Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID-19

(1) Background: Different clinical presentations in COVID-19 are described to date, from mild to severe cases. This study aims to identify different clinical phenotypes in COVID-19 pneumonia using cluster analysis and to assess the prognostic impact among identified clusters in such patients. (2) Methods: Cluster analysis including 11 phenotypic variables was performed in a large cohort of 12,066 COVID-19 patients, collected and followed-up from 1 March to 31 July 2020, from the nationwide Spanish Society of Internal Medicine (SEMI)-COVID-19 Registry. (3) Results: Of the total of 12,066 patients included in the study, most were males (7052, 58.5%) and Caucasian (10,635, 89.5%), with a mean age at diagnosis of 67 years (standard deviation (SD) 16). The main pre-admission comorbidities were arterial hypertension (6030, 50%), hyperlipidemia (4741, 39.4%) and diabetes mellitus (2309, 19.2%). The average number of days from COVID-19 symptom onset to hospital admission was 6.7 (SD 7). The triad of fever, cough, and dyspnea was present almost uniformly in all 4 clinical phenotypes identified by clustering. Cluster C1 (8737 patients, 72.4%) was the largest, and comprised patients with the triad alone. Cluster C2 (1196 patients, 9.9%) also presented with ageusia and anosmia; cluster C3 (880 patients, 7.3%) also had arthromyalgia, headache, and sore throat; and cluster C4 (1253 patients, 10.4%) also manifested with diarrhea, vomiting, and abdominal pain. Compared to each other, cluster C1 presented the highest in-hospital mortality (24.1% vs. 4.3% vs. 14.7% vs. 18.6%; p 20 bpm, lower PaO2/FiO2 at admission, higher levels of C-reactive protein (CRP) and lactate dehydrogenase (LDH), and the phenotypic cluster as independent factors for in-hospital death. (4) Conclusions: The present study identified 4 phenotypic clusters in patients with COVID-19 pneumonia, which predicted the in-hospital prognosis of clinical outcomes

Instance selection of linear complexity for big data

Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems with very large-to-massive data sets. In this paper, two new algorithms with linear complexity for instance selection purposes are presented. Both algorithms use locality-sensitive hashing to find similarities between instances. While the complexity of conventional methods (usually quadratic, O(n2), or log-linear, O(nlogn)) means that they are unable to process large-sized data sets, the new proposal shows competitive results in terms of accuracy. Even more remarkably, it shortens execution time, as the proposal manages to reduce complexity and make it linear with respect to the data set size. The new proposal has been compared with some of the best known instance selection methods for testing and has also been evaluated on large data sets (up to a million instances).Supported by the Research Projects TIN 2011-24046 and TIN 2015-67534-P from the Spanish Ministry of Economy and Competitiveness

Cancer impact prognosis on mortality in patients with acute heart failure: analysis of the epicter study

Introduction: Heart failure (HF) and cancer are currently the leading causes of death worldwide, with an increasing incidence with age. Little is known about the treatment received and the prognosis of patients with acute HF and a prior cancer diagnosis. Objective: to determine the clinical characteristics, palliative treatment received, and prognostic impact of patients with acute HF and a history of solid tumor. Methods: The EPICTER study ('Epidemiological survey of advanced heart failure') is a cross-sectional, multicenter project that consecutively collected patients admitted for acute HF in 74 Spanish hospitals. Patients were classified into two groups according to whether they met criteria for acute HF with and without solid cancer, and the groups were subsequently compared. A multivariable logistic regression analysis was conducted, using the forward stepwise method. A Kaplan-Meier survival analysis was performed to evaluate the impact of solid tumor on prognosis in patients with acute HF. Results: A total of 3127 patients were included, of which 394 patients (13%) had a prior diagnosis of some type of solid cancer. Patients with a history of cancer presented a greater frequency of weight loss at admission: 18% vs. 12% (p = 0.030). In the cancer group, functional impairment was noted more frequently: 43% vs. 35%, p = 0.039). Patients with a history of solid cancer more frequently presented with acute HF with preserved ejection fraction (65% vs. 58%, p = 0.048) than reduced or mildly reduced. In-hospital and 6-month follow-up mortality was 31% (110/357) in patients with solid cancer vs. 26% (637/2466), p = 0.046. Conclusion: Our investigation demonstrates that in-hospital mortality and mortality during 6-month follow-up in patients with acute HF were higher in those subjects with a history of concomitant solid tumor cancer diagnosis

Differences in clinical features and mortality in very old unvaccinated patients (≥ 80 years) hospitalized with COVID-19 during the first and successive waves from the multicenter SEMI-COVID-19 Registry (Spain)

Background: Old age is one of the most important risk factors for severe COVID-19. Few studies have analyzed changes in the clinical characteristics and prognosis of COVID-19 among older adults before the availability of vaccines. This work analyzes differences in clinical features and mortality in unvaccinated very old adults during the first and successive COVID-19 waves in Spain. Methods This nationwide, multicenter, retrospective cohort study analyzes unvaccinated patients >= 80 years hospitalized for COVID-19 in 150 Spanish hospitals (SEMI-COVID-19 Registry). Patients were classified according to whether they were admitted in the first wave (March 1-June 30, 2020) or successive waves (July 1-December 31, 2020). The endpoint was all-cause in-hospital mortality, expressed as the case fatality rate (CFR). Results Of the 21,461 patients hospitalized with COVID-19, 5,953 (27.7%) were >= 80 years (mean age [IQR]: 85.6 [82.3-89.2] years). Of them, 4,545 (76.3%) were admitted during the first wave and 1,408 (23.7%) during successive waves. Patients hospitalized in successive waves were older, had a greater Charlson Comorbidity Index and dependency, less cough and fever, and met fewer severity criteria at admission (qSOFA index, PO2/FiO2 ratio, inflammatory parameters). Significant differences were observed in treatments used in the first (greater use of antimalarials, lopinavir, and macrolides) and successive waves (greater use of corticosteroids, tocilizumab and remdesivir). In-hospital complications, especially acute respiratory distress syndrome and pneumonia, were less frequent in patients hospitalized in successive waves, except for heart failure. The CFR was significantly higher in the first wave (44.1% vs. 33.3%; -10.8%; p = 95 years (54.4% vs. 38.5%; -15.9%; p < 0.001). After adjustments to the model, the probability of death was 33% lower in successive waves (OR: 0.67; 95% CI: 0.57-0.79). Conclusions Mortality declined significantly between the first and successive waves in very old unvaccinated patients hospitalized with COVID-19 in Spain. This decline could be explained by a greater availability of hospital resources and more effective treatments as the pandemic progressed, although other factors such as changes in SARS-CoV-2 virulence cannot be ruled out

WHO Ordinal Scale and Inflammation Risk Categories in COVID-19

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

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

To compare coronavirus disease 2019 (COVID-19) hospitalization outcomes between persons with and without HIV. Retrospective observational cohort study in 150 hospitals in Spain. 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. 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. HIV infection was associated with a lower probability of ICU admission, invasive mechanical ventilation, or death

Real-Life Impact of Glucocorticoid Treatment in COVID-19 Mortality: A Multicenter Retrospective Study

We aimed to determine the impact of steroid use in COVID-19 in-hospital mortality, in a retrospective cohort study of the SEMICOVID19 database of admitted patients with SARS-CoV-2 laboratory-confirmed pneumonia from 131 Spanish hospitals. Patients treated with corticosteroids were compared to patients not treated with corticosteroids; and adjusted using a propensity-score for steroid treatment. From March-July 2020, 5.262 (35.26%) were treated with corticosteroids and 9.659 (64.73%) were not. In-hospital mortality overall was 20.50%; it was higher in patients treated with corticosteroids than in controls (28.5% versus 16.2%, OR 2.068 [95% confidence interval; 1.908 to 2.242]; p = 0.0001); however, when adjusting by occurrence of ARDS, mortality was significantly lower in the steroid group (43.4% versus 57.6%; OR 0.564 [95% confidence interval; 0.503 to 0.633]; p = 0.0001). Moreover, the greater the respiratory failure, the greater the impact on mortality of the steroid treatment. When adjusting these results including the propensity score as a covariate, in-hospital mortality remained significantly lower in the steroid group (OR 0.774 [0.660 to 0.907], p = 0.002). Steroid treatment reduced mortality by 24% relative to no steroid treatment (RRR 0.24). These results support the use of glucocorticoids in COVID-19 in this subgroup of patients

Does admission acetylsalicylic acid uptake in hospitalized COVID-19 patients have a protective role? Data from the Spanish SEMI-COVID-19 Registry.

Acetylsalicylic acid (ASA) is widely used in the treatment and prevention of cardiovascular disorders. Our objective is to evaluate its possible protective role, not only in mortality but also in other aspects such as inflammation, symptomatic thrombosis, and intensive care unit (ICU) admission in hospitalized COVID-19 patients. We realized an observational retrospective cohort study of 20,641 patients with COVID-19 pneumonia collected and followed-up from Mar 1st, 2020 to May 1st, 2021, from the nationwide Spanish SEMI-COVID-19 Registry. Propensity score matching (PSM) was performed to determine whether treatment with ASA affected outcomes in COVID-19 patients. On hospital admission, 3291 (15.9%) patients were receiving ASA. After PSM, 3291 patients exposed to ASA and 2885 not-exposed patients were analyzed. In-hospital mortality was higher in the ASA group (30.4 vs. 16.9%, p

Clusters of inflammation in COVID-19: descriptive analysis and prognosis on more than 15,000 patients from the Spanish SEMI-COVID-19 Registry.

Uncontrolled inflammation following COVID-19 infection is an important characteristic of the most seriously ill patients. The present study aims to describe the clusters of inflammation in COVID-19 and to analyze their prognostic role. This is a retrospective observational study including 15,691 patients with a high degree of inflammation. They were included in the Spanish SEMI-COVID-19 registry from March 1, 2020 to May 1, 2021. The primary outcome was in-hospital mortality. Hierarchical cluster analysis identified 7 clusters. C1 is characterized by lymphopenia, C2 by elevated ferritin, and C3 by elevated LDH. C4 is characterized by lymphopenia plus elevated CRP and LDH and frequently also ferritin. C5 is defined by elevated CRP, and C6 by elevated ferritin and D-dimer, and frequently also elevated CRP and LDH. Finally, C7 is characterized by an elevated D-dimer. The clusters with the highest in-hospital mortality were C4, C6, and C7 (17.4% vs. 18% vs. 15.6% vs. 36.8% vs. 17.5% vs. 39.3% vs. 26.4%). Inflammation clusters were found as independent factors for in-hospital mortality. In detail and, having cluster C1 as reference, the model revealed a worse prognosis for all other clusters: C2 (OR = 1.30, p = 0.001), C3 (OR = 1.14, p = 0.178), C4 (OR = 2.28, p