Risk factor analysis and prediction model to establish recurrence or progression of non-functioning pituitary adenomas in men after transnasal sphenoidal surgery.

This paper aims to analyze the risk factors for the recurrence or progression of non-functioning pituitary adenomas (NFPAs) in male patients after transnasal sphenoidal surgery and to develop a predictive model for prognosis. Clinical and follow-up data of 126 male patients with NFPAs treated by transnasal sphenoidal surgery from January 2011 to January 2021 in Fuzhou 900th Hospital were retrospectively analyzed. Lasso regression analysis was used to screen the best predictors, and the predictors were further screened by multivariate logistic regression analysis, and the nomogram prediction model was constructed. The performance of the model was verified by three aspects: discrimination, calibration and clinical utility by using the consistency index (C-index), receiver operating characteristic curve (ROC), calibration curve, clinical decision curve (DCA) and Clinical impact curve (CIC). Out of 126 cases, 7 (5.56%) showed postoperative tumor recurrence, and 18 (14.29%) exhibited postoperative residual regrowth (progression). Age (P = 0.024), maximum tumor diameter (P < 0.001), modified Knosp grade (P < 0.001), resection extent (P < 0.001), Ki67 (P < 0.001), pressure symptom (P < 0.001), Pre-op hypopituitarism (P = 0.048), Post-op new hypopituitarism (P = 0.017) showed significant differences among the recurrence group, the progression group, and the alleviation group. Three independent risk factors (Ki67, modified Knosp grade, and resection extent) affecting postoperative remission were used to construct a predictive model for long-term postoperative failure to remit. The C-index of the nomogram model was 0.823, suggesting that the model had a high discriminatory power, and the AUC of the area under the ROC curve was 0.9[95% CI (0.843, 0.958)]. A nomogram prediction model based on modified Knosp grading (grades 3B-4), resection extent (partial resection), and Ki-67 (≥ 3%) predicts the recurrence or progression of NFPAs in men after transnasal sphenoidal surgery.

Comment on, "Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential".

The article "Survival Prediction of Glioblastoma Patients-Are We There Yet? A Systematic Review of Prognostic Modeling for Glioblastoma and Its Clinical Potential" by Tewarie et al. (2024) critically examines the current landscape of prognostic models for glioblastoma, highlighting both advancements and challenges in their clinical application. Through a systematic review adhering to PRISMA guidelines, the authors synthesize findings from diverse studies, shedding light on the variability in model performance and the obstacles to clinical implementation. Despite these contributions, the review faces limitations due to the heterogeneity of the studies included, which complicates definitive conclusions. The authors emphasize the need for external validation and standardization, though further exploration of the persistence of these challenges and the biases in machine learning models is warranted. Future research should focus on standardizing protocols and integrating ethical considerations to enhance the clinical utility of these models, moving the field closer to practical application.

A Real-World Data-Based Analysis of Prognostic Indices as Part of Trial Eligibility Criteria in Diffuse Large B-Cell Lymphoma Patients.

OBJECTIVES: Recent front-line clinical trials used the International Prognostic Index (IPI) to identify trial-eligible patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL). However, many IPI-like variants with improved accuracy have been developed over the years for rituximab-treated patients. METHODS: We assessed the impact of International Prognostic Indices on patient enrolment in clinical trials, aiming to exclude low-risk IPI patients based on POLARIX/EPCORE DLBCL-2 trial criteria. RESULTS: We identified 2877 patients in the Danish Lymphoma Registry who would have been eligible for the POLARIX trial if patients with IPI 0-1 scores were included. IPI and NCCN-IPI assigned 33.3% and 11.9% of patients to the low-risk group, respectively. Shorter 5-year overall survival (91.4% vs. 97.5%), higher relapse rate (9.9% vs. 4.4%), and more deaths (16.1% vs. 4.4%) occurred in the low-risk IPI group compared with low-risk NCCN-IPI group. Analyzed models failed to identify true high-risk patients with poor prognosis. Similar results were found in the confirmatory cohort developed based on EPCORE DLBCL-2 trial eligibility criteria. CONCLUSION: True low-risk patients are more optimal identified by NCCN-IPI and should be excluded from front-line clinical trials due to their excellent prognosis. However, additional high-risk factors besides clinical prognostic models need to be considered when selecting trial-eligible patients.

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model.

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BCE-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

Integration of clinical and blood parameters in risk prognostication for patients receiving immunochemotherapy for extensive stage small cell lung cancer: real-world data from two centers.

BACKGROUND: Immune checkpoint inhibitors (ICIs) had modest advances in the treatment of extensive-stage small cell lung cancer (ES-SCLC) in clinical trials, but there is a lack of biomarkers for prognosis in clinical practice. METHODS: We retrospectively collected data from ES-SCLC patients who received ICIs combined chemotherapy from two centers in China, integrated clinical and blood parameters, and constructed risk prognostication for immunochemotherapy. The population was divided into high- and low-risk groups, and the performance of the model was assessed separately in the training and validation cohorts. RESULTS: Two hundred and twenty and 43 patients were included in the training and validation groups, respectively. The important predictors were screened including body mass index, liver metastases, coefficient variation of red blood cell distribution width, lactate dehydrogenase, albumin, and C-reactive protein. Predicting 1-year overall survival (OS), the AUC values under ROC for the model under training, internal validation, and external validation were 0.760, 0.732, and 0.722, respectively, and the calibration curve and clinical decision curve performed well. Applied the model to divide patients into low-risk and high-risk groups, and the median OS was 23.7 months and 9.1 months, and the median progression-free survival was 8.2 months and 4.8 months, respectively; furthermore, this ability to discriminate survival was also observed in the validation cohort. CONCLUSIONS: We constructed a novel prognostic model for ES-SCLC to predict survival employing baseline tumor burden, nutritional and inflammatory parameters, it is easily measured to screen high-risk patient populations.

An Integrated Approach Based on Clinical Data Combined with Metabolites and Biomarkers for the Assessment of Post-Operative Complications after Cardiac Surgery.

Background: Early diagnosis of post-operative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. The purpose of this study was to determine whether an integrated approach based on clinical data, along with metabolites and biomarkers, had greater predictive value than the models built on fewer data in the early diagnosis of post-operative complications after cardiac surgery. Methods: The study included patients (n = 62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass) with (n = 26) or without (n = 36) post-operative complications. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers and metabolites. Results: Multivariate PLS-DA models, predicting the presence or absence of post-operative complications, were built using clinical data, concentrations of metabolites and biomarkers, and the entire data set (ROC-AUC = 0.80, 0.71, and 0.85, respectively). For comparison, we built univariate models using the EuroScore2 and SOFA scales, concentrations of lactate, the dynamic changes of 4-hydroxyphenyllactic acid, and the sum of three sepsis-associated metabolites (ROC-AUC = 0.54, 0.79, 0.62, 0.58, and 0.70, respectively). Conclusions: The proposed complex model using the entire dataset had the best characteristics, which confirms the expediency of searching for new predictive models based on a variety of factors.

Identification of epithelial-related artificial neural network prognostic models for the prediction of bladder cancer prognosis through comprehensive analysis of single-cell and bulk RNA sequencing.

Background: Bladder cancer (BLCA) presents as a heterogeneous epithelial malignancy. Progress in the early detection and effective treatment of BLCA relies heavily on the identification of novel biomarkers. Therefore, the primary goal of this study is to pinpoint potential biomarkers for BLCA through the fusion of single-cell RNA sequencing and RNA sequencing assessments. Furthermore, the aim is to establish practical clinical prognostic models that can facilitate accurate categorization and individualized therapy for patients. Methods: In this research, training sets were acquired from the TCGA database, whereas validation sets (GSE32894) and single-cell datasets (GSE135337) were extracted from the GEO database. Single-cell analysis was utilized to obtain characteristic subpopulations along with their associated marker genes. Subsequently, a novel BLCA subtype was identified within TCGA-BLCA. Furthermore, an artificial neural network prognostic model was constructed within the TCGA-BLCA cohort and subsequently verified utilizing a validation set. Two machine learning algorithms were employed to screen hub genes. QRT-qPCR was performed to detect the gene expression levels utilized in the construction of prognostic models across various cell lines. Additionally, the cMAP database and molecular docking were utilized for searching small molecule drugs. Results: The results of single-cell analysis revealed the presence of epithelial cells in multiple subpopulations, with 1579 marker genes selected for subsequent investigations. Subsequently, four epithelial cell subtypes were identified within the TCGA-BLCA cohort. Notably, cluster A exhibited a significant survival advantage. Concurrently, an artificial neural network prognostic model comprising 17 feature genes was constructed, accurately stratifying patient risk. Patients categorized in the low-risk group demonstrated a considerable survival advantage. The ROC analysis suggested that the model has strong prognostic ability. Furthermore, the findings of the validation group align consistently with those from the training group. Two types of machine learning algorithms screened NFIC as hub genes. Forskolin, a small molecule drug that binds to NFIC, was identified by employing a cMAP database and molecular docking. Conclusion: The analysis results supplement the research on the role of epithelial cells in BLCA. An artificial neural network prognostic model containing 17 characteristic genes demonstrates the capability to accurately stratify patient risk, thereby potentially improving clinical decision-making and optimizing personalized therapeutic approaches.

From prognostication to therapeutics: Four key questions to accelerate the development of therapeutics for patients with acute-on-chronic liver failure.

Acute-on-chronic liver failure (ACLF) has come a long way as a clinical concept within the hepatology and liver transplant communities. Though the term was proposed in 1995, the first recognition of the entity along with a consensus definition emerged in 2009. Subsequently, the entity has sparked great interest, inspired several consensus conferences, and inspired national societies to form professional ACLF affinity groups (eg, special interest group). Multicenter consortia have been established all over the world to study this condition, including the North American Consortium for the Study of End-Stage Liver Disease, Chronic Liver Failure consortium, Asian Pacific Association for the Study of Liver Diseases ACLF Research Consortium, Chronic Liver disease Evolution And Registry for Events and Decompensation, and the LiverHope Consortium. Collectively, these consortia have enrolled tens of thousands of patients with or at risk for ACLF across dozens of countries and characterized in detail the predictors, pathogenesis, and progression of patients with ACLF. Perhaps most importantly, they have produced essential data characterizing the excess morbidity and mortality that patients with ACLF face, making a compelling case for the urgent need for therapeutic strategies for this condition.

Interleukin-6 in Sepsis-Promising but Yet to Be Proven.

How to cite this article: Sinha S. Interleukin-6 in Sepsis-Promising but Yet to Be Proven. Indian J Crit Care Med 2024;28(7):629-631.

Methods in DNA methylation array dataset analysis: A review.

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS).

The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors' application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.

Editor's Choice - Prognostic Factors and Models to Predict Mortality Outcomes in Patients with Peripheral Arterial Disease: A Systematic Review.

OBJECTIVE: Predicting adverse outcomes in patients with peripheral arterial disease (PAD) is a complex task owing to the heterogeneity in patient and disease characteristics. This systematic review aimed to identify prognostic factors and prognostic models to predict mortality outcomes in patients with PAD Fontaine stage I - III or Rutherford category 0 - 4. DATA SOURCES: PubMed, Embase, and Cochrane Database of Systematic Reviews were searched to identify studies examining individual prognostic factors or studies aiming to develop or validate a prognostic model for mortality outcomes in patients with PAD. REVIEW METHODS: Information on study design, patient population, prognostic factors, and prognostic model characteristics was extracted, and risk of bias was evaluated. RESULTS: Sixty nine studies investigated prognostic factors for mortality outcomes in PAD. Over 80 single prognostic factors were identified, with age as a predictor of death in most of the studies. Other common factors included sex, diabetes, and smoking status. Six studies had low risk of bias in all domains, and the remainder had an unclear or high risk of bias in at least one domain. Eight studies developed or validated a prognostic model. All models included age in their primary model, but not sex. All studies had similar discrimination levels of > 70%. Five of the studies on prognostic models had an overall high risk of bias, whereas two studies had an overall unclear risk of bias. CONCLUSION: This systematic review shows that a large number of prognostic studies have been published, with heterogeneity in patient populations, outcomes, and risk of bias. Factors such as sex, age, diabetes, hypertension, and smoking are significant in predicting mortality risk among patients with PAD Fontaine stage I - III or Rutherford category 0 - 4.

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Prognosis-based management of unexplained infertility-why not?

Up to a half of couples seeking medical assistance for infertility are diagnosed with unexplained infertility, characterized by normal ovulation, tubal patency, and semen analysis results. This condition presents a challenge in determining the optimal treatment approach. Available treatments include IUI and IVF, but guidelines vary on when to offer each. Prognosis-based management is identified as a research priority, and various prediction models have been developed to guide treatment decisions. Prognostic factors include female age, duration of subfertility, and sperm parameters, among others. Prognosis-based strategies can enhance cost-effectiveness, safety, and patient outcomes, offering less invasive options to those with good prognoses and more aggressive interventions to those with poor prognoses. However, there is a gap between research evidence and its clinical application. In this article, we discuss the application of prognosis-based management in the context of unexplained infertility, highlighting its potential to improve clinical decision-making and patient outcomes.

GRADE concept paper 8: judging the certainty of discrimination performance estimates of prognostic models in a body of validation studies.

BACKGROUND: Prognostic models incorporate multiple prognostic factors to estimate the likelihood of future events for individual patients based on their prognostic factor values. Evaluating these models crucially involves conducting studies to assess their predictive performance, like discrimination. Systematic reviews and meta-analyses of these validation studies play an essential role in selecting models for clinical practice. METHODS: In this paper, we outline 3 thresholds to determine the target for certainty rating in the discrimination of prognostic models, as observed across a body of validation studies. RESULTS AND CONCLUSION: We propose 3 thresholds when rating the certainty of evidence about a prognostic model's discrimination. The first threshold amounts to rating certainty in the model's ability to classify better than random chance. The other 2 approaches involve setting thresholds informed by other mechanisms for classification: clinician intuition or an alternative prognostic model developed for the same disease area and outcome. The choice of threshold will vary based on the context. Instead of relying on arbitrary discrimination cut-offs, our approach positions the observed discrimination within an informed spectrum, potentially aiding decisions about a prognostic model's practical utility.

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning.

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer.

Pancreatic cancer stands as one of the most lethal malignancies, characterized by delayed diagnosis, high mortality rates, limited treatment efficacy, and poor prognosis. Disulfidptosis, a recently unveiled modality of cell demise induced by disulfide stress, has emerged as a critical player intricately associated with the onset and progression of various cancer types. It has emerged as a promising candidate biomarker for cancer diagnosis, prognosis assessment, and treatment strategies. In this study, we have effectively established a prognostic risk model for pancreatic cancer by incorporating multiple differentially expressed long non-coding RNAs (DElncRNAs) closely linked to disulfide-driven cell death. Our investigation delved into the nuanced relationship between the DElncRNA-based predictive model for disulfide-driven cell death and the therapeutic responses to anticancer agents. Our findings illuminate that the high-risk subgroup exhibits heightened susceptibility to the small molecule compound AZD1208, positioning it as a prospective therapeutic agent for pancreatic cancer. Finally, we have elucidated the underlying mechanistic potential of AZD1208 in ameliorating pancreatic cancer through its targeted inhibition of the peroxisome proliferator-activated receptor-γ (PPARG) protein, employing an array of comprehensive analytical methods, including molecular docking and molecular dynamics (MD) simulations. This study explores disulfidptosis-related genes, paving the way for the development of targeted therapies for pancreatic cancer and emphasizing their significance in the field of oncology. Furthermore, through computational biology approaches, the drug AZD1208 was identified as a potential treatment targeting the PPARG protein for pancreatic cancer. This discovery opens new avenues for exploring targets and screening drugs for pancreatic cancer.

MRI radiomics in head and neck cancer from reproducibility to combined approaches.

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.

Development of prognostic models for predicting 90-day neurological function and mortality after cardiac arrest.

BACKGROUND: The survivors of cardiac arrest experienced vary extent of hypoxic ischemic brain injury causing mortality and long-term neurologic disability. However, there is still a need to develop robust and reliable prognostic models that can accurately predict these outcomes. OBJECTIVES: To establish reliable models for predicting 90-day neurological function and mortality in adult ICU patients recovering from cardiac arrest. METHODS: We enrolled patients who had recovered from cardiac arrest at Binhaiwan Central Hospital of Dongguan, from January 2018 to July 2021. The study's primary outcome was 90-day neurological function, assessed and divided into two categories using the Cerebral Performance Category (CPC) scale: either good (CPC 1-2) or poor (CPC 3-5). The secondary outcome was 90-day mortality. We analyzed the relationships between risk factors and outcomes individually. A total of four models were developed: two multivariable logistic regression models (models 1 and 2) for predicting neurological function, and two Cox regression models (models 3 and 4) for predicting mortality. Models 2 and 4 included new neurological biomarkers as predictor variables, while models 1 and 3 excluded. We evaluated calibration, discrimination, clinical utility, and relative performance to establish superiority between the models. RESULTS: Model 1 incorporates variables such as gender, site of cardiopulmonary resuscitation (CPR), total CPR time, and acute physiology and chronic health evaluation II (APACHE II) score, while model 2 includes gender, site of CPR, APACHE II score, and serum level of ubiquitin carboxy-terminal hydrolase L1 (UCH-L1). Model 2 outperforms model 1, showcasing a superior area under the receiver operating characteristic curve (AUC) of 0.97 compared to 0.83. Additionally, model 2 exhibits improved accuracy, sensitivity, and specificity. The decision curve analysis confirms the net benefit of model 2. Similarly, models 3 and 4 are designed to predict 90-day mortality. Model 3 incorporates the variables such as site of CPR, total CPR time, and APACHE II score, while model 4 includes APACHE II score, total CPR time, and serum level of UCH-L1. Model 4 outperforms model 3, showcasing an AUC of 0.926 and a C-index of 0.830. The clinical decision curve analysis also confirms the net benefit of model 4. CONCLUSIONS: By integrating new neurological biomarkers, we have successfully developed enhanced models that can predict 90-day neurological function and mortality outcomes more accurately.