Abstract: BACKGROUND: The patient underwent multiple hypoproteinemia after total hip arthroplasty, which affected postoperative healing and rehabilitation.
OBJECTIVE: To investigate and screen the risk factors for hypoproteinemia after total hip arthroplasty, and to establish a nomogram prediction model so as to provide guidance for judging whether hypoproteinemia occurs after total hip arthroplasty.
METHODS: A total of 355 patients who underwent total hip arthroplasty were included, and according to whether hypoproteinemia occurred on the first day after surgery, they were divided into 238 cases in the hypoproteinemia group and 117 cases in the normal group, with a hypoproteinemia rate of 67%. Data were collected, including age, gender, diabetes mellitus, hypertension, hyperuricemia, hyperlipidemia, anesthesia method, preoperative leukocytes, preoperative erythrocytes, preoperative hemoglobin, preoperative platelets, preoperative plasma prothrombin time, preoperative activated partial prothrombin time, preoperative international normalized ratio, preoperative thrombin time, preoperative fibrinogen, preoperative erythrocyte sedimentation rate, preoperative C-reactive protein, preoperative D-dimer,  preoperative mean corpuscular hemoglobin content,  preoperative mean corpuscular volume, operation time, body mass index,  preoperative procalcitonin, and preoperative hematocrit. SPSS 27.0 software was used for univariate analysis, followed by R language (4.3.1) to perform least absolute shrinkage and selection operator regression and 10-fold cross-validation of the observation indicators to obtain the intersection of the two risk factors. SPSS 27.0 software was used to perform multivariate binary logistic regression to obtain the final risk factors. The prediction model of hypoproteinemia after total hip arthroplasty was constructed by R language. The receiver operating characteristic curve, calibration curve, and clinical decision curve were constructed to assess the predictive model predictive ability.
RESULTS AND CONCLUSION: (1) Univariate analysis, least absolute shrinkage and selection operator regression, and multivariate logistic regression were used to screen out significant differences in age (OR=1.024, P=0.023), preoperative platelets (OR=0.995, P=0.028), and preoperative erythrocyte sedimentation rate (OR=1.031, P=0.045) in judging whether hypoproteinemia would occur after surgery (P < 0.05). (2) The nomogram prediction model was constructed based on the final risk factors screened by multivariate Logistic regression, and the prediction ability of the model was evaluated by constructing the receiver operating characteristic curve, and the area under the calculated receiver operating characteristic curve reached 0.835 (95%CI=0.779-0.891), C-index=0.835. A threshold of 0-0.83 could bring better clinical efficacy calculated by the decision curve analysis. The model has good sensitivity and accuracy, which can better identify the risk of postoperative hypoproteinemia for medical staff and patients before total hip arthroplasty.

中国组织工程研究杂志出版内容重点：人工关节；骨植入物；脊柱；骨折；内固定；数字化骨科；组织工程

Key words: total hip arthroplasty, hypoproteinemia, risk factor, predictive model, nomogram