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      Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set

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          Abstract

          During the perioperative period patients often suffer complications, including acute kidney injury (AKI), reintubation, and mortality. In order to effectively prevent these complications, high-risk patients must be readily identified. However, most current risk scores are designed to predict a single postoperative complication and often lack specificity on the patient level. In other fields, machine learning (ML) has been shown to successfully create models to predict multiple end points using a single input feature set. We hypothesized that ML can be used to create models to predict postoperative mortality, AKI, reintubation, and a combined outcome using a single set of features available at the end of surgery. A set of 46 features available at the end of surgery, including drug dosing, blood loss, vital signs, and others were extracted. Additionally, six additional features accounting for total intraoperative hypotension were extracted and trialed for different models. A total of 59,981 surgical procedures met inclusion criteria and the deep neural networks (DNN) were trained on 80% of the data, with 20% reserved for testing. The network performances were then compared to ASA Physical Status. In addition to creating separate models for each outcome, a multitask learning model was trialed that used information on all outcomes to predict the likelihood of each outcome individually. The overall rate of the examined complications in this data set was 0.79% for mortality, 22.3% (of 21,676 patients with creatinine values) for AKI, and 1.1% for reintubation. Overall, there was significant overlap between the various model types for each outcome, with no one modeling technique consistently performing the best. However, the best DNN models did beat the ASA score for all outcomes other than mortality. The highest area under the receiver operating characteristic curve (AUC) models were 0.792 (0.775–0.808) for AKI, 0.879 (0.851–0.905) for reintubation, 0.907 (0.872–0.938) for mortality, and 0.874 (0.864–0.866) for any outcome. The ASA score alone achieved AUCs of 0.652 (0.636–0.669) for AKI, 0.787 (0.757–0.818) for reintubation, 0.839 (0.804–0.875) for mortality, and 0.76 (0.748–0.773) for any outcome. Overall, the DNN architecture was able to create models that outperformed the ASA physical status to predict all outcomes based on a single feature set, consisting of objective data available at the end of surgery. No one model architecture consistently performed the best.

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          Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension.

          Intraoperative hypotension may contribute to postoperative acute kidney injury (AKI) and myocardial injury, but what blood pressures are unsafe is unclear. The authors evaluated the association between the intraoperative mean arterial pressure (MAP) and the risk of AKI and myocardial injury.
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            Relationship between Intraoperative Hypotension, Defined by Either Reduction from Baseline or Absolute Thresholds, and Acute Kidney and Myocardial Injury after Noncardiac Surgery: A Retrospective Cohort Analysis.

            How best to characterize intraoperative hypotension remains unclear. Thus, the authors assessed the relationship between myocardial and kidney injury and intraoperative absolute (mean arterial pressure [MAP]) and relative (reduction from preoperative pressure) MAP thresholds.
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              Myocardial injury after noncardiac surgery: a large, international, prospective cohort study establishing diagnostic criteria, characteristics, predictors, and 30-day outcomes.

              Myocardial injury after noncardiac surgery (MINS) was defined as prognostically relevant myocardial injury due to ischemia that occurs during or within 30 days after noncardiac surgery. The study's four objectives were to determine the diagnostic criteria, characteristics, predictors, and 30-day outcomes of MINS. In this international, prospective cohort study of 15,065 patients aged 45 yr or older who underwent in-patient noncardiac surgery, troponin T was measured during the first 3 postoperative days. Patients with a troponin T level of 0.04 ng/ml or greater (elevated "abnormal" laboratory threshold) were assessed for ischemic features (i.e., ischemic symptoms and electrocardiography findings). Patients adjudicated as having a nonischemic troponin elevation (e.g., sepsis) were excluded. To establish diagnostic criteria for MINS, the authors used Cox regression analyses in which the dependent variable was 30-day mortality (260 deaths) and independent variables included preoperative variables, perioperative complications, and potential MINS diagnostic criteria. An elevated troponin after noncardiac surgery, irrespective of the presence of an ischemic feature, independently predicted 30-day mortality. Therefore, the authors' diagnostic criterion for MINS was a peak troponin T level of 0.03 ng/ml or greater judged due to myocardial ischemia. MINS was an independent predictor of 30-day mortality (adjusted hazard ratio, 3.87; 95% CI, 2.96-5.08) and had the highest population-attributable risk (34.0%, 95% CI, 26.6-41.5) of the perioperative complications. Twelve hundred patients (8.0%) suffered MINS, and 58.2% of these patients would not have fulfilled the universal definition of myocardial infarction. Only 15.8% of patients with MINS experienced an ischemic symptom. Among adults undergoing noncardiac surgery, MINS is common and associated with substantial mortality.
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                Author and article information

                Contributors
                ihofer@mednet.ucla.edu
                Journal
                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                2398-6352
                20 April 2020
                20 April 2020
                2020
                : 3
                : 58
                Affiliations
                [1 ]ISNI 0000 0000 9632 6718, GRID grid.19006.3e, Department of Anesthesiology and Perioperative Medicine, , David Geffen School of Medicine at UCLA, ; Los Angeles, CA USA
                [2 ]ISNI 0000 0001 0668 7243, GRID grid.266093.8, Department of Biomedical Engineering, , University of California Irvine, ; Irvine, CA USA
                [3 ]ISNI 0000 0001 0668 7243, GRID grid.266093.8, Department of Computer Sciences, , University of California Irvine, ; Irvine, CA USA
                Author information
                http://orcid.org/0000-0002-8148-4590
                Article
                248
                10.1038/s41746-020-0248-0
                7170922
                32352036
                a23b1047-6d8d-4fff-b341-2c726760a71b
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 June 2019
                : 18 February 2020
                Funding
                Funded by: NIH 1R01HL144692
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                disease-free survival,health policy,translational research

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