Development of an algorithm for finding pertussis episodes in a population-based electronic health record database

• Main Authors: Chathuri Daluwatte (Author), Maryia Dvaretskaya (Author), Sam Ekhtiari (Author), Paul Hayat (Author), Martin Montmerle (Author), Sachin Mathur (Author), Denis Macina (Author)
• Format: Book
• Published: Taylor & Francis Group, 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2164-5515; 2164-554X; 10.1080/21645515.2023.2209455
• Keywords: algorithms; bordetella pertussis; diagnosis; electronic health record; machine learning; predictive modeling; Immunologic diseases. Allergy; RC581-607; Therapeutics. Pharmacology; RM1-950
• Source: Human Vaccines & Immunotherapeutics, Vol 19, Iss 1 (2023)

While tetanus-diphtheria-acellular pertussis (Tdap) vaccines for adolescents and adults were licensed in 2005 and immunization strategies proposed, the burden of pertussis in this population remains under-recognized mainly due to atypical disease presentation, undermining efforts to optimize protection through vaccination. We developed a machine learning algorithm to identify undiagnosed/misdiagnosed pertussis episodes in patients diagnosed with acute respiratory disease (ARD) using signs, diseases and symptoms from clinician notes and demographic information within electronic health-care records (Optum Humedica repository [2007-2019]). We used two patient cohorts aged ≥11 years to develop the model: a positive pertussis cohort (4,515 episodes in 4,316 patients) and a negative pertussis (ARD) cohort (4,573,445 episodes and patients), defined using ICD 9/10 codes. To improve contrast between positive pertussis and negative pertussis (ARD) episodes, only episodes with ≥7 symptoms were selected. LightGBM was used as the machine learning model for pertussis episode identification. Model validity was determined using laboratory-confirmed pertussis positive and negative cohorts. Model explainability was obtained using the Shapley additive explanations method. The predictive performance was as follows: area under the precision-recall curve, 0.24 (SD, 7 × 10−3); recall, 0.72 (SD, 4 × 10−3); precision, 0.012 (SD, 1 × 10−3); and specificity, 0.94 (SD, 7 × 10−3). The model applied to laboratory-confirmed positive and negative pertussis episodes had a specificity of 0.846. Predictive probability for pertussis increased with presence of whooping cough, whoop, and post-tussive vomiting in clinician notes, but decreased with gastrointestinal bleeding, sepsis, pulmonary symptoms, and fever. In conclusion, machine learning can help identify pertussis episodes among those diagnosed with ARD.