Emergency transcatheter arterial embolization for massive hemoptysis due to pulmonary tuberculosis and tuberculosis sequelae

Revolutionizing diagnosis of pulmonary Mycobacterium tuberculosis based on CT: a systematic review of imaging analysis through deep learning

Introduction

The mortality rate associated with Mycobacterium tuberculosis (MTB) has seen a significant rise in regions heavily affected by the disease over the past few decades. The traditional methods for diagnosing and differentiating tuberculosis (TB) remain thorny issues, particularly in areas with a high TB epidemic and inadequate resources. Processing numerous images can be time-consuming and tedious. Therefore, there is a need for automatic segmentation and classification technologies based on lung computed tomography (CT) scans to expedite and enhance the diagnosis of TB, enabling the rapid and secure identification of the condition. Deep learning (DL) offers a promising solution for automatically segmenting and classifying lung CT scans, expediting and enhancing TB diagnosis.

Methods

This review evaluates the diagnostic accuracy of DL modalities for diagnosing pulmonary tuberculosis (PTB) after searching the PubMed and Web of Science databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines.

Results

Seven articles were found and included in the review. While DL has been widely used and achieved great success in CT-based PTB diagnosis, there are still challenges to be addressed and opportunities to be explored, including data scarcity, model generalization, interpretability, and ethical concerns. Addressing these challenges requires data augmentation, interpretable models, moral frameworks, and clinical validation.

Conclusion

Further research should focus on developing robust and generalizable DL models, enhancing model interpretability, establishing ethical guidelines, and conducting clinical validation studies. DL holds great promise for transforming PTB diagnosis and improving patient outcomes.

The prevalence and risks of major comorbidities among inpatients with pulmonary tuberculosis in China from a gender and age perspective: a large-scale multicenter observational study

In clinical practice, PTB patients have concurrent many types of comorbidities such as pneumonia, liver disorder, diabetes mellitus, hematological disorder, and malnutrition. Detecting and treating specific comorbidities and preventing their development are important for PTB patients. However, the prevalence of most comorbid conditions in patients with PTB is not well described. We conducted a large-scale, multicenter, observational study to elucidate and illustrate the prevalence rates of major comorbidities in inpatients at 21 hospitals in China. The 19 specific comorbidities were selected for analysis in this patient cohort, and stratified the inpatient cohort according to age and gender. A total of 355,929 PTB inpatients were included, with a male:female ratio of 1.98 and the proportion of ≥ 65 years PTB inpatients was the most. Approximately 70% of PTB inpatients had at least one defined type of comorbidity. The prevalence of 19 specific comorbidities in inpatients with PTB was analyzed, with pneumonia being the most common comorbidity. The prevalence of most comorbidities was higher in males with PTB except thyroid disorders, mental health disorders, etc. The prevalence of defined most comorbidities in patients with PTB tended to increase with increasing age, although some specific comorbidities tended to increase initially then decrease with increasing age. Our study describes multiple clinically important comorbidities among PTB inpatients, and their prevalence between different gender and age groups. The results will enhance the clinical aptitude of physicians who treat patients with PTB to recognize, diagnose, and treat PTB comorbidities early.

Identifying a Clinical Risk Triage Score for Adult Emergency Department

Emergency triage is crucial for the treatment and prognosis of emergency patients, but its validity needs further improvement. The purpose of this study was to identify a risk score for adult triage. We conducted a regression analysis of physiological and biochemical data from 1,522 adult patients. A 60-point triage scoring model included temperature, pulse, systolic blood pressure, oxygen saturation, consciousness, dyspnea, admission mode, syncope history, chest pain or chest tightness, complexion, hematochezia or hematemesis, hemoptysis, white blood count, creatinine, bicarbonate, platelets, and creatine kinase. The area under curve in predicting ICU admission was 0.929 (95% CI [0.913-0.944]) for the derivation cohort and 0.911 (95% CI [0.884-0.938]) for the validation cohort. Four categories: critical level (≥13 points), severe level (6-12 points), urgency level (1-5 points), and sub-acute level (0 points) were divided, which significantly distinguished the severity of emergency patients.

Long-Term Outcomes of Bronchial Artery Embolization for Patients with Non-Mycobacterial Non-Fungal Infection Bronchiectasis

BACKGROUND

There is no study on the predictive factors of recurrent haemoptysis after bronchial artery embolization (BAE) with the long-term outcomes in patients with bronchiectasis (BE).

OBJECTIVES

To evaluate the long-term outcomes of BAE in BE patients without accompanying refractory active infection of mycobacteriosis and aspergillosis with analysis for the predictive factors of recurrent haemoptysis.

METHODS

Data of 106 patients with BE who underwent BAE using coils between January 2011 and December 2018 were retrospectively reviewed. The cumulative haemoptysis control rate was estimated using Kaplan-Meier methods with log-rank tests to analyze differences in recurrence-free rate between groups based on technical success and failure, bacterial colonization status, number of BE lesions, and vessels embolized to bronchial arteries (BAs) or BAs + non-bronchial systemic arteries (NBSAs).

RESULTS

Bacterial colonization was detected in approximately 60% of patients. Computed tomography showed bronchiectatic lesions with 2.9 ± 1.4 lobes. In the first series of BAE, embolization was performed in the BAs alone and BAs + NBSAs in 65.1 and 34.9% of patients, respectively, with 2.4 ± 1.4 embolized vessels in total. The median follow-up period was 1,000 (7-2,790) days. The cumulative haemoptysis control rates were 91.3, 84.2, 81.5, and 78.9% at 1, 2, 3, and 5 years, respectively. The haemoptysis control rates were higher in the technical success group than in the technical failure group (p = 0.029).

CONCLUSIONS

High haemoptysis control rates for long-term periods were obtained by embolization for all visualized abnormal arteries, regardless of the colonization status, number of bronchiectatic lobes, and target vessels, irrespective of NBSAs.

A systematic approach to the management of massive hemoptysis

Massive hemoptysis is regarded as a potentially lethal condition that requires immediate attention, and prompt action. Although minor hemoptysis is frequently encountered by most clinicians, massive hemoptysis in far less frequent and most physicians are not prepared to manage this time-sensitive clinical presentation in a systematic and timely fashion. Critical initial steps in management need to be implemented in an expedited fashion, such that patients may have a chance at a more definitive treatment. In this article, we review the definition, vascular anatomy, etiology, diagnostic evaluation, epidemiology and prognostic markers of massive hemoptysis. A systematic approach to management, stabilization and treatment options is followed. An algorithm is proposed for the management of massive hemoptysis and the importance of a multidisciplinary approach is emphasized.