Predict Patient Outcome Research Articles

Enhancing Breast Cancer Survival Prognosis Through Omic and Non-Omic Data Integration

Background and ObjectiveCancer, the second leading cause of death globally, claimed 685,000 lives among 2.3 million women affected by breast cancer in 2020. Cancer prognosis plays a pivotal role in tailoring treatments and assessing efficacy, emphasizing the need for a comprehensive understanding. The goal is to develop predictive model capable of accurately predicting patient outcomes and guiding personalized treatment strategies, thereby advancing precision medicine in breast cancer care. MethodsThis project addresses limitations in current cancer prognosis models by integrating omics and non-omics data. While existing models often neglect crucial omics data like DNA methylation and miRNA, the method utilizes the TCGA dataset to incorporate these data types along with others. Employing mRMR feature selection and CNN models for each type of data for feature extraction, features are stacked and a Random Forest classifier is employed for final prognosis. ResultThe proposed method is applied to the dataset to predict whether the patient is a long-time or a short-time survivor. This strategy showcases excellent performance, with an AUC value of 0.873, precision at 0.881, and sensitivity reaching 0.943. With an accuracy rate of 0.861, signaling an improvement of 11.96% compared to prior studies. ConclusionIn conclusion, integrating diverse data with advanced machine learning holds promise for improving breast cancer prognosis. Addressing model limitations and leveraging comprehensive datasets can enhance accuracy, paving the way for better patient care. Further refinement offers potential for significant advancements in cancer prognosis and treatment strategies.

The Glasgow Coma Scale and Full Outline of Unresponsiveness score evaluation to predict patient outcomes with neurological illnesses in intensive care units in West Bank: a prospective cross-sectional study.

Determining the clinical neurological state of the patient is essential for making decisions and forecasting results. The Glasgow Coma Scale and the Full Outline of Unresponsiveness (FOUR) Scale are commonly used tools for measuring behavioral consciousness. This study aims to compare scales among patients with neurological disorders in intensive care units (ICUs) in the West Bank. A prospective cross-sectional design was employed. All patients admitted to ICUs who met inclusion criteria were involved in this study. Data were collected from from An-Najah National University, Al-Watani, and Rafedia Hospital. Both tools were used to collect data. A total of 84 patients were assessed, 69.0% of the patients were male, and the average length of stay was 6.4 days. The mean score on the Glasgow Coma scale was 11.2 on admission 11.6 after 48 hours, and 12.2 on discharge. The mean FOUR Scale score was 12.2 on admission, 12.4 after 48 hours, and 12.5 at discharge. This study indicates that both the Glasgow Coma Scale and the FOUR scale are effective in predicting outcomes for neurologically deteriorated critically ill patients. However, the FOUR scale proved to be more reliable when assessing outcomes in ICU patients.

Surgical treatment of isolated tricuspid valve disease: indications, new evidence, and risk stratification

The tricuspid valve, long neglected as a passive structure and often termed the "forgotten valve", has recently gained attention from the international medical and cardiological community due to the association of tricuspid regurgitation with an unfavorable prognosis. Isolated tricuspid regurgitation represents a relatively unknown and variable condition, closely linked to the shape and function of the right ventricle and the state of the pulmonary circulation. Currently, guidelines are not clear regarding the optimal treatment strategy, the process of patient selection, and the surgical or transcatheter procedural timing, nor for predicting patient outcomes. Surgical procedures specifically aimed at correcting isolated tricuspid regurgitation, without other concomitant indications for open-heart surgery, have been considered complex and risky, with a high rate of postoperative complications and a poorly understood impact on patient survival and life expectancy. In this review, we will attempt to examine tricuspid valve pathology by analyzing preoperative assessment, essential for risk stratification, various surgical techniques, and outcomes.

MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma.

The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher's exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p = 0.045) and tumor volume (p = 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p ≤ 0.05). The regularized Cox regression analysis identified the "256 Angular Variance of Energy" texture feature as significantly associated with OS independently of the EC/MMMT grouping (p = 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes.

Machine Learning-Based Risk Prediction of Discharge Status for Sepsis.

As a severe inflammatory response syndrome, sepsis presents complex challenges in predicting patient outcomes due to its unclear pathogenesis and the unstable discharge status of affected individuals. In this study, we develop a machine learning-based method for predicting the discharge status of sepsis patients, aiming to improve treatment decisions. To enhance the robustness of our analysis against outliers, we incorporate robust statistical methods, specifically the minimum covariance determinant technique. We utilize the random forest imputation method to effectively manage and impute missing data. For feature selection, we employ Lasso penalized logistic regression, which efficiently identifies significant predictors and reduces model complexity, setting the stage for the application of more complex predictive methods. Our predictive analysis incorporates multiple machine learning methods, including random forest, support vector machine, and XGBoost. We compare the prediction performance of these methods with Lasso penalized logistic regression to identify the most effective approach. Each method's performance is rigorously evaluated through ten iterations of 10-fold cross-validation to ensure robust and reliable results. Our comparative analysis reveals that XGBoost surpasses the other models, demonstrating its exceptional capability to navigate the complexities of sepsis data effectively.

TME-NET: an interpretable deep neural network for predicting pan-cancer immune checkpoint inhibitor responses.

Immunotherapy with immune checkpoint inhibitors (ICIs) is increasingly used to treat various tumor types. Determining patient responses to ICIs presents a significant clinical challenge. Although components of the tumor microenvironment (TME) are used to predict patient outcomes, comprehensive assessments of the TME are frequently overlooked. Using a top-down approach, the TME was divided into five layers-outcome, immune role, cell, cellular component, and gene. Using this structure, a neural network called TME-NET was developed to predict responses to ICIs. Model parameter weights and cell ablation studies were used to investigate the influence of TME components. The model was developed and evaluated using a pan-cancer cohort of 948 patients across four cancer types, with Area Under the Curve (AUC) and accuracy as performance metrics. Results show that TME-NET surpasses established models such as support vector machine and k-nearest neighbors in AUC and accuracy. Visualization of model parameter weights showed that at the cellular layer, Th1 cells enhance immune responses, whereas myeloid-derived suppressor cells and M2 macrophages show strong immunosuppressive effects. Cell ablation studies further confirmed the impact of these cells. At the gene layer, the transcription factors STAT4 in Th1 cells and IRF4 in M2 macrophages significantly affect TME dynamics. Additionally, the cytokine-encoding genes IFNG from Th1 cells and ARG1 from M2 macrophages are crucial for modulating immune responses within the TME. Survival data from immunotherapy cohorts confirmed the prognostic ability of these markers, with p-values <0.01. In summary, TME-NET performs well in predicting immunotherapy responses and offers interpretable insights into the immunotherapy process. It can be customized at https://immbal.shinyapps.io/TME-NET.

A novel prognostic model of de novo metastatic hormone-sensitive prostate cancer to optimize treatment intensity.

The treatment and prognosis of de novo metastatic hormone-sensitive prostate cancer (mHSPC) vary. We established and validated a novel prognostic model for predicting cancer-specific survival (CSS) in patients with mHSPC using retrospective data from a contemporary cohort. 1092 Japanese patients diagnosed with de novo mHSPC between 2014 and 2020 were registered. The patients treated with androgen deprivation therapy and first-generation anti-androgens (ADT/CAB) were assigned to the Discovery (N = 467) or Validation (N = 328) cohorts. Those treated with ADT and androgen-receptor signaling inhibitors (ARSIs) were assigned to the ARSI cohort (N = 81). Using the Discovery cohort, independent prognostic factors of CSS, the extent of disease score ≥ 2 or the presence of liver metastasis; lactate dehydrogenase levels > 250U/L; a primary Gleason pattern of 5, and serum albumin levels ≤ 3.7g/dl, were identified. The prognostic model incorporating these factors showed high predictability and reproducibility in the Validation cohort. The 5-year CSS of the low-risk group was 86% and that of the high-risk group was 22%. Approximately 26.4%, 62.7%, and 10.9% of the patients in the Validation cohort defined as high-risk by the LATITUDE criteria were further grouped into high-, intermediate-, and low-risk groups by the new model with significant differences in CSS. In the ARSIs cohort, high-risk group had a significantly shorter time to castration resistance than the intermediate-risk group. The novel model based on prognostic factors can predict patient outcomes with high accuracy and reproducibility. The model may be used to optimize the treatment intensity of de novo mHSPC.

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics.

Prediction of prognosis in lung cancer using machine learning with inter-institutional generalizability: A multicenter cohort study (WJOG15121L: REAL-WIND)

ObjectivesPredicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis. Materials and MethodsThis multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities. ResultsOverall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9–17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88–0.91), 0.85 (0.84–0.87), 0.83 (0.81–0.85), and 0.85 (0.82–0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84–0.92), 0.84 (0.78–0.88), 0.84 (0.76–0.89), and 0.84 (0.75–0.90) at 180, 360, 720, and 1,080 days, respectively. ConclusionThis study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings.

UNNECESSARY USE OF ANTIMICROBIALS – COMPARISON OF THE PREDICTIVE PERFORMANCE OF QSOFA AND APACHE SCORE FOR EVALUATION OF DISEASE PROGNOSIS PATTERNS IN CRITICAL ILLNESS

In critical care, accurate prognosis assessment, including antimicrobial therapy, is vital for guiding treatment decisions. The Acute Physiology and Chronic Health Evaluation (APACHE) and quick Sequential Organ Failure Assessment (qSOFA) scores are widely used for predicting patient outcomes. Yet, their relative performance and potential implications for antimicrobial stewardship need further exploration. Objective: To evaluate the disease prognosis patterns in critically ill patients by comparing the predictive performance of APACHE and qSOFA scores and to explore their correlation with antimicrobial misuse. Methods: A comparative study was conducted at the National Hospital and Medical Center in Lahore from January to November 2023. The study included 138 critically ill patients. Demographic information, clinical parameters, and medical history were collected, explicitly focusing on APACHE and qSOFA scores recorded at admission and during follow-up. Statistical analysis, including Pearson's and Spearman's correlation coefficients and logistic regression models, was performed using RStudio to assess the predictive ability of these scores for disease progression and patient outcomes. Results: The analysis revealed a moderate positive correlation (r=0.51) between qSOFA and APACHE-II scores, suggesting that both scores align in assessing the severity of illness in critically ill patients. The predictive models indicated that combining qSOFA and APACHE-II scores enhanced the accuracy of predicting critical outcomes, such as shortness of breath (SOB), with a combined model AUC of 0.659 compared to 0.601 for the APACHE-II model alone. Conclusion: The findings underscore the value of integrating multiple clinical scoring systems in managing critically ill patients. Such integration can aid in more judicious use of antibiotics, potentially mitigating the risk of antimicrobial resistance. The study advocates incorporating these insights into clinical guidelines and decision-making processes in critical care settings.

The Power of Artificial Intelligence for Improved Patient Outcomes, Ethical Practices and Overcoming Challenges

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing patient outcomes, reducing costs, and increasing the efficiency of medical professionals. This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions. AI technologies such as Machine Learning (ML), deep learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) are becoming integral to modern healthcare practices. These technologies enable early disease detection, particularly in cases like cancer, by analyzing medical images and patient data, leading to more effective and personalized treatment strategies. Additionally, AI can predict patient outcomes by analyzing large datasets from electronic health records, providing valuable insights that can inform clinical decisions. However, the integration of AI in healthcare also presents significant ethical challenges. Issues such as data privacy, algorithmic bias, lack of transparency, and the potential for increased health inequalities need to be addressed. The World Health Organization (WHO) has provided guidelines emphasizing the ethical use of AI, highlighting the importance of designing AI systems that respect human rights and promote equity. As AI continues to advance, it is crucial to ensure its responsible and transparent use to maximize benefits and minimize risks. This review underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations to ensure that AI technologies are implemented in a manner that enhances patient care and upholds ethical standards.

Right atrial and right ventricular strain: prognostic value depends on the severity of tricuspid regurgitation.

Assessing right heart function is challenging, particularly when significant tricuspid regurgitation (TR) is present. Among available echocardiographic techniques for assessment, literatures suggests that strain imaging may be more reliable and less susceptible to loading conditions. Thus, we aimed to assess the validity of RA and RV strain relative to conventional metrics as well as their utility in predicting patient outcomes in TR. We studied 262 consecutive patients (mean age 74 ± 11.2 years, 53% male) who underwent same-day echocardiography and right heart catheterization between 2018 and 2023. We compared right heart strain to traditional metrics of RV function and subsequently correlated RA and RV strain to heart failure (HF)-related death or hospitalization, whichever came first. Over a mean follow-up of 34 ± 15 months, there were 103 deaths and HF hospitalizations. Both RA and RV strain were correlated with echocardiographic and invasive measures of right heart function. Across all patients, preserved RA strain was associated with lower risk of adverse outcomes (HR 0.763, 95% CI 0.618-0.943). Similarly, preserved RV strain was correlated with better outcomes, though this was only statistically significant in patients without severe TR or pulmonary hypertension (HR 2.450, 95% CI 1.244-4.825). Moreover, abnormal ratios of RV strain to pulmonary pressures and RV size were significantly correlated with adverse outcomes (p < 0.05 each). RA and RV strain are independently correlated with echocardiographic and invasive measures of cardiac function. Moreover, preserved RA and RV strain are likely associated with better clinical outcomes.

Cardiac magnetic resonance findings in cardiac amyloidosis.

The purpose of this review is to highlight the increasing importance of cardiac magnetic resonance (CMR) imaging in diagnosing and managing cardiac amyloidosis, especially given the recent advancements in treatment options. This review emphasizes the crucial role of late gadolinium enhancement (LGE) with phase-sensitive inversion recovery (PSIR) techniques in both diagnosing and predicting patient outcomes in cardiac amyloidosis. The review also explores promising new techniques for diagnosing early-stage disease, such as native T1 mapping and ECV quantification. Additionally, it delves into experimental techniques like diffusion tensor imaging, MR elastography, and spectroscopy. This review underscores CMR as a powerful tool for diagnosing cardiac amyloidosis, assessing risk factors, and monitoring treatment response. While LGE imaging remains the current best practice for diagnosis, emerging techniques such as T1 mapping and ECV quantification offer promise for improved detection, particularly in early stages of the disease. This has significant implications for patient management as newer therapeutic options become available for cardiac amyloidosis.

Prognostic value of brachial-ankle pulse wave velocity changes post-transcatheter aortic valve replacement.

Transcatheter aortic valve replacement (TAVR) offers a solution, especially for high-risk aortic stenosis (AS) patients. However, patient outcomes post-TAVR show variability, highlighting the need for reliable prognostic indicators. Brachial-ankle pulse wave velocity (baPWV), a measure of arterial stiffness, may predict outcomes post-TAVR. This study aims to explore baPWV's prognostic value in relation to all-cause mortality post-TAVR. This study prospectively enrolled 212 severe AS patients undergoing TAVR between September 2015 and December 2021, focusing on pre- and post-TAVR baPWV measurements to explore associations with all-cause mortality. Of the 212 patients (119 females, 93 males, mean age 85years), post-TAVR baPWV increased significantly from 1589 ± 376 to 2010 ± 521cm/s (p < 0.001). Aortic valve (AV) peak velocity and mean pressure gradient decreased, while AV area increased, indicating procedural success. Despite this, 88% of patients experienced an increase in baPWV, with higher pre-procedure AV peak velocity and mean pressure gradient identified as predictors of increased baPWV post-TAVR. Over 23months, 29 patients (14%) reached the primary endpoint of all-cause mortality. Notably, changes in baPWV, rather than baseline values, were significantly associated with event-free survival (HR: 0.64 per 1SD increase, p = 0.009). The study highlights the prognostic value of baPWV changes post-TAVR in predicting patient outcomes. Elevated baPWV post-TAVR may reflect a beneficial adaptation to altered hemodynamics, suggesting the need for individualized patient evaluation and the integration of baPWV measurements into clinical practice for improved post-TAVR management.

Examining the relationship between alterations in plasma cholesterol, vascular endothelin-1 levels, and the severity of sepsis in children: An observational study.

Considering the significant impact of total cholesterol (TC) and vascular endothelin-1 (ET-1) on children sepsis outcomes, this research aimed to explore the association between the levels of plasma cholesterol and vascular endothelin-1 and the severity of sepsis and evaluated its clinical implications. In this study, we examined 250 pediatric patients diagnosed with sepsis between February 2019 and April 2021, collecting data on their plasma levels of TC and ET-1. Depending on the observed outcomes, the participants were divided into 2 categories: a group with a positive prognosis (control group, n = 100) and a group with a negative prognosis (n = 50). We assessed the significance of plasma TC and ET-1 levels in forecasting the outcomes for these pediatric patients. Patients in the group with a poor prognosis experienced notably longer hospital stays and higher treatment expenses than those in the control group (P < .05). Within the first 24 hours of admission and again on days 3 and 7, the levels of ET-1 were significantly higher in the poor prognosis group, whereas plasma TC levels were notably lower in comparison to the control group (P < .05). A Spearman correlation analysis identified a significant correlation between the levels of plasma TC and ET-1 and the severity of sepsis among the children (P < .05). The diagnostic performance for the severity of sepsis in children, as measured by the area under the curve (AUC), was 0.805 for plasma TC, 0.777 for ET-1 levels, and 0.938 when both were combined. This investigation underscores a meaningful relationship between the levels of plasma TC and ET-1 in pediatric sepsis patients, suggesting these biomarkers are highly valuable in predicting patient outcomes. High levels of ET-1 and low levels of TC in these patients signify a grave condition and a poor prognosis.

Comparative immune profiling of pancreatic ductal adenocarcinoma progression among South African patients

BackgroundPancreatic Ductal Adenocarcinoma (PDAC) is an aggressive cancer characterized by an immunosuppressive microenvironment. Patients from specific ethnicities and population groups have poorer prognoses than others. Therefore, a better understanding of the immune landscape in such groups is necessary for disease elucidation, predicting patient outcomes and therapeutic targeting. This study investigated the expression of circulating key immune cell markers in South African PDAC patients of African ancestry.MethodsBlood samples were obtained from a total of 6 healthy volunteers (HC), 6 Chronic Pancreatitis (CP) and 34 PDAC patients consisting of 22 resectable (RPC), 8 locally advanced (LAPC) and 4 metastatic (MPC). Real-time Quantitative Polymerase Chain reactions (RT-qPCR), Metabolomics, Enzyme-Linked Immunosorbent Assay (ELISA), Reactive Oxygen Species (ROS), and Immunophenotyping assays were conducted. Statistical analysis was conducted in R (v 4.3.2). Additional analysis of single-cell RNA data from 20 patients (16 PDAC and 4 controls) was conducted to interrogate the distribution of T-cell and Natural Killer cell populations.ResultsGranulocyte and neutrophil levels were significantly elevated while lymphocytes decreased with PDAC severity. The total percentages of CD3 T-cell subpopulations (helper and double negative T-cells) decreased when compared to HC. Although both NK (p = 0.014) and NKT (p < 0.001) cell levels increased as the disease progressed, their subsets: NK CD56dimCD16− (p = 0.024) and NKTs CD56+ (p = 0.008) cell levels reduced significantly. Of note is the negative association of NK CD56dimCD16− (p < 0.001) cell levels with survival time. The gene expression analyses showed no statistically significant correlation when comparing the PDAC groups with the controls. The inflammatory status of PDAC was assessed by ROS levels of serum which were elevated in CP (p = 0.025), (RPC (p = 0.003) and LAPC (p = 0.008)) while no significant change was observed in MPC, compared to the HC group. ROS was shown to be positively correlated with GlycA (R = 0.45, p = 0.0096). Single-cell analyses showed a significant difference in the ratio of NKT cells per total cell counts in LAPC (p < 0.001) and MPC (p < 0.001) groups compared with HC, confirming observations in our sample group.ConclusionThe expression of these immune cell markers observed in this pilot study provides insight into their potential roles in tumour progression in the patient group and suggests their potential utility in the development of immunotherapeutic strategies.

Integrating Shapley Values into Machine Learning Techniques for Enhanced Predictions of Hospital Admissions

(1) Background: Predictive modeling is becoming increasingly relevant in healthcare, aiding in clinical decision making and improving patient outcomes. However, many of the most potent predictive models, such as deep learning algorithms, are inherently opaque, and their decisions are challenging to interpret. This study addresses this challenge by employing Shapley Additive Explanations (SHAP) to facilitate model interpretability while maintaining prediction accuracy. (2) Methods: We utilized Gradient Boosting Machines (GBMs) to predict patient outcomes in an emergency department setting, with a focus on model transparency to ensure actionable insights. (3) Results: Our analysis identifies “Acuity”, “Hours”, and “Age” as critical predictive features. We provide a detailed exploration of their intricate interactions and effects on the model’s predictions. The SHAP summary plots highlight that “Acuity” has the highest impact on predictions, followed by “Hours” and “Age”. Dependence plots further reveal that higher acuity levels and longer hours are associated with poorer patient outcomes, while age shows a non-linear relationship with outcomes. Additionally, SHAP interaction values uncover that the interaction between “Acuity” and “Hours” significantly influences predictions. (4) Conclusions: We employed force plots for individual-level interpretation, aligning with the current shift toward personalized medicine. This research highlights the potential of combining machine learning’s predictive power with interpretability, providing a promising route concerning a data-driven, evidence-based healthcare future.

Copeptin's role in traumatic brain injury: The promising quest for a new biomarker

ObjectiveTraumatic brain injury (TBI) necessitates reliable biomarkers to improve patient care. This study explored copeptin as a potential biomarker in TBI and its relation to vasopressin (ADH) in such patients. MethodsA cross-sectional study was conducted on 50 TBI patients. Exclusion criteria included specific medical conditions and recent traumatic events. Copeptin and ADH testing were performed within 30 days post-trauma. Patient data, Glasgow Coma Scale (GCS) scores, imaging results, and the need for surgical intervention were obtained from medical charts. ResultsCopeptin levels negatively correlated with GCS scores (ρ = − 0.313, p = 0.027), indicating a potential association with trauma severity. Copeptin levels (mean: 3.22 pmol/L, median 2.027 pmol/L, SD = 3.15) tended to be lower than those found in the normal population, suggesting possible neuroendocrine dysfunction post-TBI. ADH levels (mean: 67.93 pmol/L, median 56.474 pmol/L SD = 47.67) were higher than the normal range and associated with the need for surgery (p = 0.048). Surprisingly, copeptin and ADH levels negatively correlated (r = − 0.491; p < 0.001), potentially due to differences in degradation processes and physiological variations in TBI patients. ConclusionCopeptin shows potential as a predictive biomarker for assessing TBI severity and predicting patient outcome. However, its complex relationship with ADH in TBI requires further investigation. Careful interpretation is needed due to potential variations in excretion dynamics and metabolism. Larger studies on TBI patient cohorts are essential to validate copeptin as a reliable biomarker and improve patient care in TBI.

PANoptosis-based molecular subtype and prognostic model predict survival and immune landscape in esophageal cancer

PurposeTo establish a prognostic model to predict the survival of patients with esophageal cancer (EC).MethodsWe extracted the expression profiles of prognostic-related genes and clinicopathological data from TCGA and GEO databases. Subsequently, a comprehensive bioinformatics analysis was conducted to construct a prognostic model utilizing LASSO and multivariate Cox regression. The stability of the risk signature was validated through Kaplan-Meier and ROC curve analyses on the training, internal testing, and external testing sets. Furthermore, we developed a nomogram that incorporates the risk score and clinical features to predict the suvival. Additionally, a nomogram incorporating the risk score and relevant clinical parameters was developed to enhance survivorship prediction. Furthermore, we delved into exploring the correlation between the risk score and immune cell abundance, expression of cancer checkpoints, as well as responses to immunotherapy and chemotherapeutic agents.ResultsIn this study, we successfully identified 19 prognosis-related genes out of a pool of 65 PANoptosis-related genes (PRGs) sourced from existing literature. Through consensus clustering analysis, we classified patients into two distinct groups as PANcluster A and B. Furthermore, the risk score derived from the five PANoptosis-related signatures emerged as an independent prognostic factor among patients with EC. To enhance the prognostic accuracy, we devised a nomogram integrating the risk score with clinical risk characteristics, enabling the prediction of 1-year, 2-year, and 3-year overall survival (OS) rates. Notably, individuals classified in the high-risk group demonstrated poorer prognoses compared to their low-risk counterparts. Furthermore, the risk score displayed substantial correlations with immune cell abundance, expression levels of cancer checkpoints, and responses to immunotherapy and chemotherapeutic agents. These pivotal findings underscore the significance of considering PANoptosis-related patterns in improving prognostic assessment and predicting treatment responses in patients diagnosed with esophageal cancer.ConclusionWe constructed a reliable prognostic risk model for EC utilizing five PRGs. The developed nomogram serves as a valuable tool in predicting patient outcomes, offering crucial insights that can inform and guide treatment decisions for individuals diagnosed with EC.

Evaluating the benefit of medical adjustment on the predictive accuracy of the revised trauma index

In the early 1970s, data from emergency trauma units was used to develop the trauma index (TI) as a triage tool for non-physicians. Over the next two decades several modifications were made to the TI, and in 1990 a revised trauma index (RTI) was published. Throughout the 1990s, modifications to the RTI concerning pre-existing medical conditions (PEC) have been studied. Our study incorporated a PEC scoring system into the RTI, and evaluated its performance against using the RTI alone. The addition of PECs to the trauma index improved its accuracy in predicting overall outcome as shown by the increase in the kappa score from 0.42 to 0.73. Based on this finding, we suggest incorporating a PEC scoring system into the revised trauma index to better predict patient outcomes.