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.
With nearly four decades of progress in healthcare-associated infection prevention and control in China, the national quality control efforts in this field have been ongoing for the past ten years, advancing rapidly with significant achievements. Over the last decade, the team of infection control professionals involved in quality management and control in China has consistently expanded, accompanied by an enhancement of their skills. Management capabilities have steadily grown, and operational mechanisms have been continuously refined. As public hospitals transition into a new phase of high-quality development, emphasizing refined management models and intrinsic development of medical quality, it becomes crucial to further fortify the foundation and foster innovation in infection control work to ensure quality. This article provides an overview of the establishment and implementation of the National Center for Quality Control of Infection Prevention and Control, examines the current shortcomings and challenges in the field, and collectively explores the positioning and direction of the development of quality control efforts for infection prevention and control in China.
Objective To evaluate the current status of human resources in healthcare-associated infection prevention and control (infection control) in Jiangxi Province, and explore the impact of emergency public health events on the human resources of infection control professionals in various levels and types of medical institutions. Methods From October 1st to 31st, 2023, questionnaire and on-site interviews were conducted to investigate the human resources situation of infection control professionals in various levels and types of medical institutions in Jiangxi Province. Three stages were selected for the investigation: before the outbreak of COVID-19 (before the event, December 2019), during the event (June 2022), and after the transition of COVID-19 (after the event, June 2023), focusing on the characteristics of human resources between before the event and after the event by the comparative analysis. Results Finally, 289 medical institutions were included. There was a statistically significant difference in the number of infection control professionals in medical institutions among 2019, 2022, and 2023 (χ2=189.677, P<0.001). The number of infection control professionals in 2019 was lower than that in 2022 (P<0.001) and 2023 (P<0.001), but there was no statistically significant difference between 2022 and 2023 (P=0.242). The number of infection control professionals per thousand beds in 2019, 2022, and 2023 was 4.40, 6.16, and 5.76, respectively. There was no statistically significant difference between 2019 and 2023 in terms of professional titles, gender, educational level, or professional background (P>0.05). Conclusion Emergency public health events have promoted the increase in the number of infection control professionals, but there is no statistical significance in the professional titles, educational level, or professional background of infection control professionals.
At present, the mode of day surgery has been widely carried out in China. With the rapid turnover of patients, higher requirements have been put forward for the management of nosocomial infection. Therefore, it needs norms for the management of nosocomial infection in the day surgery ward. After 10 years of precipitation, under the guidance of the hospital infection management department, the hospital infection management system for the day surgery ward of West China Hospital of Sichuan University has been continuously researched and explored, so as to ensure the rapid turnover of patients and make the hospital infection management meet the national standards. The system includes patient management and control, environmental management and control, matters needing attention of medical staff, surgical site infection data collection, and indicators of hospital infection supervision, etc.
Objective To use bibliometrics to identify research hotspots and emerging trends in the use of artificial intelligence (AI) in healthcare-associated infections (HAI), as well as to offer a resource for more relevant research. Methods The literature on AI and HAI from the Science Citation Index Expanded database of the Web of Science Core Collection was retrieved through computer searches, covering the period from January 1, 1994, to January 22, 2024. VOSviewer (v1.6.19) and CiteSpace (v6.1. R6) software were utilized for bibliometric analysis, creating knowledge maps that include research cooperation networks and keyword analysis. Results A total of 305 documents were included, and both the number of early publications and the frequency of citations were at a very low level for a long time before showing an annual increase trend after 2018. The United States had the most published documents among the 50 countries/regions from where they were sourced. Harvard University was the scientific research institution with the most publications, while Professor Evans HL of the Medical University of South Carolina was the scholar with the most publications. Research on AI in the field of HAI primarily focused on three aspects: AI algorithms and technologies, monitoring and prediction of HAI, and the accuracy of HAI diagnosis and prediction. These findings were based on keyword co-occurrence and clustering analysis. Conclusions A new phase of AI research in the subject of HAI has begun. More in-depth research can be done in the future for the hot direction, as there is still a gap between China’s academic accomplishments in this subject and the advanced level of the world.
Objective To investigate and analyze the difficulties of nosocomial infection management in different-level medical institutions in Shanghai, and to provide scientific basis for improving the level of nosocomial infection management. Methods A questionnaire was designed to include 10 difficulties in nosocomial infection management such as professional title promotion, salary, and personnel allocation. In October 2023, the Shanghai Nosocomial Infection Quality Control Center, in collaboration with the Shanghai Hospital Association, conducted a questionnaire survey among the heads of nosocomial infection management departments in medical institutions in Shanghai. The scores of difficulties were analyzed by stratification according to hospital level, allocation and changes of full-time personnel. Results A total of 548 questionnaires were distributed, and 530 valid questionnaires were retrieved, with a recovery rate of 96.72%. There were 55 public tertiary, 93 public secondary, 169 public primary and 213 social medical institutions. The rates of full-time personnel allocation meeting standards were 76.36% (42/55), 72.04% (67/93), 31.95% (54/169), and 21.60% (46/213), respectively. There was a statistically significant difference in the rates of full-time personnel allocation meeting standards among different levels of medical institutions (χ2=105.149, P<0.001). There was no statistical difference in the total scores of nosocomial infection management difficulties among different-level medical institutions (F=1.657, P=0.176). There were statistically significant differences in the scores of difficulties in professional title promotion, cumbersome daily norms and requirements, insufficient allocation of full-time personnel, and high personnel turnover (P<0.05). Conclusions The main difficulties in nosocomial management of medical institutions at all levels in Shanghai include the difficulty in career promotion, cumbersome daily norms and requirements, insufficient allocation of full-time personnel and lack of experience. In the future, medical institutions should strengthen the allocation of full-time personnel and enhance their capabilities, provide smooth promotion channels, to promote the high-quality development of nosocomial infection management ultimately.