Clinical outcomes associated with respiratory virus detection before allogeneic hematopoietic stem cell transplant.

BACKGROUND: The management of respiratory virus infections prior to hematopoietic cell transplant (HCT) is difficult. We examined whether respiratory virus detection before HCT influenced the requirement for bronchoscopy, hospitalization, and overall survival following HCT. METHODS: Pre-HCT and weekly post-HCT nasal washes were collected through day 100 from patients with and without symptoms. Samples were tested by multiplex polymerase chain reaction for respiratory syncytial virus, parainfluenza viruses 1-4, influenza A and B, human metapneumovirus, adenovirus, and human rhinoviruses, coronaviruses, and bocavirus. RESULTS: Of 458 patients, 116 (25%) had respiratory viruses detected pre-HCT. Overall, patients with viruses detected pre-HCT had fewer days alive and out of the hospital and lower survival at day 100 (adjusted hazard ratio [aHR], 2.4; 95% confidence interval [CI], 1.3-4.5; P = .007) than patients with negative samples; this risk was also present with rhinovirus alone (aHR for mortality, 2.6; 95% CI, 1.2-5.5; P = .01). No difference in bronchoscopy incidence was seen in patients with and without respiratory viruses (aHR, 1.3; 95% CI, .8-2.0; P = .32). In symptomatic patients, those with respiratory viruses detected had increased overall mortality compared with patients without viruses detected (unadjusted HR, 3.5; 95% CI, 1.0-12.1; P = .05); among asymptomatic patients, detection of respiratory viruses was not associated with increased mortality. CONCLUSIONS: These data support routine testing for respiratory viruses among symptomatic patients before HCT, and delay of transplant with virus detection when feasible, even for detection of rhinovirus alone. Further study is needed to address whether asymptomatic patients should undergo screening for respiratory virus detection before HCT.

The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: relation to polysomnographic measurements of the apnea/hypopnea index.

BACKGROUND: Various models and questionnaires have been developed for screening specific populations for obstructive sleep apnea (OSA) as defined by the apnea/hypopnea index (AHI); however, almost every method is based upon dichotomizing a population, and none function ideally. We evaluated the possibility of using the STOP-Bang model (SBM) to classify severity of OSA into 4 categories ranging from none to severe. METHODS: Anthropomorphic data and the presence of snoring, tiredness/sleepiness, observed apneas, and hypertension were collected from 1426 patients who underwent diagnostic polysomnography. Questionnaire data for each patient was converted to the STOP-Bang equivalent with an ordinal rating of 0 to 8. Proportional odds logistic regression analysis was conducted to predict severity of sleep apnea based upon the AHI: none (AHI < 5/h), mild (AHI ≥ 5 to < 15/h), moderate (≥ 15 to < 30/h), and severe (AHI ≥ 30/h). RESULTS: Linear, curvilinear, and weighted models (R(2) = 0.245, 0.251, and 0.269, respectively) were developed that predicted AHI severity. The linear model showed a progressive increase in the probability of severe (4.4% to 81.9%) and progressive decrease in the probability of none (52.5% to 1.1%). The probability of mild or moderate OSA initially increased from 32.9% and 10.3% respectively (SBM score 0) to 39.3% (SBM score 2) and 31.8% (SBM score 4), after which there was a progressive decrease in probabilities as more patients fell into the severe category. CONCLUSIONS: The STOP-Bang model may be useful to categorize OSA severity, triage patients for diagnostic evaluation or exclude from harm.