Survival Tree Analysis Research Articles

Characterization of PSA dynamics and oncological outcomes in patients with metastatic hormone-sensitive prostate cancer treated with androgen receptor signaling inhibitors.

This study investigated the characteristics of prostate-specific antigen (PSA) dynamics when androgen receptor signaling inhibitor (ARSI), or vintage agent (bicalutamide) was used for patients with metastatic hormone-sensitive prostate cancer (mHSPC). A total of 213 mHSPC patients from each of the ARSI and bicalutamide groups treated between 2015 and 2022 were selected from multiple institutions using propensity score-matched analysis to align backgrounds. PSA progression-free survival (PFS) and overall survival (OS) were assessed. PSA level at 3months, PSA nadir level, and time to PSA nadir were examined to analyze of PSA kinetics. ARSI treatment significantly improved PSA PFS compared to bicalutamide (P = 0.0063), although no significant difference in OS was seen (P = 0.3134). No significant differences were observed between treatment groups in median PSA levels at 3months (1.47 vs 0.52ng/ml, P = 0.3042) or PSA nadir levels (0.263 vs 0.1345ng/ml, P = 0.1228). Bicalutamide treatment demonstrated longer time to nadir than ARSI in progression-free cases (median: 243 vs 213.5days, P = 0.0003). Survival tree analysis found that PSA nadir ≤ 1.5ng/ml and time to nadir ≥ 145days were the optimal cut-offs for best stratifying OS with bicalutamide, while PSA nadir ≤ 0.45ng/ml and time to nadir ≥ 70days were optimal with ARSI. No significant differences in PSA response was seen between groups; however, distinct optimal cut-offs were demonstrated for PSA nadir and time to nadir. The present findings will be useful for optimal PSA monitoring for mHSPC patients and for early identification of poor-prognosis populations.

Aortic pulse wave velocity predicts cardiovascular mortality among middle-aged metabolic syndrome subjects without overt cardiovascular disease

BackgroundThe objective of this cohort study was to assess the predictive value of main arterial markers for cardiovascular death in middle-aged subjects with metabolic syndrome (MetS).MethodsThis prospective longitudinal study analyzed data from 5829 metabolic syndrome subjects without overt cardiovascular disease aged between 40 and 64 years and enrolled in the Lithuanian High Cardiovascular Risk primary prevention program. Initial assessment comprised the evaluation of aortic pulse wave velocity (aPWV), carotid intima-media thickness (cIMT), carotid stiffness index, cardio-ankle vascular index (CAVI), ankle-brachial index (ABI), aortic augmentation index adjusted for a heart rate of 75 bpm (AIXHR75), and endothelium-dependent flow-mediated dilatation (FMD).ResultsDuring the mean follow-up period of 6.35 ± 2.99 years, 170 subjects (2.9%) had died, with 41 out of these deaths (24.1%) related to cardiovascular causes. Cox proportional hazard regression analysis revealed associations between cardiovascular deaths and increases in aPWV (HR 1.34, 95% CI 1.14–1.58, p < 0.001), CAVI (HR 1.28, 95% CI 1.09–1.50, p = 0.002), and cIMT (HR 1.004, 95% CI 1.001–1.006, p = 0.003), as well as a decrease in ABI (HR 0.020, 95% CI 0.001–0.359, p = 0.008). However, after adjustment for age and gender, only aPWV remained a statistically significant predictor. Common survival tree analysis foregrounded the predictive significance of C-reactive protein (CRP), as the primary variable associated with an increased risk of cardiovascular death, followed by aPWV and smoking as secondary and tertiary variables. The analysis also demonstrated sex-related differences: in women, the primary predictive variable was aPWV, whereas in men, CRP was identified as the primary variable, followed by CAVI and cIMT.ConclusionsThe findings of this study suggest that, among the markers of subclinical arterial damage, an increase in both aPWV and CAVI has a statistically significant predictive value for cardiovascular mortality in the middle-aged subjects with MetS. However, only aPWV demonstrated predictive value that was independent of age and gender.

Physical activity and sedentary behaviour thresholds for secondary prevention of coronary heart disease: morbidity survival tree analysis

BackgroundThere are no diagnosis-specific guidelines for moderate-to-vigorous physical activity (MVPA) and sedentary behaviour (SB) for coronary heart disease (CHD). This study aims identifying thresholds of MVPA and SB associated with cardiovascular events. MethodsCohort study including individuals with CHD. MVPA and SB were self-reported, and health registers identified cardiovascular events (2006-2022). Survival tree analyses identified thresholds of time associated with the risk of cardiovascular events. Thresholds were explored in Cox regression models. ResultsThere were 40,156 Australians, 62% men, mean age 70 years. Over 8.3 median years, 3260 non-fatal cardiac events, 5161 total cardiac events, and 14,383 major adverse cardiovascular events (MACE) occurred. Thresholds for MVPA were 122 minutes/week for non-fatal cardiac events and 94 minutes/week for total cardiac events and MACE. Meeting MVPA thresholds was associated with an 18% lower risk for non-fatal cardiac events, 29% lower risk of total cardiac events, and 23% lower risk of MACE than not reaching the thresholds. Thresholds for SB were 4 and 10 hours/day respectively for risk of total cardiac events and MACE. SB below thresholds was associated with a 14% lower risk of total cardiac events and an 18% lower risk of MACE. There were sex-specific thresholds for MVPA and SB. ConclusionTo lower cardiovascular event risk, identified MVPA thresholds were lower (94-122 minutes/week) than the public health guidelines (150 minutes/week) in individuals with CHD. The SB thresholds associated with a lower risk of total cardiac events and MACE varied between 4-10 hours/day.

Glycemic control in diabetic patients improved overall lung cancer survival across diverse populations.

The consequence of diabetes on lung cancer overall survival (OS) is debated. This retrospective study used 2 large lung cancer databases to assess comprehensively diabetes effects on lung cancer OS in diverse demographic populations, including health disparity. The University of Texas MD Anderson Cancer Center database (32 643 lung cancer patients with 11 973 patients with diabetes) was extracted from electronic health records (EHRs) using natural language processing (NLP). Associations were between diabetes and lung cancer prognostic features (age, sex, race, body mass index [BMI], insurance status, smoking, stage, and histopathology). Hemoglobin A1C (HgbA1c) and glucose levels assessed glycemic control. Validation was with a Louisiana cohort (17 768 lung cancer patients with 5402 patients with diabetes) enriched for health disparity cases. Kaplan-Meier analysis, log-rank test, multivariable Cox proportional hazard models, and survival tree analyses were employed. Lung cancer patients with diabetes exhibited marginally elevated OS or no statistically significant difference versus nondiabetic patients. When examining OS for 2 glycemic levels (HgbA1c > 7.0 or glucose > 154 mg/dL vs HgbA1c > 9.0 or glucose > 215 mg/dL), a statistically significant improvement in OS occurred in lung cancer patients with controlled versus uncontrolled glycemia (P < .0001). This improvement spanned sex, age, smoking status, insurance status, stage, race, BMI, histopathology, and therapy. Survival tree analysis revealed that obese and morbidly obese patients with controlled glycemia had higher lung cancer OS than comparison groups. These findings indicate a need for optimal glycemic control to improve lung cancer OS in diverse populations with diabetes.

Physical activity modifies cognitive impairment-associated mortality risks among chronic kidney disease

BackgroundOlder chronic kidney disease (CKD) patients frequently face unrecognized cognitive impairment and excess mortality. Physical activity (PA) reduces cognitive decline but whether PA modifies cognitive impairment-associated mortality remains unknown. MethodsFrom 2005 to 2011, 30,561 older Taiwanese CKD patients were enrolled. Patients were divided into intact cognition (≥8 scores), mild (6–7 scores), and severe (≤5 scores) cognitive impairment groups by the Short Portable Mental Status Questionnaire (SPMSQ), and were also categorized into high-PA (≥60 min/week of moderate-intensity PA), low-PA (20–60 min/week) or inactive (<20 min/week) groups. Cox regression was conducted to evaluate the individual and joint associations of cognitive impairment and PA on all-cause and cardiovascular mortality. ResultsAfter a median follow-up of 4.52 years, the all-cause mortality were higher in CKD patients with severe (multivariable-adjusted hazard ratio [aHR] 2.31; 95% confidence interval [CI] 2.05–2.60) and mild (aHR 1.74; CI 1.51–1.99) cognitive impairment than cognitively intact ones. Remarkably, decreased PA amount interacted and amplified the cognitive impairment-associated mortality risks. Notably, the high-PA status linked to lower overall mortality risks both in mild (aHR 0.65; CI 0.45–0.93) and severe (aHR 0.73; CI 0.54–0.99) cognitively-impaired patients as compared to inactivity. Survival tree analysis indicated the least mortality in those with high PA and >8 SPMSQ scores. Similar associations were found in the cardiovascular mortality. LimitationsResidual confounding and single ethnicity. ConclusionsCognitive impairment defined by SPMSQ was progressively associated with higher mortality among elderly CKD. Higher PA linked to lower cognitive impairment-associated death risks, and could be promoted for longevity benefits.

PSA doubling time 4.65 months as an optimal cut-off of Japanese nonmetastatic castration-resistant prostate cancer

A multicenter study of nonmetastatic castration-resistant prostate cancer (nmCRPC) was conducted to identify the optimal cut-off value of prostate-specific antigen (PSA) doubling time (PSADT) that correlated with the prognosis in Japanese nmCRPC. Of the 515 patients diagnosed and treated for nmCRPC at 25 participating Japanese Urological Oncology Group centers, 450 patients with complete clinical information were included. The prognostic values of clinical factors were evaluated with respect to prostate specific antigen progression-free (PFS), cancer-specific survival (CSS), and overall survival (OS). The optimal cutoff value of PSADT was identified using survival tree analysis by Python. The Median PSA and PSADT at diagnosis of nmCRPC were 3.3 ng/ml, and 5.2 months, respectively. Patients treated with novel hormonal therapy (NHT) showed significantly longer PFS (HR: hazard ratio 0.38, p < 0.0001) and PFS2 (HR 0.45, p < 0.0001) than those treated with vintage nonsteroidal antiandrogen agent (Vintage). The survival tree identified 4.65 months as the most prognostic PSADT cutoff point. Among the clinical and pathological factors PSADT of < 4.65 months remained an independent prognostic factor for OS (HR 2.96, p = 0.0003) and CSS (HR 3.66, p < 0.0001). Current data represented optimal cut-off of PSADT 4.65 months for a Japanese nmCRPC.

Survival Prognostication in Patients with Differentiated Thyroid Cancer and Distant Metastases: A SEER Population-Based Study.

Background: For patients with thyroid cancer, distant metastasis is a significant predictor of poor outcome. Since distant metastasis occurs in less than 10% of patients with differentiated thyroid cancer, correlates of survival in this vulnerable patient population remain understudied. This study aimed to identify prognostic groups among patients with differentiated thyroid cancer and distant metastases and to determine the role of, and interactions between, patient and tumor characteristics in determining survival. Methods: We identified adult patients diagnosed with differentiated thyroid cancer with distant metastases from the U.S. SEER-17 cancer registry (2010-2019). Analyses were performed using Cox proportional hazards regression, survival trees, and random survival forest. Relative importance of patient and tumor factors important for disease-specific and overall survival was assessed based on the random survival forest analyses. Results: Cohort consisted of 2411 patients with differentiated thyroid cancer with distant metastases followed for a median of 62 months. Most common histopathologic subtype (86.0%) was papillary thyroid cancer, and the most common sites of distant metastasis were the lungs (33.7%) and bone (18.9%). Cox proportional hazards model illustrated significant associations between survival and the following: patient age (p < 0.001), tumor size (p < 0.01), and site of distant metastasis (p < 0.05). Survival tree analyses identified three distinct prognostic groups based on disease-specific survival (DSS) (5-year survival of the prognostic groups was 92%, 64%, and 41%; p < 0.001) and four distinct prognostic groups based on overall survival (OS) (5-year survival of the prognostic groups was 96%, 84%, 57%, and 31%; p < 0.001). The first split in the survival trees for DSS and OS was by age at diagnosis (≤57 years vs. ≥58 years) with subsequent splits based on presence/absence of lung metastases, tumor size (≤4 cm vs. >4 cm), and patient age. A total of 558 patients (23.1%) died from thyroid cancer, and 757 patients (31.4%) died from all causes during the study period. Conclusions: This study identifies distinct prognostic groups for patients with differentiated thyroid cancer with distant metastases and highlights the importance of patient age, lung metastases, and tumor size for determining both disease-specific and overall survival. These findings inform risk stratification and treatment decision-making in this understudied patient population.

COVID-19 death risk predictors in Brazil using survival tree analysis: a retrospective cohort from 2020 to 2022

PurposeThis study analyses the survival of hospitalized patients with Severe Acute Respiratory Syndrome (SARS) due to COVID-19 and identifies the risk groups for death due to COVID-19 from the identification of potential interactions between its predictors.MethodsThis was a retrospective longitudinal study with data from 1,756,917 patients reported in the Influenza Epidemiological Surveillance Information System from 26 February 2020 to 31 December 2022. In this study, all adult and older (≥ 20 years) patients were hospitalized with SARS due to COVID-19, with death as the outcome. Survival tree analysis was used to identify potential interactions between the predictors. A model was built for each year of study.ResultsHospital lethalitywas 33.2%. The worst survival curve was observed among those who underwent invasive mechanical ventilation and were aged 80 years or older in the three years of the pandemic. Black and brown race/color were predictors of deaths in the years 2020 and 2021 when there was greater demand from the health system due to the greater number of cases.ConclusionBy applying survival tree analysis we identified several numbers of homogeneous subgroups with different risks for mortality from COVID-19. These findings show the effects of wide inequalities of access by the population, requiring effective policies for the reduction and adequate management of the disease.

A Prognostic Model To Predict Survival After Recurrence Among Patients With Recurrent Hepatocellular Carcinoma.

We sought to develop and validate a preoperative model to predict survival after recurrence (SAR) in hepatocellular carcinoma (HCC). Although HCC is characterized by recurrence as high as 60%, models to predict outcomes after recurrence remain relatively unexplored. Patients who developed recurrent HCC between 2000 and 2020 were identified from an international multi-institutional database. Clinicopathologic data on primary disease and laboratory and radiologic imaging data on recurrent disease were collected. Multivariable Cox regression analysis and internal bootstrap validation (5000 repetitions) were used to develop and validate the SARScore. Optimal Survival Tree analysis was used to characterize SAR among patients treated with various treatment modalities. Among 497 patients who developed recurrent HCC, median SAR was 41.2 months (95% CI 38.1-52.0). The presence of cirrhosis, number of primary tumors, primary macrovascular invasion, primary R1 resection margin, AFP>400ng/mL on the diagnosis of recurrent disease, radiologic extrahepatic recurrence, radiologic size and number of recurrent lesions, radiologic recurrent bilobar disease, and early recurrence (≤24 months) were included in the model. The SARScore successfully stratified 1-, 3- and 5-year SAR and demonstrated strong discriminatory ability (3-year AUC: 0.75, 95% CI 0.70-0.79). While a subset of patients benefitted from resection/ablation, Optimal Survival Tree analysis revealed that patients with high SARScore disease had the worst outcomes (5-year AUC; training: 0.79 vs. testing: 0.71). The SARScore model was made available online for ease of use and clinical applicability ( https://yutaka-endo.shinyapps.io/SARScore/ ). The SARScore demonstrated strong discriminatory ability and may be a clinically useful tool to help stratify risk and guide treatment for patients with recurrent HCC.

A Survival Tree of Advanced Melanoma Patients with Brain Metastases Treated with Immune Checkpoint Inhibitors.

The efficacy of immune checkpoint inhibitors (ICIs) in patients with advanced melanoma that develop brain metastases (BM) remains unpredictable. In this study, we aimed to identify prognostic factors in patients with melanoma BM who are treated with ICIs. Data from advanced melanoma patients with BM treated with ICIs in any line between 2013 and 2020 were obtained from the Dutch Melanoma Treatment Registry. Patients were included from the time of the treatment of BM with ICIs. Survival tree analysis was performed with clinicopathological parameters as potential classifiers and overall survival (OS) as the response variable. In total, 1278 patients were included. Most patients were treated with ipilimumab-nivolumab combination therapy (45%). The survival tree analysis resulted in 31 subgroups. The median OS ranged from 2.7 months to 35.7 months. The strongest clinical parameter associated with survival in advanced melanoma patients with BM was the serum lactate dehydrogenase (LDH) level. Patients with elevated LDH levels and symptomatic BM had the worst prognosis. The clinicopathological classifiers identified in this study can contribute to optimizing clinical studies and can aid doctors in giving an indication of the patients' survival based on their baseline and disease characteristics.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

A novel preoperative inflammation score system established for postoperative prognosis predicting of intrahepatic cholangiocarcinoma

BackgroundInflammation is implicated in tumorigenesis and has been reported as an important prognostic factor in cancers. In this study, we aimed to develop and validate a novel inflammation score (IFS) system based on 12 inflammatory markers and explore its impact on intrahepatic cholangiocarcinoma (ICC) survival after hepatectomy.MethodsClinical data of 446 ICC patients undergoing surgical treatment were collected from the Primary Liver Cancer Big Data, and then served as a training cohort to establish the IFS. Furthermore, an internal validation cohort including 175 patients was used as internal validation cohort of the IFS. A survival tree analysis was used to divide ICC patients into three groups (low-, median-, and high- IFS-score groups) according to different IFS values. Kaplan-Meier (KM) curves were used to compare the overall survival (OS) and recurrence-free survival (RFS) rates among three different groups. Cox regression analyses were applied to explore the independent risk factors influencing OS and RFS.ResultsIn the training cohort, 149 patients were in the low-IFS-score group, 187 in the median-IFS-score group, and 110 in the high-IFS-score group. KM curves showed that the high-IFS-score group had worse OS and RFS rates than those of the low- and median-IFS-score groups (P < 0.001) in both the training and validation cohorts. Moreover, multivariable Cox analyses identified high IFS as an independent risk factor for OS and RFS in the training cohort. The area under the curve values for OS prediction of IFS were 0.703 and 0.664 in the training and validation cohorts, respectively, which were higher than those of the American Joint Committee on Cancer (AJCC) 7th edition TNM stage, AJCC 8th edition TNM stage, and the Child-Pugh score.ConclusionOur results revealed the IFS was an independent risk factor for OS and RFS in patients with ICC after hepatectomy and could serve as an effective prognostic prediction system in daily clinical practice.

Prognostic stratification of oropharyngeal cancer patients in a betel nut chewing and low HPV area

BackgroundThis study aimed to establish a simple predictive model for oropharyngeal cancer (OPC) in an area with a relatively low prevalence of human papillomavirus (HPV) and frequent betel nut chewing.MethodsA total of 116 patients with OPC were recruited from the clinical research database of a referral cancer center between 2013 and 2018. Patient characteristics—including age, gender, tumor stage, differentiation, and treatment modality—were extracted from the database. Patients diagnosed after 2018 were staged using the 7th AJCC staging system to explore the impact of extra-nodal tumor extension (ENE) on survival. Immunohistochemical analysis was performed for p16, epidermal growth factor receptor (EGFR), p53, and programmed cell death ligand 1 (PD-L1). ENE status was evaluated by pathological analysis or radiological features. Primary outcome was disease-specific overall survival (OS). Univariate and multivariate Cox regression were used to establish a predictive model.ResultsMean age was 57.3 ± 9.9 years; 103 patients (88.8%) were male. P16 positive OPC was positively associated with higher PD-L1 and a tonsillar sub-site and negatively associated with betel nut chewing and cigarette smoking. In Cox regression, age, p16 status, EGFR, cT4, ENE, and cigarette smoking were significantly associated with OS. In survival tree analysis, cT stage was the most important risk stratification parameter, followed by EGFR expression and p16 status. Patients with cT4 stage or high EGFR were classified as the high-risk group and had poorest OS.ConclusionsDue to the low prevalence of HPV and popularity of betel nut chewing in Asia, the relative importance of prognostic predictors for OPC are not identical to Western countries. Identification of significant prognostic biomarkers may improve treatment.Trial registration This study was registered and approved by the Institutional Review Board (IRB) of Kaohsiung Veterans General Hospital (VGHKS19-CT9-07; date of approval: Aug 9, 2019).Graphic

Supervised learning-derived tailored risk-stratification in patients with severe secondary mitral regurgitation

Abstract Background Mitral regurgitation secondary to heart failure (sMR) has considerable impact on quality of life, heart failure (HF) rehospitalizations and mortality. A diverse burden of comorbidities suggests multifaceted aspects of individual risks. This risk-spectrum has never been studied but is essential to understand disease trajectories. Objectives To provide a comprehensive and structured decision-tree-like approach to risk-stratification in patients with severe sMR. Methods This large-scale, long-term observational study included 1317 patients with severe sMR from the entire HF spectrum (preserved, mid-range and reduced ejection fraction). Primary endpoint was all-cause mortality and survival tree analysis, a supervised learning technique, was applied to identify patient subgroups with excessive risk of mortality (Figure 1). Results Eight distinct subgroups that differed significantly in long-term survival were identified (Figure 2). Subgroup 7, characterized by younger age (≤66), higher hemoglobin (&amp;gt;12.7 g/dl) and higher albumin levels (&amp;gt;40.6 g/l) had the best survival. In contrast, subgroup 5 displayed a 20-fold risk of mortality (HR 95% CI: 20.38 ([0.78–38.52]), P&amp;lt;0.001) and presented with older age (&amp;gt;68 years) and low serum albumin (≤40.6 g/l) and higher NT-proBNP levels (≥9750 pg/ml). Results were consistent in internal and temporal validation. Conclusion Supervised machine learning reveals an unexpected heterogeneity in the sMR risk-spectrum, indicating the clinical challenges tied to severe sMR. A decision-tree-like model can guide through the risk spectrum and provide tailored risk-stratification. This structured approach provides the foundation to generate hypotheses towards improved therapeutic strategies and optimized patient care. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Austrian Science Fund

Geriatric Assessment Predictors of 1-Year Mortality in Older Adults With GI Malignancies: A Survival Tree Analysis.

Identifying older patients with GI malignancies who are at increased risk of mortality remains challenging. The goal of our study was to examine geriatric assessment (GA) predictors of 1-year mortality and explore the use of a survival tree analysis in a prospective cohort of older adults (≥ 60 years) with newly diagnosed GI malignancies. Survival tree analysis was performed to understand variable interactions and identify predictors of overall survival, computed from time of GA to death or last follow-up. Cox regression was used to estimate associations of 1-year mortality, first using a base model (age, race, cancer stage, cancer risk group, and planned chemotherapy), then using all significant predictors from the univariable analyses, and finally only those identified in survival tree analysis. A total of 478 participants met eligibility, with a mean age of 70 years. The survival tree analysis identified nutrition, cancer stage, physical and emotional health, age, and functional status as predictors of mortality. Older patients without malnutrition or depression had the best 1-year survival, whereas those with malnutrition, stage IV disease, and functional limitations had the worst 1-year survival. Our base model demonstrated good discrimination (area under curve [AUC] 0.76) but was improved with the addition of GA variables (AUC 0.82) or from survival tree analysis (AUC 0.82). Measures of function, nutrition, and mental health are important predictors of mortality in older adults with GI cancers. Using GA as part of clinical management can aid in the prediction of survival and help inform treatment decision making.

Tailored Risk Stratification in Severe Mitral Regurgitation and Heart Failure Using Supervised Learning Techniques

BackgroundSecondary mitral regurgitation (sMR) in the setting of heart failure (HF) has considerable impact on quality of life, HF rehospitalizations, and mortality. Identification of high-risk cohorts is essential to understand disease trajectories and for risk stratification. ObjectivesThis study aimed to provide a structured decision tree–like approach to risk stratification in patients with severe sMR and HF. MethodsThis observational study included 1,317 patients with severe sMR from the entire HF spectrum. Clinical, echocardiographic, and laboratory data were extracted for all patients. The primary end point was all-cause mortality. Survival tree analysis, a supervised learning technique, was applied to identify patient subgroups at risk of mortality and further stratified by HF subtype (preserved, mildly reduced, and reduced ejection fraction). ResultsUsing supervised learning (survival tree method), 8 distinct subgroups were identified that differed significantly in long-term survival. Subgroup 7, characterized by younger age (≤66 years), higher hemoglobin (>12.7 g/dL), and higher albumin levels (>40.6 g/L) had the best survival. In contrast, subgroup 5 displayed a 20-fold risk of mortality (hazard ratio: 20.38 [95% CI: 10.78-38.52]); P < 0.001 and had older age (>68 years), low serum albumin (≤40.6 g/L), and higher NT-proBNP levels (≥9,750 pg/mL). Unique subgroups were further identified for each type of HF subtypes. ConclusionsSupervised machine learning reveals heterogeneity in the sMR risk spectrum, highlighting the clinical variability in the population. A decision tree–like model can help identify differences in outcomes among subgroups and can help provide tailored risk stratification.

A Novel Signature Based on m6A RNA Methylation Regulators Reveals Distinct Prognostic Subgroups and Associates with Tumor Immunity of Patients with Pancreatic Neuroendocrine Neoplasms

Introduction: The RNA N6-methyladenosine (m6A) regulators play a crucial role in tumorigenesis and could be indicators of prognosis and therapeutic targets in various cancers. However, the expression status and prognostic value of m6A regulators have not been studied in pancreatic neuroendocrine neoplasms (PanNENs). We aimed to investigate the expression patterns and prognostic value of m6A regulators and assess their correlations with immune checkpoints and infiltrates in PanNENs. Methods: Immunohistochemistry was performed for 15 m6A regulators and immune markers using tissue microarrays obtained from 183 patients with PanNENs. The correlation between m6A protein expression and clinicopathological parameters with recurrence-free survival (RFS) was examined using a random survival forest, Cox regression model, and survival tree analysis. Results: Among the 15 m6A proteins, high expression of YTHDF2 (p < 0.001) and HNRNPC (p = 0.006) was found to be significantly associated with recurrence and served as independent risk factors in multivariate analysis. High YTHDF2 expression was associated with higher number of CD3⁺ T cells (p = 0.003), whereas high HNRNPC expression was significantly correlated with the expression of PD-L1 (p = 0.039). A YTHDF2-based signature was determined, including five patterns from survival tree analysis: patients with the LN_negYTHDF2_high signature had a 5-year RFS rate of 92.1%, whereas patients with LN_posTumorSize_{＜2.5 cm} signature had the worst 5-year RFS rate of 0% (p < 0.001). The area under receiver operating characteristic curve was 0.870 (95% confidence interval: 0.762–0.915) for the YTHDF2-based signature. The C-index was 0.978, suggesting good discrimination ability; moreover, the risk score of recurrence served as an independent prognostic factor indicating shorter RFS. Conclusions: YTHDF2 appears to serve as a promising prognostic biomarker and therapeutic target. A YTHDF2-based signature can identify distinct subgroups, which may be helpful to strategize personalized postoperative monitoring.

Biomarkers for Locally Advanced Hepatocellular Carcinoma Patients Treated with Liver-Directed Combined Radiotherapy

Introduction: In the era of biomarker-driven cancer therapy, robust biomarkers for hepatocellular carcinoma (HCC) have not been well-defined. In this hypothesis-generating study, we investigated biomarkers that can be incorporated to predict treatment outcomes in patients with locally advanced HCC who are administered liver-directed combined radiotherapy (LDCRT). Methods: Ninety-nine patients with HCC who were treated with conventional fractionation LDCRT between July 2016 and October 2018 were enrolled in this prospective single-arm study. Clinical outcomes and possible serum biomarkers, including soluble programmed cell death ligand-1 (sPD-L1), interleukin (IL)-10, IL-6, cell-free DNA (cfDNA), inter-alpha inhibitor H4, and interferon-gamma, were analyzed. The primary endpoint was disease progression, and additional endpoints were local failure-free rate, intrahepatic failure-free rate, and lung metastasis-free rate. Results: The median follow-up period was 18.7 months. The 1-year progression-free rate was 38.2%. Increasing baseline sPD-L1 per pg/mL, previous treatment history, protein induced by vitamin K absence-II >1,629 mAU/mL, and multiple tumors were the adverse factors for progression based on multivariate analysis. Survival tree analysis revealed three prognostic groups for progression, in which patients with multiple lesions and baseline sPD-L1 ≥41.07 pg/mL showed the worst outcomes. For dynamic changes in biomarker levels, sPD-L1 fold change and cfDNA fold-change values were unfavorable factors for progression. Conclusion: Baseline sPD-L1, sPD-L1 fold change, and cfDNA fold-change values showed the highest potential as biomarkers for predicting post-treatment progression after LDCRT in HCC patients. By incorporating clinical factors, these biomarkers may be useful for devising a biomarker-driven treatment paradigm in locally advanced HCC.

Unveiling the Burden of Interactions Among Clinical Risk Factors for 1-Year Mortality in Hospitalized Older Patients.

Background: Hospitalized older patients are particularly exposed to adverse health outcomes.Objective: In this study, we aimed at investigating the prognostic interactions between disability in basic activities of daily living (BADL), cognitive impairment, low handgrip strength, anticholinergic cognitive burden (ACB), and depression on 1-year mortality.Setting and Subjects: Our series consisted of 503 older patients discharged from acute care hospitals.Methods: Disability in at least one BADL, ACB, depression, cognitive impairment, and low handgrip strength was considered in the analysis. One-year mortality was investigated by Cox regression analysis and prognostic interactions among study variables were assessed by survival tree analysis.Results: Basic activities of daily living disability, ACB, cognitive impairment, and low handgrip strength were significantly associated with 1-year mortality. Survival tree analysis showed that patients with BADL disability and high ACB carried the highest risk of poor survival [hazard ratio (HR): 16.48 (2.63–74.72)], followed by patients with BADL disability and low ACB (HR: 8.43, 95% CI: 1.85–38.87). Patients with cognitive impairment and no BADL disability were characterized by a lower but still significant risk of mortality (HR: 6.61, 95% CI: 1.51–28.97) and those with high ACB scores and good cognitive and functional performance (HR: 5.28, 95% CI: 1.13–24.55).Conclusion: Basic activities of daily living dependency, cognitive impairment, and ACB score were the three main predictors of 1-year mortality among patients discharged from acute care hospitals; the interaction between BADL dependency and ACB score wasfound to significantly affect survival. Early identification of such high-risk patients may help tailor targeted interventions to counteract their detrimental effects on prognosis.

Prognostic interplay of kidney function with sarcopenia, anemia, disability and cognitive impairment. The GLISTEN study

BackgroundInteractions between chronic kidney disease (CKD) and several comorbidities may potentially affect prognosis of older hospitalized patients. This study aims at evaluating the prognostic interactions between estimated glomerular filtration rate (eGFR), anemia, sarcopenia, functional and cognitive dysfunction, and 3-year mortality among older patients discharged from acute care hospitals. MethodsOur series consisted of 504 older adults enrolled in a multicenter observational study carried out in twelve Acute Geriatric and Internal Medicine wards throughout Italy. CKD was defined as an eGFR< 60 ml/min/1.73 m2. Anemia, Short Portable Status Mental Questionnaire (SPMSQ), Basic Activities of Daily Living (BADL), sarcopenia, and Charlson index were considered in the analysis. 3-year survival was investigated by Cox regression and prognostic interactions among study variables were assessed by survival tree analysis. Accuracy of different survival models was investigated by C-index. ResultseGFR < 30 mL/min/1.73 m2, anemia, sarcopenia, SPMSQ ≥ 5, and impairment in 1 or more BADL were significantly associated with mortality. Survival tree analysis showed that patients with eGFR < 35.32 ml/min/1.73 m2 and SPMSQ ≥ 5 had the highest risk of mortality [hazard ratio (HR): 5.49, 95%CI: 3.04–9.94] followed by those with eGFR < 35.32 ml/min/1.73 m2, hemoglobin < 11.95 g/dL and SPMSQ < 5 (HR:3.65; 95%CI: 2.21–6.02) and those with eGFR 35.32–47.99 ml/min/1.73 m2 and sarcopenia (HR:3.65; 95%CI: 1.99–6.69). Survival tree leaf node membership had good accuracy in predicting the study outcome (C-index: 0.73, 95%CI:0.70-0.76). ConclusionsInteractions among study risk factors designed distinct risk profiles in older patients discharged from acute care hospitals, that may help identify patients needing targeted interventions and appropriate follow-up after discharge.