Evidence map›Paper›PMID 35996071›Full record

ArticleCanadian journal of anaesthesia = Journal canadien d'anesthesie2022

Postoperative mortality risk prediction that incorporates intraoperative vital signs: development and internal validation in a historical cohort.

Janny Xue Chen Ke, Daniel I McIsaac, Ronald B George, Paula Branco, E Francis Cook, W Scott Beattie, Robin Urquhart, David B MacDonald

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In one paragraph

Article in Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04014010 (Machine Learning Modeling of Intraoperative Hemodynamic Predictors of 30-day Mortality and Major In-hospital Morbidity After Noncardiac Surgery), which is not on this map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT04014010 completednot on this map

Machine Learning Modeling of Intraoperative Hemodynamic Predictors of 30-day Mortality and Major In-hospital Morbidity After Noncardiac Surgery: a Retrospective Population Cohort Study

TypeobservationalSponsorJanny Xue Chen KeRan2013 to 2017Enrolled35,000ConditionsPerioperative/Postoperative Complications, Morbidity, Multiple, Surgery, AnesthesiaArmsBlood pressure, Heart rate, Use of hemodynamic medications (i.e. special medications for blood pressure), Oxygen saturation by pulse oximetry (SpO2), End-tidal Carbon dioxide (EtCO2)
3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Janny Xue Chen KeDepartment of Anesthesia, Pain Management & Perioperative Medicine, Dalhousie University, Halifax, NS, Canada. janny.ke@ubc.ca.ORCID http://orcid.org/0000-0002-3121-9780
Daniel I McIsaacDepartment of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada.
Ronald B GeorgeDepartment of Anesthesia, Pain Management & Perioperative Medicine, Dalhousie University, Halifax, NS, Canada.
Paula BrancoSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.
E Francis CookT. H. Chan School of Public Health, Harvard University, Boston, MB, USA.
W Scott BeattieDepartment of Anesthesia, University of Toronto, Toronto, ON, Canada.
Robin UrquhartDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada.
David B MacDonaldDepartment of Anesthesia, Pain Management & Perioperative Medicine, Dalhousie University, Halifax, NS, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAccurate risk reassessment after surgery is crucial for postoperative planning for monitoring and disposition. Existing postoperative mortality risk prediction models using preoperative features do not incorporate intraoperative hemodynamic derangements that may alter risk stratification. Intraoperative vital signs may provide an objective and readily available prognostic resource. Our primary objective was to derive and internally validate a logistic regression (LR) model by adding intraoperative features to established preoperative predictors to predict 30-day postoperative mortality.

methodsFollowing Research Ethics Board approval, we analyzed a historical cohort that included patients aged ≥ 45 undergoing noncardiac surgery with an overnight stay at two tertiary hospitals (2013 to 2017). Features included intraoperative vital signs (blood pressure, heart rate, end-tidal carbon dioxide partial pressure, oxygen saturation, and temperature) by threshold and duration of exposure, as well as patient, surgical, and anesthetic factors. The cohort was divided temporally 75:25 into derivation and validation sets. We constructed a multivariable LR model with 30-day all-cause mortality as the outcome and evaluated performance metrics.

resultsThere were 30,619 patients in the cohort (mean [standard deviation] age, 66 [11] yr; 50.2% female; 2.0% mortality). In the validation set, the primary LR model showed a c-statistic of 0.893 (99% confidence interval [CI], 0.853 to 0.927), a Nagelkerke R-squared of 0.269, a scaled Brier score of 0.082, and an area under precision-recall curve of 0.158 (baseline 0.017 for an uninformative model). The addition of intraoperative vital signs to preoperative factors minimally improved discrimination and calibration.

conclusionWe derived and internally validated a model that incorporated vital signs to improve risk stratification after surgery. Preoperative factors were strongly predictive of mortality risk, and intraoperative predictors only minimally improved discrimination. External and prospective validations are needed. STUDY REGISTRATION: www. CLINICALTRIALS: gov (NCT04014010); registered on 10 July 2019.

Indexed as

Vital SignsAgedCohort StudiesFemaleHumansLogistic ModelsMalePrognosisRetrospective StudiesRisk AssessmentRisk Factorsinformaticsmortalityperioperative medicinerisk predictionvital signs

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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.