Frontiers in Cellular and Infection Microbiology (Mar 2022)
A Clinically Applicable Nomogram for Predicting the Risk of Invasive Mechanical Ventilation in Pneumocystis jirovecii Pneumonia
- Rongjun Wan,
- Rongjun Wan,
- Rongjun Wan,
- Rongjun Wan,
- Rongjun Wan,
- Lu Bai,
- Lu Bai,
- Lu Bai,
- Lu Bai,
- Lu Bai,
- Yusheng Yan,
- Jianmin Li,
- Qingkai Luo,
- Hua Huang,
- Lingmei Huang,
- Zhi Xiang,
- Qing Luo,
- Zi Gu,
- Qing Guo,
- Pinhua Pan,
- Pinhua Pan,
- Pinhua Pan,
- Pinhua Pan,
- Pinhua Pan,
- Rongli Lu,
- Rongli Lu,
- Rongli Lu,
- Rongli Lu,
- Rongli Lu,
- Yimin Fang,
- Yimin Fang,
- Yimin Fang,
- Yimin Fang,
- Yimin Fang,
- Chengping Hu,
- Chengping Hu,
- Chengping Hu,
- Chengping Hu,
- Chengping Hu,
- Juan Jiang,
- Juan Jiang,
- Juan Jiang,
- Juan Jiang,
- Juan Jiang,
- Yuanyuan Li,
- Yuanyuan Li,
- Yuanyuan Li,
- Yuanyuan Li,
- Yuanyuan Li
Affiliations
- Rongjun Wan
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
- Rongjun Wan
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- Rongjun Wan
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
- Rongjun Wan
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China
- Rongjun Wan
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Lu Bai
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
- Lu Bai
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- Lu Bai
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
- Lu Bai
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China
- Lu Bai
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Yusheng Yan
- Department of Pulmonary and Critical Care Medicine, First Hospital of Changsha, Changsha, China
- Jianmin Li
- Department of Pulmonary and Critical Care Medicine, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, China
- Qingkai Luo
- Department of Pulmonary and Critical Care Medicine, First People’s Hospital of Chenzhou, Chenzhou, China
- Hua Huang
- Medical Center of Tuberculosis, Second People’s Hospital of Chenzhou, Chenzhou, China
- Lingmei Huang
- 0Department of Pulmonary and Critical Care Medicine, Yueyang Central Hospital, Yueyang, China
- Zhi Xiang
- 1Department of Respiratory Medicine, First People’s Hospital of Huaihua, Huaihua, China
- Qing Luo
- 2Department of Pulmonary and Critical Care Medicine, Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
- Zi Gu
- 3Department of Pulmonary and Critical Care Medicine, Xiangtan Central Hospital, Xiangtan, China
- Qing Guo
- 4Department of Pulmonary and Critical Care Medicine, Yiyang Central Hospital, Yiyang, China
- Pinhua Pan
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
- Pinhua Pan
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- Pinhua Pan
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
- Pinhua Pan
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China
- Pinhua Pan
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Rongli Lu
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
- Rongli Lu
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- Rongli Lu
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
- Rongli Lu
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China
- Rongli Lu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Yimin Fang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
- Yimin Fang
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- Yimin Fang
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
- Yimin Fang
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China
- Yimin Fang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Chengping Hu
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
- Chengping Hu
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- Chengping Hu
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
- Chengping Hu
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China
- Chengping Hu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Juan Jiang
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
- Juan Jiang
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- Juan Jiang
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
- Juan Jiang
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China
- Juan Jiang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Yuanyuan Li
- Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
- Yuanyuan Li
- Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China
- Yuanyuan Li
- Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China
- Yuanyuan Li
- Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China
- Yuanyuan Li
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- DOI
- https://doi.org/10.3389/fcimb.2022.850741
- Journal volume & issue
-
Vol. 12
Abstract
ObjectivePneumocystis jirovecii pneumonia (PCP) is a life-threatening disease associated with a high mortality rate among immunocompromised patient populations. Invasive mechanical ventilation (IMV) is a crucial component of treatment for PCP patients with progressive hypoxemia. This study explored the risk factors for IMV and established a model for early predicting the risk of IMV among patients with PCP.MethodsA multicenter, observational cohort study was conducted in 10 hospitals in China. Patients diagnosed with PCP were included, and their baseline clinical characteristics were collected. A Boruta analysis was performed to identify potentially important clinical features associated with the use of IMV during hospitalization. Selected variables were further analyzed using univariate and multivariable logistic regression. A logistic regression model was established based on independent risk factors for IMV and visualized using a nomogram.ResultsIn total, 103 patients comprised the training cohort for model development, and 45 comprised the validation cohort to confirm the model’s performance. No significant differences were observed in baseline clinical characteristics between the training and validation cohorts. Boruta analysis identified eight clinical features associated with IMV, three of which were further confirmed to be independent risk factors for IMV, including age (odds ratio [OR] 2.615 [95% confidence interval (CI) 1.110–6.159]; p = 0.028), oxygenation index (OR 0.217 [95% CI 0.078–0.604]; p = 0.003), and serum lactate dehydrogenase level (OR 1.864 [95% CI 1.040–3.341]; p = 0.037). Incorporating these three variables, the nomogram achieved good concordance indices of 0.829 (95% CI 0.752–0.906) and 0.818 (95% CI 0.686–0.950) in predicting IMV in the training and validation cohorts, respectively, and had well-fitted calibration curves.ConclusionsThe nomogram demonstrated accurate prediction of IMV in patients with PCP. Clinical application of this model enables early identification of patients with PCP who require IMV, which, in turn, may lead to rational therapeutic choices and improved clinical outcomes.
Keywords
- Pneumocystis jirovecii pneumonia (PCP)
- invasive mechanical ventilation (IMV)
- predictive model
- nomogram
- machine learning
