success of total hip replacement (THR) for the treatment of symptomatic osteoarthritis (OA) of the hip is well recognised and the operation has been described as the operation of the 20th century [1]. There is an anticipated increased demand in surgical intervention due to the ageing population and the increase in obesity [2,3,4]. The short-term risk of dying following THR is low and has declined over the last decades [5,6,7,8,9]. The 90-day mortality for all patients, including those with surgery due to acute hip fractures and tumours around the hip, has been reported to be 0.7% in Sweden [10]. In England and Wales, the 90-day mortality for patients undergoing hip replacement because of OA was 0.3% in 2011 [5]. Systematic reviews of 30- and 90-day mortality rates in patients undergoing hip or knee replacements found an average 90-day mortality of 0.7% [9,11]. Except for the first post-operative month, elective THR patients have a better relative survival than the general population [12,13,14]. This could be explained by selection bias involved in the decision to proceed with THR; medically fit patients are more likely to be recommended for surgery then those with advanced comorbidities.
The relative mortality risk in the period after surgery is higher in males and younger patients [9,12,14,15,16]. Other factors associated with increased risk of dying up to 90 days post-operatively in THR patients are worse physical status, according to the American Society of Anaesthesiologists (ASA) classification system, and comorbidities such as congestive heart failure, diabetes with complication, renal failure, metastatic cancer, chronic obstructive pulmonary disease, dementia, liver disease, and peptic ulcer disease [5,16].
The influence of comorbidities and physical status on mortality leads to the possibility that patient-reported health status may also be a predictor of mortality after elective THR. The importance of self-reported health status has previously been investigated and been shown to be a predictor of mortality in the elderly population, as well as in the general population over the age of 16 years [17,18]. Although this has not been demonstrated in THR surgery, some studies in other fields like acute coronary syndrome have demonstrated a relationship between worse patient-reported health status and increased mortality [19,20].
The complexity of the interactions between different patient factors and their association with different outcomes (such as pain level, functional status, reoperations, death, and other adverse events) complicates accurate assessments of risks and expected benefits for individual patients. Ideally, modifiable risk factors (such as poor diabetes control, preoperative anaemia, and smoking) should be identified and adjusted in order to minimize risk of complications and improve health outcomes. However, patients should not be excluded or deterred from surgery based on generalised information that may inaccurately overestimate the risks. To facilitate the assessment of risks and benefits, a decision aid providing individualised information on expected outcomes is warranted, which in return could be beneficial in the consenting process. Hip replacement relieves pain and improves mobility, enhancing quality of life for a non-life threatening problem, and patients elect to take the risk involved in surgery. This situation contrasts with life-saving surgery for cancer or cardiac conditions. In the UK, mortality rates for individual surgeons following elective hip replacement surgery have been published, and this had led to much controversy [21,22].
The objective of this study is to investigate the relationship between patient-reported health status as determined by the five EQ-5D-3L dimensions, the self-assessment of overall health using a visual analogue scale (EQ VAS), and hip pain (according to a Visual Analogue Scale (VAS)) before THR and the risk of dying up to five years post-operatively.
Given the low number of events, it is obvious that large datasets are required to investigate if health status, as reported by the patient, is associated with risk of dying after THR. With routine nationwide prospective collection of patient-reported outcome measures (PROMs), the Swedish Hip Arthroplasty Register (SHAR) is well suited to investigate such relationships. The importance of patient-focused registries to improve health, care, and science has been re-iterated in a recent work by Nelson and colleagues and within the field of arthroplasty by Berry [23,24].

2. Materials and Methods

2.1. Data Sources, Patient Selection, and Measures

The SHAR runs a nationwide program collecting PROMs to routinely monitor patients undergoing THR in Sweden [25,26]. PROMs data collection commenced in 2002, was gradually adopted, and reached full active nationwide coverage in 2008. Patients were asked to complete a PROMs survey at their preoperative clinical visit. The response rate of this preoperative survey was 86% [25]. The data available within the SHAR databases has been linked with Statistics Sweden using the Personal Identity Number (PIN) as part of a larger research project and data have subsequently been anonymised. The process of the linkage of the different databases has been described elsewhere [27]. The short PROMs survey includes the EQ-5D-3L instrument (the descriptive system with five health dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression; and three severity levels: no problems, moderate problems, and severe problems), the EQ VAS (self-assessment of overall health ranging from 0 to 100) and a pain VAS (to assess hip pain ranging from 0 (no pain) to 100 (worst imaginable pain)). These data are prospectively collected prior to surgery as part of the pre-operative visit and recorded in the SHAR database. All patients reported to SHAR and undergoing THR for the first time between 1 January 2008 and 31 December 2012 with a diagnosis of osteoarthritis and complete pre-operative PROMs were included in this study. In order not to violate the assumption of independent observations, it was first decided that a patient could only contribute with data from one primary hip replacement. Subsequently, however, we decided to only include the first hip to reduce heterogeneity. Additional to the PROMs, we obtained data on sex, age at operation, hospital type (university, county, rural or private), date of operation, method of fixation, and date of death, if applicable. The mortality data is obtained by cross-matching with the Swedish Population Register, governed by the Swedish Tax Office [28]. Based on the unique personal identity number, we obtained data on the educational level (highest completed level of education at time of operation) through linkage to Statistics Sweden. We used low, middle, and high levels of education to respectively describe the first 9 years of education (secondary school level), an extra 2 or 3 years (college level), and higher education (university or similar). Using these selection criteria, we identified 42,862 patients for the study group (Figure 1). To a certain extent, the PROMs act as proxies for patient comorbidities. We tested the association between the dimension of the EQ-5D-3L questionnaire and the ASA class (American Society of Anaesthesiologists physical status classification) and the International Classification of Diseases 10th revision (ICD-10) based Elixhauser comorbidity index [29].

2.2. Statistical Analyses and Software

Group comparison between survivors and deceased patients were conducted with Student’s t-test for continuous variables and the χ2 test for categorical variables.
Survival data was summarised and illustrated with the help of relative survival curves. We opted for relative survival over the classical Kaplan-Meier survival curves and Cox Proportional Hazards in order to gain better insight, not only into the differences between the levels of the five EQ-5D-3L dimensions, but also to be able to relate the survival of the patients to the general population.
The relative survival ratio r(t) is defined as the observed survival in the patient group divided by the expected survival of a comparable group from the general population:
r ( t ) = S O ( t ) S p ( t )
where SO(t) denotes the observed survival in the studied group and SP(t) is the population or expected survival [30]. The population or expected survival was estimated from publicly available mortality tables tabulated for sex and age [31]. The population or expected survival was estimated from publicly available mortality tables maintained by the Human Life-Table Database (http://www.lifetable.de/) and Human Mortality Database (http://www.mortality.org).
Multivariable modelling proceeded with time-transformed Cox Proportional Hazards [32]. EQ VAS and pain VAS were measured in units of 10. Model assumptions were checked with Brownian bridges [33]. We assumed that age might have a non-linear effect on survival. We fitted a model where age was modelled with cubic splines. However, information theoretic assessment with the Bayesian Information Criterion strongly favoured the linear model; thus, this approach was dropped.
We used R v3.4 (R Core team, Vienna, Austria, 2016) for statistical calculations.

2.3. Research Ethics

This study is part of a larger research project with the overall aim of performing a multidimensional outcomes assessment following hip replacement surgery, and developing an instrument to facilitate shared decision-making before hip replacement. All the investigations were carried out following the rules of the Declaration of Helsinki. Ethical review approval was obtained from the Regional Ethical Review Board in Gothenburg, Sweden (entry number 271-14) on 9 April 2014.
In accordance with the Swedish Patient Data Act (2008:355), patients receive information about being registered and have the full right to opt-out. All patients undergoing hip replacement surgery in Sweden are, unless they opt out, per definition enrolled in the scientific studies performed by and at the SHAR.

2.4. Availability of Data and Materials

The datasets generated and analysed for the current study are not publicly available but are stored in Secure-online Data Access (SODA) servers within the Registercentrum Vastra Götaland, Gothenburg, Sweden and can be made available upon reasonable request [27].

3. Results

Of the 42,862 patients in the cohort, 1346 died in the follow-up period (range = 5.0 years, mean = 2.4 years, Standard Deviation (SD) = 1.4 years). There were statistically significant differences between survivors and deceased regarding sex, age at day of operation, hospital type, the five EQ-5D dimensions, the EQ VAS, the pain VAS, and educational level (Table 1).
As a group, THR patients had a better survival rate than the general population (Figure 2).
Females had a better survival rate than males. Of interest, the so-called protective effect of hip replacement on mortality is more profound in the more advanced age group. Compared to patients operated at university hospitals, patients operated at any other type of hospitals had a better survival rate.
Broken down by the five EQ-5D-3L dimensions, we observed differentiated survival patterns (Figure 2).
Patients who reported no problems on any of the EQ-5D-3L dimensions had a better survival rate than the general population and patients who reported moderate or severe problems. Patients who reported moderate problems on any of the EQ-5D-3L dimensions had a better survival rate than the general population and patients who reported severe problems. The worse the patient scored on any of the EQ-5D-3L dimensions, the higher the hazard rates of increased mortality became. Only a relatively small number of patients (n = 131) reported severe problems on the mobility dimension, but they were found to have worse survival than the general population; patients who reported severe problems on the self-care dimension had a slight drop in survival probability straight after the operation. In the time span of one to four years after the operation, these patients had a better survival rate than the general population; after year four, the survival chances worsened. Patients who reported severe problems on the dimensions of pain/discomfort, usual activities, and anxiety/depression had a better survival rate than the general population. This pattern was reinforced by the multivariable regression analysis (Table 2).
Reviewing the EQ VAS data, we also discovered an association with lower patient-reported overall health in the non-survival group. The pain VAS was neither clinically nor statistically significantly associated with a difference in survival.
Although we identified an association between a higher obtained educational level and improved survival chances after THR, these differences were not statistically significant and should be interpreted with caution.
We undertook a sensitivity analysis for the missing PROMs data (6506 patients) and we did not observe any significant difference in mortality between the two groups, and the baseline data was statistically significant (however, it lacked a clinically significant difference (Table 3 and Figure 3)).
We observed a strong association between the dimensions of the EQ-5D-3L questionnaire and the ASA score (p < 0.0001 for all five dimensions) and the Elixhauser index (p < 0.0001 for all 5 dimensions).

4. Discussion

Our findings suggest a lower mortality in patients operated with THR compared to the general population. This finding is consistent with previous reports [12,13,14]. The reduced mortality risk is probably due to the selection of healthier individuals that are fit enough to undergo a major elective procedure and also have a preoperative medical assessment and optimization. This effect is more profound in the more advanced age group. This is consistent with previous reports of both the Norwegian and Swedish arthroplasty registers and might well be explained by selection bias [14,15]. The worse survival rate after THR in university hospitals compared to the other non-university hospitals has been a constant in the annual reports of the SHAR (https://shpr.registercentrum.se/shar-in-english) and has been attributed to the difference in patient characteristics of the patients treated at university hospitals.
The analysis of the available data points indicates an increased mortality risk for patients who pre-operatively report more problems in the EQ-5D-3L dimensions of mobility and self-care; however, in our data there are only a limited number of patients reporting severe limitations. The level of problems on the dimensions’ usual activities—pain/discomfort and anxiety/depression—and the EQ VAS score had limited influence on the mortality risk. It is very likely that poorer pre-operative mobility is associated with increased incidence of chronic comorbidities like diabetes and cardiopulmonary disease. This assumption is corroborated by the strong association between the EQ-5D-3L dimensions and the ASA classification and Elixhauser comorbidity index. There is evidence linking an increased sedentary lifestyle with increased risk of hospitalization, cardiovascular disease, and mortality [34]. Jämsen et al. described a significant association between poorer mobility (inability to walk/walking indoors only) and increased risk of early mortality in their single centre-analysis of 1998 hip and knee replacements [16]. Nüesch et al. [35] described the influence of poor mobility on mortality in the presence of osteoarthritis of the hip. They performed a population cohort study in the southwest of England and reported the presence of walking disability as a major risk factor for increased mortality, while pain and depression had a limited influence. In the field of heart failure no association was found between self-reported decreased ability to self-care and increased mortality [36]. To the best of our knowledge, no previous reports have been found on the influence of problems with self-care on mortality in patients undergoing elective THR surgery. There has been some evidence on the influence of problems with anxiety and/or depression on mortality in patients following THR, where most studies report a lower mortality in patients with anxiety and depression [37,38]. In contrast, we found a small negative influence of self-reported problems in the anxiety or depression dimension on survival after elective THR, although this relationship did not reach statistical significance in the multivariable regression analysis. The influence of the pre-operative hip pain VAS score on post-operative mortality is negligible and likely not of any clinical nor statistical significance. Asberg et al. were unable to identify a difference in all-cause mortality between individuals with and without chronic musculoskeletal complaints in their prospective population-based cohort study, as well as their systematic review [39,40].
The findings of our study are in agreement with the historical studies from Sweden and Manitoba, based on a general population, and do suggest there is a relationship between worse self-reported health and an increased risk of dying earlier following hip replacement surgery [17,18]. Bliemel et al. studied the influence of pre-fracture quality of life and early mortality (within one year) and found there was an association between surviving the first postoperative year and the EQ-5D score [41].
There is an increased awareness of the importance and validity of arthroplasty registers and PROMs [42,43,44,45]. Arthroplasty registers traditionally report revision or mortality as the endpoint in measuring success after joint replacement, but it is accepted that revision is only a single and relatively crude marker of the success of the procedure. In a recent study comparing different hip arthroplasty registers it was noted that only few registers have so far been collecting PROMs data, despite the fact that these scores may be highly relevant in measuring the success of the intervention [43]. A change in the type of data recorded in joint registries has been advocated [46,47].
The influence of pre-operative PROMs on postoperative PROMs has been described, but so far, no association between pre-operative PROMs and mortality has been described [48,49]. With an increased interest in outcomes measurement and early postoperative mortality, it is important to realise that many factors, patient-related as well as procedure-related, might influence the outcome [9,16]. The risk of dying is certainly an important consideration for the patient and should be discussed with the individual patient as part of the consenting process.
The importance of using pre-operative recorded scores within orthopaedics was published by Jansson et al. as he described the possibility of using the EQ-5D instrument in the pre-operative setting within orthopaedic surgery, and specifically found that the postoperative scores following elective THR were improving and reaching levels of an age- and sex-matched population [50]. They suggested that the EQ-5D could be used as part of the discussions with the patients in the pre-operative setting. Using self-rated health and its association with risk of dying is likely to improve the understanding of the risk to life to the patient. Discussions should be centered on the individual situation and risk to an individual patient, and as such the increased mortality risk in patient with poorer pre-operative mobility and increased problems with self-care might well be worth mentioning [51].
A recent decision by the UK Department of Health to publish mortality figures after elective knee- and hip replacements for individual surgeons and hospitals highlights the need to consider pre-operatively-gathered outcome scores in order to explain potential variations and poorer results [21].
We used the information of a nationwide surveillance program, using prospectively collected data to analyse the association between the pre-operatively collected PROMs and the mortality up to five years after THR surgery.
The SHAR has a recognized high coverage and completeness of data (>98%) and a high response rate for the pre-operative PROMs collection (86%). We have also studied the relationship of non-responders of the PROMs program in the postoperative period and are aware that non-responders are usually having worse scores. A difference between preoperative non-responders and responders, and the observed mortality, could not be established.
As with some of the observational and register studies there is an absence of a control group, and as such we could not compare what would have happened to the patients with an increased risk of dying if no surgery would have been performed. The EQ-5D-3L (none, moderate, extreme) has only been recently (2016) upgraded to the EQ-5D-5L (none, minimal, moderate, many, extreme) within the SHAR. We are aware that the five-level survey could well cause less ceiling effect and add more nuance to this study [52].

5. Conclusions

It is increasingly recognised that registries are an essential tool to improve healthcare and science. As with all observational studies, it is difficult to prove causation, but we were able to provide associations between pre-operative, patient-reported factors and postoperative mortality. Patients reporting severe problems in mobility, self-care, and usual activity prior to an elective THR for primary osteoarthritis have a higher mortality up to five years after surgery. EQ-5D-3L responses may be useful in a multifactorial, individualized risk assessment before THR. Whilst this study does not need to be seen as a deterrent to performing surgery, it helps identify patients and patient groups that are more at risk of an earlier terminal event, compared to an age- and sex-matched population. Identifying preoperatively patients at increased risk, based on EQ-5D responses, improves the engagement between the patient and the surgeon for the decision-making process and may further improve the outcomes after THR.

Acknowledgments

No external funding was received in support of the research work. The funding for the article processing charges (APC) will be borne by the Swedish Hip Arthroplasty Register, Gothenburg, Sweden.

Author Contributions

P.C., S.N., and O.R. contributed to the conceptual design of this study; P.C., S.N., M.M., J.T., G.G., K.B., and O.R. contributed to the drafting of the manuscript; S.N. performed statistical analysis, and configured tables and graphics; P.C., J.T., and O.R. finalized the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Learmonth, I.D.; Young, C.; Rorabeck, C. The operation of the century: Total hip replacement. Lancet 2007, 370, 1508–1519. [Google Scholar] [CrossRef]
  2. Cross, M.; Smith, E.; Hoy, D.; Nolte, S.; Ackerman, I.; Fransen, M.; Bridgett, L.; Williams, S.; Guillemin, F.; Hill, C.L.; et al. The global burden of hip and knee osteoarthritis: Estimates from the global burden of disease 2010 study. Ann. Rheum. Dis. 2014, 73, 1323–1330. [Google Scholar] [CrossRef] [PubMed]
  3. Mont, M.A.; Issa, K. Updated projections of total joint arthroplasty demands in America: Commentary on an article by Steven M. Kurtz, PhD, et al.: “Impact of the economic downturn on total joint replacement demand in the united states. Updated projections to 2021”. J. Bone Jt. Surg. 2014, 96, e68.61–e68.62. [Google Scholar] [CrossRef] [PubMed]
  4. Nemes, S.; Gordon, M.; Rogmark, C.; Rolfson, O. Projections of total hip replacement in Sweden from 2013 to 2030. Acta Orthop. 2014, 85, 238–243. [Google Scholar] [CrossRef] [PubMed]
  5. Hunt, L.P.; Ben-Shlomo, Y.; Clark, E.M.; Dieppe, P.; Judge, A.; MacGregor, A.J.; Tobias, J.H.; Vernon, K.; Blom, A.W. 90-day mortality after 409,096 total hip replacements for osteoarthritis, from the national joint registry for England and Wales: A retrospective analysis. Lancet 2013, 382, 1097–1104. [Google Scholar] [CrossRef]
  6. Aynardi, M.; Pulido, L.; Parvizi, J.; Sharkey, P.F.; Rothman, R.H. Early mortality after modern total hip arthroplasty. Clin. Orthop. Relat. Res. 2009, 467, 213–218. [Google Scholar] [CrossRef] [PubMed]
  7. Aynardi, M.; Jacovides, C.L.; Huang, R.; Mortazavi, S.M.; Parvizi, J. Risk factors for early mortality following modern total hip arthroplasty. J. Arthroplast. 2013, 28, 517–520. [Google Scholar] [CrossRef] [PubMed]
  8. Lalmohamed, A.; Vestergaard, P.; De Boer, A.; Leufkens, H.G.M.; Van Staa, T.P.; De Vries, F. Changes in mortality patterns following total hip or knee arthroplasty over the past two decades: A nationwide cohort study. Arthr. Rheumatol. 2014, 66, 311–318. [Google Scholar] [CrossRef] [PubMed]
  9. Berstock, J.R.; Beswick, A.D.; Lenguerrand, E.; Whitehouse, M.R.; Blom, A.W. Mortality after total hip replacement surgery: A systematic review. Bone Jt. Res. 2014, 3, 175–182. [Google Scholar] [CrossRef] [PubMed]
  10. Garellick, G.; Karrbolm, J.; Lindabl, H.; Malcbau, H.; Rogmark, C.; Rolfson, O. Swedish Hip Arthroplasty Register Annual Report 2014; Registercentrum Västra Götaland: Gothenburg, Sweden, 2015. [Google Scholar]
  11. Singh, J.A.; Kundukulam, J.; Riddle, D.L.; Strand, V.; Tugwell, P. Early postoperative mortality following joint arthroplasty: A systematic review. J. Rheumatol. 2011, 38, 1507–1513. [Google Scholar] [CrossRef] [PubMed]
  12. Pedersen, A.B.; Baron, J.A.; Overgaard, S.; Johnsen, S.P. Short- and long-term mortality following primary total hip replacement for osteoarthritis: A Danish nationwide epidemiological study. J. Bone Jt. Surg. Ser. B 2011, 93, 172–177. [Google Scholar] [CrossRef] [PubMed]
  13. Maradit Kremers, H.; Larson, D.R.; Noureldin, M.; Schleck, C.D.; Jiranek, W.A.; Berry, D.J. Long-term mortality trends after total hip and knee arthroplasties: A population-based study. J. Arthrop. 2016, 31, 1163–1169. [Google Scholar] [CrossRef] [PubMed]
  14. Lie, S.A.; Engesaeter, L.B.; Havelin, L.I.; Gjessing, H.K.; Vollset, S.E. Mortality after total hip replacement: 0–10-year follow-up of 39,543 patients in the Norwegian arthroplasty register. Acta Orthop. Scand. 2000, 71, 19–27. [Google Scholar] [CrossRef] [PubMed]
  15. Robertsson, O.; Stefansdottir, A.; Lidgren, L.; Ranstam, J. Increased long-term mortality in patients less than 55 years old who have undergone knee replacement for osteoarthritis: Results from the Swedish knee arthroplasty register. J. Bone Jt. Surg. Br. 2007, 89, 599–603. [Google Scholar] [CrossRef] [PubMed]
  16. Jamsen, E.; Puolakka, T.; Eskelinen, A.; Jantti, P.; Kalliovalkama, J.; Nieminen, J.; Valvanne, J. Predictors of mortality following primary hip and knee replacement in the aged. A single-center analysis of 1998 primary hip and knee replacements for primary osteoarthritis. Acta Orthop. 2013, 84, 44–53. [Google Scholar] [CrossRef] [PubMed]
  17. Burstrom, B.; Fredlund, P. Self-rated health: Is it as good a predictor of subsequent mortality among adults in lower as well as in higher social classes? J. Epidemiol. Commun. Health 2001, 55, 836–840. [Google Scholar] [CrossRef]
  18. Mossey, J.M.; Shapiro, E. Self-rated health: A predictor of mortality among the elderly. Am. J. Public Health 1982, 72, 800–808. [Google Scholar] [CrossRef] [PubMed]
  19. Jerant, A.; Tancredi, D.J.; Franks, P. Mortality prediction by quality-adjusted life year compatible health measures: Findings in a nationally representative us sample. Med. Care 2011, 49, 443–450. [Google Scholar] [CrossRef] [PubMed]
  20. Pocock, S.; Bueno, H.; Licour, M.; Medina, J.; Zhang, L.; Annemans, L.; Danchin, N.; Huo, Y.; Van de Werf, F. Predictors of one-year mortality at hospital discharge after acute coronary syndromes: A new risk score from the epicor (long-term follow up of antithrombotic management patterns in acute coronary syndrome patients) study. Eur. Heart J. Acute Cardiovasc. Care 2015, 4, 509–517. [Google Scholar] [CrossRef] [PubMed][Green Version]
  21. Haddad, F.S.; Manktelow, A.R.; Skinner, J.A. Publication of surgeon level data from registers: Who benefits? Bone Jt. J. 2016, 98, 1–2. [Google Scholar] [CrossRef] [PubMed]
  22. Whitehouse, S.L.; Bolland, B.J.; Howell, J.R.; Crawford, R.W.; Timperley, A.J. Mortality following hip arthroplasty—Inappropriate use of national joint registry (NJR) data. J. Arthrop. 2014, 29, 1827–1834. [Google Scholar] [CrossRef] [PubMed]
  23. Nelson, E.C.; Dixon-Woods, M.; Batalden, P.B.; Homa, K.; Van Citters, A.D.; Morgan, T.S.; Eftimovska, E.; Fisher, E.S.; Ovretveit, J.; Harrison, W.; et al. Patient focused registries can improve health, care, and science. BMJ 2016, 354, i3319. [Google Scholar] [CrossRef] [PubMed][Green Version]
  24. Berry, D.J. Joint registries: What can we learn in 2016? Bone Jt. J. 2017, 99, 3–7. [Google Scholar] [CrossRef] [PubMed]
  25. Rolfson, O.; Kärrholm, J.; Dahlberg, L.E.; Garellick, G. Patient-reported outcomes in the Swedish hip arthroplasty register: Results of a nationwide prospective observational study. J. Bone Jt. Surg. Br. 2011, 93, 867–875. [Google Scholar] [CrossRef] [PubMed]
  26. EuroQol, G. EuroQol—A new facility for the measurement of health-related quality of life. Health Policy 1990, 16, 199–208. [Google Scholar]
  27. Cnudde, P.; Rolfson, O.; Nemes, S.; Karrholm, J.; Rehnberg, C.; Rogmark, C.; Timperley, J.; Garellick, G. Linking Swedish health data registers to establish a research database and a shared decision-making tool in hip replacement. BMC Musculoskelet. Disord. 2016, 17, 414. [Google Scholar] [CrossRef] [PubMed]
  28. Mohaddes, M.; Garellick, G.; Karrholm, J. Method of fixation does not influence the overall risk of rerevision in first-time cup revisions. Clin. Orthop. Relat. Res. 2013, 471, 3922–3931. [Google Scholar] [CrossRef] [PubMed]
  29. Bülow, E.; Rolfson, O.; Cnudde, P.; Rogmark, C.; Garellick, G.; Nemes, S. Comorbidity does not predict long-term mortality after total hip arthroplasty. Acta Orthop. 2017, 1–6. [Google Scholar] [CrossRef] [PubMed]
  30. Pohar Perme, M.; Stare, J.; Estève, J. On estimation in relative survival. Biometrics 2012, 68, 113–120. [Google Scholar] [CrossRef] [PubMed]
  31. Statistics Sweden. The Future Population of Sweden 2012–2060; Institute, F., Ed.; SCB: Stockholm, Sweden, 2012; 220p.
  32. Stare, J.; Henderson, R.; Pohar, M. An individual measure of relative survival. J. R. Stat. Soc. Ser. C 2005, 54, 115–126. [Google Scholar] [CrossRef]
  33. Stare, J.; Pohar, M.; Henderson, R. Goodness of fit of relative survival models. Stat. Med. 2005, 24, 3911–3925. [Google Scholar] [CrossRef] [PubMed]
  34. Biswas, A.; Oh, P.I.; Faulkner, G.E.; Bajaj, R.R.; Silver, M.A.; Mitchell, M.S.; Alter, D.A. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: A systematic review and meta-analysis. Ann. Int. Med. 2015, 162, 123–132. [Google Scholar] [CrossRef] [PubMed]
  35. Nuesch, E.; Dieppe, P.; Reichenbach, S.; Williams, S.; Iff, S.; Juni, P. All cause and disease specific mortality in patients with knee or hip osteoarthritis: Population based cohort study. BMJ 2011, 342, d1165. [Google Scholar] [CrossRef] [PubMed]
  36. Kessing, D.; Denollet, J.; Widdershoven, J.; Kupper, N. Self-care and all-cause mortality in patients with chronic heart failure. JACC Heart Fail. 2016, 4, 176–183. [Google Scholar] [CrossRef] [PubMed]
  37. Buller, L.T.; Best, M.J.; Klika, A.K.; Barsoum, W.K. The influence of psychiatric comorbidity on perioperative outcomes following primary total hip and knee arthroplasty; a 17-year analysis of the national hospital discharge survey database. J. Arthrop. 2015, 30, 165–170. [Google Scholar] [CrossRef] [PubMed]
  38. Stundner, O.; Kirksey, M.; Chiu, Y.L.; Mazumdar, M.; Poultsides, L.; Gerner, P.; Memtsoudis, S.G. Demographics and perioperative outcome in patients with depression and anxiety undergoing total joint arthroplasty: A population-based study. Psychosomatics 2013, 54, 149–157. [Google Scholar] [CrossRef] [PubMed]
  39. Asberg, A.N.; Stovner, L.J.; Zwart, J.A.; Winsvold, B.S.; Heuch, I.; Hagen, K. Chronic musculoskeletal complaints as a predictor of mortality-the hunt study. Pain 2016, 157, 1443–1447. [Google Scholar] [CrossRef] [PubMed]
  40. Asberg, A.N.; Heuch, I.; Hagen, K. The mortality associated with chronic widespread musculoskeletal complaints: A systematic review of the literature. Musculoskelet. Care 2017, 15, 104–113. [Google Scholar] [CrossRef] [PubMed]
  41. Bliemel, C.; Sielski, R.; Doering, B.; Dodel, R.; Balzer-Geldsetzer, M.; Ruchholtz, S.; Buecking, B. Pre-fracture quality of life predicts 1-year survival in elderly patients with hip fracture-development of a new scoring system. Osteoporos. Int. 2016, 27, 1979–1987. [Google Scholar] [CrossRef] [PubMed]
  42. Black, N. Patient reported outcome measures could help transform healthcare. BMJ 2013, 346. [Google Scholar] [CrossRef] [PubMed]
  43. Lacny, S.; Bohm, E.; Hawker, G.; Powell, J.; Marshall, D.A. Assessing the comparability of hip arthroplasty registries in order to improve the recording and monitoring of outcome. Bone Jt. J. 2016, 98, 442–451. [Google Scholar] [CrossRef] [PubMed]
  44. Pivec, R.; Johnson, A.J.; Mears, S.C.; Mont, M.A. Hip arthroplasty. Lancet 2012, 380, 1768–1777. [Google Scholar] [CrossRef]
  45. Graves, S.E. The value of arthroplasty registry data. Acta Orthop. 2010, 81, 8–9. [Google Scholar] [CrossRef] [PubMed]
  46. Cobb, J. Arthroplasty registries, patient safety and outlier surgeons: The case for change. Acta Orthop. Belg. 2015, 81, 594–599. [Google Scholar] [PubMed]
  47. Varagunam, M.; Hutchings, A.; Black, N. Do patient-reported outcomes offer a more sensitive method for comparing the outcomes of consultants than mortality? A multilevel analysis of routine data. BMJ Qual. Saf. 2015, 24, 195–202. [Google Scholar] [CrossRef] [PubMed]
  48. Greene, M.E.; Rolfson, O.; Garellick, G.; Gordon, M.; Nemes, S. Improved statistical analysis of pre- and post-treatment patient-reported outcome measures (PROMs): The applicability of piecewise linear regression splines. Qual. Life Res. 2015, 24, 567–573. [Google Scholar] [CrossRef] [PubMed]
  49. Nemes, S.; Rolfson, O.; Garellick, G. Development and validation of a shared decision—Making instrument for health—Related quality of life one year after total hip replacement based on quality registries data. J. Eval. Clin. Pract. 2016. [Google Scholar] [CrossRef] [PubMed]
  50. Jansson, K.A.; Granath, F. Health-related quality of life (EQ-5D) before and after orthopedic surgery. Acta Orthop. 2011, 82, 82–89. [Google Scholar] [CrossRef] [PubMed]
  51. Fryar, C. Clarifying the Montgomery judgment. BMJ 2015, 350, h2217. [Google Scholar] [CrossRef] [PubMed]
  52. Greene, M.E.; Rader, K.A.; Garellick, G.; Malchau, H.; Freiberg, A.A.; Rolfson, O. The EQ-5D-5L improves on the EQ-5D-3L for health-related quality-of-life assessment in patients undergoing total hip arthroplasty. Clin. Orthop. Relat. Res. 2015, 473, 3383–3390. [Google Scholar] [CrossRef] [PubMed]
Ijerph 14 00899 g001 550
Figure 1. Flowchart study design. THR: total hip replacement; OA: osteoarthritis; PROMs: patient-reported outcome measures.
Figure 1. Flowchart study design. THR: total hip replacement; OA: osteoarthritis; PROMs: patient-reported outcome measures.
Ijerph 14 00899 g001
Ijerph 14 00899 g002 550
Figure 2. Relative survival curves for the overall study population (A) and broken down by the five dimensions of the EQ-5D-3L (B–F). All our survival analyses are based on recorded PROMs preoperatively.
Figure 2. Relative survival curves for the overall study population (A) and broken down by the five dimensions of the EQ-5D-3L (B–F). All our survival analyses are based on recorded PROMs preoperatively.
Ijerph 14 00899 g002
Ijerph 14 00899 g003 550
Figure 3. Relative survival curves for the population with missing PROMs and available PROMs. All our survival analyses are based on (present or absent) preoperative recorded PROMs. Although there is a difference between the groups, the difference is not statistically significant (p = 0.07).
Figure 3. Relative survival curves for the population with missing PROMs and available PROMs. All our survival analyses are based on (present or absent) preoperative recorded PROMs. Although there is a difference between the groups, the difference is not statistically significant (p = 0.07).
Ijerph 14 00899 g003
Table
Table 1. Demographics and pre-operative, health-related quality of life of the 42,862 total hip replacement patients. The data is summarized as absolute numbers and percentages for discrete variables and means, and standard deviations (SDs) for continuous variables.
Table 1. Demographics and pre-operative, health-related quality of life of the 42,862 total hip replacement patients. The data is summarized as absolute numbers and percentages for discrete variables and means, and standard deviations (SDs) for continuous variables.
AliveDeadp-Value
n = 41,516n = 1346
Mobility (%)No problems3210 (7.7)52 (3.9)<0.001
Moderate problems38,190 (92.0)1279 (95.0)
Severe problems116 (0.3)15 (1.1)
Self-care (%)No problems32,066 (77.2)910 (67.6)<0.001
Moderate problems9102 (21.9)403 (29.9)
Severe problems348 (0.8)33 (2.5)
Usual activities (%)No problems16,086 (38.7)460 (34.2)<0.001
Moderate problems21,125 (50.9)684 (50.8)
Severe problems4305 (10.4)202 (15.0)
Pain/discomfort (%)No problems631 (1.5)16 (1.2)<0.001
Moderate problems23,822 (57.4)706 (52.5)
Severe problems17,063 (41.1)624 (46.4)
Anxiety/depression (%)No problems23,963 (57.7)711 (52.8)<0.001
Moderate problems16,079 (38.7)568 (42.2)
Severe problems1474 (3.6)67 (5.0)
EQ VAS score (SD) 54.77 (22.17)50.61 (21.76)<0.001
Pain VAS score (SD) 62.39 (15.91)62.67 (17.34)0.53
Females (%) 23,358 (56.3)633 (47.0)<0.001
Age (SD) 67.70 (10.09)75.76 (8.83)<0.001
Educational level (%)low14,018 (33.8)658 (48.9)<0.001
middle17,038 (41.0)466 (34.6)
high10,460 (25.2)222 (16.5)
Hospital (%)University3018 (7.3)117 (8.7)<0.001
County13,026 (31.4)464 (34.5)
Rural17,490 (42.1)603 (44.8)
Private7982 (19.2)162 (12.0)
Fixation (%)Cemented28,237 (68.4)1168 (87.0)<0.001
Uncemented6363 (15.4)58 (4.3)
Hybrid536 (1.3)12 (0.9)
Reverse hybrid5503 (13.3)102 (7.6)
Resurfacing639 (1.5)2 (0.1)
Table
Table 2. Results of the relative survival regression analysis on mortality after total hip replacement. The results are presented as Hazard Rates (HR) and associated 95% confidence intervals (CI).
Table 2. Results of the relative survival regression analysis on mortality after total hip replacement. The results are presented as Hazard Rates (HR) and associated 95% confidence intervals (CI).
HR95% CI
MobilityNo problemsref
Moderate problems1.461.09–1.96
Severe problems2.651.43–4.92
Self-careNo problemsref
Moderate problems1.151.01–1.31
Severe problems1.571.08–2.29
Usual activityNo problemsref
Moderate problems1.050.93–1.20
Severe problems1.281.06–1.56
Pain/discomfortNo problemsref
Moderate problems1.070.64–1.77
Severe problems1.200.71–2.00
Anxiety/depressionNo problemsref
Moderate problems1.090.96–1.22
Severe problems1.240.95–1.62
EQ VAS (in units of 10) 0.950.92–0.98
Pain VAS (in units of 10) 0.960.92–1.01
SexMaleref
Female0.860.76–0.96
Age 0.960.95–0.97
Operation Year 0.910.86–0.96
EducationLowref
Middle0.930.83–1.06
High0.850.73–1.01
HospitalUniversityref
County0.790.65–0.97
Rural0.820.67–1.00
Private0.720.56–0.91
FixationCementedref
Uncemented0.600.45–0.81
Hybrid0.800.45–1.43
Reverse hybrid0.940.76–1.16
Resurfacing0.200.05–0.84
Table
Table 3. Demographics, socioeconomics, and surgery-related data of 43,096 total hip replacement patients with PROMs data, and the 6506 total hip replacement patients without PROMs data. The data is summarized as absolute numbers and percentages for discrete variables, and means and standard deviations for continuous variables.
Table 3. Demographics, socioeconomics, and surgery-related data of 43,096 total hip replacement patients with PROMs data, and the 6506 total hip replacement patients without PROMs data. The data is summarized as absolute numbers and percentages for discrete variables, and means and standard deviations for continuous variables.
Missing PROMsAvailable PROMsp-Value
n = 6506n = 43,096
Females (%) 3676 (56.5)24,139 (56.0)0.466
Age (SD) 68.59 (11.16)67.97 (10.16)<0.001
Educational level (%)Low2407 (37.0)14,676 (34.1)<0.001
Middle2557 (39.3)17,504 (40.6)
High1453 (22.3)10,682 (24.8)
Missing89 (1.4)234 (0.5)
Hospital (%)University687 (10.6)3154 (7.3)<0.001
County2288 (35.2)13,571 (31.5)
Rural2239 (34.4)18,194 (42.2)
Private1292 (19.9)8177 (19.0)
Fixation (%)Cemented4226 (65.0)29,594 (68.7)<0.001
Uncemented1099 (16.9)6451 (15.0)
Hybrid107 (1.6)551 (1.3)
Reverse hybrid883 (13.6)5627 (13.1)
Resurfacing83 (1.3)643 (1.5)

Share and Cite

MDPI and ACS Style

Cnudde, P.; Nemes, S.; Mohaddes, M.; Timperley, J.; Garellick, G.; Burström, K.; Rolfson, O. Is Preoperative Patient-Reported Health Status Associated with Mortality after Total Hip Replacement? Int. J. Environ. Res. Public Health 2017, 14, 899. https://doi.org/10.3390/ijerph14080899

AMA Style

Cnudde P, Nemes S, Mohaddes M, Timperley J, Garellick G, Burström K, Rolfson O. Is Preoperative Patient-Reported Health Status Associated with Mortality after Total Hip Replacement? International Journal of Environmental Research and Public Health. 2017; 14(8):899. https://doi.org/10.3390/ijerph14080899

Chicago/Turabian Style

Cnudde, Peter, Szilard Nemes, Maziar Mohaddes, John Timperley, Göran Garellick, Kristina Burström, and Ola Rolfson. 2017. "Is Preoperative Patient-Reported Health Status Associated with Mortality after Total Hip Replacement?" International Journal of Environmental Research and Public Health 14, no. 8: 899. https://doi.org/10.3390/ijerph14080899

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop