Time-dependent Receiver Operating Characteristic Research Articles

Prognostic value of advanced lung cancer inflammation index in heart failure patients: A comprehensive analysis.

The prognosis of heart failure (HF) is closely linked to inflammation and nutritional status. The advanced lung cancer inflammation index (ALI) is a composite indicator consisting of several parameters used to assess inflammation and nutritional status. Our study aimed to investigate the prognostic value of ALI in HF patients. The data from Study 1, which included 1359 HF patients, were extracted from the National Health and Nutrition Examination Survey (NHANES) database spanning the years 1999 to 2018. Study 2 analysed data from patients with HF who underwent cardiac magnetic resonance imaging examinations from 2020 to 2023. Kaplan-Meier curve analysis, Cox proportional hazard model, time-dependent receiver operating characteristic (ROC) curve and restricted cubic spline (RCS) were used to evaluate the relationship between ALI and long-term prognosis of patients with HF in Study 1. Logistic regression analysis was used to evaluate the correlation between ALI and left ventricular reverse remodelling, and RCS was used to determine any dose-response relationship. Spearman correlation was used to evaluate the relationship between ALI and indicators of cardiac structural changes. Study 1 found that the average age of the patients was 68years [inter-quartile range (IQR) 58-76], the proportion of males was 54.3%, and there were 699 all-cause mortality and 293 cardiovascular mortality cases. After adjusted by multivariable Cox regression analysis, elevated ALI levels were significantly associated with increased risks of all-cause [hazard ratio (HR)=0.58, 95% confidence interval (CI)=0.42-0.79, P<0.001] and cardiovascular mortality (HR=0.61, 95% CI=0.38-0.97, P=0.036) in patients with HF. A linear negative correlation was observed between ALI and both all-cause (P=0.0011 and P<0.001, P for nonlinear=0.3993) and cardiovascular mortality (P=0.0011, P for nonlinear=0.5198). Time-dependent ROC curves showed the predictive value of ALI for all-cause mortality [area under the curve (AUC)=0.678 in 3years, AUC=0.674 in 5years and AUC=0.683 in 10years] and cardiovascular mortality (AUC=0.694 in 3years, AUC=0.685 in 5years and AUC=0.697 in 10years). Study 2 included 79 patients; the average age of the patients was 44years (IQR 35-55); and the proportion of males was 74.7%. Adjusted multivariable logistic regression analysis indicated that high ALI levels were associated with left ventricular reverse remodelling (LVRR) in patients with HF following discharge from the hospital [odds ratio (OR)=3.16, 95% CI=1.06-10.8, P=0.049]. Spearman analysis revealed a correlation between ALI and extracellular volume (ECV) (r=-0.25, P=0.023). ALI is associated with all-cause and cardiovascular mortality risk and structural changes in the heart in patients with HF.

Early prediction of acute-on-chronic liver failure development in patients with diverse chronic liver diseases

Acute-on-chronic liver failure (ACLF) is a syndrome characterized by the acute decompensation of chronic liver disease, resulting in organ failure and high short-term mortality. The progression of ACLF is dynamic and reversible in a considerable proportion of patients during hospitalization. Early detection and accurate assessment of ACLF are essential; however, ideal methods for this purpose are still lacking. Therefore, this study aimed to develop a new score for predicting the onset of ACLF in patients with various chronic liver diseases.A total of 6,188 patients with various chronic liver diseases were included in the study. Clinical and laboratory data were collected, and the occurrence of ACLF within 28 days was recorded. The Lasso-Cox regression method was employed to develop prediction models for the onset of ACLF at 7, 14, and 28 days. Among 5,221 patients without ACLF, 477 progressed to ACLF within 28 days. Seven predictors were identified as significantly associated with the occurrence of ACLF at 7, 14, and 28 days. A new scoring system was developed as follows: [NEUT ≥ 7, 109/L; 1 or 0] × 0.49 + [PLT < 100, 109/L; 1 or 0] × 0.44 + [TBIL ≥ 35, µmol/L; 1 or 0] × 0.05 + [HDL-C < 0.5, mmol/L; 1 or 0] × 1.04 – Ln[Hb, g/L] × 0.89 + [BUN > 7, mmol/L; 1 or 0] × 0.51 + Ln[INR] × 0.87 + 3.40. This new score demonstrated superior discrimination, with the C-indexes of 0.958, 0.944, and 0.938 at 7, 14, and 28 days, respectively, outperforming those of four other scores (CLIF-C-ACLF-Ds, MELD, MELD-Na, and CLIF-C-ADs score; all P < 0.001). Additionally, the new score improved in predictive accuracy, time-dependent receiver operating characteristics, probability density function evaluations, and calibration curves, making it highly predictive for the onset of ACLF at all time points. The optimal cut-off value of 9.6 effectively distinguished between high- and low-risk patients for ACLF onset. These findings were further validated in a separate cohort of patients. A new progressive score, based on seven predictors, has been developed to accurately forecast the occurrence of ACLF within 7, 14, and 28 days in patients with various chronic liver diseases. This tool may be utilized to identify high-risk patients, tailor follow-up management, and guide the escalation of care, prognostication, and transplant evaluation.

The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b in patients with advanced melanoma.

Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-α1b in patients with advanced melanoma. We retrospectively analyzed 113 patients with the American Joint Committee on Cancer (AJCC) stage III-IV melanoma who received postoperative IFN-α1b therapy between July 2009 and February 2024. Recurrence-free survival (RFS) and overall survival (OS) were assessed using Kaplan-Meier analysis. Five machine learning models (Decision Tree, Cox Proportional Hazards, Random Forest, Support Vector Machine, and LASSO regression) were developed and compared for their capacity to predict the outcomes of patients. Model performance was evaluated using concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis. The 1-year, 2-year, and 3-year RFS rates were 71.10%, 43.10%, and 31.10%, respectively. For OS, the 1-year, 2-year, and 3-year OS rates were 99.10%, 82.30%, and 75.00%, respectively. The Decision Tree (DT) model demonstrated superior predictive performance with the highest C-index of 0.792. Time-dependent ROC analysis for predicting 1-, 2-, and 3-year RFS based on the DT model is 0.77, 0.79 and 0.76, respectively. Serum albumin emerged as the important predictor of RFS. Our study demonstrates the considerable efficacy DT model for predicting the efficacy of adjuvant IFN-α1b in patients with advanced melanoma. Serum albumin was identified as a key predictive factor of the treatment efficacy.

Prognostic value of neutrophil to lymphocyte ratio and lymphocyte counts before durvalumab consolidation after radio-chemotherapy in locally advanced non-small cell lung cancer

BackgroundDurvalumab, an anti-PD-L1 immune checkpoint inhibitor, after radio-chemotherapy (RCT) has changed the management of locally advanced non-small cell lung cancer (LA NSCLC). A series of retrospective studies have investigated different cut-off of lymphocyte count (LyC) and neutrophil-to-lymphocyte ratio (NLR) to predict survival in LA NSCLC. None of these studies has validated their threshold in an independent group of patients. We wanted to assess the OS prognostic value of NLR and LyC in patients with LA NSCLC treated by RCT and durvalumab, with threshold determination and their validation in an external cohort.MethodsPatients were enrolled in four institutions between Oct. 2017 and Jan. 2022. Pre durvalumab LyC, neutrophils count (NC) and NLR were collected. To define NLR and LyC cut-off value predicting survival event, time dependent Receiver Operating Characteristics (ROC) curves was performed. Survival outcomes were estimated by the Kaplan-Meier method and differences were compared using univariate and multivariate Cox proportional hazard models.ResultsWe included 76 patients in the training set and 85 in the test set. The best cut off were 2,94 for NLR and 0,61 G/l for LyC to predict OS in the training set. For patients with NLR > 2,94, univariate analysis showed no significant deterioration in OS in either the training set (p = 0,066) or the test set (p = 0,12). Patients with LyC > 0,61 G/L, in univariate analysis, had longer OS in training set (p = 0,030) and in test set (p = 0,0062). This OS increase was not found in multivariate analysis (p = 0,057) in training set but was confirmed in test set (0,039).ConclusionLyC > 0,61 G/l is associated with longer OS for LA NSCLC patient’s treated with RCT and durvalumab in univariate analysis. In this context, a particular expectation for organs at risk sparing during RT to avoid lymphopenia seems important.Trial registrationRetrospectively registered.

Assessing the validity of METS-IR for predicting the future onset of diabetes: an analysis using time-dependent receiver operating characteristics

BackgroundThe Metabolic Insulin Resistance Score (METS-IR) is a non-invasive proxy for insulin resistance (IR) that has been newly developed in recent years and has been shown to be associated with diabetes risk. Our aim was to assess the predictive value of METS-IR for the future development of diabetes and its temporal differences in people of different sex, age, and body mass index (BMI).MethodsThe current study included 15,453 baseline non-diabetic subjects in the NAGALA cohort and then grouped according to the World Health Organization’s (WHO) recommended criteria for age and BMI. Multivariate Cox regression and time-dependent receiver operator characteristics (ROC) curves were used to analyze the value of METS-IR in assessing and predicting the risk of diabetes in people of different sexes, ages, and BMIs.Results373 individuals developed diabetes during the observation period. By multivariate COX regression analysis, the development of future diabetes was significantly associated with increased METS-IR, and this positive association was stronger in women than in men and in individuals < 45 years than in individuals ≥ 45 years; while no significant differences were observed between non-obese and overweight/obesity individuals. Using time-dependent ROC analysis we also assessed the predictive value of METS-IR for future diabetes at a total of 11-time points between 2 and 12 years. The results showed that METS-IR had a higher predictive value for the future development of diabetes in women or individuals < 45 years of age compared to men or individuals ≥ 45 years of age for almost the entire follow-up period. Furthermore, across different BMI categories, we also found that in the short term (3–5 years), METS-IR had a higher predictive value for the development of diabetes in individuals with overweight/obesity, while in the medium to long term (6–12 years), METS-IR was more accurate in predicting the development of diabetes in non-obese individuals.ConclusionsOur study showed that METS-IR was independently associated with the development of future diabetes in a non-diabetic population. METS-IR was a good predictor of diabetes, especially for women and individuals < 45 years old for predicting the future risk of developing diabetes at all times.

Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with gastric neuroendocrine neoplasms (gNENs) after diagnosis. Data on patients with gNENs were extracted and randomly divided into training and validation sets using the Surveillance, Epidemiology, and End Results database. We developed a prediction model using 10 machine learning algorithms across 101 combinations to forecast cancer-related mortality in patients with gNENs, selecting the best model using the highest mean over a sequence of time-dependent area under the receiver operating characteristic (ROC) curve (AUC). The performance of the final model was assessed through time-dependent ROC curves for discrimination and calibration curves for calibration. The maximum selection rank method was used to determine the best prognostic risk score threshold for classifying patients into high- and low-risk groups. Afterward, Kaplan-Meier analysis and log-rank test were used to compare survival rates among these groups. Our study examined 775 patients with gNENs, dividing them into training and validation sets. A training set comprised 543 patients, with a median follow-up of 42 months and cumulative mortality rates of 40.0% at 1 year, 48.6% at 3 years, and 54.0% at 5 years. A validation set comprised 232 patients, with cumulative mortality rates of 29.1% at 1 year, 43.5% at 3 years, and 53.2% at 5 years. The optimal random survival forest (RSF) model (mtry = 4, node size = 5) achieved an AUC of 0.839 for survival prediction in the training set. Comprising 11 variables such as demographics, treatment details, tumor characteristics, T staging, N staging, and M staging, the RSF model revealed high predictive accuracy with AUCs of 0.92, 0.96, and 0.96 for 1-, 3-, and 5-year survival, respectively, which was consistently reflected in the validation set with AUCs of 0.88, 0.92, and 0.89, respectively. Moreover, patients were risk-stratified. Although our RSF model effectively stratified patients into different prognostic groups, it needs external validation to confirm its utility for noninvasive prognostic prediction and risk stratification in gNENs. Further research is required to verify its broader clinical applicability.

Development of a Prognostic Nomogram for Nonneutropenic Invasive Pulmonary Aspergillosis Based on Machine Learning.

The incidence of invasive pulmonary aspergillosis (IPA) is progressively rising in the nonneutropenic population, but studies investigating relevant prognostic factors remain scarce. Participants who were hospitalized at Zhongshan City People's Hospital from January 2018 to May 2023 and diagnosed with nonneutropenic deficient IPA were included in this study. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression methods were used to select variables for constructing the predictive model. The performance of the predictive model was evaluated using the concordance index (C-index), calibration curve, time-dependent receiver operating characteristic (T-ROC) curve, area under the curve (AUC), and decision curve analysis (DCA). Finally, prognostic risk stratification was performed for nonneutropenic IPA patients, transforming the nomogram into a risk-stratified prognostic model. A total of 210 participants were included in this study, divided into training and validation cohorts at a ratio of 7.5:2.5. Lasso regression identified seven potential predictive factors, including age, comorbid bacterial pneumonia, pleural effusion, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), invasive mechanical ventilation and ICU treatment. Multivariate Cox regression analysis showed age (HR=1.02), comorbid bacterial pneumonia (HR=3.36), NLR (HR=1.02), LDH (HR=1.001), and invasive mechanical ventilation (HR=4.86) as independent predictive factors and constructed nomogram. The calibration curves show that the nomogram performs well in terms of consistency between predictions and actual observations. The T-ROC curves and DCA of the nomogram model show that the recognition ability of the nomogram model was outstanding. Participants could be classified into high and low-risk groups based on the final score of this nomogram, demonstrating the excellent risk stratification performance of our model. The nomogram model developed in this study is an effective tool for predicting mortality risk in nonneutropenic IPA patients, aiding clinicians in identifying high-risk patients and optimizing early treatment strategies.

Association of estimated glucose disposal rate with incident cardiovascular disease under different metabolic and circadian rhythm states: findings from a national population-based prospective cohort study

BackgroundRecent studies have shown that both metabolic syndrome and circadian rhythm syndrome are firmly associated with the occurrence of cardiovascular disease (CVD), with insulin resistance playing a significant role. The estimated glucose disposal rate (eGDR) is considered to be a reliable surrogate marker for insulin resistance. However, the relationship between eGDR and CVD under different metabolic and circadian rhythm states has not been thoroughly studied, and large-scale prospective cohort studies are needed to clarify this relationship.MethodsThis study is based on the China Health and Retirement Longitudinal Study (CHARLS), recruiting individuals aged 45 and above with complete eGDR data. The eGDR was calculated by the formula: eGDR(mg/kg/min) = 21.158 − (0.09 × WC) − (3.407 × hypertension) − (0.551 × HbA1c) [WC (cm), hypertension (yes = 1/no = 0), and HbA1c (%)] (Zabala et al. in Cardiovasc Diabetol 20(1):202; 2021).Participants were divided into four subgroups based on the quartiles (Q) of eGDR.The cumulative incidence rates and hazard ratios (HR) with 95% confidence intervals (CI) were calculated, with the lowest eGDR quartile (representing the highest degree of insulin resistance) as the reference. Participants were further divided into subgroups based on the diagnosis of Metabolic syndrome (MetS) or circadian syndrome (CircS) to explore the relationship between eGDR and CVD under different metabolic and circadian rhythm conditions. The dose–response relationship between eGDR and CVD incidence was investigated using a restricted cubic spline (RCS) based on a Cox regression model. Receiver operating characteristic (ROC) curves were generated to assess the predictive value of eGDR for CVD incidence. A clinical decision curve analysis (DCA) was also conducted to assess the clinical utility of the basic model.Results6507 participants were included, with a median age of 58 years [52 years, 64 years], and 55% were female. Over a median follow-up duration of 87 months, 679 first-episode CVD events were recorded, including heart disease and stroke. The RCS curves demonstrated a significant dose–response relationship between eGDR and the incidence of first-presentation CVD in different metabolic and circadian rhythm subgroups (all P-values < 0.001, non-linearity P > 0.05). eGDR exhibited a significant linear relationship with all outcomes (non-linearity P < 0.05). The Kaplan–Meier cumulative incidence curves showed that as eGDR levels increased, the cumulative incidence rates of first CVD, heart disease, and stroke gradually decreased from Q1 to Q4 groups. Significant differences were observed across all metabolic and circadian rhythm subgroups (log-rank test P < 0.001). Through the Cox proportional hazards model, we confirmed a significant association between baseline eGDR levels and first-onset CVD, heart disease, and stroke. Subgroup analyses indicated that the predictive ability of eGDR for CVD risk varied across different Body mass index (BMI) (P for interaction = 0.025) and age (P for interaction = 0.045) subgroups. Mediation analysis revealed that CircS partially mediated this association. Furthermore, time-dependent ROC curves demonstrated the potential of eGDR as a predictor of CVD risk, revealing possible differences in the model's application across different cardiovascular conditions.ConclusioneGDR is an independent predictor of CVD risk, with lower eGDR levels being closely associated with a higher risk of CVD (including heart disease and stroke). In populations with MetS or CircS, the association between lower eGDR levels and increased risk is more pronounced.

Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.

Due to the heterogeneity in the effectiveness of immunotherapy for lung cancer, identifying predictors is crucial. This study aimed to develop a machine learning model to identify predictors of overall survival in lung cancer patients treated with immune checkpoint inhibitors (ICIs). A retrospective analysis was performed on data from 1314 lung cancer patients at the Chongqing University Cancer Hospital from September 2018 to September 2022. We used the random survival forest (RSF) model to identify survival-influencing factors, using backward elimination for variable selection. A Cox proportional hazards (CPH) model was constructed using the most significant predictors. We assessed model performance and generalizability using time-dependent receiver operating characteristics (ROC) and predictive error curves. The RSF model demonstrated better predictive accuracy than the CPH (IBS 0.17 vs. 0.17; C-index 0.91 vs. 0.68), with better discrimination and prediction performance. The influential variables identified included D-dimer, Karnofsky performance status, albumin, surgery, TNM stage, platelet count, and age. The RSF model, which incorporated these variables, achieved area under the curve (AUC) scores of 0.95, 0.94, and 0.98 for 1-, 3-, and 5-year survival predictions, respectively, in the training set. The validation set showed AUCs of 0.94, 0.90, and 0.95, respectively, exceeding the performance of the CPH model. The study successfully developed a machine learning model that accurately predicted the survival benefits of ICI therapy in lung cancer patients, supporting clinical decision-making in lung cancer treatment.

Association of neutrophil-percentage-to-albumin ratio with mortality in cardiovascular disease patients complicating abnormal glucose metabolism

Abstract Background Cardiovascular disease (CVD) patients complicated with abnormal glucose metabolism are at especially higher risk of cardiovascular mortality. Therefore, it is critical to identify and manage CVD patients complicated with abnormal glucose metabolism at higher risk of mortality to improve the overall prognosis of such population. Evidence regarding neutrophil-percentage-to-albumin ratio (NPAR) and survival outcomes in CVD patients complicated with abnormal glucose metabolism remains scarce. Purpose To prospectively evaluate the association of NPAR with cardiovascular and all-cause mortality in CVD patients complicating abnormal glucose metabolism. Methods This study included 3,897 CVD patients with diabetes or pre-diabetes from the National Health and Nutrition Examination Survey (NHANES, 1999-2018). Mortality status was determined by access to NHANES public-use linked mortality files. Multivariate Cox proportional hazards regression analysis, threshold effect analysis, and stratified analysis were performed to examine the association of NPAR with cardiovascular and all-cause mortality. The prognostic value of NPAR was further assessed by time-dependent receiver operating characteristic (ROC) curve analysis. Results During a median follow-up period of 76 months, there were 703 cardiovascular deaths and 1,727 all-cause deaths. After adjusting for confounders, NPAR was positively nonlinear associated with cardiovascular and all-cause mortality. Furthermore, the thresholds of NPAR were identified for cardiovascular and all-cause mortality as 14.10 and 14.49, respectively. When the NPAR exceeded the thresholds, a 1-unit increase of NPAR was associated with a 16% and 18% greater hazard ratio (HR) for cardiovascular (HR = 1.16, 95% confidence interval (CI) = 1.08–1.23, P &amp;lt; 0.001) and all-cause mortality (HR = 1.18, 95% CI = 1.13–1.22, P &amp;lt; 0.001), respectively. The time-dependent ROC curve analysis showed that the NPAR had moderate prognostic value and outperformed the neutrophil-to-lymphocyte ratio for predicting short-term survival outcomes. Conclusions The NPAR is a superior predictor of survival outcomes in CVD patients complicated with abnormal glucose metabolism. The thresholds of 14.10 and 14.49 for cardiovascular and all-cause mortality, respectively.

The prognostic value of advanced lung cancer inflammation index in cancer survivors complicated with cardiovascular disease

Abstract Background The advanced lung cancer inflammation index (ALI) reflects systemic inflammatory and nutritional status, both of which are related to the prognosis of the disease. However, the prognostic value of ALI in cancer survivors with cardiovascular disease (CVD) remains unclear. Purpose This study was conducted to investigate the association of ALI with mortality in cancer survivors with CVD. Methods This study enrolled 955 cancer survivors with CVD from the National Health and Nutrition Examination Survey (NHANES, 1999-2018), and followed mortality status until December 31, 2019. Cox proportional hazards regression analysis and threshold effect analysis were applied to evaluate the relationship between ALI and all-cause, CVD-specific, and cancer-specific mortality. The prognostic value of ALI was further examined by time-dependent receiver operating characteristic (ROC) curve analysis. Results During a median follow-up period of 66.65 months, 490 all-cause deaths were observed, including 181 CVD-specific deaths and 121 cancer-specific deaths. After multivariate adjustment, ALI was inversely associated with all-cause, CVD-specific, and cancer-specific mortality in cancer survivors with CVD. Furthermore, L-shaped associations of ALI with mortality were discovered, with inflection points of 74.18 for all-cause and CVD-specific mortality and 55.50 for cancer-specific mortality. When the ALI was less than the inflection points, a 10-unit increase of ALI was associated with a 16%, 16%, and 25% decreased HR for all-cause, CVD-specific, and cancer-specific mortality, respectively. The time-dependent ROC curve analysis showed that the ALI had moderate predictive value for short- and long-term mortality. Conclusions The ALI is a significant prognostic indicator of mortality for cancer survivors with CVD. The findings allow for early prevention to improve the overall prognosis in such populations.

Prospective Longitudinal Analysis of Physiologic MRI-based Tumor Habitat Predicts Short-term Patient Outcomes in IDH-wildtype Glioblastoma.

This study validates MRI-based tumor habitats in predicting time-to-progression (TTP), overall survival (OS), and progression site in isocitrate dehydrogenase (IDH)-wildtype glioblastoma patients. Seventy-nine patients were prospectively enrolled between January 2020 and June 2022. MRI, including diffusion-weighted and dynamic susceptibility contrast imaging, were obtained immediately post-operation and at three serial timepoints. Voxels from cerebral blood volume and apparent diffusion coefficient maps were grouped into three habitats (hypervascular cellular, hypovascular cellular, and nonviable tissue) using k-means clustering. Pre-defined cutoffs for increases in hypervascular and hypovascular cellular habitat were applied to calculate the habitat risk score. Associations between spatiotemporal habitats, habitat risk score, TTP, and OS were investigated using Cox proportional hazards modeling. Habitat risk score was compared to tumor volume using time-dependent receiver operating characteristics analysis. Progression sites were matched with spatial habitats. Increases in hypervascular and hypovascular cellular habitats and habitat risk scores were associated with shorter TTP and OS (all p < .05). Hypovascular cellular habitat and habitat risk scores 1 and 2 independently predicted TTP (hazard ratio [HR], 4.14; p=.03, HR, 4.51; p=.001 and HR, 10.02; p<.001, respectively). Hypovascular cellular habitat and habitat risk score 2 independently predicted OS (HR, 4.01, p=.003; and HR, 3.27, p<.001, respectively). Habitat risk score outperformed tumor volume in predicting TTP (12-month AUC, 0.762 vs. 0.646, p=.048). Hypovascular cellular habitat predicted progression sites (mean Dice index: 0.31). Multiparametric physiologic MRI-based spatiotemporal tumor habitats and habitat risk scores are useful biomarkers for early tumor progression and outcomes in IDH-wildtype glioblastoma patients.

Utilization of SYNTAX Score II for Predictive Clinical Outcomes in Patients with Coronary Artery Disease and Chronic Renal Insufficiency Following Percutaneous Coronary Intervention.

The SYNTAX score II (SS II) has earned widespread recognition for use on individuals with coronary artery disease (CAD) due to its reliable predictions of 4-year all-cause mortality (ACM). This research focuses on substantiating the prognostic significance of using the SS II for patients experiencing concurrent chronic renal insufficiency (CRI) and CAD who have undergone percutaneous coronary intervention (PCI). This study retrospectively examined 2468 patients with concurrent CAD and CRI who underwent PCI. Based on their SS II, these participants were sorted into low-, medium-, and high-risk groups and monitored over a median of three years. The evaluation of the predictive precision of different SYNTAX scores for clinical outcomes in patients with CRI after PCI involved using time-dependent receiver operating characteristic (ROC) curves. These included the standard SS (SS), SS II, clinical SS (CSS), and residual SS (rSS). The primary outcomes were ACM and cardiac mortality (CM), while the secondary outcomes covered major adverse cardiovascular and cerebrovascular events (MACCEs), stroke, unplanned revascularization, and myocardial infarction (MI). Higher 5-year cumulative incidences of MACCEs, MI, CM, and ACM were observed significantly in patients in the high SS II category relative to those in the low and medium SS II categories. Multivariable Cox regression analysis confirmed that the SS II independently predicts ACM, CM, MI, and MACCEs as a prognostic marker. Additionally, the analysis of the time-dependent ROC curve demonstrated that the areas under the curve (AUC) for predicting CM and ACM were 0.772 and 0.767, respectively, which are superior to those of other SYNTAX scores (p < 0.05). As an independent predictor, the SS II is notable for its ability to forecast long-term adverse outcomes, including MACCEs, CM, ACM, and MI. For patients with coexisting CAD and CRI undergoing PCI, it provides significantly improved prognostic accuracy for 5-year ACM and CM compared to other SYNTAX scores.

Prognosis of Patients with Hepatocellular Carcinoma Treated with TACE: A New Score Combining Alpha-Fetoprotein and Des-γ-Carboxy Prothrombin.

Hepatocellular carcinoma (HCC) represents a significant global health problem, requiring precise prognostic tools for optimal treatment stratification. This study aimed to develop a new risk prediction score, called AD score, based on the serum markers alpha-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP), to offer an objective and accurate preoperative assessment of HCC in patients undergoing transarterial chemoembolization (TACE). This was a retrospective study that included 295HCC patients who were subjected to TACE (training set, n=147; testing set, n=148). Serum AFP and DCP levels were log-transformed to construct the AD score. Multivariate Cox regression analysis on cirrhosis subgroups validated the objectivity of the model. Performance comparison of established models (Child Pugh, BCLC, ALBI, Up-to-seven, Six-and-twelve, Four and seven, HAP score, mHAP-II, FAIL-T score), was assessed through time-dependent receiver operating characteristic (ROC) curves and risk stratification. The AD score, incorporating lgAFP and lgDCP, demonstrated superior predictive accuracy than the existing models. Time-dependent ROC curve revealed the consistent superiority of the AD score over a 5-year period. The risk stratification into low, intermediate, and high group based on the AD score showed a significant survival difference in both training and testing set. For HCC patients undergoing TACE, the AD score serves as an objective and straightforward prognostic tool, enhancing predictive accuracy and showcasing its clinical utility. It demonstrates potential significance as a crucial addition to preoperative risk assessment for TACE.

Necroptosis-related lncRNAs: biomarkers for predicting prognosis and immune response in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most prevalent types of lung cancer (LC), accounting for 50% of all LC cases. Despite therapeutic advancements, patients suffer from adverse drug reactions. Furthermore, the prognosis of LC patients remains poor. Necroptosis is a novel mode of cell death and is critically involved in regulating immunotherapy in patients. However, the correlation between the necroptosis-related long non-coding RNA (lncRNA) (necro-related lnc) signature (NecroLncSig) and the response of patients with LUAD to immunotherapy is unclear. This study developed a model using lncRNAs to predict the prognosis of patients with LUAD. We obtained the transcriptomic and clinical data of LUAD patients from The Cancer Genome Atlas (TCGA) database. Next, we conducted a co-expression analysis to identify the necro-related lnc. In addition, we constructed the NecroLncSig using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then we evaluated and validated the NecroLncSig using a Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) curves, principal component analysis (PCA), Gene Ontology (GO) enrichment analysis, a nomogram, and calibration curves. Finally, we used the NecroLncSig to predict the responses of patients to immunotherapy. We constructed the NecroLncSig based on seven necro-related lnc. The patients were classified into a high-risk group (HRG) and a low-risk group (LRG). The overall survival (OS) of patients in the HRG was significantly poorer in the training, testing, and entire sets (P<0.05) than that of the patients in the LRG. Univariate and multivariate Cox regression analyses demonstrated that the risk score could predict the OS of patients in an independent manner (P<0.001). Time-dependent ROC analysis demonstrated that the area under the curve values of the NecroLncSig for 1-, 2-, and 3-year OS were 0.689, 0.700, and 0.685, respectively, for the entire set. Furthermore, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm showed that the response of patients in the HRG to immunotherapy was better than that of patients in the LRG. Necro-related lnc can affect disease progression and patient prognosis. In addition, these lncRNAs can be used to design therapeutic strategies, such as immunotherapy, to treat patients with LUAD.

Pathomics signatures and cuproptosis-related genes signatures for prediction of prognosis in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor with high heterogeneity and poor prognosis, so early prediction and treatment are still difficult. Cuproptosis is a newly discovered type of programmed cell death that has been shown to be closely related to the occurrence and progression of HCC. Cancer morphology is influenced by genetic drivers, and computational pathology methods typically use tissue images such as entire slide images as input to predict clinical or genetic features. Therefore, the comprehensive analysis of pathological features and genomic data provides a feasible way to explore the potential mechanism of the tumor. The objective of this study was to develop a prediction model for HCC prognosis based on the pathomics signatures (PS) and the genomics signatures (GS). A dataset comprising 315 HCC patients was randomly divided into a training set (n=200) and a validation set (n=115). Prognostic models related to PS and GS were constructed by univariate and multivariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, univariate and multivariate Cox analyses, and nomogram were used to evaluate the predictive performance of the prognostic model. The prognostic value of the model was internally validated. A prognostic model incorporating clinical features, PS, and GS was developed using Cox regression analysis and LASSO regression analyses. Kaplan-Meier survival analysis revealed statistically significant differences in survival time between high-risk and low-risk subgroups in both the training and validation datasets (PS: P=0.003 and <0.001, respectively; GS: P=0.008 and 0.004, respectively). The time-dependent ROC curve showed favorable predictive value for survival in both the training and validation sets. The area under the ROC curves at 1, 3, and 5 years was 0.750, 0.830, and 0.870 in the training set, and 0.780, 0.810, and 0.760 in the validation set, respectively. A nomogram model based on the risk model score could effectively predict the survival probability of HCC patients. The calibration curves further demonstrated the good predictive capability of the nomogram model. The prognostic model incorporating PS and GS could effectively predict the prognosis of HCC patients.

Novel multifactor predictive model for postoperative survival in gallbladder cancer: a multi-center study

BackgroundGallbladder cancer (GBC) is a highly aggressive malignancy, with limited survival profiles after curative surgeries. This study aimed to develop a practical model for predicting the postoperative overall survival (OS) in GBC patients.MethodsPatients from three hospitals were included. Two centers (N = 102 and 100) were adopted for model development and internal validation, and the third center (N = 85) was used for external testing. Univariate and stepwise multivariate Cox regression were used for feature selection. A nomogram for 1-, 3-, and 5-year postoperative survival rates was constructed accordingly. Performance assessment included Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier curves were utilized to evaluate the risk stratification results of the nomogram. Decision curves were used to reflect the net benefit.ResultsEight factors, TNM stage, age-adjusted Charlson Comorbidity Index (aCCI), body mass index (BMI), R0 resection, blood platelet count, and serum levels of albumin, CA125, CA199 were incorporated in the nomogram. The time-dependent C-index consistently exceeded 0.70 from 6 months to 5 years, and time-dependent ROC revealed an area under the curve (AUC) of over 75% for 1-, 3-, and 5-year survival. The calibration curves, Kaplan-Meier curves and decision curves also indicated good prognostic performance and clinical benefit, surpassing traditional indicators TNM staging and CA199 levels. The reliability of results was further proved in the independent external testing set.ConclusionsThe novel nomogram exhibited good prognostic efficacy and robust generalizability in GBC patients, which might be a promising tool for aiding clinical decision-making.

A multiparametric heart failure score at baseline is associated with long-term outcome in patients with remotely monitored implantable cardioverter-defibrillators: A pooled analysis of 9 clinical trials

BackgroundTo predict worsening heart failure hospitalizations (WHFHs), the HeartInsight multiparametric algorithm calculates a heart failure (HF) Score based on temporal trends of physiologic parameters obtained through automatic daily remote monitoring of implantable cardioverter-defibrillators (ICDs). ObjectiveWe studied the association of the baseline HF Score, determined at algorithm activation, with long-term patient outcomes. MethodsData from 9 clinical trials were pooled, including 1841 ICD patients with a preimplantation ejection fraction ≤35%, New York Heart Association class II/III, and no long-standing atrial fibrillation. The primary end point was a composite of death or WHFH. ResultsAfter a median follow-up of 631 days (interquartile range, 385–865 days), there were 243 WHFHs in 173 patients (9.4%) and 122 deaths (6.6%), 52 of which (42.6%) were cardiovascular. The primary end point occurred in 265 patients (14.4%). A multivariable time-to-first-event analysis showed that a high baseline HF Score (>23, as determined by a time-dependent receiver operating characteristics curve analysis) was significantly associated with the occurrence of the primary end point (adjusted hazard ratio [HR], 2.05; 95% confidence interval [CI], 1.54–2.71; P < .0001), all-cause death (HR, 2.37; CI, 1.56–3.58; P < .0001), cardiovascular death (HR, 2.19; CI, 1.14–4.22; P = .019), and WHFH (HR, 1.91; CI, 1.35–2.71; P = .0003). In a hierarchical event analysis of all-cause death as the outcome with highest priority and WHFHs as repeated event outcomes, the win ratio was 2.47 (CI, 1.89–3.24; P < .0001). ConclusionBased on a retrospective analysis of clinical trial data with adjudicated events, baseline HF Score derived from device-monitored variables was able to stratify patients at higher long-term risk of death or WHFH.

A Longitudinal Model for a Dynamic Risk Score to Predict Delayed Cerebral Ischemia after Subarachnoid Hemorrhage.

Accurate longitudinal risk prediction for DCI (delayed cerebral ischemia) occurrence after subarachnoid hemorrhage (SAH) is essential for clinicians to administer appropriate and timely diagnostics, thereby improving treatment planning and outcome. This study aimed to develop an improved longitudinal DCI prediction model and evaluate its performance in predicting DCI between day 4 and 14 after aneurysm rupture. Two DCI classification models were trained: (1) a static model based on routinely collected demographics and SAH grading scores and (2) a dynamic model based on results from laboratory and blood gas analysis anchored at the time of DCI. A combined model was derived from these two using a voting approach. Multiple classifiers, including Logistic Regression, Support Vector Machines, Random Forests, Histogram-based Gradient Boosting, and Extremely Randomized Trees, were evaluated through cross-validation using anchored data. A leave-one-out simulation was then performed on the best-performing models to evaluate their longitudinal performance using time-dependent Receiver Operating Characteristic (ROC) analysis. The training dataset included 218 patients, with 89 of them developing DCI (41%). In the anchored ROC analysis, the combined model achieved a ROC AUC of 0.73 ± 0.05 in predicting DCI onset, the static and the dynamic model achieved a ROC AUC of 0.69 ± 0.08 and 0.66 ± 0.08, respectively. In the leave-one-out simulation experiments, the dynamic and voting model showed a highly dynamic risk score (intra-patient score range was 0.25 [0.24, 0.49] and 0.17 [0.12, 0.25] for the dynamic and the voting model, respectively, for DCI occurrence over the course of disease. In the time-dependent ROC analysis, the dynamic model performed best until day 5.4, and afterwards the voting model showed the best performance. A machine learning model for longitudinal DCI risk assessment was developed comprising a static and a dynamic sub-model. The longitudinal performance evaluation highlighted substantial time dependence in model performance, underscoring the need for a longitudinal assessment of prediction models in intensive care settings. Moreover, clinicians need to be aware of these performance variations when performing a risk assessment and weight the different model outputs correspondingly.

The association between triglyceride-glucose index and its combination with systemic inflammation indicators and all-cause and cardiovascular mortality in the general US population: NHANES 1999–2018

BackgroundThe correlation between the triglyceride-glucose (TyG) index and mortality in the general population remains controversial, with inconsistent conclusions emerging from different studies.ObjectiveThis study aims to investigate whether there is an association between the TyG index and mortality in the general population in the United States, and to explore whether a new index combining the TyG index with systemic inflammation indicators can better predict all-cause and cardiovascular mortality risks in the general population than using the TyG index alone.MethodsCalculate the systemic inflammation indicators and TyG index for each participant based on their complete blood count, as well as their triglyceride and glucose levels in a fasting state. TyG-inflammation indices were obtained by multiplying the TyG index with systemic inflammation indicators (TyG-NLR, TyG-MLR, TyG-lgPLR, TyG-lgSII, and TyG-SIRI). Based on the weighted Cox proportional hazards model, assess whether the TyG and TyG-Inflammation indices are associated with mortality risk in the general population. Restricted cubic splines (RCS) are used to clarify the dose-response relationship between the TyG and TyG-Inflammation indices and mortality, and to visualize the results. Time-dependent receiver operating characteristic (ROC) curves are used to evaluate the accuracy of the TyG and TyG-Inflammation indices in predicting adverse outcomes.ResultsThis study included 17,118 participants. Over a median follow-up period of 125 months, 2595 patients died. The TyG index was not found to be related to mortality after adjusting for potentially confounding factors. However, the TyG-inflammation indices in the highest quartile (Q4), except for TyG-lgPLR, were significantly associated with both all-cause and cardiovascular mortality, compared to those in the lowest quartile (Q1). Among them, TyG-MLR and TyG-lgSII showed the strongest correlations with all-cause mortality and cardiovascular mortality. Specifically, compared to their respective lowest quartiles (Q1), participants in the highest quartile (Q4) of TyG-MLR had a 48% increased risk of all-cause mortality (95% CI: 1.23–1.77, P for trend < 0.0001), while participants in the highest quartile (Q4) of TyG-lgSII had a 92% increased risk of cardiovascular mortality (95% CI: 1.31–2.81, P for trend < 0.001). Time-dependent ROC curve analysis showed that the TyG-MLR had the highest accuracy in predicting long-term mortality outcomes.ConclusionsThe TyG-Inflammation indices constructed based on TyG and systemic inflammation indicators are closely related to mortality in the general population and can better predict the risk of adverse outcomes. However, no association between TyG and mortality in the general population was found.