Markov chain evaluation of acute postoperative pain transition states.
Markov chain evaluation of acute postoperative pain transition states.
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DOI：
10.1097/j.pain.0000000000000429
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发表时间：
2016-03
期刊：
影响因子：
7.4
通讯作者：
Aytug H
中科院分区：
文献类型：
--
作者：
Tighe PJ;Bzdega M;Fillingim RB;Rashidi P;Aytug H
先前对急性术后疼痛动态的调查主要集中在日常疼痛评估上，因此无法检查急性疼痛强度的日间变化。我们分析了8,346例手术患者术后1至7天临床记录的476,108例术后急性疼痛强度评分，使用马尔可夫链模型来描述患者如何可能以概率方式从一种疼痛状态过渡到另一种疼痛状态。发现马尔可夫链是不可约的，正循环的，没有吸收态。从状态10到状态1的转换概率为0.0031，从状态0到状态0的转换概率为0.69。转换矩阵的对角线区域的转换密度最大，表明患者通常最有可能过渡到与当前状态相同的疼痛状态。从当前状态过渡到睡眠状态或零状态的概率密度也略有增加。检查从特定的第一次疼痛评分到目标状态所需的步数表明，总体而言，达到零状态（6.1-8.8步）或睡眠状态（9.1-11步）所需的步数少于达到轻度疼痛强度状态所需的步数。我们的研究结果表明，马尔可夫链是描述概率术后疼痛轨迹的可行方法，指出了使用马尔可夫决策过程来模拟疼痛强度等级和术后镇痛干预之间的顺序相互作用的可能性。
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.