Slow Dynamics of Acute Postoperative Pain Intensity Time Series Determined via Wavelet Analysis Are Associated With the Risk of Severe Postoperative Day 30 Pain.

BACKGROUND: Evidence suggests that increased early postoperative pain (POP) intensities are associated with increased pain in the weeks following surgery. However, it remains unclear which temporal aspects of this early POP relate to later pain experience. In this prospective cohort study, we used wavelet analysis of clinically captured POP intensity data on postoperative days 1 and 2 to characterize slow/fast dynamics of POP intensities and predict pain outcomes on postoperative day 30. METHODS: The study used clinical POP time series from the first 48 hours following surgery from 218 patients to predict their mean POP on postoperative day 30. We first used wavelet analysis to approximate the POP series and to represent the series at different time scales to characterize the early temporal profile of acute POP in the first 2 postoperative days. We then used the wavelet coefficients alongside demographic parameters as inputs to a neural network to predict the risk of severe pain 30 days after surgery. RESULTS: Slow dynamic approximation components, but not fast dynamic detailed components, were linked to pain intensity on postoperative day 30. Despite imbalanced outcome rates, using wavelet decomposition along with a neural network for classification, the model achieved an F score of 0.79 and area under the receiver operating characteristic curve of 0.74 on test-set data for classifying pain intensities on postoperative day 30. The wavelet-based approach outperformed logistic regression (F score of 0.31) and neural network (F score of 0.22) classifiers that were restricted to sociodemographic variables and linear trajectories of pain intensities. CONCLUSIONS: These findings identify latent mechanistic information within the temporal domain of clinically documented acute POP intensity ratings, which are accessible via wavelet analysis, and demonstrate that such temporal patterns inform pain outcomes at postoperative day 30.

Long-Term Postoperative Pain Prediction Using Higher-Order Singular Value Decomposition of Intraoperative Physiological Responses: Prospective Cohort Study.

BACKGROUND: Long-term postoperative pain (POP) and patient responses to pain relief medications are not yet fully understood. Although recent studies have developed an index for the nociception level of patients under general anesthesia based on multiple physiological parameters, it remains unclear whether these parameters correlate with long-term POP outcomes. OBJECTIVE: This study aims to extract unbiased and interpretable descriptions of how the dynamics of physiological parameters change over time and across patients in response to surgical procedures and intraoperative medications using a multivariate-temporal analysis. We demonstrated that there is an association (correlation) between the main features of intraoperative physiological responses and long-term POP, which has a predictive value, even without claiming causality. METHODS: We proposed a complex higher-order singular value decomposition method to accurately decompose patients' physiological responses into multivariate structures evolving over time. We used intraoperative vital signs of 175 patients from a mixed surgical cohort to extract three interconnected, low-dimensional, complex-valued descriptions of patients' physiological responses: multivariate factors, reflecting subphysiological parameters; temporal factors, reflecting common intrasurgery temporal dynamics; and patients' factors, describing interpatient changes in physiological responses. RESULTS: Adoption of the complex higher-order singular value decomposition method allowed us to clarify the dynamic correlation structure included in the intraoperative physiological responses. Instantaneous phases of the complex-valued physiological responses of 242 patients within the subspace of principal descriptors enabled us to discriminate between mild and not-mild (moderate-severe) levels of pain at postoperative days 30 and 90. Following rotation of physiological responses before projection to align with the common multivariate-temporal dynamic, the method achieved an area under curve for postoperative day 30 and 90 outcomes of 0.81 and 0.89 for thoracic surgery, 0.87 and 0.83 for orthopedic surgery, 0.87 and 0.88 for urological surgery, 0.86 and 1 for colorectal surgery, 1 and 1 for transplant surgery, and 0.83 and 0.92 for pancreatic surgery, respectively. CONCLUSIONS: By categorizing patients into different surgical groups, we identified significant surgery-related principal descriptors. Each of them potentially encodes different surgical stimulation. The dynamics of patients' physiological responses to these surgical events were linked to long-term POP development.

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.

Effects of Patient and Surgery Characteristics on Persistent Postoperative Pain: A Mediation Analysis.

OBJECTIVE: Acute postoperative pain intensity is associated with persistent postsurgical pain (PPP) risk. However, it remains unclear whether acute postoperative pain intensity mediates the relationship between clinical factors and persistent pain. MATERIALS AND METHODS: Participants from a mixed surgical population completed the Brief Pain Inventory and Pain Catastrophizing Scale before surgery, and the Brief Pain Inventory daily after surgery for 7 days and at 30 and 90 days after surgery. We considered mediation models using the mean of the worst pain intensities collected daily on each of postoperative days (PODs) 1 to 7 against outcomes of worst pain intensity at the surgical site endpoints reflecting PPP (POD 90) and subacute pain (POD 30). RESULTS: The analyzed cohort included 284 participants for the POD 90 outcome. For every unit increase of maximum acute postoperative pain intensity through PODs 1 to 7, there was a statistically significant increase of mean POD 90 pain intensity by 0.287 after controlling for confounding effects. The effects of female versus male sex (m=0.212, P=0.034), pancreatic/biliary versus colorectal surgery (m=0.459, P=0.012), thoracic cardiovascular versus colorectal surgery (m=0.31, P=0.038), every minute increase of anesthesia time (m=0.001, P=0.038), every unit increase of preoperative average pain score (m=0.012, P=0.015), and every unit increase of catastrophizing (m=0.044, P=0.042) on POD 90 pain intensity were mediated through acute PODs 1 to 7 postoperative pain intensity. DISCUSSION: Our results suggest the mediating relationship of acute postoperative pain on PPP may be predicated on select patient and surgical factors.

Odor tracking in aquatic organisms: the importance of temporal and spatial intermittency of the turbulent plume.

In aquatic and terrestrial environments, odorants are dispersed by currents that create concentration distributions that are spatially and temporally complex. Animals navigating in a plume must therefore rely upon intermittent, and time-varying information to find the source. Navigation has typically been studied as a spatial information problem, with the aim of movement towards higher mean concentrations. However, this spatial information alone, without information of the temporal dynamics of the plume, is insufficient to explain the accuracy and speed of many animals tracking odors. Recent studies have identified a subpopulation of olfactory receptor neurons (ORNs) that consist of intrinsically rhythmically active 'bursting' ORNs (bORNs) in the lobster, Panulirus argus. As a population, bORNs provide a neural mechanism dedicated to encoding the time between odor encounters. Using a numerical simulation of a large-scale plume, the lobster is used as a framework to construct a computer model to examine the utility of intermittency for orienting within a plume. Results show that plume intermittency is reliably detectable when sampling simulated odorants on the order of seconds, and provides the most information when animals search along the plume edge. Both the temporal and spatial variation in intermittency is predictably structured on scales relevant for a searching animal that encodes olfactory information utilizing bORNs, and therefore is suitable and useful as a navigational cue.

Comparison of Gaussian Processes Methods to Linear methods for Imputation of Sparse Physiological Time Series.

Physiological timeseries such as vital signs contain important information about a patient and are used in different clinical application; however, they suffer from missing values and sampling irregularity. In recent years, Gaussian Processes have been used as sophisticated nonlinear value imputation methods on time series, however there is a lack of comparison to other simpler methods. This paper compares the ability of five methods that can be used in missing data imputation in physiological time series. These models are linear interpolation as the baseline, cubic spline interpolation, and three non-linear methods: Single Task Gaussian Processes, Multi-Task Gaussian Processes, and Multivariate Imputation Chained Equations. We used seven intraoperative physiological time series from 27,481 patients. Piecewise aggregate approximation was employed as a dimensionality reduction and resampling strategy. Linear interpolation and cubic splining show overall superiority in prediction of the missing values, compared to the other complex models. The performance of the kernel-based methods suggest that they are highly sensitive to the kernel width and require incorporation of domain knowledge for fine-tuning.

Transition Icons for Time-Series Visualization and Exploratory Analysis.

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.