The Dutch hospital standardised mortality ratio (HSMR) method and cardiac surgery: benchmarking in a national cohort using hospital administration data versus a clinical database

Objective

To compare the accuracy of data from hospital administration databases and a national clinical cardiac surgery database and to compare the performance of the Dutch hospital standardised mortality ratio (HSMR) method and the logistic European System for Cardiac Operative Risk Evaluation, for the purpose of benchmarking of mortality across hospitals.

Methods

Information on all patients undergoing cardiac surgery between 1 January 2007 and 31 December 2010 in 10 centres was extracted from The Netherlands Association for Cardio-Thoracic Surgery database and the Hospital Discharge Registry. The number of cardiac surgery interventions was compared between both databases. The European System for Cardiac Operative Risk Evaluation and hospital standardised mortality ratio models were updated in the study population and compared using the C-statistic, calibration plots and the Brier-score.

Results

The number of cardiac surgery interventions performed could not be assessed using the administrative database as the intervention code was incorrect in 1.4–26.3%, depending on the type of intervention. In 7.3% no intervention code was registered. The updated administrative model was inferior to the updated clinical model with respect to discrimination (c-statistic of 0.77 vs 0.85, p<0.001) and calibration (Brier Score of 2.8% vs 2.6%, p<0.001, maximum score 3.0%). Two average performing hospitals according to the clinical model became outliers when benchmarking was performed using the administrative model.

Conclusions

In cardiac surgery, administrative data are less suitable than clinical data for the purpose of benchmarking. The use of either administrative or clinical risk-adjustment models can affect the outlier status of hospitals. Risk-adjustment models including procedure-specific clinical risk factors are recommended.

Including post-discharge mortality in calculation of hospital standardised mortality ratios: retrospective analysis of hospital episode statistics

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.

SETTING

60 Dutch hospitals.

PARTICIPANTS

1 228 815 patients discharged in the period 2008 to 2010.

MAIN OUTCOME MEASURES

In-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).

CONCLUSIONS

Applying 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.

Hospital standardized mortality ratio: consequences of adjusting hospital mortality with indirect standardization

Background

The 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.

Methods

Retrospective 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.

Results

A 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.

Conclusion

Direct 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.

A modeling approach to evaluate long-term outcome of prophylactic and on demand treatment strategies for severe hemophilia A

BACKGROUND

Severe 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 METHODS

A 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.

RESULTS

According 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.

CONCLUSIONS

The 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.

Decontamination of the digestive tract and oropharynx in ICU patients

BACKGROUND

Selective digestive tract decontamination (SDD) and selective oropharyngeal decontamination (SOD) are infection-prevention measures used in the treatment of some patients in intensive care, but reported effects on patient outcome are conflicting.

METHODS

We evaluated the effectiveness of SDD and SOD in a crossover study using cluster randomization in 13 intensive care units (ICUs), all in The Netherlands. Patients with an expected duration of intubation of more than 48 hours or an expected ICU stay of more than 72 hours were eligible. In each ICU, three regimens (SDD, SOD, and standard care) were applied in random order over the course of 6 months. Mortality at day 28 was the primary end point. SDD consisted of 4 days of intravenous cefotaxime and topical application of tobramycin, colistin, and amphotericin B in the oropharynx and stomach. SOD consisted of oropharyngeal application only of the same antibiotics. Monthly point-prevalence studies were performed to analyze antibiotic resistance.

RESULTS

A total of 5939 patients were enrolled in the study, with 1990 assigned to standard care, 1904 to SOD, and 2045 to SDD; crude mortality in the groups at day 28 was 27.5%, 26.6%, and 26.9%, respectively. In a random-effects logistic-regression model with age, sex, Acute Physiology and Chronic Health Evaluation (APACHE II) score, intubation status, and medical specialty used as covariates, odds ratios for death at day 28 in the SOD and SDD groups, as compared with the standard-care group, were 0.86 (95% confidence interval [CI], 0.74 to 0.99) and 0.83 (95% CI, 0.72 to 0.97), respectively.

CONCLUSIONS

In an ICU population in which the mortality rate associated with standard care was 27.5% at day 28, the rate was reduced by an estimated 3.5 percentage points with SDD and by 2.9 percentage points with SOD. (Controlled Clinical Trials number, ISRCTN35176830.)