INTRODUCTION Identifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all-cause dementia (ACD) conversion at 5 years. METHODS Cox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held-out data subset. RESULTS Of 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72–0.74]), and calibration (Brier score 0.18 [95% CI 0.17–0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors. DISCUSSION EHR-based prediction model had good performance in identifying 5-year MCI to ACD conversion and has potential to assist triaging of at-risk patients. Highlights * Of 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all-cause dementia within 5 years. * Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18). * Age and vascular-related morbidities were predictors of dementia conversion. * Synthetic data was comparable to real data in modeling MCI to dementia conversion. Key Points * An electronic health record–based model using demographic and co-morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all-cause dementia (ACD) within 5 years. * Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5-year conversion from MCI to ACD. * High body mass index, alcohol abuse, and sleep apnea were protective factors for 5-year conversion from MCI to ACD. * Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health-care data with minimized patient privacy concern that could accelerate scientific discoveries.
more »
« less
Estimated Effects of Comorbidities on Risk of All-cause Dementia in Patients with Mild Cognitive Impairment
Introduction:Estimating the effects of comorbidities on risk of all-cause dementia (ACD) could potentially better inform prevention strategies and identify novel risk factors compared to more common post-hoc analyses from predictive modeling. Methods:In a retrospective cohort study of patients with mild cognitive impairment (MCI) from US Veterans Affairs Medical Centers between 2009 and 2021, we used machine learning techniques from the treatment effect estimation literature to estimate individualized effects of 25 comorbidities (e.g., hypertension) on ACD risk within 10 years of MCI diagnosis. Age and healthcare utilization were adjusted for using exact matching. Results:After matching, of 19,797 MCI patients, 6,767 (34.18%) experienced ACD onset. Dyslipidemia (percentage point increase of ACD risk range across different treatment effect estimation techniques = 0.009–0.044), hypertension (range = 0.007–0.043), and diabetes (range = 0.007–0.191) consistently had non-zero average effects. Discussion:Our findings support known associations between dyslipidemia, hypertension, and diabetes that increase the risk of ACD in MCI patients, demonstrating the potential for these approaches to identify novel risk factors.
more »
« less
- Award ID(s):
- 2124127
- PAR ID:
- 10613810
- Publisher / Repository:
- Sage Journals
- Date Published:
- Journal Name:
- Sage Open Aging
- Volume:
- 11
- ISSN:
- 3049-5334
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Purpose: AI models for kidney transplant acceptance must be rigorously evaluated for bias to ensure equitable healthcare access. This study investigates demographic and clinical biases in the Final Acceptance Model (FAM), a donor-recipient matching deep learning model that complements surgeons’ decision-making process in predicting whether to accept available kidneys for their patients with end of stage renal disorder. Methods: AI models for kidney transplant acceptance must be rigorously evaluated for bias to ensure equitable healthcare access. This study investigates demographic and clinical biases in the Final Acceptance Model (FAM), a donor-recipient matching deep learning model that complements surgeons’ decision-making process in predicting whether to accept available kidneys for their patients with end of stage renal disorder. Results: There is no significant racial bias in the model’s predictions (p=1.0), indicating consistent outcome across all racial combinations between donors and recipients. Gender-related effects as shown in Figure 1, while statistically significant (p=0.008), showed minimal practical impact with mean differences below 1% in prediction probabilities. Significant difference Clinical factors involving diabetes and hypertension showed significant difference (p=4.21e-19). The combined presence of diabetes and hypertension in donors showed the largest effect on predictions (mean difference up to -0.0173, p<0.05), followed by diabetes-only conditions in donors (mean difference up to -0.0166, p<0.05). These variations in clinical factor predictions showed bias against groups with comorbidities. Conclusions: The biases observed in the model highlight the need to improve the algorithm to ensure absolute fairness in prediction.more » « less
-
Abstract Hearing loss has been associated with individual cardiovascular disease (CVD) risk factors and, to a lesser extent, CVD risk metrics. However, these relationships are understudied in clinical populations. We conducted a retrospective study of electronic health records to evaluate the relationship between hearing loss and CVD risk burden. Hearing loss was defined as puretone average (PTA 0.5,1,2,4 ) > 20 dB hearing level (HL). Optimal CVD risk was defined as nondiabetic, nonsmoking, systolic blood pressure (SBP) < 120 and diastolic (D)BP < 80 mm Hg, and total cholesterol < 180 mg/dL. Major CVD risk factors were diabetes, smoking, hypertension, and total cholesterol ≥ 240 mg/dL or statin use. We identified 6332 patients (mean age = 62.96 years; 45.5% male); 64.0% had hearing loss. Sex-stratified logistic regression adjusted for age, noise exposure, hearing aid use, and body mass index examined associations between hearing loss and CVD risk. For males, diabetes, hypertension, smoking, and ≥ 2 major CVD risk factors were associated with hearing loss. For females, diabetes, smoking, and ≥ 2 major CVD risk factors were significant risk factors. Compared to those with no CVD risk factors, there is a higher likelihood of hearing loss in patients with ≥ 2 major CVD risk factors. Future research to better understand sex dependence in the hearing loss-hypertension relationship is indicated.more » « less
-
Abstract INTRODUCTIONAlzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. METHODSGiven the genetic susceptibility of AD, a multi‐factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk‐stratified groups, focusing on patients with mild cognitive impairment (MCI). RESULTSOur risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI,APOEε4– MCI, and amyloid+ MCI. DISCUSSIONOur risk score holds great potential to improve the precision of early risk assessment. HighlightsAccurate early risk assessment is critical for the success of clinical trials.A new risk score was built from integrating amyloid imaging and genetic data.Our risk score demonstrated improved capability in early risk stratification.more » « less
-
:Diabetes has been linked to an increased risk of mild cognitive impairment (MCI), a conditioncharacterized by a subtle cognitive decline that may precede the development of dementia. Theunderlying mechanisms connecting diabetes and MCI involve complex interactions between metabolicdysregulation, inflammation, and neurodegeneration. A critical mechanism implicated in diabetes andMCI is the activation of inflammatory pathways. Chronic low-grade inflammation, as observed in diabetes,can lead to the production of pro-inflammatory cytokines such as tumor necrosis factor-alpha(TNF-α), interleukin-6 (IL-6), interleukin-1 beta (IL-1β), and interferon-gamma (IFNγ), each of whichcan exacerbate neuroinflammation and contribute to cognitive decline. A crucial enzyme involved inregulating inflammation is ADAM17, a disintegrin, and metalloproteinase, which can cleave and releaseTNF-α from its membrane-bound precursor and cause it to become activated. These processes, inturn, activate additional inflammation-related pathways, such as AKT, NF-κB, NLP3, MAPK, andJAK-STAT pathways. Recent research has provided novel insights into the role of ADAM17 in diabetesand neurodegenerative diseases. ADAM17 is upregulated in both diabetes and Alzheimer's disease,suggesting a shared mechanism and implicating inflammation as a possible contributor to muchbroader forms of pathology and pointing to a possible link between inflammation and the emergenceof MCI. This review provides an overview of the different roles of ADAM17 in diabetes-associatedmild cognitive impairment diseases. It identifies mechanistic connections through which ADAM17and associated pathways may influence the emergence of mild cognitive impairment.more » « less
An official website of the United States government

