Predictor Of Long-term Survival Research Articles

A study predicting long-term survival capacity in postoperative advanced gastric cancer patients based on MAOA and subcutaneous muscle fat characteristics

BackgroundThe prognosis of advanced gastric cancer (AGC) is relatively poor, and long-term survival depends on timely intervention. Currently, predicting survival rates remains a hot topic. The application of radiomics and immunohistochemistry-related techniques in cancer research is increasingly widespread. However, their integration for predicting long-term survival in AGC patients has not been fully explored.MethodsWe Collected 150 patients diagnosed with AGC at the Affiliated Zhongshan Hospital of Dalian University who underwent radical surgery between 2015 and 2019. Following strict inclusion and exclusion criteria, 90 patients were included in the analysis. We Collected postoperative pathological specimens from enrolled patients, analyzed the expression levels of MAOA using immunohistochemical techniques, and quantified these levels as the MAOAHScore. Obtained plain abdominal CT images from patients, delineated the region of interest at the L3 vertebral body level, and extracted radiomics features. Lasso Cox regression was used to select significant features to establish a radionics risk score, convert it into a categorical variable named risk, and use Cox regression to identify independent predictive factors for constructing a clinical prediction model. ROC, DCA, and calibration curves validated the model’s performance.ResultsThe enrolled patients had an average age of 65.71 years, including 70 males and 20 females. Multivariate Cox regression analysis revealed that risk (P = 0.001, HR = 3.303), MAOAHScore (P = 0.043, HR = 2.055), and TNM stage (P = 0.047, HR = 2.273) emerged as independent prognostic risk factors for 3-year overall survival (OS) and The Similar results were found in the analysis of 3-year disease-specific survival (DSS). The nomogram developed could predict 3-year OS and DSS rates, with areas under the ROC curve (AUCs) of 0.81 and 0.797, respectively. Joint calibration and decision curve analyses (DCA) confirmed the nomogram’s good predictive performance and clinical utility.ConclusionIntegrating immunohistochemistry and muscle fat features provides a more accurate prediction of long-term survival in gastric cancer patients. This study offers new perspectives and methods for a deeper understanding of survival prediction in AGC.

Temporal trends and prognostic impact of reperfusion modalities in Tunisian patients presenting with ST-elevation myocardial infarction: A 20-year analysis.

With the advent of reperfusion therapies, management of patients presenting with ST-elevation myocardial infarction (STEMI) has witnessed significant changes during the last decades. We sought to analyze temporal trends in reperfusion modalities and their prognostic impact over a 20-year period in patients presenting with STEMI the Monastir region (Tunisia). Patients from Monastir region presenting for STEMI were included in a 20-year (1998-2017) single center registry. Reperfusion modalities, early and long-term outcomes were studied according to five four-year periods. Out of 1734 patients with STEMI, 1370 (79%) were male and mean age was 60.3 ± 12.7 years. From 1998 to 2017, primary percutaneous coronary intervention (PCI) use significantly increased from 12.5% to 48.3% while fibrinolysis use significantly decreased from 47.6% to 31.7% (p<0.001 for both). Reperfusion delays for either fibrinolysis or primary PCI significantly decreased during the study period. In-hospital mortality significantly decreased from 13.7% during Period 1 (1998-2001) to 5.4% during Period 5 (2014-2017), (p=0.03). Long-term mortality rate (mean follow-up 49.4 ± 30.7 months) significantly decreased from 25.3% to 13% (p<0.001). In multivariate analysis, age, female gender, anemia on-presentation, akinesia/dyskinesia of the infarcted area and use of plain old balloon angioplasty were independent predictors of death at long-term follow-up whereas primary PCI use and preinfaction angina were predictors of long-term survival. In this long-term follow-up study of Tunisian patients presenting for STEMI, reperfusion delays decreased concomitantly to an increase in primary PCI use. In-hospital and long-term mortality rates significantly decreased from 1998 to 2017.

Long-Term Follow-Up of Patients with Advanced Colorectal Liver Metastasis: A Survival Analysis from the Randomized Controlled Multicenter Trial LIGRO

Objective: The objective of this study was to evaluate the long-term oncological outcomes of patients with colorectal liver metastasis (CRLM) randomized for associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) or 2-stage hepatectomy (TSH). Introduction: For advanced CRLM, TSH or ALPPS may be needed for tumor freedom. The randomized, controlled, multicenter trial LIGRO showed an increased resection rate in patients who underwent ALPPS but no difference in morbidity or mortality. The 2-year survival analysis revealed better overall survival in the ALPPS group. Here, the long-term survival analysis from the LIGRO trial is reported. Methods: In the LIGRO trial, 100 patients were randomized to TSH or ALPPS, with the option of rescue ALPPS if insufficient growth was found after the initial step of TSH. Patients were enrolled between June 2014 and August 2016. Follow-up data for this study were collected between November 2022 and February 2023. Results: In total, 16 patients were alive at the end of the follow-up period. The estimated median follow-up time was 93 months. Estimated median overall survival times were 45 months in the ALPPS group and 27 months in the TSH group (P = 0.057), with 5-year survival rates of 31% and 20%, respectively. Positive prognostic factors were liver tumor-free status at the first follow-up and rectal primary tumor. Negative prognostic factors were extrahepatic disease and increasing CLRM size. Conclusion: Liver tumor-free status is a predictor of long-term survival, along with extrahepatic disease, large CRLM size, and rectal primary tumor. Survival did not significantly differ between patients treated with ALPPS or TSH.

MSR49 Long-Term Survival Prediction in Early Breast Cancer: A Machine Learning Approach With Random Survival Forest

MSR49 Long-Term Survival Prediction in Early Breast Cancer: A Machine Learning Approach With Random Survival Forest

The role of CT-assessed sarcopenia and visceral adipose tissue in predicting long-term survival in patients undergoing elective endovascular infrarenal aortic repair.

To evaluate if ileo-psoas muscle size and visceral adipose tissue (VAT) can predict long-term survival after endovascular aneurysm repair (EVAR). Patients who underwent EVAR between 2004 and 2012 in a single centre were included. Total psoas muscle area (TPA), abdominal VAT area, subcutaneous adipose tissue (SAT), and total adipose tissue were measured on the preoperative CT. Primary endpoint was all-cause mortality. Values are presented as median and interquartile range or absolute number and percentage. Cox regression analyses were performed to assess the associations with mortality. Two hundred and eighty-four patients could be included in the study. During a median follow-up of 8 (4-11) years, 223 (79.9%) patients died. Age (P ≤ .001), cardiovascular (P = .041), cerebrovascular (P = .009), renal diseases (P = .002), and chronic obstructive pulmonary disease (P ≤ .001) were independently associated with mortality. TPA was associated with mortality in a univariate (P = .040), but not in a multivariate regression model (P = .764). No significant association was found between mortality and TPA index (P = .103) or any of the adiposity measurements with the exception of SAT (P = .040). However, SAT area loss in a multivariate analysis (P = .875). Assessment of core muscle size and VAT did not contribute to improving the prediction of long-term survival after EVAR. The finding of this study contradicts the previously claimed utility of core muscle size and VAT in predicting long-term survival after EVAR.

Notch ratio in pulmonary flow predicts long-term survival after pulmonary endarterectomy for chronic thromboembolic pulmonary hypertension.

Assessment of the pattern of the RV outflow tract Doppler provides insights into the hemodynamics of chronic thromboembolic pulmonary hypertension (CTEPH). We studied whether pre-operative assessment of timing of the pulmonary flow systolic notch by Doppler echocardiography is associated with long-term survival after pulmonary endarterectomy (PEA) for CTEPH. Fifty-nine out of 61 consecutive CETPH patients (mean age 53 ± 14 years, 34% male) whom underwent PEA between June 2002 and June 2005 were studied. Clinical, echocardiographic and hemodynamic variables were assessed pre-operatively and repeat echocardiography was performed 3 months after PEA. Notch ratio (NR) was assessed with pulsed Doppler and calculated as the time from onset of pulmonary flow until notch divided by the time from notch until end of pulmonary flow. Long-term follow-up was obtained between May 2021 and February 2022. Pre-operative mean pulmonary artery pressure (mPAP) was 45 ± 15 mmHg and pulmonary vascular resistance (PVR) was 646 ± 454 dynes.s.cm-5. Echocardiography before PEA showed that 7 patients had no notch, 33 had a NR < 1.0 and 19 had a NR > 1.0. Three months after PEA, echocardiography revealed a significant decrease in sPAP in long-term survivors with a NR < 1.0 and a NR > 1.0, while a significant increase in TAPSE/sPAP was only observed in the NR < 1.0 group. Mean long-term clinical follow-up was 14 ± 6 years. NR was significantly different between survivors and non-survivors (0.73 ± 0.25 vs. 1.1 ± 0.44, p < 0.001) but no significant differences were observed in mPAP or PVR. Long-term survival at 14 years was significantly better in patients with a NR < 1.0 compared to patients with a NR > 1.0 (83% vs. 37%, p = < 0.001). Pre-operative assessment of NR is a predictor of long-term survival in CTEPH patients undergoing PEA, with low mortality risk in patients with NR < 1.0. Long-term survivors with a NR < 1.0 and NR > 1.0 had a significant decrease in sPAP after PEA. However, the TAPSE/sPAP only significantly increased in the NR < 1.0 group. In the NR < 1.0 group, the 6-min walk test increased significantly between pre-operative and at 1-year post-operative follow-up. NR is a simple echocardiographic parameter that can be used in clinical decision-making for PEA.

Predictors of long-term survival in immune checkpoint inhibitor (ICI)-associated myocarditis.

e24026 Background: ICI-associated myocarditis carries a high morbidity and mortality, with close to half of patients experiencing major adverse cardiac events (MACE). Risk factors previously associated with short-term MACE include troponin T (TnT), neutrophil/lymphocyte ratio (NLR), and absolute lymphocyte count (ALC). In this study, we aimed to analyze the predictors of long-term survival. Methods: This single-center retrospective study included 37 patients diagnosed with ICI-associated myocarditis at Massachusetts General Hospital between March 2016 and March 2022. We collected demographics, diagnostic results, and myocarditis treatments from the electronic medical record. Patients were grouped by survival time: short-term ( &lt; 30 day), intermediate (30 to 360 day), or long-term ( &gt; 360 days) survival. We used chi-square and Kruskal-Wallis tests for descriptive statistics. Cut-off points of continuous variables were determined by ROC curve. Predictors of survival were analyzed by logistic regression models. Results: The cohort consisted of 29 males and 8 females with a median age of 75 years (IQR 68-79), and median follow-up of 5.6 months (IQR 0.5-14.9). The median time from ICI start to myocarditis onset was 50 days (IQR 36-91). Ten, 14, and 13 patients were in the short-term, intermediate, and long-term survival groups, respectively. By univariate analysis, predictors of long-term survival included initial NT-proBNP ≤ 450, NLR ≤ 4.44, ALC ≥ 1122, normal EKG QTc interval (≤ 450 ms in male or ≤ 460 ms in female), and TnT decrement ≥ 42% by day 8 of immunosuppression (Table 1). All patients with long-term survival presented with a normal left ventricular ejection fraction (LVEF ≥ 55%), which made it uninterpretable using logistic regression models. Normal LVEF was significantly associated with long-term survival by using chi-square test (p = 0.002). While TnT elevation &gt; 75x the upper limit of normal (ULN) was strongly associated with short-term survival (p = 0.001), it was not associated with long-term survival (p = 0.23). The use of second-line immunosuppression in addition to corticosteroids did not have a significant impact on long-term survival (p = 0.69). Conclusions: NLR, ALC, NT-proBNP, QTc interval, and a substantial decrease in TnT at day 8 after immunosuppression are all associated with long-term survival in ICI myocarditis. [Table: see text]

Intermediate and long-term survival prediction using prognostic scores in patients undergoing salvage TIPS for uncontrolled variceal bleeding

Intermediate and long-term survival prediction using prognostic scores in patients undergoing salvage TIPS for uncontrolled variceal bleeding

Systemic inflammation-modified PG-SGA index in the survival prediction of advanced cancers.

12056 Background: The tight association between inflammation and nutrition has been established. Whether the systemic inflammatory indicators could increase the predictive power of nutrition assessment tools in the survival of advanced cancers remains unclear. Methods: In this retrospective study, advanced cancer patients were enrolled from the West China Hospital between Nov 2019 and Sep 2023. Five systemic inflammatory indicators were selected including the systemic inflammatory index (SII), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and advanced lung cancer inflammation index (ALI). Nutritional status was evaluated by the PG-SGA. Kaplan-Meier curves, restricted cubic splines (RCS), and Cox regression analyses were used to estimate the effects of systemic inflammation indices as well as systemic inflammation-modified PG-SGA index on the survival of cancer patients. Stratified and sensitive analyses were conducted to determine the high-risk subgroups and check the robustness of the main findings. Results: In total, there were 1,065 advanced-stage cancer patients included in this study, with a mean age of 58.18 years old. Advanced lung cancer (265 cases, 24.9%) accounted for relatively higher proportions of the whole study population, followed by colorectal cancer (167 cases, 15.7%), esophagus cancer (143 cases, 13.4%), and gastric cancer (139 cases, 13.0%). With a median follow-up of 1,300 days, 501 deaths (47.04%) were observed. Multivariate Cox regression analysis showed that ALI maintained the best predictive power in predicting the all-cause mortality of advanced-stage cancer patients when compared with other systemic inflammation indicators (4th quartile: hazard ratio (HR)= 0.38, 95% confidence interval (CI): 0.29, 0.50, P&lt;0.001), with a C-index of 0.615. The novel inflammation-modified PG-SGA/ALI index showed better predictive value in identifying the high-risk subgroups of advanced-stage cancer patients, with an AUC of ROC of 0.68 in 365-day mortality, 0.65 in 730-day mortality, and 0.64 in 1300-day survival prediction, respectively. The C-index (0.626), calibration curves, and decision curve analysis validated the promising predictive accuracy of the PG-SGA/ALI index in predicting the all-cause mortality of advanced-stage cancer patients. No significant interaction was observed between subgroups. A series of sensitive analyses supported the consistent findings as we determined in the main regression. Conclusions: Our study highlights that the PG-SGA/ALI index has superior predictive value than original PG-SGA criteria in short- and long-term survival prediction of advanced cancers. The PG-SGA/ALI index would be a more accurate tool for nutritional evaluation and prognosis prediction among cancer patients during clinical practice.

ASO Visual Abstract: Predictors for Long-Term Survival After Resection of Pancreatic Ductal Adenocarcinoma: A Systematic Review and Meta-Analysis.

ASO Visual Abstract: Predictors for Long-Term Survival After Resection of Pancreatic Ductal Adenocarcinoma: A Systematic Review and Meta-Analysis.

Identifying and Validating an Angiogenesis-related Signature for the Prognosis of Head and Neck Squamous Cell Carcinoma.

The present study aimed todevelop a prognostic model for HNSCC treatment on the basis of angiogenesis-related signatures. Head and Neck Squamous Cell Carcinoma (HNSCC) is the most frequent malignancy with poor prognostic outcomes in the head and neck. Angiogenesis plays a critical role in tumorigenesis and is expected to be an effective therapeutic target. The RNA-seq dataset TCGA-HNSCC and the hallmark gene set were used for angiogenesis-related RiskScore model construction. The RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA), and the hallmark gene set was used to measure the angiogenesis score using the GSVA R package. Then, the optimal cutoff point for prognostic classification was calculated by the survminer package, and Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify angiogenesis gene modules . Multi/univariable and Lasso Cox analyses were performed to develop the RiskScore model, and the classifier efficiency was evaluated by the Receiver Operating Characteristic curve (ROC). Furthermore, a nomogram was designed for survival probability prediction, and the immune infiltration and immunotherapy differences among different risk patients were assessed. After calculating the angiogenesis score, we found that this indicator and patients' prognosis were closely correlated, especially when patients with a high angiogenesis score had a poor prognosis. Then, WGCNA identified a blue gene module positively correlated with angiogenesis. Multivariate and Lasso Cox analysis further identified 9 risk model genes for developing a RiskScore, which was used to divide low- and high- -risk groups of patients. Those with a high risk tended to show poor prognosis, immune infiltration, and higher immune escape. Finally, a nomogram was developed to optimize the risk model, and it exhibited excellent short- and long-term survival prediction performance. We constructed a reliable RiskScore model for the prognostic prediction of HNSCC patients, contributing to precise therapeutic intervention of the cancer.

Analysis and prediction of long-term survival using a clinically applicable risk score based on the Electronic Health Record

BackgroundThe long-term survival of a population assigned to a hospital can be essential to anticipate, manage, and provide appropriate hospital healthcare resources or lead preventive actions for high-risk mortality individuals. In this study, we discriminate which electronic health record variables are most relevant to predict the long-term survival of a population, and apply the results to identify high-risk mortality groups. Materials and MethodsA prospective cohort study was conducted on a population of 113,403 individuals alive on July 1st, 2018 from the General Hospital of Castellón (Spain). Considering electronic health record patients’ variables and survival days from the start date of the study, a Kaplan-Meier analysis and a multivariate Cox regression model were performed, and a risk score based on Cox coefficients was applied to predict survival over 3 years. ResultsAll significant covariates from the Cox model (91.5% c-index) were associated with increased mortality risk. Using the proposed risk score, Kaplan-Meier curves show that survival probability in the 3rd year is 99.23% (95% confidence interval (CI) 99.18–99.29) for the low-risk, 91.21% (95% CI 90.67–91.76) for medium-risk, 76.52% (95% CI 75.59–77.46) for the high-risk, and 48.61 % (95% CI 46.85–50.36) for the very high-risk groups. DiscussionThe Cox model obtained is highly predictive, and it has been found that some electronic health record variables little studied to date, such as Clinical Risk Groups, have a strong impact on survival. Regarding clinical application, the proposed risk score is particularly useful for identifying high-risk subpopulations within a large population.

Enhancing long-term survival prediction with two short-term events: Landmarking with a flexible varying coefficient model.

Patients with cardiovascular diseases who experience disease-related short-term events, such as hospitalizations, often exhibit diverse long-term survival outcomes compared to others. In this study, we aim to improve the prediction of long-term survival probability by incorporating two short-term events using a flexible varying coefficient landmark model. Our objective is to predict the long-term survival among patients who survived up to a pre-specified landmark time since the initial admission. Inverse probability weighting estimation equations are formed based on the information of the short-term outcomes before the landmark time. The kernel smoothing method with the use of cross-validation for bandwidth selection is employed to estimate the time-varying coefficients. The predictive performance of the proposed model is evaluated and compared using predictive measures: area under the receiver operating characteristic curve and Brier score. Simulation studies confirm that parameters under the landmark models can be estimated accurately and the predictive performance of the proposed method consistently outperforms existing methods that either do not incorporate or only partially incorporate information from two short-term events. We demonstrate the practical application of our model using a community-based cohort from the Atherosclerosis Risk in Communities (ARIC) study.

Short- and Long-term survival prediction in patients with acute type A aortic dissection undergoing open surgery

BackgroundAcute Type A aortic dissection (ATAAD) is a life-threatening cardiovascular disease associated with high mortality rates, where surgical intervention remains the primary life-saving treatment. However, the mortality rate for ATAAD operations continues to be alarmingly high. To address this critical issue, our study aimed to assess the correlation between preoperative laboratory examination, clinical imaging data, and postoperative mortality in ATAAD patients. Additionally, we sought to establish a reliable prediction model for evaluating the risk of postoperative death.MethodsIn this study, a total of 384 patients with acute type A aortic dissection (ATAAD) who were admitted to the emergency department for surgical treatment were included. Based on preoperative laboratory examination and clinical imaging data of ATAAD patients, logistic analysis was used to obtain independent risk factors for postoperative in-hospital death. The survival prediction model was based on cox regression analysis and displayed as a nomogram.ResultsLogistic analysis identified several independent risk factors for postoperative in-hospital death, including Marfan syndrome, previous cardiac surgery history, previous renal dialysis history, direct bilirubin, serum phosphorus, D-dimer, white blood cell, multiple aortic ruptures and age. A survival prediction model based on cox regression analysis was established and presented as a nomogram. The model exhibited good discrimination and significantly improved the prediction of death risk in ATAAD patients.ConclusionsIn this study, we developed a novel survival prediction model for acute type A aortic dissection based on preoperative clinical features. The model demonstrated good discriminatory power and improved accuracy in predicting the risk of death in ATAAD patients undergoing open surgery.

(214) - Prediction of Long-Term Survival by Three Risk Assessment Models in Patients with Pulmonary Arterial Hypertension: A SPAHR Analysis

(214) - Prediction of Long-Term Survival by Three Risk Assessment Models in Patients with Pulmonary Arterial Hypertension: A SPAHR Analysis

SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer.

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients.

Merkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual's prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model. This study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient's survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors' effect on overall survival and establish a novel CS-based nomogram. A total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41-50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance. CS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

Prognostic value of neutrophil-to-lymphocyte ratio at diagnosis in colorectal cancer: propensity score analysis.

Baseline neutrophil-to-lymphocyte ratio (NLR) at the time of colorectal cancer (CRC) diagnosis has been proposed as a predictor of long-term survival. The aim of the study was to analyse its usefulness in a homogeneous population with control of the main confounding factors. Observational study of 836 patients operated on for CRC who were divided into two groups: NLR ≤ 3.3 vs NLR > 3.3. To control for confounders, they were matched one-to-one by propensity analysis. A final sample of 526 patients remained for study. The two groups were mismatched in terms of age, comorbidity, tumour stage, rectal location, and neoadjuvant therapy. Once matching was performed, baseline NLR was statistically significantly associated with long-term survival (p < 0.001) and behaved as an independent prognostic factor for survival (p = 0.001; HR: 1,996; 95% CI: 1.32-3.00) when adjusted in a Cox regression model using age (p < 0,001; HR: 1,04; IC95%: 1,02-1,06) and the Charlson Comorbidity Index (p < 0,001; HR: 1,40; IC95%: 1,27-1,55). Neoadjuvant therapy lost its statistical significance (p = 0,137; HR: 1,59; IC95%: 0,86-2,93). A high baseline NLR (> 3.3) in patients with colorectal cancer at diagnosis represents a poor prognostic factor in terms of survival. Its use in routine practice could intensify therapeutic strategies and follow-up in these patients.

Predictors of long-term survival after resection of adenocarcinoma arising from intraductal papillary mucinous neoplasm: an international multicenter study

Predictors of long-term survival after resection of adenocarcinoma arising from intraductal papillary mucinous neoplasm: an international multicenter study

Intermediate and long-term survival prediction using prognostic scores in patients undergoing salvage TIPS for uncontrolled variceal bleeding

Intermediate and long-term survival prediction using prognostic scores in patients undergoing salvage TIPS for uncontrolled variceal bleeding