Parsimonious clinical prediction model for the diagnosis of complicated appendicitis

ObjectiveTo develop a logistic regression model that combines clinical and radiological parameters for prediction of complicated appendicitis.Methods248 patients with histologically proven uncomplicated (n = 214) 34) acute appendicitis were analyzed retrospectively. All had undergone presurgical abdominal and/or pelvic computed tomography (CT) scan, assessed by two radiologists. A using univariate multivariate analyses was developed, the strength association between independent predictors evaluated adjusted odds radio. Clinical gender, age, anorexia, vomiting, duration symptoms, right lower quadrant (RLQ) tenderness, rebound body temperature, white blood cell (WBC) count, neutrophil ratio. Radiological appendix diameter, appendicolith, caecal wall thickening, mesenteric lymphadenopathy, extraluminal air, abscess, fat stranding, periappendicular fluid.ResultsFour features (body temperature＞37.2 °C, periappendiceal fluid) included in model, yielded an area under curve (AUC) 0.87 (95% confidence interval (CI), 0.80–0.93), sensitive 88%, specificity 74%.ConclusionThe makes accurate simple possible.