Hartzel, Dustin N.

Abstract Background and Aims Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR-progression risk. Methods Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using colour Doppler videos. Methods of summating video-level classifications into study-level predictions were tested, comparing single-view with multiview approaches integrating predictions across multiple videos. Model agreement with cardiologists was assessed by weighted kappa. A separate AI system (DELINEATE-MR-Progression) analysing colour Doppler videos was developed to predict which patients with mild, mild–moderate, and moderate MR were most likely to progress to moderate–severe or severe MR with analysis by Kaplan–Meier and Cox proportional hazards models. Results A total of 71 660 TTEs with 1 203 980 colour Doppler videos were included. The weighted kappa in internal/external test sets for regurgitation classification was 0.81/0.76 for AR, 0.76/0.72 for MR, and 0.73/0.64 for TR using a multiview approach taking all colour Doppler videos in a study, demonstrating substantial agreement with cardiologist interpretation with superiority of multiview over single view approaches. In the progression analysis, the AI score stratified MR-progression risk even when controlled for clinical factors known to be associated with MR progression [internal test set hazard ratio 4.1 (95% confidence interval 2.5–6.6)]. Conclusions An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.

Significance Statement Dominantly inherited polycystic kidney and liver phenotypes occur when epithelial cells in these organs have reduction of polycystin-1 functional dosage. In a cohort of genetically unresolved polycystic liver and kidney disease, the authors identified heterozygous loss of function mutations in ALG9 . ALG9 encodes an endoplasmic reticulum enzyme that builds N -glycans, and the authors show that Alg9 inactivation results in impaired polycystin-1 maturation. Using a novel ‘genotype-first’ approach to ascertain individuals based strictly on their ALG9 genotype from a large cohort of exome-sequenced individuals, the authors find that 7/8 (88%) of ALG9 mutation carriers over age 50 have multiple kidney cysts. Together, these findings identify ALG9 as a novel human polycystic kidney and liver disease gene and support the utility of a genotype-driven approach to candidate disease gene validation and genotype-phenotype correlation. Background Mutations in PKD1 or PKD2 cause typical autosomal dominant polycystic kidney disease (ADPKD), the most common monogenic kidney disease. Dominantly inherited polycystic kidney and liver diseases on the ADPKD spectrum are also caused by mutations in at least six other genes required for protein biogenesis in the endoplasmic reticulum, the loss of which results in defective production of the PKD1 gene product, the membrane protein polycystin-1 (PC1). Methods We used whole-exome sequencing in a cohort of 122 patients with genetically unresolved clinical diagnosis of ADPKD or polycystic liver disease to identify a candidate gene, ALG9 , and in vitro cell-based assays of PC1 protein maturation to functionally validate it. For further validation, we identified carriers of ALG9 loss-of-function mutations and noncarrier matched controls in a large exome-sequenced population-based cohort and evaluated the occurrence of polycystic phenotypes in both groups. Results Two patients in the clinically defined cohort had rare loss-of-function variants in ALG9 , which encodes a protein required for addition of specific mannose molecules to the assembling N-glycan precursors in the endoplasmic reticulum lumen. In vitro assays showed that inactivation of Alg9 results in impaired maturation and defective glycosylation of PC1. Seven of the eight (88%) cases selected from the population-based cohort based on ALG9 mutation carrier state who had abdominal imaging after age 50; seven (88%) had at least four kidney cysts, compared with none in matched controls without ALG9 mutations. Conclusions ALG9 is a novel disease gene in the genetically heterogeneous ADPKD spectrum. This study supports the utility of phenotype characterization in genetically-defined cohorts to validate novel disease genes, and provide much-needed genotype-phenotype correlations.

Background: Heterologous functional validation studies of putative long-QT syndrome subtype 2–associated variants clarify their pathological potential and identify disease mechanism(s) for most variants studied. The purpose of this study is to clarify the pathological potential for rare nonsynonymous KCNH2 variants seemingly associated with sudden infant death syndrome. Methods: Genetic testing of 292 sudden infant death syndrome cases identified 9 KCNH2 variants: E90K, R181Q, A190T, G294V, R791W, P967L, R1005W, R1047L, and Q1068R. Previous studies show R181Q-, P967L-, and R1047L-Kv11.1 channels function similar to wild-type Kv11.1 channels, whereas Q1068R-Kv11.1 channels accelerate inactivation gating. We studied the biochemical and biophysical properties for E90K-, G294V-, R791W-, and R1005W-Kv11.1 channels expressed in human embryonic kidney 293 cells; examined the electronic health records of patients who were genotype positive for the sudden infant death syndrome–linked KCNH2 variants; and simulated their functional impact using computational models of the human ventricular action potential. RESULTS: Western blot and voltage-clamping analyses of cells expressing E90K-, G294V-, R791W-, and R1005W-Kv11.1 channels demonstrated these variants express and generate peak Kv11.1 current levels similar to cells expressing wild-type-Kv11.1 channels, but R791W- and R1005W-Kv11.1 channels accelerated deactivation and activation gating, respectively. Electronic health records of patients with the sudden infant death syndrome–linked KCNH2 variants showed that the patients had median heart rate–corrected QT intervals <480 ms and none had been diagnosed with long-QT syndrome or experienced cardiac arrest. Simulating the impact of dysfunctional gating variants predicted that they have little impact on ventricular action potential duration. Conclusions: We conclude that these rare Kv11.1 missense variants are not long-QT syndrome subtype 2–causative variants and therefore do not represent the pathogenic substrate for sudden infant death syndrome in the variant-positive infants.