Prediction Model and Novel Classification System for Complications after Minimally Invasive Hysterectomy.

Andrea Stuart^1,2, Karin Källen^3,4, Michael Conditt⁵, Hanna Källen Murray⁶, Jan Baekelandt^7,8

¹Institute of Clinical Sciences, Department of Obstetrics and Gynecology, Lund University, Lund, Sweden, andrea.stuart@med.lu.se.

²Department of Obstetrics and Gynecology, Helsingborg Hospital, Helsingborg, Sweden, andrea.stuart@med.lu.se.

³Institute of Clinical Sciences, Department of Obstetrics and Gynecology, Lund University, Lund, Sweden.

⁴Center for Reproductive Epidemiology, Tornblad Institutet, Lund University, Lund, Sweden.

⁵Momentis Surgical, Yoni Netanyahu 6, Or Yehuda, Israel.

⁶Savantic AB, Stockholm, Sweden.

⁷Department of Obstetrics and Gynecology, Imelda Hospital, Bonheiden, Belgium.

⁸Department of Development and Regeneration, Leuven University, Leuven, Belgium.

PubMed PMC DOI PDF

Abstract

OBJECTIVES: The aim of the study was to develop and validate a novel model for predicting complications in minimally invasive hysterectomy and to define a standardized classification system, the Hysterectomy Complication Classes (HCCs). DESIGN: This is a retrospective cohort study using merged data from four national Swedish registries. Data were randomly split into development and validation dat…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.