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Purpose: Evaluation of the prognostic significance of four different scoring systems in a real-world cohort of patients with metastatic urothelial carcinoma (mUC) or renal cell carcinoma (mRCC) undergoing immunotherapy (IO). Methods: For 120 patients with mUC (n = 67) and mRCC (n = 53) who received IO between July 2016 and December 2020 at the tertiary Urological University Medical Centre Mannheim, the following scores were recorded at pre-treatment baseline: modified Glasgow prognostic score (mGPS), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-eosinophil ratio (NER). Overall survival (time between the beginning of IO until the patients' death or last contact) was determined for every patient. Results: Kaplan-Meier analyses revealed that high baseline mGPS, SII (> 979) and NLR (> 3) were associated with poor overall survival (OS) (p < 0.05). Cox proportional hazards regression analyses showed that baseline mGPS and NLR had a significant independent prognostic influence on OS (p < 0.05), of which mGPS had a greater significance (p < 0.001, mGPS Score 2 vs. Score 0: HR 4.1, 95% CI 1.9–8.8). Although a high baseline NER (63.9) was associated with poor OS, it did not reach statistical significance. Baseline NER was also not identified as a significant score in the regression analyses. Conclusion: mGPS, SII and NLR are scoring systems that are easy to record in routine clinical practice. As they provide good prediction of OS in patients with mUC and mRCC under IO, they may allow identification of patients at high-risk and monitor them more cautiously in addition to imaging. [ABSTRACT FROM AUTHOR]

Purpose: Atherosclerosis and cancer may progress through common pathological factors. This study was performed to investigate the association between the abdominal aortic calcification (AAC) volume and outcomes following surgical treatment for pancreatic cancer. Methods: The subjects of this retrospective study were 194 patients who underwent pancreatic cancer surgery between 2007 and 2020. The AAC volume was assessed through routine preoperative computed tomography. Univariate and multivariate analyses were performed to evaluate the impact of the AAC volume on oncological outcomes. Results: A higher AAC volume (≥ 312 mm3) was identified in 66 (34%) patients, who were significantly older and had a higher prevalence of diabetes and sarcopenia. Univariate analysis revealed several risk factors for overall survival (OS), including male sex, an AAC volume ≥ 312 mm3, elevated carbohydrate antigen 19–9, prolonged operation time, increased intraoperative bleeding, lymph node metastasis, poor differentiation, and absence of adjuvant chemotherapy. Multivariate analysis identified an AAC volume ≥ 312 mm3, prolonged operation time, lymph node metastasis, poor differentiation, and absence of adjuvant chemotherapy as independent OS risk factors. The OS rate was significantly lower in the high AAC group than in the low AAC group. Conclusion: The AAC volume may serve as a preoperative prognostic indicator for patients with pancreatic cancer. [ABSTRACT FROM AUTHOR]

The prognosis for patients with hepatocellular carcinoma (HCC) depends on tumor stage and remnant liver function. However, it often includes tumor morphology, which is usually assessed with imaging studies or pathologic analysis, leading to limited predictive performance. Therefore, the aim of this study was to develop a simple and low-cost prognostic score for HCC based on serum biomarkers in routine clinical practice. A total of 3,100 patients were recruited. The least absolute shrinkage and selector operation (LASSO) algorithm was used to select the significant factors for overall survival. The prognostic score was devised based on multivariate Cox regression of the training cohort. Model performance was assessed by discrimination and calibration. Albumin (ALB), alkaline phosphatase (ALP), and alpha-fetoprotein (AFP) were selected by the LASSO algorithm. The three variables were incorporated into multivariate Cox regression to create the risk score (APP score = 0.390* ln (ALP) + 0.063* ln(AFP) - 0.033*ALB). The C-index, K-index, and time-dependent AUC of the score displayed significantly better predictive performance than 5 other models and 5 other staging systems. The model was able to stratify patients into three different risk groups. In conclusion, the APP score was developed to estimate survival probability and was used to stratify three strata with significantly different outcomes, outperforming other models in training and validation cohorts as well as different subgroups. This simple and low-cost model could help guide individualized follow-up. [ABSTRACT FROM AUTHOR]

Abstract Background Propensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis. Methods We conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment. Results The simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates. Discussion The prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data. Conclusions Using the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data.

Background: Transarterial chemoembolization (TACE) is recommended as a palliative treatment for patients of the B stage of the Barcelona Clinic Liver Cancer (BCLC) classification. Aims: To identify clinical, biological, and radiological predictors of survival in patients undergoing TACE and develop a pre-therapeutic prognostic score. Methods: 191 adult cirrhotic patients treated for HCC with TACE at the University Hospital (UH) of Clermont-Ferrand (France) from 2007–2017 were retrospectively included. We investigated the impact of baseline liver function, patient characteristics, and tumor burden on overall survival and developed a prognostic score. Results: Patients had a median age of 66 years and 126 patients were Child A. The AFP-DIAM score distinguishes two groups with a significant difference in survival time (median OS 28.3 months in patients with a score = 0 versus 17.7 months in patients with a score > 0). AFP-DIAM was validated on an external cohort, is well calibrated, and has the best discrimination capacity (C-index) as compared to NIACE, HAP, STATE, and SIX TO TWELVE. AFP-DIAM and SIX TO TWELVE are the more easy-to-use scores. When AFP-DIAM and the SIX TO TWELVE scores were tested in the same statistical model, results confirmed a better AFP-DIAM performance. Conclusions: The AFP-DIAM is an easy-to-use score which allows to distinguish two groups with different prognosis before the first TACE session. Its use could provide further support to BCLC system to guide the therapeutic strategy of patients with HCC. An easy-to-use score allows to distinguish two groups with a different median survival. Patient with a high score could receive immunotherapy as first-line treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: In patients with hepatocellular carcinoma (HCC), predicting survival after treatment is crucial for informed decision-making. This retrospective study investigated how muscle health (myosteatosis) and liver function (the albumin–bilirubin grade) influence survival outcomes in patients undergoing transarterial chemoembolization (TACE), resulting in the development of the Myo-ALBI prognostic score. The Myo-ALBI score was found to be a more accurate tool for predicting survival compared to existing methods. This study highlights the potential of the Myo-ALBI score to help clinicians personalize treatments and improve patient outcomes. Objective: This study aimed to investigate the prognostic value of preoperative myosteatosis and the albumin–bilirubin (ALBI) grade in patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE) and develop a robust prognostic score based on these factors. Methods: Patients with HCC who underwent TACE between January 2009 and December 2020 were included. Multivariate Cox regression analysis identified prognostic factors. CT-based body composition parameters were acquired from baseline abdominal CT images at the level of the third lumbar vertebra. A prognostic score (Myo-ALBI) was developed based on the presence of preoperative myosteatosis and the ALBI grade, and its prognostic value was evaluated. Results: Of 446 patients, 63% were male, and the mean age was 62.4 years. Preoperative myosteatosis was present in 41.5% of patients. The BCLC stages were mostly B (67.9%). Multivariate analysis shows that preoperative myosteatosis, ALBI grade 2, and ALBI grade 3 were independent prognostic factors. The Myo-ALBI grade was incorporated into a prognostic model, including alpha-fetoprotein and up-to-seven criteria, to generate a nomogram. The C-index of the nomogram based on the Myo-ALBI grade (0.743) was significantly higher than the non-Myo-ALBI nomogram (0.677), the up-to-seven criteria (0.653), the ALBI grade (0.616), and the Child–Pugh class (0.573) (all p < 0.05). The t-ROC curve for the nomogram was consistently superior to the other models throughout the observation period in all patients and the BCLC-B subgroup. Conclusions: The combination of preoperative CT-derived myosteatosis and ALBI grade enhances prognostication for patients with unresectable HCC undergoing TACE. The Myo-ALBI nomogram constructed in this study could support individualized prognosis prediction, assisting in treatment decision-making for HCC patients. [ABSTRACT FROM AUTHOR]

Background: Propensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis. Methods: We conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment. Results: The simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates. Discussion: The prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data. Conclusions: Using the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data. [ABSTRACT FROM AUTHOR]

Background: Identifying high-risk COVID-19 patients is critical for emergency department decision-making. Our study's primary objective was to identify new independent predictors of mortality and their predictive utility in combination with traditional pneumonia risk assessment scores and new risk scores for COVID-19 developed during the pandemic. Methods: A retrospective study was performed in two Italian University Hospitals. A multivariable logistic model was used to locate independent parameters associated with mortality. Results: Age, PaO2/FiO2, and SpO2/FiO2 ratios were found to be independent parameters associated with mortality. This study found that the Pneumonia Severity Index (PSI) was superior to many of the risk scores developed during the pandemic, for example, the International Severe Acute Respiratory Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC 4C) (AUC 0.845 vs. 0.687, p < 0.001), and to many of the risk scores already in use, for example, the National Early Warning Score 2 (NEWS2) (AUC 0.845 vs. 0.589, p < 0.001). Furthermore, our study found that the Pneumonia Severity Index had a similar performance to other risk scores, such as CRB-65 (AUC 0.845 vs. 0.823, p = 0.294). Combining the PaO2/FiO2 or SpO2/FiO2 ratios with the risk scores analyzed improved the prognostic accuracy. Conclusions: Adding the SpO2/FiO2 ratio to the traditional, validated, and already internationally known pre-pandemic prognostic scores seems to be a valid and rapid alternative to the need for developing new prognostic scores. Future research should focus on integrating these markers into existing pneumonia scores to improve their prognostic accuracy. [ABSTRACT FROM AUTHOR]

Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre‐specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs. [ABSTRACT FROM AUTHOR]

BACKGROUND Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage (ICH) in real‐world settings. METHODS AND RESULTS ML‐based models were developed to predict in‐hospital mortality in 527 patients with ICH using raw brain imaging data from brain computed tomography and clinical data. The models' performances were evaluated using the area under the receiver operating characteristic curves and calibration plots, comparing them with traditional risk scores such as the ICH score and ICH grading scale. Kaplan–Meier curves were used to examine the post‐ICH survival rates, stratified by ML‐based risk assessment. The net benefit of ML‐based models was evaluated using decision curve analysis. The area under the receiver operating characteristic curves were 0.91 (95% CI, 0.86–0.95) for the ICH score, 0.93 (95% CI, 0.89–0.97) for the ICH grading scale, 0.83 (95% CI, 0.71–0.91) for the ML‐based model fitted with raw image data only, and 0.87 (95% CI, 0.76–0.93) for the ML‐based model fitted using clinical data without specialist expertise. The area under the receiver operating characteristic curve increased significantly to 0.97 (95% CI, 0.94–0.99) when the ML model was fitted using clinical and image data assessed by specialists. All ML‐based models demonstrated good calibration, and the survival rates showed significant differences between risk groups. Decision curve analysis indicated the highest net benefit when utilizing the findings assessed by specialists. CONCLUSIONS ML‐based prediction models exhibit satisfactory performance in predicting post‐ICH in‐hospital mortality when utilizing raw imaging data or nonspecialist input. Nevertheless, incorporating specialist expertise notably improves performance.

Abstract Early prognostic assessment of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important for guiding clinical management and reducing mortality. The aim of this study was to dynamically monitor the clinical characteristics of HBV-ACLF patients, thereby allowing the construction of a novel prognostic scoring model to predict the outcome of HBV-ACLF patients. Clinical data was prospectively collected for 518 patients with HBV-ACLF and randomly divided into training and validation sets. We constructed day-1, day-2, and day-(1 + 3) prognostic score models based on dynamic time points. The prognostic risk score constructed for day-3 was found to have the best predictive ability. The factors included in this scoring system, referred to as DSM-ACLF-D3, were age, hepatic encephalopathy, alkaline phosphatase, total bilirubin, triglycerides, very low-density lipoprotein, blood glucose, neutrophil count, fibrin, and INR. ROC analysis revealed the area under the curve predicted by DSM-ACLF-D3 for 28-day and 90-day mortality (0.901 and 0.889, respectively) was significantly better than those of five other scoring systems: COSSH-ACLF IIs (0.882 and 0.836), COSSH-ACLFs (0.863 and 0.832), CLIF-C ACLF (0.838 and 0.766), MELD (0.782 and 0.762) and MELD-Na (0.756 and 0.731). Dynamic monitoring of the changes in clinical factors can therefore significantly improve the accuracy of scoring models. Evaluation of the probability density function and risk stratification by DSM-ACLF-D3 also resulted in the best predictive values for mortality. The novel DSM-ACLF-D3 prognostic scoring model based on dynamic data can improve early warning, prediction and clinical management of HBV-ACLF patients.

Abstract Background Type C hepatitis B-related acute-on-chronic liver failure (HBV-ACLF), which is based on decompensated cirrhosis, has different laboratory tests, precipitating events, organ failure and clinical outcomes. The predictors of prognosis for type C HBV-ACLF patients are different from those for other subgroups. This study aimed to construct a novel, short-term prognostic score that applied serological indicators of hepatic regeneration and noninvasive assessment of liver fibrosis to predict outcomes in patients with type C HBV-ACLF. Method Patients with type C HBV-ACLF were observed for 90 days. Demographic information, clinical examination, and laboratory test results of the enrolled patients were collected. Univariate and multivariate logistic regression were performed to identify independent prognostic factors and develop a novel prognostic scoring system. A receiver operating characteristic (ROC) curve was used to analyse the performance of the model. Results A total of 224 patients with type C HBV-ACLF were finally included. The overall survival rate within 90 days was 47.77%. Age, total bilirubin (TBil), international normalized ratio (INR), alpha-fetoprotein (AFP), white blood cell (WBC), serum sodium (Na), and aspartate aminotransferase/platelet ratio index (APRI) were found to be independent prognostic factors. According to the results of the logistic regression analysis, a new prognostic model (named the A3Twin score) was established. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.851 [95% CI (0.801-0.901)], the sensitivity was 78.8%, and the specificity was 71.8%, which were significantly higher than those of the MELD, IMELD, MELD-Na, TACIA and COSSH‐ACLF II scores (all P < 0.001). Patients with lower A3Twin scores (

Introduction: Merkel cell carcinoma (MCC) is an aggressive skin cancer with a poor prognosis, which only improved with the introduction of immunotherapies. An MCC prediction model with high diagnostic accuracy is lacking. The aim was to develop an MCC prognostic score (MCC-PS) based on combinations of previously proposed risk factors. Methods: A multicentric, retrospective study was conducted to develop MCCPS, which included age, neuron-specific enolase (NSE), C-reactive protein (CRP), creatinine, bilirubin, and international normalized ratio (INR). Creatinine, bilirubin, and INR were used to calculate the model of end-stage liver disease (MELD) score. A total of 98 patients were included in the study, including 36.7% with stage I according to American Joint Committee on Cancer 2018 (n = 36), 30.6% with stage II (n = 30), 25.5% with stage III (n = 25), and 7.1% with stage IV (n = 7). Survival data of MCC patients were correlated with selected laboratory parameters and risk factors. Primary endpoint was MCC-specific survival (MSS) and the secondary endpoint was progression-free survival. Several statistical methods were used to develop the prognostic score, including correlation analysis, Kaplan-Meier curves, Cox regression, and time-dependent receiver operating characteristic analysis. Results: The MCC-PS is based on the sum of the following baseline variables: elevated CRP (≥5.5 mg/l), elevated NSE (≥22.8 μg/l), MELD score ≥ 11, and age ≥ 75 years. An MELD score ≥ 11 was scored as 4 points, elevated NSE level as 3 points, elevated CRP level as 2 points, and age ≥ 75 years as 1 point. A high-risk group according to the MCC-PS was characterized by a score of 4 or more points. The high-risk group was associated with a worse prognosis than the lowrisk group (1-year MSS 62%, 2-year 43.1%, 5-year 17.6% as compared to 1-year MSS 79.5%, 3-year 75%, 5-year 72%). Notably, the developed MCC-PS predicts MCC outcome measures with high accuracy (3-year MSS: area under the curve (AUC) 0.934, sensitivity 87.5% and specificity 82.2%; 5-year MSS: AUC 0.93, sensitivity 89% and specificity 82%). Conclusion: MCC-PS is the first prognostic score predicting MCC outcome with a high accuracy based on five easily available laboratory parameters and patient's age. An MCC-PS of 4 or more indicates a high-risk patient associated with a poor prognosis. Conclusion: MCC-PS is the first prognostic score predicting MCC outcome with a high accuracy based on five easily available laboratory parameters and patient's age. An MCC-PS of 4 or more indicates a high-risk patient associated with a poor prognosis. [ABSTRACT FROM AUTHOR]

Objective Pediatric adrenocortical carcinoma (pACC) is rare, and prognostic stratification remains challenging. We aimed to confirm the prognostic value of the previously published pediatric scoring system (pS-GRAS) in an international multicenter cohort. Design Analysis of pS-GRAS items of pACC from 6 countries in collaboration of ENSAT-PACT, GPOH-MET, and IC-PACT. Methods We received patient data of the pS-GRAS items including survival information from 9 centers. PS-GRAS score was calculated as a sum of tumor stage (1 = 0; 2-3 = 1; 4 = 2 points), grade (Ki67 index: 0%-9% = 0; 10%-19% = 1; ≥20% = 2 points), resection status (R0 = 0; RX/R1/R2 = 1 point), age (<4 years = 0; ≥4 years = 1 point), and hormone production (androgen production = 0; glucocorticoid-/mixed-/no-hormone production = 1 point) generating 8 scores and 4 groups (1: 0-2, 2: 3-4, 3: 5, 4: 6-7). Primary endpoint was overall survival (OS). Results We included 268 patients with median age of 4 years. The analysis of the pS-GRAS score showed a significantly favorable prognosis in patients with a lower scoring compared to higher scoring groups (5-year OS: Group 1 98%; group 2 87% [hazard ratio {HR} of death 3.6, 95% CI of HR 1.6-8.2]; group 3 43% [HR of death 2.8, 95% CI 1.9-4.4]; group 4: OS 18% [HR of death 2.1, 95% CI 1.7-2.7]). In the multivariable analysis, age (HR of death 3.5, 95% CI 1.8-7.0), resection status (HR of death 5.5, 95% CI 2.7-11.1), tumor stage (HR of death 1.9, 95% CI of HR 1.2-3.0), and Ki67 index (HR of death 1.7, 95% CI 1.2-2.4) remained strong independent outcome predictors. Especially infants < 4 years showed more often low-risk constellations with a better OS for all tumor stages. Conclusion In an international multicenter study, we confirmed that the pS-GRAS score is strongly associated with overall survival among patients with pACC. Age, resection status, stage, and Ki67 index are important parameters for risk stratification. [ABSTRACT FROM AUTHOR]

Early prognostic assessment of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important for guiding clinical management and reducing mortality. The aim of this study was to dynamically monitor the clinical characteristics of HBV-ACLF patients, thereby allowing the construction of a novel prognostic scoring model to predict the outcome of HBV-ACLF patients. Clinical data was prospectively collected for 518 patients with HBV-ACLF and randomly divided into training and validation sets. We constructed day-1, day-2, and day-(1 + 3) prognostic score models based on dynamic time points. The prognostic risk score constructed for day-3 was found to have the best predictive ability. The factors included in this scoring system, referred to as DSM-ACLF-D3, were age, hepatic encephalopathy, alkaline phosphatase, total bilirubin, triglycerides, very low-density lipoprotein, blood glucose, neutrophil count, fibrin, and INR. ROC analysis revealed the area under the curve predicted by DSM-ACLF-D3 for 28-day and 90-day mortality (0.901 and 0.889, respectively) was significantly better than those of five other scoring systems: COSSH-ACLF IIs (0.882 and 0.836), COSSH-ACLFs (0.863 and 0.832), CLIF-C ACLF (0.838 and 0.766), MELD (0.782 and 0.762) and MELD-Na (0.756 and 0.731). Dynamic monitoring of the changes in clinical factors can therefore significantly improve the accuracy of scoring models. Evaluation of the probability density function and risk stratification by DSM-ACLF-D3 also resulted in the best predictive values for mortality. The novel DSM-ACLF-D3 prognostic scoring model based on dynamic data can improve early warning, prediction and clinical management of HBV-ACLF patients. [ABSTRACT FROM AUTHOR]

Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassification is recall bias, which occurs in retrospective cohort studies when study subjects may inaccurately recall their past exposure. Particularly challenging is differential recall bias in the context of self-reported binary exposures, where the bias may be directional rather than random and its extent varies according to the outcomes experienced. This paper makes several contributions: (1) it establishes bounds for the average treatment effect even when a validation study is not available; (2) it proposes multiple estimation methods across various strategies predicated on different assumptions; and (3) it suggests a sensitivity analysis technique to assess the robustness of the causal conclusion, incorporating insights from prior research. The effectiveness of these methods is demonstrated through simulation studies that explore various model misspecification scenarios. These approaches are then applied to investigate the effect of childhood physical abuse on mental health in adulthood. [ABSTRACT FROM AUTHOR]

Osteosarcoma stratification relies on clinical parameters and histological response. We developed a new personalized stratification using less invasive circulating tumor DNA (ctDNA) quantification. Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before surgery and end of treatment, were sequenced using low-passage whole-genome sequencing (lpWGS) for copy number alteration detection. We developed a prediction tool including ctDNA quantification and known clinical parameters to estimate patients' individual risk of event. ctDNA quantification at diagnosis (diagCPA) was evaluated for 183 patients of the protocol OS2006. diagCPA as a continuous variable was a major prognostic factor, independent of other clinical parameters, including metastatic status [diagCPA hazard ratio (HR) = 3.5, P = 0.002 and 3.51, P = 0.012, for progression-free survival (PFS) and overall survival (OS)]. At the time of surgery and until the end of treatment, diagCPA was also a major prognostic factor independent of histological response (diagCPA HR = 9.2, P < 0.001 and 11.6, P < 0.001, for PFS and OS). Therefore, the addition of diagCPA to metastatic status at diagnosis or poor histological response after surgery improved the prognostic stratification of patients with osteosarcoma. We developed the prediction tool PRONOS to generate individual risk estimations, showing great performance ctDNA quantification at the time of surgery and the end of treatment still required improvement to overcome the low sensitivity of lpWGS and to enable the follow-up of disease progression. The addition of ctDNA quantification to known risk factors improves the estimation of prognosis calculated by our prediction tool PRONOS. To confirm its value, an external validation in the Sarcoma 13 trial is underway. • ctDNA detection is a non-invasive procedure. • The use of lpWGS is cost-effective. • This procedure represents a considerable step in reducing therapeutic burden and trauma for patients with cancer. • PRONOS is a tool that estimates relapse risk using ctDNA at diagnosis and known clinical risk factor in osteosarcoma. [ABSTRACT FROM AUTHOR]

The purpose of this study is to illustrate how matching approaches based on different balancing scores lead to variations in the treatment effect estimators. We introduced a case study evaluating the effect of anti-thrombotic agents on the severe progression of patients with intracerebral hemorrhage. We extracted subpopulations based on propensity and prognostic scores and then estimated the relative risk between the treatment groups. Furthermore, to illustrate the situation where the treatment effect estimates varied depending on employed balancing scores, we conducted a simulation experiment. In the case study, the matching using different balancing scores extracted subpopulations with different characteristics. Then, the estimated relative risk (95% confidence interval) was 1.27 (0.98–1.94) among the propensity score matched cohort, whereas it was 0.91 (0.76–1.08) among the prognostic score matched cohort. In the simulation experiments, the results indicated that the matching schemes based on different balancing scores created distinct matched cohorts, leading to varying estimates under treatment effect heterogeneity. Moreover, the variability of the estimated effect becomes substantial when there are covariates strongly related to the dependent variable of the nuisance functions. The difference in the selected subpopulation via matching based on different balancing scores is a thoughtful factor that can result in different estimates when there is effect heterogeneity. In practice, we recommend assessing the characteristics of the matched subpopulation and employing the balancing score that can estimate the treatment effect among the target population of interest in each study. Plain Language Summary: This study aimed to understand why different methods of matching patients in a study can lead to different estimates of how well a treatment works. We used a real example of a study where the treatment's effectiveness varied for different patients and compared how different methods of matching patients affected the results. We also did a series of experiments to see how the different methods affected estimates of treatment effect when the effectiveness varied among patients. Our study found that the different matching methods extracted the different patients. The difference in the matched patients led to very different results, especially when the treatment's effectiveness was not the same for all patients. Therefore, it is important to check the characteristics of patients after matching and consider whether the treatment's effectiveness is not the same for all patients. [ABSTRACT FROM AUTHOR]

• Prevalence of mayor adverse cardiovascular events (MACE) was 7.8 % following one year of COVID-19 hospitalization. • Age, history of cardiovascular disease, hypertension, increased troponin serum levels at admission and acute renal failure during hospitalization were identified as predictors of MACE and combined in a clinical score. • HOPE-2 clinical score classified patients into three risk categories and the observed MACE rates were 1.9 %, 9.4 % and 26.3 % for low- intermediate and high-risk patients, respectively. Long-term consequences of COVID-19 are still partly known. To derive a clinical score for risk prediction of long-term major cardiac adverse events (MACE) and all cause death in COVID-19 hospitalized patients. 2573 consecutive patients were enrolled in a multicenter, international registry (HOPE-2) from January 2020 to April 2021 and identified as the derivation cohort. Five hundred and twenty-six patients from the Cardio-Covid-Italy registry were considered as external validation cohort. A long-term prognostic risk score for MACE and all cause death was derived from a multivariable regression model. Out of 2573 patients enrolled in the HOPE-2 registry, 1481 (58 %) were male, with mean age of 60±16 years. At long-term follow-up, the overall rate of patients affected by MACE and/or all cause death was 7.8 %. After multivariable regression analysis, independent predictors of MACE and all cause death were identified. The HOPE-2 prognostic score was therefore calculated by giving: 1–4 points for age class (<65 years, 65–74, 75–84, ≥85), 3 points for history of cardiovascular disease, 1 point for hypertension, 3 points for increased troponin serum levels at admission and 2 points for acute renal failure during hospitalization. Score accuracy at ROC curve analysis was 0.79 (0.74 at external validation). Stratification into 3 risk groups (<3, 3–6, >6 points) classified patients into low, intermediate and high risk. The observed MACE and all-cause death rates were 1.9 %, 9.4 % and 26.3 % for low- intermediate and high-risk patients, respectively (Log-rank test p < 0.01). The HOPE-2 prognostic score may be useful for long-term risk stratification in patients with previous COVID-19 hospitalization. High-risk patients may require a strict follow-up. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Type C hepatitis B-related acute-on-chronic liver failure (HBV-ACLF), which is based on decompensated cirrhosis, has different laboratory tests, precipitating events, organ failure and clinical outcomes. The predictors of prognosis for type C HBV-ACLF patients are different from those for other subgroups. This study aimed to construct a novel, short-term prognostic score that applied serological indicators of hepatic regeneration and noninvasive assessment of liver fibrosis to predict outcomes in patients with type C HBV-ACLF. Method: Patients with type C HBV-ACLF were observed for 90 days. Demographic information, clinical examination, and laboratory test results of the enrolled patients were collected. Univariate and multivariate logistic regression were performed to identify independent prognostic factors and develop a novel prognostic scoring system. A receiver operating characteristic (ROC) curve was used to analyse the performance of the model. Results: A total of 224 patients with type C HBV-ACLF were finally included. The overall survival rate within 90 days was 47.77%. Age, total bilirubin (TBil), international normalized ratio (INR), alpha-fetoprotein (AFP), white blood cell (WBC), serum sodium (Na), and aspartate aminotransferase/platelet ratio index (APRI) were found to be independent prognostic factors. According to the results of the logistic regression analysis, a new prognostic model (named the A3Twin score) was established. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.851 [95% CI (0.801-0.901)], the sensitivity was 78.8%, and the specificity was 71.8%, which were significantly higher than those of the MELD, IMELD, MELD-Na, TACIA and COSSH‐ACLF II scores (all P < 0.001). Patients with lower A3Twin scores (<-9.07) survived longer. Conclusions: A new prognostic scoring system for patients with type C HBV-ACLF based on seven routine indices was established in our study and can accurately predict short-term mortality and might be used to guide clinical management. [ABSTRACT FROM AUTHOR]

Abstract Background and Objective Acute myeloid leukemia (AML) is an aggressive, heterogenous hematopoetic malignancies with poor long-term prognosis. T-cell mediated tumor killing plays a key role in tumor immunity. Here, we explored the prognostic performance and functional significance of a T-cell mediated tumor killing sensitivity gene (GSTTK)-based prognostic score (TTKPI). Methods Publicly available transcriptomic data for AML were obtained from TCGA and NCBI-GEO. GSTTK were identified from the TISIDB database. Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed. Prognostic performance of the TTKPI was examined using Kaplan–Meier survival analysis, Receiver operating curves, and nomogram analysis. Association of TTKPI with clinical phenotypes, tumor immune cell infiltration patterns, checkpoint expression patterns were analysed. Drug docking was used to identify important candidate drugs based on the TTKPI-component genes. Results From 401 differentially expressed GSTTK in AML, 24 genes were identified as signature genes and used to construct the TTKPI score. High-TTKPI risk score predicted worse survival and good prognostic accuracy with AUC values ranging from 75 to 96%. Higher TTKPI scores were associated with older age and cancer stage, which showed improved prognostic performance when combined with TTKPI. High TTKPI was associated with lower naïve CD4 T cell and follicular helper T cell infiltrates and higher M2 macrophages/monocyte infiltration. Distinct patterns of immune checkpoint expression corresponded with TTKPI score groups. Three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) were identified as candidates for AML. Conclusion A T-cell mediated killing sensitivity gene-based prognostic score TTKPI showed good accuracy in predicting survival in AML. TTKPI corresponded to functional and immunological features of the tumor microenvironment including checkpoint expression patterns and should be investigated for precision medicine approaches.

Abstract Background No widely used prognostic tool exists to demonstrate the benefit of oxaliplatin plus 5-fluorouracil/leucovorin (FOLFOX4) in patients with advanced hepatocellular carcinoma (HCC). We aimed to establish a prognostic score and demonstrate the real-world efficacy of FOLFOX4 chemotherapy in Thai patients. Methods Between August 2017 and December 2021, we identified 58 FOLFOX4-treated patients with HCC. Overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) were assessed. The prognostic score was constructed by stepwise Cox proportional hazards regression analysis to select variables for the best model with the lowest Akaike information criterion from all potential variables. Results Forty-four patients (76%) received FOLFOX4 as first-line therapy. The ORR in the entire cohort was 8.6%, and the disease control rate was 29.3%. The PFS and OS were 3.7 and 4.8 months, respectively. Four clinically relevant variables were included in the new prognostic score to predict 6-month OS: L, the presence of lung metastasis; A, alcoholic cirrhosis; B, elevated total bilirubin level; and S, sorafenib-naïve status. Using the LABS score, patients were classified into low-, intermediate-, and high-risk groups, demonstrating OS values of 9.3, 4.2, and 2.1 months, respectively (p

The article highlights the limitations of using ejection fraction (EF) as a standalone metric for cardiac health, advocating for the inclusion of an associated companion (EFC) metric to provide a more comprehensive assessment. Topics include the analysis of cardiac patients, particularly those with acute myocardial infarction, the distribution of EFC for patients with mid-range EF values, and the importance of combining EF and EFC to accurately locate individual patient profiles.

Simple Summary: Despite the promising evidence of the Royal Marsden Hospital (RMH) score as a readily available prognostic biomarker in patients with cancer, the wide scale implementation in clinical practice as well as the true benefit in clinical decision-making is lacking. Therefore, we systematically reviewed the available evidence on the association between the RMH score and prognosis in patients with cancer. This comprehensive meta-analysis, encompassing over a hundred thousand patients, revealed a negative association between a higher RMH score and survival in cancer patients. The available evidence demonstrates that the RMH score is not only a selective biomarker for patients enrolled in clinical trials, but also a useful prognostic biomarker in a real-world setting. Future research should aim to validate and refine this score, ensuring its optimal application in clinical practice and decision-making. Background: Cancer remains a leading cause of death globally, necessitating the identification of prognostic biomarkers to guide treatment decisions. The Royal Marsden Hospital (RMH) score, based on readily available blood tests and clinical features, has emerged as a prognostic tool, although its performance across variable clinical scenarios is not thoroughly delineated. Therefore, we aimed to systematically assess the association between RMH score and survival in cancer patients. Methods: We conducted a systematic literature search across Pubmed, Scopus, and Web of Science databases for studies published up to 15 February 2024. We performed a meta-analysis with the generic inverse variance method with a random-effects model and reported hazard ratios (HR) with 95% confidence intervals (CI). Results: Nineteen studies encompassing 127,230 patients were included. A higher RMH score was significantly associated with worse overall survival (OS) (HR: 2.09, 95% CI: 1.87–2.33, p < 0.001) and progression-free survival (PFS) (HR: 1.80, 95% CI: 1.48–2.18, p < 0.001). This association was consistent across various subgroups, including study population (clinical trial vs. real-world cohort), geographic region, and tumor type. Conclusion: This meta-analysis, including over a hundred thousand patients, demonstrates a negative association between a higher RMH score and survival in cancer patients. The RMH score holds promise as a readily available prognostic tool across diverse cancer types and clinical settings. Future research should focus on validating and refining this score to aid clinical decision-making. [ABSTRACT FROM AUTHOR]

Despite neutrophil involvement in inflammation and tissue repair, little is understood about their inflammatory status in acute coronary syndrome (ACS) patients with poor outcomes. Hence, we investigated the potential correlation between neutrophil inflammatory markers and the prognosis of ACS patients with/without diabetes and explored whether neutrophils demonstrate a unique inflammatory phenotype in patients experiencing an adverse in-hospital outcome. The study enrolled 229 ACS patients with or without diabetes. Poor evolution was defined as either death, left ventricular ejection fraction (LVEF) <40%, Killip Class 3/4, ventricular arrhythmias, or mechanical complications. Univariate and multivariate analyses were employed to identify clinical and paraclinical factors associated with in-hospital outcomes. Neutrophils isolated from fresh blood were investigated using qPCR, Western blot, enzymatic assay, and immunofluorescence. Poor evolution post-myocardial infarction (MI) was associated with increased number, activity, and inflammatory status of neutrophils, as indicated by significant increase of Erythrocyte Sedimentation Rate (ESR), C-reactive protein (CRP), fibrinogen, interleukin-1β (IL-1β), and, interleukin-6 (IL-6). Among the patients with complicated evolution, neutrophil activity had an important prognosis value for diabetics. Neutrophils from patients with unfavorable evolution revealed a pro-inflammatory phenotype with increased expression of CCL3, IL-1β, interleukin-18 (IL-18), S100A9, intracellular cell adhesion molecule-1 (ICAM-1), matrix metalloprotease (MMP-9), of molecules essential in reactive oxygen species (ROS) production p22phox and Nox2, and increased capacity to form neutrophil extracellular traps. Inflammation is associated with adverse short-term prognosis in acute ACS, and inflammatory biomarkers exhibit greater specificity in predicting short-term outcomes in diabetics. Moreover, neutrophils from patients with unfavorable evolution exhibit distinct inflammatory patterns, suggesting that alterations in the innate immune response in this subgroup may exert detrimental effects on disease progression. [ABSTRACT FROM AUTHOR]

Predicting survival is essential to tailoring treatment for patients diagnosed with brain metastases. We have evaluated the performance of widely used, validated prognostic scoring systems (Graded Prognostic Assessment and diagnosis-specific Graded Prognostic Assessment) in over 1000 'real-world' patients treated with stereotactic radiosurgery to the brain, selected according to National Health Service commissioning criteria. Survival outcomes from our dataset were consistent with those predicted by the prognostic systems, but with certain cancer subtypes showing a significantly better survival than predicted. Although performance status remains the simplest tool for prediction, total brain tumour volume emerges as an independent prognostic factor, and a new, improved, prognostic scoring system incorporating this has been developed. • Real-world data for patients with brain metastases are consistent with the predictions made by the prognostic tool, DS-GPA. • Intracranial tumour volume is a novel independent risk factor for survival. • Incorporating tumour volume with existing prognostic factors improves a valuable pan-cancer prognostic tool. • Performance status remains a simple and robust predictor of survival. [ABSTRACT FROM AUTHOR]

Background and Objective: Acute myeloid leukemia (AML) is an aggressive, heterogenous hematopoetic malignancies with poor long-term prognosis. T-cell mediated tumor killing plays a key role in tumor immunity. Here, we explored the prognostic performance and functional significance of a T-cell mediated tumor killing sensitivity gene (GSTTK)-based prognostic score (TTKPI). Methods: Publicly available transcriptomic data for AML were obtained from TCGA and NCBI-GEO. GSTTK were identified from the TISIDB database. Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed. Prognostic performance of the TTKPI was examined using Kaplan–Meier survival analysis, Receiver operating curves, and nomogram analysis. Association of TTKPI with clinical phenotypes, tumor immune cell infiltration patterns, checkpoint expression patterns were analysed. Drug docking was used to identify important candidate drugs based on the TTKPI-component genes. Results: From 401 differentially expressed GSTTK in AML, 24 genes were identified as signature genes and used to construct the TTKPI score. High-TTKPI risk score predicted worse survival and good prognostic accuracy with AUC values ranging from 75 to 96%. Higher TTKPI scores were associated with older age and cancer stage, which showed improved prognostic performance when combined with TTKPI. High TTKPI was associated with lower naïve CD4 T cell and follicular helper T cell infiltrates and higher M2 macrophages/monocyte infiltration. Distinct patterns of immune checkpoint expression corresponded with TTKPI score groups. Three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) were identified as candidates for AML. Conclusion: A T-cell mediated killing sensitivity gene-based prognostic score TTKPI showed good accuracy in predicting survival in AML. TTKPI corresponded to functional and immunological features of the tumor microenvironment including checkpoint expression patterns and should be investigated for precision medicine approaches. [ABSTRACT FROM AUTHOR]

Prognostic estimates for older mRCC patients might be key for clinical decision-making. The outcome of realworld older mRCC patients treated with any line cabozantinib study was analyzed according to the baseline Meet-URO score. Meet-URO score is a valid prognostic tool in older mRCC patients treated with cabozantinib for informing the patient and clinical decisions. Background: The addition of neutrophil to lymphocyte ratio (NLR) and bone metastases to the IMDC classification provided by the Meet-URO score, resulted in higher prognostic accuracy in metastatic renal cell carcinoma (mRCC) patients receiving ≥2nd line nivolumab or cabozantinib in 2 retrospective analyses and 1st line nivolumab-ipilimumab in an expanded access programme. Prognostic estimates for older mRCC patients might be key for clinical decisionmaking. Methods: The outcome of real-world older (≥70 years) mRCC patients treated with any line cabozantinib within the multicenter observational prospective ZEBRA (Meet-URO 9) study was analyzed according to the baseline Meet-URO score. The primary endpoint was overall survival (OS). The discriminative ability by Harrell's c-index and calibration were assessed to compare the Meet-URO and IMDC scores. Results: A total of 104 mRCC patients received cabozantinib as 1st (38%), 2nd (20%), or ≥3rd (41%) line. With a median follow-up of 11.2 months, the median OS (mOS) was of 18.4 months. According to the IMDC score, favorable (15%), intermediate (65%) and poor-risk (19%) patients had a mOS not reached, of 15.6 and 5.7 months respectively (p = .011). According to the Meet-URO score groups, mOS was not reached in both group 1 (10%) and group 2 (25%), while in group 3 (33%), group 4 (25%) and group 5 (8%) mOS was of 13.6, 12.5, and 3.7 months, respectively (p < .001). The discriminative ability of the Meet-URO score was maintained by merging groups 1 to 2 vs. 3 to 4 vs. 5 (p < .001). The Meet-URO score (with either the original 5-group stratification or the modified 3-group one) showed higher accuracy than the IMDC score (c-index of 0.686 and 0.676 vs. 0.622). Conclusion: This analysis confirmed the prognostic accuracy of the Meet-URO score in older mRCC patients treated with cabozantinib and its role as a convenient tool for informing the patient and clinical decisions. [ABSTRACT FROM AUTHOR]

Background: There is growing evidence regarding the prognostic utility of ratios such as neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and systemic immune‐inflammatory index (SIII) in head and neck squamous cell carcinoma (HNSCC). However, most studies to date include heterogeneous series with different treatments or tumor subsites. Methods: We collected data from 201 patients with stage I–II glottic squamous cell carcinoma treated with transoral laser surgery. NLR, PLR, and SIII were calculated from preoperative cell blood count, cut‐off points were obtained by ROC curve analysis, and survival rates were calculated. Results: High NLR (p = 0.012) and SIII (p = 0.037), but not PLR (p = 0.48), were associated with worse disease‐specific survival (DSS). A similar trend was observed with overall survival (OS), although it did not reach statistical significance. On multivariable analyses, both high NLR (HR = 3.8, 95% CI = 1.5–9.9, p = 0.006) and high SIII (HR = 2.77, 95% CI = 1.1–6.9, p = 0.03) were significantly associated with shortened DSS. Conclusions: Preoperative NLR and SIII emerge as independent prognostic biomarkers for early‐stage surgically treated glottic tumors and could guide individualized follow‐up. [ABSTRACT FROM AUTHOR]

Immunotherapy combinations with tyrosine-kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) had significantly improved outcomes of patients with mRCC. Predictive and prognostic factors are crucial to improve patients' counseling and management. The present study aimed to externally validate the prognostic value of a previously developed red cell-based score, including hemoglobin (Hb), mean corpuscular volume (MCV) and red cell distribution width (RDW), in patients with mRCC treated with first-line immunotherapy combinations (TKI plus ICI or ICI plus ICI). We performed a sub-analysis of a multicentre retrospective observational study (ARON-1 project) involving patients with mRCC treated with first-line immunotherapy combinations. Uni- and multivariable Cox regression models were used to assess the correlation between the red cell-based score and progression-free survival (PFS), and overall survival (OS). Logistic regression were used to estimate the correlation between the score and the objective response rate (ORR). The prognostic impact of the red cell-based score on PFS and OS was confirmed in the whole population regardless of the immunotherapy combination used [median PFS (mPFS): 17.4 vs 8.2 months, HR 0.66, 95% CI 0.47–0.94; median OS (mOS): 42.0 vs 17.3 months, HR 0.60, 95% CI 0.39–0.92; p < 0.001 for both]. We validated the prognostic significance of the red cell-based score in patients with mRCC treated with first-line immunotherapy combinations. The score is easy to use in daily clinical practice and it might improve patient counselling. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to determine whether the RNA, 5‐methylcytosine (m5C), is involved in the progression of head and neck squamous cell carcinoma (HNSCC). Materials and methods: We used least absolute shrinkage and selection operator to establish a prognostic score (PS) model based on the m5C regulator expression. Immune scores were calculated using the estimation of stromal and immune cells in malignant tumor tissues using expression data. The biological functions of the m5C regulator, NOP2/Sun RNA methyltransferase 3 (NSUN3), were thoroughly investigated in vitro and in vivo. Results: The PS model acted as efficient prognostic factors in HNSCC. The expression of NSUN3, with the maximum weight, was found to be upregulated and indicated a poor prognosis. Meanwhile, NSUN3 knockdown inhibited the tumor proliferation and growth both in vitro and in vivo. High PS status was negatively correlated with CD8+ T, γδ+ T, and M1 macrophage percentages. NSUN3 knockdown increased the infiltration of M1 macrophages but decreased the percentage of M2 macrophages. Conclusions: The PS index is a novel and promising biomarker for predicting the prognosis and immune infiltration microenvironment in HNSCC. Moreover, NSUN3 plays a key role in this process and may serve as a potential therapeutic target for HNSCC. [ABSTRACT FROM AUTHOR]

Background: No widely used prognostic tool exists to demonstrate the benefit of oxaliplatin plus 5-fluorouracil/leucovorin (FOLFOX4) in patients with advanced hepatocellular carcinoma (HCC). We aimed to establish a prognostic score and demonstrate the real-world efficacy of FOLFOX4 chemotherapy in Thai patients. Methods: Between August 2017 and December 2021, we identified 58 FOLFOX4-treated patients with HCC. Overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) were assessed. The prognostic score was constructed by stepwise Cox proportional hazards regression analysis to select variables for the best model with the lowest Akaike information criterion from all potential variables. Results: Forty-four patients (76%) received FOLFOX4 as first-line therapy. The ORR in the entire cohort was 8.6%, and the disease control rate was 29.3%. The PFS and OS were 3.7 and 4.8 months, respectively. Four clinically relevant variables were included in the new prognostic score to predict 6-month OS: L, the presence of lung metastasis; A, alcoholic cirrhosis; B, elevated total bilirubin level; and S, sorafenib-naïve status. Using the LABS score, patients were classified into low-, intermediate-, and high-risk groups, demonstrating OS values of 9.3, 4.2, and 2.1 months, respectively (p < 0.0001). The C-index and area under the receiver-operating characteristic curve of the score were 0.71 and 0.73, respectively. Conclusions: The proposed LABS score could discriminate patients who would derive benefit from FOLFOX4 chemotherapy. FOLFOX4 chemotherapy is an option for patients who cannot receive immunotherapy and targeted therapy, particularly those with a low-risk score. However, further validation of this model via larger cohorts is warranted. [ABSTRACT FROM AUTHOR]

IntroductionMerkel cell carcinoma (MCC) is an aggressive skin cancer with a poor prognosis, which only improved with the introduction of immunotherapies. An MCC prediction model with high diagnostic accuracy is lacking. The aim was to develop an MCC prognostic score (MCC-PS) based on combinations of previously proposed risk factors.MethodsA multicentric, retrospective study was conducted to develop MCC-PS, which included age, neuron-specific enolase (NSE), C-reactive protein (CRP), creatinine, bilirubin, and international normalized ratio (INR). Creatinine, bilirubin, and INR were used to calculate the model of end-stage liver disease (MELD) score. A total of 98 patients were included in the study, including 36.7% with stage I according to American Joint Committee on Cancer 2018 (n = 36), 30.6% with stage II (n = 30), 25.5% with stage III (n = 25), and 7.1% with stage IV (n = 7). Survival data of MCC patients were correlated with selected laboratory parameters and risk factors. Primary endpoint was MCC-specific survival (MSS) and the secondary endpoint was progression-free survival. Several statistical methods were used to develop the prognostic score, including correlation analysis, Kaplan–Meier curves, Cox regression, and time-dependent receiver operating characteristic analysis.ResultsThe MCC-PS is based on the sum of the following baseline variables: elevated CRP (≥5.5 mg/l), elevated NSE (≥22.8 µg/l), MELD score ≥ 11, and age ≥ 75 years. An MELD score ≥ 11 was scored as 4 points, elevated NSE level as 3 points, elevated CRP level as 2 points, and age ≥ 75 years as 1 point. A high-risk group according to the MCC-PS was characterized by a score of 4 or more points. The high-risk group was associated with a worse prognosis than the low-risk group (1-year MSS 62%, 2-year 43.1%, 5-year 17.6% as compared to 1-year MSS 79.5%, 3-year 75%, 5-year 72%). Notably, the developed MCC-PS predicts MCC outcome measures with high accuracy (3-year MSS: area under the curve (AUC) 0.934, sensitivity 87.5% and specificity 82.2%; 5-year MSS: AUC 0.93, sensitivity 89% and specificity 82%).ConclusionMCC-PS is the first prognostic score predicting MCC outcome with a high accuracy based on five easily available laboratory parameters and patient’s age. An MCC-PS of 4 or more indicates a high-risk patient associated with a poor prognosis.

The Naples prognostic score (NPS) predicts patient survival in gastroesophageal cancer using parameters related to nutritional and inflammatory status. These parameters include risk factors for coronary endothelial dysfunction except for low total cholesterol. Therefore, we modified the score (mNPS) to include high cholesterol, a risk factor for coronary endothelial dysfunction. We aimed to evaluate the relationship between mNPS and the angiographic epicardial coronary slow flow phenomenon (CSFP). This retrospective study included 301 patients with coronary slow flow who underwent coronary angiography between 2018 and 2022. The mNPS parameters were calculated and the population was divided into three groups based on the calculated parameters. Angiographic findings were classified in the left anterior descending (LAD), circumflex (Cx), right coronary (RCA) arteries, and three coronary arteries together. Statistical analyses were performed to identify mNPS as predictors of a slow flow phenomenon. Participants were divided into mNPS Group 1 (n=63), mNPS Group 2 (n=201), and mNPS Group 3 (n=37). No significant differences were observed in age, gender, or medications among the mNPS groups. The RCA had a statistically significant association with mNPS groups for slow flow phenomenon (p=0.006). Considering all three coronary arteries, the association with mNPS groups was also significant (p=0.005). White blood cell and lymphocyte counts showed significant differences. Compared with group 1, group 3 had 4.11 times more coronary artery slow flow. Our study suggests that the mNPS, integrating nutritional and inflammatory parameters along with high cholesterol, holds promise as a potential predictor for the coronary slow flow phenomenon. This could impact risk stratification and clinical management in this patient group. [Med-Science 2023; 12(4.000): 1227-31]

OBJECTIVES: An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information. DESIGN: Multicenter retrospective observational cohort study. SETTING: Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles. PATIENTS: Coronavirus Disease 2019-confirmed and hospitalized adult patients. MEASUREMENTS AND MAIN RESULTS: We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2

Abstract Background Patients with malignant pleural effusion (MPE) typically have poor prognoses, and predicting survival is challenging. The present study aimed to identify prognostic factors of overall survival (OS) in non‐small cell lung cancer (NSCLC) patients with MPE in the time of immunotherapy and targeted therapy. Methods Data of 344 consecutive NSCLC patients with MPE on clinical, radiological, and molecular characteristics and treatment options were collected. The risk factors in the training cohort were assessed using univariate and multivariate proportional hazards analyses. A clinical prognostic score was established and validated. Results According to the results of the multivariable survival analysis, the Eastern Cooperative Oncology Group (ECOG) performance score (PS), antiangiogenic therapy, immunotherapy, and lactic dehydrogenase (LDH) in pleural fluid (CAIL) prognostic score was developed (n = 275) and subsequently validated (n = 69). Patients who underwent risk stratification into low‐, moderate‐, and high‐risk groups had median OS of 46.1, 23.1, and 9.6 months, respectively (P

BackgroundMalnutrition, despite being a common complication, is often neglected in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The objective of this study was to develop a simplified nutritional prognostic score to accurately predict mortality in HBV-ACLF patients.MethodsIn this multicenter retrospective study, clinical data from 530 HBV-ACLF patients were used to create a new prognostic score, which was then validated in two external cohorts (n = 229 and 248).ResultsFour independent factors were significantly associated with 28-day mortality in HBV-ACLF patients, forming a novel prognostic score (ALTA score = 0.187 × age—0.849 × lymphocyte count—2.033 × total cholesterol—0.148 × albumin—0.971). Notably, the AUROC of ALTA score for 28/90-day mortality (0.950/0.967) were significantly higher than those of three other ACLF prognostic scores (COSSH-ACLF II, 0.864/0.734; MELD, 0.525/0.488; MELD-Na, 0.546/0.517; all P < 0.001), and three known nutritional scores (CONUT, 0.739/0.861; OPNI, 0.279/0.157; NRS-2002, 0.322/0.286; all P < 0.001). The prediction error rates of ALTA score for 28-day mortality were significantly lower than COSSH-ACLF II (7.3%), MELD (14.4%), MELD-Na (12.7%), CONUT (9.0%), OPNI (30.6%), and NRS2002 (34.1%) scores. Further classifying ALTA score into two strata, the hazard ratios of mortality at 28/90 days were notably increased in the high-risk groups compared to the low-risk group (15.959 and 5.740). These results were then validated in two external cohorts.ConclusionALTA, as a simplified nutritional prognostic score for HBV-ACLF, demonstrates superiority over the COSSH-ACLF II and other scores in predicting short-term mortality among HBV-ACLF patients. Therefore, it may be used to guide clinical management, particularly in primary care settings.

Different prognostic scores have been applied to identify patients with non-small cell lung cancer who have a higher probability of poor outcomes. In this study, we evaluated whether the Naples Prognostic Score, a novel index that considers both inflammatory and nutritional values, was associated with long-term survival. This study presents a retrospective propensity score matching analysis of patients who underwent curative surgery for non-small cell lung cancer from January 2016 to December 2021. The score considered the following four pre-operative parameters: the neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, serum albumin, and total cholesterol. The Kaplan–Meier method and Cox regression analysis were performed to evaluate the relationship between the score and disease-free survival, overall survival, and cancer-related survival. A total of 260 patients were selected for the study, though this was reduced to 154 after propensity score matching. Post-propensity Kaplan–Meier analysis showed a significant correlation between the Naples Prognostic Score, overall survival (p = 0.018), and cancer-related survival (p = 0.007). Multivariate Cox regression analysis further validated the score as an independent prognostic indicator for both types of survival (p = 0.007 and p = 0.010, respectively). The Naples Prognostic Score proved to be an easily achievable prognostic factor of long-term survival in patients with non-small cell lung cancer after surgical treatment. [ABSTRACT FROM AUTHOR]

The Naples prognostic score (NPS) predicts patient survival in gastroesophageal cancer using parameters related to nutritional and inflammatory status. These parameters include risk factors for coronary endothelial dysfunction except for low total cholesterol. Therefore, we modified the score (mNPS) to include high cholesterol, a risk factor for coronary endothelial dysfunction. We aimed to evaluate the relationship between mNPS and the angiographic epicardial coronary slow flow phenomenon (CSFP). This retrospective study included 301 patients with coronary slow flow who underwent coronary angiography between 2018 and 2022. The mNPS parameters were calculated and the population was divided into three groups based on the calculated parameters. Angiographic findings were classified in the left anterior descending (LAD), circumflex (Cx), right coronary (RCA) arteries, and three coronary arteries together. Statistical analyses were performed to identify mNPS as predictors of a slow flow phenomenon. Participants were divided into mNPS Group 1 (n=63), mNPS Group 2 (n=201), and mNPS Group 3 (n=37). No significant differences were observed in age, gender, or medications among the mNPS groups. The RCA had a statistically significant association with mNPS groups for slow flow phenomenon (p=0.006). Considering all three coronary arteries, the association with mNPS groups was also significant (p=0.005). White blood cell and lymphocyte counts showed significant differences. Compared with group 1, group 3 had 4.11 times more coronary artery slow flow. Our study suggests that the mNPS, integrating nutritional and inflammatory parameters along with high cholesterol, holds promise as a potential predictor for the coronary slow flow phenomenon. This could impact risk stratification and clinical management in this patient group. [ABSTRACT FROM AUTHOR]

Abstract Purpose Patients with malignant melanoma brain metastases (MBMs) have poor prognoses. For MBMs, the Melanoma-molGPA is the most widely used predictive score, but its predictive value remains uncertain in patients fully treated with radiotherapy. We identified MBMs prognostic factors and modified the prognostic scoring model. Methods We retrospectively analyzed patients diagnosed with MBMs between December 2010 and November 2021 for prognostic factors influencing overall survival (OS) by univariate and multivariate analyses. Nomogram plots were based on Cox regression modeling. We evaluated overall survival (OS) using Kaplan–Meier survival curves and log-rank tests. Results The median OS (mOS) was 7.9 months. On multivariate analysis, BRAF mutation status (p

Abstract Background Immune cells are crucial components in the tumor microenvironment and have a significant impact on the outcomes of patients. Aims Here, we aimed to establish a prognostic score based on different types of tumor‐infiltrating immune cells for Endometrial Carcinoma (EC). Methods and Results We enrolled and analyzed 516 EC patients from The Cancer Genome Atlas. The relative abundance of 22 immune cells were estimated by using the CIBERSORTx algorithm. Cox regression was performed to identify potential prognostic immune cells, which were used to develop a Tumor‐infiltrating Immune Cell Score (TICS). The prognostic and incremental value of TICS for overall survival were compared with traditional prognostic factors using the C‐index and decision curves. Clustering analysis using all immune cells identified three immune landscape subtypes, which had weak correlation with survival. A TICS was constructed using CD8T cells, resting memory CD4 T cells, activated NK and activated DCs, and classified patients as low‐, moderate‐ and high‐risk subgroups. The low‐risk subgroup had higher tumor mutation burden and activation of IL2/STAT5, IL2/STAT3 and IFN‐gamma response pathways. Conversely, the high‐risk subgroup was associated with DNA copy number variation, hypoxia and EMT process. The TICS subgroups significantly predicted overall survival, which was independent of patient age, tumor stage, grade and molecular classification. Moreover, we developed a nomogram incorporating TICS and clinicopathologic factors, which significantly improved the predictive accuracy compared to the clinicopathologic model alone. Conclusion The TICS is an effective and independent prognostic predictor for EC patients and may serve as a useful supplement to clinicopathological factors and molecular subtyping.

Introduction: Little is known about the clinical characteristics of women at increased risk for abnormal uterine bleeding (UB) during anticoagulation for venous thromboembolism (VTE). Methods: We used the RIETE registry to identify the baseline characteristics of women developing abnormal UB during anticoagulation. We used logistic regression analysis to identify independent predictors for abnormal UB. Then, we built a prognostic score to identify at-risk women. Results: From March 2001 through October 2022, there were 54,372 women with VTE. During anticoagulation (median, 181 days), 318 (0.6%) developed abnormal UB (major bleeding = 88, clinically relevant non-major (CRNM) = 230). On multivariable analysis, women aged 70 kg, with uterine cancer, recent UB, anemia, estrogen-related VTE, or receiving rivaroxaban or apixaban were at increased risk for abnormal UB. Using the prognostic score, 42,273 women (78%) were at low-risk, 8,828 (16%) intermediate-, and 3,271 (6.1%) at high-risk for abnormal UB. Their rates of abnormal UB were: 0.28 (95%CI: 0.23–0.35), 1.32 (95%CI: 1.07–1.61) and 7.12 (95%CI: 5.98–8.41) bleeds per 100 patient-years, respectively. The c-statistic was 0.80 (95%CI: 0.77–0.83). The rates of major UB were: 0.06 (95%CI: 0.04–0.09), 0.43 (95%CI: 0.30–0.60) and 1.85 (95%CI: 1.31–2.53) per 100 patient-years, respectively (c-statistic: 0.84; 95%CI: 0.80–0.89). The rates of CRNM uterine bleeding were: 0.21 (95%CI: 0.17–0.26), 0.85 (95%CI: 0.65–1.08), and 5.02 (95%CI: 4.09–6.10) bleeds per 100 patient-years, respectively (c-statistic: 0.78; 95%CI: 0.75–0.82). Conclusions: Using 7 variables easily available at admission, we built a prognostic score that reliably identified women with VTE at increased risk for abnormal UB during anticoagulation.