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Published on: November 28, 2018
A Novel Integrated Perioperative Cardiovascular Risk Score (PERFORM-CV) in Non-Cardiac Surgical Patients
Andreea Boghean1, Cristian Gutu1,2, Laura Florentina Rebegea1,3
1Faculty of Medicine and Pharmacy, Research Centre in the Medical-Pharmaceutical Field, "Dunarea de Jos" University of Galati, 800008 Galati, Romania.
Background:
Perioperative cardiovascular risk assessment remains challenging in non-cardiac surgery, particularly in older patients and those with multiple comorbidities. Traditional models rely largely on clinical history and may not fully reflect current cardiovascular functional status. This study aimed to derive and assess the apparent performance of a new composite score, PERFORM-CV, integrating clinical, laboratory, and echocardiographic data.
Methods:
We conducted a prospective two-center cohort study including 503 non-cardiac surgical patients with cardiovascular comorbidity. The Revised Cardiac Risk Index (Lee/RCRI) and the AUB-HAS2 index were calculated according to their original published definitions as raw point totals ranging from 0 to 6; without additional normalization. The PERFORM-CV score was derived from univariable and multivariable analyses, with continuous predictors dichotomized using ROC-derived thresholds.
Results:
Emergency admission, chronic heart failure, and elevated serum creatinine remained independently associated with in-hospital mortality. Lower left ventricular ejection fraction, lower mitral annular plane systolic excursion (MAPSE), lower hemoglobin, and atrial fibrillation also contributed to the final composite score. ROC analysis showed good discrimination for PERFORM-CV (AUC 0.852; 95% CI 0.806-0.897; p < 0.001), comparable to Lee/RCRI (AUC 0.860; 95% CI 0.818-0.901; p < 0.001) and higher than AUB-HAS2 (AUC 0.779; 95% CI 0.731-0.826; p < 0.001).
Conclusions:
PERFORM-CV showed good apparent discrimination in the derivation cohort and may complement established bedside risk tools by incorporating echocardiographic and laboratory data. The ROC-derived thresholds should be interpreted as data-driven derivation cut-offs; resampling-based internal validation and external validation are required before broader clinical use.
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