Anja S. Lindman

The ability of the SCORE high-risk model to predict 10-year cardiovascular disease mortality in Norway.

Aims To evaluate the predictive accuracy of the Systematic Coronary Risk Evaluation (SCORE) project high-risk function in Norway. Methods and results We included 57229 individuals screened in 1985-1992 from two population-based surveys in Norway (age groups 40-49, 50-59, and 60-69 years). The data have been linked to the Norwegian Cause of Death Registry. The SCORE high-risk algorithm for the prediction of 10-year cardiovascular disease (CVD) mortality was applied, and the risk factors entered into the model were age, sex, total cholesterol, systolic blood pressure, and smoking (yes/no). The number of expected events estimated by the SCORE model (E) was compared with the observed numbers (O). The SCORE low-risk algorithm was studied for comparison. In men, the observed number of CVD deaths was 718, compared with 1464 estimated by the SCORE high-risk function (O/E ratios 0.53, 0.53 and 0.45, for age groups 40-49, 50-59 and 60-69, respectively). In women, the observed and expected numbers were 226 and 547. The O/E ratios decreased with age (ratios 0.60, 0.45 and 0.37, respectively), i.e. the overestimation increased with age. The low-risk function predicted reasonably well for men (ratios 0.85, 0.92 and 0.79, respectively), whereas an overestimation was found for women aged 50-59 and 60-69 years (ratios 0.69 and 0.56, respectively). Conclusion The SCORE high-risk model overestimated the number of CVD deaths in Norway. Before implementation in clinical practice, proper adjustments to national levels are required.

A prospective study of intake of trans -fatty acids from ruminant fat, partially hydrogenated vegetable oils, and marine oils and mortality from CVD

Trans-fatty acids (TFA) have adverse effects on blood lipids, but whether TFA from different sources are associated with risk of CVD remains unresolved. The objective of the present study was to evaluate the association between TFA intake from partially hydrogenated vegetable oils (PHVO), partially hydrogenated fish oils (PHFO) and ruminant fat (rTFA) and risks of death of CVD, CHD, cerebrovascular diseases and sudden death in the Norwegian Counties Study, a population-based cohort study. Between 1974 and 1988, participants were examined for up to three times. Fat intake was assessed with a semi-quantitative FFQ. A total of 71,464 men and women were followed up through 2007. Hazard ratios (HR) and 95 % CI were estimated with Cox regression. Energy from TFA was compared to energy from all other sources, carbohydrates or unsaturated cis-fatty acids with different multivariable models. During follow-up, 3870 subjects died of CVD, 2383 of CHD, 732 of cerebrovascular diseases and 243 of sudden death. Significant risks, comparing highest to lowest intake category, were found for: TFA from PHVO and CHD (HR 1.23 (95 % CI 1.00, 1.50)) and cerebrovascular diseases (HR 0.65 (95 % CI 0.45, 0.94)); TFA from PHFO and CVD (HR 1.14 (95 % CI 1.03, 1.26)) and cerebrovascular diseases (HR 1.32 (95 % CI 1.04, 1.69)); and rTFA intake and CVD (HR 1.30 (95 % CI 1.05, 1.61)), CHD (HR 1.50 (95 % CI 1.11, 2.03)) and sudden death (HR 2.73 (95 % CI 1.19, 6.25)) in women. These associations with rTFA intake were not significant in men (P interaction ≥ 0.01). The present study supports that TFA intake, irrespective of source, increases CVD risk. Whether TFA from PHVO decreases risk of cerebrovascular diseases warrants further investigation.

Cardiovascular risk estimation in older persons: SCORE O.P.

Aims Estimation of cardiovascular disease risk, using SCORE (Systematic COronary Risk Evaluation) is recommended by European guidelines on cardiovascular disease prevention. Risk estimation is inaccurate in older people. We hypothesized that this may be due to the assumption, inherent in current risk estimation systems, that risk factors function similarly in all age groups. We aimed to derive and validate a risk estimation function, SCORE O.P., solely from data from individuals aged 65 years and older. Methods and results 20,704 men and 20,121 women, aged 65 and over and without pre-existing coronary disease, from four representative, prospective studies of the general population were included. These were Italian, Belgian and Danish studies (from original SCORE dataset) and the CONOR (Cohort of Norway) study. The variables which remained statistically significant in Cox proportional hazards model and were included in the SCORE O.P. model were: age, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, smoking status and diabetes. SCORE O.P. showed good discrimination; area under receiver operator characteristic curve (AUROC) 0.74 (95% confidence interval: 0.73 to 0.75). Calibration was also reasonable, Hosmer–Lemeshow goodness of fit test: 17.16 (men), 22.70 (women). Compared with the original SCORE function extrapolated to the ≥65 years age group discrimination improved, p = 0.05 (men), p < 0.001 (women). Simple risk charts were constructed. On simulated external validation, performed using 10-fold cross validation, AUROC was 0.74 and predicted/observed ratio was 1.02. Conclusion SCORE O.P. provides improved accuracy in risk estimation in older people and may reduce excessive use of medication in this vulnerable population.

Ethnic differences in risk factors and total risk of cardiovascular disease based on the Norwegian CONOR study

Background: Risk of cardiovascular disease varies between ethnic groups and the aim of this study was to investigate differences in cardiovascular risk factors, and total cardiovascular risk between ethnic groups in Norway. Design: Cross-sectional study using data from the Cohort of Norway (CONOR). Methods: A sample of 62,145 participants, 40–65 years of age, originating from 11 geographical regions, were included in our study. Self-reported variables, blood samples and physical measurements were used to estimate age- and time-adjusted mean values of cardiovascular risk factors for different ethnic groups. The 10-year risks of cardiovascular mortality and cardiovascular events were calculated using the Framingham and NORRISK risk models. Results: We observed differences between ethnic groups for cardiovascular risk factors and both Framingham and NORRISK risk scores. NORRISK showed significant differences by ethnicity in women only. Immigrants from the Indian subcontinent had the lowest high-density lipoprotein (HDL) levels, the highest levels of blood glucose, triglycerides, total cholesterol/HDL ratio, waist hip ratio and diabetes prevalence. Immigrants from the former Yugoslavia had the highest Framingham scores, high blood pressure, high total cholesterol/HDL ratio, overweight measures and smoking. Low cardiovascular risk was observed among East Asian immigrants. Conclusion: The previously reported excess cardiovascular risk among immigrants from the Indian subcontinent was supported in this study. We also showed that immigrants from the former Yugoslavian countries had a higher total 10-year risk of cardiovascular events than other ethnic groups. This study adds information about ethnic groups in Norway which needs to be addressed in further research and targeted prevention strategies.

The SCORE risk model applied to recent population surveys in Norway compared to observed mortality in the general population

Aims To compare the predictions of the Systematic Coronary Risk Evaluation (SCORE) high- and low-risk functions applied to a recent population study with observed cardiovascular disease (CVD) mortality estimated from annual official mortality statistics in Norway. Methods Data were obtained from large epidemiological surveys conducted in five Norwegian counties in 2000–2003. Results A total of 32 251 men and women were investigated (aged 30–31, 40–41, 45–46, and 59–61). For men aged ≥ 59, more than 75% qualified for preventive treatment by having a 10-year risk ≥ 5%. Few women and practically no men younger than 46 years can be considered at high risk according to the SCORE risk prediction models. For men, the high-risk function overestimated and the low-risk model underestimated the CVD mortality as compared to the 10-year risks calculated from official mortality statistics (1999–2003). For women, however, both functions underestimated mortality in young individuals, whereas in the elderly an overestimation was observed. Conclusions The risk predictions depended strongly on age and gender. The SCORE high-risk function overestimates the risk of fatal CVD for men in Norway, and before implementation in clinical practice, proper adjustments to national levels are required.

Survival curves to support quality improvement in hospitals with excess 30-day mortality after acute myocardial infarction, cerebral stroke and hip fracture: a before-after study.

Objectives To evaluate survival curves (Kaplan-Meier) as a means of identifying areas in the clinical pathway amenable to quality improvement. Design Observational before–after study. Setting In Norway, annual public reporting of nationwide 30-day in-and-out-of-hospital mortality (30D) for three medical conditions started in 2011: first time acute myocardial infarction (AMI), stroke and hip fracture; reported for 2009. 12 of 61 hospitals had statistically significant lower/higher mortality compared with the hospital mean. Participants Three hospitals with significantly higher mortality requested detailed analyses for quality improvement purposes: Telemark Hospital Trust Skien (AMI and stroke), Østfold Hospital Trust Fredrikstad (stroke), Innlandet Hospital Trust Gjøvik (hip fracture). Outcome measures Survival curves, crude and risk-adjusted 30D before (2008–2009) and after (2012–2013). Interventions Unadjusted survival curves for the outlier hospitals were compared to curves based on pooled data from the other hospitals for the 30-day period 2008–2009. For patients admitted with AMI (Skien), stroke (Fredrikstad) and hip fracture (Gjøvik), the curves suggested increased mortality from the initial part of the clinical pathway. For stroke (Skien), increased mortality appeared after about 8 days. The curve profiles were thought to reflect suboptimal care in various phases in the clinical pathway. This informed improvement efforts. Results For 2008–2009, hospital-specific curves differed from other hospitals: borderline significant for AMI (p=0.064), highly significant (p≤0.005) for the remainder. After intervention, no difference was found (p>0.188). Before–after comparison of the curves within each hospital revealed a significant change for Fredrikstad (p=0.006). For the three hospitals, crude 30D declined and they were non-outliers for risk-adjusted 30D for 2013. Conclusions Survival curves as a supplement to 30D may be useful for identifying suboptimal care in the clinical pathway, and thus informing design of quality improvement projects.

30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis.

Background The Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no), as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator. Methods and Findings Three diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI), stroke and hip fracture) are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall) 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days) is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities for AMI in 2011 varied from 80.6% (in the hospital with lowest estimated survival) to 91.7% (in the hospital with highest estimated survival), whereas it ranged from 83.8% to 91.2% in 2013. Conclusions Since 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years. Public reporting of survival/mortality indicators are increasingly being used as quality measures of health care systems. Openness regarding the methods used to calculate the indicators are important, as it provides the opportunity of critically reviewing and discussing the methods in the literature. In this way, the methods employed for establishing the indicators may be improved.

Effects of dietary fat quantity and composition on fasting and postprandial levels of coagulation factor VII and serum choline-containing phospholipids

Dietary fat influences plasma levels of coagulation factor VII (FVII) and serum phospholipids (PL). It is, however, unknown if the fat-mediated changes in FVII are linked to PL. The present study aimed to investigate the effects of dietary fat on fasting and postprandial levels of activated FVII (FVIIa), FVII coagulant activity (FVIIc), FVII protein (FVIIag) and choline-containing PL (PC). In a randomized single-blinded crossover-designed study a high-fat diet (HSAFA), a low-fat diet (LSAFA), both rich in saturated fatty acids, and a high-fat diet rich in unsaturated fatty acids (HUFA) were consumed for 3 weeks. Twenty-five healthy females, in which postprandial responses were studied in a subset of twelve, were included. The HSAFA diet resulted in higher levels of fasting FVIIa and PC compared with the LSAFA and the HUFA diets (all comparisons P< or =0.01). The fasting PC levels after the LSAFA diet were also higher than after the HUFA diet (P<0.001). Postprandial levels of FVIIa and PC were highest on the HSAFA diet and different from LSAFA and HUFA (all comparisons P< or =0.05). Postprandial FVIIa was higher on the HUFA compared with the LSAFA diet (P<0.03), whereas the HUFA diet resulted in lower postprandial levels of PC than the LSAFA diet (P<0.001). Significant correlations between fasting levels of PC and FVIIc were found on all diets, whereas FVIIag was correlated to PC on the HSAFA and HUFA diet. The present results indicate that dietary fat, both quality and quantity, influences fasting and postprandial levels of FVIIa and PC. Although significant associations between fasting FVII and PC levels were found, our results do not support the assumption that postprandial FVII activation is linked to serum PC.

Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture

Background The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records. Methods We used PAS data from all Norwegian hospitals (2005–2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed. Results The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes. Conclusions This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.