A Comparison of SF-36 and SF-12 Composite Scores and Subsequent Hospitalization and Mortality Risks in Long-Term Dialysis Patients

Discussion

To our knowledge, this report is the largest cross-sectional study in patients ESRD and with SF-12 and SF-36 information with accompanying risk estimates for hospitalization and mortality. Our survey response rate of 55% compares favorably with 47.6% of the American cohort reported in the Dialysis Outcomes and Practice Pattern Study (DOPPS) (28). The responders' demographic characteristics were similar enough to that of nonresponders, and because the study population was distributed nationally, we believe that these results are potentially generalizable to the US dialysis population. Results indicate that group PCS and MCS information derived from SF-12 is highly correlated and is in agreement with those derived from SF-36 in this population. In addition, we show for the first time that implications on hospitalization or mortality HRs derived from SF-36 composite scores are equally applicable to SF-12–derived composite scores; therefore, use of PCS-12/MCS-12 in lieu of the PCS-36/MCS-36, either alone or as part of a larger questionnaire (e.g., KDQoL-36), is valid in the US ESRD population.

We confirm that, on average, PCS scores are much lower in patients with ESRD than in the general population (approximately 17 points less by PCS-36 and approximately 15 points less by PCS-12), consistent with previous reports in large US prevalent dialysis cohorts (2,3,5,6). Similarly, we confirm that average MCS scores are only slightly lower (approximately 3 to 4 points less) than in the general population. Although a decade apart, the distribution of SF-36 PCS and MCS scores from this study almost exactly mirrors that of the FMCNA cohort from 1996 (5). Furthermore, we detected a similar magnitude of decline in adjusted relative risk for death for each 1-point increase in PCS (2.4%) as reported in other large studies: 2.0% from Lowrie et al. (5), approximately 2.1% from De Oreo et al. (2) (derived from 10.4% decline per 5 points), and 2.1% in the US cohort of DOPPS by Mapes et al. (28) (derived from 21% decline per 10 points), for each 1-point increase in PCS. The HR for mortality in this study decreased by 1.3% for each incremental MCS-36 point (with 1.2% for MCS-12) and was most consistent with DOPPS data, which revealed a 1.3% lower HR per 1-point change in MCS (derived from 13% decline per 10 points) (28). Values ranged from a 1.4% decline for each 5-point increase in MCS (De Oreo et al.) to a 2% decline for each MCS 1-point increase (Lowrie et al.) (2,5); however, significant increases in death risk accrue as soon as PCS-12 falls below 44 (for PCS-36 below 40), whereas a slightly higher risk was associated with MCS-12/36 below 50.

The corresponding decline in hazard rate for first hospitalization was only by 0.4% per incremental PCS point in this study, slightly lower than the 0.9% lower hazard rate per 1-point increment of PCS from DOPPS (Lowrie et al. reported odds ratios for hospitalization and De Oreo et al. reported hospital days per patient-year) (2,5,28). The corresponding time to first hospitalization hazard rates were −0.6% lower HR per MCS point in this study and 5% lower HR per 10-point MCS increment in DOPPS. Similarly, hospitalization risk begins to increase at PCS-12/36 below 44 and MCS-12 below 50, whereas for MCS-36, scores up to 59 were associated with increased risk (of borderline significance) in adjusted models. Of note, adjustment for case-mix and laboratory variables had a larger impact on the hazard rates associated with PCS than MCS, perhaps indicative of a stronger correlation between these variables and physical well-being.

Although the PCS and MCS both are known to predict hard outcomes in ESRD, losing information provided by the eight domains of the SF-36 may decrease the ability to detect more specific changes in a patient's functional well-being. For example, one seminal study that showed QoL improvement that resulted from increased hematocrit by the use of recombinant erythropoietin in new dialysis patients would be less impressive if only PCS and MCS were reported, absent the much larger changes detected in vitality, social functioning, mental health, and physical functioning (13). Thus, the usefulness of the SF-12 in isolation will depend on the purpose of investigators, notwithstanding the logistical ease of implementation when compared with the SF-36. The Centers for Medicare and Medicaid Services Interpretative Guidelines [S&C-09-01, version 1.1, 10/03/08 (29)] memo accompanying the recently updated Conditions for Coverage for ESRD facilities (42 CFR part 494), identified the KDQoL-36 as the preferred standardized physical and mental assessment tool for psychosocial status, on the basis of recommendations from the National Quality Forum and the Centers for Medicare and Medicaid Services Clinical Performance Measures Work Group, with consideration that use of the KDQoL-36 is free from royalty fees. Loss of the eight domain scores will be offset by the addition of kidney disease–specific questions on the burden, symptoms, and effects of kidney disease on daily life (24). In this analysis, we used the scoring algorithm from the KDQoL work group, thereby providing a better understanding of the PCS-12 and MCS-12 that will eventually be obtained from implementing the KDQoL-36 (27).

Taken together, results suggest that the use of SF-12–derived component scores in ESRD are just as good as their SF-36 counterparts. Furthermore, norms and other interpretation guidelines from previous work using the SF-36 in the US dialysis population is useful in interpreting the SF-12. The strengths of this study include results that were robust, a diverse source population with a distribution that is national in scope, a relatively high survey response rate, and a contemporary time period reflecting current dialysis practices and technology; however, the study has several limitations, with the first three of them inherent to the study design: First, this was a cross-sectional study that did not take into account longitudinal changes; second, results obtained with regard to prediction of death and hospitalization hazard rates were not necessarily causal and should be interpreted with caution; third, the strengths of the association observed may have a larger variance when studying groups of a much smaller size. Fourth, this analysis does not necessarily apply to non-US patient populations, although our findings may be true in these populations, as well. Clearly there are international and intercontinental variations in scores and interpretation of scores, evident in DOPPS (6). Fifth, application and implications represented here pertain to groups of patients, and the role of SF-12 component scores as an adjunct to clinical decision making in individual patients require further investigation. We need longitudinal studies in individual patients that can also assess the sensitivity of SF-12 measurements to changes in clinical condition and interventions as well as the potential for patient fatigue or “burnout” with repeated periodic survey administration. Finally, potential “context” bias may arise from our use of the embedded SF-12 (within the SF-36) as opposed to isolated implementation of the SF-12 questionnaire; however, some reassurance may be gained from a study conducted by Ware et al. (30) in a sample of 525 patients for whom the product moment correlation between answers to isolated SF-12 questions and the SF-12 items embedded in the SF-36 was exceptionally high (r = 0.999).