The impacts of physical activity on psychological and behavioral problems, and changes in physical activity, sleep and quality of life during the COVID-19 pandemic in preschoolers, children, and adolescents: A systematic review and meta-analysis.

Background: The COVID-19 pandemic has greatly affected the level of physical activity (PA). However, little is known about its effect on health outcomes. Methods: Articles without language restrictions published from the database inception through March 16, 2022, were retrieved using the CINAHL Complete, Cochrane Library, EMBASE, Medline, PubMed, and PsycINFO databases. High-quality articles assessing the effect of PA on psychological and behavioral problems. Additionally, PA, QoL, and/or sleep problems before and during the pandemic were included. Articles without data regarding PA or involving non-general populations were excluded. The PRISMA and MOOSE guidelines were followed. Data quality of the selected articles was assessed using the Newcastle-Ottawa Scale and GRADE approach. Data were pooled using a random-effects model and sensitivity analysis if heterogenicity was high (I 2 ≥ 50%). The relationship between PA and psychological and behavioral problems; and changes in PA, QoL, and sleeping patterns before and during the pandemic in preschoolers, children, and adolescents were investigated. A meta-analysis was conducted; odds ratios (ORs), mean differences (MD), and standardized MDs (SMDs) were calculated. Results: Thirty-four articles involving 66,857 participants were included. The results showed an overall significant protective effect between PA and psychological and/or behavioral problems (OR = 0.677; 95% CI = 0.630, 0.728; p-value <0.001; I 2 = 59.79%). This relationship was also significant in the subgroup analysis of children (OR = 0.690; 95% CI = 0.632, 0.752; p-value <0.001; I 2 = 58.93%) and adolescents (OR = 0.650; 95% CI = 0.570, 0.741; p-value <0.001; I 2 = 60.85%); however, no data on the relationship in preschoolers were collected. In addition, the overall time spent on PA significantly decreased by 23.2 min per day during the COVID-19 pandemic (95% CI = -13.5, -32.9; p-value <0.001; I 2 = 99.82%). Moreover, the results showed an overall significant decrease in QoL (SMD = -0.894, 95% CI = -1.180, -0.609, p-value <0.001, I 2 = 96.64%). However, there was no significant difference in sleep duration during the COVID-19 pandemic (MD = 0.01 h per day, 95% CI = -0.027, 0.225; p-value = 0.125; I 2 = 98.48%). Conclusion: During the pandemic, less PA was contributed to poor QoL and sleep quality. However, increases in PA are associated with reduced occurrences of psychological and behavioral problems. Implementing recovery plans to address the health effect of the pandemic is essential.

Nasal anti-CD3 monoclonal antibody (Foralumab) reduces PET microglial activation and blood inflammatory biomarkers in two patients with non-active secondary progressive MS

Introduction: There are no effective treatments for non-active secondary progressive MS (SPMS), which is mediated by compartmentalized CNS inflammation, including activated microglia. We found that fully human anti-CD3 intranasal monoclonal antibody (Foralumab) suppressed disease in a chronic EAE model by dampening microglia and astrocyte inflammation. Nasal Foralumab does not enter the bloodstream or brain. A dose-finding study of nasal Foralumab in controls dosed at 10ug, 50ug and 250ug for 5 days found the drug to be safe with immune effects seen at 50ug. COVID patients dosed with 100ug of nasal Foralumab for 10 days was well-tolerated and exhibited positive effects on blood markers and lung inflammation. Objective(s): To determine if nasal Foralumab has a therapeutic effect on patients with non-active SPMS. Method(s): Two patients were identified with non-active SPMS and sustained clinical progression, despite use of approved DMT. EA1 is a 61-year-old male diagnosed for over 20 years and EA2 is a 42-year-old male diagnosed for 8 years, both last treated with ocrelizumab for 3 years. Treatment occurs in 3-week cycles with intranasal Foralumab 50ug/day administered 3x/week for 2 weeks with 1 week rest. Each cycle, clinical and neurological assessments are repeated, and imaging is repeated every 3 months. Result(s): EA1 has completed 6 months and EA2 has completed 3 months of treatment. To date, there have been no adverse reactions, local irritation, or laboratory abnormalities, and symptom progression has subsided. EA1 is feeling more stable, subjectively, and has noted improvement in lower extremity strength. EDSS, pyramidal motor score and T25FW have stabilized or improved. SDMT and 9HPT were stable during treatment. Microglial activation as measured by [F-18]PBR06 PET scan was significantly reduced 3 months after the start of nasal Foralumab, and this reduction was sustained after 7-week washout and at 6 months. Serum protein measurements of cytokines showed reduction of IFN-gamma, IL-18, IL-1s and IL-6 levels (Olink assay). Cellular immune studies showed increase in CD8 naive cells and decrease in CD8 effector cells, and alteration in gene expression as measured by single cell RNA sequencing. EA2 3-month laboratory and imaging results are pending and will be presented. Conclusion(s): Nasal Foralumab in non-active SPMS patients treated for at least 3 months reduced microglial activation, decreased levels of proinflammatory cytokines, and had positive clinical effects.

Safety and Activity of Anti-CD14 Antibody IC14 (Atibuclimab) in ALS: Experience with an Expanded Access Protocol

Introduction: IC14 (atibuclimab) is a monoclonal anti-CD14 antibody that may target T-regulatory (T-reg) cell function. A previous phase 1 trial of 10 participants with amyotrophic lateral sclerosis (ALS) demonstrated initial safety of IC14 for a single cycle of treatment. We provided longterm treatment with IC14 to 17 individuals with ALS via an expanded access protocol (EAP) and documented target engagement, safety, and disease endpoints. Method(s): Participants received intravenous IC14 every two weeks. Consistent with FDA guidelines, participants were ineligible for clinical trials and the EAP was inclusive of a broad population. Participants unable to travel to MGH due to the COVID-19 pandemic or disease progression, were transitioned to infusions in-home or local clinics. Blood samples for hematology, chemistry, and coagulation were collected to monitor safety. The Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) was administered monthly to track disease progression. Respiratory function was measured through slow vital capacity tests -data for this is limited due to the COVID-19 pandemic. Whole blood and serum were collected to determine monocyte CD14 receptor occupancy (RO), soluble CD14, and antidrug antibodies (ADA). Ex vivo T regulatory functional assays were performed with five participants. Result(s): Participants received IC14 for up to 103 weeks (average: 30.1 weeks, range: 1-103 weeks). Treatmentemergent adverse events were uncommon, mild, and self-limited. There were 18 serious adverse events (SAEs) which were related to disease progression and unrelated (17) or likely unrelated (1) to IC14. Three participants died due to disease progression. Most participants achieved >80% monocyte mCD14 RO on a 14-day dosing schedule, although one individual required more frequent dosing (every 10 days) to achieve >80% RO. ADA were detected in only one participant and were transient, low titer, and non-neutralizing. Tregs were isolated from the available longitudinal samples and assayed for suppression of CD4 T cell proliferation and cytokine production versus baseline T-reg activity. Conclusion(s): IC14 administration to ALS patients was safe and well tolerated in this EAP, with no significant changes in laboratory tests and no drug-related SAEs. Measuring RO guided dosing frequency. Preliminary data suggest IC14 enhanced T-reg activity. Additional placebo-controlled trials are required to determine the efficacy of IC14 in ALS.

Risk Factors for Infection and Health Impacts of the Coronavirus Disease 2019 (COVID-19) Pandemic in People With Autoimmune Diseases.

BACKGROUND: People with autoimmune or inflammatory conditions taking immunomodulatory/suppressive medications may have higher risk of novel coronavirus disease 2019 (COVID-19). Chronic disease care has also changed for many patients, with uncertain downstream consequences. METHODS: We included participants with autoimmune or inflammatory conditions followed by specialists at Johns Hopkins. Participants completed periodic surveys querying comorbidities, disease-modifying medications, exposures, COVID-19 testing and outcomes, social behaviors, and disruptions to healthcare. We assessed whether COVID-19 risk is higher among those on immunomodulating or suppressive agents and characterized pandemic-associated changes to care and mental health. RESULTS: In total, 265 (5.6%) developed COVID-19 over 9 months of follow-up (April-December 2020). Patient characteristics (age, race, comorbidity, medications) were associated with differences in social distancing behaviors during the pandemic. Glucocorticoid exposure was associated with higher odds of COVID-19 in models incorporating behavior and other potential confounders (odds ratio [OR]: 1.43; 95% confidence interval [CI]: 1.08, 1.89). Other medication classes were not associated with COVID-19 risk. Diabetes (OR: 1.72; 95% CI: 1.08, 2.73), cardiovascular disease (OR: 1.68; 95% CI: 1.24, 2.28), and kidney disease (OR: 1.76; 95% CI: 1.04, 2.97) were associated with higher odds of COVID-19. Of the 2156 reporting pre-pandemic utilization of infusion, mental health or rehabilitative services, 975 (45.2%) reported disruptions therein, which disproportionately affected individuals experiencing changes to employment or income. CONCLUSIONS: Glucocorticoid exposure may increase risk of COVID-19 in people with autoimmune or inflammatory conditions. Disruption to healthcare and related services was common. Those with pandemic-related reduced income may be most vulnerable to care disruptions.

Impact of COVID-19 on angioplasty service and outcome of patients treated for critical limb ischaemia

Background: Peripheral artery disease (PAD) is a major challenge worldwide and endovascular revascularization is an important component of treatment that is affected by COVID-19 restrictions. Purpose: Here, we evaluated the impact of COVID-19 restriction on angioplasty service and outcome of patients undergoing lower limb angioplasty. Methods: Consecutive patients undergoing endovascular revascularisation between August 2018-March 2021 in a UK district general hospital were analysed retrospectively. Indications for angioplasty of all patients were discussed and agreed upon in multi-disciplinary teams. We compared time from referral to angioplasty, patient and procedural characteristics, technical success, peri-procedural complications, and outcome (wound healing, major amputation, target lesion revascularization, death) in patients treated 'before' and after February 2020 (during COVID-19). Results: One hundred nineteen patients were treated 'before' (92% critical limb ischaemia [CLI];60% diabetes mellitus) and 72 were treated 'during COVID-19' (96% CLI;61% diabetes mellitus). While the total monthly number of patients treated did not change, the number of outpatients treated as day cases increased (40% to 72%) and overnight stays for social reasons decreased (16% to 10%). Treatment of hospitalized patients decreased from 44% to 18%. The percentage of outpatients treated at <14 days after referral increased from 39% to 63% and hospitalized patients treated <5 days from 47% to 54%. Neither COVID-19 nor time to procedure affected wound healing (p(log Rank) = 0.451;median time to healing 168±l25 days) and amputation free survival (p(log Rank) = 0.924;median survival 368±l30 days) in all CLI patients significantly. However, amputation-free survival was significantly worse in hospitalized as compared to outpatients (p(log Rank) <0.001;median survival 155±l20 vs 368±l30 days) with similar wound healing in those that survived (p(log Rank) = 0.340;median time to wound healing 168±l25 days). Of note, the known causes of death were sepsis (32%), pneumonia (18%), COVID pneumonia (18%), cardiac (16%) and stroke (8%). Conclusions: Adapting to COVID-19 restriction we maintained a safe and effective angioplasty service while shortening waiting times. Very high mortality rates in patients after hospitalization indicated that CLI need to be treated much earlier and more aggressively to avoid disease progression requiring hospitalization.

Neighborhood-level disparities and subway utilization during the COVID-19 pandemic in New York City.

The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. We investigate the association between neighborhood social disadvantage and the ability to socially distance, infections, and mortality in Spring 2020. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with weighted social factors associated with infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood disadvantage is also associated with a proxy of the capacity to socially isolate, NYC subway usage data. Finally, our index is associated with COVID-19-related mortality.

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.

Risk Factors for Infection and Health Impacts of the COVID-19 Pandemic in People with Autoimmune Diseases (preprint)

Background: People with autoimmune or inflammatory conditions who take immunomodulatory/suppressive medications may have a higher risk of novel coronavirus disease 2019 (COVID-19). Chronic disease care has also changed for many patients, with uncertain downstream consequences. Objective: Assess whether COVID-19 risk is higher among those on immunomodulating or suppressive agents and characterize pandemic-associated changes to care. Design: Longitudinal registry study Participants: 4666 individuals with autoimmune or inflammatory conditions followed by specialists in neurology, rheumatology, cardiology, pulmonology or gastroenterology at Johns Hopkins Measurements: Periodic surveys querying comorbidities, disease-modifying medications, exposures, COVID-19 testing and outcomes, social behaviors, and disruptions to healthcare Results: A total of 265 (5.6%) developed COVID-19 over 9 months of follow-up (April-December 2020). Patient characteristics (age, race, comorbidity, medication exposure) were associated with differences in social distancing behaviors during the pandemic. Glucocorticoid exposure was associated with higher odds of COVID-19 in multivariable models incorporating behavior and other potential confounders (OR: 1.43; 95%CI: 1.08, 1.89). Other medication classes were not associated with COVID-19 risk. Diabetes (OR: 1.72; 95%CI: 1.08, 2.73), cardiovascular disease (OR: 1.68; 95%CI: 1.24, 2.28), and chronic kidney disease (OR: 1.76; 95%CI: 1.04, 2.97) were each associated with higher odds of COVID-19. Pandemic-related disruption to care was common. Of the 2156 reporting pre-pandemic utilization of infusion, mental health or rehabilitative services, 975 (45.2%) reported disruptions. Individuals experiencing changes to employment or income were at highest odds of care disruption. Limitations: Results may not be generalizable to all patients with autoimmune or inflammatory conditions. Information was self-reported. Conclusions: Exposure to glucocorticoids may increase risk of COVID-19 in people with autoimmune or inflammatory conditions. Disruption to healthcare and related services was common. Those with pandemic-related reduced income may be most vulnerable to care disruptions.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

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.

Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic (preprint)

The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. Here, we investigate the role of neighborhood social disadvantage on the ability to socially distance, infections, and mortality. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with relative weights for social factors facilitating infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood infection risk is also associated with capacity to socially isolate, as measured by NYC subway data. Finally, infection risk is associated with COVID-19-related mortality. These analyses support that differences in capacity to socially isolate is a credible pathway between disadvantage and COVID-19 disparities.

Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization For COVID-19 (preprint)

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. Methods and Findings: Retrospective cohort study of patients with confirmed SARS-CoV-2 discharged alive from five hospitals in New York City with index hospitalization between February 27th-April 12th, 2020, with follow-up of [≥]14 days. Significance was defined as P<0.05 after multiplying P by 125 study-wide comparisons. Of 2,864 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 to patients who did not return, among those who returned there was a higher proportion of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%). 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 anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, Padjusted=0.064). 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 had higher proportion of COPD and hypertension with shorter LOS on index hospitalization, and a trend towards 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.

Acute Kidney Injury in Hospitalized Patients with COVID-19 (preprint)

Importance: Preliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described. Objective: To provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients. Design: Observational, retrospective study. Setting: Admitted to hospital between February 27 and April 15, 2020. Participants: Patients aged [≥]18 years with laboratory confirmed COVID-19 Exposures: AKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and Measures: Frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation. Results: A total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test. Conclusions and Relevance: AKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.

Analysis of hospitalized COVID-19 patients in the Mount Sinai Health System using electronic medical records (EMR) reveals important prognostic factors for improved clinical outcomes (preprint)

COVID-19 is a novel threat to human health worldwide. There is an urgent need to understand patient characteristics of having COVID-19 disease and evaluate markers of critical illness and mortality. Objective: To assess association of clinical features on patient outcomes. Design, Setting, and Participants: In this observational case series, patient-level data were extracted from electronic medical records for 28,336 patients tested for SARS-CoV-2 at the Mount Sinai Health System from 2/24/ to 4/15/2020, including 6,158 laboratory-confirmed cases. Exposures: Confirmed COVID-19 diagnosis by RT-PCR assay from nasal swabs. Main Outcomes and Measures: Effects of race on positive test rates and mortality were assessed. Among positive cases admitted to the hospital (N = 3,273), effects of patient demographics, hospital site and unit, social behavior, vital signs, lab results, and disease comorbidities on discharge and death were estimated. Results: Hispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to population base rates (p<2e-16); however, no differences in mortality rates were observed in the hospital. Outcome differed significantly between hospitals (Gray's T=248.9; p<2e-16), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR=1.05 [95% CI, 1.04-1.06]; p=1.15e-32), oxygen saturation (HR=0.985 [95% CI, 0.982-0.988]; p=1.57e-17), care in ICU areas (HR=1.58 [95% CI, 1.29-1.92]; p=7.81e-6), and elevated creatinine (HR=1.75 [95% CI, 1.47-2.10]; p=7.48e-10), alanine aminotransferase (ALT) (HR=1.002, [95% CI 1.001-1.003]; p=8.86e-5) and body-mass index (BMI) (HR=1.02, [95% CI 1.00-1.03]; p=1.09e-2). Asthma (HR=0.78 [95% CI, 0.62-0.98]; p=0.031) was significantly associated with increased length of hospital stay, but not mortality. Deceased patients were more likely to have elevated markers of inflammation. Baseline age, BMI, oxygen saturation, respiratory rate, white blood cell (WBC) count, creatinine, and ALT were significant prognostic indicators of mortality. Conclusions and Relevance: While race was associated with higher risk of infection, we did not find a racial disparity in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. We identified clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk and evaluate the impact on survival.

Clinical Characteristics of Hospitalized Covid-19 Patients in New York City (preprint)

ABSTRACT Background: The coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States. Methods Demographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive. Results A total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2nd, 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL. Conclusions This is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.