Estimation of Baseline Serum Creatinine with Machine Learning

2021 ◽  
pp. 1-10
Author(s):  
Erina Ghosh ◽  
Larry Eshelman ◽  
Stephanie Lanius ◽  
Emma Schwager ◽  
Kalyan S. Pasupathy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Serum Creatinine ◽  
Test Data ◽  
Mayo Clinic ◽  
Gradient Boosting ◽  
Baseline Serum ◽  
Baseline Serum Creatinine ◽  
Baseline Creatinine ◽  
Mimic Iii

<b><i>Introduction:</i></b> Comparing current to baseline serum creatinine is important in detecting acute kidney injury. In this study, we report a regression-based machine learning model to predict baseline serum creatinine. <b><i>Methods:</i></b> We developed and internally validated a gradient boosting model on patients admitted in Mayo Clinic intensive care units from 2005 to 2017 to predict baseline creatinine. The model was externally validated on the Medical Information Mart for Intensive Care III (MIMIC III) cohort in all ICU admissions from 2001 to 2012. The predicted baseline creatinine from the model was compared with measured serum creatinine levels. We compared the performance of our model with that of the backcalculated estimated serum creatinine from the Modification of Diet in Renal Disease (MDRD) equation. <b><i>Results:</i></b> Following ascertainment of eligibility criteria, 44,370 patients from the Mayo Clinic and 6,112 individuals from the MIMIC III cohort were enrolled. Our model used 6 features from the Mayo Clinic and MIMIC III datasets, including the presence of chronic kidney disease, weight, height, and age. Our model had significantly lower error than the MDRD backcalculation (mean absolute error [MAE] of 0.248 vs. 0.374 in the Mayo Clinic test data; MAE of 0.387 vs. 0.465 in the MIMIC III cohort) and higher correlation (intraclass correlation coefficient [ICC] of 0.559 vs. 0.050 in the Mayo Clinic test data; ICC of 0.357 vs. 0.030 in the MIMIC III cohort). <b><i>Discussion/Conclusion:</i></b> Using machine learning models, baseline serum creatinine could be estimated with higher accuracy than the backcalculated estimated serum creatinine level.

scholarly journals Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning

2020 ◽  
Author(s):  
Khaled Shawwa ◽  
Erina Ghosh ◽  
Stephanie Lanius ◽  
Emma Schwager ◽  
Larry Eshelman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
Mayo Clinic ◽  
Medical Information ◽  
Kidney Injury ◽  
Feature Model ◽  
Receiver Operating Curve ◽  
Gradient Boosting ◽  
Mimic Iii

Abstract Background Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this study is to develop and externally validate a model for predicting AKI in the ICU using patient data present prior to ICU admission. Methods We used data of 98 472 adult ICU admissions at Mayo Clinic between 1 January 2005 and 31 December 2017 and 51 801 encounters from Medical Information Mart for Intensive Care III (MIMIC-III) cohort. A gradient-boosting model was trained on 80% of the Mayo Clinic cohort using a set of features to predict AKI acquired in the ICU. Results AKI was identified in 39 307 (39.9%) encounters in the Mayo Clinic cohort. Patients who developed AKI in the ICU were older and had higher ICU and in-hospital mortality compared to patients without AKI. A 30-feature model yielded an area under the receiver operating curve of 0.690 [95% confidence interval (CI) 0.682–0.697] in the Mayo Clinic cohort set and 0.656 (95% CI 0.648–0.664) in the MIMIC-III cohort. Conclusions Using machine learning, AKI among ICU patients can be predicted using information available prior to admission. This model is independent of ICU information, making it valuable for stratifying patients at admission.

A Comparative Study of Machine Learning Techniques for Predicting Sepsis for MIMIC-III Patients

2021 ◽  
Author(s):  
Xuze Zhao ◽  
Bo Qu
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Medical Information ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Mortality And Morbidity ◽  
Extreme Gradient Boosting ◽  
Mimic Iii

Abstract Background: Sepsis is one of the dominating causes of mortality and morbidity in-hospital especially in intensive care units (ICU) patients. Therefore, a reliable decision-making model for predicting sepsis is of great importance. The purpose of this study was to develop an eXtreme Gradient Boosting (XGBoost) based model and explore whether it performs better in predicting sepsis from the time of admission in intensive care units (ICU) than other machine learning (ML) methods. Methods: The source data used for model establishment in this study were from a retrospective medical information mart for intensive care (MIMIC) III dataset, restricted to intensive care units (ICUs) patients aged between 18 and 89. Model performance of the XGBoost model was compared to logistic regression (LR), recursive neural network (RNN), and support vector machine (SVM). Then, the performances of the models were evaluated and compared by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves.Results: A total of 6430 MIMIC-III cases are included in this article, in which, 3021 cases have encountered sepsis while 3409 cases have not, respectively. As for the AUC (0.808 (95% CI): 0.767-0.848,DT), 0.802 (95%CI: 0.762-0.842，RNN), 0.790 (95%CI: 0.751-0.830,SVM), 0.775 (95%CI: 0.736-0.813,LR) , results of the models, XGBoost performs best in predicting sepsis.Conclusions: By using the DT algorithm, a more accurate prediction model can be established. Amongst other ML methods, the XGBoost model demonstrated the best ability in detecting the sepsis of the patients in ICU.

scholarly journals Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Guilan Kong ◽  
Ke Lin ◽  
Yonghua Hu
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
High Risk ◽  
Prediction Performance ◽  
Brier Score ◽  
Gradient Boosting ◽  
Hospital Death ◽  
Good Prediction ◽  
Saps Ii ◽  
Mimic Iii

Abstract Background Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sepsis in ICUs. Methods The source database used for model development and validation is the medical information mart for intensive care (MIMIC) III. We identified adult sepsis patients using the new sepsis definition Sepsis-3. A total of 86 predictor variables consisting of demographics, laboratory tests and comorbidities were used. We employed the least absolute shrinkage and selection operator (LASSO), random forest (RF), gradient boosting machine (GBM) and the traditional logistic regression (LR) method to develop prediction models. In addition, the prediction performance of the four developed models was evaluated and compared with that of an existent scoring tool – simplified acute physiology score (SAPS) II – using five different performance measures: the area under the receiver operating characteristic curve (AUROC), Brier score, sensitivity, specificity and calibration plot. Results The records of 16,688 sepsis patients in MIMIC III were used for model training and test. Amongst them, 2949 (17.7%) patients had in-hospital death. The average AUROCs of the LASSO, RF, GBM, LR and SAPS II models were 0.829, 0.829, 0.845, 0.833 and 0.77, respectively. The Brier scores of the LASSO, RF, GBM, LR and SAPS II models were 0.108, 0.109, 0.104, 0.107 and 0.146, respectively. The calibration plots showed that the GBM, LASSO and LR models had good calibration; the RF model underestimated high-risk patients; and SAPS II had the poorest calibration. Conclusion The machine learning-based models developed in this study had good prediction performance. Amongst them, the GBM model showed the best performance in predicting the risk of in-hospital death. It has the potential to assist physicians in the ICU to perform appropriate clinical interventions for critically ill sepsis patients and thus may help improve the prognoses of sepsis patients in the ICU.

P0199 : Acute kidney injury in cirrhosis: Baseline serum creatinine predicts patient outcomes

2015 ◽  
Vol 62 ◽  
pp. S380 ◽  
Author(s):  
F. Wong ◽  
J.G. O’Leary ◽  
K.R. Reddy ◽  
G. Garcia-Tsao ◽  
M.B. Fallon ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Serum Creatinine ◽  
Patient Outcomes ◽  
Kidney Injury ◽  
Baseline Serum ◽  
Baseline Serum Creatinine

scholarly journals Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Dongkai Li ◽  
Jie He ◽  
Fanglan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Set ◽  
Machine Learning Methods ◽  
Heparin Treatment ◽  
Neural Network Algorithm ◽  
Mimic Iii ◽  
Machine Learning Models

BACKGROUND Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Mo1892 Acute Kidney Injury in Cirrhosis: Baseline Serum Creatinine Predicts Patient Outcomes

2015 ◽  
Vol 148 (4) ◽  
pp. S-1075
Author(s):  
Florence Wong ◽  
Jacqueline G. O'Leary ◽  
K. Rajender Reddy ◽  
Guadalupe Garcia-Tsao ◽  
Michael B. Fallon ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Serum Creatinine ◽  
Patient Outcomes ◽  
Kidney Injury ◽  
Baseline Serum ◽  
Baseline Serum Creatinine

Machine Learning Methods for Predicting Long-term Mortality in Patients after Cardiac Surgery

2021 ◽  
Author(s):  
Yue Yu ◽  
Chi Peng ◽  
Zhiyuan Zhang ◽  
Kejia Shen ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Early Warning Systems ◽  
Gradient Boosting ◽  
Distribution Width ◽  
Extreme Gradient Boosting ◽  
Mimic Iii ◽  
Long Term Mortality

Abstract Background Establishing a mortality prediction model of patients undergoing cardiac surgery might be useful for clinicians for alerting, judgment, and intervention, while few predictive tools for long-term mortality have been developed targeting patients post-cardiac surgery. Objective We aimed to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients after cardiac surgery during a 4-year follow-up. Methods The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, prognostic scoring systems, and treatment information on the first day of ICU admission. 4-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA). Results Of 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination (RFE) and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings. Conclusions The Ada model performs best in predicting long-term mortality after cardiac surgery among the eight ML models. The ML-based algorithms might have significant application in the development of early warning systems for patients following operations.

Acute Kidney Injury in Cirrhosis: Baseline Serum Creatinine Predicts Patient Outcomes

2017 ◽  
Vol 112 (7) ◽  
pp. 1103-1110 ◽  
Author(s):  
F Wong ◽  
J G OʼLeary ◽  
K R Reddy ◽  
G Garcia-Tsao ◽  
M B Fallon ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Serum Creatinine ◽  
Patient Outcomes ◽  
Kidney Injury ◽  
Baseline Serum ◽  
Baseline Serum Creatinine

scholarly journals Angiotensin II infusion and markers of organ function in invasively ventilated COVID-19 patients

2021 ◽  
Vol 23 (2) ◽  
pp. 215-224
Author(s):  
Alberto Zangrillo ◽  
◽  
Sergio Colombo ◽  
Anna Mara Scandroglio ◽  
Evgeny Fominskiy ◽  
...  
Keyword(s):  
Angiotensin Ii ◽  
Serum Creatinine ◽  
Liver Dysfunction ◽  
Bayesian Modelling ◽  
Organ Function ◽  
Baseline Serum ◽  
Median Difference ◽  
Reactive Protein ◽  
Baseline Serum Creatinine ◽  
Ventilated Patients

OBJECTIVE: The use of angiotensin II in invasively ventilated patients with coronavirus disease 2019 (COVID-19) is controversial. Its effect on organ function is unknown. DESIGN: Prospective observational study. SETTING: Intensive care unit (ICU) of a tertiary academic hospital in Milan, Italy. PARTICIPANTS: Adult patients receiving mechanical ventilation due to COVID-19. INTERVENTIONS: Use angiotensin II either as rescue vasopressor agent or as low dose vasopressor support. MAIN OUTCOME MEASURES: Patients treated before angiotensin II was available or treated in an adjacent COVID-19 ICU served as controls. For data analysis, we applied Bayesian modelling as appropriate. We assessed the effects of angiotensin II on organ function. RESULTS: We compared 46 patients receiving angiotensin II therapy with 53 controls. Compared with controls, angiotensin II increased the mean arterial pressure (median difference, 9.05 mmHg; 95% CI, 1.87–16.22; P = 0.013) and the Pao2/Fio2 ratio (median difference, 23.17; 95% CI, 3.46–42.88; P = 0.021), and decreased the odds ratio (OR) of liver dysfunction (OR, 0.32; 95% CI, 0.09–0.94). However, angiotensin II had no effect on lactate, urinary output, serum creatinine, C-reactive protein, platelet count, or thromboembolic complications. In patients with abnormal baseline serum creatinine, Bayesian modelling showed that angiotensin II carried a 95.7% probability of reducing the use of renal replacement therapy (RRT). CONCLUSIONS: In ventilated patients with COVID-19, angiotensin II therapy increased blood pressure and Pao2/Fio2 ratios, decreased the OR of liver dysfunction, and appeared to decrease the risk of RRT use in patients with abnormal baseline serum creatinine. However, all of these findings are hypothesis-generating only. TRIAL REGISTRATION: ClinicalTrials.gov NCT04318366.

Sign in / Sign up

Export Citation Format

Share Document