Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review.

BACKGROUND: Approximately 37 million individuals in the United States (US) have chronic kidney disease (CKD). Patients with CKD have a substantial morbidity and mortality, which contributes to a huge economic burden to the healthcare system. A limited number of clinical pathways or defined workflows exist for CKD care delivery in the US, primarily due to a lower prioritization of CKD care within health systems compared with other areas (e.g., cardiovascular disease [CVD], cancer screening). CKD is a public health crisis and by the year 2040, CKD will become the fifth leading cause of years of life lost. It is therefore critical to address these challenges to improve outcomes in patients with CKD. METHODS: The CKD Leaders Network conducted a virtual, 3 h, multidisciplinary roundtable discussion with eight subject-matter experts to better understand key factors impacting CKD care delivery and barriers across the US. A premeeting survey identified topics for discussion covering the screening, diagnosis, risk stratification, and management of CKD across the care continuum. Findings from this roundtable are summarized and presented herein. RESULTS: Universal challenges exist across health systems, including a lack of awareness amongst providers and patients, constrained care team bandwidth, inadequate financial incentives for early CKD identification, non-standardized diagnostic classification and triage processes, and non-centralized patient information. Proposed solutions include highlighting immediate and long-term financial implications linked with failure to identify and address at-risk individuals, identifying and managing early-stage CKD, enhancing efforts to support guideline-based education for providers and patients, and capitalizing on next-generation solutions. CONCLUSIONS: Payers and other industry stakeholders have opportunities to contribute to optimal CKD care delivery. Beyond addressing the inadequacies that currently exist, actionable tactics can be implemented into clinical practice to improve clinical outcomes in patients at risk for or diagnosed with CKD in the US.

Derivation and external validation of a simple risk score for predicting severe acute kidney injury after intravenous cisplatin: cohort study.

OBJECTIVE: To develop and externally validate a prediction model for severe cisplatin associated acute kidney injury (CP-AKI). DESIGN: Multicenter cohort study. SETTING: Six geographically diverse major academic cancer centers across the US. PARTICIPANTS: Adults (≥18 years) receiving their first dose of intravenous cisplatin, 2006-22. MAIN OUTCOME MEASURES: The primary outcome was CP-AKI, defined as a twofold or greater increase in serum creatinine or kidney replacement therapy within 14 days of a first dose of intravenous cisplatin. Independent predictors of CP-AKI were identified using a multivariable logistic regression model, which was developed in a derivation cohort and tested in an external validation cohort. For the primary model, continuous variables were examined using restricted cubic splines. A simple risk model was also generated by converting the odds ratios from the primary model into risk points. Finally, a multivariable Cox model was used to examine the association between severity of CP-AKI and 90 day survival. RESULTS: A total of 24 717 adults were included, with 11 766 in the derivation cohort (median age 59 (interquartile range (IQR) 50-67)) and 12 951 in the validation cohort (median age 60 (IQR 50-67)). The incidence of CP-AKI was 5.2% (608/11 766) in the derivation cohort and 3.3% (421/12 951) in the validation cohort. Each of the following factors were independently associated with CP-AKI in the derivation cohort: age, hypertension, diabetes mellitus, serum creatinine level, hemoglobin level, white blood cell count, platelet count, serum albumin level, serum magnesium level, and cisplatin dose. A simple risk score consisting of nine covariates was shown to predict a higher risk of CP-AKI in a monotonic fashion in both the derivation cohort and the validation cohort. Compared with patients in the lowest risk category, those in the highest risk category showed a 24.00-fold (95% confidence interval (CI) 13.49-fold to 42.78-fold) higher odds of CP-AKI in the derivation cohort and a 17.87-fold (10.56-fold to 29.60-fold) higher odds in the validation cohort. The primary model had a C statistic of 0.75 and showed better discrimination for CP-AKI than previously published models, the C statistics for which ranged from 0.60 to 0.68 (DeLong P<0.001 for each comparison). Greater severity of CP-AKI was monotonically associated with shorter 90 day survival (adjusted hazard ratio 4.63 (95% CI 3.56 to 6.02) for stage 3 CP-AKI versus no CP-AKI). CONCLUSION: This study found that a simple risk score based on readily available variables from patients receiving intravenous cisplatin could predict the risk of severe CP-AKI, the occurrence of which is strongly associated with death.

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.

A system approach to improving guideline-directed therapy for cardio-renal-metabolic conditions: The "beyond diabetes" initiative.

Objective: Despite demonstrating improvements in cardiovascular disease, kidney disease, and survival outcomes, guideline-directed antihyperglycemic medications such as sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like-peptide-1 receptor agonists (GLP1-RA), are underutilized. Many obstacles constrain their use including lack of systematic provider and patient education, concern for medication side effects, and patient affordability. Methods: We designed a multimodality, systems-based approach to address these challenges with the goal of increasing medication utilization across the largest healthcare system in New York State. This multispecialty collaborative included provider and patient education, an electronic health record-enabled platform to identify eligible patients, and access to pharmacists for medication guidance and addressing insurance coverage barriers. Surveys were administered following grand rounds lectures and knowledge-based questionnaires were given before and after case-based sessions for housestaff, with results analyzed using a two-sided Student's t-test. Rates of first prescriptions of SGLT2i/GLP1-RA in combined and individual analyses were compared between the pre- and post-education periods (6 months prior to 3/31/2021 and 6 months post 8/19/2021), and the change in prescriptions per 100 eligible-visits was assessed using the incidence density approach. Results: Among grand rounds participants, 69.3% of respondents said they would make changes to their clinical practice. Knowledge increased by 14.7% (p-value <0.001) among housestaff following case-based sessions. An increase in SGLT2i/GLP1-RA prescribing was noted for eligible patients among internal medicine, cardiology, nephrology, and endocrinology providers, from 11.9 per 100 eligible visits in the pre-education period to 14.8 in the post-education period (absolute increase 2.9 [24.4%], incidence risk ratio 1.24 [95% CI 1.18-1.31]; p-value <0.001). Increases in prescribing rates were also seen among individual medical specialties. Conclusions: Our "Beyond Diabetes" initiative showed an improvement in provider knowledge-base and was associated with a modest, but statistically significant increase in the use of SGLT2i and GLP1-RA throughout our healthcare system.

Prevalence and Overlap of Cardiac, Renal, and Metabolic Conditions in US Adults, 1999-2020.

Importance: Individually, cardiac, renal, and metabolic (CRM) conditions are common and leading causes of death, disability, and health care-associated costs. However, the frequency with which CRM conditions coexist has not been comprehensively characterized to date. Objective: To examine the prevalence and overlap of CRM conditions among US adults currently and over time. Design, Setting, and Participants: To establish prevalence of CRM conditions, nationally representative, serial cross-sectional data included in the January 2015 through March 2020 National Health and Nutrition Examination Survey (NHANES) were evaluated in this cohort study. To assess temporal trends in CRM overlap, NHANES data between 1999-2002 and 2015-2020 were compared. Data on 11 607 nonpregnant US adults (≥20 years) were included. Data analysis occurred between November 10, 2020, and November 23, 2022. Main Outcomes and Measures: Proportion of participants with CRM conditions, overall and stratified by age, defined as cardiovascular disease (CVD), chronic kidney disease (CKD), type 2 diabetes (T2D), or all 3. Results: From 2015 through March 2020, of 11 607 US adults included in the analysis (mean [SE] age, 48.5 [0.4] years; 51.0% women), 26.3% had at least 1 CRM condition, 8.0% had at least 2 CRM conditions, and 1.5% had 3 CRM conditions. Overall, CKD plus T2D was the most common CRM dyad (3.2%), followed by CVD plus T2D (1.7%) and CVD plus CKD (1.6%). Participants with higher CRM comorbidity burden were more likely to be older and male. Among participants aged 65 years or older, 33.6% had 1 CRM condition, 17.1% had 2 CRM conditions, and 5.0% had 3 CRM conditions. Within this subset, CKD plus T2D (7.3%) was most common, followed by CVD plus CKD (6.0%) and CVD plus T2D (3.8%). The CRM comorbidity burden was disproportionately high among participants reporting non-Hispanic Black race or ethnicity, unemployment, low socioeconomic status, and no high school degree. Among participants with 3 CRM conditions, nearly one-third (30.5%) did not report statin use, and only 4.8% and 3.0% used glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors, respectively. Between 1999 and 2020, the proportion of US adults with multiple CRM conditions increased significantly (from 5.3% to 8.0%; P < .001 for trend), as did the proportion having all 3 CRM conditions (0.7% to 1.5%; P < .001 for trend). Conclusions and Relevance: This cohort study found that CRM multimorbidity is increasingly common and undertreated among US adults, highlighting the importance of collaborative and comprehensive management strategies.

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.

Use of TIMP-2 and IGFBP-7 for prediction of postoperative acute kidney injury after cardiac surgery.

BACKGROUND: 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. MATERIALS AND METHODS: 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. RESULTS: Of the 108 patients, 19 (17.6%) patients developed postoperative AKI within 48 hours of surgery. At the threshold of > 0.3 (ng/mL)2/1,000, post-cardiopulmonary bypass [TIMP-2]*[IGFBP-7] had a sensitivity of 13% and specificity of 82% for predicting postoperative AKI. Postoperative day-1 [TIMP-2]*[IGFBP-7] had a sensitivity of 47% and a specificity of 59% for predicting postoperative AKI. There were no differences in [TIMP-2]*[IGFBP-7] values at either timepoint between patients who developed postoperative AKI as compared to those who did not. CONCLUSION: Urinary [TIMP-2]*[IGFBP7] was not predictive of the risk of AKI after cardiac surgery in this single-center study population.

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.

Association of race/ethnicity with mortality in patients hospitalized with COVID-19.

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.

The effect of race coefficients on preemptive listing for kidney transplantation.

Background: Race coefficients of estimated glomerular filtration rate (eGFR) formulas may be partially responsible for racial inequality in preemptive listing for kidney transplantation. Methods: 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. Results: 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 m2 were removed. Compared with Black candidates, the adjusted odds of preemptive listing for White candidates decreased from 2.01 [95% confidence interval (95% CI) 1.78-2.26] before eGFR modulation to 1.18 (95% CI 1.0-1.39; P = 0.046) with the MDRD and 1.37 (95% CI 1.18-1.58) with the CKD-EPI equations after adjusting for race coefficients. Conclusions: Removing race coefficients in GFR estimation formulas may result in a more equitable distribution of Black candidates listed earlier on a preemptive basis.

Mortality of Mechanically Ventilated COVID-19 Patients in Traditional versus Expanded Intensive Care Units in New York.

Rationale: During the first wave of the coronavirus disease (COVID-19) pandemic in New York City, the number of mechanically ventilated COVID-19 patients rapidly surpassed the capacity of traditional intensive care units (ICUs), resulting in health systems utilizing other areas as expanded ICUs to provide critical care. Objectives: To evaluate the mortality of patients admitted to expanded ICUs compared with those admitted to traditional ICUs. Methods: Multicenter, retrospective, cohort study of mechanically ventilated patients with COVID-19 admitted to the ICUs at 11 Northwell Health hospitals in the greater New York City area between March 1, 2020 and April 30, 2020. Primary outcome was in-hospital mortality up to 28 days after intubation of COVID-19 patients. Results: Among 1,966 mechanically ventilated patients with COVID-19, 1,198 (61%) died within 28 days after intubation, 46 (2%) were transferred to other hospitals outside of the Northwell Health system, 722 (37%) survived in the hospital until 28 days or were discharged after recovery. The risk of mortality of mechanically ventilated patients admitted to expanded ICUs was not different from those admitted to traditional ICUs (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.95-1.20; P = 0.28), while hospital occupancy for critically ill patients itself was associated with increased risk of mortality (HR, 1.28; 95% CI, 1.12-1.45; P < 0.001). Conclusions: Although increased hospital occupancy for critically ill patients itself was associated with increased mortality, the risk of 28-day in-hospital mortality of mechanically ventilated patients with COVID-19 who were admitted to expanded ICUs was not different from those admitted to traditional ICUs.

Automated Identification of Patients with Advanced Illness.

Early identification of advanced illness patients within an inpatient population is essential in order to establish the patient's goals of care. Having goals of care conversations enables hospital patients to dictate a plan for care in concordance with their values and wishes. These conversations allow a patient to maintain some control, rather than be subjected to a default care process that may not be desired and may not provide benefit. In this study the performance of two approaches which identify advanced illness patients within an inpatient population were evaluated: LACE (a rule-based approach that uses L - Length of stay, A- Acuity of Admission, C- Co-morbidities, E- Emergency room visits), and a novel approach: Hospital Impairment Score (HIS). The Hospital impairment score is derived by leveraging both rule-based insights and a novel machine learning algorithm. It was identified that HIS significantly outperformed the LACE score, the current model being used in production at Northwell Health. Furthermore, we describe how the HIS model was piloted at a single hospital, was launched into production, and is being successfully used by clinicians at that hospital.

Intubated COVID-19 predictive (ICOP) score for early mortality after intubation in patients with COVID-19.

Patients with coronavirus disease 2019 (COVID-19) can have increased risk of mortality shortly after intubation. The aim of this study is to develop a model using predictors of early mortality after intubation from COVID-19. A retrospective study of 1945 intubated patients with COVID-19 admitted to 12 Northwell hospitals in the greater New York City area was performed. Logistic regression model using backward selection was applied. This study evaluated predictors of 14-day mortality after intubation for COVID-19 patients. The predictors of mortality within 14 days after intubation included older age, history of chronic kidney disease, lower mean arterial pressure or increased dose of required vasopressors, higher urea nitrogen level, higher ferritin, higher oxygen index, and abnormal pH levels. We developed and externally validated an intubated COVID-19 predictive score (ICOP). The area under the receiver operating characteristic curve was 0.75 (95% CI 0.73-0.78) in the derivation cohort and 0.71 (95% CI 0.67-0.75) in the validation cohort; both were significantly greater than corresponding values for sequential organ failure assessment (SOFA) or CURB-65 scores. The externally validated predictive score may help clinicians estimate early mortality risk after intubation and provide guidance for deciding the most effective patient therapies.

Design, development, and deployment of an indication- and kidney function-based decision support tool to optimize treatment and reduce medication dosing errors.

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.

Asthma and COVID-19: An early inpatient and outpatient experience at a US children's hospital.

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.

In-Hospital 30-Day Survival Among Young Adults With Coronavirus Disease 2019: A Cohort Study.

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.

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.