Predicting Potential Prevention Effects on Hospital Burden of Nosocomial Infections: A Multistate Modeling Approach

Optimisation of aetiological examination processes for enhanced quality and efficiency in hospitalised patients prior to antimicrobial therapy: a multicentre quasi-experimental study

OBJECTIVES

This study aimed to optimise the aetiological examination process in hospitalised patients to enhance pathogen detection quality and efficiency. The hypothesis was that process management strategies would improve specimen submission rates, quality, timeliness and antimicrobial use adjustment.

DESIGN

A multicentre quasi-experimental pre-post comparison design was used, with baseline and postoptimisation phases.

SETTING

Two hospitals in Guangming District, Shenzhen, China.

PARTICIPANTS

34 790 inpatients in the baseline and 34 361 in the postoptimisation phase, across all departments.

INTERVENTIONS

Implemented process clarification, standardisation of specimen collection/submission and multidisciplinary collaboration, with comprehensive staff training.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measures were the pathogen submission rate before antimicrobial therapy. The secondary outcome measure was the adjustment rate of antimicrobial use based on test results, specimen qualification rate and specimen submission time. These measures were evaluated before and after process optimisation.

RESULTS

Postoptimisation, key metrics improved significantly: pathogen submission rate (50.82%-71.77%), specimen qualification rate (90.20%-98.71%), submission time (192-104 min) and antimicrobial adjustment rate (74.11%-93.24%; all p<0.001).

CONCLUSIONS

Process management effectively enhanced aetiological examination quality and efficiency, with potential for widespread adoption.

TRIAL REGISTRATION NUMBER

Not applicable as this was a quasi-experimental study.

Healthcare-Associated Infections: The Role of Microbial and Environmental Factors in Infection Control-A Narrative Review

Healthcare-associated infections (HAIs), previously known as nosocomial infections, represent a significant threat to healthcare systems worldwide, prolonging patient hospital stays and the duration of antimicrobial therapy. One of the most serious consequences of HAIs is the increase in the rate of antibiotic resistance (AR) generated by the prolonged, frequent, and sometimes incorrect use of antibiotics, which leads to the selection of resistant bacteria, making treatment difficult and expensive, with direct consequences for the safety of patients and healthcare personnel. Therefore, timely and accurate diagnosis of HAIs is mandatory to develop appropriate infection prevention and control practices (IPC) and new therapeutic strategies. This review aimed to present the prevalence, risk factors, current diagnosis, including artificial intelligence (AI) and machine learning approaches, future perspectives in combating HAIs causative bacteria (phage therapy, microbiome-based interventions, and vaccination), and HAIs surveillance strategies. Also, we discussed the latest findings regarding the relationships of AR with climate change and environmental pollution in the context of the One Health approach. Phage therapy is an emerging option that can offer an alternative to ineffective antibiotic treatments for antibiotic-resistant bacteria causing HAIs. Clinical trials dealing with vaccine development for resistant bacteria have yielded conflicting results. Two promising strategies, fecal microbiota transplantation and probiotic therapy, proved highly effective against recurrent Clostridium difficile infections and have been shown to reduce HAI incidence in hospitalized patients undergoing antibiotic therapy. Artificial intelligence and machine learning systems offer promising predictive capabilities in processing large volumes of clinical, microbiological, and patient data but require robust data integration. Our paper argues that HAIs are still a global challenge, requiring stringent IPC policies, computer vision, and AI-powered tools. Despite promising avenues like integrated One Health approaches, optimized phage therapy, microbiome-based interventions, and targeted vaccine development, several knowledge gaps in clinical efficacy, standardization, and pathogen complexity remain to be answered.

How to avoid time-related types of bias in the analysis of clinical infectious diseases: demonstration and methods

BACKGROUND

Competing risk and immortal time bias can significantly affect the interpretation of analyses in infection disease research where patients may experience multiple outcomes. These biases can distort study results, leading to misestimation of event rates or therapy effects, and ultimately misinform clinical decisions.

OBJECTIVE

This article aims to explain the concepts of competing risk and immortal time bias while providing a clear understanding of how these biases arise and offering practical methods for addressing them in clinical studies. A study of patients in the intensive care unit (ICU) is used as an illustrative example.

SOURCES

References were compiled through searches of MEDLINE/PubMed and Google Scholar up to December 2024.

CONTENT

Competing risk bias occurs when a patient is at risk for more than one mutually exclusive event, such as death from different causes, which can skew survival analyses if not properly accounted for. Immortal time bias arises when periods during which the outcome cannot occur for patients with an intermediate exposure are improperly included in a survival analysis. This leads to altered effects and duration of stay estimation. In the ICU data example, ignoring competing risks results in impossible probability estimates and relative overestimation of incidence by as much as 52%. Ignoring time dependencies leads to the flawed conclusion that bloodstream infection is protective against patient death in the ICU, whereas a valid approach results in a significant harmful effect. Solutions such as multi-state models or extended Cox regression models are presented to mitigate these biases. Examples from published literature with proper handling of these biases are provided.

IMPLICATIONS

Properly accounting for competing risk and immortal time biases ensures more reliable and valid results, ultimately guiding better clinical decision-making and improving patient outcomes in the management of infectious diseases.

Predicting the effect of nosocomial infection prevention on neonatal mortality and hospital stay in Ethiopia: a prospective longitudinal study

BACKGROUND

Neonatal Nosocomial infections (NNIs) are a significant cause of morbidity and mortality for neonates in an intensive care unit. Neonatal causes of death in healthcare facilities are attributed to different factors. We aimed to investigate factors associated with NNIs, estimate the burden of NNIs, and assess how the prediction effects help to save medical mortality and length of hospital stay.

METHOD

A prospective longitudinal study was conducted and data were collected from January 2022 to June 2022 from Jimma University Medical Center (JUMC). The data were gathered in a variety of ways, including an in-person interview with the patient's caregiver, direct observations of neonatal patients, and a review of the study participants' charts. This study includes patients aged 3 to 28 days who were admitted to the JUMC neonatal ward and stayed for at least 48 h. Multi-state model formulation and multivariate logistic regression were used for data analysis.

RESULTS

A total of 545 neonates were included out of 688, and 30% (n = 164) of them acquired nosocomial infections (NIs); 98 (33%) of infected patients were born prematurely; and 71 (31.4%) were underweight at birth. NIs were higher in neonates with long hospital stay (AOR: 1.16, 95%CI: 1.13-1.20), use of urinary catheters (AOR: 3.09, 95%CI: 1.55-6.15), and undergoing surgical procedures (AOR: 2.42, 95%CI: 1.13-5.17). Patients who developed NIs had a higher risk of death (HR: 2, 95% CI: 1.31, 3.04). The burden of neonatal NIs was determined to have a risk of 0.3, a mortality rate of 9.6%, and an average duration of hospital stay of 14.6 days. Competing risk regression suggests that neonates with NIs have a significantly higher risk of death than those who are not infected (HR: 16.42, 95% CI: 8.70-30.98, p < 0.001). Assumed prevention that decreases the NIs rate in half would result in 101 lives and 1357 patient days saved from 10,000 neonatal inpatients.

CONCLUSION

Urinary catheterization and surgical procedure increased neonatal NIs. Longer hospital stay can increase the risk of NIs and can also result from the NIs. Our finding indicated that effective prevention of NIs could help reduce neonatal deaths and their hospital stays.

Investigation of multiple nosocomial infections using a semi-Markov multi-state model

BACKGROUND

The prevalence of multiple nosocomial infections (MNIs) is on the rise, however, there remains a limited comprehension regarding the associated risk factors, cumulative risk, probability of occurrence, and impact on length of stay (LOS).

METHOD

This multicenter study includes all hospitalized patients from 2020 to July 2023 in two sub-hospitals of a tertiary hospital in Guangming District, Shenzhen. The semi-Markov multi-state model (MSM) was utilized to analyze risk factors and cumulative risk of MNI, predict its occurrence probability, and calculate the extra LOS of nosocomial infection (NI).

RESULTS

The risk factors for MNI include age, community infection at admission, surgery, and combined use of antibiotics. However, the cumulative risk of MNI is lower than that of single nosocomial infection (SNI). MNI is most likely to occur within 14 days after admission. Additionally, SNI prolongs LOS by an average of 7.48 days (95% Confidence Interval, CI: 6.06-8.68 days), while MNI prolongs LOS by an average of 15.94 days (95% CI: 14.03-18.17 days). Furthermore, the more sites of infection there are, the longer the extra LOS will be.

CONCLUSION

The longer LOS and increased treatment difficulty of MNI result in a heavier disease burden for patients, necessitating targeted prevention and control measures.

Incidence and mortality of community-acquired and nosocomial infections in Japan: a nationwide medical claims database study

BACKGROUND

It is important to determine the prevalence and prognosis of community-acquired infection (CAI) and nosocomial infection (NI) to develop treatment strategies and appropriate medical policies in aging society.

METHODS

Patients hospitalized between January 2010 and December 2019, for whom culture tests were performed and antibiotics were administered, were selected using a national claims-based database. The annual trends in incidence and in-hospital mortality were calculated and evaluated by dividing the patients into four age groups.

RESULTS

Of the 73,962,409 inpatients registered in the database, 9.7% and 4.7% had CAI and NI, respectively. These incidences tended to increase across the years in both the groups. Among the patients hospitalized with infectious diseases, there was a significant increase in patients aged ≥ 85 years (CAI: + 1.04%/year and NI: + 0.94%/year, P < 0.001), while there was a significant decrease in hospitalization of patients aged ≤ 64 years (CAI: -1.63%/year and NI: -0.94%/year, P < 0.001). In-hospital mortality was significantly higher in the NI than in the CAI group (CAI: 8.3%; NI: 14.5%, adjusted mean difference 4.7%). The NI group had higher organ support, medical cost per patient, and longer duration of hospital stay. A decreasing trend in mortality was observed in both the groups (CAI: -0.53%/year and NI: -0.72%/year, P < 0.001).

CONCLUSION

The present analysis of a large Japanese claims database showed that NI is a significant burden on hospitalized patients in aging societies, emphasizing the need to address particularly on NI.

Spatial-temporal simulation for hospital infection spread and outbreaks of Clostridioides difficile

Validated and curated datasets are essential for studying the spread and control of infectious diseases in hospital settings, requiring clinical information on patients' evolution and their location. The literature shows that approaches based on Artificial Intelligence (AI) in the development of clinical-support systems have benefits that are increasingly recognized. However, there is a lack of available high-volume data, necessary for trusting such AI models. One effective method in this situation involves the simulation of realistic data. Existing simulators primarily focus on implementing compartmental epidemiological models and contact networks to validate epidemiological hypotheses. Nevertheless, other practical aspects such as the hospital building distribution, shifts or safety policies on infections has received minimal attention. In this paper, we propose a novel approach for a simulator of nosocomial infection spread, combining agent-based patient description, spatial-temporal constraints of the hospital settings, and microorganism behavior driven by epidemiological models. The predictive validity of the model was analyzed considering micro and macro-face validation, parameter calibration based on literature review, model alignment, and sensitive analysis with an expert. This simulation model is useful in monitoring infections and in the decision-making process in a hospital, by helping to detect spatial-temporal patterns and predict statistical data about the disease.

Nurses' knowledge on nosocomial infections preventive measures and its associated factors in Ghana: a cross-sectional study

BACKGROUND

Nosocomial infections (NCIs) have been associated with several adverse outcomes including extended hospitalization, persistent disability, heightened antimicrobial resistance, amplified socio-economic disruption, and elevated mortality rates. The adoption of infection prevention strategies has the greatest tendency to significantly reduce the risk and occurrence of NCIs among the population, particularly in resource constrained health systems. This study assessed nurses' knowledge on NCI preventive measures and its associated factors in Ghana.

METHODS

A cross-sectional study was conducted from July to August 2021. A sample of 237 healthcare workers in the Hohoe Municipality was selected to participate in the study. Data was collected with a questionnaire designed in Google Forms and analyzed using Stata version 16.0.

RESULTS

Overall, most of the participants (69.2%) were not knowledgeable about the preventive measures of NCIs. Nurses who were within the age group of 20-40 years [aOR = 0.25 (95% CI = 0.09-0.69), p = 0.007] and 41-60 years [aOR = 0.05 (95% CI = 0.01-0.29), p = 0.001] were significantly less likely to be knowledgeable about the preventive measures of NCIs compared to those who those aged less than 20 years. Nurses who attended in-service training or workshop were approximately 10 times more likely to be knowledgeable about preventive measures of nosocomial infection compared to those who had never attended in-service training or workshop [aOR = 9.55 (95% CI = 1.23-74.36), p = 0.031].

CONCLUSION

The study concludes that age and participation in-service training or workshop are significant factors that influence the knowledge of healthcare workers in preventive measures for nosocomial infections. These results highlight the importance of providing ongoing training and professional development opportunities to nurses to enhance their knowledge and improve their ability to prevent and control nosocomial infections. Additionally, the study emphasizes the need for targeted training programs that consider the age of nurses, to ensure that training is tailored to their specific needs.

Global prevalence of nosocomial infection: A systematic review and meta-analysis

OBJECTIVES

Hospital-acquired infections (HAIs) are significant problems as public health issues which need attention. Such infections are significant problems for society and healthcare organizations. This study aimed to carry out a systematic review and a meta-analysis to analyze the prevalence of HAIs globally.

METHODS

We conducted a comprehensive search of electronic databases including EMBASE, Scopus, PubMed and Web of Science between 2000 and June 2021. We found 7031 articles. After removing the duplicates, 5430 studies were screened based on the titles/ abstracts. Then, we systematically evaluated the full texts of the 1909 remaining studies and selected 400 records with 29,159,630 participants for meta-analysis. Random-effects model was used for the analysis, and heterogeneity analysis and publication bias test were conducted.

RESULTS

The rate of universal HAIs was 0.14 percent. The rate of HAIs is increasing by 0.06 percent annually. The highest rate of HAIs was in the AFR, while the lowest prevalence were in AMR and WPR. Besides, AFR prevalence in central Africa is higher than in other parts of the world by 0.27 (95% CI, 0.22-0.34). Besides, E. coli infected patients more than other micro-organisms such as Coagulase-negative staphylococci, Staphylococcus spp. and Pseudomonas aeruginosa. In hospital wards, Transplant, and Neonatal wards and ICU had the highest rates. The prevalence of HAIs was higher in men than in women.

CONCLUSION

We identified several essential details about the rate of HAIs in various parts of the world. The HAIs rate and the most common micro-organism were different in various contexts. However, several essential gaps were also identified. The study findings can help hospital managers and health policy makers identify the reason for HAIs and apply effective control programs to implement different plans to reduce the HAIs rate and the financial costs of such infections and save resources.

Antibacterial Screening of Isoespintanol, an Aromatic Monoterpene Isolated from Oxandra xylopioides Diels

The incidence of nosocomial infections, as well as the high mortality and drug resistance expressed by nosocomial pathogens, especially in immunocompromised patients, poses significant medical challenges. Currently, the efficacy of plant compounds with antimicrobial potential has been reported as a promising alternative therapy to traditional methods. Isoespintanol (ISO) is a monoterpene with high biological activity. Using the broth microdilution method, the antibacterial activity of ISO was examined in 90 clinical isolates, which included 14 different species: (Escherichia coli (38), Pseudomonas aeruginosa (12), Klebsiella pneumoniae (13), Acinetobacter baumannii (3), Proteus mirabilis (7), Staphylococcus epidermidis (3), Staphylococcus aureus (5), Enterococcus faecium (1), Enterococcus faecalis (1), Stenotrophomonas maltophilia (2), Citrobacter koseri (2), Serratia marcescens (1), Aeromonas hydrophila (1), and Providencia rettgeri (1). MIC90 minimum inhibitory concentration values ranged from 694.3 to 916.5 µg/mL and MIC50 values from 154.2 to 457.3 µg/mL. The eradication of mature biofilms in P. aeruginosa after 1 h of exposure to ISO was between 6.6 and 77.4%, being higher in all cases than the percentage of biofilm eradication in cells treated with ciprofloxacin, which was between 4.3 and 67.5%. ISO has antibacterial and antibiofilm potential against nosocomial bacteria and could serve as an adjuvant in the control of these pathogens.