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.
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.
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.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
BACKGROUND:For many major surgical procedures, the outcomes are better when performed by surgeons with higher procedure volumes. The purpose of this study was to examine the relations between surgeon ...procedure volume and the outcomes of Dupuytren’s surgery.
METHODS:The authors conducted an observational study from 2011 to 2014 at six dedicated hand surgery practice sites in The Netherlands. Five hundred eighty-eight patients underwent surgery for Dupuytren’s contracture performed by one of the 16 surgeons. The main exposure variable was annual surgeon volume. Outcome measures were the degree of residual contracture, full release rate, and any postoperative adverse event examined within 3 months of surgery.
RESULTS:Mean annual surgeon volume was 51 among the 16 surgeons(range; 4-86) procedures. The majority of patients had primary disease (79 percent) and underwent open fasciectomy (74 percent). Multivariable regression analyses showed that surgeon volume was linearly related to all three outcomes, and identified no optimal volume threshold. Performing 10 additional procedures annually was independently associated with nearly 0.8 degree less residual contracture (p = 0.002), 9 percent higher odds of attaining a full release (p = 0.037), and 11 percent lower odds of an adverse event (p < 0.001). Nonetheless, patient-related factors had larger impacts on all three clinical outcomes than surgeon volume.
CONCLUSIONS:In this study of practicing hand surgeons, surgeon volume varied widely, and a higher volume was associated with less postoperative residual contracture, higher full release rates, and fewer adverse events. This implies that increasing surgeon’s procedure volume provides an opportunity for improving the outcomes of Dupuytren’s surgery.
CLINICAL QUESTION/LEVEL OF EVIDENCE:Therapeutic, III.
BackgroundDespite 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.ObjectiveThis 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.MethodsUsing 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 (between-hospital variation/between-hospital+within hospital variation)×100% is classified as low (<50%), moderate (50%–75%) and high (>75%).ResultsAdmissions 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%).ConclusionCombining 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.
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.
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.
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.
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.
Observational studies of interventions are at risk for confounding by indication. The objective of the current study was to define the circumstances for the validity of methods to adjust for ...confounding by indication in observational studies.
We performed post hoc analyses of data prospectively collected from three European and North American traumatic brain injury studies including 1,725 patients. The effects of three interventions (intracranial pressure ICP monitoring, intracranial operation and primary referral) were estimated in a proportional odds regression model with the Glasgow Outcome Scale as ordinal outcome variable. Three analytical methods were compared: classical covariate adjustment, propensity score matching and instrumental variable (IV) analysis in which the percentage exposed to an intervention in each hospital was added as an independent variable, together with a random intercept for each hospital. In addition, a simulation study was performed in which the effect of a hypothetical beneficial intervention (OR 1.65) was simulated for scenarios with and without unmeasured confounders.
For all three interventions, covariate adjustment and propensity score matching resulted in negative estimates of the treatment effect (OR ranging from 0.80 to 0.92), whereas the IV approach indicated that both ICP monitoring and intracranial operation might be beneficial (OR per 10% change 1.17, 95% CI 1.01-1.42 and 1.42, 95% CI 0.95-1.97). In our simulation study, we found that covariate adjustment and propensity score matching resulted in an invalid estimate of the treatment effect in case of unmeasured confounders (OR ranging from 0.90 to 1.03). The IV approach provided an estimate in the similar direction as the simulated effect (OR per 10% change 1.04-1.05) but was statistically inefficient.
The effect estimation of interventions in observational studies strongly depends on the analytical method used. When unobserved confounding and practice variation are expected in observational multicenter studies, IV analysis should be considered.
Background
In traumatic brain injury (TBI), large between-center differences in treatment and outcome for patients managed in the intensive care unit (ICU) have been shown. The aim of this study is ...to explore if European neurotrauma centers can be clustered, based on their treatment preference in different domains of TBI care in the ICU.
Methods
Provider profiles of centers participating in the Collaborative European Neurotrauma Effectiveness Research in TBI study were used to assess correlations within and between the predefined domains: intracranial pressure monitoring, coagulation and transfusion, surgery, prophylactic antibiotics, and more general ICU treatment policies. Hierarchical clustering using Ward’s minimum variance method was applied to group data with the highest similarity. Heat maps were used to visualize whether hospitals could be grouped to uncover types of hospitals adhering to certain treatment strategies.
Results
Provider profiles were available from 66 centers in 20 different countries in Europe and Israel. Correlations within most of the predefined domains varied from low to high correlations (mean correlation coefficients 0.2–0.7). Correlations between domains were lower, with mean correlation coefficients of 0.2. Cluster analysis showed that policies could be grouped, but hospitals could not be grouped based on their preference.
Conclusions
Although correlations between treatment policies within domains were found, the failure to cluster hospitals indicates that a specific treatment choice within a domain is not a proxy for other treatment choices within or outside the domain. These results imply that studying the effects of specific TBI interventions on outcome can be based on between-center variation without being substantially confounded by other treatments.
Trial registration
We do not report the results of a health care intervention.
Response to Dr. Boriani’s Letter to the Editor Zhou, Chao; Ceyisakar, Iris E; Hovius, Steven E.R ...
Plastic and reconstructive surgery (1963),
2019-February-18, 2019-Feb-18
Journal Article
We aimed to study the associations between pre- and in-hospital tracheal intubation and outcomes in traumatic brain injury (TBI), and whether the association varied according to injury severity.
Data ...from the international prospective pan-European cohort study, Collaborative European NeuroTrauma Effectiveness Research for TBI (CENTER-TBI), were used (n=4509). For prehospital intubation, we excluded self-presenters. For in-hospital intubation, patients whose tracheas were intubated on-scene were excluded. The association between intubation and outcome was analysed with ordinal regression with adjustment for the International Mission for Prognosis and Analysis of Clinical Trials in TBI variables and extracranial injury. We assessed whether the effect of intubation varied by injury severity by testing the added value of an interaction term with likelihood ratio tests.
In the prehospital analysis, 890/3736 (24%) patients had their tracheas intubated at scene. In the in-hospital analysis, 460/2930 (16%) patients had their tracheas intubated in the emergency department. There was no adjusted overall effect on functional outcome of prehospital intubation (odds ratio=1.01; 95% confidence interval, 0.79–1.28; P=0.96), and the adjusted overall effect of in-hospital intubation was not significant (odds ratio=0.86; 95% confidence interval, 0.65–1.13; P=0.28). However, prehospital intubation was associated with better functional outcome in patients with higher thorax and abdominal Abbreviated Injury Scale scores (P=0.009 and P=0.02, respectively), whereas in-hospital intubation was associated with better outcome in patients with lower Glasgow Coma Scale scores (P=0.01): in-hospital intubation was associated with better functional outcome in patients with Glasgow Coma Scale scores of 10 or lower.
The benefits and harms of tracheal intubation should be carefully evaluated in patients with TBI to optimise benefit. This study suggests that extracranial injury should influence the decision in the prehospital setting, and level of consciousness in the in-hospital setting.
NCT02210221.
Background
Fatigue is one of the most commonly reported subjective symptoms following traumatic brain injury (TBI). The aims were to assess frequency of fatigue over the first 6 months after TBI, and ...examine whether fatigue changes could be predicted by demographic characteristics, injury severity and comorbidities.
Methods
Patients with acute TBI admitted to 65 trauma centers were enrolled in the study Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI). Subjective fatigue was measured by single item on the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), administered at baseline, three and 6 months postinjury. Patients were categorized by clinical care pathway: admitted to an emergency room (ER), a ward (ADM) or an intensive care unit (ICU). Injury severity, preinjury somatic- and psychiatric conditions, depressive and sleep problems were registered at baseline. For prediction of fatigue changes, descriptive statistics and mixed effect logistic regression analysis are reported.
Results
Fatigue was experienced by 47% of patients at baseline, 48% at 3 months and 46% at 6 months. Patients admitted to ICU had a higher probability of experiencing fatigue than those in ER and ADM strata. Females and individuals with lower age, higher education, more severe intracranial injury, preinjury somatic and psychiatric conditions, sleep disturbance and feeling depressed postinjury had a higher probability of fatigue.
Conclusion
A high and stable frequency of fatigue was found during the first 6 months after TBI. Specific socio-demographic factors, comorbidities and injury severity characteristics were predictors of fatigue in this study.
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