Factors Affecting Post Caesarean Pain Intensity among Women in the Northern Peninsular of Malaysia IC07-IC11
Dr. Hanan Hussein Jasim,
Postgraduate Student, Department of Clinical Pharmacy, School of Pharmaceutical Sciences, University Science, Malaysia.
Introduction: Caesarean section (CS) rate has increased considerably during the past years, accounting for 15% to 25% of births. During post-CS period, moderate to severe postoperative pain is a regularly reported problem. Ideally, the intensity of postoperative pain should be predicted so as to customize analgesia.
Aim: To document the CS rate, assess the pain intensity and preoperative factors that may predict post caesarean pain among women in the Obstetric unit of a Hospital Pulau Pinang in Malaysia.
Materials and Methods: A retrospective chart review of 400 caesarean deliveries was conducted between January 2013 and June 2014. The study encompassed patient’s demographic data and obstetrics data. The overall pain scores since the time of surgery (2, 4, 8, 12, 24 and 48 hours postoperatively at rest and while moving) were assessed by visual analogue scale (VAS). The data were analyzed by using SPSS software (version 21.0 for Windows).
Results: The results demonstrate that within a 48 hours postoperative period, the average pain at rest and while moving was 0.40±0.013 and 0.83±0.017 (VAS score), respectively. Logistic regression identified that a higher BMI (=30) (OR 1.056; 95% CI=1.003 to 1.113, p=0.04), an increase in operation time (> 60 minutes) (OR 1.009; 95% CI=1.000 to 1.018, p=0.049), Single women (OR 11.597; 95% CI=1.382 to 97.320, p=0.024), blood group type O (OR 1.857; 95% CI=0.543 to 2.040, p = 0.001) and general anesthesia (OR 3.689; 95% CI=1.653 to 8.232, p=0.001) were found to be independent predictors for postcaesarean pain intensity.
Conclusion: This study concluded that CS rate is 28% among women in the obstetric unit of a Hospital Pulau Pinang and the pain experienced by the study participants was mild. Moreover, the predictive factors for pain intensity may aid in identifying patients at greater risk for postoperative pain. This study concluded that the predictive methods proposed may aid in identifying patients at greater risk for postoperative pain.