Multinational prospective cohort study over 18 years of the risk factors for ventilator-associated pneumonia in 9 Asian countries: INICC findings.

BACKGROUND: 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. 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<.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. CONCLUSIONS: 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.

The impact of healthcare-associated infections on mortality in ICU: A prospective study in Asia, Africa, Eastern Europe, Latin America, and the Middle East.

BACKGROUND: The International Nosocomial Infection Control Consortium has found a high ICU mortality rate. Our aim was to identify all-cause mortality risk factors in ICU-patients. 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<.0001); ventilator-associated pneumonia (aOR:1.48; P<.0001); catheter-associated urinary tract infection (aOR:1.18;P<.0001); medical hospitalization (aOR:1.81; P<.0001); length of stay (LOS), risk rises 1% per day (aOR:1.01; P<.0001); female gender (aOR:1.09; P<.0001); age (aOR:1.012; P<.0001); central line-days, risk rises 2% per day (aOR:1.02; P<.0001); and mechanical ventilator (MV)-utilization ratio (aOR:10.46; P<.0001). Coronary ICU showed the lowest risk for mortality (aOR: 0.34;P<.0001). CONCLUSION: Some identified risk factors are unlikely to change, such as country income-level, facility ownership, hospitalization type, gender, and age. Some can be modified; Central line-associated bloodstream infection, ventilator-associated pneumonia, catheter-associated urinary tract infection, LOS, and MV-utilization. So, to lower the risk of death in ICUs, we recommend focusing on strategies to shorten the LOS, reduce MV-utilization, and use evidence-based recommendations to prevent HAIs.

CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring.

Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.