Minjae Kim1,2, Gen Li3, Sumit Mohan2,4, Zachary A Turnbull5, Ravi P Kiran2,6, Guohua Li1,2. 1. From the Department of Anesthesiology, Columbia University Medical Center, New York, New York. 2. Department of Epidemiology. 3. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York. 4. Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York. 5. Department of Anesthesiology, Weill Cornell Medicine, New York, New York. 6. Division of Colorectal Surgery, Department of Surgery, Columbia University Medical Center, New York, New York.
Abstract
BACKGROUND: Aspects of intraoperative management (eg, hypotension) are associated with acute kidney injury (AKI) in noncardiac surgery patients. However, it is unclear if and how the addition of intraoperative data affects a baseline risk prediction model for postoperative AKI. METHODS: With institutional review board (IRB) approval, an institutional cohort (2005-2015) of inpatient intra-abdominal surgery patients without preoperative AKI was identified. Data from the American College of Surgeons National Surgical Quality Improvement Program (preoperative and procedure data), Anesthesia Information Management System (intraoperative data), and electronic health record (postoperative laboratory data) were linked. The sample was split into derivation/validation (70%/30%) cohorts. AKI was defined as an increase in serum creatinine ≥0.3 mg/dL within 48 hours or >50% within 7 days of surgery. Forward logistic regression fit a baseline model incorporating preoperative variables and surgical procedure. Forward logistic regression fit a second model incorporating the previously selected baseline variables, as well as additional intraoperative variables. Intraoperative variables reflected the following aspects of intraoperative management: anesthetics, beta-blockers, blood pressure, diuretics, fluids, operative time, opioids, and vasopressors. The baseline and intraoperative models were evaluated based on statistical significance and discriminative ability (c-statistic). The risk threshold equalizing sensitivity and specificity in the intraoperative model was identified. RESULTS: Of 2691 patients in the derivation cohort, 234 (8.7%) developed AKI. The baseline model had c-statistic 0.77 (95% confidence interval [CI], 0.74-0.80). The additional variables added to the intraoperative model were significantly associated with AKI (P < .0001) and the intraoperative model had c-statistic 0.81 (95% CI, 0.78-0.83). Sensitivity and specificity were equalized at a risk threshold of 9.0% in the intraoperative model. At this threshold, the baseline model had sensitivity and specificity of 71% (95% CI, 65-76) and 69% (95% CI, 67-70), respectively, and the intraoperative model had sensitivity and specificity of 74% (95% CI, 69-80) and 74% (95% CI, 73-76), respectively. The high-risk group had an AKI risk of 18% (95% CI, 15-20) in the baseline model and 22% (95% CI, 19-25) in the intraoperative model. CONCLUSIONS: Intraoperative data, when added to a baseline risk prediction model for postoperative AKI in intra-abdominal surgery patients, improves the performance of the model.
BACKGROUND: Aspects of intraoperative management (eg, hypotension) are associated with acute kidney injury (AKI) in noncardiac surgery patients. However, it is unclear if and how the addition of intraoperative data affects a baseline risk prediction model for postoperative AKI. METHODS: With institutional review board (IRB) approval, an institutional cohort (2005-2015) of inpatient intra-abdominal surgery patients without preoperative AKI was identified. Data from the American College of Surgeons National Surgical Quality Improvement Program (preoperative and procedure data), Anesthesia Information Management System (intraoperative data), and electronic health record (postoperative laboratory data) were linked. The sample was split into derivation/validation (70%/30%) cohorts. AKI was defined as an increase in serum creatinine ≥0.3 mg/dL within 48 hours or >50% within 7 days of surgery. Forward logistic regression fit a baseline model incorporating preoperative variables and surgical procedure. Forward logistic regression fit a second model incorporating the previously selected baseline variables, as well as additional intraoperative variables. Intraoperative variables reflected the following aspects of intraoperative management: anesthetics, beta-blockers, blood pressure, diuretics, fluids, operative time, opioids, and vasopressors. The baseline and intraoperative models were evaluated based on statistical significance and discriminative ability (c-statistic). The risk threshold equalizing sensitivity and specificity in the intraoperative model was identified. RESULTS: Of 2691 patients in the derivation cohort, 234 (8.7%) developed AKI. The baseline model had c-statistic 0.77 (95% confidence interval [CI], 0.74-0.80). The additional variables added to the intraoperative model were significantly associated with AKI (P < .0001) and the intraoperative model had c-statistic 0.81 (95% CI, 0.78-0.83). Sensitivity and specificity were equalized at a risk threshold of 9.0% in the intraoperative model. At this threshold, the baseline model had sensitivity and specificity of 71% (95% CI, 65-76) and 69% (95% CI, 67-70), respectively, and the intraoperative model had sensitivity and specificity of 74% (95% CI, 69-80) and 74% (95% CI, 73-76), respectively. The high-risk group had an AKI risk of 18% (95% CI, 15-20) in the baseline model and 22% (95% CI, 19-25) in the intraoperative model. CONCLUSIONS: Intraoperative data, when added to a baseline risk prediction model for postoperative AKI in intra-abdominal surgery patients, improves the performance of the model.
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Authors: Xinyu Yan; Jeff Goldsmith; Sumit Mohan; Zachary A Turnbull; Robert E Freundlich; Frederic T Billings; Ravi P Kiran; Guohua Li; Minjae Kim Journal: Anesth Analg Date: 2022-01-01 Impact factor: 5.108