The Clinical Course of Coronavirus Disease 2019 in a US Hospital System: A Multistate Analysis

Mody, Aaloke; Lyons, Patrick G.; Guillamet, Rodrigo Vazquez; Michelson, Andrew P.; Yu, Sean; Namwase, Angella Sandra; Sinha, Pratik; Powderly, William G.; Woeltje, Keith F.; Geng, Elvin

doi:10.1093/aje/kwaa286

Cited by 18 publications

(23 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coronavirus disease 2019 has a wide clinical spectrum, with disease severities ranging from completely asymptomatic to the need for intubation and death [1][2][3][4] . For example, those with existing cardiac problems are more likely to suffer from more severe disease life courses [5][6][7][8][9][10][11] , with potential modifier effects from different medication classes [12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Zhou

Lee²,

Wang³

et al. 2021

npj Digit. Med.

View full text Add to dashboard Cite

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Zhou

Lee²,

Wang³

et al. 2021

npj Digit. Med.

View full text Add to dashboard Cite

show abstract

“…Furthermore, death is the only end event considered. [ 1 , 2 , 7 – 11 ] also consider multistate survival models with different predictors, but only with hospitalised patients and, as a consequence, cannot give any assessment of the hospitalisation risk for the general population. This comment applies also to [ 22 ], published after the submission of the first version of the present paper.…”

Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain)

et al. 2021

View full text Add to dashboard Cite

This paper analyses COVID-19 patients’ dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed.

show abstract

“…The clinical course of Covid-19 is characterized by an initial period of mild to moderate symptoms, followed by progressive respiratory failure and requirement for intensive care unit (ICU)-level organ support or death in some patients. 1,2 The majority of patients requiring hospitalization are moderately ill, not initially requiring ICU-level organ support. 3-5 Limited therapies are available to prevent clinical progression to ICU-level organ support and death among moderately ill hospitalized patients.…”

Section: Introductionmentioning

confidence: 99%

Therapeutic Anticoagulation in Non-Critically Ill Patients with Covid-19

Lawler

Goligher

Berger

et al. 2021

Preprint

View full text Add to dashboard Cite

Background Thrombo-inflammation may contribute to morbidity and mortality in Covid-19. We hypothesized that therapeutic-dose anticoagulation may improve outcomes in non-critically ill patients hospitalized for Covid-19. Methods In an open-label adaptive multiplatform randomized controlled trial, non-critically ill patients hospitalized for Covid-19, defined by the absence of critical care-level organ support at enrollment, were randomized to a pragmatic strategy of therapeutic-dose anticoagulation with heparin or usual care pharmacological thromboprophylaxis. The primary outcome combined survival to hospital discharge and days free of organ support through 21 days, which was evaluated with Bayesian statistical models according to baseline D-dimer. Results The trial was stopped when prespecified criteria for superiority were met for therapeutic-dose anticoagulation in groups defined by high (≥2-fold elevated) and low (<2-fold elevated) D-dimer. Among 2219 participants in the final analysis, the probability that therapeutic anticoagulation increased organ support-free days compared to thromboprophylaxis was 99.0% (adjusted odds ratio 1.29, 95% credible interval 1.04 to 1.61). The adjusted absolute increase in survival to hospital discharge without organ support with therapeutic-dose anticoagulation was 4.6% (95% credible interval 0.7 to 8.1). In the primary adaptive stopping groups, the final probabilities of superiority for therapeutic anticoagulation were 97.3% in the high D-dimer group and 92.9% in the low D-dimer group. Major bleeding occurred in 1.9% and 0.9% of participants randomized to therapeutic anticoagulation and thromboprophylaxis, respectively. Conclusions In non-critically ill patients with Covid-19, an initial strategy of therapeutic-dose anticoagulation with heparin increases the probability of survival to hospital discharge with reduced use of organ support.

show abstract

The Clinical Course of Coronavirus Disease 2019 in a US Hospital System: A Multistate Analysis

Cited by 18 publications

References 35 publications

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain)

Therapeutic Anticoagulation in Non-Critically Ill Patients with Covid-19

Contact Info

Product

Resources

About