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Huang SS, Qiu JY, Li SP, Ma YQ, He J, Han LN, Jiao LL, Xu C, Mao YM, Zhang YM. Microbial signatures predictive of short-term prognosis in severe pneumonia. Front Cell Infect Microbiol 2024; 14:1397717. [PMID: 39157177 PMCID: PMC11327560 DOI: 10.3389/fcimb.2024.1397717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 07/15/2024] [Indexed: 08/20/2024] Open
Abstract
Objective This retrospective cohort study aimed to investigate the composition and diversity of lung microbiota in patients with severe pneumonia and explore its association with short-term prognosis. Methods A total of 301 patients diagnosed with severe pneumonia underwent bronchoalveolar lavage fluid metagenomic next-generation sequencing (mNGS) testing from February 2022 to January 2024. After applying exclusion criteria, 236 patients were included in the study. Baseline demographic and clinical characteristics were compared between survival and non-survival groups. Microbial composition and diversity were analyzed using alpha and beta diversity metrics. Additionally, LEfSe analysis and machine learning methods were employed to identify key pathogenic microorganism associated with short-term mortality. Microbial interaction modes were assessed through network co-occurrence analysis. Results The overall 28-day mortality rate was 37.7% in severe pneumonia. Non-survival patients had a higher prevalence of hypertension and exhibited higher APACHE II and SOFA scores, higher procalcitonin (PCT), and shorter hospitalization duration. Microbial α and β diversity analysis showed no significant differences between the two groups. However, distinct species diversity patterns were observed, with the non-survival group showing a higher abundance of Acinetobacter baumannii, Klebsiella pneumoniae, and Enterococcus faecium, while the survival group had a higher prevalence of Corynebacterium striatum and Enterobacter. LEfSe analysis identified 29 distinct terms, with 10 potential markers in the non-survival group, including Pseudomonas sp. and Enterococcus durans. Machine learning models selected 16 key pathogenic bacteria, such as Klebsiella pneumoniae, significantly contributing to predicting short-term mortality. Network co-occurrence analysis revealed greater complexity in the non-survival group compared to the survival group, with differences in central genera. Conclusion Our study highlights the potential significance of lung microbiota composition in predicting short-term prognosis in severe pneumonia patients. Differences in microbial diversity and composition, along with distinct microbial interaction modes, may contribute to variations in short-term outcomes. Further research is warranted to elucidate the clinical implications and underlying mechanisms of these findings.
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Affiliation(s)
- Shen-Shen Huang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Jia-Yong Qiu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shuang-Ping Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Ya-Qing Ma
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Jun He
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Li-Na Han
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Long-Long Jiao
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Chong Xu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Yi-Min Mao
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Yong-Mei Zhang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
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Zhai Y, Lan D, Lv S, Mo L. Interpretability-based machine learning for predicting the risk of death from pulmonary inflammation in Chinese intensive care unit patients. Front Med (Lausanne) 2024; 11:1399527. [PMID: 38933112 PMCID: PMC11200536 DOI: 10.3389/fmed.2024.1399527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/13/2024] [Indexed: 06/28/2024] Open
Abstract
Objective The objective of this research was to create a machine learning predictive model that could be easily interpreted in order to precisely determine the risk of premature death in patients receiving intensive care after pulmonary inflammation. Methods In this study, information from the China intensive care units (ICU) Open Source database was used to examine data from 2790 patients who had infections between January 2019 and December 2020. A 7:3 ratio was used to randomly assign the whole patient population to training and validation groups. This study used six machine learning techniques: logistic regression, random forest, gradient boosting tree, extreme gradient boosting tree (XGBoost), multilayer perceptron, and K-nearest neighbor. A cross-validation grid search method was used to search the parameters in each model. Eight metrics were used to assess the models' performance: accuracy, precision, recall, F1 score, area under the curve (AUC) value, Brier score, Jordon's index, and calibration slope. The machine methods were ranked based on how well they performed in each of these metrics. The best-performing models were selected for interpretation using both the Shapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME) interpretable techniques. Results A subset of the study cohort's patients (120/1668, or 7.19%) died in the hospital following screening for inclusion and exclusion criteria. Using a cross-validated grid search to evaluate the six machine learning techniques, XGBoost showed good discriminative ability, achieving an accuracy score of 0.889 (0.874-0.904), precision score of 0.871 (0.849-0.893), recall score of 0.913 (0.890-0.936), F1 score of 0.891 (0.876-0.906), and AUC of 0.956 (0.939-0.973). Additionally, XGBoost exhibited excellent performance with a Brier score of 0.050, Jordon index of 0.947, and calibration slope of 1.074. It was also possible to create an interactive internet page using the XGBoost model. Conclusion By identifying patients at higher risk of early mortality, machine learning-based mortality risk prediction models have the potential to significantly improve patient care by directing clinical decision making and enabling early detection of survival and mortality issues in patients with pulmonary inflammation disease.
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Affiliation(s)
| | | | | | - Liqin Mo
- Cardiothoracic Surgery Intensive Care Unit, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Jeon ET, Lee HJ, Park TY, Jin KN, Ryu B, Lee HW, Kim DH. Machine learning-based prediction of in-ICU mortality in pneumonia patients. Sci Rep 2023; 13:11527. [PMID: 37460837 DOI: 10.1038/s41598-023-38765-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/14/2023] [Indexed: 07/20/2023] Open
Abstract
Conventional severity-of-illness scoring systems have shown suboptimal performance for predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This study aimed to develop and validate machine learning (ML) models for mortality prediction in patients with severe pneumonia. This retrospective study evaluated patients admitted to the ICU for severe pneumonia between January 2016 and December 2021. The predictive performance was analyzed by comparing the area under the receiver operating characteristic curve (AU-ROC) of ML models to that of conventional severity-of-illness scoring systems. Three ML models were evaluated: (1) logistic regression with L2 regularization, (2) gradient-boosted decision tree (LightGBM), and (3) multilayer perceptron (MLP). Among the 816 pneumonia patients included, 223 (27.3%) patients died. All ML models significantly outperformed the Simplified Acute Physiology Score II (AU-ROC: 0.650 [0.584-0.716] vs 0.820 [0.771-0.869] for logistic regression vs 0.827 [0.777-0.876] for LightGBM 0.838 [0.791-0.884] for MLP; P < 0.001). In the analysis for NRI, the LightGBM and MLP models showed superior reclassification compared with the logistic regression model in predicting in-ICU mortality in all length of stay in the ICU subgroups; all age subgroups; all subgroups with any APACHE II score, PaO2/FiO2 ratio < 200; all subgroups with or without history of respiratory disease; with or without history of CVA or dementia; treatment with mechanical ventilation, and use of inotropic agents. In conclusion, the ML models have excellent performance in predicting in-ICU mortality in patients with severe pneumonia. Moreover, this study highlights the potential advantages of selecting individual ML models for predicting in-ICU mortality in different subgroups.
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Affiliation(s)
- Eun-Tae Jeon
- Department of Radiology, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 5 Gil 20, Boramae-Road, Dongjak-gu, Seoul, South Korea
| | - Hyo Jin Lee
- Division of Respiratory and Critical Care, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 5 Gil 20, Boramae-Road, Dongjak-gu, Seoul, South Korea
| | - Tae Yun Park
- Division of Respiratory and Critical Care, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 5 Gil 20, Boramae-Road, Dongjak-gu, Seoul, South Korea
| | - Kwang Nam Jin
- Department of Radiology, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 5 Gil 20, Boramae-Road, Dongjak-gu, Seoul, South Korea
| | - Borim Ryu
- Center for Data Science, Biomedical Research Institute, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Hyun Woo Lee
- Division of Respiratory and Critical Care, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 5 Gil 20, Boramae-Road, Dongjak-gu, Seoul, South Korea.
| | - Dong Hyun Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 5 Gil 20, Boramae-Road, Dongjak-gu, Seoul, South Korea.
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Palamim CVC, Boschiero MN, Marson FAL. Epidemiological profile and risk factors associated with death in patients receiving invasive mechanical ventilation in an adult intensive care unit from Brazil: a retrospective study. Front Med (Lausanne) 2023; 10:1064120. [PMID: 37181356 PMCID: PMC10166862 DOI: 10.3389/fmed.2023.1064120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 03/28/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction Understanding the epidemiological profile and risk factors associated with invasive mechanical ventilation (IMV) is essential to manage the patients better and to improve health services. Therefore, our objective was to describe the epidemiological profile of adult patients in intensive care that required IMV in-hospital treatment. Also, to evaluate the risks associated with death and the influence of positive end-expiratory pressure (PEEP) and arterial oxygen pressure (PaO2) at admission in the clinical outcome. Methods We conducted an epidemiological study analyzing medical records of inpatients who received IMV from January 2016 to December 2019 prior to the Coronavirus Disease (COVID)-19 pandemic in Brazil. We considered the following characteristics in the statistical analysis: demographic data, diagnostic hypothesis, hospitalization data, and PEEP and PaO2 during IMV. We associated the patients' features with the risk of death using a multivariate binary logistic regression analysis. We adopted an alpha error of 0.05. Results We analyzed 1,443 medical records; out of those, 570 (39.5%) recorded the patients' deaths. The binary logistic regression was significant in predicting the patients' risk of death [X2(9) = 288.335; p < 0.001]. Among predictors, the most significant in relation to death risk were: age [elderly ≥65 years old; OR = 2.226 (95%CI = 1.728-2.867)]; male sex (OR = 0.754; 95%CI = 0.593-0.959); sepsis diagnosis (OR = 1.961; 95%CI = 1.481-2.595); need for elective surgery (OR = 0.469; 95%CI = 0.362-0.608); the presence of cerebrovascular accident (OR = 2.304; 95%CI = 1.502-3.534); time of hospital care (OR = 0.946; 95%CI = 0.935-0.956); hypoxemia at admission (OR = 1.635; 95%CI = 1.024-2.611), and PEEP >8 cmH2O at admission (OR = 2.153; 95%CI = 1.426-3.250). Conclusion The death rate of the studied intensive care unit was equivalent to that of other similar units. Regarding risk predictors, several demographic and clinical characteristics were associated with enhanced mortality in intensive care unit patients under mechanical ventilation, such as diabetes mellitus, systemic arterial hypertension, and older age. The PEEP >8 cmH2O at admission was also associated with increased mortality since this value is a marker of initially severe hypoxia.
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Affiliation(s)
- Camila Vantini Capasso Palamim
- Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, São Francisco University, Bragança Paulista, São Paulo, Brazil
- Laboratory of Human and Medical Genetics, Bragança Paulista, São Francisco University, São Paulo, Brazil
| | - Matheus Negri Boschiero
- Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, São Francisco University, Bragança Paulista, São Paulo, Brazil
- Laboratory of Human and Medical Genetics, Bragança Paulista, São Francisco University, São Paulo, Brazil
| | - Fernando Augusto Lima Marson
- Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, São Francisco University, Bragança Paulista, São Paulo, Brazil
- Laboratory of Human and Medical Genetics, Bragança Paulista, São Francisco University, São Paulo, Brazil
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He H, Li H, Zeng X, Zhao H, Zhang Y. Development and validation of a patient-reported outcome measure for patients with chronic respiratory failure: The CRF-PROM scale. Health Expect 2021; 24:1842-1858. [PMID: 34337839 PMCID: PMC8483203 DOI: 10.1111/hex.13324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/05/2021] [Accepted: 07/18/2021] [Indexed: 12/01/2022] Open
Abstract
Background Various health‐related quality‐of‐life (HRQOL) tools are used to evaluate patients with chronic respiratory failure (CRF), but there is a relative lack of tools available for the evaluation of social support and treatment in these patients. The present study focused on the development of a systematic patient‐reported outcome measure (PROM) tool for use in patients with CRF. Methods The CRF‐PROM scale conceptual framework and item bank were generated after reviewing the corresponding literature and HRQOL scales, interviewing CRF patients and focus groups. After creation of the initial scale, the items in the scale were selected through two item selection theories, and the final scale was created. The reliability, validity and feasibility of the final scale were assessed. Results The CRF‐PROM scale includes four domains (i.e., physiological domain, psychological domain, social domain and therapeutic domain) and 10 dimensions. After the item selection process, the final scale included 50 items. Cronbach's α coefficients, which were all above 0.7, indicated the reliability of the scale. The results of structural validity met the relevant standards of confirmatory factor analysis. The response rates of the preinvestigation and the formal investigation were 93.3% and 97.6%, respectively. Conclusions The CRF‐PROM scale developed in the present study is effective and reliable. It could be used widely in the posthospital management of patients, in CRF studies and in clinical trials of new medical products and interventions. Patient or Public Contribution Participants from eight different hospitals and communities participated in the development or validation phase of the CRF‐PROM scale.
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Affiliation(s)
- Hangzhi He
- Department of Health Statistics, Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Hao Li
- Department of Health Statistics, Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Xianhua Zeng
- Department of Health Statistics, Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Hui Zhao
- Respiratory Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Yanbo Zhang
- Department of Health Statistics, Shanxi Medical University, Taiyuan, Shanxi Province, China
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Lee HW, Cho YJ. The Impact of Mechanical Ventilation Duration on the Readmission to Intensive Care Unit: A Population-Based Observational Study. Tuberc Respir Dis (Seoul) 2020; 83:303-311. [PMID: 32819076 PMCID: PMC7515670 DOI: 10.4046/trd.2020.0024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 08/20/2020] [Indexed: 11/24/2022] Open
Abstract
Background If the duration of mechanical ventilation (MV) is related with the intensive care unit (ICU) readmission must be clarified. The purpose of this study was to elucidate if prolonged MV duration increases ICU readmission rate. Methods The present observational cohort study analyzed national healthcare claims data from 2006 to 2015. Critically ill patients who received MV in the ICU were classified into five groups according to the MV duration: MV for <7 days, 7–13 days, 14–20 days, 21–27 days, and ≥28 days. The rate and risk of the ICU readmission were estimated according to the MV duration using the unadjusted and adjusted analyses. Results We found that 12,929 patients had at least one episode of MV in the ICU. There was a significant linear relationship between the MV duration and the ICU readmission (R2=0.85, p=0.025). The total readmission rate was significantly higher as the MV duration is prolonged (MV for <7 days, 13.9%; for 7–13 days, 16.7%; for 14–20 days, 19.4%; for 21–27 days, 20.4%; for ≥28 days, 35.7%; p<0.001). The analyses adjusted by covariables and weighted with the multinomial propensity scores showed similar results. In the adjusted regression analysis with a Cox proportional hazards model, the MV duration was significantly related to the ICU readmission (hazard ratio, 1.058 [95% confidence interval, 1.047–1.069], p<0.001). Conclusion The rate of readmission to the ICU was significantly higher in patients who received longer durations of the MV in the ICU. In the clinical setting, closer observation of patients discharged from the ICU after prolonged periods of MV is required.
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Affiliation(s)
- Hyun Woo Lee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Young-Jae Cho
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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