Bronchiolitis obliterans syndrome is a pulmonary complication of allogeneic hematopoietic cell transplantation. Recent National Institutes of Health consensus diagnostic criteria for bronchiolitis obliterans syndrome have not been assessed in a clinical setting. Modified National Institutes of Health diagnostic consensus criteria for bronchiolitis obliterans syndrome were applied to evaluate its prevalence, risk factors and outcomes in the modern era of allogeneic hematopoietic cell transplantation. Pulmonary function tests from 1145 patients were screened to identify patients with new-onset airflow obstruction. Clinical records were reviewed to exclude pulmonary infection and other causes. The overall prevalence of bronchiolitis obliterans syndrome among all transplanted patients was 5.5%, and 14% among patients with chronic graft-versus-host disease. The median time from transplant to meeting spirometric criteria for bronchiolitis obliterans syndrome was 439 days (range 274–1690). Although many previously identified risk factors were not significantly associated, lower baseline FEV1/SVC ratio (P = 0.006), non-Caucasian race (P = 0.014), and lower circulating IgG level (P = 0.010), and presence of chronic graft versus host disease (P < 0.001) were associated with an increase in risk, with the latter associated with a 10-fold increase in risk. Multivariate analysis indicated that bronchiolitis obliterans syndrome conferred a 1.6 fold increase in risk for mortality after diagnosis. These results suggest that National Institutes of Health diagnostic criteria can reliably identify bronchiolitis obliterans syndrome, and that it is more prevalent than previously suggested. Spirometric monitoring of high-risk patients with chronic graft-versus-host disease may permit earlier detection and intervention for this often-fatal disease.

Backround Bronchiolitis obliterans syndrome (BOS) is a devastating pulmonary complication affecting long term survivors of allogeneic hematopoietic cell transplantation. Treatment of BOS with prolonged courses of high dose corticosteroids is often associated with significant morbidity. Reducing the exposure to corticosteroids may reduce treatment related morbidity. Our institution has recently begun to treat patients with emerging therapies in an effort to diminish steroid exposure. Methods We retrospectively reviewed the 6-month corticosteroid exposure, lung function, and failure rates in 8 patients with newly diagnosed BOS who were treated with a combination of fluticasone, azithromycin and montelukast (FAM) and a rapid corticosteroid taper. These patients were compared to 14 matched historical patients who received high dose corticosteroids followed by a standard taper. Results The median 6-month prednisone exposure in FAM-treated patients was 1819 mg [0 mg to 4036 mg] compared to 7163 mg [6551 mg to 7829 mg] in the control group (p = 0.002). The median FEV1 change in FAM-treated patients was 2% [−3% to 4%] compared to 1% [−4 to 5%] in the control group (p = 1.0). Discussion Prednisone exposure in FAM patients was one quarter that of a retrospective matched group of patients, with minimal change in median FEV1, suggesting that BOS may be spared of the morbidities associated with long-term corticosteroid use by using alternative agents with less side effects.

We conducted a 15-year retrospective cohort study to determine the prevalence of restrictive lung disease prior to allogeneic hematopoietic cell transplant (HCT), and to assess whether this was a risk factor for poor outcomes. 2545 patients were eligible for the analysis. Restrictive lung disease was defined as a total lung capacity (TLC) <80% of predicted normal. Chest x-rays and /or computed tomography scans were reviewed for all restricted patients to determine whether lung parenchymal abnormalities were unlikely or highly likely to cause restriction. Multivariate Cox-proportional hazard and sensitivity analyses were performed to assess the relationship between restriction and early respiratory failure and nonrelapse mortality. Restrictive lung disease was present in 194 subjects (7.6%) prior to transplantation. Among these cases, radiographically apparent abnormalities were unlikely to be the cause of the restriction in 149 (77%) subjects. In unadjusted and adjusted analyses, the presence of pulmonary restriction was significantly associated with a 2-fold increase in risk for early respiratory failure and nonrelapse mortality, suggesting that these outcomes occurring in the absence of radiographically apparent abnormalities may be related to respiratory muscle weakness. These findings suggest that pulmonary restriction should be considered as a risk factor for poor outcomes after transplant.

If RP efficacy and prostate cancer survival in the absence of screening are similar to that in the SPCG-4 trial, then overdiagnosis and lead time largely explain the lower AMD in PIVOT. If these artifacts of screening are the correct explanation, then there is a subset of case subjects that should not be treated with RP, and identifying this subset should lead to a clearer understanding of the benefit of RP in the remaining cases.

Objective: Multiple clinical prediction rules have been developed, but lack validation. This study aims to identify a set of prediction algorithms for influenza, based on electronic health record (EHR) structured data and clinical notes derived data using Unified Medical Language System (UMLS) driven natural language processing (NLP). Materials and Methods:Data were extracted from an enterprise-wide data warehouse for all patients who tested positive for influenza and were seen in ambulatory care between 2009 and 2019 (N = 7,278). A text processing pipeline was used to analyze chart notes for UMLS terms for symptoms of interest to improve data quality completeness. Three models, which step up complexity of the dataset and predictors, were tested with least absolute shrinkage and selection operator (LASSO)-selected parameters to identify predictors for influenza. Receiver operating characteristic (ROC) curves compared test accuracy across the three models.Results: Three models identified 7, 8, and 10 predictors, and the most complex model performed best. The addition of the UMLS-driven NLP symptoms data improved data quality (false negatives) and increased the number of significant predictors. NLP also increased the strength of the models, as did the addition of two-way predictor interactions. Discussion:The EHR is a feasible source for offering rapidly accessible datasets for influenza related prediction research that was used to produce a prediction model for influenza.Combining data collected in routine care with data science methods improved a prediction model for influenza, and in the future, could be used to drive diagnostics at the point of care.

Background: Lung cancer is the most common cause of cancer-related death in the United States (US), with most patients diagnosed at later stages (3 or 4). While most patients are diagnosed following symptomatic presentation, no studies have compared symptoms and physical examination signs at or prior to diagnosis from electronic health records (EHR) in the United States (US). Objective: To identify symptoms and signs in patients prior to lung cancer diagnosis in EHR data. Study Design: Case-control study. Methods: We studied 698 primary lung cancer cases in adults diagnosed between January 1, 2012 and December 31, 2019, and 6,841 controls matched by age, sex, smoking status, and type of clinic. Coded and free-text data from the EHR were extracted from 2 years prior to diagnosis date for cases and index date for controls. Univariate and multivariate conditional logistic regression were used to identify symptoms and signs associated with lung cancer. Analyses were repeated excluding symptom data from 1, 3, 6, and 12 months before the diagnosis/index dates. Results: Eleven symptoms and signs recorded during the study period were associated with a significantly higher chance of being a lung cancer case in multivariate analyses. Of these, seven were significantly associated with lung cancer six months prior to diagnosis: hemoptysis (OR 3.2, 95%CI 1.9-5.3), cough (OR 3.1, 95%CI 2.4-4.0), chest crackles or wheeze (OR 3.1, 95%CI 2.3-4.1), bone pain (OR 2.7, 95%CI 2.1-3.6), back pain (OR 2.5, 95%CI 1.9-3.2), weight loss (OR 2.1, 95%CI 1.5-2.8) and fatigue (OR 1.6, 95%CI 1.3-2.1). Conclusions: Patients diagnosed with lung cancer appear to have symptoms and signs recorded in the EHR that distinguish them from similar matched patients in ambulatory care, often six months or more before their diagnosis. These findings suggest opportunities to improve the diagnostic process for lung cancer in the US.

No abstract

Purpose: Problem drug-related behavior (PDB) among patients on chronic opioid therapy may reflect an opioid use disorder. This study assessed PDB prevalence and the relationship between PDB and ongoing prescription of opioids at a primary care clinic that implemented a multifaceted opioid management program.Methods: A chart review of patients in a chronic opioid registry assessed prevalence of different types of PDB over 2 years, and whether opioids were prescribed during the last 3 months of the 2-year study period among patients with different levels of PDB.Results: Among 233 registry patients, 84.1% exhibited PDB; 45.5% exhibited >3 types of PDB. At the end of 2 years, most registry patients were still prescribed opioids, though patients with >3 types of PDB were less likely than those without PDB to be prescribed opioids (62.3% vs. 78.4%, P ‫؍‬ 0.016).Conclusions: PDB was pervasive in this population of patients on chronic opioid therapy. Those with the most PDB, and thus with the greatest likelihood of opioid use disorder and its social and medical consequences, were the least likely to be prescribed opioids by the clinic after 2 years. Given the rising rates of illicit opioid use in the U.S., it is important that clinics work closely with their patients who display PDB, systematically assess them for opioid use disorder, and offer evidence-based treatment.