Pain and the Alzheimer's Disease and Related Dementia Spectrum in Community-Dwelling Older Americans: A Nationally Representative Study

Assessing and Managing Pain in Dementias: Insights From Interdisciplinary Text Message Communications

PURPOSE

This study aimed to explore the difference between the representation of pain assessment and management among older adults with and without Alzheimer's Disease and Related Dementias (ADRD) residing in nursing homes (NH) using interdisciplinary text messages (TMs) exchanged prior to hospital transfer.

DESIGN

A retrospective cohort study and secondary data analysis of (n = 21,000) TMs exchanged among interdisciplinary healthcare team members and survey data about NH residents who transferred to the hospital from 2016 to 2020.

METHODS

The study analyzed TMs using natural language processing tools to identify mentions of pain assessment, management, and pain medications. We used a two-sample independent t-test and a negative binomial generalized linear model to assess the association between pain mentions and various resident characteristics, including ADRD status.

RESULTS

The analysis included 694 transfer events involving 414 residents, 39% of whom had ADRD. Significant differences were found in pain mentions, with ADRD residents having fewer mentions (t = 2.38, p = .02) than those without ADRD. The generalized linear model identified statistically significant predictors (age, race, and specific comorbidities) of the number of TMs exchanged with pain mentions about NH residents. Dementia stage and avoidable transfers were not significant.

CONCLUSIONS

Our findings indicate pain is underrepresented in communications about Black residents and those with ADRD, potentially contributing to disparities in pain assessment and management in these populations.

CLINICAL IMPLICATIONS

This study indicates a need for more equitable pain practices with improved pain assessment tools and pain assessment and management, particularly with TM communication in healthcare settings.

Association of Cognitive Impairments and Alzheimer's Disease and Related Dementias With Hospital and Skilled Nursing Facility Utilization Among Community-Dwelling Older Adults

This observational cohort study examined the association between Alzheimer's disease and related dementias (ADRD) and hospital and skilled nursing facility (SNF) utilization among community-dwelling older adults aged 65 and older. Using four waves of longitudinal survey data from the 2016-2022 Health and Retirement Study conducted by the University of Michigan and multivariate, individual-level regressions, we found that the cognitive impairment but no dementia (CIND) and ADRD groups exhibited similar patterns in hospitalizations, hospital visits, and total hospital days compared to the normal cognition group. However, CIND was significantly associated with increased odds of SNF stays (odds ratio [OR], 1.22) and a higher number of SNF visits (incidence rate ratio [IRR], 1.45) than the normal cognition group. Higher odds of any SNF stay (OR, 1.26) and number of SNF visits (IRR, 1.45) were also observed for ADRD compared to normal cognition. These findings suggest that CIND or ADRD was not associated with hospital use but was significantly associated with increased SNF use.

Immediate and long-term effects of transcranial direct current stimulation on pain relief in older adults with Alzheimer's disease and related dementias: A pilot study

We simultaneously assessed the impact of transcranial direct current stimulation (tDCS) on mitigating both self-reported pain and pain behaviors to more objectively explore its effects in older adults with Alzheimer's disease and related dementias. The analysis investigated 40 participants randomly (1:1) subjected to active and sham tDCS for 20 min on 5 consecutive days. Multi-group latent transition analysis enabled the simultaneous evaluation of both pain domains in a single model and analysis of their changes as a function of intervention exposure by modeling the transition probabilities of latent classes and comparing these changes between groups. Two pain categories ("high pain" and "low pain") were identified based on the numeric rating scale and mobilization-observation-behavior-intensity-dementia scale scores. Overall, tDCS demonstrated better effects in helping participants transition to a "low pain" status during and after the intervention (∼3 months) compared with sham stimulation, demonstrating its immediate and enduring effects.

Mechanisms of comorbidity between Alzheimer's disease and pain

Clinical studies have revealed a significant correlation between pain and neurodegenerative diseases, particularly Alzheimer's disease (AD). However, due to cognitive and speech impairments, AD patients, especially those in moderate to severe stages, are often overlooked in pain management. The challenges in obtaining pain-related information from this population exacerbate the issue. Although recent clinical research has increasingly recognized the comorbidity of AD and pain, the pathological alterations and interactive mechanisms underlying this relationship remain inadequately explored. This review provides a comprehensive analysis of the clinical features and pathological mechanisms of AD with and without pain comorbidity. It examines underlying processes, including neuroinflammation, peripheral-central immune interactions, and neurotransmitter dynamics. Furthermore, it highlights current pain assessment and management strategies in AD patients. By offering a theoretical framework, this review aims to support the development of effective pain management approaches and serve as a reference for clinical interventions targeting AD-associated pain. HIGHLIGHTS: The comorbidity between AD and CP encompasses multiple interrelated biological pathways, such as neurodegeneration and inflammatory responses. The damage to neurons and synapses in AD patients influences the brain regions responsible for processing pain, thereby reducing the pain response. Neuroinflammation plays a vital role in the development of both AD and CP. Enhanced inflammatory responses have an impact on the CNS and promote sensitization. Common neurotransmitter alterations exist in the comorbidity of AD and CP, influencing cognition, emotion, and pain perception.

Association between chronic pain and dementia: a systematic review and meta-analysis

PURPOSE

Dementia and chronic pain (CP) are prevalent among older adults. However, no study has systematically reviewed the association between dementia and CP. Therefore, we performed this study to gather evidence about the potential relationship between the two.

METHODS

Two authors independently searched PubMed, Embase, and Web of Science to identify all records published up to 1 September 2022 that explored the association between CP and dementia. The methodological quality of the studies was assessed using the Newcastle Ottawa Scale (NOS). A fixed or random-effects model was used to pool the risk estimates.

RESULTS

Among the initial 3296 articles retrieved, 19 were included in the review (1 cross-sectional, and 18 cohort). The pooled result showed the risk of dementia was 1.42 times higher in CP patients (HR = 1.42, 95% CI 1.23-1.64, P < 0.001). dementia and CP subtypes, gender, and age did not significantly affect the results.

CONCLUSION

Our study shows that people who suffered from CP are at an increased risk of developing dementia, regardless of gender, age, and dementia and CP subtypes.

Association Between Documented Severe Pain and Cognitive Impairment in Home Health Care Patients: Results from the National Outcome and Assessment Information Set Data

Background: Despite the growing importance of home health care (HHC) in the care of older adults with cognitive impairment, limited evidence exists about factors associated with documented severe pain among older adults receiving HHC. Methods: This secondary data analysis used a 5% random national sample of the 2017 national Outcome and Assessment Information Set (OASIS) data. Multivariable Poisson regression model was used to examine the association between documented severe pain, cognitive impairment, and a range of sociodemographic, clinical, and cognitive factors. Results: HHC patients (n = 183,038) were mean age 79.7 years, 61.7% female, and 78.6% non-Hispanic White. In multivariable models, cognitive impairment was associated with lower likelihood of documented severe pain (prevalence ratio [PR] = 0.96, confidence interval [CI] = [0.94-0.98]). Other factors independently associated with less documented severe pain included Hispanic ethnicity (PR = 0.92, CI = [0.89-0.95]), the oldest (≥85 years) groups (PR = 0.65, CI = [0.63-0.66]), male patients (PR = 0.83, CI = [0.82-0.85]), those with a formal diagnosis of Alzheimer's Disease Related Dementias (PR = 0.67, CI = [0.65-0.69]), and patients with verbal- and speech-related difficulty (PR = 0.83, CI = [0.80-0.86]). Patients with history of falls (PR = 1.18, CI = [1.16-1.20]), positive screen on the Patient Health Questionnaire-2 (PR = 1.36, CI = [1.31-1.41]), shortness of breath (PR = 1.14, CI = [1.12-1.14]), anxiety daily (PR = 1.16, CI = [1.14-1.18]), and anxiety daily or more often (PR = 1.40, CI = [1.37-1.43]) were more likely to have documented severe pain. Conclusions: HHC patients with cognitive impairment were less likely to have documented severe pain even with a range of sociodemographic, clinical, functional, and cognitive characteristics were considered. These findings may reflect a link between cognitive impairment missed opportunities for clinicians to provide pain management. Tailored interventions are needed to better assess and manage pain in this vulnerable group of HHC patients.

The association between cognitive ability and opioid prescribing in vulnerable older adults with chronic pain in ambulatory care: a secondary data analysis using the Medical Expenditure Panel Survey

BACKGROUND

Vulnerable older adults living with Alzheimer's disease or Alzheimer's disease and related dementia (AD/ADRD) and chronic pain generally receive fewer pain medications than individuals without AD/ADRD, especially in nursing homes. Little is known about pain management in older adults with AD/ADRD in the community. The aim of the study was to examine opioid prescribing patterns in individuals with chronic pain by levels of cognitive ability in ambulatory care.

METHODS

We used the Medical Expenditure Panel Survey (MEPS), years 2002-2017, and identified three levels of cognitive impairment: no cognitive impairment (NCI), individuals reporting cognitive impairment (CI) without an AD/ADRD diagnosis, and individuals with a diagnosis of AD/ADRD. We examined any receipt of an opioid prescription and the number of opioid prescriptions using a logistic and negative binomial regression adjusting for sociodemographic and health characteristics and stratifying by three types of chronic pain (any chronic pain, severe chronic pain, and chronic pain identified through ICD 9/10 chronic pain diagnoses).

RESULTS

Among people with any chronic pain, adjusted odds of receiving an opioid for people with CI (OR 1.41, 95% confidence interval 1.31-1.52) and AD/ADRD (OR 1.23, 95% confidence interval 1.04-1.45) were higher compared to NCI. Among people with chronic pain ICD 9/10 conditions, the odds of receiving an opioid were also higher for those with CI (OR 1.43, 95% confidence interval 1.34-1.56) and AD/ADRD (OR 1.48, 95% confidence interval 1.23-1.78) compared to NCI. Among those with severe chronic pain, people with CI were more likely to receive an opioid (OR 1.17, 95% confidence interval 1.07-1.27) relative to NCI (OR 0.89, 95% confidence interval 0.75-1.06). People with AD/ADRD experiencing severe chronic pain were not more likely to receive an opioid compared to the NCI group. Adjusted predicted counts of opioid prescriptions showed more opioids in CI and AD/ADRD in all chronic pain cohorts, with the largest numbers of opioid prescriptions in the severe chronic pain and ICD 9/10 diagnoses groups.

CONCLUSIONS

The results suggest increased opioid use in people living with CI and AD/ADRD in the ambulatory care setting and potentially indicate that these individuals either require more analgesics or that opioids may be overprescribed. Further research is needed to examine pain management in this vulnerable population.

Development of a binary classifier model from extended facial codes toward video-based pain recognition in cancer patients

OBJECTIVES

The Automatic Pain Assessment (APA) relies on the exploitation of objective methods to evaluate the severity of pain and other pain-related characteristics. Facial expressions are the most investigated pain behavior features for APA. We constructed a binary classifier model for discriminating between the absence and presence of pain through video analysis.

METHODS

A brief interview lasting approximately two-minute was conducted with cancer patients, and video recordings were taken during the session. The Delaware Pain Database and UNBC-McMaster Shoulder Pain dataset were used for training. A set of 17 Action Units (AUs) was adopted. For each image, the OpenFace toolkit was used to extract the considered AUs. The collected data were grouped and split into train and test sets: 80 % of the data was used as a training set and the remaining 20 % as the validation set. For continuous estimation, the entire patient video with frame prediction values of 0 (no pain) or 1 (pain), was imported into an annotator (ELAN 6.4). The developed Neural Network classifier consists of two dense layers. The first layer contains 17 nodes associated with the facial AUs extracted by OpenFace for each image. The output layer is a classification label of "pain" (1) or "no pain" (0).

RESULTS

The classifier obtained an accuracy of ∼94 % after about 400 training epochs. The Area Under the ROC curve (AUROC) value was approximately 0.98.

CONCLUSIONS

This study demonstrated that the use of a binary classifier model developed from selected AUs can be an effective tool for evaluating cancer pain. The implementation of an APA classifier can be useful for detecting potential pain fluctuations. In the context of APA research, further investigations are necessary to refine the process and particularly to combine this data with multi-parameter analyses such as speech analysis, text analysis, and data obtained from physiological parameters.

Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept

Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts.

The Association of Cognitive Status and Post-Operative Opioid Prescribing in Older Adults

Objective

To examine the differences in opioid prescribing by cognitive status following common elective surgical procedures among Medicare beneficiaries.

Background

Older individuals commonly experience changes in cognition with age. Although opioid prescribing is common after surgery, differences in opioid prescribing after surgery by cognitive status are poorly understood.

Methods

We conducted a retrospective analysis of patients ≥65 years participating in the Health and Retirement Study (HRS) linked with Medicare claims data who underwent surgeries between January 2007 and November 2016 and had cognitive assessments before the index operation. Cognitive status was defined as normal cognition, mild cognitive impairment (MCI), or dementia. Outcomes assessed were initial perioperative opioid fill rates, refill rates, and high-risk prescriptions fill rates. The total amount of opioids filled during the 30-day postdischarge period was also assessed. Adjusted rates were estimated for patient factors using the Cochran-Armitage test for trend.

Results

Among the 1874 patients included in the analysis, 68% had normal cognition, 21.3% had MCI, and 10.7% had dementia. Patients with normal cognition (58.1%) and MCI (54.5%) had higher initial preoperative fill rates than patients with dementia (33.5%) (P < 0.001). Overall, patients with dementia had similar opioid refill rates (21%) to patients with normal cognition (24.1%) and MCI (26.5%) (P = 0.322). Although prior opioid exposure did not differ by cognitive status (P = 0.171), among patients with high chronic preoperative use, those with dementia had lower adjusted prescription sizes filled within 30 days following discharge (281 OME) than patients with normal cognition (2147 OME) and MCI (774 OME) (P < 0.001; P = 0.009 respectively). Among opioid-naive patients, patients with dementia also filled smaller prescription sizes (97 OME) compared to patients with normal cognition (205 OME) and patients with MCI (173 OME) (P < 0.001 and P = 0.019, respectively).

Conclusions

Patients with dementia are less likely to receive postoperative prescriptions, less likely to refill prescriptions, and receive prescriptions of smaller sizes compared to patients with normal cognition or MCI. A cognitive assessment is an additional tool surgeons can use to determine a patient's individualized postoperative pain control plan.

Palliative care to support the needs of adults with neurological disease

Neurological diseases cause physical, psychosocial, and spiritual or existential suffering from the time of their diagnosis. Palliative care focuses on improving quality of life for people with serious illness and their families by addressing this multidimensional suffering. Evidence from clinical trials supports the ability of palliative care to improve patient and caregiver outcomes by the use of outpatient or home-based palliative care interventions for people with motor neuron disease, multiple sclerosis, or Parkinson's disease; inpatient palliative care consultations for people with advanced dementia; telephone-based case management for people with dementia in the community; and nurse-led discussions with decision aids for people with advanced dementia in long-term care. Unfortunately, most people with neurological diseases do not get the support that they need for their palliative care under current standards of healthcare. Improving this situation requires the deployment of routine screening to identify individual palliative care needs, the integration of palliative care approaches into routine neurological care, and collaboration between neurologists and palliative care specialists. Research, education, and advocacy are also needed to raise standards of care.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.