Assessing the risk of concurrent mycoplasma pneumoniae pneumonia in children with tracheobronchial tuberculosis: retrospective study.

Objective: This study aimed to create a predictive model based on machine learning to identify the risk for tracheobronchial tuberculosis (TBTB) occurring alongside Mycoplasma pneumoniae pneumonia in pediatric patients. Methods: Clinical data from 212 pediatric patients were examined in this retrospective analysis. This cohort included 42 individuals diagnosed with TBTB and Mycoplasma pneumoniae pneumonia (combined group) and 170 patients diagnosed with lobar pneumonia alone (pneumonia group). Three predictive models, namely XGBoost, decision tree, and logistic regression, were constructed, and their performances were assessed using the receiver's operating characteristic (ROC) curve, precision-recall curve (PR), and decision curve analysis (DCA). The dataset was divided into a 7:3 ratio to test the first and second groups, utilizing them to validate the XGBoost model and to construct the nomogram model. Results: The XGBoost highlighted eight significant signatures, while the decision tree and logistic regression models identified six and five signatures, respectively. The ROC analysis revealed an area under the curve (AUC) of 0.996 for XGBoost, significantly outperforming the other models (p < 0.05). Similarly, the PR curve demonstrated the superior predictive capability of XGBoost. DCA further confirmed that XGBoost offered the highest AIC (43.226), the highest average net benefit (0.764), and the best model fit. Validation efforts confirmed the robustness of the findings, with the validation groups 1 and 2 showing ROC and PR curves with AUC of 0.997, indicating a high net benefit. The nomogram model was shown to possess significant clinical value. Conclusion: Compared to machine learning approaches, the XGBoost model demonstrated superior predictive efficacy in identifying pediatric patients at risk of concurrent TBTB and Mycoplasma pneumoniae pneumonia. The model's identification of critical signatures provides valuable insights into the pathogenesis of these conditions.

Conditional survival analysis and real-time prognosis prediction in stage III T3-T4 colon cancer patients after surgical resection: a SEER database analysis.

BACKGROUND: Conditional survival (CS) takes into consideration the duration of survival post-surgery and can provide valuable additional insights. The aim of this study was to investigate the risk factors associated with reduced one-year postoperative conditional survival in patients diagnosed with stage III T3-T4 colon cancer and real-time prognosis prediction. Furthermore, we aim to develop pertinent nomograms and predictive models. METHODS: Clinical data and survival outcomes of patients diagnosed with stage III T3-T4 colon cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2019. Patients were divided into training and validation cohorts at a ratio of 7:3. The training set consisted of a total of 11,386 patients for conditional overall survival (cOS) and 11,800 patients for conditional cancer-specific survival (cCSS), while the validation set comprised 4876 patients for cOS and 5055 patients for cCSS. Univariate and multivariate Cox regression analyses were employed to identify independent risk factors influencing one-year postoperative cOS and cCSS. Subsequently, predictive nomograms for cOS and cCSS at 2-year, 3-year, 4-year, and 5-year intervals were constructed based on the identified prognostic factors. The performance of these nomograms was rigorously assessed through metrics including the concordance index (C-index), calibration curves, and the area under curve (AUC) derived from the receiver operating characteristic (ROC) analysis. Clinical utility was further evaluated using decision curve analysis (DCA). RESULTS: A total of 18,190 patients diagnosed with stage III T3-T4 colon cancer were included in this study. Independent risk factors for one-year postoperative cOS and cCSS included age, pT stage, pN stage, pretreatment carcinoembryonic antigen (CEA) levels, receipt of chemotherapy, perineural invasion (PNI), presence of tumor deposits, the number of harvested lymph nodes, and marital status. Sex and tumor site were significantly associated with one-year postoperative cOS, while radiation therapy was notably associated with one-year postoperative cCSS. In the training cohort, the developed nomogram demonstrated a C-index of 0.701 (95% CI, 0.711-0.691) for predicting one-year postoperative cOS and 0.701 (95% CI, 0.713-0.689) for one-year postoperative cCSS. Following validation, the C-index remained robust at 0.707 (95% CI, 0.721-0.693) for one-year postoperative cOS and 0.700 (95% CI, 0.716-0.684) for one-year postoperative cCSS. ROC and calibration curves provided evidence of the model's stability and reliability. Furthermore, DCA underscored the nomogram's superior clinical utility. CONCLUSIONS: Our study developed nomograms and predictive models for postoperative stage III survival in T3-T4 colon cancer with the aim of accurately estimating conditional survival. Survival bias in our analyses may lead to overestimation of survival outcomes, which may limit the applicability of our findings.

Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study.

BACKGROUND AND AIM: This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD. METHODS: This cross-sectional study was conducted among 7701 participants with MAFLD from August 2018 to December 2023. All participants were divided into a training cohort and a validation cohort. The study compared different subgroups' demographic, anthropometric, and laboratory examination indicators and conducted logistic regression analysis to assess the correlation between independent variables and liver fibrosis. Nomograms were created using the logistic regression model. The predictive values of noninvasive models and nomograms were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). RESULTS: Four nomograms were developed for the quantitative analysis of significant liver fibrosis risk based on the multivariate logistic regression analysis results. The nomogram's area under ROC curves (AUC) was 0.710, 0.714, 0.748, and 0.715 in overall MAFLD, OW-MAFLD, Lean-MAFLD, and T2DM-MAFLD, respectively. The nomogram had a higher AUC in all MAFLD participants and OW-MAFLD than the other non-invasive scores. The DCA curve showed that the net benefit of each nomogram was higher than that of APRI and FIB-4. In the validation cohort, the AUCs of the nomograms were 0.722, 0.750, 0.719, and 0.705, respectively. CONCLUSION: APRI, FIB-4, and NFS performed poorly predicting significant fibrosis in patients with MAFLD. The new model demonstrated improved diagnostic accuracy and clinical applicability in identifying significant fibrosis in MAFLD.

Improving predictions: Enhancing in-hospital mortality forecast for ICU patients with sepsis-induced coagulopathy using a stacking ensemble model.

The incidence of sepsis-induced coagulopathy (SIC) is high, leading to increased mortality rates and prolonged hospitalization and intensive care unit (ICU) stays. Early identification of SIC patients at risk of in-hospital mortality can improve patient prognosis. The objective of this study is to develop and validate machine learning (ML) models to dynamically predict in-hospital mortality risk in SIC patients. A ML model is established based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict in-hospital mortality in SIC patients. Utilizing univariate feature selection for feature screening. The optimal model was determined by calculating the area under the curve (AUC) with a 95% confidence interval (CI). The optimal model was interpreted using Shapley Additive Explanation (SHAP) values. Among the 3112 SIC patients included in MIMIC-IV, a total of 757 (25%) patients experienced mortality during their ICU stay. Univariate feature selection helps us to pick out the 20 most critical variables from the original feature. Among the 10 developed machine learning models, the stacking ensemble model exhibited the highest AUC (0.795, 95% CI: 0.763-0.827). Anion gap and age emerged as the most significant features for predicting the mortality risk in SIC. In this study, an ML model was constructed that exhibited excellent performance in predicting in-hospital mortality risk in SIC patients. Specifically, the stacking ensemble model demonstrated superior predictive ability.

[Exploration and validation of optimal cut-off values for tPSA and fPSA/tPSA screening of prostate cancer at different ages].

Objective: To determine the total and age-specific cut-off values of total prostate specific antigen (tPSA) and the ratio of free PSA divided total PSA (fPSA/tPSA) for screening prostate cancer in China. Methods: Based on the Chinese Colorectal, Breast, Lung, Liver, and Stomach cancer Screening Trial (C-BLAST) and the Tianjin Common Cancer Case Cohort (TJ4C), males who were not diagnosed with any cancers at baseline since 2017 and received both tPSA and fPSA testes were selected. Based on Cox regression, the overall and age-specific (＜60, 60-＜70, and ≥70 years) accuracy and optimal cut-off values of tPSA and fPSA/tPSA ratio for screening prostate cancer were evaluated with time-dependent receiver operating characteristic curve (tdROC) and area under curve (AUC). Bootstrap resampling was used to internally validate the stability of the optimal cut-off value, and the PLCO study was used to externally validate the accuracy under different cut-off values. Results: A total of 5 180 participants were included in the study, and after a median follow-up of 1.48 years, a total of 332 prostate cancer patients were included. In the total population, the tdAUC of tPSA and fPSA/tPSA screening for prostate cancer were 0.852 and 0.748, respectively, with the optimal cut-off values of 5.08 ng/ml and 0.173, respectively. After age stratification, the age specific cut-off values of tPSA in the <60, 60-<70, and ≥70 age groups were 3.13, 4.82, and 11.54 ng/ml, respectively, while the age-specific cut-off values of fPSA/tPSA were 0.153, 0.135, and 0.130, respectively. Under the age-specific cut-off values, the sensitivities of tPSA screening for prostate cancer in males <60, 60-70, and ≥70 years old were 92.3%, 82.0%, and 77.6%, respectively, while the specificities were 84.7%, 81.3%, and 75.4%, respectively. The age-specific sensitivities of fPSA/tPSA for screening prostate cancer were 74.4%, 53.3%, and 55.9%, respectively, while the specificities were 83.8%, 83.7%, and 83.7%, respectively. Both bootstrap's internal validation and PLCO external validation provided similar results. The combination of tPSA and fPSA/tPSA could further improve the accuracy of screening. Conclusion: To improve the screening effects, it is recommended that age-specific cut-off values of tPSA and fPSA/tPSA should be used to screen for prostate cancer in the general risk population.

Prognosis prediction of procalcitonin within 24 h for acute diquat poisoning.

BACKGROUND: To explore the predictive value of procalcitonin (PCT) within 24 h after poisoning for prognosis of acute diquat poisoning. METHODS: This retrospective study included acute diquat poisoning patients in the Nanyang City Hospital between May 2017 and July 2021. RESULTS: Among the 45 patients included, 27 survived. The maximum PCT value within 24 h after poisoning was significantly higher in the non-survival patients [9.65 (2.63, 22.77) vs. 0.15 (0.10, 0.50) µg/mL, P < 0.001] compared to the survival patients. The area under the ROC curve (AUC) indicated that the maximum PCT value within 24 h had a good predictive value (AUC = 0.905, 95% CI: 0.808-1.000) compared to ingested quantity (AUC = 0.879, 95% CI: 0.776-0.981), serum creatinine (AUC = 0.776, 95% CI: 0.640-0.912), or APACHE II score (AUC = 0.778, 95% CI: 0.631-0.925). The predictive value of maximum PCT value within 24 h was comparable with blood lactate (AUC = 0.904, 95%CI: 0.807-1.000). CONCLUSIONS: The maximum PCT value within 24 h after poisoning might be a good predictor for the prognosis of patients with acute diquat poisoning.

Identifying the predictive role and the related drugs of oxidative stress genes in the hepatocellular carcinoma.

BACKGROUND AND AIMS: Oncogenesis and tumor development have been related to oxidative stress (OS). The potential diagnostic utility of OS genes in hepatocellular carcinoma (HCC), however, remains uncertain. As a result, this work aimed to create a novel OS related-genes signature that could be used to predict the survival of HCC patients and to screen OS related-genes drugs that might be used for HCC treatment. METHODS: We used The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database to acquire mRNA expression profiles and clinical data for this research and the GeneCards database to obtain OS related-genes. Following that, biological functions from Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on differentially expressed OS-related genes (DEOSGs). Subsequently, the prognostic risk signature was constructed based on DEOSGs from the TCGA data that were screened by using univariate cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression, and multivariate cox analysis. At the same time, we developed a prognostic nomogram of HCC patients based on risk signature and clinical-pathological characteristics. The GEO data was used for validation. We used the receiver operating characteristic (ROC) curve, calibration curves, and Kaplan-Meier (KM) survival curves to examine the prediction value of the risk signature and nomogram. Finally, we screened the differentially expressed OS genes related drugs. RESULTS: We were able to recognize 9 OS genes linked to HCC prognosis. In addition, the KM curve revealed a statistically significant difference in overall survival (OS) between the high-risk and low-risk groups. The area under the curve (AUC) shows the independent prognostic value of the risk signature model. Meanwhile, the ROC curves and calibration curves show the strong prognostic power of the nomogram. The top three drugs with negative ratings were ZM-336372, lestaurtinib, and flunisolide, all of which inversely regulate different OS gene expressions. CONCLUSION: Our findings indicate that OS related-genes have a favorable prognostic value for HCC, which sheds new light on the relationship between oxidative stress and HCC, and suggests potential therapeutic strategies for HCC patients.

Real-time crash prediction on express managed lanes of Interstate highway with anomaly detection learning.

To facilitate efficient transportation, I-4 Express is constructed separately from general use lanes in metropolitan area to improve mobility and reduce congestion. As this new infrastructure would undoubtedly change the traffic network, there is a need for more understanding of its potential safety impact. Unfortunately, many advanced real-time crash prediction models encounter an important challenge in their applicability due to their demand for a substantial volume of data for direct modeling. To tackle this challenge, we proposed a simple yet effective approach - anomaly detection learning, which formulates model as an anomaly detection problem, solves it through normality feature recognition, and predicts crashes by identifying deviations from the normal state. The proposed approach demonstrates significant improvement in the Area Under the Curve (AUC), sensitivity, and False Alarm Rate (FAR). When juxtaposed with the prevalent direct classification paradigm, our proposed anomaly detection learning (ADL) consistently outperforms in AUC (with an increase of up to 45%), sensitivity (experiencing up to a 45% increase), and FAR (reducing by up to 0.53). The most performance gain is attained through the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in an ensemble, resulting in a 0.78 AUC, 0.79 sensitivity, and a 0.22 false alarm rate. Furthermore, we analyzed model features with a game-theoretic approach illustrating the most correlated features for accurate prediction, revealing the attention of advanced convolution neural networks to occupancy features. This provided crucial insights into improving crash precaution, the findings from which not only benefit private stakeholders but also extend a promising opportunity for governmental intervention on the express lane. This work could promote express lane with more efficient resource allocation, real-time traffic management optimization, and high-risk area prioritization.

A chest CT-based nomogram for predicting survival in acute myeloid leukemia.

BACKGROUND: The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). METHODS: 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. RESULTS: Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011). CONCLUSIONS: In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study.

BACKGROUND: The objective of this research was to create and validate an interpretable prediction model for drug-induced liver injury (DILI) during tuberculosis (TB) treatment. METHODS: A dataset of TB patients from Ningbo City was used to develop models employing the eXtreme Gradient Boosting (XGBoost), random forest (RF), and the least absolute shrinkage and selection operator (LASSO) logistic algorithms. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR) alongside the decision curve. The Shapley Additive exPlanations (SHAP) method was used to interpret the variable contributions of the superior model. RESULTS: A total of 7,071 TB patients were identified from the regional healthcare dataset. The study cohort consisted of individuals with a median age of 47 years, 68.0% of whom were male, and 16.3% developed DILI. We utilized part of the high dimensional propensity score (HDPS) method to identify relevant variables and obtained a total of 424 variables. From these, 37 variables were selected for inclusion in a logistic model using LASSO. The dataset was then split into training and validation sets according to a 7:3 ratio. In the validation dataset, the XGBoost model displayed improved overall performance, with an AUROC of 0.89, an AUPR of 0.75, an F1 score of 0.57, and a Brier score of 0.07. Both SHAP analysis and XGBoost model highlighted the contribution of baseline liver-related ailments such as DILI, drug-induced hepatitis (DIH), and fatty liver disease (FLD). Age, alanine transaminase (ALT), and total bilirubin (Tbil) were also linked to DILI status. CONCLUSION: XGBoost demonstrates improved predictive performance compared to RF and LASSO logistic in this study. Moreover, the introduction of the SHAP method enhances the clinical understanding and potential application of the model. For further research, external validation and more detailed feature integration are necessary.

Machine Learning-Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma.

PURPOSE: Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS: This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS: In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION: The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis.

BACKGROUND: The continuous monitoring and recording of patients' pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. OBJECTIVE: The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. METHODS: The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. RESULTS: A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. CONCLUSIONS: This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. TRIAL REGISTRATION: PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181.

Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense's N-TidalTM capnometer. METHOD: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1-4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity. RESULTS: The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1. CONCLUSION: The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring.

Feasibility Study of Computed Tomographic Radiomics Model for the Prediction of Early and Intermediate Stage Hepatocellular Carcinoma Using BCLC Staging.

BACKGROUND: Hepatocellular carcinoma (HCC) is a serious health concern because of its high morbidity and mortality. The prognosis of HCC largely depends on the disease stage at diagnosis. Computed tomography (CT) image textural analysis is an image analysis technique that has emerged in recent years. OBJECTIVE: To probe the feasibility of a CT radiomic model for predicting early (stages 0, A) and intermediate (stage B) HCC using Barcelona Clinic Liver Cancer (BCLC) staging. METHODS: A total of 190 patients with stages 0, A, or B HCC according to CT-enhanced arterial and portal vein phase images were retrospectively assessed. The lesions were delineated manually to construct a region of interest (ROI) consisting of the entire tumor mass. Consequently, the textural profiles of the ROIs were extracted by specific software. Least absolute shrinkage and selection operator dimensionality reduction was used to screen the textural profiles and obtain the area under the receiver operating characteristic curve values. RESULTS: Within the test cohort, the area under the curve (AUC) values associated with arterial-phase images and BCLC stages 0, A, and B disease were 0.99, 0.98, and 0.99, respectively. The overall accuracy rate was 92.7%. The AUC values associated with portal vein phase images and BCLC stages 0, A, and B disease were 0.98, 0.95, and 0.99, respectively, with an overall accuracy of 90.9%. CONCLUSION: The CT radiomic model can be used to predict the BCLC stage of early-stage and intermediate-stage HCC.

Low Hounsfield unit values on computed tomography as a potential predictor of vertebral fracture in patients with rheumatoid arthritis: The KURAMA cohort study.

OBJECTIVE: Hounsfield units (HU) measured using computed tomography (CT) have gained considerable attention for the detection of osteoporosis. This study aimed to investigate whether opportunistic CT could predict vertebral fractures in patients with rheumatoid arthritis (RA). METHODS: A total of 233 patients with RA who underwent chest CT were included in this study. The HU values of the anterior 1/3 of the vertebral bodies based on the sagittal plane at T11-L2 after reconstruction were measured. The incidence of vertebral fractures was investigated with respect to the HU value. RESULTS: Vertebral fractures were identified in 32 patients during a mean follow-up period of 3.8 years. In patients who experienced vertebral fractures within 2 years of CT imaging, the HU values of the vertebral bodies (T11-L2) were lower than those in patients who did not experience fractures. Receiver operating characteristic curve analysis identified that a T11 HU value of <125 was a risk factor for vertebral fracture within 2 years. Multivariate analysis showed that a T11 HU value of <125 and the existence of prevalent vertebral fractures were significant risk factors for fracture. CONCLUSION: HU measurements of the anterior 1/3 of the vertebral body are a potential predictor for vertebral fractures in patients with RA.

Abatacept Pharmacokinetics and Exposure Response in Patients Hospitalized With COVID-19: A Secondary Analysis of the ACTIV-1 IM Randomized Clinical Trial.

Importance: The pharmacokinetics of abatacept and the association between abatacept exposure and outcomes in patients with severe COVID-19 are unknown. Objective: To characterize abatacept pharmacokinetics, relate drug exposure with clinical outcomes, and evaluate the need for dosage adjustments. Design, Setting, and Participants: This study is a secondary analysis of data from the ACTIV-1 (Accelerating COVID-19 Therapeutic Interventions and Vaccines) Immune Modulator (IM) randomized clinical trial conducted between October 16, 2020, and December 31, 2021. The trial included hospitalized adults who received abatacept in addition to standard of care for treatment of COVID-19 pneumonia. Data analysis was performed between September 2022 and February 2024. Exposure: Single intravenous infusion of abatacept (10 mg/kg with a maximum dose of 1000 mg). Main Outcomes and Measures: Mortality at day 28 was the primary outcome of interest, and time to recovery at day 28 was the secondary outcome. Drug exposure was assessed using the projected area under the serum concentration time curve over 28 days (AUC0-28). Logistic regression modeling was used to analyze the association between drug exposure and 28-day mortality, adjusted for age, sex, and disease severity. The association between time to recovery and abatacept exposure was examined using Fine-Gray modeling with death as a competing risk, and was adjusted for age, sex, and disease severity. Results: Of the 509 patients who received abatacept, 395 patients with 848 serum samples were included in the population pharmacokinetic analysis. Their median age was 55 (range, 19-89) years and most (250 [63.3%]) were men. Abatacept clearance increased with body weight and more severe disease activity at baseline. Drug exposure was higher in patients who survived vs those who died, with a median AUC0-28 of 21 428 (range, 8462-43 378) mg × h/L vs 18 262 (range, 9628-27 507) mg × h/L (P < .001). Controlling for age, sex, and disease severity, an increase of 5000 units in AUC0-28 was associated with lower odds of mortality at day 28 (OR, 0.52 [95% CI, 0.35-0.79]; P = .002). For an AUC0-28 of 19 400 mg × h/L or less, there was a higher probability of recovery at day 28 (hazard ratio, 2.63 [95% CI, 1.70-4.08] for every 5000-unit increase; P < .001). Controlling for age, sex, and disease severity, every 5000-unit increase in AUC0-28 was also associated with lower odds of a composite safety event at 28 days (OR, 0.46 [95% CI, 0.33-0.63]; P < .001). Using the dosing regimen studied in the ACTIV-1 IM trial, 121 of the 395 patients (30.6%) would not achieve an abatacept exposure of at least 19 400 mg × h/L, particularly at the extremes of body weight. Using a modified, higher-dose regimen, only 12 patients (3.0%) would not achieve the hypothesized target abatacept exposure. Conclusions and Relevance: In this study, patients who were hospitalized with severe COVID-19 and achieved higher projected abatacept exposure had reduced mortality and a higher probability of recovery with fewer safety events. However, abatacept clearance was high in this population, and the current abatacept dosing (10 mg/kg intravenously with a maximum of 1000 mg) may not achieve optimal exposure in all patients. Trial Registration: ClinicalTrials.gov Identifier: NCT04593940.

Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR) and Monocyte-to-Lymphocyte Ratio (MLR) as Biomarkers in Diagnosis Evaluation of Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Retrospective, Observational Study.

Purpose: Hierarchical management is advocated in China to effectively manage chronic obstructive pulmonary disease (COPD) patients and reduce the incidence and mortality of acute exacerbation of COPD (AE-COPD). However, primary and community hospitals often have limited access to advanced equipment and technology. Complete blood count (CBC), which is commonly used in these hospitals, offers the advantages of being cost-effective and easily accessible. This study aims to evaluate the significance of routine blood indicators in aiding of diagnosing AE-COPD. Patients and Methods: In this research, we enrolled a total of 112 patients diagnosed with AE-COPD, 92 patients with stable COPD, and a control group comprising 60 healthy individuals. Clinical characteristics, CBC parameters, and serum CRP levels were collected within two hours. To assess the associations between NLR/PLR/MLR and CRP by Spearman correlation test. The diagnostic accuracy of NLR, PLR and MLR in AE-COPD was assessed using Receiver Operating Characteristic Curve (ROC) and the area under the curve (AUC). Binary Logistic Regression analysis was conducted for the indicators of NLR, PLR and MLR. Results: We found that patients with AE-COPD had significantly higher levels of NLR, PLR and MLR in contrast to patients with stable COPD. Additionally, the study revealed a noteworthy correlation between CRP and NLR (rs=0.5319, P<0.001), PLR (rs=0.4424, P<0.001), and MLR (rs=0.4628, P<0.001). By utilizing specific cut-off values, the amalgamation of NLR, PLR and MLR augmented diagnostic sensitivity. Binary logistic regression analysis demonstrated that heightened NLR and MLR act as risk factors for the progression of AE-COPD. Conclusion: The increasing levels of NLR, PLR and MLR could function as biomarkers, akin to CRP, for diagnosis and assessment of acute exacerbations among COPD patients. Further research is required to validate this concept.

Role of radiomics in staging liver fibrosis: a meta-analysis.

BACKGROUND: Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics in staging liver fibrosis. METHOD: After literature search in electronic databases (Embase, Ovid, Science Direct, Springer, and Web of Science), studies were selected by following precise eligibility criteria. The quality of included studies was assessed, and meta-analyses were performed to achieve pooled estimates of area under receiver-operator curve (AUROC), accuracy, sensitivity, and specificity of radiomics in staging liver fibrosis compared to histopathology. RESULTS: Fifteen studies (3718 patients; age 47 years [95% confidence interval (CI): 42, 53]; 69% [95% CI: 65, 73] males) were included. AUROC values of radiomics for detecting significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4) were 0.91 [95%CI: 0.89, 0.94], 0.92 [95%CI: 0.90, 0.95], and 0.94 [95%CI: 0.93, 0.96] in training cohorts and 0.89 [95%CI: 0.83, 0.91], 0.89 [95%CI: 0.83, 0.94], and 0.93 [95%CI: 0.91, 0.95] in validation cohorts, respectively. For diagnosing significant fibrosis, advanced fibrosis, and cirrhosis the sensitivity of radiomics was 84.0% [95%CI: 76.1, 91.9], 86.9% [95%CI: 76.8, 97.0], and 92.7% [95%CI: 89.7, 95.7] in training cohorts, and 75.6% [95%CI: 67.7, 83.5], 80.0% [95%CI: 70.7, 89.3], and 92.0% [95%CI: 87.8, 96.1] in validation cohorts, respectively. Respective specificity was 88.6% [95% CI: 83.0, 94.2], 88.4% [95% CI: 81.9, 94.8], and 91.1% [95% CI: 86.8, 95.5] in training cohorts, and 86.8% [95% CI: 83.3, 90.3], 94.0% [95% CI: 89.5, 98.4], and 88.3% [95% CI: 84.4, 92.2] in validation cohorts. Limitations included use of several methods for feature selection and classification, less availability of studies evaluating a particular radiological modality, lack of a direct comparison between radiology and radiomics, and lack of external validation. CONCLUSION: Although radiomics offers good diagnostic accuracy in detecting liver fibrosis, its role in clinical practice is not as clear at present due to comparability and validation constraints.

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS: The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION: We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION: Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.

Development of a multivariable prediction model for prolonged air leak after lung resection.

OBJECTIVES: Prolonged air leak (PAL) is a common complication of lung resection. Research on predictors of PAL using a digital drainage system (DDS) remains insufficient. In this study, we investigated the predictive factors of PAL to establish a novel early postoperative prediction model for PAL. METHODS: A retrospective cohort study and validation study were conducted. We examined patients who underwent lung resection with DDS at our institute. The relationship between the clinical factors and measurements of the DDS, including the difference between the set and measured intrapleural pressure (named: additional negative pressure [ANP]) at postoperative hour (POH) 3, with PAL was analyzed. RESULTS: A total of 494 patients were enrolled, 29 of whom had PAL. Percent forced expiratory volume in 1 s <60%, ANP <1 cmH2O, air leak flow >20 mL/min and pleural adhesion findings at surgery were independent predictors of PAL according to a multivariable analysis. The PAL rate was clearly stratified according to our novel risk scoring system, which simply notes the presence of the above four factors, that is, the rate increases when the score increases. The area under the curve (AUC) of the receiver operating characteristic (ROC) analysis for this scoring system was 0.818. Analysis of the validation cohort (n = 133) revealed that this scoring system showed a sufficient ability to predict PAL. CONCLUSIONS: ANP at POH 3 is an independent predictor of PAL. Thus, the risk-scoring system proposed in this study is useful for predicting PAL in the early postoperative period.