Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients

Kim, Jong-Ho; Kwon, Young Suk; Baek, Moon Seong

doi:10.3390/jcm10102172

Cited by 13 publications

(13 citation statements)

References 38 publications

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“…Comparing MLTs in predicting hard outcomes in adult ICUs setting (e.g., in-hospital mortality) is under debate but remains under-addressed in the pediatric ICU environment. However, the results agree with the literature concerning the mortality outcome prediction via MLT models in the ICU setting [ 36 ]. For example, the gradient boosting (GB) model showed the highest ROC (0.79 (0.77–0.80)) for the 30-day mortality prediction in mechanically ventilated patients, followed by the random forest model (0.78 (0.76–0.80)).…”

Section: Discussionsupporting

confidence: 90%

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

et al. 2021

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The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their ability to predict the outcome of interest. The study involved patients admitted to PICUs between 2010 and 2020. Data were extracted from the Italian Network of Pediatric Intensive Care Units (TIPNet) registry. The algorithms considered were generalized linear model (GLM), recursive partition tree (RPART), random forest (RF), neural networks models, and extreme gradient boosting (XGB). Since the outcome is rare, upsampling and downsampling algorithms have been applied for imbalance control. For each approach, the main performance measures were reported. Among an overall sample of 29,494 subjects, only 399 developed HF during the PICU stay. The median age was about two years, and the male gender was the most prevalent. The XGB algorithm outperformed other MLTs in predicting HF development, with a median ROC measure of 0.780 (IQR 0.770–0.793). PIM 3, age, and base excess were found to be the strongest predictors of outcome. The present work provides insights for the prediction of HF development during PICU stay using machine-learning algorithms.

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Section: Discussionsupporting

confidence: 90%

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

et al. 2021

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“…In this study, we combined ML-based models with electronic medical record data to retrieve information on various clinical characteristics that affect in-hospital mortality, and we determined that the RF, XGB, and GBM models exhibited the most favorable discriminative ability with AUROCs of 0.816, 0.806, and 0.823, respectively. The accuracy of the ML models used in this study in predicting in-hospital mortality in the patients receiving CRRT is comparable to that reported in previous studies [ 7 , 10 , 22 , 23 , 24 , 25 ]. In particular, our model exhibited higher discriminative power than did the prediction model developed by Kang et al [ 7 ].…”

Section: Discussionsupporting

confidence: 86%

“…Pattharanitima et al used ML and deep learning to build a model for predicting renal replacement therapy-free survival and demonstrated that the long short-term memory model with multilayer perceptron architecture exhibited high discrimination performance with an AUC of 0.70 [ 13 ]. Studies have investigated the accuracy of other ML-based mortality prediction models in intensive care settings, including in patients with lactic acidosis [ 23 ], mechanically ventilated patients [ 24 ], and those with COVID-19 and AKI [ 25 ]. These findings emphasize the importance of ML in predicting outcomes in critical care settings, especially for patients receiving CRRT.…”

Section: Discussionmentioning

confidence: 99%

Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation

et al. 2022

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In this study, we established an explainable and personalized risk prediction model for in-hospital mortality after continuous renal replacement therapy (CRRT) initiation. This retrospective cohort study was conducted at Changhua Christian Hospital (CCH). A total of 2932 consecutive intensive care unit patients receiving CRRT between 1 January 2010, and 30 April 2021, were identified from the CCH Clinical Research Database and were included in this study. The recursive feature elimination method with 10-fold cross-validation was used and repeated five times to select the optimal subset of features for the development of machine learning (ML) models to predict in-hospital mortality after CRRT initiation. An explainable approach based on ML and the SHapley Additive exPlanation (SHAP) and a local explanation method were used to evaluate the risk of in-hospital mortality and help clinicians understand the results of ML models. The extreme gradient boosting and gradient boosting machine models exhibited a higher discrimination ability (area under curve [AUC] = 0.806, 95% CI = 0.770–0.843 and AUC = 0.823, 95% CI = 0.788–0.858, respectively). The SHAP model revealed that the Acute Physiology and Chronic Health Evaluation II score, albumin level, and the timing of CRRT initiation were the most crucial features, followed by age, potassium and creatinine levels, SPO2, mean arterial pressure, international normalized ratio, and vasopressor support use. ML models combined with SHAP and local interpretation can provide the visual interpretation of individual risk predictions, which can help clinicians understand the effect of critical features and make informed decisions for preventing in-hospital deaths.

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“…Moreover, patients who died within two weeks of starting MV were excluded from the analysis, leading to a selection bias. Several other studies exist but only predicted mortality 9 and short-term outcomes 10 , or they did not consider death in model building 7 .…”

Section: Discussionmentioning

confidence: 99%

“…The I-TRACH model previously extracted tachycardia, renal dysfunction, acidemia, and a decreased HCO 3 concentration as the main variables for constructing a scoring system 8 . In another study that reported the prediction of 30-day mortality, the essential features in the models were Acute Physiology and Chronic Health Evaluation II score, Charlson Comorbidity Index, use of norepinephrine, and base excess 9 . In our study, lactate level and anion gap were the two most important predictors in the nal VC model.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms Predict Successful Weaning from Mechanical Ventilation Before Intubation

Kim

Kim³

et al. 2022

Preprint

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Prediction of successful weaning from mechanical ventilation in advance to intubation can facilitate discussions regarding end-of-life care before unnecessary intubation. In this context, we aimed to develop a machine-learning-based model that predicts successful weaning from ventilator support based on routine clinical and laboratory data taken before or immediately after intubation. We used the Medical Information Mart for Intensive Care-IV database, including adult patients who underwent mechanical ventilation in intensive care at the Beth Israel Deaconess Medical Center, USA. Clinical and laboratory variables collected before or within 24 hours of intubation were used to develop machine-learning models that predict the probability of successful weaning within 14 days of ventilator support. Of 23,242 patients, 19,025 (81.9%) patients were successfully weaned from mechanical ventilation within 14 days. We selected 46 clinical and laboratory variables to create machine-learning models. The machine-learning-based ensemble voting classifier revealed the area under the receiver operating characteristic curve of 0.863 (95% confidence interval 0.855–0.870), which was significantly better than that of Sequential Organ Failure Assessment (0.588 [0.566–0.609]) and Simplified Acute Physiology Score II (0.749 [0.742–0.756]). The top features included lactate, anion gap, and prothrombin time. The model’s performance achieved a plateau with approximately the top 21 variables. We developed machine learning algorithms that can predict successful weaning from mechanical ventilation in advance to intubation in the intensive care unit. Our models can aid in appropriate management for patients who hesitate to decide on ventilator support or meaningless end-of-life care.

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Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients

Cited by 13 publications

References 38 publications

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation

Machine Learning Algorithms Predict Successful Weaning from Mechanical Ventilation Before Intubation

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