BACKGROUND AND AIMS:Large registries are promising tools to study the epidemiology of inflammatory bowel disease (IBD). We aimed to develop and validate machine learning models to identify IBD cases in administrative data, aiming to determine the prevalence, incidence, and mortality of IBD in the Netherlands. METHODS:We developed machine learning models for administrative data to identify IBD cases and classify them on subtype and incidence year. Models were developed in a population-based cohort and externally validated in a hospital cohort. Models were evaluated on Brier score, area under the receiver operating characteristic curve (AUC), calibration, and accuracy. The best models were used to determine the epidemiology of IBD in the Netherlands between 2013 and 2020. RESULTS:For identifying IBD cases the random forest model was best (AUC: 0.97, 95% CI [0.96; 0.97]). The gradient-boosted trees model for subtype was best (accuracy: 0.95, 95% CI [0.94; 0.95]) as was the random forest model for incidence year (0.88, 95% CI [0.86; 0.89]). The prevalence of IBD in the Netherlands was 577.6 (95% CI [566.7; 586.2]) per 100 000 on December 31, 2020, with varying prevalence across the Netherlands. Incidence of IBD was 20.1 (95% CI [18.0; 22.3]) per 100 000 in 2020 and stable over time. Mortality rates of IBD patients rose over time and were 11.6 (95% CI [10.5; 11.8]) per 1000 in 2020 as compared to 9.5 in the general population. CONCLUSION:Inflammatory bowel disease cases can be accurately identified using administrative data. The prevalence of IBD in the Netherlands is increasing slower than expected, suggesting a trend towards the epidemiological stage of Prevalence Equilibrium.

Importance Efficient care processes are crucial to minimize treatment delays and improve outcome after endovascular thrombectomy (EVT) in patients with ischemic stroke. A potential means to improve care processes is performance feedback. Objective To evaluate the effect of performance feedback to hospitals on treatment times for EVT. Design, Setting, and Participants This cluster randomized clinical trial was conducted from January 1, 2020, to June 30, 2022. Participants were consecutive adult patients with ischemic stroke who underwent EVT in 13 Dutch hospitals. No patients were excluded. Data analysis took place from March to May 2023. Intervention The intervention consisted of feedback on hospital performance using structure, process, and outcome indicators. Indicator scores were based on data from a national quality registry and compared with a benchmark. Performance feedback was provided through a dashboard for local quality improvement teams who developed and implemented improvement plans based on the feedback. Every 6 months, 3 to 4 randomly selected hospitals switched to the intervention condition. Main Outcome and Measures The primary outcome was time from door to groin puncture for all patients treated with EVT. Secondary outcomes included door-to-needle time, National Institutes of Health Stroke Scale (NIHSS) score at day 2, expanded Treatment in Cerebral Infarction (eTICI) score, and modified Rankin Scale (mRS) score at 3 months. The effect of the intervention was estimated with multivariable linear mixed models. Results A total of 4747 patients were included (intervention: 2431; control: 2316). Their mean (SD) age was 72 (13) years; 2337 (49.2%) were female and 2410 (50.8%) were male. The median (IQR) baseline NIHSS score was 14 (8-19). Median (IQR) door-to-groin puncture time under the intervention condition was 47 (25-71) minutes, compared with 52 (29-75) minutes under the control condition. The adjusted absolute reduction was 5 minutes (beta = -4.8; 95% CI, -9.5 to -0.1; P = .04), corresponding to a relative reduction of 9.2% (95% CI, -18.3% to -0.2%). Conclusion and Relevance This study found that performance feedback provided through a dashboard used by local quality improvement teams reduced door-to-groin puncture time for EVT. Implementation of performance feedback in hospitals providing EVT can improve the quality of care for ischemic stroke. Trial Registration The Netherlands Trial Register: NL9090

Background Data on variation in outcomes and costs of the treatment of inflammatory bowel disease (IBD) can be used to identify areas for cost and quality improvement. It can also help healthcare providers learn from each other and strive for equity in care. We aimed to assess the variation in outcomes and costs of IBD care between hospitals.Methods We conducted a 12-month cohort study in 8 hospitals in the Netherlands. Patients with IBD who were treated with biologics and new small molecules were included. The percentage of variation in outcomes (following the International Consortium for Health Outcomes Measurement standard set) and costs attributable to the treating hospital were analyzed with intraclass correlation coefficients (ICCs) from case mix-adjusted (generalized) linear mixed models.Results We included 1010 patients (median age 45 years, 55% female). Clinicians reported high remission rates (83%), while patient-reported rates were lower (40%). During the 12-month follow-up, 5.2% of patients used prednisolone for more than 3 months. Hospital costs (outpatient, inpatient, and medication costs) were substantial (median: 8323 per 6 months), mainly attributed to advanced therapies (6611). Most of the variation in outcomes and costs among patients could not be attributed to the treating hospitals, with ICCs typically between 0% and 2%. Instead, patient-level characteristics, often with ICCs above 50%, accounted for these variations.Conclusions Variation in outcomes and costs cannot be used to differentiate between hospitals for quality of care. Future quality improvement initiatives should look at differences in structure and process measures of care and implement patient-level interventions to improve quality of IBD care.Trial Registration Number NL8276 Variation in outcomes and costs cannot be used to differentiate between hospitals for quality of inflammatory bowel disease care. Future quality improvement initiatives should look at differences in structure and process measures and implement patient-level interventions to improve quality of inflammatory bowel disease care.

Background: Healthcare services worldwide are transforming themselves into value-based organizations. Integrated care is an important aspect of value-based healthcare (VBHC), but practical evidence-based recommendations for the successful implementation of integrated care within a VBHC context are lacking. This systematic review aims to identify how value-based integrated care (VBIC) is defined in literature, and to summarize the literature regarding the effects of VBIC, and the facilitators and barriers for its implementation. Methods: Embase, Medline ALL, Web of Science Core Collection, and Cochrane Central Register of Controlled Trails databases were searched from inception until January 2022. Empirical studies that implemented and evaluated an integrated care intervention within a VBHC context were included. Non-empirical studies were included if they described either a definition of VBIC or facilitators and barriers for its implementation. Theoretical articles and articles without an available full text were excluded. All included articles were analysed qualitatively. The Rainbow Model of Integrated Care (RMIC) was used to analyse the VBIC interventions. The quality of the articles was assessed using the Mixed Methods Appraisal Tool (MMAT). Results: After screening 1328 titles/abstract and 485 full-text articles, 24 articles were included. No articles were excluded based on quality. One article provided a definition of VBIC. Eleven studies reported—mostly positive— effects of VBIC, on clinical outcomes, patient-reported outcomes, and healthcare utilization. Nineteen studies reported facilitators and barriers for the implementation of VBIC; factors related to reimbursement and information technology (IT) infrastructure were reported most frequently. Conclusion: The concept of VBIC is not well defined. The effect of VBIC seems promising, but the exact interpretation of effect evaluations is challenged by the precedence of multicomponent interventions, multiple testing and generalizability issues. For successful implementation of VBIC, it is imperative that healthcare organizations consider investing in adequate IT infrastructure and new reimbursement models. Systematic Review Registration: PROSPERO (CRD42021259025).

Objective Acute myeloid leukaemia (AML) prognosis is enhanced with intensive remission induction chemotherapy (ICT) in eligible patients. However, ICT eligibility perceptions may differ among healthcare professionals. This nationwide, population-based study aimed to explore regional variation in ICT application and its relation with overall survival (OS).Methods and analysis We compared nine Dutch regional networks using data from the Netherlands Cancer Registry. Regional variance was assessed for the entire population and age subgroups (ie, ≤60 years and >60 years) using multivariable mixed effects logistic and Cox proportional hazard regression analyses, expressed via median OR (MOR) and median HR (MHR).Results Including all adult AML patients from 2014 to 2018 (N=4060 patients; 58% males; median age, 70 years), 1761 (43%) received ICT. ICT application varied from 36% to 57% (MOR 1.36 (95% CI 1.11 to 1.58)) across regions, with minor variations for patients aged ≤60 years (MOR 1.16 (95% CI 1.00 to 1.40)) and more extensive differences for those aged >60 years (MOR 1.43 (95% CI 1.16 to 1.63)). Median OS spanned 4.9–8.4 months across regions (MHR 1.11 (95% CI 1.00 to 1.15)), with pronounced differences in older patients (MHR 1.12 (95% CI 1.08 to 1.20)) but negligible differences in the younger group (MHR 1.02 (95% CI 1.00 to 1.14)). Survival differences for the total population and the older patients decreased to respectively, MHR 1.09 (95% CI 1.00 to 1.13) and 1.10 (95% CI 1.04 to 1.18), after additional adjustment for the probability of receiving ICT within a region, indicating approximately 10% unexplained differences.Conclusion Regional disparities in ICT application and survival exist, especially in older AML patients. However, ICT application differences partially explain survival disparities, indicating the need for more standardised ICT eligibility criteria and a better understanding of underlying causes of outcome disparities.

Background Efforts to mitigate unwarranted variation in the quality of care require insight into the 'level' (eg, patient, physician, ward, hospital) at which observed variation exists. This systematic literature review aims to synthesise the results of studies that quantify the extent to which hospitals contribute to variation in quality indicator scores.Methods Embase, Medline, Web of Science, Cochrane and Google Scholar were systematically searched from 2010 to November 2023. We included studies that reported a measure of between-hospital variation in quality indicator scores relative to total variation, typically expressed as a variance partition coefficient (VPC). The results were analysed by disease category and quality indicator type.Results In total, 8373 studies were reviewed, of which 44 met the inclusion criteria. Casemix adjusted variation was studied for multiple disease categories using 144 indicators, divided over 5 types: intermediate clinical outcomes (n=81), final clinical outcomes (n=35), processes (n=10), patient-reported experiences (n=15) and patient-reported outcomes (n=3). In addition to an analysis of between-hospital variation, eight studies also reported physician-level variation (n=54 estimates). In general, variation that could be attributed to hospitals was limited (median VPC=3%, IQR=1%-9%). Between-hospital variation was highest for process indicators (17.4%, 10.8%-33.5%) and lowest for final clinical outcomes (1.4%, 0.6%-4.2%) and patient-reported outcomes (1.0%, 0.9%-1.5%). No clear pattern could be identified in the degree of between-hospital variation by disease category. Furthermore, the studies exhibited limited attention to the reliability of observed differences in indicator scores.Conclusion Hospital-level variation in quality indicator scores is generally small relative to residual variation. However, meaningful variation between hospitals does exist for multiple indicators, especially for care processes which can be directly influenced by hospital policy. Quality improvement strategies are likely to generate more impact if preceded by level-specific and indicator-specific analyses of variation, and when absolute variation is also considered.PROSPERO registration number CRD42022315850.

Background Patients with head and neck squamous cell carcinoma (HNSCC) enter the palliative phase when cure is no longer possible or when they refuse curative treatment. The mean survival is five months, with a range of days until years. Realistic prognostic counseling enables patients to make well-considered end-of-life choices. However, physicians tend to overestimate survival. The aim of this study was to develop a prognostic model that calculates the overall survival (OS) probability of palliative HNSCC patients. Methods Patients diagnosed with incurable HNSCC or patients who refused curative treatment for HNSCC between January 1st 2006 and June 3rd 2019 were included (n = 659). Three patients were lost to follow-up. Patients were considered to have incurable HNSCC due to tumor factors (e.g. inoperability with no other curative treatment options, distant metastasis) or patient factors (e.g. the presence of severe comorbidity and/or poor performance status).Tumor and patients factors accounted for 574 patients. An additional 82 patients refused curative treatment and were also considered palliative. The effect of 17 candidate predictors was estimated in the univariable cox proportional hazard regression model. Using backwards selection with a cut-off P -value < 0.10 resulted in a final multivariable prediction model. The C-statistic was calculated to determine the discriminative performance of the model. The final model was internally validated using bootstrapping techniques. Results A total of 647 patients (98.6%) died during follow-up. Median OS time was 15.0 weeks (95% CI: 13.5;16.6). Of the 17 candidate predictors, seven were included in the final model: the reason for entering the palliative phase, the number of previous HNSCC, cT, cN, cM, weight loss in the 6 months before diagnosis, and the WHO performance status. The internally validated C-statistic was 0.66 indicating moderate discriminative ability. The model showed some optimism, with a shrinkage factor of 0.89. Conclusion This study enabled the development and internal validation of a prognostic model that predicts the OS probability in HNSCC patients in the palliative phase. This model facilitates personalized prognostic counseling in the palliative phase. External validation and qualitative research are necessary before widespread use in patient counseling and end-of-life care.

Background: Acute myeloid leukemia (AML) requires specialized care, particularly when administrating intensive remission induction chemotherapy (ICT). High-volume hospitals are presumed more adept at delivering this complex treatment, resulting in better overall survival (OS) rates. Despite its potential implications for quality improvement, research on the volume-outcome relationship in ICT administration for AML is scarce. This nationwide, populationbased study in the Netherlands explored the volume-outcome relationship in AML. Materials and methods: Data from the Netherlands Cancer Registry on adult (>= 18 years of age) ICT-treated AML patients, diagnosed between 2014 and 2018, were analyzed. Hospital volume was assessed against OS using mixedeffects Cox regression, adjusting for patient and disease characteristics (i.e. case mix), with hospital as a random effect. Results: Our study population consisted of a total of 1761 patients (57% male), with a median age of 61 years. The average annual number of ICT-treated patients varied across the 24 hospitals (range 1-56, median 13, and interquartile range 8-20 patients per hospital per year). Overall, an increase of 10 ICT-treated patients annually was associated with an 8% lower mortality risk [hazard ratio (HR) 0.92, 95% confidence interval (CI) 0.87-0.98, P = 0.01]. This association was not significant at 30-day (HR 1.02, 95% CI 0.89-1.17, P = 0.75) and 42-day (HR 0.96, 95% CI 0.85-1.08, P = 0.54) OS but became apparent Conclusions: There is a volume-outcome association within AML care. This finding could support hospital volume as a metric in AML care. However, it should be acknowledged that centralizing care is a complex process with implications for health care providers and patients. Therefore, any move toward centralization must be judiciously balanced.

PDF file, 37K, Spearmen correlation among three methylation markers as well as among methylation assay, cytology and FGFR3 assay.

PDF file, 113K, 3-plex methylation assay.

Abstract Background Treatment of IBD has improved with the introduction of biologics and small molecules, yet this has come with a considerable increase in healthcare costs. Due to the increasing cost burden, reliable nationwide epidemiological data on the prevalence of IBD is necessary to inform health policy makers, especially as the prevalence of IBD is forecasted to double between 2010 and 2030. We aimed to develop a model for identifying prevalent IBD cases in administrative data and to determine prevalence of IBD in the Netherlands. Methods Data on hospital care came from the Dutch National Hospital Care Basic Registration (Landelijke Basisregistratie Ziekenhuiszorg). This database contains data on all hospital admissions (since 1991), outpatient clinic visits (since 2017), and dispensations of biologics and small molecules (since 2015) of all hospitals in the Netherlands. Data on pathology reports were retrieved from the nationwide network and registry of histo- and cytopathology in the Netherlands (PALGA), this database contains coded pathology reports of all Dutch hospitals since 1991. These datasets were combined with a reference cohort with a verified IBD diagnosis (yes/no) and demographics for all patients. Models were trained to optimise the F-score and evaluated using five-times repeated ten-fold cross-validation. The best performing model from cross-validation was applied to assess IBD prevalence in the Netherlands. Results The reference cohort consisted of 10,155 patients, of which 3,381 were diagnosed with IBD. All models performed well in the cross-validation procedure, with F-scores of 0.870 and higher. The use of more flexible models led to improved performance, with gradient boosted trees performing best in the cross-validation procedure (Table 1). When applying the gradient boosted trees model to the general population, a prevalence of 691 per 100,000 was found for IBD in the Netherlands on 31-12-2020. Cases are unevenly distributed throughout the Netherlands (Figure 1), with the highest incidence in the south (Middle Limburg: 936 per 100,000 inhabitants) and the lowest in the northwest (Amsterdam: 545). Conclusion Prevalent IBD cases can be identified from administrative data using a gradient boosted trees model. Using this model, we have shown that prevalence of IBD in the Netherlands is increasing. However, while prior studies have predicted a growth in prevalence of 50% over the last 10 years, we found that IBD prevalence has only increased by 12% in that time range. The lower increase in prevalence may be predictive of a transition to the fourth epidemiological stage of IBD, aptly named Prevalence Equilibrium.

PDF file, 15K, ROC curve of methylation assay (dotted line) and methylation + FGFR3 assay (thick line) for the validation set.

Background and ObjectivesThe EuroQol Group 5-Dimension Self-Reported Questionnaire (EQ-5D) is a well-established instrument to assess quality of life and generates generic utility values for health states reported by patients, derived from assessments by the general public. We hypothesized that language problems and other nonmotor deficits are not captured as well as motor deficits by this system. We aimed to quantify the association between disabling neurologic deficits and the EQ-5D dimension scores and the utility score in patients with ischemic stroke.MethodsWe used data of the Interventional Management of Stroke III trial. Missing data were imputed by multiple imputation. The association between neurologic deficits (individual NIH Stroke Scale [NIHSS] item scores) and the EQ-5D-3L (5 three-level dimension scores and utility score) at 90 days was assessed with ordinal logistic regression and Tobit regression, respectively. The explained variance of each model was estimated with Nagelkerke pseudo-R-2 or R-2.ResultsIn total, 525 surviving patients were included. Complete data on both the NIHSS and EQ-5D were available for 481/525 (91.6%) patients. At 90 days, 161/491 (32.8%) patients had aphasia and 226/491 (46.0%) patients had paresis of at least 1 limb. Limb paresis, facial palsy, sensory loss, and dysarthria explained most of the variance in all EQ-5D dimension scores and the utility score. In the utility score, 8.9% of the variance was explained by neglect, 10.0% by aphasia, 10.8% by hemianopia, and 17.5%-24.1% by limb paresis.DiscussionThe impact of neurologic deficits on the EQ-5D in patients with ischemic stroke is mostly due to limb paresis, while the EQ-5D is less sensitive to other nonmotor deficits such as hemianopia, aphasia, and neglect. This may lead to overestimation of quality of life and, consequently, underestimation of the (cost-)effectiveness of treatments and interventions.

This study was performed to describe observed healthcare utilization and medical costs for patients with a cleft, compare these costs to the expected costs based on the treatment protocol, and explore the additional costs of implementing the International Consortium for Health Outcomes Measurement (ICHOM) Standard Set for Cleft Lip and Palate (CL/P). Forty patients with unilateral CL/P between 0 and 24 years of age, treated between 2012 and 2019 at Erasmus University Medical Center, were included. Healthcare services (consultations, diagnostic and surgical procedures) were counted and costs were calculated. Expected costs based on the treatment protocol were calculated by multiplying healthcare products by the product prices. Correspondingly, the additional expected costs after implementing the ICHOM Standard Set (protocol + ICHOM) were calculated. Observed costs were compared with protocol costs, and the additional expected protocol + ICHOM costs were described. The total mean costs were highest in the first year after birth (€5596), mainly due to surgeries. The mean observed total costs (€40,859) for the complete treatment (0-24 years) were 1.6 times the expected protocol costs (€25,198) due to optional, non-protocolized procedures. Hospital admissions including surgery were the main cost drivers, accounting for 42% of observed costs and 70% of expected protocol costs. Implementing the ICHOM Standard Set increased protocol-based costs by 7%.

Missing data are frequently encountered in registries that are used to compare performance across hospitals. The most appropriate method for handling missing data when analysing differences in outcomes between hospitals with a generalised linear mixed model is unclear. We aimed to compare methods for handling missing data when comparing hospitals on ordinal and dichotomous outcomes. We performed a simulation study using data from the Multicentre Randomised Controlled Trial of Endovascular Treatment for Acute Ischaemic Stroke in the Netherlands (MR CLEAN) Registry, a prospective cohort study in 17 hospitals performing endovascular therapy for ischaemic stroke in the Netherlands. The investigated methods for handling missing data, both case-mix adjustment variables and outcomes, were complete case analysis, single imputation, multiple imputation, single imputation with deletion of imputed outcomes and multiple imputation with deletion of imputed outcomes. Data were generated as missing completely at random (MCAR), missing at random and missing not at random (MNAR) in three scenarios: (1) 10% missing data in case-mix and outcome; (2) 40% missing data in case-mix and outcome; and (3) 40% missing data in case-mix and outcome with varying degree of missing data among hospitals. Bias and reliability of the methods were compared on the mean squared error (MSE, a summary measure combining bias and reliability) relative to the hospital effect estimates from the complete reference data set. For both the ordinal outcome (ie, the modified Rankin Scale) and a common dichotomised version thereof, all methods of handling missing data were biased, likely due to shrinkage of the random effects. The MSE of all methods was on average lowest under MCAR and with fewer missing data, and highest with more missing data and under MNAR. The 'multiple imputation, then deletion' method had the lowest MSE for both outcomes under all simulated patterns of missing data. Thus, when estimating hospital effects on ordinal and dichotomous outcomes in the presence of missing data, the least biased and most reliable method to handle these missing data is 'multiple imputation, then deletion'.

Background: The International Consortium for Health Outcomes Measurement has selected the self-administered comorbidity questionnaire (SCQ) to adjust case-mix when comparing outcomes of inflammatory bowel disease (IBD) treatment between healthcare providers. However, the SCQ has not been validated for use in IBD patients. Objectives: We assessed the validity of the SCQ for measuring comorbidities in IBD patients. Design: Cohort study. Methods: We assessed the criterion validity of the SCQ for IBD patients by comparing patient-reported and clinician-reported comorbidities (as noted in the electronic health record) of the 13 diseases of the SCQ using Cohen’s kappa. Construct validity was assessed using the Spearman correlation coefficient between the SCQ and the Charlson Comorbidity Index (CCI), clinician-reported SCQ, quality of life, IBD-related healthcare and productivity costs, prevalence of disability, and IBD disease activity. We assessed responsiveness by correlating changes in the SCQ with changes in healthcare costs, productivity costs, quality of life, and disease activity after 15 months. Results: We included 613 patients. At least fair agreement (κ > 0.20) was found for most comorbidities, but the agreement was slight (κ < 0.20) for stomach disease [κ = 0.19, 95% CI (−0.03; 0.41)], blood disease [κ = 0.02, 95% CI (−0.06; 0.11)], and back pain [κ = 0.18, 95% CI (0.11; 0.25)]. Correlations were found between the SCQ and the clinician-reported SCQ [ρ = 0.60, 95% CI (0.55; 0.66)], CCI [ρ = 0.39, 95% CI (0.31; 0.45)], the prevalence of disability [ρ = 0.23, 95% CI (0.15; 0.32)], and quality of life [ρ = −0.30, 95% CI (−0.37; −0.22)], but not between the SCQ and healthcare or productivity costs or disease activity (|ρ| ⩽ 0.2). A change in the SCQ after 15 months was not correlated with a change in any of the outcomes. Conclusion: The SCQ is a valid tool for measuring comorbidity in IBD patients, but face and content validity should be improved before being used to correct case-mix differences.

PDF file, 19K, Sensitivity of the methylation markers for detection of different stage and grade recurrences.

PDF file, 21K, Primers and probes used for the BS-SNaPshot assay and FGFR3 assay.

Abstract Background Standardized Mortality Ratios (SMRs) are case-mix adjusted mortality rates per hospital and are used to evaluate quality of care. However, acute care is increasingly organized on a regional level, with more severe patients admitted to specialized hospitals. We hypothesize that the current case-mix adjustment insufficiently captures differences in case-mix between non-specialized and specialized hospitals. We aim to improve the SMR by adding proxies of disease severity to the model and by calculating a regional SMR (RSMR) for acute cerebrovascular disease (CVD) and myocardial infarction (MI). Methods We used data from the Dutch National Basic Registration of Hospital Care. We selected all admissions from 2016 to 2018. SMRs and RSMRs were calculated by dividing the observed in-hospital mortality by the expected in-hospital mortality. The expected in-hospital mortality was calculated using logistic regression with adjustment for age, sex, socioeconomic status, severity of main diagnosis, urgency of admission, Charlson comorbidity index, place of residence before admission, month/year of admission, and in-hospital mortality as outcome. Results The IQR of hospital SMRs of CVD was 0.85–1.10, median 0.94, with higher SMRs for specialized hospitals (median 1.12, IQR 1.00-1.28, 71%-SMR > 1) than for non-specialized hospitals (median 0.92, IQR 0.82–1.07, 32%-SMR > 1). The IQR of RSMRs was 0.92–1.09, median 1.00. The IQR of hospital SMRs of MI was 0.76–1.14, median 0.98, with higher SMRs for specialized hospitals (median 1.00, IQR 0.89–1.25, 50%-SMR > 1 versus median 0.94, IQR 0.74–1.11, 44%-SMR > 1). The IQR of RSMRs was 0.90–1.08, median 1.00. Adjustment for proxies of disease severity mostly led to lower SMRs of specialized hospitals. Conclusion SMRs of acute regionally organized diseases do not only measure differences in quality of care between hospitals, but merely measure differences in case-mix between hospitals. Although the addition of proxies of disease severity improves the model to calculate SMRs, real disease severity scores would be preferred. However, such scores are not available in administrative data. As a consequence, the usefulness of the current SMR as quality indicator is very limited. RSMRs are potentially more useful, since they fit regional organization and might be a more valid representation of quality of care.

Background Insight into outcome variation between hospitals could help to improve quality of care. We aimed to assess the validity of early outcomes as quality indicators for acute ischemic stroke care for patients treated with endovascular therapy (EVT). Methods and Results We used data from the MR CLEAN (Multicenter Randomized Controlled Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry, a large multicenter prospective cohort study including 3279 patients with acute ischemic stroke undergoing EVT. Random effect linear and proportional odds regression were used to analyze the effect of case mix on between‐hospital differences in 2 early outcomes: the National Institutes of Health Stroke Scale (NIHSS) score at 24 to 48 hours and the expanded thrombolysis in cerebral infarction score. Between‐hospital variation in outcomes was assessed using the variance of random hospital effects (tau2). In addition, we estimated the correlation between hospitals' EVT‐patient volume and (case‐mix–adjusted) outcomes. Both early outcomes and case‐mix characteristics varied significantly across hospitals. Between‐hospital variation in the expanded thrombolysis in cerebral infarction score was not influenced by case‐mix adjustment (tau 2=0.17 in both models). In contrast, for the NIHSS score at 24 to 48 hours, case‐mix adjustment led to a decrease in variation between hospitals (tau 2 decreases from 0.19 to 0.17). Hospitals' EVT‐patient volume was strongly correlated with higher expanded thrombolysis in cerebral infarction scores (r=0.48) and weakly with lower NIHSS score at 24 to 48 hours (r=0.15). Conclusions Between‐hospital variation in NIHSS score at 24 to 48 hours is significantly influenced by case‐mix but not by patient volume. In contrast, between‐hospital variation in expanded thrombolysis in cerebral infarction score is strongly influenced by EVT‐patient volume but not by case‐mix. Both outcomes may be suitable for comparing hospitals on quality of care, provided that adequate adjustment for case‐mix is applied for NIHSS score.