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Identifying long QT syndrome using genotypic-phenotypic correlations is a life saver

Many young patients at risk of sudden death from long QT syndrome (LQTS) would benefit from a more efficient screening strategy, say the authors of a prospective study. Replacing mutation analysis with genotype-phenotype correlations would enable timely diagnosis and earlier prophylaxis for index cases and their affected relatives.

The study was performed in 40 unrelated consecutive patients using clinical and ECG data available for the index patients and their relations up to the time of referral. It assessed whether diagnosis based on the first gene elected for analysis could be increased by using known genotype-phenotype correlations and the impact on yield of positive results and on cost. It compared screening all five known causal genes for LQTS with screening for the most eligible gene first, according to published prevalences in LQTS or to phenotypic data on the index case and relatives. The data included patient age, sex, treatment and data on age at onset, triggers, and relatives’ ECG results and medical history.

Incorporating phenotypic data significantly improved the yield of positive results—70% versus 45% for published data—only slightly below the optimum yield with screening for all genes and at 80% less cost. It predicted 90% of all genotyped cases correctly.

LQTS affects 1:5–7000 people, and 6–13% of patients have affected relatives. Testing for all causal mutations is labour intensive and slow; even screening for the most likely first usually entails two screenings to get a positive result.

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Many young patients at risk of sudden death from long QT syndrome (LQTS) would benefit from a more efficient screening strategy, say the authors of a prospective study. Replacing mutation analysis with genotype-phenotype correlations would enable timely diagnosis and earlier prophylaxis for index cases and their affected relatives.

The study was performed in 40 unrelated consecutive patients using clinical and ECG data available for the index patients and their relations up to the time of referral. It assessed whether diagnosis based on the first gene elected for analysis could be increased by using known genotype-phenotype correlations and the impact on yield of positive results and on cost. It compared screening all five known causal genes for LQTS with screening for the most eligible gene first, according to published prevalences in LQTS or to phenotypic data on the index case and relatives. The data included patient age, sex, treatment and data on age at onset, triggers, and relatives’ ECG results and medical history.

Incorporating phenotypic data significantly improved the yield of positive results—70% versus 45% for published data—only slightly below the optimum yield with screening for all genes and at 80% less cost. It predicted 90% of all genotyped cases correctly.

LQTS affects 1:5–7000 people, and 6–13% of patients have affected relatives. Testing for all causal mutations is labour intensive and slow; even screening for the most likely first usually entails two screenings to get a positive result.

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