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Intraoperative Data Enhance the Detection of High-Risk Acute Kidney Injury Patients When Added to a Baseline Prediction Model.

TitleIntraoperative Data Enhance the Detection of High-Risk Acute Kidney Injury Patients When Added to a Baseline Prediction Model.
Publication TypeJournal Article
Year of Publication2021
AuthorsKim M, Li G, Mohan S, Turnbull ZA, Kiran RP, Li G
JournalAnesth Analg
Volume132
Issue2
Pagination430-441
Date Published2021 02 01
ISSN1526-7598
KeywordsAbdomen, Acute Kidney Injury, Adult, Aged, Aged, 80 and over, Biomarkers, Creatinine, Female, Humans, Male, Middle Aged, Monitoring, Intraoperative, Predictive Value of Tests, Reproducibility of Results, Retrospective Studies, Risk Assessment, Risk Factors, Surgical Procedures, Operative, Time Factors, Treatment Outcome
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.

DOI10.1213/ANE.0000000000005057
Alternate JournalAnesth Analg
PubMed ID32769380
PubMed Central IDPMC7855510
Grant ListKL2 TR001874 / TR / NCATS NIH HHS / United States