Explore the vast range of titles available.

Major extracranial injury (MEI) is common in traumatic brain injury (TBI) patients, but the effect on outcome is controversial. To assess the prognostic value of MEI on mortality after TBI in an individual patient data meta-analysis of 3 observational TBI studies (International Mission on Prognosis and Clinical Trial Design in TBI [IMPACT]), a randomized controlled trial (Corticosteroid Randomization After Significant Head Injury [CRASH]), and a trauma registry (Trauma Audit and Research Network [TARN]). MEI (extracranial injury with an Abbreviated Injury Scale ≥ 3 or requiring hospital admission) was related to mortality with logistic regression analysis, adjusted for age, Glasgow Coma Scale motor score, and pupil reactivity and stratified by TBI severity. We pooled odds ratios (ORs) with random-effects meta-analysis. We included 39,274 patients. Mortality was 25%, and 32% had MEI. MEI was a strong predictor for mortality in TARN, with adjusted odds ratios of 2.81 (95% confidence interval [CI], 2.44-3.23) in mild, 2.18 (95% CI, 1.80-2.65) in moderate, and 2.14 (95% CI, 1.95-2.35) in severe TBI patients. The prognostic effect was smaller in IMPACT and CRASH, with pooled adjusted odds ratios of 2.14 (95% CI, 0.93-4.91) in mild, 1.46 (95% CI, 1.14-1.85) in moderate, and 1.18 (95% CI, 1.03-1.55) in severe TBI. When patients who died within 6 hours after injury were excluded from TARN, the effect of MEI was comparable with IMPACT and CRASH. MEI is an important prognostic factor for mortality in TBI patients. However, the effect varies by population, which explains the controversy in the literature. The strength of the effect is smaller in patients with more severe brain injury and depends on time of inclusion in a study.

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.

BackgroundEfforts 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.MethodsEmbase, 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.ResultsIn 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.ConclusionHospital-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 numberCRD42022315850.

Stratification and minimisation are useful methods to ensure balance of risk factors between treatment arms.2–4 These methods can be beneficial in small and large trials, but for trials larger than 1000 patients little effect of minimisation on imbalance was found as compared with simple randomisation.4 One of the assumptions of Fisher’s exact test is that samples are random and independent, which is not the case in this study. The correlation between observations violates the independence assumption and will lead to standard errors (SE) that are biased upwards because tests for independent samples do not account for this correlation and will overestimate the variance of the treatment effect. While adjustment is possible using Fisher’s exact test,7 8 we suggest performing logistic regression analysis as this allows adjustment for multiple minimisation variables, does not rely on inefficient stratification,8 and can be used to determine the confidence interval around the treatment effect estimate.

Abstract 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 Lay Summary 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.

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’.

Regression discontinuity (RD) is a quasi-experimental design that may provide valid estimates of treatment effects in case of continuous outcomes. We aimed to evaluate validity and precision in the RD design for dichotomous outcomes. We performed validation studies in three large randomized controlled trials (RCTs) (Corticosteroid Randomization After Significant Head injury [CRASH], the Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries [GUSTO], and PROspective Study of Pravastatin in elderly individuals at risk of vascular disease [PROSPER]). To mimic the RD design, we selected patients above and below a cutoff (e.g., age 75 years) randomized to treatment and control, respectively. Adjusted logistic regression models using restricted cubic splines (RCS) and polynomials and local logistic regression models estimated the odds ratio (OR) for treatment, with 95% confidence intervals (CIs) to indicate precision. In CRASH, treatment increased mortality with OR 1.22 [95% CI 1.06–1.40] in the RCT. The RD estimates were 1.42 (0.94–2.16) and 1.13 (0.90–1.40) with RCS adjustment and local regression, respectively. In GUSTO, treatment reduced mortality (OR 0.83 [0.72–0.95]), with more extreme estimates in the RD analysis (OR 0.57 [0.35; 0.92] and 0.67 [0.51; 0.86]). In PROSPER, similar RCT and RD estimates were found, again with less precision in RD designs. We conclude that the RD design provides similar but substantially less precise treatment effect estimates compared with an RCT, with local regression being the preferred method of analysis.

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).

Given clinicians' frequent concerns about unfavourable outcomes, Intensive Care Unit (ICU) triage decisions in acutely ill cancer patients can be difficult, as clinicians may have doubts about the appropriateness of an ICU admission. To aid to this decision making, we studied the survival and performance status of cancer patients 2 years following an unplanned ICU admission. This was a retrospective cohort study in a large tertiary referral university hospital in the Netherlands. We categorized all adult patients with an unplanned ICU admission in 2017 into two groups: patients with or without an active malignancy. Descriptive statistics, Pearson's Chi-square tests and the Mann-Whitney U tests were used to evaluate the primary objective 2-year mortality and performance status. A good performance status was defined as ECOG performance status 0 (fully active) or 1 (restricted in physically strenuous activity but ambulatory and able to carry out light work). A multivariable binary logistic regression analysis was used to identify factors associated with 2-year mortality within cancer patients. Of the 1046 unplanned ICU admissions, 125 (12%) patients had cancer. The 2-year mortality in patients with cancer was significantly higher than in patients without cancer (72% and 42.5%, P <0.001). The median performance status at 2 years in cancer patients was 1 (IQR 0-2). Only an ECOG performance status of 2 (OR 8.94; 95% CI 1.21-65.89) was independently associated with 2-year mortality. In our study, the majority of the survivors have a good performance status 2 years after ICU admission. However, at that point, three-quarter of these cancer patients had died, and mortality in cancer patients was significantly higher than in patients without cancer. ICU admission decisions in acutely ill cancer patients should be based on performance status, severity of illness and long-term prognosis, and this should be communicated in the shared decision making. An ICU admission decision should not solely be based on the presence of a malignancy.