ArticleAnnals of surgery2019
MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery.
Article in Annals of surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 173 papers, 11 of them syntheses that pooled it.
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Who cites it
173 citing papers in PubMed, 11 syntheses or guidelines pooled it, 331 citations in OpenAlex.
- Artificial intelligence for venous thromboembolism risk stratification in surgical patients: a systematic review.Journal of thrombosis and thrombolysis · 2026Pooled it
- Precision Medicine and Artificial Intelligence in Next-Generation Cancer Surgery: A Comprehensive Analysis of Clinical Applications, Therapeutic Outcomes, and Implementation Strategies.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.British journal of anaesthesia · 2024Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbeck's archives of surgery · 2022Pooled it
- Controversies in acute kidney injury: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference.Kidney international · 2020 · on this mapGuideline
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.Intensive care medicine · 2020Pooled it
- Systematic review and narrative synthesis of surgeons' perception of postoperative outcomes and risk.BJS open · 2020Pooled it
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study.Surgery · 2019Trial
- Material type versus defect size in adult cranioplasty: a multi-institutional analysis of 30-day outcomes and risk prediction modeling.JPRAS open · 2026Article
- Decision-centered artificial intelligence for perioperative care outside the operating room: a practical review for surgeons.Journal of minimally invasive surgery · 2026Review
- Role of artificial intelligence in head and neck surgery - A narrative review.Turkish journal of surgery · 2026Article
- Prediction of postoperative pulmonary infection after video-assisted thoracoscopic anatomical pulmonary resection using an interpretable machine learning model.Journal of thoracic disease · 2026Article
- Review
- Prognostic Factors for Postoperative Complications. An Aggregate Protocol for 10 Observational Studies From the Danish TRIPLE-A Cohort of 1.2 Million Surgeries.Acta anaesthesiologica Scandinavica · 2026Article
- Development of the Novel G-CMAP Score: A Preoperative Scoring System for Predicting Postoperative Complications After Gastrectomy for Gastric Cancer.Annals of surgical oncology · 2026Article
- Integration of Precision Medicine into ERAS Pathways: A Conceptual Framework, Current Feasibility and Challenges.Journal of personalized medicine · 2026Review
113 more citing papers are in PubMed but not listed here.
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Authors and funding
16 authors at 3 institutions in 2 countries.
Funding
Abstract
objectiveTo accurately calculate the risk for postoperative complications and death after surgery in the preoperative period using machine-learning modeling of clinical data.
backgroundPostoperative complications cause a 2-fold increase in the 30-day mortality and cost, and are associated with long-term consequences. The ability to precisely forecast the risk for major complications before surgery is limited.
methodsIn a single-center cohort of 51,457 surgical patients undergoing major inpatient surgery, we have developed and validated an automated analytics framework for a preoperative risk algorithm (MySurgeryRisk) that uses existing clinical data in electronic health records to forecast patient-level probabilistic risk scores for 8 major postoperative complications (acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission >48 hours, mechanical ventilation >48 hours, wound, neurologic, and cardiovascular complications) and death up to 24 months after surgery. We used the area under the receiver characteristic curve (AUC) and predictiveness curves to evaluate model performance.
resultsMySurgeryRisk calculates probabilistic risk scores for 8 postoperative complications with AUC values ranging between 0.82 and 0.94 [99% confidence intervals (CIs) 0.81-0.94]. The model predicts the risk for death at 1, 3, 6, 12, and 24 months with AUC values ranging between 0.77 and 0.83 (99% CI 0.76-0.85).
conclusionsWe constructed an automated predictive analytics framework for machine-learning algorithm with high discriminatory ability for assessing the risk of surgical complications and death using readily available preoperative electronic health records data. The feasibility of this novel algorithm implemented in real time clinical workflow requires further testing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.