Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and assessment of existing guidelines.

BACKGROUND: The SAVVY project aims to improve the analyses of adverse events (AEs) in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). This paper summarizes key features and conclusions from the various SAVVY papers. METHODS: Summarizing several papers reporting theoretical investigations using simulations and an empirical study including randomized clinical trials from several sponsor organizations, biases from ignoring varying follow-up times or CEs are investigated. The bias of commonly used estimators of the absolute (incidence proportion and one minus Kaplan-Meier) and relative (risk and hazard ratio) AE risk is quantified. Furthermore, we provide a cursory assessment of how pertinent guidelines for the analysis of safety data deal with the features of varying follow-up time and CEs. RESULTS: SAVVY finds that for both, avoiding bias and categorization of evidence with respect to treatment effect on AE risk into categories, the choice of the estimator is key and more important than features of the underlying data such as percentage of censoring, CEs, amount of follow-up, or value of the gold-standard. CONCLUSIONS: The choice of the estimator of the cumulative AE probability and the definition of CEs are crucial. Whenever varying follow-up times and/or CEs are present in the assessment of AEs, SAVVY recommends using the Aalen-Johansen estimator (AJE) with an appropriate definition of CEs to quantify AE risk. There is an urgent need to improve pertinent clinical trial reporting guidelines for reporting AEs so that incidence proportions or one minus Kaplan-Meier estimators are finally replaced by the AJE with appropriate definition of CEs.

The efficacy of Bortezomib-based regimens on survival state in newly diagnosed multiple myeloma patients.

The prognosis of patients with multiple myeloma (MM) has significantly improved over the past ten years because of several innovative treatments, including the proteasome inhibitor Bortezomib and immunomodulatory drugs (IMiDs) like Thalidomide and Lenalidomide. The present study aimed to determine the effectiveness of Bortezomib-based regimens on survival state of MM patients. This retrospective study included 204 newly diagnosed MM patients who were registered at Nanakali Hospital for Blood Diseases and Cancer, Erbil- Iraq, between April 2008 and April 2022. The patients were split into two primary groups: those receiving treatment with Bortezomib and those not. Clinical and laboratory data, treatment type, responsiveness to induction therapy, and survival results were examined in the enrolled patients' medical records. The mean patient age was 60 years, males constituted 55.8% of the included patients. At the time of diagnosis, 98 individuals (48%) had stage 3 illness. Except for the LDH, which was noticeably higher in the non-Bortezomib group, the patients laboratory results did not substantially change between the Bortezomib and non-Bortezomib groups (p = 0.001). In patients treated with Bortezomib, the complete response (CR) rate following induction was substantially greater (35.2%) than in those treated without Bortezomib (9.1%). Compared to the non-Bortezomib group, the median survival time of the Bortezomib group was considerably greater (p < 0.001). Bortezomib has a significant role in inducing a CR before bone marrow (BM) transplantation, and it has a significant role in the survival outcome in MM.

Clinical characteristics, survival analysis and influencing factors of distant metastasis in patients with acromelanomas: A retrospective study.

The prognosis of acromelanomas (AM) is worse. The objective of this study was to investigate the clinical features of distant metastasis of AM and the factors affecting the survival and prognosis of patients. In this study, a retrospective study was conducted to select 154 AM patients admitted to Nanjing Pukou People's Hospital from January 2018 to April 2021 for clinical research. The clinical characteristics of distant metastasis were statistically analyzed, and the survival curve was drawn with 5-year follow-up outcomes. The median survival time of the patients was calculated, and the clinicopathological features and peripheral blood laboratory indexes of the surviving and dead patients were analyzed. Logistic regression model was used to analyze the risk factors affecting the prognosis of AM patients. In this study, 154 patients with AM were treated, including 88 males and 76 females, aged from 27 to 79 years old, with an average age of (59.3 ±â€…11.7) years old. Among them, 90 cases had distant metastasis. The main metastatic sites were lung (47.78%) and lymph nodes (42.22%). Among them, single site metastasis accounted for 41.11% and multiple site metastasis 58.89%. 89 cases survived and 65 cases died. The survival time was 22 months to 60 months, and the median survival time was 48.0 months. The Breslow thickness, stage at diagnosis, distant metastasis, site of metastasis and ulceration were compared between the survival group and the death group (P < .05). serum lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR) and lymphocyte monocyte ratio (LMR) were compared between the survival group and the death group (P < .05). The results of Logistic regression model showed that LDH ≥ 281 U/L, NLR ≥ 2.96, LMR ≤ 3.57, newly diagnosed stage > stage II, distant metastasis, multiple site metastasis and tumor ulcer were independent risk factors for poor prognosis of AM patients (P < .05). Patients with AM had a higher proportion of distant metastasis, mainly lung and lymph node metastasis. Increased LDH, increased NLR, decreased LMR, higher initial stage, distant metastasis, multiple site metastasis, and combined tumor ulcer were closely related to the poor prognosis of patients after surgery.

Identification of hub genes related to metastasis and prognosis of osteosarcoma and establishment of a prognostic model with bioinformatic methods.

Osteosarcoma (OS) is the most common primary malignant bone tumor occurring in children and adolescents. Improvements in our understanding of the OS pathogenesis and metastatic mechanism on the molecular level might lead to notable advances in the treatment and prognosis of OS. Biomarkers related to OS metastasis and prognosis were analyzed and identified, and a prognostic model was established through the integration of bioinformatics tools and datasets in multiple databases. 2 OS datasets were downloaded from the Gene Expression Omnibus database for data consolidation, standardization, batch effect correction, and identification of differentially expressed genes (DEGs); following that, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs; the STRING database was subsequently used for protein-protein interaction (PPI) network construction and identification of hub genes; hub gene expression was validated, and survival analysis was conducted through the employment of the TARGET database; finally, a prognostic model was established and evaluated subsequent to the screening of survival-related genes. A total of 701 DEGs were identified; by gene ontology and KEGG pathway enrichment analyses, the overlapping DEGs were enriched for 249 biological process terms, 13 cellular component terms, 35 molecular function terms, and 4 KEGG pathways; 13 hub genes were selected from the PPI network; 6 survival-related genes were identified by the survival analysis; the prognostic model suggested that 4 genes were strongly associated with the prognosis of OS. DEGs related to OS metastasis and survival were identified through bioinformatics analysis, and hub genes were further selected to establish an ideal prognostic model for OS patients. On this basis, 4 protective genes including TPM1, TPM2, TPM3, and TPM4 were yielded by the prognostic model.

Enhancing multiple myeloma staging: a novel cell death risk model approach.

The prognostication of survival trajectories in multiple myeloma (MM) patients presents a substantial clinical challenge. Leveraging transcriptomic and clinical profiles from an expansive cohort of 2,088 MM patients, sourced from the Gene Expression Omnibus and The Cancer Genome Atlas repositories, we applied a sophisticated nested lasso regression technique to construct a prognostic model predicated on 28 gene pairings intrinsic to cell death pathways, thereby deriving a quantifiable risk stratification metric. Employing a threshold of 0.15, we dichotomized the MM samples into discrete high-risk and low-risk categories. Notably, the delineated high-risk cohort exhibited a statistically significant diminution in survival duration, a finding which consistently replicated across both training and external validation datasets. The prognostic acumen of our cell death signature was further corroborated by TIME ROC analyses, with the model demonstrating robust performance, evidenced by AUC metrics consistently surpassing the 0.6 benchmark across the evaluated arrays. Further analytical rigor was applied through multivariate COX regression analyses, which ratified the cell death risk model as an independent prognostic determinant. In an innovative stratagem, we amalgamated this risk stratification with the established International Staging System (ISS), culminating in the genesis of a novel, refined ISS categorization. This tripartite classification system was subjected to comparative analysis against extant prognostic models, whereupon it manifested superior predictive precision, as reflected by an elevated C-index. In summation, our endeavors have yielded a clinically viable gene pairing model predicated on cellular mortality, which, when synthesized with the ISS, engenders an augmented prognostic tool that exhibits pronounced predictive prowess in the context of multiple myeloma.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

BACKGROUND: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

The expression characteristic and prognostic role of Siglec-15 in lung adenocarcinoma.

Sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15) has been identified as an immune suppressor and a promising candidate for immunotherapy of cancer management. However, the association between Siglec-15 expression and clinicopathological features of lung adenocarcinoma (LUAD), especially the prognostic role, is not fully elucidated. In this present study, a serial of bioinformatics analyses in both tissue and cell levels were conducted to provide an overview of Siglec-15 expression. Real-time quantitative PCR (qPCR) test, western blotting assay, and immunohistochemistry (IHC) analyses were conducted to evaluate the expression of Siglec-15 in LUAD. Survival analysis and Kaplan-Meier curve were employed to describe the prognostic parameters of LUAD. The results of bioinformatics analyses demonstrated the up-regulation of Siglec-15 expression in LUAD. The data of qPCR, western blotting, and IHC analyses further proved that the expression of Siglec-15 in LUAD tissues was significantly increased than that in noncancerous tissues. Moreover, the expression level of Siglec-15 protein in LUAD was substantially associated with TNM stage. LUAD cases with up-regulated Siglec-15 expression, positive N status, and advance TNM stage suffered a critical unfavorable prognosis. In conclusion, Siglec-15 could be identified as a novel prognostic biomarker in LUAD and targeting Siglec-15 may provide a promising strategy for LUAD immunotherapy.

Integrating Machine Learning and Traditional Survival Analysis to Identify Key Predictors of Foveal Involvement in Geographic Atrophy.

Purpose: The purpose of this study was to investigate the incidence of foveal involvement in geographic atrophy (GA) secondary to age-related macular degeneration (AMD), using machine learning to assess the importance of risk factors. Methods: Retrospective, longitudinal cohort study. Patients diagnosed with foveal-sparing GA, having GA size ≥ 0.049 mm² and follow-up ≥ 6 months, were included. Baseline GA area, distance from the fovea, and perilesional patterns were measured using fundus autofluorescence. Optical coherence tomography assessed foveal involvement, structural biomarkers, and outer retinal layers thickness. Onset of foveal involvement was recorded. Foveal survival rates were estimated using Kaplan-Meier curves. Hazard ratios (HRs) were assessed with mixed model Cox regression. Variable Importance (VIMP) was ranked with Random Survival Forests (RSF), with higher scores indicating greater predictive significance. Results: One hundred sixty-seven eyes (115 patients, average age = 75.8 ± 9.47 years) with mean follow-up of 50 ± 29 months, were included in this study. Median foveal survival time was 45 months (95% confidence interval [CI] = 38-55). Incidences of foveal involvement were 26% at 24 months and 67% at 60 months. Risk factors were GA proximity to the fovea (HR = 0.97 per 10-µm increase, 95% CI = 0.96-0.98), worse baseline visual acuity (HR = 1.37 per 0.1 LogMAR increase, 95% CI = 1.21-1.53), and thinner outer nuclear layer (HR = 0.59 per 10-µm increase, 95% CI = 0.46-0.74). RSF analysis confirmed these as main predictors (VIMP = 16.7, P = 0.002; VIMP = 6.2, P = 0.003; and VIMP = 3.4, P = 0.01). Lesser baseline GA area (HR = 1.09 per 1-mm2 increase, 95% CI = 1.01-1.16) and presence of a double layer sign (HR = 0.42, 95% CI = 0.20-0.88) were protective but less influential. Conclusions: This study identifies anatomic and functional factors impacting the risk of foveal involvement in GA. These findings may help identify at-risk patients, enabling tailored preventive strategies.

Trends in incidence and survival of childhood cancers in Khon Kaen, Thailand (2000-2019): a population-based Khon Kaen Cancer Registry study.

BACKGROUND: In Thailand, the national health care system and nationwide standard treatment protocols have evolved over time, potentially influencing the trends in the incidence and survival rates of childhood cancers. However, further investigations are required to comprehensively study these trends in Khon Kaen, Thailand. METHODS: Childhood cancer patients aged 0-14 years (n = 541) who were diagnosed with one of the five most common cancers between 2000 and 2019 from the population-based Khon Kaen Cancer Registry were enrolled. Descriptive statistics were used to analyse the demographic data, which are presented as numbers, percentages, means, and standard deviations. The trends in incidence between 2000 and 2019, including age-standardized incidence rates (ASRs) and annual percent changes (APCs), were analysed using the Joinpoint regression model. Survival analysis was performed for 5-year relative survival rates (RSRs) according to the Pohar Perme estimator and Kaplan-Meier survival curves. RESULTS: The ASRs of the overall top 5 childhood cancer groups were 67.96 and 106.12 per million person-years in 2000 and 2019, respectively. Overall, the APC significantly increased by 2.37% each year for both sexes. The overall 5-year RSRs were 60.5% for both sexes, 58.2% for males, and 63.9% for females. The highest 5-year RSR was for germ cell tumours (84.3%), whereas the lowest 5-year RSR was for neuroblastoma (29.1%). CONCLUSIONS: The incidence and survival rates of childhood cancers in Khon Kaen, Thailand, varied according to sex. The incidence trends increased over time, meanwhile, the relative survival rates rose to satisfactory levels and were comparable to those of other nations with similar financial status. The implementation of national health policies and adherence to national treatment guidelines have improved cancer diagnosis and treatment outcomes.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Survival estimation of oral cancer using fuzzy deep learning.

BACKGROUND: Oral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on clinicopathologic data of oral cancer. METHODS: Electronic medical records of 581 oral squamous cell carcinoma (OSCC) patients, treated with surgery with or without radiochemotherapy, were collected retrospectively from the Oral and Maxillofacial Surgery Clinic and the Regional Cancer Center from 2011 to 2019. The deep learning (DL) model was trained to classify survival time classes based on clinicopathologic data. Fuzzy logic was integrated into the DL model and trained to create FDL-based models to estimate the survival time classes. RESULTS: The performance of the models was evaluated on a test dataset. The performance of the DL and FDL models for estimation of survival time achieved an accuracy of 0.74 and 0.97 and an area under the receiver operating characteristic (AUC) curve of 0.84 to 1.00 and 1.00, respectively. CONCLUSIONS: The integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.

Pathology of explanted pediatric hearts: An 11-year study. Population characteristics and implications for outcomes.

BACKGROUND: As more pediatric patients become candidates for heart transplantation (HT), understanding pathological predictors of outcome and the accuracy of the pretransplantation evaluation are important to optimize utilization of scarce donor organs and improve outcomes. The authors aimed to investigate explanted heart specimens to identify pathologic predictors that may affect cardiac allograft survival after HT. METHODS: Explanted pediatric hearts obtained over an 11-year period were analyzed to understand the patient demographics, indications for transplant, and the clinical-pathological factors. RESULTS: In this study, 149 explanted hearts, 46% congenital heart defects (CHD), were studied. CHD patients were younger and mean pulmonary artery pressure and resistance were significantly lower than in cardiomyopathy patients. Twenty-one died or underwent retransplantation (14.1%). Survival was significantly higher in the cardiomyopathy group at all follow-up intervals. There were more deaths and the 1-, 5- and 7-year survival was lower in patients ≤10 years of age at HT. Early rejection was significantly higher in CHD patients exposed to homograft tissue, but not late rejection. Mortality/retransplantation rate was significantly higher and allograft survival lower in CHD hearts with excessive fibrosis of one or both ventricles. Anatomic diagnosis at pathologic examination differed from the clinical diagnosis in eight cases. CONCLUSIONS: Survival was better for the cardiomyopathy group and patients >10 years at HT. Prior homograft use was associated with a higher prevalence of early rejection. Ventricular fibrosis (of explant) was a strong predictor of outcome in the CHD group. We presented several pathologic findings in explanted pediatric hearts.

A transformer model for cause-specific hazard prediction.

BACKGROUD: Modelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains. Survival analysis in longitudinal cohorts often requires such models; notably when the data is gathered at discrete points in time and the predicted events display complex dynamics. Current models often rely on strong assumptions of proportional hazards, that is rarely verified in practice; or do not handle sequential data in a meaningful way. This study proposes a Transformer architecture for the prediction of cause-specific hazards in discrete-time competing risks. Contrary to Multilayer perceptrons that were already used for this task (DeepHit), the Transformer architecture is especially suited for handling complex relationships in sequential data, having displayed state-of-the-art performance in numerous tasks with few underlying assumptions on the task at hand. RESULTS: Using synthetic datasets of 2000-50,000 patients, we showed that our Transformer model surpassed the CoxPH, PyDTS, and DeepHit models for the prediction of cause-specific hazard, especially when the proportional assumption did not hold. The error along simulated time outlined the ability of our model to anticipate the evolution of cause-specific hazards at later time steps where few events are observed. It was also superior to current models for prediction of dementia and other psychiatric conditions in the English longitudinal study of ageing cohort using the integrated brier score and the time-dependent concordance index. We also displayed the explainability of our model's prediction using the integrated gradients method. CONCLUSIONS: Our model provided state-of-the-art prediction of cause-specific hazards, without adopting prior parametric assumptions on the hazard rates. It outperformed other models in non-proportional hazards settings for both the synthetic dataset and the longitudinal cohort study. We also observed that basic models such as CoxPH were more suited to extremely simple settings than deep learning models. Our model is therefore especially suited for survival analysis on longitudinal cohorts with complex dynamics of the covariate-to-outcome relationship, which are common in clinical practice. The integrated gradients provided the importance scores of input variables, which indicated variables guiding the model in its prediction. This model is ready to be utilized for time-to-event prediction in longitudinal cohorts.

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.

BACKGROUND: Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. METHODS: We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. RESULTS: The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. CONCLUSIONS: This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction. In this article, we propose a new hybrid feature selection approach that handles high-dimensional bioinformatics datasets for improved survival prediction. This study explores the efficacy of four distinct variable selection techniques: LASSO, RSF-vs, SCAD, and CoxBoost, in the context of non-parametric biomedical survival prediction. Leveraging these methods, we conducted comprehensive variable selection processes. Subsequently, survival analysis models-specifically CoxPH, RSF, and DeepHit NN-were employed to construct predictive models based on the selected variables. Furthermore, we introduce a novel approach wherein only variables consistently selected by a majority of the aforementioned feature selection techniques are considered. This innovative strategy, referred to as the proposed method, aims to enhance the reliability and robustness of variable selection, subsequently improving the predictive performance of the survival analysis models. To evaluate the effectiveness of the proposed method, we compare the performance of the proposed approach with the existing LASSO, RSF-vs, SCAD, and CoxBoost techniques using various performance metrics including integrated brier score (IBS), concordance index (C-Index) and integrated absolute error (IAE) for numerous high-dimensional survival datasets. The real data applications reveal that the proposed method outperforms the competing methods in terms of survival prediction accuracy.

[The clinical features, survival analysis, and geriatric assessment of 85 patients with follicular lymphoma: a single-center study].

Objective: To retrospectively analyze the clinical characteristics and prognosis of 85 newly diagnosed patients with follicular lymphoma (FL), as well as the prognostic value of comprehensive geriatric assessment (CGA) in patients with FL aged ≥ 60 years old. Methods: The clinical data and prognosis of 85 newly diagnosed FL patients admitted from August 2011 to June 2022 were collected. The clinical features, laboratory indicators, therapeutic efficacy, survival and prognostic factors of patients were statistically analyzed, and the prognosis of patients was stratified using various geriatric assessment tools. Results: ① The patients with FL were mostly middle-aged and older, with a median age of 59 (20-87) years, including 41 patients (48.2%) aged ≥60 years. The ratio of male to female was 1∶1.36. Overall, 77.6% of the patients were diagnosed with Ann Arbor stage Ⅲ-Ⅳ, and 17 cases (20.0%) were accompanied by B symptoms. Bone marrow involvement was the most common (34.1%). ②Overall, 71 patients received immunochemotherapy. The overall response rate was 86.6%, and the complete recovery rate was 47.1% of 68 evaluated patients. Disease progression or relapse in the first 2 years was observed in 23.9% of the patient. Overall, 14.1% of the patients died during follow-up. ③Of the 56 patients receiving R-CHOP-like therapies, the 3-year and 5-year progression-free survival (PFS) rates were 85.2% and 72.8%, respectively, and the 3-year and 5-year overall survival (OS) rates were 95.9% and 88.8%, respectively. The univariate analysis showed that age ≥60 years old (HR=3.430, 95% CI 1.256-9.371, P=0.016), B symptoms (HR=5.030, 95% CI 1.903-13.294, P=0.016), Prognostic Nutritional Index (PNI) <45.25 (HR=3.478, 95% CI 1.299-9.310, P=0.013), Follicular Lymphoma International Prognostic Index (FLIPI) high-risk (HR=2.918, 95% CI 1.074-7.928, P=0.036), and PRIMA-prognostic index (PRIMA-PI) high-risk (HR=2.745, 95% CI 1.057-7.129, P=0.038) significantly predicted PFS. Moreover, age ≥60 years old and B symptoms were independent risk factors for PFS. Progression of disease within 24 months (POD24) significantly predicted OS in the univariate analysis. Conclusions: FL is more common among middle-aged and older women. Age, B symptoms, PNI score, FLIPI high-risk, PRIMA-PI high-risk, and POD24 influenced PFS and OS. The CGA can be used for treatment selection and risk prognostication in older patients with FL.

Open or closed abdomen post laparotomy to control severe abdominal sepsis: a survival analysis.

INTRODUCTION: severe abdominal sepsis, accompained by diffuse peritonitis, poses a significant challenge for most surgeons. It often requires repetitive surgical interventions, leading to complications and resulting in high morbidity and mortality rates. The open abdomen technique, facilitated by applying a negative-pressure wound therapy (NPWT), reduces the duration of the initial surgical procedure, minimizes the accumulation of secretions and inflammatory mediators in the abdominal cavity and lowers the risk of abdominal compartment syndrome and its associated complications. Another approach is primary closure of the abdominal aponeurosis, which involves suturing the layers of the abdominal wall. METHODS: the objective of this study is to conduct a survival analysis comparing the treatment of severe abdominal sepsis using open abdomen technique versus primary closure after laparotomy in a public hospital in the South of Brazil. We utilized data extracted from electronic medical records to perform both descriptive and survival analysis, employing the Kaplan-Meier curve and a log-rank test. RESULTS: the study sample encompassed 75 laparotomies conducted over a span of 5 years, with 40 cases employing NPWT and 35 cases utilizing primary closure. The overall mortality rate observed was 55%. Notably, survival rates did not exhibit statistical significance when comparing the two methods, even after stratifying the data into separate analysis groups for each technique. CONCLUSION: recent publications on this subject have reported some favorable outcomes associated with the open abdomen technique underscoring the pressing need for a standardized approach to managing patients with severe, complicated abdominal sepsis.

Factores de mal pronóstico en 113 pacientes sometidos a duodenopancreatectomía cefálica por colangiocarcinoma distal / Prognostic risk factors in 113 patients undergoing cephalic duodenopancreatectomy for distal cholangiocarcinoma

Introducción El colangiocarcinoma distal es una neoplasia epitelial maligna que afecta a los conductos biliares extrahepáticos, per debajo del conducto cístico. Existe poca evidencia sobre la relación entre factores perioperatorios y peor evolución a largo plazo tras la resección quirúrgica. Objetivo Analizar los factores de riesgo de mortalidad y recidiva a largo plazo del colangiocarcinoma distal de los pacientes resecados. Material y métodos Se ha analizado una base de datos prospectiva unicéntrica de pacientes intervenidos por colangiocarcinoma distal entre los años 1990 y 2021 con la finalidad de investigar los factores de mortalidad y recidiva. Resultados Se han intervenido 113 pacientes, con una supervivencia actuarial media de 100,2 (76-124) meses tras la resección. El estudio bivariante no evidenció diferencias entre los pacientes dependiendo de la edad o variables preoperatorias estudiadas. La presencia de adenopatías afectadas fue un factor de riesgo de mortalidad a largo plazo en el estudio multivariante. La presencia de adenopatías afectadas, la recidiva tumoral y la fístula biliar durante el postoperatorio implicaron peor supervivencia actuarial al comparar las curvas de Kaplan-Meier. Conclusiones La presencia de adenopatías afectadas influyen en el pronóstico de la enfermedad. La aparición de fístula biliar durante el postoperatorio del colangiocarcinoma distal podría agravar los resultados a largo plazo, hallazgo que debe ser reafirmado en futuros estudios. (AU)

Introduction Distal cholangiocarcinoma is a malignant epithelial neoplasia that affects the extrahepatic bile ducts, below the cystic duct. No relevant relationship between perioperative factors and worse long-term outcome has been proved. Objective To analyze the risk factors for mortality and long-term recurrence of distal cholangiocarcinoma in resected patients. Materials and methods A single-center prospective database of patients operated on for distal cholangiocarcinoma between 1990 and 2021 was analyzed in order to investigate mortality and recurrence factors. Results One hundred and thirteen patients have undergone surgery, with mean actuarial survival of 100.2 (76–124) months after resection. The bivariate study did not show differences between patients depending on age or preoperative variables studied. When multivariate analysis was performed, the presence of affected adenopathy was a risk factor for long-term mortality. The presence of affected lymph nodes, tumor recurrence, and biliary fistula during the postoperative period implied worse actuarial survival when comparing the Kaplan–Meier curves. Conclusions The presence of affected lymph nodes influence the prognosis of the disease. The occurrence of biliary fistula during postoperative cholangiocarcinoma distal could aggravate long-term outcomes, a finding that should be reaffirmed in future studies. (AU)