Predictive study on discharge status of hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease

LI Shaofan; LI Lifang; HE Hangzhi; ZHANG Yaoye; YUAN Yiwei; ZHAO Hui; ZHANG Yanbo

doi:10.16462/j.cnki.zhjbkz.2024.06.011

Volume 28 Issue 6

Jun. 2024

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LI Shaofan, LI Lifang, HE Hangzhi, ZHANG Yaoye, YUAN Yiwei, ZHAO Hui, ZHANG Yanbo. Predictive study on discharge status of hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(6): 685-690. doi: 10.16462/j.cnki.zhjbkz.2024.06.011

Citation:

LI Shaofan, LI Lifang, HE Hangzhi, ZHANG Yaoye, YUAN Yiwei, ZHAO Hui, ZHANG Yanbo. Predictive study on discharge status of hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(6): 685-690. doi: 10.16462/j.cnki.zhjbkz.2024.06.011

Citation:

LI Shaofan, LI Lifang, HE Hangzhi, ZHANG Yaoye, YUAN Yiwei, ZHAO Hui, ZHANG Yanbo. Predictive study on discharge status of hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(6): 685-690. doi: 10.16462/j.cnki.zhjbkz.2024.06.011

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Predictive study on discharge status of hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease

doi: 10.16462/j.cnki.zhjbkz.2024.06.011

LI Shaofan¹,
LI Lifang²,
HE Hangzhi¹,
ZHANG Yaoye¹,
YUAN Yiwei¹,
ZHAO Hui^{2
,
,},
ZHANG Yanbo^{1, 3
,
,}

1.
Department of Biostatistics, School of Public Health, Shanxi Medical University, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan 030001, China
2.
Department of Cardiology, Pulmonary and Critical Care Medicine, Second Hospital, Shanxi Medical University, Taiyuan 030001, China
3.
Shanxi University of Chinese Medicine, Jinzhong 030619, China

Funds:

National Natural Science Foundation of China 82173631

Special of Science and Technology Cooperation and Exchange of Shanxi Province 202204041101031

More Information

Corresponding author: ZHAO Hui, E-mail: hui_zhao@sxmu.edu.cn; ZHANG Yanbo, E-mail: sxmuzyb@126.com
Received Date: 2024-02-28
Rev Recd Date: 2024-04-02

Available Online: 2024-07-13

Publish Date: 2024-06-10

Abstract

Abstract

Objective A machine learning prognosis prediction model was created by innovatively combining discharge status and length of hospital stay, to accurately predict the prognosis of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This was done in order to address the issues of difficult to obtain pulmonary function tests and large measurement error. Methods A total of 3 035 inpatients with AECOPD were recruited from the second hospital of Shanxi Medical University between October 2011 and May 2020. The outcome variable is whether or not the patient recovered and was discharged within the median length of hospitalization.The prediction model is created using five distinct machine learning models: logistic regression, support vector machine, random forest, Catboost, and multi-layer perceptron. By contrasting evaluation metrics like area under the working characteristic curve (AUROC), the optimal model is determined. In order to verify the decision curve's clinical applicability, the best model was used to assess it. Results In comparison to other machine learning models, random forest has the greatest overall prediction performance, with AUC of 0.780, accuracy of 69.69%, precision of 64.50%, recall of 75.18%, F1 score of 69.44%, and Brier score of 18.77%. The decision curve analysis has high clinical value, and the calibration curve is largely compatible with the diagonal. Conclusions The prediction model based on random forest may reliably forecast the prognosis of patients with AECOPD and provide some aid to physicians in evaluation and treatment decision-making when the important indices of the lung function test cannot be acquired.
- Chronic obstructive pulmonary disease,
- Acute exacerbation period,
- Machine learning,
- Prediction model

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References(16)

References

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