Predicting mortality among older adults hospitalized for community-acquired pneumonia: an enhanced confusion, urea, respiratory rate and blood pressure score compared with pneumonia severity index

Impact of Antibiotics Used for Acute Aspiration Bronchitis on the Prevention of Pneumonia

BACKGROUNDS

It remains unclear if antibiotics should be used for the treatment of acute aspiration bronchitis to prevent the development of pneumonia. This study aimed to assess the associations between the use of antibiotics and the development of pneumonia among patients with acute aspiration bronchitis.

METHODS

We retrospectively reviewed consecutive patients with acute aspiration bronchitis aged ≥75 years. Acute aspiration bronchitis was defined as a condition with aspiration risk, high fever (body temperature, ≥37.5 °C), respiratory symptoms, and the absence of evidence of pneumonia.

RESULTS

There was no significant difference in the incidence of pneumonia between patients treated with and without antibiotics for acute aspiration bronchitis (6/44, 14% vs. 31/143, 22%; p = 0.242). Lower estimated glomerular filtration rate (adjusted odds ratio, 0.956; 95% confidence interval, 0.920-0.993) was significantly associated with the development of pneumonia.

CONCLUSIONS

Antibiotic administration should not be routinely recommended to prevent pneumonia following acute aspiration bronchitis, and patients with decreased renal function should be closely monitored. A randomized controlled trial is necessary to validate these results.

Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit

BACKGROUND

Scores for predicting the long-term mortality of severe pneumonia are lacking. The purpose of this study is to use machine learning methods to develop new pneumonia scores to predict the 1-year mortality and hospital mortality of pneumonia patients on admission to the intensive care unit (ICU).

METHODS

The study population was screened from the MIMIC-IV and eICU databases. The main outcomes evaluated were 1-year mortality and hospital mortality in the MIMIC-IV database and hospital mortality in the eICU database. From the full data set, we separated patients diagnosed with community-acquired pneumonia (CAP) and ventilator-associated pneumonia (VAP) for subgroup analysis. We used common shallow machine learning algorithms, including logistic regression, decision tree, random forest, multilayer perceptron and XGBoost.

RESULTS

The full data set of the MIMIC-IV database contained 4697 patients, while that of the eICU database contained 13760 patients. We defined a new pneumonia score, the "Integrated CCI-APS", using a multivariate logistic regression model including six variables: metastatic solid tumor, Charlson Comorbidity Index, readmission, congestive heart failure, age, and Acute Physiology Score III. The area under the curve (AUC) and accuracy of the integrated CCI-APS were assessed in three data sets (full, CAP, and VAP) using both the test set derived from the MIMIC-IV database and the external validation set derived from the eICU database. The AUC value ranges in predicting 1-year and hospital mortality were 0.784-0.797 and 0.691-0.780, respectively, and the corresponding accuracy ranges were 0.723-0.725 and 0.641-0.718, respectively.

CONCLUSIONS

The main contribution of this study was a benchmark for using machine learning models to build pneumonia scores. Based on the idea of integrated learning, we propose a new integrated CCI-APS score for severe pneumonia. In the prediction of 1-year mortality and hospital mortality, our new pneumonia score outperformed the existing score.

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems

BACKGROUND

We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores.

METHODS

This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis.

RESULTS

Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores.

CONCLUSION

The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

Effects of endobronchial coils for endoscopic lung volume reduction on sleep in COPD patients with advanced pulmonary emphysema

PURPOSE

Treatment of advanced pulmonary emphysema with endobronchial coils can improve clinical outcomes like quality of life (QOL). Yet, patients with chronic obstructive pulmonary disease (COPD) are also known to suffer from reduced sleep quality. The effect of coil therapy on sleep has not yet been investigated. The primary aim of this study was to investigate sleep efficiency before and after coil treatment. Secondly, we investigated the effects on nocturnal breathing pattern, QOL, and physical activity.

METHODS

Polysomnography (PSG) testing was performed before (T0), 6 month after (T3), and 12 months after (T4) treatment with endobronchial coils. Further examinations included QOL by St George's Respiratory Questionnaire (SGRQ) and COPD assessment test (CAT), and physical activity using an accelerometer for 1 week after each visit.

RESULTS

Of 21 patients, 14 completed the study: 6 women; mean age 58.0 ± 4.9 years; BMI 22.6 ± 4.6 kg/m²; FEV₁ 28.6 ± 7.1% predicted; residual volume (RV) 278.2 ± 49.4% predicted. Sleep efficiency did not vary between baseline and follow-up examinations (T0 69.0 ± 15.8%; T3 70.9 ± 16.0%; T4 66.8 ± 18.9%). Non-REM respiratory rate decreased compared to baseline (T0 19.4 ± 3.9/min; T3 17.8 ± 3.5/min; T4 17.1 ± 3.1/min (p = 0.041; p = 0.030) and QOL improved meeting the minimal clinically important difference (MCID) (SGRQ, T3 -12.8 units; T4 -7.1 units; CAT: T3 -5.6 units; T4 -3.4 units). No increase in physical activity was recorded (light activity T0 31.9 ± 9.9; T3 30.8 ± 16.9; T4 26.3 ± 10.6 h/week).

CONCLUSIONS

Treatment with endobronchial coils did not influence objectively measured sleep quality or physical activity, but reduced nocturnal breathing frequency and improved QOL in severe emphysema patients.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02399514, First Posted: March 26, 2015.

A new prediction model for assessing the clinical outcomes of ICU patients with community-acquired pneumonia: a decision tree analysis

PURPOSE

We aimed to develop a new scoring index based on decision-tree analysis to predict clinical outcomes of patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU).

METHODS

Data of 3519 ICU patients with CAP were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) 2001-2012 database and analysed between 30-d survivors and non-survivors. Accuracy, sensitivity, and specificity of the new decision tree model were compared with those of CURB-65 and SOAR.

RESULTS

The newly developed classification and regression tree (CART) model identified coexisting illnesses as the most important single discriminating factor between survivors and non-survivors. The CART model area under the curve (AUC) 0.661 was superior to that of CURB-65 (0.609) and SOAR (0.589). CART sensitivity was 73.4%, and specificity 49.0%. CURB-65 and SOAR sensitivity for predicting 30-d mortality were 74.5 and 80.7%, and specificity was 42.3 and 33.9%, respectively. After smoothing, the CART model had higher sensitivity and specificity than both CURB-65 and SOAR.

CONCLUSIONS

The new CART prediction model has higher specificity and better receiver operating characteristics (ROC) curves than CURB-65 and SOAR score indices although its accuracy and sensitivity are only moderately better than the other systems. Key messages The new CART prediction model has higher specificity and better ROC curves than CURB-65 and SOAR score indices. However, accuracy and sensitivity of the new CART prediction model are only moderately better than the other systems in predicting 30-day mortality in CAP patients.

Guideline for Antibiotic Use in Adults with Community-acquired Pneumonia

Community-acquired pneumonia is common and important infectious disease in adults. This work represents an update to 2009 treatment guideline for community-acquired pneumonia in Korea. The present clinical practice guideline provides revised recommendations on the appropriate diagnosis, treatment, and prevention of community-acquired pneumonia in adults aged 19 years or older, taking into account the current situation regarding community-acquired pneumonia in Korea. This guideline may help reduce the difference in the level of treatment between medical institutions and medical staff, and enable efficient treatment. It may also reduce antibiotic resistance by preventing antibiotic misuse against acute lower respiratory tract infection in Korea.

Circulating level of high mobility group box‑1 predicts the severity of community‑acquired pneumonia: Regulation of inflammatory responses via the c‑Jun N‑terminal signaling pathway in macrophages

High mobility group box‑1 (HMGB‑1) has been reported to serve significant roles in various inflammatory diseases. However, the correlation between the circulating level of HMGB‑1 and severity of community‑acquired pneumonia (CAP) remains unclear. The present study investigated differential alterations in plasma HMGB‑1 levels of patients with CAP prior to and following antibiotic treatment, and further analyzed the association between CAP severity and HMGB‑1 levels. Furthermore, lipopolysaccharide (LPS)‑induced HMGB‑1 expression in RAW264.7 macrophages and the relevant signaling pathways were examined. Plasma HMGB‑1 levels of 90 patients with CAP and 52 healthy controls were measured using a commercial ELISA. The levels of plasma HMGB‑1 were significantly elevated in CAP patients compared with the controls, and antibiotic treatment was effective in reducing HMGB‑1 levels. Plasma HMGB‑1 correlated with the pneumonia severity index score (r=0.566, P<0.001). Furthermore, LPS‑stimulation significantly upregulated HMGB‑1 secretion via the c‑Jun N‑terminal kinase (JNK) signaling pathway in RAW264.7 macrophages, whereas pretreatment with the JNK inhibitor SP600125 markedly downregulated LPS‑induced HMGB‑1 levels. In conclusion, plasma HMGB‑1 levels may serve a role in the diagnosis and clinical assessment of CAP severity. These findings may provide information on novel targets for the treatment of CAP.

Prediction model for mortality in cancer patients with pneumonia: comparison with CURB-65 and PSI

INTRODUCTION AND OBJECTIVES

We aimed to develop a new prediction model of mortality in cancer patients with pneumonia and to compare its performance with CURB-65 and the Pneumonia Severity Index (PSI).

METHODS

Active cancer patients who were diagnosed with pneumonia at the Emergency Department (ED) from 7/1/2014 to 12/31/2014 were consecutively included. Clinical data were collected through a medical chart review. The primary outcome was the 28-day mortality, and clinical factors were analyzed using logistic regression analysis.

RESULTS

Among a total of 218 analyzed patients with a median age of 64.0 years (IQR, 56.8-71.0) and a male proportion of 72%, 42 (19.3%) died within 28 days of ED admission. By multivariate logistic regression analysis, an ECOG performance status (PS) 3 (OR: 8.54, 95% CI: 3.42-21.33) or 4 (OR: 13.17, 95% CI: 3.19-54.32), SpO₂ <90% (OR: 3.06, 95% CI: 1.17-8.00), and elevated lactic acid levels (OR: 1.42, 95% CI: 1.12-1.81) were significantly associated with mortality. With these three variables, a new prediction model with total scores ranged from 0 to 6 was generated. The area under the curve of the new prediction model was 0.840, compared with 0.673 and 0.586 for CURB-65 and PSI, respectively.

CONCLUSION

In cancer patients with pneumonia, a poor ECOG PS, SpO₂ <90%, and lactic acid elevation are independent predictors of mortality. The new prediction model, comprising three predictors, performs better in predicting mortality in cancer patients than CURB-65 or PSI.

Prognostic value of Pneumonia Severity Index, CURB-65, CRB-65, and procalcitonin in community-acquired pneumonia in Singapore

Objective:

The purpose of this study was to evaluate the performance of three severity scoring tools and procalcitonin (PCT) in severity stratification and mortality prediction among patients with community-acquired pneumonia (CAP) in Singapore.

Methods:

The method used was a retrospective observational study of all the consecutive patients with CAP admitted through the emergency department of Singapore General Hospital between 2012–2013.

Results:

Among 1902 study subjects, the overall 30-day mortality was 15.7%. The mortality rates for Pneumonia Severity Index (PSI) class I–III were 0, 0, and 3.7%, which were comparable to the original published data. CURB-65 and CRB-65 had higher mortality rates in all severity levels. In three levels of risk stratification, the low risk group of PSI (class I–III) included 42.6% of the patients with mortality rate of 1.9%, whereas the low risk group defined by CURB-65 (score 0–1) and CRB-65 (score 0) included 52.0% and 24.4% of the patients with higher mortality rates (7.3% and 4.5% respectively). PSI was the most sensitive in mortality prediction with area under receiver operating characteristic (ROC) curve of 0.82, higher than CURB-65 (0.71), CRB-65 (0.67), and PCT (0.63) ( p<0.001). The initial level of PCT was higher in non-survivors and intensive care unit (ICU)-admitted patients compared to survivors (0.91 vs 0.36 ng/ml, p<0.001) and non-ICU patients (3.70 vs 0.38 ng/ml, p<0.001). Incorporating PCT did not improve the discriminatory power of the scoring tools for mortality prediction.

Conclusions:

PSI was a reliable tool for severity stratification and morality prediction among the patients with CAP in Singapore.

Management of community-acquired pneumonia in older adults

Community-acquired pneumonia (CAP) is an increasing problem among the elderly. Multiple factors related to ageing, such as comorbidities, nutritional status and swallowing dysfunction have been implicated in the increased incidence of CAP in the older population. Moreover, mortality in patients with CAP rises dramatically with increasing age. Streptococcus pneumoniae is still the most common pathogen among the elderly, although CAP may also be caused by drug-resistant microorganisms and aspiration pneumonia. Furthermore, in the elderly CAP has a different clinical presentation, often lacking the typical acute symptoms observed in younger adults, due to the lower local and systemic inflammatory response. Several independent prognostic factors for mortality in the elderly have been identified, including factors related to pneumonia severity, inadequate response to infection, and low functional status. CAP scores and biomarkers have lower prognostic value in the elderly, and so there is a need to find new scales or to set new cut-off points for current scores in this population. Adherence to the current guidelines for CAP has a significant beneficial impact on clinical outcomes in elderly patients. Particular attention should also be paid to nutritional status, fluid administration, functional status, and comorbidity stabilizing therapy in this group of frail patients. This article presents an up-to-date review of the main aspects of CAP in elderly patients, including epidemiology, causative organisms, clinical features, and prognosis, and assesses key points for best practices for the management of the disease.