Liu Libin, Wang Chuyan, Mei Bin, Wang Jing, Xu Xiaoqun, Zhou Hongjuan, Cai Long
Centre of Laboratory Medicine, Hangzhou Red Cross Hospital, Hangzhou, Zhejiang, People's Republic of China.
Department of Clinical Laboratory, The Third People's Hospital of Lin'an District, Hangzhou, Zhejiang, People's Republic of China.
Infect Drug Resist. 2024 Aug 26;17:3701-3713. doi: 10.2147/IDR.S473027. eCollection 2024.
This study aimed to establish and validate a diagnostic nomogram for identifying false positives in the Xpert MTB/RIF (Xpert) for detection of rifampicin resistance (RIF-R).
In this retrospective study, we collected basic patient characteristics and various clinical information from the electronic medical record database. Patients were randomly divided into training and validation groups in a 7:3 ratio. LASSO regression was used to screen variables and construct a diagnostic nomogram. The ROC curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.
A total of 384 patients were included in the study, with 268 and 116 patients in the training and validation cohorts, respectively. Finally, probe mutations and probe delay were identified as the independent influencing factors. Using the mutation of probe E as a reference, probes A or C (OR = 51.07, <0.001), probe D (OR = 7.48, <0.001), and multiple probes (OR = 4.42, =0.029) were identified as factors influencing false positives in Xpert for detection of RIF-R. Taking probe delay ΔCT <4 as a reference, ΔCT (4-5.9) (OR = 17.06, =0.005) and ΔCT (6-7.9) (OR = 36.67, <0.001) were noted to be the factors influencing false positives in Xpert for detection of RIF-R. Based on these two variables, we constructed a diagnostic nomogram. The area under the curve of the nomogram model was 0.847 and 0.850 for the training and validation groups, respectively. The calibration curves were consistent. The DCA revealed that the model achieved the greatest net benefit when the threshold probability was set between 6% and 71% in the training cohort and 6% and 70% in the validation cohort.
The nomogram constructed can identify false positives in Xpert for detection of RIF-R and provides basis for clinicians to formulate diagnosis and treatment plans.
本研究旨在建立并验证一种诊断列线图，用于识别Xpert MTB/RIF（Xpert）检测利福平耐药（RIF-R）时的假阳性结果。
在这项回顾性研究中，我们从电子病历数据库收集了患者的基本特征和各种临床信息。患者按7:3的比例随机分为训练组和验证组。采用LASSO回归筛选变量并构建诊断列线图。使用ROC曲线、校准曲线和决策曲线分析（DCA）来评估列线图的性能。
本研究共纳入384例患者，训练队列和验证队列分别有268例和116例患者。最终，探针突变和探针延迟被确定为独立影响因素。以探针E的突变作为参考，探针A或C（OR = 51.07，<0.001）、探针D（OR = 7.48，<0.001）以及多个探针（OR = 4.42，=0.029）被确定为影响Xpert检测RIF-R时假阳性的因素。以探针延迟ΔCT <4作为参考，ΔCT（4 - 5.9）（OR = 17.06，=0.005）和ΔCT（6 - 7.9）（OR = 36.67，<0.001）被指出是影响Xpert检测RIF-R时假阳性的因素。基于这两个变量，我们构建了一个诊断列线图。列线图模型在训练组和验证组的曲线下面积分别为0.847和0.850。校准曲线一致。DCA显示，当训练队列的阈值概率设定在6%至71%之间，验证队列的阈值概率设定在6%至70%之间时，该模型实现了最大净效益。
构建的列线图可识别Xpert检测RIF-R时的假阳性结果，为临床医生制定诊断和治疗方案提供依据。
