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OP029 Development of a risk stratification model for Caesarean delivery women at increased risk of significant post-Caesarean pain
  1. David Chee1,
  2. Hon Sen Tan1,2,
  3. Chin Wen Tan1,2,
  4. Rehena Sultana3,
  5. Farida Ithnin1,2 and
  6. Ban Leong Sng1,2
  1. 1Department of Women’s Anaesthesia, KK Women’s and Children’s Hospital, Singapore, Singapore, Singapore
  2. 2Anaesthesiology and Perioperative Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore, Singapore
  3. 3Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore, Singapore


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Background and Aims One of the significant barriers of optimal post-Caesarean pain management is the lack of a clinically relevant risk stratification strategy for early identification of women at risk of significant post-Caesarean pain. The aim of this study is to develop a predictive model for pain score at 13-24 hours post-Caesarean, by analyzing data from our centralized enterprise analytic platform (eHIntS).

Methods We analyzed data retrieved from eHIntS dataset in 979 patients between January to July 2020 at our institution. The data included patient demographics, pre-Caesarean pain score, type of admission, duration of surgery, procedure code, pain scores at PACU and post-Caesarean 0-24th hours and adverse events.

Results Overall, 85 out of 979 (9%) women had significant pain (NRS 4-10) during their hospital stay after Caesarean delivery with spinal morphine. Specifically, there were 27 (3%) women with an outcome of significant pain on movement at 13-24 hours post-Caesarean. Univariate analysis identified factors including race, having emergency surgery, increased pain score at rest and on movement (post-Caesarean 1-12th). The multivariable model showed that Indian race as compared with Chinese (OR 4.13, 95%CI 1.36 to 12.56, p=0.0124) and having higher pain score on movement at 1-12th hours post-Caesarean (OR 3.28, 95%CI 2.04 to 5.26, p<0.001) were significant independent risk factors (AUC=0.783).

Conclusions This pilot data will need further refinement in extending into the post-Caesarean recovery period. The model also requires verification in a larger and more diverse dataset to increase the predictive power of the model.

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