External Validation of the eRADAR Risk Score for Detecting Undiagnosed Dementia in Two Real-World Healthcare Systems

Predicting Risk of Alzheimer's Diseases and Related Dementias with AI Foundation Model on Electronic Health Records

Early identification of Alzheimer's disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, named TRADE, capable of identifying risks for AD/ADRD and mild cognitive impairment (MCI), by analyzing the past sequential visit records. Amongst individuals 65 and older, our model was able to generate risk predictions for various future timeframes. On the held-out validation set, our model achieved an area under the receiver operating characteristic (AUROC) of 0.772 (95% CI: 0.770, 0.773) for identifying the AD/ADRD/MCI risks in 1 year, and AUROC of 0.735 (95% CI: 0.734, 0.736) in 5 years. The positive predictive values (PPV) in 5 years among individuals with top 1% and 5% highest estimated risks were 39.2% and 27.8%, respectively. These results demonstrate significant improvements upon the current EHR-based AD/ADRD/MCI risk assessment models, paving the way for better prognosis and management of AD/ADRD/MCI at scale.

Impact of mechanical ventilation on clinical outcomes in ICU-admitted Alzheimer's disease patients: a retrospective cohort study

Background

Alzheimer's disease (AD) is increasingly recognized as a pressing global public health issue, demanding urgent development of scientific AD management strategies. In recent years, the proportion of AD patients in Intensive Care Units (ICU) has been on the rise. Simultaneously, the use of mechanical ventilation (MV) is becoming more prevalent among this specific patient group. Considering the pathophysiological characteristics of AD, the application of MV in AD patients may lead to different outcomes. However, due to insufficient research data, the significant impact of MV on the prognosis of AD patients in the ICU remains unclear. Therefore, we conducted this study to comprehensively evaluate the potential influence of MV on the survival rate of AD patients in the ICU.

Methods

We obtained data from the MIMIC-IV database for patients diagnosed with AD. Using propensity score matching (PSM), we paired patients who received MV treatment with those who did not receive treatment. Next, we conducted Cox regression analysis to evaluate the association between MV and in-hospital mortality, 7-day mortality, 28-day mortality, 90-day mortality, 4-year mortality, length of hospital stay, and ICU stay.

Results

The data analysis involved a cohort of 641 AD patients spanning from 2008 to 2019, inclusive. Following a 1:2 propensity score matching (PSM) procedure, 300 patients were successfully paired, comprising 123 individuals who underwent MV treatment and 177 who did not. MV demonstrated an association with an elevated risk of in-hospital mortality (HR 5.782; 95% CI 2.981-11.216; p < 0.001), 7-day mortality (HR 6.353; 95% CI 3.014-13.392; p < 0.001), 28-day mortality (HR 3.210; 95% CI 1.977-5.210; p < 0.001), 90-day mortality (HR 2.334; 95% CI 1.537-3.544; p < 0.001), and 4-year mortality (HR 1.861; 95% CI 1.370-2.527; p < 0.001). Furthermore, it was associated with a prolonged length of ICU stay [3.6(2.2,5.8) vs. 2.2(1.6,3.7); p = 0.001]. In the subgroup analysis, we further confirmed the robustness of the results obtained from the overall population. Additionally, we observed a significant interaction (p-interaction <0.05) between age, admission type, aspirin use, statin use, and the use of MV.

Conclusion

In patients with AD who are receiving treatment in the ICU, the use of MV has been linked to higher short-term, medium-term, and long-term mortality rates, as well as prolong ICU stays. Therefore, it is crucial to break away from conventional thinking and meticulously consider both the medical condition and personal preferences of these vulnerable patients. Personalized treatment decisions, comprehensive communication between healthcare providers and patients, formulation of comprehensive treatment plans, and a focus on collaboration between the ICU and community organizations become imperative.

Algorithmic identification of persons with dementia for research recruitment: ethical considerations

Underdiagnosis, misdiagnosis, and patterns of social inequality that translate into unequal access to health systems all pose barriers to identifying and recruiting diverse and representative populations into research on Alzheimer's disease and Alzheimer's disease related dementias. In response, some have turned to algorithms to identify patients living with dementia using information that is associated with this condition but that is not as specific as a diagnosis. This paper explains six ethical issues associated with the use of such algorithms including the generation of new, sensitive, identifiable medical information for research purposes without participant consent, issues of justice and equity, risk, and ethical communication. It concludes with a discussion of strategies for addressing these issues and prompting valuable research.

The electronic health record Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) Brain Health Trial: Protocol for an embedded, pragmatic clinical trial of a low-cost dementia detection algorithm

BACKGROUND

About half of people living with dementia have not received a diagnosis, delaying access to treatment, education, and support. We previously developed a tool, eRADAR, which uses information in the electronic health record (EHR) to identify patients who may have undiagnosed dementia. This paper provides the protocol for an embedded, pragmatic clinical trial (ePCT) implementing eRADAR in two healthcare systems to determine whether an intervention using eRADAR increases dementia diagnosis rates and to examine the benefits and harms experienced by patients and other stakeholders.

METHODS

We will conduct an ePCT within an integrated healthcare system and replicate it in an urban academic medical center. At primary care clinics serving about 27,000 patients age 65 and above, we will randomize primary care providers (PCPs) to have their patients with high eRADAR scores receive targeted outreach (intervention) or usual care. Intervention patients will be offered a "brain health" assessment visit with a clinical research interventionist mirroring existing roles within the healthcare systems. The interventionist will make follow-up recommendations to PCPs and offer support to newly-diagnosed patients. Patients with high eRADAR scores in both study arms will be followed to identify new diagnoses of dementia in the EHR (primary outcome). Secondary outcomes include healthcare utilization from the EHR and patient, family member and clinician satisfaction assessed through surveys and interviews.

CONCLUSION

If this pragmatic trial is successful, the eRADAR tool and intervention could be adopted by other healthcare systems, potentially improving dementia detection, patient care and quality of life.

Temporal characterization of Alzheimer's Disease with sequences of clinical records

BACKGROUND

Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD.

METHODS

We conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using the transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of Machine Learning models, using Gradient Boosting Machine (GBM), to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts.

FINDINGS

In a group of 4985 patients, we identified 219 tSPM temporal representations (i.e., transitive sequences) of medical records for constructing the best classification models. The models with sequential features improved AD classification by a magnitude of 3-16 percent over the use of AD diagnosis codes alone. The computed cohort included 663 patients, 35 of whom had no record of AD. Six groups of tSPM sequences were identified for characterizing the AD cohorts.

INTERPRETATION

We present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimer's Disease. Classification algorithms developed on sequential patterns can replace standard features from EHRs to enrich phenotype modelling.

FUNDING

National Institutes of Health: the National Institute on Aging (RF1AG074372) and the National Institute of Allergy and Infectious Diseases (R01AI165535).