| Autor: |
Victoria Gomez, Ekaterine Popova, Javier Zamora, María Gómez-Rojo, David Pestaña, Gerard Urrútia, José Manuel del Rey, Ana Belen Serrano, Eva Ureta, Monica Nuñez, Borja Fernández Félix, Elisa Velasco, Javier Burgos, Alfonso Sanjuanbenito, Juan Manuel Monteagudo, Basilio de la Torre, Ángel Candela-Toha |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
BMJ Open, Vol 11, Iss 8 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2044-6055 |
| DOI: |
10.1136/bmjopen-2020-045052 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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