In Pursuit of Microbiome-based Therapies for Acute Respiratory Failure.
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ABSTRACT:
SUBMITTER: Ali F
PROVIDER: S-EPMC7737586 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
In Pursuit of Microbiome-based Therapies for Acute Respiratory Failure.
Ali Farhana F Sweeney Daniel A DA
American journal of respiratory and critical care medicine 20201201 12
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Project description:BackgroundThe recent pandemic highlights the essential nature of optimizing the use of invasive mechanical ventilation (IMV) in complex critical care settings. This review of reviews maps evidence-based practices (EBPs) that are associated with better outcomes among adult patients with acute respiratory failure or ARDS on the continuum of care, from intubation to liberation.Research questionWhat EPBs are recommended to reduce the duration of IMV and mortality rate among patients with acute respiratory failure/ARDS?Study design and methodsWe identified an initial set of reports that links EBPs to mortality rates and/or duration of IMV. We conducted a review of reviews, focusing on preappraised guidelines, meta-analyses, and systematic reviews. We searched Scopus, CINAHL, and PubMed from January 2016 to January 2019 for additional evidence that has not yet been incorporated into current guidelines.ResultsOur initial search produced 61 publications that contained 42 EBPs. We excluded 42 manuscripts during the data extraction process, primarily because they were not associated with improved patient outcomes. The remaining 19 preappraised guidelines, meta-analyses, and systematic reviews met our full inclusion criteria and spanned the continuum of IMV care from intubation to liberation. These contained 20 EBPs, a majority of which were supported with moderate levels of evidence. Of these, six EBPs focused on intubation and escalation of care, such as ventilator management and synchrony; ten EBPs reduced complications associated with IMV, which included spontaneous awakening and breathing trials and early mobility protocols; and four EBPs promoted timely extubation and postextubation recovery.InterpretationThis review describes EBPs that are associated with fewer ventilator days and/or lower mortality rates among patients who received IMV for acute respiratory failure/ARDS. Many of these EBPs are connected across the care continuum, which indicates the need to promote and assess effective implementation jointly, rather than individually.
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2020-08-18 | E-MTAB-8263 | biostudies-arrayexpress
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Project description:BackgroundEmergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored.ObjectiveThe present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort.MethodsNon-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016-18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R2). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models.ResultsOf 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899-0.901 vs 0.894, 95% CI 0.862-0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R2: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8-11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0-3.2) and $11,900 (95% CI 11,600-12,300) in costs.ConclusionsLogistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.
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