scholarly journals Prediction of Acute Kidney Injury with a Machine Learning Algorithm using Electronic Health Record Data

2017 ◽  
Author(s):  
Hamid Mohamadlou ◽  
Anna Lynn-Palevsky ◽  
Christopher Barton ◽  
Uli Chettipally ◽  
Lisa Shieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Sofa Score ◽  
Learning Algorithm ◽  
Medical Center ◽  
Kidney Injury ◽  
Kidney Damage ◽  
Prediction Algorithm ◽  
Prediction Tool ◽  
Electronic Health Record Data

AbstractBackgroundA major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified.MethodsWe used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data from inpatients at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center. We tested the algorithm’s ability to detect AKI at onset, and to predict AKI 12, 24, 48, and 72 hours before onset, and compared its 3-fold cross-validation performance to the SOFA score for AKI identification in terms of Area Under the Receiver Operating Characteristic (AUROC).ResultsThe prediction algorithm achieves AUROC of 0.872 (95% CI 0.867, 0.878) for AKI onset detection, superior to the SOFA score AUROC of 0.815 (P < 0.01). At 72 hours before onset, the algorithm achieves AUROC of 0.728 (95% CI 0.719, 0.737), compared to the SOFA score AUROC of 0.720 (P < 0.01).ConclusionsThe results of these experiments suggest that a machine-learning-based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests.

scholarly journals Elevated serum galectin-1 concentrations are associated with increased risks of mortality and acute kidney injury in critically ill patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257558
Author(s):  
Ruey-Hsing Chou ◽  
Chuan-Tsai Tsai ◽  
Ya-Wen Lu ◽  
Jiun-Yu Guo ◽  
Chi-Ting Lu ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Critically Ill ◽  
Sofa Score ◽  
Critically Ill Patients ◽  
Medical Center ◽  
Elevated Serum ◽  
Kidney Injury ◽  
The Body ◽  
Icu Admission ◽  
Increased Risk

Background Galectin-1 (Gal-1), a member of the β-galactoside binding protein family, is associated with inflammation and chronic kidney disease. However, the effect of Gal-1 on mortality and acute kidney injury (AKI) in critically-ill patients remain unclear. Methods From May 2018 to March 2020, 350 patients admitted to the medical intensive care unit (ICU) of Taipei Veterans General Hospital, a tertiary medical center, were enrolled in this study. Forty-one patients receiving long-term renal replacement therapy were excluded. Serum Gal-1 levels were determined within 24 h of ICU admission. The patients were divided into tertiles according to their serum Gal-1 levels (low, serum Gal-1 < 39 ng/ml; median, 39–70 ng/ml; high, ≥71 ng/ml). All patients were followed for 90 days or until death. Results Mortality in the ICU and at 90 days was greater among patients with elevated serum Gal-1 levels. In analyses adjusted for the body mass index, malignancy, sepsis, Sequential Organ Failure Assessment (SOFA) score, and serum lactate level, the serum Gal-1 level remained an independent predictor of 90-day mortality [median vs. low: adjusted hazard ratio (aHR) 2.11, 95% confidence interval (CI) 1.24–3.60, p = 0.006; high vs. low: aHR 3.21, 95% CI 1.90–5.42, p < 0.001]. Higher serum Gal-1 levels were also associated with a higher incidence of AKI within 48 h after ICU admission, independent of the SOFA score and renal function (median vs. low: aHR 2.77, 95% CI 1.21–6.34, p = 0.016; high vs. low: aHR 2.88, 95% CI 1.20–6.88, p = 0.017). The results were consistent among different subgroups with high and low Gal-1 levels. Conclusion Serum Gal-1 elevation at the time of ICU admission were associated with an increased risk of mortality at 90 days, and an increased incidence of AKI within 48 h after ICU admission.

scholarly journals Elevated serum galectin-1 concentrations are associated with increased risks of mortality and acute kidney injury in critically ill patients

2020 ◽  
Author(s):  
Ruey-Hsing Chou ◽  
Chuan-Tsai Tsai ◽  
Ya-Wen Lu ◽  
Jiun-Yu Guo ◽  
Chi-Ting Lu ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Critically Ill ◽  
Sofa Score ◽  
Critically Ill Patients ◽  
Medical Center ◽  
Elevated Serum ◽  
Kidney Injury ◽  
The Body ◽  
Icu Admission ◽  
Increased Risk

Abstract Background: Galectin-1 (Gal-1), a member of the β-galactoside binding protein family, is associated with inflammation and chronic kidney disease. However, the effect of Gal-1 on mortality and acute kidney injury (AKI) in critically ill patients remains unclear.Methods: From May 2018 to March 2020, 350 patients admitted to the medical intensive care unit (ICU) of Taipei Veterans General Hospital, a tertiary medical center, were enrolled in this study. Forty-one patients receiving long-term renal replacement therapy were excluded. Serum Gal-1 levels were determined within 24 h of ICU admission. The patients were divided into three equally sized groups according to their serum Gal-1 levels (low, serum Gal-1 < 39 ng/ml; median, 39–70 ng/ml; high, >71 ng/ml). All patients were followed for 90 days or until death.Results: Mortality in the ICU and at 90 days was greater among patients with elevated serum Gal-1 levels. In analyses adjusted for the body mass index, malignancy, sepsis, Sequential Organ Failure Assessment (SOFA) score, and serum lactate level, the serum Gal-1 level remained an independent predictor of 90-day mortality [median vs. low: adjusted hazard ratio (aHR) 2.11, 95% confidence interval (CI) 1.24–3.60, p = 0.006; high vs. low: aHR 3.21, 95% CI 1.90–5.42, p < 0.001]. Higher serum Gal-1 levels were also associated with a higher incidence of AKI within 48 h after ICU admission, independent of the SOFA score and renal function (median vs. low: aHR 2.77, 95% CI 1.21–6.34, p = 0.016; high vs. low: aHR 2.88, 95% CI 1.20–6.88, p = 0.017). The results were consistent among different subgroups with high and low Gal-1 levels.Conclusion: Serum Gal-1 elevation at the time of ICU admission were associated with an increased risk of mortality at 90 days, and an increased incidence of AKI within 48 h after ICU admission.

scholarly journals Pediatric Severe Sepsis Prediction Using Machine Learning

2017 ◽  
Author(s):  
Thomas Desautels ◽  
Jana Hoffman ◽  
Christopher Barton ◽  
Qingqing Mao ◽  
Melissa Jay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
San Francisco ◽  
High Performance ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Medical Center ◽  
Prediction Algorithm ◽  
Machine Learning Algorithm ◽  
Logistic Organ Dysfunction

Early detection of pediatric severe sepsis is necessary in order to administer effective treatment. In this study, we assessed the efficacy of a machine-learning-based prediction algorithm applied to electronic healthcare record (EHR) data for the prediction of severe sepsis onset. The resulting prediction performance was compared with the Pediatric Logistic Organ Dysfunction score (PELOD-2) and pediatric Systemic Inflammatory Response Syndrome score (SIRS) using cross-validation and pairwise t-tests. EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016. Patients (n = 11,127) were 2-17 years of age and 103 [0.93%] were labeled severely septic. In four-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.912 for discrimination between severely septic and control pediatric patients at onset and AUROC of 0.727 four hours before onset. Under the same measure, the prediction algorithm also significantly outperformed PELOD-2 (p < 0.05) and SIRS (p < 0.05) in the prediction of severe sepsis four hours before onset. This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction for pediatric inpatients.

782-P: Machine Learning to Identify Diabetes Patients Initiating SGLT2i at High-Risk of Acute Kidney Injury

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 782-P
Author(s):  
LANTING YANG ◽  
NICO GABRIEL ◽  
INMACULADA HERNANDEZ ◽  
ALMUT G. WINTERSTEIN ◽  
STEPHEN KIMMEL ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
High Risk ◽  
Kidney Injury ◽  
Diabetes Patients
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