ABSTRACT
BACKGROUND: Resting electrocardiogram (ECG) identification of long QT syndrome (LQTS) has limitations. Uncertainty exists on how to classify patients with borderline prolonged QT intervals. We tested if exercise testing could help serve to guide which children with borderline prolonged QT intervals may be gene positive for LQTS. METHODS: Pediatric patients (n = 139) were divided into three groups: Controls (n = 76), gene positive LQTS with borderline QTc (n = 21), and gene negative patients with borderline QTc (n = 42). Borderline QTc was defined between 440-470 (male) and 440-480 (female) ms. ECGs were recorded supine, sitting, and standing. Patients then underwent treadmill stress testing with Bruce protocol followed by a 9-minute recovery phase. RESULTS: Supine resting QTc, age, and Schwartz score for the three groups were: (a) gene positive: 446 ± 23 ms, 12.4 ± 3.4 years old, 3.2 ± 1.8; (b) gene negative: 445 ± 20 ms, 12.1 ± 2 years old, 2.0 ± 1.2; and (c) control: 400 ± 24 ms, 15.0 ± 3 years old. The three groups could be differentiated by their QTc response at two time points: standing and recovery phase at 6 minutes. Standing QTc ≥460 ms differentiated borderline prolonged QTc patients (gene positive and gene negative) from controls. Late recovery QTc ≥480 ms distinguished gene positive from gene negative patients. CONCLUSION: Exercise stress testing can be useful to identify children who are gene positive borderline LQTS from a normal population and gene negative borderline QTc children, allowing for selective gene testing in a higher risk group of patients with borderline QTc intervals and intermediate Schwartz scores.
Subject(s)
Electrocardiography , Exercise Test , Long QT Syndrome/congenital , Long QT Syndrome/diagnosis , Adolescent , Child , Female , Genetic Predisposition to Disease , Humans , Long QT Syndrome/genetics , MaleABSTRACT
BACKGROUND: The under-5 mortality rate (U5MR) is an important metric of child health and survival. Country-level estimates of U5MR are readily available, but efforts to estimate U5MR subnationally have been limited, in part, due to spatial misalignment of available data sources (e.g., use of different administrative levels, or as a result of historical boundary changes). METHODS: We analyzed all available complete and summary birth history data in surveys and censuses in six countries (Bangladesh, Cameroon, Chad, Mozambique, Uganda, and Zambia) at the finest geographic level available in each data source. We then developed small area estimation models capable of incorporating spatially misaligned data. These small area estimation models were applied to the birth history data in order to estimate trends in U5MR from 1980 to 2015 at the second administrative level in Cameroon, Chad, Mozambique, Uganda, and Zambia and at the third administrative level in Bangladesh. RESULTS: We found substantial variation in U5MR in all six countries: there was more than a two-fold difference in U5MR between the area with the highest rate and the area with the lowest rate in every country. All areas in all countries experienced declines in U5MR between 1980 and 2015, but the degree varied both within and between countries. In Cameroon, Chad, Mozambique, and Zambia we found areas with U5MRs in 2015 that were higher than in other parts of the same country in 1980. Comparing subnational U5MR to country-level targets for the Millennium Development Goals (MDG), we find that 12.8% of areas in Bangladesh did not meet the country-level target, although the country as whole did. A minority of areas in Chad, Mozambique, Uganda, and Zambia met the country-level MDG targets while these countries as a whole did not. CONCLUSIONS: Subnational estimates of U5MR reveal significant within-country variation. These estimates could be used for identifying high-need areas and positive deviants, tracking trends in geographic inequalities, and evaluating progress towards international development targets such as the Sustainable Development Goals.
