Markov chain evaluation of acute postoperative pain transition states

The Effect of Everyday-Life Social Contact on Pain

Pain is a biopsychosocial phenomenon, resulting from the interplay between physiological and psychological processes and social factors. Given that humans constantly interact with others, the effect of social factors is particularly relevant. Documenting the significance of the social modulation of pain, an increasing number of studies have investigated the effect of social contact on subjective pain intensity and pain-related physiological changes. While evidence suggests that social contact can alleviate pain, contradictory findings indicate an increase in pain intensity and a deterioration of pain coping strategies. This evidence primarily stems from studies examining the effect of social contact on pain within highly controlled laboratory conditions. Moreover, pain assessments often rely on one-time subjective reports of average pain intensity across a predefined period. Ecological momentary assessments (EMAs) can circumvent these problems, as they can capture diverse aspects of behavior and experiences multiple times a day, in real time, with high resolution, and within naturalistic and ecologically valid settings. These multiple measures allow for the examination of fluctuations of pain symptoms throughout the day in relation to affective, cognitive, behavioral, and social factors. In this opinion paper, we review the current state and future relevance of EMA-based social pain research in daily life. Specifically, we examine whether everyday-life social support reduces or enhances pain. The first part of the paper provides a comprehensive overview of the use of EMA in pain research and summarizes the main findings. The review of the relatively limited number of existing EMA studies shows that the association between pain and social contact in everyday life depends on numerous factors, including pain syndromes, temporal dynamics, the nature of social interactions, and characteristics of the interaction partners. In line with laboratory research, there is evidence that everyday-life social contact can alleviate, but also intensify pain, depending on the type of social support. Everyday-life emotional support seems to reduce pain, while extensive solicitous support was found to have opposite effects. Moreover, positive short-term effects of social support can be overshadowed by other symptoms such as fatigue. Overall, gathering and integrating experiences from a patient's social environment can offer valuable insights. These insights can help interpret dynamics in pain intensity and accompanying symptoms such as depression or fatigue. We conclude that factors determining the reducing versus enhancing effects of social contact on pain need to be investigated more thoroughly. We advocate EMA as the assessment method of the future and highlight open questions that should be addressed in future EMA studies on pain and the potential of ecological momentary interventions for pain treatment.

Characterization of sleep efficiency transitions in family caregivers

Family caregivers are at high risk of psychological distress and low sleep efficiency resulting from their caregiving responsibilities. Although psychological symptoms are associated with sleep efficiency, there is limited knowledge about the association of psychological distress with variations in sleep efficiency. We aimed to characterize the short- and long-term patterns of caregivers' sleep efficiency using Markov chain models and compare these patterns between groups with high and low psychological symptoms (i.e., depression, anxiety, and caregiving stress). Based on 7-day actigraphy data from 33 caregivers, we categorized sleep efficiency into three states, < 75% (S1), 75-84% (S2), and ≥ 85% (S3), and developed Markov chain models. Caregivers were likely to maintain a consistent sleep efficiency state from one night to the next without returning efficiently to a normal state. On average, it took 3.6-5.1 days to return to a night of normal sleep efficiency (S3) from lower states, and the long-term probability of achieving normal sleep was 42%. We observed lower probabilities of transitioning to or remaining in a normal sleep efficiency state (S3) in the high depression and anxiety groups compared to the low symptom groups. The differences in the time required to return to a normal state were inconsistent by symptom levels. The long-term probability of achieving normal sleep efficiency was significantly lower for caregivers with high depression and anxiety compared to the low symptom groups. Caregivers' sleep efficiency appears to remain relatively consistent over time and does not show rapid recovery. Caregivers with higher levels of depression and anxiety may be more vulnerable to sustained suboptimal sleep efficiency.

SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users' self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.

Time Effect on Acute Postoperative Pain After Total Knee Replacement Surgery: An Exploratory Study Using the Experience Sampling Method

OBJECTIVES

Acute postoperative pain (APP) is the main cause of postoperative dissatisfaction; however, traditional methods of pain assessment provide limited insights into the dynamics and development of APP. This study used the experience sampling method to understand the dynamics of APP over time in relation to various patient factors.

MATERIALS AND METHODS

Forty patients scheduled to undergo total knee replacement surgery were recruited in this study. Following an initial assessment, a short report questionnaire was sent to the patients through 10 digital alerts per day to assess the pain levels during 2 preoperative and the first 6 postoperative days. The data were analyzed using multilevel regression, including random intercept and slope.

RESULTS

Thirty-two patients submitted the prespecified minimum of 30% of their short reports, yielding 1217 records. The analysis revealed significant ( P <0.001) linear and quadratic decreases in APP and a quadratic time effect. The lowest between-day and within-day pain levels were observed on postoperative day 4.8 and during the time slot 3.8 or ~19:15, respectively. Significant random intercepts and slopes were noted, indicating variations in the mean pain level between patients and a decrease in pain. None of the 10 patient factors had any confounding effect.

DISCUSSION

Using the experience sampling method data combined with multilevel analysis, we were able to evaluate the postoperative pain course while considering inter-individual differences in the baseline pain level and nonlinear pain course over time. The findings of this study could aid clinicians in personalizing the treatment for APP.

Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit

Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model. For each patient, we calculated transition probabilities to characterize movement among illness states. We calculated the Shannon entropy of the transition probabilities. Using the entropy parameter, we determined phenotypes of illness dynamics based on hierarchical clustering. We also examined the association between individual entropy scores and a composite variable of negative outcomes. Entropy-based clustering identified four illness dynamic phenotypes in a cohort of 164 intensive care unit admissions where at least one sepsis event occurred. Compared to the low-risk phenotype, the high-risk phenotype was defined by the highest entropy values and had the most ill patients as defined by a composite variable of negative outcomes. Entropy was significantly associated with the negative outcome composite variable in a regression analysis. Information-theoretical approaches to characterize illness trajectories offer a novel way of assessing the complexity of a course of illness. Characterizing illness dynamics with entropy offers additional information in conjunction with static assessments of illness severity. Additional attention is needed to test and incorporate novel measures representing the dynamics of illness.

Predicting long-term postsurgical pain by examining the evolution of acute pain

BACKGROUND

Increased acute postoperative pain intensity has been associated with the development of persistent postsurgical pain (PPP) in mechanistic and clinical investigations, but it remains unclear which aspects of acute pain explain this linkage.

METHODS

We analysed clinical postoperative pain intensity assessments using symbolic aggregate approximations (SAX), a graphical way of representing changes between pain states from one patient evaluation to the next, to visualize and understand how pain intensity changes across sequential assessments are associated with the intensity of postoperative pain at 1 (M1) and 6 (M6) months after surgery. SAX-based acute pain transition patterns were compared using cosine similarity, which indicates the degree to which patterns mirror each other.

RESULTS

This single-centre prospective cohort study included 364 subjects. Patterns of acute postoperative pain sequential transitions differed between the 'None' and 'Severe' outcomes at M1 (cosine similarity 0.44) and M6 (cosine similarity 0.49). Stratifications of M6 outcomes by preoperative pain intensity, sex, age group, surgery type and catastrophising showed significant heterogeneity of pain transition patterns within and across strata. Severe-to-severe acute pain transitions were common, but not exclusive, in patients with moderate or severe pain intensity at M6.

CONCLUSIONS

Clinically, these results suggest that individual pain-state transitions, even within patient or procedural strata associated with PPP, may not alone offer good predictive information regarding PPP. Longitudinal observation in the immediate postoperative period and consideration of patient- and surgery-specific factors may help indicate which patients are at increased risk of PPP.

SIGNIFICANCE

Symbolic aggregate approximations of clinically obtained, acute postoperative pain intraday time series identify different motifs in patients suffering moderate to severe pain 6 months after surgery.

Dynamic Transitions of Pediatric Sepsis: A Markov Chain Analysis

Pediatric sepsis is a heterogeneous disease with varying physiological dynamics associated with recovery, disability, and mortality. Using risk scores generated from a sepsis prediction model to define illness states, we used Markov chain modeling to describe disease dynamics over time by describing how children transition among illness states. We analyzed 18,666 illness state transitions over 157 pediatric intensive care unit admissions in the 3 days following blood cultures for suspected sepsis. We used Shannon entropy to quantify the differences in transition matrices stratified by clinical characteristics. The population-based transition matrix based on the sepsis illness severity scores in the days following a sepsis diagnosis can describe a sepsis illness trajectory. Using the entropy based on Markov chain transition matrices, we found a different structure of dynamic transitions based on ventilator use but not age group. Stochastic modeling of transitions in sepsis illness severity scores can be useful in describing the variation in transitions made by patient and clinical characteristics.

III. Detecting Treatment Effects in Clinical Trials With Different Indices of Pain Intensity Derived From Ecological Momentary Assessment

Pain intensity represents the primary outcome in most pain clinical trials. Identifying methods to measure aspects of pain that are most sensitive to treatment may facilitate discovery of effective interventions. In this third of 3 articles examining alternative indices of pain intensity derived from ecological momentary assessments (EMA), we compare treatment effects based on Average Pain, Maximum Pain, Minimum Pain, Pain Variability, Time in High Pain, Time in Low Pain, and Pain After Wake-Up. We also examine which indices contribute to Patient Global Impressions of Change (PGIC). Data came from 2 randomized, double-blind, placebo-controlled trials examining the efficacy of milnacipran for fibromyalgia treatment; 2,084 patients provided >1 million EMA pain intensity ratings over 24 (Study 1) or 26 (Study 2) treatment weeks. Pain Variability and Time in High Pain produced significantly smaller treatment effects than Average Pain; other pain indices showed effects that were numerically smaller, but not significantly different from Average Pain. Changes in all pain indices were significantly associated with PGIC, with improvements in Maximum Pain and in Pain Variability offering small incremental contributions to understanding PGIC over Average Pain. Results suggest that different pain indices could be used to detect treatment effects in pain clinical trials. PERSPECTIVE: Alternative summary measures of pain intensity derived from EMA may broaden the scope of outcomes useful in pain clinical trials. In this analysis of a pharmacological treatment for fibromyalgia, most pain summary measures indicated similar effects; improvements in Maximum Pain and Pain Variability contributed to understanding PGIC over Average Pain.

Deep neural network architectures for forecasting analgesic response

Response to prescribed analgesic drugs varies between individuals, and choosing the right drug/dose often involves a lengthy, iterative process of trial and error. Furthermore, a significant portion of patients experience adverse events such as post-operative urinary retention (POUR) during inpatient management of acute postoperative pain. To better forecast analgesic responses, we compared conventional machine learning methods with modern neural network architectures to gauge their effectiveness at forecasting temporal patterns of postoperative pain and analgesic use, as well as predicting the risk of POUR. Our results indicate that simpler machine learning approaches might offer superior results; however, all of these techniques may play a promising role for developing smarter post-operative pain management strategies.