Development and validation of a nomogram for predicting mild cognitive impairment in middle-aged and elderly people

Demand Analysis of Self-Management Mobile Health Applications for Middle-Aged and Older Patients with Chronic Kidney Disease Based on the Kano Model

INTRODUCTION

Middle-aged and older individuals often face significant challenges in adopting digital health solutions, leading to a digital divide that hinders their ability to benefit from mobile health (mHealth) interventions. This study aimed to investigate the specific requirements of middle-aged and older patients with chronic kidney disease (CKD) for self-management through mobile health applications (mHealth apps), using the Kano model.

METHODS

A multicenter cross-sectional survey was conducted from April to September 2023 in five hospitals across Sichuan, Shandong, Guangdong, and Shaanxi provinces in China. The Kano model was employed to analyze participants' preferences regarding mHealth apps for self-management.

RESULTS

Out of 359 participants (57.1% men, predominantly aged 45-54), the study identified essential and desirable features for mHealth apps. Essential attributes include comprehensive CKD information and robust privacy protection. Key to enhancing user satisfaction is features like symptom and medication management, access to medical insurance information, and app interface simplicity. Additional attractive features for increasing app appeal include diet management, exercise guidance, and customizable text size.

CONCLUSION

This study identifies critical mHealth app features for self-management in middle-aged and older CKD patients, emphasizing the importance of user-centric design. The findings provide valuable insights for app developers to create tailored solutions that cater to the specific needs of this demographic, potentially enhancing their self-management capabilities.

Development and Validation of the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk)

Objectives: The aim was to develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aiding community healthcare workers in the early identification of individuals at high risk of mild cognitive impairment (MCI). Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively. Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). The calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained a similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801). Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.

Development of a diagnostic model for detecting mild cognitive impairment in young and middle-aged patients with obstructive sleep apnea: a prospective observational study

Objectives

Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition linked to the accelerated onset of mild cognitive impairment (MCI). However, the prevalence of undiagnosed MCI among OSA patients is high and attributable to the complexity and specialized nature of MCI diagnosis. Timely identification and intervention for MCI can potentially prevent or delay the onset of dementia. This study aimed to develop screening models for MCI in OSA patients that will be suitable for healthcare professionals in diverse settings and can be effectively utilized without specialized neurological training.

Methods

A prospective observational study was conducted at a specialized sleep medicine center from April 2021 to September 2022. Three hundred and fifty consecutive patients (age: 18-60 years) suspected OSA, underwent the Montreal Cognitive Assessment (MoCA) and polysomnography overnight. Demographic and clinical data, including polysomnographic sleep parameters and additional cognitive function assessments were collected from OSA patients. The data were divided into training (70%) and validation (30%) sets, and predictors of MCI were identified using univariate and multivariate logistic regression analyses. Models were evaluated for predictive accuracy and calibration, with nomograms for application.

Results

Two hundred and thirty-three patients with newly diagnosed OSA were enrolled. The proportion of patients with MCI was 38.2%. Three diagnostic models, each with an accompanying nomogram, were developed. Model 1 utilized body mass index (BMI) and years of education as predictors. Model 2 incorporated N1 and the score of backward task of the digital span test (DST_B) into the base of Model 1. Model 3 expanded upon Model 1 by including the total score of digital span test (DST). Each of these models exhibited robust discriminatory power and calibration. The C-statistics for Model 1, 2, and 3 were 0.803 [95% confidence interval (CI): 0.735-0.872], 0.849 (95% CI: 0.788-0.910), and 0.83 (95% CI: 0.763-0.896), respectively.

Conclusion

Three straightforward diagnostic models, each requiring only two to four easily accessible parameters, were developed that demonstrated high efficacy. These models offer a convenient diagnostic tool for healthcare professionals in diverse healthcare settings, facilitating timely and necessary further evaluation and intervention for OSA patients at an increased risk of MCI.

Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning-Based Design Thinking Study

BACKGROUND

The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literature, it has been reported that people die annually due to adverse events (AEs), and various methods exist for investigating and measuring AEs. However, some methods have a limited scope, data extraction, and the need for data standardization. In Brazil, there are few studies on the application of trigger tools, and this study is the first to create automated triggers in ambulatory care.

OBJECTIVE

This study aims to develop a machine learning (ML)-based automated trigger for outpatient health care settings in Brazil.

METHODS

A mixed methods research will be conducted within a design thinking framework and the principles will be applied in creating the automated triggers, following the stages of (1) empathize and define the problem, involving observations and inquiries to comprehend both the user and the challenge at hand; (2) ideation, where various solutions to the problem are generated; (3) prototyping, involving the construction of a minimal representation of the best solutions; (4) testing, where user feedback is obtained to refine the solution; and (5) implementation, where the refined solution is tested, changes are assessed, and scaling is considered. Furthermore, ML methods will be adopted to develop automated triggers, tailored to the local context in collaboration with an expert in the field.

RESULTS

This protocol describes a research study in its preliminary stages, prior to any data gathering and analysis. The study was approved by the members of the organizations within the institution in January 2024 and by the ethics board of the University of São Paulo and the institution where the study will take place. in May 2024. As of June 2024, stage 1 commenced with data gathering for qualitative research. A separate paper focused on explaining the method of ML will be considered after the outcomes of stages 1 and 2 in this study.

CONCLUSIONS

After the development of automated triggers in the outpatient setting, it will be possible to prevent and identify potential risks of AEs more promptly, providing valuable information. This technological innovation not only promotes advances in clinical practice but also contributes to the dissemination of techniques and knowledge related to patient safety. Additionally, health care professionals can adopt evidence-based preventive measures, reducing costs associated with AEs and hospital readmissions, enhancing productivity in outpatient care, and contributing to the safety, quality, and effectiveness of care provided. Additionally, in the future, if the outcome is successful, there is the potential to apply it in all units, as planned by the institutional organization.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/55466.

Development and validation of a risk prediction model for mild cognitive impairment in elderly patients with type 2 diabetes mellitus

BACKGROUND

The prevalence of mild cognitive impairment (MCI) is steadily increasing among elderly people with type 2 diabetes (T2DM). This study aimed to create and validate a predictive model based on a nomogram.

METHODS

This cross-sectional study collected sociodemographic characteristics, T2DM-related factors, depression, and levels of social support from 530 older adults with T2DM. We used LASSO regression and multifactorial logistic regression to determine the predictors of the model. The performance of the nomogram was evaluated using calibration curves, receiver operating characteristics (ROC), and decision curve analysis (DCA).

RESULTS

The nomogram comprised age, smoking, physical activity, social support, depression, living alone, and glycosylated hemoglobin. The AUC for the training and validation sets were 0.914 and 0.859. The DCA showed good clinical applicability.

CONCLUSIONS

This predictive nomogram has satisfactory accuracy and discrimination. Therefore, the nomogram can be intuitively and easily used to detect MCI in elderly adults with T2DM.

Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study

Disabled older adults exhibited a higher risk for cognitive impairment. Early identification is crucial in alleviating the disease burden. This study aims to develop and validate a prediction model for identifying cognitive impairment among disabled older adults. A total of 2138, 501, and 746 participants were included in the development set and two external validation sets. Logistic regression, support vector machine, random forest, and XGBoost were introduced to develop the prediction model. A nomogram was further established to demonstrate the prediction model directly and vividly. Logistic regression exhibited better predictive performance on the test set with an area under the curve of 0.875. It maintained a high level of precision (0.808), specification (0.788), sensitivity (0.770), and F1-score (0.788) compared with the machine learning models. We further simplified and established a nomogram based on the logistic regression, comprising five variables: age, daily living activities, instrumental activity of daily living, hearing impairment, and visual impairment. The areas under the curve of the nomogram were 0.871, 0.825, and 0.863 in the internal and two external validation sets, respectively. This nomogram effectively identifies the risk of cognitive impairment in disabled older adults.

The impact of frailty as a critical mediator causing postoperative neurocognitive disorders in postoperative cardiac patients

Frailty is prevalent in elderly cardiac patients and may be a critical predictor of post-operative neurocognitive disorders (PND). The aim of this review was to demonstrate the correlation of frailty with PND in postsurgical elder patients. A review of published literature and bibliometric analysis was undertaken. Electronic databases from 2009 to 2022 were searched to identify articles that evaluated the relationship between frailty and PND in aging populations. Demographic data, type of surgery performed, frailty measurement, and impact of frailty on PND were extracted from the selected studies. The quality of the studies and risk of bias were assessed by the Newcastle-Ottawa Quality Assessment Scale, and the included articles were assessed as medium to high quality. Eighty-one studies were selected for the Bibliometric review in terms of research trends and hotpots. Additionally, 35 observational studies (prospective and retrospective cohorts) were selected for this review. The mean age ranged from 63 to 84 years and included patients undergoing cardiac, orthopedic, and other surgeries who had cardiac symptoms. Regardless of how frailty was measured, the strongest evidence in terms of numbers of studies, consistency of results, and study quality was for associations between frailty and PND. This analysis found a steadily growing focus on frailty and PND research in cardiac and other patients. The observational studies account for the majority of this area, and frailty occurred in the older cardiac patients over 60 years of age, and pre-screening of frailty can be predictive of PND and mortality.

Development and validation of a nutrition-related genetic-clinical-radiological nomogram associated with behavioral and psychological symptoms in Alzheimer's disease

BACKGROUND

Few evidence is available in the early prediction models of behavioral and psychological symptoms of dementia (BPSD) in Alzheimer's disease (AD). This study aimed to develop and validate a novel genetic-clinical-radiological nomogram for evaluating BPSD in patients with AD and explore its underlying nutritional mechanism.

METHODS

This retrospective study included 165 patients with AD from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) cohort between June 1, 2021, and March 31, 2022. Data on demoimagedatas, neuropsychological assessments, single-nucleotide polymorphisms of AD risk genes, and regional brain volumes were collected. A multivariate logistic regression model identified BPSD-associated factors, for subsequently constructing a diagnostic nomogram. This nomogram was internally validated through 1000-bootstrap resampling and externally validated using a time-series split based on the CIBL cohort data between June 1, 2022, and February 1, 2023. Area under receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical applicability of the nomogram.

RESULTS

Factors independently associated with BPSD were: CETP rs1800775 (odds ratio [OR] = 4.137, 95% confidence interval [CI]: 1.276-13.415, P = 0.018), decreased Mini Nutritional Assessment score (OR = 0.187, 95% CI: 0.086-0.405, P <0.001), increased caregiver burden inventory score (OR = 8.993, 95% CI: 3.830-21.119, P <0.001), and decreased brain stem volume (OR = 0.006, 95% CI: 0.001-0.191, P = 0.004). These variables were incorporated into the nomogram. The area under the ROC curve was 0.925 (95% CI: 0.884-0.967, P <0.001) in the internal validation and 0.791 (95% CI: 0.686-0.895, P <0.001) in the external validation. The calibration plots showed favorable consistency between the prediction of nomogram and actual observations, and the DCA showed that the model was clinically useful in both validations.

CONCLUSION

A novel nomogram was established and validated based on lipid metabolism-related genes, nutritional status, and brain stem volumes, which may allow patients with AD to benefit from early triage and more intensive monitoring of BPSD.

REGISTRATION

Chictr.org.cn, ChiCTR2100049131.

Validation of the mild cognitive impairment health literacy assessment scale (MCI-HLA scale) in middle-aged and older adults

OBJECTIVES

Health literacy (HL) is associated with early prevention of mild cognitive impairment (MCI), but a targeted comprehensive assessment tool is lacking. This study aims to psychometrically evaluate the MCI-HLA scale.

METHODS

This observational study was conducted in a tertiary-level hospital's physical examination center from October to December 2020. The middle-aged and older adults (N = 550, aged 41-80 years) completed the MCI-HLA questionnaire online. The validity of the MCI-HLA scale was assessed through content validity, exploratory factor analysis, confirmatory factor analysis (CFA), convergent validity, and discriminant validity. The internal reliability was measured using Cronbach's alpha, McDonald's Omega coefficient, and split-half reliability.

RESULTS

5 factors emerged, naming: Function (7 items), Knowledge (8 items), Practice (8 items), Attitude (4 items), and Motivation (3 items), explaining 72.42% of variance. The CFA revealed that five factors of the MCI-HLA scale fit well (χ2/df=4.076, RMSEA=0.078, SRMR=0.057, CFI=0.904, TLI=0.894). Good convergent validity was suggested by the Average Variance Extracted (AVE) values exceeding 0.50. Discriminant validity was demonstrated for all the square root AVE were higher than the correlation between the two factors. Internal consistency was high (Cronbach's alpha=0.875, McDonald's Omega coefficient=0.910, split-half reliability=0.949).

CONCLUSIONS

The MCI-HLA scale takes on high reliability and validity, suitable for assessing MCI-related health literacy in middle-aged and older adults. The MCI-HLA scale could enhance MCI health literacy assessment and supports tailored interventions for improved outcomes.

Circulating antibodies to Helicobacter pylori are associated with biomarkers of neurodegeneration in cognitively intact adults

Helicobacter pylori (H. pylori) infection confers risk for Alzheimer's Disease (AD), with the mechanisms unknown. Infections are linked to the etiology of AD partly through modulating the humoral immunity post-infection. This study found increased plasma levels of tTau and pTau181 in H. Pylori infected individuals with intact cognition. Plasma antibodies to H. pylori were positively associated with Aβ40, Aβ42, tTau, and pTau181, adjusting for age, sex, education level, BMI, ApoE ε4 genotype, hypertension, diabetes mellitus, and hypercholesteremia. This study presents novel insights into the relationship between H. pylori infection and AD from an autoimmune perspective.

A predictive model for the risk of cognitive impairment in community middle-aged and older adults

Identifying individuals at high risk of cognitive impairment is essential for treatment and prevention strategies. We aimed to develop and validate a prediction model for evaluating the risk of cognitive impairment. Data were from the China Family Panel Studies (CFPS) and China Health and Retirement Longitudinal Study (CHARLS). A total of 14,265 subjects were selected for model development. The area under the curve(AUC) for the training, internal, and external validation sets were 0.775, 0.920, and 0.727, respectively. This model could be used to identify middle-aged and older adults aged 45 years and older at high risk of cognitive impairment.