Risk factor analysis and prediction model construction for hospital infections in tertiary hospitals in Gansu Province_West China Medical Journal

Authors：

LIANG Jie ¹ , WU Minghua ¹ , CAI Ling ² , ZHANG Yinghua ² ,  WANG Junling ¹ ,  ZHANG Haojun ³

1. Institute of Health Toxicology, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, P. R. China;
2. Department of Infection Management, Gansu Provincial Hospital, Lanzhou, Gansu 730000, P. R. China;
3. Office of the President, Second Provincial People’s Hospital of Gansu, Lanzhou, Gansu 730000, P. R. China;

Corresponding author：

WANG Junling, Email: wangjl@lzu.edu.cn; ZHANG Haojun, Email: haozi_523@163.com

Keywords：

Hospital infection; risk factor; logistic regression; predictive model

DOI：

10.7507/1002-0179.202401049

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Objective To explore the independent risk factors for hospital infections in tertiary hospitals in Gansu Province, and establish and validate a prediction model. Methods A total of 690 patients hospitalized with hospital infections in Gansu Provincial Hospital between January and December 2021 were selected as the infection group; matched with admission department and age at a 1∶1 ratio, 690 patients who were hospitalized during the same period without hospital infections were selected as the control group. The information including underlying diseases, endoscopic operations, blood transfusion and immunosuppressant use of the two groups were compared, the factors influencing hospital infections in hospitalized patients were analyzed through multiple logistic regression, and the logistic prediction model was established. Eighty percent of the data from Gansu Provincial Hospital were used as the training set of the model, and the remaining 20% were used as the test set for internal validation. Case data from other three hospitals in Gansu Province were used for external validation. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate the model effectiveness. Results Multiple logistic regression analysis showed that endoscopic therapeutic manipulation [odds ratio (OR)=3.360, 95% confidence interval (CI) (2.496, 4.523)], indwelling catheter [OR=3.100, 95%CI (2.352, 4.085)], organ transplantation/artifact implantation [OR=3.133, 95%CI (1.780, 5.516)], blood or blood product transfusions [OR=3.412, 95%CI (2.626, 4.434)], glucocorticoids [OR=2.253, 95%CI (1.608, 3.157)], the number of underlying diseases [OR=1.197, 95%CI (1.068, 1.342)], and the number of surgical procedures performed during hospitalization [OR=1.221, 95%CI (1.096, 1.361)] were risk factors for hospital infections. The regression equation of the prediction model was: logit(P)=–2.208+1.212×endoscopic therapeutic operations+1.131×indwelling urinary catheters+1.142×organ transplantation/artifact implantation+1.227×transfusion of blood or blood products+0.812×glucocorticosteroids+0.180×number of underlying diseases+0.200×number of surgical procedures performed during the hospitalization. The internal validation set model had a sensitivity of 72.857%, a specificity of 77.206%, an accuracy of 76.692%, and an AUC value of 0.817. The external validation model had a sensitivity of 63.705%, a specificity of 70.934%, an accuracy of 68.669%, and an AUC value of 0.726. Conclusions Endoscopic treatment operation, indwelling catheter, organ transplantation/artifact implantation, blood or blood product transfusion, glucocorticoid, number of underlying diseases, and number of surgical cases during hospitalization are influencing factors of hospital infections. The model can effectively predict the occurrence of hospital infections and guide the clinic to take preventive measures to reduce the occurrence of hospital infections.

Citation： LIANG Jie, WU Minghua, CAI Ling, ZHANG Yinghua, WANG Junling, ZHANG Haojun. Risk factor analysis and prediction model construction for hospital infections in tertiary hospitals in Gansu Province. West China Medical Journal, 2024, 39(3): 406-411. doi: 10.7507/1002-0179.202401049 Copy

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21.	王美霞, 米宏霏, 高晓东, 等. 某三级综合医院医院感染横断面调查及预测模型构建. 公共卫生与预防医学, 2021, 32(5): 56-60.
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1. Gilbert GL, Kerridge I. Hospital infection control: old problem - evolving challenges. Intern Med J, 2020, 50(1): 105-107.
2. Abban MK, Ayerakwa EA, Mosi L, et al. The burden of hospital acquired infections and antimicrobial resistance. Heliyon, 2023, 9(10): e20561.
3. Hazard D, von Cube M, Kaier K, et al. Predicting potential prevention effects on hospital burden of nosocomial infections: a multistate modeling approach. Value Health, 2021, 24(6): 830-838.
4. Liu X, Spencer A, Long Y, et al. A systematic review and meta-analysis of disease burden of healthcare-associated infections in China: an economic burden perspective from general hospitals. J Hosp Infect, 2022, 123: 1-11.
5. Glowicz JB, Landon E, Sickbert-Bennett EE, et al. SHEA/IDSA/APIC Practice Recommendation: strategies to prevent healthcare-associated infections through hand hygiene: 2022 update. Infect Control Hosp Epidemiol, 2023, 44(3): 355-376.
6. Saran S, Gurjar M, Azim A, et al. Structural risk factors for hospital-acquired infections in intensive care unit. HERD, 2021, 14(2): 328-336.
7. 中华人民共和国卫生部. 医院感染诊断标准(试行). 中华医学杂志, 2001, 81(5): 314-320.
8. Tan XYD, Wiseman T, Betihavas V. Risk factors for nosocomial infections and/or sepsis in adult burns patients: an integrative review. Intensive Crit Care Nurs, 2022, 73: 103292.
9. Kollef MH, Torres A, Shorr AF, et al. Nosocomial infection. Crit Care Med, 2021, 49(2): 169-187.
10. Rossi G, Nguyen Y, Lafont E, et al. Large retrospective study analysing predictive factors of primary treatment failure, recurrence and death in pyogenic liver abscesses. Infection, 2022, 50(5): 1205-1215.
11. Yang DN, Zhong LM, Huang FQ. Clinical effect of standardized nursing for lymphoma patients and the influencing factors of nosocomial infection. Medicine (Baltimore), 2023, 102(3): e32624.
12. 赖莎, 周忠良, 张小龙, 等. 非计划再住院的界定与控制策略. 卫生经济研究, 2022, 39(7): 41-45.
13. 吕林芳, 张汉阳. 2020 年北京市某三甲医院医院感染直接经济损失分析. 中华医院感染学杂志, 2023, 33(6): 933-937.
14. 林臻, 祝晓强, 陈致宁, 等. 基于 DRG 管理的肿瘤相关疾病医院感染直接经济负担评价. 中华医院感染学杂志, 2023, 33(9): 1417-1421.
15. Avasarala SK, Muscarella LF, Mehta AC. Sans standardization: effective endoscope reprocessing. Respiration, 2021, 100(12): 1208-1217.
16. Eickhoff A, Jung M. Status quo of infections via endoscopes in 2022. A real problem?. Gastroenterologe, 2022, 17(1): 3-8.
17. Dettori M, Riccardi N, Canetti D, et al. Infections in lung transplanted patients: a review. Pulmonology, 2022.
18. Jang SC, Oh BC, Nam JH, et al. Clinical impact and economic burden of post-transplant infections following heart transplantation: a retrospective nationwide cohort study. J Heart Lung Transplant, 2022, 41(11): 1601-1610.
19. Le Gal S, Toubas D, Totet A, et al. Pneumocystis infection outbreaks in organ transplantation units in france: a nation-wide survey. Clin Infect Dis, 2020, 70(10): 2216-2220.
20. Vinson AJ, Kiberd BA, Tennankore KK. Panic in the pandemic: when should kidney transplant programs close?. Kidney Int Rep, 2021, 6(5): 1232-1241.
21. 王美霞, 米宏霏, 高晓东, 等. 某三级综合医院医院感染横断面调查及预测模型构建. 公共卫生与预防医学, 2021, 32(5): 56-60.
22. Schwingshackl A, Meduri GU. Rationale for prolonged glucocorticoid use in pediatric ARDS: what the adults can teach us. Front Pediatr, 2016, 4: 58.
23. Tang Q, Chen Q, Li Y, et al. Association between glucocorticoids and mortality in patients with severe pneumonia: a systematic review and meta-analysis based on randomized controlled trials. Comput Math Methods Med, 2022, 2022: 1191205.

West China Medical Journal

Risk factor analysis and prediction model construction for hospital infections in tertiary hospitals in Gansu Province

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