A Retrospective analysis of DIC score and SIC score in prediction of COVID-19 severity
Abstract
Background
The novel Disseminated Intravascular Coagulation (DIC) score [platelet count, prolonged prothrombin time, D-dimer, and fibrinogen] and Sepsis Induced Coagulopathy (SIC) score [platelet count, International normalized ratio, and Sequential organ failure assessment score] are markers of coagulopathy, which, for the first time, are explored in line with the COVID-19 disease outcomes. The correlation of D-dimer with these findings is also studied.
Patients and methods
A retrospective analysis of hospital-based records of 168 COVID-19 patients. Data including D-dimer, routine investigations, DIC and SIC scorings (all within three days of admission) were collected and correlated with the outcomes. The study was conducted in a tertiary care center catering to population of North India.
Results
Higher DIC score (1·59 ± 1·18 v/s 0·96 ± 1·18), SIC score (1·60 ± 0·89 v/s 0·63 ± 0·99), and D-dimer titers (1321·33 ± 1627·89 v/s 583·66 ± 777·71 ng/ml) were significantly associated with severe COVID-19 disease (P<0·05). DIC score and SIC score ≥ 1, and D-dimer ≥ 1315 ng/ml for severe disease; DIC score ≥ 1, SIC score ≥ 2, and D-dimer ≥ 600 ng/ml for Pulmonary Embolism (PE); and DIC score and SIC score ≥ 1, and D-dimer level ≥ 990 ng/ml for mortality were the respective cut-off values we found from our study.
Conclusion
Higher DIC scores, SIC scores, and D-dimer values are associated with severe COVID-19 disease, in-hospital mortality, and PE risk. They can serve as easily accessible early markers of severe disease and prioritize hospital admissions in the presently overburdened scenario, and may be used to develop prognostic prediction models.
Highlights
DIC scores, SIC scores, and D-dimer values are hereby studied in association with COVID-19 disease severity, in-hospital mortality, and PE risk. They serve as easily accessible early markers of severe disease and prioritize hospital admissions in the presently overburdened scenario, and may be used to develop prognostic prediction models.
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