Trends in Venous Thromboembolism Anticoagulation in Patients Hospitalized With COVID-19

Importance: Venous thromboembolism (VTE) is a common complication of COVID-19. It is not well understood how hospitals have managed VTE prevention and the effect of prevention strategies on mortality. Objective: To characterize frequency, variation across hospitals, and change over time in VTE prophylaxis and treatment-dose anticoagulation in patients hospitalized for COVID-19, as well as the association of anticoagulation strategies with in-hospital and 60-day mortality. Design, Setting, and Participants: This cohort study of adults hospitalized with COVID-19 used a pseudorandom sample from 30 US hospitals in the state of Michigan participating in a collaborative quality initiative. Data analyzed were from patients hospitalized between March 7, 2020, and June 17, 2020. Data were analyzed through March 2021. Exposures: Nonadherence to VTE prophylaxis (defined as missing ≥2 days of VTE prophylaxis) and receipt of treatment-dose or prophylactic-dose anticoagulants vs no anticoagulation during hospitalization. Main Outcomes and Measures: The effect of nonadherence and anticoagulation strategies on in-hospital and 60-day mortality was assessed using multinomial logit models with inverse probability of treatment weighting. Results: Of a total 1351 patients with COVID-19 included (median [IQR] age, 64 [52-75] years; 47.7% women, 48.9% Black patients), only 18 (1.3%) had a confirmed VTE, and 219 (16.2%) received treatment-dose anticoagulation. Use of treatment-dose anticoagulation without imaging ranged from 0% to 29% across hospitals and increased over time (adjusted odds ratio [aOR], 1.46; 95% CI, 1.31-1.61 per week). Of 1127 patients who ever received anticoagulation, 392 (34.8%) missed 2 or more days of prophylaxis. Missed prophylaxis varied from 11% to 61% across hospitals and decreased markedly over time (aOR, 0.89; 95% CI, 0.82-0.97 per week). VTE nonadherence was associated with higher 60-day (adjusted hazard ratio [aHR], 1.31; 95% CI, 1.03-1.67) but not in-hospital mortality (aHR, 0.97; 95% CI, 0.91-1.03). Receiving any dose of anticoagulation (vs no anticoagulation) was associated with lower in-hospital mortality (only prophylactic dose: aHR, 0.36; 95% CI, 0.26-0.52; any treatment dose: aHR, 0.38; 95% CI, 0.25-0.58). However, only the prophylactic dose of anticoagulation remained associated with lower mortality at 60 days (prophylactic dose: aHR, 0.71; 95% CI, 0.51-0.90; treatment dose: aHR, 0.92; 95% CI, 0.63-1.35). Conclusions and Relevance: This large, multicenter cohort of patients hospitalized with COVID-19, found evidence of rapid dissemination and implementation of anticoagulation strategies, including use of treatment-dose anticoagulation. As only prophylactic-dose anticoagulation was associated with lower 60-day mortality, prophylactic dosing strategies may be optimal for patients hospitalized with COVID-19

Derivation and external validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19: A multicenter retrospective cohort study

ABSTRACT: As severe acute respiratory syndrome coronavirus 2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with 2019 novel coronavirus (COVID-19) that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals. In this retrospective observational study, we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for severe acute respiratory syndrome coronavirus 2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model. Two thousand one hundred and ninety-three patients in the Mi-COVID19 registry met our inclusion criteria. The derivation and validation sets ultimately included 1690 and 398 patients, respectively, with mortality rates of 19.6% and 18.6%, respectively. The average age of participants in the study after exclusions was 64 years old, and the participants were 48% female, 49% Black, and 87% non-Hispanic. Our final model includes the patient\u27s age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital\u27s COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The area under the receiver operating characteristics curve for the derivation and validation sets were .796 (95% confidence interval, .767-.826) and .829 (95% confidence interval, .782-.876) respectively. We conclude that the risk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter

The South Carolina Multigenerational Linked Birth Dataset: Developing Social Mobility Measures Across Generations to Understand Racial/Ethnic Disparities in Adverse Birth Outcomes in the US South

Objectives To describe the creation of a multigenerational linked dataset with social mobility measures for South Carolina (SC), as an example for states in the South and other areas of the country. Methods Using unique identifiers, we linked birth certificates along the maternal line using SC birth certificate data from 1989 to 2014, and compared the subset of records for which linking was possible with two comparison groups on sociodemographic and birth outcome measures. We created four multi-generational social mobility measures using maternal education, paternal education, presence of paternal information, and a summary score incorporating the prior three measures plus payment source for births after 2004. We compared social mobility measures by race/ethnicity. Results Of the 1,366,288 singleton birth certificates in SC from 1989 to 2014, we linked 103,194, resulting in 61,229 unique three-generation units. Mothers and fathers were younger and had lower education, and low birth weight was more common, in the multigenerational linked dataset than in the two comparison groups. Based on the social mobility summary score, only 6.3% of White families were always disadvantaged, compared to 30.4% of Black families and 13.2% of Hispanic families. Moreover, 32.8% of White families were upwardly mobile and 39.1% of Black families were upwardly mobile, but only 29.9% of Hispanic families were upwardly mobile. Conclusions for Practice When states are able to link individuals, birth certificate data may be an excellent source for examining population-level relationships between social mobility and adverse birth outcomes. Due to its location in the Deep South, the multigenerational SC dataset may be particularly useful for understanding racial/ethnic difference in social mobility and birth outcomes

COVID-19 risk index (CRI): a simple and validated emergency department risk score that predicts mortality and the need for mechanical ventilation

Although certain risk factors have been associated with morbidity and mortality, validated emergency department (ED) derived risk prediction models specific to coronavirus disease 2019 (COVID-19) are lacking. The objective of this study is to describe and externally validate the COVID-19 risk index (CRI). A large retrospective longitudinal cohort study was performed to analyze consecutively hospitalized patients with COVID-19. Multivariate regression using clinical data elements from the ED was used to create the CRI. The results were validated with an external cohort of 1799 patients from the MI-COVID19 database. The primary outcome was the composite of the need for mechanical ventilation or inpatient mortality, and the secondary outcome was inpatient mortality. A total of 1020 patients were included in the derivation cohort. A total of 236 (23%) patients in the derivation cohort required mechanical ventilation or died. Variables independently associated with the primary outcome were age ≥ 65 years, chronic obstructive pulmonary disease, chronic kidney disease, cerebrovascular disease, initial D-dimer \u3e 1.1 µg/mL, platelet count \u3c 150 K/µL, and severity of SpO2:FiO2 ratio. The derivation cohort had an area under the receiver operator characteristic curve (AUC) of 0.83, and 0.74 in the external validation cohort Calibration shows close adherence between the observed and expected primary outcomes within the validation cohort. The CRI is a novel disease-specific tool that assesses the risk for mechanical ventilation or death in hospitalized patients with COVID-19. Discrimination of the score may change given continuous updates in contemporary COVID-19 management and outcomes