FACE-Q for Measuring Patient-reported Outcomes after Facial Skin Cancer Surgery: Cross-cultural Validation.

Background: Facial skin cancer and its surgical treatment can affect health-related quality of life. The FACE-Q Skin Cancer Module is a patient-reported outcome measure that measures different aspects of health-related quality of life and has recently been translated into Dutch. This study aimed to evaluate the performance of the translated version in a Dutch cohort using modern psychometric measurement theory (Rasch). Methods: Dutch participants with facial skin cancer were prospectively recruited and asked to complete the translated FACE-Q Skin Cancer Module. The following assumptions of the Rasch model were tested: unidimensionality, local independence, and monotonicity. Response thresholds, fit statistics, internal consistency, floor and ceiling effects, and targeting were assessed for all scales and items within the scales. Responsiveness was tested for the "cancer worry" scale. Results: In total, 259 patients completed the preoperative questionnaire and were included in the analysis. All five scales assessed showed a good or sufficient fit to the Rasch model. Unidimensionality and monotonicity were present for all scales. Some items showed a local dependency. Most of the scales demonstrate ordered item thresholds and appropriate fit statistics. Conclusions: The FACE-Q Skin Cancer Module is a well-designed patient-reported outcome measure that shows psychometric validity for the translated version in a Dutch cohort, using classical and modern test theory.

Development and Validation of a Novel Literature-Based Method to Identify Disparity-Sensitive Surgical Quality Metrics.

BACKGROUND: Disparity in surgical care impedes the delivery of uniformly high-quality care. Metrics that quantify disparity in care can help identify areas for needed intervention. A literature-based Disparity-Sensitive Score (DSS) system for surgical care was adapted by the Metrics for Equitable Access and Care in Surgery (MEASUR) group. The alignment between the MEASUR DSS and Delphi ratings of an expert advisory panel (EAP) regarding the disparity sensitivity of surgical quality metrics was assessed. STUDY DESIGN: Using DSS criteria MEASUR co-investigators scored 534 surgical metrics which were subsequently rated by the EAP. All scores were converted to a 9-point scale. Agreement between the new measurement technique (ie DSS) and an established subjective technique (ie importance and validity ratings) were assessed using the Bland-Altman method, adjusting for the linear relationship between the paired difference and the paired average. The limit of agreement (LOA) was set at 1.96 SD (95%). RESULTS: The percentage of DSS scores inside the LOA was 96.8% (LOA, 0.02 points) for the importance rating and 94.6% (LOA, 1.5 points) for the validity rating. In comparison, 94.4% of the 2 subjective EAP ratings were inside the LOA (0.7 points). CONCLUSIONS: Applying the MEASUR DSS criteria using available literature allowed for identification of disparity-sensitive surgical metrics. The results suggest that this literature-based method of selecting quality metrics may be comparable to more complex consensus-based Delphi methods. In fields with robust literature, literature-based composite scores may be used to select quality metrics rather than assembling consensus panels.

Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data.

Contrary to national guidelines, women with ovarian cancer often receive treatment at the end of life, potentially due to the difficulty in accurately estimating prognosis. We trained machine learning algorithms to guide prognosis by predicting 180-day mortality for women with ovarian cancer using patient-reported outcomes (PRO) data. We collected data from a single academic cancer institution in the United States. Women completed biopsychosocial PRO measures every 90 days. We randomly partitioned our dataset into training and testing samples. We used synthetic minority oversampling to reduce class imbalance in the training dataset. We fitted training data to six machine learning algorithms and combined their classifications on the testing dataset into an unweighted voting ensemble. We assessed each algorithm's accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) using testing data. We recruited 245 patients who completed 1319 PRO assessments. The final voting ensemble produced state-of-the-art results on the task of predicting 180-day mortality for ovarian cancer paitents (Accuracy = 0.79, Sensitivity = 0.71, Specificity = 0.80, AUROC = 0.76). The algorithm correctly identified 25 of the 35 women in the testing dataset who died within 180 days of assessment. Machine learning algorithms trained using PRO data offer encouraging performance in predicting whether a woman with ovarian cancer will die within 180 days. This model could be used to drive data-driven end-of-life care and address current shortcomings in care delivery. Our model demonstrates the potential of biopsychosocial PROM information to make substantial contributions to oncology prediction modeling. This model could inform clinical decision-making Future research is needed to validate these findings in a larger, more diverse sample.

The Use of the FACE-Q Aesthetic: A Narrative Review.

INTRODUCTION: In the past decade there has been an increasing interest in the field of patient-reported outcome measures (PROMs) which are now commonly used alongside traditional outcome measures, such as morbidity and mortality. Since the FACE-Q Aesthetic development in 2010, it has been widely used in clinical practice and research, measuring the quality of life and patient satisfaction. It quantifies the impact and change across different aspects of cosmetic facial surgery and minimally invasive treatments. We review how researchers have utilized the FACE-Q Aesthetic module to date, and aim to understand better whether and how it has enhanced our understanding and practice of aesthetic facial procedures. METHODS: We performed a systematic search of the literature. Publications that used the FACE-Q Aesthetic module to evaluate patient outcomes were included. Publications about the development of PROMs or modifications of the FACE-Q Aesthetic, translation or validation studies of the FACE-Q Aesthetic scales, papers not published in English, reviews, comments/discussions, or letters to the editor were excluded. RESULTS: Our search produced 1189 different articles; 70 remained after applying in- and exclusion criteria. Significant findings and associations were further explored. The need for evidence-based patient-reported outcome caused a growing uptake of the FACE-Q Aesthetic in cosmetic surgery and dermatology an increasing amount of evidence concerning facelift surgery, botulinum toxin, rhinoplasty, soft tissue fillers, scar treatments, and experimental areas. DISCUSSION: The FACE-Q Aesthetic has been used to contribute substantial evidence about the outcome from the patient perspective in cosmetic facial surgery and minimally invasive treatments. The FACE-Q Aesthetic holds great potential to improve quality of care and may fundamentally change the way we measure success in plastic surgery and dermatology. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Developing Machine Learning Algorithms to Support Patient-centered, Value-based Carpal Tunnel Decompression Surgery.

Background: Carpal tunnel syndrome (CTS) is extremely common and typically treated with carpal tunnel decompression (CTD). Although 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. 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. A range of machine learning algorithms was developed using the training data and evaluated with the test data. We also used a machine learning technique called chi-squared automatic interaction detection to develop flowcharts that could help clinicians and patients to understand the chances of a patient improving with surgery. Results: The top performing models predicted functional and symptomatic improvement with accuracies of 0.718 (95% confidence interval 0.660, 0.771) and 0.759 (95% confidence interval 0.708, 0.810), respectively. The chi-squared automatic interaction detection flowcharts could provide valuable clinical insights from as little as two preoperative questions. Conclusions: Patient-reported outcome measures 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.

Patient-reported outcomes measures used in facial vascularized composite allotransplantation: A systematic literature review.

BACKGROUND: Facial vascularized composite allotransplantation (fVCA) is a life-enhancing procedure performed to improve quality of life (QOL). Patient-reported outcome measures (PROMs) are tools used to assess QOL from the patients' perspective, and are increasingly recognized as an important clinical metric to assess outcomes of treatment. A systematic literature review was performed to identify and appraise the content of PROMs used in fVCA. METHODS: We searched PUBMED/Medline, CINAHL, Embase, PsychInfo, and Web of Science from their inception through to June 2020. Included studies used a PROM in candidates and recipients of fVCA of any gender or age. We excluded abstracts, reviews, editorials, and dissertations. Items from each PROM were extracted and coded, using top-level codes and subcodes, to develop a preliminary conceptual framework of QOL concerns in fVCA, and to guide future PROM selection. RESULTS: Title and abstract screening of 6089 publications resulted in 16 studies that met inclusion criteria. Review of the 16 studies identified 38 PROMs, none of which were developed for fVCA. Review of the coded content for each PROM identified six top-level codes (appearance, facial function, physical, psychological and social health, and experience of care) and 16 subcodes, making up the preliminary conceptual framework. CONCLUSION: There are currently no PROMs designed to measure QOL concerns of fVCA candidates and recipients. Findings from this systematic review will be used to inform an interview guide for use in qualitative interviews to elicit and refine important concepts related to QOL in fVCA.

Deeper Understanding of Appearance in Orofacial Clefts: A Structural Equation Model of the CLEFT-Q Appearance Scales.

BACKGROUND: The CLEFT-Q is a patient-reported outcome measure with seven scales measuring elements of facial appearance in cleft lip and/or palate. We built on the validated CLEFT-Q structural model to describe conceptual relationships between these scales, and tested our hypothesis through structural equation modeling (SEM). In our hypothesized model, the appearance of the nose, nostrils, teeth, jaw, lips, and cleft lip scar all contribute to overall facial appearance. METHODS: We included 640 participants from the international CLEFT-Q field test. Model fit was assessed using weighted least squares mean and variance adjusted regression. The model was then refined through modification indices. The fit of the hypothesized model was confirmed in an independent sample of 452 participants. RESULTS: The refined model demonstrated excellent fit to the data (comparative fit index 0.999, Tucker-Lewis index 0.999, root mean square error of approximation 0.036 and standardized root mean square residual 0.036). The confirmatory analysis also demonstrated excellent model fit. CONCLUSION: Our structural model, based on a clinical understanding of appearance in orofacial clefting, aligns with CLEFT-Q field test data. This supports the instrument's use and the exploration of a wider range of applications, such as multidimensional computerized adaptive testing.

Applying Computerized Adaptive Testing to the FACE-Q Skin Cancer Module: Individualizing Patient-Reported Outcome Measures in Facial Surgery.

BACKGROUND: Skin cancer is among the most frequently occurring malignancies worldwide, which creates a great need for an effective patient-reported outcome measure. Providing shorter questionnaires reduces patient burden and increases patients' willingness to complete forms. The authors set out to use computerized adaptive testing to reduce the number of items needed to predict results for scales of the FACE-Q Skin Cancer Module, a validated patient-reported outcome measure that measures health-related quality of life and patient satisfaction in facial surgery. METHODS: Computerized adaptive testing generates tailored questionnaires for patients in real time based on their responses to previous questions. The authors used an open-source computerized adaptive testing simulation software to run item responses for the five scales from the FACE-Q Skin Cancer Module (i.e., scar appraisal, satisfaction with facial appearance, appearance-related psychosocial distress, cancer worry, and satisfaction with information about appearance). Each simulation continued to administer items until prespecified levels of precision were met, estimated by standard error. Mean and maximum item reductions between the original fixed-length short forms and the simulated versions were evaluated. RESULTS: The number of questions that patients needed to answer to complete the FACE-Q Skin Oncology Module was reduced from 41 items in the original form to a mean of 23 ± 0.55 items (range, 15 to 29) using the computerized adaptive testing version. Simulated computerized adaptive testing scores maintained a high correlation (0.98 to 0.99) with the score from the fixed-length short forms. CONCLUSIONS: Applying computerized adaptive testing to the FACE-Q Skin Cancer Module can reduce the length of assessment by more than 50 percent, with virtually no loss in precision. It is likely to play a critical role in the implementation in clinical practice.

Optimising the computerised adaptive test to reliably reduce the burden of administering the CLEFT-Q: A Monte Carlo simulation study.

BACKGROUND: Computerised adaptive testing (CAT) has the potential to transform plastic surgery outcome measurement by making patient-reported outcome measures (PROMs) shorter, individualised and more accurate than pen-and-paper questionnaires. OBJECTIVES: This paper reports the results of two optimisation studies for the CLEFT-Q CAT, a CAT intended for use in the field of cleft lip and/or palate. Specifically, we aimed to identify the optimal score estimation and item selection methods for using this CAT in clinical practice. These represent two major components of any CAT algorithm. METHOD: Monte Carlo simulations were performed using simulated data in the R statistical computing environment and incorporated a range of score estimation and item selection techniques. The performance and accuracy of the CAT was assessed by mean items administered, correlation between CAT scores and paired linear assessment scores, and the root mean squared deviation (RMSD) of these score pairs. RESULTS: The accuracy of the CLEFT-Q CAT was not significantly affected by the choice of score estimation or item selection method. Sub-scales which originally contain more items were amenable to greater item reduction with CAT. CONCLUSION: This study shows that score estimation and item selection methods that need minimal processing power can be used in the CLEFT-Q CAT without compromising accuracy. This means that the CLEFT-Q CAT could be administered quickly and efficiently with basic hardware demands. We recommend the use of less computationally intensive techniques in future CLEFT-Q CAT studies.

Computerised adaptive testing accurately predicts CLEFT-Q scores by selecting fewer, more patient-focused questions.

BACKGROUND: The International Consortium for Health Outcome Measurement (ICHOM) has recently agreed upon a core outcome set for the comprehensive appraisal of cleft care, which puts a greater emphasis on patient-reported outcome measures (PROMs) and, in particular, the CLEFT-Q. The CLEFT-Q comprises 12 scales with a total of 110 items, aimed to be answered by children as young as 8 years old. OBJECTIVE: In this study, we aimed to use computerised adaptive testing (CAT) to reduce the number of items needed to predict results for each CLEFT-Q scale. METHOD: We used an open-source CAT simulation package to run item responses over each of the full-length scales and its CAT counterpart at varying degrees of precision, estimated by standard error (SE). The mean number of items needed to achieve a given SE was recorded for each scale's CAT, and the correlations between results from the full-length scales and those predicted by the CAT versions were calculated. RESULTS: Using CATs for each of the 12 CLEFT-Q scales, we reduced the number of questions that participants needed to answer, that is, from 110 to a mean of 43.1 (range 34-60, SE < 0.55) while maintaining a 97% correlation between scores obtained with CAT and full-length scales. CONCLUSIONS: CAT is likely to play a fundamental role in the uptake of PROMs into clinical practice given the high degree of accuracy achievable with substantially fewer items.

Streamlining the Assessment of Patient-Reported Outcomes in Weight Loss and Body Contouring Patients: Applying Computerized Adaptive Testing to the BODY-Q.

BACKGROUND: The BODY-Q is a widely used patient-reported outcome measure of surgical outcomes in weight loss and body contouring patients. Reducing the length of the BODY-Q assessment could overcome implementation barriers in busy clinics. A shorter BODY-Q could be achieved by using computerized adaptive testing, a method to shorten and tailor assessments while maintaining reliability and accuracy. In this study, the authors apply computerized adaptive testing to the BODY-Q and assess computerized adaptive testing performance in terms of item reduction and accuracy. METHODS: Parameters describing the psychometric properties of 138 BODY-Q items (i.e., questions) were derived from the original validation sample (n = 734). The 138 items are arranged into 18 scales reflecting Appearance, Quality of Life, and Experience of Care domains. The authors simulated 1000 administrations of the computerized adaptive testing until a stopping rule, reflecting assessment accuracy of standard error less than 0.55, was met. The authors describe the reduction of assessment length in terms of the mean and range of items administered. The authors assessed accuracy by determining correlation between full test and computerized adaptive testing scores. RESULTS: The authors ran 54 simulations. Mean item reduction was 36.9 percent (51 items; range, 48 to 138 items). Highest item reduction was achieved for the Experience of Care domain (56.2 percent, 22.5 items). Correlation between full test scores and the BODY-Q computerized adaptive test scores averaged 0.99. CONCLUSIONS: Substantial item reduction is possible by using BODY-Q computerized adaptive testing. Reduced assessment length using BODY-Q computerized adaptive testing could reduce patient burden while preserving the accuracy of clinical patient-reported outcomes for patients undergoing weight loss and body contouring operations.

Machine learning in medicine: a practical introduction.

BACKGROUND: Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data. METHODS: We demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples. We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment. RESULTS: The trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble. CONCLUSIONS: We use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.