Developing a Cognitive Profile for Early Detection of Cognitive Decline: Identifying Key Risk Factors and Target Populations

doi:10.21203/rs.3.rs-4816658/v1

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Developing a Cognitive Profile for Early Detection of Cognitive Decline: Identifying Key Risk Factors and Target Populations

https://doi.org/10.21203/rs.3.rs-4816658/v1

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Purpose: Early screening for cognitive decline (CD) is crucial for managing dementia risk factors and preventing disease progression. This study seeks to create a detailed cognitive profile for patients who are candidates for CD screening, with a focus on identifying emerging cognitive deficits and their underlying causes.

Methods: We first performed a systematic review of CD screening practices within community pharmacies. Following this, we analyzed data from 286 individuals who were screened after the introduction of these practices across 18 community pharmacies. This analysis aimed to pinpoint factors influencing patients who report subjective memory complaints (SMC).

Results: Our findings reveal that CD screening is not widely promoted globally, and participation among pharmacies remains limited. We identified specific characteristics associated with a higher risk of subjective cognitive decline (SCD), including age, usage of internet and social media, sleep patterns, and educational attainment. These factors are essential for defining the profile of individuals who would benefit most from CD screening services.

Conclusion: Enhancing early dementia detection in primary care settings may be achieved by focusing on individuals with limited engagement with internet and social media, irregular sleep patterns, and lower levels of education. Targeting these groups could significantly improve the effectiveness of CD screening programs.

cognitive decline

screening

internet

social media

sleep

education

About half of elderly individuals report memory complaints without functional decline and they are referred to as having Subjective Memory Complaint (SMC) [1]. SMC involves personal memory difficulties without formal examination, potentially indicating early dementia [2]. As it is subjective, it requires scientifically validated psychometric tests for accurate assessment of Mild Cognitive Impairment (MCI) [3].

Recently, Subjective Cognitive Decline (SCD) was coined to describe worsening cognitive perception in cognitively normal older individuals, possibly indicating underlying neuronal damage preceding Objective Cognitive Decline (OCD). Research shows individuals with SCD have a higher risk of developing MCI and doubled dementia risk [4]. Therefore, patient's subjective perception may be sufficient to indicate the need for additional testing [5].

Because age is the main risk factor of dementia and there is currently no definitive treatment available, its progression is considered unstoppable [6]. Research indicates dementia development is influenced by modifiable lifestyle factors, suggesting that with an effective public health strategy, prevention is feasible [7]. Early detection of dementia offers benefits such as prompt medication initiation, financial planning, support group engagement, and lifestyle changes, potentially reducing economic impact and enhancing quality of life for patients and caregivers [8]. For this purpose, screening tests are widely used in primary care [9].

To design an effective Cognitive Decline (CD) screening strategy, variables' effect sizes are compared, and machine learning techniques are used for variable selection. Relative risk, Cohen’s d, and logistic regression are key methodologies for identifying variables associated with dementia and making accurate predictions [9].

Machine learning offers advanced CD modelling techniques such as stepwise logistic regression, LASSO regression, and classification trees. However, these methods may lack robustness and interpretability. Other approaches like random forest and artificial neural networks use all variables as inputs, but their interpretation can be challenging. Certain methods have been developed to unravel these algorithms and decipher the information they employ, providing insight into factors associated with CD [10–12]

Our objective was to conduct a systematic review on CD screening in community pharmacy and develop a cognitive profile of eligible patients, identifying emerging cognitive deficits.

1. Cognitive Impairment Screening in Community Pharmacy. Systematic review

Data sources and searches

A systematic review of CD screening in community pharmacy was conducted in PubMed, Web of Science (WOS) and Scopus databases, following PRISMA guideline [13]. This review was carried out between 10 and 15 August 2023. The keywords used were: “community pharmacy” AND “cognitive impairment” AND/OR “subjective cognitive decline”. A flow sheet of the search is presented as Fig. 1. Manuscripts published during the last 10 years (2013–2023) with a screening intervention in Primary Care were included. Papers with no community pharmacist present in the study or not written in English or Spanish were excluded. A total of 103 records were first identified: 15 in PubMed, 57 in WOS and 31 in Scopus. Besides, 1 handsearching full-text article was included. After removing any duplicates, 64 records were selected. For the tittle-based screening, those in which an early detection was not developed were eliminated. Of the remaining 24 records, only those that considered community pharmacy as the service provider were selected. Thus, 17 full-text articles were assessed for eligibility. From them, 6 manuscripts proposing a screening design but with no study results, no community pharmacy participation or with cognitive screening results from a previous study were excluded.

2. Implementation of the Pharmaceutical Care Service for Subjective Cognitive Decline Screening

Data sources and searches

The project was initiated in February of 2020 in conjunction with 18 community pharmacies. Within a 16-month timeframe, the participating pharmacists screened patients with SMC during their working day.

Study selection

Inclusion criteria were age 50 years or older, SMC referred by themselves or by a family member/caregiver and willingness to participate. Conversely, exclusion criteria were diagnosis of dementia, severe sensory deficits such as blindness or deafness and physical disability interfering with the performance of the tests.

Patient description

The service was offered to regular patients of the pharmacy who met the selection criteria. Likewise, patients directly referred by physicians who had been previously informed of the project were also included [14].

Referrals for SMC and age of 50 years or older were used as inclusion criteria, aiming to detect early CD stages.

Principio del formulario

Community pharmacists’ participation and training

Community pharmacies in the region of Valencia were invited to participate voluntarily through a call made by the Official Illustrious College of Pharmacists in Valencia (MICOF).

Participating pharmacists underwent a 3-hour live webinar by MICOF, covering project introduction, dementia detection, and a standardized work protocol explanation.

Principio del formulario

Study design

A cross-sectional observational study was conducted. The study was reviewed and approved by the Institutional Review Board (IRB) of Universidad CEU Cardenal Herrera (CEEI21/198). All subjects gave written informed consent in accordance with the Declaration of Helsinki.

To encourage patient participation, posters and brochures announced CD screening service availability in pharmacies and pharmacists offered the service from the counter during daily practice to interested patients.

Pharmacists informed patients about the study and service using the Patient Information Sheet, addressing any concerns before obtaining their consent, and conducted MCI screening tests during a 30-minute interview in the pharmacy's Pharmaceutical Care Custom Zone, followed by referral to primary care physicians for further evaluation and diagnosis confirmation.

Assessment tools for cognitive impairment screening

To assess whether there is any suspicion of MCI, the following screening tests were carried out: Memory Impairment Screening (MIS), Semantic Verbal Fluency (SVF) and Short Portable Mental State Questionnaire (SPMSQ).

MIS validated into Spanish by Böhm [15]. Maximum score: 8. Cut off point: 4 or less. Sensitivity: 74%. Specificity: 96%. In a first phase, participants must read aloud four related words that belong to four different categories. After an unstructured distraction a free recall is made, and semantic keys can be given for those words or categories that cannot remember. Freely remembered items receive 2 points and those remembered after the key, 1 point, so that it comprises a path from 0 to 8 points.

SVF validated into Spanish by López Pérez-Díaz [16]. Cut off point: 10. Sensitivity: 74%. Specificity: 80%. It measures the number of items of a category that the subject can recall in one minute [17].

SPMSQ validated into Spanish by Martínez de la Iglesia [18]. Maximum score: 10. Cut-off point: 3 errors (4 errors for illiterate). Sensitivity: 85.7%. Specificity: 79.3%. It assesses different aspects of intellectual functioning, including short-term memory, long-term memory, information about current events, orientation, and the ability to perform a serial mathematical work [19].

The rationale for using three tests was to detect as many true positives as possible. Subjects with at least one positive result in these tests were classified as suspected MCI patients. An assessment of key attributes of brief cognitive tests and their suitability for clinical application was also conducted before selecting those to be incorporated into the project [20] (Table 1).

Furthermore, the interview encompassed additional lifestyle variables (physical exercise, reading, cognitive stimulation games, playing a musical instrument, dietary habits, social relationships, smoking, alcohol consumption, use of internet and social media) and dietary habits to gather information on factors associated with dementia.

In addition, 14 Point Mediterranean Diet Adherence Screen (MEDAS-14) and 5 Point Geriatric Depression Scale (GDS-5) were performed to obtain objective data on nutrition and depression factors, respectively.

MEDAS-14 validated into Spanish by Schröder [21]. It is the 14-point scale of adherence to the Mediterranean diet, designed in the PREDIMED study [22].

GDS-5 validated into Spanish by Ortega [23]. Maximum score: 5. Cut-off point: 2. Sensitivity: 82%. Specificity: 98%. It is the short version of GDS-30 and quantifies depressive symptoms in older adults through 5 questions.

In our study, CD screening targets patients with SCD by referring SMC. Those who test positive are identified as having suspected MCI, indicating results compatible with OCD.

Data Synthesis and Analysis

To ensure a proper registration of information, data collection was carried out through ATENFARMA®, a computer platform designed by MICOF for the development of Pharmaceutical Healthcare Services.

For the collected variables, a description of the effect sizes of the collected variables is calculated and the data are shown depending on the CD condition and the p-value of the appropriate test. Due to the lack of normality for most of the quantitative variables, the rank biserial correlation was reported as effect size. Depending on Shapiro-Wilk’s normality test, an appropriate hypothesis testing is performed. The t-test of independent samples is carried out for those variables whose p-value with Shapiro-Wilk’s test is not lower than 0.05; otherwise, the nonparametric test Mann-Whitney U test is performed. For qualitative variables, the chi-squared test or Fisher’s exact test are performed depending on meeting the conditions of the chi-squared test or not and as an effect size is used the odds ratio.

To determine the profile of patients with suspected MCI, we selected the ranger package to assess the importance of these variables. One of the measures is based on the mean decreased accuracy after a random reordering of the variable as it is described by Breiman [24] and the other one is an improved version of the Gini index [25] unbiased with respect to the number of categories and the range of quantitative variables. With that purpose, a random forest with 50000 trees was trained with all the data. The parameter controlling the number of variables used to decide a split at each point was set to the default value, which is the square root of the number of variables. After train the random forest, the mentioned indices of importance were calculated.

1. Cognitive decline screening in pharmacies. Systematic review.

Table 1 presents the results of our data extraction, including first author, year of publication, type of study, country, type of population, number of patients screened, duration of the study, number of community pharmacies participating, screening method and percentage of positive results after SCD screening.

Table 1

Characteristics of the studies included.
Citation	Study type	Country	Population	n	Duration	Number of Community Pharmacies participating	Screening method	Positive screening results
Breslow, 2013 (26)	Pilot	EEUU	Community population aged 60–75 with no previous diagnosis of memory impairment and not taking a prescribed medication for dementia	26		2	MMSE, category (animal) fluency test, clock-drawing test	4%
Moreno, 2013 (9)	Cross-sectional	Spain	Community population aged ≥ 65	245	3 months	4	SPMSQ, MMSE	12.6%
Rickles, 2014 (8)		United States	Community population identified as at risk of developing AD	161	6 months	12	Three-Word Recall, Clock Draw Test, Animal Fluency Test	44.1%
Climent, 2015 (27)	Cross-sectional observational	Spain	Community population aged ≥ 65	729	3 months	14	SPMSQ, MMSE	17.6%
Climent, 2018 (4)	Cross-sectional	Spain	Non-institutionalized people aged ≥ 65 who went regularly to the pharmacy	728	1 year	14	SPMSQ, MMSE	17.4%
Feijoo, 2019 (28)	Observational transversal	Spain	Non-institutionalised patients aged ≥ 65 in the community pharmacy setting	729	2 years	13	SPMSQ, MMSE	17.6%
Ramos, 2021 (29)	Comparative	Spain	Community population aged ≥ 50 with SMC	281	1 year	19	MIS, SPMSQ, SVF	29.84%
Ramos, 2021 (14)	Cross-sectional	Spain	Adults aged > 50 years with SMC	497	17 months	19	MIS, SPMSQ, SVF	30.8%
Mačeková, 2022 (30)	Pilot	Slovakia	Patients aged ≥ 60	222	1 year	16	MoCA	41%
Agüera, 2023 (31)	Cross-sectional observational	Spain	Caregivers of persons aged > 70 not previously diagnosed with CI and not living in a nursing home or hospitalized	910		197	IQ-CODE	38.5%
Bragazzi, 2023 (32)	Observational, cross-sectional, multicenter	Italy	Clients attending a network of community pharmacies that had agreed to offer neuropsychological screening	185			MoCA, BSRT, ROCF	48.6% MoCA, 10.3% BSRT immediate recall, 8.6% BSRT delayed recall, 21.1% ROCF

(MMSE: Mini-Mental State Examination; SPMSQ: Short Portable Mental State Questionnaire of Pfeiffer; AD: Alzheimer Disease; MIS: Memory Impairment Screen; SVF: Semantic Verbal Fluency; IQ-CODE: Informant Questionnaire on Cognitive Decline; SMC: Subjective Memory Complaint; MoCA: Montreal Cognitive Assessment; BSRT: Babcock Story Recall Test; ROCF: Rey–Osterrieth complex figure)

It is noted that few countries decide to promote CD screening service in community pharmacies. In those areas that do so, there is a small number of pharmacies participating. As in our study, several tests were used to increase de accuracy of the overall process. Nevertheless, we can appreciate differences between the screening results in each study, depending on the questionnaires used.

2. Cognitive decline screening in Community Pharmacy as a Pharmaceutical Care Service

A total of 18 pharmacies adhered to our study, with their staff being trained to administer three cognitive tests: SVF, SPQMS, and MIS. After that training, they screened 286 users with SMC consenting to participate in the study. Among them, 79 (27.6%) were identified as suspected MCI cases. With the data collected a comparison of the cognitive test is presented, hence the general characteristics of the three tests are described in Appendix Exhibit A1 and the results are summarized in three contingency tables and a panel presented in Appendix A. The SVF test showed a greater degree of agreement with the other tests according to Cohen’s Kappa, but error rates comparing with MIS/SPQMS studied through McNemar’s test suggest SVF is classifying differently from the other tests. Therefore, the three tests are collecting different group of individuals with an OCD, which indicates the complexity of the assessment landscape of the CD. These results can be consulted in Appendix A.

While studies on the detection of suspected MCI in community pharmacy are limited, the significance of CD screening using psychometric tests in primary care has been emphasized. Additionally, the promotion of cognitive training programs to develop preventive strategies and compensatory measures for mitigating cognitive impairment in old age has been highlighted [9].

Only the United States, Slovakia, Italy, and Spain studies on CD screening in community pharmacy have been published [4, 8, 9, 14, 26–32]. In Spain, both studies of Ramos shifted their inclusion criteria from the general population to individuals with SMC. [14, 29]. In Agüera's study, older individuals who reported memory loss had a 4.3 times higher risk of cognitive impairment, based on interviews conducted with their caregivers rather than the patients themselves. [31]. In the United States, the screening targeted patients with risk factors for memory decline [8].

Early intervention, involving a combination of pharmacological treatments and cognitive stimulation, has the potential to slow disease progression and provide patients with a better prognosis and quality of life. This implies that the disease should be diagnosed in its early stages [14]. Based on this premise, the intervention in our study was made as specific and personal as possible. The assessments performed required a total of 30 minutes and provided complete and diverse information on the patient's cognitive status and associated risk factors. The number of patients identified with suspected CD, and healthy patients was comparable regardless of the screening questionnaire used. However, the incorporation of multiple questionnaires in the screening process resulted in a wider and more comprehensive range of detection.

The MIS shows strong validity for early dementia detection through significant associations with hippocampal and entorhinal volumes. Extensively researched, it is a leading choice for primary care practitioners. Likewise, the SPMSQ performs well in excellently in geriatric settings. The inclusion of verbal fluency tests, such as SVF, contributes valuable qualitative and quantitative insights, enriching the overall assessment process [33].

As reflected by our study, a high-level of education has been associated with a reduced risk of positive scores for suspected CD [7]. It should be considered that less time in education is defined by Livingston and colleagues as no secondary school education and it is believed to contribute to a lower cognitive reserve, although it has not yet been clearly defined whether education beyond secondary level provides additional protection. Our results are also in line with other studies that have found a statistically significant association between sustained internet use and enhanced cognitive function [34]. However, the use of digital technologies may offer fewer advantages to individuals with dementia compared to those with MCI, underscoring the significance of this factor during the initial phases of dementia [35]. Likewise, it has also been reported higher risk of dementia in individuals who sleep more than 10 hours or less than 5 hours [7].

The main contribution of this study lies in the development of a cognitive profile for CD screening eligibility via decision trees. It analyzes dementia-associated factors, focusing on those with high discriminatory capacity. The model's replicability enables the consolidation of CD screening for patients aged 61 or older, considering internet use, education, and sleep duration to objectivize SCD. However, it cannot definitively determine whether the patient suffers from MCI and referral to a neurologist is necessary for further evaluation and to confirm this suspicion [8] and initiate early intervention, combining pharmacological treatments and cognitive stimulation, to slow disease progression and improve prognosis and quality of life [14].

Limitations include slower service implementation in some pharmacies due to SARS-CoV-2 pandemic and a smaller-than-expected sample size. Unlike previous projects, community pharmacists handled screening alongside other duties, potentially affecting efficiency [14].

Involvement of pharmacists trained in early detection of MCI allows for timely health assessments and selecting appropriate candidates for cognitive screening can further increase effectiveness.

Beyond age, which is undeniably associated with dementia, individuals who have limited internet or social media usage, sleep more than 10 hours or less than 5 hours per day and possess lower educational attainment may be considered ideal candidates for CI screening, thus contributing to the early dementia detection.

Author Contribution

Conceptualization: MGP, FJMA, HR, JSL, LMR . Methodology. MGP, FJMA, JSL, LMR. Software: FJMA. Validation: MGP, FJMA, HR, JSL, LMR. Formal análisis: FJMA. Investigation: MGP JSL, LMR. Resources: JSL, LMR. Data Curation. FJMA. Writing-Original Draft: MGP. Writing – Review & Editing; MGP, FJMA, HR, JSL, LMR. Visualization: MGP. FJMA Supervision: JSL. LMR. Project Administration: LMR. Funding Acquisition: JSL, LMR.

Acknowledgement

We extend our sincere gratitude to all the healthcare professionals and patients who participated in this study. We also wish to acknowledge Cathedra DeCo MICOF-CEU UCH for their partial funding and support of this research.

Data Availability:

The datasets generated and analyzed during the current study are available upon request.

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No competing interests reported.

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Developing a Cognitive Profile for Early Detection of Cognitive Decline: Identifying Key Risk Factors and Target Populations

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Abstract

Figures

1 Introduction

2 Methods

1. Cognitive Impairment Screening in Community Pharmacy. Systematic review

2. Implementation of the Pharmaceutical Care Service for Subjective Cognitive Decline Screening

3 Results

1. Cognitive decline screening in pharmacies. Systematic review.

2. Cognitive decline screening in Community Pharmacy as a Pharmaceutical Care Service

4 Discussion

5 Conclusion

Declarations

Author Contribution

Acknowledgement

Data Availability:

References

Additional Declarations

Supplementary Files

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