title: Markov chain evaluation of acute postoperative pain transition states.
abstract: Previous investigations on acute postoperative pain dynamicity have focused on daily pain assessments, and so were unable to examine intraday variations in acute pain intensity. We analyzed 476,108 postoperative acute pain intensity ratings, which were clinically documented on postoperative days 1 to 7 from 8346 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 0 to state 0. 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 0 from the current state. An 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 0 (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 using Markov chains is 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.
authors.affiliation: Pain Research and Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA J. Crayton Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, FL, USA Department of Information Systems and Operations Management, Warrington College of Business Administration, University of Florida, Gainesville, FL, USA.
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Tighe
authors.forename: Matthew
authors.initials: M
authors.lastname: Bzdega
authors.forename: Roger B
authors.initials: RB
authors.lastname: Fillingim
authors.forename: Parisa
authors.initials: P
authors.lastname: Rashidi
authors.forename: Haldun
authors.initials: H
authors.lastname: Aytug
mesh_terms: D059787:Acute Pain / Q000175:diagnosis* / Q000453:epidemiology; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D010147:Pain Measurement / Q000379:methods*; D010149:Postoperative Pain / Q000175:diagnosis* / Q000453:epidemiology; D012189:Retrospective Studies; D055815:Young Adult