Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany

BACKGROUND: EQ-5D health state utilities (HSU) are commonly used in health economics to compute quality-adjusted life years (QALYs). The EQ-5D, which is country-specific, can be derived directly or by mapping from self-reported health-related quality of life (HRQoL) scales such as the PROMIS-29 profile. The PROMIS-29 from the Patient Reported Outcome Measures Information System is a comprehensive assessment of self-reported health with excellent psychometric properties. We sought to find optimal models predicting the EQ-5D-5L crosswalk from the PROMIS-29 in the United Kingdom, France, and Germany and compared the prediction performances with that of a US model. METHODS: We collected EQ-5D-5L and PROMIS-29 profiles and three samples representative of the general populations in the UK (n = 1509), France (n = 1501), and Germany (n = 1502). We used stepwise regression with backward selection to find the best models to predict the EQ-5D-5L crosswalk from all seven PROMIS-29 domains. We investigated the agreement between the observed and predicted EQ-5D-5L crosswalk in all three countries using various indices for the prediction performance, including Bland-Altman plots to examine the performance along the HSU continuum. RESULTS: The EQ-5D-5L crosswalk was best predicted in France (nRMSEFRA = 0.075, nMAEFRA = 0.052), followed by the UK (nRMSEUK = 0.076, nMAEUK = 0.053) and Germany (nRMSEGER = 0.079, nMAEGER = 0.051). The Bland-Altman plots show that the inclusion of higher-order effects reduced the overprediction of low HSU scores. CONCLUSIONS: Our models provide a valid method to predict the EQ-5D-5L crosswalk from the PROMIS-29 for the UK, France, and Germany

Recursive partitioning vs computerized adaptive testing to reduce the burden of health assessments in cleft lip and/or palate : comparative simulation study

Background: Computerized adaptive testing (CAT) has been shown to deliver short, accurate, and personalized versions of the CLEFT-Q patient-reported outcome measure for children and young adults born with a cleft lip and/or palate. Decision trees may integrate clinician-reported data (eg, age, gender, cleft type, and planned treatments) to make these assessments even shorter and more accurate. Objective: We aimed to create decision tree models incorporating clinician-reported data into adaptive CLEFT-Q assessments and compare their accuracy to traditional CAT models. Methods: We used relevant clinician-reported data and patient-reported item responses from the CLEFT-Q field test to train and test decision tree models using recursive partitioning. We compared the prediction accuracy of decision trees to CAT assessments of similar length. Participant scores from the full-length questionnaire were used as ground truth. Accuracy was assessed through Pearson’s correlation coefficient of predicted and ground truth scores, mean absolute error, root mean squared error, and a two-tailed Wilcoxon signed-rank test comparing squared error. Results: Decision trees demonstrated poorer accuracy than CAT comparators and generally made data splits based on item responses rather than clinician-reported data. Conclusions: When predicting CLEFT-Q scores, individual item responses are generally more informative than clinician-reported data. Decision trees that make binary splits are at risk of underfitting polytomous patient-reported outcome measure data and demonstrated poorer performance than CATs in this study

Using machine learning algorithms to support the delivery of patient-centered and value-based care for carpal tunnel syndrome

Background: Carpal tunnel syndrome (CTS) is extremely common and typically treated with carpal tunnel decompression (CTD). While generally an effective treatment, up to 25% of patients do not experience meaningful benefit. Given the prevalence, this amounts to considerable morbidity and cost without return. Being able to reliably predict which patients would benefit from CTD preoperatively would support more patient-centered and value-based care. Materials and methods: We used registry data from 1916 consecutive patients undergoing CTD for CTS at a regional hand center between 2010 and 2019. Improvement was defined as change exceeding the respective QuickDASH subscale’s minimal important change estimate. Predictors included a range of clinical, demographic and patient-reported variables. Data were split into training (75%) and test (25%) sets. An elastic net, K-nearest neighbors algorithm, support vector machine, random forest (XGB), and neural network were trained on bootstraps of the training set and evaluated on the test set. We developed Chi squared automatic interaction detection (CHAID) trees for the same classification tasks, as readily-implementable clinical decision support tools. Results: XGB models predicted functional and symptomatic improvement with accuracies of 0.718 [95% CI 0.660, 0.771] and 0.759 [95% CI 0.708, 0.810] respectively. CHAID trees could provide valuable clinical insights from as little as two preoperative questions. Conclusions: Contemporary psychometrics and machine learning can support patient-centered and value-based healthcare. Our algorithms can be used for expectation management and to rationalize treatment risks and costs associated with CTD

Factors associated with the development, progression, and outcome of dupuytren disease treatment: a systematic review

Background: The factors typically considered to be associated with Dupuytren disease have been described, such as those in the “Dupuytren diathesis.” However, the quality of studies describing them has not been appraised. This systematic review aimed to analyze the evidence for all factors investigated for potential association with the development, progression, outcome of treatment, or recurrence of Dupuytren disease. Methods: A systematic review of the Cochrane Central Register of Controlled Trials, MEDLINE, Embase, and Cumulative Index to Nursing and Allied Health Literature databases was conducted using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses–compliant methodology up to September of 2019. Articles were screened in duplicate. Prognostic studies were quality assessed using the Quality in Prognosis Study tool. Results: This study identified 2301 records; 51 met full inclusion criteria reporting data related to 54,491 patients with Dupuytren disease. In total, 46 candidate factors associated with the development of Dupuytren disease were identified. There was inconsistent evidence between the association of Dupuytren disease and the presence of “classic” diathesis factors. The quality of included studies varied, and the generalizability of studies was low. There was little evidence describing the factors associated with functional outcome. Conclusions: This systematic review challenges conventional notions of diathesis factors. Traditional diathesis factors are associated with disease development and recurrence, although they are not significantly associated with poor outcome following intervention based on the current evidence.</p

Effectiveness of routine provision of feedback from patient‐reported outcome measurements for cancer care improvement: a systematic review and meta-analysis

Abstract Background Research shows that feeding back patient-reported outcome information to clinicians and/or patients could be associated with improved care processes and patient outcomes. Quantitative syntheses of intervention effects on oncology patient outcomes are lacking. Objective To determine the effects of patient-reported outcome measure (PROM) feedback intervention on oncology patient outcomes. Data sources We identified relevant studies from 116 references included in our previous Cochrane review assessing the intervention for the general population. In May 2022, we conducted a systematic search in five bibliography databases using predefined keywords for additional studies published after the Cochrane review. Study selection We included randomized controlled trials evaluating the effects of PROM feedback intervention on processes and outcomes of care for oncology patients. Data extraction and synthesis We used the meta-analytic approach to synthesize across studies measuring the same outcomes. We estimated pooled effects of the intervention on outcomes using Cohen’s d for continuous data and risk ratio (RR) with a 95% confidence interval for dichotomous data. We used a descriptive approach to summarize studies which reported insufficient data for a meta-analysis. Main outcome(s) and measures(s) Health-related quality of life (HRQL), symptoms, patient-healthcare provider communication, number of visits and hospitalizations, number of adverse events, and overall survival. Results We included 29 studies involving 7071 cancer participants. A small number of studies was available for each metanalysis (median = 3 studies, ranging from 2 to 9 studies) due to heterogeneity in the evaluation of the trials. We found that the intervention improved HRQL (Cohen’s d = 0.23, 95% CI 0.11–0.34), mental functioning (Cohen’s d = 0.14, 95% CI 0.02–0.26), patient-healthcare provider communication (Cohen’s d = 0.41, 95% CI 0.20–0.62), and 1-year overall survival (OR = 0.64, 95% CI 0.48–0.86). The risk of bias across studies was considerable in the domains of allocation concealment, blinding, and intervention contamination. Conclusions and relevance Although we found evidence to support the intervention for highly relevant outcomes, our conclusions are tempered by the high risk of bias relating mainly to intervention design. PROM feedback for oncology patients may improve processes and outcomes for cancer patients but more high-quality evidence is required