The Application of the Ten Group Classification System (TGCS) in Caesarean Delivery Case Mix Adjustment. A Multicenter Prospective Study

• DOI: 10.1371/journal.pone.0062364
• Publication Date: 2013-06-05T19:23:54Z
• Authors: Gianpaolo Maso; Salvatore Alberico; Lorenzo Monasta; Luca Ronfani; Marcella Montico; Caterina Businelli; Valentina Soini; Monica Piccoli; Carmine Gigli; Daniele Domini; Claudio Fiscella; Sara Casarsa; Carlo Zompicchiatti; Michela De Agostinis; Attilio D'Atri; Raffaela Mugittu; Santo La Valle; Cristina Di Leonardo; Valter Adamo; Silvia Smiroldo; Giovanni Del Frate; Monica Olivuzzi; Silvio Giove; Maria Parente; Daniele Bassini; Simona Melazzini; Secondo Guaschino; Francesco De Seta; Sergio Demarini; Laura Travan; Diego Marchesoni; Alberto Rossi; Giorgio Simon; Sandro Zicari; Giorgio Tamburlini
• Keywords: Bonferroni correction

Background Caesarean delivery (CD) rates are commonly used as an indicator of quality in obstetric care and risk adjustment evaluation is recommended to assess inter-institutional variations. The aim this study was evaluate whether the Ten Group classification system (TGCS) can be case-mix adjustment. Methods Standardized data on 15,255 deliveries from 11 different regional centers were prospectively collected. Crude Risk Ratios CDs calculated for each center. Two multiple logistic regression models herein considered by using: Model 1- maternal (age, Body Mass Index), variables (gestational age, fetal presentation, single or multiple, previous scar, parity, neonatal birth weight) presence factors; 2- TGCS either with without characteristics factors. Receiver Operating Characteristic (ROC) curves multivariate analyses diagnostic accuracy model. null hypothesis that Areas under ROC Curve (AUC) not other verified a Chi Square test post hoc pairwise comparisons using Bonferroni correction. Results CD showed all had significantly higher than referent. Both reduced these However two methods ranked institutions differently: model 1 2 (adjusted TGCS) identified respectively nine eight referent slightly AUCs (0.8758 0.8929 respectively). In adjusted characteristics/presence factors, three similar best AUC (0.9024). Conclusions might reliable variable adjust rates. addition factors substantially increase predictive discrimination