Kaplan-Meier Research Articles

Impaired sensitivity to thyroid hormone correlates to all-cause mortality in euthyroid individuals with chronic kidney disease

BackgroundThis study aimed to investigate the association between central sensitivity to thyroid hormones and all-cause mortality in euthyroid patients with chronic kidney disease (CKD).Methods​Data on thyroid function indicators and all-cause mortality for CKD patients were extracted from the NHANES database (2007–2012). Central sensitivities to thyroid hormones were mainly evaluated by Thyroid Feedback Quantile-based Index (TFQI). The Kaplan–Meier method, Cox proportional hazards regression model and subgroup analysis were performed to explore the potential associations between thyroid hormone sensitivity and all-cause mortality.ResultsA total of 1303 euthyroid CKD patients were enrolled in this study. After a median follow-up of 115 months, 503 participants died. The Kaplan-Meier analysis demonstrated significant variations in survival rates among different levels of TFQI (P = 0.0015). Cox regression analysis showed that increased levels of TFQI were independent risk factors for all-cause mortality after adjusting for multiple confounding factors (HR = 1.40, 95% CI 1.10–1.79, P = 0.007). Subgroup analysis did not reveal any significant variation in the association between TFQI and all-cause mortality between the subgroups assessed (P for interaction > 0.05).ConclusionOur study suggests that impaired thyroid hormone sensitivity might be linked to increased mortality in euthyroid CKD patients. Further research is needed to confirm and explore this association.

Ensemble of machine learning techniques to predict survival in kidney transplant recipients

Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in this patient group. This article proposes the application of clustering techniques and feature selection to predict the survival outcomes of kidney transplant recipients based on machine learning techniques and mainstream statistical methods. First, feature selection techniques (Boruta, Random Survival Forest and Elastic Net) are used to detect the most relevant variables. Subsequently, each set of variables obtained by each feature selection technique is used as input for the clustering algorithms used (Consensus Clustering, Self-Organizing Map and Agglomerative Clustering) to determine which combination of feature selection, clustering algorithm and number of clusters maximizes intercluster variability. Next, the mechanism called False Clustering Discovery Reduction is applied to obtain the minimum number of statistically differentiable populations after applying a control metric. This metric is based on a variance test to confirm that reducing the number of clusters does not generate significant losses in the heterogeneity obtained. This approach was applied to the Organ Procurement and Transplantation Network medical dataset (n = 11,332). The combination of Random Survival Forest and consensus clustering yielded the optimal result of 4 clusters starting from 8 initial ones. Finally, for each population, Kaplan-Meier survival curves are generated to predict the survival of new patients based on the predictions of the XGBoost classifier, with an overall multi-class AUC of 98.11%.

Relapses and serious adverse events during rituximab maintenance therapy in ANCA-associated vasculitis: a multicentre retrospective study.

There are limited real-life data regarding the efficacy and safety of rituximab (RTX) as a remission-maintenance agent in microscopic-polyangiitis (ΜPA) and granulomatosis-with-polyangiitis (GPA). We aimed to estimate the incidence and risk factors for relapses, as well serious-adverse-events (SAEs) in MPA/GPA patients during RTX-maintenance. Retrospective cohort of newly-diagnosed/relapsing GPA/MPA patients who received RTX-maintenance (≥1 RTX-cycle, ≥6 months follow-up) following complete-remission (Birmingham-Vasculitis-Activity-Score-version-3 = 0 plus prednisolone ≤7.5 mg/day) with induction regimens. SAEs included serious-infections, COVID-19-associated hospitalizations, deaths, cardiovascular-events, malignancies and hypogammaglobulinemia. Incidence-rates (IR) and relapse-free survival through Kaplan-Meier plots were estimated. Cox-regression was conducted to investigate factors associated with the time-to-relapse. 101 patients were included; 48% females, 69% GPA, 53% newly diagnosed, median age: 63 years. During follow-up (294.5 patient-years, median: 3 RTX-cycles), 30 relapses (57% major) occurred among 24 patients (24%, IR 10.2/100 patient-years). Kidney involvement (adjusted-Hazard-Ratio/aHR: 0.20; 95% CI: 0.06-0.74, p= 0.016), prior induction with RTX plus cyclophosphamide (vs RTX monotherapy: aHR = 0.02; 95% CI: 0.001-0.43, p= 0.012) and shorter time-interval until complete-remission (aHR = 1.07; 95% CI: 1.01-1.14, p= 0.023) were associated with decreased relapse-risk. We recorded 17 serious-infections (IR 5.8/100 patient-years), 11 COVID-19-associated hospitalizations (IR 3.7/100 patient-years), 4 malignancies (IR 1.4/100 patient-years), 6 cardiovascular-events (IR 2/100 patient-years) and 10 deaths (IR 3.4/100 patient-years). In this real-world study, relapses during RTX-maintenance occurred in approximately in 1 out of 4 patients. Kidney involvement, induction with RTX plus cyclophosphamide and earlier achievement of complete-remission were associated with lower relapse-risk. Serious-infections rate was consistent with previous reports, whereas an increased rate of COVID19-associated hospitalizations was observed.

Analysis of the efficacy and safety of dual immunotherapy in patients with driver gene and programmed death ligand-1 double negative advanced non-small cell lung cancer

Objective: To discuss the efficacy and safety of the dual immunotherapy of nivolumab plus ipilimumab in patients with advanced non-small cell lung cancer (NSCLC) who are double negative for driver gene and programmed death-ligand 1 (PD-L1) expression. Methods: We conducted a retrospective collection of clinical data for 61 patients with advanced NSCLC who were negative for both driver genes and PD-L1 and received dual immunotherapy with nivolumab plus ipilimumab at the First Affiliated Hospital of Guangzhou Medical University from January 2019 to June 2023. Based on treatment conditions, patients were divided into first-line and non-first-line dual immunotherapy groups. Patients were followed up monthly, with the follow-up period ending on October 1, 2023. The efficacy was evaluated using Solid Tumor Response Evaluation Criteria, and adverse reactions were assessed according to the Common Terminology Criteria for Adverse Events developed by the National Cancer Institute in the United States. Survival curves were plotted using the Kaplan-Meier method, and the log-rank test was used to compare the differences in progression-free survival (PFS) and overall survival (OS) between first-line and non-first-line dual immunotherapy patients. The influence factors of PFS were analyzed using a multivariate Cox proportional hazards regression model. Results: Among the 61 NSCLC patients, 49 were male (80.3%), with an age range of 23-88 years [(65.3±7.4) years]. There were 14 cases (23.0%) classified as stage ⅢC and 47 cases (77.0%) classified as stage Ⅳ according to TNM staging. Forty cases (65.6%) received non-first-line treatment. The objective response rate (ORR) was 24.6% (15/61), and the disease control rate (DCR) was 52.5% (32/61). All 61 patients were followed up, with a median follow-up time of 17.8 months. The median PFS was 6.0 months (95%CI: 5.5-6.4 months), and the median OS was 17.0 months (95%CI: 14.8-19.2 months). For patients receiving first-line dual immunotherapy, the median PFS was longer than for those receiving non-first-line dual immunotherapy [7.0 months (95%CI: 6.0-7.9 months) vs 4.0 months (95%CI: 3.3-4.6 months), P<0.001]; similarly, the median OS for patients receiving first-line dual immunotherapy was longer than for those receiving non-first-line dual immunotherapy [19.0 months (95%CI: 18.1-19.9 months) vs 13.0 months (95%CI: 10.8-15.1 months), P<0.001]. Multivariate Cox risk regression model analysis showed that distant tumor metastasis (HR=1.414, 95%CI: 1.253-1.725), non-first-line dual immunotherapy (HR=1.412, 95%CI: 1.184-1.652), and tumor mutation burden<10 mut/Mb (HR=1.328, 95%CI: 1.151-1.546) were risk factors for PFS, while non-squamous carcinoma (HR=0.917, 95%CI: 0.823-0.984) was a protective factor for PFS. Immune-related adverse reactions occurred in 41 cases (67.2%), including 21 cases (32.8%) of grade 3-4 adverse reactions. Eight cases (13.1%) discontinued treatment, and there were no deaths. Conclusions: Dual immunotherapy with nivolumab plus ipilimumab can be a treatment option for driver gene and PD-L1 double-negative advanced NSCLC. Distant tumor metastasis, non-first-line dual immunotherapy, and tumor mutation burden<10 mut/Mb are risk factors affecting patients' PFS, while non-squamous cell carcinoma is a protective factor affecting patients' PFS.

Real-World Drug Survival of Biologics and Targeted Synthetic Disease-Modifying Anti-rheumatic Drugs Among Patients with Psoriatic Arthritis.

While the variety of biologics (b) and targeted synthetic (ts) disease-modifying anti-rheumatic drugs (DMARDs) available for patients with psoriatic arthritis (PsA) has proved to be efficacious in randomized clinical trials, there is a growing importance to understand the benefits and potential drawbacks of these different therapies in real-world settings, which includes bio-experienced and older patients as well. To evaluate the real-world adherence, drug survival, and discontinuation risk of bDMARDs and tsDMARDs among patients with PsA, comprising both younger and older patients. A retrospective studyusing a computerized database. Treatment-naïve and treatment-experiencedpatients with PsA, younger and older than 60 years, who initiated treatment with bDMARDs [TNF-α inhibitors (TNF-αis), IL-17 inhibitors (IL-17is), IL-12/23 inhibitors (IL-12/23i)] or tsDMARDs (the PDE-4 inhibitor apremilast) during 2015-2018 were included. Adherence was assessed using the proportion of days covered (PDC) method. Time to discontinuation was analyzed using Kaplan-Meier estimates. Risk of discontinuation was estimated by Cox proportional hazard model. We identified 427 eligible patients (22.2 % were older than 60 years), utilizing 673 treatment lines. The proportion of adherent patients (PDC ≥0.8) was similar (62.1-66.5%) across all lines of therapy and across different biologics (70.0-72.0%), while apremilast showed the lowest, in both treatment-naïve and experienced settings (43.6% and 25.5%, respectively). The Kaplan-Meier analysis showed that in the treatment-naïve TNF-αis had higher drug survival compared with apremilast (P = 0.032). Apremilast also had the lowest drug survival in the treatment-experienced group (P < 0.0001). Kaplan-Meier analysis by age groups showed similar drug survival rates in older (≥ 60 years) and younger (age < 60 years) patients, regardless of treatment-experience status. The multivariable model showed that apremilast had increased risk for discontinuation compared with TNF-αis. Adherence, drug survival and risk for discontinuation were similar for all included bDMARDs, regardless of treatment experience status, while apremilast showed lower rates and increased risk. Adherence and discontinuation rate were similar in older and younger patients. With the variety of drug modes of action available for patients with PsA, these findings may assist caregivers in selecting the appropriate treatment.

Survival characteristics of Wilms Tumor, a reference developed from a longitudinal cohort study

BackgroundWilms tumor (WT) survival has been affected by the evolution in clinical and biological prognostic factors. Significant differences in survival rates indicate the need for further efforts to reduce these disparities. This study aims to evaluate the clinicopathological data impact on survival among patients after Wilm's diagnosis.MethodsThe study utilized the SEERStat Database to identify Wilms tumor patients, applying SEERStat software version 8.3.9.2 for data extraction. Selection criteria involved specific codes based on the International Classification of Diseases for Oncology (ICDO-3), excluding cases with unknown SEER stage, incomplete survival data, unknown size, or lymph node status. Statistical analyses, including Kaplan–Meier estimates and Cox regression models, were conducted using R software version 3.5. Standardized mortality ratios (SMR) were computed with SEER*Stat software, and relative and conditional survival analyses were performed to evaluate long-term survival outcomes.ResultsOf 2273 patients diagnosed with Wilms tumor, (1219 patients, 53.6% were females with an average age group of 3–8 years (50.2%). The overall mean survival after five years of diagnosis was 93.6% (2.6–94.7), and the overall mean survival rate was 92.5% (91.3–93.8) after ten years of diagnosis. Renal cancers were identified as the leading cause of death (77.3%), followed by nonrenal cancers (11%) and noncancer causes (11%). Additionally, robust relative survival rates of 98.10%, 92.80%, and 91.3% at one, five, and ten years, respectively, were observed, with corresponding five-year conditional survival rates indicating an increasing likelihood of survival with each additional year post-diagnosis. Univariate Cox regression identified significant prognostic factors: superior CSS for patients below 3 years (cHR 0.48) and poorer CSS for those older than 15 years (cHR 2.72), distant spread (cHR 10.24), regional spread (cHR 3.09), and unknown stage (cHR 4.97). In the multivariate model, age was not a significant predictor, but distant spread (aHR 9.22), regional spread (aHR 2.84), and unknown stage (aHR 4.98) were associated with worse CSS compared to localized tumors.ConclusionThis study delving into WT survival dynamics reveals a multifaceted landscape influenced by clinicopathological variables. This comprehensive understanding emphasizes the imperative for ongoing research and personalized interventions to refine survival rates and address nuanced challenges across age, stage, and tumor spread in WT patients.

Identification and validation of genes associated with prognosis of cisplatin-resistant ovarian cancer

PurposeTo investigate the role of prognostic genes related to cisplatin resistance in ovarian cancer during disease progression.MethodThe gene expression profile of the NCI-60 cell line was acquired through comprehensive analysis of the GEO database accession GSE116439. We performed a thorough analysis of gene expression differences in samples from seven individuals exposed to cisplatin concentrations of 0 nM compared to seven samples exposed to 15000 nM over a 24-h period. Key genes were initially identified through LASSO regression, followed by their enrichment through differential gene function analysis (GO) and pathway enrichment analysis (KEGG). Subsequently, a prognostic risk model was established for these key genes. The prognostic model's performance was assessed through K-M survival curves and ROC curves. To examine the variance in immune cell infiltration between the high and low-risk groups, CIBERSORTx analysis was employed. Finally, validation of prognostic gene expression in cisplatin-resistant ovarian cancer was carried out using clinical samples, employing RT-qPCR and Western Blot techniques.ResultsA total of 132 differential genes were found between cisplatin resistance and control group, and 8 key prognostic genes were selected by analysis, namely VPS13B, PLGRKT, CDKAL1, TBC1D22A, TAP1, PPP3CA, CUX1 and PPP1R15A. The efficacy of the risk assessment model derived from prognostic biomarkers, as indicated by favorable performance on both Kaplan–Meier survival curves and ROC curves. Significant variations in the abundance of Macrophages M1, T cells CD4 memory resting, T cells follicular helper, and T cells gamma delta were observed between the high and low-risk groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, confirming differential expression of the identified eight key genes between the two groups.ConclusionVPS13B, TBC1D22A, PPP3CA, CUX1 and PPP1R15A were identified as poor prognostic genes of cisplatin resistance in ovarian cancer, while PLGRKT, CDKAL1 and TAP1 were identified as good prognostic genes. This offers a novel perspective for future advancements in ovarian cancer treatment, suggesting potential avenues for the development of new therapeutic targets.

Abnormal Histopathological Expression of Klotho, Ferroptosis, and Circadian Clock Regulators in Pancreatic Ductal Adenocarcinoma: Prognostic Implications and Correlation Analyses

Pancreatic ductal adenocarcinoma (PDAC) is an extremely lethal tumor with increasing incidence, presenting numerous clinical challenges. The histopathological examination of novel, unexplored biomarkers offers a promising avenue for research, with significant translational potential for improving patient outcomes. In this study, we evaluated the prognostic significance of ferroptosis markers (TFRC, ALOX-5, ACSL-4, and GPX-4), circadian clock regulators (CLOCK, BMAL1, PER1, PER2), and KLOTHO in a retrospective cohort of 41 patients deceased by PDAC. Immunohistochemical techniques (IHC) and multiple statistical analyses (Kaplan–Meier curves, correlograms, and multinomial linear regression models) were performed. Our findings reveal that ferroptosis markers are directly associated with PDAC mortality, while circadian regulators and KLOTHO are inversely associated. Notably, TFRC emerged as the strongest risk marker associated with mortality (HR = 35.905), whereas CLOCK was identified as the most significant protective marker (HR = 0.01832). Correlation analyses indicate that ferroptosis markers are positively correlated with each other, as are circadian regulators, which also positively correlate with KLOTHO expression. In contrast, KLOTHO and circadian regulators exhibit inverse correlations with ferroptosis markers. Among the clinical variables examined, only the presence of chronic pathologies showed an association with the expression patterns of several proteins studied. These findings underscore the complexity of PDAC pathogenesis and highlight the need for further research into the specific molecular mechanisms driving disease progression.

Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis

Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package “ConsensusClusterPlus” on the bulk RNA-seq data. Hub genes were discerned through the application of the R package “limma” and univariate regression analysis, with the calculation of risk scores carried out using the R package “survminer”. To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan–Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages “ESTIMATE” and “GSVA” were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.

Establishment of a nomogram model for predicting distant metastasis in pancreatic ductal adenocarcinoma: a comparative analysis of different lymph node staging systems based on the SEER database

The purpose of this study was to compare the predictive value of different lymph node staging systems and to develop an optimal prognostic nomogram for predicting distant metastasis in pancreatic ductal adenocarcinoma (PDAC). Our study involved 6364 patients selected from the Surveillance, Epidemiology, and End Results (SEER) database and 126 patients from China. Independent risk factors for distant metastasis were screened by univariate and multivariate logistic regression analyses, and a model-based comparison of different lymph node staging systems was conducted. Furthermore, we developed a nomogram for predicting distant metastasis using the optimal performance lymph node staging system. The lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), age, primary site, grade, tumor size, American Joint Committee on Cancer (AJCC) 7th Edition T stage, and radiotherapy recipient status were significant predictors of distant metastasis in PDAC patients. The model with the LODDS was a better fit than the model with the LNR. We developed a nomogram model based on LODDS and six clinical parameters. The area under the curve (AUC) and concordance index (C-index) of 0.753 indicated that this model satisfied the discrimination criteria. Kaplan–Meier curves indicate a significant difference in OS among patients with different metastasis risks. LODDS seems to have a superior ability to predict distant metastasis in PDAC patients compared with the AJCC 8th Edition N stage, PLN and LNR staging systems. Moreover, we developed a nomogram model for predicting distant metastasis. Clinicians can use the model to detect patients at high risk of distant metastasis and to make further clinical decisions.

Identifying prognostic predictors for postoperative pituitary neuroendocrine tumour recurrence: an integrated clinical, radiological, and immunohistochemistry assessment

Objective Pituitary neuroendocrine tumours (PitNETs) are the second most common type of intracranial tumour. Several studies have explored the prognostic factors for PitNETs. However, prognostic factors for postoperative PitNET recurrence remain not fully understood. This study aimed to explore potential prognostic factors for PitNET recurrence, such as surrounding tissue invasion and the extent of surgical resection in patients with postoperative PitNETs. Methods We included 106 patients who underwent PitNET surgery between 2013 and 2018, dividing them into two groups: those with recurrence and those without recurrence. Tumours were classified based on demographics, neuroradiological, and immunohistological characteristics. Univariate and multivariate analyses were used to determine factors predicting recurrence. Kaplan–Meier plots and log-rank tests were used to analyse each independent factor based on the cumulative 5-year recurrence rate. Results During the 5-year follow-up period, 29.2% of the patients (n = 31) had disease recurrence. Univariate analysis showed that predictors of recurrence included cavernous and sphenoid sinus invasions, optic chiasm compression, larger tumour volume, giant adenoma >4 cm, and gross total resection (GTR). Multivariate analysis showed that lactotroph tumour type, sphenoid sinus invasion, and GTR were independent predictors. Kaplan–Meier analysis revealed significant differences in the 5-year recurrence rate among the three independent predictors, with significantly lower recurrence rate in patients with lactotroph tumours and GTR, and a significantly higher recurrence risk in patients with sphenoid sinus invasion. Conclusions Lactotroph tumour type, sphenoid sinus invasion, and GTR are independent predictors of postoperative PitNET recurrence. This study provides insights into the factors affecting postoperative PitNET recurrence.

Assessment of survival prediction after surgery in spinal metastases patients using the Global Spine Study Tumor Group (GSTSG) risk calculator; an external validation from a tertiary cancer hospital.

We aim to validate the Global Spine Tumor Study Group (GSTSG) score compared to previous prognostic scoring systems in spinal metastasis. We conducted a retrospective study from January 2013 to December 2022. The survival prediction was compared between the GSTSG, Tomita Score, Revised Tokuhashi Score, and Skeletal Oncology Research Group (SORG) Nomogram. Single-variable factors associated with survival rate were analyzed using univariate Cox regression and multivariable Cox proportional hazard model. Receiver operating characteristic was used for external validity analysis at 3, 6, 12, and 24months. The overall survival rate was reported using the Kaplan-Meier survival curve. 248 spinal metastasis patients were included. The mean age was 59.23 ± 12.55years. The mean duration of follow-up time was 470.29 ± 441.98days. The external validity of GSTSG was the highest at all follow-up times (sufficiently accurate AUC > 0.7), which was about the same as SORG at 3months (both AUC of GSTSG and SORG = 0.76) and higher than modified Tokuhashi and Tomita score at 12months (AUC of GSTSG = 0.78, SORG = 0.71, Tomita = 0.64, and modified Tokuhashi = 0.61, respectively). From our study, the Multivariate Cox regression analysis indicates that the significant factors related to survival rate are regular analgesic use of weak opioids, lung metastasis, and previous chemotherapy. Compared to other traditional spinal metastases prognostic scoring systems, GSTSG shows the highest AUC for external validity in all follow-up times up to 24months.

Hip Displacement After Triradiate Closure in Ambulatory Cerebral Palsy: Who Needs Continued Surveillance?

Hip surveillance in cerebral palsy (CP) is an accepted practice with evidence-based guidelines implemented. For the skeletally immature with open triradiate cartilage (TRC), recommendations for radiographic surveillance stemmed from population-based studies. For nonambulatory CP, progression of hip displacement after skeletal maturity has been reported; less is known for ambulatory CP. We aimed to determine the prevalence and risk factors associated with progressive hip displacement after TRC closure, a proxy for skeletal maturity, for ambulatory CP. This is a retrospective cohort study of patients with ambulatory CP (Gross Motor Function Classification System I-III), with unilateral or bilateral involvement, hypertonic motor type, regular hip surveillance (≥3 radiographs after age 10 yr, 1 before TRC closure, ≥1 after age 16 yr), and 2-year follow-up post-TRC closure. The primary outcome was migration percentage (MP). Other variables included previous preventative/reconstructive surgery, topographic pattern, sex, scoliosis, epilepsy, and ventriculoperitoneal shunt. An "unsuccessful hip" was defined by MP ≥30%, MP progression ≥10%, and/or requiring reconstructive surgery after TRC closure. Statistical analyses included chi-square and multivariate Cox regression. Kaplan-Meier survivorship curves were also determined. Receiver operating characteristic analysis was used to determine the MP threshold for progression to an "unsuccessful hip" after TRC closure. Seventy-six patients (39.5% female) met the inclusion criteria, mean follow-up 4.7±2.1 years after TRC closure. Sixteen (21.1%) patients had an unsuccessful hip outcome. By chi-square analysis, diplegia (P=0.002) and epilepsy (P=0.04) were risk factors for an unsuccessful hip. By multivariate analysis, only first MP after TRC closure (P<0.001) was a significant risk factor for progression to an unsuccessful hip; MP ≥28% being the determined threshold (receiver operating characteristic curve analysis, area under curve: 0.845, P<0.02). The risk of MP progression after skeletal maturity is relatively high (21%), similar to nonambulatory CP. Annual hip surveillance radiographs after TRC closure should continue for Gross Motor Function Classification System I-III with an MP ≥28% after TRC closure, especially for bilateral CP and epilepsy. III.

Optimizing Glioblastoma, IDH-wildtype Treatment Outcomes : A Radiomics and Support Vector Machine -Based Approach to Overall Survival Estimation.

Glioblastoma multiforme (GBM), particularly the IDH-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches. This study utilizes a Support Vector Machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (< 12 Months) and long (>=12 Months) survivors. A dataset comprising multi-parametric MRI (mpMRI) scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, FLAIR, and T1-Gd sequences. Low variance features were removed, and Recursive Feature Elimination (RFE) was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT promoter methylation status were integrated to enhance prediction accuracy. The model showed reasonable results in terms of cross-validated AUC of 0.84 (95% CI: 0.80-0.90) with (p-value < 0.001) effectively categorizing patients into short and long survivors. Log-rank test (Chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen's d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value<0.0001. The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

The impact of socioeconomic determinants on the access to care and survival in patients with spinal chordomas- a national cancer database analysis.

Chordomas are rare malignant neoplasms primarily treated surgically. Disparities related to race and socioeconomic status, may affect patient outcomes. This study aims to identify prognostic factors for access to care and survival in patients with spinal chordomas. The NCDB database was queried between the years 2004 and 2017. Kaplan-Meier curves were constructed to compare survival probabilities among different groups, based on race and socioeconomic determinents. 1769 patients were identified, with 87% being White, 5% Hispanic, 4% Black, and Asian each. The mean age was 61.3 years. Most patients received care at academic/research centers and lived in a large metropolitan area, with no difference between races. A significantly higher percentage of Black patients did not undergo surgery (p < 0.001), with no statistically significant difference in survival between races (p = 0.97). A higher survival probability was seen in patients with other government insurances (p < 0.0001), in higher income quartiles (p < 0.0001), in metropolitan areas (p = 0.023), and at an academic/research center (p < 0.0001). A lower survival probability was seen in patients who are uninsured, in rural areas, and at community cancer programs (p < 0.0001). This study highlights disparities in access to surgical intervention for patients with spinal chordomas, especially among Black individuals. It emphasizes the significant impact of insurance status and income on access to surgical care and highlights geographical and institutional variations in survival rates. Addressing socioeconomic differences is crucial for fostering equity in neurosurgical outcomes.

Association between waist triglyceride index, body mass index, dietary inflammatory index, and triglyceride- glucose index with chronic kidney disease: the 1999-2018 cohort study from NHANES

PurposeTo compare the dietary inflammatory index (DII), triglyceride glucose index (TyG), waist triglyceride index (WTI), and body mass index (BMI) in predicting the survival of chronic kidney disease (CKD).MethodologyInclusion of 23,099 participants from the NHANES database who met specific criteria. Baseline was established using quartiles of DII index. The relationship between DII index, WTI index, TyG index, and BMI index with mortality rate in CKD patients was evaluated using Kaplan-Meier curves. Univariate and multivariate COX regression risk models were used to study the relationship between DII index, WTI index, and TyG index with mortality risk in CKD patients. Stratification of eGFR by age and gender was conducted to investigate the association between DII index, WTI index, and TyG index with mortality risk in CKD patients. Restricted cubic spline analysis was used to study the correlation between DII index, WTI index, and TyG index with mortality risk in CKD patients.ResultsThe incidence of CKD increased with the increase of DII index, WTI index and TyG index. After multivariable adjustment, the fourth quartile of DII index, TyG index and WTI index showed the highest risk for CKD [DII: hazard ratio (HR) 1.36, 95% confidential interval (CI) (1.23–1.51); TyG: HR 1.21; 95% CI (1.07–1.37); WTI: HR 1.29; 95% CI (1.13–1.46)]. There was no difference in the risk of developing CKD between the obese group (BMI ≥24 kg/m2) and the normal weight group (P&amp;gt;0.05).ConclusionThis study has identified a significant association between elevated DII index, WTI index, and TyG index with the risk of CKD. Furthermore, the DII index demonstrated superior prognostic capability in predicting CKD compared to other indicators.

The Glasgow Prognostic Score as a Predictor of Survival After Chemoradiotherapy for Limited-Disease Small Cell Lung Cancer.

Established biomarkers for predicting chemoradiotherapy efficacy for limited-disease small cell lung cancer (LD-SCLC) are lacking. The inflammation-based Glasgow Prognostic Score (GPS), comprising serum C-reactive protein (CRP) and albumin levels, can predict survival in advanced cancer. This study investigated whether metabolic and inflammatory markers, including the GPS, can predict the efficacy of chemoradiotherapy in patients with LD-SCLC. We retrospectively analyzed 124 patients who underwent chemoradiotherapy for LD-SCLC at two institutions between April 2007 and June 2021, and assessed the prognostic significance of various metabolic and inflammatory markers. The GPS was calculated using the CRP and albumin concentrations, and categorized as follows: 0, CRP&lt;1.0 mg/dL and albumin≥3.5 mg/dL; 1, elevated CRP or decreased albumin; and 2, CRP≥1.0 mg/dL and albumin&lt;3.5 mg/dL. Differences in progression-free survival (PFS) and overall survival (OS) were examined using Kaplan-Meier curves and Cox proportional-hazard models. The overall response rate was 95.1% (95% confidence interval [CI]: 89.6-97.9%). The median PFS and OS from chemoradiotherapy initiation were 12.6 (95% CI: 9.9-15.4) and 29.0 (95% CI: 24.8-45.5) months, respectively. The GPS demonstrated independent predictive ability for the effectiveness of chemoradiotherapy, wherein favorable scores (GPS 0-1) were significantly correlated with superior PFS and OS compared to unfavorable scores (GPS 2: PFS: 14.8 vs. 6.7 months, p=0.0001; OS: 35.4 vs. 11.0 months, p&lt;0.0001). This preliminary examination revealed that the GPS was significantly associated with PFS and OS in patients undergoing chemoradiotherapy for LD-SCLC, indicating its potential utility in assessing the therapeutic outcomes in LD-SCLC.

Characterizing relationships between T-cell inflammation and outcomes in patients with high-risk neuroblastoma according to mesenchymal and adrenergic signatures.

Recent insights have identified adrenergic (ADRN) and mesenchymal (MES) cell lineages as distinct biologic cell types and T-cell inflammation as a prognostic marker in neuroblastoma. We hypothesized that elucidating unique and overlapping aspects of these biologic features could serve as novel biomarkers for informing ongoing efforts to improve therapeutic approaches for children with high-risk neuroblastoma. We identified lineage-specific, single-stranded super-enhancers to define ADRN and MES specific genes. Publicly available RNA-seq of diagnostic tumor biopsies was used in Discovery and Validation cohorts. Each tumor was assigned a relative MES score and T-cell-inflamed (TCI) score. Survival was assessed using the Kaplan-Meier method, and differences were assessed by the log-rank test. Inflammation scores were correlated with MES scores and anticorrelated with MYCN-amplification in both cohorts. Among patients with high-risk, ADRN tumors, those with TCI tumors had superior overall survival to those with non-inflamed tumors. A similar, but non-significant, trend was observed in the Validation cohort. Conversely, there was no difference according to TCI status in the MES cohort in either the Discover or Validation Cohorts. High inflammation scores were correlated with improved survival in some patients with high-risk, ADRN but not MES neuroblastoma. Our findings bolster support for further developing T-cell based immunotherapy-based approaches for children with high-risk neuroblastoma of varying MES and ADRN expression.

Radiofrequency Ablation for the Treatment of Benign Thyroid Nodules: 10-Year Experience.

Background: Longer follow-up after radiofrequency ablation (RFA) of benign thyroid nodules is needed to understand regrowth and other causes of delayed surgery and long-term complications. Methods: This retrospective study included consecutive patients treated with RFA for symptomatic benign nonfunctioning thyroid nodules between March 2007 and December 2010. RFA was performed according to the standard protocol. We followed up patients at 1, 6, and 12 months, then yearly, until August 2022, and calculated the volume reduction ratio (VRR) at each follow-up. We assessed the incidence of regrowth according to three published criteria, delayed surgery, and complications. The Kaplan-Meier method was used to evaluate the cumulative incidence of regrowth, and univariable and multivariable Cox regression analyses were performed to identify risk factors for regrowth. Results: This study included 421 patients (mean age, 47 ± 13 years; 372 women) with 456 nodules (mean volume, 21 ± 23 mL). The median follow-up period was 90 months (interquartile range, 24-143 months). The mean VRR was 81% at 2 years, 90% at 5 years, and 94% at ≥10 years. Overall regrowth was noted in 12% (53/456) of nodules and was treated with repeat RFA (n = 33) or surgery (n = 4) or left under observation (n = 16). Thyroid nodules with ≥20 mL initial volume had significantly higher risk of regrowth compared with nodules with <10 mL initial volume (hazard ratio, 2.315 [95% confidence interval, 1.183-4.530]; p = 0.014 on multivariable Cox regression analysis). Delayed surgery was performed in 6% (26/421) of patients because of regrowth and/or persistent symptoms (n = 4) or newly detected thyroid tumors (n = 22), one benign and 21 malignant. The overall complication rate was 2.4% (10/421), with no procedure-related deaths or long-term complications. Conclusion: RFA is safe and effective for treating benign thyroid nodules, with a high VRR at long-term follow-up. Regular follow-up after initial success is warranted because of the possibility of regrowth of ablated nodules and the need for delayed surgery in some patients.

Clinical features and prognosis of patients with myeloid neoplasms harboring t(7;11)(p15;p15) translocation: a single-center retrospective study

BackgroundFor myeloid neoplasms with t(7;11)(p15;p15) translocation, the prognosis is quite dismal. Because these tumors are rare, most occurrences are reported as single cases. Clinical results and optimal treatment approaches remain elusive. This study endeavors to elucidate the clinical implications and prognosis of this cytogenetic aberration.MethodsThis study retrospectively analyzed 23 cases of myeloid neoplasm with t(7;11)(p15;p15). Clinicopathological characteristics, genetic alterations, and outcomes were evaluated, and the Kaplan-Meier method was employed to construct survival curves.ResultsOf these, nine cases were newly diagnosed acute myeloid leukemia (ND AML), seven presented with relapsed refractory AML (R/R AML), four had myelodysplastic syndrome (MDS), two had secondary AML, and one exhibited a mixed germinoma associated with MDS. Patients with t(7;11)(p15;p15) in AML were primarily younger females who preferred subtype M2. Interestingly, these patients had decreased hemoglobin and red blood cell counts, along with markedly elevated levels of lactic dehydrogenase and interleukin-6, and exhibited the expression of CD117. R/R AML patients exhibited a higher likelihood of additional chromosome abnormalities (ACAs) besides t(7;11). WT1 and FLT3-ITD were the most commonly found mutated genes, and 10 of those instances showed evidence of the NUP98::HOXA9 fusion gene. The composite complete remission rate was 66.7% (12/18), while the cumulative graft survival rate was 100% (4/4). However, the survival outcomes were dismal. Interestingly, the median overall survival for R/R AML patients was 4.0 months (95% CI: 1.7–6.4). Additionally, the type of AML diagnosis or the presence of ACAs or molecular prognostic stratification did not significantly influence clinical outcomes (p = 0.066, p = 0.585, p = 0.570, respectively).ConclusionMyeloid leukemia with t(7;11) exhibits unique clinical features, cytogenetic properties, and molecular genetic characteristics. These survival outcomes were dismal. R/R AML patients have a limited lifespan. For myeloid patients with t(7;11), targeted therapy or transplantation may be an effective course of treatment.