Itinai.com light and shadow chase in a bright clinical trial 46d6fec8 e34f 4900 920c bc826aa5cb79 2
Itinai.com light and shadow chase in a bright clinical trial 46d6fec8 e34f 4900 920c bc826aa5cb79 2

Chest Computed Tomography Findings of Disseminated Histoplasmosis in Advanced HIV Disease

Background

Disseminated histoplasmosis is a serious infection that can be life-threatening, especially for people with advanced HIV. Diagnosing this condition can be challenging due to unclear symptoms and limitations in traditional testing methods.

Study Objectives

This study looked at chest CT scans of patients with disseminated histoplasmosis and advanced HIV to identify common patterns that could help in diagnosis.

Patients and Methods

The study involved 38 patients who were part of a clinical trial using a treatment called liposomal amphotericin B. High-resolution chest CT scans were analyzed by two radiologists, focusing on lung issues like nodules and other abnormalities.

Results

The majority of patients were male, with an average age of about 38 years. They showed severe immune suppression, with very low CD4 cell counts. Most patients had respiratory symptoms and significant weight loss. All patients had abnormal chest CT findings, including:

  • Small miliary nodules (89.5%)
  • Ground-glass opacity (86.8%)
  • Large nodules (73.8%)
  • Cavitated nodules (26.3%)
  • Lymph node enlargement (84.2%)
  • Pleural effusions (23.7%)

Additionally, co-infections were found in 36.8% of the patients.

Conclusions

Chest CT scans show varied and often non-specific findings in disseminated histoplasmosis, which can make diagnosis difficult. However, certain patterns, like diffuse micronodular appearances, should raise suspicion for this disease, especially in patients with weakened immune systems.

Opportunities for Improvement

Based on the trial data, clinics can focus on:

  • Defining clear, measurable outcomes for diagnosing disseminated histoplasmosis.
  • Setting specific goals for patient care based on CT findings.
  • Selecting AI tools that fit clinical needs for better diagnosis and management.
  • Implementing a step-by-step approach to pilot AI solutions and track their effectiveness.

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