Applying the Global Leadership Initiative on Malnutrition criteria in patients admitted with SARS-CoV-2 infection using computed tomography imaging.

BACKGROUND: Patients with low muscle mass and acute SARS-CoV-2 infection meet the Global Leadership Initiative on Malnutrition (GLIM) etiologic and phenotypic criteria to diagnose malnutrition, respectively. However, available cut-points to classify individuals with low muscle mass are not straightforward. Using computed tomography (CT) to determine low muscularity, we assessed the prevalence of malnutrition using the GLIM framework and associations with clinical outcomes. METHODS: A retrospective cohort was conducted gathering patient data from various clinical resources. Patients admitted to the COVID-19 unit (March 2020 to June 2020) with appropriate/evaluable CT studies (chest or abdomen/pelvis) within the first 5 days of admission were considered eligible. Sex- and vertebral-specific skeletal muscle indices (SMI; cm2 /m2 ) from healthy controls were used to determine low muscle mass. Injury-adjusted SMI were derived, extrapolated from cancer cut-points and explored. Descriptive statistics and mediation analyses were completed. RESULTS: Patients (n = 141) were 58.2 years of age and racially diverse. Obesity (46%), diabetes (40%), and cardiovascular disease (68%) were prevalent. Using healthy controls and injury-adjusted SMI, malnutrition prevalence was 26% (n = 36/141) and 50% (n = 71/141), respectively. Mediation analyses demonstrated a significant reduction in the effect of malnutrition on outcomes in the presence of Acute Physiology and Chronic Health Evaluation II, supporting the mediating effects of severity of illness intensive care unit (ICU) admission, ICU length of stay, mechanical ventilation, complex respiratory support, discharge status (all P values = 0.03), and 28-day mortality (P = 0.04). CONCLUSIONS: Future studies involving the GLIM criteria should consider these collective findings in their design, analyses, and implementation.

Design and rationale of the CHILL phase II trial of hypothermia and neuromuscular blockade for acute respiratory distress syndrome.

The Cooling to Help Injured Lungs (CHILL) trial is an open label, two group, parallel design multicenter, randomized phase IIB clinical trial assessing the efficacy and safety of targeted temperature management with combined external cooling and neuromuscular blockade to block shivering in patients with early moderate-severe acute respiratory distress syndrome (ARDS). This report provides the background and rationale for the clinical trial and outlines the methods using the Consolidated Standards of Reporting Trials guidelines. Key design challenges include: [1] protocolizing important co-interventions; [2] incorporation of patients with COVID-19 as the cause of ARDS; [3] inability to blind the investigators; and [4] ability to obtain timely informed consent from patients or legally authorized representatives early in the disease process. Results of the Reevaluation of Systemic Early Neuromuscular Blockade (ROSE) trial informed the decision to mandate sedation and neuromuscular blockade only in the group assigned to therapeutic hypothermia and proceed without this mandate in the control group assigned to a usual temperature management protocol. Previous trials conducted in National Heart, Lung, and Blood Institute ARDS Clinical Trials (ARDSNet) and Prevention and Early Treatment of Acute Lung Injury (PETAL) Networks informed ventilator management, ventilation liberation and fluid management protocols. Since ARDS due to COVID-19 is a common cause of ARDS during pandemic surges and shares many features with ARDS from other causes, patients with ARDS due to COVID-19 are included. Finally, a stepwise approach to obtaining informed consent prior to documenting critical hypoxemia was adopted to facilitate enrollment and reduce the number of candidates excluded because eligibility time window expiration.

Risk factors and outcomes of acute respiratory distress syndrome in critically ill patients with cirrhosis.

AIM: Prior randomized controlled trials of acute respiratory distress syndrome (ARDS) excluded critically ill patients with cirrhosis. Data regarding risk factors for ARDS development and outcomes from ARDS in patients with cirrhosis are scarce. We sought to characterize outcomes from ARDS in patients with cirrhosis. METHODS: An observational cohort of patients with cirrhosis admitted to an intensive care unit at a high-volume liver transplant center between 1 January 2012 and 31 December 2014 were reviewed. ARDS cases were identified according to the Berlin definition. Potential risk factors were examined in multivariable logistic regression analysis for ARDS development. Outcomes including in-hospital mortality were compared between ARDS and non-ARDS patients. RESULTS: A total of 559 patients met the inclusion criteria and 45 (8.1%) developed ARDS. Differences between ARDS and non-ARDS patients included sepsis, Model for End-Stage Liver Disease - Sodium score, and Sequential Organ Failure Assessment score. In-hospital mortality was higher in cirrhotic patients with ARDS compared with those without ARDS (82.2% vs. 27.6%, P < 0.001). In multivariable analysis, acute-on-chronic liver failure (OR 8.69, 95% CI 2.28-33.18, P < 0.01) and shock on intensive care unit admission (OR 3.13, 95% CI 1.57-6.24, P = 0.001) were associated with ARDS development, whereas etiology of cirrhosis or alcohol use were not. CONCLUSIONS: Acute-on-chronic liver failure and shock on intensive care unit admission were risk factors for ARDS development, whereas etiology of cirrhosis and alcohol were not. Mortality from ARDS was markedly increased in patients with cirrhosis. Early recognition and treatment for infection might be important for improving the high mortality in this group of patients.

Helping Patients With ESRD and Earlier Stages of CKD to Quit Smoking.

Among the many adverse effects of tobacco exposure is the increased risk for progression of kidney disease. Individuals with chronic kidney disease (CKD), who already face increased cardiovascular event rates compared to the general population, are at even greater risk if they smoke. Despite these risks and the increased focus on smoking cessation in the general population in recent years, national guidelines have not specifically targeted individuals with CKD. There are similarly sparse data specific to individuals with CKD regarding the safety and efficacy of evidence-based smoking cessation modalities. This review aims to identify the risks of nicotine dependence in individuals with CKD and the potential benefits of smoking cessation; discuss current strategies for smoking cessation, including behavioral and pharmacologic therapies such as varenicline; and extrapolate these interventions to the unique challenges of this population. Much of the data presented stem from evidence for the general population but are described with additional consideration in dosing of nicotine replacement therapy, as well as non-nicotine pharmacotherapy and treatment modality for individuals with CKD.

A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.

Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports. The objective of this study was to determine whether natural language processing (NLP) with machine learning performs better than a traditional keyword model for ARDS identification. Linguistic pre-processing of reports was performed and text features were inputs to machine learning classifiers tuned using 10-fold cross-validation on 80% of the sample size and tested in the remaining 20%. A cohort of 533 patients was evaluated, with a data corpus of 9,255 radiology reports. The traditional model had an accuracy of 67.3% (95% CI: 58.3-76.3) with a positive predictive value (PPV) of 41.7% (95% CI: 27.7-55.6). The best NLP model had an accuracy of 83.0% (95% CI: 75.9-90.2) with a PPV of 71.4% (95% CI: 52.1-90.8). A computable phenotype for ARDS with NLP may identify more cases than the traditional model.