Objective To identify the prevalence of cranial nerve (CN) palsy and its associated factors in malignant otitis externa (MOE). Methods In a retrospective study, records of MOE patients from 2011 to 2014 were reviewed. MOE and CN involvement were evaluated based on patient demographics, clinical, and paraclinical data. Results Overall, 119 MOE patients with a mean age of 65.9 ± 11.3 were included. 69.7% were male, and 63.0% had a history of diabetes. The most common symptoms and signs were otalgia (97.5%), otorrhea (44.5%), and ear canal erythema/edema (24.4%). Thirty‐three patients (27.7%) had CN involvement. The facial nerve was mostly involved (26.1%). Skull base osteomyelitis (SBO) was present in 59 patients. When excluding patients younger than 30 and older than 80, age decade was correlated with CN palsy. 66.9% of patients with CN palsy and 65.6% without CN palsy were male, which was significantly different. The following factors were not significantly different between patients with and without CN palsy: Comorbidities, signs and symptoms, diagnostic delay, erythrocyte sedimentation rate level, fasting blood sugar, hemoglobin A1c level, antifungal therapy, hospitalization duration, and SBO on imaging. Tinnitus was correlated with SBO evidence on imaging (specificity: 96.7%). Conclusion CN involvement occurs in about three out of 10 MOE patients. Male gender and advanced age may be related to a higher incidence rate of CN palsy. Tinnitus can be a specific indicator of SBO. These findings could help in better decision‐making for early interventions. Level of Evidence 4.
The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate implementation of MES and validated it using ClinVar and UK-Biobank (UKBB) data. We tested concordance of MaxSpliZer predictions with ClinVar classifications of pathogenicity of variants in LDLR and FBN1 with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid lowering medication use. MaxSpliZer prediction was concordant with the ClinVar classification in 96% of LDLR variants and 98% of FBN1 variants. In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify splice variants in a rapid automated fashion.
The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate implementation of MES and validated it using ClinVar and UK-Biobank (UKBB) data. We tested concordance of MaxSpliZer predictions with ClinVar classifications of benign/likely benign (B/LB) and pathogenic/likely pathogenic (P/LP) for LDLR variants with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid lowering medication use. MaxSpliZer prediction was concordant with the ClinVar classification in 128 of 138 P/LP variants (sensitivity 93%) and 432 of 436 B/LB variants (specificity 99%). In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify LDLR splice variants in a rapid automated fashion.
Background: The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict the effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate the implementation of MES, and validated it using ClinVar and UK-Biobank (UKBB) data. Method: We tested concordance of MaxSpliZer predictions with ClinVar classifications of benign/likely benign (B/LB) and pathogenic/likely pathogenic (P/LP) for LDLR variants with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid-lowering medication use. Results: MaxSpliZer prediction was concordant with the ClinVar classification in 128 of 138 P/LP variants (sensitivity 93%) and 432 of 436 B/LB variants (specificity 99%). In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. Conclusion: MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify LDLR splice variants in a rapid automated fashion.
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