Lal, Amos

IMPORTANCE: At the start of the coronavirus disease 2019 pandemic, medications repurposed for management of coronavirus disease 2019 were used in the absence of clinical trial evidence. OBJECTIVES: To describe the variation and evolution in use of repurposed medications for coronavirus disease 2019. DESIGN, SETTING, AND PARTICIPANTS: Observational cohort study of adults hospitalized with coronavirus disease 2019 between February 15, 2020, and April 12, 2021, across 76 United States and international hospitals within the Society of Critical Care Medicine’s Discovery Viral Infection and Respiratory Illness Universal Study coronavirus disease 2019 registry. MAIN OUTCOMES AND MEASURES: Hospital variation was quantified using multivariable adjusted random effects logistic regression models and unsupervised clustering. Repurposed medications included antivirals, corticosteroids, hydroxychloroquine, immunomodulators, and therapeutic dose anticoagulants. RESULTS: Among 7,069 adults hospitalized with coronavirus disease 2019, 1,979 (28%) received antivirals, 2,876 (41%) received corticosteroids, 1,779 (25%) received hydroxychloroquine, 620 (9%) received immunomodulators, and 2,154 (31%) received therapeutic dose anticoagulants. Contribution of hospital site to risk-adjusted variation was 46% for antivirals, 30% for corticosteroids, 48% for hydroxychloroquine, 46% for immunomodulators, and 52% for therapeutic dose anticoagulants. Compared with the early pandemic, the later pandemic practice phenotypes converged with increased use of antivirals (odds ratio, 3.14; 95% CI, 2.40–4.10) and corticosteroids (odds ratio, 5.43; 95% CI, 4.23–6.97), with decreased use of hydroxychloroquine (odds ratio, 0.02; 95% CI, 0.01–0.04) and immunomodulators (odds ratio, 0.49; 95% CI, 0.34–0.70). There was no clinically significant change in the use of therapeutic dose anticoagulants (odds ratio, 1.01; 95% CI, 1.01–1.02). There were no differences in risk-adjusted mortality between hospitals with high rates of repurposed medication use compared with hospitals with low rates of use. CONCLUSIONS AND RELEVANCE: Hospital variation in the use of repurposed medications varied widely across hospitals early in the pandemic and later converged with the emergence of randomized clinical trials. Platforms developed for rapid activation and enrollment in clinical trials of repurposed medications are needed prior to the next pandemic to expedite effective, evidence-based practice.

BACKGROUND: Several markers of oxygenation are used as prognostic markers in acute hypoxemic respiratory failure. Real-world use is limited by the need for invasive measurements and unreliable availability in the electronic health record. A pragmatic, reliable, and accurate marker of acute hypoxemic respiratory failure is needed to facilitate epidemiologic studies, clinical trials, and shared decision-making with patients. F IO 2 is easily obtained at the bedside and from the electronic health record. The F IO 2 trajectory may be a valuable marker of recovery in patients with acute hypoxemic respiratory failure. METHODS: This was a historical cohort study of adult subjects admitted to an ICU with acute hypoxemic respiratory failure secondary to community-acquired pneumonia and/or ARDS. RESULTS: Our study included 2,670 subjects. F IO 2 and S pO 2 were consistently more available than was P aO 2 in the electronic health record: (F IO 2 vs S pO 2 vs P aO 2 : 100 vs 100 vs 72.8% on day 1, and 100 vs 99 vs 21% on day 5). A worsening F IO 2 trajectory was associated with reduced ventilator-free days. From days 2 to 5, every increase in F IO 2 by 10% from the previous day was associated with fewer ventilator-free days (on day 2: adjusted mean –1.25 [95% CI –1.45 to –1.05] d, P < .001). The S pO 2 /F IO 2 trajectory also provided prognostic information. On days 3 – 5, an increase in S pO 2 /F IO 2 from the previous day was associated with increased ventilator-free days (on day 3: adjusted mean 2.09 (95% CI 1.44–2.74) d; P < .001). S pO 2 /F IO 2 models did not add predictive information compared with models with F IO 2 alone (on day 2: adjusted F IO 2 vs S pO 2 /F IO 2 R2 0.122 vs 0.119; and on day 3: 0.153 vs 0.163). CONCLUSIONS: F IO 2 and S pO 2 /F IO 2 are pragmatic and readily available intermediate prognostic markers in acute hypoxic respiratory failure. The F IO 2 trajectory in the first 5 d of ICU admission provided important prognostic information (ventilator-free days). Although the S pO 2 /F IO 2 trajectory was also associated with ventilator-free days, it did not provide more information than the F IO 2 trajectory alone.

Two adenovirus-based vaccines, ChAdOx1 nCoV-19 and Ad26.COV2.S, and two mRNA-based vaccines, BNT162b2 and mRNA.1273, have been approved by the European Medicines Agency (EMA), and are invaluable in preventing and reducing the incidence of coronavirus disease-2019 (COVID-19). Recent reports have pointed to thrombosis with associated thrombocytopenia as an adverse effect occurring at a low frequency in some individuals after vaccination. The causes of such events may be related to SARS-CoV-2 spike protein interactions with different C-type lectin receptors, heparan sulfate proteoglycans (HSPGs) and the CD147 receptor, or to different soluble splice variants of the spike protein, adenovirus vector interactions with the CD46 receptor or platelet factor 4 antibodies. Similar findings have been reported for several viral diseases after vaccine administration. In addition, immunological mechanisms elicited by viral vectors related to cellular delivery could play a relevant role in individuals with certain genetic backgrounds. Although rare, the potential COVID-19 vaccine-induced immune thrombotic thrombocytopenia (VITT) requires immediate validation, especially in risk groups, such as the elderly, chronic smokers, and individuals with pre-existing incidences of thrombocytopenia; and if necessary, a reformulation of existing vaccines.

OBJECTIVES: Accurate identification of acute respiratory distress syndrome is essential for understanding its epidemiology, patterns of care, and outcomes. We aimed to design a computable phenotyping strategy to detect acute respiratory distress syndrome in electronic health records of critically ill patients. DESIGN: This is a retrospective cohort study. Using a near real-time copy of the electronic health record, we developed a computable phenotyping strategy to detect acute respiratory distress syndrome based on the Berlin definition. SETTING: Twenty multidisciplinary ICUs in Mayo Clinic Health System. SUBJECTS: The phenotyping strategy was applied to 196,487 consecutive admissions from year 2009 to 2019. INTERVENTIONS: The acute respiratory distress syndrome cohort generated by this novel strategy was compared with the acute respiratory distress syndrome cohort documented by clinicians during the same period. The sensitivity and specificity of the phenotyping strategy were calculated in randomly selected patient cohort (50 patients) using the results from manual medical record review as gold standard. MEASUREMENTS AND MAIN RESULTS: Among the patients who did not have acute respiratory distress syndrome documented, the computable phenotyping strategy identified 3,169 adult patients who met the Berlin definition, 676 patients (21.3%) were classified to have severe acute respiratory distress syndrome (Pao 2/Fio 2 ratio ≤ 100), 1,535 patients (48.4%) had moderate acute respiratory distress syndrome (100 < Pao 2/Fio 2 ratio ≤ 200), and 958 patients (30.2%) had mild acute respiratory distress syndrome (200 < Pao 2/Fio 2 ratio ≤ 300). The phenotyping strategy achieved a sensitivity of 94.4%, specificity of 96.9%, positive predictive value of 94.4%, and negative predictive value of 96.9% in a randomly selected patient cohort. The clinicians documented acute respiratory distress syndrome in 1,257 adult patients during the study period. The clinician documentation rate of acute respiratory distress syndrome was 28.4%. Compared with the clinicians’ documentation, the phenotyping strategy identified a cohort that had higher acuity and complexity of illness suggested by higher Sequential Organ Failure Assessment score (9 vs 7; p < 0.0001), higher Acute Physiology and Chronic Health Evaluation score (76 vs 63; p < 0.0001), higher rate of requiring invasive mechanical ventilation (99.1% vs 71.8%; p < 0.0001), higher ICU mortality (20.6% vs 16.8%; p < 0.0001), and longer ICU length of stay (5.1 vs 4.2 d; p < 0.0001). CONCLUSIONS: Our rule-based computable phenotyping strategy can accurately detect acute respiratory distress syndrome in critically ill patients in the setting of high clinical complexity. This strategy can be applied to enhance early recognition of acute respiratory distress syndrome and to facilitate best-care delivery and clinical research in acute respiratory distress syndrome.

Objectives: Our preliminary data and observational studies suggested an increasing “off label” use of oral midodrine as a vasopressor sparing agent in various groups of critically ill patients, including those with sepsis. We designed this clinical trial to evaluate the feasibility of use of midodrine hydrochloride in early sepsis to reduce the duration for IV vasopressors and decrease ICU and hospital length of stay. Design: Pilot, two-center, placebo-controlled, double blinded randomized clinical trial. Setting: Medical ICUs at Mayo Clinic Rochester and Cleveland Clinic Abu Dhabi were the study sites. Patients and Methods: Adult patients (≥ 18 yr old) were included within 24 hours of meeting the Sepsis-3 definition if the mean arterial pressure remained less than 70 mm Hg despite receiving timely antibiotics and initial IV fluid bolus of 30 cc/kg. Intervention: Three doses of 10 mg midodrine versus placebo were administered. Measurements and Main Results: Total 32 patients were randomized into midodrine (n = 17) and placebo groups (n = 15). There were no major differences in baseline variables between the groups except for higher baseline creatinine in the midodrine group (2.0 ± 0.9 mg/dL) versus placebo group (1.4 ± 0.6 mg /dL), p = 0.03. The median duration of IV vasopressor requirement was 14.5 ± 8.1 hours in midodrine group versus 18.8 ± 7.1 hours in the placebo group, p value equals to 0.19. Patients in the midodrine group needed 729 ± 963 norepinephrine equivalent compared with 983 ± 1,569 norepinephrine equivalent in the placebo group, p value equals to 0.59. ICU length of stay was 2.29 days (interquartile range, 1.65–3.9 d) in the midodrine group, compared with 2.45 days (interquartile range, 1.6–3.2 d) in the placebo group, p value equals to 0.36. No serious adverse events were observed in either group. Conclusions: Phase II clinical trial powered for clinical outcomes (duration of vasopressor use, need for central venous catheter, and ICU and hospital length of stay) is justified.

Phylogenetic analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is focused on a single isolate of bat coronaviruses (bat CoVs) which does not adequately represent genetically related coronaviruses (CoVs) [...]

OBJECTIVES: To describe the outcomes of hospitalized patients in a multicenter, international coronavirus disease 2019 registry. DESIGN: Cross-sectional observational study including coronavirus disease 2019 patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus-2 infection between February 15, 2020, and November 30, 2020, according to age and type of organ support therapies. SETTING: About 168 hospitals in 16 countries within the Society of Critical Care Medicine’s Discovery Viral Infection and Respiratory Illness University Study coronavirus disease 2019 registry. PATIENTS: Adult hospitalized coronavirus disease 2019 patients who did and did not require various types and combinations of organ support (mechanical ventilation, renal replacement therapy, vasopressors, and extracorporeal membrane oxygenation). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcome was hospital mortality. Secondary outcomes were discharge home with or without assistance and hospital length of stay. Risk-adjusted variation in hospital mortality for patients receiving invasive mechanical ventilation was assessed by using multilevel models with hospitals as a random effect, adjusted for age, race/ethnicity, sex, and comorbidities. Among 20,608 patients with coronavirus disease 2019, the mean (± sd ) age was 60.5 (±17), 11,1887 (54.3%) were men, 8,745 (42.4%) were admitted to the ICU, and 3,906 (19%) died in the hospital. Hospital mortality was 8.2% for patients receiving no organ support ( n = 15,001). The most common organ support therapy was invasive mechanical ventilation ( n = 5,005; 24.3%), with a hospital mortality of 49.8%. Mortality ranged from 40.8% among patients receiving only invasive mechanical ventilation ( n =1,749) to 71.6% for patients receiving invasive mechanical ventilation, vasoactive drugs, and new renal replacement therapy ( n = 655). Mortality was 39% for patients receiving extracorporeal membrane oxygenation ( n = 389). Rates of discharge home ranged from 73.5% for patients who did not require organ support therapies to 29.8% for patients who only received invasive mechanical ventilation, and 8.8% for invasive mechanical ventilation, vasoactive drugs, and renal replacement; 10.8% of patients older than 74 years who received invasive mechanical ventilation were discharged home. Median hospital length of stay for patients on mechanical ventilation was 17.1 days (9.7-28 d). Adjusted interhospital variation in mortality among patients receiving invasive mechanical ventilation was large (median odds ratio 1.69). CONCLUSIONS: Coronavirus disease 2019 prognosis varies by age and level of organ support. Interhospital variation in mortality of mechanically ventilated patients was not explained by patient characteristics and requires further evaluation.

Objectives: To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis. Design: Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin. Setting: Medical ICU of a large quaternary- care academic medical center in the United States. Patients or Subjects: Adult (> 18 year yr old), medical ICU patients were included in the study. Interventions: No additional interventions were made beyond the standard of care for this study. Measurements and Main Results: During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54–66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0–14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%). Conclusions: We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients.