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To identify central-line (CL)–associated bloodstream infection (CLABSI) incidence and risk factors in low- and middle-income countries (LMICs).
From July 1, 1998, to February 12, 2022, we conducted a multinational multicenter prospective cohort study using online standardized surveillance system and unified forms.
The study included 728 ICUs of 286 hospitals in 147 cities in 41 African, Asian, Eastern European, Latin American, and Middle Eastern countries.
In total, 278,241 patients followed during 1,815,043 patient days acquired 3,537 CLABSIs.
For the CLABSI rate, we used CL days as the denominator and the number of CLABSIs as the numerator. Using multiple logistic regression, outcomes are shown as adjusted odds ratios (aORs).
The pooled CLABSI rate was 4.82 CLABSIs per 1,000 CL days, which is significantly higher than that reported by the Centers for Disease Control and Prevention National Healthcare Safety Network (CDC NHSN). We analyzed 11 variables, and the following variables were independently and significantly associated with CLABSI: length of stay (LOS), risk increasing 3% daily (aOR, 1.03; 95% CI, 1.03–1.04; P < .0001), number of CL days, risk increasing 4% per CL day (aOR, 1.04; 95% CI, 1.03–1.04; P < .0001), surgical hospitalization (aOR, 1.12; 95% CI, 1.03–1.21; P < .0001), tracheostomy use (aOR, 1.52; 95% CI, 1.23–1.88; P < .0001), hospitalization at a publicly owned facility (aOR, 3.04; 95% CI, 2.31–4.01; P <.0001) or at a teaching hospital (aOR, 2.91; 95% CI, 2.22–3.83; P < .0001), hospitalization in a middle-income country (aOR, 2.41; 95% CI, 2.09–2.77; P < .0001). The ICU type with highest risk was adult oncology (aOR, 4.35; 95% CI, 3.11–6.09; P < .0001), followed by pediatric oncology (aOR, 2.51;95% CI, 1.57–3.99; P < .0001), and pediatric (aOR, 2.34; 95% CI, 1.81–3.01; P < .0001). The CL type with the highest risk was internal-jugular (aOR, 3.01; 95% CI, 2.71–3.33; P < .0001), followed by femoral (aOR, 2.29; 95% CI, 1.96–2.68; P < .0001). Peripherally inserted central catheter (PICC) was the CL with the lowest CLABSI risk (aOR, 1.48; 95% CI, 1.02–2.18; P = .04).
The following CLABSI risk factors are unlikely to change: country income level, facility ownership, hospitalization type, and ICU type. These findings suggest a focus on reducing LOS, CL days, and tracheostomy; using PICC instead of internal-jugular or femoral CL; and implementing evidence-based CLABSI prevention recommendations.
Rates of ventilator-associated pneumonia (VAP) in low- and middle-income countries (LMIC) are several times above those of high-income countries. The objective of this study was to identify risk factors (RFs) for VAP cases in ICUs of LMICs.
Prospective cohort study.
This study was conducted across 743 ICUs of 282 hospitals in 144 cities in 42 Asian, African, European, Latin American, and Middle Eastern countries.
The study included patients admitted to ICUs across 24 years.
In total, 289,643 patients were followed during 1,951,405 patient days and acquired 8,236 VAPs. We analyzed 10 independent variables. Multiple logistic regression identified the following independent VAP RFs: male sex (adjusted odds ratio [aOR], 1.22; 95% confidence interval [CI], 1.16–1.28; P < .0001); longer length of stay (LOS), which increased the risk 7% per day (aOR, 1.07; 95% CI, 1.07–1.08; P < .0001); mechanical ventilation (MV) utilization ratio (aOR, 1.27; 95% CI, 1.23–1.31; P < .0001); continuous positive airway pressure (CPAP), which was associated with the highest risk (aOR, 13.38; 95% CI, 11.57–15.48; P < .0001); tracheostomy connected to a MV, which was associated with the next-highest risk (aOR, 8.31; 95% CI, 7.21–9.58; P < .0001); endotracheal tube connected to a MV (aOR, 6.76; 95% CI, 6.34–7.21; P < .0001); surgical hospitalization (aOR, 1.23; 95% CI, 1.17–1.29; P < .0001); admission to a public hospital (aOR, 1.59; 95% CI, 1.35-1.86; P < .0001); middle-income country (aOR, 1.22; 95% CI, 15–1.29; P < .0001); admission to an adult-oncology ICU, which was associated with the highest risk (aOR, 4.05; 95% CI, 3.22–5.09; P < .0001), admission to a neurologic ICU, which was associated with the next-highest risk (aOR, 2.48; 95% CI, 1.78–3.45; P < .0001); and admission to a respiratory ICU (aOR, 2.35; 95% CI, 1.79–3.07; P < .0001). Admission to a coronary ICU showed the lowest risk (aOR, 0.63; 95% CI, 0.51–0.77; P < .0001).
Some identified VAP RFs are unlikely to change: sex, hospitalization type, ICU type, facility ownership, and country income level. Based on our results, we recommend focusing on strategies to reduce LOS, to reduce the MV utilization ratio, to limit CPAP use and implementing a set of evidence-based VAP prevention recommendations.
To identify risk factors for mortality in intensive care units (ICUs) in Asia.
Prospective cohort study.
The study included 317 ICUs of 96 hospitals in 44 cities in 9 countries of Asia: China, India, Malaysia, Mongolia, Nepal, Pakistan, Philippines, Sri Lanka, Thailand, and Vietnam.
Patients aged >18 years admitted to ICUs.
In total, 157,667 patients were followed during 957,517 patient days, and 8,157 HAIs occurred. In multiple logistic regression, the following variables were associated with an increased mortality risk: central-line–associated bloodstream infection (CLABSI; aOR, 2.36; P < .0001), ventilator-associated event (VAE; aOR, 1.51; P < .0001), catheter-associated urinary tract infection (CAUTI; aOR, 1.04; P < .0001), and female sex (aOR, 1.06; P < .0001). Older age increased mortality risk by 1% per year (aOR, 1.01; P < .0001). Length of stay (LOS) increased mortality risk by 1% per bed day (aOR, 1.01; P < .0001). Central-line days increased mortality risk by 2% per central-line day (aOR, 1.02; P < .0001). Urinary catheter days increased mortality risk by 4% per urinary catheter day (aOR, 1.04; P < .0001). The highest mortality risks were associated with mechanical ventilation utilization ratio (aOR, 12.48; P < .0001), upper middle-income country (aOR, 1.09; P = .033), surgical hospitalization (aOR, 2.17; P < .0001), pediatric oncology ICU (aOR, 9.90; P < .0001), and adult oncology ICU (aOR, 4.52; P < .0001). Patients at university hospitals had the lowest mortality risk (aOR, 0.61; P < .0001).
Some variables associated with an increased mortality risk are unlikely to change, such as age, sex, national economy, hospitalization type, and ICU type. Some other variables can be modified, such as LOS, central-line use, urinary catheter use, and mechanical ventilation as well as and acquisition of CLABSI, VAE, or CAUTI. To reduce mortality risk, we shall focus on strategies to reduce LOS; strategies to reduce central-line, urinary catheter, and mechanical ventilation use; and HAI prevention recommendations.
The myotendinous junction (MTJ) is a highly specialized region of the locomotor apparatus. Here, we investigated the ultrastructural and molecular effects in the MTJ region after static stretching prior to the ladder-based resistance training. Thirty-two male, 60-day old Wistar rats were divided into four groups: Sedentary, Resistance Training, Stretching, and Stretching-Resistance Training. The gastrocnemius muscle was processed for transmission electron microscopy techniques and Western blot assay. We observed that the static stretching prior to the ladder-based resistance training increased the MTJ components, the fibroblast growth factor (FGF)-2 and FGF-6 protein expression. Also, we demonstrated the lower transforming growth factor expression and no difference in the lysyl oxidase expression after combined training. The MTJ alterations in response to combined training demonstrate adaptive mechanisms which can be used for the prescription or development of methods to reduce or prevent injuries in humans and promote the myotendinous interface benefit.
Background: Healthcare-associated infections (HAIs) are a major global threat to patient safety. Systematic surveillance is crucial for understanding HAI rates and antimicrobial resistance trends and to guide infection prevention and control (IPC) activities based on local epidemiology. In India, no standardized national HAI surveillance system was in place before 2017. Methods: Public and private hospitals from across 21 states in India were recruited to participate in an HAI surveillance network. Baseline assessments followed by trainings ensured that basic microbiology and IPC implementation capacity existed at all sites. Standardized surveillance protocols for central-line–associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs) were modified from the NHSN for the Indian context. IPC nurses were trained to implement surveillance protocols. Data were reported through a locally developed web portal. Standardized external data quality checks were performed to assure data quality. Results: Between May 2017 and April 2019, 109 ICUs from 37 hospitals (29 public and 8 private) enrolled in the network, of which 33 were teaching hospitals with >500 beds. The network recorded 679,109 patient days, 212,081 central-line days, and 387,092 urinary catheter days. Overall, 4,301 bloodstream infection (BSI) events and 1,402 urinary tract infection (UTI) events were reported. The network CLABSI rate was 9.4 per 1,000 central-line days and the CAUTI rate was 3.4 per 1,000 catheter days. The central-line utilization ratio was 0.31 and the urinary catheter utilization ratio was 0.57. Moreover, 3,542 (73%) of 4,742 pathogens reported from BSIs and 868 (53%) of 1,644 pathogens reported from UTIs were gram negative. Also, 1,680 (26.3%) of all 6,386 pathogens reported were Enterobacteriaceae. Of 1,486 Enterobacteriaceae with complete antibiotic susceptibility testing data reported, 832 (57%) were carbapenem resistant. Of 951 Enterobacteriaceae subjected to colistin broth microdilution testing, 62 (7%) were colistin resistant. The surveillance platform identified 2 separate hospital-level HAI outbreaks; one caused by colistin-resistant K. pneumoniae and another due to Burkholderia cepacia. Phased expansion of surveillance to additional hospitals continues. Conclusions: HAI surveillance was successfully implemented across a national network of diverse hospitals using modified NHSN protocols. Surveillance data are being used to understand HAI burden and trends at the facility and national levels, to inform public policy, and to direct efforts to implement effective hospital IPC activities. This network approach to HAI surveillance may provide lessons to other countries or contexts with limited surveillance capacity.
To report the International Nosocomial Infection Control Consortium surveillance data from 40 hospitals (20 cities) in India 2004–2013.
Surveillance using US National Healthcare Safety Network’s criteria and definitions, and International Nosocomial Infection Control Consortium methodology.
We collected data from 236,700 ICU patients for 970,713 bed-days
Pooled device-associated healthcare-associated infection rates for adult and pediatric ICUs were 5.1 central line–associated bloodstream infections (CLABSIs)/1,000 central line–days, 9.4 cases of ventilator-associated pneumonia (VAPs)/1,000 mechanical ventilator–days, and 2.1 catheter-associated urinary tract infections/1,000 urinary catheter–days
In neonatal ICUs (NICUs) pooled rates were 36.2 CLABSIs/1,000 central line–days and 1.9 VAPs/1,000 mechanical ventilator–days
Extra length of stay in adult and pediatric ICUs was 9.5 for CLABSI, 9.1 for VAP, and 10.0 for catheter-associated urinary tract infections. Extra length of stay in NICUs was 14.7 for CLABSI and 38.7 for VAP
Crude extra mortality was 16.3% for CLABSI, 22.7% for VAP, and 6.6% for catheter-associated urinary tract infections in adult and pediatric ICUs, and 1.2% for CLABSI and 8.3% for VAP in NICUs
Pooled device use ratios were 0.21 for mechanical ventilator, 0.39 for central line, and 0.53 for urinary catheter in adult and pediatric ICUs; and 0.07 for mechanical ventilator and 0.06 for central line in NICUs.
Despite a lower device use ratio in our ICUs, our device-associated healthcare-associated infection rates are higher than National Healthcare Safety Network, but lower than International Nosocomial Infection Control Consortium Report.
Infect. Control Hosp. Epidemiol. 2016;37(2):172–181