Evaluating hospital performance based on excess cause-specific incidence

Formal evaluation of hospital performance in specific types of care is becoming an indispensable tool for quality assurance in the health care system. When the prime concern lies in reducing the risk of a cause-specific event, we propose to evaluate performance in terms of an average excess cumulative incidence, referring to the center's observed patient mix. Its intuitive interpretation helps give meaning to the evaluation results and facilitates the determination of important benchmarks for hospital performance. We apply it to the evaluation of cerebrovascular deaths after stroke in Swedish stroke centers, using data from Riksstroke, the Swedish stroke registry

Estimation with Cox models: cause-specific survival analysis with misclassified cause of failure.

While epidemiologic and clinical research often aims to analyze predictors of specific endpoints, time-to-the-specific-event analysis can be hampered by problems with cause ascertainment. Under typical assumptions of competing risks analysis (and missing-data settings), we correct the cause-specific proportional hazards analysis when information on the reliability of diagnosis is available. Our method avoids bias in effect estimates at low cost in variance, thus offering a perspective for better-informed decision making. The ratio of different cause-specific hazards can be estimated flexibly for this purpose. It thus complements an all-cause analysis. In a sensitivity analysis, this approach can reveal the likely extent and direction of the bias of a standard cause-specific analysis when the diagnosis is suspect. These 2 uses are illustrated in a randomized vaccine trial and an epidemiologic cohort study, respectively

Differences in cardiovascular risk factors and socioeconomic status do not explain the increased risk of death after a first stroke in diabetic patients : results from the Swedish Stroke Register

AIMS/HYPOTHESIS: This study compared survival rates and causes of death after stroke in diabetic and non-diabetic patients in Sweden. We hypothesised that differences in cardiovascular risk factors, acute stroke management or socioeconomic status (SES) could explain the higher risk of death after stroke in diabetic patients. METHODS: The study included 155,806 first-ever stroke patients from the Swedish Stroke Register between 2001 and 2009. Individual patient information on SES was retrieved from Statistics Sweden. Survival was followed until 2010 (532,140 person-years) with a median follow-up time of 35 months. Multiple Cox regression was used to analyse survival adjusting for differences in background characteristics, in-hospital treatment, SES and year of stroke. Causes of death were analysed using cause-specific proportional hazard models. RESULTS: The risk of death after stroke increased in diabetic patients (HR 1.28, 95% CI 1.25, 1.31), and this risk was greater in younger patients and in women. Differences in background characteristics, cardiovascular risk factors, in-hospital treatment and SES did not explain the increased risk of death after stroke (HR 1.35, 95% CI 1.32, 1.37) after adjustments. Diabetic patients had an increased probability of dying from cerebrovascular disease and even higher probabilities of dying from other circulatory causes and all other causes except cancer. CONCLUSIONS/INTERPRETATION: Differences in cardiovascular risk factors, acute stroke management and SES do not explain the lower survival after stroke in diabetic compared with non-diabetic patients. Diabetic patients are at higher risk of dying from cardiovascular causes and all other causes of death, other than cancer

The Importance of Integrating Clinical Relevance and Statistical Significance in the Assessment of Quality of Care--Illustrated Using the Swedish Stroke Register.

When profiling hospital performance, quality inicators are commonly evaluated through hospital-specific adjusted means with confidence intervals. When identifying deviations from a norm, large hospitals can have statistically significant results even for clinically irrelevant deviations while important deviations in small hospitals can remain undiscovered. We have used data from the Swedish Stroke Register (Riksstroke) to illustrate the properties of a benchmarking method that integrates considerations of both clinical relevance and level of statistical significance.The performance measure used was case-mix adjusted risk of death or dependency in activities of daily living within 3 months after stroke. A hospital was labeled as having outlying performance if its case-mix adjusted risk exceeded a benchmark value with a specified statistical confidence level. The benchmark was expressed relative to the population risk and should reflect the clinically relevant deviation that is to be detected. A simulation study based on Riksstroke patient data from 2008-2009 was performed to investigate the effect of the choice of the statistical confidence level and benchmark value on the diagnostic properties of the method.Simulations were based on 18,309 patients in 76 hospitals. The widely used setting, comparing 95% confidence intervals to the national average, resulted in low sensitivity (0.252) and high specificity (0.991). There were large variations in sensitivity and specificity for different requirements of statistical confidence. Lowering statistical confidence improved sensitivity with a relatively smaller loss of specificity. Variations due to different benchmark values were smaller, especially for sensitivity. This allows the choice of a clinically relevant benchmark to be driven by clinical factors without major concerns about sufficiently reliable evidence.The study emphasizes the importance of combining clinical relevance and level of statistical confidence when profiling hospital performance. To guide the decision process a web-based tool that gives ROC-curves for different scenarios is provided

Effect of Stacked Sodium Bicarbonate Loading on Repeated All-out Exercise

The purpose of this study was to evaluate whether NaHCO3, administered via a 9-h stacked loading protocol (i.e. repeated supplementation with small doses in order to obtain a gradual increase in blood [HCO3-]), has an ergogenic effect on repeated all-out exercise. Twelve physically active males were randomly assigned to receive either NaHCO3 (BIC) or placebo (PL) in a double-blind cross-over design. NaHCO3 supplementation was divided in three identical 3-h cycles: a 6.3 g bolus at the start, followed by 2.1 g doses at +1-h and +2-h, yielding a total NaHCO3 intake of 0.4 g·kg-1 BM over 9-h. At the end of each cycle, participants performed 2-min all-out cycling. Capillary blood samples were analyzed for [HCO3-], pH and [La-]. Pre-exercise blood [HCO3-] in PL decreased from 25.6±0.2 mmol·L-1 in bout 1 to 23.6±0.2 mmol·L-1 in bout 4, while increasing from 25.5±0.2 to 31.2±0.4 mmol·L-1 in BIC (P<0.05). Concomitantly, pre-exercise pH values gradually decreased in PL (from 7.41±0.00 to 7.39±0.01) and increased in BIC (from 7.41±0.01 to 7.47±0.01; P<0.05). Mean power output of the four bouts was higher in BIC (428±20 W) than in PL (420±20 W; P<0.05). The ergogenic effect on repeated all-out exercise occurred in the absence of gastrointestinal distress.status: publishe

Patient characteristics (means and proportions) at the hospital level.

<p>Patient characteristics (means and proportions) at the hospital level.</p