Objectives To assess the consequences of applying different mortality timeframes on standardised mortality ratios of individual hospitals and, secondarily, to evaluate the association between in-hospital standardised mortality ratios and early post-discharge mortality rate, length of hospital stay, and transfer rate.Design Retrospective analysis of routinely collected hospital data to compare observed deaths in 50 diagnostic categories with deaths predicted by a case mix adjustment method. Main outcome measuresIn-hospital standardised mortality ratio, 30 days post-admission standardised mortality ratio, and 30 days post-discharge standardised mortality ratio.Results Compared with the in-hospital standardised mortality ratio, 33% of the hospitals were categorised differently with the 30 days post-admission standardised mortality ratio and 22% were categorised differently with the 30 days post-discharge standardised mortality ratio.A positive association was found between in-hospital standardised mortality ratio and length of hospital stay (Pearson correlation coefficient 0.33; P=0.01), and an inverse association was found between in-hospital standardised mortality ratio and early post-discharge mortality (Pearson correlation coefficient −0.37; P=0.004). ConclusionsApplying different mortality timeframes resulted in differences in standardised mortality ratios and differences in judgment regarding the performance of individual hospitals. Furthermore, associations between in-hospital standardised mortality rates, length of stay, and early post-discharge mortality rates were found. Combining these findings suggests that standardised mortality ratios based on in-hospital mortality are subject to so-called "discharge bias." Hence, early post-discharge mortality should be included in the calculation of standardised mortality ratios. IntroductionIn the past few decades, quality of care in hospitals has been subject to growing attention from physicians and regulators. In various countries, standardised mortality ratios are used in an

The online version of this article has a Supplementary Appendix. BackgroundSevere hemophilia requires life-long treatment with expensive clotting factor concentrates; studies comparing effects of different therapeutic strategies over decades are very difficult to perform. A simulation model was developed to evaluate the long-term outcome of on demand, prophylactic and mixed treatment strategies for patients with severe hemophilia A. Design and MethodsA computer model was developed based on individual patients' data from a Dutch cohort study in which intermediate dose prophylaxis was used and a French cohort study in which on demand treatment was used, and multivariate regression analyses. This model simulated individual patients' life expectancy, onset of bleeding, life-time joint bleeds, radiological outcome and concentrate use according to the different treatment strategies. ResultsAccording to the model, life-time on demand treatment would result in an average of 1,494 joint bleeds during the hemophiliac's life, and consumption of 4.9 million IU of factor VIII concentrate. In contrast, life-time intermediate dose prophylaxis resulted in a mean of 357 joint bleeds and factor consumption of 8.3 million IU. A multiple switch strategy (between prophylactic and on demand treatment based on bleeding pattern) resulted in a mean number of 395 joint bleeds and factor consumption of 6.6 million IU. The estimated proportion of patients with Pettersson scores over 28 points was 32% for both the prophylactic and the multiple switching strategies, compared to 76% for continuous on demand treatment. ConclusionsThe present model allows evaluation of the impact of various treatment strategies on patients' joint bleeds and clotting factor consumption. It may be expanded with additional data to allow more precise estimates and include economic evaluations of treatment strategies.Key words: hemophilia, prophylaxis, on demand, arthropathy, cost, outcome.Citation: Fischer K, Pouw ME, Lewandowski D, Janssen MP, van den Berg HM, and van Hout BA. A modeling approach to evaluate long-term outcome of prophylactic and on demand treatment strategies for severe hemophilia A. Haematologica 2011;96(5):738-743. doi:10.3324/haematol.2010 A modeling approach to evaluate long-term outcome of prophylactic and on demand treatment strategies for severe hemophilia A

BackgroundThe hospital standardized mortality ratio (HSMR) is developed to evaluate and improve hospital quality. Different methods can be used to standardize the hospital mortality ratio. Our aim was to assess the validity and applicability of directly and indirectly standardized hospital mortality ratios.MethodsRetrospective scenario analysis using routinely collected hospital data to compare deaths predicted by the indirectly standardized case-mix adjustment method with observed deaths. Discharges from Dutch hospitals in the period 2003–2009 were used to estimate the underlying prediction models. We analysed variation in indirectly standardized hospital mortality ratios (HSMRs) when changing the case-mix distributions using different scenarios. Sixty-one Dutch hospitals were included in our scenario analysis.ResultsA numerical example showed that when interaction between hospital and case-mix is present and case-mix differs between hospitals, indirectly standardized HSMRs vary between hospitals providing the same quality of care. In empirical data analysis, the differences between directly and indirectly standardized HSMRs for individual hospitals were limited.ConclusionDirect standardization is not affected by the presence of interaction between hospital and case-mix and is therefore theoretically preferable over indirect standardization. Since direct standardization is practically impossible when multiple predictors are included in the case-mix adjustment model, indirect standardization is the only available method to compute the HSMR. Before interpreting such indirectly standardized HSMRs the case-mix distributions of individual hospitals and the presence of interactions between hospital and case-mix should be assessed.