Dear editor,
As COVID-19 pandemic continues to escalate, hospitals around the world confront with the need to attend an increasing number of patients. Therefore, we read with much interest the recent study published in the Journal of Infection by Galloway JB et al., reinforcing the importance of stratifying patients to ease their management and their incorporation to potential clinical trials1. For this purpose, these authors developed a valuable and complex risk score based on twelve parameters, including, among others, age, gender, diabetes mellitus, hypertension, and chronic lung disease. Since knowing the risk of clinical deterioration can assist medical decisions about appropriate level of care, predictive models for COVID-19 are becoming notably frequent. However, many of them are notably biased, non-validated, or present a construction lacking in clarity2 , 3. Moreover, they often conclude that male older patients with comorbidities are more likely to experience unfavourable outcomes4 , 5, even when such determinants are already well-known predictors of worse result in community-acquired pneumonia6. Although the medical assessment of patients must always address demographics and underlying comorbidities, it is known that the evaluation of disease severity and prognosis should not only depend on the above-mentioned risk markers.
Our aim was to help clinicians rapidly identify which patients, attended for the first time in an emergency room and regardless of their age, sex, or comorbid conditions, are more likely to be transferred to the intensive care unit (ICU) or to die, and are therefore candidates for a close monitoring and for the administration of the best available therapy. Thus, we focused on the simplest and readily available hemodynamic and laboratory features to build a quick prognostic equation that, based on five independent predictors, was able to estimate the probability of ICU admission or death among adult COVID-19 inpatients.
Briefly, we conducted a prospective cohort study in Virgen del Rocío University Hospital, a Spanish tertiary-care-teaching centre, where 244 consecutive patients, diagnosed of COVID-19, were enrolled from February 21 to April 8, 2020, and followed-up for 28 days. Data were recorded at the emergency room or upon hospital admission. Primary endpoints were favourable (disease improvement, full recovery and discharge, and/or maintenance of non-critical status) and unfavourable (death and/or ICU admission) clinical outcomes. The study protocol was approved by the Ethics Committee (C.I. 0771-N-20) and complied the Declaration of Helsinki. Further information on study design, statistical approach, and internal validation is provided in the Supplementary materials text, Supplementary Table S1, and Supplementary Table S2.
Patients’ characteristics are shown in Table 1 . One-hundred-thirty-two (54.1%) were male and median age was 64 (IQR 55–76) years. Older, institutionalized, solid organ transplant recipients, and hypertensive patients were more likely to develop an unfavourable clinical outcome. Dyspnoea, diastolic hypotension, tachycardia, tachypnoea, low peripheral capillary oxygen saturation (SpO2), chest bilateral infiltrates, high qSOFA and CURB-65 scores were also closely linked to a worse prognosis. Leucocytosis, neutrophilia, lymphocytopenia, thrombocytopenia, and high values of neutrophil-to-lymphocyte ratio, C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), d-dimer, creatinine, and aspartate aminotransferase were more frequent in the unfavourable outcome group. Forty-three (17.6%) patients were admitted to the ICU. The occurrence of ICU transfer by age-group was: <50 years, n = 6 (15.4%); 50–64 years, n = 17 (20.0%); ≥65 years, n = 20 (16.7%). Overall mortality rate (12.7%) and short-term mortality distribution are described in Supplementary Figure S1.
Table 1.
Characteristics of the cohort versus clinical outcome.
| Total (n = 244) |
Clinical outcome |
OR/MD (95% CI) | p value | ||
| Favourable (n = 179) | Unfavourable (n = 65) | ||||
| Demographics | |||||
| Age, years | 64 (55–76) | 62 (16) | 70 (14) | 8 (4–12) | <0.001 |
| Age group ≥65 years | 120 (49.2%) | 78 (43.6%) | 42 (64.6%) | 2.37 (1.31–4.26) | 0.004 |
| Male sex | 132 (54.1%) | 93 (52.0%) | 39 (60.0%) | 1.39 (0.78–2.47) | 0.265 |
| Underlying conditions | |||||
| Smoking history | 18 (7.4%) | 13 (7.3%) | 5 (7.7%) | 1.06 (0.36–3.11) | 1.000 |
| Drinking history | 8 (3.3%) | 4 (2.2%) | 4 (6.2%) | 2.87 (0.70–11.82) | 0.266 |
| Diabetes mellitus | 46 (18.9%) | 32 (17.9%) | 14 (21.5%) | 1.26 (0.62–2.55) | 0.518 |
| Hypertension | 122 (50.0%) | 82 (45.8%) | 40 (61.5%) | 1.89 (1.06–3.38) | 0.030 |
| Malignancy | 19 (7.8%) | 11 (6.1%) | 8 (12.3%) | 2.14 (0.82–5.59) | 0.112 |
| Cerebrovascular disease | 11 (4.5%) | 7 (3.9%) | 4 (6.2%) | 1.61 (0.46–5.70) | 0.691 |
| Dementia | 20 (8.2%) | 14 (7.8%) | 6 (9.2%) | 1.20 (0.44–3.26) | 0.723 |
| COPD | 10 (4.1%) | 6 (3.4%) | 4 (6.2%) | 1.89 (0.52–6.93) | 0.541 |
| OSA | 3 (1.2%) | 3 (1.7%) | 0 | .. | 0.694 |
| Asthma | 10 (4.1%) | 10 (5.6%) | 0 | .. | 0.114 |
| Chronic cardiopathy | 44 (18.0%) | 29 (16.2%) | 15 (23.1%) | 1.55 (0.77–3.13) | 0.217 |
| Chronic renal impairment | 18 (7.4%) | 14 (7.8%) | 4 (6.2%) | 0.77 (0.25–2.44) | 0.870 |
| Chronic liver impairment | 9 (3.7%) | 6 (3.4%) | 3 (4.6%) | 1.40 (0.34–5.75) | 0.937 |
| Connective tissue disease | 13 (5.3%) | 9 (5.0%) | 4 (6.2%) | 1.24 (0.37–4.17) | 0.981 |
| SOT | 5 (2.0%) | 1 (0.6%) | 4 (6.2%) | 11.67 (1.28–106.46) | 0.027 |
| Residence in a socio-sanitary/geriatric centre | 35 (14.3%) | 19 (10.6%) | 16 (24.6%) | 2.75 (1.32–5.75) | 0.006 |
| Charlson Index ≥3 | 139 (57.0%) | 92 (51.4%) | 47 (72.3%) | 2.47 (1.33–4.58) | 0.004 |
| Previous treatment | |||||
| ACEi | 47 (19.3%) | 35 (19.6%) | 12 (18.5%) | 0.93 (0.45–1.93) | 0.848 |
| Statins | 40 (16.4%) | 26 (14.5%) | 14 (21.5%) | 1.62 (0.78–3.33) | 0.191 |
| Immunosuppressive drugs | 30 (12.3%) | 21 (11.7%) | 9 (13.8%) | 1.21 (0.52–2.80) | 0.675 |
| Clinical symptoms at diagnosis | |||||
| Time from symptoms onset to hospital admission, days | 7 (5–11) | 8 (5–12) | 7 (4–10) | 2 (0–4) | 0.051 |
| Rhinorrhoea | 15 (6.1%) | 13 (7.3%) | 2 (3.1%) | 0.41 (0.09–1.85) | 0.367 |
| Odynophagia | 17 (7.0%) | 12 (6.7%) | 5 (7.7%) | 1.16 (0.39–3.43) | 1.000 |
| Cough | 175 (71.7%) | 132 (73.7%) | 43 (66.2%) | 0.70 (0.38–1.28) | 0.245 |
| Expectoration | 25 (10.2%) | 18 (10.1%) | 7 (10.8%) | 1.08 (0.43–2.72) | 0.871 |
| Pleuritic chest pain | 12 (4.9%) | 10 (5.6%) | 2 (3.1%) | 0.54 (0.11–2.52) | 0.641 |
| Dyspnoea | 118 (48.4%) | 78 (43.6%) | 40 (61.5%) | 2.07 (1.16–3.70) | 0.013 |
| Diarrhoea | 41 (16.8%) | 35 (19.6%) | 6 (9.2%) | 0.42 (0.17–1.05) | 0.057 |
| Vomits | 17 (7.0%) | 16 (8.9%) | 1 (1.5%) | 0.16 (0.02–1.23) | 0.085 |
| Arthromyalgia | 54 (22.1%) | 39 (21.8%) | 15 (23.1%) | 1.08 (0.55–2.12) | 0.830 |
| Weakness | 62 (25.4%) | 48 (26.8%) | 14 (21.5%) | 0.75 (0.38–1.48) | 0.403 |
| Headache | 43 (17.6%) | 30 (16.8%) | 13 (20.0%) | 1.24 (0.60–2.56) | 0.557 |
| Impaired consciousness | 8 (3.3%) | 4 (2.2%) | 4 (6.2%) | 2.87 (0.70–11.82) | 0.266 |
| Anosmia | 26 (10.7%) | 21 (11.7%) | 5 (7.7%) | 0.63 (0.23–1.74) | 0.366 |
| Dysgeusia | 27 (11.1%) | 23 (12.8%) | 4 (6.2%) | 0.45 (0.15–1.34) | 0.141 |
| Vital signs, exploration, and severity scores at diagnosis | |||||
| Temperature, °C | 36.7 (36.0–37.7) | 36.7 (36.0–37.7) | 37.0 (1.2) | 0.2 (−0.1–0.6) | 0.211 |
| Temperature >37.5 °C | 72 (30.0%) | 50 (28.4%) | 22 (34.4%) | 1.32 (0.72–2.43) | 0.372 |
| SBP <90 mmHg | 9 (3.7%) | 5 (2.8%) | 4 (6.5%) | 2.40 (0.62–9.24) | 0.357 |
| DBP <60 mmHg | 25 (10.4%) | 11 (6.1%) | 14 (22.6%) | 4.46 (1.90–10.45) | <0.001 |
| HR >100 bpm | 61 (25.3%) | 38 (21.6%) | 23 (35.4%) | 1.99 (1.07–3.71) | 0.029 |
| RR >20 bpm | 37 (15.6%) | 14 (7.9%) | 23 (38.3%) | 7.24 (3.40–15.39) | <0.001 |
| SpO2, % | 95 (92–97) | 96 (94–97) | 90 (85–93) | 7 (5–9) | <0.001 |
| SpO2 <95% | 114 (47.1%) | 56 (31.6%) | 58 (89.2%) | 17.90 (7.68–41.71) | <0.001 |
| Pathological respiratory exploration | 153 (62.7%) | 113 (63.1%) | 40 (61.5%) | 0.94 (0.52–1.68) | 0.820 |
| qSOFA ≥2 | 23 (9.4%) | 12 (6.7%) | 11 (16.9%) | 2.84 (1.18–6.79) | 0.016 |
| CURB-65 ≥ 2 | 74 (30.3%) | 38 (21.2%) | 36 (55.4%) | 4.61 (2.51–8.45) | <0.001 |
| Chest x-ray findings | |||||
| Dominant interstitial pattern | 145 (59.4%) | 103 (57.5%) | 42 (64.6%) | 1.35 (0.75–2.43) | 0.320 |
| Dominant alveolar pattern | 69 (28.3%) | 49 (27.4%) | 20 (30.8%) | 1.18 (0.63–2.19) | 0.603 |
| Unilateral infiltrates | 41 (16.8%) | 36 (20.1%) | 5 (7.7%) | 0.33 (0.12–0.88) | 0.022 |
| Bilateral infiltrates | 173 (70.9%) | 116 (64.8%) | 57 (87.7%) | 3.87 (1.74–8.62) | 0.001 |
| Laboratory results | |||||
| WBC count, x109 per L | 6.8 (4.9–9.1) | 6.5 (4.8–8.5) | 7.8 (5.0–11.7) | 3.9 (0.1–7.7) | 0.046 |
| WBC count >11.0 × 109 per L | 34 (14.0%) | 14 (7.9%) | 20 (30.8%) | 5.21 (2.44–11.12) | <0.001 |
| Neutrophil count, x109 per L | 5.0 (3.4–7.1) | 4.6 (3.3–6.3) | 6.7 (3.7–9.7) | 0.2 (−10.3–10.7) | 0.972 |
| Neutrophil count >7.5 × 109 per L | 52 (21.5%) | 25 (14.1%) | 27 (41.5%) | 4.32 (2.26–8.27) | <0.001 |
| Lymphocyte count, x109 per L | 1.1 (0.7–1.5) | 1.1 (0.8–1.6) | 0.8 (0.6–1.3) | 3.1 (−1.1–7.3) | 0.147 |
| Lymphocyte count <1.0 × 109 per L | 111 (45.7%) | 69 (38.8%) | 42 (64.0%) | 2.89 (1.60–5.21) | <0.001 |
| NLR | 4.4 (2.7–7.9) | 3.7 (2.4–6.8) | 6.5 (3.7–12.4) | 11.3 (−9.6–32.3) | 0.284 |
| NLR >3.04 | 161 (66.5%) | 108 (61.0%) | 53 (81.5%) | 2.82 (1.41–5.66) | 0.003 |
| Platelet count, x109 per L | 201 (163–264) | 200 (165–265) | 201 (155–265) | 6 (−18–31) | 0.603 |
| Platelet count <130 × 109 per L | 22 (9.1%) | 12 (6.8%) | 10 (15.4%) | 2.49 (1.02–6.07) | 0.040 |
| CRP, mg/L | 69 (32–149) | 54 (23–112) | 175 (68–256) | 124 (56–193) | 0.001 |
| CRP ≥100 mg/L | 89 (37.9%) | 51 (29.5%) | 38 (61.3%) | 3.79 (2.07–6.95) | <0.001 |
| Ferritin, ng/mL | 521.0 (248.3–1158.7) | 419.3 (227.4–977.6) | 824.5 (405.7–1712.2) | 356.7 (10.8–702.6) | 0.043 |
| Ferritin ≥1000 ng/mL | 46 (30.5%) | 24 (22.6%) | 22 (48.9%) | 3.27 (1.56–6.85) | 0.001 |
| D-dimer, ng/mL | 790 (473–1650) | 730 (460–1455) | 1160 (678–2333) | 3019 (−875–6912) | 0.126 |
| D-dimer ≥600 ng/mL | 143 (64.7%) | 98 (59.4%) | 45 (80.4%) | 2.80 (1.35–5.80) | 0.005 |
| LDH, UI/L | 321 (244–424) | 297 (234–377) | 420 (321–516) | 129 (73–186) | <0.001 |
| LDH ≥300 UI/L | 130 (58.0%) | 81 (48.8%) | 49 (84.5%) | 5.71 (2.64–12.38) | <0.001 |
| Creatinine >1.3 mg/dL | 47 (21.5%) | 28 (17.7%) | 19 (31.1%) | 2.10 (1.07–4.14) | 0.030 |
| AST, UI/L | 30 (23–52) | 27 (22–45) | 44 (30–64) | 25 (3–48) | 0.028 |
| AST >30 UI/L | 104 (49.8%) | 61 (40.7%) | 43 (72.9%) | 3.92 (2.03–7.59) | <0.001 |
| ALT, UI/L | 28 (18–46) | 24 (18–47) | 33 (22–46) | 3 (−14–21) | 0.691 |
| ALT >40 UI/L | 63 (30.1%) | 43 (28.7%) | 20 (22.9%) | 1.28 (0.67–2.43) | 0.458 |
| Hospital stay | |||||
| ALOS, days | 7 (3–13) | 6 (3–9) | 16 (6–29) | 11 (7–15) | <0.001 |
| LOS >30 days | 21 (8.6%) | 6 (3.4%) | 15 (23.1%) | 8.65 (3.19–23.46) | <0.001 |
| Treatments administered | |||||
| Initial antiviral treatment | |||||
| None | 20 (8.2%) | 11 (6.1%) | 9 (13.8%) | 2.46 (0.97–6.23) | 0.053 |
| LPV/r monotherapy | 7 (2.9%) | 6 (3.4%) | 1 (1.5%) | 0.45 (0.05–3.82) | 0.752 |
| HCQ monotherapy | 37 (15.2%) | 34 (19.0%) | 3 (4.6%) | 0.21 (0.06–0.70) | 0.006 |
| LPV/r + HCQ | 132 (54.1%) | 106 (59.2%) | 26 (40.0%) | 0.46 (0.26–0.82) | 0.008 |
| LPV/r + HCQ + IFN-β | 48 (19.7%) | 22 (12.3%) | 26 (40.0%) | 4.76 (2.44–9.27) | <0.001 |
| Time from symptoms onset to start of antiviral treatment, days | 8 (6–12) | 8 (6–12) | 8 (6–11) | 1 (−1–3) | 0.553 |
| Antiviral treatment added during hospitalization | |||||
| LPV/r | 10 (4.1%) | 9 (5.0%) | 1 (1.5%) | 0.30 (0.04–2.38) | 0.395 |
| IFN-β | 15 (6.1%) | 6 (3.4%) | 9 (13.8%) | 4.63 (1.58–13.59) | 0.003 |
| Remdesivir | 2 (0.8%) | 0 | 2 (3.1%) | .. | 0.120 |
| Anti-inflammatory treatment added during hospitalization | |||||
| Tocilizumab | 28 (11.5%) | 0 | 28 (43.1%) | .. | <0.001 |
| Azithromycin | 83 (34.0%) | 43 (24.0%) | 40 (61.5%) | 5.06 (2.76–9.28) | <0.001 |
| Steroid therapy | 61 (25.0%) | 32 (17.9%) | 29 (44.6%) | 3.70 (1.99–6.88) | <0.001 |
| Oxygen support | |||||
| HFT in ward | 61 (25.0%) | 19 (10.6%) | 42 (64.6%) | 15.38 (7.67–30.85) | <0.001 |
| NIMV in ward | 20 (8.2%) | 6 (3.4%) | 14 (21.5%) | 7.92 (2.89–21.65) | <0.001 |
| IMV | 28 (11.5%) | 0 | 28 (43.1%) | .. | <0.001 |
| Complications | |||||
| ARDS | 36 (14.8%) | 4 (2.2%) | 32 (49.2%) | 42.42 (14.07–127.96) | <0.001 |
| Multiorgan failure | 2 (0.8%) | 0 | 2 (3.1%) | .. | 0.120 |
| Septic shock | 5 (2.0%) | 1 (0.6%) | 4 (6.2%) | 11.67 (1.28–106.46) | 0.027 |
| Acute kidney injury | 5 (2.0%) | 3 (1.7%) | 2 (3.1%) | 1.86 (0.30–11.41) | 0.864 |
Data are n (%), median (IQR), mean (SD), or odds ratio/mean difference (95% CI), according to indication. p values (two-tailed) were calculated by χ2-test, Yates´ Correction for Continuity, Student´s t-test, or Welch´s t-test, as appropriate. OR=odds ratio. MD=mean difference. CODP=chronic obstructive pulmonary disease. OSA=obstructive sleep apnoea. SOT=solid organ transplant. ACEi=angiotensin-converting-enzyme inhibitors. SBP=systolic blood pressure. DBP=diastolic blood pressure. HR=heart rate. RR=respiratory rate. SpO2=peripheral capillary oxygen saturation. WBC=white blood cell. NLR=neutrophil-to-lymphocyte ratio. CRP=C-reactive protein. LDH=lactate dehydrogenase. AST=aspartate aminotransferase. ALT=alanine aminotransferase. ALOS=average length of stay. LOS=length of stay. LPV/r=lopinavir/ritonavir. HCQ=hydroxychloroquine. IFN-β=beta interferon. HFT=high flow therapy. NIMV=non-invasive mechanical ventilation. IMV=invasive mechanical ventilation. ARDS=acute respiratory distress syndrome. Data were missing for symptoms onset in one (0.4%) patient, for temperature in four (1.6%), for blood pressure in three (1.2%), HR in three (1.2%), RR in seven (2.9%), SpO2 in two (0.8%) WBC count in one (0.4%), neutrophil count in two (0.8%), lymphocyte count in one (0.4%), platelet count in three (1.2%), CRP in nine (3.7%), ferritin in 93 (38.1%), d-dimer in 23 (9.4%), LDH in 20 (8.2%), creatinine in 25 (10.2%), and for liver enzymes in 35 (14.3%) patients.
Twenty-three categorical variables were identified as potential independent predictors of unfavourable outcome in univariable logistic regression analysis (Supplementary Table S3). We found significant differences between the survival functions of SpO2 <95% (log-rank, p<0.003) and CRP ≥100 mg/L (p = 0.015), and the adjusted Cox regression analysis showed that hypoxemic patients and those presenting high CRP were indeed more likely to die earlier (Supplementary Figure S2). The prognosis model was composed of five predictors, demonstrated as independent risk factors in the adjusted multivariable logistic regression analysis: SpO2 <95%, neutrophil count >7.5 × 109 per L, platelet count <130 × 109 per L, LDH ≥300 UI/L, and CRP ≥100 mg/L (Supplementary Figure S3). A final model description, its overall apparent performance, and the explanation on how to implement it are presented in Table 2 .
Table 2.
Final prognosis model description.
| B (SE) | W2(df) | OR (95% CI) | p value | |
| SpO2 <95% | 3.075 (0.539) | 32.509 (1) | 21.66 (7.52–62.33) | <0.001 |
| Neutrophil count >7.5 × 109 per L | 1.324 (0.478) | 7.674 (1) | 3.76 (1.47–9.59) | 0.006 |
| Platelet count <130 × 109 per L | 1.492 (0.649) | 5.280 (1) | 4.45 (1.25–15.87) | 0.022 |
| LDH ≥300 UI/L | 0.981 (0.491) | 3.991 (1) | 2.67 (1.02–6.98) | 0.046 |
| CRP ≥100 mg/L | 0.916 (0.434) | 4.466 (1) | 2.50 (1.07–5.85) | 0.035 |
| Constant | −4.655 (0.656) | 50.423 (1) | .. | .. |
Variables in the final multivariable logistic regression model are accompanied by the beta coefficient (SE), Wald-statistic (df), adjusted odds ratio (95% CI), and two-tailed p value. Information concerning the constant is provided as beta coefficient (SE) and Wald-statistic (df). B=beta coefficient. SE=standard error. W2=Wald-statistic. df=degrees of freedom. OR=odds ratio. SpO2=peripheral capillary oxygen saturation. LDH=lactate dehydrogenase. CRP=C-reactive protein.
The model was composed of five variables (therefore 13 events per variable) demonstrated as independent risk factors in the multivariable logistic regression analysis: SpO2 <95%, neutrophil count >7.5 × 109 per L, platelet count <130 × 109 per L, LDH ≥300 UI/L, and CRP ≥100 mg/L (Supplementary Figure S3). It reported an overall apparent performance of 82.9% (sensitivity 62.5%, specificity 90.1%, PPV 68.6%, NPV 87.4%). Its discrimination power (C-index) was expressed by an AUC-ROC of 0.891 (standard error 0.020, 95% CI 0.847–0.936; p<0.001) (Supplementary Figure S4). The variables included were explanatory, being −2LL=151.615 (χ2 96.208, df 5; p<0.001), and contributed to giving the model an ability to explain roughly 53% of the variation of the outcome (Nagelkerke R2 0.526). The model was a good fit to the dataset (Hosmer-Lemeshow χ2 1.130, df 5; p = 0.951), which could also be tested visually by the calibration plot (Supplementary Figure S5). After 100 iterations of bootstrapping, model optimism was estimated <0.01 (SD 0.02), indicating minimal overfitting to the data. The optimism-corrected performance was of 0.885. The final equation to estimate the probability (0 to 1) of unfavourable outcome was: Logit (logarithm of the odds) (pi) = −4.655 + 3.075 (SpO2 <95%) + 1.324 (neutrophil count >7.5 × 109 per L) + 1.492 (platelet count <130 × 109 per L) + 0.981 (LDH ≥300 UI/L) + 0.916 (CRP ≥100 mg/L). Thus, filling each term of the equation with 1 or 0 regarding if the respective condition is present or not, patients can be assigned a probability of critical disease or fatality on the basis of information from the initial history and quickly available laboratory examinations.
Unlike the rest of prognosis models published1 , 3 , 7, 8, 9, that included already well-established and globally accepted clinical predictors of severity6, we opted for incorporating exclusively the explanatory variables that were directly related to the pathogenesis of COVID-19. In this respect, the biological plausibility of hypoxemia, thrombocytopenia, neutrophilia, and high levels of LDH and CRP, coupled with their important role in disease progression, make our selected variables of great interest for further research on SARS-CoV-2 damaging mechanisms and therapeutic targets. Hypoxemia and high LDH, expression of tissue damage, contributed to build Ji et al., Liang et al. and Galloway et al. predictive tools1 , 7 , 9. Gong et al. and Galloway et al., like us, included CRP and neutrophil count as inflammation markers in their model, but in conjunction with other not as easily accessible predictors, like albumin1 , 3. The low platelet count, despite its likely interlinkage with thrombosis in the pathogenesis of COVID-19, has not yet been thoroughly explored. Zhao et al. discussed the tendency of these cells to decrease in critically ill patients10. Our study goes one step further and offers thrombocytopenia at the moment of hospital admission as a main predictor for short-term adverse clinical outcomes.
In conclusion, we derived and validated a prognostic model, including five common features obtained in the first patient's evaluation at the emergency room, with high sensitivity and specificity to discriminate individuals that might develop critical disease or die, from those with a favourable course. This model, besides the complete clinical evaluation of the patient by the physician, could be helpful for guiding prompt decision-making, improve the management of COVID-19 patients, alleviate insufficiency of medical resources, and reduce mortality.
CRediT authorship contribution statement
Sonsoles Salto-Alejandre: Investigation, Writing - original draft, Writing - review & editing. Cristina Roca-Oporto: Data curation, Writing - review & editing. Guillermo Martín-Gutiérrez: Data curation, Writing - review & editing. María Dolores Avilés: Data curation, Writing - review & editing. Carmen Gómez-González: Data curation, Writing - review & editing. María Dolores Navarro-Amuedo: Data curation, Writing - review & editing. Julia Praena-Segovia: Data curation, Writing - review & editing. José Molina: Data curation, Writing - review & editing. María Paniagua-García: Data curation, Writing - review & editing. Horacio García-Delgado: Data curation, Writing - review & editing. Antonio Domínguez-Petit: Data curation, Writing - review & editing. Jerónimo Pachón: Conceptualization, Visualization, Investigation, Writing - original draft, Writing - review & editing. José Miguel Cisneros: Conceptualization, Visualization, Writing - review & editing.
Declaration of Competing Interest
None.
Acknowledgments
Acknowledgments
Not applicable.
Funding
This work was supported by National Plan R + D + I 2013–2016 and Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministry of Economy, Industry, and Competitiveness, Spanish Network for Research in Infectious Diseases [REIPI RD16/0016/0009]; cofinanced by European Development Regional Fund “A way to achieve Europe”, Operative program Intelligent Growth 2014–2020; and supported by a grant from the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación, Proyectos de Investigación sobre el SARS-CoV-2 y la enfermedad COVID-19 [COV20/00370] to SS-A.
Availability of data and materials
Data are available on request.
Ethics approval
The study protocol was approved by the Ethics Committee of Virgen Macarena and Virgen del Rocío University Hospitals (C.I. 0771-N-20) and complied the Declaration of Helsinki.
Consent for publication
All authors have approved the manuscript and its publication.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jinf.2020.09.023.
Appendix. Supplementary materials
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Data Availability Statement
Data are available on request.