Hemoglobin Concentration and the Incidence of Stroke in the General Japanese Population: The Jichi Medical School Cohort Study

Sato, Fumitaka; Nakamura, Yosikazu; Kayaba, Kazunori; Ishikawa, Shizukiyo

doi:10.2188/jea.je20200346

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…Bhupesh et al found that women with lower preoperative plasma hemoglobin concentrations exhibited increased susceptibility to stroke [13]. Similarly, a Japanese trial revealed that low hemoglobin concentrations were linked to an increased risk of stroke in adults [14]. Moreover, a metaanalysis provided compelling evidence supporting the association between admission anemia and both ischemic and hemorrhagic stroke, as well as elevated mortality rates related to stroke [15].…”

Section: Discussionmentioning

confidence: 99%

Advancing Clinical Decision-Making: Utilizing Machine Learning to Predict Postoperative Stroke Following Craniotomy

Lu,

Huang,

Luo

et al. 2024

Preprint

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Objection: Postoperative stroke (PS) represents a significant and grave complication, which often remains challenging to detect until clear clinical symptoms emerge. The early identification of populations at high risk for perioperative stroke is essential for enabling timely intervention and enhancing postoperative outcomes. This study seeks to employ machine learning (ML) techniques to create a predictive model for PS following elective craniotomy. Methods This study encompassed a total of 1,349 cases that underwent elective craniotomy between January 2013 and August 2021. Perioperative data, encompassing demographics, etiology, laboratory results, comorbidities, and medications, were utilized to construct predictive models. Nine distinct machine learning models were developed for the prediction of postoperative stroke (PS) and assessed based on the area under the receiver-operating characteristic curve (AUC), along with sensitivity, specificity, and accuracy metrics. Results Among the 1,349 patients included in the study, 137 cases (10.2%) were diagnosed with postoperative stroke (PS), which was associated with a worse prognosis. Of the nine machine learning prediction models evaluated, the logistic regression (LR) model exhibited superior performance, as indicated by an area under the receiver-operating characteristic curve (AUC) value of 0.741 (0.64–0.85), and competitive performance metrics, including an accuracy of 0.668, sensitivity of 0.650, and specificity of 0.670. Notably, feature importance analysis identified "preoperative albumin," "ASA classification," and "preoperative hemoglobin" as the top three factors contributing to the prediction of PS. Conclusion Our study successfully developed a real-time and easily accessible parameter requiring LR-based PS prediction model for post-elective craniotomy patients.

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Section: Discussionmentioning

confidence: 99%

Advancing Clinical Decision-Making: Utilizing Machine Learning to Predict Postoperative Stroke Following Craniotomy

Lu,

Huang,

Luo

et al. 2024

Preprint

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“…Secondly, their study did not consider the impact of renal function, TG, UA, PLT, and HGB on the relationship between the non-HDL-c ratio and incident stroke when adjusting covariates, unlike our research. However, previous studies have identified these variables as stroke risk factors ( 18 – 22 ). Furthermore, it is imperative to recognize that the application of linear regression analysis may be susceptible to the impact of non-linear associations, resulting in discrepancies in the established linear relationship.…”

Section: Discussionmentioning

confidence: 99%

“…However, it is important to note that this study only adjusted for age, smoking, gender, hypertension, body mass index (BMI), drinking, diabetes, and high-sensitivity C-reactive protein (hs-CRP) when analyzing the relationship between the non-HDL-c/HDL-c ratio and stroke. Other significant factors known to influence stroke, such as renal function ( 18 ), triglyceride (TG) levels ( 19 ), uric acid (UA) levels ( 20 ), platelet count (PLT) ( 21 ), and hemoglobin concentration (HGB) ( 22 ), were not accounted for in the analysis. Furthermore, the nature of the relationship between the non-HDL-c/HDL-c ratio and stroke, whether it is linear or non-linear, requires further investigation.…”

Section: Introductionmentioning

confidence: 99%

The non-linear link between non-high-density lipoprotein to high-density lipoprotein ratio and the risk of stroke in middle-aged and older adults in Chinese: a prospective cohort study from the China health and retirement longitudinal study

Wang,

Han,

Cao

et al. 2024

Front. Endocrinol.

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ObjectiveThis study aims to assess the association between the non-HDL-c/HDL-c ratio and stroke risk among middle-aged and older adults participating in the China Health and Retirement Longitudinal Study (CHARLS).MethodsThis study conducted a prospective cohort analysis, enrolling a total of 10,183 participants who met the designated criteria from CHARLS between 2011 and 2012. We then used the Cox proportional-hazards regression model to explore the relationship between baseline non-HDL-c/HDL-c ratio and stroke risk. Using a Cox proportional hazards regression with cubic spline function, we were able to identify the non-linear relationship between the non-HDL-c/HDL-c ratio and stroke occurrence. A series of sensitivity analyses were also carried out.ResultsThe average age of the participants included in this study was 59.16 ± 9.35 years, and 4,735 individuals (46.68%) were male. Over a median follow-up period of 7.0 years, a total of 1,191 people (11.70%) experienced a stroke. Using a Cox proportional hazards regression model that was fully adjusted, we found no statistically significant correlation between the non-HDL-c/HDL-c ratio and the risk of stroke (HR=1.022; 95% CI 0.964, 1.083). Nevertheless, we did observe a non-linear relationship and saturation effect between the non-HDL-c/HDL-c ratio and stroke. Employing a two-piece Cox proportional hazards regression model and a recursive algorithm, we determined an inflection point of 2.685 for the non-HDL-c/HDL-c ratio. In instances where the non-HDL-c/HDL-c ratio fell below 2.685, for every 1-unit decrease in the non-HDL-c/HDL-c ratio, the likelihood of stroke decreased by 21.4% (HR=1.214, 95% CI: 1.039-1.418). In contrast, when the non-HDL-c/HDL-c ratio exceeded 2.685, there was no statistically significant change in the risk of stroke for each unit decrease in the non-HDL-c/HDL-c ratio (HR: 0.967, 95% CI: 0.897-1.042). The consistency of these findings across multiple sensitivity analyses suggests their robustness.ConclusionThis study unveils a non-linear relationship between the non-HDL-c/HDL-c ratio and stroke risk in middle-aged and older adults in China. Specifically, when the non-HDL-c/HDL-c ratio was below 2.685, a significant and clearly positive association with stroke risk was observed. Additionally, maintaining the non-HDL-c/HDL-c ratio below 2.685 could potentially lead to a substantial reduction in the risk of stroke.

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Validity and Reliability of the Child and Adolescent Version of the Iron Intake Scale (CIIS) as an Educational Tool

Okabe,

Ito,

Kameta

et al. 2024

J Prim Care Community Health

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Introduction/Objective: In Asia, 42% of young children suffer from iron deficiency anemia. Children have an increased requirement for iron intake because of growth and physical activity. Education plays an important role in anemia prevention and in ensuring children are aware of appropriate iron intake and the iron content of different foods. As a tool for this purpose, we adapted the adult version of the Revised Iron Intake Scale (RIIS) to create the Child and Adolescent Version of the Iron Intake Scale (CIIS), using illustrations to help children recognize the foods listed in the CIIS. We aimed to evaluate the validity and reliability of this new scale. Methods: We conducted a cross-sectional study using a self-administered questionnaire to examine the criterion-related validity of the CIIS. We used Spearman’s rank correlation coefficient to compare iron intake estimated by the CIIS with that calculated by the Brief-type Diet History Questionnaire (BDHQ-15y), which assesses respondents’ dietary habits over the past month and is standardized among Japanese children. The survey was repeated twice to examine reliability. Results: We found a moderate positive correlation for iron intake between the CIIS and BDHQ-15y, with a correlation coefficient of .52 (n = 258, P < .001). Cronbach’s alpha coefficient was .718. The CIIS reproducibility test yielded a correlation coefficient of .67. Conclusion: Our results indicated that the CIIS was valid, reliable, and reproducible. We therefore believe that the scale can be used to improve education about iron deficiency anemia and thereby reduce anemia rates among children and adolescents.

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Hemoglobin Concentration and the Incidence of Stroke in the General Japanese Population: The Jichi Medical School Cohort Study

Abstract: This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record.

Cited by 3 publications

References 32 publications

Advancing Clinical Decision-Making: Utilizing Machine Learning to Predict Postoperative Stroke Following Craniotomy

Advancing Clinical Decision-Making: Utilizing Machine Learning to Predict Postoperative Stroke Following Craniotomy

The non-linear link between non-high-density lipoprotein to high-density lipoprotein ratio and the risk of stroke in middle-aged and older adults in Chinese: a prospective cohort study from the China health and retirement longitudinal study

Validity and Reliability of the Child and Adolescent Version of the Iron Intake Scale (CIIS) as an Educational Tool

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