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https://www.selleckchem.com/products/ipi-145-ink1197.html f occurrence of adverse events was associated with an increase in risk-standardized expenditures of $103 (95% CI, $57-$150) for AMI, $100 (95% CI, $29-$172) for HF, and $152 (95% CI, $73-$232) for pneumonia per discharge. Conclusions and Relevance Hospitals with high adverse event rates were more likely to have high 30-day episode-of-care Medicare expenditures for patients discharged with AMI, HF, or pneumonia.BACKGROUND Exact numbers of breast cancer (BC) recurrences are currently unknown at the population-level, since they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence.We present the first systematic review and meta-analysis of publications estimating BC recurrence at the population-level using algorithms based on administrative data. METHODS The systematic literature search followed Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model (GLMM) to obtain a pooled estimate of accuracy. RESULTS Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%), compared to studies using detection rules without specified model (58.8%). The GLMM for all recurrence types reported an accuracy of 92.2% (95%CI 88.4-94.8%). CONCLUSION Publications reporting algorithms for detecting BC recurrence are limited in number and heterogeneous. A thorough analysis of the existing

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