Keywords COVID-19 - SARS-CoV-2 - disseminated intravascular coagulation - antithrombin - D-dimers
Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease
2019 (COVID-19).[1 ]
[2 ]
[3 ] The SARS-CoV-2 pandemic has put the health care systems worldwide under extremely
high stress. COVID-19 is characterized by acute pneumonia which may progress to respiratory
failure and life-threatening complications, including acute respiratory distress syndrome
and multisystem organ failure with fatal outcome.[4 ]
[5 ] COVID-19 should be regarded as a systemic disease involving multiple systems.[6 ]
[7 ]
[8 ] COVID-19 is associated with excessive inflammation, platelet activation, endothelial
dysfunction, blood coagulation activation, and fibrin formation.[9 ]
[10 ]
[11 ] Current management of patients hospitalized in an intensive care unit (ICU) is based
on supportive care and the mortality rate may be high.[12 ]
[13 ] Blood hypercoagulability—documented by an increase in D-dimers and a decrease in
antithrombin (AT)—is frequently encountered among COVID-19 patients.[14 ]
Identification of patients with COVID-19 being at high risk for clinical deterioration
is a challenging issue for an earlier adapted treatment and positive clinical outcome.[15 ]
[16 ] These patients could benefit from earlier appropriate oxygen support, antithrombotic
agents, and compassionate-use therapies, including antiretrovirals, anti-inflammatory
drugs, immunomodulatory compounds, and convalescent plasma.[17 ]
[18 ]
[19 ]
[20 ]
[21 ]
In this prospective observational study conducted at the COVID-19 center of Tenon
University Hospital in Paris, we aimed to identify the most relevant clinical and
hematological risk factors for worsening of COVID-19 by constructing an accurate risk
assessment tool.
Methods
Participants
In the set-up of the COVID-19 center at Tenon University Hospital (APHP.6, Sorbonne
University, Paris), we designed a prospective observational cohort study and enrolled
all admitted patients in two phases: (1) between March 18 and April 5, 2020 to constitute
the derivation cohort and (b) between April 6 and April 21 to constitute the validation
cohort which was composed only of new patients. According to the follow-up during
the course of the disease, two groups became evident: (1) good prognosis group (G-group) with patients hospitalized at the conventional COVID-19 ward. G-group patients
were assessed on the first admission day. (2) Worsening disease group (W-group) included patients admitted to the ICU from the emergency departments since
they presented with clinically deteriorated COVID-19. W-group patients were assessed
on the first day of admission.
Definitions
All patients had laboratory-confirmed COVID-19 infection and were hospitalized either
in the conventional COVID-19 ward of the medical department or in the COVID-19 ICU.
A confirmed case of COVID-19 was defined by a positive result on a reverse transcriptase
polymerase chain reaction (rt-PCR) assay from a specimen collected on a nasopharyngeal
swab and imaging of well-documented pneumonia according the Fleischner Society consensus
statement.[22 ] Pregnant women, patients receiving anticoagulant treatment, and patients with cytopenia
due to current anticancer treatment were excluded. All patients hospitalized in the
conventional medical department or in the ICU routinely received thromboprophylaxis
with body-weight-adapted enoxaparin. All clinical and biological data were cross-checked
with the electronic files using the ORBIS software (Agfa Healthcare) and the GLIMS
laboratory information system (MIPS France) of Tenon University Hospital.
Hematological Parameters
In the set-up of the COVID-19 center at Tenon University Hospital, all patients were
evaluated daily with a “COAG-COVID” panel composed of tests of various parameters:
prothrombin time (PT), fibrinogen, D-dimers, AT activity, protein C (PC) activity,
and platelet count. These tests are predictors of compensated disseminated intravascular
coagulation (DIC) according to the International Society on Thrombosis and Haemostasis
(ISTH) score (compensated DIC-ISTH).[23 ] In addition to platelets, all other hemogram parameters were also analyzed. Blood
samples were routinely obtained via atraumatic antecubital venipuncture or from the
central vein catheter. For coagulation tests, blood was collected in 3.5 mL Vacuette
tubes containing 0.109 mol/L trisodium citrate—one volume trisodium citrate to nine
volumes of blood—(Greiner Bio-One, Courtaboeuf, France) and centrifuged at 2,000 g for 20 minutes at room temperature for platelet-poor plasma (PPP) preparation. Within
30 minutes upon preparation, PPP samples were assessed for blood coagulation tests
on the STA-R Max instrument from Stago (Asnières-sur-Seine, France) according to manufacturer's
instructions. PT was assessed with chronometric assay using the STA-NeoPTimal reagent
(ref: 01165). Fibrinogen was measured with the Clauss-based chronometric assay using
the Liquid FIB reagent (ref: 00673). D-dimers were measured with turbidimetric assay
using the STA-Liatest D-Di Plus reagent (ref: 00662). AT and PC activities were measured
with the amidolytic assays STACHROM ATIII (ref: 00596, from Stago, Asnières, France)
and BIOPHEN PC (LRT) (ref. 221205, from Hyphen BioMed, Neuville-sur-Oise, France),
respectively. Hemogram parameters and platelet count were assessed on whole blood
collected in 4 mL EDTA BD Vacutainer tubes (Becton-Dickinson, Le Pont-de-Claix, France)
using the Sysmex XN-3100 instrument (Paris, France). All hematological tests were
performed at the ISO certified Central Haematological Laboratory of Thrombosis Center
at Tenon University Hospital in Paris.
Ethics
The protocol of the study was in accordance with the commitment of the Helsinki Declaration
and all patients received care according to the recommended institutional practice
during the COVID-19 pandemic. All hematological tests were performed in the frame
of routine monitoring of patients as decided by the local institutional board for
the management of the COVID-19 patients. This study was approved by the institutional
ethics board. The observational design of the study did not impose the need for getting
informed consent from individual patients.
Outcomes
The study end point was disease worsening requiring ICU admission. Disease worsening
requiring ICU admission was defined according to the following criteria[16 ]: acute hypoxemic respiratory failure judged on the basis of increased need for oxygen
supply more than 9 L/minute or clinical signs of respiratory insufficiency (shortness
of breath, respiratory rate ≥ 30 times/min), arterial oxygen saturation (resting status)
≤ 92%, shock, myocardial dysfunction, bacterial or viral coinfections, and acute kidney
injury.
Statistical Analysis
Data from the first hospitalization day for the G-group and the first day on ICU admission
for the W-group were analyzed. The normal ranges of the COAG-COVID panel of tests
have been established by the Thrombosis Center of Tenon University Hospital, according
to the requirements for the good quality of laboratory practice. The number of patients
included in the derivation cohort was calculated according to the following assumptions:
(1) the model had to be constructed according to the rule of thumb, the so-called
events per variable (EPV) 10–1 and (2) less than 10 variables should be included in
the model in order for it to be easy to use.[24 ]
[25 ]
[26 ] Thus, at least 70 patients were required to be enrolled in the W-group to respond
to the above conditions accommodating at maximum a seven-variable model. Continuous
variables were summarized as median (interquartile range) and categorical variables
as frequency and percentage. Because of the deviation from normality (as evidenced
by the Shapiro-Wilk test), the comparison of continuous variables between patients
with worsening disease and those hospitalized at the conventional ward was performed
using the Mann–Whitney–Wilcoxon test for independent samples. Regarding the associations
between disease worsening and hematological parameters at baseline, the latter were
converted to binary variables on the basis of laboratory normal values. Univariate
and multivariate logistic regression analyses were performed to evaluate the independent
associations between disease worsening (dependent variable) and the examined hematological
parameters (independent variables) as binary variables: 1 (yes) or 0 (no). In the
multivariate approach aiming to create a score predicting disease worsening, stepwise
selection of variables was performed on the basis of the Akaike information criterion
(AIC). To obtain the score, β coefficients of the final logistic regression model
were rounded and rescaled from the logarithm of the odds ratios (ORs). Receiver operating
characteristic (ROC) curve analysis was subsequently undertaken; the area under the
ROC curve (AUC) was estimated to evaluate model discrimination performance. The optimal
cut-off level was identified through the maximization of unweighted Youden's index
by calculating sensitivity, specificity, positive predictive value, (PPV) and negative
predictive value (NPV). Calibration of the model was examined with the Hosmer–Lemeshow
test and the respective plot of expected versus observed probability was constructed,
for the G-group and W-group. The level of statistical significance was set at 0.05.
Data were analyzed using the STATA/SE version 13 statistical software (Stata Corp.,
College Station, Texas, United States).
Results
Derivation Cohort
Among 330 patients with confirmed COVID-19 disease, 310 patients who responded to
the inclusion criteria were enrolled in the derivation cohort. The remaining 20 patients
were excluded because of pregnancy (n = 5), pancytopenia due to chemotherapy (n = 6), or oral anticoagulant treatment with direct oral anticoagulants or vitamin
K antagonists (n = 9). The G-group included 208 patients. The W-group consisted of 102 patients; 87%
of these patients were admitted to the ICU directly from the emergency department.
Males were 113 out of 208 patients in the G-group and 76 out of 102 patients in the
W-group. Age ranged from 19 to 95 years in the G-group and from 30 to 93 years in
the W-group.
Validation Cohort
The validation cohort included 120 patients stratified in the G-group (n = 89) and the W-group (n = 31); 90% of patients in the W-group were admitted to the ICU directly from the
emergency department. Males were 58 out of 89 patients in the G-group and 25 out of
31 patients in the W-group. Age ranged from 21 to 95 years in the G-group and from
30 to 98 years in the W-group.
The derivation and validation cohorts were comparable regarding the age and sex distribution
in each one of the two groups (G- and W-groups). Detailed epidemiological, clinical,
and hematological characteristics of the derivation and validation cohorts are shown
in [Tables 1 ] and [2 ]. Very few patients received compassionated or antiviral treatments. Among the G-group
patients, five received treatment with lopinavir-ritonavir, one was treated with remdesivir,
and another one received hydroxychloroquine. Among the patients in W-group, only four
received lopinavir-ritonavir. Upon hospitalization all patients received thromboprophylaxis
with low-molecular-weight heparin (LMWH; enoxaparin). The dose of enoxaparin was adapted
according to the evolution of D-dimers and the levels of the anti-Xa activity. Moreover,
patients with AT deficiency (AT activity lower than 50%) upon admission or during
hospitalization received treatment with AT concentrate according to the protocol published
elsewhere.[14 ]
Table 1
Demographic data, cardiovascular risk factors, comorbidities, and DIC rates in hospitalized
COVID-19 patient enrolled in the derivation cohort hospitalized in conventional ward
(G-group) or presenting worsening disease (W-group)
Derivation cohort
Validation cohort
G-group
W-group
p -Value
G-group
W-group
p -Value
(n = 208)
(n = 102)
(n = 89)
(n = 31)
Demographics
Gender male
54.3% (113/208)
74.5% (76/102)
–
66.3% (59/89)
80.6% (25/31)
Age (y)[a ]
19–95 (median: 66)
30–93 (median: 61)
0.06
21–95 (median: 63)
30–80 (median: 59)
0.06
Cardiovascular risk factors
Hypertension
39.5% (82/208)
64.6% (66/102)
<0.001
42.7% (38/89)
61.3% (19/31)
0.001
Diabetes
15.8 (33/208)
33.3% (34/102)
0.006
21.3% (19/89)
12.9% (4/31)
0.01
Obesity
7.8% (16/208)
31.5% (32/102)
<0.001
10.1% (9/89)
32.2% (10/31)
<0.001
Active smoking
4.4% (9/208)
3.9% (4/102)
0.612
4.5% (4/89)
3.2% (1/31)
0.5
Comorbidities
Chronic renal disease
7.3% (17/208)
18.8% (19/102)
0.003
11.2% (10/89)
19.4% (6/31)
0.002
Chronic obstructive pulmonary disease
8.3% (17/208)
10.7% (11/102)
0.548
12.4% (11/89)
15.1% (5/31)
0.62
Active cancer
12.6% (26/208)
1.9% (2/102)
0.003
10,1% (9/89)
3.2% (1/31)
0.001
End-stage renal disease
3.9% (8/208)
8.8% (9/102)
0.105
2.2% (2/89)
9.7% (3/31)
0.051
Coagulopathy
Compensated DIC (DIC-ISTH score ≥5)
8.2% (17/208)
28.4% (29/102)
<0.001
2.2% (2/89)
3.2% (1/31)
0.3
Abbreviations: BMI, body mass index; DIC, disseminated intravascular coagulation;
ISTH, International Society on Thrombosis and Haemostasis; VTE, venous thromboembolism.
a Values for age are in the minimum and maximum range.
Table 2
Variations of hematological parameters in the conventional group (G-group) and worsening
disease group (W-group) of COVID-19 patients
Derivation cohort
Validation cohort
Normal range
G-group (n = 208)
W- group (n = 102)
p- Value
G-group (n = 89)
W-group (n = 31)
p -Value
Blood coagulation parameters
PT (s)
<13.6
14.0 (13.3–14.8)
14.6 (13.9–15.6)
0.0002
14.6 (13.5–15.7)
14.9 (13.6–16.1)
0.001
Fibrinogen (g/L)
1.8–4.0
5.9 (4.7–7.0)
6.9 (6.0–7.6)
<0.0001
6.1 (5.1–7.2)
7.1 (6.1–7.8)
0.001
D-dimers (ng/mL)
<500 for age under 60 years
1,171 (608–2,136)
1,881 (1,074–3,662)
<0.0001
2,434 (923–3,082)
2,599 (1,200–3,600)
0.05
Antithrombin (%)
80–120
96 (84–107)
88 (79–99)
0.0003
95 (83–107)
90 (80–100)
0.06
Protein C (%)
70–130
97 (79–113)
88 (71–100)
0.0002
101 (81–115)
101 (80–116)
0.2
Blood cell parameters
Hemoglobin (g/dL)
12.0–16.0
12.6 (11.3–13.7)
12.1 (10.4–13.4)
0.019
12.4 (11.2–13.5)
10.5 (8.3–11.8)
0.01
Hematocrit (%)
35.0–47.0
37.1 (34.2–40.3)
35.4 (31.1–38.8)
0.006
38.1 (35.1–41.0)
28.3 (28.–36.2)
0.002
Red blood cells (× 109 /L)
4.0–5.2
4.3 (4.0–4.8)
4.2 (3.7–4.6)
0.039
4.5 (4.2–5.1)
3.9 (3.5–4.3)
0.02
Platelets (× 109 /L)
150–400
240 (179–315)
211 (151–257)
0.0003
272 (182–350)
276 (200–340)
0.2
White blood cells (× 109 /L)
4.0–10.0
6.8 (5.1–8.7)
7.7 (6.1–10.5)
0.013
6.5 (5.0–8.6)
8.1 (6.9–11.2)
0.01
Neutrophils (× 109 /L)
1.5–7.0
4.9 (3.4–6.6)
6.2 (4.8–8.9)
0.0001
4.7 (3.2–6.4)
6.5 (4.9–9.1)
0.001
Lymphocytes (× 109 /L)
1.5–4.0
1.04 (0.74–1.52)
0.76 (0.53–1.10)
<0.0001
1.26 (1.12–1.72)
0.97 (0.52–1.32)
0.001
Monocytes (× 109 /L)
0.1–1.0
0.49 (0.32–0.68)
0.29 (0.19–0.49)
<0.0001
0.50 (0.33–0.69)
0.30 (0.18–0.51)
0.002
Eosinophils (× 109 /L)
0.03–0.7
0.02 (0–0.08)
0.01 (0–0.04)
0.004
0.02 (0–0.09)
0.01 (0–0.4)
0.001
Basophils (× 109 /L)
<0.1
0.01 (0.01–0.02)
0.01 (0–0.02)
0.065
0.01 (0.01–0.02)
0.01 (0–0.02)
0.06
Abbreviation: PT, prothrombin time.
In both cohorts, there were no missing values of the COAG-COVID biomarkers and clinical
predictors. A follow-up of at least 12 days was predicted. However, the follow-up
for the most recently enrolled patients was shorter because France was still at the
peak of COVID-19 public health crisis when the database closed and data analysis was
performed. Male gender representation was significantly higher in the W-group as compared
with the G-group. Obesity (body mass index [BMI] > 30), arterial hypertension, and
diabetes were significantly more frequent in the W-group as compared with the G-group.
Chronic renal insufficiency and cardiovascular disease were more frequent in the W-group
as compared with the G-group. Interestingly, very few patients in both groups were
active smokers. Data are summarized in [Table 1 ].
Derivation of the COMPASS-COVID-19 Risk Assessment Model
Derivation of the COMPASS-COVID-19 Risk Assessment Model
In the derivation cohort, compensated DIC was diagnosed in 8.2% of patients in the
G-group and in 28.2% of patients in the W-group (p = 0.001; [Table 1 ]). Compared with the G-group, the patients in the W-group had significantly lower
levels of AT, PC, platelets, lymphocyte, monocyte, and red blood cell counts, as well
as hemoglobin and hematocrit. They had significantly higher levels of fibrinogen,
D-dimers, white blood cells, and neutrophil counts. No difference was noted in eosinophil
and basophil counts. Data are summarized in [Table 2 ].
Clinical Predictors for Disease Worsening
The univariate analysis showed that the risk for worsening disease was increased in
men compared with women: OR = 2.43, 95% confidence interval (CI): 1.44 to 4.10. Among
clinical predictors, obesity (OR = 5.44, 95% CI: 2.77–10.67), hypertension (OR = 2.79,
95% CI: 1.69–4.62), and diabetes (OR = 2.13, 95% CI: 1.23–3.69) were significantly
related with an increased risk for disease worsening. Surprisingly current smoking
was not a significant risk factor for clinical deterioration of COVID-19 patients.
Among comorbidities, chronic kidney disease (OR = 2.92, 95%CI:1.40–6.09) was a risk
factor for disease worsening ([Table 3 ]).
Table 3
Univariate analysis of COVID-19 patients determining the risk factors associated with
worsening disease
Examined parameters
Compared categories
OR (95% CI)
p -Value
Demographics
Gender
Male vs. female
2.43 (1.44–4.10)
0.001
Age (y)
≥70 vs. <70
0.48 (0.28–0.81)
0.006
Cardiovascular risk factors
Obesity
Yes vs. no
5.44 (2.77–10.67)
<0.001
Hypertension
Yes vs. no
2.79 (1.69–4.62)
<0.001
Diabetes mellitus
Yes vs. no
2.13 (1.23–3.69)
0.007
Current smoking
Yes vs. no
0.71 (0.19–2.68)
0.614
Comorbidities
Chronic kidney disease
Yes vs. no
2.92 (1.40–6.09)
0.004
Chronic obstructive pulmonary disease
Yes vs. no
1.29 (0.57–2.92)
0.549
Coagulopathy
Compensated DIC-ISTH score ≥ 5[a ]
Yes vs. no
4.58 (2.09–10.07)
<0.001
Blood coagulation parameters
PT (s)
Elevated >3 seconds compared with the normal limit versus not
2.43 (1.15–5.13)
0.021
Fibrinogen (g/L)
>4 vs. ≤4
9.50 (2.23–40.54)
0.002
D-dimers (ng/mL)
Elevated vs. normal in age-specific norm[b ]
7.65 (2.67–21.87)
<0.001
Protein C (%)
<70 vs. ≥70
2.51 (1.30–4.83)
0.006
Antithrombin (%)
<80 vs. ≥80
2.13 (1.18–3.85)
0.012
Blood cell parameters
Hemoglobin (g/dL)
<11 vs. ≥11
2.09 (1.18–3.69)
0.011
Hematocrit (%)
<40 vs. ≥40
1.58 (0.86–2.89)
0.138
Red blood cells (x109 /L)
<4 vs. ≥4
1.50 (0.89–2.52)
0.128
Platelets (× 109 /L)
<100 vs. ≥100
7.60 (1.55–37.35)
0.012
Platelets (× 109 /L)
<150 vs. ≥150
3.54 (1.77–7.10)
<0.001
White blood cells (× 109 /L)
>10 vs. ≤10
1.86 (1.04–3.33)
0.036
Neutrophils (× 109 /L)
>7 vs. ≤7
2.43 (1.42–4.18)
0.001
Lymphocytes (× 109 /L)
<1.5 vs. ≥1.5
3.37 (1.58–7.21)
0.002
Lymphocytes (× 109 /L)
<1 vs. ≥1
2.49 (1.48–4.20)
0.001
Monocytes (× 109 /L)
>1 vs. ≤1
0.84 (0.35–1.99)
0.687
Eosinophils (× 109 /L)
>0.07 vs. ≤0.07
0.56 (0.30–1.03)
0.060
Basophils (× 109 /L)
>0.01 vs. ≤0.01
0.69 (0.41–1.16)
0.160
Abbreviations: CI, confidence interval; DIC, disseminated intravascular coagulation;
ISTH, International Society on Thrombosis and Haemostasis; PT, prothrombin time.
Note: Values are odds ratio and 95% confidence intervals.
a Assuming COVID-19 is associated with DIC, therefore a +2 term was added for all participants.
b Age-adapted threshold for D-dimers: >500 for patients under 60 years, >600 for age
60–69, >700 for age 70–79, >800 for age 80–89, and >900 for age 90–99.
Hematological Predictors for Disease Worsening
Increase of fibrinogen (OR = 9.50, 95% CI: 2.23–40.54) and D-dimers levels (OR = 7.65,
95% CI: 2.67–21.87), a platelet count lower than 100 × 109 /L (OR = 7.60, 95% CI: 1.55–37.35), and a positive compensated DIC-ISTH score (OR = 4.58,
95% CI: 2.09–10.07) were major determinants of disease worsening risk. Deficiency
of AT activity (OR = 2.13, 95%CI: 1.18–3.85), PT prolongation (OR = 2.43, 95% CI:
1.15–5.13), leukocytosis (OR = 1.86, 95% CI: 1.04–3.33), and lymphopenia with lymphocyte
count lower than 1.5 × 109 /L (OR = 3.37, CI: 1.58–7.21) were significant risk factors for worsening disease
([Table 3 ]).
COMPASS-COVID-19 Score
A multivariate logistic regression analysis led to the derivation of a risk assessment
model (RAM) for the identification of COVID-19 patients at high risk for worsening
disease. The multivariate analysis retained obesity (BMI ≥ 30; OR = 6.56, 95% CI:
2.98–14.46; p < 0.001), male gender (OR = 2.59, 95% CI: 1.29–5.21; p = 0.007), compensated DIC-ISTH score ≥ 5 (OR = 2.58, 95% CI: 1.07–6.21; p = 0.034), lymphocyte count <1 × 109 /L (OR = 2.21, 95% CI: 1.17–4.19; p = 0.015), and Hb < 11 g/dL (OR = 2.25, 95% CI: 1.13–4.48; p = 0.021) as significant predictors of worsening disease. Multivariate logistic regression
analysis led to the following equation:
log (odds for worsening disease) = −2.6 + 1.9 * (obesity) + 1.0 * (male gender) + 0.9
* (DIC-ISTH score ≥ 5) + 0.8 (lymphocytes <1 × 109 /L) + 0.8 * (Hb < 11 g/dL).
The COMPASS-COVID-19 RAM was formulated by calculating an integer numeric value for
each predictor according to the value of its multiple regression coefficients ([Table 4 ]). The score ranged between 0 and 54 points with a cut-off at 18 points and stratified
COVID-19 patients into high and low risk for worsening disease. The COMPASS-COVID-19
score calculator is available online at the web site: www.medupdate.eu . [Table 5 ] shows a simplified profile of patients with COVID-19 at high risk (score ≥ 18) or
low risk (score < 18) of disease worsening.
Table 4
COMPASS-COVID-19 score for the evaluation of the risk for worsening disease in COVID-19
patients
COMPASS-COVID-19 RAM
Predictors for risk of worsening disease
Score
Obesity (BMI > 30)
19
Male gender
10
Compensated DIC-ISTH score ≥ 5
9
Confirmed COVID-19
2
Thrombocytopenia (platelets < 100,000/μL)
1
Prothrombin time prolongation (> control + 3 s):
1
D-dimer increase (>500 for age <60 y; >600 ng/mL for age 60–59 y; >600 ng/mL for age
60–69 y; >700 ng/mL for age 70–79 y; >800 ng/mL for age 80–89 y; >900 ng/mL for age
90–99 y)
1
Antithrombin decrease (< lower normal limit established by the laboratory)
1
Protein C decrease (< lower normal limit established by the laboratory)
1
Total
≥5
Lymphocytes < 109 /L
8
Hemoglobin < 11 g/dL
8
Total
≥18: high risk
<18: low risk
Abbreviations: BMI, body mass index; DIC, disseminated intravascular coagulation;
ISTH, International Society on Thrombosis and Haemostasis.
Table 5
Simplified profile of patients with COVID-19 at high or low risk for disease worsening
according to the COMPASS-COVID-19 risk assessment model
Patients with COVID-19 at high risk for disease worsening (COMPASS-COVID-19 score≥18)
Obese (BMI > 30), any sex, any examined comorbidities
Nonobese, male with one or more of: compensated DIC-ISTH ≥ 5, lymphopenia, anemiaa
Nonobese female with all three of: compensated DIC-ISTH ≥ 5, lymphopenia and anemiaa
Patients with COVID-19 at low risk for disease worsening (COMPASS-COVID-19 score < 18)
Nonobese male, compensated DIC-ISTH < 5, without lymphopenia, without anemia
Nonobese female with none, one, or two of: compensated DIC-ISTH ≥ 5, lymphopenia,
anemia
Abbreviations: BMI, body mass index; DIC, disseminated intravascular coagulation;
ISTH, International Society on Thrombosis and Haemostasis.
Qualitative Characteristics of the COMPASS-COVID-19 Score
The COMPASS-COVID-19 score at the cut-off value for high-risk level (≥18) had 81%
sensitivity, 60% specificity, 88% NPV, and 47% PPV. According to the Hosmer–Lemeshow
test, a p = 0.797 showed that the score was well calibrated. Plotting the expected worsening
cases, according to the score, against the observed worsening cases, as well as the
expected against the observed number of patients whose condition did not worsen, confirmed
the good calibration of the score with a Pearson's r
2 = 0.965 for worsening cases and r
2 = 0.992 for nonworsening cases ([Fig. 1, Frame A ]). The ROC curve was plotted to evaluate the discrimination ability of the score
between the high-risk and low-risk populations for disease deterioration. The AUC
was equal to 0.77, indicating a very good discrimination capacity ([Fig. 1, Frame B ]). The model with the score minimized the AIC (AIC = 1.033) compared with all other
examined logistic regression models, including a univariate model based on the DIC-ISTH
score (AIC = 1.191), denoting the substantial improvement through the addition of
parameters such as obesity, gender, lymphocyte count, and hemoglobin levels, adopted
in the COMPASS-COVID-19 score.
Fig. 1 Qualitative characteristics of the COMPASS-COVID-19 score. Frame A: plot presenting
the expected (through the logistic regression equation underlying the score) versus
observed probability in worsening cases (circles ) and nonworsening cases (triangles ). Frame B: the ROC analysis of the model (area under the curve = 0.77).
Validation of the COMPASS-COVID-19 Score
Patients included in the validation cohort were prospectively assessed with the COMPASS-COVID-19
score. The score at the cut-off value of 18 points identified as high risk for disease
worsening; 90% of patients at the W-group and 38% of the patients at the G-group.
The sensitivity and the specificity of the score were 94 and 58% respectively and
the NPV and PPVs were 96 and 45%, respectively.
Discussion
Development of prognostic tools and biomarkers for the prediction of COVID-19 trajectory
from the time of symptom onset is a difficult task needing urgent response.[15 ]
[16 ] To anticipate this challenge, we performed this prospective observational cohort
study which led to the derivation and validation of the COMPASS-COVID-19 RAM.
We showed that in COVID-19 patients disease worsening is related to the presence of
cardiovascular risk factors (i.e., arterial hypertension, diabetes, and obesity) and
blood hypercoagulability. The present study showed for the first time that compensated
DIC, diagnosed according to the ISTH criteria,[23 ] was already present in 8 and 28% of COVID-19 patients when admitted at the medical
conventional ward and the ICU, respectively. Thus, compensated DIC is an independent
risk factor for disease worsening. This figure completes the substantial role of blood
coagulation activation and DIC in the poor prognosis of COVID-19 patients.[27 ]
[28 ] Our study underlines that COVID-19 is associated with enhanced blood hypercoagulability
documented by the consumption of natural coagulation inhibitors (particularly AT)
and the marked increase of D-dimers. This concept is further supported by data from
recent postmortem analysis in COVID-19 patients that showed endothelial cell activation
and microcirculation abnormalities implicating blood hypercoagulability in the process
of disease aggravation.[29 ]
[30 ]
[31 ] Consequently, our data justify the monitoring of hypercoagulability biomarkers and
the need for an early application of antithrombotic treatment in COVID-19 patients.
Moreover, close monitoring of AT levels—the most potent heparin cofactor—and its administration
in the case of deficiency is mandatory to preserve the treatment efficacy of LMWH.
In fact, intravenous administration of AT concentrates could be an effective supportive
strategy for the management of DIC in patients with severe COVID-19.[14 ]
Our study led to the derivation of the COMPASS-COVID-19 score which includes the following
easily assessable predictors: presence of obesity (BMI ≥ 30), gender, hemoglobin,
lymphocyte count, platelet count, PT, D-dimers, AT, and PC activity. The COMPASS-COVID-19
score accurately identified COVID-19 patients at high risk for disease worsening.
The hematological predictors of the score can be easily measured in nonspecialized
hematological laboratories. This score is feasible in all health care structures equipped
with a routine hematological laboratory. At the cut-off of 18 points, the score has
a very good discriminating capacity to stratify patients at high and low risks for
disease aggravation, with an AUC value of 0.77, a sensitivity of 81%, and a specificity
of 60%. These qualitative characteristics together with the feasibility of measuring
routine hematological parameters designate the COMPASS-COVID-19 score as a useful
clinical tool, promptly identifying at least 80% of patients in the medical ward as
being at high risk for disease worsening and as requiring an optimized targeted management.
The COMPASS-COVID-19 score calculator is available online at the web site www.medupdate.eu . The COMPASS-COVID-19 score for the prediction of disease worsening in hospitalized
patients was developed according the TRIPOD reporting guidelines.[32 ] Data analysis was performed taking into consideration the major conclusions of the
systematic review and critical appraisal of prediction models for diagnosis and prognosis
of COVID-19 infection published by Wynants et al.[33 ] The prospective design of our study is a strength for the derivation of the new
RAM since all patients were tested with the COAG-COVID panel which provided information
on hematological alterations together with specific evaluation of biomarkers of hypercoagulability.
Moreover, this design allowed the evaluation of the presence of compensated DIC on
the first hospitalization day of patients either at the conventional COVID-19 medical
ward or at the ICU. The COMPASS-COVID-19 score is not applicable in patients receiving
anticoagulant treatment with direct oral anticoagulants or vitamin K antagonists because
these antithrombotic agents introduce prolongation of PT. Moreover, treatment with
direct oral anticoagulants (DOACs) induces a variable degree of overestimation of
PC activity when a clotting based assay is used. However, the low number of patients
with COVID-19 on anticoagulant treatment admitted in our center did not allow the
evaluation of the above-mentioned conditions on the accuracy of the COMPASS-COVID-19
score. This is an issue that has to be evaluated in the external validation of the
score.
Time to event analysis is considered to be the optimal methodology for the elaboration
of predictive scores allowing for administrative censoring in a competing risk framework.[33 ] However, this strategy was practically unfeasible during the actual phase of the
pandemic, which had an extreme pressure on our hospital and on the availability of
ICU beds in the city of Paris. For this reason, the cohort design was selected.
External validation is the optimal strategy to control the accuracy of predictive
models. However, in the actual phase of the pandemic, this validation strategy is
practically impossible to apply. For this reason, we set up an independent validation
cohort of patients selected from new patients hospitalized at the COVID-16 center
at Tenon University hospital between April 6 and April 21. The assessment of patients
enrolled in the validation cohort showed that the COMPASS-COVID-19 score accurately
predicted patients at high risk for disease worsening with a very high sensitivity
reaching up to 96%. An independent multicenter external validation of the COMPASS-COVID-19
score is ongoing.
Demographics and epidemiological characteristics of the patients enrolled in our study
were similar to those described in recently published studies from United States and
China.[4 ]
[34 ]
[35 ]
[36 ]
[37 ] Moreover, the low frequency of active smokers found in our cohorts was also reported
in recent studies.[34 ]
[38 ] These similarities further support that patients enrolled in our cohorts are representative
of those suffering from COVID-19 in the community and support the generalizability
of our findings. Moreover, very limited exclusion criteria were applied, yielding
our cohort representative of the population of COVID-19 patients requiring hospitalization.
These characteristics of our study allow implementation of the COMPASS-COVID-19 score
across different settings and populations.
We aimed to derive an original, simple, and easy-to-use RAM based on clinical predictors
and concrete hematological parameters closely related to mechanisms implicated in
COVID-19 pathogenesis. Indeed, available evidence so far has reinforced the importance
of blood coagulation, endothelial cell activation, white blood cell alterations, and
hypoxia in the deterioration of COVID-19 patients. An enhanced inflammatory reaction
with associated-cytokine storm has a central role in COVID-19 patients' worsening.
This has also been extensively described in the analysis of biochemical biomarkers
of inflammation such as ferritin. Nevertheless, the inflammatory process is reflected
upon some of the hematological parameters studied, such as fibrinogen levels, platelets,
and white blood cell counts. To the best of our knowledge, two studies have been published
to date aiming to elaborate a prediction tool for disease severity in patients hospitalized
with COVID-19 and are based on the evaluation of biochemical biomarkers.[39 ]
[40 ] Both studies included very limited numbers of patients, which hardly allowed (if
so) sufficient statistical power to identify score predictors by applying the rule-of-thumb
(the so-called EPV 10–1) method for predictive score derivation. In contrast, the
COMPASS-COVID-19 score derived from a robust cohort with a sufficient number of patients
in the two groups allowing accurate identification of the most pertinent biological
and clinical predictors by following the above-mentioned rule.
Despite its original nature, this study bears some limitations. First, in our approach,
an unweighted Youden's index was used to establish the optimal cut-off in the score,
allocating equal importance to sensitivity and specificity, and yielding a sensitivity
of 81% and a specificity of 60%; alternative, weighted approaches prioritizing for
instance sensitivity over specificity could lead to other cut-off values.[41 ] Another limitation pertains to the number of patients and the single-center design
of the study as well as the short admission time, which were imposed by the urgent
character of the SARS-CoV-2 epidemic.
Regarding the external validity and generalizability of findings, it should be underlined
that the COMPASS-COVID-19 score is applicable in patients receiving heparin treatment
since all patients hospitalized in a conventional medical department or in an ICU
routinely receive thromboprophylaxis with body weight-adapted enoxaparin as in our
setting. At the actual phase of the score development, the score is not applicable
to pregnant women, patients on anticoagulant treatment with VKA or DOAC, and patients
with cytopenia due to current anticancer treatment, since these groups of patients
were excluded from this study. The applicability of the score on these special groups
of patients will be explored in the forthcoming studies.
In conclusion, the present study provides an accurate RAM for early identification
of patients with COVID-19 being at high risk of disease worsening that responds to
the criteria established by the TRIPOD guidelines. Contextualized application of the
COMPASS-COVID-19 score will provide a useful clinical decision-making tool for earlier
and targeted application of treatments including antithrombotic agents. As stated
by Wang et al in a recently published clinical trial on the efficacy and safety of
remdesivir, earlier administration of antiviral drugs might be a strategy for successful
phase III trials.[42 ] The COMPASS-COVID-19 score will be a helpful tool for the identification of patients
eligible for phase III trials. The COMPASS-COVID-19 score is based on the presence
of pertinent clinical risk factors such as obesity and male gender and also on simple,
easy-to-measure hematological and blood coagulation biomarkers, and it can be applied
at any level of the health care system. Implementation of the COMPASS-COVID-19 score
will allow an easy and rapid identification of a great majority of COVID-19 patients
at risk for disease worsening and who may shortly require ICU admission.
What is known about this topic?
What does this paper add?
The COMPASS-COVID-19 score derived from a prospective study is composed of easily
assessable clinical and hematological predictors.
The COMPASS-COVID-19 score has high sensitivity (81%) for the identification of hospitalized
patients at high risk of disease worsening.
