Page 245 - Haematologica Vol. 107 - September 2022

LETTER TO THE EDITOR
A validated clinical-genetic score for assessing the risk of thrombosis in patients with COVID-19 receiving thromboprophylaxis
Venous thromboembolic events (VTE) have emerged as a common complication among patients hospitalized for COVID-19 with an estimated incidence of 14-31%, increas- ing disease severity and mortality.1 The incidence is even higher in critically ill patients admitted to intensive care units (ICU),1,2 including those provided thromboprophylaxis at the moment of hospital admission.2 Therefore, the ef- fectiveness of anticoagulant prophylaxis is actually unclear due to no significant reduction in thrombotic complications despite prophylactic therapy.2,3 These studies, however, face a major limitation: the lack of tools for assessing the risk of VTE. Many variables affect the ap- pearance of a VTE, both clinical and genetic.4 With this in mind, the present work examines whether the Thrombo inCode (TiC) score, which combines genetic and clinical risk variables and has shown the capacity to predict VTE in different populations,5,6,7 is of use in predicting VTE in patients with COVID-19 who were administered prophy- lactic anticoagulation therapy at the time of hospital ad- mission.
The PRECIS_COVID19 cohort consists of 734 patients; all aged over 18 years, with a confirmed diagnosis of COVID- 19, all of whom were admitted to the Hospital de la Santa Creu i Sant Pau (Barcelona, Spain) between April and July 2020. COVID-19 was confirmed by real-time reverse-tran- scription polymerase chain reaction (PCR) assays using nasal and pharyngeal swabs. All patients were adminis- tered standard thromboprophylactic treatment at the time of their admission to hospital following international recommendations.8
A total of 279 patients had a D-Dimer value below 1,000 ng/mL (validated threshold to rule out VTE)9 and were thus considered as non-VTE (control) patients. Of the re- mainder, 382 patients had D-Dimer levels of >1,000 ng/mL and were excluded from further analyses, and 73 suffered a VTE event during hospitalization (either a deep venous thrombosis or a pulmonary embolism). Diagnoses were confirmed using Doppler ultrasonography, magnetic res- onance, arteriography, phlebography, pulmonary gammag- raphy and computed tomography pulmonary angiography. The total number of subjects in the study was therefore n=352 (279 controls plus 73 cases). A total of five models were therefore compared, the details of which are de- scribed below:
1. Genetic risk score (GRS)
The twelve genetic variants reported by Soria et al.5, in-
cluding the variants rs6025, rs118203905, rs118203906, rs1799963, rs121909548 (chr1:173873176:C:A, SERPINC1), rs1801020 (chr5:176836532:A:G, F12), rs5985 (chr6:6318795:C:A, F13), rs2232698 (chr14:94756669:G:A, SERPINE10) and four variants providing the ABO:A1 haplo- type (additive effect of A1 allele).
2. Clinical risk score (CRS)
Five clinical variables were assessed: age, sex, obesity, smoking habit, and diabetes. These variables have been shown to be associated with VTE and have been reported to be useful in estimating VTE risk.7 Smoking habit was codified as a dichotomic variable (smoker/non-smoker); obesity was defined as body mass index >30.
3. Thrombo inCode model (TiC)
A combination of the variables in both the genetic risk score (GRS) and the clinical risk score (CRS) described in the models 1 and 2. The original Thrombo inCode (TiC) model also includes family history, oral contraceptive use and pregnancy, but were not evaluated, as they were dif- ficult to obtain in the COVID19 context.
4. Factor V Leiden plus prothrombin score
The classic genetic thrombophilia model based on the Factor V Leiden (FVL) (rs6025; chr1:169519049:T:C, F5) and prothrombin (PT) G20210A (rs1799963; chr11:46761055:G:A, F2) mutations.
5. Factor V Leiden plus prothrombin plus clinical risk score
Combination of the variables in the FVL+PT and the CRS models explained before.
A descriptive analysis of both the genetic and clinical vari- ables was performed, and the relationship with VTE as- sessed by the Chi-squared test for bivariate associations. The same method was used to evaluate the association between ICU admission and mortality rates (at 30 and 90 days after hospital admission), as well as the association between VTE and mortality rates and ICU admission. Sig- nificance was set at P<0.05.
All risk models were constructed by including the cor- responding variables as additive linear predictors of VTE using logistic regression. The predictive capacity of the different models was examined using receiver operating curves (ROC), employing the optimal cut-off based on the Youden index.11 The significance of the predictive capacity of each score was measured by comparing with a random model (area under the ROC curve [AUC] of 0.5), using the DeLong test.13 In addition to determining test sensitivity
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