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Wang Y, Wang G, Zhao Y, Wang C, Chen C, Ding Y, Lin J, You J, Gao S, Pang X. A deep learning model for predicting multidrug-resistant organism infection in critically ill patients. J Intensive Care 2023; 11:49. [PMID: 37941079 PMCID: PMC10633993 DOI: 10.1186/s40560-023-00695-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/12/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND This study aimed to apply the backpropagation neural network (BPNN) to develop a model for predicting multidrug-resistant organism (MDRO) infection in critically ill patients. METHODS This study collected patient information admitted to the intensive care unit (ICU) of the Affiliated Hospital of Qingdao University from August 2021 to January 2022. All patients enrolled were divided randomly into a training set (80%) and a test set (20%). The least absolute shrinkage and selection operator and stepwise regression analysis were used to determine the independent risk factors for MDRO infection. A BPNN model was constructed based on these factors. Then, we externally validated this model in patients from May 2022 to July 2022 over the same center. The model performance was evaluated by the calibration curve, the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS In the primary cohort, 688 patients were enrolled, including 109 (15.84%) MDRO infection patients. Risk factors for MDRO infection, as determined by the primary cohort, included length of hospitalization, length of ICU stay, long-term bed rest, antibiotics use before ICU, acute physiology and chronic health evaluation II, invasive operation before ICU, quantity of antibiotics, chronic lung disease, and hypoproteinemia. There were 238 patients in the validation set, including 31 (13.03%) MDRO infection patients. This BPNN model yielded good calibration. The AUC of the training set, the test set and the validation set were 0.889 (95% CI 0.852-0.925), 0.919 (95% CI 0.856-0.983), and 0.811 (95% CI 0.731-0.891), respectively. CONCLUSIONS This study confirmed nine independent risk factors for MDRO infection. The BPNN model performed well and was potentially used to predict MDRO infection in ICU patients.
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Affiliation(s)
- Yaxi Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Gang Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Yuxiao Zhao
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Cheng Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Chen Chen
- School of Nursing, Qingdao University, No. 38 Dengzhou Road, Qingdao, 266021, China
| | - Yaoyao Ding
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Jing Lin
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Jingjing You
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Silong Gao
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China.
| | - Xufeng Pang
- Department of Hospital-Acquired Infection Control, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China.
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Wu B, Peng M, Tong Y, Wang X, Ding Y, Cheng X. Distribution of bacteria and risk factors in patients with multidrug-resistant pneumonia in a single center rehabilitation ward. Medicine (Baltimore) 2023; 102:e35023. [PMID: 37682183 PMCID: PMC10489429 DOI: 10.1097/md.0000000000035023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/09/2023] [Indexed: 09/09/2023] Open
Abstract
Stroke patients may have dysphagia and frequent aspiration increasing exposure to antibiotics and the chance of multidrug-resistant (MDR) bacteria infection. This study investigated clinical risk factors and related antibiotic use of MDR bacteria infection in stroke patients in the rehabilitation ward, hoping that it can help prevent and reduce the condition of MDR bacteria. A retrospective cohort study was conducted using the database of stroke patients with pneumonia admitted to the rehabilitation ward from January 1, 2020, to June 30, 2022. The selected stroke patients were divided into the MDR and non-MDR groups. Analyze the infection bacteria of the 2 groups. Forward logistic regression was applied to identify possible independent MDR bacteria infection risk factors. A total of 323 patients were included. The top 3 common MDR pathogens were Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii. Almost all Pseudomonas aeruginosa and Acinetobacter baumannii are resistant to ertapenem. National Institute of Health stroke scale at admission was associated with MDR bacteria infection pneumonia (OR [odds ratio] = 1.078, 95%CI [1.017, 1.142]). Long-term tracheotomy (OR = 2.695, 95%CI [1.232, 5.897]), hypoalbuminemia (OR = 473, 95%CI [1.318, 4.642]), and bilateral cerebral hemisphere stroke (OR = 4.021, 95%CI [2.009, 8.048]) were significant clinical risk factors of MDR pneumonia after stroke. The detection rate of MDR bacteria has increased. Understanding the distribution and drug resistance of MDR bacteria in stroke patients with pneumonia in the neurological rehabilitation ward and the related susceptibility of MDR bacteria infection is necessary. This way, the treatment plan can be adjusted more timely, avoiding the abuse of antibiotics.
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Affiliation(s)
- Bangqi Wu
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Maohan Peng
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Department of Respiratory and Critical Care Medicine, Pengzhou People’s Hospital, Pengzhou, China
| | - Yuanyuan Tong
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xuhui Wang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yi Ding
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xinyue Cheng
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
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Risk Factors of Multidrug-Resistant Bacteria in Lower Respiratory Tract Infections: A Systematic Review and Meta-Analysis. CANADIAN JOURNAL OF INFECTIOUS DISEASES & MEDICAL MICROBIOLOGY 2020; 2020:7268519. [PMID: 32670442 PMCID: PMC7345606 DOI: 10.1155/2020/7268519] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/13/2020] [Accepted: 05/26/2020] [Indexed: 12/13/2022]
Abstract
Background Multidrug-resistant (MDR) bacteria are the main cause of lower respiratory tract infections (LRTIs) with high mortality. The purpose of this study is to identify the risk factors associated with MDR by performing a systematic review and meta-analysis. Methods PubMed, EMBASE (via Ovid), and Cochrane Library were systematically searched for studies on the risk factors for MDR bacteria in LRTIs as of November 30, 2019. Literature screening, data abstraction, and quality assessment of the eligible studies were performed independently by two researchers. Results A total of 3,607 articles were retrieved, of which 21 articles representing 20 cohort studies published in English were included after title/abstract and full-text screening. Among the 21 articles involving 7,650 patients and 1,360 MDR organisms, ten reported the risk factors for MDR Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB), ten for MDR GNB, and one for MDR GPB. The meta-analysis results suggested that prior antibiotic treatment, inappropriate antibiotic therapy, chronic lung disease, chronic liver disease and cerebral disease, prior MDR and PA infection/colonization, recent hospitalization, longer hospitalization stay, endotracheal tracheostomy and mechanical ventilation, tube feeding, nursing home residence, and higher disease severity score were independent risk factors for MDR bacteria. Conclusions This review identified fourteen clinical factors that might increase the risk of MDR bacteria in patients with LRTIs. Clinicians could take into account these factors when selecting antibiotics for patients and determine whether coverage for MDR bacteria is required. More well-designed studies are needed to confirm the various risk factors for MDR bacteria in the future.
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Li X, Chen B, Zhang S, Li X, Chang J, Tang Y, Wu Y, Lu X. Rapid Detection of Respiratory Pathogens for Community-Acquired Pneumonia by Capillary Electrophoresis-Based Multiplex PCR. SLAS Technol 2018; 24:105-116. [PMID: 30048599 DOI: 10.1177/2472630318787452] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Community-acquired pneumonia (CAP) is a common infectious disease linked to high rates of morbidity and mortality. Fast and accurate identification of the pathogens responsible for CAP will aid in diagnosis. We established a capillary electrophoresis-based multiplex PCR (CEMP) panel to enable the detection of viral and bacterial pathogens associated with CAP. The assay simultaneously detects and identifies the 13 common unculturable CAP viral and bacterial pathogens within 4 h. We evaluated the performance of a commercially available panel with 314 samples collected from CAP patients. We compared the results to those obtained with the liquid chip-based Luminex xTAG Respiratory Viral Panel (RVP) Fast Kit (for viruses) and the agarose gel-based Seegene PneumoBacter ACE Detection Kit (for atypical bacteria). All positive samples were further verified by the Sanger sequencing method. The sensitivity, specificity, positive predictive value, and negative predictive value of CEMP were 97.31%, 100%, 100%, and 99.85%, respectively. CEMP provides a rapid and accurate method for the high-throughput detection of pathogens in patients with CAP.
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Affiliation(s)
- Xue Li
- 1 Department of Laboratory Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, China.,2 College of Medicine, Capital Medical University, Beijing, China
| | - Bo Chen
- 3 Ningbo HEALTH Gene Technologies Co., Ltd., Ningbo, China
| | - Shaoya Zhang
- 1 Department of Laboratory Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xiuyuan Li
- 1 Department of Laboratory Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, China.,2 College of Medicine, Capital Medical University, Beijing, China
| | - Junxia Chang
- 4 Department of Laboratory Medicine, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Yanyan Tang
- 1 Department of Laboratory Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, China.,2 College of Medicine, Capital Medical University, Beijing, China
| | - Yong Wu
- 3 Ningbo HEALTH Gene Technologies Co., Ltd., Ningbo, China
| | - Xinxin Lu
- 1 Department of Laboratory Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, China.,2 College of Medicine, Capital Medical University, Beijing, China
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