WHO Ordinal Scale and Inflammation Risk Categories in COVID-19. Comparative Study of the Severity Scales.

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.

The Use of Corticosteroids or Tocilizumab in COVID-19 Based on Inflammatory Markers.

BACKGROUND: The inflammatory cascade is the main cause of death in COVID-19 patients. Corticosteroids (CS) and tocilizumab (TCZ) are available to treat this escalation but which patients to administer it remains undefined. OBJECTIVE: We aimed to evaluate the efficacy of immunosuppressive/anti-inflammatory therapy in COVID-19, based on the degree of inflammation. DESIGN: A retrospective cohort study with data on patients collected and followed up from March 1st, 2020, to May 1st, 2021, from the nationwide Spanish SEMI-COVID-19 Registry. Patients under treatment with CS vs. those under CS plus TCZ were compared. Effectiveness was explored in 3 risk categories (low, intermediate, high) based on lymphocyte count, C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, and D-dimer values. PATIENTS: A total of 21,962 patients were included in the Registry by May 2021. Of these, 5940 met the inclusion criteria for the present study (5332 were treated with CS and 608 with CS plus TCZ). MAIN MEASURES: The primary outcome of the study was in-hospital mortality. Secondary outcomes were the composite variable of in-hospital mortality, requirement for high-flow nasal cannula (HFNC), non-invasive mechanical ventilation (NIMV), invasive mechanical ventilation (IMV), or intensive care unit (ICU) admission. KEY RESULTS: A total of 5940 met the inclusion criteria for the present study (5332 were treated with CS and 608 with CS plus TCZ). No significant differences were observed in either the low/intermediate-risk category (1.5% vs. 7.4%, p=0.175) or the high-risk category (23.1% vs. 20%, p=0.223) after propensity score matching. A statistically significant lower mortality was observed in the very high-risk category (31.9% vs. 23.9%, p=0.049). CONCLUSIONS: The prescription of CS alone or in combination with TCZ should be based on the degrees of inflammation and reserve the CS plus TCZ combination for patients at high and especially very high risk.

Inadequate use of antibiotics in the covid-19 era: effectiveness of antibiotic therapy.

BACKGROUND: Since December 2019, the COVID-19 pandemic has changed the concept of medicine. This work aims to analyze the use of antibiotics in patients admitted to the hospital due to SARS-CoV-2 infection. METHODS: This work analyzes the use and effectiveness of antibiotics in hospitalized patients with COVID-19 based on data from the SEMI-COVID-19 registry, an initiative to generate knowledge about this disease using data from electronic medical records. Our primary endpoint was all-cause in-hospital mortality according to antibiotic use. The secondary endpoint was the effect of macrolides on mortality. RESULTS: Of 13,932 patients, antibiotics were used in 12,238. The overall death rate was 20.7% and higher among those taking antibiotics (87.8%). Higher mortality was observed with use of all antibiotics (OR 1.40, 95% CI 1.21-1.62; p < .001) except macrolides, which had a higher survival rate (OR 0.70, 95% CI 0.64-0.76; p < .001). The decision to start antibiotics was influenced by presence of increased inflammatory markers and any kind of infiltrate on an x-ray. Patients receiving antibiotics required respiratory support and were transferred to intensive care units more often. CONCLUSIONS: Bacterial co-infection was uncommon among COVID-19 patients, yet use of antibiotics was high. There is insufficient evidence to support widespread use of empiric antibiotics in these patients. Most may not require empiric treatment and if they do, there is promising evidence regarding azithromycin as a potential COVID-19 treatment.

Prognostic Value of the PROFUND Index for 30-Day Mortality in Acute Heart Failure.

Background and Objectives: The prevalence and incidence of heart failure (HF) have been increasing in recent years as the population ages. These patients show a distinct profile of comorbidity, which makes their care more complex. In recent years, the PROFUND index, a specific tool for estimating the mortality rate at one year in pluripathology patients, has been developed. The aim of this study was to evaluate the prognostic value of the PROFUND index and of in-hospital and 30-day mortality after discharge of patients admitted for acute heart failure (AHF). Materials and Methods: A prospective multicenter longitudinal study was performed that included patients admitted with AHF and ≥2 comorbid conditions. Clinical, analytical, and prognostic variables were collected. The PROFUND index was collected in all patients and rates of in-hospital and 30-day mortality after discharge were analyzed. A bivariate analysis was performed with quantitative variables between patients who died and those who survived at the 30-day follow-up. A logistic regression analysis was performed with the variables that obtained statistical significance in the bivariate analysis between deceased and surviving subjects. Results: A total of 128 patients were included. Mean age was 80.5 +/- 9.98 years, and women represented 51.6%. The mean PROFUND index was 5.26 +/- 4.5. The mortality rate was 8.6% in-hospital and 20.3% at 30 days. Preserved left ventricular ejection fraction was found in 60.9%. In the sample studied, there were patients with a PROFUND score < 7 predominated (89 patients (70%) versus 39 patients (31%) with a PROFUND score ≥ 7). Thirteen patients (15%) with a PROFUND score < 7 died versus the 13 (33%) with a PROFUND score ≥ 7, p = 0.03. Twelve patients (15%) with a PROFUND score < 7 required readmission versus 12 patients (35%) with a PROFUND score ≥ 7, p = 0.02. The ROC curve of the PROFUND index for in-hospital mortality and 30-day follow-up in patients with AHF showed AUC 0.63, CI: 95% (0.508-0.764), p <0.033. Conclusions: The PROFUND index is a clinical tool that may be useful for predicting short-term mortality in elderly patients with AHF. Further studies with larger simple sizes are required to validate these results.

Prognostic Value of D-dimer to Lymphocyte Ratio (DLR) in Hospitalized Coronavirus Disease 2019 (COVID-19) Patients: A Validation Study in a National Cohort.

BACKGROUND: This study aimed to validate the role of the D-dimer to lymphocyte ratio (DLR) for mortality prediction in a large national cohort of hospitalized coronavirus disease 2019 (COVID-19) patients. METHODS: A retrospective, multicenter, observational study that included hospitalized patients due to SARS-CoV-2 infection in Spain was conducted from March 2020 to March 2022. All biomarkers and laboratory indices analyzed were measured once at admission. RESULTS: A total of 10,575 COVID-19 patients were included in this study. The mean age of participants was 66.9 (±16) years, and 58.6% (6202 patients) of them were male. The overall mortality rate was 16.3% (n = 1726 patients). Intensive care unit admission was needed in 10.5% (n = 1106 patients), non-invasive mechanical ventilation was required in 8.8% (n = 923 patients), and orotracheal intubation was required in 7.5% (789 patients). DLR presented a c-statistic of 0.69 (95% CI, 0.68-0.71) for in-hospital mortality with an optimal cut-off above 1. Multivariate analysis showed an independent association for in-hospital mortality for DLR > 1 (adjusted OR 2.09, 95% CI 1.09-4.04; p = 0.03); in the same way, survival analysis showed a higher mortality risk for DLR > 1 (HR 2.24; 95% CI 2.03-2.47; p < 0.01). Further, no other laboratory indices showed an independent association for mortality in multivariate analysis. CONCLUSIONS: This study confirmed the usefulness of DLR as a prognostic biomarker for mortality associated with SARS-CoV-2 infection, being an accessible, cost-effective, and easy-to-use biomarker in daily clinical practice.

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study.

Background: The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection. Methods: We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients. Results: The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients. Conclusions: The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves. Funding: University of Vienna.

Mortality reduction in older COVID-19-patients hospitalized in Spain during the second pandemic wave from the SEMI-COVID-19 Registry.

In 2020, the COVID-19 pandemic followed a two-wave pattern in most countries. Hospital admission for COVID-19 in one wave or another could have affected mortality, especially among the older persons. The objective of this study was to evaluate whether the admission of older patients during the different waves, before SARS-CoV-2 vaccination was available, was associated with a different mortality. We compared the mortality rates of patients hospitalized during 2020 before (first wave) and after (second wave) July 7, 2020, included in the SEMI-COVID-19 Registry, a large, multicenter, retrospective cohort of patients admitted to 126 Spanish hospitals for COVID-19. A multivariate logistic regression analysis was performed to control for changes in either the patient or disease profile. As of December 26, 2022, 22,494 patients had been included (17,784 from the first wave and 4710 from the second one). Overall mortality was 20.4% in the first wave and 17.2% in the second wave (risk difference (RD) - 3.2%; 95% confidence interval (95% CI) - 4.4 to - 2.0). Only patients aged 70 and older (10,973 patients: 8571 in the first wave and 2386 in the second wave) had a significant reduction in mortality (RD - 7.6%; 95% CI - 9.7 to - 5.5) (unadjusted relative risk reduction: 21.6%). After adjusting for age, comorbidities, variables related to the severity of the disease, and treatment received, admission during the second wave remained a protective factor. In Spain, patients aged 70 years and older admitted during the second wave of the COVID-19 pandemic had a significantly lower risk of mortality, except in severely dependent persons in need of corticosteroid treatment. This effect is independent of patient characteristics, disease severity, or treatment received. This suggests a protective effect of a better standard of care, greater clinical expertise, or a lesser degree of healthcare system overload.

Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry.

The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.

Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry.

COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.

COVID-19 Severity and Survival over Time in Patients with Hematologic Malignancies: A Population-Based Registry Study.

Mortality rates for COVID-19 have declined over time in the general population, but data in patients with hematologic malignancies are contradictory. We identified independent prognostic factors for COVID-19 severity and survival in unvaccinated patients with hematologic malignancies, compared mortality rates over time and versus non-cancer inpatients, and investigated post COVID-19 condition. Data were analyzed from 1166 consecutive, eligible patients with hematologic malignancies from the population-based HEMATO-MADRID registry, Spain, with COVID-19 prior to vaccination roll-out, stratified into early (February-June 2020; n = 769 (66%)) and later (July 2020-February 2021; n = 397 (34%)) cohorts. Propensity-score matched non-cancer patients were identified from the SEMI-COVID registry. A lower proportion of patients were hospitalized in the later waves (54.2%) compared to the earlier (88.6%), OR 0.15, 95%CI 0.11-0.20. The proportion of hospitalized patients admitted to the ICU was higher in the later cohort (103/215, 47.9%) compared with the early cohort (170/681, 25.0%, 2.77; 2.01-3.82). The reduced 30-day mortality between early and later cohorts of non-cancer inpatients (29.6% vs. 12.6%, OR 0.34; 0.22-0.53) was not paralleled in inpatients with hematologic malignancies (32.3% vs. 34.8%, OR 1.12; 0.81-1.5). Among evaluable patients, 27.3% had post COVID-19 condition. These findings will help inform evidence-based preventive and therapeutic strategies for patients with hematologic malignancies and COVID-19 diagnosis.

Usefulness of Multi-Organ Point-of-Care Ultrasound as a Complement to the Decision-Making Process in Internal Medicine.

Accumulated data show the utility of diagnostic multi-organ point-of-care ultrasound (PoCUS) in the assessment of patients admitted to an internal medicine ward. We assessed whether multi-organ PoCUS (lung, cardiac, and abdomen) provides relevant diagnostic and/or therapeutic information in patients admitted for any reason to an internal medicine ward. We conducted a prospective, observational, and single-center study, at a secondary hospital. Multi-organ PoCUS was performed during the first 24 h of admission. The sonographer had access to the patients' medical history, physical examination, and basic complementary tests performed in the Emergency Department (laboratory, X-ray, electrocardiogram). We considered a relevant ultrasound finding if it implied a significant diagnostic and/or therapeutic change. In the second semester of 2019, we enrolled 310 patients, 48.7% were male and the mean age was 70.5 years. Relevant ultrasound findings were detected in 86 patients (27.7%) and in 60 (19.3%) triggered a therapeutic change. These findings were associated with an older age (Mantel−Haenszel χ2 = 25.6; p < 0.001) and higher degree of dependency (Mantel−Haenszel χ2 = 5.7; p = 0.017). Multi-organ PoCUS provides relevant diagnostic information, complementing traditional physical examination, and facilitates therapy adjustment, regardless of the cause of admission. Multi-organ PoCUS to be useful need to be systematically integrated into the decision-making process in internal medicine.

The Effects of a Therapeutic Strategy Guided by Lung Ultrasound on 6-Month Outcomes in Patients with Heart Failure: Results from the EPICC Randomized Controlled Trial.

Background: Pulmonary congestion (PC) is associated with an increased risk of hospitalization and death in patients with heart failure (HF). Lung ultrasound is highly sensitive for detecting PC. The aim of this study is to evaluate whether lung ultrasound-guided therapy improves 6-month outcomes in patients with HF. Methods: A randomized, multicenter, single-blind clinical trial in patients discharged after hospitalization for decompensated HF. Participants were assigned 1:1 to receive treatment guided according to the presence of lung ultrasound signs of congestion (semi-quantitative evaluation of B lines and the presence of pleural effusion) versus standard of care (SOC). The primary endpoint was the combination of cardiovascular death, readmission, or emergency department or day hospital visit due to worsening HF at 6 months. In September 2020, after an interim analysis, patient recruitment was stopped. Results: A total of 79 patients were randomized (mean age 81.2 +/− 9 years) and 41 patients (51.8%) showed a left ventricular ejection fraction >50%. The primary endpoint occurred in 11 patients (29.7%) in the SOC group and in 11 patients (26.1%) in the LUS group (log-rank = 0.83). Regarding nonserious adverse events, no significant differences were found. Conclusions: LUS-guided diuretic therapy after hospital discharge due to ADHF did not show any benefit in survival or a need for intravenous diuretics compared with SOC.

Coronavirus disease 2019 hospitalization outcomes in persons with and without HIV in Spain.

OBJECTIVE: To compare coronavirus disease 2019 (COVID-19) hospitalization outcomes between persons with and without HIV. DESIGN: Retrospective observational cohort study in 150 hospitals in Spain. METHODS: Patients admitted from 1 March to 8 October 2020 with COVID-19 diagnosis confirmed by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 positive) PCR test in respiratory tract samples. The primary data source was the COVID-19 Sociedad Española de Medicina Interna's registry (SEMI-COVID-19). Demographics, comorbidities, vital signs, laboratory parameters, and clinical severity as well as treatments received during admission, treatment duration, ICU admission, use of invasive mechanical ventilation, and death were recorded. Factors associated with mortality and the composite of ICU admission, invasive mechanical ventilation, and death, were analyzed. RESULTS: Data from 16 563 admissions were collected, 98 (0.59%) of which were of persons with HIV infection. These patients were younger, the percentage of male patients was higher, and their Charlson comorbidity index was also higher. Rates of mortality and composite outcome of ICU admission, invasive mechanical ventilation or death were lower among patients with HIV infection. In the logistic regression analysis, HIV infection was associated with an adjusted odds ratio of 0.53 [95% confidence interval (CI) 0.29-0.96] for the composite outcome. CONCLUSION: HIV infection was associated with a lower probability of ICU admission, invasive mechanical ventilation, or death.

Prognostic Significance of Lung and Cava Vein Ultrasound in Elderly Patients Admitted for Acute Heart Failure: PROFUND-IC Registry Analysis.

Introduction: Heart failure is an extremely prevalent disease in the elderly population of the world. Most patients present signs and symptoms of decompensation of the disease due to worsening congestion. This congestion has been clinically assessed through clinical signs and symptoms and complementary imaging tests, such as chest radiography. Recently, pulmonary and inferior vena cava ultrasound has been shown to be useful in assessing congestion but its prognostic significance in elderly patients has been less well evaluated. Objectives: This study aims to compare the clinical and radiological characteristics and predictive values for mortality in patients admitted for heart failure through the determination of B lines by lung ultrasound and the degree of collapsibility of the inferior vena cava (IVC). Secondarily, the study aims to assess the prediction of 30-day mortality based on the diameter of the IVC by means of the ROC curve. Methods: This is an observational cohort study based on data collected in the PROFUND-IC study, a nationwide multicentric registry of patients admitted with decompensated heart failure. Data were collected from these patients between October 2020 and April 2022. Results: A total of 482 patients were entered into the PROFUND-IC registry between October 2020 and April 2022. Bedside clinical ultrasound was performed during admission in 301 patients (64.3%). The number of patients with more than 6 B-lines on lung ultrasound amounted to 194 (66%). Statistically significant differences in 30-day mortality (22.1% vs. 9.2%; p = 0.01) were found in these patients. The sum of patients with IVC collapsibility of less than 50% amounted to 195 (67%). Regarding prognostic value, collapsibility data were significant for the number of admissions in the last year (12.5% vs. 5.5%; p = 0.04), in-hospital mortality (10.1% vs. 3.3%, p = 0.04) and 30-day mortality (22.6% vs. 8.1%; p < 0.01), but not for readmissions. Regarding the prognostic value of IVC diameter for 30-day mortality, the area under the ROC curve (AUC) was 0.73, with a p < 0.01. The curve cut-off point with the highest sensitivity (70%) and specificity (70.3%) was for an IVC value of 22.5 mm. In the logistic regression analysis, we observed that the variable most associated with patient survival at 30 days was the presence of a collapsible inferior vena cava, with more than 50% OR 0.359 (CI 0.139−0.926; p = 0.034). Conclusions: The subgroups of patients analyzed with more than six B lines per field and IVC collapsibility less than or equal to 50%, as measured by clinical ultrasound, had higher 30-day mortality rates than patients who did not fall into these subgroups. IVC diameter may be a good independent predictor of 30-day mortality in patients with decompensated heart failure. Comparing both ultrasound variables, it seems that in our population, the assessment of the inferior vena cava may be more associated with short-term prognosis than the pulmonary congestion variables assessed by B lines.

The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach.

BACKGROUND: The individual influence of a variety of comorbidities on COVID-19 patient outcomes has already been analyzed in previous works in an isolated way. We aim to determine if different associations of diseases influence the outcomes of inpatients with COVID-19. METHODS: Retrospective cohort multicenter study based on clinical practice. Data were taken from the SEMI-COVID-19 Registry, which includes most consecutive patients with confirmed COVID-19 hospitalized and discharged in Spain. Two machine learning algorithms were applied in order to classify comorbidities and patients (Random Forest -RF algorithm, and Gaussian mixed model by clustering -GMM-). The primary endpoint was a composite of either, all-cause death or intensive care unit admission during the period of hospitalization. The sample was randomly divided into training and test sets to determine the most important comorbidities related to the primary endpoint, grow several clusters with these comorbidities based on discriminant analysis and GMM, and compare these clusters. RESULTS: A total of 16,455 inpatients (57.4% women and 42.6% men) were analyzed. According to the RF algorithm, the most important comorbidities were heart failure/atrial fibrillation (HF/AF), vascular diseases, and neurodegenerative diseases. There were six clusters: three included patients who met the primary endpoint (clusters 4, 5, and 6) and three included patients who did not (clusters 1, 2, and 3). Patients with HF/AF, vascular diseases, and neurodegenerative diseases were distributed among clusters 3, 4 and 5. Patients in cluster 5 also had kidney, liver, and acid peptic diseases as well as a chronic obstructive pulmonary disease; it was the cluster with the worst prognosis. CONCLUSION: The interplay of several comorbidities may affect the outcome and complications of inpatients with COVID-19.

Use of glucocorticoids megadoses in SARS-CoV-2 infection in a spanish registry: SEMI-COVID-19.

OBJECTIVE: To describe the impact of different doses of corticosteroids on the evolution of patients with COVID-19 pneumonia, based on the potential benefit of the non-genomic mechanism of these drugs at higher doses. METHODS: Observational study using data collected from the SEMI-COVID-19 Registry. We evaluated the epidemiological, radiological and analytical scenario between patients treated with megadoses therapy of corticosteroids vs low-dose of corticosteroids and the development of complications. The primary endpoint was all-cause in-hospital mortality according to use of corticosteroids megadoses. RESULTS: Of a total of 14,921 patients, corticosteroids were used in 5,262 (35.3%). Of them, 2,216 (46%) specifically received megadoses. Age was a factor that differed between those who received megadoses therapy versus those who did not in a significant manner (69 years [IQR 59-79] vs 73 years [IQR 61-83]; p < .001). Radiological and analytical findings showed a higher use of megadoses therapy among patients with an interstitial infiltrate and elevated inflammatory markers associated with COVID-19. In the univariate study it appears that steroid use is associated with increased mortality (OR 2.07 95% CI 1.91-2.24 p < .001) and megadose use with increased survival (OR 0.84 95% CI 0.75-0.96, p 0.011), but when adjusting for possible confounding factors, it is observed that the use of megadoses is also associated with higher mortality (OR 1.54, 95% CI 1.32-1.80; p < .001). There is no difference between megadoses and low-dose (p .298). Although, there are differences in the use of megadoses versus low-dose in terms of complications, mainly infectious, with fewer pneumonias and sepsis in the megadoses group (OR 0.82 95% CI 0.71-0.95; p < .001 and OR 0.80 95% CI 0.65-0.97; p < .001) respectively. CONCLUSION: There is no difference in mortality with megadoses versus low-dose, but there is a lower incidence of infectious complications with glucocorticoid megadoses.

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.

While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.

Ethnicity and Clinical Outcomes in Patients Hospitalized for COVID-19 in Spain: Results from the Multicenter SEMI-COVID-19 Registry.

(1) Background: This work aims to analyze clinical outcomes according to ethnic groups in patients hospitalized for COVID-19 in Spain. (2) Methods: This nationwide, retrospective, multicenter, observational study analyzed hospitalized patients with confirmed COVID-19 in 150 Spanish hospitals (SEMI-COVID-19 Registry) from 1 March 2020 to 31 December 2021. Clinical outcomes were assessed according to ethnicity (Latin Americans, Sub-Saharan Africans, Asians, North Africans, Europeans). The outcomes were in-hospital mortality (IHM), intensive care unit (ICU) admission, and the use of invasive mechanical ventilation (IMV). Associations between ethnic groups and clinical outcomes adjusted for patient characteristics and baseline Charlson Comorbidity Index values and wave were evaluated using logistic regression. (3) Results: Of 23,953 patients (median age 69.5 years, 42.9% women), 7.0% were Latin American, 1.2% were North African, 0.5% were Asian, 0.5% were Sub-Saharan African, and 89.7% were European. Ethnic minority patients were significantly younger than European patients (median (IQR) age 49.1 (40.5−58.9) to 57.1 (44.1−67.1) vs. 71.5 (59.5−81.4) years, p < 0.001). The unadjusted IHM was higher in European (21.6%) versus North African (11.4%), Asian (10.9%), Latin American (7.1%), and Sub-Saharan African (3.2%) patients. After further adjustment, the IHM was lower in Sub-Saharan African (OR 0.28 (0.10−0.79), p = 0.017) versus European patients, while ICU admission rates were higher in Latin American and North African versus European patients (OR (95%CI) 1.37 (1.17−1.60), p < 0.001) and (OR (95%CI) 1.74 (1.26−2.41), p < 0.001). Moreover, Latin American patients were 39% more likely than European patients to use IMV (OR (95%CI) 1.43 (1.21−1.71), p < 0.001). (4) Conclusion: The adjusted IHM was similar in all groups except for Sub-Saharan Africans, who had lower IHM. Latin American patients were admitted to the ICU and required IMV more often.

Effect of newer antihyperglycemic drugs on liver steatosis indices in patients with diabetes and obesity.

AIM: To assess the efficacy of sodium-glucose cotransporter-2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptors agonist (GLP-1RA) therapy on liver steatosis measured by fatty liver index (FLI) and hepatic steatosis index (HSI) at 26 weeks in outpatients with diabetes and obesity. METHODS: Observational, prospective, multicenter study. Patients with steatosis determined by FLI (values <30 rule out and >60 indicate steatosis) and HIS (values <30 rule out and >36 indicate steatosis) who received combination therapy were included. Patients were stratified into three groups according to the sequential order of treatment. We used robust statistical methods. RESULTS: In our final report we included 174 patients (58.6% males), mean age 61.9 (10) years. Baseline body mass index, waist circumference and weight were 36.5 (6.8) kg/m2, 117.5 (15.1) cm and 99.4 (20.5) kg, respectively. One hundred percent of patients had altered biomarkers of fatty liver scores (FLI 96 [13] and HSI 49.2 [8.5]). At 26 weeks, significant reductions in FLI (-4.5 [95% CI 3.5-5.9] p < .001) and HSI (-2.4 [95% CI 1.6-3.2] p < .001) were found in the total sample and pre-specified treatment and FLI cut-off point subgroups. CONCLUSION: Our results show a beneficial effect of the combination of GLP-1RAs plus SGLT2is on liver steatosis that goes beyond glucose control, and it is related mainly to weight loss, a decline in biomarkers and reductions in abdominal circumference. For many patients, early detection is essential to improving outcomes in NAFLD and could allow us to select the most efficient treatment options.