Construction of wound drainage tube time delay prediction model for patients with breast cancer
10.3760/cma.j.cn211501-20220218-00431
- VernacularTitle:乳腺癌术后患者伤口引流拔管时间延迟预测模型的构建
- Author:
Xinjie JIA
1
;
Qing WANG
;
Miao WANG
;
Xin HE
Author Information
1. 天津医科大学肿瘤医院乳腺二科 国家恶性肿瘤临床医学研究中心 天津市肿瘤防治重点实验室 天津市恶性肿瘤临床医学研究中心 乳腺癌防治教育部重点实验室，天津 300060
- Keywords:
Breast neoplasms;
Risk factors;
Surgical wound;
Drainage;
Forecasting;
Extubation time
- From:
Chinese Journal of Practical Nursing
2023;39(2):101-106
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the independent influencing factors of wound drainage tube time delay in patients with breast cancer and establish a predictive model.Methods:Patients admitted to Tianjin Medical University Cancer Hospital from January to November 2021 were selected as the research objects. They were divided into sword modeling group (156 cases) and verification group (86 cases) according to the admission time. Delayed time to postoperative wound drainage and extubation in breast cancer patients was the end point, 95 cases of 156 patients in the modeling group whose extubation time was less than or equal to 7 days were set as the normal group, 61 cases whose extubation time were more than 7 days were set as delayed group, and the influencing factors of the two groups were compared to establish the prediction model, Hosmer-Lemeshow test was conduct to verify the fitting effect, used the ROC curve to verify the prediction model performance.Results:Univariate and multivariate Cox proportional hazards regression analysis showed that patients' high BMI, related basic disease history, operation mode, axillary lymph node dissection, breast tumor size (T3, T4) and drainage fluid volume 48 hours (≥50 ml) after operation were independent influencing factors for wound drainage tube time delay ( P<0.05). The prediction model was P=0.822, and the area under the ROC curve was 0.877, and the Youden index was 0.605, the sensitivity was 0.736, and the specificity was 0.869. The research data of 86 cases in the validation group were used as the test set for internal and external validation of the model, and the model verification was 96.51%. Conclusion:This prediction model has a good effect, providing a reference basis for clinical medical workers.
