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Background: In patients with acute respiratory distress syndrome (ARDS), low tidal volume ventilation has been associated with reduced mortality. Driving pressure (tidal volume normalized to respiratory system compliance) may be an even stronger predictor of ARDS survival than tidal volume. We sought to study whether these associations hold true in acute respiratory failure patients without ARDS. Methods: This is a retrospectively cohort analysis of mechanically ventilated adult patients admitted to ICUs from 12 hospitals over 2 years. We used natural language processing of chest radiograph reports and data from the electronic medical record to identify patients who had ARDS. We used multivariable logistic regression and generalized linear models to estimate associations between tidal volume, driving pressure, and respiratory system compliance with adjusted 30-day mortality using covariates of Acute Physiology Score (APS), Charlson Comorbidity Index (CCI), age, and PaO 2 /FiO 2 ratio. Results: We studied 2641 patients; 48% had ARDS (n = 1273). Patients with ARDS had higher mean APS (25 vs. 23, p < .001) but similar CCI (4 vs. 3, p = 0.6) scores. For non-ARDS patients, tidal volume was associated with increased adjusted mortality (OR 1.18 per 1 mL/kg PBW increase in tidal volume, CI 1.04 to 1.35, p = 0.010). We observed no association between driving pressure or respiratory compliance and mortality in patients without ARDS. In ARDS patients, both ΔP (OR1.1, CI 1.06-1.14, p < 0.001) and tidal volume (OR 1.17, CI 1.04-1.31, p = 0.007) were associated with mortality. Conclusions: In a large retrospective analysis of critically ill non-ARDS patients receiving mechanical ventilation, we found that tidal volume was associated with 30-day mortality, while driving pressure was not.

Background: Lung-protective ventilation (LPV) improves outcomes for patients with acute respiratory distress syndrome (ARDS) through the administration of low tidal volumes (≤ 6.5 ml/kg predicted body weight [PBW]) with co-titration of positive end-expiratory pressure and fraction of inspired oxygen. Many patients with ARDS, however, are not managed with LPV. The purpose of this study was to understand the implementation barriers and facilitators to the use of LPV and a computerized LPV clinical decision support (CDS) tool in intensive care units (ICUs) in preparation for a pilot hybrid implementation-effectiveness clinical trial. Methods: We performed an explanatory sequential mixed methods study from June 2018 to March 2019 to evaluate the variation in LPV adherence across 17 ICUs in an integrated healthcare system with > 4000 mechanically ventilated patients annually. We analyzed 47 key informant interviews of ICU physicians, respiratory therapists (RTs), and nurses in 3 of the ICUs using a qualitative content analysis paradigm to investigate site variation as defined by adherence level (low, medium, high) and to identify barriers and facilitators to LPV and LPV CDS tool use. Results: Forty-two percent of patients had an initial set tidal volume of ≤ 6.5 ml/kg PBW during the measurement period (site range 21-80%). LPV CDS tool use was 28% (site range 6-91%). This study's main findings revealed multi-factorial facilitators and barriers to use that varied by ICU site adherence level. The primary facilitator was that LPV and the LPV CDS tool could be used on all mechanically ventilated patients. Barriers included a persistent gap between clinician attitudes regarding the use of LPV and actual use, the perceived loss of autonomy associated with using a computerized protocol, the nature of physician-RT interaction in ventilation management, and the lack of clear organization measures of success.

We describe the use of a frame-based knowledge representation to construct an adequately-explicit bedside clinical decision support application for ventilator weaning. The application consists of a data entry form, a knowledge base, an inference engine, and a patient database. The knowledge base contains database queries, a data dictionary, and decision frames. A frame consists of a title, a list of findings necessary to make a decision or carry out an action, and a logic or mathematical statement to determine its output. Frames for knowledge representation are advantageous because they can be created, visualized, and conceptualized as self-contained entities that correspond to accepted medical constructs. They facilitate knowledge engineering and provide understandable explanations of protocol outputs for clinicians. Our frames are elements of a hierarchical decision process. In addition to running diagnostic and therapeutic logic, frames can run database queries, make changes to the user interface, and modify computer variables.