METHODS: We conducted a retrospective observational study in our multi-disciplinary Pediatric Intensive Care Unit (ICU) from January 2015 to December 2018. All patients from birth to 16 years of age who were admitted to the pediatric ICU were included. The Kidney Disease Improving Global Outcomes (KDIGO) definition was considered as the reference standard. We compared the incidence data assessed by KDIGO, pediatric risk, injury, failure, loss of kidney function and end- stage renal disease (pRIFLE) and pediatric reference change value optimised for AKI (pROCK).
RESULTS: Out of 7505 patients, 9.2% developed AKI by KDIGO criteria. The majority (59.8%) presented with stage 1 AKI. Recovery from AKI was observed in 70.4% of patients within 7 days from diagnosis. Both pRIFLE and pROCK were less sensitive compared to KDIGO criteria for the classification of AKI. Patients who met all three-KDIGO, pRIFLE and pROCK criteria had a high mortality rate (35.0%).
CONCLUSION: Close to one in ten patients admitted to the pediatric ICU met AKI criteria according to KDIGO. In about 30% of patients, AKI persisted beyond 7 days. Follow-up of patients with persistent kidney function reduction at hospital discharge is needed to reveal the long-term morbidity due to AKI in the pediatric ICU.
DESIGN: Single-center retrospective observational study.
SETTING: Thirty-six-bed surgical/medical tertiary PICU.
PATIENTS: Children from birth to less than or equal to 16 years old admitted between 2015 and 2018.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Clinical data were extracted from the PICU clinical information system. Patients with baseline creatinine at admission greater than 20 micromol/L above the calculated normal creatinine level were classified as "high risk of acute kidney injury." Models were created to predict acute kidney injury at admission and on day 1. Out of the 7,505 children admitted during the study period, 738 patients (9.8%) were classified as high risk of acute kidney injury at admission and 690 (9.2%) developed acute kidney injury during PICU admission. Compared to Kidney Disease: Improving Global Outcomes criteria as the reference standard, high risk of acute kidney injury had a lower sensitivity and higher specificity compared with renal angina index greater than or equal to 8 on day 1. For the admission model, the adjusted odds ratio of developing acute kidney injury for high risk of acute kidney injury was 4.2 (95% CI, 3.3-5.2). The adjusted odds ratio in the noncardiac cohort for high risk of acute kidney injury was 7.3 (95% CI, 5.5-9.7). For the day 1 model, odds ratios for high risk of acute kidney injury and renal angina index greater than or equal to 8 were 3.3 (95% CI, 2.6-4.2) and 3.1 (95% CI, 2.4-3.8), respectively.
CONCLUSIONS: The relationship between high risk of acute kidney injury and acute kidney injury needs further evaluation. High risk of acute kidney injury performed better in the noncardiac cohort.
MATERIALS AND METHODS: A total of 110 nasopharyngeal swabs (NPS) were collected from children aged one month to 12 years old who were admitted with ARI in UKMMC during a one-year period. The two qPCR assays were conducted in parallel.
RESULTS: Ninety-seven samples (88.2%) were positive by QIAstat-Dx RP and 86 (78.2%) by RespiFinder assay. The overall agreement on both assays was substantial (kappa value: 0.769) with excellent concordance rate of 96.95%. Using both assays, hRV/EV, INF A/H1N1 and RSV were the most common pathogens detected. Influenza A/H1N1 infection was significantly seen higher in older children (age group > 60 months old) (53.3%, p-value < 0.05). Meanwhile, RSV and hRV/EV infection were seen among below one-year-old children. Co-infections by two to four pathogens were detected in 17 (17.5%) samples by QIAstat-Dx RP and 12 (14%) samples by RespiFinder, mainly involving hRV/EV. Bacterial detection was observed only in 5 (4.5%) and 6 (5.4%) samples by QIAstat-Dx RP and RespiFinder, respectively, with Mycoplasma pneumoniae the most common detected.
CONCLUSION: The overall performance of the two qPCR assays was comparable and showed excellent agreement. Both detected various clinically important respiratory pathogens in a single test with simultaneous multiple infection detection. The use of qPCR as a routine diagnostic test can improve diagnosis and management.
METHODS: 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).
RESULTS: 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
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.
METHODS: From January 1, 2014, to February 12, 2022, we conducted a prospective cohort study. To estimate CAUTI incidence, the number of UC days was the denominator, and CAUTI was the numerator. To estimate CAUTI RFs, we analyzed 11 variables using multiple logistic regression.
RESULTS: 84,920 patients hospitalized for 499,272 patient days acquired 869 CAUTIs. The pooled CAUTI rate per 1,000 UC-days was 3.08; for those using suprapubic-catheters (4.11); indwelling-catheters (2.65); trauma-ICU (10.55), neurologic-ICU (7.17), neurosurgical-ICU (5.28); in lower-middle-income countries (3.05); in upper-middle-income countries (1.71); at public-hospitals (5.98), at private-hospitals (3.09), at teaching-hospitals (2.04). The following variables were identified as CAUTI RFs: Age (adjusted odds ratio [aOR] = 1.01; 95% CI = 1.01-1.02; P
METHODS: We implemented the INICC multidimensional approach, incorporating an 11-component bundle, in 122 ICUs spanning nine Asian countries. We computed the CLABSI rate using the CDC/NSHN definition and criteria. The CLABSI rate per 1000 CL-days was calculated at baseline and throughout different phases of the intervention, including the 2nd month, 3rd month, 4-16 month, and 17-29 month periods. A two-sample t-test was employed to compare baseline CLABSI rates with intervention rates. Additionally, we utilized a generalized linear mixed model with a Poisson distribution to analyze the association between exposure and outcome.
RESULTS: A total of 124,946 patients were hospitalized over 717,270 patient-days, with 238,595 central line (CL)-days recorded. The rates of CLABSI per 1000 CL-days significantly decreased from 16.64 during the baseline period to 6.51 in the 2nd month (RR = 0.39; 95% CI = 0.36-0.42; p
METHODS: Multinational, multicenter, prospective cohort study at 786 ICUs of 312 hospitals in 147 cities in 37 Latin American, Asian, African, Middle Eastern, and European countries.
RESULTS: Between 07/01/1998 and 02/12/2022, 300,827 patients, followed during 2,167,397 patient-days, acquired 21,371 HAIs. Following mortality risk factors were identified in multiple logistic regression: Central line-associated bloodstream infection (aOR:1.84; P
METHODS: We implemented a strategy involving a 9-element bundle, education, surveillance of CAUTI rates and clinical outcomes, monitoring compliance with bundle components, feedback of CAUTI rates and performance feedback. This was executed in 299 ICUs across 32 low- and middle-income countries. The dependent variable was CAUTI per 1,000 UC days, assessed at baseline and throughout the intervention, in the second month, third month, 4 to 15 months, 16 to 27 months, and 28 to 39 months. Comparisons were made using a 2-sample t test, and the exposure-outcome relationship was explored using a generalized linear mixed model with a Poisson distribution.
RESULTS: Over the course of 978,364 patient days, 150,258 patients utilized 652,053 UC-days. The rates of CAUTI per 1,000 UC days were measured. The rates decreased from 14.89 during the baseline period to 5.51 in the second month (risk ratio [RR] = 0.37; 95% confidence interval [CI] = 0.34-0.39; P
METHODS: Prospective intensive care unit patient data collected via International Nosocomial Infection Control Consortium Surveillance Online System. Centers for Disease Control and Prevention/National Health Care Safety Network definitions applied for device-associated health care-associated infections (DA-HAI).
RESULTS: We gathered data from 204,770 patients, 1,480,620 patient days, 936,976 central line (CL)-days, 637,850 mechanical ventilators (MV)-days, and 1,005,589 urinary catheter (UC)-days. Our results showed 4,270 CL-associated bloodstream infections, 7,635 ventilator-associated pneumonia, and 3,005 UC-associated urinary tract infections. The combined rates of DA-HAIs were 7.28%, and 10.07 DA-HAIs per 1,000 patient days. CL-associated bloodstream infections occurred at 4.55 per 1,000 CL-days, ventilator-associated pneumonias at 11.96 per 1,000 MV-days, and UC-associated urinary tract infections at 2.91 per 1,000 UC days. In terms of resistance, Pseudomonas aeruginosa showed 50.73% resistance to imipenem, 44.99% to ceftazidime, 37.95% to ciprofloxacin, and 34.05% to amikacin. Meanwhile, Klebsiella spp had resistance rates of 48.29% to imipenem, 72.03% to ceftazidime, 61.78% to ciprofloxacin, and 40.32% to amikacin. Coagulase-negative Staphylococci and Staphylococcus aureus displayed oxacillin resistance in 81.33% and 53.83% of cases, respectively.
CONCLUSIONS: The high rates of DA-HAI and bacterial resistance emphasize the ongoing need for continued efforts to control them.
DESIGN: Prospective cohort study.
SETTING: This study was conducted across 743 ICUs of 282 hospitals in 144 cities in 42 Asian, African, European, Latin American, and Middle Eastern countries.
PARTICIPANTS: The study included patients admitted to ICUs across 24 years.
RESULTS: 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).
CONCLUSIONS: 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.