Abstract
Background
The WHO ordinal severity scale has been used to predict mortality and guide trials in COVID-19. However, it has its limitations.
Objective
The present study aims to compare three classificatory and predictive models: the WHO ordinal severity scale, the model based on inflammation grades, and the hybrid model.
Design
Retrospective cohort study with patient data collected and followed up from March 1, 2020, to May 1, 2021, from the nationwide SEMI-COVID-19 Registry. The primary study outcome was in-hospital mortality. As this was a hospital-based study, the patients included corresponded to categories 3 to 7 of the WHO ordinal scale. Categories 6 and 7 were grouped in the same category.
Key Results
A total of 17,225 patients were included in the study. Patients classified as high risk in each of the WHO categories according to the degree of inflammation were as follows: 63.8% vs. 79.9% vs. 90.2% vs. 95.1% (p<0.001). In-hospital mortality for WHO ordinal scale categories 3 to 6/7 was as follows: 0.8% vs. 24.3% vs. 45.3% vs. 34% (p<0.001). In-hospital mortality for the combined categories of ordinal scale 3a to 5b was as follows: 0.4% vs. 1.1% vs. 11.2% vs. 27.5% vs. 35.5% vs. 41.1% (p<0.001). The predictive regression model for in-hospital mortality with our proposed combined ordinal scale reached an AUC=0.871, superior to the two models separately.
Conclusions
The present study proposes a new severity grading scale for COVID-19 hospitalized patients. In our opinion, it is the most informative, representative, and predictive scale in COVID-19 patients to date.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11606-022-07511-7.
KEY WORDS: COVID-19, prognosis, WHO ordinal scale, inflammation
INTRODUCTION
COVID-19 is a disease with a viral trigger that causes an inflammatory escalation leading to acute respiratory distress syndrome (ARDS) in some patients.1 From the beginning of the pandemic, WHO proposed a severity classification based on the respiratory status of patients.2 This strategy facilitated the therapeutic approach and the prediction of clinical worsening during admission in patients with COVID-19. On the other hand, it also served as a guide for the clinical trials (RCT) of the different therapies that have been proposed during this time.3
However, COVID-19 is a particularly clinic-functional-radiological dissociated disease and characteristically produces a well-tolerated hypoxemia that does not reflect the underlying severity. A severity classification strategy has recently been proposed by our group based on the degrees of analytical inflammation.4
The present study aimed to compare both strategies and a model based on a combination of both.
METHODS
Study Design, Patient Selection, and Data Collection
This is a retrospective cohort study with data on patients collected and followed up from March 1, 2020, to May 1, 2021, from the nationwide Spanish SEMI-COVID-19 Registry. The characteristics of the patients included in this registry have been extensively described previously.5 This is a multicenter, nationwide registry with over 150 hospitals registered so far. All included patients were diagnosed by polymerase chain reaction (PCR) test taken from a nasopharyngeal sample, sputum, or bronchoalveolar lavage. The analytical data collected in the present study correspond to the analysis upon admission as well. The collection of data from each patient in terms of laboratory data, treatments, and outcomes was verified by the principal investigator of each center through the review of clinical records.
All participating centers in the register received confirmation from the relevant Ethics Committees, including Bellvitge University Hospital (PR 128/20).
WHO Ordinal Scale
The WHO ordinal clinical severity scale was collected at the time of hospital admission. The 9 points of the scale are as follows: 0: no clinical or virological evidence of infection; 1: ambulatory, no activity limitation; 2: ambulatory, activity limitation; 3: hospitalized, no oxygen therapy; 4: hospitalized, oxygen mask or nasal prongs; 5: hospitalized, noninvasive mechanical ventilation (NIMV) or high-flow nasal cannula (HFNC); 6: hospitalized, intubation and invasive mechanical ventilation (IMV); 7: hospitalized, IMV + additional support such as pressors or extracardiac membranous oxygenation (ECMO); 8: death.
As this was a hospital-based study, the patients included corresponded to categories 3 to 7 of the WHO ordinal scale. Categories 6 and 7 were grouped in the same category because there were few patients in each category and also because of difficulties in differentiating between the two in our database.
Degrees of Inflammation
We previously reported the 3 categories of risk (low, intermediate, and high risk) based on the total lymphocyte count, and the C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, and D-dimer values taken at the time of admission.4
Combined Ordinal Scale of Severity
The scale we propose in the present study combines the WHO ordinal scale and the degrees of inflammation. It thus results in 6 categories in hospitalized population by COVID-19 (Fig. 1): 3a: hospitalized, no oxygen therapy and not high risk of inflammation; 3b: hospitalized, no oxygen therapy and high risk of inflammation; 4a: hospitalized, oxygen mask or nasal cannula and not high risk of inflammation; 4b: hospitalized, oxygen mask or nasal prongs and high risk of inflammation; 5a: hospitalized, NIMV, HFNC, IMV, ICU, ECMO, or pressors and not high risk of inflammation; 5b: hospitalized, NIMV, HFNC, IMV, ICU, ECMO, or pressors and high risk of inflammation.
Figure 1.
Severity classification algorithm in hospitalized patients with COVID-19 combining the WHO ordinal scale and inflammatory risk categories.
Treatments Prescribed
Regarding antiviral treatment, the use of antivirals (lopinavir/ritonavir,6 remdesivir3), hydroxychloroquine,7 azithromycin,7 corticosteroids (CS),8 and tocilizumab (TCZ)9–11 was allowed according to the recommendations of the Spanish Ministry of Health.
Outcomes
The primary outcome of the study was in-hospital mortality. Secondary outcomes were the requirement of HFNC, NIMV, IMV, ECMO, and intensive care unit (ICU) admission.
Statistical Analysis
Categorical variables were expressed as absolute numbers and percentages. Continuous variables are expressed as mean plus standard deviation (SD) in the case of parametric distribution or median [IQR] in the case of non-parametric distribution. Differences among groups were assessed using the chi-square test for categorical variables and ANOVA or Kruskal-Wallis test as appropriate for continuous variables. p values< 0.05 indicated statistical significance.
For the study of risk factors associated with in-hospital mortality, univariate and multivariate binary logistic regression was performed. For the latter, variables with p<0.10 in the univariate study plus age and gender were included. The differences in mortality were shown graphically using Kaplan-Meier curves with their log-rank test (event: death; censored data: hospital discharge). Missing data were treated with multiple imputations.
Statistical analysis was performed by IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, USA: IBM Corp.
RESULTS
General Data and Symptoms Between Groups
A total of 21,962 patients were included in the Registry by May 2021. Of these, 17,225 patients had community (non-nosocomial) COVID-19 infection and their baseline oxygenation data were collected in the database for inclusion in the present study (Figure S1). Table 1 shows the differences in overall baseline data between the different WHO severity categories. Table 1 shows the differences in overall baseline data between the different WHO severity categories.
Table 1.
General Data Between Groups in the WHO Ordinal Scale
| WHO ordinal scale | |||||
|---|---|---|---|---|---|
| 3 | 4 | 5 | 6/7 | p value | |
| n | 5391 | 11,438 | 234 | 162 | |
| Age, median [IQR] | 60.7 [48.8–73.3] | 72.3 [60.5–81.9] | 68.8 [60–77.7] | 63.7 [53.4–72.7] | <0.001 |
| Gender (males), n (%) | 2803 (52) | 6862 (60) | 168 (71.8) | 118 (72.8) | <0.001 |
| Days from onset to admission, median [IQR] | 7 [4–10] | 6 [4–9] | 7 [4–10] | 7 [4–9] | <0.001 |
| BMI, median [IQR] | 28 [25–31.7] | 28.7 [25.7–32.3] | 29.9 [27.1–33.4] | 30.2 [26.7–33.1] | <0.001 |
| Race, n (%) | <0.001 | ||||
| Caucasian | 4582 (85) | 10,421 (91.1) | 192 (82.1) | 138 (85.2) | |
| Black | 46 (0.9) | 50 (0.4) | 1 (0.4) | 0 | |
| Hispanic | 639 (11.9) | 808 (7.1) | 28 (12) | 14 (8.6) | |
| Asian | 40 (0.7) | 41 (0.4) | 3 (1.3) | 2 (1.2) | |
| Others | 84 (1.6) | 118 (1) | 10 (4.3) | 8 (4.9) | |
| Smoking behavior, n (%) | <0.001 | ||||
| Never smoker | 4060 (75.3) | 7691 (67.2) | 142 (60.7) | 99 (61.1) | |
| Former smoker | 1052 (19.5) | 3221 (28.2) | 79 (33.8) | 52 (32.1) | |
| Current smoker | 279 (5.2) | 526 (4.6) | 13 (5.6) | 11 (6.8) | |
| Degree of dependency, n (%) | <0.001 | ||||
| None or mild | 4890 (90.7) | 9159 (80.1) | 198 (84.6) | 158 (97.5) | |
| Moderate | 283 (5.2) | 1273 (11.1) | 23 (9.8) | 3 (1.9) | |
| Severe | 218 (4) | 1006 (8.8) | 13 (5.6) | 1 (0.6) | |
| Arterial hypertension, n (%) | 2126 (39.4) | 6587 (57.6) | 137 (58.5) | 79 (48.8) | <0.001 |
| Dyslipidemia, n (%) | 1760 (32.6) | 4967 (43.4) | 113 (48.3) | 73 (45.1) | <0.001 |
| Diabetes mellitus, n (%) | 792 (14.7) | 2682 (23.4) | 66 (28.2) | 47 (29) | <0.001 |
| Ischemic cardiopathy, n (%) | 287 (5.3) | 1008 (8.8) | 13 (5.6) | 11 (6.8) | <0.001 |
| Chronic heart failure, n (%) | 185 (3.4) | 900 (7.9) | 16 (6.8) | 5 (3.1) | <0.001 |
| Chronic liver disease, n (%) | 169 (3.1) | 416 (3.6) | 6 (2.6) | 4 (2.5) | 0.281 |
| Severe chronic renal failure, n (%) | 206 (3.8) | 755 (6.6) | 16 (6.8) | 3 (1.9) | <0.001 |
| Dementia, n (%) | 283 (5.2) | 1338 (11.7) | 8 (3.4) | 2 (1.2) | <0.001 |
| Cancer, n (%) | 406 (7.5) | 1143 (10) | 17 (7.3) | 7 (4.3) | <0.001 |
| COPD, n (%) | 164 (3) | 965 (8.4) | 22 (9.4) | 10 (6.2) | <0.001 |
| Asthma, n (%) | 409 (7.6) | 759 (6.6) | 16 (6.8) | 16 (9.9) | 0.064 |
| OSAS, n (%) | 218 (4) | 797 (7) | 24 (10.3) | 16 (9.9) | <0.001 |
| Charlson index, median [IQR] | 0 [0–1] | 1 [0–2] | 1 [0–2] | 0 [0–1] | <0.001 |
IQR interquartile range, BMI body mass index
Symptoms at the time of hospital admission are shown in Table S1. WHO category 3 presented less frequently dyspnea (48.9% vs. 62.7% vs. 88.9% vs. 80.2%; p<0.001) as well as tachypnea at admission (18.6% vs. 36.9% vs. 72.2% vs. 75.3%; p<0.001). On the contrary, they presented more frequently arthromyalgia (35.4% vs. 28.1% vs. 26.5% vs. 29.6%; p<0.001), ageusia (13.4% vs. 7.4% vs. 12.8% vs. 9.3%; p<0. 001), anosmia (12.2% vs. 6.2% vs. 13.2% vs. 8%; p<0.001), sore throat (11.7% vs. 8.4% vs. 6% vs. 8%; p<0.001), and headache (15.9% vs. 10.7% vs. 9.8% vs. 11.1%; p<0.001).
Lab Tests Between Groups
Table 2 shows the analytical parameters between the different WHO categories. As expected, the higher the severity, the lower the PaO2/FiO2 and lymphocyte count and the higher the CRP, LDH, and ferritin. It is noteworthy that between WHO categories 5 and 6/7 this progression in the analytical figures was not observed. As for D-dimer, differences were observed between the 4 groups but the differences were not progressive in parallel to the severity on the scale.
Table 2.
Lab Tests Upon Admission Between Groups in the WHO Ordinal Scale
| WHO ordinal scale | |||||
|---|---|---|---|---|---|
| 3 | 4 | 5 | 6/7 | p value | |
| PaO2/FiO2, median [IQR] | 322 [265–378] | 286 [230–338] | 227 [133–300] | 241 [151–287] | <0.001 |
| Lymphocytes ×106/L, median [IQR] | 1090 [800–1460] | 900 [620–1200] | 825 [580–1115] | 745 [529–1093] | <0.001 |
| CRP mg/L, median [IQR] | 39 [12–88] | 78 [30–146] | 131 [75–220] | 141 [79–228] | <0.001 |
| LDH U/L, median [IQR] | 288 [226–378] | 342 [262–462] | 414 [326–523] | 445 [334–600] | <0.001 |
| Ferritin mcg/L, median [IQR] | 522 [179–1191] | 785 [300–1494] | 1203 [577–1867] | 1027 [433–1734] | <0.001 |
| D-dimer ng/mL, median [IQR] | 546 [275–1170] | 700 [350–1586] | 596 [316–1210] | 725 [430–1372] | <0.001 |
| Risk categories of inflammation, n (%) | |||||
| Low risk | 313 (5.8) | 178 (1.6) | 1 (0.4) | 0 | <0.001 |
| Intermediate risk | 1638 (30.4) | 2119 (18.5) | 22 (9.4) | 8 (4.9) | <0.001 |
| High risk | 3440 (63.8) | 9141 (79.9) | 211 (90.2) | 154 (95.1) | <0.001 |
CRP C-reactive protein, LDH lactate dehydrogenase, IQR interquartile range
The patients classified as high risk in each of the WHO categories according to the degree of inflammation were as follows: 63.8% vs. 79.9% vs. 90.2% vs. 95.1% (p<0.001) (Table 2; Fig. 2). The high-risk parameters elevated in each of the WHO categories are detailed in Table S2. Likewise, the correlation between the WHO clinical severity categories and the inflammatory parameters is detailed in Table S3.
Figure 2.
Risk categories of inflammation by the WHO ordinal scale.
Treatments Between Groups
As expected, those treatments with greater evidence of effectiveness (CS, TCZ, remdesivir, and heparin) were more frequently used in higher severity categories (Table S4).
Outcomes Between Groups
The outcomes are shown in Table 3 and Figure S2–4. The first part of the table shows the outcomes for each of the WHO severity categories. In-hospital mortality for categories 3 to 6/7 was as follows: 0.8% vs. 24.3% vs. 45.3% vs. 34% (p<0.001). The second part of the table shows the outcomes for the proposed combined scale with degrees of inflammation and WHO categories. In-hospital mortality for categories 3a to 5b was as follows: 0.4% vs. 1.1% vs. 11.2% vs. 27.5% vs. 35.5% vs. 41.1% (p<0.001). Survival between the different categories in the proposed model is depicted in Figure 3.
Table 3.
Outcomes Between Groups in the WHO Ordinal Scale and the Combined Ordinal Scale
| WHO ordinal scale | |||||||
| 3 | 4 | 5 | 6/7 | p value | |||
| Primary outcome, n (%) | |||||||
| In-hospital mortality | 45 (0.8) | 2774 (24.3) | 106 (45.3) | 55 (34) | <0.001 | ||
| Secondary outcomes, n (%) | |||||||
| HFNC | 108 (2) | 1305 (11.4) | 195 (83.3) | 80 (49.4) | <0.001 | ||
| NIMV | 37 (0.7) | 815 (7.1) | 138 (59) | 51 (31.5) | <0.001 | ||
| IMV | 29 (0.5) | 1086 (9.5) | 71 (30.3) | 110 (67.9) | <0.001 | ||
| ICU admission | 50 (0.9) | 1382 (12.1) | 87 (37.2) | 162 (100) | <0.001 | ||
| ECMO | 3 (0.1) | 13 (0.1) | 4 (1.7) | 2 (1.2) | <0.001 | ||
| Combined ordinal scale | |||||||
| 3a | 3b | 4a | 4b | 5a | 5b | p value | |
| Primary outcome, n (%) | |||||||
| In-hospital mortality | 7 (0.4) | 38 (1.1) | 258 (11.2) | 2516 (27.5) | 11 (35.5) | 150 (41.1) | <0.001 |
| Secondary outcomes, n (%) | |||||||
| HFNC | 23 (1.2) | 85 (2.5) | 173 (7.5) | 1132 (12.4) | 21 (67.7) | 254 (69.6) | <0.001 |
| NIMV | 6 (0.3) | 31 (0.9) | 114 (5) | 701 (7.7) | 11 (35.5) | 178 (48.8) | <0.001 |
| IMV | 6 (0.3) | 23 (0.7) | 110 (4.8) | 976 (10.7) | 5 (16.1) | 176 (48.2) | <0.001 |
| ICU admission | 9 (0.5) | 41 (1.2) | 155 (6.7) | 1227 (13.4) | 12 (38.7) | 237 (64.9) | <0.001 |
| ECMO | 0 | 3 (0.1) | 1 (0.1) | 12 (0.1) | 0 | 6 (1.6) | <0.001 |
HFNC high-flow nasal cannula, NIMV noninvasive mechanical ventilation, IMV invasive mechanical ventilation, ICU intensive care unit, ECMO extracardiac membranous oxygenation
Figure 3.
Kaplan-Meier curves of the combined ordinal scale of severity. Log-rank test=936.76, p<0.001.
Risk Factors for In-Hospital Mortality
Table 4 shows the results of the multivariate analysis of the 3 models (degrees of inflammation vs. WHO vs. combined model). In the previous univariate study, age, sex, BMI, race, smoking behavior, degree of dependency, different comorbidities, Charlson index, tachypnea upon admission, and the use of different therapies (CS, TCZ, and remdesivir) were analyzed. All of them were significant in the univariate study so they were entered in the multivariate models.
Table 4.
Risk Factors for In-Hospital Mortality Based on the Categories of Inflammation vs. WHO Ordinal Scale. Multivariate Analysis
| Model based on the risk categories of inflammation | Model based on the WHO ordinal scale | Model including the WHO scale and categories of inflammation | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | OR (95% CI) | p value | |
| Age | 1.07 (1.06–1.07) | <0.001 | 1.07 (1.06–1.07) | <0.001 | 1.07 (1.06–1.07) | <0.001 |
| Gender (female) | 0.65 (0.59–0.72) | <0.001 | 0.66 (0.60–0.73) | <0.001 | 0.70 (0.63–0.77) | <0.001 |
| BMI | 1.03 (1.02–1.04) | <0.001 | 1.02 (1.01–1.03) | <0.001 | 1.02 (1.01–1.03) | <0.001 |
| Race | NS | NS | NS | |||
| Caucasian (ref.) | ||||||
| Black | ||||||
| Hispanic | ||||||
| Asian | ||||||
| Others | ||||||
| Dependency | ||||||
| No | 1 ref. | 1 ref. | 1 ref. | |||
| Moderate | 1.46 (1.27–1.67) | <0.001 | 1.45 (1.26–1.67) | <0.001 | 1.49 (1.29–1.72) | <0.001 |
| Severe | 1.92 (1.65–2.23) | <0.001 | 1.90 (1.62–2.22) | <0.001 | 1.98 (1.70–2.32) | <0.001 |
| Arterial hypertension | NS | NS | NS | |||
| Dyslipidemia | NS | NS | NS | |||
| Diabetes mellitus | NS | NS | NS | |||
| Ischemic cardiopathy | 1.19 (1.02–1.38) | 0.024 | 1.24 (1.06–1.44) | 0.007 | 1.25 (1.07–1.46) | 0.005 |
| Chronic heart failure | NS | NS | NS | |||
| Chronic liver disease | NS | NS | NS | |||
| Severe chronic renal failure | NS | NS | NS | |||
| Dementia | NS | NS | NS | |||
| Cancer | NS | NS | NS | |||
| COPD | NS | NS | NS | |||
| Asthma | 0.74 (0.60–0.90) | 0.003 | 0.69 (0.56–0.85) | <0.001 | 0.72 (0.58–0.88) | 0.002 |
| OSAS | NS | NS | NS | |||
| Charlson index | 1.14 (1.12–1.17) | <0.001 | 1.16 (1.13–1.19) | <0.001 | 1.15 (1.12–1.18) | <0.001 |
| Respiratory rate >20 rpm | 2.77 (2.53–3.04) | <0.001 | 2.52 (2.29–2.77) | <0.001 | 2.37 (2.16–2.61) | <0.001 |
| Tocilizumab | 1.83 (1.59–2.11) | <0.001 | 1.57 (1.36–1.81) | <0.001 | 1.53 (1.32–1.76) | <0.001 |
| Corticosteroids | 1.49 (1.35–1.64) | <0.001 | 1.29 (1.17–1.42) | <0.001 | 1.24 (1.13–1.38) | <0.001 |
| Remdesivir | 0.50 (0.40–0.64) | <0.001 | 0.43 (0.34–0.55) | <0.001 | 0.45 (0.36–0.58) | <0.001 |
| Categories of risk | - | |||||
| Low risk | 1 ref. | 1 ref. | ||||
| Intermediate risk | 1.81 (1.02–3.22) | 0.044 | 1.55 (0.85–2.83) | 0.153 | ||
| High risk | 5 (2.85–8.77) | <0.001 | 3.95 (2.19–7.10) | <0.001 | ||
| WHO ordinal scale | - | |||||
| 3 | 1 ref. | 1 ref. | ||||
| 4 | 21.9 (16.2–29.6) | <0.001 | 20.9 (15.4–28.2) | <0.001 | ||
| 5 | 65.3 (42.8–99.5) | <0.001 | 60.4 (39.6–92.2) | <0.001 | ||
| 6/7 | 60.7 (38–96.8) | <0.001 | 53.4 (33.4–85.3) | <0.001 | ||
| AUC | 0.840 | 0.866 | 0.871 | |||
AUC area under curve, BMI body mass index, NS not significant, COPD chronic obstructive pulmonary disease, OSAS obstructive sleep apnea syndrome
The in-hospital mortality predictive model that included the degrees of inflammation reached an area under the curve (AUC) of 0.840. The model that included the WHO severity categories reached an AUC=0.866. The model that included both variables reached an AUC=0.871. Table S5 shows the regression model with our proposal combined with categories 3a to 5b. The AUC of this model was also 0.871 (Figure S5).
DISCUSSION
In the present study, we demonstrate and propose a new and better method of classifying the clinical severity of patients admitted for COVID-19 according to respiratory status and degrees of analytical inflammation. We believe that it is more in line with reality and better defines subgroups of patients who were previously grouped in the same category. It also allows progress to be made in therapeutic strategies since it identifies patients at risk without waiting for subsequent respiratory deterioration.
Several severity scores have been proposed in the last 2 decades to assess community-acquired pneumonia severity: Pneumonia Severity Index (PSI),12 CURB-65,13 A-DROP,14 SMART-COP,15 NEWS2,16 and qSOFA.17 They focus on the respiratory state and organ failure. They were not developed for COVID-19 and, therefore, the inflammatory response is not represented in them. Thus, and although the AUC is high for some of them as predictive tool in COVID-19,18 it is not a good tool in some inflamed patients with not yet impaired respiratory condition. Besides, these scores cannot lead trials focused on immunosuppressants in COVID-19. The WHO severity scale has been a good classifying and predictive tool for patients with COVID-19.2 However, it has some shortcomings that should be recalled. On the one hand, category 4 defines patients hospitalized in a conventional ward with oxygen therapy. Obviously, a patient with nasal cannula at 2 L per minute is not the same as a patient with a 50% mask or with a reservoir, but in this classification they are included in the same group. On the other hand, and as we showed in our article, categories 5-6-7 do not differ so much in their basal and analytical characteristics and in their outcomes, and neither are these progressive.4 Besides, we cannot forget it is not a validated scale. For this reason, we believe that these subcategories are informative of the resources used but do not provide added informative value of their clinical, analytical, or predictive characteristics and, therefore, should be grouped in the same category. Finally, the predictive leap in in-hospital mortality between categories 3 and 4, and subsequently 4 and 5, is very large. This means that, for simplicity’s sake, there are groups of patients that have been grouped with others and are not completely well represented.
The alternative model based on degrees of inflammation is a different approach to the disease and its severity.4 It has the advantage of being able to identify patients at high risk who have not yet presented respiratory deterioration. In this sense, Figure S1 shows how in categories with mild and moderate disease there are about 60% and 80%, respectively, of patients characterized as high risk according to analytical degrees of inflammation.
The model we propose is a combination of both models. It is more informative and representative of the real severity of each patient. In addition, the multivariate study performed shows a slightly higher AUC than the other 2 models. Not only should it be a practical tool for clinical use, but we believe it should be the basis for RCTs, many of which have failed for these reasons. Especially those RCTs assessing immunosuppressants such as CS and TCZ without including and defining patient inflammation well could have benefited greatly from this clinical-analytical approach.8–11
The advantage of a model based on degrees of inflammation is the fact that the escalation of inflammation precedes respiratory deterioration. Thus, clinicians can detect earlier those patients susceptible to clinical worsening in the following days. On the other hand, COVID-19 is a clinico-functional-radiological dissociated disease. Thus, a patient with significant hypoxemia can tolerate it acceptably without requiring additional ventilatory resources as in other diseases. It can also guide the anti-inflammatory/immunosuppressive therapies accepted in the treatment of COVID-19. Certainly, not all patients die from the accompanying inflammation of the disease; there are other accompanying predictive variables and, therefore, the WHO classification based on respiratory status and oxygen/ventilation support also provides predictive power. Surely this combination is what makes the hybrid model the most complete and predictive.
Our study has some obvious strengths. The patient sample is very large and comes from a wide range of hospitals of all types nationwide. The database is very large and includes numerous clinical and analytical variables at the time of hospital admission.
Our study also has some limitations that deserve comment. First, it is a retrospective study. Second, being a multicenter study, it gives a good idea of what COVID-19 has been in our country but introduces a certain degree of heterogeneity when it comes to including the data.
In conclusion, the present study proposes a new severity classification scale for patients hospitalized by COVID-19. In our opinion, it is the most informative, representative, and predictive scale in COVID-19 patients to date.
Supplementary Information
(DOCX 102 kb)
Acknowledgements
We gratefully acknowledge all the investigators who participated in the SEMI-COVID-19 Registry. We also thank the SEMI-COVID-19 Registry Coordinating Center for their quality control data, logistic, and administrative support.
Abbreviations
- ARDS
acute respiratory distress syndrome
- AUC
area under the curve
- BMI
body mass index
- COPD
chronic obstructive pulmonary disease
- CRP
C-reactive protein
- CS
corticosteroids
- ECMO
extracardiac membranous oxygenation
- HFNC
high-flow nasal cannula
- ICU
intensive care unit
- IMV
invasive mechanical ventilation
- IQR
interquartile range
- LDH
lactate dehydrogenase
- LMWH
low-molecular-weight heparin
- NIMV
noninvasive mechanical ventilation
- OSAS
obstructive sleep apnea syndrome
- PCR
polymerase chain reaction
- RCT
randomized clinical trial
- SD
standard deviation
- SEMI
Spanish Society of Internal Medicine
- TCZ
tocilizumab
- WHO
World Health Organization
Declarations
Conflict of Interest
The authors declare that they do not have a conflict of interest.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Manuel Rubio-Rivas, Email: mrubio@bellvitgehospital.cat.
José María Mora-Luján, Email: jmora@bellvitgehospital.cat.
Francesc Formiga, Email: fformiga@bellvitgehospital.cat.
Coral Arévalo-Cañas, Email: cocoralarevalo@gmail.com.
Juan Manuel Lebrón Ramos, Email: jmlebronr@gmail.com.
María Victoria Villalba García, Email: mvictoria.villalba@salud.madrid.org.
Eva Mª Fonseca Aizpuru, Email: evamfonseca@yahoo.es.
Jesús Díez-Manglano, Email: jdiez@aragon.es.
Francisco Arnalich Fernández, Email: francisco.arnalich@gmail.com.
Juan Luis Romero Cabrera, Email: Juanluroca855@gmail.com.
Gema María García García, Email: geminway21@hotmail.com.
Paula M. Pesqueira Fontan, Email: paulapesqueira@hotmail.con.
Juan Antonio Vargas Núñez, Email: juanantonio.vargas@uam.es.
Santiago Jesús Freire Castro, Email: santiago.freire.castro@sergas.es.
José Loureiro Amigo, Email: ahores@gmail.com.
Maria de los Reyes Pascual Pérez, Email: cperezb@coma.es.
José N. Alcalá Pedrajas, Email: jnalcala58@hotmail.com.
Daniel Encinas-Sánchez, Email: dencinas@saludcastillayleon.es.
Carmen Mella Pérez, Email: mellacarmen@gmail.com.
Javier Ena, Email: ena_jav@yahoo.es.
Anyuli Gracia Gutiérrez, Email: agraciagut@gmail.com.
María José Esteban Giner, Email: mestebanginer@gmail.com.
José F. Varona, Email: jfvarona@hmhospitales.com.
Jesús Millán Núñez-Cortés, Email: jesus.millan@salud.madrid.org.
José-Manuel Casas-Rojo, Email: jm.casas@gmail.com.
References
- 1.Hu B, Huang S, Yin L. The Cytokine Storm and COVID-19. J Med Virol. 2020 Jun 27;10.1002/jmv.26232. Online ahead of print. [DOI] [PMC free article] [PubMed]
- 2.World Health Organization. WHO R&D Blueprint Novel Coronavirus COVID-19 Therapeutic Trial Synopsis 2020a, available at https://www.who.int/blueprint/priority-diseases/key-action/COVID-19_Treatment_Trial_Design_Master_Protocol_synopsis_Final_18022020.
- 3.Beigel JH, Tomashek KM, Dodd LE, et al. Remdesivir for the treatment of COVID-19 - final report. N Engl J Med. 2020;383:1813–26. doi: 10.1056/NEJMoa2007764. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Rubio-Rivas M, Corbella X, Formiga F, et al. Risk categories in COVID-19 based on degrees of inflammation: data on more than 17,000 patients from the Spanish SEMI-COVID-19 Registry. J Clin Med. 2021;10:2214. doi: 10.3390/jcm10102214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Casas-Rojo JM, Antón-Santos JM, Millán-Núñez-Cortés J, et al. Clinical characteristics of patients hospitalized with COVID-19 in Spain: results from the SEMI-COVID-19 Registry. Rev Clin Esp. 2020;220:480–94. doi: 10.1016/j.rce.2020.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Lora-Tamayo J, Maestro G, Lalueza A, et al. Early lopinavir/ritonavir does not reduce mortality in COVID-19 patients: results of a large multicenter study. J Infect. 2021;S0163-4453(21):00077–3. doi: 10.1016/j.jinf.2021.02.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Gautret P, Hoang VT, Lagier JC, Raoult D. Effect of hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial, an update with an intention-to-treat analysis and clinical outcomes. Int J Antimicrob Agents. 2021;57:106239. doi: 10.1016/j.ijantimicag.2020.106239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.RECOVERY Collaborative Group. Horby P, Lim WS, Emberson JR, et al. Dexamethasone in hospitalized patients with COVID-19. N Engl J Med. 2021;384:693–704. doi: 10.1056/NEJMoa2021436. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Rubio-Rivas M, Ronda M, Padulles A, et al. Beneficial effect of corticosteroids in preventing mortality in patients receiving tocilizumab to treat severe COVID-19 illness. Int J Infect Dis. 2020;101:290–7. doi: 10.1016/j.ijid.2020.09.1486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.REMAP-CAP Investigators, Gordon AC, Mouncey PR, Al-Beidh F, et al. Interleukin-6 receptor antagonists in critically ill patients with COVID-19. N Engl J Med. 2021 Feb 25:NEJMoa2100433. 10.1056/NEJMc2108482 [DOI] [PMC free article] [PubMed]
- 11.RECOVERY Collaborative Group Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. Lancet. 2021;397:1637–45. doi: 10.1016/S0140-6736(21)00676-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Fine MJ, Auble TE, Yealy DM, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336:243–250. doi: 10.1056/NEJM199701233360402. [DOI] [PubMed] [Google Scholar]
- 13.Lim WS, van der Eerden MM, Laing R, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58:377–382. doi: 10.1136/thorax.58.5.377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Miyashita N, Matsushima T, Oka M, et al. The JRS guidelines for the management of community-acquired pneumonia in adults: an update and new recommendations. Intern Med. 2006;45:419–428. doi: 10.2169/internalmedicine.45.1691. [DOI] [PubMed] [Google Scholar]
- 15.Charles PGP, Wolfe R, Whitby M, et al. SMART-COP: a tool for predicting the need for intensive respiratory or vasopressor support in community-acquired pneumonia. Clin Infect Dis. 2008;47:375–384. doi: 10.1086/589754. [DOI] [PubMed] [Google Scholar]
- 16.National Health Service (NHS) England. Resources to support the safe adoption of the revised National Early Warning Score (NEWS2) n.d.. www.england.nhs.uk/wp-content/uploads/2019/12/Patient_Safety_Alert_-_adoption_of_NEWS2.pdf Date last updated: 25 April 2018; date last accessed: 2 April 2020.
- 17.Rudd KE, Seymour CW, Aluisio AR, et al. Association of the Quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) score with excess hospital mortality in adults with suspected infection in low- and middle-income countries. JAMA. 2018;319:2202–2211. doi: 10.1001/jama.2018.6229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Fan G, Tu C, Zhou F, et al. Comparison of severity scores for COVID-19 patients with pneumonia: a retrospective study. Eur Respir J. 2020;56(3):2002113. doi: 10.1183/13993003.02113-2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
(DOCX 102 kb)