Identification of Predictive Factors for Patient-Reported Outcomes in the Prospective Australian Breast Device Registry
| Autor: | Ying Khu, Colin Moore, Arul Earnest, Pragya Gartoulla, Jessy Hansen, Gillian Farrell, Elisabeth Elder, Ingrid Hopper, Susannah Ahern |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
business.industry Breast Implants Australia Outcome measures General Medicine Benchmarking Logistic regression Identification (information) Humans Medicine Surgery Medical physics Patient Reported Outcome Measures Prospective Studies Registries Implant Breast reconstruction business Prospective cohort study |
| Zdroj: | Aesthetic Surgery Journal. 42:470-480 |
| ISSN: | 1527-330X 1090-820X |
| Popis: | Background Patient-reported outcome measures (PROMs) are an important tool for evaluating outcomes following breast device procedures and are used by breast device registries. PROMs can assist with device monitoring through benchmarked outcomes but need to account for demographic and clinical factors that may affect PROM responses. Objectives This study aimed to develop appropriate risk-adjustment models for the benchmarking of PROM data to accurately track device outcomes and identify outliers in an equitable manner. Methods Data for this study were obtained from the Australian Breast Device Registry, which consists of a large prospective cohort of patients with primary breast implants. The 5-question BREAST-Q implant surveillance module was used to assess PROMs at 1 year following implant insertion. Logistic regression models were used to evaluate associations between demographic and clinical characteristics and PROMs separately by implant indication. Final multivariate risk-adjustment models were built sequentially, assessing the independent significant association of these variables. Results In total, 2221 reconstructive and 12,045 aesthetic primary breast implants with complete 1-year follow-up PROMs were included in the study. Indication for operation (post-cancer, risk reduction, or developmental deformity) was included in the final model for all reconstructive implant PROMs. Site type (private or public hospital) was included in the final breast reconstruction model for look, rippling, and tightness. Age at operation was included in the reconstruction models for rippling and tightness and in the aesthetic models for look, rippling, pain, and tightness. Conclusions These multivariate models will be useful for equitable benchmarking of breast devices by PROMs to help track device performance. Level of Evidence: 2 |
| Databáze: | OpenAIRE |
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