BACKGROUND Bathing intensive care unit (ICU) patients with 2% chlorhexidine gluconate (CHG)-impregnated cloths decreases the risk of healthcare-associated bacteremia and multidrug-resistant organism transmission. Hospitals employ different methods of CHG bathing, and few studies have evaluated whether those methods yield comparable results. OBJECTIVE To determine whether 3 different CHG skin cleansing methods yield similar residual CHG concentrations and bacterial densities on skin. DESIGN Prospective, randomized 2-center study with blinded assessment. PARTICIPANTS AND SETTING Healthcare personnel in surgical ICUs at 2 tertiary-care teaching hospitals in Chicago, Illinois, and Boston, Massachusetts, from July 2015 to January 2016. INTERVENTION Cleansing skin of one forearm with no-rinse 2% CHG-impregnated polyester cloth (method A) versus 4% CHG liquid cleansing with rinsing on the contralateral arm, applied with either non-antiseptic-impregnated cellulose/polyester cloth (method B) or cotton washcloth dampened with sterile water (method C). RESULTS In total, 63 participants (126 forearms) received method A on 1 forearm (n=63). On the contralateral forearm, 33 participants received method B and 30 participants received method C. Immediately and 6 hours after cleansing, method A yielded the highest residual CHG concentrations (2500 µg/mL and 1250 µg/mL, respectively) and lowest bacterial densities compared to methods B or C (P<.001). CONCLUSION In healthy volunteers, cleansing with 2% CHG-impregnated cloths yielded higher residual CHG concentrations and lower bacterial densities than cleansing with 4% CHG liquid applied with either of 2 different cloth types and followed by rinsing. The relevance of these differences to clinical outcomes remains to be determined. Infect Control Hosp Epidemiol 2018;39:405-411.

Background

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

Methods

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

Findings

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group).

Interpretation

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

Funding

US National Institutes of Health and European Society of Intensive Care Medicine.