Cost-effectiveness of cardiac resynchronisation therapy for patients with moderate-to-severe heart failure: a lifetime Markov model

1-7Objective To assess the cost-effectiveness of cardiac resynchronisation therapy (CRT) both with CRT-P (biventricular pacemaker only) and with CRT-D (biventricular pacemaker with defibrillator) in patients with New York Heart Association (NYHA) functional class III/IV from a Belgian healthcare-payer perspective. Methods A lifetime Markov model was designed to calculate the cost–utility of both interventions. In the reference case, the treatment effect was based on the Comparison of Medical Therapy, Pacing and Defibrillation in Heart Failure trial. Costs were based on real-world data. Pharmacoeconomic guidelines were applied, including probabilistic modelling and sensitivity analyses. Results Compared with optimal medical treatment, on average 1.31 quality-adjusted life-years (QALY) are gained with CRT-P at an additional cost of €14 700, resulting in an incremental cost-effectiveness ratio (ICER) of about €11 200/QALY. As compared with CRT-P, CRT-D treatment adds on average an additional 0.55 QALYs at an extra cost of €30 900 resulting in an ICER of €57 000/QALY. This result was very sensitive to the incremental clinical benefit of the defibrillator function on top of CRT. Conclusions Based on efficiency arguments, CRT-P can be recommended for NYHA class III and IV patients if there is a willingness to pay more than €11 000/QALY. Even though CRT-D may offer a survival benefit over CRT-P, the incremental clinical benefit appears to be too marginal to warrant a threefold-higher device price for CRT-D. Further clinical research should focus on the added value of CRT-D over CRT-P

Prospectief bepalen van de honoraria van ziekenhuisartsen op basis van klinische paden en guidelines : makkelijker gezegd dan gedaan

153 p.ill

Clustering pathology groups on hospital stay similarity : Short Report

38 p.ill.,FOREWORD 1 -- SHORT REPORT 2 -- TABLE OF CONTENTS 2 -- LIST OF FIGURES 4 -- LIST OF TABLES .4 -- 1. INTRODUCTION 5 -- 1.1. BACKGROUND 5 -- 1.2. SCOPE AND OBJECTIVES .7 -- 1.3. METHODS .7 -- 2. BACKGROUND ON HOSPITAL PAYMENT IN BELGIUM 7 -- 2.1. THE BUDGET OF FINANCIAL MEANS .7 -- 2.2. PHYSICIAN FEES .8 -- 2.3. MIXED PAYMENTS FOR PHARMACEUTICAL SPECIALTIES .10 -- 2.4. PAYMENTS FOR DAY-CARE STAYS .10 -- 3. DEFINING THREE CLUSTERS 11 -- 3.1. WHAT DATA IS USED? 11 -- 3.1.1. Minimal Hospital Data (MZG – RHM) 11 -- 3.1.2. Hospital Billing Data (AZV – SHA and ADH – HJA) 12 -- 3.1.3. Technical Cell data (TCT) 12 -- 3.1.4. Analysis data set 13 -- 3.2. CLUSTER ANALYSIS METHOD 15 -- 3.2.1. What is a cluster analysis? 15 -- 3.2.2. Choice of cluster method .15 -- 3.2.3. Which variables to include? 15 -- 3.2.4. Cluster validation 16 -- 3.2.5. Cross-border cases .17 -- 3.3. CLUSTER ANALYSIS RESULTS 17 -- 3.3.1. Variable selection with HINoV 17 -- 3.3.2. Description of the three clusters 18 -- 3.3.3. Validation of the three clusters 22 -- 3.4. CAN THE CLUSTERS BE USED AS OUTLINES FOR THREE DIFFERENT PAYMENT SYSTEMS? 23 -- 4. ASSESSING APR-DRGS FOR A LUMP SUM PAYMENT PER STAY .25 -- 4.1. SCOPE .25 -- 4.2. VISUALISING VARIABILITY OF APR-DRG-SOIS IN THE LOW VARIABILITY CLUSTER 26 -- 4.2.1. Finding patterns of low within and between hospital variability 26 -- 4.2.2. Visualising variability on the original scale of the variable 26 -- 4.3. AN EXAMPLE: APR-DRG 301 – HIP JOINT REPLACEMENT 29 -- 5. DISCUSSION AND CONCLUSION 32 -- 5.1. THREE CLUSTERS 32 -- 5.1.1. Conclusion 32 -- 5.1.2. Data improvements for future analysis .32 -- 5.2. APR-DRG-SOIS ELIGIBLE FOR A LUMP SUM PER STAY 32 -- 5.2.1. Conclusion 32 -- 5.2.2. Next steps toward a lump sum per stay 32 -- 5.2.3. Implementation issues 34 -- 5.3. GENERAL CONCLUSION .35 -- RECOMMENDATIONS 36 -- REFERENCES .3

Clustering des groupes de pathologies selon les similarités de séjours hospitaliers : Synthèse

41 p.ill.,Le KCE publie une première étude exploratoire sur le clustering des séjours hospitaliers en prévision de la réforme du financement des hôpitaux. Une analyse de variabilité a également été proposée pour le cluster à faible variabilité, afin d’identifier les groupes de pathologies pouvant être candidates à un forfait prospectif.PRÉFACE 1 -- SYNTHÈSE 2 -- 1. INTRODUCTION 5 -- 1.1. CONTEXTE 5 -- 1.2. CHAMP DE L’ETUDE ET OBJECTIFS 7 -- 1.3. METHODES 7 -- 2. LE FINANCEMENT DES HÔPITAUX EN BELGIQUE 8 -- 2.1. LE BUDGET DES MOYENS FINANCIERS (BMF) 8 -- 2.2. HONORAIRES DES MEDECINS 9 -- 2.3. FINANCEMENT MIXTE POUR LES SPECIALITES PHARMACEUTIQUES 10 -- 2.4. FINANCEMENT DE L’HOSPITALISATION DE JOUR 10 -- 3. DÉFINITION DES TROIS CLUSTERS 12 -- 3.1. QUELLES SONT LES DONNÉES UTILISÉES ? 12 -- 3.1.1. Données hospitalières minimales (RHM) 12 -- 3.1.2. Données de facturation des hôpitaux (SHA et HJA) 12 -- 3.1.3. Données de la cellule technique de traitement (TCT) 13 -- 3.1.4. Données de l’analyse 13 -- 3.2. MÉTHODE D’ANALYSE DE CLUSTERS 16 -- 3.2.1. Qu’est-ce qu’une analyse de clusters ? 16 -- 3.2.2. Choix de la méthode de clustering 16 -- 3.2.3. Quelles variables prendre en compte ? 16 -- 3.2.4. Validation des clusters 17 -- 3.2.5. Intrus 18 -- 3.3. RÉSULTATS DE L’ANALYSE DE CLUSTERS 18 -- 3.3.1. Variables identifiées par l’HINoV 18 -- 3.3.2. Description des trois clusters 19 -- 3.3.3. Validation des trois clusters 23 -- 3.4. LES CLUSTERS PEUVENT-ILS SERVIR DE BASE À TROIS SYSTÈMES DE FINANCEMENT DIFFÉRENTS ? 24 -- 4. ÉVALUER LES APR-DRG POUR UN PAIEMENT FORFAITAIRE PAR SÉJOUR 26 -- 4.1. CHAMP D’APPLICATION 26 -- 4.2. VISUALISATION DE LA VARIABILITÉ DES APR-DRG-SOI DU CLUSTER DE SOINS À BASSE VARIABILITÉ 27 -- 4.2.1. Identifier des modèles à basse variabilité intra et interhospitalière 27 -- 4.2.2. Visualiser la variabilité sur l’échelle d’origine de la variable 27 -- 4.3. EXEMPLE CONCRET : APR-DRG 301 – ARTHROPLASTIE DE LA HANCHE 30 -- 5. DISCUSSION ET CONCLUSION 33 -- 5.1. TROIS CLUSTERS 33 -- 5.1.1. Conclusion 33 -- 5.1.2. Améliorer les données pour de prochaines analyses 33 -- 5.2. APR-DRG-SOI ÉLIGIBLES POUR UN FORFAIT PAR SÉJOUR 33 -- 5.2.1. Conclusion 33 -- 5.2.2. Prochaines étapes vers un forfait par séjour 33 -- 5.2.3. Aspects liés à la mise en œuvre 36 -- 5.3. CONCLUSION GÉNÉRALE 36 -- RECOMMANDATIONS 38 -- REFERENCES 4

Utilisation des itinéraires cliniques et guides de bonne pratique afin de déterminer de manière prospective les honoraires des médecins hospitaliers : plus facile à dire qu'à faire.

152 p.ill

A multi-criteria decision approach for ranking unmet needs in healthcare

Early temporary reimbursement (ETR) schemes for new interventions targeting high unmet needs are increasingly applied in pharmaceutical policy. Crucial for these schemes is the assessment of unmet healthcare needs of patients and society. This study develops and tests a multi-criteria decision approach (MCDA) for assessing therapeutic and societal needs. The Belgian unmet needs commission, responsible for creating a list of unmet needs for the ETR programme, has tested this methodology to assess the needs in eight health conditions. For therapeutic need, three criteria were included (impact of the condition on quality of life and on life expectancy and inconvenience of current treatment); for societal need two criteria (condition-related healthcare expenditures per patient, prevalence). The results show that the proposed MCDA is feasible and acceptable for the unmet needs commission. Clear definitions of the criteria and regular repetition of these is needed to avoid variable interpretation of the criteria by the commission members. Quality assessment of the evidence is desired. Rankings resulting from the application have face validity. Considering therapeutic need separately from societal need is considered appropriate. Policy makers should consider the use of MCDA in assessing healthcare needs. MCDA improves the transparency and accountability of the decision making processes and is practical and feasible.status: publishe