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The following cell cycle arrest urinary biomarkers, tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP-7), have been used for early detection of acute kidney injury (AKI) in critically ill patients. The purpose of this study is to validate the use of these urinary biomarkers in patients undergoing open heart surgery.In a single-center prospective observational study, urine samples were collected in 108 consecutive patients who underwent open heart surgery immediately after separation from cardiopulmonary bypass and on postoperative day 1, and were sent for the biomarker [TIMP-2]*[IGFBP7] analysis. Acute kidney injury was defined based on KDIGO criteria, and levels of [TIMP-2]*[IGFBP7] were analyzed for the ability to predict AKI.Of the 108 patients, 19 (17.6%) patients developed postoperative AKI within 48 hours of surgery. At the threshold of0.3 (ng/mL)Urinary [TIMP-2]*[IGFBP7] was not predictive of the risk of AKI after cardiac surgery in this single-center study population.

Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.

There is a need to discriminate which COVID-19 inpatients are at higher risk for venous thromboembolism (VTE) to inform prophylaxis strategies. The IMPROVE-DD VTE risk assessment model (RAM) has previously demonstrated good discrimination in non-COVID populations. We aimed to externally validate the IMPROVE-DD VTE RAM in medical patients hospitalized with COVID-19. This retrospective cohort study evaluated the IMPROVE-DD VTE RAM in adult patients with COVID-19 admitted to one of thirteen Northwell Health hospitals in the New York metropolitan area between March 1, 2020 and April 27, 2020. VTE was defined as new-onset symptomatic deep venous thrombosis or pulmonary embolism. To assess the predictive value of the RAM, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Of 9407 patients who met study criteria, 274 patients developed VTE with a prevalence of 2.91%. The VTE rate was 0.41% for IMPROVE-DD score 0–1 (low risk), 1.21% for score 2–3 (moderate risk), and 5.30% for score ≥ 4 (high risk). Approximately 45.7% of patients were classified as high VTE risk, 33.3% moderate risk, and 21.0% low risk. Discrimination of low versus moderate-high VTE risk demonstrated sensitivity 0.971, specificity 0.215, PPV 0.036, and NPV 0.996. ROC AUC was 0.703. In this external validation study, the IMPROVE-DD VTE RAM demonstrated very good discrimination to identify hospitalized COVID-19 patients at low, moderate, and high VTE risk.

Venous thromboembolism (VTE) has emerged as an important issue in patients with COVID-19. The purpose of this study is to identify the incidence of VTE and mortality in COVID-19 patients initially presenting to a large health system. Our retrospective study included adult patients (excluding patients presenting with obstetric/gynecologic conditions) across a multihospital health system in the New York Metropolitan Region from March 1-April 27, 2020. VTE and mortality rates within 8 h of assessment were described. In 10,871 adults with COVID-19, 118 patients (1.09%) were diagnosed with symptomatic VTE (101 pulmonary embolism, 17 deep vein thrombosis events) and 28 patients (0.26%) died during initial assessment. Among these 146 patients, 64.4% were males, 56.8% were 60 years or older, 15.1% had a BMI > 35, and 11.6% were admitted to the intensive care unit. Comorbidities included hypertension (46.6%), diabetes (24.7%), hyperlipidemia (14.4%), chronic lung disease (12.3%), coronary artery disease (11.0%), and prior VTE (7.5%). Key medications included corticosteroids (22.6%), statins (21.2%), antiplatelets (20.6%), and anticoagulants (20.6%). Highest D-Dimer was greater than six times the upper limit of normal in 51.4%. Statin and antiplatelet use were associated with decreased VTE or mortality (each p

Supplemental Digital Content is available in the text., Background and aims Liver chemistry abnormalities (LCA) are common in patients with coronavirus disease 2019 (COVID-19), but their causes and clinical impact have not been adequately studied. We assessed the associations between LCA and clinical characteristics, inflammatory serum markers, in-hospital mortality. Methods Ten thousand eight hundred fifty-six adult patients with COVID-19 hospitalized in 13 hospitals in New York (1 March to 27 April 2020) were analyzed retrospectively. Abnormalities of liver chemistries [aspartate aminotransferase (AST), alanine aminotransferase, alkaline phosphatase, or total bilirubin] were defined as absent, mild-moderate (at least one value up to four times elevated), or severe. Results LCA were mild-moderate in 63.9% and severe in 7.6% at admission. Risk factors for severe LCA were male sex and chronic liver disease. Conversely, hypertension and diabetes mellitus were less likely associated with severe LCA. AST elevation correlated weakly to modestly with inflammatory markers. On adjusted analysis, in-hospital mortality was 1.56 times and 1.87 times increased in patients with mild-to-moderate and severe LCA, respectively. Diabetes, hypertension, male sex, and age greater than 60 years was associated with incremental risk of mortality with increase severity of LCA, especially in the first week of hospitalization. HTN was not associated with increased in-hospital mortality unless LCA was present. Conclusion Increasing severity of LCA on hospital admission predicts early in-hospital mortality in COVID-19 patients. Mortality associated with the known risk factors, hypertension, diabetes, male sex, and old age was accentuated in the presence of LCA. AST correlated modestly with inflammatory markers.

Background Antithrombotic guidance statements for hospitalized patients with coronavirus disease 2019 (COVID-19) suggest a universal thromboprophylactic strategy with potential to escalate doses in high-risk patients. To date, no clear approach exists to discriminate patients at high risk for venous thromboembolism (VTE). Objectives The objective of this study is to externally validate the IMPROVE-DD risk assessment model (RAM) for VTE in a large cohort of hospitalized patients with COVID-19 within a multihospital health system. Methods This retrospective cohort study evaluated the IMPROVE-DD RAM on adult inpatients with COVID-19 hospitalized between March 1, 2020, and April 27, 2020. Diagnosis of VTE was defined by new acute deep venous thrombosis or pulmonary embolism by Radiology Department imaging or point-of-care ultrasound. The receiver operating characteristic (ROC) curve was plotted and area under the curve (AUC) calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using standard methods. Results A total of 9407 patients were included, with a VTE prevalence of 2.9%. The VTE rate was 0.4% for IMPROVE-DD score 0-1 (low risk), 1.3% for score 2-3 (moderate risk), and 5.3% for score ≥ 4 (high risk). Approximately 45% of the total population scored high VTE risk, while 21% scored low VTE risk. IMPROVE-DD discrimination of low versus medium/high risk showed sensitivity of 0.971, specificity of 0.218, PPV of 0.036, and NPV of 0.996. ROC AUC was 0.702. Conclusions The IMPROVE-DD VTE RAM demonstrated very good discrimination to identify hospitalized patients with COVID-19 as low, moderate, and high VTE risk in this large external validation study with potential to individualize thromboprophylactic strategies.

Background We aimed to identify the prevalence and predictors of venous thromboembolism (VTE) or mortality in hospitalized coronavirus disease 2019 (COVID-19) patients. Methods A retrospective cohort study of hospitalized adult patients admitted to an integrated health care network in the New York metropolitan region between March 1, 2020 and April 27, 2020. The final analysis included 9,407 patients with an overall VTE rate of 2.9% (2.4% in the medical ward and 4.9% in the intensive care unit [ICU]) and a VTE or mortality rate of 26.1%. Most patients received prophylactic-dose thromboprophylaxis. Multivariable analysis showed significantly reduced VTE or mortality with Black race, history of hypertension, angiotensin converting enzyme/angiotensin receptor blocker use, and initial prophylactic anticoagulation. It also showed significantly increased VTE or mortality with age 60 years or greater, Charlson Comorbidity Index (CCI) of 3 or greater, patients on Medicare, history of heart failure, history of cerebrovascular disease, body mass index greater than 35, steroid use, antirheumatologic medication use, hydroxychloroquine use, maximum D-dimer four times or greater than the upper limit of normal (ULN), ICU level of care, increasing creatinine, and decreasing platelet counts. Conclusion In our large cohort of hospitalized COVID-19 patients, the overall in-hospital VTE rate was 2.9% (4.9% in the ICU) and a VTE or mortality rate of 26.1%. Key predictors of VTE or mortality included advanced age, increasing CCI, history of cardiovascular disease, ICU level of care, and elevated maximum D-dimer with a cutoff at least four times the ULN. Use of prophylactic-dose anticoagulation but not treatment-dose anticoagulation was associated with reduced VTE or mortality.

Highlights • Patients with COVID-19 with elevated troponin have markedly increased risk of death. • Patients with elevated troponin were frequently admitted to intensive care units. • Risk with elevated troponin is independent of acute phase and inflammatory markers. • Risk with elevated troponin is independent of baseline cardiovascular disease., Elevations in troponin levels have been shown to predict mortality in patients with coronavirus disease 2019 (COVID-19). The role of inflammation in myocardial injury remains unclear. We sought to determine the association of elevated troponin with mortality in a large, ethnically diverse population of patients hospitalized with COVID-19, and to determine the association of elevated inflammatory markers with increased troponin levels. We reviewed all patients admitted at our health system with COVID-19 from March 1 to April 27, 2020, who had a troponin assessment within 48 hours of admission. We used logistic regression to calculate odds ratios (ORs) for mortality during hospitalization, controlling for demographics, co-morbidities, and markers of inflammation. Of 11,159 patients hospitalized with COVID-19, 6,247 had a troponin assessment within 48 hours. Of these, 4,426 (71%) patients had normal, 919 (15%) had mildly elevated, and 902 (14%) had severely elevated troponin. Acute phase and inflammatory markers were significantly elevated in patients with mildly and severely elevated troponin compared with normal troponin. Patients with elevated troponin had significantly increased odds of death for mildly elevated compared with normal troponin (adjusted OR, 2.06; 95% confidence interval, 1.68 to 2.53; p < 0.001) and for severely elevated compared with normal troponin (OR, 4.51; 95% confidence interval, 3.66 to 5.54; p < 0.001) independently of elevation in inflammatory markers. In conclusion, patients hospitalized with COVID-19 and elevated troponin had markedly increased mortality compared with patients with normal troponin levels. This risk was independent of cardiovascular co-morbidities and elevated markers of inflammation.

Immune checkpoint inhibitors (ICIs), immunomodulatory antibodies that are used to enhance the immune system, have substantially improved the prognosis of patients with advanced malignancy. Although we are aware that there is no renal clearance of these agents (1), the data on the use of ICIs in patients with ESKD on hemodialysis (HD) as well as those on peritoneal dialysis (PD) is sparse. Published reports on the use of ICIs in patients with ESKD are limited to case reports and case series: one from Korea (2) and the second from Italy (3). We describe our single-center experience of ICI use in patients with ESKD and summarize the current literature of ICI use in this population. We used data from a large health system in the state of New York comprising 23 hospitals and >700 ambulatory facilities. Data for this study were obtained from the enterprise inpatient electronic health record (Sunrise Clinical Manager; Allscripts, Chicago, IL), which covers 13 of the hospitals, including the oncology infusion centers. Using an analytics database, we identified all patients with a minimum of one ESKD diagnosis code (Insertional Classification of Diseases 10th Edition, N18.6) who received at least one of the following agents between 2012 and 2019: ipilimumab, nivolumab, pembrolizumab, or atezolizumab. We included both prevalent patients on dialysis (those already receiving dialysis during the time of ICI initiation) and incident patients on dialysis (those who started dialysis after ICI initiation). Charts were reviewed manually to confirm that patients were on HD or PD during the ICI therapy. Clinical details such as demographics, comorbidities, cancer type, immune-related adverse …

Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.

Objective Improving the patient experience has become an essential component of any healthcare system’s performance metrics portfolio. In this study, we developed a machine learning model to predict a patient’s response to the Hospital Consumer Assessment of Healthcare Providers and Systems survey’s “Doctor Communications” domain questions while simultaneously identifying most impactful providers in a network. Materials and Methods This is an observational study of patients admitted to a single tertiary care hospital between 2016 and 2020. Using machine learning algorithms, electronic health record data were used to predict patient responses to Hospital Consumer Assessment of Healthcare Providers and Systems survey questions in the doctor domain, and patients who are at risk for responding negatively were identified. Model performance was assessed by area under receiver-operating characteristic curve. Social network analysis metrics were also used to identify providers most impactful to patient experience. Results Using a random forest algorithm, patients’ responses to the following 3 questions were predicted: “During this hospital stay how often did doctors. 1) treat you with courtesy and respect? 2) explain things in a way that you could understand? 3) listen carefully to you?” with areas under the receiver-operating characteristic curve of 0.876, 0.819, and 0.819, respectively. Social network analysis found that doctors with higher centrality appear to have an outsized influence on patient experience, as measured by rank in the random forest model in the doctor domain. Conclusions A machine learning algorithm identified patients at risk of a negative experience. Furthermore, a doctor social network framework provides metrics for identifying those providers that are most influential on the patient experience.

There is minimal information on coronavirus disease 2019 (COVID‐19) in immunocompromised individuals. We have studied 10 patients treated at 12 adult care hospitals. Ten kidney transplant recipients tested positive for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) by polymerase chain reaction, and 9 were admitted. The median age was 57 (interquartile range [IQR] 47‐67), 60% were male, 40% Caucasian, and 30% Black/African American. Median time from transplant to COVID‐19 testing was 2822 days (IQR 1272‐4592). The most common symptom was fever, followed by cough, myalgia, chills, and fatigue. The most common chest X‐ray and computed tomography abnormality was multifocal patchy opacities. Three patients had no abnormal findings. Leukopenia was seen in 20% of patients, and allograft function was stable in 50% of patients. Nine patients were on tacrolimus and a mycophenolic antimetabolite, and 70% were on prednisone. Hospitalized patients had their antimetabolite agent stopped. All hospitalized patients received hydroxychloroquine and azithromycin. Three patients died (30%), and 5 (50%) developed acute kidney injury. Kidney transplant recipients infected with COVID‐19 should be monitored closely in the setting of lowered immunosuppression. Most individuals required hospitalization and presenting symptoms were similar to those of nontransplant individuals.

Objective To evaluate racial and ethnic differences in mortality among patients hospitalized with coronavirus disease 2019 (COVID-19) after adjusting for baseline characteristics and comorbidities. Methods This retrospective cohort study at 13 acute care facilities in the New York City metropolitan area included sequentially hospitalized patients between March 1, 2020, and April 27, 2020. Last day of follow up was July 31, 2020. Patient demographic information, including race/ethnicity and comorbidities, were collected. The primary outcome was in-hospital mortality. Results A total of 10 869 patients were included in the study (median age, 65 years [interquartile range (IQR) 54–77; range, 18–107 years]; 40.5% female). In adjusted time-to-event analysis, increased age, male sex, insurance type (Medicare and Self-Pay), unknown smoking status, and a higher score on the Charlson Comorbidity Index were significantly associated with higher in-hospital mortality. Adjusted risk of hospital mortality for Black, Asian, Hispanic, multiracial/other, and unknown race/ethnicity patients were similar to risk for White patients. Conclusions In a large diverse cohort of patients hospitalized with COVID-19, patients from racial/ethnic minorities experienced similar mortality risk as White patients.

Race coefficients of estimated glomerular filtration rate (eGFR) formulas may be partially responsible for racial inequality in preemptive listing for kidney transplantation.We used the Scientific Registry of Transplant Recipients database to evaluate differences in racial distribution of preemptive listing before and after application of the Modification of Diet in Renal Disease (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) race coefficients to all preemptively listed non-Black kidney transplant candidates (eGFR modulation). Odds of preemptive listing were calculated by race, with Black as the reference before and after eGFR modulation. Variables known to influence preemptive listing were included in the model.Among 385 087 kidney-alone transplant candidates from 1 January 2010 to 2 December 2020, 118 329 (30.7%) candidates were identified as preemptively listed (71.7% White, 19% Black, 7.8% Asian, 0.6% multi-racial, 0.6% Native American and 0.3% Pacific Islander). After eGFR modulation, non-Black patients with an eGFR ≥20 mL/min/1.73 mRemoving race coefficients in GFR estimation formulas may result in a more equitable distribution of Black candidates listed earlier on a preemptive basis.

BackgroundImmune checkpoint inhibitor-associated acute kidney injury (ICPi-AKI) has emerged as an important toxicity among patients with cancer.MethodsWe collected data on 429 patients with ICPi-AKI and 429 control patients who received ICPis contemporaneously but who did not develop ICPi-AKI from 30 sites in 10 countries. Multivariable logistic regression was used to identify predictors of ICPi-AKI and its recovery. A multivariable Cox model was used to estimate the effect of ICPi rechallenge versus no rechallenge on survival following ICPi-AKI.ResultsICPi-AKI occurred at a median of 16 weeks (IQR 8–32) following ICPi initiation. Lower baseline estimated glomerular filtration rate, proton pump inhibitor (PPI) use, and extrarenal immune-related adverse events (irAEs) were each associated with a higher risk of ICPi-AKI. Acute tubulointerstitial nephritis was the most common lesion on kidney biopsy (125/151 biopsied patients [82.7%]). Renal recovery occurred in 276 patients (64.3%) at a median of 7 weeks (IQR 3–10) following ICPi-AKI. Treatment with corticosteroids within 14 days following ICPi-AKI diagnosis was associated with higher odds of renal recovery (adjusted OR 2.64; 95% CI 1.58 to 4.41). Among patients treated with corticosteroids, early initiation of corticosteroids (within 3 days of ICPi-AKI) was associated with a higher odds of renal recovery compared with later initiation (more than 3 days following ICPi-AKI) (adjusted OR 2.09; 95% CI 1.16 to 3.79). Of 121 patients rechallenged, 20 (16.5%) developed recurrent ICPi-AKI. There was no difference in survival among patients rechallenged versus those not rechallenged following ICPi-AKI.ConclusionsPatients who developed ICPi-AKI were more likely to have impaired renal function at baseline, use a PPI, and have extrarenal irAEs. Two-thirds of patients had renal recovery following ICPi-AKI. Treatment with corticosteroids was associated with improved renal recovery.

Background Initially, persistent asthma was deemed a risk factor for severe COVID-19 disease. However, data suggests that asthmatics do not have an increased risk of COVID-19 infection or disease. There is a paucity of data describing pediatric asthmatics with COVID-19. Objective The objectives of this study were to determine the prevalence of asthma among hospitalized children with acute symptomatic COVID-19, compare demographic and clinical outcomes between asthmatics and nonasthmatics, and characterize behaviors of our outpatient pediatric population. Methods We conducted a single-center retrospective study of pediatric patients admitted to the Cohen Children's Medical Center at Northwell Health with symptomatic COVID-19 within 4 months of the surge beginning in March 2020 and a retrospective analysis of pediatric asthma outpatients seen in the previous 6 months. Baseline demographic variables and clinical outcomes for inpatients, and medication compliance, health behaviors, and asthma control for outpatients were collected. Results Thirty-eight inpatients and 95 outpatients were included. The inpatient prevalence of asthma was 34.2%. Asthmatics were less likely to have abnormal chest x-rays (CXRs), require oxygen support, and be treated with remdesivir. Among outpatients, 41% reported improved asthma control and decreased rescue medication use, with no COVID-19 hospitalizations, despite six suspected infections. Conclusions Among children hospitalized for acute symptomatic COVID-19 at our institution, 34.2% had a diagnosis of asthma. Asthmatics did not have a more severe course and required a lower level of care. Outpatients had improved medication compliance and control and a low risk of hospitalization. Biological and behavioral factors may have mitigated against severe disease.

Background Our objective was to characterize young adult patients hospitalized with coronavirus disease 2019 (COVID-19) and identify predictors of survival at 30 days. Methods This retrospective cohort study took place at 12 acute care hospitals in the New York City area. Patients aged 18–39 hospitalized with confirmed COVID-19 between March 1 and April 27, 2020 were included in the study. Demographic, clinical, and outcome data were extracted from electronic health record reports. Results A total of 1013 patients were included in the study (median age, 33 years; interquartile range [IQR], 28–36; 52% female). At the study end point, 940 (92.8%) patients were discharged alive, 18 (1.8%) remained hospitalized, 5 (0.5%) were transferred to another acute care facility, and 50 (4.9%) died. The most common comorbidities in hospitalized young adult patients were obesity (51.2%), diabetes mellitus (14.8%), and hypertension (13%). Multivariable analysis revealed that obesity (adjusted hazard ratio [aHR], 2.71; 95% confidence interval [CI], 1.28–5.73; P = .002) and Charlson comorbidity index score (aHR, 1.20; 95% CI, 1.07–1.35; P = .002) were independent predictors of in-hospital 30-day mortality. Conclusions Obesity was identified as the strongest negative predictor of 30-day in-hospital survival in young adults with COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented and historic public health crisis that continues to expand and evolve. The National Kidney Foundation held a 2-part continuing medical education live virtual symposium on July 16 and July 24, 2020, to address the multiple challenges of COVID-19 in the context of advanced chronic kidney disease. Faculty addressed the pathophysiology, impact, risks, and management of COVID-19 as it relates to advanced kidney disease. Testing, risk mitigation, and inpatient and outpatient management were also addressed. This concise review addresses major findings of the symposium along with certain updates regarding vaccinations since then. These findings include: (1) severe COVID-19 infection has been associated with acute kidney injury, (2) it is essential to prevent and actively manage acute kidney injury to decrease mortality in these critically ill patients, (3) management of patients with advanced kidney disease should be geared toward minimizing their risk for exposure while making sure they are receiving adequate treatments, and (4) patients with kidney disease, especially ones in advanced stages, should be prioritized for vaccination.

ImportanceNeutralizing monoclonal antibody (MAB) therapies may benefit patients with mild to moderate COVID-19 at high risk for progressing to severe COVID-19 and/or hospitalization. Studies documenting approaches to deliver MAB infusions as well as demonstrating their efficacy are lacking.ObjectiveWe describe our experience and patient outcomes of almost 3,000 patients who received MAB infusion therapy at Northwell Health, a large integrated health care system in New York.Design, Setting, and ParticipantsThis is a descriptive study of adult patients who received MAB therapy between November 20, 2020, to January 31, 2021, and a retrospective cohort survival analysis comparing patients who received MAB therapy prior to admission versus those who did not. A multivariable Cox model with inverse probability weighting according to the propensity score including covariates (sociodemographic, comorbidities, and presenting vital signs) was used.Main outcomes and measuresThe primary outcome was in-hospital mortality; additional evaluations included ED utilization and hospitalization within 28 days of a positive COVID-19 test for patients who received MAB therapy.ResultsDuring the study period, 2818 adult patients received MAB infusion. Following therapy and within 28 days of COVID-19 test, 123 patients (4.4%) presented to the ED and were released and 145 patients (5.1%) were hospitalized. These 145 patients were compared with 200 controls who were eligible for but did not receive MAB therapy, and were hospitalized. In the MAB group, 16 (11%) patients met the primary outcome of in-hospital mortality, versus 21 (10.5%) in the control group. In an unadjusted Cox model, the hazard ratio (HR) for time to in-hospital mortality for the MAB group was 1.38 (95% confidence interval [95% CI] 0.696-2.719). Models adjusting for demographics (HR 1.1, 95% CI 0.53-2.23), demographics and Charlson Comorbidity Index (CCI) (HR 1.22, 95% CI 0.573-2.59), and with inverse probability weighting according to propensity scores (HR 1.19, 95% CI 0.619-2.29) did not demonstrate significance. The hospitalization rate was 4.4% for patients who received MAB therapy within 0-4 days, 5% within 5-7 days, and 6.1% within ≥8 days of symptom onset (p-value = 0.15).Conclusions and relevanceEstablishing the capability to provide neutralizing MAB infusion therapy requires significant planning and coordination. While this therapy may be an important treatment option for early mild to moderate COVID-19 in high-risk patients, further investigations are needed to define the optimal timing of MAB treatment in order to reduce hospitalization and mortality.

Background: Smaller studies suggest lower morbidity and mortality associated with coronavirus disease 2019 (COVID-19) in women. Our aim is to assess the impact of female sex on outcomes in a large cohort of patients hospitalized with COVID-19. Materials and Methods: This is a retrospective observational cohort study of 10,630 adult patients hospitalized with a confirmed COVID-19 polymerase chain reaction between March 1, 2020 and April 27, 2020, with follow-up conducted through June 4, 2020. Logistic regression was used to examine the relationship between sex and the primary outcomes, including length of stay, admission to intensive care unit (ICU), need for mechanical ventilation, pressor requirement, and all-cause mortality as well as major adverse events and in-hospital COVID-19 treatments. Results: In the multivariable analysis, women had 27% lower odds of in-hospital mortality (odds ratio [OR] = 0.73, 95% confidence interval [CI] 0.66-0.81; p < 0.001), 24% lower odds of ICU admission (OR = 0.76, 95% CI 0.69-0.84; p < 0.001), 26% lower odds of mechanical ventilation (OR = 0.74, 95% CI 0.66-0.82; p < 0.001), and 25% lower odds of vasopressor requirement (OR = 0.75, 95% CI 0.67-0.84; p < 0.001). Women had 34% less odds of having acute cardiac injury (OR = 0.66, 95% CI 0.59-0.74; p < 0.001; n = 7,289), 16% less odds of acute kidney injury (OR = 0.84, 95% CI 0.76-0.92; p < 0.001; n = 9,840), and 27% less odds of venous thromboembolism (OR = 0.73, 95% CI 0.56-0.96; p < 0.02; c-statistic 0.85, n = 9,407). Conclusions: Female sex is associated with lower odds of in-hospital outcomes, major adverse events, and all-cause mortality. There may be protective mechanisms inherent to female sex, which explain differences in COVID-19 outcomes.

Delivering clinical decision support (CDS) at the point of care has long been considered a major advantage of computerized physician order entry (CPOE). Despite the widespread implementation of CPOE, medication ordering errors and associated adverse events still occur at an unacceptable level. Previous attempts at indication- and kidney function-based dosing have mostly employed intrusive CDS, including interruptive alerts with poor usability. This descriptive work describes the design, development, and deployment of the Adult Dosing Methodology (ADM) module, a novel CDS tool that provides indication- and kidney-based dosing at the time of order entry. Inclusion of several antimicrobials in the initial set of medications allowed for the additional goal of optimizing therapy duration for appropriate antimicrobial stewardship. The CDS aims to decrease order entry errors and burden on providers by offering automatic dose and frequency recommendations, integration within the native electronic health record, and reasonable knowledge maintenance requirements. Following implementation, early utilization demonstrated high acceptance of automated recommendations, with up to 96% of provided automated recommendations accepted by users.

Background and aims Gastrointestinal (GI) bleeding has been observed amongst patients hospitalized with COVID-19. Recently, anticoagulation has shown to decrease mortality, but it is unclear whether this contributes to increased GI bleeding. The aims of this study are: (i) to examine whether there are risk factors for GI bleeding in COVID-19 patients and (ii) to study whether there is a mortality difference between hospitalized patients with COVID-19 with and without GI bleeding. Methods This is a propensity score matched case-control study from a large health system in the New York metropolitan area between March 1st and April 27th. COVID-19 patients with GI bleeding were matched 1:1 to COVID-19 patients without bleeding using a propensity score that took into account comorbidities, demographics, GI bleeding risk factors and length of stay. Results Of 11, 158 hospitalized with COVID-19, 314 patients were identified with GI bleeding. The point prevalence of GI bleeding was 3%. There were no identifiable risk factors for GI bleeding. Use of anticoagulation medication or antiplatelet agents was not associated with increased risk of GI bleeding in COVID-19 patients. For patients who developed a GI bleed during the hospitalization, there was an increased mortality risk in the GI bleeding group (OR 1.58, P = 0.02). Conclusion Use of anticoagulation or antiplatelet agents was not risk factors for GI bleeding in a large cohort of hospitalized COVID-19 patients. Those with GI bleeding during the hospitalization had increased mortality.

Objective Coronavirus disease 2019 [COVID-19] infection in patients with chronic liver disease [CLD] may precipitate acute-on-chronic liver failure [ACLF]. In a large multi-center cohort of COVID-19-infected patients, we aim to analyze (1) the outcomes of patients with underlying CLD [with and without cirrhosis] and (2) the development and impact of ACLF on in-hospital mortality. Design We identified 192 adults with CLD from among 10,859 patients with confirmed COVID-19 infection (admitted to any of 12 hospitals in a New York health care system between March 1, 2020 and April 27, 2020). ACLF was defined using the EASL-CLIF Consortium definition. Patient follow-up was through April 30, 2020, or until the date of discharge, transfer, or death. Results Of the 84 patients with cirrhosis, 32 [38%] developed ACLF, with respiratory failure [39%] and renal failure [26%] being the most common. Hispanic/Latino ethnicity was particularly at higher risk of in-hospital mortality [adjusted HR 4.92, 95% 1.27–19.09, p

OBJECTIVE: This study examined the association between BMI and clinical outcomes among patients with coronavirus disease 2019 (COVID-19) infection METHODS: A total of 10,861 patients with COVID-19 infection who were admitted to the Northwell Health system hospitals between March 1, 2020, and April 27, 2020, were included in this study BMI was classified as underweight, normal weight, overweight, and obesity classes I, II, and III Primary outcomes were invasive mechanical ventilation (IMV) and death RESULTS: A total of 243 (2 2%) patients were underweight, 2,507 (23 1%) were normal weight, 4,021 (37 0%) had overweight, 2,345 (21 6%) had obesity class I, 990 (9 1%) had obesity class II, and 755 (7 0%) had obesity class III Patients who had overweight (odds ratio [OR] = 1 27 [95% CI: 1 11-1 46]), obesity class I (OR = 1 48 [95% CI: 1 27-1 72]), obesity class II (OR = 1 89 [95% CI: 1 56-2 28]), and obesity class III (OR = 2 31 [95% CI: 1 88-2 85]) had an increased risk of requiring IMV Underweight and obesity classes II and III were statistically associated with death (OR = 1 44 [95% CI: 1 08-1 92];OR = 1 25 [95% CI: 1 03-1 52];OR = 1 61 [95% CI: 1 30-2 00], respectively) Among patients who were on IMV, BMI was not associated with inpatient deaths CONCLUSIONS: Patients who are underweight or who have obesity are at risk for mechanical ventilation and death, suggesting that pulmonary complications (indicated by IMV) are a significant contributor for poor outcomes in COVID-19 infection

Background: Opioid use has emerged as a leading cause of death in the US. Given that 1 in 300 opioid-naive patients exposed to opioids after cesarean birth will become persistent users, hospitals should strive to limit exposure to these medications. We set out to evaluate whether transitioning to a standardized order set based on multimodal combination analgesic therapy decreases the exposure to opioids after cesarean delivery. Methods: Our health system&rsquo, s post-cesarean pain management electronic medical record (EMR) order set was changed from standing NSAIDs (Ibuprofen 600 mg every 6 h) and additional acetaminophen and opioid medications (Oxycodone 5 mg/acetaminophen 325 mg every 3 h or Oxycodone 10 mg/acetaminophen 650 mg every 6 h for moderate and severe pain, respectively) as needed (PRN) to a multimodal combination therapy with acetaminophen (975 mg every 6 h) and NSAIDs (Ibuprofen 600 mg every 6 h) as primary analgesics and opioids PRN (Oxycodone immediate release (IR) 5 mg every 3 h for moderate to severe pain). We performed a retrospective analysis across seven hospitals comparing inpatient opioid use, administration of other analgesics, and severe pain episodes (pain score &ge, 7) between the patients who were treated before and after implementation of the multimodal order set. Chi square and Student t-test were used for statistical analysis with significance determined as p &lt, 0.05. Results: A total of 12,898 cesarean births were included (8696 prior and 4202 after implementation). The multimodal order set was associated with marked decrease in the incidence of post cesarean opioid use (45.4% vs. 67.5%, p &lt, 0.0001), lower average opioid dose (26.7 mg vs. 36.6 mg of oxycodone, 0.0001), and increased dose of acetaminophen (8422 mg vs. 4563 mg, 0.0001), while severe pain scores were less frequent (46.3% vs. 56.6%, p &lt, 0.0001). Conclusions: Multimodal analgesic therapy for post-cesarean pain management reduces inpatient opioid use while improving pain control. Incorporation of a multimodal order set as a default in the EMR facilitates effective and widespread implementation on a large scale. Obstetric units should consider standardizing post-cesarean pain management orders to include routine (not PRN) multimodal combination therapy with acetaminophen and NSAIDs as primary analgesics.