Merging Two Models of One-Dimensional Convolutional Neural Networks to Improve the Differential Diagnosis between Acute Asthma and Bronchitis in Preschool Children.

(1) Background: Acute asthma and bronchitis are common infectious diseases in children that affect lower respiratory tract infections (LRTIs), especially in preschool children (below six years). These diseases can be caused by viral or bacterial infections and are considered one of the main reasons for the increase in the number of deaths among children due to the rapid spread of infection, especially in low- and middle-income countries (LMICs). People sometimes confuse acute bronchitis and asthma because there are many overlapping symptoms, such as coughing, runny nose, chills, wheezing, and shortness of breath; therefore, many junior doctors face difficulty differentiating between cases of children in the emergency departments. This study aims to find a solution to improve the differential diagnosis between acute asthma and bronchitis, reducing time, effort, and money. The dataset was generated with 512 prospective cases in Iraq by a consultant pediatrician at Fallujah Teaching Hospital for Women and Children; each case contains 12 clinical features. The data collection period for this study lasted four months, from March 2022 to June 2022. (2) Methods: A novel method is proposed for merging two one-dimensional convolutional neural networks (2-1D-CNNs) and comparing the results with merging one-dimensional neural networks with long short-term memory (1D-CNNs + LSTM). (3) Results: The merged results (2-1D-CNNs) show an accuracy of 99.72% with AUC 1.0, then we merged 1D-CNNs with LSTM models to obtain the accuracy of 99.44% with AUC 99.96%. (4) Conclusions: The merging of 2-1D-CNNs is better because the hyperparameters of both models will be combined; therefore, high accuracy results will be obtained. The 1D-CNNs is the best artificial neural network technique for textual data, especially in healthcare; this study will help enhance junior and practitioner doctors' capabilities by the rapid detection and differentiation between acute bronchitis and asthma without referring to the consultant pediatrician in the hospitals.

Development of a Large Pneumatocele in a Patient Recovering from COVID-19 Pneumonitis.

A 39-year-old man presented with severe COVID-19 pneumonitis requiring hospital admission. He represented three days following discharge with sudden onset breathlessness and chest pain. Initial imaging suggested the presence of a left pneumothorax. Following further clinical decline a plan was made to insert a CT guided chest drain. However, imaging in the prone position for the procedure unexpectedly revealed a large left lower lobe pneumatocele with only a very small pneumothorax. Events and appearances suggest that this is a rare case of delayed COVID-19 pneumonitis-related pneumatocele formation. We will discuss the clinical significance of this entity. LEARNING POINTS: Pneumatocele formation should be considered in patients presenting with new respiratory symptoms after completing therapy for COVID-19 pneumonitis.Performing CT examinations with patients in different positions may be required to help exclude the possibility of pneumatocele formation when a loculated pneumothorax is suspected on the supine CT images.

The bronchiectasis severity index. An international derivation and validation study.

RATIONALE: There are no risk stratification tools for morbidity and mortality in bronchiectasis. Identifying patients at risk of exacerbations, hospital admissions, and mortality is vital for future research. OBJECTIVES: This study describes the derivation and validation of the Bronchiectasis Severity Index (BSI). METHODS: Derivation of the BSI used data from a prospective cohort study (Edinburgh, UK, 2008-2012) enrolling 608 patients. Cox proportional hazard regression was used to identify independent predictors of mortality and hospitalization over 4-year follow-up. The score was validated in independent cohorts from Dundee, UK (n = 218); Leuven, Belgium (n = 253); Monza, Italy (n = 105); and Newcastle, UK (n = 126). MEASUREMENTS AND MAIN RESULTS: Independent predictors of future hospitalization were prior hospital admissions, Medical Research Council dyspnea score greater than or equal to 4, FEV1 < 30% predicted, Pseudomonas aeruginosa colonization, colonization with other pathogenic organisms, and three or more lobes involved on high-resolution computed tomography. Independent predictors of mortality were older age, low FEV1, lower body mass index, prior hospitalization, and three or more exacerbations in the year before the study. The derived BSI predicted mortality and hospitalization: area under the receiver operator characteristic curve (AUC) 0.80 (95% confidence interval, 0.74-0.86) for mortality and AUC 0.88 (95% confidence interval, 0.84-0.91) for hospitalization, respectively. There was a clear difference in exacerbation frequency and quality of life using the St. George's Respiratory Questionnaire between patients classified as low, intermediate, and high risk by the score (P < 0.0001 for all comparisons). In the validation cohorts, the AUC for mortality ranged from 0.81 to 0.84 and for hospitalization from 0.80 to 0.88. CONCLUSIONS: The BSI is a useful clinical predictive tool that identifies patients at risk of future mortality, hospitalization, and exacerbations across healthcare systems.

Simplification of the IDSA/ATS criteria for severe CAP using meta-analysis and observational data.

The 2007 Infectious Diseases Society of America (IDSA)/American Thoracic Society (ATS) guidelines proposed "minor" criteria to predict intensive care unit (ICU) admission in patients with community-acquired pneumonia. These criteria were based on expert opinion. Consequently, the authors of the guidelines asked investigators to determine whether the score could be simplified by excluding noncontributory variables. Each IDSA/ATS minor criterion was validated using a random effects meta-analysis of seven studies. Variables present in <5% of cases or that were nonsignificantly associated with mortality/ICU admission were excluded. A simplified score excluding these variables was tested for prediction of mortality and ICU admission in an established database. Prediction was assessed using the area under the receiver operator characteristic curve (AUC). Leukopenia (<4000 cells·mm(-3)), thrombocytopenia (<100,000 cells·mm(-3)) and hypothermia <36°C occurred in <5% of cases. A simplified score excluding these variables was performed similarly for prediction of mortality, AUC 0.77 (95% CI 0.73-0.81) versus 0.78 (95% CI 0.74-0.82) (p=0.9) and intensive care unit admission, AUC 0.85 (95% CI 0.82-0.87) versus 0.85 (95% CI 0.82-0.88) (p=0.9). Additional predictors suggested by the IDSA/ATS were associated with mortality and ICU admission, but only incorporating acidosis (pH <7.35) altered the AUC (0.82 (95% CI 0.78-0.86) (p=0.6) for mortality and 0.86 (95% CI 0.82-0.88) (p=0.8) for ICU admission). No improvements were statistically significant. The IDSA/ATS criteria can be simplified by removing three infrequent variables.

Healthcare-associated pneumonia does not accurately identify potentially resistant pathogens: a systematic review and meta-analysis.

BACKGROUND: The 2005 American Thoracic Society/Infectious Diseases Society of America guidelines introduced a concept of healthcare-associated pneumonia (HCAP) to define patients at higher risk of antibiotic-resistant pathogens, thus requiring broad spectrum therapy. There has been no systematic evaluation of the ability of this definition to identify antibiotic-resistant pathogens. METHODS: We conducted a systematic review and meta-analysis of studies comparing the frequency of resistant pathogens (defined as methicillin-resistant Staphylococcus aureus, Enterobacteriaceae, and Pseudomonas aeruginosa) in populations with HCAP compared with populations with community-acquired pneumonia (CAP). Predictive accuracy was evaluated using the area under the receiver operator characteristic curve (AUC). The frequencies of pathogens in each group were pooled using a random effects model. RESULTS: Twenty-four studies were included (n = 22 456). Overall study quality was poor. HCAP was associated with an increased risk of methicillin-resistant S. aureus (odds ratio [OR], 4.72; 95% confidence interval [CI], 3.69-6.04) enterobactericeae (OR, 2.11; 95% CI, 1.69-2.63), and P. aeruginosa (OR, 2.75; 95% CI, 2.04-3.72; all P < .0001), but these analyses were confounded by publication bias. The discriminatory ability of HCAP for resistant pathogens was low (AUC, 0.70; 95% CI, 0.69-0.71) and was lower in high-quality (AUC, 0.66; 95% CI, 0.62-0.70) and prospective studies (AUC, 0.64; 95% CI 0.62-0.66). After adjustment for age and comorbidities, mortality was not increased in HCAP (OR, 1.20; 95% CI, 0.85-1.70; P = .30). CONCLUSIONS: The HCAP concept is based on predominantly low-quality evidence and does not accurately identify resistant pathogens. Mortality in HCAP does not appear to be due to a higher frequency of resistant pathogens.