Of rough starts and smooth finishes: correlations between post-anesthesia care unit and postoperative days 1-5 pain scores

Markov chain evaluation of acute postoperative pain transition states

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.

Clinically derived early postoperative pain trajectories differ by age, sex, and type of surgery

The objective of this study was to determine the effects of age, sex, and type of surgery on postoperative pain trajectories derived in a clinical setting from pain assessments in the first 24 hours after surgery. This study is a retrospective cohort study using a large electronic medical records system to collect and analyze surgical case data. The sample population included adult patients undergoing nonambulatory nonobstetric surgery in a single institution over a 1-year period. Analyses of postoperative pain trajectories were performed using a linear mixed-effects model. Pain score observations (91,708) from 7293 patients were included in the statistical analysis. On average, the pain score decreased about 0.042 (95% confidence interval [CI]: -0.044 to -0.040) points on the numerical rating scale (NRS) per hour after surgery for the first 24 postoperative hours. The pain score reported by male patients was approximately 0.27 (95% CI: -0.380 to -0.168) NRS points lower than that reported by females. Pain scores significantly decreased over time in all age groups, with a slightly more rapid decrease for younger patients. Pain trajectories differed by anatomic location of surgery, ranging from -0.054 (95% CI: -0.062 to -0.046) NRS units per hour for integumentary and nervous surgery to -0.104 (95% CI: -0.110 to -0.098) NRS units per hour for digestive surgery, and a positive trajectory (0.02 [95% CI: 0.016 to 0.024] NRS units per hour) for musculoskeletal surgery. Our data support the important role of time after surgery in considering the influence of biopsychosocial and clinical factors on acute postoperative pain.

Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain

BACKGROUND

Given their ability to process highly dimensional datasets with hundreds of variables, machine learning algorithms may offer one solution to the vexing challenge of predicting postoperative pain.

METHODS

Here, we report on the application of machine learning algorithms to predict postoperative pain outcomes in a retrospective cohort of 8,071 surgical patients using 796 clinical variables. Five algorithms were compared in terms of their ability to forecast moderate to severe postoperative pain: Least Absolute Shrinkage and Selection Operator (LASSO), gradient-boosted decision tree, support vector machine, neural network, and k-nearest neighbor (k-NN), with logistic regression included for baseline comparison.

RESULTS

In forecasting moderate to severe postoperative pain for postoperative day (POD) 1, the LASSO algorithm, using all 796 variables, had the highest accuracy with an area under the receiver-operating curve (ROC) of 0.704. Next, the gradient-boosted decision tree had an ROC of 0.665 and the k-NN algorithm had an ROC of 0.643. For POD 3, the LASSO algorithm, using all variables, again had the highest accuracy, with an ROC of 0.727. Logistic regression had a lower ROC of 0.5 for predicting pain outcomes on POD 1 and 3.

CONCLUSIONS

Machine learning algorithms, when combined with complex and heterogeneous data from electronic medical record systems, can forecast acute postoperative pain outcomes with accuracies similar to methods that rely only on variables specifically collected for pain outcome prediction.