What Are the Real Issues in Providing Extracorporeal Membrane Oxygenation (ECMO) Support: A Survey.

INTRODUCTION: By using a novel survey our study aimed to assess the challenges ECMO and Critical Care (CC) teams face when initiating and managing patient's ECMO support. METHODS: A qualitative survey-based observational study was performed of members of 2 Critical Care Medicine organizations involved in decision-making around the practice of Extracorporeal Membrane Oxygenation (ECMO). The range of exploratory questions covered ethical principles of informed consent, autonomy and goals of care discussions, beneficence, non-maleficence (offering life-sustaining treatments in end-of-life care), and justice (insurance-related limitations of treatment). Questions also covered pragmatic practice and quality improvement areas, such as exploring whether palliative care or ethics teams were involved in such decision-making. RESULTS: 305 members received the survey links, and a total of 61 completed surveys were received, for an overall response rate of 20% among all eligible members. Only 70% of the participants who manage ECMO patients are involved in the ECMO initiation decision process. The majority do not involve Ethics or Palliative care at the initial ECMO initiation decision step. Of the ethical and moral dilemmas reported, the majority revolved around 1. Prognostication of patients receiving VV and VA ECMO support, 2. Lack of knowledge of patient's wishes and goals, 3. Disconnect between expectations of families and outcomes and 4. Staff moral distress around when to stop ECMO in case of futility. CONCLUSION: Our survey highlights areas of distress and dilemma which have been stressed before in the initiation, management, and outcomes of ECMO patients, however with the increasing use of this modality of cardiopulmonary mechanical support being offered, the survey results can offer a guidance using sound ethical principles.

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19.

OBJECTIVE: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). METHODS: We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. RESULTS: The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). CONCLUSION: A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

Responding to COVID-19 Through Interhospital Resource Coordination: A Mixed-Methods Evaluation.

OBJECTIVES: The COVID-19 pandemic stressed hospital operations, requiring rapid innovations to address rise in demand and specialized COVID-19 services while maintaining access to hospital-based care and facilitating expertise. We aimed to describe a novel hospital system approach to managing the COVID-19 pandemic, including multihospital coordination capability and transfer of COVID-19 patients to a single, dedicated hospital. METHODS: We included patients who tested positive for SARS-CoV-2 by polymerase chain reaction admitted to a 12-hospital network including a dedicated COVID-19 hospital. Our primary outcome was adherence to local guidelines, including admission risk stratification, anticoagulation, and dexamethasone treatment assessed by differences-in-differences analysis after guideline dissemination. We evaluated outcomes and health care worker satisfaction. Finally, we assessed barriers to safe transfer including transfer across different electronic health record systems. RESULTS: During the study, the system admitted a total of 1209 patients. Of these, 56.3% underwent transfer, supported by a physician-led System Operations Center. Patients who were transferred were older (P = 0.001) and had similar risk-adjusted mortality rates. Guideline adherence after dissemination was higher among patients who underwent transfer: admission risk stratification (P < 0.001), anticoagulation (P < 0.001), and dexamethasone administration (P = 0.003). Transfer across electronic health record systems was a perceived barrier to safety and reduced quality. Providers positively viewed our transfer approach. CONCLUSIONS: With standardized communication, interhospital transfers can be a safe and effective method of cohorting COVID-19 patients, are well received by health care providers, and have the potential to improve care quality.

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

OBJECTIVE: With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution. MATERIALS AND METHODS: Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger. RESULTS: This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems. DISCUSSION: Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime. CONCLUSION: This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.

Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles.

PURPOSE: Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. METHODS: This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes. RESULTS: The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III. CONCLUSION: We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.

Characterizing COVID-19 Clinical Phenotypes and Associated Comorbidities and Complication Profiles.

BACKGROUND: There is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes. OBJECTIVE: Identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. DESIGN, SETTINGS, AND PARTICIPANTS: Retrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020. METHODS: Ensemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes. Main outcomes and measures: Phenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality. RESULTS: The database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III. When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (p<0.001), renal (p<0.001), and cardiac (p<0.001) comorbidities were most commonly characterized by phenotype I. The adjusted odds of respiratory (p<0.001), renal (p<0.001), and metabolic (p<0.001) complications were highest for patients with phenotype I, followed by phenotype II. Patients with phenotype I had a far greater odds of hepatic (p<0.001) and hematological (p=0.02) complications than the other two phenotypes. Phenotypes I and II were associated with 7.30-fold (HR: 7.30, 95% CI: (3.11-17.17), p<0.001) and 2.57-fold (HR: 2.57, 95% CI: (1.10-6.00), p=0.03) increases in the hazard of death, respectively, when compared to phenotype III. CONCLUSION: In this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.

The association between ASA status and other risk stratification models on postoperative intensive care unit outcomes.

BACKGROUND: There is limited medical literature investigating the association between perioperative risk stratification methods and surgical intensive care unit (SICU) outcomes. Our hypothesis contends that routine assessments such as higher ASA physical status classification, surgical risk as defined by American College of Cardiology/American Heart Association guidelines, and simplified Revised Cardiac Index (SRCI) can reliably be associated with SICU outcomes. METHODS: We performed a chart review of all patients 18 years or older admitted to the SICU between October 1, 2010, and March 1, 2011. We collected demographic and preoperative clinical data: age, sex, ASA physical status class, surgical risk, and SRCI. Outcome data included our primary end point, SICU length of stay, and secondary end points: mechanical ventilation and vasopressor treatment duration, number of acquired organ dysfunctions (NOD), readmission to the intensive care unit (ICU) within 7 days, SICU mortality, and 30-day mortality. Regression analysis and nonparametric tests were used, and P < 0.05 was considered significant. RESULTS: We screened 239 patients and included 220 patients in the study. The patients' mean age was 58 ± 16 years. There were 32% emergent surgery and 5% readmissions to the SICU within 7 days. The SICU mortality and the 30-day mortality were 3.2%. There was a significant difference between SICU length of stay (2.9 ± 2.1 vs 5.9 ± 7.4, P = 0.007), mechanical ventilation (0.9 ± 2.0 vs 3.4 ± 6.8, P = 0.01), and NOD (0 [0-2] vs 1 [0-5], P < 0.001) based on ASA physical status class (≤ 2 vs ≥ 3). Outcomes significantly associated with ASA physical status class after adjusting for confounders were: SICU length of stay (incidence rate ratio [IRR] = 1.79, 95% confidence interval [CI], 1.35-2.39, P < 0.001), mechanical ventilation (IRR = 2.57, 95% CI, 1.69-3.92, P < 0.001), vasopressor treatment (IRR = 3.57, 95% CI, 1.84-6. 94, P < 0.001), NOD (IRR = 1.71, 95% CI, 1.46-1.99, P < 0.001), and readmission to ICU (odds ratio = 3.39, 95% CI, 1.04-11.09, P = 0.04). We found significant association between surgery risk and NOD (IRR = 1.56, 95% CI, 1.29-1.89, P < 0.001, and adjusted IRR = 1.31, 95% CI, 1.05-1.64, P = 0.02). SRCI was not significantly associated with SICU outcomes. CONCLUSIONS: Our study revealed that ASA physical status class is associated with increased SICU length of stay, mechanical ventilation, vasopressor treatment duration, NOD, readmission to ICU, and surgery risk is associated with NOD.

Inadequate antibiotic therapy in solid organ transplant recipients is associated with a higher mortality rate.

BACKGROUND: Inadequate antibiotic therapy and failure to administer antibiotics in a timely fashion have been associated with substantial mortality rates in patients in the intensive care unit (ICU). We analyzed the infection pattern in solid organ transplant recipients as well as the impact of antibiotic resistance and inadequate antibiotic treatment on mortality rates and morbidity outcomes. METHODS: Charts of adult solid organ transplant recipients in 2006 from a single institution were reviewed. Data on patients with bacterial and fungal infections acquired within one year after transplantation were compared with the primary outcome of death within 28 days. Statistical analysis included nonparametric tests (Wilcoxon rank sum, Fisher exact, and chi-square) and multivariable logistic regression with p < 0.05 considered significant. RESULTS: Of the 366 patients, 114 (31%) had a total of 208 bacterial or fungal infections, and 44 of them (39%) were admitted to the ICU. Our primary endpoint, the 28-day mortality rate, was 8% overall, whereas the six-month mortality rate was 11%. Patients treated inadequately with antibiotics had a significantly higher mortality rate. The leading causes of infection were multiple organisms, coagulase-negative Staphylococcus, and E. coli, of which 76% were resistant to antibiotics. Antibiotic-resistant infections were associated with longer hospital stays (p = 0.04), intravenous antibiotic use prior to infection (p = 0.04), nucleotide synthesis inhibitor use (p = 0.02), ICU admission (p < 0.01), and respiratory failure (p = 0.03). Most infections were treated inadequately initially (69%) but treated adequately at 24 h (56%). Inadequate antibiotic treatment was significantly associated with younger age (p = 0.04), prior intravenous antibiotic use (p = 0.04), longer stay prior to infection (p = 0.05), and cardiovascular shock (p = 0.014). Inadequate antibiotic therapy at 24 h was associated with a higher mortality rate (14% vs. 2%; p = 0.03) and a trend toward longer ICU and in-hospital stays. CONCLUSIONS: Most bacterial and fungal infections were resistant to antibiotics and were treated inadequately initially. Prior intravenous antibiotic use and longer stay prior to infection were associated with antibiotic resistance and inadequate antibiotic therapy. Failure to provide adequate antibiotic treatment within 24 h had a significant impact on the 28-day mortality rate and was associated with other detrimental clinical outcomes.