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Abstract Background There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The identification of specific phenotypes underlying in the population might contribute towards a better understanding and characterization of the different courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumonia using machine learning clustering, and to study their association with subsequent clinical outcomes as severity and mortality. Methods Multicentric observational, prospective, longitudinal, cohort study conducted in four hospitals in Spain. We included adult patients admitted for in-hospital stay due to SARS-CoV-2 pneumonia. We collected a broad spectrum of variables to describe exhaustively each case: patient demographics, comorbidities, symptoms, physiological status, baseline examinations (blood analytics, arterial gas test), etc. For the development and internal validation of the clustering/phenotype models, the dataset was split into training and test sets (50% each). We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. The optimal cluster model parameters –including k, the number of phenotypes– were chosen automatically, by maximizing the average Silhouette score across the training set. Results We enrolled 1548 patients, each of them characterized by 92 clinical attributes (d=109 features after variable encoding). Our clustering algorithm identified k=3 distinct phenotypes and 18 strongly informative variables: Phenotype A (788 cases [50.9% prevalence] – age $$\sim$$ ∼ 57, Charlson comorbidity $$\sim$$ ∼ 1, pneumonia CURB-65 score $$\sim$$ ∼ 0 to 1, respiratory rate at admission $$\sim$$ ∼ 18 min-1, FiO2 $$\sim$$ ∼ 21%, C-reactive protein CRP $$\sim$$ ∼ 49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] – age $$\sim$$ ∼ 75, Charlson $$\sim$$ ∼ 5, CURB-65 $$\sim$$ ∼ 1 to 2, respiration $$\sim$$ ∼ 20 min-1, FiO2 $$\sim$$ ∼ 21%, CRP $$\sim$$ ∼ 101.5 mg/dL); and phenotype C (140 cases [9.0%] – age $$\sim$$ ∼ 71, Charlson $$\sim$$ ∼ 4, CURB-65 $$\sim$$ ∼ 0 to 2, respiration $$\sim$$ ∼ 30 min-1, FiO2 $$\sim$$ ∼ 38%, CRP $$\sim$$ ∼ 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clinical outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher frequencies of unfavourable evolution in phenotype C with respect to B, as well as more unfavourable in phenotype B than in A. Conclusion A compound unsupervised clustering technique (including a fully-automated optimization of its internal parameters) revealed the existence of three distinct groups of patients – phenotypes. In turn, these showed strong associations with the clinical severity in the progression of pneumonia, and with mortality.

Objectives: To analyze the differences in short- and long-term prognosis and the predictors of survival between patients with community-acquired Legionella and Streptococcus pneumoniae pneumonia, diagnosed early by urinary antigen testing (UAT). Methods: Prospective multicenter study conducted in immunocompetent patients hospitalized with community-acquired Legionella or pneumococcal pneumonia (L-CAP or P-CAP) between 2002-2020. All cases were diagnosed based on positive UAT. Results: We included 1452 patients, 260 with community-acquired Legionella pneumonia (L-CAP) and 1192 with community-acquired pneumococcal pneumonia (P-CAP). The 30-day mortality was higher for L-CAP (6.2%) than for P-CAP (5%). After discharge and during the median follow-up durations of 11.4 and 8.43 years, 32.4% and 47.9% of patients with L-CAP and P-CAP died, and 82.3% and 97.4% died earlier than expected, respectively. The independent risk factors for shorter long-term survival were age >65 years, chronic obstructive pulmonary disease, cardiac arrhythmia, and congestive heart failure in L-CAP and the same first three factors plus nursing home residence, cancer, diabetes mellitus, cerebrovascular disease, altered mental status, blood urea nitrogen ≥30 mg/dl, and congestive heart failure as a cardiac complication during hospitalization in P-CAP. Conclusion: In patients diagnosed early by UAT, the long-term survival after L-CAP or P-CAP was shorter (particularly after P-CAP) than expected, and this shorter survival was mainly associated with age and comorbidities.

Newer higher valency pneumococcal conjugate vaccines (PCVs) have the potential to reduce the adult community-acquired pneumonia (CAP) burden. We describe the evolution and distribution of adult community-acquired pneumonia (CAP) serotypes in Spain, focusing on serotypes contained in the 20-valent PCV (PCV20). This was a prospective, observational study of chest X-ray (CXR)-confirmed CAP in immunocompetent adults hospitalized in one of four Spanish hospitals between November 2016 and November 2020. Pneumococci were isolated from cultures and detected in urine using BinaxNow® and Pfizer serotype-specific urinary antigen tests UAD1 and UAD2. We included 1948 adults hospitalized with CXR-CAP. The median age was 69.0 years (IQR: 24 years). At least one comorbidity was present in 84.8% (n = 1653) of patients. At admission, 76.1% of patients had complicated pneumonia. Pneumococcus was identified in 34.9% (n = 680) of study participants. The PCV20 vaccine-type CAP occurred in 23.9% (n = 465) of all patients, 68.4% (n = 465) of patients with pneumococcal CAP, and 82.2% (83/101) of patients who had pneumococcus identified by culture. Serotypes 8 (n = 153; 7.9% of all CAP) and 3 (n = 152; 7.8% of all CAP) were the most frequently identified. Pneumococcus is a common cause of hospitalized CAP among Spanish adults and serotypes contained in PCV20 caused the majority of pneumococcal CAP.

ABSTRACT: Objective: To analyse differences in clinical presentation and outcome between bacteraemic pneumococcal community-acquired pneumonia (B-PCAP) and sSvere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pneumonia. Methods: This observational multi-centre study was conducted on patients hospitalized with B-PCAP between 2000 and 2020 and SARS-CoV-2 pneumonia in 2020. Thirty-day survival, predictors of mortality, and intensive care unit (ICU) admission were compared. Results: In total, 663 patients with B-PCAP and 1561 patients with SARS-CoV-2 pneumonia were included in this study. Patients with B-PCAP had more severe disease, a higher ICU admission rate and more complications. Patients with SARS-CoV-2 pneumonia had higher in-hospital mortality (10.8% vs 6.8%; P=0.004). Among patients admitted to the ICU, the need for invasive mechanical ventilation (69.7% vs 36.2%; P

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient's C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels -saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2-, the neutrophil-to-lymphocyte ratio (NLR) -to certain extent, also neutrophil and lymphocyte counts separately-, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.

The clinical evolution of patients infected with the Severe Acute Respiratory Coronavirus type 2 (SARS-CoV-2) depends on the complex interplay between viral and host factors. The evolution to less aggressive but better-transmitted viral variants, and the presence of immune memory responses in a growing number of vaccinated and/or virus-exposed individuals, has caused the pandemic to slowly wane in virulence. However, there are still patients with risk factors or comorbidities that put them at risk of poor outcomes in the event of having the coronavirus infectious disease 2019 (COVID-19). Among the different treatment options for patients with COVID-19, virus-targeted measures include antiviral drugs or monoclonal antibodies that may be provided in the early days of infection. The present expert consensus is based on a review of all the literature published between 1 July 2021 and 15 February 2022 that was carried out to establish the characteristics of patients, in terms of presence of risk factors or comorbidities, that may make them candidates for receiving any of the virus-targeted measures available in order to prevent a fatal outcome, such as severe disease or death. A total of 119 studies were included from the review of the literature and 159 were from the additional independent review carried out by the panelists a posteriori. Conditions found related to strong recommendation of the use of virus-targeted measures in the first days of COVID-19 were age above 80 years, or above 65 years with another risk factor; antineoplastic chemotherapy or active malignancy; HIV infection with CD4+ cell counts < 200/mm3; and treatment with anti-CD20 immunosuppressive drugs. There is also a strong recommendation against using the studied interventions in HIV-infected patients with a CD4+ nadir 3 or treatment with other immunosuppressants. Indications of therapies against SARS-CoV-2, regardless of vaccination status or history of infection, may still exist for some populations, even after COVID-19 has been declared to no longer be a global health emergency by the WHO.

Abstract Background The optimal duration of antibiotic treatment for community-acquired pneumonia (CAP) is not well established. The aim of this study was to assess the impact of reducing the duration of antibiotic treatment on long-term prognosis in patients hospitalized with CAP. Methods This was a multicenter study assessing complications developed during 1 year of patients previously hospitalized with CAP who had been included in a randomized clinical trial concerning the duration of antibiotic treatment. Mortality at 90 days, at 180 days and at 1 year was analyzed, as well as new admissions and cardiovascular complications. A subanalysis was carried out in one of the hospitals by measuring C-reactive protein (CRP), procalcitonin (PCT) and proadrenomedullin (proADM) at admission, at day 5 and at day 30. Results A total of 312 patients were included, 150 in the control group and 162 in the intervention group. Ninety day, 180 day and 1-year mortality in the per-protocol analysis were 8 (2.57%), 10 (3.22%) and 14 (4.50%), respectively. There were no significant differences between both groups in terms of 1-year mortality (p = 0.94), new admissions (p = 0.84) or cardiovascular events (p = 0.33). No differences were observed between biomarker level differences from day 5 to day 30 (CRP p = 0.29; PCT p = 0.44; proADM p = 0.52). Conclusions Reducing antibiotic treatment in hospitalized patients with CAP based on clinical stability criteria is safe, without leading to a greater number of long-term complications.

After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513). Non-hospitalized recovered patients do not show any metabolic fingerprint associated with the disease or immune alterations. Acute patients are characterized by the metabolic and lipidomic dysregulation that accompanies the exacerbated immunological response, resulting in a slow recovery time with a maximum probability of around 62 days. As a manifestation of the heterogeneity in the metabolic phenoreversion, age and severity become factors that modulate their normalization time which, in turn, correlates with changes in the atherogenesis-associated chemokine MCP-1. Our results are consistent with a model where the slow metabolic normalization in acute patients results in enhanced atherosclerotic risk, in line with the recent observation of an elevated number of cardiovascular episodes found in post-COVID-19 cohorts.

An amendment to this paper has been published and can be accessed via the original article.

Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer–Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer–Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice. Registration: ClinicalTrials.gov Identifier: NCT04463706. [ABSTRACT FROM AUTHOR]