Adapting summary scores for the PROMIS-29 v2.0 for use among older adults with multiple chronic conditions

Abstract

Purpose

The patient-reported outcomes measurement information system 29-item profile (PROMIS-29 v2.0) is a widely used health-related quality of life (HRQoL) measure. Summary scores for physical and mental HRQoL have recently been developed for the PROMIS-29 using a general population. Our purpose was to adapt these summary scores to a population of older adults with multiple chronic conditions.

Methods

We collected the PROMIS-29 v2.0 for 1359 primary care patients age 65+ with at least 2 of 13 chronic conditions. PROMIS-29 has 7 domains, plus a single-item pain intensity scale. We used exploratory factor analysis (EFA), followed by confirmatory factor analysis (CFA), to examine the number of factors that best captured these eight scores. We used previous results from a recent study by Hays et al. (Qual Life Res 27:1885–1891, 2018) to standardize scoring coefficients, normed to the general population.

Results

The mean age was 80.7, and 67% of participants were age 80 or older. Our results indicated a 2-factor solution, with these factors representing physical and mental HRQoL, respectively. We call these factors the physical health score (PHS) and the mental health score (MHS). We normed these summary scores to the general US population. The mean MHS for our population of was 50.1, similar to the US population, while the mean PHS was 42.2, almost a full standard deviation below the US population.

Conclusions

We describe the adaptation of physical and mental health summary scores of the PROMIS-29 for use with a population of older adults with multiple chronic conditions.

Appendix

Appendix 1: Conditions used in this study

See Table 5.

Table 5 Layman’s terms and ICD-10 codes for chronic conditions

Development of code list

To develop an appropriate list of codes for the present study, we started with a set of codes developed by Quan et al., which were based on those originally established by Elixhauser for risk adjustment of chronic conditions [29]. We further modified the Quan set of codes in consultation with clinically trained members of our team and other practicing clinicians at RAND.

We modified the Quan set of codes in two main ways. First, we added codes to capture some conditions that were not included by Quan. The purpose of risk adjustment models such as the Elixhauser/Quan model is to detect chronic conditions that drive hospitalizations, costs, or mortality. Our purpose here, in contrast, was to detect conditions that would have an important effect on HRQoL. We therefore added codes to the Quan set to capture certain conditions that can impact HRQoL but are not a major cause of hospitalization or death, such as osteoarthritis, osteoporosis, and sciatica.

Second, we added codes to extend the spectrum of disease for some conditions that were already part of the Quan model, because the spectrum of disease that we needed to detect was different. Quan was interested in detecting only the most severe manifestations of disease, because these tend to drive morbidity and mortality. For our study, we were also interested in capturing less severe manifestations of disease, because those can impact HRQoL as well. For ischemic heart disease, for example, we added the codes for angina pectoris to the codes that Quan had used to capture more severe manifestations of disease, such as myocardial infarction. We reasoned that, although angina pectoris is less severe, it could still meaningfully impact HRQoL.

Appendix 2: Comparison of survey responders with non-responders based on data derived from the KPCO electronic medical record

See Tables 6 and 7.

Table 6 Bivariate comparisons of survey responders and non-responders based on measured characteristics

Table 7 Odds ratios for responding to the initial survey (n = 3749; 1359 responders and 2390 non-responders)