Cochrane corner: Biomarkers to improve preoperative prediction of major cardiac outcomes after non-cardiac surgery
The revised cardiac risk index (RCRI) is such a prediction model developed over 20 years ago.2 It preoperatively predicts in-hospital MACE using six predictors, namely high-risk surgery (ie, intraperitoneal, intrathoracic or supra-inguinal vascular surgery), history of ischaemic heart disease, histo...
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Veröffentlicht in: | Heart (British Cardiac Society) 2022-07, Vol.108 (16), p.1257-1259 |
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Zusammenfassung: | The revised cardiac risk index (RCRI) is such a prediction model developed over 20 years ago.2 It preoperatively predicts in-hospital MACE using six predictors, namely high-risk surgery (ie, intraperitoneal, intrathoracic or supra-inguinal vascular surgery), history of ischaemic heart disease, history of congestive heart failure, history of cerebrovascular disease, insulin therapy for diabetes mellitus and preoperative serum creatinine ≥2 mg/dL. ASA, American Society of Anesthesiologists classification; BNP, B type natriuretic peptide; MACE, major adverse cardiac event; N, number; NSQIP-MICA, National Surgical Quality Improvement to predict Myocardial Infarction or Cardiac Arrest; NT-proBNP, N‐terminal pro‐B type natriuretic peptide; RCRI, Revised Cardiac Risk Index; VSG-CRI, Vascular Study Group of New England Cardiac Risk Index. [...]the use of standardised endpoints in prediction research in non-cardiac surgery patients is mandatory. The StEP initiative provides guidance on standardised definitions for cardiovascular endpoints in perioperative medicine and defined MACE as a composite of cardiac death, myocardial infarction, non-fatal cardiac arrest and coronary revascularisation within 30 days after the index surgery.5 Besides standardised endpoints, following guidance on the reporting of primary prediction model studies such as TRIPOD and PROBAST is strongly advised to facilitate (individual patient data and aggregate) meta-analyses and to improve the generalisability and comparability between studies, and thus the clinical application of prediction models and biomarkers. |
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ISSN: | 1355-6037 1468-201X |
DOI: | 10.1136/heartjnl-2022-321055 |