While medication regimen complexity, as measured by a novel medication regimen complexity-intensive care unit (MRC-ICU) score, correlates with baseline severity of illness and mortality, whether the MRC-ICU improves hospital mortality prediction is not known. After characterizing the association between MRC-ICU, severity of illness and hospital mortality we sought to evaluate the incremental benefit of adding MRC-ICU to illness severity-based hospital mortality prediction models. This was a single-center, observational cohort study of adult intensive care units (ICUs). A random sample of 991 adults admitted ≥ 24 h to the ICU from 10/2015 to 10/2020 were included. The logistic regression models for the primary outcome of mortality were assessed via area under the receiver operating characteristic (AUROC). Medication regimen complexity was evaluated daily using the MRC-ICU. This previously validated index is a weighted summation of medications prescribed in the first 24 h of ICU stay [e.g., a patient prescribed insulin (1 point) and vancomycin (3 points) has a MRC-ICU = 4 points]. Baseline demographic features (e.g., age, sex, ICU type) were collected and severity of illness (based on worst values within the first 24 h of ICU admission) was characterized using both the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Sequential Organ Failure Assessment (SOFA) score. Univariate analysis of 991 patients revealed every one-point increase in the average 24-h MRC-ICU score was associated with a 5% increase in hospital mortality [Odds Ratio (OR) 1.05, 95% confidence interval 1.02–1.08, p = 0.002]. The model including MRC-ICU, APACHE II and SOFA had a AUROC for mortality of 0.81 whereas the model including only APACHE-II and SOFA had a AUROC for mortality of 0.76. Medication regimen complexity is associated with increased hospital mortality. A prediction model including medication regimen complexity only modestly improves hospital mortality prediction.

Background: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed ‘pharmacophenotypes’) correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality). Methods: This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate. Results: A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay (p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6. Conclusion: The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.

OBJECTIVES: Increasing time to mechanical ventilation and high-flow nasal cannula use may be associated with mortality in coronavirus disease 2019. We examined the impact of time to intubation and use of high-flow nasal cannula on clinical outcomes in patients with coronavirus disease 2019. DESIGN: Retrospective cohort study. SETTING: Six coronavirus disease 2019-specific ICUs across four university-affiliated hospitals in Atlanta, Georgia. PATIENTS: Adults with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection who received high-flow nasal cannula or mechanical ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 231 patients admitted to the ICU, 109 (47.2%) were treated with high-flow nasal cannula and 97 (42.0%) were intubated without preceding high-flow nasal cannula use. Of those managed with high-flow nasal cannula, 78 (71.6%) ultimately received mechanical ventilation. In total, 175 patients received mechanical ventilation; 44.6% were female, 66.3% were Black, and the median age was 66 years (interquartile range, 56-75 yr). Seventy-six patients (43.4%) were intubated within 8 hours of ICU admission, 57 (32.6%) between 8 and 24 hours of admission, and 42 (24.0%) greater than or equal to 24 hours after admission. Patients intubated within 8 hours were more likely to have diabetes, chronic comorbidities, and higher admission Sequential Organ Failure Assessment scores. Mortality did not differ by time to intubation (≤ 8 hr: 38.2%; 8-24 hr: 31.6%; ≥ 24 hr: 38.1%; p = 0.7), and there was no association between time to intubation and mortality in adjusted analysis. Similarly, there was no difference in initial static compliance, duration of mechanical ventilation, or ICU length of stay by timing of intubation. High-flow nasal cannula use prior to intubation was not associated with mortality. CONCLUSIONS: In this cohort of critically ill patients with coronavirus disease 2019, neither time from ICU admission to intubation nor high-flow nasal cannula use were associated with increased mortality. This study provides evidence that coronavirus disease 2019 respiratory failure can be managed similarly to hypoxic respiratory failure of other etiologies.

Objectives: To critically assess available high-level clinical studies regarding RBC transfusion strategies, with a focus on hemoglobin transfusion thresholds in the ICU. Data Sources: Source data were obtained from a PubMed literature review. Study Selection: English language studies addressing RBC transfusions in the ICU with a focus on the most recent relevant studies. Data Extraction: Relevant studies were reviewed and the following aspects of each study were identified, abstracted, and analyzed: study design, methods, results, and implications for critical care practice. Data Synthesis: Approximately 30–50% of ICU patients receive a transfusion during their hospitalization with anemia being the indication for 75% of transfusions. A significant body of clinical research evidence supports using a restrictive transfusion strategy (e.g., hemoglobin threshold < 7g/dL) compared with a more liberal approach (e.g., hemoglobin threshold < 10g/dL). A restrictive strategy (hemoglobin < 7g/dL) is recommended in patients with sepsis and gastrointestinal bleeds. A slightly higher restrictive threshold is recommended in cardiac surgery (hemoglobin < 7.5g/dL) and stable cardiovascular disease (hemoglobin < 8g/dL). Although restrictive strategies are generally supported in hematologic malignancies, acute neurologic injury, and burns, more definitive studies are needed, including acute coronary syndrome. Massive transfusion protocols are the mainstay of treatment for hemorrhagic shock; however, the exact RBC to fresh frozen plasma ratio is still unclear. There are also emerging complimentary practices including nontransfusion strategies to avoid and treat anemia and the reemergence of whole blood transfusion. Conclusions: The current literature supports the use of restrictive transfusion strategies in the majority of critically ill populations. Continued studies of optimal transfusion strategies in various patient populations, coupled with the integration of novel complementary ICU practices, will continue to enhance our ability to treat critically ill patients.

BACKGROUND: Multisystem inflammatory syndrome in adults (MIS-A) is a severe condition temporally associated with SARS-CoV-2 infection. METHODS: In this retrospective cohort study, we applied the U.S. Centers for Disease Control and Prevention (CDC) case definition to identify diagnosed and undiagnosed MIS-A cases among adults discharged April 2020-January 2021 from four Atlanta, Georgia hospitals affiliated with a single medical center. Non-MIS-A COVID-19 hospitalizations were identified using International Classification of Diseases, Tenth Revision encounter code U07.1. We calculated the ratio of MIS-A to COVID-19 hospitalizations, compared demographic characteristics of the two cohorts, and described clinical characteristics of MIS-A patients. RESULTS: We identified 11 MIS-A cases, none of which were diagnosed by the treatment team, and 5,755 COVID-19 hospitalizations (ratio 1: 523). Compared with patients with COVID-19, patients with MIS-A were more likely to be younger than 50 years (72.7% vs. 26.1%, p < 0.01) and to be non-Hispanic Black persons (81.8% vs. 50.0%, p = 0.04). Ten patients with MIS-A (90.9%) had at least one underlying medical condition. Two MIS-A patients (18.2%) had a previous episode of laboratory-confirmed COVID-19, occurring 37 and 55 days prior to admission. All MIS-A patients developed left ventricular systolic dysfunction. None had documented mucocutaneous involvement. All required intensive care, all received systemic corticosteroids, eight (72.7%) required mechanical ventilation, two (18.2%) required mechanical cardiovascular circulatory support, and none received intravenous immunoglobulin. Two (18.2%) died or were discharged to hospice. CONCLUSIONS: MIS-A is severe but likely underrecognized complication of SARS-CoV-2 infection. Improved recognition of MIS-A is needed to quantify its burden and identify populations at highest risk.

Objectives: To identify circumstances in which repeated measures of organ failure would improve mortality prediction in ICU patients. Design: Retrospective cohort study, with external validation in a deidentified ICU database. Setting: Eleven ICUs in three university hospitals within an academic healthcare system in 2014. Patients: Adults (18 yr old or older) who satisfied the following criteria: 1) two of four systemic inflammatory response syndrome criteria plus an ordered blood culture, all within 24 hours of hospital admission; and 2) ICU admission for at least 2 calendar days, within 72 hours of emergency department presentation. Intervention: None Measurements and Main Results: Data were collected until death, ICU discharge, or the seventh ICU day, whichever came first. The highest Sequential Organ Failure Assessment score from the ICU admission day (ICU day 1) was included in a multivariable model controlling for other covariates. The worst Sequential Organ Failure Assessment scores from the first 7 days after ICU admission were incrementally added and retained if they obtained statistical significance (p < 0.05). The cohort was divided into seven subcohorts to facilitate statistical comparison using the integrated discriminatory index. Of the 1,290 derivation cohort patients, 83 patients (6.4%) died in the ICU, compared with 949 of the 8,441 patients (11.2%) in the validation cohort. Incremental addition of Sequential Organ Failure Assessment data up to ICU day 5 improved the integrated discriminatory index in the validation cohort. Adding ICU day 6 or 7 Sequential Organ Failure Assessment data did not further improve model performance. Conclusions: Serial organ failure data improve prediction of ICU mortality, but a point exists after which further data no longer improve ICU mortality prediction of early sepsis.

The coronavirus disease (COVID-19) pandemic has forced healthcare systems to develop strategies to allocate critical care resources when demand outstrips supply (1). The pandemic has also disproportionately impacted Black patients (2, 3), for whom baseline health disparities are well documented and largely driven by inequity in social determinants of health. Concerns about the potential for inequity in resource allocation were raised early in the pandemic, especially if morbidity limiting near-term survival was factored into allocation decisions. Two mitigation strategies to avoid inequity in allocation have been proposed: eliminating consideration of expected survival beyond 1 year and incorporating measures of social disadvantage such as the Area Deprivation Index (ADI) (2, 4, 5).

We compared the characteristics of hospitalized and nonhospitalized patients who had coronavirus disease in Atlanta, Georgia, USA. We found that risk for hospitalization increased with a patient's age and number of concurrent conditions. We also found a potential association between hospitalization and high hemoglobin A1c levels in persons with diabetes.

Objectives: Administrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices for sepsis and organ dysfunction may confound efforts to estimate sepsis rates, compare outcomes, and perform risk adjustment. We evaluated hospital variation in the sensitivity of claims data relative to clinical data from electronic health records and its impact on outcome comparisons. Design, Setting, and Patients: Retrospective cohort study of 4.3 million adult encounters at 193 U.S. hospitals in 2013-2014. Interventions: None. Measurements and Main Results: Sepsis was defined using electronic health record-derived clinical indicators of presumed infection (blood culture draws and antibiotic administrations) and concurrent organ dysfunction (vasopressors, mechanical ventilation, doubling in creatinine, doubling in bilirubin to ≥ 2.0 mg/dL, decrease in platelets to < 100 cells/µL, or lactate ≥ 2.0 mmol/L). We compared claims for sepsis prevalence and mortality rates between both methods. All estimates were reliability adjusted to account for random variation using hierarchical logistic regression modeling. The sensitivity of hospitals' claims data was low and variable: median 30% (range, 5-54%) for sepsis, 66% (range, 26-84%) for acute kidney injury, 39% (range, 16-60%) for thrombocytopenia, 36% (range, 29-44%) for hepatic injury, and 66% (range, 29-84%) for shock. Correlation between claims and clinical data was moderate for sepsis prevalence (Pearson coefficient, 0.64) and mortality (0.61). Among hospitals in the lowest sepsis mortality quartile by claims, 46% shifted to higher mortality quartiles using clinical data. Using implicit sepsis criteria based on infection and organ dysfunction codes also yielded major differences versus clinical data. Conclusions: Variation in the accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospitals' sepsis rates and outcomes. Using objective clinical data may facilitate more meaningful hospital comparisons.

Accurately diagnosing urinary tract infections (UTIs) in hospitalized patients remains challenging, requiring correlation of frequently nonspecific symptoms and laboratory findings. Urine cultures (UCs) are often ordered indiscriminately, especially in patients with urinary catheters, despite the Infectious Diseases Society of America guidelines recommending against routine screening for asymptomatic bacteriuria (ASB).1,2 Positive UCs can be difficult for providers to ignore, leading to unnecessary antibiotic treatment of ASB.2,3 Using diagnostic stewardship to limit UCs to situations with a positive urinalysis (UA) can reduce inappropriate UCs since the absence of pyuria suggests the absence of infection.4-6 We assessed the impact of the implementation of a UA with reflex to UC algorithm (reflex intervention) on UC ordering practices, diagnostic efficiency, and UTIs using a quasi-experimental design.