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Non-infectious Parenchymal Lung Disease

  • G. Dalpiaz
  • M. Piolanti
Chapter
Part of the Medical Radiology book series (MEDRAD)

Abstract

Acute dyspnea is a common presenting complaint in the emergency room, emergency medicine and intensive care. It may have a cardiovascular or a non-cardiovascular origin, the latter including pulmonary parenchymal diseases. Depending on the cause, it may be associated with fever, cough, hemoptysis, and/or chest pain, with a duration of symptoms that can range from hours to days.

Prompt identification of the underlying cause of acute dyspnea is essential in guiding appropriate therapy and management, as patients may rapidly progress to acute respiratory failure. Evaluation with chest radiography is vital for initial assessment and may reveal diffuse parenchymal abnormalities, which may require further assessment with computed tomography (HRCT).

Acute non-infectious parenchymal lung diseases are often overlooked and may be under-diagnosed. Their diagnosis requires the evaluation, along with the HRCT pattern, of the clinical and laboratory features and of the bronchoalveolar lavage. Biopsy may be necessary in more complex cases.

Although the most frequent cause of diffuse non-infectious parenchymal lung involvement is acute hydrostatic pulmonary edema, there is a wide variety of diseases that may be encountered, including acute drug toxicity, hypersensitivity pneumonitis (HP), acute respiratory distress syndrome (ARDS) and diffuse alveolar hemorrhage (DAH). In trauma patients, fat embolism syndrome (FES) must be taken into account. Acute respiratory failure is an eventuality that can occur during the course of chronic lung diseases (UIP for example), which may have been unknown until then.

Keywords

Acute Respiratory Distress Syndrome Pulmonary Capillary Wedge Pressure Hypersensitivity Pneumonitis Diffuse Alveolar Damage Diffuse Alveolar Hemorrhage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Radiologia Ospedale BellariaAUSL BolognaItaly
  2. 2.Radiologia Ospedale MaggioreAUSL BolognaItaly

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