Dataset Information

Clinical and infarction patterns of PFO-related cryptogenic strokes and a prediction model.

ABSTRACT: Objectives:The higher than expected PFO rate in CS patients has raised concerns that paradoxical embolism maybe the pathophysiologic mechanism for strokes. However, only a small proportion of pathogenic PFOs cause CS. Therefore, accurate recognition of patients with pathogenic PFOs among all CS patients could guide clinical decision making in selecting the most appropriate treatment. The aim of this study was to devise a new algorithm to stratify cryptogenic stroke (CS) patients into pathogenic patent foramen ovale (p-PFO)- and non-p-PFO-related patients. Methods:A total of 1201 patients with acute ischemic stroke were recruited from two different medical centers, and 253 CS patients were identified. Of the 253 patients, 111 were diagnosed with PFO using contrast transcranial Doppler. Data on medical histories, neuroimaging and laboratory tests were compared in CS patients with or without PFO. Results:Compared with PFO-negative CS patients, PFO-positive CS patients showed younger onset age, lower incidence of hypertension and dyslipidemia, characteristic infarction pattern in magnetic resonance imaging and specifically altered platelet activity and coagulation function. Based on the above information, we constructed a PFO judgment formula (Hr-PFOJ) by means of feature weight estimation and predictive performance evaluation to predict pathogenic PFO in CS patients with a sensitivity of 76.3% and a specificity of 66.5%. Interpretations:Hr-PFOJ judgment formula is a useful screening tool for identification of patients with pathogenic PFO who may benefit from PFO-related treatment.

SUBMITTER: He D

PROVIDER: S-EPMC6243387 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

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Clinical and infarction patterns of PFO-related cryptogenic strokes and a prediction model.

He Dan D Shi Qiang Q Xu Guangjing G Hu Zheng Z Li Xuefei X Li Qian Q Guo Yinping Y Xu Shabei S Lin Yongbo Y Yu Zhiyuan Z Wang Wei W Luo Xiang X

Annals of clinical and translational neurology 20180924 11

<h4>Objectives</h4>The higher than expected PFO rate in CS patients has raised concerns that paradoxical embolism maybe the pathophysiologic mechanism for strokes. However, only a small proportion of pathogenic PFOs cause CS. Therefore, accurate recognition of patients with pathogenic PFOs among all CS patients could guide clinical decision making in selecting the most appropriate treatment. The aim of this study was to devise a new algorithm to stratify cryptogenic stroke (CS) patients into pat ...[more]

PMID: 30480027

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Project description:ImportanceThe Million Hearts Model paid health care organizations to assess and reduce cardiovascular disease (CVD) risk. Model effects on long-term outcomes are unknown.ObjectiveTo estimate model effects on first-time myocardial infarctions (MIs) and strokes and Medicare spending over a period up to 5 years.Design, setting, and participantsThis pragmatic cluster-randomized trial ran from 2017 to 2021, with organizations assigned to a model intervention group or standard care control group. Randomized organizations included 516 US-based primary care and specialty practices, health centers, and hospital-based outpatient clinics participating voluntarily. Of these organizations, 342 entered patients into the study population, which included Medicare fee-for-service beneficiaries aged 40 to 79 years with no previous MI or stroke and with high or medium CVD risk (a 10-year predicted probability of MI or stroke [ie, CVD risk score] ≥15%) in 2017-2018.InterventionOrganizations agreed to perform guideline-concordant care, including routine CVD risk assessment and cardiovascular care management for high-risk patients. The Centers for Medicare & Medicaid Services paid organizations to calculate CVD risk scores for Medicare fee-for-service beneficiaries. CMS further rewarded organizations for reducing risk among high-risk beneficiaries (CVD risk score ≥30%).Main outcomes and measuresOutcomes included first-time CVD events (MIs, strokes, and transient ischemic attacks) identified in Medicare claims, combined first-time CVD events from claims and CVD deaths (coronary heart disease or cerebrovascular disease deaths) identified using the National Death Index, and Medicare Parts A and B spending for CVD events and overall. Outcomes were measured through 2021.ResultsHigh- and medium-risk model intervention beneficiaries (n = 130 578) and standard care control beneficiaries (n = 88 286) were similar in age (median age, 72-73 y), sex (58%-59% men), race (7%-8% Black), and baseline CVD risk score (median, 24%). The probability of a first-time CVD event within 5 years was 0.3 percentage points lower for intervention beneficiaries than control beneficiaries (3.3% relative effect; adjusted hazard ratio [HR], 0.97 [90% CI, 0.93-1.00]; P = .09). The 5-year probability of combined first-time CVD events and CVD deaths was 0.4 percentage points lower in the intervention group (4.2% relative effect; HR, 0.96 [90% CI, 0.93-0.99]; P = .02). Medicare spending for CVD events was similar between the groups (effect estimate, -$1.83 per beneficiary per month [90% CI, -$3.97 to -$0.30]; P = .16), as was overall Medicare spending including model payments (effect estimate, $2.11 per beneficiary per month [90% CI, -$16.66 to $20.89]; P = .85).Conclusions and relevanceThe Million Hearts Model, which encouraged and paid for CVD risk assessment and reduction, reduced first-time MIs and strokes. Results support guidelines to use risk scores for CVD primary prevention.Trial registrationClinicalTrials.gov Identifier: NCT04047147.

Project description:Background and purposeOne-fifth of ischemic strokes are embolic strokes of undetermined source (ESUS). Their theoretical causes can be classified as cardioembolic versus noncardioembolic. This distinction has important implications, but the categories' proportions are unknown.MethodsUsing data from the Cornell Acute Stroke Academic Registry, we trained a machine-learning algorithm to distinguish cardioembolic versus non-cardioembolic strokes, then applied the algorithm to ESUS cases to determine the predicted proportion with an occult cardioembolic source. A panel of neurologists adjudicated stroke etiologies using standard criteria. We trained a machine learning classifier using data on demographics, comorbidities, vitals, laboratory results, and echocardiograms. An ensemble predictive method including L1 regularization, gradient-boosted decision tree ensemble (XGBoost), random forests, and multivariate adaptive splines was used. Random search and cross-validation were used to tune hyperparameters. Model performance was assessed using cross-validation among cases of known etiology. We applied the final algorithm to an independent set of ESUS cases to determine the predicted mechanism (cardioembolic or not). To assess our classifier's validity, we correlated the predicted probability of a cardioembolic source with the eventual post-ESUS diagnosis of atrial fibrillation.ResultsAmong 1083 strokes with known etiologies, our classifier distinguished cardioembolic versus noncardioembolic cases with excellent accuracy (area under the curve, 0.85). Applied to 580 ESUS cases, the classifier predicted that 44% (95% credibility interval, 39%-49%) resulted from cardiac embolism. Individual ESUS patients' predicted likelihood of cardiac embolism was associated with eventual atrial fibrillation detection (OR per 10% increase, 1.27 [95% CI, 1.03-1.57]; c-statistic, 0.68 [95% CI, 0.58-0.78]). ESUS patients with high predicted probability of cardiac embolism were older and had more coronary and peripheral vascular disease, lower ejection fractions, larger left atria, lower blood pressures, and higher creatinine levels.ConclusionsA machine learning estimator that distinguished known cardioembolic versus noncardioembolic strokes indirectly estimated that 44% of ESUS cases were cardioembolic.

Dataset Information

Clinical and infarction patterns of PFO-related cryptogenic strokes and a prediction model.

Publications

Clinical and infarction patterns of PFO-related cryptogenic strokes and a prediction model.

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