Age and Gender Confound PROMIS Scores in Spine Patients With Back and Neck Pain

STUDY DESIGN

This was a single-center retrospective review.

OBJECTIVES

To explore how age and gender affect PROMIS scores compared with traditional health-related quality of life (HRQL) in spine patients.

METHODS

Patients presenting with a primary complaint of back pain (BP) or neck pain (NP) were included. Legacy HRQLs were Oswestry Disability Index (ODI), Neck Disability Index (NDI), and Visual Analogue Scale (VAS). PROMIS Physical Function (PF), Pain Intensity (Int), and Pain Interference (Inf) were also administered to patients in a clinical setting. Patients were grouped by chief complaint, age (18-44, 45-64, 65+ years) and gender. Two parallel analyses were conducted to identify the effects of age and gender on patient-reported outcomes. Age groups were compared after propensity-score matching by VAS-pain and gender. Separately, genders were compared after propensity-score matching by age and VAS-pain.

RESULTS

A total of 484 BP and 128 NP patients were matched into gender cohorts (n = 201 in each BP group, 46 in each NP group). Among BP patients, female patients demonstrated worse disability by ODI (44.15 vs 38.45, P = .005); PROMIS-PF did not differ by gender. Among NP patients, neither legacy HRQLs nor PROMIS differed by gender when controlling for NP and age. BP and NP patients were matched into age cohorts (n = 135 in each BP group and n = 14 in each BP group). Among BP patients, ANOVA revealed differences between groups when controlling for BP and gender: ODI (P < .001), PROMIS-PF (P = .018), PROMIS-Int (P < .001) PROMIS-Inf (P < .001). Among NP patients, matched age groups differed significantly in terms of NDI (P = .032) and PROMIS-PF (P = .022) but not PROMIS-Int or PROMIS-Inf.

CONCLUSIONS

Age and gender confound traditional HRQLs as well as PROMIS domains. However, PROMIS offers age and gender-specific scores, which traditional HRQLs lack.

Patient-reported outcome 2 years after lung transplantation: does the underlying diagnosis matter?

Purpose

Transplantation has the potential to produce profound effects on survival and health-related quality of life (HRQL). The inclusion of the patient’s perspective may play an important role in the assessment of the effectiveness of lung transplantation. Patient perspectives are assessed by patient-reported outcome measures, including HRQL measures. We describe how patients’ HRQL among different diagnosis groups can be used by clinicians to monitor and evaluate the outcomes associated with transplantation.

Methods

Consecutive lung transplant recipients attending the lung transplant outpatient clinic in a tertiary institution completed the 15-item Health Utilities Index (HUI) questionnaire on a touchscreen computer. The results were available to clinicians at every patient visit. The HUI3 covers a range of severity and comorbidities in eight dimensions of health status. Overall HUI3 scores are on a scale in which dead = 0.00 and perfect health = 1.00; disability categories range from no disability = 1 to severe disability <0.70. Single-attribute and overall HUI3 scores were used to compare patients’ HRQL among different diagnosis groups. Random-effect models with time since transplant as a random variable and age, gender, underlying diagnoses, infections, and broncholitis obliterans syndrome as fixed variables were built to identify determinants of health status at 2-years posttransplantation.

Results

Two hundred and fourteen lung transplant recipients of whom 61% were male with a mean age of 52 (19–75) years were included in the study. Chronic obstructive pulmonary disease and cystic fibrosis patients displayed moderate disability, while pulmonary fibrosis and pulmonary arterial hypertension patients displayed severe disability. Patients with chronic obstructive pulmonary disease had the worst pain level, whereas patients with pulmonary fibrosis had the worst emotion and cognition levels. A random-effect model confirmed that development of broncholitis obliterans syndrome was the most important determinant of health status (P = 0.03) compared to other variables, such as cytomegalovirus infections and underlying diagnoses.

Conclusion

Descriptions of patients’ HRQL among different diagnosis groups could be used by clinicians to assist individualized patient care.