Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

OBJECTIVES : Administrative claims data are prone to underestimate the burden of nontuberculous mycobacterial pulmonary disease (NTM-PD).

METHODS : We developed machine learning-based algorithms using historical claims data from cases with NTM-PD to predict patients with a high probability of having previously undiagnosed NTM-PD and to assess actual prevalence and incidence. Adults with incident NTM-PD were classified from a representative 5% sample of the German population covered by statutory health insurance during 2011-2016 by the International Classification of Diseases, 10th revision code A31.0. Pre-diagnosis characteristics (patient demographics, comorbidities, diagnostic and therapeutic procedures, and medications) were extracted and compared to that of a control group without NTM-PD to identify risk factors.

RESULTS : Applying a random forest model (area under the curve 0.847; total error 19.4%) and a risk threshold of >99%, prevalence and incidence rates in 2016 increased 5-fold and 9-fold to 19 and 15 cases/100,000 population, respectively, for both coded and non-coded vs. coded cases alone.

CONCLUSIONS : The use of a machine learning-based algorithm applied to German statutory health insurance claims data predicted a considerable number of previously unreported NTM-PD cases with high probabilty.

Ringshausen Felix C, Ewen Raphael, Multmeier Jan, Monga Bondo, Obradovic Marko, van der Laan Roald, Diel Roland

2021-Jan-11

Epidemiology, Insurance claims analysis, Machine learning, Nontuberculous mycobacteria, Nontuberculous mycobacterium infections, Probability learning