ArticleJournal of clinical anesthesia2023
Development of an automated, general-purpose prediction tool for postoperative respiratory failure using machine learning: A retrospective cohort study.
Article in Journal of clinical anesthesia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
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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
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Risk assessment and prevention of postoperative and postprocedural respiratory failure: recommendations from an international expert panel.British journal of anaesthesia · 2026Guideline
- Prediction models for postoperative pulmonary complications: a systematic review and meta-analysis.British journal of anaesthesia · 2025Pooled it
- Artificial intelligence for predicting perioperative anaesthetic complications and supporting clinical decision-making: a scoping review.Journal of clinical monitoring and computing · 2026Review
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- A web-based two-stage prediction model for postoperative pulmonary complications using preoperative and intraoperative data.Journal of anesthesia and translational medicine · 2026Article
- Automating eligibility assessment and enrollment for sugammadex administration within an integrated perioperative workflow.JAMIA open · 2026Article
- Predicting Unplanned Intubations in Rib Fracture Patients: An Interpretable Machine Learning Approach.The American surgeon · 2026Article
- Prognostic value of the ARISCAT score for postoperative pneumonia in patients with esophageal squamous cell carcinoma: a retrospective cohort study.European journal of medical research · 2025Article
- Article
- PRESCO: an online tool for predicting severe pulmonary complications and survival after cancer surgery.Frontiers in oncology · 2025Article
- Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety.BMC anesthesiology · 2024Article
- A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population.CHEST critical care · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
STUDY
objectivePostoperative respiratory failure is a major surgical complication and key quality metric. Existing prediction tools underperform, are limited to specific populations, and necessitate manual calculation. This limits their implementation. We aimed to create an improved, machine learning powered prediction tool with ideal characteristics for automated calculation. DESIGN, SETTING, AND PATIENTS: We retrospectively reviewed 101,455 anesthetic procedures from 1/2018 to 6/2021. The primary outcome was the Standardized Endpoints in Perioperative Medicine consensus definition for postoperative respiratory failure. Secondary outcomes were respiratory quality metrics from the National Surgery Quality Improvement Sample, Society of Thoracic Surgeons, and CMS. We abstracted from the electronic health record 26 procedural and physiologic variables previously identified as respiratory failure risk factors. We randomly split the cohort and used the Random Forest method to predict the composite outcome in the training cohort. We coined this the RESPIRE model and measured its accuracy in the validation cohort using area under the receiver operating curve (AUROC) analysis, among other measures, and compared this with ARISCAT and SPORC-1, two leading prediction tools. We compared performance in a validation cohort using score cut-offs determined in a separate test cohort. MAIN
resultsThe RESPIRE model exhibited superior accuracy with an AUROC of 0.93 (95% CI, 0.92-0.95) compared to 0.82 for both ARISCAT and SPORC-1 (P-for-difference < 0.0001 for both). At comparable 80-90% sensitivities, RESPIRE had higher positive predictive value (11%, 95% CI: 10-12%) and lower false positive rate (12%, 95% CI: 12-13%) compared to 4% and 37% for both ARISCAT and SPORC-1. The RESPIRE model also better predicted the established quality metrics for postoperative respiratory failure.
conclusionsWe developed a general-purpose, machine learning powered prediction tool with superior performance for research and quality-based definitions of postoperative respiratory failure.
Indexed as
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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.