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.

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.

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.

[Model for estimation of cardiovascular risk in Norway]. / Modell for estimering av kardiovaskulaer risiko i Norge.

BACKGROUND: Guidelines for prevention of cardiovascular disease (CVD) include calculation of total risk. A new risk model based on updated Norwegian data is needed, as the European SCORE function overestimates the risk of fatal CVD in Norway. NORRISK for 10-year CVD mortality is presented. It includes gender, age and smoking and levels of systolic blood pressure and serumtotal cholesterol. MATERIAL AND METHODS: NORRISK is based on national age- and sex specific mortality rates from Statistics Norway (1999-2003), mean levels of risk factors from Norwegian Health Surveys (2000-03) and relative risks from mortality follow-up of Norwegian Cardiovascular Screenings (1985-2002). The model is adjusted to the mortality level in the period 1999-2003 and is compared with the SCORE model. RESULTS: 10-year risk estimates calculated from NORRISK fall between SCORE high- and low-risk estimates and increase strongly with age. Very few persons below 50 years of age have a 10-year risk above 5% (European limit for high risk). More than half of men aged 60 years have estimated risks above this limit, while only 7% of 60-year-old women exceed the limit. Even if the risk limit is reduced to 1% for younger age groups, very few women below 50 years of age have risks above the limit. INTERPRETATION: NORRISK is more adapted to the current situation in Norway than the SCORE model and may be a useful and relevant tool in Norwegian clinical practice.

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.