scholarly journals Prediction of recurrent stroke among ischemic stroke patients with atrial fibrillation: Development and validation of a risk score model

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258377
Author(s):  
Beom Joon Kim ◽  
Keon-Joo Lee ◽  
Eun Lyeong Park ◽  
Kanta Tanaka ◽  
Masatoshi Koga ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Prediction Model ◽  
Risk Prediction ◽  
Recurrent Events ◽  
Recurrent Stroke ◽  
External Validation ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
Stroke Recurrence

Background There is currently no validated risk prediction model for recurrent events among patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). Considering that the application of conventional risk scores has contextual limitations, new strategies are needed to develop such a model. Here, we set out to develop and validate a comprehensive risk prediction model for stroke recurrence in AIS patients with AF. Methods AIS patients with AF were collected from multicenter registries in South Korea and Japan. A developmental dataset was constructed with 5648 registered cases from both countries for the period 2011‒2014. An external validation dataset was also created, consisting of Korean AIS subjects with AF registered between 2015 and 2018. Event outcomes were collected during 1 year after the index stroke. A multivariable prediction model was developed using the Fine–Gray subdistribution hazard model with non-stroke mortality as a competing risk. The model incorporated 21 clinical variables and was further validated, calibrated, and revised using the external validation dataset. Results The developmental dataset consisted of 4483 Korean and 1165 Japanese patients (mean age, 74.3 ± 10.2 years; male 53%); 338 patients (6%) had recurrent stroke and 903 (16%) died. The clinical profiles of the external validation set (n = 3668) were comparable to those of the developmental dataset. The c-statistics of the final model was 0.68 (95% confidence interval, 0.66 ‒0.71). The developed prediction model did not show better discriminative ability for predicting stroke recurrence than the conventional risk prediction tools (CHADS2, CHA2DS2-VASc, and ATRIA). Conclusions Neither conventional risk stratification tools nor our newly developed comprehensive prediction model using available clinical factors seemed to be suitable for identifying patients at high risk of recurrent ischemic stroke among AIS patients with AF in this modern direct oral anticoagulant era. Detailed individual information, including imaging, may be warranted to build a more robust and precise risk prediction model for stroke survivors with AF.

scholarly journals A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaona Jia ◽  
Mirza Mansoor Baig ◽  
Farhaan Mirza ◽  
Hamid GholamHosseini
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Model ◽  
Clinical Laboratory ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
Cvd Risk

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

scholarly journals Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

2017 ◽  
Vol 128 (5) ◽  
pp. 1140-1145 ◽  
Author(s):  
Japke F. Petersen ◽  
Martijn M. Stuiver ◽  
Adriana J. Timmermans ◽  
Amy Chen ◽  
Hongzhen Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prediction Model ◽  
Risk Prediction ◽  
External Validation ◽  
Risk Prediction Model ◽  
Larynx Cancer

scholarly journals The M6 risk prediction model and two-step strategy to characterize pregnancies of unknown location: a multicentre external validation study

Authorea ◽  
2020 ◽  
Author(s):  
Evangelia Christodoulou ◽  
Shabnam Bobdiwala ◽  
Christopher Kyriacou ◽  
Jessica Farren ◽  
Nicola Mitchell Jones ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Validation Study ◽  
External Validation ◽  
Risk Prediction Model ◽  
External Validation Study

Abstract 17956: External Validation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Matthew J Kolek ◽  
Amy J Graves ◽  
Aihua Bian ◽  
Pedro L Teixeira ◽  
Moore B Shoemaker ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Prediction Model ◽  
Electronic Medical Records ◽  
Risk Prediction ◽  
Medical Records ◽  
Risk Model ◽  
External Validation ◽  
Left Ventricular ◽  
Cox Proportional Hazards Model ◽  
Ambulatory Setting

Background: Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and healthcare costs. Accurate prediction of incident AF might enhance patient care and improve outcomes. We aimed to externally validate the AF risk model developed by the CHARGE-AF investigators utilizing a large repository of electronic medical records (EMR). Methods: Using a database of de-identified EMRs, we conducted a retrospective cohort study of subjects serially followed in internal medicine clinics at our institution (minimum 3 visits in a 24 month window). Subjects were followed for incident AF from 2005 until 2010. We applied the published CHARGE-AF Cox proportional hazards model beta coefficients to our cohort. Predictors included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval. Calibration and discrimination were assessed by generating calibration plots and calculating C-statistics. Results: The study included 33,494 subjects with median age 57 years (25th to 75th percentile: 49 - 67), 57% women, 86% whites, and 14% African Americans. During the mean follow-up period of 4.8 ± 0.85 years, 2455 (7.3%) subjects developed AF. After correcting for baseline hazard, the CHARGE-AF model over-predicted AF at the highest risk deciles but was otherwise well-calibrated (Figure) and showed good discrimination, with a C-statistic of 0.746 (95% confidence interval: 0.738 to 0.754). Conclusion: From clinical factors readily accessible in a large de-identified EMR repository, we externally validated the CHARGE-AF risk prediction model to identify individuals at risk for developing AF in an ambulatory setting. These data not only provide strong validation for the CHARGE-AF risk prediction tool, but also indicate that the tool, and thus primary prevention strategies, can be implemented in an EMR context.

Abstract W MP65: Moderate to Severe Left Atrial Enlargement is Associated with Cardioembolic or Cryptogenic Stroke Recurrence in a Multiethnic Cohort: The Northern Manhattan Study

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Shadi Yaghi ◽  
Yeseon P Moon ◽  
Consuelo Mora-McLaughlin ◽  
Joshua Z Willey ◽  
Marco R Di Tullio ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Left Atrial ◽  
Recurrent Stroke ◽  
Cryptogenic Stroke ◽  
Cardioembolic Stroke ◽  
Stroke Recurrence ◽  
Stroke Patients ◽  
Stroke Subtypes ◽  
Multiethnic Cohort

Background: While left atrial (LA) enlargement increases incident stroke risk, the association with recurrent stroke is unclear. Our aim was to determine the association of LA enlargement (LAE) with stroke recurrence risk and recurrent stroke subtypes likely related to embolism (cryptogenic or cardioembolic). Methods: We enrolled 655 first ischemic stroke patients in the Northern Manhattan Stroke Study. LA size was measured by two-dimensional echocardiogram as part of the clinical evaluation and patients were followed annually for up to 5 years. LA size adjusted for sex and body surface area was categorized into three groups: normal (52.7%), mild LAE (31.6%), and moderate to severe LAE (15.7%). The outcomes were total recurrent stroke, and recurrent combined cryptogenic or cardioembolic stroke. Cox proportional hazard models assessed the association between LA size and risk of stroke recurrence. Results: Of 655 patients, LA size data was present in 529 (81%). Mean age was 69 ± 13 years; 46% were male and 18% had atrial fibrillation. Over a median of 4 years, recurrent stroke occurred in 83 patients (16%), 29 were cardioembolic or cryptogenic stroke. After adjusting for baseline demographics and risk factors including atrial fibrillation and congestive heart failure, compared to normal LA size, moderate to severe LAE was associated with greater risk of recurrent combined cardioembolic or cryptogenic stroke (adjusted HR 2. 99, 95% CI 1. 10 to 8.13), but not with risk of total stroke recurrence (adjusted HR 1.18, 95% CI 0.60 to 2.32). Mild LAE was not associated with either total stroke recurrence or the combined recurrent cryptogenic or cardioembolic stroke subtypes. Conclusion: Moderate to severe LAE is an independent marker of recurrent cardioembolic or cryptogenic stroke in a multiethnic cohort of ischemic stroke patients. Future research is needed to determine if anticoagulant use reduces the risk of recurrence in ischemic stroke patients with moderate to severe LAE.

scholarly journals External Validation Of The Risk-Prediction Model For Hepatocellular Carcinoma [Hcc] From The Reveal Hcv Study

2013 ◽  
Vol 16 (3) ◽  
pp. A12
Author(s):  
T. Matsuda ◽  
I. Tonnu-Mihara ◽  
Y. Yuan ◽  
P. Hines ◽  
S.L. Saab ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Prediction ◽  
External Validation ◽  
Risk Prediction Model
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