A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis

Abstract

Purpose: This study aimed to develop and validate a scoring system based on a nomogram of common clinical metrics to discriminate between active pulmonary tuberculosis (APTB) and inactive pulmonary tuberculosis (IPTB).

Patients and methods: A total of 1096 patients with pulmonary tuberculosis (PTB) admitted to Wuhan Jinyintan Hospital between January 2017 and December 2019 were included in this study. Of these patients with PTB, 744 were included in the training cohort (70%; 458 patients with APTB, and 286 patients with IPTB), and 352 were included in the validation cohort (30%; 220 patients with APTB, and 132 patients with IPTB). Data from 744 patients from the training cohort were used to establish the diagnostic model. Routine blood examination indices and biochemical indicators were collected to construct a diagnostic model using the nomogram, which was then transformed into a scoring system. Furthermore, data from 352 patients from the validation cohort were used to validate the scoring system.

Results: Six variables were selected to construct the prediction model. In the scoring system, the mean corpuscular volume, erythrocyte sedimentation rate, albumin level, adenosine deaminase level, monocyte-to-high-density lipoprotein ratio, and high-sensitivity C-reactive protein-to-lymphocyte ratio were 6, 4, 7, 5, 5, and 10, respectively. When the cut-off value was 15.5, the scoring system for recognizing APTB and IPTB exhibited excellent diagnostic performance. The area under the curve, specificity, and sensitivity of the training cohort were 0.919, 84.06%, and 86.36%, respectively, whereas those of the validation cohort were 0.900, 82.73, and 86.36%, respectively.

Conclusion: This study successfully constructed a scoring system for distinguishing APTB from IPTB that performed well.

Keywords: Active pulmonary tuberculosis; differential diagnosis; inactive pulmonary tuberculosis; nomogram; scoring system.

Figure 1

Flowchart of participant selection and the performance of the steps (A); Flowchart of the diagnostic criteria for APTB and IPTB (B). APTB, active pulmonary tuberculosis; IPTB, inactive pulmonary tuberculosis; NTM, non-tuberculous mycobacteria; DCA, decision curve analysis; ROC, receiver operating characteristic. AUC, area under the curve; DR, drug resistance.

Figure 2

The performance of parameters in the training cohort. (A) The comparison between APTB and IPTB in the training cohort. The values represented the median after normalization to range between 0 and 1. *P < 0.05 and **P < 0.001; (B) The ROC analysis for significant parameters in univariate logistic regression analyses. Curves in the upper indicated that the levels of these indicators are higher in APTB than in IPTB. Curves in the bottom indicated that the levels of these indicators are lower in APTB than in IPTB.

Figure 3

Calibration and clinical use of a diagnostic nomogram for the identification of APTB and IPTB. (A) Diagnostic nomogram for discriminating APTB from IPTB. (B) The ROC analyses for the Diagnostic model. (C) Calibration curve of the diagnostic nomogram. (D) DCA of the diagnostic nomogram.

Figure 4

Discrimination and calibration of the scoring system for discrimination of APTB and IPTB. ROC curves of the scoring system in the training cohort (A) and validation cohort (B). Calibration curves of the scoring system in the training cohort (C) and validation cohort (D).