Good overall morbidity prediction with the POSSUM scoring system in patients having a total hip or knee replacement - a prospective study in 227 patients

POSSUM评分系统对接受全髋关节或全膝关节置换术患者的总体并发症预测效果良好——一项纳入227例患者的前瞻性研究

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Abstract

PURPOSE: The Physiological and Operation Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and P (Portsmouth)-POSSUM predict the risks of complications and mortality 30 days after surgery. The purpose of this study was to evaluate the POSSUM and P-POSSUM scoring systems in patients who underwent surgery for a total hip or knee replacement. PATIENTS AND METHODS: A total of 227 patients with an elective primary total hip or knee replacement were included. The predicted postoperative morbidity was analyzed in these patients and compared with the observed value 30 days after surgery. Logistic regression analysis was used to assess the correlation of variables and outcome. RESULTS: The number of patients undergoing total hip or knee replacement was equally distributed with a mean age of 66.4±12.5 years; 57% of patients were females. Postoperative complications occurred in 49 patients, and POSSUM predicted 49 cases with an observed-over-expected ratio of 1.0. The average total POSSUM score was 27.4±4.4 in patients with complications and 26.8±3.5 in patients without complications (P=0.340). Wound infection (n=18), urinary tract infection (n=7), and pulmonary embolus (n=5) were the most common complications. The operation magnitude variable had the highest mean POSSUM score making it the most relevant variable. Age and blood loss and echocardiogram had the largest variance among the assessed variables. CONCLUSION: POSSUM accurately predicted morbidities in patients undergoing elective primary total hip or knee replacement. The risk for wound infection, urinary retention, and pulmonary embolus should be considered during hospitalization. The computerized POSSUM system provides case-mix-adjusted morbidity predictions for groups and, hence, serves as a useful tool for surgical audits and large-scale benchmarking.

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