Rule-Based Identification of Individuals with Mild Cognitive Impairment or Alzheimer's Disease Using Clinical Notes from the United States Veterans Affairs Healthcare System

Extracting Critical Information from Unstructured Clinicians' Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study

BACKGROUND

The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or "hidden" in unstructured text fields and not readily available for clinicians to act upon.

OBJECTIVE

We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data.

METHODS

We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians' notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, "mild dementia" and "advanced Alzheimer disease"). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm.

RESULTS

We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93% (2090/9115) of patients were documented with mild ADRD, 20.87% (1902/9115) were documented with moderate or severe ADRD, and 56.20% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95%, specificity of >95%, sensitivity of >90%, and an F1-score of >83%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91%, specificity of >80%, sensitivity of >88%, and F1-score of >92%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence.

CONCLUSIONS

Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems.

Clinical Characterization of Veterans With Alzheimer Disease by Disease Severity in the United States

PURPOSE

We aimed to examine the clinical characteristics of US veterans who underwent neurocognitive test score-based assessments of Alzheimer disease (AD) stage in the Veterans Affairs Healthcare System (VAHS).

METHODS

Test dates for specific stages of AD were referenced as index dates to study behavioral and psychological symptoms of dementia (BPSD) and other patient characteristics related to utilization/work-up and time to death.

PATIENTS

We identified veterans with AD and neurocognitive evaluations using the VAHS Electronic Health Record (EHR).

RESULTS

Anxiety and sleep disorders/disturbances were the most documented BPSDs across all AD severity stages. Magnetic resonance imaging, neurology and psychiatry consultations, and neuropsychiatric evaluations were slightly higher in veterans with mild AD than in those at later stages. The overall average time to death from the first AD severity record was 5 years for mild and 4 years for moderate/severe AD.

CONCLUSION

We found differences in clinical symptoms, healthcare utilization, and survival among the mild, moderate, and severe stages of AD. These differences are limited by the low documentation of BPSDs among veterans with test score-based AD stages. These data support the hypothesis that our cohorts represent coherent subgroups of patients with AD based on disease severity.

Prevalence of Mild Cognitive Impairment and Alzheimer's Disease Identified in Veterans in the United States

Background

Diagnostic codes can be instrumental for case identification in Alzheimer's disease (AD) research; however, this method has known limitations and cannot distinguish between disease stages. Clinical notes may offer more detailed information including AD severity and can complement diagnostic codes for case identification.

Objective

To estimate prevalence of mild cognitive impairment (MCI) and AD using diagnostics codes and clinical notes available in the electronic healthcare record (EHR).

Methods

This was a retrospective study in the Veterans Affairs Healthcare System (VAHS). Health records from Veterans aged 65 years or older were reviewed during Fiscal Years (FY) 2010-2019. Overall, 274,736 and 469,569 Veterans were identified based on a rule-based algorithm as having at least one clinical note for MCI and AD, respectively; 201,211 and 149,779 Veterans had a diagnostic code for MCI and AD, respectively. During FY 2011-2018, likely MCI or AD diagnosis was defined by≥2 qualifiers (i.e., notes and/or codes)≥30 days apart. Veterans with only 1 qualifier were considered as suspected MCI/AD.

Results

Over the 8-year study, 147,106 and 207,225 Veterans had likely MCI and AD, respectively. From 2011 to 2018, yearly MCI prevalence increased from 0.9% to 2.2%; yearly AD prevalence slightly decreased from 2.4% to 2.1%; mild AD changed from 22.9% to 26.8%, moderate AD changed from 26.5% to 29.1%, and severe AD changed from 24.6% to 30.7.

Conclusions

The relative distribution of AD severities was stable over time. Accurate prevalence estimation is critical for healthcare resource allocation and facilitating patients receiving innovative medicines.

Alzheimer's Disease Stage Transitions Among United States Veterans

BACKGROUND

Alzheimer's disease (AD) and related dementias are progressive neurological disorders with stage-specific clinical features and challenges. An important knowledge gap is the "window of time" within which patients transition from mild cognitive impairment or mild AD to moderate or severe AD. Better characterization/establishment of transition times would help clinicians initiating treatments, including anti-amyloid therapy.

OBJECTIVE

To describe cognitive test score-based AD stage transitions in Veterans with AD in the US Veterans Affairs Healthcare System (VAHS).

METHODS

This retrospective analysis (2010-2019) identified Veterans with AD from the VAHS Electronic Health Record (EHR) notes. AD stage was based on Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), or Saint Louis University Mental Status (SLUMS) Examination scores in the EHR.

RESULTS

We identified 296,519 Veterans with cognitive test-based AD staging. Over the 10-year study, the proportion of veterans with MMSE scores declined from 24.9% to 9.5% while those with SLUMS rose from 9.0% to 17.8%; and MoCA rose from 5.0% to 25.4%. The average forward transition times between each stage were approximately 2-4 years, whether assessed by MMSE, MoCA, or SLUMS.

CONCLUSION

The average transition time for cognitive test-based assessments of initial cognitive decline, early-stage AD, and moderate/severe AD in the VAHS is 2-4 years. In view of the short window for introducing disease-modifying therapy and the significant benefits of early treatment of AD, our data suggest a critical need for treatment guidelines in the management of AD.