A novel single-cell model reveals ferroptosis-associated biomarkers for individualized therapy and prognostic prediction in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a pressing need for improved therapeutic response and prognosis prediction. This study delves into a novel predictive model related to ferroptosis, a regulated cell death mechanism disrupting metabolic processes. RESULTS: Single-cell sequencing data analysis identified subpopulations of HCC cells exhibiting activated ferroptosis and distinct gene expression patterns compared to normal tissues. Utilizing the LASSO-Cox algorithm, we constructed a model with 10 single-cell biomarkers associated with ferroptosis, namely STMN1, S100A10, FABP5, CAPG, RGCC, ENO1, ANXA5, UTRN, CXCR3, and ITM2A. Comprehensive analyses using these biomarkers revealed variations in immune infiltration, tumor mutation burden, drug sensitivity, and biological functional profiles between risk groups. Specific associations were established between particular immune cell subtypes and certain gene expression patterns. Treatment response analyses indicated potential benefits from anti-tumor immune therapy for the low-risk group and chemotherapy advantages for the high-risk group. CONCLUSIONS: The integration of this single-cell level model with clinicopathological features enabled accurate overall survival prediction and effective risk stratification in HCC patients. Our findings illuminate the potential of ferroptosis-related genes in tailoring therapy and prognosis prediction for HCC, offering novel insights into the intricate interplay among ferroptosis, immune response, and HCC progression.

Comparison of predictive models using logistic regression, classification tree, and structural equation model for severe dengue.

BACKGROUND: The aim of this study was to compare the predictive performance of three statistical models-logistic regression, classification tree, and structural equation model (SEM)-in predicting severe dengue illness. METHODS/FINDINGS: We adopted modified classification of dengue illness severity based on WHO 1997 guideline. Predictive models were constructed using demographic factors and laboratory indicators on the day of fever occurrence. We developed statistical predictive models using data from two hospital cohorts in Thailand, consisting of 257 Thai children. Different predictive models for each category of severe dengue illness were developed employing logistic regression, classification tree, and SEM. The probability of discrimination of each model for severe output of disease was analyzed with external validation data sets from 55 and 700 patients not used in model development. From external validation using predictors on the day of presentation to the hospital, the area under the receiver operating characteristic curve was between 0.65 and 0.84 for the regression model. It was between 0.73 and 0.85 for SEM models. Classification tree models showed good results of sensitivity, ranging from 0.95 to 0.99. However, they showed poor specificity ranging from 0.10 to 0.44. CONCLUSIONS: Our study showed that SEM is comparable to logistic regression or classification tree, which was widely used for more severe form of dengue prediction.

Genome-wide association study of exercise-induced skeletal muscle hypertrophy and the construction of predictive model.

The aim of the current study was to investigate interindividual differences in muscle thickness of the rectus femoris (MTRF) following 12 wk of resistance training (RT) or high-intensity interval training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-wk exercise period. A genome-wide association study was used to identify variants associated with the MTRF response, separately for RT and HIIT. Using the polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for the MTRF response were constructed using random forest (RF), support vector mac (SVM), and generalized linear model (GLM) in 10 cross-validated approaches. MTRF increased significantly after both RT (8.8%, P < 0.05) and HIIT (5.3%, P < 0.05), but with considerable interindividual differences (RT: -13.5 to 38.4%, HIIT: -14.2 to 30.7%). Eleven lead single-nucleotide polymorphisms in RT and eight lead single-nucleotide polymorphisms in HIIT were identified at a significance level of P < 1 × 10-5. The PPS was associated with the MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance compared with RF models (P < 0.05), and the GLM demonstrated optimal performance with an area under curve of 0.809 (95% confidence interval: 0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.NEW & NOTEWORTHY The interindividual variability induced muscle hypertrophy by resistance training (RT) or high-intensity interval training (HIIT) and the associated genetic architecture remain uncertain. We identified genetic variants that underlie RT- or HIIT-induced muscle hypertrophy and established them as pivotal factors influencing the response regardless of the training type. The genetic-phenotype predictive model developed has the potential to identify nonresponders or individuals with low responsiveness before engaging in exercise training.

Identifying individual's distractor suppression using functional connectivity between anatomical large-scale brain regions.

Distractor suppression (DS) is crucial in goal-oriented behaviors, referring to the ability to suppress irrelevant information. Current evidence points to the prefrontal cortex as an origin region of DS, while subcortical, occipital, and temporal regions are also implicated. The present study aimed to examine the contribution of communications between these brain regions to visual DS. To do it, we recruited two independent cohorts of participants for the study. One cohort participated in a visual search experiment where a salient distractor triggering distractor suppression to measure their DS and the other cohort filled out a Cognitive Failure Questionnaire to assess distractibility in daily life. Both cohorts collected resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate function connectivity (FC) underlying DS. First, we generated predictive models of the DS measured in visual search task using resting-state functional connectivity between large anatomical regions. It turned out that the models could successfully predict individual's DS, indicated by a significant correlation between the actual and predicted DS (r = 0.32, p < 0.01). Importantly, Prefrontal-Temporal, Insula-Limbic and Parietal-Occipital connections contributed to the prediction model. Furthermore, the model could also predict individual's daily distractibility in the other independent cohort (r = -0.34, p < 0.05). Our findings showed the efficiency of the predictive models of distractor suppression encompassing connections between large anatomical regions and highlighted the importance of the communications between attention-related and visual information processing regions in distractor suppression. Current findings may potentially provide neurobiological markers of visual distractor suppression.

Multicancer screening test based on the detection of circulating non haematological proliferating atypical cells.

BACKGROUND: the problem in early diagnosis of sporadic cancer is understanding the individual's risk to develop disease. In response to this need, global scientific research is focusing on developing predictive models based on non-invasive screening tests. A tentative solution to the problem may be a cancer screening blood-based test able to discover those cell requirements triggering subclinical and clinical onset latency, at the stage when the cell disorder, i.e. atypical epithelial hyperplasia, is still in a subclinical stage of proliferative dysregulation. METHODS: a well-established procedure to identify proliferating circulating tumor cells was deployed to measure the cell proliferation of circulating non-haematological cells which may suggest tumor pathology. Moreover, the data collected were processed by a supervised machine learning model to make the prediction. RESULTS: the developed test combining circulating non-haematological cell proliferation data and artificial intelligence shows 98.8% of accuracy, 100% sensitivity, and 95% specificity. CONCLUSION: this proof of concept study demonstrates that integration of innovative non invasive methods and predictive-models can be decisive in assessing the health status of an individual, and achieve cutting-edge results in cancer prevention and management.

The predictive value of mBDNF for major adverse cardiovascular events in stable coronary artery disease patients with depressive symptoms: A single-center, 5-year follow-up study.

BACKGROUND: Myokines play vital roles in both stable coronary artery disease (SCAD) and depression. Meanwhile, there is a pressing necessity to find effective biomarkers for early predictor of major adverse cardiovascular events (MACE) in SCAD patients with depressive symptoms. METHODS: A single-center, 5-year follow-up study was investigated. MACE was defined as composite end points, including cardiovascular death, non-fatal stroke, non-fatal myocardial infarction, coronary artery revascularization, or hospitalization for unstable angina. RESULTS: A total of 116 SCAD patients were enrolled, consisting of 30 cases (25.9%) without depressive symptoms and 86 cases (74.1%) with depressive symptoms. During the follow-up, 3 patients (2.6%) were lost. Out of 113 patients, 51 (45.1%) experienced MACE. In the subgroup of 84 SCAD patients with depressive symptoms, 44 cases (52.4%) of MACE were observed. Finally, mature brain-derived neurotrophic factor (mBDNF), pro-brain-derived neurotrophic factor, receptor activator of nuclear factor-κB ligand, smoking history, hypertension and cystatin C were incorporated into the predictive model. CONCLUSIONS: Depressive symptoms represent an independent risk factor for MACE in patients with SCAD. Additionally, low mBDNF expression may be an important early predictor for MACE in SCAD patients with depressive symptoms. The predictive model may exhibit a commendable predictive performance for MACE in SCAD patients with depressive symptoms.

Development of an MRI-Based Prediction Model for Anti-TNF Treatment Failure in Perianal Crohn's Disease: A Multicenter Study.

BACKGROUND & AIMS: Clinical and radiologic variables associated with perianal fistula (PAF) outcomes are poorly understood. We developed prediction models for anti-tumor necrosis factor (TNF) treatment failure in patients with Crohn's disease-related PAF. METHODS: In a multicenter retrospective study between 2005 and 2022 we included biologic-naive adults (>17 years) who initiated their first anti-TNF therapy for PAF after pelvic magnetic resonance imaging (MRI). Pretreatment MRI studies were prospectively reread centrally by blinded radiologists. We developed and internally validated a prediction model based on clinical and radiologic parameters to predict the likelihood of anti-TNF treatment failure, clinically, at 6 months. We compared our model and a simplified version of MRI parameters alone with existing imaging-based PAF activity indices (MAGNIFI-CD and modified Van Assche MRI scores) by De Long statistical test. RESULTS: We included 221 patients: 32 ± 14 years, 60% males, 76% complex fistulas; 68% treated with infliximab and 32% treated with adalimumab. Treatment failure occurred in 102 (46%) patients. Our prediction model included age at PAF diagnosis, time to initiate anti-TNF treatment, and smoking and 8 MRI characteristics (supra/extrasphincteric anatomy, fistula length >4.3 cm, primary tracts >1, secondary tracts >1, external openings >1, tract hyperintensity on T1-weighted imaging, horseshoe anatomy, and collections >1.3 cm). Our full and simplified MRI models had fair discriminatory capacity for anti-TNF treatment failure (concordance statistic, 0.67 and 0.65, respectively) and outperformed MAGNIFI-CD (P = .002 and < .0005) and modified Van Assche MRI scores (P < .0001 and < .0001), respectively. CONCLUSIONS: Our risk prediction models consisting of clinical and/or radiologic variables accurately predict treatment failure in patients with PAF.

Development and Validation of a Scoring System to Predict Response to Obeticholic Acid in Primary Biliary Cholangitis.

BACKGROUND & AIMS: Obeticholic acid (OCA) is the only licensed second-line therapy for primary biliary cholangitis (PBC). With novel therapeutics in advanced development, clinical tools are needed to tailor the treatment algorithm. We aimed to derive and externally validate the OCA response score (ORS) for predicting the response probability of individuals with PBC to OCA. METHODS: We used data from the Italian RECAPITULATE (N = 441) and the IBER-PBC (N = 244) OCA real-world prospective cohorts to derive/validate a score including widely available variables obtained either pre-treatment (ORS) or also after 6 months of treatment (ORS+). Multivariable Cox regressions with backward selection were applied to obtain parsimonious predictive models. The predicted outcomes were biochemical response according to POISE (alkaline phosphatase [ALP]/upper limit of normal [ULN]<1.67 with a reduction of at least 15%, and normal bilirubin), or ALP/ULN<1.67, or normal range criteria (NR: normal ALP, alanine aminotransferase [ALT], and bilirubin) up to 24 months. RESULTS: Depending on the response criteria, ORS included age, pruritus, cirrhosis, ALP/ULN, ALT/ULN, GGT/ULN, and bilirubin. ORS+ also included ALP/ULN and bilirubin after 6 months of OCA therapy. Internally validated c-statistics for ORS were 0.75, 0.78, and 0.72 for POISE, ALP/ULN<1.67, and NR response, which raised to 0.83, 0.88, and 0.81 with ORS+, respectively. The respective performances in validation were 0.70, 0.72, and 0.71 for ORS and 0.80, 0.84, and 0.78 for ORS+. Results were consistent across groups with mild/severe disease. CONCLUSIONS: We developed and externally validated a scoring system capable to predict OCA response according to different criteria. This tool will enhance a stratified second-line therapy model to streamline standard care and trial delivery in PBC.

A Machine Learning Algorithm Avoids Unnecessary Paracentesis for Exclusion of SBP in Cirrhosis in Resource-limited Settings.

BACKGROUND & AIMS: Despite the poor prognosis associated with missed or delayed spontaneous bacterial peritonitis (SBP) diagnosis, <15% get timely paracentesis, which persists despite guidelines/education in the United States. Measures to exclude SBP non-invasively where timely paracentesis cannot be performed could streamline this burden. METHODS: Using Veterans Health Administration Corporate Data Warehouse (VHA-CDW) we included patients with cirrhosis between 2009 and 2019 who underwent timely paracentesis and collected relevant clinical information (demographics, cirrhosis severity, medications, vitals, and comorbidities). XGBoost-models were trained on 75% of the primary cohort, with 25% reserved for testing. The final model was further validated in 2 cohorts: Validation cohort #1: In VHA-CDW, those without prior SBP who received 2nd early paracentesis, and Validation cohort #2: Prospective data from 276 non-electively admitted University hospital patients. RESULTS: Negative predictive values (NPVs) at 5%,10%, and 15% probability cutoffs were examined. Primary cohort: n = 9643 (mean age, 63.1 ± 8.7 years; 97.2% men; SBP, 15.0%) received first early paracentesis. Testing-set NPVs for SBP were 96.5%, 93.0%, and 91.6% at the 5%, 10%, and 15% probability thresholds, respectively. In Validation cohort #1: n = 2844 (mean age, 63.14 ± 8.37 years; 97.1% male; SBP, 9.7%) with NPVs were 98.8%, 95.3%, and 94.5%. In Validation cohort #2: n = 276 (mean age, 56.08 ± 9.09; 59.6% male; SBP, 7.6%) with NPVs were 100%, 98.9%, and 98.0% The final machine learning model showed the greatest net benefit on decision-curve analyses. CONCLUSIONS: A machine learning model generated using routinely collected variables excluded SBP with high NPV. Applying this model could ease the need to provide paracentesis in resource-limited settings by excluding those unlikely to have SBP.

Generation and External Validation of a Histologic Transformation Risk Model for Patients with Follicular Lymphoma.

Follicular lymphoma (FL) is the most frequent indolent lymphoma. Some patients (10%-15%) experience histologic transformation (HT) to a more aggressive lymphoma, usually diffuse large B-cell lymphoma (DLBCL). This study aimed to validate and improve a genetic risk model to predict HT at diagnosis.We collected mutational data from diagnosis biopsies of 64 FL patients. We combined them with the data from a previously published cohort (total n = 104; 62 from nontransformed and 42 from patients who did transform to DLBCL). This combined cohort was used to develop a nomogram to estimate the risk of HT. Prognostic mutated genes and clinical variables were assessed using Cox regression analysis to generate a risk model. The model was internally validated by bootstrapping and externally validated in an independent cohort. Its performance was evaluated using a concordance index and a calibration curve. The clinicogenetic nomogram included the mutational status of 3 genes (HIST1HE1, KMT2D, and TNFSR14) and high-risk Follicular Lymphoma International Prognostic Index and predicted HT with a concordance index of 0.746. Patients were classified as being at low or high risk of transformation. The probability HT function at 24 months was 0.90 in the low-risk group vs 0.51 in the high-risk group and, at 60 months, 0.71 vs 0.15, respectively. In the external validation cohort, the probability HT function in the low-risk group was 0.86 vs 0.54 in the high-risk group at 24 months, and 0.71 vs 0.32 at 60 months. The concordance index in the external cohort was 0.552. In conclusion, we propose a clinicogenetic risk model to predict FL HT to DLBLC, combining genetic alterations in HIST1H1E, KMT2D, and TNFRSF14 genes and clinical features (Follicular Lymphoma International Prognostic Index) at diagnosis. This model could improve the management of FL patients and allow treatment strategies that would prevent or delay transformation.

Development and validation of predictive models for myopia onset and progression using extensive 15-year refractive data in children and adolescents.

BACKGROUND: Global myopia prevalence poses a substantial public health burden with vision-threatening complications, necessitating effective prevention and control strategies. Precise prediction of spherical equivalent (SE), myopia, and high myopia onset is vital for proactive clinical interventions. METHODS: We reviewed electronic medical records of pediatric and adolescent patients who underwent cycloplegic refraction measurements at the Eye & Ear, Nose, and Throat Hospital of Fudan University between January 2005 and December 2019. Patients aged 3-18 years who met the inclusion criteria were enrolled in this study. To predict the SE and onset of myopia and high myopia in a specific year, two distinct models, random forest (RF) and the gradient boosted tree algorithm (XGBoost), were trained and validated based on variables such as age at baseline, and SE at various intervals. Outputs included SE, the onset of myopia, and high myopia up to 15 years post-initial examination. Age-stratified analyses and feature importance assessments were conducted to augment the clinical significance of the models. RESULTS: The study enrolled 88,250 individuals with 408,255 refraction records. The XGBoost-based SE prediction model consistently demonstrated robust and better performance than RF over 15 years, maintaining an R2 exceeding 0.729, and a Mean Absolute Error ranging from 0.078 to 1.802 in the test set. Myopia onset prediction exhibited strong area under the curve (AUC) values between 0.845 and 0.953 over 15 years, and high myopia onset prediction showed robust AUC values (0.807-0.997 over 13 years, with the 14th year at 0.765), emphasizing the models' effectiveness across age groups and temporal dimensions on the test set. Additionally, our classification models exhibited excellent calibration, as evidenced by consistently low brier score values, all falling below 0.25. Moreover, our findings underscore the importance of commencing regular examinations at an early age to predict high myopia. CONCLUSIONS: The XGBoost predictive models exhibited high accuracy in predicting SE, onset of myopia, and high myopia among children and adolescents aged 3-18 years. Our findings emphasize the importance of early and regular examinations at a young age for predicting high myopia, thereby providing valuable insights for clinical practice.

Clinical data mining: challenges, opportunities, and recommendations for translational applications.

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Predictive models for personalized precision medical intervention in spontaneous regression stages of cervical precancerous lesions.

BACKGROUND: During the prolonged period from Human Papillomavirus (HPV) infection to cervical cancer development, Low-Grade Squamous Intraepithelial Lesion (LSIL) stage provides a critical opportunity for cervical cancer prevention, giving the high potential for reversal in this stage. However, there is few research and a lack of clear guidelines on appropriate intervention strategies at this stage, underscoring the need for real-time prognostic predictions and personalized treatments to promote lesion reversal. METHODS: We have established a prospective cohort. Since 2018, we have been collecting clinical data and pathological images of HPV-infected patients, followed by tracking the progression of their cervical lesions. In constructing our predictive models, we applied logistic regression and six machine learning models, evaluating each model's predictive performance using metrics such as the Area Under the Curve (AUC). We also employed the SHAP method for interpretative analysis of the prediction results. Additionally, the model identifies key factors influencing the progression of the lesions. RESULTS: Model comparisons highlighted the superior performance of Random Forests (RF) and Support Vector Machines (SVM), both in clinical parameter and pathological image-based predictions. Notably, the RF model, which integrates pathological images and clinical multi-parameters, achieved the highest AUC of 0.866. Another significant finding was the substantial impact of sleep quality on the spontaneous clearance of HPV and regression of LSIL. CONCLUSIONS: In contrast to current cervical cancer prediction models, our model's prognostic capabilities extend to the spontaneous regression stage of cervical cancer. This model aids clinicians in real-time monitoring of lesions and in developing personalized treatment or follow-up plans by assessing individual risk factors, thus fostering lesion spontaneous reversal and aiding in cervical cancer prevention and reduction.

Enhanced predictive validity of integrative models for refractory hyperthyroidism considering baseline and early therapy characteristics: a prospective cohort study.

BACKGROUND: A subset of Graves' disease (GD) patients develops refractory hyperthyroidism, posing challenges in treatment decisions. The predictive value of baseline characteristics and early therapy indicators in identifying high risk individuals is an area worth exploration. METHODS: A prospective cohort study (2018-2022) involved 597 newly diagnosed adult GD patients undergoing methimazole (MMI) treatment. Baseline characteristics and 3-month therapy parameters were utilized to develop predictive models for refractory GD, considering antithyroid drug (ATD) dosage regimens. RESULTS: Among 346 patients analyzed, 49.7% developed ATD-refractory GD, marked by recurrence and sustained Thyrotropin Receptor Antibody (TRAb) positivity. Key baseline factors, including younger age, Graves' ophthalmopathy (GO), larger goiter size, and higher initial free triiodothyronine (fT3), free thyroxine (fT4), and TRAb levels, were all significantly associated with an increased risk of refractory GD, forming the baseline predictive model (Model A). Subsequent analysis based on MMI cumulative dosage at 3 months resulted in two subgroups: a high cumulative dosage group (average ≥ 20 mg/day) and a medium-low cumulative dosage group (average < 20 mg/day). Absolute values, percentage changes, and cumulative values of thyroid function and autoantibodies at 3 months were analyzed. Two combined predictive models, Model B (high cumulative dosage) and Model C (medium-low cumulative dosage), were developed based on stepwise regression and multivariate analysis, incorporating additional 3-month parameters beyond the baseline. In both groups, these combined models outperformed the baseline model in terms of discriminative ability (measured by AUC), concordance with actual outcomes (66.2% comprehensive improvement), and risk classification accuracy (especially for Class I and II patients with baseline predictive risk < 71%). The reliability of the above models was confirmed through additional analysis using random forests. This study also explored ATD dosage regimens, revealing differences in refractory outcomes between predicted risk groups. However, adjusting MMI dosage after early risk assessment did not conclusively improve the prognosis of refractory GD. CONCLUSION: Integrating baseline and early therapy characteristics enhances the predictive capability for refractory GD outcomes. The study provides valuable insights into refining risk assessment and guiding personalized treatment decisions for GD patients.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

A multiparametric risk table for loss of clinical remission status in patients with rheumatoid arthritis: A starter study post-hoc analysis.

OBJECTIVE: This post-hoc analysis was carried out on data acquired in the longitudinal Sonographic Tenosynovitis/arthritis Assessment in Rheumatoid Arthritis Patients in Remission (STARTER) study. Its primary aim was to determine the predictive clinical and MSUS features factors for disease flare in RA patients in clinical remission, whilst its secondary aim was to evaluate the probability of disease flare based on clinical and MSUS features. METHODS: The analysis included a total of 389 RA patients in DAS28-defined remission. All patients underwent a MSUS examination according to OMERACT guidelines. Logistic regression and results presented as OR and 95%CI were used for the evaluation of the association between selected variables and disease flare. Significant clinical and MSUS features were incorporated into a risk table to predict disease flare within 12 months in RA remission patients. RESULTS: Within 12 months, 137(35%) RA patients experienced a disease flare. RA patients who experienced a flare disease differed from persistent remission for ACPA positivity (75.9%vs62.3%; p= 0.007), percentage of sustained clinical remission at baseline (44.1%vs68.5%; p= 0.001) and synovium PD signal presence (58.4%vs33.3%; p< 0.001). Based on these results, the three features were considered in a predictive model of disease flare with adjOR 3.064(95%CI 1.728-5.432). Finally, a risk table was constructed including the three significant predictive factors of disease flare within 12 months from the enrolment. CONCLUSION: An adaptive flare prediction model tool, based on data available in outpatient setting, were developed as a multiparametric risk table. If confirmed by the external validation, this tool might support the definition of therapeutic strategies in RA patients in DAS28-defined remission status.

Two-Year Outcomes and Biomarker Analysis of Locally Advanced Gastric and Gastroesophageal Junction Adenocarcinoma After Neoadjuvant Chemotherapy and Immunotherapy from the Phase II WuhanUHGI001 Trial.

BACKGROUND: This study reports the 2-year outcomes and biomarker analysis results of patients with locally advanced gastric and gastroesophageal junction (G/GEJ) adenocarcinoma who received neoadjuvant chemotherapy and immunotherapy in a phase II WuhanUHGI001 trial. METHODS: Eligible patients with cT3/4aN+M0 locally advanced G/GEJ adenocarcinoma were screened, enrolled, and treated with 3 cycles of neoadjuvant tislelizumab and SOX followed by D2 gastrectomy and another 5 cycles of postoperative adjuvant SOX. The primary endpoint was major pathological response. RESULTS: Of the 49 included patients, 24 (49.0%) achieved major pathological response and 13 (26.5%) achieved pathological complete response. During a median follow-up of 26.8 months, the 2-year progression-free survival (PFS) and overall survival (OS) rates were 69.4% and 81.2%, respectively. Grade 3-4 adverse events occurred in six patients (12.2%) during the neoadjuvant period, eight patients (17.0%) during the postoperative period, and seven patients (15.2%) during the adjuvant period. Biomarker analysis revealed that the pathological complete response showed no association with 2-year PFS and OS. Major pathological response showed a potentially strong association with improved 2-year PFS and OS rates. In addition, preoperative circulating tumor cells combined with pathological responses are helpful in prognosis assessment. In addition, our results showed that T downstaging, lymphocyte-to-monocyte ratio, and CD3+ T cells were independent factors that affect PFS. The signet ring cell component (SRCC), T downstaging, and neutrophil-to-lymphocyte ratio were independent factors affecting OS. Prognostic nomograms of PFS and OS constructed based on the multivariate Cox regression results demonstrated suitable calibration and discrimination ability. CONCLUSIONS: Neoadjuvant tislelizumab plus SOX exhibits promising efficacy and acceptable toxicity in patients with locally advanced G/GEJ adenocarcinoma. In addition, our study established a prognostic risk signature and nomograms based on clinicopathological characteristics, which can accurately predict patient outcomes and aid in personalized treatment planning.

Predictive model for CAR-T cell therapy success in patients with relapsed/refractory B-cell acute lymphoblastic leukaemia.

Chimeric antigen receptor T-cell (CAR-T) therapy has demonstrated remarkable efficacy in treating relapsed/refractory acute B-cell lymphoblastic leukaemia (R/R B-ALL). However, a subset of patients does not benefit from CAR-T therapy. Our study aims to identify predictive indicators and establish a model to evaluate the feasibility of CAR-T therapy. Fifty-five R/R B-ALL patients and 22 healthy donors were enrolled. Peripheral blood lymphocyte subsets were analysed using flow cytometry. Sensitivity, specificity, accuracy, positive and negative predictive values and receiver operating characteristic (ROC) areas under the curve (AUC) were determined to evaluate the predictive values of the indicators. We identified B lymphocyte, regulatory T cell (Treg) and peripheral blood minimal residual leukaemia cells (B-MRD) as indicators for predicting the success of CAR-T cell preparation with AUC 0.936, 0.857 and 0.914. Furthermore, a model based on CD3+ T count, CD4+ T/CD8+ T ratio, Treg and extramedullary diseases (EMD) was used to predict the response to CAR-T therapy with AUC of 0.938. Notably, a model based on CD4+ T/CD8+ T ratio, B, Treg and EMD were used in predicting the success of CAR-T therapy with AUC 0.966 [0.908-1.000], with specificity (92.59%) and sensitivity (91.67%). In the validated group, the predictive model predicted the success of CAR-T therapy with specificity (90.91%) and sensitivity (100%). We have identified several predictive indicators for CAR-T cell therapy success and a model has demonstrated robust predictive capacity for the success of CAR-T therapy. These results show great potential for guiding informed clinical decisions in the field of CAR-T cell therapy.

A Novel Predictive Model for Acute Kidney Injury Following Surgery of the Aorta.

Background: Acute kidney injury (AKI) frequently occurs after aortic surgery and has a significant impact on patient outcomes. Early detection or prediction of AKI is crucial for timely interventions. This study aims to develop and validate a novel model for predicting AKI following aortic surgery. Methods: We enrolled 156 patients who underwent on-pump aortic surgery in our hospital from February 2023 to April 2023. Postoperative levels of eight cytokines related to macrophage polarization analyzed using a multiplex cytokine assay. All-subset regression was used to select the optimal cytokines to predict AKI. A logistic regression model incorporating the selected cytokines was used for internal validation in combination with a bootstrapping technique. The model's ability to discriminate between cases of AKI and non-AKI was assessed using receiver operating characteristic (ROC) curve analysis. Results: Of the 156 patients, 109 (69.87%) developed postoperative AKI. Interferon-gamma (IFN- γ ) and interleukin-4 (IL-4) were identified as candidate AKI predictors. The cytokine-based model including IFN- γ and IL-4 demonstrated excellent discrimination (C-statistic: 0.90) and good calibration (Brier score: 0.11). A clinical nomogram was generated, and decision curve analysis revealed that the cytokine-based model outperformed the clinical factor-based model in terms of net benefit. Moreover, both IFN- γ and IL-4 emerged as independent risk factors for AKI. Patients in the second and third tertiles of IFN- γ and IL-4 concentrations had a significantly higher risk of severe AKI, a higher likelihood of requiring renal replacement therapy, or experiencing in-hospital death. These patients also had extended durations of mechanical ventilation and intensive care unit stays, compared with those in the first tertile (all p for group trend < 0.001). Conclusions: We successfully established a novel and powerful predictive model for AKI, and demonstrating the significance of IFN- γ and IL-4 as valuable clinical markers. These cytokines not only predict the risk of AKI following aortic surgery but are also linked to adverse in-hospital outcomes. This model offers a promising avenue for the early identification of high-risk patients, potentially improving clinical decision-making and patient care.

A single-center, retrospective study of hospitalized patients with lower respiratory tract infections: clinical assessment of metagenomic next-generation sequencing and identification of risk factors in patients.

INTRODUCTION: Lower respiratory tract infections(LRTIs) in adults are complicated by diverse pathogens that challenge traditional detection methods, which are often slow and insensitive. Metagenomic next-generation sequencing (mNGS) offers a comprehensive, high-throughput, and unbiased approach to pathogen identification. This retrospective study evaluates the diagnostic efficacy of mNGS compared to conventional microbiological testing (CMT) in LRTIs, aiming to enhance detection accuracy and enable early clinical prediction. METHODS: In our retrospective single-center analysis, 451 patients with suspected LRTIs underwent mNGS testing from July 2020 to July 2023. We assessed the pathogen spectrum and compared the diagnostic efficacy of mNGS to CMT, with clinical comprehensive diagnosis serving as the reference standard. The study analyzed mNGS performance in lung tissue biopsies and bronchoalveolar lavage fluid (BALF) from cases suspected of lung infection. Patients were stratified into two groups based on clinical outcomes (improvement or mortality), and we compared clinical data and conventional laboratory indices between groups. A predictive model and nomogram for the prognosis of LRTIs were constructed using univariate followed by multivariate logistic regression, with model predictive accuracy evaluated by the area under the ROC curve (AUC). RESULTS: (1) Comparative Analysis of mNGS versus CMT: In a comprehensive analysis of 510 specimens, where 59 cases were concurrently collected from lung tissue biopsies and BALF, the study highlights the diagnostic superiority of mNGS over CMT. Specifically, mNGS demonstrated significantly higher sensitivity and specificity in BALF samples (82.86% vs. 44.42% and 52.00% vs. 21.05%, respectively, p < 0.001) alongside greater positive and negative predictive values (96.71% vs. 79.55% and 15.12% vs. 5.19%, respectively, p < 0.01). Additionally, when comparing simultaneous testing of lung tissue biopsies and BALF, mNGS showed enhanced sensitivity in BALF (84.21% vs. 57.41%), whereas lung tissues offered higher specificity (80.00% vs. 50.00%). (2) Analysis of Infectious Species in Patients from This Study: The study also notes a concerning incidence of lung abscesses and identifies Epstein-Barr virus (EBV), Fusobacterium nucleatum, Mycoplasma pneumoniae, Chlamydia psittaci, and Haemophilus influenzae as the most common pathogens, with Klebsiella pneumoniae emerging as the predominant bacterial culprit. Among herpes viruses, EBV and herpes virus 7 (HHV-7) were most frequently detected, with HHV-7 more prevalent in immunocompromised individuals. (3) Risk Factors for Adverse Prognosis and a Mortality Risk Prediction Model in Patients with LRTIs: We identified key risk factors for poor prognosis in lower respiratory tract infection patients, with significant findings including delayed time to mNGS testing, low lymphocyte percentage, presence of chronic lung disease, multiple comorbidities, false-negative CMT results, and positive herpesvirus affecting patient outcomes. We also developed a nomogram model with good consistency and high accuracy (AUC of 0.825) for predicting mortality risk in these patients, offering a valuable clinical tool for assessing prognosis. CONCLUSION: The study underscores mNGS as a superior tool for lower respiratory tract infection diagnosis, exhibiting higher sensitivity and specificity than traditional methods.