Article

The revised cardiac risk index delivers what it promised.

Annals of internal medicine (Impact Factor: 16.1). 01/2010; 152(1):57-8. DOI: 10.1059/0003-4819-152-1-201001050-00013
Source: PubMed
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    ABSTRACT: To develop a 30-day mortality risk index for noncardiac surgery that can be used to communicate risk information to patients and guide clinical management at the "point-of-care," and that can be used by surgeons and hospitals to internally audit their quality of care. Clinicians rely on the Revised Cardiac Risk Index to quantify the risk of cardiac complications in patients undergoing noncardiac surgery. Because mortality from noncardiac causes accounts for many perioperative deaths, there is also a need for a simple bedside risk index to predict 30-day all-cause mortality after noncardiac surgery. Retrospective cohort study of 298,772 patients undergoing noncardiac surgery during 2005 to 2007 using the American College of Surgeons National Surgical Quality Improvement Program database. The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index was derived empirically and includes three risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. Patients with risk scores less than 5 had a predicted risk of mortality less than 0.50%, whereas patients with a risk score of 5 to 6 had a risk of mortality between 1.5% and 4.0%. Patients with a risk score greater than 6 had risk of mortality more than 10%. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set. Thirty-day mortality after noncardiac surgery can be accurately predicted using a simple and accurate risk score based on information readily available at the bedside. This risk index may play a useful role in facilitating shared decision making, developing and implementing risk-reduction strategies, and guiding quality improvement efforts.
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    ABSTRACT: To better characterize patient understanding of their risk of cardiac complications from non-cardiac surgery and to develop a patient driven clinical decision support system for preoperative patient risk management. A patient-driven preoperative self-assessment decision support tool for perioperative assessment was created. Patient' self-perception of cardiac risk and self-report data for risk factors were compared with gold standard preoperative physician assessment to evaluate agreement. The patient generated cardiac risk profile was used for risk score generation and had excellent agreement with the expert physician assessment. However, patient subjective self-perception risk of cardiovascular complications had poor agreement with expert assessment. A patient driven cardiac risk assessment tool provides a high degree of agreement with expert provider assessment demonstrating clinical feasibility. The limited agreement between provider risk assessment and patient self-perception underscores a need for further work including focused preoperative patient education on cardiac risk.
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    ABSTRACT: External validation of published risk stratification models is essential to determine their generalizability. This study evaluates the performance of the Risk Stratification Indices (RSIs) and 30-day mortality Risk Quantification Index (RQI). 108,423 adult hospital admissions with anesthetics were identified (2006–2011). RSIs for mortality and length-of-stay endpoints were calculated using published methodology. 91,128 adult, noncardiac inpatient surgeries were identified with administrative data required for RQI calculation. RSI in-hospital mortality and RQI 30-day mortality Brier scores were 0.308 and 0.017, respectively. RSI discrimination, by area under the receiver operating curves, was excellent at 0.966 (95% CI, 0.963–0.970) for in-hospital mortality, 0.903 (0.896–0.909) for 30-day mortality, 0.866 (0.861–0.870) for 1-yr mortality, and 0.884 (0.882–0.886) for length-of-stay. RSI calibration, however, was poor overall (17% predicted in-hospital mortality vs. 1.5% observed after inclusion of the regression constant) as demonstrated by calibration plots. Removal of self-fulfilling diagnosis and procedure codes (20,001 of 108,423; 20%) yielded similar results. RQIs were calculated for only 62,640 of 91,128 patients (68.7%) due to unmatched procedure codes. Patients with unmatched codes were younger, had higher American Society of Anesthesiologists physical status and 30-day mortality. The area under the receiver operating curve for 30-day mortality RQI was 0.888 (0.879–0.897). The model also demonstrated good calibration. Performance of a restricted index, Procedure Severity Score + American Society of Anesthesiologists physical status, performed as well as the original RQI model (age + American Society of Anesthesiologists + Procedure Severity Score). Although the RSIs demonstrated excellent discrimination, poor calibration limits their generalizability. The 30-day mortality RQI performed well with age providing a limited contribution.
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