One heartbeat away from a prediction model for cardiovascular diseases in patients with chronic kidney disease: a systematic review

Introduction: Patients with chronic kidney disease (CKD) have a high risk of cardiovascular disease (CVD). Prediction models, combining clinical and laboratory characteristics, are commonly used to estimate an individual's CVD risk. However, these models are not specifically developed for patients with CKD and may therefore be less accurate. In this review, we aim to give an overview of CVD prognostic studies available, and their methodological quality, specifically for patients with CKD. Methods: MEDLINE was searched for papers reporting CVD prognostic studies in patients with CKD published between 2012 and 2021. Characteristics regarding patients, study design, outcome measurement, and prediction models were compared between included studies. The risk of bias of studies reporting on prognostic factors or the development/validation of a prediction model was assessed with, respectively, the QUIPS and PROBAST tool. Results: In total, 134 studies were included, of which 123 studies tested the incremental value of one or more predictors to existing models or common risk factors, while only 11 studies reported on the development or validation of a prediction model. Substantial heterogeneity in cohort and study characteristics, such as sample size, event rate, and definition of outcome measurements, was observed across studies. The most common predictors were age (87%), sex (75%), diabetes (70%), and estimated glomerular filtration rate (69%). Most of the studies on prognostic factors have methodological shortcomings, mostly due to a lack of reporting on clinical and methodological information. Of the 11 studies on prediction models, six developed and internally validated a model and four externally validated existing or developed models. Only one study on prognostic models showed a low risk of bias and high applicability. Conclusion: A large quantity of prognostic studies has been published, yet their usefulness remains unclear due to incomplete presentation, and lack of external validation of prognostic models. Our review can be used to select the most appropriate prognostic model depending on the patient population, outcome, and risk of bias. Future collaborative efforts should aim at improving existing models by externally validating them, evaluating the addition of new predictors, and assessment of the clinical impact. Registration: We have registered the protocol of our systematic review on PROSPERO (CRD42021228043)

Oral Antiplatelet Therapy in Secondary Prevention of Cardiovascular Events: An Assessment from the Payer's Perspective

Background: A wide variety of oral antiplatelet trials have been carried out, and a large number of cost-effectiveness estimates based on them have been published. Objective: To assess the cost effectiveness of oral antiplatelet treatments in the prevention of cardiovascular events. Methods: A comprehensive literature search was carried out in PubMed and the Cochrane Library and the data reviewed. Cost-effectiveness or cost-utility studies of oral antiplatelets published since 2000 were selected. Cost-effectiveness analyses from the perspective of the UK NHS were then carried out using a Markov model with a 6-month cycle length and a lifetime horizon. Inputs from the CAPRIE, CHARISMA, (PCI)-CURE, CREDO, COMMIT, CLARITY, ESPS 2 and ESPRIT trials were included. All estimates of cost found (per event avoided, per QALY gained or per life-year gained) were included. Results were analysed in light of the National Institute for Health and Clinical Excellence (NICE) guidelines for the use of antiplatelets for the prevention of cardiovascular events and all estimates were updated to Lstg (year 2006 values) for easy comparison. Results: Of the initial 141 studies found, 21 were included in the initial review. The literature and the Markov model subsequently used suggest that aspirin (acetylsalicylic acid) dominates placebo for the secondary prevention of cardiovascular events, as it is effective, is also less costly and is as well tolerated as placebo. Additionally, in periods or patients with elevated risk, more intensive treatment with clopidogrel (alone or together with aspirin) is cost effective compared with aspirin alone for the secondary prevention of ischaemic events. For secondary stroke prevention, combination therapy with aspirin and dipyridamole has a favourable incremental cost-effectiveness ratio (ICER) when compared with aspirin alone and, based on an indirect comparison, also when compared with clopidogrel. Conclusions: The cost-effectiveness estimates presented in this article support the NICE guidelines for the use of antiplatelets for the prevention of cardiovascular events. Based on these pharmacoeconomic data alone, aspirin should be prescribed for primary or secondary prevention among patients at high risk of cardiovascular events, dipyridamole for the secondary prevention of stroke (for a maximum of 5 years), and clopidogrel for the treatment of symptomatic cardiovascular disease or acute coronary syndrome (for a maximum of 2 years). The cost effectiveness of antiplatelets hinges on the patient's initial risk, the risk reduction associated with treatment, and the price of the treatment. Evidence suggests that the cost effectiveness of antiplatelets can be optimized by individualising the treatment decision based on patient risk and expected risk reduction.Antiplatelets, Aspirin, Cardiovascular-disorders, Clopidogrel, Cost-effectiveness, Cost-utility, Dipyridamole

Modelling approaches : the case of schizophrenia

Schizophrenia is a chronic disease characterized by periods of relative stability interrupted by acute episodes (or relapses). The course of the disease may vary considerably between patients. Patient histories show considerable inter- and even intra-individual variability. We provide a critical assessment of the advantages and disadvantages of three modelling techniques that have been used in schizophrenia: decision trees, (cohort and micro-simulation) Markov models and discrete event simulation models. These modelling techniques are compared in terms of building time, data requirements, medico-scientific experience, simulation time, clinical representation, and their ability to deal with patient heterogeneity, the timing of events, prior events, patient interaction, interaction between co-variates and variability (first-order uncertainty).We note that, depending on the research question, the optimal modelling approach should be selected based on the expected differences between the comparators, the number of co-variates, the number of patient subgroups, the interactions between co-variates, and simulation time. Finally, it is argued that in case micro-simulation is required for the cost-effectiveness analysis of schizophrenia treatments, a discrete event simulation model is best suited to accurately capture all of the relevant interdependencies in this chronic, highly heterogeneous disease with limited long-term follow-up data.

Modelling approaches - The case of schizophrenia: the case of schizophrenia

Schizophrenia is a chronic disease characterized by periods of relative stability interrupted by acute episodes (or relapses). The course of the disease may vary considerably between patients. Patient histories show considerable inter- and even intra-individual variability. We provide a critical assessment of the advantages and disadvantages of three modelling techniques that have been used in schizophrenia: decision trees, (cohort and micro-simulation) Markov models and discrete event simulation models. These modelling techniques are compared in terms of building time, data requirements, medico-scientific experience, simulation time, clinical representation, and their ability to deal with patient heterogeneity, the timing of events, prior events, patient interaction, interaction between covariates and variability (first-order uncertainty). We note that, depending on the research question, the optimal modelling approach should be selected based on the expected differences between the comparators, the number of co-variates, the number of patient subgroups, the interactions between co-variates, and simulation time. Finally, it is argued that in case micro-simulation is required for the cost-effectiveness analysis of schizophrenia treatments, a discrete event simulation model is best suited to accurately capture all of the relevant interdependencies in this chronic, highly heterogeneous disease with limited long-term follow-up data

The Cost-Effectiveness of Atypicals in the UK

AbstractBackgroundIn 2002, the National Institute for Health and Clinical Excellence (NICE), recommended atypical antipsychotics over conventional ones for first-line schizophrenia treatment, based on their lower risk of extrapyramidal symptoms.ObjectiveTo estimate the incremental cost-effectiveness of atypical relative to conventional antipsychotics for the treatment of schizophrenia in the UK.MethodsA discrete event simulation (DES) model was adopted to reflect the treatment of schizophrenia in the UK. The model estimates symptoms (using the Positive and Negative Symptom Score [PANSS]), psychiatrist visits, pharmacological treatment and treatment location, number and duration of psychotic relapses, level of compliance, quality-adjusted life-years (QALYs), and side effects over a 5-year time period. Probabilistic sensitivity analyses were carried out. Following NICE's “atypical” recommendation, the cost-effectiveness of atypical versus conventional antipsychotics was estimated in a scenario analysis, assuming both groups differ only in side-effect profile.ResultsWhen comparing conventional and atypical antipsychotics, the model predicts that the latter would decrease 5-year costs by £1633 per patient and result in a QALY gain of 0.101. The probabilistic sensitivity analysis suggests these results are robust. The sensitivity analyses indicate that incremental costs and effects are most sensitive to the differential efficacy of atypicals and conventionals, as measured by PANSS. When it is assumed that the only differences between atypicals and conventionals are found in side-effect profiles, the incremental cost-effectiveness ratio of the atypicals is £45,000 per QALY gained.ConclusionAccording to this DES model for schizophrenia, atypical antipsychotics are cost-effective compared to the conventional antipsychotics. The assumptions used in the model need further validation through large naturalistic based studies with reasonable follow-up to determine the real-life differences between atypicals and conventional antipsychotics

Modelling Approaches: The Case of Schizophrenia

Schizophrenia is a chronic disease characterized by periods of relative stability interrupted by acute episodes (or relapses). The course of the disease may vary considerably between patients. Patient histories show considerable inter- and even intra-individual variability. We provide a critical assessment of the advantages and disadvantages of three modelling techniques that have been used in schizophrenia: decision trees, (cohort and micro-simulation) Markov models and discrete event simulation models. These modelling techniques are compared in terms of building time, data requirements, medico-scientific experience, simulation time, clinical representation, and their ability to deal with patient heterogeneity, the timing of events, prior events, patient interaction, interaction between co-variates and variability (first-order uncertainty). We note that, depending on the research question, the optimal modelling approach should be selected based on the expected differences between the comparators, the number of co-variates, the number of patient subgroups, the interactions between co-variates, and simulation time. Finally, it is argued that in case micro-simulation is required for the cost-effectiveness analysis of schizophrenia treatments, a discrete event simulation model is best suited to accurately capture all of the relevant interdependencies in this chronic, highly heterogeneous disease with limited long-term follow-up data.Antipsychotics, Cost-effectiveness, Decision-analysis, Discrete-event-simulation, Markov-model, Modelling, Schizophrenia