Bayesian Modeling of Muscle Biopsy Contracture Testing for Malignant Hyperthermia Susceptibility

Abstract

Background Phenotyping malignant hyperthermia (MH) by contracture testing has a low but quantifiable degree of inaccuracy, measured by its sensitivity and specificity. Quantifying the limitations inherent in diagnostic testing for MH can help resolve issues in clinical practice, such as the interpretation of a negative test and the apparent lack of complete genetic linkage to RYR1. Methods Bayesian models, mathematical descriptions of the outcome of diagnostic testing, were constructed. The inputs to the model include patient factors, summarized in a single number called pretest probability (PTP), and sensitivity and specificity that specify the accuracy of the entire test process. The outputs of the model include positive predictive value (PPV) and negative predictive value (NPV), which are numeric expressions of diagnostic certainty of positive and negative test results. A special case was constructed for equivocal results. Results The PPV, NPV, and efficiency of contracture testing for MH are functions of PTP, sensitivity, and specificity. The NPV is high for all clinical PTP, whereas PPV is clinically useful for moderate to high PTP. Conclusions Diagnostic contracture testing for MH is clinically useful because of high NPV and can exclude MH with near certainty. For MH probands, the clinical grading scale for MH may guide PTP estimation, whereas for relatives of probands, PTP is a function of kinship to a known MH-susceptible relative. A sequential testing strategy optimizes diagnostic information by maximizing PTP within a pedigree. Incomplete testing of parents of an MH susceptible child can pose a significant risk of false-negative results for the untested parent. Even with optimal pedigree testing strategies, the PPV drift effect results in a considerable source of phenotypic uncertainty for genetic linkage studies.