A population-based study relevant to seasonal variations in causes of death in children undergoing surgery for congenital cardiac malformations

Originally published in the journal Cardiology in the Young, Cambridge University Press http://journals.cambridge.org/action/displayJournal?jid=CTYAims: Our objectives were, first, to study seasonal distribution of perioperative deaths within 30 days after surgery, and late death, in children undergoing surgery for congenitally malformed hearts, and second, to study the causes of late death. Methods: We analysed a retrospective cohort of 1,753 children with congenital cardiac malformations born and undergoing surgery in the period from 1990 through 2002 with a special focus on the causes of late death. The data was obtained from the registry of congenital cardiac malformations at Rikshospitalet, Oslo, and the Norwegian Medical Birth Registry. The mean follow-up from birth was 8.1 years, with a range from zero to 15.2 years. Results: During the period of follow-up, 204 (11.6%) of the children died having undergone previous surgery. Of these 124 (7.1%) died in the perioperative period, and 80 (4.5%) were late deaths. There were 56 late deaths during the 6 coldest months, compared with 24 during the 6 warmest months (p < 0.01). There was no significant seasonal variation in perioperative deaths. Respiratory infection was the most common cause of late death, and occurred in 25 children, of whom 24 died during the 6 coldest months. Of the 8 sudden late deaths, 7 occurred during the 6 coldest months. There was no seasonal variation for the other causes of death. Conclusions: In children undergoing surgery for congenital cardiac malformations in Norway, there is a seasonal variation in late death, with a higher proportion occurring in the coldest months. Death related to respiratory infections predominantly occurs in the winter season, and is the overall most common cause of late death

Categorisation of continuous exposure variables revisited. A response to the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study

Background Although the general statistical advice is to keep continuous exposure variables as continuous in statistical analyses, categorisation is still a common approach in medical research. In a recent paper from the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study, categorisation of body mass index (BMI) was used when analysing the effect of BMI on adverse pregnancy outcomes. The lowest category, labelled "underweight", was used as the reference category. Methods The present paper gives a summary of reasons for categorisation and methodological drawbacks of this approach. We also discuss the choice of reference category and alternative analyses. We exemplify our arguments by a reanalysis of results from the HAPO paper. Results Categorisation of continuous exposure data results in loss of power and other methodological challenges. An unfortunate choice of reference category can give additional lack of precision and obscure the interpretation of risk estimates. A highlighted odds ratio (OR) in the HAPO study is the OR for birth weight >90th percentile for women in the highest compared to the lowest BMI category ("obese class III" versus "underweight"). This estimate was OR = 4.55 and OR = 3.52, with two different multiple logistic regression models. When using the "normal weight" category as the reference, our corresponding estimates were OR = 2.03 and OR = 1.62, respectively. Moreover, our choice of reference category also gave narrower confidence intervals. Summary Due to several methodological drawbacks, categorisation should be avoided. Modern statistical analyses should be used to analyse continuous exposure data, and to explore non-linear relations. If continuous data are categorised, special attention must be given to the choice of reference category

Genetic determinants of glucose levels in pregnancy: genetic risk scores analysis and GWAS in the Norwegian STORK cohort

Objective: Hyperglycaemia during pregnancy increases the risk of adverse health outcomes in mother and child, but the genetic aetiology is scarcely studied. Our aims were to (1) assess the overlapping genetic aetiology between the pregnant and non-pregnant population and (2) assess the importance of genome-wide polygenic contributions to glucose traits during pregnancy, by exploring whether genetic risk scores (GRSs) for fasting glucose (FG), 2-h glucose (2hG), type 2 diabetes (T2D) and BMI in non-pregnant individuals were associated with glucose measures in pregnant women. Methods: We genotyped 529 Norwegian pregnant women and constructed GRS from known genome-wide significant variants and SNPs weakly associated (p>5×10−) with FG, 2hG, BMI and T2D from external genome-wide association studies (GWAS) and examined the association between these scores and glucose measures at gestational weeks 14-16 and 30-32. We also performed GWAS of FG, 2hG and shape information from the glucose curve during an oral glucose tolerance test (OGTT). Results: GRS explained similar variance during pregnancy as in the non-pregnant population (~5%). GRS and GRS explained up to 1.3% of the variation in the glucose traits in pregnancy. If we included variants more weakly associated with these traits, GRS and GRS explained up to 2.4% of the variation in the glucose traits in pregnancy, highlighting the importance of polygenic contributions. Conclusions: Our results suggest overlap in the genetic aetiology of FG in pregnant and non-pregnant individuals. This was less apparent with 2hG, suggesting potential differences in postprandial glucose metabolism inside and outside of pregnancy

Shape information from glucose curves: Functional data analysis compared with traditional summary measures

Background Plasma glucose levels are important measures in medical care and research, and are often obtained from oral glucose tolerance tests (OGTT) with repeated measurements over 2–3 hours. It is common practice to use simple summary measures of OGTT curves. However, different OGTT curves can yield similar summary measures, and information of physiological or clinical interest may be lost. Our mean aim was to extract information inherent in the shape of OGTT glucose curves, compare it with the information from simple summary measures, and explore the clinical usefulness of such information. Methods OGTTs with five glucose measurements over two hours were recorded for 974 healthy pregnant women in their first trimester. For each woman, the five measurements were transformed into smooth OGTT glucose curves by functional data analysis (FDA), a collection of statistical methods developed specifically to analyse curve data. The essential modes of temporal variation between OGTT glucose curves were extracted by functional principal component analysis. The resultant functional principal component (FPC) scores were compared with commonly used simple summary measures: fasting and two-hour (2-h) values, area under the curve (AUC) and simple shape index (2-h minus 90-min values, or 90-min minus 60-min values). Clinical usefulness of FDA was explored by regression analyses of glucose tolerance later in pregnancy. Results Over 99% of the variation between individually fitted curves was expressed in the first three FPCs, interpreted physiologically as “general level” (FPC1), “time to peak” (FPC2) and “oscillations” (FPC3). FPC1 scores correlated strongly with AUC (r=0.999), but less with the other simple summary measures (−0.42≤r≤0.79). FPC2 scores gave shape information not captured by simple summary measures (−0.12≤r≤0.40). FPC2 scores, but not FPC1 nor the simple summary measures, discriminated between women who did and did not develop gestational diabetes later in pregnancy. Conclusions FDA of OGTT glucose curves in early pregnancy extracted shape information that was not identified by commonly used simple summary measures. This information discriminated between women with and without gestational diabetes later in pregnancy