Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

Steyerberg, Ewout W.; Moons, Karel G.M.; Windt, D.A.W.M. van der; Hayden, Jill A.; Perel, Pablo; Schroter, Sara; Riley, Richard D; Hemingway, Harry; Altman, Douglas G.

doi:10.1371/journal.pmed.1001381

Cited by 1,159 publications

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References 78 publications

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“…A recent series on prognosis research [293][294][295][296] discusses how a single prognostic factor rarely predicts individual outcome risk accurately, and usually does not suitably discriminate between high-risk and low-risk individuals, as in this report. This is why prognostic models are developed, as they utilise multiple prognostic factors in combination to improve individual risk prediction accuracy and to better discriminate the underlying risk across individuals.…”

Section: Consideration On the Usefulness Of Prognostic Factorsmentioning

confidence: 99%

“…[293][294][295][296] For instance, they help to define disease at diagnosis; they aid the design and analysis of trials; they are confounders to consider in observational studies and unbalanced trials; and they are the building blocks of risk prediction models. The designation of 'aspirin resistance' based on a PFT is a potential prognostic factor, but it should ideally be considered in conjunction with any other factors identified as prognostic of future adverse clinical outcomes.…”

Section: Consideration On the Usefulness Of Prognostic Factorsmentioning

confidence: 99%

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The prognostic utility of tests of platelet function for the detection of ‘aspirin resistance’ in patients with established cardiovascular or cerebrovascular disease: a systematic review and economic evaluation

Dretzke

Riley

Lordkipanidzé

et al. 2015

Health Technology Assessment

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BackgroundThe use of aspirin is well established for secondary prevention of cardiovascular disease. However, a proportion of patients suffer repeat cardiovascular events despite being prescribed aspirin treatment. It is uncertain whether or not this is due to an inherent inability of aspirin to sufficiently modify platelet activity. This report aims to investigate whether or not insufficient platelet function inhibition by aspirin (‘aspirin resistance‘), as defined using platelet function tests (PFTs), is linked to the occurrence of adverse clinical outcomes, and further, whether or not patients at risk of future adverse clinical events can be identified through PFTs.ObjectivesTo review systematically the clinical effectiveness and cost-effectiveness evidence regarding the association between PFT designation of ‘aspirin resistance’ and the risk of adverse clinical outcome(s) in patients prescribed aspirin therapy. To undertake exploratory model-based cost-effectiveness analysis on the use of PFTs.Data sourcesBibliographic databases (e.g. MEDLINE from inception and EMBASE from 1980), conference proceedings and ongoing trial registries up to April 2012.MethodsStandard systematic review methods were used for identifying clinical and cost studies. A risk-of-bias assessment tool was adapted from checklists for prognostic and diagnostic studies. (Un)adjusted odds and hazard ratios for the association between ‘aspirin resistance’, for different PFTs, and clinical outcomes are presented; however, heterogeneity between studies precluded pooling of results. A speculative economic model of a PFT and change of therapy strategy was developed.ResultsOne hundred and eight relevant studies using a variety of PFTs, 58 in patients on aspirin monotherapy, were analysed in detail. Results indicated that some PFTs may have some prognostic utility, i.e. a trend for more clinical events to be associated with groups classified as ‘aspirin resistant’. Methodological and clinical heterogeneity prevented a quantitative summary of prognostic effect. Study-level effect sizes were generally small and absolute outcome risk was not substantially different between ‘aspirin resistant’ and ‘aspirin sensitive’ designations.No studies on the cost-effectiveness of PFTs for ‘aspirin resistance’ were identified. Based on assumptions of PFTs being able to accurately identify patients at high risk of clinical events and such patients benefiting from treatment modification, the economic model found that a test–treat strategy was likely to be cost-effective. However, neither assumption is currently evidence based.LimitationsPoor or incomplete reporting of studies suggests a potentially large volume of inaccessible data. Analyses were confined to studies on patients prescribed aspirin as sole antiplatelet therapy at the time of PFT. Clinical and methodological heterogeneity across studies precluded meta-analysis. Given the lack of robust data the economic modelling was speculative.ConclusionsAlthough evidence indicates that some PFTs may have some prognostic value, methodological and clinical heterogeneity between studies and different approaches to analyses create confusion and inconsistency in prognostic results, and prevented a quantitative summary of their prognostic effect. Protocol-driven and adequately powered primary studies are needed, using standardised methods of measurements to evaluate the prognostic ability of each test in the same population(s), and ideally presenting individual patient data. For any PFT to inform individual risk prediction, it will likely need to be considered in combination with other prognostic factors, within a prognostic model.Study registrationThis study is registered as PROSPERO 2012:CRD42012002151.FundingThe National Institute for Health Research Health Technology Assessment programme.

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Section: Consideration On the Usefulness Of Prognostic Factorsmentioning

confidence: 99%

Section: Consideration On the Usefulness Of Prognostic Factorsmentioning

confidence: 99%

The prognostic utility of tests of platelet function for the detection of ‘aspirin resistance’ in patients with established cardiovascular or cerebrovascular disease: a systematic review and economic evaluation

Dretzke

Riley

Lordkipanidzé

et al. 2015

Health Technology Assessment

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“…Tamaño de la muestra: a diferencia de las pruebas de hipótesis convencionales para experimentos clínicos u otros estudios de asociación, no existe en los modelos predictivos una formulación estándar que permita estimar el número de individuos necesario para detectar la "diferencia que se espera encontrar" (13). Es claro que un criterio fundamental es el número absoluto de sujetos que presentan el desenlace de interés, más que el total de individuos en riesgo que componen la muestra, y que el modelo es más confiable si se desarrolla con bases de datos grandes y de calidad, aunque no exista una definición única para ese "grande" (3,10). En términos generales, de acuerdo con consideraciones como el tipo de predictores, su distribución y variabilidad, la necesidad de transformaciones o la presencia de interacciones, podría considerarse necesario tener de 10 a 50 desenlaces por cada variable independiente predictora en el modelo (10).…”

Section: Desarrollo De Un Modelounclassified

“…La implementación de un modelo puede limitarse cuando la medición de los predictores no está disponible en el cuidado de rutina o si dicha medición es costosa. Un modelo complejo o que dificulta la toma de decisiones es difícil de usar en el contexto clínico (3,15). Para su aplicabilidad clínica, un puntaje de riesgo debe ser creíble, bien calibrado, con buena habilidad discriminativa, generalizable, y proveer a los clínicos información para mejorar la toma de decisiones terapéuticas (14).…”

Section: Evaluación Del Impacto Y La Aplicabilidad Clínica Del Modelounclassified

Ronda clínica y epidemiológica: aproximación a los modelos de predicción clínica

Isaza-Jaramillo

Jaimes

2017

iatreia

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RESUMENLa investigación relacionada con el pronóstico se puede clasificar así: fundamental, que revela diferencias en desenlaces de salud; de factores pronósticos, que identifica y caracteriza variables; de desarrollo, validación y utilidad de modelos predictivos; y finalmente, de medicina estratificada, donde se establecen grupos de riesgo que comparten un factor asociado con el desenlace de interés. El desenlace en salud, o la condición de enfermedad que tendrá un individuo se pueden predecir considerando ciertas características asociadas, con anterioridad o simultáneamente, con dicho desenlace. Lo anterior puede hacerse mediante modelos predictivos pronósticos o diagnósticos. El desarrollo de un modelo predictivo exige cuidado en la selección, definición, medición y categorización de las variables predictoras; en la exploración de interacciones; en el número de variables que se van a incluir; en el cálculo del tamaño de la muestra; en el manejo de los datos perdidos; en las pruebas estadísticas que se van a usar y en la forma de presentación del modelo. El modelo así desarrollado se debe validar en un grupo diferente de pacientes para establecer su calibración, discriminación y utilidad. PALABRAS CLAVEDiagnóstico; Índice; Modelo; Predicción; Pronóstico; Regla SUMMARY Clinical and epidemiological round: Approach to clinical prediction modelsResearch related to prognosis can be classified as follows: fundamental, which shows differences in health outcomes; prognostic factors, which identifies and characterizes variables;

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“…A prognostic model is a methodology to amalgamate multiple prognostic factors for an outcome and allows for an estimate of risk for this outcome to be calculated for an individual, within a specific timeframe (91). A good prognostic model provides accurate predictions that allows for informed decisions by clinicians, patients and care givers to improve outcomes, as well as supports clinical research by improving design of clinical trials (92) and case-mix adjustment (93).…”

Section: Prognostic Model Researchmentioning

confidence: 99%

Frailty Assessment in Acute Care

2015

The Gerontologist

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IntroductionFor some people, ageing is associated with the experience of increased co-morbidity, functional impairment, poor resilience and heightened vulnerability to external stressors, resulting in reduced lifespan as well as health-span. This frailty phenomenon poses challenges to health care systems in the form of increased patient complexity and resource utilisation. The acute care setting, characterised by time-pressure and high patient turn-over, is under strain and struggles to recognise and subsequently reliably intervene, to prevent, reverse or halt the decline of this vulnerable cohort. MethodsThis mixed-methods study probes existing evidence and 'real-world' processes with a systematic review of frailty assessments developed or validated in the acute care setting and a survey of contemporaneous clinical practice in London Acute Medical Units. Content validation and understanding of contextual factors for ideal frailty assessment in acute care is explored using Delphi consensus and Focus Group methodology respectively. The resultant model is developed on existing retrospective national Hospital Episode Statistics data, and prospectively tested on observational data in a local Acute Medical Unit setting. ResultsExisting frailty scores are preponderantly biophysical in nature, and have poor predictive power for adverse outcomes in the acute care setting. In clinical practice, single-dimension assessment tools predominate. Frailty syndromes and previous high resource utilisation in the form of a simple, clinically relevant tool useful to the multidisciplinary team gain consensus as optimal assessment for the setting. Retrospective testing of the frailty model displays moderate predictive powers for adverse events (inpatient mortality, emergency readmission and institutionalisation) and prospective testing provides concurrent (Frailty Index, Age, Co-Morbidity) and comparative predictive validity (Frailty Index, Co-Morbidity, 2 admission National Early Warning Score) with existing risk stratification models in this setting. ConclusionsA risk prediction model based on frailty syndromes and previous high resource utilisation is a valid, feasible and useful for the acute care setting.

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Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

Cited by 1,159 publications

References 78 publications

The prognostic utility of tests of platelet function for the detection of ‘aspirin resistance’ in patients with established cardiovascular or cerebrovascular disease: a systematic review and economic evaluation

The prognostic utility of tests of platelet function for the detection of ‘aspirin resistance’ in patients with established cardiovascular or cerebrovascular disease: a systematic review and economic evaluation

Ronda clínica y epidemiológica: aproximación a los modelos de predicción clínica

Frailty Assessment in Acute Care

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