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: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.

Project description:Chemotherapy-related hospitalizations in patients with advanced cancer are common, distressing, and costly. Methods to identify patients at high risk of chemotherapy toxic effects will permit development of targeted strategies to prevent chemotherapy-related hospitalizations.To demonstrate the feasibility of using readily available clinical data to assess patient-specific risk of chemotherapy-related hospitalization.Nested case-control study conducted from January 2003 through December 2011 at the Mass General/North Shore Cancer Center, a community-based cancer center in northeastern Massachusetts. The parent cohort included 1579 consecutive patients with advanced solid-tumor cancer receiving palliative-intent chemotherapy. Case patients (n?=?146) included all patients from the parent cohort who experienced a chemotherapy-related hospitalization. Controls (n?=?292) were randomly selected from 1433 patients who did not experience a chemotherapy-related hospitalization.Putative risk factors for chemotherapy-related hospitalization-including patient characteristics, treatment characteristics, and pretreatment laboratory values-were abstracted from medical records. Multivariable logistic regression was used to model the patient-specific risk of chemotherapy-related hospitalization.Chemotherapy-related hospitalization, as adjudicated by the oncology clinical care team within a systematic quality-assessment program.A total of 146 (9.2%) of 1579 patients from the parent cohort experienced a chemotherapy-related hospitalization. In multivariate regression, 7 variables were significantly associated with chemotherapy-related hospitalization: age, Charlson comorbidity score, creatinine clearance, calcium level, below-normal white blood cell and/or platelet count, polychemotherapy (vs monotherapy), and receipt of camptothecin chemotherapy. The median predicted risk of chemotherapy-related hospitalization was 6.0% (interquartile range [IQR], 3.6%-11.4%) in control patients and 14.7% (IQR, 6.8%-22.5%) in case patients. The bootstrap-adjusted C statistic was 0.71 (95% CI, 0.66-0.75). At a risk threshold of 15%, the model exhibited a sensitivity of 49% (95% CI, 41%-57%) and a specificity of 85% (95% CI, 81%-89%) for predicting chemotherapy-related hospitalization.In patients initiating palliative chemotherapy for cancer, readily available clinical data were associated with the patient-specific risk of chemotherapy-related hospitalization. External validation and evaluation in the context of a clinical decision support tool are warranted.

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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