Validation of a predictive rule for the management of community-acquired pneumonia

Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score

BACKGROUND

Pneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for prediction. We previously introduced the Blood Culture Prediction Index (BCPI) model, leveraging solely on complete blood count (CBC) and differential leukocyte count (DC), demonstrating its effectiveness in predicting bacteremia. Nevertheless, its potential in assessing pneumonia remains unexplored. Therefore, this study aims to compare the effectiveness of BCPI and CURB-65 in assessing pneumonia severity in an emergency department (ED) setting and develop an integrated ML model to enhance efficiency.

METHODS

This retrospective study was conducted at a 3400-bed tertiary medical center in Taiwan. Data from 9,352 patients with pneumonia in the ED between 2019 and 2021 were analyzed in this study. We utilized the BCPI model, which was trained on CBC/DC data, and computed CURB-65 scores for each patient to compare their prognosis prediction capabilities. Subsequently, we developed a novel Cox regression model to predict in-hospital mortality, integrating the BCPI model and CURB-65 scores, aiming to assess whether this integration enhances predictive performance.

RESULTS

The predictive performance of the BCPI model and CURB-65 score for the 30-day mortality rate in ED patients and the in-hospital mortality rate among admitted patients was comparable across all risk categories. However, the Cox regression model demonstrated an improved area under the ROC curve (AUC) of 0.713 than that of CURB-65 (0.668) for in-hospital mortality (p<0.001). In the lowest risk group (CURB-65=0), the Cox regression model outperformed CURB-65, with a significantly lower mortality rate (2.9% vs. 7.7%, p<0.001).

CONCLUSIONS

The BCPI model, constructed using CBC/DC data and ML techniques, performs comparably to the widely utilized CURB-65 in predicting outcomes for patients with pneumonia in the ED. Furthermore, by integrating the CURB-65 score and BCPI model into a Cox regression model, we demonstrated improved prediction capabilities, particularly for low-risk patients. Given its simple parameters and easy training process, the Cox regression model may be a more effective prediction tool for classifying patients with pneumonia in the emergency room.

Time to Clinical Stability in Children With Community-Acquired Pneumonia

BACKGROUND AND OBJECTIVES

Time to clinical stability (TCS) is a commonly used outcome in adults with community-acquired pneumonia (CAP), yet few studies have evaluated TCS in children. Our objective was to determine the association between TCS and disease severity in children with suspected CAP, as well as factors associated with reaching early stability.

METHODS

This is a prospective cohort study of children (aged 3 months to 18 years) hospitalized with suspected CAP. TCS parameters included temperature, heart rate, respiratory rate, and hypoxemia with the use of supplemental oxygen. TCS was defined as time from admission to parameter normalization. The association of TCS with severity and clinical factors associated with earlier TCS were evaluated.

RESULTS

Of 571 children, 187 (32.7%) had at least 1 abnormal parameter at discharge, and none had ≥3 abnormal discharge parameters. A greater proportion of infants (90 [93%]) had all 4 parameters stable at discharge compared with 12- to 18-year-old youths (21 [49%]). The median TCS for each parameter was <24 hours. Younger age, absence of vomiting, diffusely decreased breath sounds, and normal capillary refill were associated with earlier TCS. Children who did not reach stability were not more likely to revisit after discharge.

CONCLUSIONS

A TCS outcome consisting of physiologic variables may be useful for objectively assessing disease recovery and clinical readiness for discharge among children hospitalized with CAP. TCS may decrease length of stay if implemented to guide discharge decisions. Clinicians can consider factors associated with earlier TCS for management decisions.

Adiponectin as a predictor of mortality and readmission in patients with community-acquired pneumonia: a prospective cohort study

Background

Adiponectin is secreted by adipocytes and is inversely associated with obesity. Given the association between low body mass index (BMI) and higher mortality risk after community-acquired pneumonia (CAP), we hypothesized that high adiponectin levels are associated with a higher risk of adverse clinical outcomes in patients with CAP.

Methods

In a prospective cohort study of 502 patients hospitalized with CAP, adiponectin was measured in serum at admission. The associations between adiponectin and clinical outcomes were estimated with logistic regression analyses adjusted for age, sex, and measures of obesity (BMI, waist circumference or body fat percentage).

Results

Adiponectin was associated with higher 90-day mortality for each 1 μg/mL increase [OR 1.02, 95% CI (1.00, 1.04), p = 0.048] independent of age and sex. Likewise, adiponectin was associated with a higher risk of 90-day readmission for each 1 μg/mL increase [OR 1.02, 95% CI (1.01, 1.04), p = 0.007] independent of age and sex. The association between adiponectin and 90-day mortality disappeared, while the association with 90-day readmission remained after adjusting for adiposity.

Conclusion

Adiponectin was positively associated with mortality and readmission. The association with mortality depended on low body fat, whereas the association with readmission risk was independent of obesity.

The Role of Biomarkers in the Diagnosis and Management of Pneumonia

Biomarkers are used in the diagnosis, severity determination, and prognosis for patients with community-acquired pneumonia (CAP). Selected biomarkers may indicate a bacterial infection and need for antibiotic therapy (C-reactive protein, procalcitonin, soluble triggering receptor expressed on myeloid cells). Biomarkers can differentiate CAP patients who require hospital admission and severe CAP requiring intensive care unit admission. Biomarker-guided antibiotic therapy may limit antibiotic exposure without compromising outcome and thus improve antibiotic stewardship. The authors discuss the role of biomarkers in diagnosing, determining severity, defining the prognosis, and limiting antibiotic exposure in CAP and ventilator-associated pneumonia patients.

Elevated lactate dehydrogenase predicts pneumonia in spontaneous intracerebral hemorrhage

Background

Although a variety of risk factors for pneumonia after spontaneous intracerebral hemorrhage have been established, an objective and easily obtainable predictor is still needed. Lactate dehydrogenase is a nonspecific inflammatory biomarker. In this study, we aimed to assess the association between lactate dehydrogenase and pneumonia in spontaneous intracerebral hemorrhage patients.

Methods

Our study was a retrospective, multicenter cohort study, undertaken in 7562 patients diagnosed with spontaneous intracerebral hemorrhage from 3 hospitals. All serum Lactate dehydrogenase was collected within 7 days from admission and divided into four groups as quartile(Q). We conducted a multivariable logistic regression analysis to assess the association of Lactate dehydrogenase with pneumonia.

Results

Among a total of 7562 patients, 2971 (39.3%) patients were diagnosed with pneumonia. All grades of elevated lactate dehydrogenase were associated with increased raw and risk-adjusted risk of pneumonia. Multiple logistic regression analysis showed odds ratios for Q2-Q4 compared with Q1 were 1.21 (95% CI, 1.04-1.42), 1.64(95% CI, 1.41-1.92), and 1.92 (95% CI, 1.63-2.25) respectively. The odds ratio after adjustment was 4.42 (95% CI, 2.94-6.64) when lactate dehydrogenase was a continuous variable after log-transformed.

Conclusions

Elevated lactate dehydrogenase is significantly associated with an increase in the odds of pneumonia and has a predictive value for severe pneumonia in patients with pneumonia. Lactate dehydrogenase may be used to predict pneumonia events in spontaneous intracerebral hemorrhage patients as a laboratory marker.

The Battle of the Pneumonia Predictors: A Comprehensive Meta-Analysis Comparing the Pneumonia Severity Index (PSI) and the CURB-65 Score in Predicting Mortality and the Need for ICU Support

The CURB-65 (confusion, uremia, respiratory rate, blood pressure, age ≥ 65 years) score and the pneumonia severity index (PSI) are widely used and recommended in predicting 30-day mortality and the need for intensive care support in community-acquired pneumonia. This study aims to compare the performance of these two severity scores in both mortality prediction and the need for intensive care support. A systematic review and meta-analysis was carried out, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines, and PubMed, Scopus, ScienceDirect, and Google Scholar were searched for articles published from 2012 to 2022. The reference lists of the included studies were also searched to retrieve possible additional studies. Twenty-five studies reporting prognostic information for CURB 65 and PSI were identified. ReviewManager (RevMan) 5.4.1 was used to produce risk ratios, and a random effects model was used to pool them. Both PSI and CURB-65 showed a high strength in identifying high-risk patients. However, CURB-65 was slightly better in early mortality prediction and had more sensitivity (96.7%) and specificity (89.3%) in predicting admission to intensive care support. Thus, CURB-65 seems to be the preferred tool in predicting mortality and the need for admission into intensive care support.

Multicentre study on the accuracy of lung ultrasound in the diagnosis and monitoring of respiratory sequelae in the medium and long term in patients with COVID-19

Introduction

Lung ultrasound (LUS) has proven to be a more sensitive tool than radiography (X-ray) to detect alveolar-interstitial involvement in COVID-19 pneumonia. However, its usefulness in the detection of possible pulmonary alterations after overcoming the acute phase of COVID-19 is unknown. In this study we proposed studying the utility of LUS in the medium- and long-term follow-up of a cohort of patients hospitalized with COVID-19 pneumonia.

Materials and methods

This was a prospective, multicentre study that included patients, aged over 18 years, at 3 ± 1 and 12 ± 1 months after discharge after treatment for COVID-19 pneumonia. Demographic variables, the disease severity, and analytical, radiographic, and functional clinical details were collected. LUS was performed at each visit and 14 areas were evaluated and classified with a scoring system whose global sum was referred to as the "lung score." Two-dimensional shear wave elastography (2D-SWE) was performed in 2 anterior areas and in 2 posterior areas in a subgroup of patients. The results were compared with high-resolution computed tomography (CT) images reported by an expert radiologist.

Results

A total of 233 patients were included, of whom 76 (32.6%) required Intensive Care Unit (ICU) admission; 58 (24.9%) of them were intubated and non-invasive respiratory support was also necessary in 58 cases (24.9%). Compared with the results from CT images, when performed in the medium term, LUS showed a sensitivity (S) of 89.7%, specificity (E) 50%, and an area under the curve (AUC) of 78.8%, while the diagnostic usefulness of X-ray showed an S of 78% and E of 47%. Most of the patients improved in the long-term evaluation, with LUS showing an efficacy with an S of 76% and E of 74%, while the X-ray presented an S of 71% and E of 50%. 2D-SWE data were available in 108 (61.7%) patients, in whom we found a non-significant tendency toward the presentation of a higher shear wave velocity among those who developed interstitial alterations, with a median kPa of 22.76 ± 15.49) versus 19.45 ± 11.39; p = 0.1).

Conclusion

Lung ultrasound could be implemented as a first-line procedure in the evaluation of interstitial lung sequelae after COVID-19 pneumonia.

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Background

The aim of our study was to externally validate the predictive capability of five developed coronavirus disease 2019 (COVID-19)-specific prognostic tools, including the COVID-19 Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), Shang COVID severity score, COVID-intubation risk score-neutrophil/lymphocyte ratio (IRS-NLR), inflammation-based score, and ventilation in COVID estimator (VICE) score.

Methods

The medical records of all patients hospitalized for a laboratory-confirmed COVID-19 diagnosis between May 2021 and June 2021 were retrospectively analyzed. Data were extracted within the first 24 h of admission, and five different scores were calculated. The primary and secondary outcomes were 30-day mortality and mechanical ventilation, respectively.

Results

A total of 285 patients were enrolled in our cohort. Sixty-five patients (22.8%) were intubated with ventilator support, and the 30-day mortality rate was 8.8%. The Shang COVID severity score had the highest numerical area under the receiver operator characteristic (AUC-ROC) (AUC 0.836) curve to predict 30-day mortality, followed by the SEIMC score (AUC 0.807) and VICE score (AUC 0.804). For intubation, both the VICE and COVID-IRS-NLR scores had the highest AUC (AUC 0.82) compared to the inflammation-based score (AUC 0.69). The 30-day mortality increased steadily according to higher Shang COVID severity scores and SEIMC scores. The intubation rate exceeded 50% in the patients stratified by higher VICE scores and COVID-IRS-NLR score quintiles.

Conclusion

The discriminative performances of the SEIMC score and Shang COVID severity score are good for predicting the 30-day mortality of hospitalized COVID-19 patients. The COVID-IRS-NLR and VICE showed good performance for predicting invasive mechanical ventilation (IMV).

How and when to manage respiratory infections out of hospital

Lower respiratory infections include acute bronchitis, influenza, community-acquired pneumonia, acute exacerbation of COPD and acute exacerbation of bronchiectasis. They are a major cause of death worldwide and often affect the most vulnerable: children, elderly and the impoverished. In this paper, we review the clinical presentation, diagnosis, severity assessment and treatment of adult outpatients with lower respiratory infections. The paper is divided into sections on specific lower respiratory infections, but we also dedicate a section to COVID-19 given the importance of the ongoing pandemic. Lower respiratory infections are heterogeneous entities, carry different risks for adverse events, and require different management strategies. For instance, while patients with acute bronchitis are rarely admitted to hospital and generally do not require antimicrobials, approximately 40% of patients seen for community-acquired pneumonia require admission. Clinicians caring for patients with lower respiratory infections face several challenges, including an increasing population of patients with immunosuppression, potential need for diagnostic tests that may not be readily available, antibiotic resistance and social aspects that place these patients at higher risk. Management principles for patients with lower respiratory infections include knowledge of local surveillance data, strategic use of diagnostic tests according to surveillance data, and judicious use of antimicrobials.

Comparison of CURB-65 Pneumonia Severity Score, Quick COVID-19 Severity Index, and Brescia-COVID Respiratory Severity Scale in Emergently Hospitalized COVID-19 Patients with Pneumonia

Objective

This study aimed to assess the performance of the CURB-65, the quick COVID-19 severity index (qCSI), and the Brescia-COVID respiratory severity scale (BCRSS) scores in predicting ICU (intensive care unit) hospitalization and in-hospital mortality in emergently hospitalized patients with COVID-19 pneumonia.

Materials and Methods

We retrospectively reviewed the emergently hospitalized 258 patients with COVID-19 pneumonia consecutively. The required sample size was calculated to compare the areas under the two ROC (receiver operating characteristic) curves (AUC) using the MedCalc 20.0 program (MedCalc Software Ltd., Ostend, Belgium). In addition, we actualized ROC analyses of the CURB-65, the qCSI, and the BCRSS scores and compared the ROC curves of these three scores.

Results

The median age of the patients was 73, and 63.6% (n=164) were male. Of 258 patients, 29.5% (n=76) were hospitalized in the intensive care unit (ICU), and 15.9% (n=41) died. The CURB-65 and the qCSI scores predicted ICU admission at a moderate level (p≤0.001; AUC values were 0.743 and 0.723, respectively). However, the predictive effect of the BCRSS score for ICU admission was lower (p≤0.001; AUC value was 0.667). The CURB-65 predicted in-hospital mortality at a moderate level ( p≤0.001; AUC value was 0.762). However, the predictive effect of the qCSI and the BCRSS scores for in-hospital mortality were lower ( p≤0.001 and p=0.012, respectively; AUC values were 0.655 and 0.612, respectively).

Conclusion

The CURB-65 score predicted ICU hospitalization and in-hospital mortality better than the qCSI and the BCRSS scores. Also, the qCSI score predicted ICU admission better than the BCRSS score.The predictive effect of the BCRSS score was the lowest. We recommend future studies to evaluate the value and utility of COVID-19 risk classification models.

Using ISARIC 4C mortality score to predict dynamic changes in mortality risk in COVID-19 patients during hospital admission

As SARS-CoV-2 infections continue to cause hospital admissions around the world, there is a continued need to accurately assess those at highest risk of death to guide resource use and clinical management. The ISARIC 4C mortality score provides mortality risk prediction at admission to hospital based on demographic and physiological parameters. Here we evaluate dynamic use of the 4C score at different points following admission. Score components were extracted for 6,373 patients admitted to Barts Health NHS Trust hospitals between 1st August 2020 and 19th July 2021 and total score calculated every 48 hours for 28 days. Area under the receiver operating characteristic (AUC) statistics were used to evaluate discrimination of the score at admission and subsequent inpatient days. Patients who were still in hospital at day 6 were more likely to die if they had a higher score at day 6 than others also still in hospital who had the same score at admission. Discrimination of dynamic scoring in those still in hospital was superior with the area under the curve 0.71 (95% CI 0.69-0.74) at admission and 0.82 (0.80-0.85) by day 8. Clinically useful changes in the dynamic parts of the score are unlikely to be associated with subject-level measurements. Dynamic use of the ISARIC 4C score is likely to provide accurate and timely information on mortality risk during a patient's hospital admission.

A framework for evaluating predictive models

Predictive models provide estimates on an individual's probability of having a disease or developing a disease/disease outcome. Clinicians often use them to support clinical decision-making. Many prediction models are published annually; online versions of models (such as MDCalc and QxMD) facilitate their use at the point of care. However, before using a model, the clinician should first establish that the model has undergone external validation demonstrating satisfactory predictive performance. Ideally, the model should also demonstrate improved outcomes from an impact analysis. This article summarizes the basic steps of predictive model evaluation, and is followed by an application example.

The Use of the SMART-COP Score in Predicting Severity Outcomes Among Patients With Community-Acquired Pneumonia: A Meta-Analysis

Pneumonia is a pathological process of interstitial lung tissue and distal airway and alveolar infection and infiltration. SMART-COP (systolic blood pressure, multilobar infiltrates, albumin, respiratory rate, tachycardia, confusion, oxygen, and pH) is a severity score method designed to identify individuals who require intensive respiratory or vasopressor support (IRVS) support due to pneumonia. Therefore, it is important for management decisions in pneumonia. This meta-analysis was conducted to determine the performance of the SMART-COP score in predicting the prognosis and severity of patients presenting with community-acquired pneumonia (CAP). The current meta-analysis was performed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search was conducted using Medline, Embase, and CINAHL to identify relevant studies assessing the validity of the SMART-COP score in predicting the severity of patients with CAP. Overall, nine studies were included in the current meta-analysis. A pooled sensitivity of the SMART-COP score to predict the use of IRVS is 89% (95% CI: 84%-92%) while its specificity is 68% (95% CI: 65%-70%). The pooled sensitivity of the SMART-COP score to predict 30-day mortality is 92% (95% CI: 89%-94%) while its specificity is 39% (95% CI: 37%-42%). To summarize, SMART-COP is a new, eight-variable instrument that appears to accurately identify patients with CAP who will require IRVS and 30-day mortality. Our findings show that SMART-COP will be a valuable tool for clinicians in accurately predicting illness severity in CAP patients as compared to other scoring systems. SMART-COP can be useful to identify patients who need urgent management.

Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia

Background: The ability to assess adverse outcomes in patients with community-acquired pneumonia (CAP) could improve clinical decision-making to enhance clinical practice, but the studies remain insufficient, and similarly, few machine learning (ML) models have been developed.

Objective: We aimed to explore the effectiveness of predicting adverse outcomes in CAP through ML models.

Methods: A total of 2,302 adults with CAP who were prospectively recruited between January 2012 and March 2015 across three cities in South America were extracted from DryadData. After a 70:30 training set: test set split of the data, nine ML algorithms were executed and their diagnostic accuracy was measured mainly by the area under the curve (AUC). The nine ML algorithms included decision trees, random forests, extreme gradient boosting (XGBoost), support vector machines, Naïve Bayes, K-nearest neighbors, ridge regression, logistic regression without regularization, and neural networks. The adverse outcomes included hospital admission, mortality, ICU admission, and one-year post-enrollment status.

Results: The XGBoost algorithm had the best performance in predicting hospital admission. Its AUC reached 0.921, and accuracy, precision, recall, and F1-score were better than those of other models. In the prediction of ICU admission, a model trained with the XGBoost algorithm showed the best performance with AUC 0.801. XGBoost algorithm also did a good job at predicting one-year post-enrollment status. The results of AUC, accuracy, precision, recall, and F1-score indicated the algorithm had high accuracy and precision. In addition, the best performance was seen by the neural network algorithm when predicting death (AUC 0.831).

Conclusions: ML algorithms, particularly the XGBoost algorithm, were feasible and effective in predicting adverse outcomes of CAP patients. The ML models based on available common clinical features had great potential to guide individual treatment and subsequent clinical decisions.

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.

The pneumonia severity index: Assessment and comparison to popular machine learning classifiers

INTRODUCTION

Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size.

METHODS

In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34,720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC).

RESULTS

Machine learning classifiers, such as Random Forest, provided a statistically highly(p < 0.001) significant improvement (∼33% in PR AUC and ∼6% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI).

DISCUSSION

Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.

Comparison between the Severity Scoring Systems A-DROP and CURB-65 for Predicting Safe Discharge from the Emergency Department in Patients with Community-Acquired Pneumonia

Background

In most community-acquired pneumonia (CAP) treatment guidelines, the Pneumonia Severity Index (PSI) and CURB-65 are used as prognostic tools. Recently, simpler and more effective predictive tools for CAP treatment, such as the A-DROP scoring system, have been developed. However, no study has performed a comparative evaluation to identify the superior tool for predicting when patients can be discharged safely.

Objectives

To compare the performances of A-DROP and CURB-65, simple predictive tools for CAP, based on 30-day death rates and 72-hour revisit rates for CAP following discharge from the emergency department (ED).

Method

This single-center retrospective observational study enrolled patients who were at least 18 years old and diagnosed with CAP at the Songklanagarind Hospital ED from January 2015 to April 2021. Following a severity assessment using the A-DROP and CURB-65 scoring systems, the 30-day mortality rates and 72-hour revisit rates after discharge from the ED were compared.

Results

A total of 408 patients were enrolled in this study. Six (1.47%) died within 30 days after presentation, whereas 29 (7.1%) returned to the ED within 72 hours after discharge. Most patients (72%) who revisited the ED were over the age of 65 years. The areas under the receiver operating characteristic curves for the prediction of 30-day mortality were 0.756 (95% confidence interval [CI]: 0.526–0.987) and 0.808 (95% CI: 0.647–0.970) for A-DROP and CURB-65, respectively. The areas under the receiver operating characteristic curves for the prediction of 72-hour revisit were 0.617 (95% confidence interval [CI]: 0.507–0.728) and 0.639 (95% CI: 0.536–0.743) for A-DROP and CURB-65, respectively.

Conclusion

A-DROP and CURB-65 yield similar results and can be used to assess low-risk patients with CAP for discharge from the ED. Older patients, even those with low-risk scores, should be particularly considered for admission to a short-term observation unit or ward.

Title: Effects of Antibiotics on Respiratory Symptoms in Terminally Ill Cancer Patients With Pneumonia: A Multicenter Cohort Study

CONTEXT

Patients in the terminal stages of cancer are frequently affected by infection, especially pneumonia; but the effects of antibiotics on respiratory symptoms and factors associated with improvement are still unclear.

OBJECTIVES

This study aimed to clarify the effects of antibiotics on respiratory symptoms of terminally ill cancer patients with pneumonia, and to explore factors associated with the improvement.

METHODS

This was a prospective cohort study in which we consecutively recruited terminally ill cancer patients diagnosed with pneumonia and treated with antibiotics at one of 23 palliative care units across Japan. At the baseline and Day 3, primarily responsible palliative care specialists recorded patient backgrounds, the results of physical and laboratory examination, and patient symptoms using the Support Team Assessment Schedule. Improvement was defined as improvement of dyspnea, cough, and sputum production on Day 3.

RESULTS

Among all 1896 patients admitted during the study periods, 137 patients (7.2%) were enrolled into this study. Improvement was achieved in 65 patients (47.4%; 95% confidence intervals, 39-56). Univariate analyses revealed that the Palliative Prognostic Index (PPI), respiratory rate (RR), and oxygen requirement were significantly associated with the improvement. A multiple logistic regression analysis identified that PPI score of ≤ 6 and RR of <20 were independently associated with the improvement (odds ratios, 4.4 [1.6-12] and 2.5 [1.1-5.5], respectively).

CONCLUSION

Antibiotics may relieve respiratory symptoms from pneumonia in approximately half of the terminally ill cancer patients. PPI and respiratory rate are useful to identify the patients who received benefits of antibiotics.

Prognostic Value of Histidine-Rich Glycoprotein for Community-Acquired Pneumonia

Purpose

Histidine-rich glycoprotein (HRG) is abundant in serum and has been implicated in several processes including blood coagulation and immune response. This prospective study is aimed at exploring HRG as a biomarker in patients hospitalized for community-acquired pneumonia (CAP).

Methods

A total of 160 patients (73 severe CAP, 57 nonsevere CAP), and 30 healthy controls were enrolled in 2019. Demographic and clinical data were recorded for all patients. Serum HRG concentration was measured upon admission using ELISA.

Results

HRG levels were significantly lower in severe CAP patients compared with other groups, regardless of etiology, and were negatively correlated with serum interleukin-6 and disease severity index scores. Combination of CURB-65, PSI, and APACHE II scores with HRG values significantly improved the accuracy of predicting 30-day mortality in these patients. Cox regression analysis showed that HRG could serve as an independent risk factor for 30-day mortality. Notably, patients with HRG ≤ 16.92 μg/mL had significantly lower cumulative survival than those with HRG > 16.92 μg/mL.

Conclusion

Serum HRG levels are lower in patients with severe CAP and are negatively correlated with disease severity scores. Measurement of HRG upon admission can provide valuable prognostic information for patients with CAP.

The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study

BACKGROUND

The current COVID-19 pandemic is unprecedented; under resource-constrained settings, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients; however, there are only few risk scores derived from a substantially large electronic health record (EHR) data set, using simplified predictors as input.

OBJECTIVE

The objectives of this study were to develop and validate simplified machine learning algorithms that predict COVID-19 adverse outcomes; to evaluate the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration of the algorithms; and to derive clinically meaningful thresholds.

METHODS

We performed machine learning model development and validation via a cohort study using multicenter, patient-level, longitudinal EHRs from the Optum COVID-19 database that provides anonymized, longitudinal EHR from across the United States. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, intensive care unit (ICU) admission, respiratory failure, and mechanical ventilator usages at inpatient setting. Data from patients who were admitted from February 1, 2020, to September 7, 2020, were randomly sampled into development, validation, and test data sets; data collected from September 7, 2020, to November 15, 2020, were reserved as the postdevelopment prospective test data set.

RESULTS

Of the 3.7 million patients in the analysis, 585,867 patients were diagnosed or tested positive for SARS-CoV-2, and 50,703 adult patients were hospitalized with COVID-19 between February 1 and November 15, 2020. Among the study cohort (n=50,703), there were 6204 deaths, 9564 ICU admissions, 6478 mechanically ventilated or EMCO patients, and 25,169 patients developed acute respiratory distress syndrome or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC 0.89, 95% CI 0.89-0.89 on the test data set [n=10,752]), consistent prediction through the second wave of the pandemic from September to November (AUC 0.85, 95% CI 0.85-0.86) on the postdevelopment prospective test data set [n=14,863], great clinical relevance, and utility. Besides, a comprehensive set of 386 input covariates from baseline or at admission were included in the analysis; the end-to-end pipeline automates feature selection and model development. The parsimonious model with only 10 input predictors produced comparably accurate predictions; these 10 predictors (age, blood urea nitrogen, SpO₂, systolic and diastolic blood pressures, respiration rate, pulse, temperature, albumin, and major cognitive disorder excluding stroke) are commonly measured and concordant with recognized risk factors for COVID-19.

CONCLUSIONS

The systematic approach and rigorous validation demonstrate consistent model performance to predict even beyond the period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated, and reliable prediction model based on only 10 clinical features as a prognostic tool to stratifying patients with COVID-19 into intermediate-, high-, and very high-risk groups. This simple predictive tool is shared with a wider health care community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize health care resources.

Significance of the Modified NUTRIC Score for Predicting Clinical Outcomes in Patients with Severe Community-Acquired Pneumonia

Nutritional status could affect clinical outcomes in critical patients. We aimed to determine the prognostic accuracy of the modified Nutrition Risk in Critically Ill (mNUTRIC) score for hospital mortality and treatment outcomes in patients with severe community-acquired pneumonia (SCAP) compared to other clinical prediction rules. We enrolled SCAP patients in a multi-center setting retrospectively. The mNUTRIC score and clinical prediction rules for pneumonia, as well as clinical factors, were calculated and recorded. Clinical outcomes, including mortality status and treatment outcome, were assessed after the patient was discharged. We used the receiver operating characteristic (ROC) curve method and multivariate logistic regression analysis to determine the prognostic accuracy of the mNUTRIC score for predicting clinical outcomes compared to clinical prediction rules, while 815 SCAP patients were enrolled. ROC curve analysis showed that the mNUTRIC score was the most effective at predicting each clinical outcome and had the highest area under the ROC curve value. The cut-off value for predicting clinical outcomes was 5.5. By multivariate logistic regression analysis, the mNUTRIC score was also an independent predictor of both clinical outcomes in SCAP patients. We concluded that the mNUTRIC score is a better prognostic factor for predicting clinical outcomes in SCAP patients compared to other clinical prediction rules.

Development and validation of a new scoring system for prognostic prediction of community-acquired pneumonia in older adults

The discriminative power of CURB-65 for mortality in community-acquired pneumonia (CAP) is suspected to decrease with age. However, a useful prognostic prediction model for older patients with CAP has not been established. This study aimed to develop and validate a new scoring system for predicting mortality in older patients with CAP. We recruited two prospective cohorts including patients aged ≥ 65 years and hospitalized with CAP. In the derivation (n = 872) and validation cohorts (n = 1,158), the average age was 82.0 and 80.6 years and the 30-day mortality rate was 7.6% (n = 66) and 7.4% (n = 86), respectively. A new scoring system was developed based on factors associated with 30-day mortality, identified by multivariate analysis in the derivation cohort. This scoring system named CHUBA comprised five variables: confusion, hypoxemia (SpO₂ ≤ 90% or PaO₂ ≤ 60 mmHg), blood urea nitrogen ≥ 30 mg/dL, bedridden state, and serum albumin level ≤ 3.0 g/dL. With regard to 30-day mortality, the area under the receiver operating characteristic curve for CURB-65 and CHUBA was 0.672 (95% confidence interval, 0.607–0.732) and 0.809 (95% confidence interval, 0.751–0.856; P < 0.001), respectively. The effectiveness of CHUBA was statistically confirmed in the external validation cohort. In conclusion, a simpler novel scoring system, CHUBA, was established for predicting mortality in older patients with CAP.

A literature review of severity scores for adults with influenza or community-acquired pneumonia - implications for influenza vaccines and therapeutics

Influenza vaccination and antiviral therapeutics may attenuate disease, decreasing severity of illness in vaccinated and treated persons. Standardized assessment tools, definitions of disease severity, and clinical endpoints would support characterizing the attenuating effects of influenza vaccines and antivirals. We review potential clinical parameters and endpoints that may be useful for ordinal scales evaluating attenuating effects of influenza vaccines and antivirals in hospital-based studies. In studies of influenza and community-acquired pneumonia, common physiologic parameters that predicted outcomes such as mortality, ICU admission, complications, and duration of stay included vital signs (hypotension, tachypnea, fever, hypoxia), laboratory results (blood urea nitrogen, platelets, serum sodium), and radiographic findings of infiltrates or effusions. Ordinal scales based on these parameters may be useful endpoints for evaluating attenuating effects of influenza vaccines and therapeutics. Factors such as clinical and policy relevance, reproducibility, and specificity of measurements should be considered when creating a standardized ordinal scale for assessment.

Prehospital hypoxemia, measured by pulse oximetry, predicts hospital outcomes during the New York City COVID-19 pandemic

OBJECTIVE

To determine if oxygen saturation (out-of-hospital SpO2), measured by New York City (NYC) 9-1-1 Emergency Medical Services (EMS), was an independent predictor of coronavirus disease 2019 (COVID-19) in-hospital mortality and length of stay, after controlling for the competing risk of death. If so, out-of-hospital SpO2 could be useful for initial triage.

METHODS

A population-based longitudinal study of adult patients transported by EMS to emergency departments (ED) between March 5 and April 30, 2020 (the NYC COVID-19 peak period). Inclusion required EMS prehospital SpO2 measurement while breathing room air, transport to emergency department, and a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription polymerase chain reaction test. Multivariable logistic regression modeled mortality as a function of prehospital SpO2, controlling for covariates (age, sex, race/ethnicity, and comorbidities). A competing risk model also was performed to estimate the absolute risks of out-of-hospital SpO2 on the cumulative incidence of being discharged from the hospital alive.

RESULTS

In 1673 patients, out-of-hospital SpO2 and age were independent predictors of in-hospital mortality and length of stay, after controlling for the competing risk of death. Among patients ≥66 years old, the probability of death was 26% with an out-of-hospital SpO2 >90% versus 54% with an out-of-hospital SpO2 ≤90%. Among patients <66 years old, the probability of death was 11.5% with an out-of-hospital SpO2 >90% versus 31% with an out-of-hospital SpO2 ≤ 90%. An out-of-hospital SpO2 level ≤90% was associated with over 50% decreased likelihood of being discharged alive, regardless of age.

CONCLUSIONS

Out-of-hospital SpO2 and age predicted in-hospital mortality and length of stay: An out-of-hospital SpO2 ≤90% strongly supports a triage decision for immediate hospital admission. For out-of-hospital SpO2 >90%, the decision to admit depends on multiple factors, including age, resource availability (outpatient vs inpatient), and the potential impact of new treatments.

[Pneumonia in old age]

Die Pneumonie ist eine bei alten Menschen häufige und schwere Erkrankung. Sie steht in dieser Patientengruppe an vierter Stelle der zum Tode führenden Erkrankungen. Die Diagnose kann oft aufgrund einer atypischen klinischen Präsentation schwierig sein. Daher sollte bei jeder Verschlechterung eines alten Menschen ursächlich auch an eine Pneumonie gedacht werden. Geriatrische Probleme wie Gebrechlichkeit und physische und psychische Einschränkungen sollten ebenso erfasst werden wie die soziale Situation, da alle diese Faktoren prognoserelevant sind. Prognostisch ungünstiger verlaufen Pneumonien, die im Pflegeheim oder von Pflegebedürftigen erworben wurden. Sie gelten zwar als ambulant erworben, sollten dennoch besonders beachtet werden. Die Behandlung unterscheidet sich nicht grundsätzlich von der Behandlung jüngerer Patient, sollte aber besondere Situationen wie den in einer Patientenverfügung festgelegten Patientenwunsch bei der Therapieplanung berücksichtigen. Gerade ältere Menschen zeigen unter einer COVID(coronavirus disease)-19-Infektion oft atypische klinische Bilder, sodass bei akuten Veränderungen im Alter auch daran zu denken ist.

The prognostic value of serum albumin levels and respiratory rate for community-acquired pneumonia: A prospective, multi-center study

Community-acquired pneumonia (CAP) is a respiratory disease frequently requiring hospital admission, and a significant cause of death worldwide. This study aimed to investigate the prognostic value of clinical indicators. A prospective, multi-center study was conducted (January 2017-December 2018) where patient demographic and clinical data were recorded (N = 366). The 30-day mortality rate was 5.46%. Cox Regression analyses showed that serum albumin (ALB) and respiratory rate (RR) were independent prognostic variables for 30-day survival in patients with CAP. Albumin negatively correlated with the Pneumonia Severity Index (PSI) and CURB-65 scores using Pearson and Spearman tests. Survival curves showed that a RR >24 breaths/min or ALB ≤30 g/L were associated with a significantly higher risk of mortality. The area-under-the-curve (AUC) for predicting 30-day mortality in patients with CAP was 0.762, 0.763, 0.790, and 0.784 for ALB, RR, PSI, and CURB-65, respectively. The AUC for the prediction of 30-day mortality using ALB combined with PSI, CURB-65 scores, and RR was 0.822 (95% CI 0.731-0.912), 0.847 (95% CI 0.755-0.938), and 0.847 (95% CI 0.738-0.955), respectively. Albumin and RR were found to be reliable prognostic factors for CAP. This combination showed equal predictive value when compared to adding ALB assessment to PSI and CURB-65 scores, which could improve their prognostic accuracy.

Validation of pneumonia prognostic scores in a statewide cohort of hospitalised patients with COVID-19

OBJECTIVE

We aimed to externally validate the predictive performance of two recently developed COVID-19-specific prognostic tools, the COVID-GRAM and CALL scores, and prior prognostic scores for community-acquired pneumonia (CURB-65), viral pneumonia (MuBLSTA) and H1N1 influenza pneumonia (Influenza risk score) in a contemporary US cohort.

METHODS

We included 257 hospitalised patients with laboratory-confirmed COVID-19 pneumonia from three teaching hospitals in Rhode Island. We extracted data from within the first 24 hours of admission. Variables were excluded if values were missing in >20% of cases, otherwise, missing values were imputed. One hundred and fifteen patients with complete data after imputation were used for the primary analysis. Sensitivity analysis was performed after the exclusion of one variable (LDH) in the complete dataset (n = 257). Primary and secondary outcomes were in-hospital mortality and critical illness (mechanical ventilation or death), respectively.

RESULTS

Only the areas under the receiver-operating characteristic curves (RO-AUC) of COVID-GRAM (RO-AUC = 0.775, 95% CI 0.525-0.915) for in-hospital death, and CURB65 for in-hospital death (RO-AUC = 0.842, 95% CI 0.674-0.932) or critical illness (RO-AUC = 0.766, 95% CI 0.584-0.884) were significantly better than random. Sensitivity analysis yielded similar trends. Calibration plots showed better agreement between the estimated and observed probability of in-hospital death for CURB65, compared with COVID-GRAM. The negative predictive value (NPV) of CURB65 ≥2 was 97.2% for in-hospital death and 88.1% for critical illness.

CONCLUSIONS

The COVID-GRAM score demonstrated acceptable predictive performance for in-hospital death. The CURB65 score had better prognostic utility for in-hospital death and critical illness. The high NPV of CURB65 values ≥2 may be useful in triaging and allocation of resources.

The Neutrophil-to-Lymphocyte Ratio Determines Clinical Efficacy of Corticosteroid Therapy in Patients with COVID-19

Corticosteroid therapy is now recommended as a treatment in patients with severe COVID-19. But one key question is how to objectively identify severely ill patients who may benefit from such therapy. Here, we assigned 12,862 COVID-19 cases from 21 hospitals in Hubei Province equally to a training and a validation cohort. We found that a neutrophil-to-lymphocyte ratio (NLR) > 6.11 at admission discriminated a higher risk for mortality. Importantly, however, corticosteroid treatment in such individuals was associated with a lower risk of 60-day all-cause mortality. Conversely, in individuals with an NLR ≤ 6.11 or with type 2 diabetes, corticosteroid treatment was not associated with reduced mortality, but rather increased risks of hyperglycemia and infections. These results show that in the studied cohort corticosteroid treatment is associated with beneficial outcomes in a subset of COVID-19 patients who are non-diabetic and with severe symptoms as defined by NLR.

Clinical Policy: Critical Issues in the Management of Adult Patients Presenting to the Emergency Department With Community-Acquired Pneumonia

This clinical policy from the American College of Emergency Physicians is a revision of the 2009 "Clinical Policy: Critical Issues in the Management of Adult Patients Presenting to the Emergency Department With Community-Acquired Pneumonia." A writing subcommittee conducted a systematic review of the literature to derive evidence-based recommendations to answer the following clinical questions: (1) In the adult emergency department patient diagnosed with community-acquired pneumonia, what clinical decision aids can inform the determination of patient disposition? (2) In the adult emergency department patient with community-acquired pneumonia, what biomarkers can be used to direct initial antimicrobial therapy? (3) In the adult emergency department patient diagnosed with community-acquired pneumonia, does a single dose of parenteral antibiotics in the emergency department followed by oral treatment versus oral treatment alone improve outcomes? Evidence was graded and recommendations were made based on the strength of the available data.

Performance of the quick COVID-19 severity index and the Brescia-COVID respiratory severity scale in hospitalized patients with COVID-19 in a community hospital setting

OBJECTIVE

To evaluate the performance of the Quick COVID-19 Severity Index (qCSI) and the Brescia-COVID Respiratory Severity Scale (BCRSS) in predicting intensive care unit (ICU) admissions and in-hospital mortality in patients with coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

This was a retrospective cohort study of 313 consecutive hospitalized adult patients (18 years or older) with confirmed COVID-19. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminatory power of the qCSI score and BCRSS prediction rule compared to the CURB-65 score for predicting mortality and intensive care unit admission.

RESULTS

The overall in-hospital fatality rate was 32.3%, and the ICU admission rate was 31.3%. The CURB-65 score had the highest numerical AUC to predict in-hospital mortality (AUC 0.781) compared to the qCSI score (AUC 0.711) and the BCRSS prediction rule (AUC 0.663). For ICU admission, the qCSI score had the highest numerical AUC (AUC 0.761) compared to the BCRSS prediction rule (AUC 0.735) and the CURB-65 score (AUC 0.629).

CONCLUSIONS

The CURB-65 and qCSI scoring systems showed a good performance for predicting in-hospital mortality. The qCSI score and the BCRSS prediction rule showed a good performance for predicting ICU admission.

Utility of established prognostic scores in COVID-19 hospital admissions: multicentre prospective evaluation of CURB-65, NEWS2 and qSOFA

Introduction

The COVID-19 pandemic is ongoing, yet, due to the lack of a COVID-19-specific tool, clinicians must use pre-existing illness severity scores for initial prognostication. However, the validity of such scores in COVID-19 is unknown.

Methods

The North West Collaborative Organisation for Respiratory Research performed a multicentre prospective evaluation of adult patients admitted to the hospital with confirmed COVID-19 during a 2-week period in April 2020. Clinical variables measured as part of usual care at presentation to the hospital were recorded, including the Confusion, Urea, Respiratory Rate, Blood Pressure and Age Above or Below 65 Years (CURB-65), National Early Warning Score 2 (NEWS2) and Quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) scores. The primary outcome of interest was 30-day mortality.

Results

Data were collected for 830 people with COVID-19 admitted across seven hospitals. By 30 days, a total of 300 (36.1%) had died and 142 (17.1%) had been in the intensive care unit. All scores underestimated mortality compared with pre-COVID-19 cohorts, and overall prognostic performance was generally poor. Among the ‘low-risk’ categories (CURB-65 score<2, NEWS2<5 and qSOFA score<2), 30-day mortality was 16.7%, 32.9% and 21.4%, respectively. NEWS2≥5 had a negative predictive value of 98% for early mortality. Multivariable logistic regression identified features of respiratory compromise rather than circulatory collapse as most relevant prognostic variables.

Conclusion

In the setting of COVID-19, existing prognostic scores underestimated risk. The design of new prognostic tools should focus on features of respiratory compromise rather than circulatory collapse. We provide a baseline set of variables which are relevant to COVID-19 outcomes and may be used as a basis for developing a bespoke COVID-19 prognostication tool.

Comparison of clinical characteristics between coronavirus disease 2019 pneumonia and community-acquired pneumonia

OBJECTIVE

Coronavirus disease 2019 (COVID-19) has high morbidity and mortality, and spreads rapidly in the community to result in a large number of infection cases. This study aimed to compare clinical features in adult patients with coronavirus disease 2019 (COVID-19) pneumonia to those in adult patients with community-acquired pneumonia (CAP).

METHODS

Clinical presentations, laboratory findings, imaging features, complications, treatment and outcomes were compared between patients with COVID-19 pneumonia and patients with CAP. The study group of patients with COVID-19 pneumonia consisted of 120 patients. One hundred and thirty-four patients with CAP were enrolled for comparison.

RESULTS

Patients with COVID-19 pneumonia had lower levels of abnormal laboratory parameters (white blood cell count, lymphocyte count, procalcitonin level, erythrocyte sedimentation rate and C-reactive protein level) and more extensive radiographic involvement. More severe respiratory compromise resulted in a higher rate of intensive care unit admission, acute respiratory distress syndrome (ARDS) and mechanical ventilation (36% vs 15%, 34% vs 15% and 32% vs 12%, respectively; all p < .05). The 30 day mortality was more than twice as high in patients with COVID-19 pneumonia (12% versus 5%; p = .063), despite not reaching a statistically significant difference.

CONCLUSIONS

Lower levels of abnormal laboratory parameters, more extensive radiographic involvement, more severe respiratory compromise, and higher rates of ICU admission, ARDS and mechanical ventilation are key characteristics that distinguish patients with COVID-19-associated pneumonia from patients with CAP.

Assessing the need for transfer to the intensive care unit for Coronavirus-19 disease: Epidemiology and risk factors

Background

Although many patients with coronavirus disease 2019 (Covid-19) require direct admission to the intensive care unit (ICU), some are sent after admission. Clinicians require an understanding of this phenomenon and various risk stratification approaches for recognizing these subjects.

Methods

We examined all Covid-19 patients sent initially to a ward who subsequently required care in the ICU. We examined the timing transfer and attempted to develop a risk score based on baseline variables to predict progressive disease. We evaluated the utility of the CURB-65 score at identifying the need for ICU transfer.

Results

The cohort included 245 subjects (mean age 59.0 ± 14.2 years, 61.2% male) and 20% were eventually sent to the ICU. The median time to transfer was 2.5 days. Approximately 1/3rd of patients were not moved until day 4 or later and the main reason for transfer (79.2%) was worsening respiratory failure. A baseline absolute lymphocyte count (ALC) of ≤0.8 10³/ml and a serum ferritin ≥1000 ng/ml were independently associated with ICU transfer. Co-morbid illnesses did not correlate with eventual ICU care. Neither a risk score based on a low ALC and/or high ferritin nor the CURB-65 score performed well at predicting need for transfer.

Conclusion

Covid-19 patients admitted to general wards face a significant risk for deterioration necessitating ICU admission and respiratory failure can occur late in this disease. Neither baseline clinical factors nor the CURB-65 score perform well as screening tests to categorize these subjects as likely to progress to ICU care.

Few bacterial co-infections but frequent empiric antibiotic use in the early phase of hospitalized patients with COVID-19: results from a multicentre retrospective cohort study in The Netherlands

BACKGROUND

Knowledge on bacterial co-infections in COVID-19 is crucial to use antibiotics appropriately. Therefore, we aimed to determine the incidence of bacterial co-infections, antibiotic use and application of antimicrobial stewardship principles in hospitalized patients with COVID-19.

METHODS

We performed a retrospective observational study in four hospitals (1 university, 2 non-university teaching, 1 non-teaching hospital) in the Netherlands from March to May 2020 including consecutive patients with PCR-confirmed COVID-19. Data on first microbiological investigations obtained at the discretion of the physician and antibiotic use in the first week of hospital admission were collected.

RESULTS

Twelve (1.2%) of the 925 patients included had a documented bacterial co-infection (75.0% pneumonia) within the first week. Microbiological testing was performed in 749 (81%) patients: sputum cultures in 105 (11.4%), blood cultures in 711 (76.9%), pneumococcal urinary antigen testing in 202 (21.8%), and Legionella urinary antigen testing in 199 (21.5%) patients, with clear variation between hospitals. On presentation 556 (60.1%; range 33.3-73.4%) patients received antibiotics for a median duration of 2 days (IQR 1-4). Intravenous to oral switch was performed in 41 of 413 (9.9%) patients who received intravenous treatment >48 h. Mean adherence to the local guideline on empiric antibiotic therapy on day 1 was on average 60.3% (range 45.3%-74.7%).

CONCLUSIONS

On presentation to the hospital bacterial co-infections are rare, while empiric antibiotic use is abundant. This implies that in patients with COVID-19 empiric antibiotic should be withheld. This has the potential to dramatically reduce the current overuse of antibiotics in the COVID-19 pandemic.

CURB-65 may serve as a useful prognostic marker in COVID-19 patients within Wuhan, China: a retrospective cohort study

A recently developed pneumonia caused by SARS-CoV-2 has quickly spread across the world. Unfortunately, a simplified risk score that could easily be used in primary care or general practice settings has not been developed. The objective of this study is to identify a simplified risk score that could easily be used to quickly triage severe COVID-19 patients. All severe and critical adult patients with laboratory-confirmed COVID-19 on the West campus of Union Hospital, Wuhan, China, from 28 January 2020 to 29 February 2020 were included in this study. Clinical data and laboratory results were obtained. CURB-65 pneumonia score was calculated. Univariate logistic regressions were applied to explore risk factors associated with in-hospital death. We used the receiver operating characteristic curve and multivariate COX-PH model to analyse risk factors for in-hospital death. A total of 74 patients (31 died, 43 survived) were finally included in the study. We observed that compared with survivors, non-survivors were older and illustrated higher respiratory rate, neutrophil-to-lymphocyte ratio, D-dimer and lactate dehydrogenase (LDH), but lower SpO₂ as well as impaired liver function, especially synthesis function. CURB-65 showed good performance for predicting in-hospital death (area under curve 0.81, 95% confidence interval (CI) 0.71–0.91). CURB-65 ⩾ 2 may serve as a cut-off value for prediction of in-hospital death in severe patients with COVID-19 (sensitivity 68%, specificity 81%, F1 score 0.7). CURB-65 (hazard ratio (HR) 1.61; 95% CI 1.05–2.46), LDH (HR 1.003; 95% CI 1.001–1.004) and albumin (HR 0.9; 95% CI 0.81–1) were risk factors for in-hospital death in severe patients with COVID-19. Our study indicates CURB-65 may serve as a useful prognostic marker in COVID-19 patients, which could be used to quickly triage severe patients in primary care or general practice settings.

Effectiveness of oseltamivir in reduction of complications and 30-day mortality in severe seasonal influenza infection

OBJECTIVES

The benefit of oseltamivir treatment in patients admitted with influenza virus infection and the design of studies addressing this issue have been questioned extensively. As the burden of influenza disease is substantial and oseltamivir treatment is biologically plausible, this study assessed the clinical benefit of oseltamivir treatment in adult patients admitted with severe seasonal influenza virus infection in daily practice.

PATIENTS AND METHODS

A multi-centre, retrospective cohort study was conducted to compare the effectiveness of treatment with and without oseltamivir <48 h after admission in patients admitted with laboratory-confirmed influenza virus infection in three large hospitals in the Netherlands. Propensity score matching was used to compare clinically relevant outcome variables.

RESULTS

In total, 390 patients were included in this study, of whom 80% had comorbidities. Thirty-day mortality, as well as the composite endpoint of 30-day mortality or intensive care unit admission >48 h after admission, were reduced by 9% (P=0.04) and 11% (P=0.02), respectively. Length of hospital stay and in-hospital mortality rates all showed a trend towards reduction. The median duration between symptom onset and initiation of treatment was 3 days.

CONCLUSIONS

This study supports that, in daily practice, patients admitted with influenza virus infection should be treated with oseltamivir within 48 h of admission, even if they have had complaints for >48 h.

Non-lytic antibiotic treatment in community-acquired pneumococcal pneumonia does not attenuate inflammation: the PRISTINE trial

Background

The inflammatory response in pneumococcal infection is primarily driven by immunoreactive bacterial cell wall components [lipoteichoic acid (LTA)]. An acute release of these components occurs when pneumococcal infection is treated with β-lactam antibiotics.

Objectives

We hypothesized that non-lytic rifampicin compared with lytic β-lactam antibiotic treatment would attenuate the inflammatory response in patients with pneumococcal pneumonia.

Methods

In the PRISTINE (Pneumonia treated with RIfampicin aTtenuates INflammation) trial, a randomized, therapeutic controlled, exploratory study in patients with community-acquired pneumococcal pneumonia, we looked at LTA release and inflammatory and clinical response during treatment with both rifampicin and β-lactam compared with treatment with β-lactam antibiotics only. The trial is registered in the Dutch trial registry, number NTR3751 (European Clinical Trials Database number 2012-003067-22).

Results

Forty-one patients with community-acquired pneumonia were included; 17 of them had pneumococcal pneumonia. LTA release, LTA-mediated inflammatory responses, clinical outcomes, inflammatory biomarkers and transcription profiles were not different between treatment groups.

Conclusions

The PRISTINE study demonstrated the feasibility of adding rifampicin to β-lactam antibiotics in the treatment of community-acquired pneumococcal pneumonia, but, despite solid in vitro and experimental animal research evidence, failed to demonstrate a difference in plasma LTA concentrations and subsequent inflammatory and clinical responses. Most likely, an inhibitory effect of human plasma contributes to the low immune response in these patients. In addition, LTA plasma concentration could be too low to mount a response via Toll-like receptor 2 in vitro, but may nonetheless have an effect in vivo.

Pneumonic versus Nonpneumonic Exacerbations of Chronic Obstructive Pulmonary Disease

Patients with chronic obstructive pulmonary disease (COPD) often suffer acute exacerbations (AECOPD) and community-acquired pneumonia (CAP), named nonpneumonic and pneumonic exacerbations of COPD, respectively. Abnormal host defense mechanisms may play a role in the specificity of the systemic inflammatory response. Given the association of this aspect to some biomarkers at admission (e.g., C-reactive protein), it can be used to help to discriminate AECOPD and CAP, especially in cases with doubtful infiltrates and advanced lung impairment. Fever, sputum purulence, chills, and pleuritic pain are typical clinical features of CAP in a patient with COPD, whereas isolated dyspnea at admission has been reported to predict AECOPD. Although CAP may have a worse outcome in terms of mortality (in hospital and short term), length of hospitalization, and early readmission rates, this has only been confirmed in a few prospective studies. There is a lack of methodologically sound research confirming the impact of severe AECOPD and COPD + CAP. Here, we review studies reporting head-to-head comparisons between AECOPD and CAP + COPD in hospitalized patients. We focus on the epidemiology, risk factors, systemic inflammatory response, clinical and microbiological characteristics, outcomes, and treatment approaches. Finally, we briefly discuss some proposals on how we should orient research in the future.

FluA-p score: a novel prediction rule for mortality in influenza A-related pneumonia patients

BACKGROUND

The pneumonia severity index (PSI) and the CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥ 65 years) score have been shown to predict mortality in community-acquired pneumonia. Their ability to predict influenza-related pneumonia, however, is less well-established.

METHODS

A total of 693 laboratory-confirmed FluA-p patients diagnosed between Jan 2013 and Dec 2018 and recruited from five teaching hospitals in China were included in the study. The sample included 494 patients in the derivation cohort and 199 patients in the validation cohort. The prediction rule was established based on independent risk factors for 30-day mortality in FluA-p patients from the derivation cohort.

RESULTS

The 30-day mortality of FluA-p patients was 19.6% (136/693). The FluA-p score was based on a multivariate logistic regression model designed to predict mortality. Results indicated the following significant predictors (regression statistics and point contributions toward total score in parentheses): blood urea nitrogen > 7 mmol/L (OR 1.604, 95% CI 1.150-4.492, p = 0.040; 1 points), pO₂/F_iO₂ ≤ 250 mmHg (OR 2.649, 95% CI 1.103-5.142, p = 0.022; 2 points), cardiovascular disease (OR 3.967, 95% CI 1.269-7.322, p < 0.001; 3 points), arterial PH < 7.35 (OR 3.959, 95% CI 1.393-7.332, p < 0.001; 3 points), smoking history (OR 5.176, 95% CI 2.604-11.838, p = 0.001; 4 points), lymphocytes < 0.8 × 10⁹/L (OR 8.391, 95% CI 3.271-16.212, p < 0.001; 5 points), and early neurominidase inhibitor therapy (OR 0.567, 95% CI 0.202-0.833, p = 0.005; - 2 points). Seven points was used as the cut-off value for mortality risk stratification. The model showed a sensitivity of 0.941, a specificity of 0.762, and overall better predictive performance than the PSI risk class (AUROC = 0.908 vs 0.560, p < 0.001) and the CURB-65 score (AUROC = 0.908 vs 0.777, p < 0.001).

CONCLUSIONS

Our results showed that a FluA-p score was easy to derive and that it served as a reliable prediction rule for 30-day mortality in FluA-p patients. The score could also effectively stratify FluA-p patients into relevant risk categories and thereby help treatment providers to make more rational clinical decisions.

Efficacy of the quick sequential organ failure assessment for predicting clinical outcomes among community-acquired pneumonia patients presenting in the emergency department

Background

The study aimed to investigate the predictive value of the quick sequential organ failure assessment (qSOFA) for clinical outcomes in emergency patients with community-acquired pneumonia (CAP).

Methods

A total of 742 CAP cases from the emergency department (ED) were enrolled in this study. The scoring systems including the qSOFA, SOFA and CURB-65 (confusion, urea, respiratory rate, blood pressure and age) were used to predict the prognostic outcomes of CAP in ICU-admission, acute respiratory distress syndrome (ARDS) and 28-day mortality. According to the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, the accuracies of prediction of the scoring systems were analyzed among CAP patients.

Results

The AUC values of the qSOFA, SOFA and CURB-65 scores for ICU-admission among CAP patients were 0.712 (95%CI: 0.678–0.745, P < 0.001), 0.744 (95%CI: 0.711–0.775, P < 0.001) and 0.705 (95%CI: 0.671–0.738, P < 0.001), respectively. For ARDS, the AUC values of the qSOFA, SOFA and CURB-65 scores were 0.730 (95%CI: 0.697–0.762, P < 0.001), 0.724 (95%CI: 0.690–0.756, P < 0.001) and 0.749 (95%CI: 0.716–0.780, P < 0.001), respectively. After 28 days of follow-up, the AUC values of the qSOFA, SOFA and CURB-65 scores for 28-day mortality were 0.602 (95%CI: 0.566–0.638, P < 0.001), 0.587 (95%CI: 0.551–0.623, P < 0.001) and 0.614 (95%CI: 0.577–0.649, P < 0.001) in turn. There were no statistical differences between qSOFA and SOFA scores for predicting ICU-admission (Z = 1.482, P = 0.138), ARDS (Z = 0.321, P = 0.748) and 28-day mortality (Z = 0.573, P = 0.567). Moreover, we found no differences to predict the ICU-admission (Z = 0.370, P = 0.712), ARDS (Z = 0.900, P = 0.368) and 28-day mortality (Z = 0.768, P = 0.442) using qSOFA or CURB-65 scores.

Conclusion

qSOFA was not inferior to SOFA or CURB-65 scores in predicting the ICU-admission, ARDS and 28-day mortality of patients presenting in the ED with CAP.

[Fatal severe community-acquired pneumonia: risk factors, clinical characteristics and medical errors of hospital patients]

Community-acquired pneumonia (CAP) is the most common disease and potentially life-threatening infection in the worldwide. In the Nizhny Novgorod region, no analysis of the causes of mortality and medical errors of severe CAP patients.

AIM

To analyze the patients structure who died severe CAP in hospitals of the Nizhny Novgorod region, to identify the leading risk factors, to assess the clinical characteristics of fatal severe CAP and medical errors according to medical records of patients from 20152016.

MATERIALS AND METHODS

This was a retrospective study of medical records of 139 patients with fatal severe CAP from medical organizations of the Nizhny Novgorod region. The 72 patients died in 2015. The mortality rate from pneumonia was 67 cases in 2016.

RESULTS

The key predictors of the fatal severe CAP in patients of the Nizhny Novgorod region identified: socio-demographic status (men of working age, unemployed, smoking, alcohol and drug dependence), late treatment and hospitalization, tachypnea, hypotension, tachycardia, confusion, leukocytosis or leukopenia, thrombocytopenia, anemia, hyperglycemia, bilateral lung damage, pleural effusion, acute respiratory failure. The leading medical errors in fatal CAP were incorrect assessment of the severity of the patients condition, untimely CAP, non-monitoring of SpO2 on the first day of hospitalization, late transfer of patients to the intensive care unit, there was no influenza therapy, inadequate starting antibacterial therapy.

CONCLUSION

The main ways to avoid or minimize medical errors and reduce the mortality of patients with TVP is strict adherence to clinical recommendations, active preventive measures, diagnosis and treatment of chronic diseases.

Post Hoc Assessment of Time to Clinical Response Among Adults Hospitalized with Community-Acquired Bacterial Pneumonia Who Received Either Lefamulin or Moxifloxacin in 2 Phase III Randomized, Double-Blind, Double-Dummy Clinical Trials

Time to clinical response, a proxy for hospital "discharge readiness," was compared between CABP inpatients who received lefamulin or moxifloxacin in the Lefamulin Evaluation Against Pneumonia (LEAP) trials. The analysis included 926 inpatients. A short and comparable median time to clinical response (4 days) was observed in both treatment groups.

The effect of inhaled corticosteroids in the outcomes of community-acquired pneumonia: ICCAP study (TURKCAP Database)

OBJECTIVE

We aimed to investigate the effect of inhaled corticosteroids (ICS) in the outcomes of community-acquired pneumonia (CAP), as well as to determine if ICS usage is exist among the risk factors for mortality in those patients.

MATERIALS AND METHODS

In this retrospective cross-sectional multicentre study, 1069 hospitalised CAP patients were investigated using CAP Database of Turkish Thoracic Society (TURKCAP Database). The patients were divided into two groups, depending on their ICS use. The data were analysed by appropriate statistical methods.

RESULTS

172 (75.8%) of the 227 patients who were on ICS had COPD and 37 (16.3%) had asthma. There were fewer patients with fever among ICS-users compared to non-ICS users (P = 0.013), and less muscle pain (P = 0.015) and fewer GIS symptoms (P = 0.022). No statistically significant difference was found between ICS use/ type of ICS and the duration of hospitalisation (P = 0.286). The multivariate regression analysis showed that patients using ICS had lower body temperature and, less crackles/bronchial sound. In the multivariate logistic regression model lung cancer (OR: 6.75), glucose (OR: 1.01) and CURB-65 (OR: 1.72) were significantly associated with mortality in the CAP patients. ICS usage were not found to be associated with mortality.

CONCLUSION

The use of ICS by the patients with CAP admitted to the hospital is not independently related with any radiological pattern, hospitalisation duration and mortality. ICS usage may diminish fever response and may suppress the findings of crackles and/or bronchial sounds. This needs further confirmation.

Pneumonia

Pneumonia is a common cause of hospital admission and community-acquired pneumonia (CAP) is a growing health problem in developed country and worldwide. Elderly patients suffer from more severe disease, require intensive care unit (ICU) admission, and exhibit higher mortality compared with their younger counterparts. The immunological changes that occur with age called “immunosenescence” (decreased efficiency of the adaptive and innate immune systems) are known to be responsible for the increased susceptibility of elderly persons to infectious diseases and for their limited response to vaccines [1].

Elevated progranulin as a novel biomarker to predict poor prognosis in community-acquired pneumonia

PURPOSE

Prognostic biomarkers help triage initial patients and inform targeted therapy selection. Here, we explored the role of progranulin (PGRN)-implicated in processes ranging from inflammation to neurodegeneration-in patients with community-acquired pneumonia (CAP).

METHODS

A prospective observational cohort study was conducted during 2017. Patients who required invasive mechanical ventilation and/or had septic shock and were discharged from the hospital were cohort II. Those who died at the hospital were cohort III. Remaining patients discharged from the hospital were cohort I. The primary endpoint was that patients progressed to served as cohort II; the secondary endpoint was that patients progressed to served as cohort III. Serum PGRN levels were detected by ELISA.

RESULTS

A total of 280 patients constituted the study cohort. 194 (69.3%) were categorized into cohort I, 61 (21.8%) were categorized into cohort II, and 25 (8.9%) were categorized into cohort III. Serum PGRN levels were increased in CAP patients, independently of etiology. Adjusting for clinical parameters, the odds ratios (95%CI) of cohort III and combined cohort II-III were 34.968 (3.743-326.692) and 3.741 (1.496-9.351), respectively, comparing lowest-to-highest quartile PGRN levels. PGRN exhibited high accuracy in predicting 30-day mortality, with AUC 0.862. PGRN combined with CURB-65 or PSI significantly improved prediction performance. Cox proportional regression analysis showed PGRN was an independent predictor for 30-day mortality risk. Cox survival curves confirmed PGRN ≥89.51 ng/mL had a significantly higher mortality rate than PGRN <89.51 ng/mL.

CONCLUSION

Higher PGRN levels at admission were associated with higher odds of poor prognosis. PGRN can improve the prognostic power of CURB-65 or PSI, so PGRN could be apparently a prognostic biomarker for assisting triage of CAP patients.

Meta-analysis of Calibration, Discrimination, and Stratum-Specific Likelihood Ratios for the CRB-65 Score

BACKGROUND

The CRB-65 score is recommended as a decision support tool to help identify patients with community-acquired pneumonia (CAP) who can safely be treated as outpatients.

OBJECTIVE

To perform an updated meta-analysis of the accuracy, discrimination, and calibration of the CRB-65 score using a novel approach to calculation of stratum-specific likelihood ratios.

DESIGN

Meta-analysis of accuracy, discrimination, and calibration.

METHODS

We searched PubMed, Google, previous systematic reviews, and reference lists of included studies. Data was abstracted and quality assessed in parallel by two investigators. The quality assessment used an adaptation of the TRIPOD and PROBAST criteria. Measures of discrimination, calibration, and stratum-specific likelihood ratios are reported.

KEY RESULTS

Twenty-nine studies met our inclusion criteria and provided usable data. Most studies were set in Europe, none in North America, and 12 were judged to be at low risk of bias. The pooled estimate of area under the receiver operating characteristic curve was 0.74 (95% CI 0.71-0.77) for all studies. Calibration was good although there was significant heterogeneity; the pooled estimate of the ratio of observed to expected mortality for all studies was 1.04 (95% CI 0.91-1.19). The corresponding values for studies at low risk of bias where patients could be treated as outpatients or inpatients were 0.76 (0.70-0.81) and 0.88 (0.69-1.13). Summary estimates of stratum-specific likelihood ratios for all studies were 0.19 for the low-risk group, 1.1 for the moderate-risk group, and 4.5 for the high-risk group, and 0.13, 1.3, and 5.6 for studies at low risk of bias where patients could be treated as outpatients or inpatients.

CONCLUSIONS

The CRB-65 is useful for identifying low-risk patients for outpatient therapy. Given a 4% overall mortality risk, patients classified as low risk by the CRB-65 had an outpatient mortality risk of no more than 0.5%.

Viral prevalence, clinical profiles and comparison of severity scores for predicting the mortality of adults with severe acute respiratory infections

Background/aim

The aim of this study was to determine the accuracy of severity scores for predicting the 28-day mortality among adults with severe acute respiratory infection (SARI) admitted to the emergency department.

Materials and methods

This study included 159 consecutive adult patients with SARI admitted to the emergency department of a tertiary hospital. A standard form was filled out in order to record demographic information, clinical parameters, laboratory tests, and radiographic findings of the patients. CURB-65, PSI, SIRS, qSOFA, SOFA and APACHE II scores were compared between the survivor and nonsurvivor groups.

Results

Of 159 patients included in the study, 38.4% were positive for respiratory viruses and 28.3% were positive for influenza viruses. 35.8% of the patients were admitted to an intensive care unit (ICU) and the mortality rate was 36.5%. The area under the receiver operating characteristic curve of CURB-65, PSI, SIRS criteria, qSOFA, SOFA and APACHE II scores were 0.717, 0.712, 0.607, 0.683, 0.755, and 0.748, respectively in predicting mortality and 0.759, 0.744, 0.583, 0.728, 0.741, and 0.731, respectively in predicting ICU admission.

Conclusion

SOFA and APACHE II were more accurate than SIRS in predicting the 28-day mortality among adults with SARI. There was no significant difference among these scores in terms of other multivariate comparisons.

Mortality Predictive Scores for Community-Acquired Pneumonia in Children

PURPOSE OF REVIEW

The use of severity score for the staging of pneumonias has emerged as a necessity for the physician caring for this disease. Although there are several established prognostic scoring systems for community-acquired pneumonia in adults, the availability for children are scarce.

RECENT FINDINGS

Recently, scoring system for risk stratification of children with pneumonia were developed in low- and middle-income countries. They use clinical variables that represent known risk factors for severe outcomes of respiratory illness in children, such as hypoxemia, chest indrawing, refusal to feed, malnutrition, age, and stage of HIV disease among others factors. Although they showed good discriminating power and are very useful in low-resource settings, the characteristics of the patients, the local epidemiology of concurrent diseases, the social conditions, and the facilities of the hospitals make them not applicable to developed countries. A new prognostic scale for estimating mortality based on the modified PIRO scale used in adults with pneumonia can be useful for developed countries. Although several scoring systems for the estimation of mortality in childhood CAP were developed in the last years, most of them come from developing countries and the results are not applicable to patients with pneumonia in developed countries. Prospective studies applying scores adapted to the reality of the developed countries are needed.