Survival Analysis Of Patients Research Articles

ECG-based machine learning model for AF identification in patients with first ischemic stroke

Background: The recurrence rate of strokes associated with atrial fibrillation (AF) can be substantially reduced through the administration of oral anticoagulants. However, previous studies have not demonstrated a clear benefit from the universal application of oral anticoagulants in patients with embolic stroke of undetermined source. Timely detection of AF remains a challenge in patients with stroke. Aim: This study aims to develop a convolutional neural network (CNN) model to accurately identify patients with AF using a 12-lead sinus-rhythm electrocardiogram (ECG) recorded around the time of the first ischemic stroke. Additionally, this study also evaluates the model’s ability to predict future occurrence of AF. Methods: A CNN model was trained with ECG data from patients at Taipei Veterans General Hospital. External validation was performed on ischemic stroke patients from National Taiwan University Hospital. The model's performance was assessed for detecting AF at the stroke event and predicting future AF occurrences. Results: The model demonstrated an area under curve (AUC) of 0.91 for internal validation and 0.69 for external validation in identifying AF at the stroke event, with sensitivity and negative predictive value both achieving 97%. Kaplan-Meier survival analysis of patients without a prior diagnosis of AF revealed a significant increase in future AF incidence among the high-risk group identified by the model (adjusted hazard ratio: 4.06; 95% confidence interval: 2.74-6.00). Conclusions: The CNN model effectively identifies AF in stroke patients using 12-lead ECGs and predicts future AF events, facilitating early anticoagulation therapy and potentially reducing recurrent stroke risk. Further prospective studies are warranted to confirm these findings.

Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis

Background and objectivesThe aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer. MethodsFour-hundred twenty-two patients from “Lung1” dataset were included in the study. A 3D convolutional autoencoder (AE) was built and features from the latent space extracted for further analysis. Radiomic features were derived from the 3D volume of the tumor region using PyRadiomics. Both radiomic and AE-based features underwent feature selection, by removing: i) highly correlated and ii) constant features. The selected variables were then used to derive both mono-domain (radiomics, AE and clinic) and multi-domain signatures fitting a Cox Proportional Hazard model with LASSO penalization and evaluated considering the concordance (C)-index as performance metric. ResultsBoth mono-domain and multi-domain signatures could significantly differentiate high risk from low risk patients. Among the mono-domain signatures, the highest hazard ratio (HR) in the test set was obtained using radiomics (HR = 1.5428) followed by the AE-based signature (HR = 1.5012) and the clinical signature (HR = 1.4770). The best overall performance was achieved by combining all three signatures, resulting in the highest HR (HR = 1.7383), while the combination of AE-based and clinical signatures yielded the highest C-index (C-index = 0.6309). ConclusionsThese preliminary results show that combining information carried by AE, radiomic and clinical domain shows potential for improving the prediction of overall survival in NSCLC patients.

Cox Regression Model with Time-varying Coefficients Applied to Survival Estimation of Heart Failure

This study focuses on survival analysis of heart failure patients. All the patients, who are over 40 years old, have NYHA classes III or IV stages of heart failure. Cox regression model with time-varying coefficients is applied to deal with these data, which is updated in 2015. Kaplan Meier curves are plotted to screen out categorical variables that violates PH assumptions. As for continuous variables violating PH assumption, Schoenfeld residual plot is drawn to realize time stratification. Therefore, for each time period, cox proportional hazard model can be fit to estimate mortality and find out the key factors promoting or inhibiting death event of heart failure. As a result, age, blood pressure, serum creatinine, and ejection fraction are the significant features of hazard of death led by heart failure, which is validated by Wald test, Likelihood ratio test, and Log-rank test. Discrimination ability is also characterized by ROC curves and AUC finally.

Novel insights of phenylalanine in Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) and cardiovascular complications

Abstract Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is linked to cardiovascular risk, necessitating an exploration of potential metabolic contributors, such as amino acids. While amino acids are implicated in metabolic disorders, their specific relationship with MASLD and subsequent cardiovascular complications is not well-defined. We aimed to delineate the association between circulating amino acids and MASLD, identifying amino acids that could potentially link MASLD to cardiovascular issues. Methods Utilizing UK Biobank data, we tested the association of ten amino acids with MASLD in a cohort of 72,626 MASLD cases and 128,102 controls. Afterwards, we performed Mendelian Randomization (MR) using genome-wide association study (GWAS) data from 8,434 MASLD cases and 770,180 controls, and GWAS of amino acids including 115,052 participants from UK Biobank. Multiple MR methods, including MR-Egger, Cochran's Q-test, and MR-PRESSO, were applied to assess pleiotropy and heterogeneity. Survival analysis in MASLD patients assessed the link between significant amino acid and incident severe cardiovascular outcomes, including stroke, heart failure, myocardial infarction, and mortality, using three different covariate combinations: Model 1 included age, sex, and fasting time; Model 2 included in addition smoking, alcohol use, education, physical activity, and Model 3 included body mass index (BMI) in addition to model 2. Results Nine out of the ten amino acids were statistically significantly associated with MASLD across all three models (p<6.25×10^-3). Among them valine, leucine, isoleucine, total branched-chain amino acids, tyrosine, alanine, and phenylalanine were positively associated with MASLD, while glutamine and glycine were negatively associated with MASLD. The genetic predisposition of MASLD was significantly associated with increased phenylalanine (beta=0.05, p=4.0×10^-4) and tyrosine (beta=0.15, p=2.0×10^-3), suggesting that alterations in these amino acids may result from the progression of MASLD. We also detected heterogeneity (Cochran’s Q-test: p=8.4×10^-9) and pleiotropy (MR-Egger: p=0.02) for tyrosine, but not for phenylalanine. Furthermore, phenylalanine levels in MASLD patients were significantly (p<6.25×10^-3) associated with the incidence of heart failure, stroke, and mortality, but not with myocardial infarction across all three models. We did not identify a significant association between baseline tyrosine levels and any cardiovascular complications or mortality across the three models (Figure 1). Conclusion Our findings indicate a strong association between certain amino acids and MASLD, with phenylalanine, in particular, emerging as a potential mediator of cardiovascular risk in MASLD patients. The absence of heterogeneity and pleiotropy for phenylalanine strengthens its candidacy as a biomarker for future cardiovascular complications in MASLD.

A Single-Center Analysis of Patient Characteristics and Overall Survival in Patients with Resectable Gallbladder Cancer.

Gallbladder cancer (GBC) is a rare and aggressive hepatobiliary malignancy with poor prognosis. The symptoms of GBC are insidious and non-specific in its early stages, and most patients are diagnosed at advanced or late stages. Surgical resection is the only potentially curative treatment for GBC for select patients. There is a lack of robust data for patients with GBC, leading to heterogenous practices in management strategies and outcomes. In this study, we aimed to identify patient characteristics and cumulative overall survival (OS) in patients with GBC who underwent surgical resection with curative intent. All adult patients (age ≥18 years) with localized or locoregionally advanced GBC who underwent definitive surgery with curative intent at our tertiary institution between 1/2013 and 12/2023 were retrospectively identified. Clinical, laboratory, radiology, histopathology, treatment, and survival data were collected from electronic medical records. Postoperative data included the use of adjuvant chemotherapy or radiotherapy, and patient survival mortality at a cut-off date of 1 February, 2024, calculated from the date of curative surgery. Continuous variables are reported as median and quartile 1 (Q1) and quartile 3 (Q3), while categorical variables are reported as counts and percentages. A total of 94 patients with GBC were included in the study. Median age was 71 (62-77) years and 58 (61.7%) patients were female. Median tumor size was 3.3 (1.9-5.0) cm. Perineural invasion was seen in 48.9% and vascular invasion in 38.3% of patients. A positive surgical margin was present in 50% of the patients, and incidental GBC (IGBC) was seen in 48.9% of patients. Tumor grade was well differentiated in 7.6%, moderately differentiated in 53.3%, and poorly differentiated in 39.1% of the patients. Patients with stage T1a (2.1%) and T1b (11.7%) tumors comprised the minority, and the majority of the tumors were stage T2 (55.3%), followed by T3 (31.9%). A total of 60.6% of patients with GBC underwent adjuvant chemotherapy, and 17% underwent adjuvant radiotherapy after surgical resection. Overall, 62 (66.0%) patients died, and the median OS was 1.88 years. The 1-year OS was 68.7%, 3-year OS was 37.4%, and 5-year OS was 32.2%. A higher absolute median OS was seen in patients who had adjuvant chemotherapy (2.1 years) compared to no chemotherapy (1.9 years); however, this finding was not statistically significant (p = 0.36). The median survival was 2.3 years in IGBC compared to 1.6 years in non-IGBC (p = 0.63). GBC is an aggressive hepatobiliary malignancy that is often diagnosed at advanced stages. Our study showed high rates of local and systemic involvement and high mortality, and the need for prospective and randomized studies on adjuvant therapies to assess their survival benefit. Real-world patient data remain important to identify patients at risk of worse outcomes and to stratify risks prior to surgery.

Evaluation of albumin and lactate dehydrogenase in comparison with cytokeratin 19 fragments and neuron‐specific enolase as diagnostic biomarkers for non‐small cell lung cancer

AbstractBackgroundThe diagnosis of non‐small cell lung cancer (NSCLC) is a clinical issue that requires attention, and more practical and effective biomarkers need to be selected to assist in diagnosis. This study aimed to examine the diagnostic value of serum albumin (ALB), lactate dehydrogenase (LDH), cytokeratin 19 fragments (CYFRA21‐1), and neuron‐specific enolase (NSE) for NSCLC.MethodsThe clinical data of 1048 NSCLC patients and 1125 healthy subjects were extracted from electronic medical records. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic significance of ALB, LDH, CYFRA21‐1, and NSE for NSCLC. The Cancer Genome Atlas (TCGA) data, which included mRNA profiles for ALB and LDH expression, were acquired from TCGA Program. Finally, interactive survival scatter plots and survival analyses for NSCLC patients were evaluated using the Human Protein Atlas and Kaplan–Meier Plotter.ResultsSignificant differences were noted in the levels of ALB and LDH between NSCLC patients and healthy controls. The areas under the ROC curves (AUCs) for ALB and LDH were 0.754 (95% CI: 0.734–0.774) and 0.681 (95% CI: 0.658–0.704), respectively. Moreover, the combination of ALB and LDH raised the AUC to 0.804 (95% CI: 0.785–0.823), and the incorporation of CYFRA21‐1 and NSE further increased the AUC to 0.903 (95% CI: 0.890–0.916). Notably, ALB and LDH might be related to the overall survival of NSCLC patients.ConclusionThis study revealed that ALB and LDH in NSCLC patient serum could improve the diagnostic accuracy of conventional biomarkers for NSCLC.

MATHEMATICAL MODELLING OF SURVIVAL IN LOW GRADE GLIOMAS AT MALIGNANT TRANSFORMATION WITH XGBOOST

Abstract AIMS To develop non-linear machine learning models using the XGBoost algorithm to predict a continuous (overall survival (OS) and a binary survival outcome (OS > 5 years) using clinical, molecular and genetic, and radiomic data. METHOD Patients with LGGs treated at a single institution (2005-2020) with histology and MRIs at the time of malignant transformation (MT) were retrospectively included in this study. MRIs underwent in-house tumour segmentation pipeline with radiomic feature extraction of whole-tumour, enhancing, non-enhancing and oedema components, and masked disconnectome map components. Patients were split into training and testing sets for the development of the survival models, which were assessed with mean absolute error (MAE) and root mean square error (RMSE) for the prediction of OS; and receiver operating characteristics analysis for the prediction of OS > 5 years. RESULTS Of 553 patients, 415 patients were included in the training set and 138 patients in the testing set. The XGB Regressor model was able to predict overall survival (OS) from the time of malignant transformation (tMRI) with an MAE of 953 days (RMSE: 1163 days). The XGB Classifier model was able to predict the probability of OS > 5 years from tMRI with an accuracy of 64% (sensitivity: 58%, specificity: 70%). Age, IDH1 mutation, 1p/19q co- deletion, regularity of tumour shape, and disconnectome-related perilesional components were most predictive of survival outcome. CONCLUSION This study has investigated the predictive capabilities of clinical, molecular and genetic, and radiomic data to develop survival analysis models, using XGBoost, to predict OS and OS > 5 years in patients with LGG at tMRI. We corroborate previous findings that age, 1p/19q co-deletion and IDH1 mutation are positive prognosticators for survival. However, further investigation into the radiomics of the disconnectome, especially of the perilesional oedema compartment, presents an intriguing and novel avenue for survival analysis of patients with LGG.

Graph based recurrent network for context specific synthetic lethality prediction.

The concept of synthetic lethality (SL) has been successfully used for targeted therapies. To further explore SL for cancer therapy, identifying more SL interactions with therapeutic potential are essential. Recently, graph neural network-based deep learning methods have been proposed for SL prediction, which reduce the SL search space of wet-lab based methods. However, these methods ignore that most SL interactions depend strongly on genetic context, which limits the application of the predicted results. In this study, we proposed a graph recurrent network-based model for specific context-dependent SL prediction (SLGRN). In particular, we introduced a Graph Recurrent Network-based encoder to acquire a context-specific, low-dimensional feature representation for each node, facilitating the prediction of novel SL. SLGRN leveraged gate recurrent unit (GRU) and it incorporated a context-dependent-level state to effectively integrate information from all nodes. As a result, SLGRN outperforms the state-of-the-arts models for SL prediction. We subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival analysis. Through in vitro experiments and retrospective clinical analysis, we emphasize the potential clinical significance of this context-specific SL prediction model.

Updated overall survival and ctDNA analysis in patients with EGFR T790M-positive advanced non-small cell lung cancer treated with lazertinib in the phase 1/2 LASER201 study.

Lazertinib is a potent, irreversible, third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) with significant efficacy in patients with EGFR T790M-mutated non-small cell lung cancer (NSCLC). This is the final overall survival (OS) report from the phase 1/2 LASER201 study in patients with advanced NSCLC with disease progression on or after prior EGFR TKI therapy. Eligible patients were aged ≥ 20years, with advanced EGFR-mutated NSCLC and previous therapy with EGFR TKI. Patients in this integrated analysis received oral lazertinib 240mg/day. Endpoints included efficacy and safety; exploratory analyses included associations between circulating EGFR-mutant tumor DNA (ctDNA) and efficacy parameters. This integrated analysis included 78 patients in Korea who received second- or later-line lazertinib. The median OS was 38.9months; estimated survival rates at 12, 24, and 36months were 89.5%, 73.9%, and 52.8%, respectively. The cumulative 12-month incidence of central nervous system progression was 9.4%. EGFR-mutant ctDNA was detected in 46 patients (62.2%) at baseline. The presence of ctDNA at baseline significantly predicted progression-free survival (PFS), disease control rate (DCR), and OS. PFS, response rate, and DCR were significantly associated with EGFR-mutant ctDNA clearance at cycle 3; PFS and OS were significantly associated with ctDNA clearance at cycle 5. The safety profile of lazertinib 240mg/day was consistent with previous findings. Lazertinib is a promising treatment option for patients with EGFR T790M-positive NSCLC following disease progression on prior EGFR-directed TKIs. Patients in LASER201 experienced prolonged OS, regardless of their EGFR mutation, brain metastases, or prior brain radiation status. Clearance of plasma EGFR mutations after lazertinib was associated with patient outcomes. ClinicalTrials.gov identifier NCT03046992.

Characterizing the genomic landscape through the lens of FOLR1 status in low and high grade serous ovarian carcinoma

ObjectiveTargeted therapy in folate receptor alpha (FOLR1)-positive high grade serous ovarian carcinoma (HGSOC) is now a mainstay for platinum-resistant disease. However, the rate of FOLR1-positivity in low grade serous ovarian carcinoma (LGSOC) is not well documented. Less common than HGSOC, LGSOC tends to respond poorly to traditional platinum-based chemotherapeutic regimens, particularly in recurrence. Thus, there is an urgent need to identify molecular targets that may assist in identifying more efficacious treatments for LGSOC. In this work, we assessed the genomic and transcriptomic landscapes in FOLR1-positive/negative LGSOC compared to its high-grade counterpart. MethodsUsing a large precision oncology database, next-generation sequencing and immunohistochemistry was performed on a cohort of 281 LGSOC and 5086 HGSOC. Associated MAPK activation was calculated based on NGS results and patient survival analysis was completed stratified by molecular alteration. ResultsCompared with LGSOC (24.6 %), HGSOC tumors have significantly higher prevalence of FOLR1+ status (43.5 %) and significantly higher PD-L1+ status. Conversely, LGSOC had higher prevalence of KRAS and NRAS mutations, with a near exclusivity for BRAF mutation compared to HGSOC. FOLR1- LGSOC and HGSOC had similar prevalences of T cell-inflamed tumors, though FOLR1+ LGSOC had a significantly lower prevalence of T-Cell inflamed tumors than FOLR1+ HGSOC. MAPK activation, quantified via MAPK activation score (MPAS), was significantly higher in low-grade tumors compared to HGSOC, yet no difference between FOLR1+ vs FOLR1- LGSOC was observed. ConclusionsThough less than in high-grade disease, a notable portion of low-grade tumors were FOLR1+, suggesting FOLR1 expression in LGSOC could be a viable target for this rare histology, particularly in the recurrent setting.

Disorder of Glucose Metabolism and Therapy: Implications on the Natural History of Advanced Chronic Liver Disease

Introduction: Hepatogenous diabetes (HD) is a disorder of glucose metabolism (DGM) that develops as a complication of advanced chronic liver disease (ACLD) with an estimated prevalence of 20–70%. It appears to be associated with a larger number of decompensations, but its impact on the natural history of the disease is unclear. The treatment of DGM is hampered by the fact that some therapeutic agents are associated with a risk of complications in ACLD. The aim of this work was to study DGM in a population of patients with ACLD: prevalence, liver disease decompensation episodes, mortality analysis and study of the impact of antidiabetic therapies in patients with ACLD who developed DGM. Materials and Methods: A cohort of consecutive patients with ACLD without previous DGM, who attended a Hepatology clinic in the period of January to June 2015 was selected. Follow-up was carried out for 5 years. Data on age, gender, date of diagnosis and etiology of ACLD, Child-Pugh and MELD-Na classifications at enrollment, development of DGM, and antidiabetic therapy were collected. Logistic regression models for hospitalizations due to decompensated ACLD, ascites, hepatic encephalopathy (HE), upper gastrointestinal bleeding (UGB), hepatocellular carcinoma (HCC), portal vein thrombosis (PVT), infectious complications, acute-on-chronic liver failure (ACLF), and death were built. A survival analysis for patients with and without DGM was also performed. Treatment effectiveness for patients with DGM was assessed. Results: Initially, 221 patients were included, 154 (69.7%) of whom developed DGM after the diagnosis of ACLD. DGM patients presented a significantly higher number of hospitalizations. Odds ratio (OR) for death was not significantly related with DGM. At 5 years of follow-up, 68.9% of patients with DGM were alive, against 81.8% without DGM (p = 0.087). From the 154 patients who were diagnosed with DGM, 42.9% were not receiving pharmacological treatment for DGM. Treated patients were prescribed with either biguanides (34.8%), a SGLT2 inhibitor (8.6%), or insulin (7.7%). Only 1 patient was treated with a GLP-1 analogue. A tendency of OR favoring treatment was observed for biguanides and SGLT2 inhibitors in all outcomes except ascites. In the univariable analysis, the use of biguanides was associated with lower risk of death (OR: 0.84 [95% CI: 0.73–0.96]) and HE (OR: 0.85 [95% CI: 0.73–0.98]). Conclusion: DGM occurs with high prevalence in patients with ACLD and it seems to be related to more hospitalizations, which highlights the importance of its early identification and appropriate therapeutic approach. In the absence of contraindications, biguanides should be considered for treatment of patients with ACLD and DGM as they appear to be associated with a tendency to better outcomes and may present some advantage in terms of survival.

Survival Analysis on Lung Cancer Patients in Damaturu-Nigeria

Cancer is one of the leading causes of death by lung cancer in particular, is the leading cause of cancer death in both men and women, accounting for 23% of all cancer deaths in 2019 according to the Centers for Disease Control and Prevention. One particular problem with lung cancer is that it usually has a poor prognosis. With such a deadly disease, it is crucial to predict the survival likelihood of cancer patients. However, this is not an easy task due to the many factors affecting the disease progression. Survival time has become an essential outcome of clinical trial, which began to emerge among the latter half of the 20th century. A present study was carried out on the survival analysis for patients with lung cancer. The data was obtained from Yobe State Specialist Hospital, Damaturu where each sample was collected from the recipients of the treatment of radical prostatectomy. The Kaplan Meier method was used to obtain and estimate the survival function and median. The log-rank test was used to test the differences in the survival curves. The cox proportional hazard (PH) model provided an effective covariate on the hazard function. As a result of cox PH model, the influence of standard clinical prognostic factors is based on the hazard rate of lung cancer patients. We performed rigorous cross-examination on each feature's relationship and the model for each feature type using data analysis information and survival analysis models. For each feature type, we used one representative survival analysis model from semi-parametric methods (Cox proportional hazards model), one from non-parametric methods (Kaplan-Meier estimator), and one from machine learning approaches (random survival forests). Using the results obtained from these different methods, we identified the best feature types and model combinations to get the top performance for various follow-up periods. The best model is Cox proportional hazards model based on the AIC and log-likelihood functions respectively.

Long-term outcome of 2-year survivors after allogeneic hematopoietic cell transplantation for acute leukemia.

Information on late complications in patients with acute leukemia who have undergone allogeneic hematopoietic cell transplantation (HCT) is limited. We performed a left-truncated analysis of long-term survival in patients with acute leukemia who were alive and disease-free 2 years after HCT. We included 2701 patients with acute lymphoblastic leukemia (ALL) and 9027 patients with acute myeloid leukemia (AML) who underwent HCT between 2005 and 2012. The 10-year overall survival (OS) rate was 81.3% for ALL and 76.2% for AML, with the main causes of late mortality being relapse (ALL-33.9%, AML-44.9%) and chronic graft-versus-host disease (ALL-29%, AML-18%). At 10 years, HCT-related mortality was 16.8% and 20.4%, respectively. Older age and unrelated donor transplantation were associated with a worse prognosis for both types of leukemia. In addition, transplantation in the second or third complete remission and peripheral blood HSC for ALL are associated with worse outcomes. Similarly, adverse cytogenetics, female donor to male patient combination, and reduced intensity conditioning in AML contribute to poor prognosis. We conclude that 2-year survival in remission after HCT for acute leukemia is encouraging, with OS of nearly 80% at 10 years. However, the long-term mortality risk of HCT survivors remains significantly higher than that of the age-matched general population. These findings underscore the importance of tailoring transplantation strategies to improve long-term outcomes in patients with acute leukemia undergoing HCT.

Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database

PurposeTo explore the characteristics of the clinical phenotype of ARDS based on Machine Learning. MethodsThis is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1, Day 3, and Day 7 after the diagnosis of ARDS, respectively. Using the Calinski-Harabasz criterion, Gap Statistic, and Silhouette Coefficient, we determine the optimal clustering number k value. By the K-means cluster analysis to derive clinical phenotype, we analyzed the data collected within the first 24 h. We compared it with the survival of cases under the Berlin standard classification, and also examined the phenotypic conversion within the first 24 h, on day 3, and on day 7 after the diagnosis of ARDS. ResultsWe collected 5054 cases and derived three clinical phenotypes using K-means cluster analysis. Phenotype-I is characterized by fewer abnormal laboratory indicators, higher oxygen partial pressure, oxygenation index, APACHE IV score, systolic and diastolic blood pressure, and lower respiratory rate and heart rate. Phenotype-II is characterized by elevated white blood cell count, blood glucose, creatinine, temperature, heart rate, and respiratory rate. Phenotype-III is characterized by elevated age, partial pressure of carbon dioxide, bicarbonate, GCS score, albumin. The differences in ICU length of stay and in-hospital mortality were significantly different between the three phenotypes (P < 0.05), with phenotype I having the lowest in-hospital mortality (10 %) and phenotype II having the highest (31.8 %). To compare the survival analysis of ARDS patients classified by phenotype and those classified according to Berlin criteria. The results showed that the differences in survival between phenotypes were statistically significant (P < 0.05) under phenotypic classification. ConclusionsThe clinical classification of ARDS based on K-means clustering analysis is beneficial for further identifying ARDS patients with different characteristics. Compared to the Berlin standard, the new clinical classification of ARDS provides a clearer display of the survival status of different types of patients, which helps to predict patient prognosis.

P85-2 Survival analysis of HER2 receptor negative and HER2 receptor low breast cancer patients at a Philippine hospital

P85-2 Survival analysis of HER2 receptor negative and HER2 receptor low breast cancer patients at a Philippine hospital

Effect of laterality on the postoperative survival of non-small cell lung cancer patients undergoing pneumonectomy.

Pneumonectomy is one of the important surgical methods for non-small cell lung cancer (NSCLC). This study evaluated the effects of laterality on the short- and long-term survival of NSCLC patients undergoing pneumonectomy. We reviewed the Surveillance, Epidemiology, and End Results database to retrieve the data of patients who underwent pneumonectomy for stage I-III NSCLC from 2004 to 2015. Propensity score matching (PSM) was used to reduce the selection bias. Logistic regression was used to analyze the correlation between laterality and mortality at 3, 6, and 9 months. The Kaplan-Meier curve was used to further assess the effect of laterality on overall survival (OS). A total of 4,763 patients met the enrollment criteria [right-sided, 1,988 (41.7%); left-sided, 2,775 (58.3%)]. After PSM, 1,911 patients for each side were included in the further analysis. The first 6 months following pneumonectomy was the main period of death, with 32.0% (428/1,336) and 19.9% (250/1,258) of right- and left-sided deaths occurring during this period. The logistic regression analysis showed that right-sided pneumonectomy was an independent risk factor for 3- (P<0.001) and 6-month (P<0.001) mortality. However, laterality had no significant effect on postoperative death at 7-9 months (P=0.82). In the total cohort, right-sided patients had worse OS (P<0.001), but the subgroup survival analysis of patients with a follow-up period >6 months revealed that laterality had no statistically significant effect on OS (P=0.75). Right-sided pneumonectomy was associated with a higher perioperative mortality risk that lasted about 6 months. After that period, laterality was not observed to have a significant prognostic effect on the OS of patients undergoing pneumonectomy.

Comparative overall survival analysis of chordomas of the base of the skull from the Surveillance, Epidemiology, and End Results (SEER) program between 2000 and 2020

Chordomas of the skull base are rare, slow growing, locally invasive cancers with limited long-term survival analysis reported in the literature. We seek to provide comparative survival analysis of patients on a long-term (20-year) basis using population-level data. The Surveillance, Epidemiology, and End Results (SEER) program was queried for cases of chordoma relegated to the base of the skull, diagnosed between 2000 and 2020. Demographic, disease, and treatment information were analyzed using Cox proportional hazards and log-rank comparisons. 630 patients with chordoma of the skull base were identified. Age ≤ 49 years at diagnosis was associated with increased five-, 10-, and 20-year overall survival (hazard ratio (HR) = 0.39, 0.33, and 0.30, respectively; p < 0.001 for all). Treatment with surgery and adjuvant radiotherapy was associated with increased five-, 10-, and 20-year survival (HR = 0.71, 0.79, and 0.79, respectively; p < 0.001 for all). On univariate analysis, widowed patients had decreased survival (20-year overall survival = 34.8% [15.3%-34.8%] compared to married patients (74.4% [68.1%-80.8%]. Surgery remains the primary treatment associated with increased survival among patients with chordoma of the skull base, with adjuvant radiotherapy serving a complimentary role. Demographic factors such as marital status are also associated with changes in survival.

A Five-Year Survival Analysis of Patients with Gastric Cancer in Kerman Province

Purpose: Gastric Adenocarcinoma (GAC) is the second most frequent cause of cancer-related deaths worldwide. Determining survival and its associated prognostic factors provides a basis for further interventions to prolong survival among patients with GAC. In this study, we aimed to perform a 5-year survival analysis among GAC patients in Kerman, Iran. Materials and Methods: This retrospective multi-centric study was conducted on all patients with GAC who were referred to Afzalipour, Bahonar, and Shafa Hospitals in Kerman, Iran in 2009-2019. The 5-year survival rates were calculated based on prognostic factors, including age, histopathology, stage/grade of the tumor, metastatic status, and surgical procedures using the Kaplan-Meier analysis and log-rank test. Results: The 5-year survival rate of GAC patients with total gastrectomy was higher than those with subtotal gastrectomy (P = 0.03). Also, the 5-year survival rate was substantially improved after lymph node dissection (P &lt; 0.001). Overall survival has not been significantly different in terms of age, sex, grade, histological type, clinical T stage, lymphovascular invasion, and perineural invasion. Conclusion: Overall survival was different for the two surgical procedures and lymph node dissection. Therefore, total gastrectomy and lymph node dissection are significant independent prognostic factors for overall survival in patients with GAC.

Clinical value of 18F-FDG PET/CT in patients with newly diagnosed acute leukemia.

To explore the correlation between semi-quantitative parameters of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) scans findings and the clinical features of patients with acute leukemia (AL), as well as to evaluate the clinical utility of 18F-FDG PET/CT in the management of AL. A retrospective study was conducted with 44 patients newly diagnosed with acute leukemia (AL) at Zhongnan Hospital of Wuhan University between January 2019 and August 2024. Multivariate analysis revealed that age at diagnosis of AL (odds ratio [OR]: 0.888, P < 0.01) and percentage of blasts in the peripheral blood (PB) (OR: 1.061, P < 0.05) were independent predictors of the appearance of active extramedullary disease (EMD). Kaplan-Meier survival analysis for patients with EMD(+) indicated that those with organ infiltration beyond the lymph nodes experienced markedly reduced overall survival (OS) compared to those without such infiltration (157 days and 806 days, respectively). Furthermore, in the AL subgroup with EMD, the ratio of the maximum standardized uptake value (SUVmax) in the bone marrow (BM) to SUVmax of the liver emerged as an independent prognostic factor for OS (Hazard ratio [HR]: 2.372; 95% confidence interval [CI]: 1.079-5.214, P < 0.05). 18F-FDG PET/CT offers the benefits of being non-invasive and highly sensitive for the thorough evaluation of disease status in patients newly diagnosed with AL. Furthermore, the SUVmax BM/liver ratio is of significant clinical importance for prognosticating outcomes in patients with AL presenting EMD.

Inflammatory markers related to survival in breast cancer patients: Peru

Introduction Breast cancer is a disease with high global prevalence. Clinical inflammatory biomarkers have been proposed as prognostic indicators in oncology. This research aims to determine the relationship between inflammatory markers and overall survival in breast cancer patients from four representative hospitals in Lima, Peru. Methods This is a multicentric, analytical, longitudinal retrospective cohort study with survival analysis in female patients with breast cancer, from 2015 to 2020, who had received at least one complete treatment regimen. The dependent variable was overall survival, and the independent variables were inflammatory markers neutrophil lymphocyte ratio, platelet lymphocyte ratio (PLR), albumin, and red cell distribution width; intervening variables included age, clinical stage, molecular subtype, and other known prognostic factors. The Kaplan–Meier method was applied to generate survival curves with the Log-Rank test, and finally, Cox regression, to find crude and adjusted hazard ratios (HR). Results Of 705 evaluated patients, 618 were analyzed. The mean age was 56.6 ± 12.3 years, 18.0% of patients were pure HER2 positive, 39.3% luminal A, 29.9% luminal B, 11.0% triple-negative, and 81.4% showed overweight and obesity. The average overall survival was 51.1 months. In the multivariate analysis, factors significantly related to lower overall survival were PLR &gt; 150 (adjusted HR: 2.33; 95% confidence interval (CI): 1.22, 4.44) and stage III (adjusted HR: 4.15; 95% CI: 1.35, 12.83). Conclusions The Elevated Platelet-Lymphocyte Index and advanced clinical stage were associated with lower overall survival in breast cancer patients. Furthermore, PLR &gt;150 proved to be an independent prognostic factor for mortality.