Novel RNA biomarkers improve discrimination of children with tuberculosis disease from those with non-TB pneumonia after in vitro stimulation.

The diagnosis of pediatric tuberculosis (TB) poses a challenge for clinical teams worldwide. TB-mediated changes in the expression of host genes in the peripheral blood can serve as diagnostic biomarkers and can provide better insights into the host immune mechanisms of childhood TB. Peripheral blood mononuclear cells (PBMCs) from children (n=102) with microbiologically confirmed TB disease, TB infection (TBI), pneumonia, and healthy controls (HC) were stimulated with either the Purified Protein Derivative (PPD) or the Early Secretory Antigen 6kDa-Culture Filtrate Protein 10 (ESAT6-CFP10) complex of Mycobacterium tuberculosis (Mtb). RNA was extracted and quantified using gene expression microarrays. Differential expression analysis was performed comparing microbiologically confirmed TB to the other diagnostic groups for the stimulated and unstimulated samples. Using variable selection, we identified sparse diagnostic gene signatures; one gene (PID1) was able to distinguish TB from pneumonia after ESAT6-CFP10 stimulation with an AUC of 100% in the test set, while a combination of two genes (STAT1 and IFI44) achieved an AUC of 91.7% (CI95% 75.0%-100%) in the test set after PPD stimulation. The number of significantly differentially expressed (SDE) genes was higher when contrasting TB to pneumonia or HC in stimulated samples, compared to unstimulated ones, leading to a larger pool of candidate diagnostic biomarkers. Our approach provides enlightened aspects of peripheral TB-specific responses and can form the basis for a point of care test meeting the World Health Organization (WHO) Target Product Profile (TPP) for pediatric TB.

Efficient federated learning for pediatric pneumonia on chest X-ray classification.

According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia.

[Early diagnostic and prognosis prediction of circ_0054633 for acute lung injury/acute respiratory distress syndrome in children with severe pneumonia].

OBJECTIVE: To explore the value of circ_0054633 in early diagnosis and prognosis prediction of acute lung injury/acute respiratory distress syndrome (ALI/ARDS) in children with severe pneumonia. METHODS: A retrospective case-control study was conducted on children with diagnosed severe pneumonia admitted to Tianjin Children's Hospital from July 1, 2022, to February 29, 2024. The clinical data was collected by electronic medical record system and clinical follow-up, including gender, age, lung injury prediction score (LIPS), pediatric critical illness score (PCIS), serum circ_0054633, interleukin-6 (IL-6), the indicators of the arterial blood-gas analysis, oxygenation index (PaO2/FiO2) within 24 hours of admission and the survival status of 28 days. According to whether ALI/ARDS occurred, they were divided into the ALI/ARDS group and the non-ALI/ARDS group. The differences of clinical data between the two groups were compared, and multivariate Logistic regression was used to analyze the risk factors for ALI/ARDS in children with severe pneumonia. The receiver operator characteristic curve (ROC curve) will be used to explore the early diagnostic value of ALI/ARDS in children with severe pneumonia. The patients of ALI/ARDS were divided into mild group, moderate group and severe group according to the level of PaO2/FiO2. The levels of serum circ_0054633 and IL-6 in various severity ALI/ARDS were compared. The differences of serum circ_0054633, IL-6 levels, PCIS score and LIPS score were compared between the two groups of ALI/ARDS patients according to different prognoses in 28 days, as well as the correlation between various risk factors and circ_0054633. RESULTS: A total 74 children with severe pneumonia were included, with 34 cases in the ALI/ARDS group and 40 cases in the non-ALI/ARDS group. In ALI/ARDS group, there were 9 cases in the mild group, 15 cases in the moderate group and 10 cases in the severe group; while 12 cases died and 22 cases survived after 28 days. The serum circ_0054633, IL-6 level and LIPS score were higher in the ALI/ARDS group than the non-ALI/ARDS group, while the PCIS score was lower, and the two groups had significant difference. Multivariate Logistic regression analysis showed that circ_0054633 was independent predictors of ALI/ARDS in children with severe pneumonia [odds ratio (OR) = 3.853, 95% confidence interval (95%CI) was 1.912-7.805, P = 0.017]. ROC curve analysis showed that the cut-off values for circ_0054633 in the diagnosis of ALI/ARDS were 3.955, sensitivity was 79.4%, specificity was 92.5%, area under the ROC curve (AUC) was 0.892. The serum circ_0054633 and IL-6 levels were higher in the children who died in 28 days than the children who were survived, while the PCIS score was lower, and the two groups had significant difference. Spearman correlation analysis showed that the level of circ_0054633 in children with ALI/ARDS was positively correlated with 28-day mortality and IL-6 (r value was 0.675, 0.763, respectively, all P < 0.001), but negatively correlated with PCIS score (r = -0.626, P < 0.001), while no significant correlation with LIPS score (r = 0.389, P = 0.023). CONCLUSIONS: The level of serum circ_0054633 has a better value in early diagnosis and prognosis prediction of ALI/ARDS caused in children with severe pneumonia.

A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study.

Purpose: To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from community-acquired pneumonia (CAP). Methods: A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value. Conclusions: This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB.

Increased plasma AACT level as an indicator of poor prognosis in patients hospitalised with community-acquired pneumonia: a multicentre prospective cohort study.

BACKGROUND AND OBJECTIVE: Community-acquired pneumonia (CAP) is a common respiratory disease that frequently requires hospitalisation, and is a significant cause of death worldwide. This study aimed to evaluate the usefulness of alpha-1-antichymotrypsin (AACT) as a diagnostic and prognostic biomarker of CAP. METHODS: We conducted a multicentre prospective cohort study in patients hospitalised with CAP. Plasma AACT levels were measured using a quantitative enzyme-linked immunosorbent assay. Receiver-operating characteristic (ROC) curves and Cox proportional hazards regression were used to assess the association between plasma AACT levels and CAP diagnosis and prognosis. RESULTS: A total of 274 patients with CAP were enrolled in the study. AACT levels were elevated in patients with CAP, especially those with severe CAP and non-survivors. The area under the curve (AUC) of AACT and CRP for diagnosing CAP was 0.755 and 0.843. Cox regression showed that CURB-65 and AACT levels were independent predictors of 30-day mortality. ROC curves showed that plasma AACT levels had the highest accuracy for predicting acute respiratory distress syndrome (ARDS), with an AUC of 0.862. Combining AACT with Pneumonia Severity Index and CURB-65 significantly improved their predictive accuracy for predicting 30-day mortality. CONCLUSION: Plasma AACT levels are elevated in patients with CAP, but plasma AACT level is inferior to the C-reactive protein level for diagnosing CAP. The AACT level can reliably predict the occurrence of ARDS and 30-day mortality in patients with CAP.

Metagenomics in the Diagnosis of Pneumonia: Protocol for a Systematic Review.

BACKGROUND: Causative pathogens are currently identified in only a minority of pneumonia cases, which affects antimicrobial stewardship. Metagenomic next-generation sequencing (mNGS) has potential to enhance pathogen detection due to its sensitivity and broad applicability. However, while studies have shown improved sensitivity compared with conventional microbiological methods for pneumonia diagnosis, it remains unclear whether this can translate into clinical benefit. Most existing studies focus on patients who are ventilated, readily allowing for analysis of bronchoalveolar lavage fluid (BALF). The impact of sample type on the use of metagenomic analysis remains poorly defined. Similarly, previous studies rarely differentiate between the types of pneumonia involved-community-acquired pneumonia (CAP), hospital-acquired pneumonia (HAP), or ventilator-associated pneumonia (VAP)-which have different clinical profiles. OBJECTIVE: This study aims to determine the clinical use of mNGS in CAP, HAP, and VAP, compared with traditional microbiological methods. METHODS: We aim to review all studies (excluding case reports of a series of fewer than 10 people) of adult patients with suspected or confirmed pneumonia that compare metagenomic analysis with traditional microbiology techniques, including culture, antigen-based testing, and polymerase chain reaction-based assays. Relevant studies will be identified through systematic searches of the Embase, MEDLINE, Scopus, and Cochrane CENTRAL databases. Screening of titles, abstracts, and subsequent review of eligible full texts will be done by 2 separate reviewers (SQ and 1 of AL, CJ, or CH), with a third clinician (ES) providing adjudication in case of disagreement. Our focus is on the clinical use of metagenomics for patients with CAP, HAP, and VAP. Data extracted will focus on clinically important outcomes-pathogen positivity rate, laboratory turnaround time, impact on clinical decision-making, length of stay, and 30-day mortality. Subgroup analyses will be performed based on the type of pneumonia (CAP, HAP, or VAP) and sample type used. The risk of bias will be assessed using the QUADAS-2 tool for diagnostic accuracy studies. Outcome data will be combined in a random-effects meta-analysis, and where this is not possible, a narrative synthesis will be undertaken. RESULTS: The searches were completed with the assistance of a medical librarian on January 13, 2024, returning 5750 records. Screening and data extraction are anticipated to be completed by September 2024. CONCLUSIONS: Despite significant promise, the impact of metagenomic analysis on clinical pathways remains unclear. Furthermore, it is unclear whether the use of this technique will alter depending on whether the pneumonia is a CAP, HAP, or VAP or the sample type that is collected. This systematic review will assess the current evidence base to support the benefit of clinical outcomes for metagenomic analysis, depending on the setting of pneumonia diagnosis or specimen type used. It will identify areas where further research is needed to advance this methodology into routine care. TRIAL REGISTRATION: PROSPERO CRD42023488096; https://tinyurl.com/3suy7cma. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57334.

The value of surfactant protein a in evaluating the severity and prognosis in community-acquired pneumonia patients.

BACKGROUND: Previous research has discovered that surfactant protein A (SP-A) is involved in the pathophysiology processes of certain lung illnesses. However, no definitive clinical studies have delved into the function of SP-A in individuals afflicted with community-acquired pneumonia (CAP). A prospective cohort study was used to investigate the relationships between blood SP-A levels and the severity and prognosis among CAP patients. MATERIALS AND METHODS: This study included 260 patients with CAP. Clinical traits and demographic data were examined during hospitalization. The concentrations of serum SP-A and serum interleukin-6 (IL-6) were determined by enzyme-linked immunosorbent assay (ELISA). In addition, to evaluate the severity of CAP, a variety of scores, including the CURB-65, PSI, SMART-COP, and APACHE II, were employed. RESULTS: The serum levels of SP-A at admission exhibited a gradual decline as the severity scores of CAP increased. Through Spearman correlation analysis, we observed an association between serum SP-A and some clinical indicators among CAP patients. Furthermore, results from a multiple linear regression model suggested changes in PSI scores (-17.868 scores, 95% CI: -32.743, -2.993) affect serum SP-A more than CURB-65 (-0.547 scores, 95% CI: -0.964, -0.131), SMART-COP (-1.097 scores, 95% CI: -1.889, -0.304) and APACHE II (-3.475 scores, 95% CI: -5.874, -1.075) with age, hypertension, diabetes mellitus, cerebral infarction, coronary heart disease, and bronchitis adjusted. In addition, the prognosis in CAP patients was monitored. Throughout their hospital stay, higher serum levels of SP-A decreased the risks of mechanical ventilation (RR: 0.315; 95% CI: 0.106, 0.937), vasoactive agents (RR: 0.165; 95% CI: 0.034, 0.790), intensive care unit (ICU) admissions (RR: 0.218; 95% CI: 0.066, 0.717) and longer hospital stays (RR: 0.397; 95% CI: 0.167, 0.945). CONCLUSION: In CAP patients, inverse dose-response correlations exist between serum SP-A levels with severity scores as well as prognosis at admission, suggesting that SP-A may take part in the CAP pathophysiological processes. Moreover, lower serum SP-A on admission is associated with an elevated prognostic risk of mechanical ventilation, the use of vasoactive agents, longer hospital stays, ICU admission, and mortality. Therefore, as a biomarker, SP-A may have the potential to predict the severity and poor prognosis of CAP patients.

The optimal antibiotic treatment duration for community-acquired pneumonia in adults diagnosed in general practice in Denmark (CAP-D): an open-label, pragmatic, randomised controlled trial.

BACKGROUND: Use of antibiotics is the main driver of antimicrobial resistance which is considered one of the biggest threats to human health. In Denmark, most antibiotics are prescribed in general practice. Acute lower respiratory tract infections, including community-acquired pneumonia (CAP), are among the most frequent indications for antibiotic prescribing. Phenoxymethylpenicillin is established as first-line treatment in general practice in Denmark. However, the treatment duration with phenoxymethylpenicillin is mostly based on traditions. Both 5 and 7 days of treatment is recommended in Danish guidelines, and when asking the general practitioners about what treatment duration, they prescribe the variation is even bigger. Several hospital-based studies have proven short course (≤ 6 days) antibiotic treatment non-inferior to long course (≥ 7 days) treatment of CAP. No evidence exists on the optimal treatment duration for CAP in non-hospitalised patients. This randomised controlled trial aim to investigate the optimal treatment duration with phenoxymethylpenicillin for CAP in adults diagnosed in general practice in Denmark. METHODS: This is an open-label, pragmatic, randomised controlled, five-arm DURATIONS trial. Participants will be recruited from at least 24 general practices in Denmark. Eligible participants are adults, with no pre-existing lung disease, presenting with symptoms of CAP, and in whom the general practitioner finds it relevant to treat with antibiotics. The study will compare treatment with phenoxymethylpenicillin 1.2 MIE q.i.d. in 3, 4, 5, 6, and 7 days. DISCUSSION: This study will provide evidence for the optimal antibiotic treatment duration of CAP in general practice and inform future guidelines on CAP in all countries using phenoxymethylpenicillin for the treatment of acute respiratory tract infections in adults. The results of this study might also be used to guide treatment recommendations in other countries using phenoxymethylpenicillin. Moreover, a (potential) reduction in antibiotic use might lower the development of antimicrobial resistance, increase patient treatment adherence, reduce risks of adverse events, and lower the economical exp TRIAL REGISTRATION: ClinicalTrials.gov: NCT06295120. Registered 28 February 2024.  The Scientific Ethics Committee for the North Denmark Region: N-20230039.

[New developments in the diagnosis and treatment of community-acquired pneumonia]. / Nouveautés dans le diagnostic et le traitement de la pneumonie acquise en communauté.

Community acquired pneumonia is a well-known entity in internal medicine. It represents 1.2 cases per 1000 inhabitants every year, and up to 14 cases per 100 inhabitants in people older than 65 years old. Despite our exposition to the disease almost daily, it is still the leading cause of death related to an infection. In 2019, The American Thoracic Society proposed a revision of its guidelines, especially concerning the diagnosis and the treatment of community acquired pneumonia. It is the latest academic society revision. Further-more, the SARS-CoV-2 pandemia has extended our knowledge of pulmonary infection and brought an adaptation of our practice.

La pneumonie acquise en communauté (PAC) est une entité bien connue de la médecine interne générale. En effet, elle représente 1,2 cas pour 1000 habitants chaque année, et jusqu'à 14 cas pour 1000 habitants chez les plus de 65 ans. Elle reste la première cause de mortalité liée à une infection et est responsable d'une morbidité importante. En 2019, l'American Thoracic Society propose une révision de ses guidelines, notamment sur le diagnostic et les traitements de la PAC. Il s'agit de la dernière revue de société savante. De plus, la pandémie de SARS-CoV-2 a enrichi notre compréhension des infections pulmonaires et a conduit à une adaptation de nos pratiques.

Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach.

Evaluating Community-Acquired Pneumonia (CAP) is crucial for determining appropriate treatment methods. In this study, we established a machine learning model using radiomics and clinical features to rapidly and accurately identify Severe Community-Acquired Pneumonia (SCAP). A total of 174 CAP patients were included in the study, with 64 cases classified as SCAP. Radiomic features were extracted from chest CT scans using radiomics techniques, and screened to remove irrelevant features. Additionally, clinical indicators of patients were similarly screened and constituted the clinical feature set. Subsequently, eight common machine learning models were employed to complete the SCAP identification task. Specifically, interpretability analysis was conducted on the models. In the end, we screened out 15 radiomic features (such as LeastAxisLength, Maximum2DDiameterColumn and ZonePercentage) and two clinical features: Lymphocyte (p = 0.041) and Albumin (p = 0.044). Using radiomic features as inputs in model predictions yielded the highest AUC of 0.85 on the test set. When using the clinical feature set as model inputs, the AUC was 0.82. Combining the two sets of features as model inputs, Ada Boost achieved the best performance with an AUC of 0.89. Our study demonstrates that combining radiomics and clinical data using machine learning methods can more accurately identify SCAP patients.

Contributions of individual qSOFA elements to assessment of severity and for prediction of mortality.

BACKGROUND: The quick sequential [sepsis-related] organ failure assessment (qSOFA) acts as a prompt to consider possible sepsis. The contributions of individual qSOFA elements to assessment of severity and for prediction of mortality remain unknown. METHODS: A total of 3974 patients with community-acquired pneumonia were recruited to an observational prospective cohort study. The area under the receiver operating characteristic curve (AUROC), odds ratio, relative risk and Youden's index were employed to assess discrimination. RESULTS: Respiratory rate ≥22/min demonstrated the most superior diagnostic value, indicated by largest odds ratio, relative risk and AUROC, and maximum Youden's index for mortality. However, the indices for altered mentation and systolic blood pressure (SBP) ≤100 mm Hg decreased notably in turn. The predictive validities of respiratory rate ≥22/min, altered mentation and SBP ≤100 mm Hg were good, adequate and poor for mortality, indicated by AUROC (0.837, 0.734 and 0.671, respectively). Respiratory rate ≥22/min showed the strongest associations with SOFA scores, pneumonia severity index, hospital length of stay and costs. However, SBP ≤100 mm Hg was most weakly correlated with the indices. CONCLUSIONS: Respiratory rate ≥22/min made the greatest contribution to parsimonious qSOFA to assess severity and predict mortality. However, the contributions of altered mentation and SBP ≤100 mm Hg decreased strikingly in turn. It is the first known prospective evidence of the contributions of individual qSOFA elements to assessment of severity and for prediction of mortality, which might have implications for more accurate clinical triage decisions.

Respiratory rate ≥22/min demonstrated the most superior diagnostic value.Respiratory rate ≥22/min showed the strongest association with severity.Respiratory rate ≥22/min, altered mentation and SBP ≤100 mm Hg predicted mortality well, adequately and poorly, respectively.

[A case report of prolonged pneumonia].

Takayasu's arteritis-pulmonary artery involvement (TA-PAI) is a chronic, progressive, inflammatory disease affecting the pulmonary artery and its branches. Patients typically present with non-specific respiratory symptoms, such as fever, dyspnea, and chest pain, leading to a high rate of misdiagnosis. The diagnosis of TA-PAI is currently based on the diagnostic criteria of aortitis and imaging evidence of pulmonary artery involvement. However, pulmonary artery involvement is not typically included in the diagnostic criteria for aortitis, which may lead to a significant underestimation of the diagnostic rate of TA-PAI, particularly in cases where pulmonary artery involvement is the only manifestation. This article reports the case of a 26-year-old female patient who presented with recurrent chest pain and fever. She was initially diagnosed with pneumonia in a foreign hospital but did not show significant improvement after four months of treatment. Eventually, she was diagnosed with pulmonary artery involvement in aortitis and was stabilized with hormones, immunosuppressive drugs, and pulmonary vascular intervention. By analyzing the clinical features and diagnostic and therapeutic approaches of this case, and reviewing the relevant literature, clinicians can improve their understanding of TA-PAI.

Bronchoalveolar lavage fluid analysis in patients with checkpoint inhibitor pneumonitis.

BACKGROUND: Checkpoint inhibitor pneumonitis (CIP) is a relatively uncommon but potentially life-threatening immune-related adverse event (irAE). Lung biopsies have not been commonly performed for CIP patients. Bronchoalveolar lavage fluid (BALF) analysis is a useful diagnostic approach for interstitial lung disease. However, BALF features were inconsistent across different studies. METHODS: We retrospectively reviewed the medical records of 154 patients with pathologically confirmed malignancies and suffering from CIPs between July 2018 and December 2022. Patients who had bronchoalveolar lavage (BAL) data available were enrolled in our study. Patient clinical, laboratory, radiological and follow-up data were reviewed and analyzed. RESULTS: The BALF differential cell count and lymphocyte subset analysis were performed for 42 CIP patients. There were 32 males (76.2%). The mean age at diagnosis of CIP was 62.0 ± 10.4 (range: 31-78) years. The median time to onset of CIP was 98.5 days after the start of immunotherapy. There were 18 patients (42.9%) with low-grade CIPs and 24 patients (57.1%) with high-grade CIPs. The mean lymphocyte percentage was 36.7 ± 22.5%. There were 34 (81%) CIP patients with a lymphocytic cellular pattern. The median ratio of CD3+CD4+/CD3+CD8+ lymphocytes was 0.5 (0.3, 1.0). The ratio was less than 1.0 for 31 CIP patients (73.8%). However, there was no significant difference in the BALF features between patients with low-grade CIPs and those with high-grade CIPs. CONCLUSIONS: The CD3+CD8+ lymphocytosis pattern was the main inflammatory profile in the BALF of CIP patients in this cohort. Targeting CD3+CD8+ lymphocytes might be a treatment option for CIPs.

Comparison of targeted next-generation sequencing and metagenomic next-generation sequencing in the identification of pathogens in pneumonia after congenital heart surgery: a comparative diagnostic accuracy study.

BACKGROUND: This study aimed to compare targeted next-generation sequencing (tNGS) with metagenomic next-generation sequencing (mNGS) for pathogen detection in infants with severe postoperative pneumonia after congenital heart surgery. METHODS: We conducted a retrospective observational study using data from the electronic medical record system of infants who developed severe pneumonia after surgery for congenital heart disease from August 2021 to August 2022. Infants were divided into tNGS and mNGS groups based on the pathogen detection methods. The primary outcome was the efficiency of pathogen detection, and the secondary outcomes were the timeliness and cost of each method. RESULTS: In the study, 91 infants were included, with tNGS detecting pathogens in 84.6% (77/91) and mNGS in 81.3% (74/91) of cases (P = 0.55). No significant differences were found in sensitivity, specificity, PPA, and NPA between the two methods (P > 0.05). tNGS identified five strains with resistance genes, while mNGS detected one strain. Furthermore, tNGS had a faster detection time (12 vs. 24 h) and lower cost ($150 vs. $500) compared to mNGS. CONCLUSION: tNGS offers similar sensitivity to mNGS but with greater efficiency and cost-effectiveness, making it a promising approach for respiratory pathogen detection.

How can we optimize the diagnostic and therapeutic approach to pneumonia? Expert opinion-based recommendations.

Pneumonia continues to be one of the most frequent infectious syndromes and a relevant cause of death and health resources utilization. The OPENIN ("Optimización de procesos clínicos para el diagnóstico y tratamiento de infecciones") Group is composed of Infectious Diseases specialists and Microbiologists and aims at generating recommendations that can contribute to improve the approach to processes with high impact on the health system. Such task relies on a critical review of the available scientific evidence. The first Group meeting (held in October 2023) aimed at answering the following questions: Can we optimize the syndromic and microbiological diagnosis of pneumonia? Is it feasible to safely shorten the length of antibiotic therapy? And, is there any role for the immunomodulatory strategies based on the adjuvant use of steroids, macrolides or immunoglobulins? The present review summarizes the literature reviewed for that meeting and offers a series of expert recommendations.

Development and testing of a deep learning algorithm to detect lung consolidation among children with pneumonia using hand-held ultrasound.

BACKGROUND AND OBJECTIVES: Severe pneumonia is the leading cause of death among young children worldwide, disproportionately impacting children who lack access to advanced diagnostic imaging. Here our objectives were to develop and test the accuracy of an artificial intelligence algorithm for detecting features of pulmonary consolidation on point-of-care lung ultrasounds among hospitalized children. METHODS: This was a prospective, multicenter center study conducted at academic Emergency Department and Pediatric inpatient or intensive care units between 2018-2020. Pediatric participants from 18 months to 17 years old with suspicion of lower respiratory tract infection were enrolled. Bedside lung ultrasounds were performed using a Philips handheld Lumify C5-2 transducer and standardized protocol to collect video loops from twelve lung zones, and lung features at both the video and frame levels annotated. Data from both affected and unaffected lung fields were split at the participant level into training, tuning, and holdout sets used to train, tune hyperparameters, and test an algorithm for detection of consolidation features. Data collected from adults with lower respiratory tract disease were added to enrich the training set. Algorithm performance at the video level to detect consolidation on lung ultrasound was determined using reference standard diagnosis of positive or negative pneumonia derived from clinical data. RESULTS: Data from 107 pediatric participants yielded 117 unique exams and contributed 604 positive and 589 negative videos for consolidation that were utilized for the algorithm development process. Overall accuracy for the model for identification and localization of consolidation was 88.5%, with sensitivity 88%, specificity 89%, positive predictive value 89%, and negative predictive value 87%. CONCLUSIONS: Our algorithm demonstrated high accuracy for identification of consolidation features on pediatric chest ultrasound in children with pneumonia. Automated diagnostic support on an ultraportable point-of-care device has important implications for global health, particularly in austere settings.

Clinical performance of existing diagnostic criteria for pneumonia in older emergency patients: A prospective cohort study.

BACKGROUND: Pneumonia accounts for over half a million older adult emergency department (ED) visits annually, but ED pneumonia diagnosis is inaccurate. Geriatric-specific pneumonia diagnostic criteria exist for other settings; no prospective data exist to determine if application in the older adult ED population is feasible. The objective was to prospectively evaluate the utility of four current diagnostic criteria (Loeb; Modified McGeer; Infectious Disease Society of America/American Thoracic Society; American College of Emergency Physicians) in older adult ED patients. METHODS: This was a prospective, observational cohort study of older adult ED patients ≥65 years of age in two U.S. EDs with suspected pneumonia defined as having chest radiography ordered and treating physician suspicion. The standard we used for defining the presence, absence, or inability to determine a diagnosis of pneumonia diagnosis was expert physician chart adjudication. We report the summary statistics for demographic characteristics and symptoms/exam findings and sensitivity, specificity, and likelihood ratios with 95% confidence intervals of the existing diagnostic criteria. Pre-specified cutoff values of a positive LR >10 and a negative LR <0.3 were considered clinically significant. RESULTS: Of 135 patients enrolled, 27 had pneumonia by adjudicator review. Typical patient-reported pneumonia symptoms, such as fever (18.5%) and new/worse cough (51.9%), were not consistently present in pneumonia. The IDSA/ATS and ACEP criteria had positive LR >10 and negative LR <0.3; however, all confidence intervals included pre-specified cutoffs. CONCLUSIONS: Older adults presented to the ED with low frequency of typical pneumonia symptoms. Although existing diagnostic definitions had promising test characteristics, they may not perform well enough for clinical application without refinement.

Exhaled Volatile Organic Compounds Detection in Pneumonia Screening: A Comprehensive Meta-analysis.

BACKGROUND: Pneumonia is a common lower respiratory tract infection, and early diagnosis is crucial for timely treatment and improved prognosis. Traditional diagnostic methods for pneumonia, such as chest imaging and microbiological examinations, have certain limitations. Exhaled volatile organic compounds (VOCs) detection, as an emerging non-invasive diagnostic technique, has shown potential application value in pneumonia screening. METHOD: A systematic search was conducted on PubMed, Embase, Cochrane Library, and Web of Science, with the retrieval time up to March 2024. The inclusion criteria were diagnostic studies evaluating exhaled VOCs detection for the diagnosis of pneumonia, regardless of the trial design type. A meta-analysis was performed using a bivariate model for sensitivity and specificity. RESULTS: A total of 14 diagnostic studies were included in this meta-analysis. The pooled results demonstrated that exhaled VOCs detection had a combined sensitivity of 0.94 (95% CI: 0.92-0.95) and a combined specificity of 0.83 (95% CI: 0.81-0.84) in pneumonia screening, with an area under the summary receiver operating characteristic (SROC) curve (AUC) of 0.96. The pooled diagnostic odds ratio (DOR) was 104.37 (95% CI: 27.93-390.03), and the pooled positive and negative likelihood ratios (LR) were 8.98 (95% CI: 3.88-20.80) and 0.11 (95% CI: 0.05-0.22), indicating a high diagnostic performance. CONCLUSION: This study highlights the potential of exhaled VOCs detection as an effective, non-invasive screening method for pneumonia, which could facilitate future diagnosis in pneumonia. Further high-quality, large-scale studies are required to confirm the clinical utility of exhaled VOCs detection in pneumonia screening. STUDY REGISTRATION: PROSPERO, Review no. CRD42024520498.