Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity.

The present study aims to assess the treatment outcome of patients with diabetes and tuberculosis (TB-DM) at an early stage using machine learning (ML) based on electronic medical records (EMRs). A total of 429 patients were included at Chongqing Public Health Medical Center. The random-forest-based Boruta algorithm was employed to select the essential variables, and four models with a fivefold cross-validation scheme were used for modeling and model evaluation. Furthermore, we adopted SHapley additive explanations to interpret results from the tree-based model. 9 features out of 69 candidate features were chosen as predictors. Among these predictors, the type of resistance was the most important feature, followed by activated partial throm-boplastic time (APTT), thrombin time (TT), platelet distribution width (PDW), and prothrombin time (PT). All the models we established performed above an AUC 0.7 with good predictive performance. XGBoost, the optimal performing model, predicts the risk of treatment failure in the test set with an AUC 0.9281. This study suggests that machine learning approach (XGBoost) presented in this study identifies patients with TB-DM at higher risk of treatment failure at an early stage based on EMRs. The application of a convenient and economy EMRs based on machine learning provides new insight into TB-DM treatment strategies in low and middle-income countries.

Development, assessment and validation of a novel prediction nomogram model for risk identification of tracheobronchial tuberculosis in patients with pulmonary tuberculosis.

OBJECTIVE: Tracheobronchial tuberculosis (TBTB), a specific subtype of pulmonary tuberculosis (PTB), can lead to bronchial stenosis or bronchial occlusion if not identified early. However, there is currently no available means for predicting the risk of associated TBTB in PTB patients. The objective of this study was to establish a risk prediction nomogram model for estimating the associated TBTB risk in every PTB patient. METHODS: A retrospective cohort study was conducted with 2153 PTB patients. Optimised characteristics were selected using least absolute shrinkage and selection operator regression. Multivariate logistic regression was applied to build a predictive nomogram model. Discrimination, calibration and clinical usefulness of the prediction model were assessed using C-statistics, receiver operator characteristic curves, calibration plots and decision analysis. The developed model was validated both internally and externally. RESULTS: Among all PTB patients who underwent bronchoscopies (n=2153), 40.36% (n=869) were diagnosed with TBTB. A nomogram model incorporating 11 predictors was developed and displayed good discrimination with a C-statistics of 0.782, a sensitivity of 0.661 and a specificity of 0.762 and good calibration with a calibration-in-the-large of 0.052 and a calibration slope of 0.957. Model's discrimination was favourable in both internal (C-statistics, 0.782) and external (C-statistics, 0.806) validation. External validation showed satisfactory accuracy (sensitivity, 0.690; specificity, 0.804) in independent cohort. Decision curve analysis showed that the model was clinically useful when intervention was decided on at the exacerbation possibility threshold of 2.3%-99.2%. A clinical impact curve demonstrated that our model predicted high-risk estimates and true positives. CONCLUSION: We developed a novel and convenient risk prediction nomogram model that enhances the risk assessment of associated TBTB in PTB patients. This nomogram can help identify high-risk PTB patients who may benefit from early bronchoscopy and aggressive treatment to prevent disease progression.

Association Between Promoter Methylation of Vitamin D Metabolic Pathway Genes and Tuberculosis and Diabetes Comorbidity in a Chinese Han Population: A Case-Control Study.

Background: Deficiency vitamin D and hyperglycemia could be related to weakened innate immune response and aggravate the progression of tuberculosis (TB). This study hypothesized that DNA promoter methylation of the pivotal genes in the vitamin D metabolic pathway might be related to diabetes and tuberculosis co-morbidity (TB-DM) susceptibility. Methods: A total of 50 TB-DM and 50 healthy subjects (HS) were included in the present study. Targeted bisulfite sequencing was applied to detect the methylation of the promoter regions of candidate genes in the vitamin D metabolic pathway (CYP24A1, CYP27B1, CYP2R1, DHCR7, and VDR) in whole blood. Results: The overall methylation level of candidate genes in this study was lower in patients with TB-DM than HS, except for CYP2R1. The results of the ROC demonstrated the potential of CYP24A1, CYP27B1, DHCR7, and VDR promoter methylation as a biomarker for diagnosing TB-DM, with all the AUC above 0.7. In subgroup analysis, we found that lower circulating vitamin D is related to a low level of CYP24A1, CYP27B1, and DHCR7 promoter methylation in patients with TB-DM. With decreasing methylation level, risk of TB-DM was significantly increased (odds ratio, 95% CI 0.343, 0.144-0.821 for CYP24A1; 0.461, 0.275-0.773 for CYP27B1; 0.09, 0.015-0.530 for DHCR7; 0.006, 0.0003-0.115 for VDR). Besides, our results revealed that there was a significant correlation between DNA promoter methylation of selected genes in the vitamin D metabolic pathway and platelet indices in TB-DM. However, there was no correlation between DNA methylation of the four genes and fasting glucose and HbA1c. Conclusion: Our results could suggest that the selected genes in the vitamin D metabolic pathway may be involved in the pathological process of TB-DM, but independent of the process of hyperglycemia to impaired immune responses to Mtb.

Diagnosis of pulmonary tuberculosis via identification of core genes and pathways utilizing blood transcriptional signatures: a multicohort analysis.

BACKGROUND: Blood transcriptomics can be used for confirmation of tuberculosis diagnosis or sputumless triage, and a comparison of their practical diagnostic accuracy is needed to assess their usefulness. In this study, we investigated potential biomarkers to improve our understanding of the pathogenesis of active pulmonary tuberculosis (PTB) using bioinformatics methods. METHODS: Differentially expressed genes (DEGs) were analyzed between PTB and healthy controls (HCs) based on two microarray datasets. Pathways and functional annotation of DEGs were identified and ten hub genes were selected. They were further analyzed and selected, then verified with an independent sample set. Finally, their diagnostic power was further evaluated between PTB and HCs or other diseases. RESULTS: 62 DEGs mostly related to type I IFN pathway, IFN-γ-mediated pathway, etc. in GO term and immune process, and especially RIG-I-like receptor pathway were acquired. Among them, OAS1, IFIT1 and IFIT3 were upregulated and were the main risk factors for predicting PTB, with adjusted risk ratios of 1.36, 3.10, and 1.32, respectively. These results further verified that peripheral blood mRNA expression levels of OAS1, IFIT1 and IFIT3 were significantly higher in PTB patients than HCs (all P < 0.01). The performance of a combination of these three genes (three-gene set) had exceeded that of all pairwise combinations of them in discriminating TB from HCs, with mean AUC reaching as high as 0.975 with a sensitivity of 94.4% and a specificity of 100%. The good discernibility capacity was evaluated d via 7 independent datasets with an AUC of 0.902, as well as mean sensitivity of 87.9% and mean specificity of 90.2%. In regards to discriminating PTB from other diseases (i.e., initially considered to be possible TB, but rejected in differential diagnosis), the three-gene set equally exhibited an overall strong ability to separate PTB from other diseases with an AUC of 0.999 (sensitivity: 99.0%; specificity: 100%) in the training set, and 0.974 with a sensitivity of 96.4% and a specificity of 98.6% in the test set. CONCLUSION: The described commonalities and unique signatures in the blood profiles of PTB and the other control samples have considerable implications for PTB biosignature design and future diagnosis, and provide insights into the biological processes underlying PTB.

Association of TyG Index with CT Features in Patients with Tuberculosis and Diabetes Mellitus.

BACKGROUND: The purpose of this study was to investigate the association of the triglyceride glucose (TyG) index, a surrogate marker of insulin resistance (IR) with a high sensitivity of 96.5% and a specificity of 85.0% for the diagnosis of IR, with computed tomography (CT) features in patients with tuberculosis and diabetes mellitus. METHODS: A total of 247 subjects were enrolled from July, 2020 to May, 2021. The basic clinical features and CT features were analyzed. In addition, multivariate logistic regression analysis models were employed to evaluate the association of the TyG indicator with CT features in participants. RESULTS: In the quartile groups of TyG index, air bronchial sign detection rate was 11.7%, 14.5%, 23.2%, and 44.1%; large segmented leafy shadow detection rate was 27.9%, 40.6%, 46.4%, and 66.2%; thick-walled cavity was found in 38.2%, 43.4%, 57.9%, and 69.1%; the rate of multiple cavities was 17.6%, 27.5%, 36.2%, 52.9%; the rate of lymph node enlargement was 22.1%, 17.4%, 28.9%, and 38.2%, respectively. In addition, the positive relation with the TyG index and the prevalence of abnormal CT signs was observed in the fully adjusted model: TyG, per one-unit increase: air bronchial sign: adjusted odds ratio (AOR) 3.92, 95% CI 1-15.35, P = 0.049; multiple cavities: AOR 4.1, 95% CI 1.26-13.31, P = 0.019; thick-walled cavity: AOR 2.89, 95% CI 1.05-8.03, P = 0.041. In quartile of TyG index, compared with patients in quartile 1, the AOR (95% CI) values for air bronchial sign in quartile 4 was 8.1 (1.7-44), p = 0.011; multiple cavities was 7.1 (1.7-32), p = 0.008; thick-walled cavity was 7.8 (1.9-34.7), p = 0.005. CONCLUSION: The present study showed that an increased TyG index was positively related to the severity of patients with T2DM-PTB.

Incidence, laboratory diagnosis and predictors of tracheobronchial tuberculosis in patients with pulmonary tuberculosis in Chongqing, China.

Tracheobronchial tuberculosis (TBTB) is reported in 10-40% of patients with pulmonary tuberculosis (PTB). Due to its non-specific presentation, the diagnosis and management are frequently delayed. The aim of the present study was to investigate the incidence, predictors and laboratory diagnosis of concomitant TBTB and PTB in Chongqing, China. Bronchoscopy was performed in all patients with newly diagnosed or relapsed PTB in order to detect TBTB between January 2018 and April 2019 in a sub-tertiary hospital in Chongqing, China. The clinical characteristics and laboratory data were analyzed to identify predictors and determine the diagnostic yield of TBTB. A total of 341 (31.4%) of the 1,085 patients with PTB who underwent the bronchoscopic examination presented with concomitant TBTB. The parameters of female sex [odds ratio (OR)=2.57], clinical symptoms (OR=6.26) and atelectasis (OR=4.3) were independent predictors of TBTB. Cough (OR=32.48) and atelectasis (OR=3.14) were independent predictors of TBTB-associated tracheobronchial stenosis. The diagnostic yields of sputum smear, bronchial brush smear, sputum culture, GeneXpert Mycobacterium tuberculosis/rifampicin resistance (GX) using sputum, GX using brushings and in bronchial brush culture used for the diagnosis of TBTB were 44.2, 44.2, 63.5, 57.7, 71.2 and 75%, respectively. GX brushings had higher diagnostic yields compared with sputum or brush smears; however, there was no significant difference between sputum/brushings cultures and GX with sputum. The incidence of TBTB in PTB was 31.4% in Chongqing, China. The parameters of female sex, atelectasis and cough were the major predictors of concomitant TBTB and associated tracheobronchial stenosis. Although GX is an accurate and rapid test to detect TBTB, additional laboratory techniques should also be adopted to improve diagnostic yields in the detection of TBTB in patients with PTB.

Clinical and Epidemiological Characteristics of COVID-19 Patients in Chongqing China.

Objectives: To study in-depth the clinical and epidemiological characteristics of pneumonia resulting from COVID-19 and provide evidence for effective public health decisions. Methods: This was a retrospective, single-center research study. Participants were enrolled from patients presenting at the Chongqing Public Health Medical Treatment Center from Jan 24 to Feb 7, 2020, and were confirmed as having COVID-19. Results: A total of 114 COVID-19 patients (99 mild, 4 severe, 11 critical) of which 56 (56/114; 49.1%) were male, 58 (58/114; 50.9%) were female with a mean age of 46.05 years. Twenty nine (29/114; 25.44%) patients suffered from chronic diseases. Neutrophils counts in 23.68% (27/114) of patients were abnormally low and abnormally high in 21.05% (24/114). Erythrocyte sedimentation rate and the C-reactive protein levels were abnormally elevated in 76.5% (62/81) and 62.9% (66/105) of patients, respectively. Creatine kinase isoenzymes (CK-MB), pro-brain natriuretic peptide (pro-BNP) and troponin levels were above the normal range in 7.10% (8/112), 66.7% (10/15), and 100% of patients, respectively. The percentage of patients in which the partial pressure of oxygen (PaO2)/fraction of inspired O2(FiO2) ratio exceeded 200 was 60%. A total of 91 (91/114; 79.82%) patients displayed severe bilateral pneumonia, 52 (52/114; 45.61%) exhibited ground-glass opacity, and pulmonary consolidation was observed in 4 (3.51%) patients. Differences in shortness of breath, insomnia, inappetence, the procalcitonin (PCT) levels, FiO2 and PaO2/FiO2 among the three groups were statistically significant (p < 0.05). Differences between the mild and severe groups was observed in neutrophil and lymphocyte counts, CD4 expression, and levels of C-reactive protein, alanine aminotransferase, aspartate aminotransferase and albumin (P < 0.05). Between the mild and critical groups, differences were observed in neutrophils, platelets, and CD4 expression (P < 0.05). A difference in C-reactive protein levels between severe and critical groups was also found (P < 0.05). Conclusions: In the majority of cases no gender differences were observed and mostly the symptoms were mild. Evidence of efficient human-to-human virus transmission was found. The elderly with comorbidities were more prone to develop into severe or critical illness. Age and comorbidity may be risk factors for poor outcome.