分析某地区多重耐药性肺结核治疗情况，构建治疗依从性的预测模型。

选取2019年3月至2022年12月我院收治的148例多重耐药性肺结核患者为对象，Morisky药物依从性量表(Morisky medication adherence scale, MMAS)<6分39例为观察组，MMAS 6～8分109例为对照组，对比两组人口社会学资料和临床资料，多因素Logistic回归分析治疗依从性的影响因素，构建多重耐药性肺结核治疗依从性不良的列线图预测模型。

痰培养81例(54.73%)转阴，MMAS评分为(6.15±1.03)分，67例(45.27%)未转阴，MMAS评分为(5.76±1.05)分(P<0.05)。观察组年龄[(51.05±8.23) vs. (46.18±7.59)]岁和服药种数(>6种)[27例(69.23%)vs. 52例(47.71%)]及服药后不良反应[12例(30.77%)vs. 16例(14.68%)]高于对照组，受教育年限[(9.65±2.62) vs. (11.20±3.17)]年、个人平均月收入(<3 000元)[19例(48.72%)vs. 21例(19.27%)]、SSRS评分[(35.15±4.02) vs. (40.36±6.18)]分和疾病宣教比[18例(46.15%)vs. 71例(65.14%)]低于对照组(P<0.05)。多因素Logistic回归分析显示：年龄(OR＝1.158)、受教育年限(OR＝0.815)、个人平均月收入(<3 000元)(OR＝27.408)和SSRS评分(OR＝0.860)是多重耐药性肺结核治疗依从性的影响因素(P<0.05)。列线图预测模型显示：年龄增加5岁，列线图模型评分增加9分；受教育年限减少2年，列线图模型评分增加5分；个人平均月收入<3 000元对应列线图评分27.5分；SSRS评分降低5分，列线图模型增加13.5分。ROC曲线结果显示AUC为0.871(95%CI：0.811～0.931)；校准曲线斜率和Hosmer-Lemeshow拟合优度检验(χ²＝7.347，P＝0.500)显示模型预测多重耐药性肺结核治疗依从性不良的发生风险一致性良好。

多重耐药性肺结核患者治疗6个月后转阴54.73%；年龄增大和受教育年限、个人平均月收入及社会支持水平低是治疗依从性不良的危险因素，列线图模型可用于临床预估治疗依从性不良风险，具有临床意义。

To analyze the treatment situation of multi-drug resistant pulmonary tuberculosis in a certain area, and to build a prediction model for the treatment compliance of such patients.

All of 148 patients with multidrug-resistant pulmonary tuberculosis admitted to our hospital from March 2019 to December 2022 were selected as the research object, and the treatment status of the patients was followed up for 6 months. 39 patients with Morisky medication adherence scale (MMAS) <6 points were the observation group, and 109 patients with MMAS 6-8 points were the control group, the influencing factors of treatment compliance were confirmed by multivariate Logistic regression equation, which was used to construct a nomogram prediction model of poor treatment compliance of patients with drug-resistant tuberculosis.

Sputum culture in 81 cases (54.73%) turned negative, MMAS score was (6.15±1.03), 67 cases (45.27%) did not turn negative, MMAS score was (5.76±1.05) points (P<0.05). The age of the observation group [(51.05±8.23) vs. (46.18±7.59)] years, the number of drug types (>6) [27 cases (69.23%) vs. 52 cases (47.71%)] and the adverse reactions after medication [12 cases (30.77%) vs. 16 cases (14.68%)] were higher than those of the control group. Years of education [(9.65±2.62) vs. (11.20±3.17)], average monthly personal income (<3 000 yuan) [19 cases (48.72%) vs. 21 cases (19.27%)], SSRS score [(35.15±4.02) vs. (40.36±6.18)] and the proportion of disease education [18 cases (46.15%) vs. 71 cases (65.14%)] were lower than those in the control group (P<0.05). Multivariate Logistic regression coefficient analysis showed that age (OR=1.158), years of education (OR=0.815), personal average monthly income (<3, 000 yuan) (OR=27.408) and SSRS score (OR=0.860) were independent influencing factors of treatment compliance of patients with multidrug-resistant pulmonary tuberculosis (P<0.05). The nomogram prediction model shows that the score of nomogram model increases by 9 points every time the patient′s age increases by 5 years; For every 2 years of education, the score of nomogram model will increase by 5 points; The nomogram score corresponding to an average monthly income of less than 3, 000 yuan is 27.5 points; For every 5 points of SSRS score reduction, the nomogram model increases by 13.5 points. The ROC curve shows that the AUC of the nomogram prediction model is 0.871 (95%CI: 0.811-0.931). The slope of calibration curve and Hosmer-Lemeshow goodness-of-fit test (χ²=7.347, P=0.500) showed that the nomogram model predicted the risk of treatment noncompliance in patients with multi-drug-resistant tuberculosis and the actual risk was in good agreement.

After 6 months of treatment, 54.73% of patients with multi-drug-resistant pulmonary tuberculosis turned negative. Age, years of education, average monthly income and low level of social support are the independent risk factors of patients′poor treatment compliance. The nomogram model of patients′poor treatment compliance formed by integrating these risk factors can be applied to predict the risk of patients′poor treatment compliance in clinic and provide value for improving the treatment effect of diseases.