Markov chain evaluation of acute postoperative pain transition states

Abstract

Prior investigations on acute postoperative pain dynamicity have focused on daily pain assessments, and so were unable to examine intra-day variations in acute pain intensity. We analyzed 476,108 postoperative acute pain intensity ratings clinically documented on postoperative days 1 to 7 from 8,346 surgical patients using Markov Chain modeling to describe how patients are likely to transition from one pain state to another in a probabilistic fashion. The Markov Chain was found to be irreducible and positive recurrent, with no absorbing states. Transition probabilities ranged from 0.0031 for the transition from state 10 to state 1, to 0.69 for the transition from state zero to state zero. The greatest density of transitions was noted in the diagonal region of the transition matrix, suggesting that patients were generally most likely to transition to the same pain state as their current state. There were also slightly increased probability densities in transitioning to a state of asleep or zero from the current state. Examination of the number of steps required to traverse from a particular first pain score to a target state suggested that overall, fewer steps were required to reach a state of zero (range 6.1–8.8 steps) or asleep (range 9.1–11) than were required to reach a mild pain intensity state. Our results suggest that Markov Chains are a feasible method for describing probabilistic postoperative pain trajectories, pointing toward the possibility of using Markov decision processes to model sequential interactions between pain intensity ratings and postoperative analgesic interventions.

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

“…Additional measures capturing temporal dynamics of pain can be derived from EMA but these were not considered here because they require more specialized analyses. For example, novel applications of time-series analyses have been shown to capture unique temporal features of pain intensity, including the persistence (e.g., autocorrelation) of pain states and the amplitude of shifts between elevated and reduced pain states, which may provide important avenues for future efforts to optimize the detection of efficacious treatments [30,43,55].…”

High-resolution, field approaches for assessing pain: Ecological Momentary Assessment

“…For acute pain, a natural application is tracking dynamic changes in postsurgical pain within and across days with momentary assessments, as illustrated in a recent study. 49 This study analyzed momentary pain reports of over 8000 patients, which were recorded by medical staff every 4 hours on postoperative days 1 through 7 and documented in patients' electronic medical records. Using Markov Chain modeling, they were able to predict the course of pain states during postoperative recovery.…”

“…Eleven original articles were found that reported results from 9 different projects (see Supplemental Table 1, available at http://links.lww.com/PAIN/B159 ). 6 , 7 , 9 , 15 , 19 , 28 , 38 , 42 , 49 , 55 All the studies were observational designs and most investigated pain intensity, 9 , 15 , 19 , 28 , 34 , 38 , 42 , 49 , 55 pain interference, 7 , 19 , 27 and pain behavior, 19 or examined relationships between pain and other domains (such as sleep 55 or social support 9 ). One study took a more methodological approach investigating the feasibility of promising statistical models.…”

Deep neural network architectures for forecasting analgesic response

“…Tighe et al used Markov decision processes to model pain transition states [9]. Intraday fluctuations and short-term analgesic response remains an open area of research, one in which machine learning may play an important role.…”