Incidence of healthcare-associated infections in a tertiary hospital in Beijing, China: results from a real-time surveillance system

Temporal patterns and clinical characteristics of healthcare-associated infections in surgery patients: A retrospective study in a major Chinese tertiary hospital

Background

Given the preventable nature of most healthcare-associated infections (HAIs), it is crucial to understand their characteristics and temporal patterns to reduce their occurrence.

Methods

A retrospective analysis of medical record cover pages from a Chinese hospital information system was conducted for surgery inpatients from 2010 to 2019. Association rules mining (ARM) was employed to explore the association between disease, procedure, and HAIs. Joinpoint models were used to estimate the annual HAI trend. The time series of each type of HAI was decomposed to analyze the temporal patterns of HAIs.

Results

The study included data from 623,290 surgery inpatients over 10 years, and a significant decline in the HAI rate was observed. Compared with patients without HAIs, those with HAIs had a longer length of stay (29 days vs. 9 days), higher medical costs (96226.57 CNY vs. 22351.98 CNY), and an increased risk of death (6.42% vs. 0.18%). The most common diseases for each type of HAI differed, although bone marrow and spleen operations were the most frequent procedures for most HAI types. ARM detected that some uncommon diagnoses could strongly associate with HAIs. The time series pattern varied for each type of HAI, with the peak occurring in January for respiratory system infections, and in August and July for surgical site and bloodstream infections, respectively.

Conclusions

Our findings demonstrate that HAIs impose a significant burden on surgery patients. The differing time series patterns for each type of HAI highlight the importance of tailored surveillance strategies for specific types of HAI.

The incidence of surgical site infections in China

Surgical site infections (SSIs) are a common type of healthcare-associated infection. We performed a literature review to demonstrate the incidence of SSIs in mainland China based on studies since 2010. We included 231 eligible studies with ≥30 postoperative patients, comprising 14 providing overall SSI data regardless of surgical sites and 217 reporting SSIs for a specific site. We found that the overall SSI incidence was 2.91% (median; interquartile range: 1.05%, 4.57%) or 3.18% (pooled; 95% confidence interval: 1.85%, 4.51%) and the SSI incidence varied remarkably according to the surgical site between the lowest (median, 1.00%; pooled, 1.69%) in thyroid surgeries and the highest (median, 14.89%; pooled, 12.54%) in colorectal procedures. We uncovered that Enterobacterales and staphylococci were the most common types of micro-organisms associated with SSIs after various abdominal surgeries and cardiac or neurological procedures, respectively. We identified two, nine, and five studies addressing the impact of SSIs on mortality, the length of stay (LOS) in hospital, and additional healthcare-related economic burden, respectively, all of which demonstrated increased mortality, prolonged LOS, and elevated medical costs associated with SSIs among affected patients. Our findings illustrate that SSIs remain a relatively common, serious threat to patient safety in China, requiring more action. To tackle SSIs, we propose to establish a nationwide network for SSI surveillance using unified criteria with the aid of informatic techniques and to tailor and implement countermeasures based on local data and observation. We highlight that the impact of SSIs in China warrants further study.

Prevalence and mortality rate of healthcare-associated infections among COVID-19 patients: a retrospective cohort community-based approach

Background

The prevalence of HAI among COVID-19 patients ranged between 4.8% and 42.8% with the highest occurrence observed in critically ill patients. The present study aimed to evaluate the clinical features of HAI in severe and critical COVID-19 patients, their microbiological characteristics, and the attributable risk factors.

Methods

This is an analytical observational, retrospective single-center, cohort study that included 723 patients with severe-critical COVID-19 admitted to Saint George Hospital between September 2020 and February 2021. Data collection included demographic variables (sex, age), comorbidities, laboratory findings, HAI types and agents, COVID-19 treatment modalities, hospitalization settings, length of stay, and mortality rate. Data was analyzed using SPSS version 25.

Results

The prevalence of patients developing HAI was 7.3% (53 of 723). Five types of nosocomial bacterial infections were tracked noting ventilator-associated pneumonia (41.26%), catheter-associated urinary tract infection (28.6%), hospital-acquired pneumonia (17.44%), catheter-related bloodstream infection (6.35%), and bloodstream infection (6.35%). Binary logistic analysis showed that HAI are statistically affected by four factors noting patients' age (p = 0.039), Length of Stay (p < 0.001), BIPAP (p = 0.019), and mechanical ventilation (p < 0.001). The risk of having HAI increases 3.930 times in case of mechanical ventilation, 2.366 times in case of BIPAP, 1.148 times when the LOS increases 1 day, and 1.029 times when the age is higher with 1 year.

Conclusion

Since the prevalence of HAI is high among severe and critical COVID-19 patients, it is important to prepare a treatment with diagnostic, preventative, and control measures for this infection.

Impact of Nosocomial Infection on in-Hospital Mortality Rate in Adult Patients Under Venoarterial Extracorporeal Membrane Oxygenation After Cardiac Surgery

Objective

There was no consensus on the impact of nosocomial infection on In-hospital mortality rate in patients receiving ECMO. This study aimed to investigate the impact of nosocomial infection (NI) on In-hospital mortality rate in adult patients receiving venoarterial extracorporeal membrane oxygenation (VA-ECMO) after cardiac surgery.

Materials and Methods

This retrospective study included 503 adult patients who underwent VA-ECMO after cardiac surgery. The impact of time-dependent NIs on In-hospital mortality rate within 28 days of ECMO initiation was investigated using a Cox regression model. The cumulative incidence function for death was compared between patients with NIs and those without NIs using a competing risk model.

Results

Within 28 days after ECMO initiation, 206 (41.0%) patients developed NIs, and 220 (43.7%) patients died. The prevalence rates of NIs were 27.8% and 20.3% during and after ECMO therapy, respectively. The incidence rates of NIs during and after ECMO therapy were 49‰ and 25‰, respectively. Time-dependent NI was an independent risk factor for predicting death (hazard ratio = 1.05, 95% confidence interval = 1.00-1.11). The cumulative incidence of death in patients with NI was significantly higher than that in patients without NI at each time point within 28 days of ECMO initiation. (Z = 5.816, P = 0.0159).

Conclusion

NI was a common complication in adult patients who received VA-ECMO after cardiac surgery, and time-dependent NI was an independent risk factor for predicting mortality in these patients. Using a competing risk model, we confirmed that NIs increased the risk of In-hospital mortality rate in these patients.

Incidence of Urinary Tract Infection Among Patients: Systematic Review and Meta-Analysis

Healthcare-associated infection is one of the most common and severe threats to patients' health and remains a significant challenge for healthcare providers. Among healthcare-associated infections, urinary tract infection (UTI) is one of the most common infections. This study aimed to determine the global incidence of UTI among patients. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline was used to perform this systematic review and meta-analysis. The articles were searched from April 4 to August 5, 2022, from electronic databases (Scopus, PubMed, Web of Science, Google Scholar, DOAJ, and MedNar) using Boolean logic operators, MeSH terms, and keywords. The quality of the study was assessed using the JBI Critical Assessment tool. One thousand nine ninety three articles were retrieved from the electronic databases, of which 38 articles conducted on 981 221 patients were included in the current study. The study found the global pooled incidence of UTI accounted for 1.6%. Based on the subgroup analysis by survey period and WHO region, the highest incidence of UTI was reported in the African Region [3.6%] and among studies conducted between 1996 and 2001 [3.7%]. This study revealed the overall pooled incidence of UTI was 1.6%. The highest incidence of UTI (3.6%) was reported in the African region. This indicates that there is a need to implement safety measures.

Epidemiology of Healthcare-Associated Infections and Adherence to the HAI Prevention Strategies

Healthcare-associated infections are widely considered one of the most common unfavorable outcomes of healthcare delivery. Ventilator-associated pneumonia, central line-associated bloodstream infections, and catheter-associated urinary tract infections are examples of healthcare-associated infections. The current study was a retrospective study conducted at a public hospital in Unaizah, Saudi Arabia, to investigate the frequency of healthcare-associated illnesses and adherence to healthcare-associated infection prevention techniques in the year 2021. Surgical site infections occurred at a rate of 0.1%. The average number of catheter-associated urinary tract infections per 1000 catheter days was 0.76. The average number of central line-associated bloodstream infections per 1000 central line days was 2.6. The rate of ventilator-associated pneumonia was 1.1 per 1000 ventilator days on average. The average number of infections caused by multidrug-resistant organisms per 1000 patient days was 2.8. Compliance rates were 94%, 100%, 99%, and 76% for ventilator-associated pneumonia, central line-associated bloodstream infections, catheter-associated urinary tract infections, and hand hygiene bundles, respectively. It is critical to participate in more educational events and workshops, particularly those that emphasize hand cleanliness and personal safety equipment.

Effect of the "Normalized Epidemic Prevention and Control Requirements" on hospital-acquired and community-acquired infections in China

BACKGROUND

No studies have yet reported the effect of prevention and control measures, which were implemented to combat COVID-19, on the prevention and control of common HAIs. We aimed to examine the effect of the "Normalized Epidemic Prevention and Control Requirements" (implemented in May 2020) by comparison of hospital-acquired infections (HAIs) and community-acquired infections (CAIs) in China during 2018, 2019, and 2020.

METHODS

Data of inpatients before and after implementation of new requirements were retrospectively analyzed, including infection rate, use of alcohol-based hand cleaner, anatomical sites of infections, pathogen species, infection by multi-drug resistant species, and use of different antibiotics.

RESULTS

The HAI rate was significantly higher in 2020 than in 2018 and 2019 (P < 0.05), and the CAI rate was significantly higher in 2019 and 2020 than in 2018 (P < 0.001). Lower respiratory tract infections were the most common HAI during all years, with no significant changes over time. Lower respiratory tract infections were also the most common CAI, but were significantly more common in 2018 and 2019 than 2020 (P < 0.001). There were no changes in upper respiratory tract infections among HAIs or CAIs. Most HAIs and CAIs were from Gram-negative bacteria, and the percentages of fungal infections were greater in 2019 and 2020 than 2018. MRSA infections were more common in 2020 than in 2018 and 2019 (P < 0.05). The utilization rate and usage days of antibiotics decreased over time (P < 0.001) and the culture rate of microbial specimens before antibiotic usage increased over time (P < 0.001).

CONCLUSIONS

The new prevention and control requirements provided important benefits during the COVID-19 pandemic. However, their effects on HAIs were not obvious.

Incidence of Healthcare-Associated Infections (HAIs) and the adherence to the HAIs' prevention strategies in a military hospital in Alkharj

Background

Healthcare-associated infections (HAI) are considered one of the most common adverse events in health care service provision. In order to prevent the occurrence of HAIs, it is important to implement several prevention strategies.

Objectives

This study aims to determine the incidence of healthcare-associated infections in a military hospital in Alkharj and the adherence to the HAIs' prevention strategies.

Methods

This study included exporting data for all infected cases confirmed by the infection disease specialists in 2019. The data were collected from the reports that were written by infection control unit and infectious disease department.

Results

The rate of healthcare associated infections (HAIs) in 2019 was 0.43% of total patient admissions. The rate of central line associated bloodstream infections in 2019 was 1.15 per 1000 central line days. The rate of catheter associated urinary tract infections in 2019 was 1.00 per 1000 catheter days. The rate of ventilator associated pneumonia in 2019 was 2.11 per 1000 ventilator days and the rate of surgical site infections in 2019 was 0.41 %.

Conclusion

The rate of overall healthcare-associated infections (HAI) was low. The compliance rate of health care workers to preventive measures that control HAIs was generally high but there was a need for more awareness particularly regarding personal protective equipment and hand hygiene. So it is important to attend more awareness activities and workshops particularly regarding personal protective equipment and hand hygiene. Furthermore, infection control unit and infectious disease department in the hospital should support the robust HAI prevention programs.

Antimicrobial resistance pattern in healthcare-associated infections: investigation of in-hospital risk factors

Background and Objectives:

Antimicrobial resistance (AMR) is an increasing threat for efficient treatment of infections. Determining the epidemiology of healthcare-associated infections and causative agents in various hospital wards helps appropriate selection of antimicrobial agents.

Materials and Methods:

This retrospective study was performed by analyzing antibiograms from March 2017 to March 2018 among patients admitted to the different wards of Imam Khomeini Hospital Complex in Tehran, Iran.

Results:

Among 2440 hospital acquired infections, 59.3% were Gram-negative bacilli: E. coli (n = 469, 22.2%), K. pneumoniae (n = 457, 21.7%), Acinetobacter spp. (n = 282, 13.4%), P. aeruginosa (n = 139, 6.6%) and important Gram-positive bacteria were Enterococcus spp. (n = 216, 10.2%), S. aureus (n = 148, 7%), S. epidermidis (n = 118, 5.6). Generally, there was a high antimicrobial resistance in bacterial isolates in this study. Methicillin resistant Staphylococcus aureus (MRSA) was 56.3 % and MRSE 62.9 %. Vancomycin resistant enterococci (VRE) was 60.7%. K. pneumoniae-ESBL was 79.6% and its resistance to carbapenem was 38.4%. E. coli-ESBL was 42% and its resistance to carbapenems was 2.3%. P. aeruginosa resistance to ceftazidime was 74.4%, to fluroquinolones 63.3%, to aminoglycosides 64.8%, to piperacillin tazobactam 47.6% and to carbapenems 62.1%. Acinetobacter baumannii resistance to ceftazidime was 98.7%, to fluroquinolones 97%, to aminoglycosides 95.9%, to ampicillin sulbactam 84%, to carbapenems 96.4% and to colistin 4%.

Conclusion:

The study revealed an alarming rate of resistance to the commonly used antimicrobial agents used in treating HAIs. Also the relationship between AMR and some risk factors and thus taking steps towards controlling them have been shown.

Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients' characteristics at ICU admission. We used data from the "Italian Nosocomial Infections Surveillance in Intensive Care Units" network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient's origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

Analysis of the Risk Factors for Nosocomial Bacterial Infection in Patients with COVID-19 in a Tertiary Hospital

Background

Infection surveillance and risk factor analysis are among the most important prerequisites for the prevention and treatment of nosocomial bacteria infections, which are the demands for both infected and non-infected patients.

Purpose

To explore the risk factors for nosocomial bacterial infection of patients with COVID-19, and further to provide a theoretical basis for scientific prevention and control of nosocomial bacterial infection.

Methods

Between 10 January 2020 and 9 March 2020, we collected data of 212 patients with COVID-19 and then explored the influence of age, gender, length of stay, use of ventilator, urinary catheterization, central venous catheterization, white blood cell (WBC) count and procalcitonin on the nosocomial bacterial infection of patients with COVID-19 by a retrospective study.

Results

There were 212 confirmed cases of COVID-19, of which 31 cases had nosocomial bacterial infections, with an incidence of 14.62%. The most common types of nosocomial bacterial infections were lower respiratory tract (12 cases, 38.71%), which was the most frequent site, followed by urinary tract (10 cases, 32.26%), blood stream (7 cases, 22.58%), upper respiratory tract (1 case, 3.23%) and gastrointestinal tract infection (1 case, 3.23%). The incidence of nosocomial bacterial infection was significantly correlated with age, arteriovenous catheterization, urinary catheterization, WBC count and procalcitonin. Moreover, multivariate analysis confirmed that WBC (OR 8.38, 95% CI 1.07 to 65.55), procalcitonin (OR 4.92, 95% CI 1.39 to 17.33) and urinary catheterization (OR 25.38, 95% CI 5.09 to 126.53) were independent risk factors for the nosocomial bacterial infection of patients with COVID-19.

Conclusion

Understanding the risk factors for nosocomial bacterial infection of patients with COVID-19 and strengthening the monitoring of various susceptible factors are helpful to control the occurrence of nosocomial bacterial infection in the COVID-19 isolation wards.

Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections

Background

Hospital-acquired bloodstream infection (BSI) is associated with high morbidity and mortality and increases patients’ length of stay (LOS) and hospital charges. Our goals were to calculate LOS and charges attributable to BSI and compare results among different models.

Methods

A retrospective observational cohort study was conducted in 2017 in a large general hospital, in Beijing. Using patient-level data, we compared the attributable LOS and charges of BSI with three models: 1) conventional non-matching, 2) propensity score matching controlling for the impact of potential confounding variables, and 3) risk set matching controlling for time-varying covariates and matching based on propensity score and infection time.

Results

The study included 118,600 patient admissions, 557 (0.47%) with BSI. Six hundred fourteen microorganisms were cultured from patients with BSI. Escherichia coli was the most common bacteria (106, 17.26%). Among multi-drug resistant bacteria, carbapenem-resistant Acinetobacter baumannii (CRAB) was the most common (42, 38.53%). In the conventional non-matching model, the excess LOS and charges associated with BSI were 25.06 days (P < 0.05) and US$22041.73 (P < 0.05), respectively. After matching, the mean LOS and charges attributable to BSI both decreased. When infection time was incorporated into the risk set matching model, the excess LOS and charges were 16.86 days (P < 0.05) and US$15909.21 (P < 0.05), respectively.

Conclusion

This is the first study to consider time-dependent bias in estimating excess LOS and charges attributable to BSI in a Chinese hospital setting. We found matching on infection time can reduce bias.