Concurrent Validity of PROMIS and LD-SRS Scores in Pediatric Patients With Lower Limb Differences

BACKGROUND

We sought to assess the concurrent validity of select Patient Reported Outcomes Measurement Information System (PROMIS) domains and Limb Deformity-Scoliosis Research Society (LD-SRS) scores.

METHODS

We prospectively administered PROMIS-25 (including anxiety, depression, fatigue, pain interference, peer relationships, and physical function) and LD-SRS questionnaires to 46 consecutive pediatric patients with lower limb differences, presenting to a single surgeon for reconstruction. Concurrent validity between various subdomains of the 2 outcome measures was assessed through Pearson's correlation, with significance defined as P <0.05. The strength of correlation was interpreted by Evans criteria: absolute r value <0.20 indicating very weak correlation; 0.20 to 0.39, weak; 0.40 to 0.59, moderate; 0.60 to 0.79, strong; and 0.8 or greater indicating very strong correlation.

RESULTS

The LD-SRS Pain, Function, and Mental Health domains most strongly correlated with the PROMIS pain interference (r=-0.79, P<0.001), physical function (r=0.74, P<0.001), and anxiety (r=-0.68, P <0.001) domains, respectively. In addition, LD-SRS pain strongly correlated with PROMIS physical function (r=0.61, P<0.001) and LD-SRS function with PROMIS pain interference (r=-0.72, P<0.001). All PROMIS domains significantly correlated with total LD-SRS scores. PROMIS pain interference (r=-0.79, P <0.001), physical function (r=0.67, P <0.001), and fatigue (r=-0.60, P <0.001) domains demonstrated the strongest correlations with the total LD-SRS score.

CONCLUSIONS

The significant concurrent validity between LD-SRS and multiple PROMIS domains suggests considerable overlap, and perhaps redundancy, between these 2 outcome measures. Given the high degree of concordance and the advantage of computer adaptive testing (CAT) in mitigating administrative burden and survey fatigue, along with the ability to compare outcomes across a wider group of children with a variety of underlying diagnoses, select PROMIS domains may be a viable alternative to LD-SRS score for assessing patient-reported outcomes when treating pediatric patients with lower limb deformities. A larger, multi-center study including pediatric patients with lower limb differences from a diverse background, including age, etiology, native language, and ethnicity, would be helpful to externally validate our findings.

LEVEL OF EVIDENCE

Level-I.

Automating Patient-reported Data Collection: Does it Work?

OBJECTIVE

There are several electronic patient-reported outcomes (ePROs) vendors that are being used at institutions to automate data collection. However, there is little known about their success in collecting patient-reported outcomes (PROs) and it is unknown which patients are more likely to complete these surveys. In this study, we assessed rates of PRO completion, as well as determined factors that contributed to the completion of baseline and follow-up surveys.

METHODS

We queried our ePRO platform to assess rates of completion for baseline and follow-up surveys for patients from October 2019 to June 2022. All baseline surveys were administered before pediatric orthopaedic procedures, and follow-up surveys were sent at 3 months, 6 months, 1 year, and 2 years after surgery to patients with baseline data. Descriptive statistics were used to summarize the data. Univariate and multivariate analyses were performed to assess differences in patients who did and did not complete surveys.

RESULTS

This study included 1313 patients during the study period. Baseline surveys were completed by 66% of the cohort (n = 873 patients). There was a significant difference in race/ethnicity and language spoken in the patients who did and did not complete baseline surveys (P < 0.01) with lower rates of completion in African American, Hispanic, and Spanish-speaking patients. At least one follow-up was obtained for 68% of patients with baseline surveys (n = 597 patients). There were significant differences in completion rates based on race/ethnicity (P = 0.03) and language spoken (P = 0.01). There were lower rates of baseline completion for patients with government insurance in our multivariate analysis (odds ratio: 0.6, P < 0.01).

CONCLUSION

Baseline and follow-up PRO data can be obtained from the majority of patients using automated ePRO platforms. However, additional focus is needed on collecting data from traditionally underrepresented patient groups to better understand outcomes in these patient populations.

LEVEL OF EVIDENCE

Level III-retrospective cohort study.

Trends in Pediatric Primary Care Visits During the COVID-19 Pandemic: Opportunity to Address Adolescent Behavioral Health Through Telemedicine

BACKGROUND AND OBJECTIVES

The COVID-19 pandemic impacted the volume and nature of pediatric primary care visits nationwide. This study aimed to identify trends in pediatric visits at our institution during the pandemic to reveal opportunities to improve care of children and adolescents.

METHODS

We performed a retrospective chart review of all pediatric visits conducted at a single family medicine clinic within a large academic medical center in Northern California from January 1, 2019, through September 30, 2021. Data collected for each visit included age, sex, type of visit (preventive or problem-focused), reason for visit (if problem-focused), and mode of visit (in-person or telehealth). We analyzed data using descriptive statistics and χ2 tests.

RESULTS

A total of 4,844 pediatric visits occurred during the study period. Visit volume dropped 9% from 2019 to 2020 and recovered to prepandemic levels in 2021. During the study period from 2019 to 2021, the percentage of problem-focused visits increased from 30% to 37% (P=.008) among adolescents, driven largely by an increase in the percentage of behavioral health visits from 14% to 29% (P<.001). We found no significant changes in the age or sex of patients seen. Telemedicine visit volume decreased from 2020 to 2021 in all age categories except for adolescents, which remained stable at 43% of all visits.

CONCLUSIONS

A sharp increase in behavioral health concerns among adolescents stands out as the most notable impact of COVID-19 on pediatric care at our institution. Our findings raise questions about how behavioral health care can be optimized for adolescents in the postpandemic era.

A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

OBJECTIVE

We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals.

RESEARCH DESIGN AND METHODS

We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables.

RESULTS

Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002).

CONCLUSIONS

The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.