A deficient immune response to SARS-CoV-2 in the nasopharynx is associated with severe COVID-19 pneumonia.

OBJECTIVES: We analyzed the expression of inflammatory and antiviral genes in the nasopharynx of SARS-CoV-2 infected patients and their association with the severity of COVID-19 pneumonia. METHODS: We conducted a cross-sectional study on 223 SARS-CoV-2 infected patients. Clinical data were collected from medical records, and nasopharyngeal samples were collected in the first 24 hours after admission to the emergency room. The gene expression of eight proinflammatory/antiviral genes (plasminogen activator urokinase receptor [PLAUR], interleukin [IL]-6, IL-8, interferon [IFN]-ß, IFN-stimulated gene 15 [ISG15], retinoic acid-inducible gene I [RIG-I], C-C motif ligand 5 [CCL5], and chemokine C-X-C motif ligand 10 [CXCL10]) were quantified by real-time polymerase chain reaction. Outcome variables were: (i) pneumonia; (ii) severe pneumonia or acute respiratory distress syndrome. Statistical analysis was performed using multivariate logistic regression analyses. RESULTS: We enrolled 84 mild, 88 moderate, and 51 severe/critical cases. High expression of PLAUR (adjusted odds ratio [aOR] = 1.25; P = 0.032, risk factor) and low expression of CXCL10 (aOR = 0.89; P = 0.048, protective factor) were associated with pneumonia. Furthermore, lower values of ISG15 (aOR = 0.88, P = 0.021), RIG-I (aOR = 0.87, P = 0.034), CCL5 (aOR = 0.73, P <0.001), and CXCL10 (aOR = 0.84, P = 0.002) were risk factors for severe pneumonia/acute respiratory distress syndrome. CONCLUSION: An unbalanced early innate immune response to SARS-CoV-2 in the nasopharynx, characterized by high expression of PLAUR and low expression of antiviral genes (ISG15 and RIG-I), and chemokines (CCL5 and CXCL10), was associated with COVID-19 severity.

Antibody levels to SARS-CoV-2 spike protein in mothers and children from delivery to six months later.

INTRODUCTION: Pregnant women are vulnerable to severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection. Neutralizing antibodies against the SARS-CoV-2 spike (S) protein protect from severe disease. This study analyzes the antibody titers to SARS-CoV-2 S protein in pregnant women and their newborns at delivery, and six months later. METHODS: We conducted a prospective study on pregnant women with confirmed SARS-CoV-2 infection and newborns. Antibody (IgG, IgM, and IgA) titers were determined using immunoassays in serum and milk samples. An angiotensin-converting enzyme 2 (ACE2) receptor-binding inhibition assay to the S protein was performed on the same serum and milk samples. RESULTS: At birth, antibodies to SARS-CoV-2 spike protein were detected in 81.9% of mothers' sera, 78.9% of cord blood samples, and 63.2% of milk samples. Symptomatic women had higher antibody titers (IgG, IgM, and IgA) than the asymptomatic ones (P < 0.05). At six months postpartum, IgG levels decreased drastically in children's serum (P < 0.001) but remained high in mothers' serum. Antibody titers correlated positively with its capacity to inhibit the ACE2-spike protein interaction at baseline in maternal sera (R2  = 0.203; P < 0.001), cord sera (R2  = 0.378; P < 0.001), and milk (R2  = 0.564; P < 0.001), and at six months in maternal sera (R2  = 0.600; P < 0.001). CONCLUSIONS: High antibody levels against SARS-CoV-2 spike protein were found in most pregnant women. Due to the efficient transfer of IgG to cord blood and high IgA titers in breast milk, neonates may be passively immunized to SARS-CoV-2 infection. Our findings could guide newborn management and maternal vaccination policies.

Effect of viral storm in patients admitted to intensive care units with severe COVID-19 in Spain: a multicentre, prospective, cohort study

Background The contribution of the virus to the pathogenesis of severe COVID-19 is still unclear. We aimed to evaluate associations between viral RNA load in plasma and host response, complications, and deaths in critically ill patients with COVID-19. Methods We did a prospective cohort study across 23 hospitals in Spain. We included patients aged 18 years or older with laboratory-confirmed SARS-CoV-2 infection who were admitted to an intensive care unit between March 16, 2020, and Feb 27, 2021. RNA of the SARS-CoV-2 nucleocapsid region 1 (N1) was quantified in plasma samples collected from patients in the first 48 h following admission, using digital PCR. Patients were grouped on the basis of N1 quantity: VIR-N1-Zero (<1 N1 copies per mL), VIR-N1-Low (1–2747 N1 copies per mL), and VIR-N1-Storm (>2747 N1 copies per mL). The primary outcome was all-cause death within 90 days after admission. We evaluated odds ratios (ORs) for the primary outcome between groups using a logistic regression analysis. Findings 1068 patients met the inclusion criteria, of whom 117 had insufficient plasma samples and 115 had key information missing. 836 patients were included in the analysis, of whom 403 (48%) were in the VIR-N1-Low group, 283 (34%) were in the VIR-N1-Storm group, and 150 (18%) were in the VIR-N1-Zero group. Overall, patients in the VIR-N1-Storm group had the most severe disease: 266 (94%) of 283 patients received invasive mechanical ventilation (IMV), 116 (41%) developed acute kidney injury, 180 (65%) had secondary infections, and 148 (52%) died within 90 days. Patients in the VIR-N1-Zero group had the least severe disease: 81 (54%) of 150 received IMV, 34 (23%) developed acute kidney injury, 47 (32%) had secondary infections, and 26 (17%) died within 90 days (OR for death 0·30, 95% CI 0·16–0·55;p<0·0001, compared with the VIR-N1-Storm group). 106 (26%) of 403 patients in the VIR-N1-Low group died within 90 days (OR for death 0·39, 95% CI 0·26–0·57;p<0·0001, compared with the VIR-N1-Storm group). Interpretation The presence of a so-called viral storm is associated with increased all-cause death in patients admitted to the intensive care unit with severe COVID-19. Preventing this viral storm could help to reduce poor outcomes. Viral storm could be an enrichment marker for treatment with antivirals or purification devices to remove viral components from the blood. Funding Instituto de Salud Carlos III, Canadian Institutes of Health Research, Li Ka-Shing Foundation, Research Nova Scotia, and European Society of Clinical Microbiology and Infectious Diseases. Translation For the Spanish translation of the see Supplementary Materials section.

Environmental factors are associated to hospital outcomes in COVID-19 patients during lockdown and post-lockdown in 2020: A nationwide study.

OBJECTIVE: This study analyzed, at a postcode detailed level, the relation-ship between short-term exposure to environmental factors and hospital ad-missions, in-hospital mortality, ICU admission, and ICU mortality due to COVID-19 during the lockdown and post-lockdown 2020 period in Spain. METHODS: We performed a nationwide population-based retrospective study on 208,744 patients admitted to Spanish hospitals due to COVID-19 based on the Minimum Basic Data Set (MBDS) during the first two waves of the pandemic in 2020. Environmental data were obtained from Copernicus Atmosphere Monitoring Service. The association was assessed by a generalized additive model. RESULTS: PM2.5 was the most critical environmental factor related to hospital admissions and hospital mortality due to COVID-19 during the lockdown in Spain, PM10, NO2, and SO2and also showed associations. The effect was considerably reduced during the post-lockdown period. ICU admissions in COVID-19 patients were mainly associated with PM2.5, PM10, NO2, and SO2 during the lockdown as well. During the lockdown, exposure to PM2.5 and PM10 were the most critical environmental factors related to ICU mortality in COVID-19. CONCLUSION: Short-term exposure to air pollutants impacts COVID-19 out-comes during the lockdown, especially PM2.5, PM10, NO2, and SO2. These pollutants are associated with hospital admission, hospital mortality and ICU admission, while ICU mortality is mainly associated with PM2.5 and PM10. Our findings reveal the importance of monitoring air pollutants in respiratory infectious diseases.

RELATIVE TELOMERE LENGTH IMPACT ON MORTALITY OF COVID-19: SEX DIFFERENCES.

INTRODUCTION: Increasing age is associated with severity and higher mortality of COVID-19. Telomere shortening is associated with higher risk of infections and may be used to identify those patients who are more likely to die. We evaluated the association between relative telomere length (RTL) and COVID-19 mortality. METHODS: RTL was measured in patients hospitalized because of COVID-19. We used Kaplan-Meier method to analyze survival probabilities, and Cox regression to investigate the association between RTL and mortality (30 and 90 days). RESULTS: 608 patients were included in the analysis (mean age =72.5 years, 41.1% women, and 53.8% Caucasic). During the study period, 75 people died from COVID-19 and 533 survived. Lower RTL was associated with a higher risk of death in women either at 30 (adjusted HR (aHR)=3.33; 95%CI=1.05-10.00; p=0.040) and at 90 days (aHR=3.57; 95%CI=1.23-11.11; p=0.019). CONCLUSION: Lower RTL was associated with a higher risk of dying of COVID-19 in women. This finding suggests that RTL has an essential role in the prognosis of this subset of the population. This article is protected by copyright. All rights reserved.

Metabolic Profiling at COVID-19 Onset Shows Disease Severity and Sex-Specific Dysregulation.

Background: metabolic changes through SARS-CoV-2 infection has been reported but not fully comprehended. This metabolic dysregulation affects multiple organs during COVID-19 and its early detection can be used as a prognosis marker of severity. Therefore, we aimed to characterize metabolic and cytokine profile at COVID-19 onset and its relationship with disease severity to identify metabolic profiles predicting disease progression. Material and Methods: we performed a retrospective cross-sectional study in 123 COVID-19 patients which were stratified as asymptomatic/mild, moderate and severe according to the highest COVID-19 severity status, and a group of healthy controls. We performed an untargeted plasma metabolic profiling (gas chromatography and capillary electrophoresis-mass spectrometry (GC and CE-MS)) and cytokine evaluation. Results: After data filtering and identification we observed 105 metabolites dysregulated (66 GC-MS and 40 CE-MS) which shown different expression patterns for each COVID-19 severity status. These metabolites belonged to different metabolic pathways including amino acid, energy, and nitrogen metabolism among others. Severity-specific metabolic dysregulation was observed, as an increased transformation of L-tryptophan into L-kynurenine. Thus, metabolic profiling at hospital admission differentiate between severe and moderate patients in the later phase of worse evolution. Several plasma pro-inflammatory biomarkers showed significant correlation with deregulated metabolites, specially with L-kynurenine and L-tryptophan. Finally, we describe a strong sex-related dysregulation of metabolites, cytokines and chemokines between severe and moderate patients. In conclusion, metabolic profiling of COVID-19 patients at disease onset is a powerful tool to unravel the SARS-CoV-2 molecular pathogenesis. Conclusions: This technique makes it possible to identify metabolic phenoconversion that predicts disease progression and explains the pronounced pathogenesis differences between sexes.

Environmental factors linked to hospital admissions in young children due to acute viral lower respiratory infections: A bidirectional case-crossover study.

OBJECTIVE: This study evaluated the association of the short-term exposure to environmental factors (relative humidity, temperature, NO2, SO2, O3, PM10, and CO) with hospital admissions due to acute viral lower respiratory infections (ALRI) in children under two years before the COVID-19 era. METHODS: We performed a bidirectional case-crossover study in 30,445 children with ALRI under two years of age in the Spanish Minimum Basic Data Set (MBDS) from 2013 to 2015. Environmental data were obtained from Spain's State Meteorological Agency (AEMET). The association was assessed by conditional logistic regression. RESULTS: Lower temperature one week before the day of the event (hospital admission) (q-value = 0.012) and higher relative humidity one week (q-value = 0.003) and two weeks (q-value<0.001) before the day of the event were related to a higher odds of hospital admissions. Higher NO2 levels two weeks before the event were associated with hospital admissions (q-value<0.001). Moreover, higher concentrations on the day of the event for SO2 (compared to lag time of 1-week (q-value = 0.026) and 2-weeks (q-value<0.001)), O3 (compared to lag time of 3-days (q-value<0.001), 1-week (q-value<0.001), and 2-weeks (q-value<0.001)), and PM10 (compared to lag time of 2-weeks (q-value<0.001)) were related to an increased odds of hospital admissions for viral ALRI. CONCLUSION: Short-term exposure to environmental factors (climatic conditions and ambient air contaminants) was linked to a higher likelihood of hospital admissions due to ALRI. Our findings emphasize the importance of monitoring environmental factors to assess the odds of ALRI hospital admissions and plan public health resources.

Plasma miRNA profile at COVID-19 onset predicts severity status and mortality.

BACKGROUND: MicroRNAs (miRNAs) have a crucial role in regulating immune response against infectious diseases, showing changes early in disease onset and before the detection of the pathogen. Thus, we aimed to analyze the plasma miRNA profile at COVID-19 onset to identify miRNAs as early prognostic biomarkers of severity and survival. METHODS AND RESULTS: Plasma miRNome of 96 COVID-19 patients that developed asymptomatic/mild, moderate and severe disease was sequenced together with a group of healthy controls. Plasma immune-related biomarkers were also assessed. COVID-19 patients showed 200 significant differentially expressed (SDE) miRNAs concerning healthy controls, with upregulated putative targets of SARS-CoV-2, and inflammatory miRNAs. Among COVID-19 patients, 75 SDE miRNAs were observed in asymptomatic/mild compared to symptomatic patients, which were involved in platelet aggregation and cytokine pathways, among others. Moreover, 137 SDE miRNAs were identified between severe and moderate patients, where miRNAs targeting the SARS CoV-2 genome were the most strongly disrupted. Finally, we constructed a mortality predictive risk score (miRNA-MRS) with ten miRNAs. Patients with higher values had a higher risk of 90-days mortality (hazard ratio = 4.60; p-value < 0.001). Besides, the discriminant power of miRNA-MRS was significantly higher than the observed for age and gender (AUROC = 0.970 vs. 0.881; p = 0.042). CONCLUSIONS: SARS-CoV-2 infection deeply disturbs the plasma miRNome from an early stage of COVID-19, making miRNAs highly valuable as early predictors of severity and mortality.

High SARS-CoV-2 Viral Load and Low CCL5 Expression Levels in the Upper Respiratory Tract Are Associated With COVID-19 Severity.

Mucosal immune response in the upper respiratory tract is crucial for initial control of viral replication, clearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and progression of coronavirus disease 2019 (COVID-19). We analyzed SARS-CoV-2 RNA load and expression of selected immune genes in the upper respiratory tract (nasopharynx) of 255 SARS-CoV-2-infected patients and evaluated their association with severe COVID-19. SARS-CoV-2 replication in nasopharyngeal mucosa induces expression of several innate immune genes. High SARS-CoV-2 viral load and low CCL5 expression levels were associated with intensive care unit admission or death, although CCL5 was the best predictor of COVID-19 severity.

Are Reduced Levels of Coagulation Proteins Upon Admission Linked to COVID-19 Severity and Mortality?

Background: The link between coagulation system disorders and COVID-19 has not yet been fully elucidated. Aim: Evaluating the association of non-previously reported coagulation proteins with COVID-19 severity and mortality. Design: Cross-sectional study of 134 COVID-19 patients recruited at admission and classified according to the highest COVID-19 severity reached (asymptomatic/mild, moderate, or severe) and 16 healthy control individuals. Methods: Coagulation proteins levels (antithrombin, prothrombin, factor_XI, factor_XII, and factor_XIII) and CRP were measured in plasma by the ProcartaPlex Panel (Invitrogen) multiplex immunoassay upon diagnosis. Results: We found higher levels of antithrombin, prothrombin, factor XI, factor XII, and factor XIII in asymptomatic/mild and moderate COVID-19 patients compared to healthy individuals. Interestingly, decreased levels of antithrombin and factors XI, XII, and XIII were observed in those patients who eventually developed severe illness. Additionally, survival models showed us that patients with lower levels of these coagulation proteins had an increased risk of death. Conclusion: COVID-19 provokes early increments of some specific coagulation proteins in most patients. However, lower levels of these proteins at diagnosis might "paradoxically" imply a higher risk of progression to severe disease and COVID-19-related mortality.

Low anti-SARS-CoV-2 S antibody levels predict increased mortality and dissemination of viral components in the blood of critical COVID-19 patients.

BACKGROUND: Anti-SARS-CoV-2 S antibodies prevent viral replication. Critically ill COVID-19 patients show viral material in plasma, associated with a dysregulated host response. If these antibodies influence survival and viral dissemination in ICU-COVID patients is unknown. PATIENTS/METHODS: We studied the impact of anti-SARS-CoV-2 S antibodies levels on survival, viral RNA-load in plasma, and N-antigenaemia in 92 COVID-19 patients over ICU admission. RESULTS: Frequency of N-antigenaemia was >2.5-fold higher in absence of antibodies. Antibodies correlated inversely with viral RNA-load in plasma, representing a protective factor against mortality (adjusted HR [CI 95%], p): (S IgM [AUC ≥ 60]: 0.44 [0.22; 0.88], 0.020); (S IgG [AUC ≥ 237]: 0.31 [0.16; 0.61], <0.001). Viral RNA-load in plasma and N-antigenaemia predicted increased mortality: (N1-viral load [≥2.156 copies/ml]: 2.25 [1.16; 4.36], 0.016); (N-antigenaemia: 2.45 [1.27; 4.69], 0.007). CONCLUSIONS: Low anti-SARS-CoV-2 S antibody levels predict mortality in critical COVID-19. Our findings support that these antibodies contribute to prevent systemic dissemination of SARS-CoV-2.

Age-Adjusted Endothelial Activation and Stress Index for Coronavirus Disease 2019 at Admission Is a Reliable Predictor for 28-Day Mortality in Hospitalized Patients With Coronavirus Disease 2019.

Background: Endothelial Activation and Stress Index (EASIX) predict death in patients undergoing allogeneic hematopoietic stem cell transplantation who develop endothelial complications. Because coronavirus disease 2019 (COVID-19) patients also have coagulopathy and endotheliitis, we aimed to assess whether EASIX predicts death within 28 days in hospitalized COVID-19 patients. Methods: We performed a retrospective study on COVID-19 patients from two different cohorts [derivation (n = 1,200 patients) and validation (n = 1,830 patients)]. The endpoint was death within 28 days. The main factors were EASIX [(lactate dehydrogenase * creatinine)/thrombocytes] and aEASIX-COVID (EASIX * age), which were log2-transformed for analysis. Results: Log2-EASIX and log2-aEASIX-COVID were independently associated with an increased risk of death in both cohorts (p < 0.001). Log2-aEASIX-COVID showed a good predictive performance for 28-day mortality both in the derivation cohort (area under the receiver-operating characteristic = 0.827) and in the validation cohort (area under the receiver-operating characteristic = 0.820), with better predictive performance than log2-EASIX (p < 0.001). For log2 aEASIX-COVID, patients with low/moderate risk (<6) had a 28-day mortality probability of 5.3% [95% confidence interval (95% CI) = 4-6.5%], high (6-7) of 17.2% (95% CI = 14.7-19.6%), and very high (>7) of 47.6% (95% CI = 44.2-50.9%). The cutoff of log2 aEASIX-COVID = 6 showed a positive predictive value of 31.7% and negative predictive value of 94.7%, and log2 aEASIX-COVID = 7 showed a positive predictive value of 47.6% and negative predictive value of 89.8%. Conclusion: Both EASIX and aEASIX-COVID were associated with death within 28 days in hospitalized COVID-19 patients. However, aEASIX-COVID had significantly better predictive performance than EASIX, particularly for discarding death. Thus, aEASIX-COVID could be a reliable predictor of death that could help to manage COVID-19 patients.

Prevalence and factors associated with SARS-CoV-2 seropositivity in the Spanish HIV Research Network Cohort.

OBJECTIVES: We aimed to assess the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and factors associated with seropositivity and asymptomatic coronavirus disease 2019 (COVID-19) among people with HIV (PWH). METHODS: This was a cross-sectional study carried out within the cohort of the Spanish HIV Research Network. Participants were consecutive PWH with plasma collected from 1st April to 30th September 2020. We determined SARS-CoV-2 antibodies (Abs) in plasma. Illness severity (NIH criteria) was assessed by a review of medical records and, if needed, participant interviews. Multivariable logistic regression analysis was used to identify predictors of seropositivity among the following variables: sex, age, country of birth, education level, comorbidities (hypertension, chronic heart disease, diabetes mellitus, non-AIDS-related cancer, chronic kidney disease, cirrhosis), route of HIV acquisition, prior AIDS, CD4+ cell count, HIV viral load, nucleoside/nucleotide reverse transcriptase inhibitor (N [t]RTI) backbone, type of third antiretroviral drug, and month of sample collection. RESULTS: Of 1076 PWH (88.0% males, median age 43 years, 97.7% on antiretroviral therapy, median CD4+ 688 cells/mm3, 91.4% undetectable HIV viral load), SARS-CoV-2 Abs were detected in 91 PWH, a seroprevalence of 8.5% (95%CI 6.9-10.3%). Forty-five infections (45.0%) were asymptomatic. Variables independently associated with SARS-CoV-2 seropositivity were birth in Latin American countries versus Spain (adjusted odds ratio (aOR) 2.30, 95%CI 1.41-3.76, p 0.001), and therapy with tenofovir disoproxil fumarate plus emtricitabine (TDF/FTC) versus tenofovir alafenamide (TAF)/FTC as the N(t)RTI backbone (aOR 0.49, 95%CI 0.26-0.94, p 0.031). CONCLUSIONS: Many SARS-CoV-2 infections among PWH were asymptomatic, and birth in Latin American countries increased the risk of SARS-CoV-2 seropositivity. Our analysis, adjusted by comorbidities and other variables, suggests that TDF/FTC may prevent SARS-CoV-2 infection among PWH.

Viral RNA load in plasma is associated with critical illness and a dysregulated host response in COVID-19.

BACKGROUND: COVID-19 can course with respiratory and extrapulmonary disease. SARS-CoV-2 RNA is detected in respiratory samples but also in blood, stool and urine. Severe COVID-19 is characterized by a dysregulated host response to this virus. We studied whether viral RNAemia or viral RNA load in plasma is associated with severe COVID-19 and also to this dysregulated response. METHODS: A total of 250 patients with COVID-19 were recruited (50 outpatients, 100 hospitalized ward patients and 100 critically ill). Viral RNA detection and quantification in plasma was performed using droplet digital PCR, targeting the N1 and N2 regions of the SARS-CoV-2 nucleoprotein gene. The association between SARS-CoV-2 RNAemia and viral RNA load in plasma with severity was evaluated by multivariate logistic regression. Correlations between viral RNA load and biomarkers evidencing dysregulation of host response were evaluated by calculating the Spearman correlation coefficients. RESULTS: The frequency of viral RNAemia was higher in the critically ill patients (78%) compared to ward patients (27%) and outpatients (2%) (p < 0.001). Critical patients had higher viral RNA loads in plasma than non-critically ill patients, with non-survivors showing the highest values. When outpatients and ward patients were compared, viral RNAemia did not show significant associations in the multivariate analysis. In contrast, when ward patients were compared with ICU patients, both viral RNAemia and viral RNA load in plasma were associated with critical illness (OR [CI 95%], p): RNAemia (3.92 [1.183-12.968], 0.025), viral RNA load (N1) (1.962 [1.244-3.096], 0.004); viral RNA load (N2) (2.229 [1.382-3.595], 0.001). Viral RNA load in plasma correlated with higher levels of chemokines (CXCL10, CCL2), biomarkers indicative of a systemic inflammatory response (IL-6, CRP, ferritin), activation of NK cells (IL-15), endothelial dysfunction (VCAM-1, angiopoietin-2, ICAM-1), coagulation activation (D-Dimer and INR), tissue damage (LDH, GPT), neutrophil response (neutrophils counts, myeloperoxidase, GM-CSF) and immunodepression (PD-L1, IL-10, lymphopenia and monocytopenia). CONCLUSIONS: SARS-CoV-2 RNAemia and viral RNA load in plasma are associated with critical illness in COVID-19. Viral RNA load in plasma correlates with key signatures of dysregulated host responses, suggesting a major role of uncontrolled viral replication in the pathogenesis of this disease.

The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19.

This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. METHODS: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient's death, thus making the results easy to interpret. RESULTS: Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. CONCLUSIONS: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.

The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. Methods: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient"s death, thus making the results easy to interpret. Results. Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. Conclusions: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.