Resilience, Survival and Functional Independence in Older Adults Facing Critical Illness.

BACKGROUND: Older adults surviving critical illness often experience new or worsening functional impairments. Modifiable positive psychological constructs, like resilience, may mitigate post-intensive care morbidity. RESEARCH QUESTION: Is pre-ICU resilience associated with: (1) post-ICU survival; (2) the drop in post-ICU functional independence; and (3) a lesser decline of independence before versus after the ICU? STUDY DESIGN AND METHODS: We performed a retrospective cohort study using Medicare-linked Health and Retirement Study surveys from 2006-2018. We included Older adults ≥65 years admitted to an ICU. We calculated resilience before ICU admission. The resilience measure was defined from the Simplified Resilience Score which was previously adapted and validated for the Health and Retirement Study. Resilience was scored using the leave-behind survey normalized to 0 (lowest resilience) to 12 (highest resilience) point scale. Outcomes were survival and probability of functional independence. We modeled survival using Gompertz models and independence using joint survival models adjusting for sociodemographic and clinical variables. We estimated average marginal effects to determine independence probabilities. RESULTS: Across 3,409 patients ≥65 years old admitted to ICUs, pre-existing frailty (30.5%) and cognitive impairment (24.3%) were common. Most patients were previously independent (82.7%). Mechanical ventilation occurred in 14.8% and sepsis in 43.2%. Highest versus lowest resilience had lower risk of post-ICU mortality (aHR 0.81 95% CI [0.70, 0.94]). Higher resilience was associated with greater likelihood in post-ICU independence (estimated probability of independence 5 years post-ICU in highest-to-lowest resilience: 0.53 CI 95% [0.33, 0.74], 0.47 [0.26, 0.68], 0.49 [0.28, 0.70], 0.36 [0.17, 0.55] p<0.01). Resilience was not associated with a difference in the drop of independence across resilience groups, nor a difference in declines of independence post-ICU. INTERPRETATION: ICU survivors with higher resilience had increased rates of survival and functional independence, though the slope of functional decline did not differ by resilience group pre- to post-ICU.

Beyond the ICU Rotation: The Importance of Trainee Involvement in Post-Intensive Care Unit Clinics.

Medical learners are vital to the care of critically ill patients in the intensive care unit (ICU). Although these learners are exposed to the challenges and stresses of acute ICU management, they do not typically experience the benefits of following ICU patients and families longitudinally after their ICU rotation. Post-ICU clinics and recovery programs may fill this crucial gap in trainee education. These clinics have emerged as an appealing approach to potentially support patient recovery, enhance provider satisfaction, and provide feedback on vital lessons learned in long-term follow-up to improve the quality of ICU care. Notably, the effect of such a program on trainee education has not been explored. In this article, we propose a framework for medical learner participation in post-ICU follow-up based on the Accreditation Council for Graduate Medical Education milestones and discuss the potential benefits, including: education about post-ICU recovery, including post-intensive care syndrome and post-intensive care syndrome-family; experience in quality improvement to enhance ICU care by understanding long-term outcomes; engagement in reflection; and mitigation of compassion fatigue and burnout.

Six-Month Impairment in Cognition, Mental Health, and Physical Function Following COVID-19-Associated Respiratory Failure.

To determine the prevalence and extent of impairments impacting health-related quality of life among survivors of COVID-19 who required mechanical ventilation, 6 months after hospital discharge. DESIGN: Multicenter, prospective cohort study, enrolling adults 18 years old or older with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection who received mechanical ventilation for 48 hours or more and survived to hospital discharge. Eligible patients were contacted 6 months after discharge for telephone-based interviews from March 2020 to December 2020. Assessments included: Montreal Cognitive Assessment-Blind, Hospital Anxiety and Depression Scale, Impact of Event Scale-6, EuroQOL 5 domain quality-of-life questionnaire, and components of the Multidimensional Dyspnea Profile. SETTING: Two tertiary academic health systems. PATIENTS: Of 173 eligible survivors, a random sample of 63 were contacted and 60 consented and completed interviews. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Mean age was 57 + 13 years and mean duration of invasive mechanical ventilation was 14 + 8.2 days. Six months post-discharge, 48 patients (80%; 95% CI, 68-88%) met criteria for post-intensive care syndrome (PICS), with one or more domains impaired. Among patients with PICS, 28 (47%; 95% CI, 35-59%) were impaired in at least 2 domains, and 12 (20%; 95% CI, 12-32%) impaired in all three domains. Significant symptoms of post-traumatic stress were present in 20 patients (33%; 95% CI, 23-46%), anxiety in 23 (38%; 95% CI, 27-51%), and depression in 25 (42%; 95% CI, 30-54%). Thirty-three patients (55%; 95% CI, 42-67%) had impairments in physical activity; 25 patients (42%; 95% CI, 30-54%) demonstrated cognitive impairment. CONCLUSIONS: Eighty percent of COVID-19 survivors who required mechanical ventilation demonstrated PICS 6 months after hospital discharge. Patients were commonly impaired in multiple PICS domains as well as coexisting mental health domains.

Characteristics of Post-ICU and Post-COVID Recovery Clinics in 29 U.S. Health Systems.

The multifaceted long-term impairments resulting from critical illness and COVID-19 require interdisciplinary management approaches in the recovery phase of illness. Operational insights into the structure and process of recovery clinics (RCs) from heterogeneous health systems are needed. This study describes the structure and process characteristics of existing and newly implemented ICU-RCs and COVID-RCs in a subset of large health systems in the United States. DESIGN: Cross-sectional survey. SETTING: Thirty-nine RCs, representing a combined 156 hospitals within 29 health systems participated. PATIENTS: None. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: RC demographics, referral criteria, and operating characteristics were collected, including measures used to assess physical, psychologic, and cognitive recoveries. Thirty-nine RC surveys were completed (94% response rate). ICU-RC teams included physicians, pharmacists, social workers, physical therapists, and advanced practice providers. Funding sources for ICU-RCs included clinical billing (n = 20, 77%), volunteer staff support (n = 15, 58%), institutional staff/space support (n = 13, 46%), and grant or foundation funding (n = 3, 12%). Forty-six percent of RCs report patient visit durations of 1 hour or longer. ICU-RC teams reported use of validated scales to assess psychologic recovery (93%), physical recovery (89%), and cognitive recovery (86%) more often in standard visits compared with COVID-RC teams (psychologic, 54%; physical, 69%; and cognitive, 46%). CONCLUSIONS: Operating structures of RCs vary, though almost all describe modest capacity and reliance on volunteerism and discretionary institutional support. ICU- and COVID-RCs in the United States employ varied funding sources and endorse different assessment measures during visits to guide care coordination. Common features include integration of ICU clinicians, interdisciplinary approach, and focus on severe critical illness. The heterogeneity in RC structures and processes contributes to future research on the optimal structure and process to achieve the best postintensive care syndrome and postacute sequelae of COVID outcomes.

Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records.

Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient's ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.

Transpulmonary pressure measurements and lung mechanics in patients with early ARDS and SARS-CoV-2.

PURPOSE: Acute Respiratory Distress Syndrome (ARDS) secondary to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has demonstrated variable oxygenation and respiratory-system mechanics without investigation of transpulmonary and chest-wall mechanics. This study describes lung, chest wall and respiratory-system mechanics in patients with SARS-CoV-2 and ARDS. METHODS: Data was collected from forty patients with confirmed SARS-CoV-2 and ARDS at Beth Israel Deaconess Medical Center in Boston, Massachusetts. Esophageal balloons were placed to estimate pleural and transpulmonary pressures. Clinical characteristics, respiratory-system, transpulmonary, and chest-wall mechanics were measured over the first week. RESULTS: Patients had moderate-severe ARDS (PaO2/FiO2 123[98-149]) and were critically ill (APACHE IV 108 [94-128] and SOFA 12 [11-13]). PaO2/FiO2 improved over the first week (150 mmHg [122.9-182] to 185 mmHg [138-228] (p = 0.035)). Respiratory system (30-35 ml/cm H2O), lung (40-50 ml/cm H2O) and chest wall (120-150 ml/cm H2O) compliance remained similar over the first week. Elevated basal pleural pressures correlated with BMI. Patients required prolonged mechanical ventilation (14.5 days [9.5-19.0]), with a mortality of 32.5%. CONCLUSIONS: Patients displayed normal chest-wall mechanics, with increased basal pleural pressure. Respiratory system and lung mechanics were similar to known existing ARDS cohorts. The wide range of respiratory system mechanics illustrates the inherent heterogeneity that is consistent with typical ARDS.

Data-driven curation process for describing the blood glucose management in the intensive care unit.

Analysis of real-world glucose and insulin clinical data recorded in electronic medical records can provide insights into tailored approaches to clinical care, yet presents many analytic challenges. This work makes publicly available a dataset that contains the curated entries of blood glucose readings and administered insulin on a per-patient basis during ICU admissions in the Medical Information Mart for Intensive Care (MIMIC-III) database version 1.4. Also, the present study details the data curation process used to extract and match glucose values to insulin therapy. The curation process includes the creation of glucose-insulin pairing rules according to clinical expert-defined physiologic and pharmacologic parameters. Through this approach, it was possible to align nearly 76% of insulin events to a preceding blood glucose reading for nearly 9,600 critically ill patients. This work has the potential to reveal trends in real-world practice for the management of blood glucose. This data extraction and processing serve as a framework for future studies of glucose and insulin in the intensive care unit.

Evidence-Based Management of the Critically Ill Adult With SARS-CoV-2 Infection.

Human infection by the novel viral pathogen SARS-CoV-2 results in a clinical syndrome termed Coronavirus Disease 2019 (COVID-19). Although the majority of COVID-19 cases are self-limiting, a substantial minority of patients develop disease severe enough to require intensive care. Features of critical illness associated with COVID-19 include hypoxemic respiratory failure, acute respiratory distress syndrome (ARDS), shock, and multiple organ dysfunction syndrome (MODS). In most (but not all) respects critically ill patients with COVID-19 resemble critically ill patients with ARDS due to other causes and are optimally managed with standard, evidence-based critical care protocols. However, there is naturally an intense interest in developing specific therapies for severe COVID-19. Here we synthesize the rapidly expanding literature around the pathophysiology, clinical presentation, and management of COVID-19 with a focus on those points most relevant for intensivists tasked with caring for these patients. We specifically highlight evidence-based approaches that we believe should guide the identification, triage, respiratory support, and general ICU care of critically ill patients infected with SARS-CoV-2. In addition, in light of the pressing need and growing enthusiasm for targeted COVID-19 therapies, we review the biological basis, plausibility, and clinical evidence underlying these novel treatment approaches.

Lung Histopathology in Coronavirus Disease 2019 as Compared With Severe Acute Respiratory Sydrome and H1N1 Influenza: A Systematic Review.

BACKGROUND: Patients with severe coronavirus disease 2019 (COVID-19) have respiratory failure with hypoxemia and acute bilateral pulmonary infiltrates, consistent with ARDS. Respiratory failure in COVID-19 might represent a novel pathologic entity. RESEARCH QUESTION: How does the lung histopathology described in COVID-19 compare with the lung histopathology described in SARS and H1N1 influenza? STUDY DESIGN AND METHODS: We conducted a systematic review to characterize the lung histopathologic features of COVID-19 and compare them against findings of other recent viral pandemics, H1N1 influenza and SARS. We systematically searched MEDLINE and PubMed for studies published up to June 24, 2020, using search terms for COVID-19, H1N1 influenza, and SARS with keywords for pathology, biopsy, and autopsy. Using PRISMA-Individual Participant Data guidelines, our systematic review analysis included 26 articles representing 171 COVID-19 patients; 20 articles representing 287 H1N1 patients; and eight articles representing 64 SARS patients. RESULTS: In COVID-19, acute-phase diffuse alveolar damage (DAD) was reported in 88% of patients, which was similar to the proportion of cases with DAD in both H1N1 (90%) and SARS (98%). Pulmonary microthrombi were reported in 57% of COVID-19 and 58% of SARS patients, as compared with 24% of H1N1 influenza patients. INTERPRETATION: DAD, the histologic correlate of ARDS, is the predominant histopathologic pattern identified in lung pathology from patients with COVID-19, H1N1 influenza, and SARS. Microthrombi were reported more frequently in both patients with COVID-19 and SARS as compared with H1N1 influenza. Future work is needed to validate this histopathologic finding and, if confirmed, elucidate the mechanistic underpinnings and characterize any associations with clinically important outcomes.