Predicting treatment failure risk in a Chinese Drug-Resistant Tuberculosis with surgical therapy: Development and assessment of a new predictive nomogram.

BACKGROUND

The aim of this study was to develop and internally validate a treatment failure risk nomogram in a Chinese population of patients with Drug-Resistant Tuberculosis with surgical therapy.

METHODS

We developed a prediction model based on a dataset of 132 drug-resistant tuberculosis (DR-TB) patients. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the treatment failure risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.

FINDINGS

Predictors contained in the prediction nomogram included Lesion, Treatment history, Recurrent chest infection (RCI) and Multidrug-resistant tuberculosis (MDR-TB) or Extensively drug-resistant tuberculosis (XDR-TB). The model displayed good discrimination with a C-index of 0.905 and good calibration. A high C-index value of 0.876 could still be reached in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when an intervention was decided at the treatment failure possibility threshold of 1%.

INTERPRETATION

This study developed a novel nomogram with relatively good accuracy to help clinicians access the risk of treatment failure in MDR/XDR-TB patients when starting surgery. With an estimate of individual risk, clinicians and patients can make more suitable decisions regarding surgery. This nomogram requires external validation, and further research is needed to determine whether the nomogram is appropriate for predicting surgery risk in MDR/XDR-TB patients.