Developing an explainable machine learning model to predict the mechanical ventilation duration of patients with ARDS in intensive care units

Wang, Zichen; Zhang, Luming; Yang, Rui; Cheng, Hongtao; Wang, Hao; Yin, Haiyan; Lyu, Jun

doi:10.1016/j.hrtlng.2022.11.005

Cited by 14 publications

(16 citation statements)

References 34 publications

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“…Currently, many predictive intubation models are based on data from Intensive Care Unit (ICU), such as MIMIC-IV, eICU, HIRID, ANZICS, and PIC [23][24][25]. Despite the valuable insights gained from this study, it is important to acknowledge its limitations.…”

Section: Discussionmentioning

confidence: 99%

A Risk Prediction Model for Efficient Intubation in the Emergency Department: A Five-Year Single-Center Retrospective Analysis

Ding,

Feng,

Yang

et al. 2023

Preprint

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Background To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning (ML) algorithms. Methods This study was conducted in an academic tertiary hospital in Hangzhou, China. Critical ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022. The demographic characteristics, distribution of organ dysfunction, parameters for different organs’ examination, and status of mechanical ventilation were recorded. These patients were assigned to the positive and negative groups according to ventilation support. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop the prediction model and compared it with other algorithms, such as logistic regression (LR), artificial neural network (ANN), and random forest (RF). SHapley Additive exPlanations (SHAP) was used to analyze the risk factors of intubated critically ill patients in the ED. Then, the best performance of the predictive model was used for auxiliary diagnosis. Results Of 14,589 critically ill patients, 10212 comprised the training group and 4377 comprised the test group; 2289 intubated patients were obtained from the electronic medical records. The mean age, the mean scores of vital signs, the parameters of different organs, and blood oxygen examination results differed significantly between the two groups (p < 0.05). The white blood cell count, international normalized ratio, respiratory rate, and pH are the top four risk factors for intubation in critically ill patients. Based on the risk factors in different predictive models, the XGBoost model showed the highest AUROC (0.8353) for predicting ED intubation. Conclusions For critically ill patients in the ED, the proposed model can predict potential intubation based on the risk factors in the clinically predictive model.

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Section: Discussionmentioning

confidence: 99%

A Risk Prediction Model for Efficient Intubation in the Emergency Department: A Five-Year Single-Center Retrospective Analysis

Ding,

Feng,

Yang

et al. 2023

Preprint

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“…ML has become increasingly popular in developing predictive models for various diseases [13][14][15]. In this study, our objective was to use ML to develop an effective model capable of identifying the potential occurrence of AKI in patients with ARDS.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, a study constructed a prognostic model for sepsis-induced ARDS patients using ML to predict the occurrence of AKI within 48 hours of admission to the ICU, achieving a high AUC of 0.86 and accuracy of 0.81 [23]. Moreover, several other studies have demonstrated the ability of ML to predict the duration of mechanical ventilation in ARDS, indicating that it can provide early and accurate predictions for MV duration in ARDS [13,24]. Overall, these ndings highlight the potential of machine learning algorithms to improve prognostic accuracy and guide clinical decision-making in ARDS.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome

Wei

Zhang

Dong

et al. 2023

Preprint

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Background Acute kidney injury (AKI) can make cases of acute respiratory distress syndrome (ARDS) more complex, and the combination of the two can significantly worsen the prognosis. Our objective, therefore, is to utilize machine learning techniques to construct models that can promptly identify the risk of AKI in ARDS patients, and provide guidance for early intervention and treatment, ultimately leading to improved prognosis. Method We obtained data regarding ARDS patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database and utilized 11 machine learning (ML) algorithms to construct our predictive models. We selected the best model based on various metrics, and visualized the importance of its features using Shapley additive explanations (SHAP). We then created a more concise model using fewer variables, and optimized it using hyperparameter optimization (HPO). Additionally, we developed a web-based calculator to facilitate clinical usage. Result A total of 928 ARDS patients were included in the analysis, of whom 179 (19.3%) developed AKI during hospitalization. A total of 43 features were used to build the model. Among all models, XGBoost performed the best. We used the top 10 features to build a compact model with an area under the curve (AUC) of 0.838, which improved to an AUC of 0.848 after the HPO. Conclusion Machine learning algorithms, especially XGBoost, are reliable tools for predicting AKI in ARDS patients. The compact model still retains excellent predictive ability, and the web-based calculator makes clinical usage more convenient.

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“…Despite extensive modeling and a large number of clinically relevant features, the discrimination using ML approaches for predicting responders to the prone position in mechanically ventilated patients with COVID-19 [ 14 ] was very poor. Scarce studies have evaluated the role of ML in predicting the duration of MV in ARDS patients [ 11 , 15 , 16 , 17 ]. Our primary goal was to compare the performance of logistic regression and three powerful ML approaches for the development, testing, and external validation of a model to predict the duration of MV > 14 days after diagnosis of moderate/severe ARDS.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study

Villar,

González-Martín,

Fernández

et al. 2024

JCM

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Background: The ability to predict a long duration of mechanical ventilation (MV) by clinicians is very limited. We assessed the value of machine learning (ML) for early prediction of the duration of MV > 14 days in patients with moderate-to-severe acute respiratory distress syndrome (ARDS). Methods: This is a development, testing, and external validation study using data from 1173 patients on MV ≥ 3 days with moderate-to-severe ARDS. We first developed and tested prediction models in 920 ARDS patients using relevant features captured at the time of moderate/severe ARDS diagnosis, at 24 h and 72 h after diagnosis with logistic regression, and Multilayer Perceptron, Support Vector Machine, and Random Forest ML techniques. For external validation, we used an independent cohort of 253 patients on MV ≥ 3 days with moderate/severe ARDS. Results: A total of 441 patients (48%) from the derivation cohort (n = 920) and 100 patients (40%) from the validation cohort (n = 253) were mechanically ventilated for >14 days [median 14 days (IQR 8–25) vs. 13 days (IQR 7–21), respectively]. The best early prediction model was obtained with data collected at 72 h after moderate/severe ARDS diagnosis. Multilayer Perceptron risk modeling identified major prognostic factors for the duration of MV > 14 days, including PaO2/FiO2, PaCO2, pH, and positive end-expiratory pressure. Predictions of the duration of MV > 14 days showed modest discrimination [AUC 0.71 (95%CI 0.65–0.76)]. Conclusions: Prolonged MV duration in moderate/severe ARDS patients remains difficult to predict early even with ML techniques such as Multilayer Perceptron and using data at 72 h of diagnosis. More research is needed to identify markers for predicting the length of MV. This study was registered on 14 August 2023 at ClinicalTrials.gov (NCT NCT05993377).

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Developing an explainable machine learning model to predict the mechanical ventilation duration of patients with ARDS in intensive care units

Cited by 14 publications

References 34 publications

A Risk Prediction Model for Efficient Intubation in the Emergency Department: A Five-Year Single-Center Retrospective Analysis

A Risk Prediction Model for Efficient Intubation in the Emergency Department: A Five-Year Single-Center Retrospective Analysis

Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome

Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study

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