Optimal Timing of Anticoagulant Treatment After Intracerebral Hemorrhage in Patients With Atrial Fibrillation.

BACKGROUND AND PURPOSE: This study aims to provide observational data on the relationship between the timing of antithrombotic treatment and the competing risks of severe thrombotic and hemorrhagic events in a cohort of Swedish patients with atrial fibrillation and intracerebral hemorrhage (ICH). METHODS: Patients with atrial fibrillation and a first-ever ICH were identified in the Swedish Stroke Register, Riksstroke, 2005 to 2012. Riksstroke was linked with other national registers to find information on treatment, comorbidity, and outcome. The optimal timing of treatment in patients with low and high thromboembolic risk was described through cumulative incidence functions separately for thrombotic and hemorrhagic events and for the combined end point vascular death or nonfatal stroke. RESULTS: The study included 2619 ICH survivors with atrial fibrillation with 5759 person-years of follow-up. Anticoagulant treatment was associated with a reduced risk of vascular death and nonfatal stroke in high-risk patients with no significantly increased risk of severe hemorrhage. The benefit seemed to be greatest when treatment was started 7 to 8 weeks after ICH. For high-risk women, the total risk of vascular death or stroke recurrence within 3 years was 17.0% when anticoagulant treatment was initiated 8 weeks after ICH and 28.6% without any antithrombotic treatment (95% confidence interval for difference, 1.4%-21.8%). For high-risk men, the corresponding risks were 14.3% versus 23.6% (95% confidence interval for difference, 0.4%-18.2%). CONCLUSIONS: This nationwide observational study suggests that anticoagulant treatment may be initiated 7 to 8 weeks after ICH in patients with atrial fibrillation to optimize the benefit from treatment and minimize risk.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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.

Preventable proportion of severe infections acquired in intensive care units: case-mix adjusted estimations from patient-based surveillance data.

BACKGROUND: More than 10% of patients admitted to intensive care units (ICUs) experience a severe, healthcare-associated infection, such as ventilator-associated pneumonia (VAP) or bloodstream infection (BSI). What could be a public health target for prevention is hotly debated, because properly adjusting for intrinsic risk factors in the patient population is difficult. We aimed to estimate the proportion of ICU-acquired VAP and BSI cases that are amenable to prevention in routine conditions. METHODS: We analyzed routine data collected prospectively according to the European standard protocol for patient-based surveillance of healthcare-acquired infections in ICUs. We computed the number of infections to be expected if, after adjustment for case mix, the infection incidence in ICUs with higher infection rates could be reduced to that of the top-tenth-percentile-ranked ICU. Computations came from model-based simulation of individual patient profiles over time in the ICU. The preventable proportion was computed as the number of observed cases minus the number of expected cases divided by the number of observed cases. RESULTS: Data for 78,222 patients admitted for more than 2 days to 525 ICUs in 6 European countries from 2005 to 2008 were available for analysis. We calculated that 52% of VAP and 69% of BSI was preventable. CONCLUSIONS: Our pragmatic, if highly conservative, estimates quantify the potential for prevention of VAP and BSI in routine conditions, assuming that variation in infection incidence between ICUs can be eliminated with improved quality of care, apart from variation attributable to differential case mix.

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.

Radiation-induced myosin IIA expression stimulates collagen type I matrix reorganization.

BACKGROUND AND PURPOSE: Extracellular matrix (ECM) reorganization critically contributes to breast cancer (BC) progression and radiotherapy response. We investigated the molecular background and functional consequences of collagen type I (col-I) reorganization by irradiated breast cancer cells (BCC). MATERIALS AND METHODS: Radiation-induced (RI) col-I reorganization was evaluated for MCF-7/6, MCF-7/AZ, T47D and SK-BR-3 BCC. Phase-contrast microscopy and a stressed matrix contraction assay were used for visualization and quantification of col-I reorganization. Cell-matrix interactions were assessed by the inhibition of ß1 integrin (neutralizing antibody 'P5D2') or focal adhesion kinase (FAK; GSK22560098 small molecule kinase inhibitor). The role of the actomyosin cytoskeleton was explored by western blotting analysis of myosin II expression and activity; and by gene silencing of myosin IIA and pharmacological inhibition of the actomyosin system (blebbistatin, cytochalasin D). BCC death was evaluated by propidium iodide staining. RESULTS: We observed a radiation dose-dependent increase of col-I reorganization by BCC. ß1 Integrin/FAK-mediated cell-matrix interactions are essential for RI col-I reorganization. Irradiated BCC are characterized by increased myosin IIA expression and myosin IIA-dependent col-I reorganization. Moreover, RI col-I reorganization by BCC is associated with decreased BCC death, as suggested by pharmacological targeting of the ß1 integrin/FAK/myosin IIA pathway. CONCLUSIONS: Our data indicate the role of myosin IIA in col-I reorganization by irradiated BCC and reciprocal BCC death.

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.

Design and testing for clinical trials faced with misclassified causes of death.

With clinical trials under pressure to produce more convincing results faster, we reexamine relative efficiencies for the semiparametric comparison of cause-specific rather than all-cause mortality events, observing that in many settings misclassification of cause of failure is not negligible. By incorporating known misclassification rates, we derive an adapted logrank test that optimizes power when the alternative treatment effect is confined to the cause-specific hazard. We derive sample size calculations for this test as well as for the corresponding all-cause mortality and naive cause-specific logrank test which ignores the misclassification. This may lead to new options at the design stage which we discuss. We reexamine a recently closed vaccine trial in this light and find the sample size needed for the new test to be 32% smaller than for the equivalent all-cause analysis, leading to a reduction of 41 224 participants.

Designing a sequentially randomized study with adherence enhancing interventions for diabetes patients.

Adaptive treatment strategies can change treatment prescription over time in response to intermediate outcomes. They are the natural choice for treating chronic diseases or for prevention, since the condition of subjects tends to change over the long term. Similarly, flexible intervention strategies are vital for generating or sustaining better adherence in long term treatment settings. When a cost-efficient first-line treatment is available, for instance, good adherence is expected to help delay or avoid second-line treatment.Sequentially randomized trials enable unbiased evaluation of how to best adapt adherence supporting interventions to a history of outcomes and adherence with the goal to optimize future treatment response. In this paper we propose and study different sequential designs targeting cost-efficient control of type II diabetes under first-line treatment through two different classes of adherence support: by (bio)technical and by behavioural means. We study their respective and joint impact first through double factorial adaptive designs, where interventions are triggered by an elevated risk of current treatment failure predicted by poor surrogate response.We develop the double factorial design and several derived designs that are more cost-efficient in the context of managed care of diabetes patients. We evaluate the marginal responses over time to different adaptive treatment strategies by means of doubly robust estimators. We consider sample sizes needed to thus detect realistic and worthwhile effects and discuss the relative practical and theoretical merits of the separate designs.