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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%.

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.

Contains fulltext : 287821.pdf (Publisher’s version ) (Open Access), INTRODUCTION: Various statistical approaches can be used to deal with unmeasured confounding when estimating treatment effects in observational studies, each with its own pros and cons. This study aimed to compare treatment effects as estimated by different statistical approaches for two interventions in observational stroke care data. PATIENTS AND METHODS: We used prospectively collected data from the MR CLEAN registry including all patients (n = 3279) with ischemic stroke who underwent endovascular treatment (EVT) from 2014 to 2017 in 17 Dutch hospitals. Treatment effects of two interventions - i.e., receiving an intravenous thrombolytic (IVT) and undergoing general anesthesia (GA) before EVT - on good functional outcome (modified Rankin Scale

Background: Instrumental variable (IV) analysis holds the potential to estimate treatment effects from observational data. IV analysis potentially circumvents unmeasured confounding but makes a number of assumptions, such as that the IV shares no common cause with the outcome. When using treatment preference as an instrument, a common cause, such as a preference regarding related treatments, may exist. We aimed to explore the validity and precision of a variant of IV analysis where we additionally adjust for the provider: adjusted IV analysis. Methods: A treatment effect on an ordinal outcome was simulated (beta − 0.5 in logistic regression) for 15.000 patients, based on a large data set (the IMPACT data, n = 8799) using different scenarios including measured and unmeasured confounders, and a common cause of IV and outcome. We compared estimated treatment effects with patient-level adjustment for confounders, IV with treatment preference as the instrument, and adjusted IV, with hospital added as a fixed effect in the regression models. Results: The use of patient-level adjustment resulted in biased estimates for all the analyses that included unmeasured confounders, IV analysis was less confounded, but also less reliable. With correlation between treatment preference and hospital characteristics (a common cause) estimates were skewed for regular IV analysis, but not for adjusted IV analysis. Conclusion: When using IV analysis for comparing hospitals, some limitations of regular IV analysis can be overcome by adjusting for a common cause. Trial registration: We do not report the results of a health care intervention.

Background: Previous studies describe a large variation in the proportion of patients undergoing treatment with curative intent for esophageal (EC) and gastric cancer (GC). Since centralization of surgical care was initiated and more awareness regarding hospital practice variation was potentially present, we hypothesized that hospital practice variation for potentially curable EC and GC patients changed over time. Methods: Patients with potentially curable EC (n = 10,115) or GC (n = 3988) diagnosed between 2012–2017 were selected from the Netherlands Cancer Registry. Multilevel multivariable logistic regression was used to analyze the differences in the probability of treatment with curative intent between hospitals of diagnosis over time, comparing 2012−2014 with 2015−2017. Relative survival (RS) between hospitals with different probabilities of treatment with curative intent were compared. Results: The range of pro

Background: There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples. Methods: We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers. Results: In the IMPACT study (9578 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale (‘unfavorable outcome’), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37 to 63% less patients. Conclusions: Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements. Trial registration: We do not report the results of a health care intervention.

Background Between-center variation in outcome may offer opportunities to identify variation in quality of care. By intervening on these quality differences, patient outcomes may be improved. However, whether observed differences in outcome reflect the true quality improvement potential is not known for many diseases. Therefore, we aimed to analyze the effect of differences in performance on structure and processes of care, and case-mix on between-center differences in outcome after endovascular treatment (EVT) for ischemic stroke. Methods In this observational cohort study, ischemic stroke patients who received EVT between 2014 and 2017 in all 17 Dutch EVT-centers were included. Primary outcome was the modified Rankin Scale, ranging from 0 (no symptoms) to 6 (death), at 90 days. We used random effect proportional odds regression modelling, to analyze the effect of differences in structure indicators (center volume and year of admission), process indicators (time to treatment and use of general anesthesia) and case-mix, by tracking changes in tau2, which represents the amount of between-center variation in outcome. Results Three thousand two hundred seventy-nine patients were included. Performance on structure and process indicators varied significantly between EVT-centers (P < 0.001). Predicted probability of good functional outcome (modified Rankin Scale 0–2 at 90 days), which can be interpreted as an overall measure of a center’s case-mix, varied significantly between 17 and 50% across centers. The amount of between-center variation (tau2) was estimated at 0.040 in a model only accounting for random variation. This estimate more than doubled after adding case-mix variables (tau2: 0.086) to the model, while a small amount of between-center variation was explained by variation in performance on structure and process indicators (tau2: 0.081 and 0.089, respectively). This indicates that variation in case-mix affects the differences in outcome to a much larger extent.

Objectives: The aim of this study was to evaluate the psychometric performance of the patient- and parent-reported measures in the International Consortium for Health Outcomes Measurement (ICHOM) Standard Set for Cleft Care, and to identify ways of improving concept coverage. Methods: Data from 714 patients with cleft lip and/or palate, aged 8 to 9, 10 to 12.5, and 22 years were collected between November 2015 and April 2019 at Erasmus University Medical Center, Boston Children's Hospital, Duke Children's Hospital, and from participating sites in the CLEFT-Q Phase 3 study. The Standard Set includes 9 CLEFT-Q scales, the Nasal Obstruction Symptom Evaluation (NOSE) questionnaire, the Child Oral Health Impact Profile–Oral Symptoms Scale (COHIP-OSS), and the Intelligibility in Context Scale (ICS). Targeting, item-fit statistics, thresholds for item responses, and measurement precision (PSI) were analyzed using Rasch measurement theory. Results: The proportion of the sample to score within each instruments range of measurement varied from 69% (ICS) to 92% (CLEFT-Q teeth and COHIP-OSS). Specific problems with individual items within the NOSE and COHIP-OSS questionnaires were noted, such as poor item fit to the Rasch model and disordered thresholds (6 of 10). Reliability measured with PSI was above 0.82 for the ICS and all but one CLEFT-Q scale (speech distress). PSIs were lowest for the COHIP-OSS (0.43) and NOSE questionnaire (0.35). Conclusion: The patient- and parent-reported components within the facial appearance, psychosocial function, and speech domains are valid measures; however, the facial function and oral health domains are not sufficiently covered by the CLEFT-Q eating and drinking, NOSE, and COHIP-OSS, and these questionnaires may not be accurate enough to stratify cleft-related outcomes.

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites., QC 20211001