We conducted a retrospective cohort study of 66 patients with known COVID-19 disease, from March 10th to May 16th 2020 in Clinique Saint-Pierre Ottignies in Belgium. Twenty patients were admitted in Intensive Care Unit and 44 in Medicine Department. Sixty-three patients were positive for a nasopharyngeal RT-PCR SARS-CoV-2 test. One patient included in the study was negative for RT-PCR SARS-CoV-2 but had an IgA and IgG ELISA-test positive for SARS-CoV-2 and ground-glass opacities on the chest X-ray. Patients characteristics are displayed in Table 1.
Table 1
|
USI n = 20
|
NON USI n = 44
|
white blood cells ( x 1000/ mm3)
|
8,6
|
6,3
|
neutrophils ( x 1000/ mm3)
|
7,2
|
6,6
|
Lymphocytes ( x 1000/ mm3)
|
0,9
|
1,1
|
Platelets (( x 1000/ mm3))
|
241,3
|
212,1
|
LDH ( U/L)
|
557,1
|
333,3
|
CRP (mg/L)
|
159,1
|
99,4
|
pct lung injury CT scan (%)
|
53,3
|
36,6
|
density score lung injury CT scan
|
2,3
|
1,95
|
Age (years)
|
64,4
|
66,5
|
death (n)
|
6 (20%)
|
11 (25%)
|
Sex M (n)
|
17 (85%)
|
22 (50%)
|
PCR+ (n)
|
19 (95%)
|
43 (97,7%)
|
Blood type
• A+
• A-
• B+
• B-
• O+
• O-
• AB
|
55%
5%
5%
0%
30%
5%
0%
|
37,5%
4,7%
7,8%
0%
45,3%
3,1%
1,6%
|
number of days of sickness before d0
|
7,3
|
5,8
|
Asthenia (n)
|
11 (55%)
|
37 (84,1%)
|
Pyrexia (n)
|
15 (75%)
|
32 (72,7%)
|
Dyspnea (n)
|
13 (65%)
|
29 (65,9%)
|
dry cough (n)
|
15 (75%)
|
31 (70,5%)
|
chest pain (n)
|
3 (15%)
|
3 (6,8%)
|
digestive signs (n)
|
5 (20%)
|
22 (50%)
|
Anosmia (n)
|
0
|
5 (11,4%)
|
Dysgueusia (n)
|
0
|
5 (11,4%)
|
Confusion (n)
|
1 (5%)
|
6 (13,6%)
|
Trip or contact < 1 month (n)
|
13 (65%)
|
27 (61,4%)
|
Tobacco (n)
|
2 (10%)
|
3 (6,8%)
|
Hypertension (n)
|
12 (60%)
|
21 (47,7%)
|
Diabetes(n)
|
7 (35%)
|
8 (18,2%)
|
Depression (n)
|
2 (10%)
|
17 (38,6%)
|
ACE inhibitors (n)
|
1 (5%)
|
12 ( 27,3%)
|
antagonists of ATR2 (n)
|
5 (20%)
|
7 (16%)
|
NSAI drug (n)
|
3 (15%)
|
2 (6,8%)
|
Immunosuppressor (n)
|
0 (0%)
|
3 (6,8%)
|
SpO2 emergy departement (%)
|
84,4
|
91,9
|
NRL (n)
|
10,8
|
7,3
|
LCR (n)
|
14,2
|
34,06
|
lactates v (n) (mmol/L)
|
2,6
|
1,62
|
lactates a (n) (mmol/L)
|
2,2
|
1,64
|
total bilirubin (n) (mg/dL)
|
0,7
|
0,66
|
direct bilirubin (n) (mg/dL)
|
0,2
|
0,2
|
Liang score (n)
|
155,0
|
115,5
|
All patients were followed from their admission at the emergency ward until they get out of the hospital or until their death. No patient was excluded from the cohort. The follow-up ended when patients leaved the hospital or died. The starting date of accrual and the end-date of accrual were reported for each patient.
Clinique Saint-Pierre Ottignies is a 425-bed regional general hospital with a capacity of 15 intensive care beds, which was increased to 25 beds during the pandemic. It has a mission of para-university training of junior medical specialists. It covers a catchment area of circa 400,000 patients, in the region of Brabant Wallon (Wallonia, Belgium).
Criteria of admission to the ICU
Twenty-two patients with COVID-19 were admitted to the ICU. For 19 patients the reason was a respiratory failure defined as (i) ambient oxygen saturation (SpO2) < 88% with nasal cannula oxygen therapy > 5 l/min; (ii) PaO2 < 50 mmHg and/or a ratio PaO2/FiO2 < 150; (iii) respiratory rate > 40/min. For one patient, the reason was a post-traumatic cerebral hemorrhage, associated with altered neurological status (defined as Glasgow coma scale < 8/15) requiring mechanical ventilation in order to protect the airway. For another one, the cause was the postoperative management of an empyema drainage developed in a context of bacterial pneumonia complicating a SARS-CoV-2 virus infection. A third patient presented a status epilepticus in the context of probable alcohol withdrawal.
Criteria of non-admission to the ICU
Clinique Saint-Pierre Ottignies has long-proven guide-lines for admission to ICU. Given the nature of the health emergency, the principle of distributive justice7 was applied and each patient admitted to the emergency department was immediately classified as "eligible for intensive care" or "not eligible for intensive care", taking into account his or her previous history and quality of life. The criteria for ineligibility were: (i) presence of a prior incurable disease; (ii) limitation of functional autonomy; and (iii) advanced dementia. Two patients had criteria for intensive care hospitalization upon admission to the emergency room, the other patients were first hospitalized in a non-intensive care unit.
The patients admitted to intensive care all had the clinical criteria mentioned above, with a pre-established maximalist therapeutic plan. Other patients with the same clinical criteria but with a care plan with therapeutic limitations were not admitted. These therapeutic projects were discussed collegially between medical specialties issued from emergency department, internal medicine and critical unit.
The advantage of a simple predictive score upon admission could be useful in decision-making regarding a therapeutic plan.
Assessment of lung injury
A senior radiologist analyzed Thoracic Computer Tomographies (TCT) according to two criteria: percentage of lung injury (continuous variable from 0 to 100%) and density of lung injury (factor variable with 3 grades: 1 = light density; 2 = moderate density; 3 = high density).
Data collection
Data collection tried to adhere as tightly as possible to the TRIPOD Adherence extraction form (https://www.tripod-statement.org/wp-content/uploads/2020/03/TRIPOD-Adherence-assessment-form_V-2018_12.pdf). At their admission, patients were questioned about their usual medication and their health condition. The body mass index was computed. Collected variables are the following: age, gender, ethnic group, weight, body mass index, number of days with symptoms before hospitalization, asthenia, pyrexia, dyspnea, chest pain, aspecific digestive symptoms, anosmia, ageusia, confusion, Travel or contact < one month, cigarette consumption (Y or N), hypertension, diabetes, mental status (depression), angiotensin-converting-enzyme inhibitors, angiotensin II receptor antagonists, non-steroidal anti-inflammatory drugs, immunosuppressive drugs, SpO2 (%), TCT % of lung injury, TCT density of lung injury, blood type, white blood cells, neutrophils, lymphocytes, blood platelets, fibrinogen, ferritin, triglycerides, LDH, troponin, CRP, neutrophil-to-lymphocyte ratio, lymphocyte-to-CRP ratio, bilirubin and lactates. The dates of admission to ICU and death were recorded.
In the ICU, the use of chloroquine, hydroxychloroquine, azithromycin, clarithromycin, remdesevir and antibacterial antibiotics (piperacill-tazobactam, meropenem, ciprofloxacin, ceftazidime, amoxicillin clavulanate) was recorded on a daily basis.
As mentioned previously, the clinical risk score developed by Liang et al1 was computed retrospectively for all COVID-19 patients at their admission to the emergency ward. This clinical risk score was compared to our own models.
Statistical analysis
The number of collected variables being 40, the number of possible predictive models8 is extremely large. Consequently, testing all of these models is not computationally feasible and a preselection of potential predictive models was performed using a recently developed method9. Among these models, we decided to consider four models that were in line with medical practice and presented suitable prediction accuracy for ICU admission. These models are logistic regressions based on the following variables: (i) LDH, (ii) LDH + sex, (iii) LDH + sex + venous lactate, and (iv) respiratory impairment score (a combination of percentage of lung injury, density of lung injury, and ambient oxygen saturation). Similarly, three models were suited to predict death: (i) Neutrophil Lymphocyte Ratio + Tobacco; (ii) Liang score + Neutrophil Lymphocyte Ratio + Tobacco; and (iii) respiratory impairment score.
As previously mentioned, logistic regressions were used in our study and this techniques allows to addressed to our research questions based on binary variables. All analysis were performed using the R software version 4.0.1 (https://cran.r-project.org/). This regression technique is a widely used statistical tool that allows for multivariate analysis and modeling of a binary dependent variable. The multivariate analysis estimates coefficients (for example, log odds or hazard ratios) for each predictor included in the final model and adjusts them with respect to the other predictors in the model. The coefficients quantify the contribution of each predictor to the outcome risk estimation10. The caveats to consider when assessing the results of a logistic regression analysis are well explained in Tolles et al.11. Theoretically, every variable collected in the study could be a candidate predictor. However, to reduce the risk of false positive findings and improve model performance, the Events Per Variable (EPV) must be considered. A rule of thumb of 10 individuals per event is commonly applied12. However recent studies have shown that EPV does not have a strong relation with metrics of predictive performance, and is not an appropriate criterion for binary prediction model development studies13. According to Vittinghof et al.14, predictive performance problems are fairly frequent with 2–4 EPV, uncommon with 5–9 EPV, and still observed with 10–16 EPV. The rule of thumb of 10 individuals per event can then be relaxed. In our study, EPV ranges from 7 to 20. The binomial family logit function and the maximum likelihood approach were used to compute the regression coefficients. The coefficients are then equivalent to the relative risk of the outcome in the exposed group (COVID-19 patients).
Patient and Public Involvement
-
Research questions were elaborated a posteriori due to the inadequacy of the predictive scores published in the literature for the Belgian population. The optimal use of resources on the one hand, and the optimal clinical management of patients on the other, made it necessary to develop predictive scores applicable to local realities. Belgian patients were thus in the position to benefit from evidence-based expertise that could be generalized to our hospital's population area.
-
This study is a monocentric retrospective cohort study. Patients were not involved in the study design.
-
Admission to ICU and clinical management were based on strict ethical considerations, and patient care and treatment were in no way affected by participation to the study. Patients and their relatives were at every step of the disease informed by the nursing and medical staff of the reasons of treatment options, and their informed consent was obtained.
-
All participants in the study are involved in the same team. Results were shared and commented on in a collegial and transparent way throughout the study.