scholarly journals Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach

2021 ◽  
Vol 8 (1) ◽  
pp. e000761
Author(s):  
Hao Du ◽  
Kewin Tien Ho Siah ◽  
Valencia Zhang Ru-Yan ◽  
Readon Teh ◽  
Christopher Yu En Tan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Receiver Operating Curve ◽  
Study Cohort ◽  
Risk Of Death ◽  
Severity Scores ◽  
Mimic Iii ◽  
Care Database

Research objectivesClostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database.MethodologyThe demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model.Summary of resultsFrom 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models.ConclusionOur machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.

scholarly journals Value of early critical care transthoracic echocardiography for patients undergoing mechanical ventilation: a retrospective study

BMJ Open ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. e048646
Author(s):  
Hao Jiang ◽  
Wen Xu ◽  
Wenjing Chen ◽  
Lingling Pan ◽  
Xueshu Yu ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Critical Care ◽  
Intensive Care ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Transthoracic Echocardiography ◽  
Secondary Outcome ◽  
Favourable Effect ◽  
Icu Admission ◽  
Mimic Iii

ObjectivesTo evaluate whether early intensive care transthoracic echocardiography (TTE) can improve the prognosis of patients with mechanical ventilation (MV).DesignA retrospective cohort study.SettingPatients undergoing MV for more than 48 hours, based on the Medical Information Mart for Intensive Care III (MIMIC-III) database and the eICU Collaborative Research Database (eICU-CRD), were selected.Participants2931 and 6236 patients were recruited from the MIMIC-III database and the eICU database, respectively.Primary and secondary outcome measuresThe primary outcome was in-hospital mortality. Secondary outcomes were 30-day mortality from the date of ICU admission, days free of MV and vasopressors 30 days after ICU admission, use of vasoactive drugs, total intravenous fluid and ventilator settings during the first day of MV.ResultsWe used propensity score matching to analyse the association between early TTE and in-hospital mortality and sensitivity analysis, including the inverse probability weighting model and covariate balancing propensity score model, to ensure the robustness of our findings. The adjusted OR showed a favourable effect between the early TTE group and in-hospital mortality (MIMIC: OR 0.78; 95% CI 0.65 to 0.94, p=0.01; eICU-CRD: OR 0.76; 95% CI 0.67 to 0.86, p<0.01). Early TTE was also associated with 30-day mortality in the MIMIC database (OR 0.71, 95% CI 0.57 to 0.88, p=0.001). Furthermore, those who had early TTE had both more ventilation-free days (only in eICU-CRD: 23.48 vs 24.57, p<0.01) and more vasopressor-free days (MIMIC: 18.22 vs 20.64, p=0.005; eICU-CRD: 27.37 vs 28.59, p<0.001) than the control group (TTE applied outside of the early TTE and no TTE at all).ConclusionsEarly application of critical care TTE during MV is beneficial for improving in-hospital mortality. Further investigation with prospectively collected data is required to validate this relationship.

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.

scholarly journals Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhixuan Zeng ◽  
Shuo Yao ◽  
Jianfei Zheng ◽  
Xun Gong
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Mortality Prediction ◽  
Research Database ◽  
Training Set ◽  
Icu Patients ◽  
Test Set ◽  
Saps Ii ◽  
Severity Scores ◽  
Mimic Iii

Abstract Background Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. Methods Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration. Results Twelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II. Conclusions The blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Improvement of APACHE II score system for disease severity based on XGBoost algorithm

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yan Luo ◽  
Zhiyu Wang ◽  
Cong Wang
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Patient Outcomes ◽  
External Validation ◽  
Apache Ii ◽  
Validation Dataset ◽  
Apache Ii Score ◽  
Severity Scores ◽  
The Impact ◽  
Improve Patient

Abstract Background Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system. Methods We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model. Results We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good. Conclusions As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient outcomes.

scholarly journals Association Between Oxygen Partial Pressure Trajectories and Short-Term Outcomes in Patients With Hemorrhagic Brain Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Guolong Cai ◽  
Weizhe Ru ◽  
Qianghong Xu ◽  
Jiong Wu ◽  
Shijin Gong ◽  
...  
Keyword(s):  
Intensive Care ◽  
Brain Injury ◽  
Hospital Mortality ◽  
Oxygen Partial Pressure ◽  
Partial Pressure ◽  
Medical Information ◽  
Logistic Models ◽  
Neurological Outcomes ◽  
Risk Of Death ◽  
Logistic Analysis

Objectives: Arterial hyperoxia is reportedly a risk factor for poor outcomes in patients with hemorrhagic brain injury (HBI). However, most previous studies have only evaluated the effects of hyperoxia using static oxygen partial pressure (PaO2) values. This study aimed to investigate the association between overall dynamic oxygenation status and HBI outcomes, using longitudinal PaO2 data.Methods: Data were extracted from the Medical Information Mart for Intensive Care III database. Longitudinal PaO2 data obtained within 72 h of admission to an intensive care unit were analyzed, using a group-based trajectory approach. In-hospital mortality was used as the primary outcomes. Multivariable logistic models were used to explore the association between PaO2 trajectory and outcomes.Results: Data of 2,028 patients with HBI were analyzed. Three PaO2 trajectory types were identified: Traj-1 (mild hyperoxia), Traj-2 (transient severe hyperoxia), and Traj-3 (persistent severe hyperoxia). The initial and maximum PaO2 of patients with Traj-2 and Traj-3 were similar and significantly higher than those of patients with Traj-1. However, PaO2 in patients with Traj-2 decreased more rapidly than in patients with Traj-3. The crude in-hospital mortality was the lowest for patients with Traj-1 and highest for patients with Traj-3 (365/1,303, 209/640, and 43/85 for Traj-1, Traj-2, and Traj-3, respectively; p &lt; 0.001), and the mean Glasgow Coma Scale score at discharge (GCSdis) was highest for patients with Traj-1 and lowest in patients with Traj-3 (13 [7–15], 11 [6–15], and 7 [3–14] for Traj-1, Traj-2, and Traj-3, respectively; p &lt; 0.001). The multivariable model revealed that the risk of death was higher in patients with Traj-3 than in patients with Traj-1 (odds ratio [OR]: 3.3, 95% confidence interval [CI]: 1.9–5.8) but similar for patients with Traj-1 and Traj-2. Similarly, the logistic analysis indicated the worst neurological outcomes in patients with Traj-3 (OR: 3.6, 95% CI: 2.0–6.4, relative to Traj-1), but similar neurological outcomes for patients in Traj-1 and Traj-2.Conclusion: Persistent, but not transient severe arterial hyperoxia, was associated with poor outcome in patients with HBI.

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.

scholarly journals A machine learning predictive model of in-hospital mortality in patients with sepsis complicated by anemia: a retrospective study based on the MIMIC-III database

2021 ◽  
Author(s):  
Xiaobin Liu ◽  
Yu Zhao ◽  
Yingyi Qin ◽  
Dan Wang ◽  
Xi Yin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Predictive Model ◽  
Clinical Data ◽  
Predictive Models ◽  
Goodness Of Fit ◽  
Advantages And Disadvantages ◽  
Extreme Gradient Boosting ◽  
Comorbidity Index ◽  
Mimic Iii

Abstract BackgroudPatients with sepsis complicated by anemia have a higher risk of mortality. It is clinically important to study the risk factors associated with the prognosis of this disease. The aim of this study was to establish a predictive model of mortality during hospitalization by extracting clinical data from the Medical Information Mart for Intensive Care III (MIMIC-III) database. MethodsThe clinical data of patients with sepsis complicated by anemia in the MIMIC-III database were retrospectively analyzed. Indexes were screened by stepwise logistic regression (LR), and machine learning predictive models such as Decision Tree (DT), Random Forests (RF), and eXtreme Gradient Boosting (XGBoost) were developed and compared, identifying advantages and disadvantages of each model. ResultsA total of 13,547 patients with sepsis complicated by anemia were included in the study, among which 1,827 died during hospitalization and 11,720 were still alive at discharge. The preliminary stepwise regression model selected 20 clinical indexes, including Elixhauser comorbidity index, maximum blood urea nitrogen (BUN), and maximum hemoglobin reduction. The predictive models showed good discriminative ability (area under the receiver operating characteristic curve [AUROC]:LR, 0.777; DT, 0.726; RF, 0.788; XGBoost, 0.815) and goodness of fit (area under the precision-recall curve [AUPRC]: LR, 0.350; DT, 0.290; RF, 0.400; XGBoost, 0.428). The Shapley Additive exPlanation (SHAP) values in the XGBoost model showed that Elixhauser comorbidity index, maximum BUN, maximum hemoglobin reduction, ventilator use within 24 hours of admission, and age were significant features for predicting in-hospital mortality in patients with sepsis complicated by anemia. ConclusionsThe XGBoost model had better discrimination ability and goodness of fit when compared with other models. Machine learning algorithms have significant practical value in the development of an early warning system for patients with sepsis complicated by anemia.

scholarly journals Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units

2021 ◽  
Vol 11 ◽  
Author(s):  
Ximing Nie ◽  
Yuan Cai ◽  
Jingyi Liu ◽  
Xiran Liu ◽  
Jiahui Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Random Forest ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Cerebral Hemorrhage ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Mortality Prediction ◽  
Machine Learning Algorithms

Objectives: This study aims to investigate whether the machine learning algorithms could provide an optimal early mortality prediction method compared with other scoring systems for patients with cerebral hemorrhage in intensive care units in clinical practice.Methods: Between 2008 and 2012, from Intensive Care III (MIMIC-III) database, all cerebral hemorrhage patients monitored with the MetaVision system and admitted to intensive care units were enrolled in this study. The calibration, discrimination, and risk classification of predicted hospital mortality based on machine learning algorithms were assessed. The primary outcome was hospital mortality. Model performance was assessed with accuracy and receiver operating characteristic curve analysis.Results: Of 760 cerebral hemorrhage patients enrolled from MIMIC database [mean age, 68.2 years (SD, ±15.5)], 383 (50.4%) patients died in hospital, and 377 (49.6%) patients survived. The area under the receiver operating characteristic curve (AUC) of six machine learning algorithms was 0.600 (nearest neighbors), 0.617 (decision tree), 0.655 (neural net), 0.671(AdaBoost), 0.819 (random forest), and 0.725 (gcForest). The AUC was 0.423 for Acute Physiology and Chronic Health Evaluation II score. The random forest had the highest specificity and accuracy, as well as the greatest AUC, showing the best ability to predict in-hospital mortality.Conclusions: Compared with conventional scoring system and the other five machine learning algorithms in this study, random forest algorithm had better performance in predicting in-hospital mortality for cerebral hemorrhage patients in intensive care units, and thus further research should be conducted on random forest algorithm.

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