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

Advancements in the Management of Severe Community-Acquired Pneumonia: A Comprehensive Narrative Review

Pneumonia, classified as a lower respiratory tract illness, affects different parts of the bronchial system as well as alveoli and can present with varying severities depending on co-morbidities and causative pathogens. It can be broadly classified using the setting in which it was acquired, namely the community or hospital setting, the former being more common and spreading through person-to-person droplet transmission. Community-acquired pneumonia (CAP) is currently the fourth leading cause of death worldwide, and its high mortality makes continual insight into the management of the condition worthwhile. This review explores the literature specifically for severe CAP (sCAP) and delves into the diagnosis, various modalities of treatment, and management of the condition. This condition can be defined as pneumonia requiring mechanical ventilation in the ICU and/or presenting with sepsis and organ failure due to pneumonia. The disease process is characterized by inflammation of the lung parenchyma, initiated by a combination of pathogens and lowered local defenses. Acute diagnosis of the condition is vital in reducing negative patient outcomes, namely through clinical presentation, blood/sputum cultures, imaging modalities such as computed tomography scan, and inflammatory markers, identifying common causative pathogens such as Streptococcus pneumoniae, rhinovirus, Legionella, and viral influenza. Pathogens such as Escherichia coli should also be investigated in patients with chronic obstructive pulmonary disease. The mainstay of treating sCAP includes rapid ICU admission once a diagnosis has been confirmed, initiating sepsis protocol, and treatment with combined empiric antibiotic regimens consisting of beta-lactams and macrolides. Corticosteroid use alongside antibiotics shows promise in reducing inflammation, but its use has to be judged on a case-by-case basis. New drugs such as omadacycline, delafloxacin, and zabofloxacin have shown valid evidence for the treatment of resistant causative organisms. The main guidelines for preventing sCAP include maintaining a healthy lifestyle, and annual pneumococcal and influenza vaccines are recommended for the most vulnerable patient groups, such as those with COPD and immunosuppression.

A 29-MRNA HOST RESPONSE WHOLE-BLOOD SIGNATURE IMPROVES PREDICTION OF 28-DAY MORTALITY AND 7-DAY INTENSIVE CARE UNIT CARE IN ADULTS PRESENTING TO THE EMERGENCY DEPARTMENT WITH SUSPECTED ACUTE INFECTION AND/OR SEPSIS

ABSTRACT

Background: Risk stratification of emergency department patients with suspected acute infections and/or suspected sepsis remains challenging. We prospectively validated a 29-messenger RNA host response classifier for predicting severity in these patients. Methods: We enrolled adults presenting with suspected acute infections and at least one vital sign abnormality to six emergency departments in Greece. Twenty-nine target host RNAs were quantified on NanoString nCounter and analyzed with the Inflammatix Severity 2 (IMX-SEV-2) classifier to determine risk scores as low, moderate, and high severity. Performance of IMX-SEV-2 for prediction of 28-day mortality was compared with that of lactate, procalcitonin, and quick sequential organ failure assessment (qSOFA). Results: A total of 397 individuals were enrolled; 38 individuals (9.6%) died within 28 days. Inflammatix Severity 2 classifier predicted 28-day mortality with an area under the receiver operator characteristics curve of 0.82 (95% confidence interval [CI], 0.74-0.90) compared with lactate, 0.66 (95% CI, 0.54-0.77); procalcitonin, 0.67 (95% CI, 0.57-0.78); and qSOFA, 0.81 (95% CI, 0.72-0.89). Combining qSOFA with IMX-SEV-2 improved prognostic accuracy from 0.81 to 0.89 (95% CI, 0.82-0.96). The high-severity (rule-in) interpretation band of IMX-SEV-2 demonstrated 96.9% specificity for predicting 28-day mortality, whereas the low-severity (rule-out) band had a sensitivity of 78.9%. Similarly, IMX-SEV-2 alone accurately predicted the need for day-7 intensive care unit care and further boosted overall accuracy when combined with qSOFA. Conclusions: Inflammatix Severity 2 classifier predicted 28-day mortality and 7-day intensive care unit care with high accuracy and boosted the accuracy of clinical scores when used in combination.

Real-time interactive artificial intelligence of things-based prediction for adverse outcomes in adult patients with pneumonia in the emergency department

BACKGROUND

Artificial intelligence of things (AIoT) may be a solution for predicting adverse outcomes in emergency department (ED) patients with pneumonia; however, this issue remains unclear. Therefore, we conducted this study to clarify it.

METHODS

We identified 52,626 adult ED patients with pneumonia from three hospitals between 2010 and 2019 for this study. Thirty-three feature variables from electronic medical records were used to construct an artificial intelligence (AI) model to predict sepsis or septic shock, respiratory failure, and mortality. After comparisons of the predictive accuracies among logistic regression, random forest, support-vector machine (SVM), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost), we selected the best one to build the model. We further combined the AI model with the Internet of things as AIoT, added an interactive mode, and implemented it in the hospital information system to assist clinicians with decision making in real time. We also compared the AIoT-based model with the confusion-urea-respiratory rate-blood pressure-65 (CURB-65) and pneumonia severity index (PSI) for predicting mortality.

RESULTS

The best AI algorithms were random forest for sepsis or septic shock (area under the curve [AUC] = 0.781), LightGBM for respiratory failure (AUC = 0.847), and mortality (AUC = 0.835). The AIoT-based model represented better performance than CURB-65 and PSI indicators for predicting mortality (0.835 vs. 0.681 and 0.835 vs. 0.728).

CONCLUSIONS

A real-time interactive AIoT-based model might be a better tool for predicting adverse outcomes in ED patients with pneumonia. Further validation in other populations is warranted.