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2.
Medicine (Baltimore) ; 100(40): e27422, 2021 Oct 08.
Article in English | MEDLINE | ID: covidwho-1462561

ABSTRACT

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's age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital's 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.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Age Factors , Aged , Aged, 80 and over , Body Mass Index , Comorbidity , Creatinine/blood , Female , Health Behavior , Humans , Logistic Models , Male , Michigan/epidemiology , Middle Aged , Oximetry , Prognosis , ROC Curve , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Sex Factors , Socioeconomic Factors
3.
J Thromb Thrombolysis ; 2021 Sep 23.
Article in English | MEDLINE | ID: covidwho-1432598

ABSTRACT

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 > 1.1 µg/mL, platelet count < 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.

5.
JAMA Netw Open ; 4(6): e2111788, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1265353

ABSTRACT

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.


Subject(s)
Anticoagulants/therapeutic use , COVID-19/complications , Hospitalization/trends , SARS-CoV-2 , Venous Thromboembolism/prevention & control , Aged , COVID-19/epidemiology , Female , Hospital Mortality/trends , Humans , Incidence , Male , Middle Aged , Retrospective Studies , Survival Rate/trends , United States/epidemiology , Venous Thromboembolism/epidemiology , Venous Thromboembolism/etiology
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