Authors: Maxim A. Terekhov et al
Anesthesiology 11 2015, Vol.123, 1059-1066. doi:10.1097/ALN.0000000000000858
Background: Estimating surgical risk is critical for perioperative decision making and risk stratification. Current risk-adjustment measures do not integrate dynamic clinical parameters along with baseline patient characteristics, which may allow a more accurate prediction of surgical risk. The goal of this study was to determine whether the preoperative Risk Quantification Index (RQI) and Present-On-Admission Risk (POARisk) models would be improved by including the intraoperative Surgical Apgar Score (SAS).
Methods: The authors identified adult patients admitted after noncardiac surgery. The RQI and POARisk were calculated using published methodologies, and model performance was compared with and without the SAS. Relative quality was measured using Akaike and Bayesian information criteria. Calibration was compared by the Brier score. Discrimination was compared by the area under the receiver operating curves (AUROCs) using a bootstrapping procedure for bias correction.
Results: SAS alone was a statistically significant predictor of both 30-day mortality and in-hospital mortality (P < 0.0001). The RQI had excellent discrimination with an AUROC of 0.8433, which increased to 0.8529 with the addition of the SAS. The POARisk had excellent discrimination with an AUROC of 0.8608, which increased to 0.8645 by including the SAS. Similarly, overall performance and relative quality increased.
Conclusions: While AUROC values increased, the RQI and POARisk preoperative risk models were not meaningfully improved by adding intraoperative risk using the SAS. In addition to the estimated blood loss, lowest heart rate, and lowest mean arterial pressure, other dynamic clinical parameters from the patient’s intraoperative course may need to be combined with procedural risk estimate models to improve risk stratification.
Both the Risk Quantification Index and Present-On-Admission Risk predicted mortality well. Adding the Surgical Apgar Score did not substantively improve predictions.
What We Already Know about This Topic
- The Risk Quantification Index and Present-On-Admission Risk Index predict postoperative mortality based on administrative data only
- The Surgical Apgar Score estimates risk from estimated blood loss, lowest heart rate, and lowest mean arterial pressure
- It remains unknown whether adding intraoperative details (which are harder to obtain) to administrative data improves predictions by either model
What This Article Tells Us That Is New
- Both the Risk Quantification Index and Present-On-Admission Risk Index predicted mortality well
- Adding the Surgical Apgar Score did not substantively improve predictions
ESTIMATING surgical risk is critical for both preoperative and postoperative decision making. There is a growing need for more accurate risk stratification with the adoption of new payment methodologies, such as population health management, bundled payments, and value-based purchasing. Specific indices have been described for surgical risk, which include the Risk Stratification Indices (RSIs) the Risk Quantification Indices (RQIs) the Present-On-Admission Risk (POARisk) model and models from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP).
The Surgical Apgar Score (SAS) uses estimated blood loss (EBL), lowest heart rate (HR), and lowest mean arterial pressure (MAP) in calculating value on a 10-point scale that is predictive of surgical outcomes. These routinely available intraoperative data can provide an objective means of measuring and communicating patient risk from surgery. The SAS has been validated in multiple patient populations and is used to predict mortality, morbidity, and intensive care unit admission. The predictive performance of this validated intraoperative risk model has not been characterized in a general surgical population when used in conjunction with preoperative risk indices.
The goal of this study was two fold: to externally validate the POARisk model and to determine whether the preoperative risk estimates would be improved by incorporating intraoperative risk estimates by evaluating the performance of the RQI and POARisk models with and without the SAS.
Leave a Reply
You must be logged in to post a comment.