scholarly journals Prognostic factors and prediction models for acute aortic dissection: a systematic review

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e042435
Author(s):  
Yan Ren ◽  
Shiyao Huang ◽  
Qianrui Li ◽  
Chunrong Liu ◽  
Ling Li ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Aortic Dissection ◽  
Acute Aortic Dissection ◽  
Prediction Models ◽  
Statistical Significance ◽  
Model Development ◽  
Model Performance ◽  
Model Studies ◽  
Significance Threshold ◽  
Cohort Study Design

ObjectiveOur study aimed to systematically review the methodological characteristics of studies that identified prognostic factors or developed or validated models for predicting mortalities among patients with acute aortic dissection (AAD), which would inform future work.Design/settingA methodological review of published studies.MethodsWe searched PubMed and EMBASE from inception to June 2020 for studies about prognostic factors or prediction models on mortality among patients with AAD. Two reviewers independently collected the information about methodological characteristics. We also documented the information about the performance of the prognostic factors or prediction models.ResultsThirty-two studies were included, of which 18 evaluated the performance of prognostic factors, and 14 developed or validated prediction models. Of the 32 studies, 23 (72%) were single-centre studies, 22 (69%) used data from electronic medical records, 19 (59%) chose retrospective cohort study design, 26 (81%) did not report missing predictor data and 5 (16%) that reported missing predictor data used complete-case analysis. Among the 14 prediction model studies, only 3 (21%) had the event per variable over 20, and only 5 (36%) reported both discrimination and calibration statistics. Among model development studies, 3 (27%) did not report statistical methods, 3 (27%) exclusively used statistical significance threshold for selecting predictors and 7 (64%) did not report the methods for handling continuous predictors. Most prediction models were considered at high risk of bias. The performance of prognostic factors showed varying discrimination (AUC 0.58 to 0.95), and the performance of prediction models also varied substantially (AUC 0.49 to 0.91). Only six studies reported calibration statistic.ConclusionsThe methods used for prognostic studies on mortality among patients with AAD—including prediction models or prognostic factor studies—were suboptimal, and the model performance highly varied. Substantial efforts are warranted to improve the use of the methods in this population.

scholarly journals Incidence, Risk Factors and Outcomes of Postoperative Headache After Stanford Type a Acute Aortic Dissection Surgery

2021 ◽  
Vol 8 ◽  
Author(s):  
Dashuai Wang ◽  
Sheng Le ◽  
Jingjing Luo ◽  
Xing Chen ◽  
Rui Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Practice ◽  
Aortic Dissection ◽  
Acute Aortic Dissection ◽  
Prediction Models ◽  
Multivariate Logistic Regression Analysis ◽  
Type A ◽  
Smoking History ◽  
Incidence Risk ◽  
Postoperative Headache

Background: Postoperative headache (POH) is common in clinical practice, however, no studies about POH after Stanford type A acute aortic dissection surgery (AADS) exist. This study aims to describe the incidence, risk factors and outcomes of POH after AADS, and to construct two prediction models.Methods: Adults who underwent AADS from 2016 to 2020 in four tertiary hospitals were enrolled. Training and validation sets were randomly assigned according to a 7:3 ratio. Risk factors were identified by univariate and multivariate logistic regression analysis. Nomograms were constructed and validated on the basis of independent predictors.Results: POH developed in 380 of the 1,476 included patients (25.7%). Poorer outcomes were observed in patients with POH. Eight independent predictors for POH after AADS were identified when both preoperative and intraoperative variables were analyzed, including younger age, female sex, smoking history, chronic headache history, cerebrovascular disease, use of deep hypothermic circulatory arrest, more blood transfusion, and longer cardiopulmonary bypass time. White blood cell and platelet count were also identified as significant predictors when intraoperative variables were excluded from the multivariate analysis. A full nomogram and a preoperative nomogram were constructed based on these independent predictors, both demonstrating good discrimination, calibration, clinical usefulness, and were well validated. Risk stratification was performed and three risk intervals were defined based on the full nomogram and clinical practice.Conclusions: POH was common after AADS, portending poorer outcomes. Two nomograms predicting POH were developed and validated, which may have clinical utility in risk evaluation, early prevention, and doctor-patient communication.

scholarly journals Prediction models for exacerbations in patients with COPD

2017 ◽  
Vol 26 (143) ◽  
pp. 160061 ◽  
Author(s):  
Beniamino Guerra ◽  
Violeta Gaveikaite ◽  
Camilla Bianchi ◽  
Milo A. Puhan
Keyword(s):  
Prediction Models ◽  
Inhaled Corticosteroids ◽  
Personalised Medicine ◽  
Model Development ◽  
Model Performance ◽  
Adverse Outcomes ◽  
Chronic Obstructive ◽  
High Quality ◽  
Obstructive Pulmonary Disease ◽  
The Individual

Personalised medicine aims to tailor medical decisions to the individual patient. A possible approach is to stratify patients according to the risk of adverse outcomes such as exacerbations in chronic obstructive pulmonary disease (COPD). Risk-stratified approaches are particularly attractive for drugs like inhaled corticosteroids or phosphodiesterase-4 inhibitors that reduce exacerbations but are associated with harms. However, it is currently not clear which models are best to predict exacerbations in patients with COPD. Therefore, our aim was to identify and critically appraise studies on models that predict exacerbations in COPD patients. Out of 1382 studies, 25 studies with 27 prediction models were included. The prediction models showed great heterogeneity in terms of number and type of predictors, time horizon, statistical methods and measures of prediction model performance. Only two out of 25 studies validated the developed model, and only one out of 27 models provided estimates of individual exacerbation risk, only three out of 27 prediction models used high-quality statistical approaches for model development and evaluation. Overall, none of the existing models fulfilled the requirements for risk-stratified treatment to personalise COPD care. A more harmonised approach to develop and validate high- quality prediction models is needed to move personalised COPD medicine forward.

scholarly journals A Wavelet-Based Approach to Streamflow Event Identification and Modeled Timing Error Evaluation

2020 ◽  
Author(s):  
Erin Towler ◽  
James L. McCreight
Keyword(s):  
Wavelet Transform ◽  
Model Evaluation ◽  
Statistical Significance ◽  
Model Development ◽  
Model Performance ◽  
Water Model ◽  
Timing Error ◽  
Discharge Data ◽  
Event Identification ◽  
Timing Errors

Abstract. Streamflow timing errors (in the units of time) are rarely explicitly evaluated, but are useful for model evaluation and development. Wavelet-based approaches have been shown to reliably quantify timing errors in streamflow simulations, but have not been applied in a systematic way that is suitable for model evaluation. This paper provides a step-by-step methodology that objectively identifies events, and then estimates timing errors for those events, in a way that can be applied to large-sample, high-resolution predictions. Step 1 applies the wavelet transform to the observations, and uses statistical significance to identify observed events. Step 2 utilizes the cross-wavelet transform to calculate the timing errors for the events identified in Step 1. The approach also includes a quantification of the confidence in the timing error estimates. The methodology is illustrated using real and simulated stream discharge data from several locations to highlight key method features. The method groups event timing errors by dominant timescales, which can be used to identify the potential processes contributing to the timing errors and the associated model development needs. For instance, timing errors that are associated with the diurnal melt cycle are identified. The method is also useful for documenting and evaluating model performance in terms of defined standards. This is illustrated by showing version-over-version performance of the National Water Model (NWM) in terms of timing errors.

scholarly journals A wavelet-based approach to streamflow event identification and modeled timing error evaluation

2021 ◽  
Vol 25 (5) ◽  
pp. 2599-2615
Author(s):  
Erin Towler ◽  
James L. McCreight
Keyword(s):  
Wavelet Transform ◽  
Model Evaluation ◽  
Statistical Significance ◽  
Model Development ◽  
Model Performance ◽  
Water Model ◽  
Discharge Data ◽  
Event Identification ◽  
Timing Errors ◽  
Stream Discharge

Abstract. Streamflow timing errors (in the units of time) are rarely explicitly evaluated but are useful for model evaluation and development. Wavelet-based approaches have been shown to reliably quantify timing errors in streamflow simulations but have not been applied in a systematic way that is suitable for model evaluation. This paper provides a step-by-step methodology that objectively identifies events, and then estimates timing errors for those events, in a way that can be applied to large-sample, high-resolution predictions. Step 1 applies the wavelet transform to the observations and uses statistical significance to identify observed events. Step 2 utilizes the cross-wavelet transform to calculate the timing errors for the events identified in step 1; this includes the diagnostic of model event hits, and timing errors are only assessed for hits. The methodology is illustrated using real and simulated stream discharge data from several locations to highlight key method features. The method groups event timing errors by dominant timescales, which can be used to identify the potential processes contributing to the timing errors and the associated model development needs. For instance, timing errors that are associated with the diurnal melt cycle are identified. The method is also useful for documenting and evaluating model performance in terms of defined standards. This is illustrated by showing the version-over-version performance of the National Water Model (NWM) in terms of timing errors.

scholarly journals Predicting inhospital admission at the emergency department: a systematic review

2021 ◽  
pp. emermed-2020-210902
Author(s):  
Anniek Brink ◽  
Jelmer Alsma ◽  
Lodewijk AAM van Attekum ◽  
Wichor M Bramer ◽  
Robert Zietse ◽  
...  
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Data Extraction ◽  
Model Development ◽  
Model Performance ◽  
Area Under The Curve ◽  
Registration Number ◽  
Modelling Studies ◽  
Assess Quality ◽  
The Uk

BackgroundED crowding has potential detrimental consequences for both patient care and staff. Advancing disposition can reduce crowding. This may be achieved by using prediction models for admission. This systematic review aims to present an overview of prediction models for admission at the ED. Furthermore, we aimed to identify the best prediction tool based on its performance, validation, calibration and clinical usability.MethodsWe included observational studies published in Embase.com, Medline Ovid, Cochrane CENTRAL, Web of Science Core Collection or Google scholar, in which admission models were developed or validated in a general medical population in European EDs including the UK. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to assess quality of model development. Model performance was presented as discrimination and calibration. The search was performed on 11 October 2020.ResultsIn total, 18 539 articles were identified. We included 11 studies, describing 16 different models, comprising the development of 9 models and 12 external validations of 11 models. The risk of bias of the development studies was considered low to medium. Discrimination, as represented by the area under the curve ranged from 0.630 to 0.878. Calibration was assessed in seven models and was strong. The best performing models are the models of Lucke et al and Cameron et al. These models combine clinical applicability, by inclusion of readily available parameters, and appropriate discrimination, calibration and validation.ConclusionNone of the models are yet implemented in EDs. Further research is needed to assess the applicability and implementation of the best performing models in the ED.Systematic review registration numberPROSPERO CRD42017057975.

Sign in / Sign up

Export Citation Format

Share Document