Impact of Operative Approach on Acute Kidney Injury Risk Prediction Models for Colectomy.

INTRODUCTION: Acute kidney injury (AKI) is a serious postoperative complication associated with increased morbidity and mortality. Identifying patients at risk for AKI is important for risk stratification and management. This study aimed to develop an AKI risk prediction model for colectomy and determine if the operative approach (laparoscopic versus open) alters the influence of predictive factors through an interaction term analysis. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was analyzed from 2005 to 2019. Patients undergoing laparoscopic and open colectomy were identified and propensity score matched. Multivariable logistic regression identified significant preoperative demographic, comorbidity, and laboratory value predictors of AKI. The predictive ability of a baseline model consisting of these variables was compared to a proposed model incorporating interaction terms between operative approach and predictor variables using the likelihood ratio test, c-statistic, and Brier score. Shapley Additive Explanations values assessed relative importance of significant predictors. RESULTS: 252,372 patients were included in the analysis. Significant AKI predictors were hypertension, age, sex, race, body mass index, smoking, diabetes, preoperative sepsis, Congestive heart failure, preoperative creatinine, preoperative albumin, and operative approach (P < 0.001). The proposed model with interaction terms had improved predictive ability per the likelihood ratio test (P < 0.05) but had no statistically significant interaction terms. C-statistic and Brier scores did not improve. Shapley Additive Explanations analysis showed hypertension had the highest importance. The importance of age and diabetes showed some variation between operative approaches. CONCLUSIONS: While the inclusion of interaction terms collectively improved AKI prediction, no individual operative approach interaction terms were significant. Including operative approach interactions may enhance predictive ability of AKI risk models for colectomy.

Hypoalbuminemia improves the ACS-NSQIP surgical risk calculator for gastrectomy.

BACKGROUND: The ACS-NSQIP Surgical Risk Calculator (SRC) is used to predict surgical outcomes, but its accuracy in gastrectomy has been questioned.1,2 We investigated if adding hypoalbuminemia enhances its predictive ability in gastrectomy. METHODS: We identified gastrectomy patients from the ACS-NSQIP database from 2005 to 2019. We constructed pairs of logistic regression models: one with the existing 21 preoperative risk factors from the SRC and another with the addition of hypoalbuminemia. We evaluated improvement using Likelihood Ratio Test (LRT), Brier scores, and c-statistics. RESULTS: Of 18,070 gastrectomy patients, 34.5 â€‹% had hypoalbuminemia. Hypoalbuminemia patients had 2.34 higher odds of mortality and 1.79 higher odds of morbidity. Adding hypoalbuminemia to the RC model statistically improved predictions for mortality, cumulative morbidity, pulmonary, renal, and wound complications (LRT p â€‹< â€‹0.001). It did not improve predictions for cardiac complications (LRT p â€‹= â€‹0.11) CONCLUSION: Hypoalbuminemia should be considered as an additional variable to the ACS-NSQIP SRC for gastrectomy.

Association of Preoperative Blood Transfusion on Postoperative Outcomes in Emergency General Surgery.

INTRODUCTION: Emergency general surgery (EGS) patients often present with anemia, in which preoperative transfusions are performed to mitigate anemia-associated risks. However, transfusions have also been noted to cause worse postoperative outcomes. This study examined how transfusion-associated outcomes vary at different levels of anemia. MATERIALS AND METHODS: The American College of Surgeons National Surgical Quality Improvement Program database from 2005 to 2019 was used to identify patients who had undergone any of 12 major EGS procedures using Current Procedural Terminology codes. Patients were divided into two cohorts based on receipt of preoperative transfusion. Cohorts were subdivided into anemia severity levels and propensity score-matched within each using patient demographic and comorbidity variables. We analyzed 30-day postoperative outcomes, including morbidity, mortality, and return to odds ratio (OR), using univariate Chi-squared tests, Wilcoxon signed-rank tests, and multivariate logistic regression analyses. RESULTS: 595,407 EGS cases were identified. Receiving preoperative transfusion were 44.45% (n = 3058) of severely anemic, 10.94% (n = 9076) of moderately anemic, 1.34% (n = 1370) of mildly anemic, and 0.174% (n = 704) of no anemia patients. Transfusion resulted in an increased overall morbidity in the severe (OR 1.54), moderate (OR 1.50), mild (OR 1.71), and no anemia (OR 1.85) groups. Mortality increased in the moderate (OR 1.27), mild (OR 1.61), and no anemia (OR 1.76) subgroups. In severe anemia, transfusion status and mortality were not significantly associated. CONCLUSIONS: Transfusion is associated with higher morbidity and mortality rates in those with higher hematocrit levels, even after controlling for pre-existing comorbidities. A restrictive transfusion strategy should be considered to avoid risks for those with a hematocrit level more than 24%.