Speaking of the "Devil": Diagnostic Errors in Interstitial Lung Diseases.

Interstitial lung diseases are respiratory diseases, which affect the normal lung parenchyma and can lead to significant pulmonary fibrosis, chronic respiratory failure, pulmonary hypertension, and ultimately death. Reuniting more than 200 entities, interstitial lung diseases pose a significant challenge to the clinician, as they represent rare diseases with vague and insidious respiratory symptoms. As such, there are many diagnostic errors that can appear along the journey of the patient with ILD, which leads to significant delays with implications for the prognosis and the quality of life of the patient.

Exploring the Role of Biomarkers Associated with Alveolar Damage and Dysfunction in Idiopathic Pulmonary Fibrosis-A Systematic Review.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is one of the most aggressive forms of interstitial lung diseases (ILDs), marked by an ongoing, chronic fibrotic process within the lung tissue. IPF leads to an irreversible deterioration of lung function, ultimately resulting in an increased mortality rate. Therefore, the focus has shifted towards the biomarkers that might contribute to the early diagnosis, risk assessment, prognosis, and tracking of the treatment progress, including those associated with epithelial injury. METHODS: We conducted this review through a systematic search of the relevant literature using established databases such as PubMed, Scopus, and Web of Science. Selected articles were assessed, with data extracted and synthesized to provide an overview of the current understanding of the existing biomarkers for IPF. RESULTS: Signs of epithelial cell damage hold promise as relevant biomarkers for IPF, consequently offering valuable support in its clinical care. Their global and standardized utilization remains limited due to a lack of comprehensive information of their implications in IPF. CONCLUSIONS: Recognizing the aggressive nature of IPF among interstitial lung diseases and its profound impact on lung function and mortality, the exploration of biomarkers becomes pivotal for early diagnosis, risk assessment, prognostic evaluation, and therapy monitoring.

A Debate on Surgical and Nonsurgical Approaches for Obstructive Sleep Apnea: A Comprehensive Review.

Regular and unobstructed breathing during the night is the prerequisite for an undisturbed and restful sleep. The most prevalent nocturnal breathing disturbance with morbid consequences is obstructive sleep apnea syndrome. The prevalence of obstructive sleep apnea (OSA) is increasing, and a significant number of patients with OSA are undiagnosed. On the other hand, the "obesity epidemic" is a growing concern globally. A sleep test is required to diagnose sleep apnea and to individualize therapy. A multidisciplinary approach is the key to success. This narrative review presents a debate on whether surgery is a friend or a foe in the treatment of sleep apnea. Depending on the type and severity of the apnea, the causal factor, and the presence of obesity and hypercapnia as well as the associated pathologies, the optimal therapeutic method is determined for each individual case. The article concludes that each case is unique, and there is no ideal method. Positive pressure ventilation, although a therapeutic gold standard, has its disadvantages extensively discussed in this paper. Nevertheless, it is necessary prior to any surgical intervention, either for the curative treatment of the causal factor of apnea or for elective surgery for another condition. The anesthetic risks associated with the presence of apnea and obesity should not be neglected and should form the basis for decision making regarding surgical interventions for the treatment of sleep apnea.

Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience.

BACKGROUND AND OBJECTIVES: In the early period after liver transplantation, patients are exposed to a high rate of complications and several scores are currently available to predict adverse postoperative outcomes. However, an ideal, universally accepted and validated score to predict adverse events in liver transplant recipients with hepatitis C is lacking. Therefore, we aimed to establish and validate a machine learning (ML) model to predict short-term outcomes of hepatitis C patients who underwent liver transplantation. MATERIALS AND METHODS: We conducted a retrospective observational two-center cohort study involving hepatitis C patients who underwent liver transplantation. Based on clinical and laboratory parameters, the dataset was used to train a deep-learning model for predicting short-term postoperative complications (within one month following liver transplantation). Adverse events prediction in the postoperative setting was the primary study outcome. RESULTS: A total of 90 liver transplant recipients with hepatitis C were enrolled in the present study, 80 patients in the training cohort and ten in the validation cohort, respectively. The age range of the participants was 12-68 years, 51 (56,7%) were male, and 39 (43.3%) were female. Throughout the 85 training epochs, the model achieved a very good performance, with the accuracy ranging between 99.76% and 100%. After testing the model on the validation set, the deep-learning classifier confirmed the performance in predicting postoperative complications, achieving an accuracy of 100% on unseen data. CONCLUSIONS: We successfully developed a ML model to predict postoperative complications following liver transplantation in hepatitis C patients. The model demonstrated an excellent performance for accurate adverse event prediction. Consequently, the present study constitutes the foundation for careful and non-invasive identification of high-risk patients who might benefit from a more intensive postoperative monitoring strategy.

Cardiovascular Involvement in Tuberculosis: From Pathophysiology to Diagnosis and Complications-A Narrative Review.

Although primarily a lung disease, extra-pulmonary tuberculosis (TB) can affect any organ or system. Of these, cardiovascular complications associated with disease or drug toxicity significantly worsen the prognosis. Approximately 60% of patients with TB have a cardiovascular disease, the most common associated pathological entities being pericarditis, myocarditis, and coronary artery disease. We searched the electronic databases PubMed, MEDLINE, and EMBASE for studies that evaluated the impact of TB on the cardiovascular system, from pathophysiological mechanisms to clinical and paraclinical diagnosis of cardiovascular involvement as well as the management of cardiotoxicity associated with antituberculosis medication. The occurrence of pericarditis in all its forms and the possibility of developing constrictive pericarditis, the association of concomitant myocarditis with severe systolic dysfunction and complication with acute heart failure phenomena, and the long-term development of aortic aneurysms with risk of complications, as well as drug-induced toxicity, pose complex additional problems in the management of patients with TB. In the era of multidisciplinarity and polymedication, evidence-based medicine provides various tools that facilitate an integrative management that allows early diagnosis and treatment of cardiac pathologies associated with TB.

The Risk of Sarcoidosis Misdiagnosis and the Harmful Effect of Corticosteroids When the Disease Picture Is Incomplete.

Sarcoidosis is a complex granulomatous disease of unknown etiology. Due to the heterogeneity of the disease, the diagnosis remains challenging in many cases, often at the physician's discretion, requiring a thorough and complex investigation. Many other granulomatous diseases have the potential to mimic sarcoidosis, whether infectious, occupational, or autoimmune diseases and starting an unnecessary corticosteroid treatment can worsen the patient's prognosis, leading to side effects that can be harder to treat than the actual disease.