13 research outputs found

    Standardized approach to extract candidate outcomes from literature for a standard outcome set:a case- and simulation study

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    Aims: Standard outcome sets enable the value-based evaluation of health care delivery. Whereas the attainment of expert opinion has been structured using methods such as the modified-Delphi process, standardized guidelines for extraction of candidate outcomes from literature are lacking. As such, we aimed to describe an approach to obtain a comprehensive list of candidate outcomes for potential inclusion in standard outcome sets. Methods: This study describes an iterative saturation approach, using randomly selected batches from a systematic literature search to develop a long list of candidate outcomes to evaluate healthcare. This approach can be preceded with an optional benchmark review of relevant registries and Clinical Practice Guidelines and data visualization techniques (e.g. as a WordCloud) to potentially decrease the number of iterations. The development of the International Consortium of Health Outcome Measures Heart valve disease set is used to illustrate the approach. Batch cutoff choices of the iterative saturation approach were validated using data of 1000 simulated cases. Results: Simulation showed that on average 98% (range 92–100%) saturation is reached using a 100-article batch initially, with 25 articles in the subsequent batches. On average 4.7 repeating rounds (range 1–9) of 25 new articles were necessary to achieve saturation if no outcomes are first identified from a benchmark review or a data visualization. Conclusion: In this paper a standardized approach is proposed to identify relevant candidate outcomes for a standard outcome set. This approach creates a balance between comprehensiveness and feasibility in conducting literature reviews for the identification of candidate outcomes.</p

    Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data

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    BACKGROUND: Physicians utilize different types of information to predict patient prognosis. For example: confronted with a new patient suffering from severe aortic stenosis (AS), the cardiologist considers not only the severity of the AS but also patient characteristics, medical history, and markers such as BNP. Intuitively, doctors adjust their prediction of prognosis over time, with the change in clinical status, aortic valve area and BNP at each outpatient clinic visit. With the help of novel statistical approaches to model outcomes, it is now possible to construct dynamic event prediction models, employing longitudinal data such as AVA and BNP, and mimicking the dynamic adjustment of prognosis as employed intuitively by cardiologists. We illustrate dynamic prediction of patient survival and freedom from intervention, using baseline patient characteristics and longitudinal BNP data that are becoming available over time, from a cohort of patients with severe aortic stenosis. METHODS: A 3-step approach was employed: (1) construction of a mixed-effects model to describe temporal BNP progression, (2) jointly modeling the mixed-effects model with time-to-event data (death and freedom from intervention), and (3) using the joint model to build subject-specific prediction risk models. The dataset used for this purpose includes 191 patients with severe aortic stenosis who were followed over a 3-year time period. RESULTS: In the mixed-effects model BNP was significantly influenced by time, baseline patient age, gender, LV fractional ejection fraction and creatinine. Additionally, the joint model showed that an increasing BNP trend over time was found to be a significant predictor of death. CONCLUSIONS: By jointly modeling longitudinal data with time-to-event outcomes it is possible to construct individualized dynamic event prediction models that renew over time with accumulating evidence. It provides a potentially valuable evidence-based tool for everyday use in medical practice.status: publishe

    Report of the Dutch experience with the Ross procedure in 343 patients

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    Objective: Limited information is available on outcome after auto-raft aortic valve replacement, in particular with respect to the durability of the autograft and of the allograft used to reconstruct the right ventricular outflow tract. A retrospective follow-up study of all patients who underwent a Ross procedure in the Netherlands since 1988 was done to obtain an overview of the Dutch experience with this procedure. Methods: From 1988 to January 2000, 348 Ross procedures were performed in nine centers in the Netherlands. Pre-operative, per-operative and follow-up data from 343 patients in seven centers (99% of all Dutch autograft patients) were collected and analyzed. Results: Mean patient age was 26 years (SD 14, range 0-58) and male/female ratio was 2.1. Bicuspid valve or other congenital heart valve disease was the most common indication for operation. The root replacement technique was used in 95% of patients and concomitant procedures were done in 12%. Hospital mortality was 2.6% (N = 9). Mean follow-up was 4 years (median 3.8. SD 2.8. range 0-12.5). Overall cumulative survival was 96% at 1 year (95% confidence interval (CI) 94-98%) and 94% at 5 and 7 post-operative years, respectively (95% CI 91-97%). At last follow-up, 87% of the surviving patients was in New York Heart Association (NYHA) class I. Independent predictors of overall mortality were pre-operative NYHA class IV/V and longer perfusion time. Autograft reoperation had to be performed in 14 patients and reintervention on the pulmonary allograft in 10 patients. Freedom from any valve-related reintervention was 88% at 7 years (95% CI 81-94%). Conclusions: The Dutch experience with the Ross procedure is favorable, with low operative mortality and good mid-term results. Although both the autograft in aortic position and the allograft in the right ventricular outflow tract have a limited durability, this has not yet resulted in considerable reoperation rates and associated morbidity and mortality. (C) 2002 Elsevier Science B.V. All rights reserved
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