Evaluation of the Clinical Utility of the Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP): A Case-Based Pilot Assessment.

PURPOSE: The Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP) provides patient-specific survival predictions and evidence-based recommendations to guide multidisciplinary management for symptomatic bone metastases. We assessed the clinical utility of the BMETS-DSP through a pilot prepost design in a simulated clinical environment. METHODS: Ten Radiation Oncology physicians reviewed 55 patient cases at two time points: without and then with the use of BMETS-DSP. Assessment included 12-month survival estimate, confidence in and likelihood of sharing estimates with patients, and recommendations for open surgery, systemic therapy, hospice referral, and radiotherapy (RT) regimen. Paired statistics compared pre- versus post-DSP outcomes. Reported statistical significance is P < .05. RESULTS: Pre- versus post-DSP, overestimation of true minus estimated survival time was significantly reduced (mean difference -2.1 [standard deviation 4.1] v -1 month [standard deviation 3.5]). Prediction accuracy was significantly improved at cut points of < 3 (72 v 79%), ≤ 6 (64 v 71%), and ≥ 12 months (70 v 81%). Median ratings of confidence in and likelihood of sharing prognosis significantly increased. Significantly greater concordance was seen in matching use of 1-fraction RT with the true survival < 3 months (70 v 76%) and < 10-fraction RT with the true survival < 12 months (55 v 62%) and appropriate use of open surgery (47% v 53%), without significant changes in selection of hospice referral or systemic therapy. CONCLUSION: This pilot study demonstrates that BMETS-DSP significantly improved physician survival estimation accuracy, prognostic confidence, likelihood of sharing prognosis, and use of prognosis-appropriate RT regimens in the care of symptomatic bone metastases, supporting future multi-institutional validation of the platform.

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

OBJECTIVE: We explore whether a knowledge-discovery approach building a Classification and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer (HNC) patients treated with radiation therapy (RT) is feasible. METHODS AND MATERIALS: HNC patients from 2007 to 2015 were identified from a prospectively collected database Oncospace. Two prediction models at different time points were developed to predict weight loss ≥5 kg at 3 months post-RT by CART algorithm: (1) during RT planning using patient demographic, delineated dose data, planning target volume-organs at risk shape relationships data and (2) at the end of treatment (EOT) using additional on-treatment toxicities and quality of life data. RESULTS: Among 391 patients identified, WL predictors during RT planning were International Classification of Diseases diagnosis; dose to masticatory and superior constrictor muscles, larynx, and parotid; and age. At EOT, patient-reported oral intake, diagnosis, N stage, nausea, pain, dose to larynx, parotid, and low-dose planning target volume-larynx distance were significant predictive factors. The area under the curve during RT and EOT was 0.773 and 0.821, respectively. CONCLUSIONS: We demonstrate the feasibility and potential value of an informatics infrastructure that has facilitated insight into the prediction of WL using the CART algorithm. The prediction accuracy significantly improved with the inclusion of additional treatment-related data and has the potential to be leveraged as a strategy to develop a learning health system.

Prospective evaluation of patient reported swallow function with the Functional Assessment of Cancer Therapy (FACT), MD Anderson Dysphagia Inventory (MDADI) and the Sydney Swallow Questionnaire (SSQ) in head and neck cancer patients.

OBJECTIVES: The Functional Assessment of Cancer Therapy (FACT) instrument is comprised of a group of related and overlapping quality of life (QoL) questionnaires including a core general form, head and neck cancer (HNC)-specific items, and an expert-selected index (FACT-HNSI). Understanding how these relate to more HNC-specific instruments such as the MD Anderson Dysphagia Inventory (MDADI) and Sydney Swallow Questionnaire (SSQ) is vital for guiding their use in clinical trials. MATERIALS AND METHODS: HNC patients concurrently completed MDADI, SSQ, and FACT questionnaires at radiation oncology clinic visits (2015-2016). Spearman correlation coefficients were calculated between each FACT instrument and MDADI or SSQ. Unsupervised k-means cluster analyses were performed to identify clusters of similar QoL responses. Principal component analysis (PCA) identified the degree of variability explained by each instrument. RESULTS: We identified 631 instances (363 patients) where the questionnaires were completed concurrently. Correlations between the various FACT measures and SSQ or MDADI were all significant (p < 0.001), but FACT HNC-specific subscale and FACT-HNSI showed the strongest correlation with MDADI and SSQ. Clustering identified 3 distinct groups of responses when combining instruments either pairwise or three-way. PCA revealed that MDADI and FACT HNC-specific subscale provide similar and likely redundant information. CONCLUSION: FACT HNC-subscale and FACT-HNSI may be preferable over other FACT measures for use in clinical trials where patient-reported swallow function is evaluated. MDADI and FACT provide similar insights into HNC patient QoL while SSQ provides additional, complementary information which could serve to better stratify patients into groups with high, medium, and low QoL outcomes.

Measuring health care quality: comparing and contrasting the medical and the marketing approaches.

Health care quality, a key concept for medical practice and research, is also a widely used construct in health care administration and marketing research. We explored discipline-specific differences in the definition of quality, with the intent of finding a more unified approach. We summarized definitions and basic conceptual approaches to quality in both disciplines and then compared them on several attributes: basic goals, sources of measurement, role of patient perceptions, role of health care personnel, and need for risk adjustment. We developed a conceptual model combining the 2 approaches. Both disciplines could benefit from broadening their outcome measures. Patient satisfaction deserves more attention from medical researchers, whereas marketing approaches should go beyond using patient satisfaction as the only outcome of interest. It is conceptually feasible to integrate medical and marketing approaches to quality, with important insights resulting from this integration.