Validation of a United Kingdom Model to Predict Mortality in Incident Dialysis Patients in the Dialysis Outcomes and Practice Patterns Study Cohort: Introduction of a Clinical Risk Score

Patients with kidney failure represent a heterogeneous group, in which many factors, including age, the cause of kidney disease, comorbidities, and so forth, result in a wide variation of mortality risk.1Goodkin D.A. Young E.W. Kurokawa K. Prütz K.G. Levin N.W. Mortality among hemodialysis patients in Europe, Japan, and the United States: case-mix effects.Am J Kidney Dis. 2004; 44: 16-21Google Scholar A number of predictive models are available to assess the patient’s individual risk of mortality at the time of dialysis initiation.2Anderson R.T. Cleek H. Pajouhi A.S. et al.Prediction of risk of death for patients starting dialysis: a systematic review and meta-analysis.Clin J Am Soc Nephrol. 2019; 14: 1213-1227Google Scholar However, few are applicable to patients treated with hemodialysis (HD) and peritoneal dialysis, many include nonroutinely available variables, and most importantly, few have been externally validated in independent cohorts, thus leaving their applicability and validity in clinical practice unanswered. Previously, we published a model to predict mortality with high accuracy in incident dialysis patients in the United Kingdom Renal Registry (UKRR) by employing routinely available variables (age, sex, race, and cause of kidney disease), comorbidities (diabetes, cardiovascular disease, and smoking), and laboratory measures (creatinine, hemoglobin, albumin, and calcium).3Wagner M. Ansell D. Kent D.M. et al.Predicting mortality in incident dialysis patients: an analysis of the United Kingdom Renal Registry.Am J Kidney Dis. 2011; 57: 894-902Google Scholar Here, we briefly report the external validation of a United Kingdom (UK) model in the international cohort of the Dialysis Outcomes and Practice Patterns Study (DOPPS). We also translated the model into a clinical risk score. The validation data set consisted of 3,612 patients participating in DOPPS phase 2 (enrollment 2002-2004) who received HD treatment 3 months after dialysis initiation, similar to the UK model.3Wagner M. Ansell D. Kent D.M. et al.Predicting mortality in incident dialysis patients: an analysis of the United Kingdom Renal Registry.Am J Kidney Dis. 2011; 57: 894-902Google Scholar,4Pisoni R.L. Gillespie B.W. Dickinson D.M. Chen K. Kutner M.H. Wolfe R.A. The Dialysis Outcomes and Practice Patterns Study (DOPPS): design, data elements, and methodology.Am J Kidney Dis. 2004; 44: 7-15Google Scholar We restricted the UKRR data set to HD patients because peritoneal dialysis patients are not enrolled in DOPPS. The UK model was validated by exploring C-statistics (discrimination) and d’Agostino and Nam5D'Agostino R.B. Nam B.H. Evaluation of the Performance of Survival Analysis Models: Discrimination and Calibration Measures. Elsevier, 2004Google Scholar χ2 statistics (calibration) for 1- and 3-year mortality, as the data allowed.6Pencina M.J. D'Agostino R.B. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.Stat Med. 2004; 23: 2109-2123Google Scholar The original (fixed) coefficients were applied; yet, the baseline hazard function of DOPPS and its subsets (North America, Europe, Japan, and Australia/New Zealand) were considered (ie, recalibration). Finally, the UK model was transformed into a clinical score (see Item S1 for details in methodology and statistical analysis).7Sullivan L.M. Massaro J.M. D'Agostino Sr R.B. Presentation of multivariate data for clinical use: the Framingham Study risk score functions.Stat Med. 2004; 23: 1631-1660Google Scholar Patient characteristics and the outcomes of DOPPS, DOPPS by continent, and the HD cohort of UKRR are displayed in Table 1. A total of 675 (18.8%) patients from DOPPS and 1,193 (31.7%) patients from UKRR died within 3 years (1-year mortality, 355 [9.8%] and 468 [12.4%], respectively; Fig S1). The UK prediction model proved to have high accuracy in DOPPS (C-statistic, 0.74; χ2 statistic, 9.3) for 1-year mortality, while discrimination and calibration were adequate in patients from Europe (C-statistic, 0.74; χ2 statistic, 7.1), Japan (C-statistic, 0.82; χ2 statistic, 2.6), and Australia/New Zealand (C-statistic, 0.80; χ2 statistic, 2.9) but modest in North American patients (C-statistic, 0.69; χ2 statistic, 17.7). The model also indicated better performance in European patients for 3-year mortality (C-statistic, 0.71; χ2 statistic, 15.5) than in North American patients (C-statistic, 0.68, χ2 statistic, 8.79) (Table S1, Fig S2). We translated the UK prediction model into a clinical risk score (Fig 1), which indicated adequate performance in the original UKRR development (C-statistic, 0.74; χ2 statistic, 2.3) and validation (C-statistic, 0.72; χ2 statistic, 1.0) data sets, in HD as well as peritoneal dialysis patients (Table S2).3Wagner M. Ansell D. Kent D.M. et al.Predicting mortality in incident dialysis patients: an analysis of the United Kingdom Renal Registry.Am J Kidney Dis. 2011; 57: 894-902Google ScholarTable 1Patient Characteristics of DOPPS Phase 2 and the HD Cohort of the UK Renal RegistryDOPPS 2n = 3,612DOPPS 2 Can/USn = 1,241DOPPS 2 EuropeaBelgium, France, Germany, Italy, Spain, Sweden, United Kingdom.n = 1,776DOPPS 2 Japann = 431DOPPS 2 Aus/NZn = 164P ValueAcross DOPPS 2 ContinentsUK Renal Registry - HD n = 3,769P Value DOPPS 2 vs UKRR-HDP Value DOPPS 2 Europe vs UKRR-HDAge66 (54-75)64 (53-75)68 (55-75)64 (55-73)62 (48-72)<0.00166 (53-75)0.370.002Male sex60.3%56.1%61.7%66.8%60.7%<0.00161.6%0.280.94BMI, kg/m224.5 (21.5-28.3)26.1 (22.4-30.9)24.6 (21.8-27.7)21.2 (19.3-23.2)25.4 (23.0-28.6)<0.00125.6 (22.3-30.0)<0.001<0.001Race White74.2%67.0%96.5%0%82.3%<0.00172.9%<0.001<0.001 Black8.8%23.1%1.7%0%0%4.5% Chinese/Japanese13.5%3.0%0.8%99.8%3.1%0.6% Asian (Indian subcontinent)0.3%0.5%0.1%0 %1.8%8.3% Other/unknown3.2% / 0%6.4%0.8%0.2%12.8%2.2% / 11.5%Cause of kidney disease Diabetes29.9%38.9%21.5%41.1%22.6%<0.00120.1%<0.001<0.001 Glomerulonephritis13.5%6.9%13.6%32.0%13.4%10.0% Polycystic kidney disease4.5%2.8%5.9%3.0%6.7%6.1% Pyelonephritis3.2%1.6%4.5%2.3%4.3%8.8% Renovascular disease18.9%25.1%18.7%3.3%14.0%16.8% Other12.7%9.4%16.2%5.6%18.3%15.8% Uncertain/missing17.4%15.2%19.8%12.8%20.3%21.9%Modality changebChange from PD to HD within the first 90 days of RRT.2.3%1.8%2.6%1.1%5.8%0.011.4%0.060.003Vascular accesscAt enrollment DOPPS. Fistula43.3%17.2%50.8%82.4%53.0%<0.001NA---- Synth. graft6.2%11.4%3.5%2.6%6.0% Bov. graft0.4%0.9%0.1%0%0% Cuffed cath.31.6%54.5%23.8%0.2%27.5% Temp. cath.18.1%15.8%21.5%13.2%13.4% Other0.5%0.3%0.4%1.6%0%ComorbiditiesDiabetesdIncluding diabetes as cause of kidney disease.44.3%58.4%34.2%47.6%38.4%<0.00129.1%<0.001<0.001CVDeDefinitions of DOPPS (Cerebrovascular disease; Ischemic Heart Disease: angina, previous myocardial infarction, previous CABG or angioplasty; Peripheral Vascular Disease: PVD diagnosis, claudication, non-coronary angioplasty. vascular graft or aneurysm, amputation for PVD) and UKRR (any of angina, previous myocardial infarction, previous CABG or angioplasty, cerebrovascular disease, claudication, ischemic or neuropathic ulcers, non-coronary angioplasty, vascular graft or aneurysm, amputation for PVD).47.7%55.2%47.0%30.8%43.8%<0.00137.7%<0.001<0.001 Ischemic heart disease32.0%39.3%31.1%16.4%28.8%<0.001na Cerebrovascular disease14.9%16.3%14.7%12.6%12.2%0.20na Peripheral artery disease25.0%28.7%26.3%8.9%25.6%<0.001naSmokingfActive smoker or stopped <1 year ago.18.6%18.8%16.9%24.1%20.1%<0.00116.5%<0.001<0.001LaboratorygMeasurements of treatment quarter 2, except creatinine.Hemoglobin, g/dL10.8 ± 1.811.5 ± 1.710.7 ± 1.69.6 ± 1.510.6 ± 1.7<0.00111.0 ± 1.7<0.001<0.001Albumin, g/L3.6 (3.2-3.9)3.6 (3.2-3.9)3.6 (3.2-3.9)3.7 (3.3-4.0)3.5 (3.2-3.8)<0.0013.6 (3.2-3.9)0.580.55Calcium, mg/dL8.94 (8.42-9.50)8.94 (8.42-9.42)9.10 (8.54-9.66)8.42 (7.90-8.82)9.22 (8.58-9.86)<0.0019.50 (9.06-10.06)<0.001<0.001Creatinine, mg/dL6.7 (5.2-8.7)6.1 (4.6-8.1)6.7 (5.3-8.5)8.0 (6.5-9.9)7.3 (6.0-9.6)<0.0017.2 (5.7-8.9)<0.001<0.001Outcomes within 3 yDeath18.8%22.5%20.0%5.4%12.3%<0.00131.7%<0.001<0.001End of observation61.1%56.7%59.8%84.6%46.0%49.0%Kidney transplantation6.0%4.9%8.1%0.7%6.1%9.9%Recovery of renal function1.0%1.3%1.1%0.2%0.6%1.3%Lost to follow-uphLost to follow-up, withdrawal of RRT, change to non DOPPS dialysis unit (DOPPS only).10.4%10.9%9.3%8.6%22.7%1.1%Switch to PD2.8%3.7%1.8%0.2%12.3%7.1%Note: Data are %, median (interquartile range) or mean ± standard deviation. P values of Χ2-test, Kruskal-Wallis-test, and ANOVA, as appropriate. Abbreviations: Aus, Australia; Can, Canada; HD, hemodialysis; NA, not available; NZ, New Zealand; PD, peritoneal dialysis; RRT, renal replacement therapy; US, United States.a Belgium, France, Germany, Italy, Spain, Sweden, United Kingdom.b Change from PD to HD within the first 90 days of RRT.c At enrollment DOPPS.d Including diabetes as cause of kidney disease.e Definitions of DOPPS (Cerebrovascular disease; Ischemic Heart Disease: angina, previous myocardial infarction, previous CABG or angioplasty; Peripheral Vascular Disease: PVD diagnosis, claudication, non-coronary angioplasty. vascular graft or aneurysm, amputation for PVD) and UKRR (any of angina, previous myocardial infarction, previous CABG or angioplasty, cerebrovascular disease, claudication, ischemic or neuropathic ulcers, non-coronary angioplasty, vascular graft or aneurysm, amputation for PVD).f Active smoker or stopped <1 year ago.g Measurements of treatment quarter 2, except creatinine.h Lost to follow-up, withdrawal of RRT, change to non DOPPS dialysis unit (DOPPS only). Open table in a new tab Note: Data are %, median (interquartile range) or mean ± standard deviation. P values of Χ2-test, Kruskal-Wallis-test, and ANOVA, as appropriate. Abbreviations: Aus, Australia; Can, Canada; HD, hemodialysis; NA, not available; NZ, New Zealand; PD, peritoneal dialysis; RRT, renal replacement therapy; US, United States. Our analyses showed that basic patient characteristics and laboratory variables are sufficient to accurately predict mortality in incident dialysis patients in various international settings. The UK prediction model was also externally validated in the NECOSAD cohort, in which, however, the more recent AROii model, which was developed in European HD patients, indicated higher performance measures.8Ramspek C.L. Voskamp P.W. Van Ittersum F.J. Krediet R.T. Dekker F.W. Van Diepen M. Prediction models for the mortality risk in chronic dialysis patients: a systematic review and independent external validation study.Clin Epidemiol. 2017; 9: 451-464Google Scholar,9Floege J. Gillespie I.A. Kronenberg F. et al.Development and validation of a predictive mortality risk score from a European hemodialysis cohort.Kidney Int. 2015; 87: 996-1008Google Scholar Yet, conclusions drawn from the results of a prediction model should be applied to patients with caution because to our knowledge, none of these standardized models have ever been tested prospectively and in a randomized controlled trial to guide clinical decision making regarding whether to apply more or less therapy. However, the proposed UK clinical risk score can help researchers and clinicians in the field of HD and peritoneal dialysis to describe the underlying baseline mortality risk at the time of dialysis inception. Research idea and study design: MW, NT, DMK; data acquisition: DF, RLP; data analysis/interpretation: MW, NT, GvG; statistical analysis: MW; supervision or mentorship: CW. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. MW received funding through grant Z-2/37 of the Interdisciplinary Center for Clinical Research (IZKF) at the University Hospital Würzburg, Germany. The authors declare that they have no relevant financial interests. We thank all the United Kingdom renal centers for providing data to the United Kingdom Renal Registry as well as all patients and their caregivers of the Dialysis Outcomes and Practice Patterns Study. The support of Brian Bieber and Francesca Tentori at the Ann Arbor Research Group, which helped with the data management of the DOPPS data set, is gratefully acknowledged. We also thank Hocine Tighiouart at Tufts Medical Center, Boston, who provided the SAS macros for prediction model performance and helped with the risk score. Parts of the results were presented at the 44th annual meeting of the American Society of Nephrology (November 8-13, 2011, Philadelphia, PA). Received January 9, 2021. Evaluated by 3 external peer reviewers, with editorial input from an Acting Editor-in-Chief (Editorial Board Member Nwamaka D. Eneanya, MD, MPH). Accepted in revised form December 6, 2021.The involvement of an Acting Editor-in-Chief to handle the peer-review and decision-making processes was to comply with Kidney Medicine’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies. 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