Left ventricular hypertrophy (LVH) is common among older adults. Amidst all causes, Fabry disease (FD) should be considered when LVH occurs with family history, specific clinical manifestations, or cardiac alert signs. Here, we report a case of a 76-year-old male who presented late onset concentric LVH with symptomatic high-grade atrioventricular (AV) block. After dual-chamber pacemaker implantation, interrogation revealed frequent right ventricular (RV) pacing with a wide QRS duration. The patient developed heart failure symptoms with rapid deterioration of LV systolic function. Pacing-induced cardiomyopathy (PICM) was suspected, and the pacemaker was upgraded to biventricular pacing. Further FD surveys were performed, including biochemical examinations, cardiac biopsies, and genetic sequencing, and the patient was ultimately diagnosed with a cardiac variant of FD. Particularly, we strongly suggest that physiologic pacing should be initially considered for patients with FD who have symptomatic high-grade AV block, rather than traditional RV pacing to prevent PICM.

Purpose: To investigate whether widely available lumbar spine radiography can predict lumbar spinal canal stenosis (LSCS). Methods: This retrospective, single-center cross-sectional study included patients who underwent both lumbar spine radiography and magnetic resonance imaging. Correlations between radiographic and MRI parameters and their associations with LSCS were analyzed with adjustment for age and sex. The predictive performance of machine learning algorithms was evaluated with nested cross-validation. Results: Among 201 subjects, 104 had L4/5 LSCS and 97 did not; the LSCS group was 18 years older. After adjustment for age and sex, static spondylolisthesis (aOR 5.62, p < 0.001), average disc height (aOR 1.23, p = 0.021), and dynamic lordotic angle (aOR 0.96, p = 0.011) remained significant predictors, and adding radiographic parameters to age and sex increased the AUC from 0.85 to 0.91 (p = 0.007). Static spondylolisthesis remained significant in an age- and sex-matched subset (aOR 4.81). In nested cross-validation, machine learning models combining radiographic parameters with age and sex achieved an accuracy of 80.7% (sensitivity 82.9%, specificity 78.4%, AUC 0.87), compared with 76.2% for radiographic parameters alone. Conclusions: Lumbar spine radiography provides information on LSCS that is additional to age. Radiographic signs reflecting the stabilization phase of spinal degeneration, particularly static spondylolisthesis and decreased dynamic lordosis, were the most robust predictors. External validation is required before clinical use.