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Prediction models for clinical outcome after a carotid revascularisation procedure: A systematic review.

ABSTRACT: Introduction:Prediction models for clinical outcome after carotid artery stenting or carotid endarterectomy could aid physicians in estimating peri- and postprocedural risks in individual patients. We aimed to identify existing prediction models for short- and long-term outcome after carotid artery stenting or carotid endarterectomy in patients with symptomatic or asymptomatic carotid stenosis, and to summarise their most important predictors and predictive performance. Patients and methods:We performed a systematic literature search for studies that developed a prediction model or risk score published until 22 December 2016. Eligible prediction models had to predict the risk of vascular events with at least one patient characteristic. Results:We identified 37 studies that developed 46 prediction models. Thirty-four (74%) models were developed in carotid endarterectomy patients; 27 of these (59%) predicted short-term (in-hospital or within 30 days) risk. Most commonly predicted outcome was stroke or death (n = 12; 26%). Age (n = 31; 67%), diabetes mellitus (n = 21; 46%), heart failure (n = 16; 35%), and contralateral carotid stenosis ≥50% or occlusion (n = 16; 35%) were most commonly used as predictors. For 25 models (54%), it was unclear how missing data were handled; a complete case analysis was performed in 15 (33%) of the remaining 21 models. Twenty-eight (61%) models reported the full regression formula or risk score with risk classification. Twenty-one (46%) models were validated internally and 12 (26%) externally. Discriminative performance (c-statistic) ranged from 0.66 to 0.94 for models after carotid artery stenting and from 0.58 to 0.74 for models after carotid endarterectomy. The c-statistic ranged from 0.55 to 0.72 for the external validations. Discussion:Age, diabetes mellitus, heart failure, and contralateral carotid stenosis ≥50% or occlusion were most often used as predictors in all models. Discriminative performance (c-statistic) was higher for prediction models after carotid artery stenting than after carotid endarterectomy. Conclusion:The clinical usefulness of most prediction models for short- or long-term outcome after carotid artery stenting or carotid endarterectomy remains unclear because of incomplete reporting, methodological limitations, and lack of external validation.

SUBMITTER: Volkers EJ

PROVIDER: S-EPMC5992733 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionAcute liver failure is a rare disease with high mortality and liver transplantation is the only life saving therapy. Accurate prognosis of ALF is crucial for proper intervention.AimTo identify and characterize newly developed prognostic models of mortality for ALF patients, assess study quality, identify important variables and provide recommendations for the development of improved models in the future.MethodsThe online databases MEDLINE® (1950-2012) and EMBASE® (1980-2012) were searched for English-language articles that reported original data from clinical trials or observational studies on prognostic models in ALF patients. Studies were included if they developed a new model or modified existing prognostic models. The studies were evaluated based on an existing framework for scoring the methodological and reporting quality of prognostic models.ResultsTwenty studies were included, of which 18 reported on newly developed models, 1 on modification of the Kings College Criteria (KCC) and 1 on the Model for End-Stage Liver Disease (MELD). Ten studies compared the newly developed models to previously existing models (e.g. KCC); they all reported that the new models were superior. In the 12-point methodological quality score, only one study scored full points. On the 38-point reporting score, no study scored full points. There was a general lack of reporting on missing values. In addition, none of the studies used performance measures for calibration and accuracy (e.g. Hosmer-Lemeshow statistics, Brier score), and only 5 studies used the AUC as a measure of discrimination.ConclusionsThere are many studies on prognostic models for ALF but they show methodological and reporting limitations. Future studies could be improved by better reporting and handling of missing data, the inclusion of model calibration aspects, use of absolute risk measures, explicit considerations for variable selection, the use of a more extensive set of reference models and more thorough validation.

Project description:Background and purposePrediction models for outcome of patients with acute ischemic stroke who will undergo endovascular treatment have been developed to improve patient management. The aim of the current study is to provide an overview of preintervention models for functional outcome after endovascular treatment and to validate these models with data from daily clinical practice.MethodsWe systematically searched within Medline, Embase, Cochrane, Web of Science, to include prediction models. Models identified from the search were validated in the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) registry, which includes all patients treated with endovascular treatment within 6.5 hours after stroke onset in the Netherlands between March 2014 and November 2017. Predictive performance was evaluated according to discrimination (area under the curve) and calibration (slope and intercept of the calibration curve). Good functional outcome was defined as a score of 0-2 or 0-3 on the modified Rankin Scale depending on the model.ResultsAfter screening 3468 publications, 19 models were included in this validation. Variables included in the models mainly addressed clinical and imaging characteristics at baseline. In the validation cohort of 3156 patients, discriminative performance ranged from 0.61 (SPAN-100 [Stroke Prognostication Using Age and NIH Stroke Scale]) to 0.80 (MR PREDICTS). Best-calibrated models were THRIVE (The Totaled Health Risks in Vascular Events; intercept -0.06 [95% CI, -0.14 to 0.02]; slope 0.84 [95% CI, 0.75-0.95]), THRIVE-c (intercept 0.08 [95% CI, -0.02 to 0.17]; slope 0.71 [95% CI, 0.65-0.77]), Stroke Checkerboard score (intercept -0.05 [95% CI, -0.13 to 0.03]; slope 0.97 [95% CI, 0.88-1.08]), and MR PREDICTS (intercept 0.43 [95% CI, 0.33-0.52]; slope 0.93 [95% CI, 0.85-1.01]).ConclusionsThe THRIVE-c score and MR PREDICTS both showed a good combination of discrimination and calibration and were, therefore, superior in predicting functional outcome for patients with ischemic stroke after endovascular treatment within 6.5 hours. Since models used different predictors and several models had relatively good predictive performance, the decision on which model to use in practice may also depend on simplicity of the model, data availability, and the comparability of the population and setting.

Project description:BackgroundAlthough imaging techniques provide information about the morphology and stability of carotid plaque, they are operator dependent and may miss certain subtleties. A variety of radiomics models for carotid plaque have recently been proposed for identifying vulnerable plaques and predicting cardiovascular and cerebrovascular diseases. The purpose of this review was to assess the risk of bias, reporting, and methodological quality of radiomics models for carotid atherosclerosis plaques.MethodsA systematic search was carried out to identify available literature published in PubMed, Web of Science, and the Cochrane Library up to March 2023. Studies that developed and/or validated machine learning models based on radiomics data to identify and/or predict unfavorable cerebral and cardiovascular events in carotid plaque were included. The basic information of each piece of included literature was identified, and the reporting quality, risk of bias, and radiomics methodology quality were assessed according the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) checklist, the Prediction Model Risk of Bias Assessment Tool (PROBAST), and the radiomics quality score (RQS), respectively.ResultsA total of 2,738 patients from 19 studies were included. The mean overall TRIPOD adherence rate was 66.1% (standard deviation 12.8%), with a range of 45-87%. All studies had a high overall risk of bias, with the analysis domain being the most common source of bias. The mean RQS was 9.89 (standard deviation 5.70), accounting for 27.4% of the possible maximum value of 36. The mean area under the curve for diagnostic or predictive properties of these included radiomics models was 0.876±0.09, with a range of 0.741-0.989.ConclusionsRadiomics models may have value in the assessment of carotid plaque, the overall scientific validity and reporting quality of current carotid plaque radiomics reports are still lacking, and many barriers must be overcome before these models can be applied in clinical practice.

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Prediction models for clinical outcome after a carotid revascularisation procedure: A systematic review.

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