Delayed diagnosis of immune mediated necrotizing myopathy (IMNM) leads to increased morbidity. Patients with chronic course without HMGCR-IgG (3-hydroxy-3-methylglutaryl-coenzyme-A reductase) or SRP54-IgGs (signal recognition particle) are often challenging to diagnose. Immunotherapy response can also be difficult to assess. We created a statistical model to assist IMNM diagnosis. Electrical myotonia vs fibrillations were reviewed as biomarkers for immunotherapy treatment response. Identified were 119 IMNM cases and 938 other-myopathy patients. Inclusion criteria included all having electrophysiological evaluations, muscle biopsies showing inflammatory/necrotizing myopathies, comprehensively recorded neurological examinations, and creatine kinase values. Electrical myotonia was recorded in 56% (67/119) of retrospective and 67% (20/30) of our validation IMNM cohorts, and significantly (p < 0.001) favored IMNM over other myopathies: sporadic inclusion body myositis (odds ratio = 4.78); dermatomyositis (odds ratio = 10.61); nonspecific inflammatory myopathies (odds ratio = 8.46); limb-girdle muscular dystrophies (odds ratio = 5.34) or mitochondrial myopathies (odds ratio = 14.17). Electrical myotonia occurred in IMNM seropositive (HMGCR-IgG 70%, 37/53; SRP-IgG 29%, 5/17) and seronegative (51%, 25/49). Multivariate regression analysis of 20 variables identified 8 (including electrical myotonia) in combination accurately predicted IMNM (97.1% area-under-curve). The model was validated in a separate cohort of 30 IMNM cases. Delayed diagnosis of cases with electrical myotonia occurred in 24% (16/67, mean 8 months; range 0-194). Half (8/19) had chronic course and were seronegative, with high model prediction (>86%) at first visit. Inherited myopathies were commonly first suspected in them. Follow-up evaluation in patients with electrical myotonia on immunotherapy was available in 19 (median 21 months, range 2-124) which reduced from 36% (58/162) of muscles to 7% (8/121; p < 0.001). Reduced myotonia correlated with immunotherapy response in 64% (9/14) as well as with median creatine kinase reduction of 1779 U/L (range 401–9238, p < 0.001). Modeling clinical features with electrical myotonia is especially helpful in IMNM diagnostic suspicion among chronic indolent and seronegative cases. Electrical myotonia favors IMNM diagnosis and can serve as an adjuvant immunotherapy biomarker.

BackgroundRespiratory pathogens are a common cause of disease. Currently there is not a practical tool to predict the putative etiology of each case with an inexpensive, fast point-of-care assay. Here, we describe a decision support tool that enables the prediction of both bacterial and viral respiratory pathogen infections in a single patient, using a Machine Learning model.MethodsThe data were obtained from the Hadassah-Hebrew University Medical Center during a period of 10 years beginning from 2007 and contained more than 40,000 patients from a 1,000,000-population community for whom specimens were tested by either PCR or culture. The pathogens included were, H. influenzae; M. catarrhalis; S. pneumoniae; M. pneumoniae; Adenovirus; Human metapneumovirus; Influenza H1N1, A, B; parainfluenza 1,2 and 3; and RSV. We then created a Machine-Learning algorithm to simulate the spread of infection in the entire Jerusalem area. We defined transmission areas based on geographical distances of patients’ home-addresses. Then we prospectively tested the tool accuracy over a 4-month period, in addition to real-time improvement of the model.ResultsInitial model was created based on gender, age, home addresses and the diagnostics test results. We then reconstructed a putative spread pattern for each of the pathogens that can be correlated to potential “transmission routes.” The initial prediction tool had an AUC for most pathogens around 0.85. It ranged from 0.75 to 0.8 for the bacterial and 0.82 to 0.89 for the viral pathogens. In almost all pathogens the NPV was 0.98–0.99. We then tested the decision support tool prospectively over four consecutive months (January to April 2019—1,700 patients with respiratory complaints from whom samples were sent to the lab). While the AUC in the prospective cohort was 0.81 on average, the NPV remained high on 0.98.ConclusionThe implementation of the decision support tool on respiratory pathogen diagnostics enables better prediction of patients not infected with either viral or bacterial pathogens. The use of such a tool can save more than 50% of diagnostic tests expenses as well as real-time mapping of disease spread. Improvement of the Machine Learning protocol may further promote the optimization of positive predictive values.Disclosures All authors: No reported disclosures.