Web of Science: 6 cites, Scopus: 10 cites, Google Scholar: cites,
Preoperative clinical model to predict myocardial injury after non-cardiac surgery : A retrospective analysis from the MANAGE cohort in a Spanish hospital
Serrano, Ana Belén (IRYCIS)
Gomez-Rojo, María (IRYCIS)
Ureta, Eva (IRYCIS)
Nuñez, Mónica (IRYCIS)
Fernández-Félix, Borja (IRYCIS)
Velasco, Elisa (IRYCIS)
Burgos, Javier (IRYCIS)
Popova, Ekaterine (Institut d'Investigació Biomèdica Sant Pau)
Urrútia, Gerard (Institut d'Investigació Biomèdica Sant Pau)
Gómez Dos Santos, Victoria (IRYCIS)
Del Rey, José Manuel (IRYCIS)
Sanjuanbenito, Alfonso (IRYCIS)
Zamora, Javier (University of Birmingham. Institute of Metabolism and Systems Researchs)
Monteagudo, Juan Manuel (IRYCIS)
Pestaña, David (Universidad de Alcalá)
De La Torre, Basilio (IRYCIS)
Candela-Toha, Angel (IRYCIS)
Universitat Autònoma de Barcelona

Data: 2021
Resum: 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Publicat a: BMJ open, Vol. 11 Núm. 8 (april 2021) , p. e045052, ISSN 2044-6055

DOI: 10.1136/bmjopen-2020-045052
PMID: 34348944


8 p, 729.8 KB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut de Recerca Sant Pau
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 Registre creat el 2023-02-17, darrera modificació el 2024-05-03



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