Objective To construct a Risk Score(RPOPS)model for Primary Osteoporosis(POP)and to evaluate its predictive value for POP.Methods A retrospective analysis was conducted on 249 subjects from the initial osteoporosis screening population in our hospital,from May 2021 to October 2023.They were divided into the normal group(n=120)and the osteoporosis group(n=129)based on BMD T-score.Their general data and peripheral blood osteoporosis related parameters including neutrophil count(N),lymphocyte count(L),neutrophil/lymphocyte ratio(NLR),platelet count(P),platelet/lymphocyte ratio(PLR),and red blood cell distribution width(RDW)were collected.The differences between the groups were compared through Nonparametric Testing,Spearman Correlation Analysis is used to analyze the correlation between various parameters.Binary Multivariate Logistic Stepwise Regression Analysis was used to predict independent high-risk factors of POP.A RPOPS prediction model for evaluating POP was established,and its receiver operating characteristic(ROC)curve was plotted to evaluate the diagnostic effectiveness of the RSPOPS prediction model.Results In 294 patients,there was significant difference between groups(All P value<0.001),except for RDW and PLR(All P>0.05).The correlation analysis showed that N and NLR(r=0.585,P=0.000),L and NLR(r=-0.594,P=0.000).It was found that age,BMI,PLT,NLR,t-PINP,and gender were independent risk factors for the occurrence of POP by stepwise Logistic Regression Analysis(P<0.05 for all).A model RSPOPS=-1.658+0.080×Age+0.921×Gender-0.220×BMI-0.008×PLT+1.053×NLR+0.045×t-PINP was established.The goodness of fit of the Hosmer Lemeshow test model was good(P=0.530).When the ideal cutoff value of the RPOPS model was 0.04,the area under the curve(AUC)was 0.905,and the predicted sensitivity and specificity were 81.4%and 84.2%,respectively.Conclusion The RPOPS model is reasonably constructed.It is simple and feasible,and suitable for predicting and screening POPs in grassroots hospitals.
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