Prognosis of hospitalized patients with community-acquired pneumonia

Survival at 3, 6 and 12 months in patients diagnosed with community-acquired pneumonia in Colombia: a retrospective cohort study

BACKGROUND

The primary aim of this study is to assess the survival rates of individuals diagnosed with Community-Acquired Pneumonia (CAP) post-hospitalization in Colombia. Additionally, explore potential risk factors associated with decreased long-term survival.

METHODS

A retrospective cohort study was conducted in a hospital in Colombia, evaluating survival at 3, 6 and 12 months in CAP patients, using the Kaplan-Meier method. Stratifications were made by age, sex, comorbidity, and severity. The comparison of survival curves was performed using the Log-Rank test, a multivariate analysis with Cox regression was performed to study possible risk factors that affected 12-month survival in patients with CAP.

RESULTS

3688 subjects were admitted, with a mortality of 16.3 % per year. Survival at three, six, and twelve months was 92.9 % (95 % CI 92-93 %), 88.8 % (95 % CI 87-90 %), and 84.2 % (95 % CI 82-85 %), respectively. Analysis stratified by pneumonia severity index, 12-month survival was 98.7 % in Class I, 95.6 % in Class II, 87.41 % in Class III, 77.1 % in Class IV, and 65.8 % in class-V (p < 0.001). Cox-regression showed that being male (HR = 1.44; 95 % CI 1.22‒1.70; p < 0.001), an elevated pneumonia severity index (HR = 4.22; 95 % CI 1.89‒9.43; p < 0.001), a high comorbidity index (HR = 2.29; 95 % CI 1.89‒2.84; p < 0.001) and vasopressor requirement (HR = 2.22; 95 % CI < 0.001) were associated with a lower survival at twelve months of follow-up.

CONCLUSION

Survival in patients with CAP who require hospitalization decreases at 3, 6, and 12 months of follow-up, being lower in patients older than 65 years, men, high comorbidity, and in subjects with severe presentation of the disease.

Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning

Objectives

The assessment of accurate mortality risk is essential for managing pneumonia patients with connective tissue disease (CTD) treated with glucocorticoids or/and immunosuppressants. This study aimed to construct a nomogram for predicting 90-day mortality in pneumonia patients using machine learning.

Methods

Data were obtained from the DRYAD database. Pneumonia patients with CTD were screened. The samples were randomly divided into a training cohort (70%) and a validation cohort (30%). A univariate Cox regression analysis was used to screen for prognostic variables in the training cohort. Prognostic variables were entered into the least absolute shrinkage and selection operator (Lasso) and a random survival forest (RSF) analysis was used to screen important prognostic variables. The overlapping prognostic variables of the two algorithms were entered into the stepwise Cox regression analysis to screen the main prognostic variables and construct a model. Model predictive power was assessed using the C-index, the calibration curve, and the clinical subgroup analysis (age, gender, interstitial lung disease, diabetes mellitus). The clinical benefits of the model were assessed using a decision curve analysis (DCA). Similarly, the C-index was calculated and the calibration curve was plotted to verify the model stability in the validation cohort.

Results

A total of 368 pneumonia patients with CTD (training cohort: 247; validation cohort: 121) treated with glucocorticoids or/and immunosuppressants were included. The univariate Cox regression analysis obtained 19 prognostic variables. Lasso and RSF algorithms obtained eight overlapping variables. The overlapping variables were entered into a stepwise Cox regression to obtain five variables (fever, cyanosis, blood urea nitrogen, ganciclovir treatment, and anti-pseudomonas treatment), and a prognostic model was constructed based on the five variables. The C-index of the construction nomogram of the training cohort was 0.808. The calibration curve, DCA results, and clinical subgroup analysis showed that the model also had good predictive power. Similarly, the C-index of the model in the validation cohort was 0.762 and the calibration curve had good predictive value.

Conclusion

In this study, the nomogram developed performed well in predicting the 90-day risk of death in pneumonia patients with CTD treated with glucocorticoids or/and immunosuppressants.

CURB-65 and Long-Term Mortality of Community-Acquired Pneumonia: A Retrospective Study on Hospitalized Patients

Background Community-acquired pneumonia remains a significant factor in global mortality. Several clinical scoring models are used for predicting pneumonia severity and mortality, aiding in the clinical decision relative to the therapeutic approach, including the CURB-65 score. However, currently, no models exist to identify high-risk patients relative to long-term prognosis when recent evidence reveals a significantly higher mortality rate in the first year after community-acquired pneumonia (CAP) hospitalization. Purpose of the study The purpose of this study is to evaluate the application of the CURB-65 scoring model in our population and examine its potential to predict prognosis and subsequent mortality 6 months after hospitalization. Other potential factors influencing mortality during and after hospitalization were characterized: patient demographics, nosocomial infections, readmissions, and identified pathogens. Study design We conducted a retrospective observational study, enrolling 130 patients admitted with a diagnosis of CAP in the department of internal medicine of Centro Hospitalar Universitário Cova da Beira between January and December of 2018. Consultation of electronic medical records was used to calculate the CURB-65 score on admission at the first hospitalization by CAP, categorizing patients into five risk groups. Mortality and readmission were evaluated after 30, 90, and 180 days. Key results High-risk patients (CURB>2) accounted for 96.9% of our study population. Inpatient mortality of 13%, increasing to 21.5% after six months, was similar to previous studies and was correlated to the CURB-65 score on admission. A microbiologic agent was identified in 37% of cases, with 53% isolates of Streptococcus (S.) pneumoniae. Conclusions Identifying high-risk patients is important for more individualized healthcare and management. The CURB-65 score, only validated for a short-term (30 days) prediction, demonstrates a potential to also predict mortality and rehospitalization in the six-month period after hospitalization, as supported by our findings and previous studies.

Acute organ injury and long-term sequelae of severe pneumococcal infections

Streptococcus pneumoniae (Spn) is a major public health problem, as it is a main cause of otitis media, community-acquired pneumonia, bacteremia, sepsis, and meningitis. Acute episodes of pneumococcal disease have been demonstrated to cause organ damage with lingering negative consequences. Cytotoxic products released by the bacterium, biomechanical and physiological stress resulting from infection, and the corresponding inflammatory response together contribute to organ damage accrued during infection. The collective result of this damage can be acutely life-threatening, but among survivors, it also contributes to the long-lasting sequelae of pneumococcal disease. These include the development of new morbidities or exacerbation of pre-existing conditions such as COPD, heart disease, and neurological impairments. Currently, pneumonia is ranked as the 9^th leading cause of death, but this estimate only considers short-term mortality and likely underestimates the true long-term impact of disease. Herein, we review the data that indicates damage incurred during acute pneumococcal infection can result in long-term sequelae which reduces quality of life and life expectancy among pneumococcal disease survivors.

Social network and severe lower respiratory tract infections in older adults: findings from a Swedish longitudinal population-based study

OBJECTIVES

To investigate the association between social network and the risk and prognosis of severe lower respiratory tract infections (LRTIs) in older adults.

METHODS

Data from Swedish hospital records were matched with the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). Social network was operationalized as social connections and social support, based on different self-reported variables, and categorized as low, medium, and high. The risk of severe LRTI and related outcomes were assessed using Cox, Poisson, and logistic regression models where appropriate.

RESULTS

A total of 362 individuals experienced LRTI-related hospitalizations between 2001 and 2016 (479 total hospitalizations). High levels of social support decreased the hazard of incident LRTI by 29% (hazard ratio 0.71, 95% confidence interval [CI] 0.52-0.96), the hospital length of stay by 21% (incidence rate ratio 0.79, 95% CI 0.65-0.97), and the risk of 30-day mortality by 92% (odds ratio 0.08, 95% CI 0.01-0.68), but was a risk factor for 30-day readmission (odds ratio 3.16, 95% CI 1.38-7.24). High levels of social connections were associated with a higher risk of incident LRTI in women and those with dementia and/or slow walking speed (P_interaction <0.05).

CONCLUSION

Older individuals' quality of social support seems to be a stronger determinant of LRTI incidence and prognosis than the quantity of their social connections. These findings may inform evidence-based policies aimed at preventing LRTIs in older adults.

Prognostic value of blood urea nitrogen-to-serum albumin ratio for mortality of pneumonia in patients receiving glucocorticoids: Secondary analysis based on a retrospective cohort study

INTRODUCTION

Previous studies have revealed that blood urea nitrogen-to-serum albumin ratio (BUN/ALB) is one of major risk factors of mortality in pneumonia. However, there are fewer scientific research about the correlation between BUN/ALB ratio and outcome of pneumonia in patients receiving glucocorticoids. This study was undertaken to explore the prognostic value of BUN/ALB ratio for mortality of pneumonia in patients receiving glucocorticoids.

METHODS

The present study was a retrospective cohort study. 1397 subjects receiving glucocorticoids alone or glucocorticoids and other immunosuppressants from six secondary and tertiary academic hospitals in China were analyzed. The endpoint of the study was 30-day mortality. It was noted that the entire study was completed by Li et al. and uploaded the data to the DATADRYAD website. The author only used this data for secondary analysis.

RESULTS

After adjusting potential confounders (age, sex, WBC, persistent lymphocytopenia, PLT, ALT, AST, Cr, high-dose steroid use, and COPD), non-linear relationship was detected between BUN/ALB ratio and 30-day mortality, whose point was 0.753. The effect sizes and the confidence intervals on the left and right sides of inflection point were 23.110 (7.157, 74.623) and 0.410 (0.074, 2.283), respectively. Subgroup analysis revealed the positive association was stronger among subjects with connective tissue disease.

CONCLUSIONS

The relationship between BUN/ALB ratio and 30-day mortality of pneumonia in patients receiving glucocorticoids is non-linear. BUN/ALB ratio is positively related with 30-day mortality when BUN/ALB ratio is less than 0.753.

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power.

Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death.

Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients.

Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.

Effects of fluid and drinking on pneumonia mortality in older adults: A systematic review and meta-analysis

Background and aims

Methods

Results

Conclusions

Age, Pulse, Urea, and Albumin Score: A Tool for Predicting the Short-Term and Long-Term Outcomes of Community-Acquired Pneumonia Patients With Diabetes

OBJECTIVE

The predictive performances of CURB-65 and pneumonia severity index (PSI) were poor in patients with diabetes. This study aimed to develop a tool for predicting the short-term and long-term outcomes of CAP in patients with diabetes.

METHODS

A retrospective study was conducted on 531 CAP patients with type 2 diabetes. The short-term outcome was in-hospital mortality. The long-term outcome was 24-month all-cause death. The APUA score was calculated according to the levels of Age (0-2 points), Pulse (0-2 points), Urea (0-2 points), and Albumin (0-4 points). The area under curves (AUCs) were used to evaluate the abilities of the APUA score for predicting short-term outcomes. Cox regression models were used for modeling relationships between the APUA score and 24-month mortality.

RESULTS

The AUC of the APUA score for predicting in-hospital mortality was 0.807 in patients with type 2 diabetes (P<0.001). The AUC of the APUA score was higher than the AUCs of CURB-65 and PSI class (P<0.05). The long-term mortality increased with the risk stratification of the APUA score (low-risk group (0-1 points) 11.5%, intermediate risk group (2-4 points) 16.9%, high risk group (≥5 points) 28.8%, P<0.05). Compared with patients in the low-risk group, patients in the high-risk group had significantly increased risk of long-term death, HR (95%CI) was 2.093 (1.041~4.208, P=0.038).

CONCLUSION

The APUA score is a simple and accurate tool for predicting short-term and long-term outcomes of CAP patients with diabetes.

Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia

Background

Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality worldwide. Although there are many predictors of death for CAP, there are still some limitations. This study aimed to build a simple and accurate model based on available and common clinical-related feature variables for predicting CAP mortality by adopting machine learning techniques.

Methods

This was a single-center retrospective study. The data used in this study were collected from all patients (≥18 years) with CAP admitted to research hospitals between January 2012 and April 2020. Each patient had 62 clinical-related features, including clinical diagnostic and treatment features. Patients were divided into two endpoints, and by using Tensorflow2.4.1 as the modeling framework, a three-layer fully connected neural network (FCNN) was built as a base model for classification. For a comprehensive comparison, seven classical machine learning methods and their integrated stacking patterns were introduced to model and compare the same training and test data.

Results

A total of 3997 patients with CAP were included; 205 (5.12%) died in the hospital. After performing deep learning methods, this study established an ensemble FCNN model based on 12 FCNNs. By comparing with seven classical machine learning methods, the area under the curve of the ensemble FCNN was 0.975 when using deep learning algorithms to classify poor from good prognosis based on available and common clinical-related feature variables. The predicted outcome was poor prognosis if the ControlNet's poor prognosis score was greater than the cutoff value of 0.50. To confirm the scientificity of the ensemble FCNN model, this study analyzed the weight of random forest features and found that mainstream prognostic features still held weight, although the model is perfect after integrating other factors considered less important by previous studies.

Conclusion

This study used deep learning algorithms to classify prognosis based on available and common clinical-related feature variables in patients with CAP with high accuracy and good generalizability. Every clinical-related feature is important to the model.

A descriptive and validation study of a predictive model of severity of SARS-COV-2 infection

Objectives

The strain the SARS-COV-2 pandemic is putting on hospitals requires that predictive values are identified for a rapid triage and management of patients at a higher risk of developing severe COVID-19. We developed and validated a prognostic model of COVID-19 severity.

Methods

A descriptive, comparative study of patients with positive vs. negative PCR-RT for SARS-COV-2 and of patients who developed moderate vs. severe COVID-19 was conducted. The model was built based on analytical and demographic data and comorbidities of patients seen in an Emergency Department with symptoms consistent with COVID-19. A logistic regression model was designed from data of the COVID-19-positive cohort.

Results

The sample was composed of 410 COVID-positive patients (303 with moderate disease and 107 with severe disease) and 81 COVID-negative patients. The predictive variables identified included lactate dehydrogenase, C-reactive protein, total proteins, urea, and platelets. Internal calibration showed an area under the ROC curve (AUC) of 0.88 (CI 95%: 0.85-0.92), with a rate of correct classifications of 85.2% for a cut-off value of 0.5. External validation (100 patients) yielded an AUC of 0.79 (95% CI: 0.71-0.89), with a rate of correct classifications of 73%.

Conclusions

The predictive model identifies patients at a higher risk of developing severe COVID-19 at Emergency Department, with a first blood test and common parameters used in a clinical laboratory. This model may be a valuable tool for clinical planning and decision-making.

Interrupted time-series analyses of routine vaccination program for elderly pneumonia patients in Japan; an ecological study using aggregated nationwide inpatient data

A national routine pneumococcal pneumonia immunization program started in Japan in 2014. It targeted the population aged ≥65 years and used a 23-valent pneumococcal polysaccharide vaccine; PPSV23. However, its effectiveness was not well defined because of the lack of a comprehensive database on the PPSV23 vaccination status of each subject. We used interrupted time-series analyses to assess the changes in the incidence and prognosis of elderly patients hospitalized for pneumonia before and after initiation of the program. First, we estimated the PPSV23 coverage rates in subjects aged ≥65 years based on the number of shipped PPSV23 syringes and the estimated population in each prefecture. The estimated coverage rates reached around 40% in 2014 for the 3 Tohoku prefectures, while those in the other prefectures remained below 20%. After the national routine immunization program started, the estimated coverage rate increased significantly in every prefecture and exceeded 40% in 2017. Next, we aggregated the data extracted from the Japanese Diagnosis Procedure Combination database from April 2011 through February 2017 for hospitalized pneumonia patients aged ≥65 years. The data included data from 655,746 patients, excluding those in the 3 Tohoku prefectures. Interrupted time-series analyses found no change in the incidence of hospitalized pneumonia patients and in-hospital mortality after the vaccination program, but there was a decrease in the in-hospital mortality of pneumonia patients with severe comorbidities defined by the modified Charlson comorbidity index. These results suggest an association between the vaccination program and an improved outcome in hospitalized elderly pneumonia patients with severe comorbidities in Japan.

Blood urea nitrogen-to-serum albumin ratio and A-DROP are useful in assessing the severity of Pneumocystis pneumonia in patients without human immunodeficiency virus infection

INTRODUCTION

There is an increasing incidence of Pneumocystis pneumonia (PcP) among individuals without human immunodeficiency virus (HIV) infection (non-HIV PcP). However, prognostic factors for patients with non-HIV PcP have not been identified. Moreover, A-DROP (for classifying the severity of community-acquired pneumonia) or the blood urea nitrogen-to-serum albumin ratio (BUN/Alb), which is reported to be a predictor of mortality of community-acquired pneumonia, has not been established as an efficient prognostic factor in patients with non-HIV PcP. In this study, we analyzed the prognostic factors for non-HIV PcP and evaluated the prognostic ability of A-DROP and the BUN/Alb ratio.

METHODS

This retrospective study involved a chart review of the medical records of 102 patients diagnosed with non-HIV PcP between January 2003 and May 2019 at five medical facilities.

RESULTS

Overall, 102 patients were involved in this study. The 30-day mortality rate for non-HIV PcP was 20.5% in this study population. Compared with survivors, non-survivors had significantly lower serum albumin levels and significantly higher age, corticosteroid dosage at the PcP onset, alveolar-arterial oxygen gradient, A-DROP score, lactate dehydrogenase levels, blood urea nitrogen levels, and BUN/Alb ratio. Multivariate analysis showed that a high BUN/Alb ratio at treatment initiation was significantly associated with 30-day mortality risk. The receiver operating characteristic curves showed that A-DROP score had the highest prognostic ability in estimating 30-day mortality.

CONCLUSIONS

In patients with non-HIV PcP, a high BUN/Alb ratio is an independent prognostic predictor of mortality risk, and A-DROP is useful for classifying the severity.

Impact of Protein Energy Malnutrition on Outcomes of Adults With Viral Pneumonia: A Nationwide Retrospective Analysis

Background Community-acquired pneumonia (CAP) is associated with significant morbidity and mortality. Viral organisms have been identified as the causal pathogen in approximately 20% of CAP. Nutritional status plays an important role in the response to pneumonia. This study aims to identify whether protein energy malnutrition (PEM) is an independent risk factor for mortality and morbidity in viral CAP. Materials and methods This was a retrospective cohort study involving adult hospitalizations for viral CAP in the United States using the Nationwide Inpatient Sample (NIS) database. This cohort was further divided based on the presence or absence of a secondary discharge diagnosis of PEM. The primary outcome was inpatient mortality. Secondary outcomes included the rate of mechanical ventilation among other complications. Results The in-hospital mortality for viral CAP was 2.22%. Patients with PEM had over two-fold high adjusted odds of inpatient mortality (aOR: 2.42, 95% CI: 1.746-3.351, p < 0.001) compared with patients without PEM. Patients with PEM had higher adjusted odds of having septic shock (aOR: 3.34, 95% CI: 2.158-5.160, p < 0.001). NSTEMI (aOR: 1.75, 95% CI: 1.163-2.621, p = 0.007), need for mechanical ventilation (aOR: 3.13, 95% CI: 2.448-4.006, p < 0.001), CVA (aOR: 3.49, 95% CI: 1.687-7.220, p = 0.001), DVT (aOR: 2.19, 95% CI: 1.453-3.295, p < 0.001), and PE (aOR: 2.24, 95% CI: 1.152-4.357, p = 0.017) relative to patients without PEM. Conclusion In conclusion, coexisting PEM is associated with a higher rate of in-hospital morbidity and mortality in patients with viral CAP. Early identification and treatment of nutritional deficiencies can lead to improved outcomes and reduced costs.

Specific pathogens as predictors of poor long-term prognosis after hospital discharge for community-acquired pneumonia

BACKGROUND

Some studies have reported that long-term prognosis after pneumonia is poor. Our aim was to determine predictors of long-term outcomes with special attention to community-acquired pneumonia (CAP) etiology.

METHODS

We studied 1930 patients who were hospitalized with CAP from January 2002 through November 2017 at Saitama Cardiovascular and Respiratory Center and were discharged alive. We conducted a retrospective study for calculation of survival rate using the Kaplan-Meier method and analysis of prognostic factors by multivariate analysis using a Cox proportional hazard model.

RESULTS

The median follow-up period was 442.5 (range 1-5514) days. During this period, 321 patients died. Median survival time was 11.9 years, and 1-year and 5-year survival rates were 93.8% and 74.0%, respectively. Among the patients' demographics factors, old age, poor performance status (PS) score, pneumococcal vaccination history, some underlying respiratory diseases, and chronic heart failure were significant independent factors of poor prognosis. Among pathogens, Streptococcus pneumoniae (hazard ratio [HR]: 1.35, 95% confidence interval [CI]: 1.03, 3.07, P = 0.038) and Pseudomonas aeruginosa (HR: 1.68, 95% CI: 1.07, 2.64, P = 0.024) were significant independent factors of poor prognosis, whereas influenza virus tended to predict a good prognosis (HR: 0.60, 95% CI: 0.36, 1.02, P = 0.058). Respiratory disease accounted for 59% of all causes of death after CAP, and the rate of death from pneumonia was the largest at 22%.

CONCLUSION

Not only age, general condition, and comorbidities but also specific pathogens were predictors of long-term prognosis after hospital discharge for CAP.

Prognostic factors in elderly patients admitted to the intensive care unit with community-acquired pneumonia

OBJECTIVE

We aimed to determine the clinical, radiological and laboratory findings that may indicate poor prognosis in severe community acquired pneumonia (CAP) requiring intensified care to reduce the risk of death.

METHODS

The medical histories, demographic characteristics and laboratory values of over 65 years old patients admitted to the intensive care unit (ICU) and diagnosed with CAP were recorded.

RESULTS

Total of 86 patients were included in the study. Among those patients 39 were discharged from the ICU with health but 47 were expired. Diastolic blood pressure was significantly lower in expired patients (p = 0.044). In multivariate analysis, older age (>78 years) (p = 0.004), at admission elevated blood glucose (>108 mg/dL) levels (p = 0.048), decreased serum albumin (<3.5 g/dL) levels (p = 0.043), elevated serum procalcitonin levels (>0.63 μg/L) (p = 0.034) and in blood gas analysis decreased pH (<7.35) (p = 0.042)and increased lactate (>2mmol/L) (p = 0.001) were the significant risk factors for in-ICU mortality.

CONCLUSIONS

At old age, blood glucose and procalcitonin levels increased at the time of admission, serum albumin levels decreased, pH decreased in blood gas analysis and lactate levels increased, and significant mortality determinants in CAP patients over 65 years of age who applied to the intensive care unit.

Blood urea nitrogen to albumin ratio is a predictor of in-hospital mortality in older emergency department patients

INTRODUCTION

In this study, we aimed to evaluate the first measured blood urea nitrogen (BUN)/albumin ratio in the emergency department (ED) as a predictor of in-hospital mortality in older ED patients.

METHODS

This retrospective observational study was conducted at a university hospital ED. Consecutive patients aged 65 and over who visited the ED in a three-month period were included in the study. The BUN, albumin, creatinine, and estimated glomerular filtration rate (eGFR) of patients were recorded. The primary end point of the study was in-hospital mortality.

RESULTS

A total of 1253 patients were included in the statistical analyses of the study. Non-survivors had increased BUN levels (32.9 (23.3-55.4) vs. 20.2 (15.4-28.3) mg/dL, p < 0.001), decreased albumin levels (3.27 (2.74-3.75) vs. 3.96 (3.52-4.25) g/dL, p < 0.001), and increased BUN/albumin ratios (10.19 (6.56-18.94) vs. 5.21 (3.88-7.72) mg/g, p < 0.001) compared to survivors. An increased BUN/albumin ratio was a powerful predictor of in-hospital mortality with an area under the curve of 0.793 (95% CI: 0.753-0.833). Malignancy (OR: 2.39; 95% CI: 1.59-3.74, p < 0.001), albumin level < 3.5 g/dL (OR: 2.75; 95% CI: 1.74-4.36, p < 0.001), and BUN/albumin ratio > 6.25 (OR: 2.82; 95% CI: 1.22-6.50, p < 0.015) were found to be independent predictors of in-hospital mortality in older ED patients.

CONCLUSION

According to our findings, older patients with a BUN level > 23 mg/dL, an albumin level < 3.5 g/dL, and a BUN/albumin ratio > 6.25 mg/g in the ED have a higher risk of in-hospital mortality. Additionally, the BUN/albumin ratio is a more powerful independent predictor of in-hospital mortality than the BUN level, albumin level, creatinine level, and eGFR in older ED patients.

Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients

The crude mortality rate in critical pneumonia cases with coronavirus disease 2019 (COVID-19) reaches 49%. This study aimed to test whether levels of blood urea nitrogen (BUN) in combination with D-dimer were predictors of in-hospital mortality in COVID-19 patients. The clinical characteristics of 305 COVID-19 patients were analysed and were compared between the survivor and non-survivor groups. Of the 305 patients, 85 (27.9%) died and 220 (72.1%) were discharged from hospital. Compared with discharged cases, non-survivor cases were older and their BUN and D-dimer levels were significantly higher (P < 0.0001). Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses identified BUN and D-dimer levels as independent risk factors for poor prognosis. Kaplan-Meier analysis showed that elevated levels of BUN and D-dimer were associated with increased mortality (log-rank, P < 0.0001). The area under the curve for BUN combined with D-dimer was 0.94 (95% CI 0.90-0.97), with a sensitivity of 85% and specificity of 91%. Based on BUN and D-dimer levels on admission, a nomogram model was developed that showed good discrimination, with a concordance index of 0.94. Together, initial BUN and D-dimer levels were associated with mortality in COVID-19 patients. The combination of BUN ≥ 4.6 mmol/L and D-dimer ≥ 0.845 μg/mL appears to identify patients at high risk of in-hospital mortality, therefore it may prove to be a powerful risk assessment tool for severe COVID-19 patients.

Risk Factors for Reoperation in Patients Hospitalized for Odontogenic Infections

PURPOSE

One of the most common adverse events after treatment of severe odontogenic infections is the need for a second procedure. The identification of risk factors for reoperation could help surgeons predict the need for reoperation or even tailor treatment to avoid this complication altogether. The purpose of this study was to identify risk factors associated with reoperation in patients hospitalized with odontogenic infections.

PATIENTS AND METHODS

We designed a retrospective cohort study from an eligible population of all patients treated at our institution for an odontogenic infection with incision and drainage under general anesthesia from August 1, 2015, to June 30, 2019. The primary outcome variable was a return to the operating room because of treatment failure. The potential predictor variables included demographic characteristics, history or physical examination findings from admission, admission laboratory values, initial computed tomography results, and medications provided during treatment. Statistical analysis was performed using the χ² test and logistic regression, and from these results, a multiple logistic regression model was created.

RESULTS

A total of 223 patients were included in the study. Men comprised 50.7% of the study population, and the average age was 38.9 ± 13.3 years. The mean number of involved spaces was 2.6 ± 1.7 spaces. Reoperation was performed in 25 patients (11.2%). In the adjusted model, an increased number of involved spaces (odds ratio [OR], 1.9; 95% confidence interval [CI], 1.3 to 2.7) and increased age (OR, 1.05; 95% CI, 1.004 to 1.09) were associated with increased odds of reoperation, and antibiotic therapy with a penicillin-type antibiotic (OR, 0.1; 95% CI, 0.04 to 0.5) and dexamethasone use (OR, 0.9; 95% CI, 0.8 to 0.98) were negatively associated with reoperation.

CONCLUSIONS

Further studies could be directed at determining whether certain timing or dosing regimens of dexamethasone could be helpful in reducing adverse outcomes in patients with odontogenic infections or determining feasible strategies when penicillin allergies are reported.

Incidence and Risk of Pneumococcal Pneumonia in Adults with Distinct Underlying Medical Conditions: A Population-Based Study

PURPOSE

This study investigated the incidence of pneumococcal pneumonia requiring hospitalisation among middle-aged and older adults with and without specific underlying medical conditions, evaluating the influence of these conditions in the risk of developing pneumonia.

METHODS

Population-based prospective cohort study included 2,025,730 individuals ≥ 50 years around Catalonia, Spain. The Catalonian information system for the development of research in primary care (SIDIAP) was used to establish baseline characteristics of the cohort (comorbidities and underlying medical conditions). Hospitalisations from pneumococcal pneumonia occurred among cohort members between 01/01/2015 and 31/12/2015 were collected from hospital discharge codes of 68 reference Catalonian hospitals. Cox regression was used to estimate the association between baseline conditions and the risk of developing pneumonia.

RESULTS

Global incidence rate (IR) of hospitalised pneumococcal pneumonia was 82.8 cases per 100,000 persons-year. Maximum IRs (per 100,000 persons-year) emerged among persons with haematological neoplasia (837.4), immunodeficiency (709.2), HIV infection (474.7), severe renal disease (407.5) and chronic pulmonary disease (305.7). In the multivariable analyses, apart from increasing age, HIV infection (hazard ratio [HR] 6.78), haematological neoplasia (HR 6.30), prior all-cause pneumonia (HR 5.27), immunodeficiency (HR 4.57) and chronic pulmonary disease (HR 2.89) were the conditions most strongly associated with an increasing risk. Pneumococcal vaccination did not emerge associated with a reduced risk in our study population (nor PPsV23 neither PCV13).

CONCLUSION

Old age, immunocompromising conditions and chronic pulmonary/respiratory disease are major risk factors for pneumococcal pneumonia in adults. Our data underline the need for better prevention strategies in these persons.

Indirect costs of adult pneumococcal disease and the productivity-based rate of return to the 13-valent pneumococcal conjugate vaccine for adults in Turkey

Productivity benefits of health technologies are ignored in typical economic evaluations from a health payer’s perspective, risking undervaluation. We conduct a productivity-based cost-benefit analysis from a societal perspective and estimate indirect costs of adult pneumococcal disease, vaccination benefits from the adult 13-valent pneumococcal conjugate vaccine (PCV13 Adult), and rates of return to PCV13 Adult for a range of hypothetical vaccination costs. Our context is Turkey’s funding PCV13 for the elderly and for non-elderly adults with select comorbidities within the Ministry of Health’s National Immunization Program. We use a Markov model with one-year cycles. Indirect costs from death or disability equal the expected present discounted value of lifetime losses in the infected individual’s paid and unpaid work and in caregivers’ paid work. Vaccination benefits comprise averted indirect costs. Rates of return equal vaccination benefits divided by vaccination costs, minus one. Input parameters are from public data sources. We model comorbidities’ effects by scalar multiplication of the parameters of the general population. Indirect costs per treatment episode of inpatient community-acquired pneumonia (CAP), bacteremia, and meningitis – but not for outpatient CAP – approach or exceed Turkish per capita gross domestic product. Vaccination benefits equal $207.02 per vaccination in 2017 US dollars. The rate of return is positive for all hypothetical costs below this. Results are sensitive to herd effects from pediatric vaccination and vaccine efficacy rates. For a wide range of hypothetical vaccination costs, the rate of return compares favorably with those of other global development interventions with well-established strong investment cases.

Blood albumin as a prognostic factor among unselected medically treated inpatients

Aim: The aim of this study is to determine the prognostic value of blood albumin (BA) in an unselected population of inpatients. Materials & methods: We performed prospective analysis of the medical documentation of 7279 patients hospitalized between July 2014 and September 2017. Results: Individuals with BA ≥3.35 mg/dl had significantly lower risk of in-hospital death (odds ratio [OR]: 0.22; 95% CI: 0.19-0.27; p < 0.001) and 14-day readmission (OR: 0.64; 95% CI: 0.55-0.77; p < 0.0001). BA concentration was the strongest favorable factor predicting inpatient survival in a Cox hazard regression model (OR: 0.43; 95% CI: 0.36-0.50; p < 0.001), did not correlate with body mass index and actual-to-ideal bodyweight ratio and was strongly affected by numerous non-nutrient factors. Conclusion: BA concentration showed similar or better predictive and diagnostic power in relation to all-cause in-hospital mortality and 14-day readmission among inpatients than selected multifactorial scores.

Prognostic significance of malnutrition for long-term mortality in community-acquired pneumonia: a propensity score matched analysis

BACKGROUND/AIMS

The impact of malnutrition on the outcome of hospitalized adults with community-acquired pneumonia (CAP) has not been fully investigated. This study evaluated the prevalence and prognostic significance of malnutrition in a Korean population with CAP.

METHODS

In total, 198 patients with CAP from November 2014 to September 2015 were analyzed retrospectively. We assessed the prevalence of malnutrition and the risk factors for 2-year mortality. Furthermore, we divided the patients into two groups: elderly (age ≥ 65 years, n = 131) and non-elderly (age < 65 years, n = 67). Subgroup analyses were performed in the elderly group through propensity score matching.

RESULTS

The prevalence of malnutrition was 39.4%, and the proportion of patients with malnutrition was significantly higher (53.4% vs. 11.9%, p < 0.001) in the elderly group than in the non-elderly group. In-hospital mortality, 1-year mortality, and 2-year mortality rates were 4.5%, 19.2%, and 26.8%, respectively. Multivariate Cox regression analyses revealed that malnutrition (odds ratio [OR], 2.52; 95% confidence interval [CI], 1.39 to 4.60; p = 0.002) and the Charlson comorbidity index score (OR, 1.30; 95% CI, 1.17 to 1.45; p < 0.001) were associated with 2-year mortality.

CONCLUSION

Malnutrition was common and associated with a poor long-term outcome in patients with CAP, particularly the elderly. A routine nutritional assessment at admission is mandatory as a first step for appropriate nutritional therapy.