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Predicting proliferative vitreoretinopathy: temporal and external validation of models based on genetic and clinical variables
  1. Jimena Rojas1,
  2. Itziar Fernandez2,
  3. Jose C Pastor1,2,3,
  4. Robert E MacLaren4,
  5. Yashin Ramkissoon4,
  6. Steven Harsum4,
  7. David G Charteris4,
  8. Jan C Van Meurs5,
  9. Sankha Amarakoon5,
  10. Jose Garcia-Arumi6,
  11. Jose M Ruiz-Moreno7,
  12. Amandio Rocha-Sousa8,
  13. Maria Brion9,10,
  14. Angel Carracedo9,10,
  15. for the Genetics on PVR Study Group (web file)
  1. 1IOBA (Eye Institute), University of Valladolid, Valladolid, Spain
  2. 2Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (Ciber BBN), Valladolid, Spain
  3. 3Clinic University Hospital of Valladolid, Valladolid, Spain
  4. 4Moorfields Eye Hospital and UCL Institute of Ophthalmology NIHR Biomedical Research Centre, London, UK
  5. 5The Rotterdam Eye Hospital and Erasmus University Medical Center, Rotterdam, The Netherlands
  6. 6Valle de Hebron University Hospital, Barcelona, Spain
  7. 7VISSUM, Alicante, University of Castile-La Mancha, Albacete, Spain
  8. 8Department of Senses Organs, Faculty of Medicine, University of Porto, São João Hospital, Porto, Portugal
  9. 9Medicina Xenómica, Complexo Hospitalario Universitario de Santiago, IDIS, Santiago de Compostela, Spain
  10. 10University of Santiago de Compostela, Galician Public Foundation for Genomic Medicine, CIBERER, Santiago de Compostela, Spain
  1. Correspondence to Jimena Rojas, Paseo de Belén 17, Campus Universitario Miguel Delibes, Edificio IOBA, Valladolid CP 47007, Spain; jimena{at}ioba.med.uva.es

Abstract

Purpose To validate three models for predicting proliferative vitreoretinopathy (PVR) based on the analysis of genotypic data and relevant clinical characteristics.

Methods The validation series consisted of data from 546 patients operated on from primary rhegmatogenous retinal detachment (RRD) coming from centres in the Netherlands, Portugal, Spain and the UK. Temporal and geographical validation was performed. The discrimination capability of each model was analysed and compared with the original series, using a receiver operating curve. Then, clinical variables were combined in order to improve the predictive capability. A risk reclassification analysis was performed with and without each one of the variables. Reclassification of patients was compared and models were readjusted in the original series. Readjusted models were further validated.

Results One of the models showed good predictability in the temporal sample as well as in the original series (area under the curve (AUC) original=0.7352; AUC temporal=0.6457, 95% CI 50.17 to 78.97). When clinical variables were included, only pre-existent PVR improves the predictability of this model in the validation series (temporal and geographical samples) (AUC original=0.7940 vs AUC temporal=0.7744 and AUC geographical=0.7152). The other models showed acceptable AUC values when clinical variables were included although they were less accurate than in the original series.

Conclusions Genetic profiling of patients with RRD can improve the predictability of PVR in addition to the well-known clinical biomarkers. This validated formula could be a new tool in our current clinical practice in order to identify those patients at high risk of developing PVR.

  • Diagnostic tests/Investigation
  • Genetics
  • Retina

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