Dataset Information

Validation of an admission coagulation panel for risk stratification of COVID-19 patients.

ABSTRACT:

Background

There is limited data on the markers of coagulation and hemostatic activation (MOCHA) profile in Coronavirus disease 2019 (COVID-19) and its ability to identify COVID-19 patients at risk for thrombotic events and other complications.

Methods

Hospitalized patients with confirmed SARS-COV-2 from four Atlanta hospitals were included in this observational cohort study and underwent admission testing of MOCHA parameters (plasma d-dimer, prothrombin fragment 1.2, thrombin-antithrombin complex, fibrin monomer). Clinical outcomes included deep vein thrombosis, pulmonary embolism, myocardial infarction, ischemic stroke, access line thrombosis, ICU admission, intubation and mortality.

Main results

Of 276 patients (mean age 59 ± 6.4 years, 47% female, 62% African American), 45 (16%) had a thrombotic endpoint. Each MOCHA parameter was independently associated with a thrombotic event (p<0.05) and ≥ 2 abnormalities was associated with thrombotic endpoints (OR 3.3, 95% CI 1.2-8.8) as were admission D-dimer ≥ 2000 ng/mL (OR 3.1, 95% CI 1.5-6.6) and ≥ 3000 ng/mL (OR 3.6, 95% CI 1.6-7.9). However, only ≥ 2 MOCHA abnormalities were associated with ICU admission (OR 3.0, 95% CI 1.7-5.2) and intubation (OR 3.2, 95% CI 1.6-6.4). MOCHA and D-dimer cutoffs were not associated with mortality. MOCHA with <2 abnormalities (26% of the cohort) had 89% sensitivity and 93% negative predictive value for a thrombotic endpoint.

Conclusions

An admission MOCHA profile is useful to risk-stratify COVID-19 patients for thrombotic complications and more effective than isolated d-dimer for predicting risk of ICU admission and intubation.

SUBMITTER: Alabyad D

PROVIDER: S-EPMC7979266 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Risk prediction is an essential part of clinical care, in order to allocate resources and provide care appropriately. During the COVID-19 pandemic risk prediction became a matter of political and public debate as a major clinical need to guide medical and organizational decisions. We previously presented a simplified risk stratification score based on a nomogram developed in Wuhan, China in the early phase of the pandemic. Here we aimed to validate this simplified risk stratification score in a larger patient cohort from one city in Austria. Age, oxygen saturation, C-reactive protein levels and creatinine levels were used to estimate the in-hospital mortality risk for COVID-19 patients in a point based score: 1 point per age decade, 4 points for oxygen saturation <92%, 8 points for CRP > 10 mg/l and 4 points for creatinine > 84 μmol/l. Between June 2020 and March 2021, during the "second wave" of the pandemic, 1,472 patients with SARS-CoV-2 infection were admitted to two hospitals in Graz, Austria. In 961 patients the necessary dataset to calculate the simplified risk stratification score was available. In this cohort, as in the cohort that was used to develop the score, a score above 22 was associated with a significantly higher mortality (p < 0.001). Cox regression confirmed that an increase of one point in the risk stratification score increases the 28-day-mortality risk approximately 1.2-fold. Patients who were categorized as high risk (≥22 points) showed a 3-4 fold increased mortality risk. Our simplified risk stratification score performed well in a separate, larger validation cohort. We therefore propose that our risk stratification score, that contains only two routine laboratory parameter, age and oxygen saturation as variables can be a useful and easy to implement tool for COVID-19 risk stratification and beyond. The clinical usefulness of a risk prediction/stratification tool needs to be assessed prospectively (https://www.cbmed.at/covid-19-risk-calculator/).

Project description:(1) Background: Extracorporeal membrane oxygenation (ECMO) therapy in intensive care units (ICUs) remains the last treatment option for Coronavirus disease 2019 (COVID-19) patients with severely affected lungs but is highly resource demanding. Early risk stratification for the need of ECMO therapy upon admission to the hospital using artificial intelligence (AI)-based computed tomography (CT) assessment and clinical scores is beneficial for patient assessment and resource management; (2) Methods: Retrospective single-center study with 95 confirmed COVID-19 patients admitted to the participating ICUs. Patients requiring ECMO therapy (n = 14) during ICU stay versus patients without ECMO treatment (n = 81) were evaluated for discriminative clinical prediction parameters and AI-based CT imaging features and their diagnostic potential to predict ECMO therapy. Reported patient data include clinical scores, AI-based CT findings and patient outcomes; (3) Results: Patients subsequently allocated to ECMO therapy had significantly higher sequential organ failure (SOFA) scores (p < 0.001) and significantly lower oxygenation indices on admission (p = 0.009) than patients with standard ICU therapy. The median time from hospital admission to ECMO placement was 1.4 days (IQR 0.2-4.0). The percentage of lung involvement on AI-based CT assessment on admission to the hospital was significantly higher in ECMO patients (p < 0.001). In binary logistic regression analyses for ECMO prediction including age, sex, body mass index (BMI), SOFA score on admission, lactate on admission and percentage of lung involvement on admission CTs, only SOFA score (OR 1.32, 95% CI 1.08-1.62) and lung involvement (OR 1.06, 95% CI 1.01-1.11) were significantly associated with subsequent ECMO allocation. Receiver operating characteristic (ROC) curves showed an area under the curve (AUC) of 0.83 (95% CI 0.73-0.94) for lung involvement on admission CT and 0.82 (95% CI 0.72-0.91) for SOFA scores on ICU admission. A combined parameter of SOFA on ICU admission and lung involvement on admission CT yielded an AUC of 0.91 (0.84-0.97) with a sensitivity of 0.93 and a specificity of 0.84 for ECMO prediction; (4) Conclusions: AI-based assessment of lung involvement on CT scans on admission to the hospital and SOFA scoring, especially if combined, can be used as risk stratification tools for subsequent requirement for ECMO therapy in patients with severe COVID-19 disease to improve resource management in ICU settings.

Project description:BackgroundIdentification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients.MethodsA retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration.ResultsCompared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)).ConclusionsThe COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.

Project description:IntroductionIn the context of the COVID-19 pandemic, early identification of patients who are likely to get worse is a major concern. Severity mainly depends on the development of acute respiratory distress syndrome (ARDS) with a predominance of subpleural lesions. Lung point-of-care ultrasonography (L-POCUS) is highly effective in detecting pulmonary peripheral patterns and may be appropriate for examining patients with COVID-19. We suggest that L-POCUS performed during the initial examination may identify patients with COVID-19 who are at a high risk of complicated treatment or unfavourable evolution.Methods and analysisPoint-of-care ultrasonography for risk stratification of non-critical COVID-19 patients on admission is a prospective, multicentre study. Adult patients visiting the emergency department (ED) of participating centres for suspected or confirmed COVID-19 are assessed for inclusion. Included patients have L-POCUS performed within 48 hours following ED admission. The severity of lung damage is assessed using the L-POCUS score based on 36 points for ARDS. Apart from the L-POCUS score assessment, patients are treated as recommended by the WHO. For hospitalised patients, a second L-POCUS is performed at day 5±3. A follow-up is carried out on day 14, and the patient's status according to the Ordinal Scale for Clinical Improvement for COVID-19 from the WHO is recorded.The primary outcome is the rate of patients requiring intubation or who are dead from any cause during the 14 days following inclusion. We will determine the area under the ROC curve of L-POCUS.Ethics and disseminationThe protocol has been approved by the French and Belgian Ethics Committees and is carried out in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. The study is funding by a grant from the French Health Ministry, and its findings will be disseminated in peer-reviewed journals and at scientific conferences.Trial registration numberNCT04338100.