We aimed to assess the prognostic role of the neutrophil/lymphocyte ratio (NLR) in community-acquired pneumonia (CAP) via a single-center retrospective cohort of hospitalized adult patients from 1/2009 to 12/2019. Patients were dichotomized into lower NLR (≤12) and higher NLR (>12). The primary outcome was mortality. ICU admission and hospital- and ICU-free days were secondary outcomes. The pneumonia severity index (PSI) and the NLR’s ability to predict outcomes was also tested. An NLR ≤12 was observed in 2513 (62.2%) patients and >12 in 1526 (37.8%). After adjusting for PSI, the NLR was not associated with hospital mortality (odds ratio [OR] 1.115; 95% confidence interval [CI] 0.774, 1.606; p = 0.559), but it was associated with a higher risk of ICU admission (OR 1.405; 95% CI 1.216, 1.624; p < 0.001). The PSI demonstrated acceptable discrimination for mortality (area under the receiver operating characteristic curve [AUC] 0.78; 95% CI 0.75, 0.82) which was not improved by adding the NLR (AUC 0.78; 95% CI 0.75, 0.82, p = 0.4476). The PSI’s performance in predicting ICU admission was also acceptable (AUC 0.75; 95% CI 0.74, 0.77) and improved by including the NLR (AUC 0.76, 95% CI 0.74, 0.77, p = 0.008), although with limited clinical significance. The NLR was not superior to the PSI for predicting mortality in hospitalized CAP patients.

Background/Objectives: Interstitial lung disease (ILD) occurs commonly in systemic sclerosis (SSc) and is the leading cause of mortality. There are limited data on the accuracy of lung auscultation in identifying the presence of ILD in patients with SSc. Methods: We retrospectively identified patients with newly diagnosed SSc who had documented lung auscultation findings and chest high-resolution computed tomography (HRCT) available for review. Diagnoses were made by rheumatologists at Mayo Clinic, Rochester, Minnesota, USA over a 4-year period. Pulmonary function measurements included lung volumes, spirometry, and single-breath diffusing capacity. Results: Among 151 patients with SSc (median age, 62 years), 72.2% were female and 55.0% were never smokers. Limited cutaneous SSc was the most common phenotype (67.3%). Seventy (46.4%) patients were evaluated by pulmonologists. There was evidence of ILD by HRCT in 69 patients (45.7%); the most common pattern of ILD was fibrotic nonspecific interstitial pneumonia (59.2%). Respiratory symptoms were present in 46.4% of those with ILD compared to 15.9% among those without. The sensitivity and specificity for crackles heard by rheumatologists in detecting ILD were 50.7% and 97.6%, respectively; for pulmonologists, 71.4% and 85.7%, respectively. Presence of crackles was associated with high positive predictive values (94.6% for rheumatologists vs. 92.1% for pulmonologists, respectively); negative predictive values were moderate (70.2% vs. 56.3%, respectively). Crackles correlated with lower pulmonary function measures but did not differ across ILD patterns. Conclusions: Detection of crackles on lung auscultation appears to be a specific and moderately sensitive indicator of ILD (often asymptomatic) in patients with newly diagnosed SSc. The presence of crackles correlates with worse pulmonary function but may be absent in early ILD.

Recent advances in sleep medicine have led to the development of various validated diagnostic devices for obstructive sleep apnea (OSA). Despite limitations, there has been an increase in the use of home sleep apnea tests (HSAT). The accuracy of HSAT may be limited by the inability of its automated signal analysis to differentiate between artifacts and the target physiological signal, leading to erroneous test interpretations with management implications. We present a 60-year-old male with a mechanical heart valve diagnosed with OSA. Six months post-intervention follow-up, The HSAT report indicated loud snoring 100% of the time. After carefully reviewing the raw HSAT data and history, the source of the non-physiological high-level sound was identified as the mechanical heart valve, and the patient was reassured. This case highlights how trusting only automated analysis can miss important information and arrive at misleading conclusions.