When Using DOPPS Slides • We welcome the use of DOPPS slides as we value the distribution of our research for the benefit of patient care and renal research • Because the DOPPS data and analyses on the following slides are published in the public domain, we ask that you honor our attached guidelines when using DOPPS slides for your own research purposes
DOPPS Slide Use Guidelines • Modifying DOPPS data, analyses, tables, and graphics in any form is not permitted without prior approval from the DOPPS coordinating center staff at Arbor Research • Each DOPPS slide used must include the citation of the associated publication and feature the corresponding DOPPS logo
The Key Comorbid Conditions Predictive of Survival among Hemodialysis Patients Dana Miskulin, Jennifer Bragg-Gresham, Brenda W. Gillespie, Francesca Tentori, Ronald L. Pisoni, HocineTighiouart, Andrew S. Levey Friedrich K. Port Clinical Journal of the American Society of Nephrology 4 (11): 1818-26, 2009
Introduction • The extent of information on comorbid conditions that should be used to risk adjust comparisons made across dialysis populations is unclear • In view of the high prevalence of comorbidity in this population, simply accounting for a few broadly defined conditions may not be sufficient to control for case-mix differences across groups • Abstracting information about comorbid illnesses from the medical record can be time consuming, particularly if a large number of conditions are under consideration • The aim of this study was to identify the key comorbid conditions associated with survival through consideration of an extensive list of conditions, and to compare the prognostic information provided by comorbidity with that provided by routinely measured laboratory and clinical parameters
DOPPS Background (1) • Longitudinal study of hemodialysis patients and HD unit practices. • Uniform international data collection. • Major outcomes: mortality, hospitalization, vascular access, quality of life • Goal: Identify HD practice patterns associated with improved patient outcomes (adjusted for patient mix) • DOPPS is supported by scientific research grants from Amgen (since 1996), Genzyme (since 2009), and Kyowa Hakko Kirin (since 1999, in Japan) without restrictions on publications • Coordinated by the Arbor Research Collaborative for Health (Ann Arbor, MI USA)
DOPPS Background (2) DOPPS 1: France, Germany, Italy, Japan, Spain, UK, US DOPPS 2, 3: DOPPS 1 Countries + Australia/New Zealand, Belgium, Canada, Sweden
Methods (1) • Sample:The current analyses were restricted to the DOPPS I (1996-2001), DOPPS II (2002-2004) and DOPPS III (2005-present) U.S. dialysis populations (n=7,685) because between-country differences in prevalence and relationships of comorbid conditions with mortality were noted previously • Outcome:all-cause mortality • Analysis: (1) Relationships of comorbid conditions with mortality were assessed using Cox proportional hazards regression stratified by study phase (2) The SCORE selection method was used to fit successive models adding one comorbid condition at a time to a demographics adjusted model until all 45 comorbid conditions were incorporated (3) Forwards stepwise selection was then employed to build a demographic and comorbidity-adjusted model, retaining variables significant at a p-value of <0.01
Methods (2) • Analysis(cont): (4) Laboratory and clinical parameters were added in subsequent models (5) An attributable fraction (AF) was calculated for each condition left in the model in order to rank its importance (6) Predictive accuracy was based on the c-statistic, a measure of discrimination, and the generalized Nagelkerke R2, a measure of explained variance • Adjustments:age, race, ESRD duration, cause of ESRD, s. albumin, phosphate, creatinine, pre-dialysis systolic blood pressure, body mass index, type of vascular access, inability to ambulate, inability to transfer, and nursing home resident, stratified by study phase, accounting for facility clustering effects
Table 1: Characteristics of the Development and Validation Study Populations (1)
Table 1: Characteristics of the Development and Validation Study Populations (2)
Table 1: Characteristics of the Development and Validation Study Populations (3)
Table 1: Characteristics of the Development and Validation Study Populations (4)
Figure 1: R-Square by Number of Variables in Model Validation Development Demographics and 17 comorbid conditions = 96% of explained variation Adjusted for age, sex, race, and time on ESRD
Table 2: Mortality Risk Attributable to Comorbid Conditions(1)
Table 2: Mortality Risk Attributable to Comorbid Conditions(2) Abbrevations: CHF (congestive heart failure), PVD (peripheral vascular disease) &In a model adjusted for age, race, sex, and time since the start of dialysis and each of the comorbid conditions shown in the table. * AF = Attributable Fraction calculated as pd((RR-1)/RR) where pd is the prevalence of the condition. See Methods for details. ˆ Absence of hypertension: defined as absence of diagnosis of hypertension in medical records.
Table 3: Predictive Accuracy of Different Risk Factors for 3-Year Survival in the Validation Dataset * Includes: serum albumin, phosphate, creatinine, pre-dialysis systolic blood pressure, body mass index, type of vascular access. ^Includes: inability to ambulate, inability to transfer, nursing home resident.
Table 4: Comparison of Comorbid Conditions on the DOPPS Shortlist, Charlson Index and Form 2728 (1)
Table 4: Comparison of Comorbid Conditions on the DOPPS Shortlist, Charlson Index and Form 2728 (2)
Table 4: Comparison of Comorbid Conditions on the DOPPS Shortlist, Charlson Index and Form 2728 (3) Abbreviations: DM=Diabetes Mellitus; CHF=Congestive heart failure +CVD=Cerebral Vascular Disease (includes history of stroke/ TIA) to be distinguished from Stroke with Deficit, which was significant %Definitions are not identical to those used in DOPPS but were considered a reasonable match ^ The CCI and Form 2728 definitions are “any history of CHF” not specifically “CHF in the past year”.
Table 5: Comparison of Different Comorbidity Measures &Includes items in the multivariable model, shown in Table 2. ^Scored as described in reference 3.
Conclusions • A relatively small list of comorbid conditions provides equivalent discrimination and explained variance for survival as a more extensive characterization of comorbidity • Comorbidity adds a modest amount of independent prognostic information to the survival model that cannot be substituted by clinical/laboratory parameters
Acknowledgements • We wish to thank the DOPPS study coordinators and medical directors for their dedication and hard work, and we are grateful to the patients who completed questionnaires • DOPPS is supported by scientific research grants from Amgen (since 1996), Kyowa Hakko Kirin (since 1999, in Japan), and Genzyme (since 2009) without restrictions on publications