Abstract
BackgroundDiagnosis of neuromuscular diseases in primary care is often challenging. Rare diseases such as Pompe disease are easily overlooked by the general practitioner. We therefore aimed to develop a diagnostic support tool using patient-oriented questions and combined data mining algorithms recognizing answer patterns in individuals with selected neuromuscular diseases. A multicenter prospective study for the proof of concept was conducted thereafter.MethodsFirst, 16 interviews with patients were conducted focusing on their pre-diagnostic observations and experiences. From these interviews, we developed a questionnaire with 46 items. Then, patients with diagnosed neuromuscular diseases as well as patients without such a disease answered the questionnaire to establish a database for data mining. For proof of concept, initially only six diagnoses were chosen (myotonic dystrophy and myotonia (MdMy), Pompe disease (MP), amyotrophic lateral sclerosis (ALS), polyneuropathy (PNP), spinal muscular atrophy (SMA), other neuromuscular diseases, and no neuromuscular disease (NND). A prospective study was performed to validate the automated malleable system, which included six different classification methods combined in a fusion algorithm proposing a final diagnosis. Finally, new diagnoses were incorporated into the system.ResultsIn total, questionnaires from 210 individuals were used to train the system. 89.5 % correct diagnoses were achieved during cross-validation. The sensitivity of the system was 93–97 % for individuals with MP, with MdMy and without neuromuscular diseases, but only 69 % in SMA and 81 % in ALS patients. In the prospective trial, 57/64 (89 %) diagnoses were predicted correctly by the computerized system. All questions, or rather all answers, increased the diagnostic accuracy of the system, with the best results reached by the fusion of different classifier methods. Receiver operating curve (ROC) and p-value analyses confirmed the results.ConclusionA questionnaire-based diagnostic support tool using data mining methods exhibited good results in predicting selected neuromuscular diseases. Due to the variety of neuromuscular diseases, additional studies are required to measure beneficial effects in the clinical setting.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0268-5) contains supplementary material, which is available to authorized users.
Highlights
Diagnosis of neuromuscular diseases in primary care is often challenging
Among various narratives belonging to same category, the questions were created to generate a questionnaire consisting of 46 items reflecting all categories (Table 1 and Additional file 1)
Creation of a novel questionnaire The six most important questions in this study are displayed in Table 1
Summary
Diagnosis of neuromuscular diseases in primary care is often challenging. Rare diseases such as Pompe disease are overlooked by the general practitioner. Patients with late-onset Pompe disease [glycogen storage disease II, acid-maltase deficiency (MP)], motor neuron disease, muscular dystrophy, or other neuromuscular diseases frequently experience diagnostic delay [1,2,3,4]. The rarity of these diseases together with clinical variability, atypical presentations, or lack of time for a thorough examination and medical history taking contribute to the delay in diagnosis. One goal of this study was to integrate the past medical history into a diagnostic tool and to combine it with modern statistical technologies. The practical experiences of the patient should be closely integrated into the diagnostic process
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