South African Journal of Surgery
On-line version ISSN 2078-5151
KONG, V Y et al. Developing a clinical model to predict the need for relaparotomy in severe intra-abdominal sepsis secondary to complicated appendicitis. S. Afr. j. surg. [online]. 2014, vol.52, n.4, pp. 91-95. ISSN 2078-5151. http://dx.doi.org/10.7196/sajs.2116.
INTRODUCTION: Complex intra-abdominal sepsis secondary to acute appendicitis is common in South Africa, and management frequently involves relaparotomy. The decision to perform relaparotomy is often difficult, and this study aimed to develop a clinical model to aid the decision-making process. METHODS: The study was conducted from January 2008 to December 2012 at Edendale Hospital, Pietermaritzburg. All patients with intraoperatively confirmed acute appendicitis and all patients in this group who subsequently underwent relaparotomy were included. The clinical course, intraoperative findings and outcome of all patients were recorded until discharge (or death). Using a combination of preoperative and intraoperative parameters, a clinical model was developed to predict the need for relaparotomy. RESULTS: Of the total of 1 000 patients identified, 54.1% were males. The median age for all patients was 21 years. Of 406 relaparotomies, 227 (55.9%) were planned and 179 (44.1%) on demand (expectant treatment). In the relaparotomy group, 367 patients (90.4%) had positive findings. Logistic regression analysis showed that the following four factors accurately predicted the need for subsequent relaparotomy: patients referred from any rural centre, duration of illness >5 days, heart rate >120 bpm, and perforation associated with generalised intra-abdominal sepsis. This model had a predictive value of >90%. CONCLUSIONS: We have constructed a model that uses clinical data available at initial laparotomy to predict the need for subsequent relaparotomy in patients with complicated acute appendicitis. It is hoped that this model can be integrated into routine clinical practice, but further study is first needed to validate this model.