Preoperative clinical model to predict myocardial injury after non-cardiac surgery: a retrospective analysis from the MANAGE cohort in a Spanish hospital

Ana Belen Serrano*, Maria Gomez-Rojo, Eva Ureta, Monica Nuñez, Borja Fernández Félix, Elisa Velasco, Javier Burgos, Ekaterine Popova, Gerard Urrutia, Victoria Gomez, Jose Manuel Del Rey, Alfonso Sanjuanbenito, Javier Zamora, Juan Manuel Monteagudo, David Pestaña, Basilio De La Torre, Ángel Candela-Toha

*Corresponding author for this work

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Abstract

Objectives To determine preoperative factors associated to myocardial injury after non-cardiac surgery (MINS) and to develop a prediction model of MINS. Design Retrospective analysis. Setting Tertiary hospital in Spain. Participants Patients aged ≥45 years undergoing major non-cardiac surgery and with at least two measures of troponin levels within the first 3 days of the postoperative period. All patients were screened for the MANAGE trial. Primary and secondary outcome measures We used multivariable logistic regression analysis to study risk factors associated with MINS and created a score predicting the preoperative risk for MINS and a nomogram to facilitate bed-side use. We used Least Absolute Shrinkage and Selection Operator method to choose the factors included in the predictive model with MINS as dependent variable. The predictive ability of the model was evaluated. Discrimination was assessed with the area under the receiver operating characteristic curve (AUC) and calibration was visually assessed using calibration plots representing deciles of predicted probability of MINS against the observed rate in each risk group and the calibration-in-the-large (CITL) and the calibration slope. We created a nomogram to facilitate obtaining risk estimates for patients at pre-anaesthesia evaluation. Results Our cohort included 3633 patients recruited from 9 September 2014 to 17 July 2017. The incidence of MINS was 9%. Preoperative risk factors that increased the risk of MINS were age, American Status Anaesthesiology classification and vascular surgery. The predictive model showed good performance in terms of discrimination (AUC=0.720; 95% CI: 0.69 to 0.75) and calibration slope=1.043 (95% CI: 0.90 to 1.18) and CITL=0.00 (95% CI: -0.12 to 0.12). Conclusions Our predictive model based on routinely preoperative information is highly affordable and might be a useful tool to identify moderate-high risk patients before surgery. However, external validation is needed before implementation.

Original languageEnglish
Article numbere045052
JournalBMJ open
Volume11
Issue number8
DOIs
Publication statusPublished - 4 Aug 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 International Association for Bear Research and Management. All rights reserved.

Keywords

  • myocardial infarction
  • preventive medicine
  • surgery

ASJC Scopus subject areas

  • General Medicine

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