Predictors of peri-operative cardiac events and development of a scoring tool for patients with chronic kidney disease undergoing non-cardiac surgeries: A prospective observational multicentre study.

Autor: Deo AS; Department of Anaesthesiology, NU Hospitals, Bengaluru, Karnataka, India., Kashyapi R; Department of Anaesthesiology, Deenanath Mangeshkar Hospital and Research Centre, Pune, Maharashtra, India., Joshi V; Department Biostatistics, NU Hospitals, Bengaluru, Karnataka, India., Balakundi P; Department of Anaesthesiology, NU Hospitals, Bengaluru, Karnataka, India., Raman P; Department of Anaesthesiology, NU Hospitals, Bengaluru, Karnataka, India.
Jazyk: angličtina
Zdroj: Indian journal of anaesthesia [Indian J Anaesth] 2022 Apr; Vol. 66 (4), pp. 278-289. Date of Electronic Publication: 2022 Apr 20.
DOI: 10.4103/ija.ija_1031_21
Abstrakt: Background and Aims: Cardiovascular diseases are the leading causes of morbidity and mortality in chronic kidney disease (CKD) patients. Our aim was to derive predictors of cardiac morbidity, mortality, cardiac complications and to develop/validate a scoring tool in patients with CKD undergoing non-cardiac surgery.
Methods: A prospective observational multicentre study was done on 770 patients with CKD. The primary outcome ("Event") was one or more than one of sudden cardiac death, pulmonary oedema, acute coronary syndrome, arrhythmia and 30-day mortality. Secondary outcome was hypertension and hypotension. Predictors of cardiac risk were identified. A scoring tool was developed on the 2018 dataset and was validated on the 2019 dataset.
Results: The overall incidence of cardiac events was 290 (37.66%) whereas the incidence of major adverse cardiac and cerebrovascular events was 15.04%. Mortality due to cardiac cause was 13 (1.68%). On multivariate regression analysis, seven perioperative variables had significant association with increased risk of events: age > 65 years ( P = 0.004), metabolic equivalents (METS) ≤4 ( P ≤0.032), emergency surgery ( P =0.032), mean arterial pressure >119 ( P = 0.001), echocardiographic scoring ( P = 0.054), type of anaesthesia ( P ≤ 0.0001) and type of surgery ( P = 0.056). Using these variables, a risk stratification tool was developed. C statistics showed favourable predictive accuracy (0.714) and the model showed good calibration.
Conclusion: This risk scoring tool based on preoperative variables will help to predict the risk of events in high-risk CKD patients undergoing non-cardiac surgery. This will help in better counselling and optimisation.
Competing Interests: There are no conflicts of interest.
(Copyright: © 2022 Indian Journal of Anaesthesia.)
Databáze: MEDLINE
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