SynthesisAnesthesiology2024
Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.
Synthesis in Anesthesiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- AI-Based Sepsis Prediction in Hospitalized Adults: Systematic Review, Subgroup Meta-Analysis, and Contextual Analysis of Clinical Burden.Journal of medical Internet research · 2026Pooled it
- Prediction of Factors Influencing the Incidence of Diabetic Foot Ulcers Using Classical Statistical and Machine Learning Approaches: A Systematic Review.International wound journal · 2026Pooled it
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Machine Learning for Predicting Venous Thromboembolism After Joint Arthroplasty: Systematic Review of Clinical Applicability and Model Performance.JMIR medical informatics · 2026Pooled it
- Pooled it
- Comprehensive overview of artificial intelligence in surgery: a systematic review and perspectives.Pflugers Archiv : European journal of physiology · 2025Pooled it
- A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.PLoS medicine · 2025Pooled it
- Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.British journal of anaesthesia · 2024Pooled it
- Dose-conditioned machine-learning prediction of a composite target sedation state in patients undergoing propofol-sedated gastrointestinal endoscopy.Annals of medicine · 2026Observational
- Clinical AI and Precision Medicine: Philosophical Questions About Labels, Disease, and Evidence.Journal of medical systems · 2026Article
- Development and Clinical Utility of Machine Learning Models for Prediction of Same-Day Discharge in Outpatient Hip and Knee Replacement: A Prognostic Study.Acta anaesthesiologica Scandinavica · 2026Article
- Review
- Development and external validation of the NEO-READY model to predict date of discharge among premature neonatal intensive care patients.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Machine learning-based prediction of 30-day mortality in critically ill patients with rheumatoid arthritis.Clinical rheumatology · 2026Article
- Interpretable machine learning models for predicting perioperative myocardial injury in non-cardiac surgery.European heart journal. Digital health · 2026Article
- Algorithmic bias in surgical risk prediction models and its impact on patient safety: a review.Patient safety in surgery · 2026Review
- Prospective validation and real-time implementation of an automated machine learning postoperative mortality prediction model.British journal of anaesthesia · 2026Article
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- Cohort profile: PeRiOperative sTress risk assEssment and Clinical decision cohorT (PROTECT), a multi-center observational study based on real-world data.BMC geriatrics · 2026Observational
- Comparison of human-AI agreement in ASA scoring by gender and duration of clinical experience: a real-world study.BMC medical informatics and decision making · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundThe utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation.
methodsA multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies.
resultsA total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications.
conclusionsThe findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE:
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Registered trials
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.