Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.

OBJECTIVES: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations. DESIGN: Single-center prospective pragmatic nonrandomized clustered clinical trial. SETTING: Academic tertiary care medical center. PATIENTS: Adult patients admitted to four medical-surgical units. Assignment to intervention or control arms was determined by initial unit admission. INTERVENTIONS: Real-time alerts stratified according to predicted likelihood of deterioration sent either to the primary team or directly to the rapid response team (RRT). Clinical care and interventions were at the providers' discretion. For the control units, alerts were generated but not sent, and standard RRT activation criteria were used. MEASUREMENTS AND MAIN RESULTS: The primary outcome was the rate of escalation per 1000 patient bed days. Secondary outcomes included the frequency of orders for fluids, medications, and diagnostic tests, and combined in-hospital and 30-day mortality. Propensity score modeling with stabilized inverse probability of treatment weight (IPTW) was used to account for differences between groups. Data from 2740 patients enrolled between July 2019 and March 2020 were analyzed (1488 intervention, 1252 control). Average age was 66.3 years and 1428 participants (52%) were female. The rate of escalation was 12.3 vs. 11.3 per 1000 patient bed days (difference, 1.0; 95% CI, -2.8 to 4.7) and IPTW adjusted incidence rate ratio 1.43 (95% CI, 1.16-1.78; p < 0.001). Patients in the intervention group were more likely to receive cardiovascular medication orders (16.1% vs. 11.3%; 4.7%; 95% CI, 2.1-7.4%) and IPTW adjusted relative risk (RR) (1.74; 95% CI, 1.39-2.18; p < 0.001). Combined in-hospital and 30-day-mortality was lower in the intervention group (7% vs. 9.3%; -2.4%; 95% CI, -4.5% to -0.2%) and IPTW adjusted RR (0.76; 95% CI, 0.58-0.99; p = 0.045). CONCLUSIONS: Real-time machine learning alerts do not reduce the rate of escalation but may reduce mortality.

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.

BACKGROUND: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. OBJECTIVE: To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. DESIGN: Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. SETTING: Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). PATIENTS: 130 000 critical care admissions across both health systems. INTERVENTION: Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. MEASUREMENTS: Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. RESULTS: At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. LIMITATIONS: In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. CONCLUSION: In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. PRIMARY FUNDING SOURCE: National Center for Advancing Translational Sciences.

Plasma Renin Activity Increases With Cardiopulmonary Bypass and is Associated With Vasoplegia After Cardiac Surgery.

OBJECTIVES: To describe the trend in plasma renin activity over time in patients undergoing cardiac surgery on cardiopulmonary bypass, and to investigate if increased plasma renin activity is associated with postcardiopulmonary bypass vasoplegia. DESIGN: A prospective cohort study. SETTING: Patients were enrolled from June 2020 to May 2021 at a tertiary cardiac surgical institution. PATIENTS: A cohort of 100 adult patients undergoing cardiac surgery on cardiopulmonary bypass. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Plasma renin activity was measured at 5 time points: baseline, postoperatively, and at midnight on postoperative days 1, 2, and 3. Plasma renin activity and delta plasma renin activity were correlated with the incidence of vasoplegia and clinical outcomes. The median plasma renin activity increased approximately 3 times from baseline immediately after cardiac surgery, remained elevated on postoperative days 0, 1, and 2, and began to downtrend on postoperative day 3. Plasma renin activity was approximately 3 times higher at all measured time points in patients who developed vasoplegia versus those who did not. CONCLUSIONS: In patients undergoing cardiac surgery on cardiopulmonary bypass, plasma renin activity increased postoperatively and remained elevated through postoperative day 2. Additionally, patients with vasoplegic syndrome after cardiac surgery on cardiopulmonary bypass had more robust elevations in plasma renin activity than nonvasoplegic patients. These findings support the need for randomized controlled trials to determine if patients undergoing cardiac surgery with high plasma renin activity may benefit from targeted treatment with therapies such as synthetic angiotensin II.

Automated electrocardiogram signal quality assessment based on Fourier analysis and template matching.

We developed and tested a novel template matching approach for signal quality assessment on electrocardiogram (ECG) data. A computational method was developed that uses a sinusoidal approximation to the QRS complex to generate a correlation value at every point of an ECG. The strength of this correlation can be numerically adapted into a 'score' for each segment of an ECG, which can be used to stratify signal quality. The algorithm was tested on lead II ECGs of intensive care unit (ICU) patients admitted to the Mount Sinai Hospital (MSH) from January to July 2020 and on records from the MIT BIH arrhythmia database. The algorithm was found to be 98.9% specific and 99% sensitive on test data from the MSH ICU patients. The routine performs in linear O(n) time and occupies O(1) heap space in runtime. This approach can be used to lower the burden of pre-processing in ECG signal analysis. Given its runtime (O(n)) and memory (O(1)) complexity, there are potential applications for signal quality stratification and arrhythmia detection in wearable devices or smartphones.

A Web-Based Reporting System for Reviewing Local Practice Patterns of Anesthesiologists Derived from the Electronic Medical Record.

After completion of training, anesthesiologists may have fewer opportunities to see how colleagues practice, and their breadth of case experiences may also diminish due to specialization. We created a web-based reporting system based on data extracted from electronic anesthesia records that allows practitioners to see how other clinicians practice in similar cases. One year after implementation, the system continues to be utilized by clinicians.

Self-reported Race/Ethnicity and Intraoperative Occult Hypoxemia: A Retrospective Cohort Study.

BACKGROUND: Pulse oximetry is ubiquitous in anesthesia and is generally a reliable noninvasive measure of arterial oxygen saturation. Concerns regarding the impact of skin pigmentation and race/ethnicity on the accuracy of pulse oximeter accuracy exist. The authors hypothesized a greater prevalence of occult hypoxemia (arterial oxygen saturation [Sao2] less than 88% despite oxygen saturation measured by pulse oximetry [Spo2] greater than 92%) in patients undergoing anesthesia who self-reported a race/ethnicity other than White. METHODS: Demographic and physiologic data, including self-reported race/ethnicity, were extracted from a departmental data warehouse for patients receiving an anesthetic that included at least one arterial blood gas between January 2008 and December 2019. Calculated Sao2 values were paired with concurrent Spo2 values for each patient. Analysis to determine whether Black, Hispanic, Asian, or Other race/ethnicities were associated with occult hypoxemia relative to White race/ethnicity within the Spo2 range of 92 to 100% was completed. RESULTS: In total, 151,070 paired Sao2-Spo2 readings (70,722 White; 16,011 Black; 21,223 Hispanic; 8,121 Asian; 34,993 Other) from 46,253 unique patients were analyzed. The prevalence of occult hypoxemia was significantly higher in Black (339 of 16,011 [2.1%]) and Hispanic (383 of 21,223 [1.8%]) versus White (791 of 70,722 [1.1%]) paired Sao2-Spo2 readings (P < 0.001 for both). In the multivariable analysis, Black (odds ratio, 1.44 [95% CI, 1.11 to 1.87]; P = 0.006) and Hispanic (odds ratio, 1.31 [95% CI, 1.03 to 1.68]; P = 0.031) race/ethnicity were associated with occult hypoxemia. Asian and Other race/ethnicity were not associated with occult hypoxemia. CONCLUSIONS: Self-reported Black and Hispanic race/ethnicity are associated with a greater prevalence of intraoperative occult hypoxemia in the Spo2 range of 92 to 100% when compared with self-reported White race/ethnicity.

Obesity as a mortality risk factor in the medical ward: a case control study.

BACKGROUND: Research regarding the association between severe obesity and in-hospital mortality is inconsistent. We evaluated the impact of body mass index (BMI) levels on mortality in the medical wards. The analysis was performed separately before and during the COVID-19 pandemic. METHODS: We retrospectively retrieved data of adult patients admitted to the medical wards at the Mount Sinai Health System in New York City. The study was conducted between January 1, 2011, to March 23, 2021. Patients were divided into two sub-cohorts: pre-COVID-19 and during-COVID-19. Patients were then clustered into groups based on BMI ranges. A multivariate logistic regression analysis compared the mortality rate among the BMI groups, before and during the pandemic. RESULTS: Overall, 179,288 patients were admitted to the medical wards and had a recorded BMI measurement. 149,098 were admitted before the COVID-19 pandemic and 30,190 during the pandemic. Pre-pandemic, multivariate analysis showed a "J curve" between BMI and mortality. Severe obesity (BMI > 40) had an aOR of 0.8 (95% CI:0.7-1.0, p = 0.018) compared to the normal BMI group. In contrast, during the pandemic, the analysis showed a "U curve" between BMI and mortality. Severe obesity had an aOR of 1.7 (95% CI:1.3-2.4, p < 0.001) compared to the normal BMI group. CONCLUSIONS: Medical ward patients with severe obesity have a lower risk for mortality compared to patients with normal BMI. However, this does not apply during COVID-19, where obesity was a leading risk factor for mortality in the medical wards. It is important for the internal medicine physician to understand the intricacies of the association between obesity and medical ward mortality.

Perioperative risk and antiplatelet management in patients undergoing non-cardiac surgery within 1 year of PCI.

Perioperative cardiovascular complications are important causes of morbidity and mortality associated with non-cardiac surgery, especially in patients with recent percutaneous coronary intervention (PCI). We aimed to illustrate the types and timing of different surgeries occurring after PCI, and to evaluate the risk of thrombotic and bleeding events according to the perioperative antiplatelet management. Patients undergoing urgent or elective non-cardiac surgery within 1 year of PCI at a tertiary-care center between 2011 and 2018 were included. The primary outcome was major adverse cardiac events (MACE; composite of death, myocardial infarction, or stent thrombosis) at 30 days. Perioperative bleeding was defined as ≥ 2 units of blood transfusion. A total of 1092 surgeries corresponding to 747 patients were classified by surgical risk (low: 50.9%, intermediate: 38.4%, high: 10.7%) and priority (elective: 88.5%, urgent/emergent: 11.5%). High-risk and urgent/emergent surgeries tended to occur earlier post-PCI compared to low-risk and elective ones, and were associated with an increased risk of both MACE and bleeding. Preoperative interruption of antiplatelet therapy (of any kind) occurred in 44.6% of all NCS and was more likely for procedures occurring later post-PCI and at intermediate risk. There was no significant association between interruption of antiplatelet therapy and adverse cardiac events. Among patients undergoing NCS within 1 year of PCI, perioperative ischemic and bleeding events primarily depend on the estimated surgical risk and urgency of the procedure, which are increased early after PCI. Preoperative antiplatelet interruption was not associated with an increased risk of cardiac events.

AKI in Hospitalized Patients with COVID-19.

BACKGROUND: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described. METHODS: This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. RESULTS: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. CONCLUSIONS: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.

How businesses are working together to deliver NASA/JPL-designed ventilators to the world in the fight against COVID-19.

In response to the COVID-19 pandemic, NASA Jet Propulsion Laboratory (JPL) engineers had embarked on an ambitious project to design a reliable, easy-to-use, and low-cost ventilator that was made of readily available parts to address the unexpected global shortage of these lifesaving devices. After successfully designing and building the VITAL (Ventilator Intervention Technology Accessible Locally) ventilator in record time, FDA Emergency Use Authorization (EUA) was obtained and then the license to manufacture and sell these ventilators was made available to select companies through a competitive process. STARK Industries, LLC (STARK), located in Columbus, OH, USA, was one of only eight U.S. companies to be selected to receive this worldwide license. Motivated by its mission to improve human health and well-being through innovated medical technologies, STARK accepted the challenge of further developing the VITAL technology and manufacturing the ventilators in large quantities and making them available to those in need around the world. To this end, Spiritus Medical, Inc (Spiritus) was spun off from STARK to focus on the ventilator business. Through collaborative efforts with various corporate, academic, governmental, and non-profit partners, Spiritus was able to successfully begin manufacturing and selling its ventilators. Due to its low-cost nature and its straightforward design, this ventilator is ideal for use in developing countries where ventilators are in short supply and affordability is a major consideration. This is a story of how NASA's ingenuity, based on space-based know-how and experience, was used to rapidly design this innovative ventilator. And by forging partnerships with highly qualified and motivated partners such as STARK and Spiritus, NASA has succeeded in translating this work into technology that could potentially save thousands of lives in the fight against the COVID-19 pandemic.

Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry.

The emergence of genomic data in biobanks and health systems offers new ways to derive medically important phenotypes, including acute phenotypes occurring during inpatient clinical care. Here we study the genetic underpinnings of the rapid response to phenylephrine, an α1-adrenergic receptor agonist commonly used to treat hypotension during anesthesia and surgery. We quantified this response by extracting blood pressure (BP) measurements 5 min before and after the administration of phenylephrine. Based on this derived phenotype, we show that systematic differences exist between self-reported ancestry groups: European-Americans (EA; n = 1387) have a significantly higher systolic response to phenylephrine than African-Americans (AA; n = 1217) and Hispanic/Latinos (HA; n = 1713) (31.3% increase, p value < 6e-08 and 22.9% increase, p value < 5e-05 respectively), after adjusting for genetic ancestry, demographics, and relevant clinical covariates. We performed a genome-wide association study to investigate genetic factors underlying individual differences in this derived phenotype. We discovered genome-wide significant association signals in loci and genes previously associated with BP measured in ambulatory settings, and a general enrichment of association in these genes. Finally, we discovered two low frequency variants, present at ~1% in EAs and AAs, respectively, where patients carrying one copy of these variants show no phenylephrine response. This work demonstrates our ability to derive a quantitative phenotype suited for comparative statistics and genome-wide association studies from dense clinical and physiological measures captured for managing patients during surgery. We identify genetic variants underlying non response to phenylephrine, with implications for preemptive pharmacogenomic screening to improve safety during surgery.

MUST-Plus: A Machine Learning Classifier That Improves Malnutrition Screening in Acute Care Facilities.

OBJECTIVE: Malnutrition among hospital patients, a frequent, yet under-diagnosed problem is associated with adverse impact on patient outcome and health care costs. Development of highly accurate malnutrition screening tools is, therefore, essential for its timely detection, for providing nutritional care, and for addressing the concerns related to the suboptimal predictive value of the conventional screening tools, such as the Malnutrition Universal Screening Tool (MUST). We aimed to develop a machine learning (ML) based classifier (MUST-Plus) for more accurate prediction of malnutrition. METHOD: A retrospective cohort with inpatient data consisting of anthropometric, lab biochemistry, clinical data, and demographics from adult (≥ 18 years) admissions at a large tertiary health care system between January 2017 and July 2018 was used. The registered dietitian (RD) nutritional assessments were used as the gold standard outcome label. The cohort was randomly split (70:30) into training and test sets. A random forest model was trained using 10-fold cross-validation on training set, and its predictive performance on test set was compared to MUST. RESULTS: In all, 13.3% of admissions were associated with malnutrition in the test cohort. MUST-Plus provided 73.07% (95% confidence interval [CI]: 69.61%-76.33%) sensitivity, 76.89% (95% CI: 75.64%-78.11%) specificity, and 83.5% (95% CI: 82.0%-85.0%) area under the receiver operating curve (AUC). Compared to classic MUST, MUST-Plus demonstrated 30% higher sensitivity, 6% higher specificity, and 17% increased AUC. CONCLUSIONS: ML-based MUST-Plus provided superior performance in identifying malnutrition compared to the classic MUST. The tool can be used for improving the operational efficiency of RDs by timely referrals of high-risk patients.

Association between COVID-19 diagnosis and presenting chief complaint from New York City triage data.

BACKGROUND AND AIM: New York City (NYC) is an epicenter of the COVID-19 pandemic in the United States. Proper triage of patients with possible COVID-19 via chief complaint is critical but not fully optimized. This study aimed to investigate the association between presentation by chief complaints and COVID-19 status. METHODS: We retrospectively analyzed adult emergency department (ED) patient visits from five different NYC hospital campuses from March 1, 2020 to May 13, 2020 of patients who underwent nasopharyngeal COVID-19 RT-PCR testing. The positive and negative COVID-19 cohorts were then assessed for different chief complaints obtained from structured triage data. Sub-analysis was performed for patients older than 65 and within chief complaints with high mortality. RESULTS: Of 11,992 ED patient visits who received COVID-19 testing, 6524/11992 (54.4%) were COVID-19 positive. 73.5% of fever, 67.7% of shortness of breath, and 65% of cough had COVID-19, but others included 57.5% of weakness/fall/altered mental status, 55.5% of glycemic control, and 51.4% of gastrointestinal symptoms. In patients over 65, 76.7% of diarrhea, 73.7% of fatigue, and 69.3% of weakness had COVID-19. 45.5% of dehydration, 40.5% of altered mental status, 27% of fall, and 24.6% of hyperglycemia patients experienced mortality. CONCLUSION: A novel high risk COVID-19 patient population was identified from chief complaint data, which is different from current suggested CDC guidelines, and may help triage systems to better isolate COVID-19 patients. Older patients with COVID-19 infection presented with more atypical complaints warranting special consideration. COVID-19 was associated with higher mortality in a unique group of complaints also warranting special consideration.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

BACKGROUND: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. OBJECTIVE: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. METHODS: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. RESULTS: Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01). CONCLUSIONS: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Regional cerebral oximetry is consistent across self-reported racial groups and predicts 30-day mortality in cardiac surgery: a retrospective analysis.

Darker skin pigmentation appears to cause underestimation of regional oxygen saturation (rSO2) for certain cerebral oximetry devices. This presents a risk of triggering unindicated interventions and may limit its utility for predicting adverse outcomes. Our goal was to quantify the impact of self-reported race on oximetry measurements during cardiac surgery and elucidate whether race has a mediating role in the association of rSO2 with mortality. Data was extracted from our department's data warehouse for adult patients who underwent on-pump cardiac surgery between June 2014 and June 2018. Intraoperative rSO2 was recorded every 15 s throughout all cases. After grouping patients by self-reported race, multiple linear regression modeling was utilized to assess the association between race and mean pre-bypass rSO2 while controlling for various perioperative variables. The role of mean pre-bypass rSO2 for predicting 30-day mortality was evaluated via multiple logistic regression, and the threshold for rSO2 was selected by maximizing F1 score. There were 4267 patients included. Compared to Caucasian patients, the unadjusted difference in mean pre-bypass rSO2 was - 0.6% (95% CI - 1.3 to 0.04) for African American patients, - 1.8% (- 2.7 to - 0.9) for Asian patients, 0.1% (- 0.8 to 1.0) for Hispanic patients, - 1.6% (- 3.0 to - 0.4) for Indian/South Asian patients, and - 1.4% (- 3.7 to 0.9) for Pacific Islander patients. After adjusting for perioperative variables, differences in rSO2 readings less than 2% were observed between racial groups. Mean pre-bypass rSO2 under 63% was an independent predictor of higher 30-day mortality risk (OR: 2.86, CI 1.39 to 5.53, p = 0.003), and the interaction variable between rSO2 and race was not statistically significant (p = 0.299). Cerebral oximetry measurements are more consistent across racial groups than previously reported, supporting its utility for intraoperative monitoring and risk stratification. Pre-intervention rSO2 is associated with increased 30-day mortality at a higher threshold than previously reported and was not significantly impacted by self-reported race.

Monitoring of COVID-19-Associated Coagulopathy and Anticoagulation with Thromboelastometry.

INTRODUCTION: Thrombosis occurs frequently in COVID-19. While the exact mechanism is unclear, 3 processes seem to play important roles in sepsis-related thrombosis and mortality: tissue factor expression on circulating monocytes and microparticles, hypercoagulability (increased clot firmness), and hypofibrinolysis. Rotational thromboelastometry is a point-of-care viscoelastic technique that uses the viscoelastic properties of blood to monitor coagulation. Using various assays, viscoelastometry could monitor this triad of changes in severely ill, COVID-19-positive patients. Similarly, with the increased incidence of coagulopathy, many patients are placed on anticoagulants, making management more difficult depending on the agents utilized. Viscoelastometry might also be used in these settings to monitor anticoagulation status and guide therapy, as it has in other areas. CASE PRESENTATION: We present a case series of 6 patients with different stages of disease and different management plans. These cases occurred at the height of the pandemic in New York City, which limited testing abilities. We first discuss the idea of using the NaHEPTEM test as a marker of tissue factor expression in COVID-19. We then present cases where patients are on different anticoagulants and review how viscoelastometry might be used in a patient on anticoagulation with COVID-19. CONCLUSION: In a disease such as COVID-19, which has profound effects on hemostasis and coagulation, viscoelastometry may aid in patient triage, disease course monitoring, and anticoagulation management.

Managing COVID-19 from the epicenter: adaptations and suggestions based on experience.

In March 2020, the New York City metropolitan area became the epicenter of the United States' SARS-CoV-2 pandemic and the surge of new cases threatened to overwhelm the area's hospital systems. This article describes how an anesthesiology department at a large urban academic hospital rapidly adapted and deployed to meet the threat head-on. Topics included are preparatory efforts, development of a team-based staffing model, and a new strategy for resource management. While still maintaining a fully functioning operating theater, discrete teams were deployed to both COVID-19 and non-COVID-19 intensive care units, rapid response/airway management team, the difficult airway response team, and labor and delivery. Additional topics include the creation of a temporary 'pop-up' anesthesiology-run COVID-19 intensive care unit utilizing anesthesia machines for monitoring and ventilatory support as well as the development of a simulation and innovation team that was instrumental in the rapid prototyping of a controlled split-ventilation system and conversion of readily available BIPAP units into emergency ventilators. As the course of the disease is uncertain, the goal of this article is to assist others in preparation for what may come next with COVID-19 as well as potential future pandemics.

Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19.

BACKGROUND: Data on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care. OBJECTIVE: To describe clinical characteristics of patients with COVID-19 who returned to the emergency department (ED) or required readmission within 14 days of discharge. DESIGN: Retrospective cohort study of SARS-COV-2-positive patients with index hospitalization between February 27 and April 12, 2020, with ≥ 14-day follow-up. Significance was defined as P < 0.05 after multiplying P by 125 study-wide comparisons. PARTICIPANTS: Hospitalized patients with confirmed SARS-CoV-2 discharged alive from five New York City hospitals. MAIN MEASURES: Readmission or return to ED following discharge. RESULTS: Of 2864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared with patients who did not return, there were higher proportions of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%) among those who returned. Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs 6.7 [3.5, 11.5] days; Padjusted = 0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; Padjusted = 0.001). A trend towards association between absence of in-hospital treatment-dose anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, Padjusted = 0.06). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively. CONCLUSIONS: Return to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned more likely had COPD and hypertension, shorter LOS on index-hospitalization, and lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS, and anticoagulation are associated with reduced readmissions.

Differential Ventilation Using Flow Control Valves as a Potential Bridge to Full Ventilatory Support during the COVID-19 Crisis.

BACKGROUND: During the COVID-19 pandemic, ventilator sharing was suggested to increase availability of mechanical ventilation. The safety and feasibility of ventilator sharing is unknown. METHODS: A single ventilator in pressure control mode was used with flow control valves to simultaneously ventilate two patients with different lung compliances. The system was first evaluated using high-fidelity human patient simulator mannequins and then tested for 1 h in two pairs of COVID-19 patients with acute respiratory failure. Patients were matched on positive end-expiratory pressure, fractional inspired oxygen tension, and respiratory rate. Tidal volume and peak airway pressure (PMAX) were recorded from each patient using separate independent spirometers and arterial blood gas samples drawn at 0, 30, and 60 min. The authors assessed acid-base status, oxygenation, tidal volume, and PMAX for each patient. Stability was assessed by calculating the coefficient of variation. RESULTS: The valves performed as expected in simulation, providing a stable tidal volume of 400 ml each to two mannequins with compliance ratios varying from 20:20 to 20:90 ml/cm H2O. The system was then tested in two pairs of patients. Pair 1 was a 49-yr-old woman, ideal body weight 46 kg, and a 55-yr-old man, ideal body weight 64 kg, with lung compliance 27 ml/cm H2O versus 35 ml/cm H2O. The coefficient of variation for tidal volume was 0.2 to 1.7%, and for PMAX 0 to 1.1%. Pair 2 was a 32-yr-old man, ideal body weight 62 kg, and a 56-yr-old woman, ideal body weight 46 kg, with lung compliance 12 ml/cm H2O versus 21 ml/cm H2O. The coefficient of variation for tidal volume was 0.4 to 5.6%, and for PMAX 0 to 2.1%. CONCLUSIONS: Differential ventilation using a single ventilator is feasible. Flow control valves enable delivery of stable tidal volume and PMAX similar to those provided by individual ventilators.

Intraoperative Electronic Alerts Improve Compliance With National Quality Program Measure for Perioperative Temperature Management.

BACKGROUND: Reimbursement for anesthesia services has been shifting from a fee-for-service model to a value-based model that ties payment to quality metrics. The Centers for Medicare & Medicaid Service's (CMS) value-based payment program includes a quality measure for perioperative temperature management (Measure #424, Perioperative Temperature Management). Compliance may impose new challenges in clinical practice, data collection, and reporting. We investigated the impact of an electronic decision-support tool on adherence to this emerging standard. METHODS: In this retrospective observational study, perioperative temperature data were collected from cases eligible for reporting this measure to CMS from a single academic medical center before and after the implementation of an electronic decision-support tool that prompted temperature measurement and maintenance of normothermia. Proportions of measure compliance were assessed using segmented regression analysis. Proportions of intraoperative temperature measurement were also assessed, and multivariable logistic regression was performed to assess the association between patient and surgical factors and measure compliance. RESULTS: A total of 24,755 cases eligible for reporting in 2017 were assessed, and 25,274 cases from 2016 were included as an extended baseline. Segmented time-series regression did not show a significant baseline trend in measure compliance. Introduction of the alerts was associated with an increase in overall compliance from 84.4% (95% confidence interval [CI], 83.6%-85.2%) to 92.4% (91.4%-93.4%), and an increase in intraoperative compliance from 26.8% (25.8%-27.8%) to 71.0% (69.6%-72.4%). The association between the alerts and overall compliance was also present on multivariable analysis. CONCLUSIONS: Implementation of an intraoperative decision-support tool was associated with statistically significant improvement in the maintenance of normothermia in cases eligible for reporting to CMS. This led to improved compliance with Measure #424 and suggests that electronic alerts can help practices improve their performance and payment bonus eligibility.