Cost-utility of osteoarticular allograft versus endoprosthetic reconstruction for primary bone sarcoma of the knee: A markov analysis.

BACKGROUND: The most cost-effective reconstruction after resection of bone sarcoma is unknown. The goal of this study was to compare the cost effectiveness of osteoarticular allograft to endoprosthetic reconstruction of the proximal tibia or distal femur. METHODS: A Markov model was used. Revision and complication rates were taken from existing studies. Costs were based on Medicare reimbursement rates and implant prices. Health-state utilities were derived from the Health Utilities Index 3 survey with additional assumptions. Incremental cost-effectiveness ratios (ICER) were used with less than $100 000 per quality-adjusted life year (QALY) considered cost-effective. Sensitivity analyses were performed for comparison over a range of costs, utilities, complication rates, and revisions rates. RESULTS: Osteoarticular allografts, and a 30% price-discounted endoprosthesis were cost-effective with ICERs of $92.59 and $6 114.77. One-way sensitivity analysis revealed discounted endoprostheses were favored if allografts cost over $21 900 or endoprostheses cost less than $51 900. Allograft reconstruction was favored over discounted endoprosthetic reconstruction if the allograft complication rate was less than 1.3%. Allografts were more cost-effective than full-price endoprostheses. CONCLUSIONS: Osteoarticular allografts and price-discounted endoprosthetic reconstructions are cost-effective. Sensitivity analysis, using plausible complication and revision rates, favored the use of discounted endoprostheses over allografts. Allografts are more cost-effective than full-price endoprostheses.

A systematic literature review of machine learning in online personal health data.

OBJECTIVE: User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations. MATERIALS AND METHODS: We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review. RESULTS: We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support. CONCLUSIONS: The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.