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Objective: To identify risk factors for mortality in intensive care units (ICUs) in Asia. Design: Prospective cohort study. Setting: 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. Participants: Patients aged >18 years admitted to ICUs. Results: 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). Conclusions: 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.

Ventilator associated pneumonia (VAP) rates in Asia are several times above those of US. The objective of this study is to identify VAP risk factors.We conducted a prospective cohort study, between March 27, 2004 and November 2, 2022, in 279 ICUs of 95 hospitals in 44 cities in 9 Asian countries (China, India, Malaysia, Mongolia, Nepal, Pakistan, Philippines, Sri Lanka, Thailand, Vietnam).153,717 patients, followed during 892,996 patient-days, acquired 3,369 VAPs. We analyzed 10 independent variables. Using multiple logistic regression we identified following independent VAP RFs= Age, rising VAP risk 1% per year (aOR=1.01; 95%CI=1.00-1.01, P.0001); male gender (OR=1.17; 95%CI=1.08-1.26, P.0001); length of stay, rising VAP risk 7% daily (aOR=1.07; 95%CI=1.06-1.07, P.0001); mechanical ventilation (MV) device utilization (DU) ratio (OR=1.43; 95%CI=1.36-1.51; p.0001); tracheostomy connected to a MV (OR=11.17; 95%CI=9.55-14.27; p.0001); public (OR=1.84; 95%CI=1.49-2.26, P.0001), and private (OR=1.57; 95%CI=1.29-1.91, P.0001) compared with teaching hospitals; upper-middle income country (OR=1.86; 95%CI=1.63-2.14, P.0001). Regarding ICUs, Medical-Surgical (OR=4.61; 95%CI=3.43-6.17; P.0001), Neurologic (OR=3.76; 95%CI=2.43-5.82; P.0001), Medical (OR=2.78; 95%CI=2.04-3.79; P.0001), and Neuro-Surgical (OR=2.33; 95%CI=1.61-3.92; P.0001) showed the highest risk.Some identified VAP RFs are unlikely to change= age, gender, ICU type, facility ownership, country income level. Based on our results, we recommend limit use of tracheostomy, reducing LOS, reducing the MV/DU ratio, and implementing an evidence-based set of VAP prevention recommendations.

Background: Our objective was to identify central line (CL)-associated bloodstream infections (CLABSI) rates and risk factors (RF) in Asia. Methods: From 03/27/2004 to 02/11/2022, we conducted a multinational multicenter prospective cohort study in 281 ICUs of 95 hospitals in 44 cities in 9 Asian countries (China, India, Malaysia, Mongolia, Nepal, Pakistan, Philippines, Sri Lanka, Thailand, and Vietnam). For estimation of CLABSI rate we used CL-days as denominator and number of CLABSI as numerator. To estimate CLABSI RF for we analyzed the data using multiple logistic regression, and outcomes are shown as adjusted odds ratios (aOR). Results: A total of 150,142 patients, hospitalized 853,604 days, acquired 1514 CLABSIs. Pooled CLABSI rate per 1000 CL-days was 5.08; per type of catheter were: femoral: 6.23; temporary hemodialysis: 4.08; jugular: 4.01; arterial: 3.14; PICC: 2.47; subclavian: 2.02. The highest rates were femoral, temporary for hemodialysis, and jugular, and the lowest PICC and subclavian. We analyzed following variables: Gender, age, length of stay (LOS) before CLABSI acquisition, CL-days before CLABSI acquisition, CL-device utilization ratio, CL-type, tracheostomy use, hospitalization type, ICU type, facility ownership and World Bank classifications by income level. Following were independently associated with CLABSI: LOS before CLABSI acquisition, rising risk 4% daily (aOR = 1.04; 95% CI = 1.03–1.04; p Conclusions: The following CLABSI RFs are unlikely to change: country income level, facility-ownership, hospitalization type, and ICU type. Based on these findings it is suggested to focus on reducing LOS, CL-days, and tracheostomy; using subclavian or PICC instead of internal-jugular or femoral; and implementing evidence-based CLABSI prevention recommendations.

Introduction Coronavirus disease-2019 (COVID-19) systemic illness caused by a novel coronavirus severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has been spreading across the world. The objective of this study is to identify the clinical and laboratory variables as predictors of in-hospital death at the time of admission in a tertiary care hospital in India. Materials and methods Demographic profile, clinical, and laboratory variables of 425 patients admitted from April to June 2020 with symptoms and laboratory-confirmed diagnosis through real-time polymerase chain reaction (RT-PCR) were studied. Descriptive statistics, an association of these variables, logistic regression, and CART models were developed to identify early predictors of in-hospital death. Results Twenty-two patients (5.17%) had expired in course of their hospital stay. The median age [interquartile range (IQR)] of the patients admitted was 49 years (21–77 years). Gender distribution was male — 73.38% (mortality rate 5.83%) and female—26.62% (mortality rate 3.34%). The study shows higher association for age (>47 years) [odds ratio (OR) 4.52], male gender (OR 1.78), shortness of breath (OR 2.02), oxygen saturation 24 (OR 5.31), comorbidities like diabetes (OR 2.70), hypertension (OR 2.12), and coronary artery disease (OR 3.18) toward overall mortality. The significant associations in laboratory variables include lymphopenia (