Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study
Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Repu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang S [verfasserIn] Li J [verfasserIn] Dai J [verfasserIn] Zhang X [verfasserIn] Tang W [verfasserIn] Liu Y [verfasserIn] Wu X [verfasserIn] Fan X [verfasserIn] |
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Englisch |
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2023 |
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In: Infection and Drug Resistance - Dove Medical Press, 2009, (2023), Seite 6549-6566 |
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| Übergeordnetes Werk: |
year:2023 ; pages:6549-6566 |
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| 520 | |a Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model | ||
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(DE-627)DOAJ090935942 (DE-599)DOAJed987afef0684e82be77eb2a21c461fd DE-627 ger DE-627 rakwb eng RC109-216 Wang S verfasserin aut Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model multi-drug resistant bacteria risk factors prediction model. Infectious and parasitic diseases Li J verfasserin aut Dai J verfasserin aut Zhang X verfasserin aut Tang W verfasserin aut Li J verfasserin aut Liu Y verfasserin aut Wu X verfasserin aut Fan X verfasserin aut In Infection and Drug Resistance Dove Medical Press, 2009 (2023), Seite 6549-6566 (DE-627)600305996 (DE-600)2494856-1 11786973 nnns year:2023 pages:6549-6566 https://doaj.org/article/ed987afef0684e82be77eb2a21c461fd kostenfrei https://www.dovepress.com/establishment-and-validation-of-models-for-the-risk-of-multi-drug-resi-peer-reviewed-fulltext-article-IDR kostenfrei https://doaj.org/toc/1178-6973 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2023 6549-6566 |
| spelling |
(DE-627)DOAJ090935942 (DE-599)DOAJed987afef0684e82be77eb2a21c461fd DE-627 ger DE-627 rakwb eng RC109-216 Wang S verfasserin aut Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model multi-drug resistant bacteria risk factors prediction model. Infectious and parasitic diseases Li J verfasserin aut Dai J verfasserin aut Zhang X verfasserin aut Tang W verfasserin aut Li J verfasserin aut Liu Y verfasserin aut Wu X verfasserin aut Fan X verfasserin aut In Infection and Drug Resistance Dove Medical Press, 2009 (2023), Seite 6549-6566 (DE-627)600305996 (DE-600)2494856-1 11786973 nnns year:2023 pages:6549-6566 https://doaj.org/article/ed987afef0684e82be77eb2a21c461fd kostenfrei https://www.dovepress.com/establishment-and-validation-of-models-for-the-risk-of-multi-drug-resi-peer-reviewed-fulltext-article-IDR kostenfrei https://doaj.org/toc/1178-6973 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2023 6549-6566 |
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(DE-627)DOAJ090935942 (DE-599)DOAJed987afef0684e82be77eb2a21c461fd DE-627 ger DE-627 rakwb eng RC109-216 Wang S verfasserin aut Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model multi-drug resistant bacteria risk factors prediction model. Infectious and parasitic diseases Li J verfasserin aut Dai J verfasserin aut Zhang X verfasserin aut Tang W verfasserin aut Li J verfasserin aut Liu Y verfasserin aut Wu X verfasserin aut Fan X verfasserin aut In Infection and Drug Resistance Dove Medical Press, 2009 (2023), Seite 6549-6566 (DE-627)600305996 (DE-600)2494856-1 11786973 nnns year:2023 pages:6549-6566 https://doaj.org/article/ed987afef0684e82be77eb2a21c461fd kostenfrei https://www.dovepress.com/establishment-and-validation-of-models-for-the-risk-of-multi-drug-resi-peer-reviewed-fulltext-article-IDR kostenfrei https://doaj.org/toc/1178-6973 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2023 6549-6566 |
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(DE-627)DOAJ090935942 (DE-599)DOAJed987afef0684e82be77eb2a21c461fd DE-627 ger DE-627 rakwb eng RC109-216 Wang S verfasserin aut Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model multi-drug resistant bacteria risk factors prediction model. Infectious and parasitic diseases Li J verfasserin aut Dai J verfasserin aut Zhang X verfasserin aut Tang W verfasserin aut Li J verfasserin aut Liu Y verfasserin aut Wu X verfasserin aut Fan X verfasserin aut In Infection and Drug Resistance Dove Medical Press, 2009 (2023), Seite 6549-6566 (DE-627)600305996 (DE-600)2494856-1 11786973 nnns year:2023 pages:6549-6566 https://doaj.org/article/ed987afef0684e82be77eb2a21c461fd kostenfrei https://www.dovepress.com/establishment-and-validation-of-models-for-the-risk-of-multi-drug-resi-peer-reviewed-fulltext-article-IDR kostenfrei https://doaj.org/toc/1178-6973 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2023 6549-6566 |
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(DE-627)DOAJ090935942 (DE-599)DOAJed987afef0684e82be77eb2a21c461fd DE-627 ger DE-627 rakwb eng RC109-216 Wang S verfasserin aut Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model multi-drug resistant bacteria risk factors prediction model. Infectious and parasitic diseases Li J verfasserin aut Dai J verfasserin aut Zhang X verfasserin aut Tang W verfasserin aut Li J verfasserin aut Liu Y verfasserin aut Wu X verfasserin aut Fan X verfasserin aut In Infection and Drug Resistance Dove Medical Press, 2009 (2023), Seite 6549-6566 (DE-627)600305996 (DE-600)2494856-1 11786973 nnns year:2023 pages:6549-6566 https://doaj.org/article/ed987afef0684e82be77eb2a21c461fd kostenfrei https://www.dovepress.com/establishment-and-validation-of-models-for-the-risk-of-multi-drug-resi-peer-reviewed-fulltext-article-IDR kostenfrei https://doaj.org/toc/1178-6973 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2023 6549-6566 |
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Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study |
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Infection and Drug Resistance |
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Wang S Li J Dai J Zhang X Tang W Liu Y Wu X Fan X |
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establishment and validation of models for the risk of multi-drug resistant bacteria infection and prognosis in elderly patients with pulmonary infection: a multicenter retrospective study |
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RC109-216 |
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Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study |
| abstract |
Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model |
| abstractGer |
Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model |
| abstract_unstemmed |
Shu Wang,1,2,* Jing Li,3,4,* Jinghong Dai,3 Xuemin Zhang,5 Wenjuan Tang,6 Jing Li,7 Yu Liu,3 Xufeng Wu,8 Xiaoyun Fan1,9 1The Department of Geriatric Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People’s Republic of China; 2Department of Geriatrics, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui Province, People’s Republic of China; 3Department of Geriatrics, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 4Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 5The Department of Respiratory and Critical Care Medicine, Fuyang Hospital Affiliated to Anhui Medical University, Fuyang, Anhui Province, People’s Republic of China; 6The Department of Respiratory and Critical care medicine, Anqing Municipal Hospital, Anqing, Anhui Province, People’s Republic of China; 7Department of Geriatrics, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui Province, People’s Republic of China; 8Department of Intensive Care Unit, Hefei Binhu Hospital, Hefei, Anhui Province, People’s Republic of China; 9Key Laboratory of Geriatric Molecular Medicine of Anhui Province, Hefei, Anhui, 230022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xufeng Wu; Xiaoyun Fan, Email 15155109772163.com; 13956988552@126.comPurpose: The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.Patients and Methods: This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.Results: The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.Conclusion: In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.Keywords: multi-drug resistant bacteria, risk factors, prediction model |
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Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study |
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