The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus

Maimaitituerxun, Rehanguli; Chen, Wenhang; Xiang, Jingsha; Xie, Yu; Kaminga, Atipatsa Chiwanda; Wu, Xin; Chen, Letao; Yang, Jianzhou; Liu, Aizhong; Dai, Wenjie

doi:10.1111/1753-0407.13384

Cited by 7 publications

(8 citation statements)

References 72 publications

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“… 11 And the other proposed a nomogram based on age, marital status, per capita monthly household income, diabetes duration, diabetic retinopathy, anxiety, and depression, which showed an optimal diagnosis of MCI (C-index 0.83). 12 Similar to the above models, the risk score in this study was also developed using easily available clinical predictors and presented similar predictive ability. Although both nomogram and risk scoring tables can present the prediction model in an intuitive way, the application of risk scoring tables are more convenient for medical staff in busy clinical practice, such as the well-known Framingham risk score.…”

Section: Discussionmentioning

confidence: 98%

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Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool

Ding,

Yu,

et al. 2024

DMSO

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Aim Numerous evidence suggests that diabetes increases the risk of cognitive impairment. This study aimed to develop and validate a multivariable risk score model to identify mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM). Methods This cross-sectional study included 1256 inpatients (age: 57.5 ± 11.2 years) with T2DM in a tertiary care hospital in China. MCI was diagnosed according to the criteria recommended by the National Institute on Aging-Alzheimer’s Association Workgroup, and a MoCA score of 19–25 indicated MCI. Participants were randomly allocated into the derivation and validation sets at 7:3 ratio. Logistic regression models were used to identify predictors for MCI in the derivation set. A scoring system based on the predictors’ beta coefficient was developed. Predictive ability of the risk score was tested by discrimination and calibration methods. Results Totally 880 (285 with MCI, 32.4%) and 376 (167 with MCI, 33.8%) patients were allocated in the derivation and validation set, respectively. Age, education, HbA 1c , self-reported history of severe hypoglycemia, and microvascular disease were identified as predictors for MCI and constituted the risk score. The AUCs (95% CI) of the risk score were 0.751 (0.717, 0.784) in derivation set and 0.776 (0.727, 0.824) in validation set. The risk score showed good apparent calibration of observed and predicted MCI probabilities and was capable of stratifying individuals into 3 risk categories by two cut-off points (low risk: ≤ 3, medium risk: 4–13, and high risk ≥ 14). Conclusion The risk score based on age, education, HbA 1c , self-reported history of severe hypoglycemia, and microvascular disease can effectively assess MCI risk in adults with T2DM at different age. It can serve as a practical prescreening tool for early detection of MCI in daily diabetes care.

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Section: Discussionmentioning

confidence: 98%

“…However, previous studies mainly focused on risk factors or biomarkers associated with MCI in patients with diabetes, and few multivariate risk prediction models for MCI have been developed in T2DM patients. 11 , 12 …”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool

Ding,

Yu,

et al. 2024

DMSO

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“…Our study utilized the MoCA to assess cognitive function and found that the prevalence of mild VCI was higher (58.7%) than that of MCI in T2DM patients. 32 , 33 One reason for this change might be that the MoCA was more sensitive to the diagnosis of MCI than the mini-mental state examination (MMSE). 34 …”

Section: Discussionmentioning

confidence: 99%

“…Second, total CSVD burden score, an important predictor of T2DM-related cognitive impairment, was taken into account in our study, whereas others do not. 33 , 43 , 44 Third, the online dynamic nomogram that we constructed is more convenient and intuitive for clinical applications.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram Including Total Cerebral Small Vessel Disease Burden Score for Predicting Mild Vascular Cognitive Impairment in Patients with Type 2 Diabetes Mellitus

Teng,

Feng,

Xie

et al. 2024

DMSO

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Background Total cerebral small vessel disease (CSVD) burden score is an important predictor of vascular cognitive impairment (VCI). However, few predictive models of VCI in type 2 diabetes mellitus (T2DM) patients have included the total CSVD burden score, especially in the early stage of VCI. Objective To develop and validate a nomogram that includes the total CSVD burden score to predict mild VCI in patients with T2DM. Methods A total of 322 eligible participants with T2DM who were divided into mild and normal cognitive groups were enrolled in this retrospective study. Demographic data, laboratory data and imaging markers of CSVD were collected. The total CSVD burden score was calculated by combining the different CSVD markers. Step-backward multivariable logistic regression analysis with the Akaike information criterion was applied to select significant predictors and develop a best-fit predictive nomogram. The performance of the nomogram was assessed in terms of discriminative ability, calibrated ability, and clinical usefulness. Results The nomogram model consisted of five variables: age, education, hemoglobin A1c level, serum homocysteine level, and total CSVD burden score. A nomogram with these variables showed good discriminative ability (area under the receiver operating characteristic curve was 0.801 in internal verification). In addition, the Hosmer-Lemeshow test ( χ 2 =9.226, P =0.417) and bootstrap-corrected calibration plot indicated that the nomogram had good calibration. The Brier score of the predictive model was 0.178. Decision curve analysis demonstrated that when the threshold probability ranged between 16% and 98%, the use of the nomogram to predict mild VCI in patients with T2DM provide a greater net benefit. Conclusions The nomogram, composed of age, education, stroke, HbA1c level, Hcy level, and total CSVD burden score, had good predictive accuracy and may provide clinicians with a practical tool for predicting the risk of mild VCI in T2DM patients.

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“…Clinical decision curve analysis (DCA) will be used to measure the clinical applicability of the prediction model with ‘rmda’ R package. 59 …”

Section: Statistical Analysis Methodsmentioning

confidence: 99%

Study protocol for a prediction model for mild cognitive impairment in older adults with diabetes mellitus and construction of a nurse-led screening system: a prospective observational study

Miao,

Lu,

et al. 2024

BMJ Open

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IntroductionWith an increasing number of older adults in China, the number of people with cognitive impairment is also increasing. To decrease the risk of dementia, it is necessary to timely detect mild cognitive impairment (MCI), which is the preliminary stage of dementia. The prevalence of MCI is relatively high among older adults with diabetes mellitus (DM); however, no effective screening strategy has been designed for this population. This study will construct a nurse-led screening system to detect MCI in community-dwelling older adults with DM in a timely manner.Methods and analysisA total of 642 participants with DM will be recruited (n=449 for development, n=193 for validation). The participants will be divided into MCI and none-MCI groups. The candidate predictors will include demographic variables, lifestyle factors, history of diseases, physical examinations, laboratory tests and neuropsychological tests. Univariate analysis, least absolute shrinkage and selection operator regression screening, and multivariate logistic regression analysis will be conducted to identify the outcome indicators. Based on the multivariate logistic regression equation, we will develop a traditional model as a comparison criterion for the machine learning models. The Hosmer-Lemeshow goodness-of-fit test and calibration curve will be used to evaluate the calibration. Sensitivity, specificity, area under the curves and clinical decision curve analysis will be performed for all models. We will report the sensitivity, specificity, area under the curve and decision curve analysis of the validation dataset. A prediction model with better performance will be adopted to form the nurse-led screening system.Ethics and disseminationThis prospective study has received institutional approval of the Medical Ethics Committee of Qidong Hospital of TCM (QDSZYY-LL-20220621). Study results will be disseminated through conference presentations, Chinese Clinical Trial Registry and publication.Trial registration numberChiCTR2200062855.

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The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus

Cited by 7 publications

References 72 publications

Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool

Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool

A Nomogram Including Total Cerebral Small Vessel Disease Burden Score for Predicting Mild Vascular Cognitive Impairment in Patients with Type 2 Diabetes Mellitus

Study protocol for a prediction model for mild cognitive impairment in older adults with diabetes mellitus and construction of a nurse-led screening system: a prospective observational study

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