Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York.

BACKGROUND: COVID-19 is a challenging disease to characterize given its wide-ranging heterogeneous symptomatology. Several studies have attempted to extract clinical phenotypes but often relied on data from small patient cohorts, usually limited to only one viral variant and utilizing a static snapshot of patient data. OBJECTIVE: This study aimed to identify clinical phenotypes of hospitalized COVID-19 patients and investigate their longitudinal dynamics throughout the pandemic, with the goal to relate these phenotypes to clinical outcomes and treatment strategies. METHODS: We utilized routinely collected demographic and clinical data throughout the hospitalization of 38,077 patients admitted between 3/2020 to 5/2022, in 12 New York hospitals. Uniform Manifold Approximation and Projection and agglomerative hierarchical clustering were used to derive the clusters, followed by exploratory data analysis to compare the prevalence of comorbidities and treatments per cluster. RESULTS: 4 distinct clinical phenotypes remained robust in multi-site validation and were associated with different mortality rates. The temporal progression of these phenotypes throughout the COVID-19 pandemic demonstrated increased variability across the waves of the three dominant viral variants (alpha, delta, omicron). Longitudinal analysis evaluating changes in clinical phenotypes of each patient throughout the course of a 4-week hospital stay exemplified the dynamic nature of the disease progression. Factors such as sex, race/ethnicity and specific treatment modalities revealed significant and clinically relevant differences between the observed phenotypes. CONCLUSIONS: Our proposed methodology has the potential of enabling clinicians and policy makers to draw evidence-based conclusions for guiding treatment modalities in a dynamic fashion.

Application of a Machine Learning Algorithm to Develop and Validate a Prediction Model for Ambulatory Non-Arrivals.

BACKGROUND: Non-arrivals to scheduled ambulatory visits are common and lead to a discontinuity of care, poor health outcomes, and increased subsequent healthcare utilization. Reducing non-arrivals is important given their association with poorer health outcomes and cost to health systems. OBJECTIVE: To develop and validate a prediction model for ambulatory non-arrivals. DESIGN: Retrospective cohort study. PATIENTS OR SUBJECTS: Patients at an integrated health system who had an outpatient visit scheduled from January 1, 2020, to February 28, 2022. MAIN MEASURES: Non-arrivals to scheduled appointments. KEY RESULTS: There were over 4.3 million ambulatory appointments from 1.2 million adult patients. Patients with appointment non-arrivals were more likely to be single, racial/ethnic minorities, and not having an established primary care provider compared to those who arrived at their appointments. A prediction model using the XGBoost machine learning algorithm had the highest AUC value (0.768 [0.767-0.770]). Using SHAP values, the most impactful features in the model include rescheduled appointments, lead time (number of days from scheduled to appointment date), appointment provider, number of days since last appointment with the same department, and a patient's prior appointment status within the same department. Scheduling visits close to an appointment date is predicted to be less likely to result in a non-arrival. Overall, the prediction model calibrated well for each department, especially over the operationally relevant probability range of 0 to 40%. Departments with fewer observations and lower non-arrival rates generally had a worse calibration. CONCLUSIONS: Using a machine learning algorithm, we developed a prediction model for non-arrivals to scheduled ambulatory appointments usable for all medical specialties. The proposed prediction model can be deployed within an electronic health system or integrated into other dashboards to reduce non-arrivals. Future work will focus on the implementation and application of the model to reduce non-arrivals.

Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients.

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.

Early Experience With Neutralizing Monoclonal Antibody Therapy for COVID-19: Retrospective Cohort Survival Analysis and Descriptive Study.

BACKGROUND: Neutralizing monoclonal antibody (MAB) therapies may benefit patients with mild to moderate COVID-19 at high risk for progressing to severe COVID-19 or hospitalization. Studies documenting approaches to deliver MAB infusions and demonstrating their efficacy are lacking. OBJECTIVE: We describe our experience and the outcomes of almost 3000 patients who received MAB infusion therapy at Northwell Health, a large integrated health care system in New York. METHODS: This 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. The primary outcome was in-hospital mortality; additional evaluations included emergency department use and hospitalization within 28 days of a positive COVID-19 test for patients who received MAB therapy. RESULTS: During the study period, 2818 adult patients received MAB infusion. Following therapy and within 28 days of a COVID-19 test, 123 (4.4%) patients presented to the emergency department and were released, and 145 (5.1%) patients 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% CI 0.696-2.719). Models adjusting for demographics (HR 1.1, 95% CI 0.53-2.23), demographics and Charlson Comorbidity Index (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 to 4 days, 5% within 5 to 7 days, and 6.1% in ≥8 days of symptom onset (P=.15). CONCLUSIONS: Establishing the capability to provide neutralizing MAB infusion therapy requires substantial planning and coordination. Although this therapy may be an important treatment option for early mild to moderate COVID-19 in patients who are at high risk, further investigations are needed to define the optimal timing of MAB treatment to reduce hospitalization and mortality.

External validation of the IMPROVE-DD risk assessment model for venous thromboembolism among inpatients with COVID-19.

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.

Incidence of Venous Thromboembolism and Mortality in Patients with Initial Presentation of COVID-19.

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 < 0.01). In COVID-19 patients who initially presented to a large multihospital health system, the overall symptomatic VTE and mortality rate was over 1.0%. Statin and antiplatelet use were associated with decreased VTE or mortality. The potential benefits of antithrombotics in high risk COVID-19 patients during the pre-hospitalization period deserves study.

Validation of the IMPROVE-DD risk assessment model for venous thromboembolism among hospitalized patients with COVID-19.

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.

Examination of patient characteristics and hydroxychloroquine use based on the US Food and Drug Administration's recommendation: a cross-sectional analysis in New York.

OBJECTIVE: To describe the pattern of hydroxychloroquine use and examine the association between hydroxychloroquine use and clinical outcomes arising from changes in the US Food and Drug Administration (FDA)'s recommendation during the coronavirus disease 2019 (COVID-19) pandemic. DESIGN: A retrospective cross-sectional analysis. SETTING AND PARTICIPANTS: We included hospitalised adult patients at Northwell Health hospitals with confirmed COVID-19 infections between 1 March 2020 and 11 May 2020. We categorised changes in the FDA's recommendation as pre-FDA approval (1 March 2020-27 March 2020), FDA approval (28 March 2020-23 April 2020), and FDA warning (24 April 2020-11 May 2020). The hydroxychloroquine-treated group received at least one dose within 48 hours of hospital admission. PRIMARY OUTCOME: A composite of intubation and inpatient death. RESULTS: The percentages of patients who were treated with hydroxychloroquine were 192/2202 (8.7%) pre-FDA approval, 2902/6741 (43.0%) FDA approval, and 176/1066 (16.5%) FDA warning period (p<0.001). Using propensity score matching, there was a higher rate of the composite outcome among patients treated with hydroxychloroquine (49/192, 25.5%) compared with no hydroxychloroquine (66/384, 17.2%) in the pre-FDA approval period (p=0.03) but not in the FDA approval period (25.5% vs 22.6%, p=0.08) or the FDA warning (21.0% vs 15.1%, p=0.11) periods. Coincidently, there was an increase in number of patients with COVID-19 and disease severity during the FDA approval period (24.1% during FDA approval vs 21.4% during pre-FDA approval period had the composite outcome). Hydroxychloroquine use was associated with increased odds of the composite outcome during the pre-FDA approval period (OR=1.65 (95% CI 1.09 to 2.51)) but not during the FDA approval (OR=1.17 (95% CI 0.99 to 1.39)) and FDA warning (OR=1.50 (95% CI 0.94 to 2.39)) periods. CONCLUSIONS: Hydroxychloroquine use was associated with adverse clinical outcomes only during the pre-FDA approval period but not during the FDA approval and warning periods, even after adjusting for concurrent changes in the percentage of patients with COVID-19 treated with hydroxychloroquine and the number (and disease severity) of hospitalised patients with COVID-19 infections.

Examination of Post-discharge Follow-up Appointment Status and 30-Day Readmission.

BACKGROUND: Post-hospital discharge follow-up appointments are intended to evaluate patients' recovery following a hospitalization, but it is unclear how appointment statuses are associated with readmissions. OBJECTIVE: To examine the association between post-discharge ambulatory follow-up status, (1) having a scheduled appointment and (2) arriving to said appointment, and 30-day readmission. DESIGN AND SETTING: A retrospective cohort study of patients hospitalized at 12 hospitals in an Integrated Delivery Network and their ambulatory appointments in that same network. PATIENTS AND MAIN MEASURES: We included 50,772 patients who had an ambulatory appointment within 18 months of an inpatient admission in 2018. Primary outcome was readmission within 30 days post-discharge. KEY RESULTS: There were 32,108 (63.2%) patients with scheduled follow-up appointments and 18,664 (36.8%) patients with no follow-up; 28,313 (88.2%) patients arrived, 3149 (9.8%) missed, and 646 (2.0%) were readmitted prior to their scheduled appointments. Overall 30-day readmission rate was 7.3%; 6.0% [5.75-6.31] for those who arrived, 8.8% [8.44-9.25] for those without follow-up, and 10.3% [9.28-11.40] for those who missed a scheduled appointment (p < 0.001). After adjusting for covariates, patients who arrived at their appointment in the first week following discharge were significantly less likely to be readmitted than those not having any follow-up scheduled (medical adjusted hazard ratio (aHR) 0.57 [0.47-0.69], p < 0.001; surgical aHR 0.58 [0.44-0.75], p < 0.001) There was an increased risk at weeks 3 and 4 for medical patients who arrived at a follow-up compared to those with no follow-up scheduled (week 3 aHR 1.29 [1.10-1.51], p = 0.001; week 4 aHR 1.46 [1.26-1.70], p < 0.001). CONCLUSIONS: The benefit of patients arriving to their post-discharge appointments compared with patients who missed their follow-up visits or had no follow-up scheduled, is only significant during first week post-discharge, suggesting that coordination within 1 week of discharge is critical in reducing 30-day readmissions.

Prevalence and Predictors of Venous Thromboembolism or Mortality in Hospitalized COVID-19 Patients.

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.

Gastrointestinal bleeding in hospitalized COVID-19 patients: a propensity score matched cohort study.

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.

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

BACKGROUND: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. MAIN BODY: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. CONCLUSION: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.

Development and Validation of a Survival Calculator for Hospitalized Patients with COVID-19.

BACKGROUND: Chinese studies reported predictors of severe disease and mortality associated with coronavirus disease 2019 (COVID-19). A generalizable and simple survival calculator based on data from US patients hospitalized with COVID-19 has not yet been introduced. OBJECTIVE: Develop and validate a clinical tool to predict 7-day survival in patients hospitalized with COVID-19. DESIGN: Retrospective and prospective cohort study. SETTING: Thirteen acute care hospitals in the New York City area. PARTICIPANTS: Adult patients hospitalized with a confirmed diagnosis of COVID-19. The development and internal validation cohort included patients hospitalized between March 1 and May 6, 2020. The external validation cohort included patients hospitalized between March 1 and May 5, 2020. MEASUREMENTS: Demographic, laboratory, clinical, and outcome data were extracted from the electronic health record. Optimal predictors and performance were identified using least absolute shrinkage and selection operator (LASSO) regression with receiver operating characteristic curves and measurements of area under the curve (AUC). RESULTS: The development and internal validation cohort included 11 095 patients with a median age of 65 years [interquartile range (IQR) 54-77]. Overall 7-day survival was 89%. Serum blood urea nitrogen, age, absolute neutrophil count, red cell distribution width, oxygen saturation, and serum sodium were identified as the 6 optimal of 42 possible predictors of survival. These factors constitute the NOCOS (Northwell COVID-19 Survival) Calculator. Performance in the internal validation, prospective validation, and external validation were marked by AUCs of 0.86, 0.82, and 0.82, respectively. LIMITATIONS: All participants were hospitalized within the New York City area. CONCLUSIONS: The NOCOS Calculator uses 6 factors routinely available at hospital admission to predict 7-day survival for patients hospitalized with COVID-19. The calculator is publicly available at https://feinstein.northwell.edu/NOCOS.

Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area.

Importance: There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19). Objective: To describe the clinical characteristics and outcomes of patients with COVID-19 hospitalized in a US health care system. Design, Setting, and Participants: Case series of patients with COVID-19 admitted to 12 hospitals in New York City, Long Island, and Westchester County, New York, within the Northwell Health system. The study included all sequentially hospitalized patients between March 1, 2020, and April 4, 2020, inclusive of these dates. Exposures: Confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample among patients requiring admission. Main Outcomes and Measures: Clinical outcomes during hospitalization, such as invasive mechanical ventilation, kidney replacement therapy, and death. Demographics, baseline comorbidities, presenting vital signs, and test results were also collected. Results: A total of 5700 patients were included (median age, 63 years [interquartile range {IQR}, 52-75; range, 0-107 years]; 39.7% female). The most common comorbidities were hypertension (3026; 56.6%), obesity (1737; 41.7%), and diabetes (1808; 33.8%). At triage, 30.7% of patients were febrile, 17.3% had a respiratory rate greater than 24 breaths/min, and 27.8% received supplemental oxygen. The rate of respiratory virus co-infection was 2.1%. Outcomes were assessed for 2634 patients who were discharged or had died at the study end point. During hospitalization, 373 patients (14.2%) (median age, 68 years [IQR, 56-78]; 33.5% female) were treated in the intensive care unit care, 320 (12.2%) received invasive mechanical ventilation, 81 (3.2%) were treated with kidney replacement therapy, and 553 (21%) died. As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital. The median postdischarge follow-up time was 4.4 days (IQR, 2.2-9.3). A total of 45 patients (2.2%) were readmitted during the study period. The median time to readmission was 3 days (IQR, 1.0-4.5) for readmitted patients. Among the 3066 patients who remained hospitalized at the final study follow-up date (median age, 65 years [IQR, 54-75]), the median follow-up at time of censoring was 4.5 days (IQR, 2.4-8.1). Conclusions and Relevance: This case series provides characteristics and early outcomes of sequentially hospitalized patients with confirmed COVID-19 in the New York City area.