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Machine Learning-Based Model for Predicting Prolonged Mechanical Ventilation in Patients with Congestive Heart Failure

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Abstract

Background

Mechanical ventilation (MV) is widely used to relieve respiratory failure in patients with congestive heart failure (CHF). Prolonged MV (PMV) is associated with a poor prognosis. We aimed to establish a prediction model based on machine learning (ML) algorithms for the early identification of patients with CHF requiring PMV.

Methods

Twelve commonly used ML algorithms were used to build the prediction model. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the key features. We examined the area under the curve (AUC) statistics to evaluate the prediction performance. Data from another database were used to conduct external validation.

Results

We screened out 10 key features from the initial 65 variables via LASSO regression to improve the practicability of the model. The CatBoost model showed the best performance for predicting PMV among the 12 commonly used ML algorithms, with favorable discrimination (AUC = 0.790) and calibration (Brier score = 0.154). Moreover, hospital mortality could be accurately predicted using the CatBoost model as well (AUC = 0.844). In the external validation, the CatBoost model also showed satisfactory prediction performance (AUC = 0.780), suggesting certain generalizability of the model. Finally, a nomogram with risk classification of PMV was shown in this study.

Conclusion

The present study developed and validated a CatBoost model, which could accurately predict PMV in mechanically ventilated patients with CHF. Moreover, this model has a favorable performance in predicting hospital mortality in these patients.

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Data availability

The data in of this study could be found in https://physionet.org/content/mimiciv/1.0/ and https://physionet.org/content/eicu-crd/2.0/

Code Availability

The code can be available upon reasonable request to the corresponding author.

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Funding

This project was supported by the High-level Hospital Project of Fuwai Hospital, Chinese Academy of Medical Sciences (2022-GSP-GG-25).

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Authors and Affiliations

  1. Chinese Academy of Medical Sciences, Peking Union Medical College, National Center for Cardiovascular Diseases, Fu Wai Hospital, Beijing, 100037, China

    Le Li, Bin Tu, Yulong Xiong, Zhao Hu, Zhenghao Zhang, Shangyu Liu & Yan Yao

Authors
  1. Le Li
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  2. Bin Tu
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  3. Yulong Xiong
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  4. Zhao Hu
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  5. Zhenghao Zhang
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  6. Shangyu Liu
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  7. Yan Yao
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Contributions

This study was designed by LL. ZZH, YLX, ZH, SYL, and BT were responsible for data collation and statistical analysis. LL wrote the first draft. YY reviewed and checked the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yan Yao.

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Ethics Approval and Consent to Participate

Because the study was an analysis of the third-party anonymized publicly available database with pre-existing institutional review board (IRB) approval, IRB approval from our institution was exempted.

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All the authors have agreed to publish this article.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Li, L., Tu, B., Xiong, Y. et al. Machine Learning-Based Model for Predicting Prolonged Mechanical Ventilation in Patients with Congestive Heart Failure. Cardiovasc Drugs Ther 38, 359–369 (2024). https://doi.org/10.1007/s10557-022-07399-9

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