Impact of severe lymphopenia on the early prediction of clinical outcome in hospitalized patients with pneumococcal community-acquired pneumonia.

PURPOSE: To evaluate the impact of an optimal and reproducible cutoff value set according to a predefined lymphopenia scale as an early predictor of in-hospital mortality and other outcomes in patients hospitalized with pneumococcal pneumonia and positive urinary antigen at admission to the emergency department. METHODS: An observational cohort study was conducted based on analysis of a prospective registry of consecutive immunocompetent adults hospitalized for pneumococcal pneumonia in two tertiary hospitals. Generalized additive models were constructed to assess the smooth relationship between in-hospital mortality and lymphopenia. RESULTS: We included 1173 patients. Lymphopenia on admission was documented in 686 (58.4%). No significant differences were observed between groups regarding the presence of comorbidities. Overall, 299 (25.5%) patients were admitted to intensive care and 90 (7.6%) required invasive mechanical ventilation. Fifty-nine (5%) patients died, among them 23 (38.9%) in the first 72 h after admission. A lymphocyte count < 500/µL, documented in 282 (24%) patients, was the predefined cutoff point that best predicted in-hospital mortality. After adjustment, these patients had higher rates of intensive care admission (OR 2.9; 95% CI 1.9-4.3), invasive mechanical ventilation (OR 2.2; 95% CI 1.2-3.9), septic shock (OR 1.8; 95% CI 1.1-2.9), treatment failure (OR 2.1; 95% CI 1.2-3.5), and in-hospital mortality (OR 2.2; 95% 1.1-4.9). Severe lymphopenia outperformed PSI score in predicting early and 30-day mortality in patients classified in the higher-risk classes. CONCLUSION: Lymphocyte count < 500/µL could be used as a reproducible predictor of complicated clinical course in patients with an early diagnosis of pneumococcal pneumonia.

Impact of prior SARS-COV-2 infection and vaccination on COVID-19 hospital admission and mortality amongst nursing home residents.

BACKGROUND: Nursing home residents (NHRs) have experienced disproportionately high risk of severe outcomes due to COVID-19 infection. AIM: We investigated the impact of COVID-19 vaccinations and previous SARS-CoV-2 episodes in preventing hospitalization and mortality in NHRs. METHODS: Retrospective study of a cohort of all NHRs in our area who were alive at the start of the vaccination campaign. The first three doses of SARS-CoV-2 vaccine and prior COVID-19 infections were registered. The main outcomes were hospital admission and mortality during each follow up. Random effects time-varying Cox models adjusted for age, sex, and comorbidities were fitted to estimate hazard ratios (HRs) according to vaccination status. RESULTS: COVID-19 hospitalization and death rates for unvaccinated NHRs were respectively 2.39 and 1.42 per 10,000 person-days, falling after administration of the second dose (0.37 and 0.34) and rising with the third dose (1.08 and 0.8). Rates were much lower amongst people who had previously had COVID-19. Adjusted HRs indicated a significant decrease in hospital admission amongst those with a two- and three-dose status; those who had had a previous COVID-19 infection had even lower hospital admission rates. Death rates decreased as NHRs received two and three doses, and the probability of death was much lower among those who had previously had the infection. CONCLUSIONS: The effectiveness of current vaccines against severe COVID-19 disease in NHRs remains high and SARS-CoV-2 episodes prior to vaccination entail a major reduction in hospitalization and mortality rates. The protection conferred by vaccines appears to decline in the following months. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04463706.

COVID-19 in young and middle-aged adults: predictors of poor outcome and clinical differences.

INTRODUCTION: Young and middle-aged adults are the largest group of patients infected with SARS-CoV-2 and some of them develop severe disease. OBJECTIVE: To investigate clinical manifestations in adults aged 18-65 years hospitalized for COVID-19 and identify predictors of poor outcome. Secondary objectives: to explore differences compared to the disease in elderly patients and the suitability of the commonly used community-acquired pneumonia prognostic scales in younger populations. METHODS: Multicenter prospective registry of consecutive patients hospitalized for COVID-19 pneumonia aged 18-65 years between March and May 2020. We considered a composite outcome of "poor outcome" including intensive care unit admission and/or use of noninvasive ventilation, continuous positive airway pressure or high flow nasal cannula oxygen and/or death. RESULTS: We identified 513 patients < 65 years of age, from a cohort of 993 patients. 102 had poor outcomes (19.8%) and 3.9% died. 78% and 55% of patients with poor outcomes were classified as low risk based on CURB and PSI scores, respectively. A multivariate Cox regression model identified six independent factors associated with poor outcome: heart disease, absence of chest pain or anosmia, low oxygen saturation, high LDH and lymphocyte count < 800/mL. CONCLUSIONS: COVID-19 in younger patients carries significant morbidity and differs in some respects from this disease in the elderly. Baseline heart disease is a relevant risk factor, while anosmia and pleuritic pain are associated to better prognosis. Hypoxemia, LDH and lymphocyte count are predictors of poor outcome. We consider that CURB and PSI scores are not suitable criteria for deciding admission in this population.

Q Fever (Coxiella Burnetii).

Q fever is a zoonotic infectious disease caused by the Coxiella burnetii bacterium. It is an obligate intracellular pathogen with a high infection capacity that proliferates exclusively in an acidified medium, forming a lysosome-like vacuole. It presents a peculiar phenomenon called "antigenic phase variation," produced by a modification in the complexity of the membrane lipopolysaccharides. Q fever can be found worldwide and presents variable clinical features and geographical distribution. It mostly affects people in rural areas who are in contact with animals. The most common type of transmission to humans is via the inhalation of aerosols containing the pathogen, especially those formed from placental derivatives. Wild animals, domestic animals, and ticks are the principal reservoirs.Diagnosis is mainly made by indirect methods such as serology or by direct methods such as microbiological cultures or tests that detect the specific DNA. Typically, there are two clinical presentations: the acute disease, which is more frequent and often asymptomatic, and a persistent focalized infection in 4 to 5% of patients, generally with a poor evolution. Treatment of the acute form in both children and adults consists of administering doxycycline, while persistent focalized infection should be treated with at least two antibiotics, such as doxycycline and hydroxychloroquine. Several measures should be undertaken to minimize exposure among people working with animals or handling birth products. Different vaccines have been developed to prevent infection, though few data are available.

Impact of reducing the duration of antibiotic treatment on the long-term prognosis of community acquired pneumonia.

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.

Streptococcus pneumoniae antigen in urine: diagnostic usefulness and impact on outcome of bacteraemic pneumococcal pneumonia in a large series of adult patients.

BACKGROUND AND OBJECTIVE: Urinary pneumococcal antigen detection provides good results in the diagnosis of pneumococcal pneumonia but has rarely been used in bacteraemic pneumococcal pneumonia and it is not known whether it is associated with outcome in this type of pneumonia. Our objectives were to assess the usefulness of an immunochromatographic technique for detecting the pneumococcal antigen in urine in a large prospective study of patients with bacteraemic pneumococcal pneumonia and explore any potential association with outcomes. METHODS: This study, carried out over 8 years, included all adult immunocompetent patients admitted for bacteraemic pneumococcal pneumonia. An immunochromatographic test for the Streptococcus pneumoniae antigen in urine was performed in the first 24 h. The sensitivity of test was assessed and patients were divided into two groups according to test results to explore differences on admission and during the course of the illness using logistic regression models. RESULTS: Of the 350 patients with bacteraemic pneumococcal pneumonia included, 261 (74.6%) were positive for the antigen. Patient characteristics were very similar on admission and differences in severity (Pneumonia Severity Index) were not statistically significant. In the adjusted analysis, antigen-positive patients had a higher risk of intensive care unit admission, treatment failure and adverse outcome. CONCLUSIONS: The sensitivity of the immunochromatographic urinary antigen test was 74.6% and positive results were associated with poorer clinical outcome. We therefore recommend systematic use of this test when pneumonia is diagnosed in the emergency department.

Basic host response parameters to classify mortality risk in COVID-19 and community-acquired pneumonia.

Improved phenotyping in pneumonia is necessary to strengthen risk assessment. Via a feasible and multidimensional approach with basic parameters, we aimed to evaluate the effect of host response at admission on severity stratification in COVID-19 and community-acquired pneumonia (CAP). Three COVID-19 and one CAP multicenter cohorts including hospitalized patients were recruited. Three easily available variables reflecting different pathophysiologic mechanisms-immune, inflammation, and respiratory-were selected (absolute lymphocyte count [ALC], C-reactive protein [CRP] and, SpO2/FiO2). In-hospital mortality and intensive care unit (ICU) admission were analyzed as outcomes. A multivariable, penalized maximum likelihood logistic regression was performed with ALC (< 724 lymphocytes/mm3), CRP (> 60 mg/L), and, SpO2/FiO2 (< 450). A total of 1452, 1222 and 462 patients were included in the three COVID-19 and 1292 in the CAP cohort for the analysis. Mortality ranged between 4 and 32% (0 to 3 abnormal biomarkers) and 0-9% in SARS-CoV-2 pneumonia and CAP, respectively. In the first COVID-19 cohort, adjusted for age and sex, we observed an increased odds ratio for in-hospital mortality in COVID-19 with elevated biomarkers altered (OR 1.8, 3, and 6.3 with 1, 2, and 3 abnormal biomarkers, respectively). The model had an AUROC of 0.83. Comparable findings were found for ICU admission, with an AUROC of 0.76. These results were confirmed in the other COVID-19 cohorts Similar OR trends were reported in the CAP cohort; however, results were not statistically significant. Assessing the host response via accessible biomarkers is a simple and rapidly applicable approach for pneumonia.

Short- and long-term prognosis of patients with community-acquired Legionella or pneumococcal pneumonia diagnosed by urinary antigen testing.

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.

Bacterial Patterns and Empiric Antibiotic Use in COPD Patients With Community-Acquired Pneumonia.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) is strongly associated with the development of community-acquired pneumonia (CAP). Limited data are available on risk factors for difficult to manage bacteria such as Pseudomonas aeruginosa in COPD patients with CAP. Our objective was to assess the microbiological patterns associated with risk factors that determine empiric antibiotic therapy in hospitalized COPD patients with CAP. METHODS: We performed a secondary data analysis of an international, multicenter, observational, point-prevalence study involving hospitalized COPD patients with CAP from March to June 2015. After identifying the risk factors associated with different microorganisms, we developed a scoring system to guide decision-making about empiric anti-pseudomonal antibiotic therapy in this population. RESULTS: We enrolled 689 hospitalized COPD patients with CAP with documented microbiological testing. The most frequent microorganisms isolated were Streptococcus pneumoniae (8%) and Gram-negative bacteria (8%), P. aeruginosa (7%) and Haemophilus influenzae (3%). We developed a scoring system incorporating the variables independently associated with P. aeruginosa that include a previous P. aeruginosa isolation or infection (OR 14.2 [95%CI 5.7-35.2]), hospitalization in the past 12 months (OR 3.7 [1.5-9.2]), and bronchiectasis (OR 3.2 [1.4-7.2]). Empiric anti-pseudomonal antibiotics were overutilized in COPD patients with CAP. The new scoring system has the potential to reduce empiric anti-pseudomonal antibiotic use from 54.1% to 6.2%. CONCLUSIONS: COPD patients with CAP present different microbiological profiles associated with unique risk factors. Anti-pseudomonal treatment is a critical decision when selecting empiric antibiotic therapy. We developed a COPD scoring system to guide decision-making about empiric anti-pseudomonal antibiotic therapy.

Is the long-term mortality similar in COVID-19 and community-acquired pneumonia?

Introduction: There are no data on the association of type of pneumonia and long-term mortality by the type of pneumonia (COVID-19 or community-acquired pneumonia [CAP]) on long-term mortality after an adjustment for potential confounding variables. We aimed to assess the type of pneumonia and risk factors for long-term mortality in patients who were hospitalized in conventional ward and later discharged. Methods: Retrospective analysis of two prospective and multicentre cohorts of hospitalized patients with COVID-19 and CAP. The main outcome under study was 1-year mortality in hospitalized patients in conventional ward and later discharged. We adjusted a Bayesian logistic regression model to assess associations between the type of pneumonia and 1-year mortality controlling for confounders. Results: The study included a total of 1,693 and 2,374 discharged patients in the COVID-19 and CAP cohorts, respectively. Of these, 1,525 (90.1%) and 2,249 (95%) patients underwent analysis. Until 1-year follow-up, 69 (4.5%) and 148 (6.6%) patients from the COVID-19 and CAP cohorts, respectively, died (p = 0.008). However, the Bayesian model showed a low probability of effect (PE) of finding relevant differences in long-term mortality between CAP and COVID-19 (odds ratio 1.127, 95% credibility interval 0.862-1.591; PE = 0.774). Conclusion: COVID-19 and CAP have similar long-term mortality after adjusting for potential confounders.

Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19.

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.

Estimating the risk of bacteraemia in hospitalised patients with pneumococcal pneumonia.

OBJECTIVE: To construct a prediction model for bacteraemia in patients with pneumococcal community-acquired pneumonia (P-CAP) based on variables easily obtained at hospital admission. METHODS: This prospective observational multicentre derivation-validation study was conducted in patients hospitalised with P-CAP between 2000 and 2020. All cases were diagnosed based on positive urinary antigen tests in the emergency department and had blood cultures taken on admission. A risk score to predict bacteraemia was developed. RESULTS: We included 1783 patients with P-CAP (1195 in the derivation and 588 in the validation cohort). A third (33.3%) of the patients had bacteraemia. In the multivariate analysis, the following were identified as independent factors associated with bacteraemia: no influenza vaccination the last year, no pneumococcal vaccination in the last 5 years, blood urea nitrogen (BUN) ≥30 mg/dL, sodium <130 mmol/L, lymphocyte count <800/µl, C-reactive protein ≥200 mg/L, respiratory failure, pleural effusion and no antibiotic treatment before admission. The score yielded good discrimination (AUC 0.732; 95% CI: 0.695-0.769) and calibration (Hosmer-Lemeshow p-value 0.801), with similar performance in the validation cohort (AUC 0.764; 95% CI:0.719-0.809). CONCLUSIONS: We found nine predictive factors easily obtained on hospital admission that could help achieve early identification of bacteraemia. The prediction model provides a useful tool to guide diagnostic decisions.

Early Recognition of Low-Risk SARS-CoV-2 Pneumonia: A Model Validated With Initial Data and Infectious Diseases Society of America/American Thoracic Society Minor Criteria.

BACKGROUND: A shortage of beds in ICUs and conventional wards during the COVID-19 pandemic led to a collapse of health care resources. RESEARCH QUESTION: Can admission data and minor criteria by the Infectious Diseases Society of America (IDSA) and the American Thoracic Society (ATS) help identify patients with low-risk SARS-CoV-2 pneumonia? STUDY DESIGN AND METHODS: This multicenter cohort study included 1,274 patients in a derivation cohort and 830 (first wave) and 754 (second wave) patients in two validation cohorts. A multinomial regression analysis was performed on the derivation cohort to compare the following patients: those admitted to the ward (assessed as low risk); those admitted to the ICU directly; those transferred to the ICU after general ward admission; and those who died. A regression analysis identified independent factors for low-risk pneumonia. The model was subsequently validated. RESULTS: In the derivation cohort, similarities existed among those either directly admitted or transferred to the ICU and those who died. These patients could, therefore, be merged into one group. Five independently associated factors were identified as being predictors of low risk (not dying and/or requiring ICU admission) (ORs, with 95% CIs): peripheral blood oxygen saturation/Fio2 > 450 (0.233; 0.149-0.364); < 3 IDSA/ATS minor criteria (0.231; 0.146-0.365); lymphocyte count > 723 cells/mL (0.539; 0.360-0.806); urea level < 40 mg/dL (0.651; 0.426-0.996); and C-reactive protein level < 60 mg/L (0.454; 0.285-0.724). The areas under the curve were 0.802 (0.769-0.835) in the derivation cohort, and 0.779 (0.742-0.816) and 0.801 (0.757-0.845) for the validation cohorts (first and second waves, respectively). INTERPRETATION: Initial biochemical findings and the application of < 3 IDSA/ATS minor criteria make early identification of low-risk SARS-CoV-2 pneumonia (approximately 80% of hospitalized patients) feasible. This scenario could facilitate and streamline health care resource allocation for patients with COVID-19.

Utility of Differential White Cell Count and Cell Population Data for Ruling Out COVID-19 Infection in Patients With Community-Acquired Pneumonia.

INTRODUCTION: The main aim of this study was to assess the utility of differential white cell count and cell population data (CPD) for the detection of COVID-19 in patients admitted for community-acquired pneumonia (CAP) of different etiologies. METHODS: This was a multicenter, observational, prospective study of adults aged ≥18 years admitted to three teaching hospitals in Spain from November 2019 to November 2021 with a diagnosis of CAP. At baseline, a Sysmex XN-20 analyzer was used to obtain detailed information related to the activation status and functional activity of white cells. RESULTS: The sample was split into derivation and validation cohorts of 1065 and 717 patients, respectively. In the derivation cohort, COVID-19 was confirmed in 791 patients and ruled out in 274 patients, with mean ages of 62.13 (14.37) and 65.42 (16.62) years, respectively (p<0.001). There were significant differences in all CPD parameters except MO-Y. The multivariate prediction model showed that lower NE-X, NE-WY, LY-Z, LY-WY, MO-WX, MO-WY, and MO-Z values and neutrophil-to-lymphocyte ratio were related to COVID-19 etiology with an AUC of 0.819 (0.790, 0.846). No significant differences were found comparing this model to another including biomarkers (p=0.18). CONCLUSIONS: Abnormalities in white blood cell morphology based on a few cell population data values as well as NLR were able to accurately identify COVID-19 etiology. Moreover, systemic inflammation biomarkers currently used were unable to improve the predictive ability. We conclude that new peripheral blood biomarkers can help determine the etiology of CAP fast and inexpensively.

Comparison of the prognostic performance of the CURB-65 and a modified version of the pneumonia severity index designed to identify high-risk patients using the International Community-Acquired Pneumonia Collaboration Cohort.

BACKGROUND: Although the PSI and CURB-65 represent well-validated prediction rules for pneumonia prognosis, PSI was designed to identify patients at low risk and CURB- 65 patients at high risk of mortality. We compared the prognostic performance of a modified version of the PSI designed to identify high-risk patients (i.e., PSI-HR) to CURB-65 in predicting short-term mortality. METHODS: Using data from 6 pneumonia cohorts, we designed PSI-HR as a 6-class prediction rule using the original prognostic weights of all PSI variables and modifying the risk score thresholds to define risk classes. We calculated the proportion of low-risk and high-risk patients using CURB-65 and PSI-HR and 30-day mortality in these subgroups. We compared the rules' sensitivity, specificity, positive and negative predictive values for mortality at all risk class thresholds and assessed discriminatory power using areas under their receiver operating characteristic curves (AUROCs). RESULTS: Among 13,874 patients with pneumonia, 1,036 (7.5%) died. For PSI-HR versus CURB-65, aggregate mortality was lower in low-risk patients (1.6% vs. 2.2%, p = 0.005) and higher in high-risk patients (36.5% vs. 32.2%, p = 0.27). PSI-HR had higher sensitivities than CURB-65 at all thresholds; PSI-HR also had higher specificities at the 3 lowest thresholds and specificities within 0.5% points of CURB-65 at the 2 highest thresholds. The AUROC was larger for PSI-HR than CURB- 65 (0.82 vs. 0.77, p < 0.0001). CONCLUSIONS: PSI-HR demonstrated superior prognostic accuracy to CURB-65 at the lower end of the severity spectrum and identified high-risk patients with nonsignificant higher short-term mortality at the higher end.

Predictors of mortality of COVID-19 in the general population and nursing homes.

The factors that predispose an individual to a higher risk of death from COVID-19 are poorly understood. The goal of the study was to identify factors associated with risk of death among patients with COVID-19. This is a retrospective cohort study of people with laboratory-confirmed SARS-CoV-2 infection from February to May 22, 2020. Data retrieved for this study included patient sociodemographic data, baseline comorbidities, baseline treatments, other background data on care provided in hospital or primary care settings, and vital status. Main outcome was deaths until June 29, 2020. In the multivariable model based on nursing home residents, predictors of mortality were being male, older than 80 years, admitted to a hospital for COVID-19, and having cardiovascular disease, kidney disease or dementia while taking anticoagulants or lipid-lowering drugs at baseline was protective. The AUC was 0.754 for the risk score based on this model and 0.717 in the validation subsample. Predictors of death among people from the general population were being male and/or older than 60 years, having been hospitalized in the month before admission for COVID-19, being admitted to a hospital for COVID-19, having cardiovascular disease, dementia, respiratory disease, liver disease, diabetes with organ damage, or cancer while being on anticoagulants was protective. The AUC was 0.941 for this model's risk score and 0.938 in the validation subsample. Our risk scores could help physicians identify high-risk groups and establish preventive measures and better follow-up for patients at high risk of dying.ClinicalTrials.gov Identifier: NCT04463706.

Non-bacteremic pneumococcal pneumonia: general characteristics and early predictive factors for poor outcome.

Purpose: Nowadays, most cases of pneumococcal community-acquired pneumonia (PCAP) are diagnosed by positive urinary antigen. Our aims were to analyse process of care in patients hospitalised with non-bacteremic PCAP (NB-PCAP) and identify factors associated with poor outcome (PO) in this population.Methods: We conducted a prospective study, including patients hospitalised for NB-PCAP (positive urinary antigen and negative blood culture) over a 15 year period. We performed multivariate analysis of predisposing factors for PO, defined as need for mechanical ventilation and/or shock and/or in-hospital death.Results: Of the 638 patients included, 4.1% died in hospital and 12.8% had PO. Host-related factors were similar in patients with and without PO, but patients with PO had higher illness severity on admission. Adjusted analysis revealed the following independent factors associated with PO: being a nursing home resident (OR: 6.156; 95% CI: 1.827-20.750; p = .003), respiratory rate ≥30 breaths/min (OR: 3.030; 95% CI: 1.554-5.910; p = .001), systolic blood pressure <90 mmHg (OR: 4.789; 95% CI: 1.967-11.660; p = .001), diastolic blood pressure <60 mmHg (OR: 2.820; 95% CI: 1.329-5.986; p = .007), pulse rate ≥125 beats/min (OR: 3.476; 95% CI: 1.607-7.518; p = .002), pH <7.35 (OR: 9.323; 95% CI: 3.680-23.622; p < .001), leukocytes <4000/µL (OR: 10.007; 95% CI: 2.960-33.835; p < .001), and severe inflammation (OR: 2.364; 95% CI 1.234-4.526; p = .009). The area under the curve for predicting PO was 0.890 (95% CI: 0.851-0.929).Conclusions: Since patients with PO seem different and had worse in-hospital course, we identified eight independent risk factors for PO measurable on admission.

Factors influencing long-term survival after hospitalization with pneumococcal pneumonia.

OBJECTIVE: To assess survival and identify predictors of survival more than 30-days after discharge in a cohort of consecutive patients diagnosed with pneumococcal pneumonia. METHODS: Observational study including all consecutive immunocompetent adult patients surviving more than 30-days after hospitalization. The bacteriological diagnosis was based on the results of urinary antigen testing and/or blood culture. Life expectancy was calculated for each patient considering their sex, age and date of discharge. RESULTS: We included 1114 patients that survived more than 30- days after discharge. Of them, 431 (38.6%) died during follow-up (median follow-up of 6.7 years). Age, history of cancer, liver disease, chronic renal disease, chronic obstructive pulmonary disease, cerebrovascular disease, atrial arrhythmia and coronary disease, red cell distribution width (RDW) > 15%, positive blood culture, hematocrit < 30% and living in a nursing home were independent risk factors for reduced long-term survival after hospital discharge. Cumulative 1-, 3- and 5-year survival rates were 93.9%, 85.3% and 76%, respectively. Among non-survivors, 361 (83.8%) died earlier than expected given their life expectancy. CONCLUSIONS: Survival after hospital discharge is mainly associated with age and comorbidities. The findings of bacteremia and elevated RDW on admission could help identify patients at high risk of long-term mortality.

COPD classification models and mortality prediction capacity.

OBJECTIVE: Our aim was to assess the impact of comorbidities on existing COPD prognosis scores. PATIENTS AND METHODS: A total of 543 patients with COPD (FEV1 <80% and FEV1/FVC <70%) were included between January 2003 and January 2004. Patients were stable for at least 6 weeks before inclusion and were followed for 5 years without any intervention by the research team. Comorbidities and causes of death were established from medical reports or information from primary care medical records. The GOLD system and the body mass index, obstruction, dyspnea and exercise (BODE) index were used for COPD classification. Patients were also classified into four clusters depending on the respiratory disease and comorbidities. Cluster analysis was performed by combining multiple correspondence analyses and automatic classification. Receiver operating characteristic curves and the area under the curve (AUC) were calculated for each model, and the DeLong test was used to evaluate differences between AUCs. Improvement in prediction ability was analyzed by the DeLong test, category-free net reclassification improvement and the integrated discrimination index. RESULTS: Among the 543 patients enrolled, 521 (96%) were male, with a mean age of 68 years, mean body mass index 28.3 and mean FEV1% 55%. A total of 167 patients died during the study follow-up. Comorbidities were prevalent in our cohort, with a mean Charlson index of 2.4. The most prevalent comorbidities were hypertension, diabetes mellitus and cardiovascular diseases. On comparing the BODE index, GOLDABCD, GOLD2017 and cluster analysis for predicting mortality, cluster system was found to be superior compared with GOLD2017 (0.654 vs 0.722, P=0.006), without significant differences between other classification models. When cardiovascular comorbidities and chronic renal failure were added to the existing scores, their prognostic capacity was statistically superior (P<0.001). CONCLUSION: Comorbidities should be taken into account in COPD management scores due to their prevalence and impact on mortality.