Automatic Adjudication of Symptom-Based Exacerbations in Bronchiectasis Patients Treated with Azithromycin

• Activity Number: 503
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Mark Wheldon* and Alain C. Vandal and Anne-Cecile Bourien and Lata Jayaram and Noel Karalus and Cecilia Tong and Hans Hockey and Conroy Wong
• Companies: Auckland University of Technology and Auckland University of Technology and Ecole Nationale de la Statistique et de l'Analyse de l'Information and Monash Health and Waikato District Health Board and Centre for Clinical Trials and Effective Practice and Biometrics Matters Ltd. and Counties Manukau District Health Board
• Keywords: logistic regression ; predictive model ; bronchiectasis ; exacerbation ; respiratory condition

Abstract

The EMBRACE multi-center RCT evaluated the effect of azithromycin on frequency of event-based exacerbations (EBEs), lung function and health-related quality of life in adult patients with non-cystic fibrosis bronchiectasis. The treatment was effective in lowering the rate of EBEs relative to placebo (Wong et al., Lancet 2012 380(9842):660--7). An EBE, a binary outcome, is a sustained worsening of condition requiring treatment with antibiotics. Respiratory condition is defined, partly, in terms of daily sputum volume, sputum purulence, and dyspnoea, recorded daily in diaries kept by participants. Quality of life, as measured by the St. George's respiratory questionnaire, was also recorded in the diaries. Here, we use the daily diary entries to build a statistical model to estimate the probability that an EBE is imminent. A definition of symptom-based exacerbation (SBE) is developed by comparing the ROC curves of logistic regression models with EBE as the response and respiratory condition over different time windows as the predictors. Predictive accuracy is estimated using cross-validation. The new SBE definition is validated against self-reported quality of life.