Thrombus perviousness is strongly associated with functional outcome and intravenous alteplase treatment success in patients with acute ischemic stroke. Accuracy of thrombus attenuation increase (TAI) assessment may be compromised by a heterogeneous thrombus composition and interobserver variations of currently used manual measurements. We hypothesized that TAI is more strongly associated with clinical outcomes when evaluated on the entire thrombus. In 195 patients, five TAI measures were performed: one manual by placing three regions of interest (TAI_manual) and four automated ones assessing densities from the entire thrombus. The automated TAI measures were calculated by comparing quartiles; Q1, Q2, and Q3 of the non-contrast and contrast enhanced thrombus density distribution and using the lag of the maximum of the cross correlations (MCC). Associations with functional outcome (mRS at 90 days) were assessed with univariate and multivariable analyses. All entire TAI measures were significantly associated with functional outcome with odd ratios (OR) of 1.63(95 %CI:1.19–2.25, p = 0.003) for Q1, 1.56(95 %CI:1.16–2.10, p = 0.003) for Q2, 1.24(95 %CI:1.00–1.54, p = 0.045) for Q3, and 1.70(95 %CI:1.24–2.34, p = 0.001) for MCC per 10 HU increase in univariate models. TAI_manual was not significantly associated with functional outcome (p = 0.055). In the multivariable logistic regression models including age, NIHSS, and recanalization, only TAI measures derived from the entire thrombus were independently associated with favorable outcome; OR of 1.64(95 %CI:1.01–2.66, p = 0.048) for Q2 and 1.82(1.13–2.95, p = 0.014) for MCC per 10 HU increase of thrombus attenuation. The novel perviousness measures of the entire thrombus are more strongly associated with functional outcome than the traditional manual perviousness assessments.

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Background: Comparing outcomes across hospitals to learn from best performing hospitals can be valuable. However, reliably identifying best performance is challenging. This study assesses the possibility to distinguish best performing hospitals on single outcomes and consistency of performance on different outcomes. Methods: Data were derived from the Dutch ColoRectal Audit 2013–2015. Outcomes considered were textbook outcome (colon), (circumferential) resection margins, (serious) complications, mortality, and ‘failure to rescue’. To include uncertainty in rankings, random effect logistic regression models were used to calculate expected ranks (ERs), for each hospital and outcome. Rankability was calculated for each outcome, as a measure of reliability of ranking. Furthermore, correlation between ERs on different outcomes was assessed. Correlation was considered weak <0.40, moderate between 0.40 - 0.59 and strong >0.60. Results: The study included 32 143 patients; of whom 11 373 were treated in 2015 across 84 hospitals, 8181 colon and 3192 rectal cancer patients. In this one-year period ‘Postoperative complications’ had the highest rankability for colon (57%) and rectal (41%) surgery. No (group of) hospital(s) had the highest ER(s) on all outcomes. Correlation between ERs of outcomes was moderate in 2 (of 25) and strong in 4 (of 25) combinations. Rankability of colorectal mortality increased from 14% in 2015 to 35% when data over 2013–2015 were used. Conclusion: The highest reliability of identifying best performance based on an outcome was 57%. However, the balance between reliability and relevance of outcomes is vulnerable. No (group of) hospital(s) could be identified as best performer on all outcomes. Performance was not consistent on outcomes.@en

Background Despite widespread use of quality indicators, it remains unclear to what extent they can reliably distinguish hospitals on true differences in performance. Rankability measures what part of variation in performance reflects 'true' hospital differences in outcomes versus random noise. Objective This study sought to assess whether combining data into composites or including data from multiple years improves the reliability of ranking quality indicators for hospital care. Methods Using the Dutch National Medical Registration (2007-2012) for stroke, colorectal carcinoma, heart failure, acute myocardial infarction and total hiparthroplasty (THA)/ total knee arthroplasty (TKA) in osteoarthritis (OA), we calculated the rankability for in-hospital mortality, 30-day acute readmission and prolonged length of stay (LOS) for single years and 3-year periods and for a dichotomous and ordinal composite measure in which mortality, readmission and prolonged LOS were combined. Rankability, defined as (betweenhospital variation/between-hospital+within hospital variation)×100% is classified as low (<50%), moderate (50%-75%) and high (>75%). Results Admissions from 555 053 patients treated in 95 hospitals were included. The rankability for mortality was generally low or moderate, varying from less than 1% for patients with OA undergoing THA/TKA in 2011 to 71% for stroke in 2010. Rankability for acute readmission was low, except for acute myocardial infarction in 2009 (51%) and 2012 (62%). Rankability for prolonged LOS was at least moderate. Combining multiple years improved rankability but still remained low in eight cases for both mortality and acute readmission. Combining the individual indicators into the dichotomous composite, all diagnoses had at least moderate rankability (range: 51%-96%). For the ordinal composite, only heart failure had low rankability (46% in 2008) (range: 46%-95%). Conclusion Combining multiple years or into multiple indicators results in more reliable ranking of hospitals, particularly compared with mortality and acute readmission in single years, thereby improving the ability to detect true hospital differences. The composite measures provide more information and more reliable rankings than combining multiple years of individual indicators.@en

Background: Report cards on the health care system increasingly report provider-specific performance on indicators that measure the quality of health care delivered. A natural reaction to the publishing of hospital-specific performance on a given indicator is to create 'league tables' that rank hospitals according to their performance. However, many indicators have been shown to have low to moderate rankability, meaning that they cannot be used to accurately rank hospitals. Our objective was to define conditions for improving the ability to rank hospitals by combining several binary indicators with low to moderate rankability. Methods: Monte Carlo simulations to examine the rankability of composite ordinal indicators created by pooling three binary indicators with low to moderate rankability. We considered scenarios in which the prevalences of the three binary indicators were 0.05, 0.10, and 0.25 and the within-hospital correlation between these indicators varied between - 0.25 and 0.90. Results: Creation of an ordinal indicator with high rankability was possible when the three component binary indicators were strongly correlated with one another (the within-hospital correlation in indicators was at least 0.5). When the binary indicators were independent or weakly correlated with one another (the within-hospital correlation in indicators was less than 0.5), the rankability of the composite ordinal indicator was often less than at least one of its binary components. The rankability of the composite indicator was most affected by the rankability of the most prevalent indicator and the magnitude of the within-hospital correlation between the indicators. Conclusions: Pooling highly-correlated binary indicators can result in a composite ordinal indicator with high rankability. Otherwise, the composite ordinal indicator may have lower rankability than some of its constituent components. It is recommended that binary indicators be combined to increase rankability only if they represent the same concept of quality of care.@en

Background: Hospital mortality, readmission and length of stay (LOS) are commonly used measures for quality of care. We aimed to disentangle the correlations between these interrelated measures and propose a new way of combining them to evaluate the quality of hospital care. Methods: We analyzed administrative data from the Global Comparators Project from 26 hospitals on patients discharged between 2007 and 2012. We correlated standardized and risk-adjusted hospital outcomes on mortality, readmission and long LOS. We constructed a composite measure with 5 levels, based on literature review and expert advice, from survival without readmission and normal LOS (best) to mortality (worst outcome). This composite measure was analyzed using ordinal regression, to obtain a standardized outcome measure to compare hospitals. Results: Overall, we observed a 3.1% mortality rate, 7.8% readmission rate (in survivors) and 20.8% long LOS rate among 4,327,105 admissions. Mortality and LOS were correlated at the patient and the hospital level. A patient in the upper quartile LOS had higher odds of mortality (odds ratio = 1.45, 95% confidence interval 1.43-1.47) than those in the lowest quartile. Hospitals with a high standardized mortality had higher proportions of long LOS (r = 0.79, p < 0.01). Readmission rates did not correlate with either mortality or long LOS rates. The interquartile range of the standardized ordinal composite outcome was 74-117. The composite outcome had similar or better reliability in ranking hospitals than individual outcomes. Conclusions: Correlations between different outcome measures are complex and differ between hospital- and patient-level. The proposed composite measure combines three outcomes in an ordinal fashion for a more comprehensive and reliable view of hospital performance than its component indicators.@en

Conclusions: Although readmission rates are a promising quality indicator, several methodological concerns identified in this study need to be addressed, especially when the indicator is intended for accountability or pay for performance. We recommend investing resources in accurate data registration, improved indicator description, and bundling outcome measures to provide a more complete picture of hospital care.Introduction: Hospital readmission rates are increasingly used for both quality improvement and cost control. However, the validity of readmission rates as a measure of quality of hospital care is not evident. We aimed to give an overview of the different methodological aspects in the definition and measurement of readmission rates that need to be considered when interpreting readmission rates as a reflection of quality of care.Methods: We conducted a systematic literature review, using the bibliographic databases Embase, Medline OvidSP, Web-of- Science, Cochrane central and PubMed for the period of January 2001 to May 2013.Results: The search resulted in 102 included papers. We found that definition of the context in which readmissions are used as a quality indicator is crucial. This context includes the patient group and the specific aspects of care of which the quality is aimed to be assessed. Methodological flaws like unreliable data and insufficient case-mix correction may confound the comparison of readmission rates between hospitals. Another problem occurs when the basic distinction between planned and unplanned readmissions cannot be made. Finally, the multi-faceted nature of quality of care and the correlation between readmissions and other outcomes limit the indicator's validity.@en