Predictor Of Survival In Patients Research Articles

Clinicopathological features, prognostic factors, and prognostic survival prediction in patients with extrahepatic bile duct cancer liver metastasis.

Extrahepatic bile duct cancer (EBDC) is a compound malignant tumor mainly consisting of extrahepatic cholangiocarcinoma and gallbladder carcinoma. Most EBDC patients are diagnosed at an advanced stage characterized by distant metastases, and the liver is one of the common sites of metastasis. Hence, the purpose of this study is to investigate the clinicopathological features, identify prognostic risk factors, and assess the long-term prognosis of extrahepatic bile duct cancer liver metastasis (EBDCLM). We identified 1922 eligible EBDCLM patients from the SEER database.Cox regression models were used to predict independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS),and Kaplan-Meier survival curves were drawn. A nomogram was constructed based on the results of multivariate Cox analysis, and the predictive effect of the nomogram was evaluated. Age, surgery, chemotherapy, brain metastasis, and lung metastasis were common independent prognostic factors for OS and CSS, and radiotherapy and bone metastasis were independent prognostic factors for CSS. The Kaplan-Meier survival curves showed a significant increase in survival for patients aged less than or equal to 70 years, undergoing surgery and chemotherapy, and without lung metastases. The results showed that the nomogram constructed by us had good predictability and ha d strong clinical application value. Our study identified age, surgery, chemotherapy, brain metastasis, and lung metastasis as independent prognostic factors for EBDCLM patients. The nomogram can accurately predict the survival probability, which is helpful for clinicians to assess the prognosis of patients with advanced EBDC and provide personalized clinical decisions.

A secure and light‐weight patient survival prediction in Internet of Medical Things framework

A secure and light‐weight patient survival prediction in Internet of Medical Things framework

#1756 Development and internal validation of machine learning algorithms for mortality prediction model of people with DM and CKD

Abstract Background and Aims Diabetes Mellitus (DM) and Chronic Kidney Disease (CKD) are leading causes of morbidity and mortality, presenting challenges in patient management. The aim of this study is to develop a machine learning-based predictive model for mortality in DM and CKD patients, improving early intervention and treatment personalization. Method 3,637 participants with DM and CKD from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018 were included. All-cause mortality was ascertained by linkage to National Death Index records through 31 December 2019 in NHANES. The dataset included clinical profiles, demographic details, and laboratory results of patients with DM and CKD. We performed 100 random groupings, dividing the data into a 75% training set and a 25% validation set each time. We then visualized the results of 100 AUC measurements using a boxplot. The objective was to forecast patient survival over 1, 3, 5, and 10-year horizons using a variety of machine learning algorithms. The algorithms tested included Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Multinomial Naive Bayes (MNB), Bayesian Network Classifier (BNC), AdaBoost (Ada), Gradient Boosting (Gradient), and Extreme Gradient Boosting (XG). The performance of each algorithm was evaluated based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), with the results visualized in boxplot format. Results Upon result shown as the boxplots, it becomes evident that the Random Forest (RF) algorithm consistently exhibits one of the highest AUCs on average across all prediction intervals, suggesting a strong and stable predictive capacity. In the 1-year survival prediction, the RF algorithm demonstrates a highest AUC, implying consistent performance. The 3 and 5-year predictions reveal a slight dip in AUC for most models, including RF, but it still remains one of the top-performing algorithms with fewer outliers than XG and Gradient Boosting. The mean AUC of RF is particularly noteworthy at the 10-year mark, where most algorithms struggle with prediction accuracy, as indicated by lower median AUCs and greater variability. Notably, the RF algorithm identified six major factors for 1-year survival prediction: systolic blood pressure, uric acid, blood urea nitrogen, lactate dehydrogenase, lymphocyte count and total cholesterol, as well as three major factors for 10-year survival prediction: age, lymphocyte and gamma-glutamyl transferase. Conclusion Overall, the study indicates that while machine learning can be a powerful tool for survival prediction in patients with DM and CKD, the choice of algorithm is crucial, with RF standing out for its consistent and reliable performance.

Timing of magnetic resonance imaging in pregnancy for outcome prediction in congenital diaphragmatic hernia.

To evaluate the impact of the timing of MRI on the prediction of survival and morbidity in patients with CDH, and whether serial measurements have a beneficial value. This retrospective cohort study was conducted in two perinatal centers, in Germany and Italy. It included 354 patients with isolated CDH having at least one fetal MRI. The severity was assessed with the observed-to-expected total fetal lung volume (o/e TFLV) measured by two experienced double-blinded operators. The cohort was divided into three groups according to the gestational age (GA) at which the MRI was performed (< 27, 27-32, and > 32weeks' gestation [WG]). The accuracy for the prediction of survival at discharge and morbidity was analyzed with receiver operating characteristic (ROC) curves. Multiple logistic regression analyses and propensity score matching examined the population for balance. The effect of repeated MRI was evaluated in ninety-seven cases. There were no significant differences in the prediction of survival when the o/e TFLV was measured before 27, between 27 and 32, and after 32 WG (area under the curve [AUC]: 0.77, 0.79, and 0.77, respectively). After adjustment for confounding factors, it was seen, that GA at MRI was not associated with survival at discharge, but the risk of mortality was higher with an intrathoracic liver position (adjusted odds ratio [aOR]: 0.30, 95% confidence interval [95%CI] 0.12-0.78), lower GA at birth (aOR 1.48, 95%CI 1.24-1.78) and lower o/e TFLV (aOR 1.13, 95%CI 1.06-1.20). ROC curves showed comparable prediction accuracy for the different timepoints in pregnancy for pulmonary hypertension, the need of extracorporeal membrane oxygenation, and feeding aids. Serial measurements revealed no difference in change rate of the o/e TFLV according to survival. The timing of MRI does not affect the prediction of survival rate or morbidity as the o/e TFLV does not change during pregnancy. Clinicians could choose any gestational age starting mid second trimester for the assessment of severity and counseling.

#426 “Predict-PD”: a machine learning tool to predict patient survival in peritoneal dialysis patients

Abstract Background and Aims With the rising prevalence of chronic kidney disease (CKD), particularly among the elderly, the need for effective renal replacement therapy has become increasingly crucial. Peritoneal dialysis (PD), recognized as a gentler alternative to haemodialysis, offers advantages in cognitive preservation and overall quality of life. Patient survival on PD, a tangible outcome measure, gains particular importance, especially in an aging demographic confronting end-stage kidney disease. Predicting survival thus becomes vital in facilitating individualized treatment planning and has implications on resource allocation and patient counseling. Method Our study analysed data from the UK Renal Registry and included 22 711 incident dialysis patients choosing PD between 2007 and 2022. Our objective was to employ Artificial Intelligence (AI) algorithms in developing an advanced risk stratification indicator with high predictive capabilities, specifically tailored for use in the United Kingdom's transplant selection procedure. We conducted experiments using three distinct machine learning models and evaluated their performance in terms of calibration and discrimination, utilizing metrics such as the integrated Brier score (IBS) and Harrell's concordance index. We assessed the potential clinical utility using decision curve analysis. Results The XGBoost model demonstrated excellent predictive efficacy for patient survival, with a concordance of 0.78. Further scrutiny revealed AUC values at 1 year (0.81), 3 years (0.78), and 5 years (0.77). The integrated Brier score, a comprehensive measure of predictive accuracy, was 0.09. There was no statistical difference between the actual and predicted survival probabilities (P = 0.72). Decision curve analysis accentuated the model's clinical applicability. Conclusion This study illustrates the potential of machine learning in prognosticating patient survival in PD, offering a pathway towards individualized patient-centric management in nephrology. The findings advocate for the integration of data-driven methodologies in clinical decision-making, paving the way for enhanced personalized care in CKD management and can serve as a guide for future research.

Evaluating nomogram models for predicting survival outcomes in gastric gastrointestinal stromal tumors with SEER database analysis

Gastrointestinal stromal tumors (GISTs) predominantly develop in the stomach. While nomogram offer tremendous therapeutic promise, there is yet no ideal nomogram comparison customized specifically for handling categorical data and model selection related gastric GISTs. (1) We selected 5463 patients with gastric GISTs from the SEER Research Plus database spanning from 2000 to 2020; (2) We proposed an advanced missing data imputation algorithm specifically designed for categorical variables; (3) We constructed five Cox nomogram models, each employing distinct methods for the selection and modeling of categorical variables, including Cox (Two-Stage), Lasso-Cox, Ridge-Cox, Elastic Net-Cox, and Cox With Lasso; (4) We conducted a comprehensive comparison of both overall survival (OS) and cancer-specific survival (CSS) tasks at six different time points; (5) To ensure robustness, we performed 50 randomized splits for each task, maintaining a 7:3 ratio between the training and test cohorts with no discernible statistical differences. Among the five models, the Cox (Two-Stage) nomogram contains the fewest features. Notably, at Near-term, Mid-term, and Long-term intervals, the Cox (Two-Stage) model attains the highest Area Under the Curve (AUC), top-1 ratio, and top-3 ratio in both OS and CSS tasks. For the prediction of survival in patients with gastric GISTs, the Cox (Two-Stage) nomogram stands as a simple, stable, and accurate predictive model with substantial promise for clinical application. To enhance the clinical utility and accessibility of our findings, we have deployed the nomogram model online, allowing healthcare professionals and researchers worldwide to access and utilize this predictive tool.

CA-125 elimination rate constant K (KELIM) as a promising predictor of complete cytoreduction after neoadjuvant chemotherapy in advanced ovarian cancer patients: a retrospective study from two Chinese hospitals

BackgroundThe modeled CA-125 elimination constant K (KELIM) is a potential marker of tumor chemosensitivity in ovarian cancer patients treated with neoadjuvant chemotherapy (NACT) before interval surgery. The objective of this study was to externally validate the KELIM (rate of elimination of CA-125) score in patients with high-grade serous ovarian cancer (HGSC) undergoing NACT and explore its relation to the completeness of IDS and survival.MethodsThe study was based on a retrospective cohort of 133 patients treated for advanced HGSC, International Federation of Gynecology and Obstetrics (FIGO) stages III–IV, with neoadjuvant chemotherapy, folllowed by interval surgery, in two centres in China. CA-125 concentrations at baseline and during neoadjuvant chemotherapy were collected. We used standardized (std) KELIM for subsequent analysis. Clinicopathologic parameters were collected, and Kaplan‒Meier survival analyses were performed for PFS and OS.ResultsKELIM was an independent predictor of the probability of complete surgery and survival in our cohort. The median std KELIM score of patients with complete surgery was significantly higher than that of patients with incomplete IDS (1.20 vs. 0.71, P < 0.001). Multivariate analysis showed that a std KELIM score \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\ge $$\\end{document}0.925 was an independent predictive factor for achieving complete resection (OR = 5.480; 95% CI, 2.409–12.466, P < 0.001) and better PFS (HR = 0.544; 95% CI: 0.349–0.849, P = 0.007) and OS (HR = 0.484; 95% CI: 0.251–0.930, P = 0.030).ConclusionsThe tumor-primary tumor chemosensitivity, assessed by the modeled CA-125 KELIM, calculated during NACT, is a major parameter to consider for decision-making regarding IDS attempts and predicting patient survival.

Enterotoxin-related genes PPFIA4 and SCN3B promote colorectal cancer development and progression.

To identify the role of enterotoxin-related genes in colorectal cancer (CRC) development and progression. Upregulated differentially expressed genes shared by three out of five Gene Expression Omnibus (GEO) data sets were included to screen the key enterotoxin-induced oncogenes (EIOGs) according to criteria oncogene definition, enrichment, and protein-protein interaction (PPI) network analysis, followed by prognosis survival, immune infiltration, and protential drugs analyses was performed via integration of RNA-sequencing data and The Cancer Genome Atlas-derived clinical profiles. We screened nine common key EIOGs from at least three GEO data sets. A Cox proportional hazards regression models verified that more alive cases, decreased overall survival, and highest 4-year survival prediction in CRC patients with high-risk score. Protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4 (PPFIA4), STY11, SCN3B, and SPTBN5 were shared in the same PPI network. Immune infiltration results showed that SCN3B and synaptotagmin 11 expression were obviously associated with B cell, macrophage, myeloid dendritic cell, neutrophils, and T cell CD4+ and CD8+ in both colon adenocarcinoma and rectal adenocarcinoma. CHIR-99021, MLN4924, and YK4-279 were identified as the potential drugs for treatment. Finally, upregulated EIOGs genes PPFIA4 and SCN3B were found in colon adenocarcinoma and PPFIA4 and SCN3B were proved to promote cell proliferation and migration in vitro. We demonstrated here that EIOGs promoting a malignancy phenotype was related with poor survival and prognosis in CRC, which might be served as novel therapeutic targets in CRC management.

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier.

The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients' clinical characteristics and findings to determine treatment plans, making accurate predictions essential for efficient resource utilization and optimal patient support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, and the Softmax Classifier was developed for predicting patient survival. The architecture was evaluated using the Haberman's Survival dataset and the Echocardiogram dataset from UCI. The results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, and Gradient Bagging applied to the same datasets. The findings indicate that the proposed architecture outperforms other Machine Learning methods in predicting patient survival for both datasets and surpasses the results reported in the literature for the Haberman's Survival dataset. In the light of the findings obtained, the models obtained with the proposed architecture can be used as a decision support system in determining patient care and applied methods.

Surveying haemoperfusion impact on COVID-19 from machine learning using Shapley values.

Haemoperfusion (HP) is an innovative extracorporeal therapy that utilizes special cartridges to filter the blood, effectively removing pro-inflammatory cytokines, toxins, and pathogens in COVID-19 patients. This retrospective cohort study aimed to assess the clinical benefits of HP for severe COVID-19 cases using Shapley values for machine learning models. The research involved 578 inpatients (≥ 20years old) admitted to Baqiyatallah hospital (Tehran, Iran). The control group (359 patients) received standard treatment, including high doses of corticosteroids (a single 500mg methylprednisolone pulse, followed by 250mg for 2days), categorized as regimen (I). On the other hand, the HP group (219 patients) received regimen II, consisting of the same corticosteroid treatment (regimen I) along with haemoperfusion using Cytosorb H300. The frequency of haemoperfusion sessions varied based on the type of lung involvement determined by chest CT scans. In addition, the value function defines the Shapley value of the th feature for the query point , where the input matrix features represent individual characteristics, drugs, and history and clinical conditions of the patient. Our data showed a favorable clinical response in the HP group compared to the control group. Notably, one-to-three sessions of HP using the CytoSorb® 300 cartridge led to reduced ventilation requirements and mortality rates in severe COVID-19 patients. Shapley values were calculated to evaluate the contribution of haemoperfusion among other factors, such as side effects, medications, and individual characteristics, to COVID-19 patient outcomes. In addition, there is a significant difference between the two groups among the treatments and medications used remdesivir, adalimumab, tocilizumab, favipiravir, Interferon beta-1a, enoxaparin prophylaxis, enoxaparin full dose, heparin prophylaxis, and heparin full dose (P < 0.05). It seems that haemoperfusion has a positive impact on the reduction of inflammation markers and renal functional such as ferritin and creatinine, respectively, as well as D-dimer and WBC levels in the HP group were significantly lower than the control group. The findings indicated that haemoperfusion played a crucial role in predicting patient survival, making it a significant feature in classifying patients' prognoses.

Bellmunt risk score as a survival predictor in patients with metastatic castration-resistant prostate cancer.

The prognosis of metastatic castration-resistant prostate cancer (mCRPC) is influenced by numerous individual factors. Despite various proposed prognostic models, the clinical application of these remains limited, probably due to complexity. Our study aimed to evaluate the predictive value of the Bellmunt risk score, which is well-known for urothelial carcinoma and easily assessed, in mCRPC patients. The Bellmunt risk score was calculated from three risk factors (Eastern Cooperative Oncology Group Performance Status (ECOG PS) ≥1, serum hemoglobin <10 g/dL, presence of liver metastases) in 125 patients who received first-line mCRPC treatment between 2005 and 2023. In addition, a modified score was established (one point each for hemoglobin <10 g/dL and the presence of liver metastases added to the ECOG PS). Associations with overall survival (OS) under first- and second-line therapy were tested using Cox regression analyzes, log-rank tests, concordance index (C-index) and time-dependent receiver operating characteristic. There is a significant correlation between the level of the Bellmunt risk score and shorter OS (hazard ratio: 3.23, 95% confidence interval: 2.06-5.05; log-rank p < 0.001; C-index: 0.724). The semi-quantitative modified risk score showed even better prognostic discrimination (log-rank p < 0.001, C-index: 0.764). The score and its dynamics were also predictive in the second-line setting (log-rank p < 0.001 and = 0.01; C-index: 0.742 and 0.595). The Bellmunt risk score is easy to assess and provides useful prognostic information in mCRPC, and can support physicians in their treatment decisions.

Comparison of different 2D muscle indexes measured at the level of the 3rd lumbar vertebra in survival prediction in patients with renal cell carcinoma.

Low computed tomography (CT)-determined muscle mass, commonly determined with height-adjusted muscle indexes (MIs), predicts worse survival in several cancers and has been suggested as a prognostic assessment tool. Although several MIs measured at the level of the 3rd lumbar vertebra (L3) are commonly used, it remains unestablished how different L3-determined MIs perform in survival prognostication compared to each other. The objective of this study was to investigate the performance of different MIs for survival prognostication in renal cell carcinoma (RCC). We retrospectively enrolled 214 consecutive patients with RCC. We determined three L3-MIs (psoas muscle index (PMI), psoas muscle index and erector spinae index (PMI+ESI), and whole skeletal muscle index (SMI)) from preoperative CT scans. Categorization of those with low and normal muscle mass was based on the Youden Index sex-specific MI cut-offs. We determined sensitivity, specificity, and accuracy metrics for predicting 1-year, 5-year, and overall survival (OS) using Cox regression models. Low PMI, PMI+ESI, and SMI significantly predicted decreased 1-year, 5-year, and OS in uni- and multivariate models. PMI+ESI and SMI were more accurate than PMI in males, and PMI and PMI+ESI were more accurate than SMI in females in the prediction of 1-year survival. However, there were no differences in accuracies between MIs in 5-year and OS prediction. PMI+ESI performed well overall in short-term prognostication, but there were no differences between the MIs in long-term prognostication. We recommend the use of PMI+ESI for muscle evaluation, particularly when SMI cannot be evaluated.

Stellae-123 gene expression signature improved risk stratification in Taiwanese acute myeloid leukemia patients

The European Leukemia Net recommendations provide valuable guidance in treatment decisions of patients with acute myeloid leukemia (AML). However, the genetic complexity and heterogeneity of AML are not fully covered, notwithstanding that gene expression analysis is crucial in the risk stratification of AML. The Stellae-123 score, an AI-based model that captures gene expression patterns, has demonstrated robust survival predictions in AML patients across four western-population cohorts. This study aims to evaluate the applicability of Stellae-123 in a Taiwanese cohort. The Stellae-123 model was applied to 304 de novo AML patients diagnosed and treated at the National Taiwan University Hospital. We find that the pretrained (BeatAML-based) model achieved c-indexes of 0.631 and 0.632 for the prediction of overall survival (OS) and relapse-free survival (RFS), respectively. Model retraining within our cohort further improve the cross-validated c-indexes to 0.667 and 0.667 for OS and RFS prediction, respectively. Multivariable analysis identify both pretrained and retrained models as independent prognostic biomarkers. We further show that incorporating age, Stellae-123, and ELN classification remarkably improves risk stratification, revealing c-indices of 0.73 and 0.728 for OS and RFS, respectively. In summary, the Stellae-123 gene expression signature is a valuable prognostic tool for AML patients and model retraining can improve the accuracy and applicability of the model in different populations.

THOC6 is a novel biomarker of glioma and a target of anti-glioma drugs: An analysis based on bioinformatics and molecular docking.

Glioma is a typical malignant tumor of the nervous system. It is of great significance to identify new biomarkers for accurate diagnosis of glioma. In this context, THOC6 has been studied as a highly diagnostic prognostic biomarker, which contributes to improve the dilemma in diagnosing gliomas. We used online databases and a variety of statistical methods, such as Wilcoxon rank sum test, Dunn test and t test. We analyzed the mutation, location and expression profile of THOC6, revealing the network of THOC6 interaction with disease. Wilcoxon rank sum test showed that THOC6 is highly expressed in gliomas (P < 0.001). Dunn test, Wilcoxon rank sum test and t test showed that THOC6 expression was correlated with multiple clinical features. Logistic regression analysis further confirmed that THOC6 gene expression was a categorical dependent variable related to clinical features of poor prognosis. Kaplan-Meier survival analysis showed that the overall survival (OS) of glioma patients with high expression of THOC6 was poor (P < 0.001). Both univariate (P < 0.001) and multivariate (P = 0.04) Cox analysis confirmed that THOC6 gene expression was an independent risk factor for OS in patients with glioma. ROC curve analysis showed that THOC6 had a high diagnostic value in glioma (AUC = 0.915). Based on this, we constructed a nomogram to predict patient survival. Enrichment analysis showed that THOC6 expression was associated with multiple signal pathways. Immuno-infiltration analysis showed that the expression of THOC6 in glioma was closely related to the infiltration level of multiple immune cells. Molecular docking results showed that THOC6 might be the target of anti-glioma drugs. THOC6 is a novel diagnostic factor and prognostic biomarker of glioma.

Individualized survival prediction and surgery recommendation for patients with glioblastoma.

There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients. This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection. We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation. The BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40-7.39; hazard ratio (HR): 0.71; 95% CI, 0.65-0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group. The ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

Role of invasive hemodynamics monitoring in sepsis intensive care

The objective was to study the effect of early planned use of transpulmonary thermodilution (TPTD) and therapeutic measures to stabilize blood circulation on the clinical outcome of sepsis.Materials and methods. The cohort study involved 132 patients with abdominal sepsis with SOFA &gt;7 and blood lactate &gt;1.6 mmol/L. Septic shock was diagnosed in 56 % of patients. TPTD in the early periods of intensive care was began in 53.8 % of patients. Logistic regression and ROC-analysis were used to process the data.Results. Early use of invasive monitoring (OR 2,3715, 95 % CI 1,1107–5,0635, p=0,026, AUC 0.655) and infusion volume &gt;43 ml/kg per day (OR 1.0313, 95 % CI 1.0073–1.0558, p=0.01, AUC 0.677) were predictors of survival in patients with abdominal sepsis. The use of TPTD compared to patients of group II was accompanied by an increase in the daily infusion volume (53.7 [38.1–63.5] vs 38.2 [29.9–47.2], ml/kg per day, p = 0.0001), more frequent use of inotropic drugs (39.4 vs 16.4 %, p = 0.004), and higher level of the inotropic scale (0 [0–4.7] vs 0 [0–0], p = 0.01). There were no differences in the frequency of prescription (57.7 vs 65.5 %, p = 0.376) and dosages (0.2 [0.1–0.4] vs 0.3 [0.2–0.4] μg/kg/min, p = 0.554) of norepinephrine. Twenty-eight-day mortality in groups I and II was 31 and 50.8 % (p = 0.022), hospital mortality was 32.9 and 54.0 % (p = 0.014).Conclusion. When assessed by SOFA &gt; 7 points and lactatemia &gt; 1.6 mmol/L, the onset of TPTD and infusion volume &gt; 43 mL/kg/day increase the likelihood of survival of patients with abdominal sepsis, as a result, 28-day and hospital mortality decrease by 1.6 times. The use of invasive monitoring of central hemodynamics in this clinical situation is accompanied by an increase in the prescription of inotropes by 2.4 times with an unchanged intensity of norepinephrine use.

Grading Cytologic Epithelial Atypia in Pancreatic Mucinous Cysts Predicts Patient Survival: Correlation With Histologic, Molecular, and Clinical Follow-Up

Cytologic examination of epithelial cells in cyst fluids from pancreatic mucinous cysts is the optimal method for identifying high-grade atypia (HGA), which may represent histologic high-grade dysplasia (HGD) or invasive carcinoma and thereby classify the cyst as high risk, warranting surgical resection. Cytologic features of HGA were previously described at our institution in 2013 and implemented thereafter, but performance of grading with these criteria has not yet been reported. In total, 1322 pancreatic cyst fluid specimens (2014-2021) were identified; all pathology reports and relevant clinical data were reviewed in detail; and 230 unique cysts (217 patients) contained neoplastic mucinous epithelium. Of the 230 cysts, 178 had low-grade atypia (LGA), and 52 had HGA. Ninety-seven cysts had histologic follow-up: 77 (79%) were resections and 20 (21%) were diagnostic surgical biopsies only. Moreover, 92 (95%) were confirmed neoplastic mucinous cysts, 3 were adenocarcinomas, and 2 were benign entities. Among histologically confirmed neoplastic mucinous cysts, 58 had low-grade dysplasia (LGD); 34 had HGD, of which 14 also had invasive carcinoma. A significantly higher proportion of cysts with HGA (63%) demonstrated at least HGD on follow-up compared to those with LGA (26%, P < .001). The sensitivity and specificity of HGA for accurately classifying a high-risk cyst were 54% and 81%, respectively. Of the 230 cysts, 146 (64%) cysts had corresponding next-generation sequencing results; 31% of HGA cysts harbored a high-risk mutation (TP53, CDKN2A, and/or SMAD4) vs 7% of LGA cysts (P < .001). Among cysts without histologic confirmation, 25% of HGA cysts had high-risk mutation vs 7% of LGA cysts. The grade of cytologic atypia was predictive of overall survival and recurrence-free survival (P < .001 and P = .020, respectively). Implementation of cytologic criteria for HGA in pancreatic mucinous cysts has relatively low sensitivity but modest specificity for classifying a high-risk cyst. Although high-risk mutations were more commonly found in cysts with HGA, their frequency is overall low. Thus, evaluating the degree of cytologic atypia, which is predictive of patient survival, provides significant value and informs patient outcomes.

Endocrine Immune-Related Adverse Events Are Independent Predictors of Survival in Patients with Lung Cancer.

Lung cancer (LC) is a serious health problem worldwide. Survival outcomes have improved over time due to the widespread use of novel therapeutic agents, including immune checkpoint inhibitors (ICIs). Endocrine immune-related adverse events (e-irAEs) are common in LC patients treated with ICIs. We performed a retrospective study of patients with LC who received treatment with ICIs at a tertiary referral center between January 2014 and October 2023. In total, 983 LC patients were included in the study. E-irAEs presented at a median time of 4.1 months and included hypothyroidism (15.6%), hyperthyroidism (4.3%), adrenal insufficiency (0.4%), hypophysitis (0.4%), and diabetes mellitus (0.2%). These toxicities were not related to the duration of treatment or the type of ICIs. Most (97.6%) e-irAEs were mild (grade 1-2). Median overall survival (OS) was higher in LC patients who experienced e-irAEs (31.6 months) compared to those who did not (10.8 months). The difference remained statistically significant in the 3-month (HR: 0.42) and 6-month landmark analysis (HR: 0.51). The OS advantage was observed in both patients with NSCLC (HR: 0.36) and SCLC (HR: 0.27). Additional research is needed to validate the role of e-irAEs as an independent predictor of survival outcomes in patients with LC.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers

PurposeThe objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis. MethodsAML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs. Results816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups. ConclusionLncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Deriving and interpreting robust features for survival prediction of brain tumor patients

AbstractAccurate prediction of survival days (SD) is vital for planning treatments in glioma patients, as type‐IV tumors typically have a poor prognosis and meager survival rates. SD prediction is challenging and heavily dependent on the extracted feature sets. Additionally, comprehending the behavior of complex machine learning models is a vital yet challenging aspect, particularly to integrate such models into the medical domain responsibly. Therefore, this study develops a robust feature set and an ensemble‐based regressor model to predict patients' SD accurately. We aim to understand how these features behave and contribute to predicting SD. To accomplish this, we employed post‐hoc interpretable techniques, precisely Shapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), and Accumulated Local Effects (ALE) plots. Furthermore, we introduced an investigation to establish a direct connection between radiomic features and their biological significance to enhance the interpretability of radiomic features. The best SD scores on the BraTS2020 training set are 0.504 for accuracy, 59927.38 mean squared error (MSE), 20101.86 median squared error (medianSE), and 0.725 Spearman ranking coefficient (SRC). The validation set's accuracy is 0.586, MSE is 76529.43, medianSE is 41402.78, and SRC is 0.52. The proposed predictor model exhibited superior performance compared with leading contemporary approaches across multiple performance metrics.