Platypnea-Orthodeoxia syndrome (POS) is a rare clinical entity characterized by dyspnea and arterial desaturation while in the upright position. The various pathophysiologic mechanisms leading to POS has puzzled clinicians for years. The hypoxia in POS has been attributed to the mixing of the deoxygenated venous blood with the oxygenated arterial blood via a shunt. The primary mechanisms of POS in these patients can be broadly classified based on intracardiac abnormalities, extracardiac abnormalities and miscellaneous etiologies. A Patent Foramen Ovale (PFO) was the most common reported site of an intracardiac shunt. In addition to PFO, intracardiac shunt leading to POS has been reported from either an Atrial Septal Defect (ASD) or an Atrial Septal Aneurysm (ASA). Most patients with an intracardiac shunt also demonstrated a secondary anatomic or a functional defect. Extracardiac causes of POS included intra-pulmonary arteriovenous malformations and lung parenchymal diseases. A systematic evaluation is necessary to identify the underlying cause and institute an appropriate intervention. We conducted a review of literature and reviewed 239 cases of POS. In this article, we review the etiology and pathophysiology of POS and also summarize the diagnostic algorithms and treatment modalities available for early diagnosis and prompt treatment of patients presenting with symptoms of platypnea and/or orthodeoxia.

Pulmonary artery aneurysm is a rare but important entity in the spectrum of pulmonary vascular diseases. The etiologies can be varied and patients can present with non-specific symptoms with the diagnosis being incidental. There is limited consensus regarding the diagnostic criteria and follow-up imaging for patients diagnosed with this entity. Further the management strategies can be variable depending upon underlying disease, etiology, center dependent expertise, and resources available. We review the etiologies, epidemiology, classification, clinical manifestations, and imaging features of pulmonary artery aneurysm. We also review the current management strategies and suggest an algorithmic approach to these patients.

Pneumomediastinum (PM) is defined as the presence of free air in the mediastinal cavity. It is often regarded as a revealing sign of a more serious medical condition. PM is broken down into two categories, one, with an instigating event, referred to as secondary PM. The other is when free air is discovered in the mediastinal cavity without a clear etiology, referred to as spontaneous pneumomediastinum (SPM). Often misdiagnosed due to the vague nature of presenting symptoms, SPM must be part of the differential diagnosis of a chest pain patient to expedite discovery and if necessary, management. A MedLine/PubMED search was performed identifying all relevant articles with “SPM” in the title. Six case series were reviewed to determine what clinical scenario constitutes a possible case of SPM. Results showed that almost all patients with SPM exhibited some chest pain, but Hamman’s crunch was present in only one-fifth of patients. Patients with certain pre-existing pulmonary diseases showed a greater propensity for the presence of free air in the mediastinal cavity. SPM must be diagnosed and managed promptly due to rare, but serious complications and any chest pain with an unknown etiology should contain SPM in the differential diagnosis.

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed. Objectives: To develop and validate a deep learning method to improve the management of IPNs. Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions. Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4–90.7%) and 91.9% (95% CI, 88.7–94.7%), compared with 78.1% (95% CI, 68.7–86.4%) and 81.9 (95% CI, 76.1–87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts. Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.

Socioeconomic status (SES) is defined as an individual's social or economic standing, and is a measure of an individual's or family's social or economic position or rank in a social group. It is a composite of several measures including income, education, occupation, location of residence or housing. Studies have found a lower SES has been linked to disproportionate access to health care in many diseases. There is emerging data in pulmonary diseases such as COPD, asthma, cystic fibrosis, pulmonary hypertension and other chronic respiratory conditions that allude to a similar observation noted in other chronic diseases. In the setting of COPD, SES has an inverse relationship with COPD prevalence, mortality, health utilization costs and HRQoL. Asthma and cystic fibrosis show an increased severity and hospitalizations in relationship to a lower SES. Similar observations were seen in sarcoidosis, PHTN and obstructive sleep apnea. There remains a limited data on non-CF bronchiectasis and interstitial lung diseases. Population SES may be gauged by various measures such as education, occupation, marital status but no value is more indicative than income. Currently guidelines and management algorithms do not factor the effect of SES in the disease process. Despite the great amount of data available, a standardized method must be created to include SES in the prognostic calculations and management of chronic pulmonary diseases.