Construction and validation of a risk prediction model for sarcopenia in colorectal cancer patients
Objective:To investigate the incidence of sarcopenia in postoperative colorectal cancer patients and its influencing factors,to construct a prediction model for sarcopenia and to complete the validation.Methods:A total of 450 colorectal cancer patients admitted between January 2022 and December 2023 were prospectively enrolled in this study.The incidence and influencing factors of sarcopenia were analyzed statistically.A risk prediction model for sarcopenia was constructed using logistic regression,displayed and validated with a nomogram.Results:In this study,a total of 315 colorectal cancer patients were included in the modeling group,ranging in age from 37 to 89 years old,with an average age of(67.09±9.06)years old.Among these,144 were females(45.71%)and 171 were males(54.29%).Sarcopenia was present in 72 patients(22.86%).Univariate analysis identified tumor stage,smoking history,drinking history,combined diabetes,nutritional status and physical exercise as independent risk factors for sarcopenia in colorectal cancer patients(P<0.05).Logistic regression analysis confirmed that Tumor stage,smoking history,drinking history and diabetes mellitus were risk factors for sarcopenia in colorectal cancer patients(P<0.05),while nutritional status and physical exercise were protective factors for sarcopenia in colorectal cancer patients(P<0.05).In the verification group,the area under the curve(AUC)was 0.880(95%CI:0.823,0.933),Yoden index was 0.613,and sensitivity and specificity were 87.4%and 73.9%,respectively.Hosmer-Lemeshow goodness of fit test indicated χ2=4.664,P=0.795.Conclusion:This study established a risk prediction model for sarcopenia in colorectal cancer patients.Tumor stage,smoking history,drinking history,diabetes mellitus,nutritional status and physical exercise were influencing factors for sarcopenia in colorectal cancer patients.The predictive model can assist clinical nurses in identifying high-risk patients and implementing timely interventions.
Colorectal cancerSarcopeniaNomogramRisk prediction model
NETL
NSTL
万方数据
