Bogumił Kamiński

Influence of Gender on Baseline Features and Clinical Outcomes among 17,370 Patients with Confirmed Ischaemic Stroke in the International Stroke Trial

Aim: We sought to determine whether there were differences between men and women with acute stroke in their baseline characteristics and outcome in a large cohort of patients randomized in the International Stroke Trial (IST). Methods: Of the 19,435 patients randomized in the IST, 17,370 had an ischemic stroke confirmed by CT scan or autopsy (8,003 female and 9,367 male). In males and females, we compared baseline characteristics (age, frequency of atrial fibrillation, pre-stroke administration of aspirin and systolic blood pressure, conscious level, stroke syndrome) and outcome at 14 days and 6 months (death, complications, dependency, recovery, place of residence). We developed a specific logistic regression model to adjust for case-mix in order to evaluate the separate influence of gender on outcome. Results: Female patients were older, suffered more frequently from atrial fibrillation, had higher systolic blood pressure at randomization and generally had more severe strokes (a higher proportion were unconscious or drowsy or had a total anterior circulation syndrome). Females had higher 14-day and 6-month case fatality and were more likely to be dead or dependent at six months (and consequently more likely to require institutional or residential care). Gender was an independent predictor of death or dependency at 6 months. Conclusions: The adverse effect of female gender on outcome indicates that further research to explore the underlying biological mechanism is justified, and that more intensive acute and long-term treatment may be needed to improve outcome among female patients with stroke.

Impact of diabetes on survival in patients with ST-segment elevation myocardial infarction treated by primary angioplasty: Insights from the POLISH STEMI registry

BACKGROUND It has been shown that, among patients with ST-segment elevation myocardial infarction (STEMI), diabetes is associated with a significantly higher mortality. The aim of this study was to investigate in a large cohort of patients the impact of diabetes on mortality in a large cohort of patients with STEMI treated with primary angioplasty. METHODS Our population is represented by consecutive patients with STEMI treated by primary angioplasty and enrolled in the POLISH registry in 2003. All clinical, angiographic, and follow-up data were prospectively collected. Diagnosis of diabetes was based on history of diabetes at admission. RESULTS Among 7193 patients, 877 (12.2%) had diabetes at admission. Diabetes was associated with more advanced age (p<0.0001), higher prevalence of female gender (p<0.0001), hyperlipidemia (p<0.0001), shock at presentation (p<0.0001), renal failure (p<0.0001), previous myocardial infarction (p<0.0001), more often treated after 6h from symptom onset (p<0.0001). Diabetes was associated with more extensive coronary artery disease (p<0.0001), less often treated with stenting (p<0.0001). Diabetes was significantly associated with impaired epicardial reperfusion (TIMI 0-2: OR [95% CI]=1.81 [1.5-2.18], p<0.0001), that persisted after correction for baseline confounding factors (OR [95% CI]=1.33 [1.075-1.64], p=0.009). At a mean follow-up of 524+/-194 days, diabetes was associated with higher mortality (unadjusted cumulative mortality: 23.5% vs. 12.6%, unadjusted HR=1.95 [1.66-2.3], p<0.0001), that persisted after correction for confounding factors (adjusted cumulative mortality: 13.3% vs. 10.7%, adjusted HR=1.23 [1.04-1.46], p=0.013). CONCLUSIONS This study shows that among STEMI treated by primary angioplasty diabetes is independently associated with impaired epicardial reperfusion and higher mortality.

Acute Ischemic Stroke Care and Outcome in Centers Participating in the Polish National Stroke Prevention and Treatment Registry

Background and Purpose— Significant intercenter variability in quality of care and stroke outcomes was found in many countries. The aim of the study was to compare the acute ischemic stroke care and outcomes in centers participating in the Polish National Stroke Prevention and Treatment Registry. Methods— The World Health Organization Stepwise Approach to Stroke Surveillance–based questionnaire was used to collect data on patients admitted to participating centers between December 1, 2001, and July 31, 2002. To ensure data quality, only centers reporting representative sample of patients were analyzed. Ischemic stroke patient characteristics, in-hospital care, and early outcomes (adjusted for case mix) were compared for participating centers. Results— There were 26 of 48 centers that met inclusion criteria, with a total of 8736 patients (52% women; mean age 71 years, with a range among institutions from 68 to 75 years). Significant differences between centers were observed for distribution of risk factors and in-hospital care. The rates for death and poor outcome (defined as a Rankin score ≥3 or death) ranged from 8.0% to 31.8% and from 44.2% to 74.7%, respectively. After adjusting for case mix, the death or poor outcome prognoses remained significantly different between centers. Conclusions— The observed significant differences between Polish stroke centers indicate the need for improvement of patient education, effective stroke risk factor control, and standardized in-hospital care.

A method for the updating of stochastic kriging metamodels

Two standard approaches to predicting the expected values of simulation outputs are either execution of the simulation itself or the use of a metamodel. In this work we propose a methodology that enables both approaches to be combined. When a prediction for a new input is required the procedure is to augment the metamodel forecast with additional simulation outputs for a given input. The key benefit of the method is that it is possible to reach the desired prediction accuracy at a new input faster than in the case when no initial metamodel is present. We show that such a procedure is computationally simple and can be applied to, for instance, web-based simulations, where response time to user actions is often crucial.

On optimization of simulation execution on Amazon EC2 spot market

Abstract Large scale simulations require considerable amounts of computing power and often cloud services are utilized to perform them. In such settings the execution costs can be significantly decreased through the use of the Amazon spot price market. Its downside is that Amazon can interrupt the user’s computations when her bid price is too low. This poses a problem in finding an on-line bidding algorithm that balances the computation cost and the simulation experiment completion time. We identify key drivers governing the spot prices on Amazon EC2 and using these insights propose an adaptive bidding strategy that simultaneously minimizes the computation cost and the delays due to computation termination. We show that bidding close to a spot price and dynamically switching between instances is a strategy that is efficient and simple to implement in practice. In the paper we present a simulator of the EC2 spot pricing mechanism. The simulator can be easily used to develop and test other bidding strategies on Amazon spot price market.

Cost‐effectiveness acceptability curves – caveats quantified

Cost-effectiveness acceptability curves (CEACs) have become widely used in applied health technology assessment and at the same time are criticized as unreliable decision-making tool. In this paper we show how using CEACs differs from maximizing expected net benefit (NB) and when it can lead to inconsistent decisions. In the case of comparing two alternatives we show the limits of the discrepancy between CEAC and expected NB approach and link it with expected value of perfect information. We also show how the shape of CEAC is influenced by the skewness of estimate of expected NB distribution, the correlation between cost and effect estimates and their variance. In the case of more than two options we show when using CEACs can lead to non-transitive choices in pair-wise comparisons and when it lacks independence of irrelevant alternatives property in joint comparisons.

Influence of different antiplatelet treatment regimens for primary percutaneous coronary intervention on all-cause mortality

AIMS The aim of this analysis was to examine the influence of different in-cath-lab antiplatelet regimens for the primary percutaneous coronary intervention (PCI) on all-cause mortality. METHODS AND RESULTS The study group consisted of 7193 patients (pts) undergoing primary PCI in 38 centres in 2003 in Poland. All patients received pretreatment with 300 mg of aspirin, 992 pts (14%) received glycoprotein (GP) IIb/IIIa inhibitors, 2690 pts (37%) were treated with 300 mg loading dose of clopidogrel, and 1566 (22%) received combined antiplatelet treatment with both GP IIb/IIIa inhibitors and clopidogrel. Remaining 1945 patients (27%) did not receive GP IIb/IIIa inhibitors or clopidogrel. Primary endpoint of the study was all-cause mortality up to 1 year from ST-segment elevation myocardial infarction (STEMI). One year mortality rates in the four groups were: 10.4%, 9.0%, 9.7%, and 15.3%, respectively. Propensity-adjusted survival analysis showed significant reduction of mortality for combination therapy with GP IIb/IIIa inhibitors and clopidogrel, clopidogrel alone, and GP IIb/IIIa inhibitors alone over aspirin alone. No additive effect on survival was seen for a combination therapy with GP IIb/IIIa inhibitors and clopidogrel in comparison to treatment with clopidogrel alone. CONCLUSION In this large cohort, multicentre STEMI registry in-cath-lab use of GP IIb/IIIa inhibitors and clopidogrel alone or in combination was associated with the reduction of 1 year all-cause mortality in the setting of primary PCI in comparison with aspirin only. However, the use of GP IIb/IIIa inhibitors on top of 300 mg loading dose of clopidogrel did not further reduce mortality.

Pulse pressure--independent predictor of poor early outcome and mortality following ischemic stroke.

Background: Pulse pressure (PP) in acute stroke may be related to the outcome. The link between PP in the first week following ischemic stroke and early outcome was assessed. Methods: We calculated mean PPs during the first 7 days after stroke onset in 1,677 patients. Poor outcome at hospital discharge was defined as a modified Rankin scale score of 3 or more points or death. Logistic regression was developed to evaluate PP as an independent predictor of early outcome. Results: For patients with poor outcomes the mean PP during the first week was higher than that for patients with non-poor outcomes. A logistic regression model confirmed that elevated mean PP was independently associated with poor outcome at discharge and 30-day mortality. Conclusion: Elevated PP during the acute phase of ischemic stroke is an independent predictor of poor early outcome at hospital discharge and 30-day mortality.

A framework for sensitivity analysis of decision trees

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Fuzzy approach to decision analysis with multiple criteria and uncertainty in health technology assessment

Decision making in health technology assessment (HTA) involves multiple criteria (clinical outcomes vs. cost) and risk (criteria measured with estimation error). A survey conducted among Polish HTA experts shows that opinions how to trade off health against money should be treated as fuzzy. We propose an approach that allows to introduce fuzziness into decision making process in HTA. Specifically, in the paper we (i) define a fuzzy preference relation between health technologies using an axiomatic approach; (ii) link it to the fuzzy willingness-to-pay and willingness-to-accept notions and show the survey results in Poland eliciting these; (iii) incorportate uncertainty additionally to fuzziness and define two concepts to support decision making: fuzzy expected net benefit and fuzzy expected acceptability (the counterparts of expected net benefit and cost-effectiveness acceptability curves, CEACs, often used in HTA). Illustrative examples show that our fuzzy approach may remove some problems with other methods (CEACs possibly being non-monotonic) and better illustrate the amount of uncertainty present in the decision problem. Our framework can be used in other multiple criteria decision problems under risk where trade-off coefficients between criteria are subjectively chosen.