Early temporal characteristics of elderly patient cognitive impairment in electronic health records

Abstract

Background: The aging population has led to an increase in cognitive impairment (CI) resulting in significant costs to patients, their families, and society. A research endeavor on a large cohort to better understand the frequency and severity of CI is urgent to respond to the health needs of this population. However, little is known about temporal trends of patient health functions (i.e., activity of daily living [ADL]) and how these trends are associated with the onset of CI in elderly patients. Also, the use of a rich source of clinical free text in electronic health records (EHRs) to facilitate CI research has not been well explored. The aim of this study is to characterize and better understand early signals of elderly patient CI by examining temporal trends of patient ADL and analyzing topics of patient medical conditions in clinical free text using topic models.

Methods: The study cohort consists of physician-diagnosed CI patients (n = 1,435) and cognitively unimpaired (CU) patients (n = 1,435) matched by age and sex, selected from patients 65 years of age or older at the time of enrollment in the Mayo Clinic Biobank. A corpus analysis was performed to examine the basic statistics of event types and practice settings where the physician first diagnosed CI. We analyzed the distribution of ADL in three different age groups over time before the development of CI. Furthermore, we applied three different topic modeling approaches on clinical free text to examine how patients' medical conditions change over time when they were close to CI diagnosis.

Results: The trajectories of ADL deterioration became steeper in CI patients than CU patients approximately 1 to 1.5 year(s) before the actual physician diagnosis of CI. The topic modeling showed that the topic terms were mostly correlated and captured the underlying semantics relevant to CI when approaching to CI diagnosis.

Conclusions: There exist notable differences in temporal trends of basic and instrumental ADL between CI and CU patients. The trajectories of certain individual ADL, such as bathing and responsibility of own medication, were closely associated with CI development. The topic terms obtained by topic modeling methods from clinical free text have a potential to show how CI patients' conditions evolve and reveal overlooked conditions when they close to CI diagnosis.

Keywords: Activity of daily living; Cognitive impairment; Deep learning; Early diagnosis; Topic modeling.

Fig. 1

Distribution of the first CI diagnosis (CON: consult, SV: subsequent visit, LE: limited exam, ME: multi-system evaluation, SUP: supervisory, SE: specialty evaluation, ADM: admission; GIM: general internal medicine)

Fig. 2

Distribution of b-ADL and i-ADL for CI and CU patient groups (x-axis is year(s) before the 1st physicain-diagnosed CI for CI patients and the latest visit for CU patients; y-axis is a ratio of patients who have a deteriorated ADL)

Fig. 3

ADL distributions for CU and CI patient groups (x-axis is year(s) before the 1st physicain-diagnosed CI for CI patients and the latest clinical visit for CU patients; y-axis is a ratio of patients who have a deteriorated ADL)

Fig. 4

Aggregated term frequencies. The first table shows the frequency one year before CI development, middle table is the frequency two year before CI development. Last table is the result which terms repeated most

Fig. 5

Topic terms for CI patients - TKM (Experiment 1)

Fig. 6

Topic terms for CI patients - KATE (Experiment 1)

Fig. 7

Topic terms for CI patients - LDA (Experiment 1)

Fig. 8

Topic terms in the TKM model (Experiment 2)

Fig. 9

Topic terms in the KATE model (Experiment 2)

Fig. 10

Topic terms in the LDA model (Experiment 2)