M D Del Toro1, C Peñas2, A Conde-Albarracín3, J Palomino4, F Brun5, S Sánchez6, J Rodríguez-Baño2. 1. Unidad Clínica de Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen Macarena, CSIC, Departamento de Medicina, Universidad de Sevilla, Instituto de Biomedicina de Sevilla (IBiS), Seville, Spain. Electronic address: mdeltoro@us.es. 2. Unidad Clínica de Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen Macarena, CSIC, Departamento de Medicina, Universidad de Sevilla, Instituto de Biomedicina de Sevilla (IBiS), Seville, Spain. 3. Unidad Clínica de Cirugía Ortopédica y Traumatología, Hospital Universitario Virgen Macarena, Seville, Spain. 4. Unidad Clínica de Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen del Rocío, CSIC, Instituto de Biomedicina de Sevilla (IBiS), Seville, Spain. 5. Servicio de Medicina Interna, Hospital Universitario Puerta del Mar, Cádiz, Spain. 6. Unidad Clínica de Cirugía Ortopédica y Traumatología, Hospital Universitario Puerta del Mar, Cádiz, Spain.
Abstract
OBJECTIVES: To develop and validate baseline, perioperative and at-discharge risk-scoring systems for postsurgical prosthetic joint infection (PJI) in patients undergoing arthroplasty. METHODS: A multicentre prospective cohort study of patients undergoing hip and knee arthroplasty was performed. Patients were randomly assigned (2:1) to a derivation cohort (DC) or a validation cohort (VC). Multivariable predictive models of PJI were constructed at baseline (preoperative period), perioperative (adding perioperative variables) and at-discharge (adding wound state at discharge). The predictive ability of the models and scores was evaluated by area under the receiving operating characteristic curves (AUROC). RESULTS: The DC and VC included 2324 and 1245 patients, respectively. Baseline model included total hip arthroplasty (THA), revision arthroplasty (RA), Charlson index and obesity. The AUROC for the score was 0.75 and 0.78 in the DC and VC, respectively. Perioperative model included THA, RA, obesity, National Nosocomial Infections Surveillance (NNIS) index ≥2, significant wound bleeding and superficial surgical site infection; the AUROC was 0.81 and 0.77 in the DC and VC, respectively. The at-discharge model included THA, RA, obesity, NNIS index ≥2, superficial surgical site infection and high-risk wound; the AUROC was 0.82 and 0.84 in the DC and VC, respectively. A score ≥8 points provided 99% negative predictive values for all models. CONCLUSIONS: Simple scores for predicting PJI at three different moments of care in patients undergoing arthroplasty were developed and validated. The scores allow early and accurate identification of high-risk individuals in whom enhanced preventive measures and follow-up may be needed. Further external validation is needed.
OBJECTIVES: To develop and validate baseline, perioperative and at-discharge risk-scoring systems for postsurgical prosthetic joint infection (PJI) in patients undergoing arthroplasty. METHODS: A multicentre prospective cohort study of patients undergoing hip and knee arthroplasty was performed. Patients were randomly assigned (2:1) to a derivation cohort (DC) or a validation cohort (VC). Multivariable predictive models of PJI were constructed at baseline (preoperative period), perioperative (adding perioperative variables) and at-discharge (adding wound state at discharge). The predictive ability of the models and scores was evaluated by area under the receiving operating characteristic curves (AUROC). RESULTS: The DC and VC included 2324 and 1245 patients, respectively. Baseline model included total hip arthroplasty (THA), revision arthroplasty (RA), Charlson index and obesity. The AUROC for the score was 0.75 and 0.78 in the DC and VC, respectively. Perioperative model included THA, RA, obesity, National Nosocomial Infections Surveillance (NNIS) index ≥2, significant wound bleeding and superficial surgical site infection; the AUROC was 0.81 and 0.77 in the DC and VC, respectively. The at-discharge model included THA, RA, obesity, NNIS index ≥2, superficial surgical site infection and high-risk wound; the AUROC was 0.82 and 0.84 in the DC and VC, respectively. A score ≥8 points provided 99% negative predictive values for all models. CONCLUSIONS: Simple scores for predicting PJI at three different moments of care in patients undergoing arthroplasty were developed and validated. The scores allow early and accurate identification of high-risk individuals in whom enhanced preventive measures and follow-up may be needed. Further external validation is needed.