Laboratory tests as short-term correlates of stroke

Sughrue, Trevor; Swiernik, Michael A.; Huang, Yang; Brody, James P.

doi:10.1186/s12883-016-0619-y

Cited by 9 publications

(8 citation statements)

References 47 publications

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“…Neutrophils are, therefore, also of considerable importance as targets for treating and preventing ischemic stroke [ 29 ]. A study by Sughrue et al [ 10 ] produced results similar to ours regarding the positive association between neutrophils and stroke occurrence. Hemoglobin levels can predict the risk of stroke.…”

Section: Discussionsupporting

confidence: 87%

“…There have been no studies suggesting that lymphocyte percentage can be a predictor of stroke, but different studies have examined the use of immune cells as biomarkers to predict stroke outcome [ 26 , 27 ]. There is one study that showed a negative correlation between hematocrit and stroke occurrence [ 10 ]. Folate deficiency has various clinical manifestations.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have been able to identify independent laboratory tests that are correlated with stroke using descriptive statistical analysis. Sughrue et al [ 10 ] conducted a retrospective study in 2013 that identified 35 tests with a statistically significant correlation with a future stroke diagnosis. The most informative were for various types of cholesterol.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models

Alanazi¹,

Abdou²,

Luo³

2021

JMIR Form Res

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Background Stroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests. Objective The aim of this study was to apply computational methods using machine learning techniques to predict stroke from lab test data. Methods We used the National Health and Nutrition Examination Survey data sets with three different data selection methods (ie, without data resampling, with data imputation, and with data resampling) to develop predictive models. We used four machine learning classifiers and six performance measures to evaluate the performance of the models. Results We found that accurate and sensitive machine learning models can be created to predict stroke from lab test data. Our results show that the data resampling approach performed the best compared to the other two data selection techniques. Prediction with the random forest algorithm, which was the best algorithm tested, achieved an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of 0.96, 0.97, 0.96, 0.75, 0.99, and 0.97, respectively, when all of the attributes were used. Conclusions The predictive model, built using data from lab tests, was easy to use and had high accuracy. In future studies, we aim to use data that reflect different types of stroke and to explore the data to build a prediction model for each type.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models

Alanazi¹,

Abdou²,

Luo³

2021

JMIR Form Res

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“…The study of Zhao et al (2018) has shown that higher levels of HDL are associated with decreased risk of stroke, but some studies have suggested that higher levels of total cholesterol, HDL, LDL, and non-HDL cholesterol significantly decrease the incidence of stroke. They have also explained that higher levels of non-HDL cholesterol can be a significant risk factor for the cardiac disease but not necessarily for stroke (Gordon et al, 1977;Sughrue et al, 2016). This can describe that cardiac disease could cause a higher mortality rate in patients with stroke.…”

Section: Discussionmentioning

confidence: 99%

Causes of mortality in patients after first‐ever stroke: A retrospective population‐based study

et al. 2021

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Background: Stroke is the third most common cause of death in developed countries and it is the most common cause of disability in the adult population of Iran. In this study, we aimed to evaluate the effects of age, sex, and other predisposing risk factors on mortality after stroke. Methods:We studied 1572 patients with first-ever stroke during a 7-year period from January 2008 to December 2014. Patients' medical records including demographic information, past medical history, physical examination, and laboratory testing were reviewed. We analyzed the correlation of qualitative and quantitative variables with sex and mortality.Results: Of all patients, 252 (16%) died during the hospital stay and of the remaining 1320 patients, 453 (34.3%) died during the follow-up period. There was no significant correlation between mortality and sex (p = .508). Descriptively, the number of women was higher in all age groups except in the age group 55-64 years. No significant correlation was observed between sex and age group (p = .748). We also observed a significant association between age group and mortality (p < .001). Hypertension is the most prevalent disease in both men and women. Higher levels of creatinine, urea, fasting blood sugar, neutrophils, cholesterol, and LDL significantly increase and higher levels of lymphocytes, platelets, RBCs, hemoglobin, and triglyceride significantly decrease the mortality. Conclusion:There are no sex differences in mortality after first-ever stroke. Elderly patients need more support and attention due to greater stroke mortality. Complete blood count, lipid profile and blood levels of urea, creatinine, and fasting blood sugar may be useful in predicting mortality after first-ever stroke.

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“…Mann–Whitney U test or t ‐test was utilized for differences between groups of continuous variables, and the χ 2 test or Fisher's exact test was applied for categorical variables. Compared to serum creatinine levels, eGFR was more suitable for evaluating renal function 17 . Therefore, eGFR was used for developing the model to avoid multicollinearity and improve its performance.…”

Section: Methodsmentioning

confidence: 99%

A model for predicting postoperative persistent acute kidney injury (AKI) in AKI after cardiac surgery patients with normal baseline renal function

Chen,

Mo,

Chu

et al. 2023

Clinical Cardiology

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BackgroundPersistent acute kidney injury (AKI) after cardiac surgery is not uncommon and linked to poor outcomes.HypothesisThe purpose was to develop a model for predicting postoperative persistent AKI in patients with normal baseline renal function who experienced AKI after cardiac surgery.MethodsData from 5368 patients with normal renal function at baseline who experienced AKI after cardiopulmonary bypass cardiac surgery in our hospital were retrospectively evaluated. Among them, 3768 patients were randomly assigned to develop the model, while the remaining patients were used to validate the model. The new model was developed using logistic regression with variables selected using least absolute shrinkage and selection operator regression.ResultsThe incidence of persistent AKI was 50.6% in the development group. Nine variables were selected for the model, including age, hypertension, diabetes, coronary heart disease, cardiopulmonary bypass time, AKI stage at initial diagnosis after cardiac surgery, postoperative serum magnesium level of <0.8 mmol/L, postoperative duration of mechanical ventilation, and postoperative intra‐aortic balloon pump use. The model's performance was good in the validation group. The area under the receiver operating characteristic curve was 0.761 (95% confidence interval: 0.737–0.784). Observations and predictions from the model agreed well in the calibration plot. The model was also clinically useful based on decision curve analysis.ConclusionsIt is feasible by using the model to identify persistent AKI after cardiac surgery in patients with normal baseline renal function who experienced postoperative AKI, which may aid in patient stratification and individualized precision treatment strategy.

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Laboratory tests as short-term correlates of stroke

Cited by 9 publications

References 47 publications

Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models

Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models

Causes of mortality in patients after first‐ever stroke: A retrospective population‐based study

A model for predicting postoperative persistent acute kidney injury (AKI) in AKI after cardiac surgery patients with normal baseline renal function

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