|
|
|
| A prediction model of multidrug resistant bacterial for inpatients with pneumonia |
| BAI Ruiying1, SHENG Haiyan2
|
1. Graduate School, Bengbu Medical University, Bengbu, Anhui 233030, China;
2. The Second Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233000, China |
|
|
|
|
Abstract Objective To create a prediction model of multidrug resistant bacterial infections for inpatients with pneumonia, so as to provide the reference for the early identification and intervention of multidrug resistant bacterial infections. Methods The inpatients with pneumonia in the Second Affiliated Hospital of Bengbu Medical University from October 2022 to June 2024 were selected as the research subjects. Basic information and clinical data of the patients were collected. Respiratory secretions were collected for etiological culture and drug sensitivity tests to analyze the infection situation of multidrug resistant bacteria. LASSO regression and a multivariable logistic regression model were used to screen predictive factors and establish a predictive model of multidrug resistant bacterial infections for inpatients with pneumonia. The predictive effect of the model was assessed by receiver operating characteristic (ROC) curve, calibration curve, and decision curve. Results A total of 368 inpatients with pneumonia were recruited, including 215 males (58.42%) and 153 females (41.58%). The median age was 71.00 (interquartile range, 20.00) years. There were 168 cases of multidrug resistant bacterial infections detected, with a detection rate of 45.65%. The multivariable logistic regression analysis showed that long-term bedridden patients (OR=2.699, 95%CI: 1.120-6.504), use of antibiotics within 30 days (OR=8.623, 95%CI: 2.949-25.216), respiratory failure (OR=2.407, 95%CI: 1.058-5.478), intensive care unit treatment (OR=3.995, 95%CI: 1.313-12.161), and hypoproteinemia (OR=2.129, 95%CI: 1.012-4.480) were predict factors of multidrug resistant bacterial infections for inpatients with pneumonia. The area under the ROC curve of the established multidrug resistant bacterial infection prediction model was 0.909 (95%CI: 0.879-0.939). The calibration curve after repeated sampling calibration approached the standard curve, and the predicted values were highly consistent with the measured values. The decision curve showed that when the probability threshold is 0.27-0.99, the clinical net benefit for predicting the risk of multidrug resistant bacterial infection is relatively high. Conclusion The prediction model of multidrug resistant bacteria infection constructed has a good predictive value for multidrug resistant bacterial infection among inpatients with pneumonia.
|
|
Received: 25 November 2024
Revised: 07 April 2025
Published: 23 May 2025
|
|
|
|
|
|
[1] 黄勋,邓子德,倪语星,等.多重耐药菌医院感染预防与控制中国专家共识[J].中国感染控制杂志,2015,14(1):1-9.
HUANG X,DENG Z D,NI Y X,et al.Chinese experts' consensus on prevention and control of multidrug resistance organism healthcare-associated infection[J].Chin J Infect Control,2015,14(1):1-9.(in Chinese)
[2] BONNET M,ECKERT C,TOURNEBIZE R,et al.Decolonization of asymptomatic carriage of multi-drug resistant bacteria by bacteriophages?[J/OL].Front Microbiol,2023,14[2025-04-07].https://doi.org/10.3389/fmicb.2023.1266416.
[3] 王丹,朱丹,夏敏,等.预防综合ICU多重耐药菌医院感染的成本效益分析[J].中国感染控制杂志,2021,20(12):1119-1125.
WANG D,ZHU D,XIA M,et al.Cost-benefit of prevention of multidrug-resistant organism healthcare-associated infection in a general intensive care unit[J].Chin J Infect Control,2021,20(12):1119-1125.(in Chinese)
[4] 黄琦,杨天富,姜成安,等.基于证据的强化培训与监测管理对医院多重耐药菌感染防控的影响研究[J].现代医院,2025,25(1):140-142,147.
HUANG Q,YANG T F,JIANG C A,et al.Study on the impact of evidence-based reinforcement training and monitoring management on the prevention and control of hospital-acquired multidrug-resistant organism infections[J].Mod Hosp J,2025,25(1):140-142,147.(in Chinese)
[5] 段滢,胡明霞.某院多重耐药菌医院感染的流行病学分析[J].抗感染药学,2024,21(7):714-717.
DUAN Y,HU M X.Epidemiological analysis of healthcare-associated infections caused by multidrug-resistant organisms in a hospital[J].Ant iInfect Pharm,2024,21(7):714-717.(in Chinese)
[6] 刘平香,沈湘波,欧政.某院高龄肺部感染患者多重耐药菌的检出情况及其危险因素分析[J].抗感染药学,2023,20(11):1199-1202.
LIU P X,SHEN X B,OU Z.Analysis of the detection of multidrug-resistant bacteria and their risk factors in elderly patients with lung infection in a hospital[J].Anti Infect Pharm,2023,20(11):1199-1202.(in Chinese)
[7] 于翠香,王西艳.《中国成人医院获得性肺炎与呼吸机相关性肺炎诊断和治疗指南(2018年版)》解读[J].中国医刊,2021,56(9):951-953.
YU C X,WANG X Y.Interpretation of the Guidelines for the Diagnosis and Treatment of ventilator-Associated Pneumonia in Adult Hospitals of China(2018)[J].Chin J Med,2021,56(9):951-953.(in Chinese)
[8] 曹季平. 基于《社区获得性肺炎诊断和治疗指南(2016版)》对医院成人CAP患者抗菌药物使用的相关因素分析[J].抗感染药学,2020,17(3):323-327.
CAO J P.Analysis of relevant factor of antibiotic medication in adult patients with CAP in hospital based on Guidelines of CAP 2016 Edition[J].Anti Infect Pharm,2020,17(3):323-327.(in Chinese)
[9] Clinical and Laboratory Standards Institute.Performance standards.for antimicrobial susceptibility testing[EB/OL].[2025-04-07]https://clsi.org/search/?term=Performance+standards+for+antimicrobial+susceptibility+testing.31sted.CLSI+supplement+M10&tab=products-services.
[10] EL MEKES A,ZAHLANE K,AIT SAID L,et al.The clinical and epidemiological risk factors of infections due to multi-drug resistant bacteria in an adult intensive care unit of University Hospital Center in Marrakesh-Morocco[J].J Infect Public Health,2020,13(4):637-643.
[11] CHINTALAPUDI N,ANGELONI U,BATTINENI G,et al.LASSO regression modeling on prediction of medical terms among seafarers' health documents using tidy text mining[J].Bioengineering(Basel),2022,9(3):124-137.
[12] NTUMY M Y,TETTEH J,AGUADZE S,et al.Predictive model for genital tract infections among men and women in Ghana:an application of LASSO penalized cross-validation regression model[J/OL].Epidemiol Infect,2024,152[2025-04-07].https://doi.org/10.1017/S0950268824001444.
[13] 孙卫锋,吕锐,胡喜荣,等.老年住院脑卒中患者医院获得性肺炎病原菌分布及耐药性分析[J].预防医学,2021,33(12):1256-1259.
SUN W F,LYU R,HU X R,et al.Distribution and drug resistance of hospital-acquired pneumonia pathogens in elderly stroke patients[J].China Prev Med J,2021,33(12):1256-1259.(in Chinese)
[14] GOLLI A L,CRISTEA O M,ZLATIAN O,et al.Prevalence of multidrug-resistant pathogens causing bloodstream infections in an intensive care unit[J].Infect Drug Resist,2022,15:5981-5992.
[15] 张静,李雨珂,王臻,等.重症肺炎患者多药耐药菌感染的病原学及影响因素模型构建[J].中华医院感染学杂志,2023,33(23):3552-3556.
ZHANG J,LI Y K,WANG Z,et al.Etiology of multi-drug resistant bacteria infection in severe pneumonia patients and the construction of influencing factors model[J].Chin J Nosocomiol,2023,33(23):3552-3556.(in Chinese)
[16] 何亚盛,张红霞,倪银,等.急诊重症监护病房住院患者医院感染的预测模型研究[J].预防医学,2022,34(9):919-922.
HE Y S,ZHANG H X,NI Y,et al.A model to predict nosocomial infections among inpatients in emergency intensive care units[J].China Prev Med J,2022,34(9):919-922.(in Chinese)
[17] BABA Y,ISHIGURO T,GOCHI M,et al.A 72-year-old woman with respiratory failure and bilateral ground-glass opacities[J].Chest,2020,158(1):41-45.
[18] 林芳,刘素贞,江海燕.老年2型糖尿病患者营养不良的影响因素分析[J].预防医学,2024,36(1):61-64,69.
LIN F,LIU S Z,JIANG H Y.Factors affecting malnutrition among elderly patients with type 2 diabetes mellitus[J].China Prev Med J,2024,36(1):61-64,69.(in Chinese) |
|
|
|
