Background: The clinical presentation of COVID‐19 ranges from a mild, self‐limiting disease, to multiple organ failure and death. Most severe COVID‐19 cases present low lymphocytes counts and high leukocytes counts, and accumulated evidence suggests that in a subgroup of patients presenting severe COVID‐19, there may be a hyperinflammatory response driving a severe hypercytokinaemia which may be, at least in part, signalling the presence of an underlying endothelial dysfunction. In this context, available data suggest a prognostic role of neutrophil‐lymphocyte ratio (NLR) in various inflammatory diseases and oncological processes. Following this rationale, we hypothesized that NLR, as a marker of endothelial dysfunction, may be useful in identifying patients with a poor prognosis in hospitalized COVID‐19 cases.Design: A retrospective observational study performed at Hospital Universitario HM Puerta del Sur, Madrid, Spain, which included 119 patients with COVID‐19 from 1 March to 31 March 2020. Patients were categorized according to WHO R&D Expert Group.Results: Forty‐five (12.1%) patients experienced severe acute respiratory failure requiring respiratory support. Forty‐seven (12.6%) patients died. Those with worse outcomes were older (P = .002) and presented significantly higher NLR at admission (P = .001), greater increase in Peak NLR (P < .001) and higher increasing speed of NLR (P = .003) compared with follow‐up patients. In a multivariable logistic regression, age, cardiovascular disease and C‐reactive protein at admission and Peak NLR were significantly associated with death.Conclusions: NLR is an easily measurable, available, cost‐effective and reliable parameter, which continuous monitoring could be useful for the diagnosis and treatment of COVID‐19.

The clinical impact of COVID-19 disease calls for the identification of routine variables to identify patients at increased risk of death. Current understanding of moderate-to-severe COVID-19 pathophysiology points toward an underlying cytokine release driving a hyperinflammatory and procoagulant state. In this scenario, white blood cells and platelets play a direct role as effectors of such inflammation and thrombotic response. We investigate whether hemogram-derived ratios such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio and the systemic immune-inflammation index may help to identify patients at risk of fatal outcomes. Activated platelets and neutrophils may be playing a decisive role during the thromboinflammatory phase of COVID-19 so, in addition, we introduce and validate a novel marker, the neutrophil-to-platelet ratio (NPR). Two thousand and eighty-eight hospitalized patients with COVID-19 admitted at any of the hospitals of HM Hospitales group in Spain, from March 1 to June 10, 2020, were categorized according to the primary outcome of in-hospital death. Baseline values, as well as the rate of increase of the four ratios analyzed were significantly higher at hospital admission in patients who died than in those who were discharged (p<0.0001). In multivariable logistic regression models, NLR (OR 1.05; 95% CI 1.02 to 1.08, p=0.00035) and NPR (OR 1.23; 95% CI 1.12 to 1.36, p<0.0001) were significantly and independently associated with in-hospital mortality. According to our results, hemogram-derived ratios obtained at hospital admission, as well as the rate of change during hospitalization, may easily detect, primarily using NLR and the novel NPR, patients with COVID-19 at high risk of in-hospital mortality.

Background: The vast impact of COVID-19 call for the identification of clinical parameter that can help predict a torpid evolution. Among these, endothelial injury has been proposed as one of the main pathophysiological mechanisms underlying the disease, promoting a hyperinflammatory and prothrombotic state leading to worse clinical outcomes. Leukocytes and platelets play a key role in inflammation and thrombogenesis, hence the objective of the current study was to study whether neutrophil-to-lymphocyte ratio (NLR), platelets-to-lymphocyte ratio (PLR), the systemic immune-inflammation index (SII) as well as the new parameter neutrophil-to-platelet ratio (NPR), could help identify patients who at risk of admission at Intensive Care Units. Methods: A retrospective observational study was performed at HM Hospitales including electronic health records from 2245 patients admitted due to COVID-19 from March 1 to June 10, 2020. Patients were divided into two groups, admitted at ICU or not. Results: Patients who were admitted at the ICU had significantly higher values in all hemogram-derived ratios at the moment of hospital admission compared to those who did not need ICU admission. Specifically, we found significant differences in NLR (6.9 [4–11.7] vs 4.1 [2.6–7.6], p < 0.0001), PLR (2 [1.4–3.3] vs 1.9 [1.3–2.9], p = 0.023), NPR (3 [2.1–4.2] vs 2.3 [1.6–3.2], p < 0.0001) and SII (13 [6.5–25.7] vs 9 [4.9–17.5], p < 0.0001) compared to those who did not require ICU admission. After multivariable logistic regression models, NPR was the hemogram-derived ratio with the highest predictive value of ICU admission, (OR 1.11 (95% CI: 0.98–1.22, p = 0.055). Conclusions: Simple, hemogram-derived ratios obtained from early hemogram at hospital admission, especially the novelty NPR, have shown to be useful predictors of risk of ICU admission in patients hospitalized due to COVID-19.

Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the training cohort and 645 to validation cohort. Four different models were generated to predict in-hospital mortality. Following variables were included: age, sex, oxygen saturation, level of C-reactive-protein, neutrophil-to-platelet-ratio (NPR), neutrophil-to-lymphocyte-ratio (NLR) and the rate of changes of both hemogram ratios (VNLR and VNPR) during the first week after admission. The accuracy of the models in predicting in-hospital mortality were evaluated using the area under the receiver-operator-characteristic curve (AUC). AUC for models including NLR and NPR performed similarly in both cohorts: NLR 0.873 (95% CI: 0.849–0.898), NPR 0.875 (95% CI: 0.851–0.899) in training cohort and NLR 0.856 (95% CI: 0.818–0.895), NPR 0.863 (95% CI: 0.826–0.901) in validation cohort. AUC was 0.885 (95% CI: 0.885–0.919) for VNLR and 0.891 (95% CI: 0.861–0.922) for VNPR in the validation cohort. According to our results, models are useful in predicting in-hospital mortality risk due to COVID-19. The RIM Score proposed is a simple, widely available tool that can help identify patients at risk of fatal outcomes.

This study aimed to create an individualized analysis model of the risk of intensive care unit (ICU) admission or death for coronavirus disease 2019 (COVID-19) patients as a tool for the rapid clinical management of hospitalized patients in order to achieve a resilience of medical resources. This is an observational, analytical, retrospective cohort study with longitudinal follow-up. Data were collected from the medical records of 3489 patients diagnosed with COVID-19 using RT-qPCR in the period of highest community transmission recorded in Europe to date: February–June 2020. The study was carried out in in two health areas of hospital care in the Madrid region: the central area of the Madrid capital (Hospitales de Madrid del Grupo HM Hospitales (CH-HM), n = 1931) and the metropolitan area of Madrid (Hospital Universitario Príncipe de Asturias (MH-HUPA) n = 1558). By using a regression model, we observed how the different patient variables had unequal importance. Among all the analyzed variables, basal oxygen saturation was found to have the highest relative importance with a value of 20.3%, followed by age (17.7%), lymphocyte/leukocyte ratio (14.4%), CRP value (12.5%), comorbidities (12.5%), and leukocyte count (8.9%). Three levels of risk of ICU/death were established: low-risk level (20%). At the high-risk level, 13% needed ICU admission, 29% died, and 37% had an ICU–death outcome. This predictive model allowed us to individualize the risk for worse outcome for hospitalized patients affected by COVID-19.