Early changes in hospital resuscitation practices during the COVID-19 pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic resulted in many disruptions in care for patients experiencing in-hospital cardiac arrest (IHCA). We sought to identify changes made in hospital resuscitation practices during progression of the COVID-19 pandemic. Methods: We conducted a descriptive qualitative study using in-depth interviews of clinical staff leadership involved with resuscitation care at a select group of U.S. acute care hospitals in the national American Heart Association Get With The Guidelines-Resuscitation registry for IHCA. We focused interviews on resuscitation practice changes for IHCA since the initiation of the COVID-19 pandemic. We used rapid analysis techniques for qualitative data summarization and analysis. Results: A total of 6 hospitals were included with interviews conducted with both physicians and nurses between November 2020 and April 2021. Three topical themes related to shifts in resuscitation practice through the COVID-19 pandemic were identified: 1) ensuring patient and provider safety and wellness (e.g., use of personal protective equipment); 2) changing protocols and training for routine educational practices (e.g., alterations in mock codes and team member roles); and 3) goals of care and end of life discussions (e.g., challenges with visitor and family policies). We found advances in leveraging technology use as an important topic that helped institutions address challenges across all 3 themes. Conclusions: Early on, the COVID-19 pandemic resulted in many changes to resuscitation practices at hospitals placing an emphasis on enhanced safety, training, and end of life planning. These lessons have implications for understanding how systems may be better designed for resuscitation efforts.

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.

OBJECTIVE: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing. DESIGN: Retrospective cohort study. SETTING: One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21. PARTICIPANTS: 33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19. MAIN OUTCOME MEASURES: An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early. RESULTS: 9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge. CONCLUSION: A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

In-Hospital Cardiac Arrest Survival in the United States During and After the Initial Novel Coronavirus Disease 2019 Pandemic Surge.

BACKGROUND: Recent reports on challenges in resuscitation care at hospitals severely affected by the novel coronavirus disease 2019 (COVID-19) pandemic raise questions about how the pandemic affected outcomes for in-hospital cardiac arrest throughout the United States. METHODS: Within Get With The Guidelines-Resuscitation, we conducted a retrospective cohort study to compare in-hospital cardiac arrest survival during the presurge (January 1-February 29), surge (March 1-May 15) and immediate postsurge (May 16-June 30) periods in 2020 compared to 2015 to 2019. Monthly COVID-19 mortality rates for each hospital's county were categorized, per 1 000 000 residents, as low (0-10), moderate (11-50), high (51-100), or very high (>100). Using hierarchical regression models, we compared rates of survival to discharge in 2020 versus 2015 to 2019 for each period. RESULTS: Of 61 586 in-hospital cardiac arrests, 21 208 (4309 in 2020), 26 459 (5949 in 2020), and 13 919 (2686 in 2020) occurred in the presurge, surge, and postsurge periods, respectively. During the presurge period, 24.2% survived to discharge in 2020 versus 24.7% in 2015 to 2019 (adjusted odds ratio, 1.12 [95% CI, 1.02-1.22]). In contrast, during the surge period, 19.6% survived to discharge in 2020 versus 26.0% in 2015 to 2019 (adjusted odds ratio, 0.81 [0.75-0.88]). Lower survival was most pronounced in communities with high (28% lower survival) and very high (42% lower survival) monthly COVID-19 mortality rates (interaction P<0.001). Resuscitation times were shorter (median: 22 versus 25 minutes; P<0.001), and delayed epinephrine treatment was more prevalent (11.3% versus 9.9%; P=0.004) during the surge period. Survival was lower even when patients with confirmed/suspected COVID-19 infection were excluded from analyses. During the postsurge period, survival rates were similar in 2020 versus 2015 to 2019 (22.3% versus 25.8%; adjusted odds ratio, 0.93 [0.83-1.04]), including communities with high COVID-19 mortality (interaction P=0.16). CONCLUSIONS: Early during the pandemic, rates of survival to discharge for IHCA decreased, even among patients without COVID-19 infection, highlighting the early impact of the COVID-19 pandemic on in-hospital resuscitation.

Impact of the three COVID-19 surges in 2020 on in-hospital cardiac arrest survival in the United States.

BACKGROUND: Studies have reported lower survival for in-hospital cardiac arrest (IHCA) during the initial COVID-19 surge. Whether the pandemic reduced IHCA survival during subsequent surges and in areas with lower COVID-19 rates is unknown. METHODS: Within Get-With-The-Guidelines®-Resuscitation, we identified 22,899 and 79,736 IHCAs during March to December in 2020 and 2015-2019, respectively. Using hierarchical regression, we compared risk-adjusted rates of survival to discharge in 2020 vs. 2015-19 during five COVID-19 periods: Surge 1 (March to mid-May), post-Surge 1 (mid-May to June), Surge 2 (July to mid-August), post-Surge 2 (mid-August to mid-October), and Surge 3 (mid-October to December). Monthly COVID-19 mortality rates for each hospital's county were categorized, per 1,000,000 residents, as very low (0-10), low (11-50), moderate (51-100), or high (>100). RESULTS: During each COVID-19 surge period in 2020, rates of survival to discharge for IHCA were lower, as compared with the same period in 2015-2019: Surge 1: adjusted OR: 0.81 (0.75-0.88); Surge 2: adjusted OR: 0.88 (0.79-0.97), Surge 3: adjusted OR: 0.79 (0.73-0.86). Lower survival was most pronounced at hospitals located in counties with moderate to high monthly COVID-19 mortality rates. In contrast, during the two post-surge periods, survival rates were similar in 2020 vs. 2015-2019: post-Surge 1: adjusted OR 0.93 (0.83-1.04) and post-Surge 2: adjusted OR 0.94 (0.86-1.03), even at hospitals with the highest county-level COVID-19 mortality rates. CONCLUSIONS: During the three COVID-19 surges in the U.S. during 2020, rates of survival to discharge for IHCA dropped substantially, especially in communities with moderate to high COVID-19 mortality rates.

Association of COVID-19 Hospitalization Volume and Case Growth at US Hospitals with Patient Outcomes.

BACKGROUND: Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions. METHODS: We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR). RESULTS: There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03). CONCLUSIONS: An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.

Incidence, Predictors, and Outcomes of In-Hospital Cardiac Arrest in COVID-19 Patients Admitted to Intensive and Non-Intensive Care Units: Insights From the AHA COVID-19 CVD Registry.

Background Limited information is available regarding in-hospital cardiac arrest (IHCA) in patients with COVID-19. Methods and Results We leveraged the American Heart Association COVID-19 Cardiovascular Disease (AHA COVID-19 CVD) Registry to conduct a cohort study of adults hospitalized for COVID-19. IHCA was defined as those with documentation of cardiac arrest requiring medication or electrical shock for resuscitation. Mixed effects models with random intercepts were used to identify independent predictors of IHCA and mortality while accounting for clustering at the hospital level. The study cohort included 8518 patients (6080 not in the intensive care unit [ICU]) with mean age of 61.5 years (SD 17.5). IHCA occurred in 509 (5.9%) patients overall with 375 (73.7%) in the ICU and 134 (26.3%) patients not in the ICU. The majority of patients at the time of ICHA were not in a shockable rhythm (76.5%). Independent predictors of IHCA included older age, Hispanic ethnicity (odds ratio [OR], 1.9; CI, 1.4-2.4; P<0.001), and non-Hispanic Black race (OR, 1.5; CI, 1.1-1.9; P=0.004). Other predictors included oxygen use on admission, quick Sequential Organ Failure Assessment score on admission, and hypertension. Overall, 35 (6.9%) patients with IHCA survived to discharge, with 9.1% for ICU and 0.7% for non-ICU patients. Conclusions Older age, Black race, and Hispanic ethnicity are independent predictors of IHCA in patients with COVID-19. Although the incidence is much lower than in ICU patients, approximately one-quarter of IHCA events in patients with COVID-19 occur in non-ICU settings, with the latter having a substantially lower survival to discharge rate.

Rate of COVID-19 Infection in Patients With ST-Segment Elevation Myocardial Infarction.

BACKGROUND: The incidence of coronavirus disease 2019 (COVID-19) in patients with ST-segment elevation myocardial infarction (STEMI) has not been fully described. METHODS: All patients with STEMI undergoing primary percutaneous coronary intervention (PCI) in Ontario, Canada between March 1 and September 30, 2020 were included. Rates of positive COVID-19 tests from January 1, 2020 to the date of STEMI presentation were ascertained. For comparison, COVID-19 results were also evaluated in the adult Ontario population between January 1, 2020 and September 30, 2020, using provincial laboratory testing data. RESULTS: There were 3606 unique patients presenting with STEMI and receiving PCI in Ontario, Canada during the study period. Sixteen patients (0.44%) tested positive for COVID-19. The background infection rate among all 12,448,541 Ontario residents was similar, at 0.34%. CONCLUSIONS: The results of this population-based analysis suggest that proceeding with primary PCI with appropriate infection control practices is reasonable when community infection rates are low.

CONTEXTE: L'incidence de la maladie à coronavirus 2019 (COVID-19) chez les patients présentant un infarctus du myocarde avec élévation du segment ST (STEMI) n'a pas été entièrement décrite. MÉTHODOLOGIE: Tous les patients atteints de STEMI ayant subi une intervention coronarienne percutanée (ICP) primaire en Ontario (Canada) entre le 1er mars et le 30 septembre 2020 ont été inclus. Les taux de tests positifs à la COVID entre le 1er janvier 2020 et le moment de la présentation du STEMI ont été vérifiés. Aux fins de comparaison, les résultats des tests de dépistage de la COVID-19 ont également été évalués au sein de la population adulte de l'Ontario entre le 1er janvier 2020 et le 30 septembre 2020 au moyen des données des laboratoires provinciaux. RÉSULTATS: Pendant la période d'étude, 3 606 patients présentant un STEMI et ayant subi une ICP en Ontario (Canada) ont été recensés. Seize patients (0,44 %) ont reçu un résultat positif au test de dépistage de la COVID-19. Le taux d'infection parmi les 12 448 541 résidents de l'Ontario était similaire, soit 0,34 %. CONCLUSIONS: Les résultats de cette analyse populationnelle portent à penser qu'il est raisonnable de procéder à une ICP primaire avec des mesures appropriées de contrôle des infections lorsque les taux d'infection dans la collectivité sont faibles.

A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine.

BACKGROUND: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race. METHODS: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center. RESULTS: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. CONCLUSIONS: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.

Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19.

Rationale: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the coronavirus disease (COVID-19) pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. Objectives: To independently evaluate the EDI in hospitalized patients with COVID-19 overall and in disproportionately affected subgroups. Methods: We studied adult patients admitted with COVID-19 to units other than the intensive care unit at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of intensive care unit-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. Results: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. The median age of the cohort was 64 (interquartile range, 53-75) with 168 (43%) Black patients and 169 (43%) women. The area under the receiver-operating characteristic curve of the EDI was 0.79 (95% confidence interval, 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a sensitivity of 39% and a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. Conclusions: We found the EDI identifies small subsets of high-risk and low-risk patients with COVID-19 with good discrimination, although its clinical use as an early warning system is limited by low sensitivity. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among patients with COVID-19.

Outcomes for Out-of-Hospital Cardiac Arrest in the United States During the Coronavirus Disease 2019 Pandemic.

Importance: Recent reports from communities severely affected by the coronavirus disease 2019 (COVID-19) pandemic found lower rates of sustained return of spontaneous circulation (ROSC) for out-of-hospital cardiac arrest (OHCA). Whether the pandemic has affected OHCA outcomes more broadly is unknown. Objective: To assess the association between the COVID-19 pandemic and OHCA outcomes, including in areas with low and moderate COVID-19 disease burden. Design, Setting, and Participants: This study used a large US registry of OHCAs to compare outcomes during the pandemic period of March 16 through April 30, 2020, with those from March 16 through April 30, 2019. Cases were geocoded to US counties, and the COVID-19 mortality rate in each county was categorized as very low (0-25 per million residents), low (26-100 per million residents), moderate (101-250 per million residents), high (251-500 per million residents), or very high (>500 per million residents). As additional controls, the study compared OHCA outcomes during the prepandemic period (January through February) and peripandemic period (March 1 through 15). Exposure: The COVID-19 pandemic. Main Outcomes and Measures: Sustained ROSC (≥20 minutes), survival to discharge, and OHCA incidence. Results: A total of 19 303 OHCAs occurred from March 16 through April 30 in both years, with 9863 cases in 2020 (mean [SD] age, 62.6 [19.3] years; 6040 men [61.3%]) and 9440 in 2019 (mean [SD] age, 62.2 [19.2] years; 5922 men [62.7%]). During the pandemic, rates of sustained ROSC were lower than in 2019 (23.0% vs 29.8%; adjusted rate ratio, 0.82 [95% CI, 0.78-0.87]; P < .001). Sustained ROSC rates were lower by between 21% (286 of 1429 [20.0%] in 2020 vs 305 of 1130 [27.0%] in 2019; adjusted RR, 0.79 [95% CI, 0.65-0.97]) and 33% (149 of 863 [17.3%] in 2020 vs 192 of 667 [28.8%] in 2019; adjusted RR, 0.67 [95% CI, 0.56-0.80]) in communities with high or very high COVID-19 mortality, respectively; however, rates of sustained ROSC were also lower by 11% (583 of 2317 [25.2%] in 2020 vs 740 of 2549 [29.0%] in 2019; adjusted RR, 0.89 [95% CI, 0.81-0.98]) to 15% (889 of 3495 [25.4%] in 2020 vs 1109 of 3532 [31.4%] in 2019; adjusted RR, 0.85 [95% CI, 0.78-0.93]) in communities with very low and low COVID-19 mortality. Among emergency medical services agencies with complete data on hospital survival (7085 total patients), survival to discharge was lower during the pandemic compared with 2019 (6.6% vs 9.8%; adjusted RR, 0.83 [95% CI, 0.69-1.00]; P = .048), primarily in communities with moderate to very high COVID-19 mortality (interaction P = .049). Incidence of OHCA was higher than in 2019, but the increase was largely observed in communities with high COVID-19 mortality (adjusted mean difference, 38.6 [95% CI, 37.1-40.1] per million residents) and very high COVID-19 mortality (adjusted mean difference, 28.7 [95% CI, 26.7-30.6] per million residents). In contrast, there was no difference in rates of sustained ROSC or survival to discharge during the prepandemic and peripandemic periods in 2020 vs 2019. Conclusions and Relevance: Early during the pandemic, rates of sustained ROSC for OHCA were lower throughout the US, even in communities with low COVID-19 mortality rates. Overall survival was lower, primarily in communities with moderate or high COVID-19 mortality.

Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System.

Importance: Black patients are overrepresented in the number of COVID-19 infections, hospitalizations, and deaths in the US. Reasons for this disparity may be due to underlying comorbidities or sociodemographic factors that require further exploration. Objective: To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes. Design, Setting, and Participants: This retrospective cohort study used comparative groups of patients tested or treated for COVID-19 at the University of Michigan from March 10, 2020, to April 22, 2020, with an outcome update through July 28, 2020. A group of randomly selected untested individuals were included for comparison. Examined factors included race/ethnicity, age, smoking, alcohol consumption, comorbidities, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), and residential-level socioeconomic characteristics. Exposure: In-house polymerase chain reaction (PCR) tests, commercial antibody tests, nasopharynx or oropharynx PCR deployed by the Michigan Department of Health and Human Services and reverse transcription-PCR tests performed in external labs. Main Outcomes and Measures: The main outcomes were being tested for COVID-19, having test results positive for COVID-19 or being diagnosed with COVID-19, being hospitalized for COVID-19, requiring intensive care unit (ICU) admission for COVID-19, and COVID-19-related mortality (including inpatient and outpatient). Medical comorbidities were defined from the International Classification of Diseases, Ninth Revision, and International Classification of Diseases, Tenth Revision, codes and were aggregated into a comorbidity score. Associations with COVID-19 outcomes were examined using odds ratios (ORs). Results: Of 5698 patients tested for COVID-19 (mean [SD] age, 47.4 [20.9] years; 2167 [38.0%] men; mean [SD] BMI, 30.0 [8.0]), most were non-Hispanic White (3740 patients [65.6%]) or non-Hispanic Black (1058 patients [18.6%]). The comparison group included 7168 individuals who were not tested (mean [SD] age, 43.1 [24.1] years; 3257 [45.4%] men; mean [SD] BMI, 28.5 [7.1]). Among 1139 patients diagnosed with COVID-19, 492 (43.2%) were White and 442 (38.8%) were Black; 523 (45.9%) were hospitalized, 283 (24.7%) were admitted to the ICU, and 88 (7.7%) died. Adjusting for age, sex, socioeconomic status, and comorbidity score, Black patients were more likely to be hospitalized compared with White patients (OR, 1.72 [95% CI, 1.15-2.58]; P = .009). In addition to older age, male sex, and obesity, living in densely populated areas was associated with increased risk of hospitalization (OR, 1.10 [95% CI, 1.01-1.19]; P = .02). In the overall population, higher risk of hospitalization was also observed in patients with preexisting type 2 diabetes (OR, 1.82 [95% CI, 1.25-2.64]; P = .02) and kidney disease (OR, 2.87 [95% CI, 1.87-4.42]; P < .001). Compared with White patients, obesity was associated with higher risk of having test results positive for COVID-19 among Black patients (White: OR, 1.37 [95% CI, 1.01-1.84]; P = .04. Black: OR, 3.11 [95% CI, 1.64-5.90]; P < .001; P for interaction = .02). Having any cancer was associated with higher risk of positive COVID-19 test results for Black patients (OR, 1.82 [95% CI, 1.19-2.78]; P = .005) but not White patients (OR, 1.08 [95% CI, 0.84-1.40]; P = .53; P for interaction = .04). Overall comorbidity burden was associated with higher risk of hospitalization in White patients (OR, 1.30 [95% CI, 1.11-1.53]; P = .001) but not in Black patients (OR, 0.99 [95% CI, 0.83-1.17]; P = .88; P for interaction = .02), as was type 2 diabetes (White: OR, 2.59 [95% CI, 1.49-4.48]; P < .001; Black: OR, 1.17 [95% CI, 0.66-2.06]; P = .59; P for interaction = .046). No statistically significant racial differences were found in ICU admission and mortality based on adjusted analysis. Conclusions and Relevance: These findings suggest that preexisting type 2 diabetes or kidney diseases and living in high-population density areas were associated with higher risk for COVID-19 hospitalization. Associations of risk factors with COVID-19 outcomes differed by race.

In-hospital cardiac arrest in critically ill patients with covid-19: multicenter cohort study.

OBJECTIVES: To estimate the incidence, risk factors, and outcomes associated with in-hospital cardiac arrest and cardiopulmonary resuscitation in critically ill adults with coronavirus disease 2019 (covid-19). DESIGN: Multicenter cohort study. SETTING: Intensive care units at 68 geographically diverse hospitals across the United States. PARTICIPANTS: Critically ill adults (age ≥18 years) with laboratory confirmed covid-19. MAIN OUTCOME MEASURES: In-hospital cardiac arrest within 14 days of admission to an intensive care unit and in-hospital mortality. RESULTS: Among 5019 critically ill patients with covid-19, 14.0% (701/5019) had in-hospital cardiac arrest, 57.1% (400/701) of whom received cardiopulmonary resuscitation. Patients who had in-hospital cardiac arrest were older (mean age 63 (standard deviation 14) v 60 (15) years), had more comorbidities, and were more likely to be admitted to a hospital with a smaller number of intensive care unit beds compared with those who did not have in-hospital cardiac arrest. Patients who received cardiopulmonary resuscitation were younger than those who did not (mean age 61 (standard deviation 14) v 67 (14) years). The most common rhythms at the time of cardiopulmonary resuscitation were pulseless electrical activity (49.8%, 199/400) and asystole (23.8%, 95/400). 48 of the 400 patients (12.0%) who received cardiopulmonary resuscitation survived to hospital discharge, and only 7.0% (28/400) survived to hospital discharge with normal or mildly impaired neurological status. Survival to hospital discharge differed by age, with 21.2% (11/52) of patients younger than 45 years surviving compared with 2.9% (1/34) of those aged 80 or older. CONCLUSIONS: Cardiac arrest is common in critically ill patients with covid-19 and is associated with poor survival, particularly among older patients.

COVID-19 outcomes, risk factors and associations by race: a comprehensive analysis using electronic health records data in Michigan Medicine.

IMPORTANCE: Blacks/African-Americans are overrepresented in the number of COVID-19 infections, hospitalizations and deaths. Reasons for this disparity have not been well-characterized but may be due to underlying comorbidities or sociodemographic factors. OBJECTIVE: To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes. DESIGN: A retrospective cohort study with comparative control groups. SETTING: Patients tested for COVID-19 at University of Michigan Medicine from March 10, 2020 to April 22, 2020. PARTICIPANTS: 5,698 tested patients and two sets of comparison groups who were not tested for COVID-19: randomly selected unmatched controls (n = 7,211) and frequency-matched controls by race, age, and sex (n = 13,351). Main Outcomes and Measures: We identified factors associated with testing and testing positive for COVID-19, being hospitalized, requiring intensive care unit (ICU) admission, and mortality (in/out-patient during the time frame). Factors included race/ethnicity, age, smoking, alcohol consumption, healthcare utilization, and residential-level socioeconomic characteristics (SES; i.e., education, unemployment, population density, and poverty rate). Medical comorbidities were defined from the International Classification of Diseases (ICD) codes, and were aggregated into a comorbidity score. RESULTS: Of 5,698 patients, (median age, 47 years; 38% male; mean BMI, 30.1), the majority were non-Hispanic Whites (NHW, 59.2%) and non-Hispanic Black/African-Americans (NHAA, 17.2%). Among 1,119 diagnosed, there were 41.2% NHW and 37.4% NHAA; 44.8% hospitalized, 20.6% admitted to ICU, and 3.8% died. Adjusting for age, sex, and SES, NHAA were 1.66 times more likely to be hospitalized (95% CI, 1.09-2.52; P=.02), 1.52 times more likely to enter ICU (95% CI, 0.92-2.52; P=.10). In addition to older age, male sex and obesity, high population density neighborhood (OR, 1.27 associated with one SD change [95% CI, 1.20-1.76]; P=.02) was associated with hospitalization. Pre-existing kidney disease led to 2.55 times higher risk of hospitalization (95% CI, 1.62-4.02; P<.001) in the overall population and 11.9 times higher mortality risk in NHAA (95% CI, 2.2-64.7, P=.004). CONCLUSIONS AND RELEVANCE: Pre-existing type II diabetes/kidney diseases and living in high population density areas were associated with high risk for COVID-19 susceptibility and poor prognosis. Association of risk factors with COVID-19 outcomes differed by race. NHAA patients were disproportionately affected by obesity and kidney disease.

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine.

BACKGROUND: We perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race. METHODS: The study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 2020 at a large academic medical center. RESULTS: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with ICU admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. CONCLUSIONS: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.