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Background and Aims The main barrier preventing optimal pain management is the inability to identify and manage patients at elevated risk of significant pain in a timely manner, thereby compounding pain-related morbidity. Our aim was to develop a predictive model for pain score at postoperative 13-36th hours by analysing data from our centralized enterprise analytic platform (eHIntS).

Methods We analysed postoperative data retrieved from eHIntS in 667 patients between January to July 2020, comprising demographic, type of admission, method of surgery (minimally invasive/open), duration of surgery, procedure code, pain scores at PACU, postoperative pain scores at 0-12th hours (at rest, on movement), number of analgesia attempts at postoperative 12th hour, and delivered analgesia at postoperative 12th hour.

Results A total of 102 (15.3%) patients had at least one pain score of >3 at postoperative 13-36th hours, with average and maximum pain score of 2.4 (SD 0.9) and 5.0 (SD 1.4), as compared with those having pain scores 0-3 at postoperative 13-36th hours (average: 1.3 (SD 0.6); maximum: 2.4 (SD 0.9)). The multivariable model showed that Malay race as compared with Chinese, having ovarian surgery, increased PCA morphine dose at 12th hour, and having higher maximum pain score at movement at postoperative 0-12th hours were independently associated with maximum pain score on movement at postoperative 13-36th hours >3 (significant pain), with an AUC of 0.731.

Conclusions This model needs to be verified and validated in a larger and more diverse dataset to increase the predictive power of the model.

Attachment 2022-2505 20220922 NR.pdf