IntroductionThe Swedish National Quality Register in Gynecological Surgery (GynOp) is widely used in gynecological and obstetric departments throughout Sweden. With a coverage rate of 91%-98%, it collects a substantial amount of data, fulfilling a crucial role in quality assurance and research. This study aimed to assess the internal validity of key variables within the GynOp register.Material and MethodsA multicenter, single-blinded study was conducted across 10 healthcare providers. Data were collected for 150 patients at each center, with 25 patients selected from each sub-register, totaling 1500 patients. Source data were extracted from medical records and compared to the data in the GynOp register for validation. Agreement was evaluated by calculating the exact agreement and the within-interval agreement using Pearson correlation (0-1) for numerical variables and Cohen's kappa (0-1) for ordinal variables.ResultsThe primary finding was that the overall validity of key variables in the GynOp register was high when compared with source data. For example, the correlation between source data and GynOp register data was 1.00 for uterine weight and 0.97 for date of delivery, and exact agreement was 97% for grade of perineal rupture and 100% for pathological-anatomical diagnosis. Some variables such as ASA-classification, genital prolapse stage, and devices for intrauterine surgery showed moderate agreement.ConclusionsThe comparison between the GynOp register and source data demonstrates strong internal validity. This study has identified several areas for improvement, leading to the initiation of an update in the registration module of the sub-register for intrauterine surgery. Additionally, certain categorical variables, such as ASA classification and prolapse stage, should be grouped and presented as dichotomized data to enhance data quality. In the future, integrating extraction of data from the Swedish Perioperative Register (SPOR) and providing education to urogynecologists on the POP-Q system could further contribute to the improvement of data quality.