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Improving Identification of Patients at Low Risk for Major Cardiac Events After Noncardiac Surgery Using Intraoperative Data
Author(s) -
Navathe Amol S,
Lei Victor J,
Fleisher Lee A,
Luong ThaiBinh,
Chen Xinwei,
Kennedy Edward,
Volpp Kevin G,
Polsky Daniel E,
Groeneveld Peter W,
Weiner Mark,
Holmes John H,
Neuman Mark D
Publication year - 2020
Publication title -
journal of hospital medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.128
H-Index - 65
eISSN - 1553-5606
pISSN - 1553-5592
DOI - 10.12788/jhm.3459
Subject(s) - medicine , mace , perioperative , receiver operating characteristic , logistic regression , risk assessment , retrospective cohort study , risk stratification , emergency medicine , cohort , surgery , percutaneous coronary intervention , myocardial infarction , computer security , computer science
BACKGROUND/OBJECTIVE Risk‐stratification tools for cardiac complications after noncardiac surgery based on preoperative risk factors are used to inform postoperative management. However, there is limited evidence on whether risk stratification can be improved by incorporating data collected intraoperatively, particularly for low‐risk patients. METHODS We conducted a retrospective cohort study of adults who underwent noncardiac surgery between 2014 and 2018 at four hospitals in the United States. Logistic regression with elastic net selection was used to classify in‐hospital major adverse cardiovascular events (MACE) using preoperative and intraoperative data (“perioperative model”). We compared model performance to standard risk stratification tools and professional society guidelines that do not use intraoperative data. RESULTS Of 72,909 patients, 558 (0.77%) experienced MACE. Those with MACE were older and less likely to be female. The perioperative model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI, 0.85‐0.92). This was higher than the Lee Revised Cardiac Risk Index (RCRI) AUC of 0.79 (95% CI, 0.74‐0.84; P < .001 for AUC comparison). There were more MACE complications in the top decile (n = 1,465) of the perioperative model's predicted risk compared with that of the RCRI model (n = 58 vs 43). Additionally, the perioperative model identified 2,341 of 7,597 (31%) patients as low risk who did not experience MACE but were recommended to receive postoperative biomarker testing by a risk factor–based guideline algorithm. CONCLUSIONS Addition of intraoperative data to preoperative data improved prediction of cardiovascular complication outcomes after noncardiac surgery and could potentially help reduce unnecessary postoperative testing.