Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019–2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795–1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434–0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model’s prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes.

SETTING: A tertiary care academic medical centre.OBJECTIVE: To evaluate the clinical usefulness of C-X-C motif chemokine receptor 3 (CXCR3) ligands in tuberculous pleural effusion (TPE).DESIGN: We recruited 336 patients with pleural effusion due to various causes. Concentrations of interferon-gamma (IFN-γ) and the CXCR3 ligands CXCL9 and CXCL11 were determined using enzyme immunoassays; adenosine deaminase (ADA) activity was measured in pleural fluid and serum.RESULTS: TPE was diagnosed in 106 patients. Non-TB conditions included lung cancer (n = 95), para-pneumonic effusion (n = 52), non-lung malignancy (n = 30), other exudate (n = 24) and transudate (n = 29) disorders. All marker levels in serum samples and pleural fluid were significantly higher in the TPE group. Analyses of receiver operating characteristic curves for differentiating TPE from non-TB effusions produced the following results for the area under the curve (AUC) for CXCL9, CXCL11, IFN-γ and ADA, respectively: 0.982, 0.952, 0.982, and 0.952. Marker AUCs in lymphocytic exudates were also high. Combining the levels of CXCL9, IFN-γ and ADA in pleural fluid improved the diagnostic performance. Serum levels of CXCL9 had the highest AUC (0.848) for diagnosing TPE.CONCLUSION: Levels of CXR3 ligands in pleural effusion are useful for diagnosing TPE.

Interferon (IFN)-γ-inducible chemokines in the CXCR3/ligand axis are involved in cell-mediated immunity and play a significant role in the progression of cancer. We enrolled patients with lung cancer (n = 144) and healthy volunteers as the controls (n = 140). Initial blood samples were collected and concentrations of IFN-γ and IFN-γ-inducible chemokines CXCL9, CXCL10, and CXCL11 were measured using enzyme-linked immunosorbent assay. Of patients with lung cancer, 125 had non-small cell lung cancer (NSCLC) and 19 had small cell lung cancer. The area under the curve (AUC) (95% CI) of CXCL9 was 0.83 (0.80–0.89) for differentiating lung cancer patients from controls. The levels of all the markers were significantly higher in NSCLC patients with stage IV than in those with stages I–III. A Kaplan-Meier survival analysis showed that NSCLC cancer patients with higher levels of all markers showed poorer survival than those with lower levels. In Cox multivariate analysis of patients with NSCLC, independent prognostic factors for overall survival were CXCL9 and CXCL11. CXCL9 was the only independent prognostic factor for cancer-specific survival. Serum IFN-γ-inducible chemokines may be useful as clinical markers of metastasis and prognosis in NSCLC, and CXCL9 levels showed the most significant results.

We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data’s variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70–0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.

Background: Pulmonary tuberculosis (PTB) poses a global health challenge owing to the time-intensive nature of traditional diagnostic tests such as smear and culture tests, which can require hours to weeks to yield results. Objective: This study aimed to use artificial intelligence (AI)–based chest radiography (CXR) to evaluate the infectivity of patients with PTB more quickly and accurately compared with traditional methods such as smear and culture tests. Methods: We used DenseNet121 and visualization techniques such as gradient-weighted class activation mapping and local interpretable model-agnostic explanations to demonstrate the decision-making process of the model. We analyzed 36,142 CXR images of 4492 patients with PTB obtained from Severance Hospital, focusing specifically on the lung region through segmentation and cropping with TransUNet. We used data from 2004 to 2020 to train the model, data from 2021 for testing, and data from 2022 to 2023 for internal validation. In addition, we used 1978 CXR images of 299 patients with PTB obtained from Yongin Severance Hospital for external validation. Results: In the internal validation, the model achieved an accuracy of 73.27%, an area under the receiver operating characteristic curve of 0.79, and an area under the precision-recall curve of 0.77. In the external validation, it exhibited an accuracy of 70.29%, an area under the receiver operating characteristic curve of 0.77, and an area under the precision-recall curve of 0.8. In addition, gradient-weighted class activation mapping and local interpretable model-agnostic explanations provided insights into the decision-making process of the AI model. Conclusions: This proposed AI tool offers a rapid and accurate alternative for evaluating PTB infectivity through CXR, with significant implications for enhancing screening efficiency by evaluating infectivity before sputum test results in clinical settings, compared with traditional smear and culture tests.

Han C, et al. Yonsei Med J. 2024 Nov;66:e3. https://doi.org/10.3349/ymj.2024.0126