Using heart rate variability to develop a predictive model for post-operative cardiovascular complications following major abdominal surgery: A pilot study.

BACKGROUND: Heart Rate Variability (HRV) is a dynamic reflection of heart rhythm regulation by various physiological inputs. HRV deviations have been found to correlate with clinical outcomes in patients under physiological stresses. Perioperative cardiovascular complications occur in up to 5% of adult patients undergoing abdominal surgery and are associated with significantly increased mortality. This pilot study aimed to develop a predictive model for post-operative cardiovascular complications using HRV parameters for early risk stratification and aid post-operative clinical decision-making. METHODS: Adult patients admitted to High Dependency Units after elective major abdominal surgery were recruited. The primary composite outcome was defined as cardiovascular complications within 7 days post-operatively. ECG monitoring for HRV parameters was conducted at three time points (pre-operative, immediately post-operative, and post-operative day 1) and analyzed based on outcome group and time interactions. Candidate HRV predictors were included in a multivariable logistic regression analysis incorporating a stepwise selection algorithm. RESULTS: 89 patients were included in the analysis, with 8 experiencing cardiovascular complications. Three HRV parameters, when measured immediately post-operatively and composited with patient age, provided the basis for a predictive model with AUC of 0.980 (95% CI: 0.953, 1.00). The negative predictive value was 1.00 at a statistically optimal predicted probability cut-off point of 0.16. CONCLUSION: Our model holds potential for accelerating clinical decision-making and aiding in patient triaging post-operatively, using easily acquired HRV parameters. Risk stratification with our model may enable safe early step-down care in patients assessed to have a low risk profile of post-operative cardiovascular complications.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study.

INTRODUCTION: Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models. MATERIAL AND METHODS: We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed. RESULTS: A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods. CONCLUSIONS: The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

[Establishment and verification of a prognostic nomogram for survival of tongue squamous cell carcinoma patients who underwent cervical dissection].

OBJECTIVE: To evaluate the prognostic significance of inflammatory biomarkers, prognostic nutritional index and clinicopathological characteristics in tongue squamous cell carcinoma (TSCC) patients who underwent cervical dissection. METHODS: The retrospective cohort study consisted of 297 patients undergoing tumor resection for TSCC between January 2017 and July 2018. The study population was divided into the training set and validation set by 7 :3 randomly. The peripheral blood indices of interest were preoperative neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammation score (SIS) and prognostic nutritional index (PNI). Kaplan-Meier survival analysis and multivariable Cox regression analysis were used to evaluate independent prognostic factors for overall survival (OS) and disease-specific survival (DSS). The nomogram's accuracy was internally validated using concordance index, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration plot and decision curve analysis. RESULTS: According to the univariate Cox regression analysis, clinical TNM stage, clinical T category, clinical N category, differentiation grade, depth of invasion (DOI), tumor size and pre-treatment PNI were the prognostic factors of TSCC. Multivariate Cox regression analysis revealed that pre-treatment PNI, clinical N category, DOI and tumor size were independent prognostic factors for OS or DSS (P < 0.05). Positive neck nodal status (N≥1), PNI≤50.65 and DOI > 2.4 cm were associated with the poorer 5-year OS, while a positive neck nodal status (N≥1), PNI≤50.65 and tumor size > 3.4 cm were associated with poorer 5-year DSS. The concordance index of the nomograms based on independent prognostic factors was 0.708 (95%CI, 0.625-0.791) for OS and 0.717 (95%CI, 0.600-0.834) for DSS. The C-indexes for external validation of OS and DSS were 0.659 (95%CI, 0.550-0.767) and 0.780 (95%CI, 0.669-0.890), respectively. The 1-, 3- and 5-year time-dependent ROC analyses (AUC = 0.66, 0.71 and 0.72, and AUC = 0.68, 0.77 and 0.79, respectively) of the nomogram for the OS and DSS pronounced robust discriminative ability of the model. The calibration curves showed good agreement between the predicted and actual observations of OS and DSS, while the decision curve confirmed its pronounced application value. CONCLUSION: Pre-treatment PNI, clinical N category, DOI and tumor size can potentially be used to predict OS and DSS of patients with TSCC. The prognostic nomogram based on these variables exhibited good accurary in predicting OS and DSS in patients with TSCC who underwent cervical dissection. They are effective tools for predicting survival and helps to choose appropriate treatment strategies to improve the prognosis.

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma.

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci.

Backgrounds: Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population. Methods: A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes. Results: Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable. Conclusion: We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

Clinical Significance of a Novel Vasculogenic Mimicry-Based Prognostic Model in Hepatocellular Carcinoma.

BACKGROUND: Vasculogenic mimicry, a novel neovascularization pattern of aggressive tumors, is associated with poor clinical outcomes. OBJECTIVE: The aim of this research was to establish a new model, termed VC score, to predict the prognosis, Tumor Microenvironment (TME) components, and immunotherapeutic response in Hepatocellular Carcinoma (HCC). METHODS: The expression data of the public databases were used to develop the prognostic model. Consensus clustering was performed to confirm the molecular subtypes with ideal clustering efficacy. The high- and low-risk groups were stratified utilizing the VC score. Various methodologies, including survival analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Dysfunction and Exclusion scores (TIDE), Immunophenoscore (IPS), and nomogram, were utilized for verification of the model performance and to characterize the immune status of HCC tissues. GSEA was performed to mine functional pathway information. RESULTS: The survival and immune characteristics varied between the three molecular subtypes. A five-gene signature (TPX2, CDC20, CFHR4, SPP1, and NQO1) was verified to function as an independent predictive factor for the prognosis of patients with HCC. The high-risk group exhibited lower Overall Survival (OS) rates and higher mortality rates in comparison to the low-risk group. Patients in the low-risk group were predicted to benefit from immune checkpoint inhibitor therapy and exhibit increased sensitivity to immunotherapy. Enrichment analysis revealed that signaling pathways linked to the cell cycle and DNA replication processes exhibited enrichment in the high-risk group. CONCLUSIONS: The VC score holds the potential to establish individualized treatment plans and clinical management strategies for patients with HCC.

[Comparison of different prediction models for the occurrence of nausea and vomiting in the postoperative phase : A systematic qualitative comparison based on prospectively defined quality indicators]. / Vergleich verschiedener Vorhersagemodelle für das Auftreten von Übelkeit und Erbrechen in der postoperativen Phase : Ein systematischer qualitativer Vergleich anhand prospektiv definierter Qualitätsindikatoren.

BACKGROUND: Various prognostic prediction models exist for evaluating the risk of nausea and vomiting in the postoperative period (PONV). So far, no systematic comparison of these prognostic scores is available. METHOD: A systematic literature search was carried out in seven medical databases to find publications on prognostic PONV models. Identified scores were assessed against prospectively defined quality criteria, including generalizability, validation and clinical relevance of the models. RESULTS: The literature search revealed 62 relevant publications with a total of 81,834 patients which could be assigned to 8 prognostic models. The simplified Apfel score performed best, primarily because it was extensively validated. The Van den Bosch score and Sinclair score tied for second place. The simplified Koivuranta score was in third place. CONCLUSION: The qualitative analysis highlights the strengths and weaknesses of each prediction system based on predetermined standardized quality criteria.

Integrated bioinformatics analysis identifies a Ferroptosis-related gene signature as prognosis model and potential therapeutic target of bladder cancer.

Background: Bladder cancer (BLCA) is one of the most prevalent cancers worldwide. Ferroptosis is a newly discovered form of non-apoptotic cell death that plays an important role in tumors. However, the prognostic value of ferroptosis-related genes (FRGs) in BLCA has not yet been well studied. Method and materials: In this study, we performed consensus clustering based on FRGS and categorized BLCA patients into 2 clusters (C1 and C2). Immune cell infiltration score and immune score for each sample were computed using the CIBERSORT and ESTIMATE methods. Functional annotation of differentially expressed genes were performed by Gene Ontology (GO) and KEGG pathway enrichment analysis. Protein expression validation were confirmed in Human Protein Atlas. Gene expression validation were performed by qPCR in human bladder cancer cell lines lysis samples. Result: C2 had a significant survival advantage and higher immune infiltration levels than C1. Additionally, C2 showed substantially higher expression levels of immune checkpoint markers than C1. According to the Cox and LASSO regression analyses, a novel ferroptosis-related prognostic signature was developed to predict the prognosis of BLCA effectively. High-risk and low-risk groups were divided according to risk scores. Kaplan-Meier survival analyses showed that the high-risk group had a shorter overall survival than the low-risk group throughout the cohort. Furthermore, a nomogram combining risk score and clinical features was developed. Finally, SLC39A7 was identified as a potential target in bladder cancer. Discussion: In conclusion, we identified two ferroptosis-clusters with different prognoses using consensus clustering in BLCA. We also developed a ferroptosis-related prognostic signature and nomogram, which could indicate the outcome.

The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews.

OBJECTIVES: To conduct a methodological overview of reviews to evaluate the reporting completeness and transparency of systematic reviews (SRs) of prognostic prediction models (PPMs) for COVID-19. STUDY DESIGN AND SETTING: MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Epistemonikos (epistemonikos.org) were searched for SRs of PPMs for COVID-19 until December 31, 2022. The risk of bias in systematic reviews tool was used to assess the risk of bias. The protocol for this overview was uploaded in the Open Science Framework (https://osf.io/7y94c). RESULTS: Ten SRs were retrieved; none of them synthesized the results in a meta-analysis. For most of the studies, there was absence of a predefined protocol and missing information on study selection, data collection process, and reporting of primary studies and models included, while only one SR had its data publicly available. In addition, for the majority of the SRs, the overall risk of bias was judged as being high. The overall corrected covered area was 6.3% showing a small amount of overlapping among the SRs. CONCLUSION: The reporting completeness and transparency of SRs of PPMs for COVID-19 was poor. Guidance is urgently required, with increased awareness and education of minimum reporting standards and quality criteria. Specific focus is needed in predefined protocol, information on study selection and data collection process, and in the reporting of findings to improve the quality of SRs of PPMs for COVID-19.

The construction, validation and promotion of the nomogram prognosis prediction model of UCEC, and the experimental verification of the expression and knockdown of the key gene GPX4.

Background: Adequate prognostic prediction of Uterine Corpus Endometrial Carcinoma (UCEC) is crucial for informing clinical decision-making. However, there is a scarcity of research on the utilization of a nomogram prognostic evaluation model that incorporates pyroptosis-related genes (PRGs) in UCEC. Methods: By analyzing data from UCEC patients in the TCGA database, four PRGs associated with prognosis were identified. Subsequently, a "risk score" was developed using these four PRGs and LASSO. Ordinary and web-based dynamic nomogram prognosis prediction models were constructed. The discrimination, calibration, clinical benefit, and promotional value of the selected GPX4 were validated. The expression level of GPX4 in UCEC cell lines was subsequently verified. The effects of GPX4 knock-down on the malignant biological behavior of UCEC cells were assessed. Results: Four key PRGs and a "risk score" were identified, with the "risk score" calculated as (-0.4323) * GPX4 + (0.2385) * GSDME + (0.0525) * NLRP2 + (-0.3299) * NOD2. The nomogram prognosis prediction model, incorporating the "risk score," "age," and "FIGO stage," demonstrated moderate predictive performance (AUC >0.7), good calibration, and clinical significance for 1, 3, and 5-year survival. The web-based dynamic nomogram demonstrated significant promotional value (https://shibaolu.shinyapps.io/DynamicNomogramForUCEC/). UCEC cells exhibited abnormally elevated expression of GPX4, and the knockdown of GPX4 effectively suppressed malignant biological activities, including proliferation and migration, while inducing apoptosis. The findings from tumorigenic experiments conducted on nude mice further validated the results obtained from cellular experiments. Conclusion: Following validation, the nomogram prognosis prediction model, which relies on four pivotal PRGs, demonstrated a high degree of accuracy in forecasting the precise probability of prognosis for patients with UCEC. Additionally, the web-based dynamic nomogram exhibited considerable potential for promotion. Notably, the key gene GPX4 exhibited characteristics of a potential oncogene in UCEC, as it facilitated malignant biological behavior and impeded apoptosis.

A novel nomogram model based on Ki-67 characteristic expression to predict prognosis in head and neck squamous cell carcinoma.

Objectives: This study aimed to examine Ki-67's correlation with clinicopathological characteristics of head and neck squamous cell carcinoma (HNSCC), evaluate its prognostic significance, and develop a Ki-67 integrated prognostic model. Methods: The retrospective study included 764 HNSCC patients hospitalized from 2012 to 2022. Data were sourced from medical records and immunohistochemical analysis of surgical specimens. Results: Ki-67 expression was significantly associated with sex, pathological grade, clinical stage, and metastasis, but not with age or recurrence. Higher Ki-67 levels were linked to poorer prognosis, as indicated by Kaplan-Meier survival analysis. Utilizing a Cox proportional hazards model, four prognostic factors were identified: age, recurrence, metastasis, and Ki-67 expression. These factors were used to construct a prognostic model and a nomogram. The model's predictive accuracy was confirmed by a high concordance index and a reliable calibration curve. Conclusion: Ki-67 expression in HNSCC patients correlates with several clinicopathological features and serves as a negative prognostic marker. A prognostic model incorporating Ki-67 was successfully developed, offering a new tool for patient prognosis assessment in HNSCC.

ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions.

INTRODUCTION: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. OBJECTIVES: Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. RESULTS: ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57-24.88, P = 3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95% CI: 0.805-0.883), AUC5-year = 0.816 (95% CI: 0.775-0.857), and C-index = 0.803 (95% CI: 0.776-0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038-1.083) and 1.090 (95% CI: 1.057-1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 19‰, NB5-year = 62‰; NR3-year = 69.21%, NR5-year = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. CONCLUSION: ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.

The suboptimal clinical applicability of prognostic prediction models for severe postpartum hemorrhage: a meta-epidemiological study.

OBJECTIVES: To systematically investigate clinical applicability of the current prognostic prediction models for severe postpartum hemorrhage (SPPH). STUDY DESIGN AND SETTING: A meta-epidemiological study of prognostic prediction models was conducted for SPPH. A pre-designed structured questionnaire was adopted to extract the study characteristics, predictors and the outcome, modeling methods, predictive performance, the classification ability for high-risk individuals, and clinical use scenarios. The risk of bias among studies was assessed by the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: Twenty-two studies containing 27 prediction models were included. The number of predictors in the final models varied from 3 to 53. However, one-third of the models (11) did not clearly specify the timing of predictor measurement. Calibration was found to be lacking in 10 (37.0%) models. Among the 20 models with an incidence rate of predicted outcomes below 15.0%, none of the models estimated the area under the precision-recall curve, and all reported positive predictive values were below 40.0%. Only two (7.4%) models specified the target clinical setting, while seven (25.9%) models clarified the intended timing of model use. Lastly, all 22 studies were deemed to be at high risk of bias. CONCLUSION: Current SPPH prediction models have limited clinical applicability due to methodological flaws, including unclear predictor measurement, inadequate calibration assessment, and insufficient evaluation of classification ability. Additionally, there is a lack of clarity regarding the timing for model use, target users, and clinical settings. These limitations raise concerns about the reliability and usefulness of these models in real-world clinical practice.

Prognostic Value of Insulin Growth Factor-Like Receptor 1 (IGFLR1) in Stage II and III Colorectal Cancer and Its Association with Immune Cell Infiltration.

IGFLR1 is a novel biomarker, and some evidences suggested that is involved in the immune microenvironment of CRC. Here, we explored the expression of IGFLR1 and its association with the prognosis as well as immune cell infiltration in CRC, with the aim to provide a basis for further studies on IGFLR1. Immunohistochemical staining for IGFLR1, TIM-3, FOXP3, CD4, CD8, and PD-1 was performed in eligible tissues to analyze the expression of IGFLR1 and its association with prognosis and immune cell infiltration. Then, we screened colon cancer samples from TCGA and grouped patients according to IGFLR1-related genes. We also evaluated the co-expression and immune-related pathways of IGFLR1 to identify the potential mechanism of it in CRC. When P < 0.05, the results were considered statistically significant. IGFLR1 and IGFLR1-related genes were associated with the prognosis and immune cell infiltration (P < 0.05). In stage II and III CRC tissue and normal tissue, we found (1) IGFLR1 was expressed in both the cell membrane and cytoplasm and which was differentially expressed between cancer tissue and normal tissue. IGFLR1 expression was associated with the expression of FOXP3, CD8, and gender but was not associated with microsatellite instability. (2) IGFLR1 was an independent prognostic factor and patients with high IGFLR1 had a better prognosis. (3) A model including IGFLR1, FOXP3, PD-1, and CD4 showed good prognostic stratification ability. (4) There was a significant interaction between IGFLR1 and GATA3, and IGFLR1 had a significant co-expression with related factors in the INFR pathway. IGFLR1 has emerged as a new molecule related to disease prognosis and immune cell infiltration in CRC patients and showed a good ability to predict the prognosis of patients.

Construction of a nomogram with IrAE and clinic character to predict the survival of advanced G/GEJ adenocarcinoma patients undergoing anti-PD-1 treatment.

Objective: This study aimed to develop and validate a survival prediction model and nomogram to predict survival in patients with advanced gastric or gastroesophageal junction (G/GEJ) adenocarcinoma undergoing treatment with anti-programmed cell death 1 receptor (PD-1). This model incorporates immune-related adverse events (irAEs) alongside common clinical characteristics as predictive factors. Method: A dataset comprising 255 adult patients diagnosed with advanced G/GEJ adenocarcinoma was assembled. The irAEs affecting overall survival (OS) to a significant degree were identified and integrated as a candidate variable, together with 12 other candidate variables. These included gender, age, Eastern cooperative oncology group performance status (ECOG PS) score, tumor stage, human epidermal growth factor receptor 2 (HER2) expression status, presence of peritoneal and liver metastases, year and line of anti-PD-1 treatment, neutrophil-to-lymphocyte ratio (NLR), controlling nutritional status (CONUT) score, and Charlson comorbidity index (CCI). To mitigate timing bias related to irAEs, landmark analysis was employed. Variable selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to pinpoint significant predictors, and the variance inflation factor was applied to address multicollinearity. Subsequently, a Cox regression analysis utilizing the forward likelihood ratio method was conducted to develop a survival prediction model, excluding variables that failed to satisfy the proportional hazards (PH) assumption. The model was developed using the entire dataset, then internally validated through bootstrap resampling and externally validated with a cohort from another Hospital. Furthermore, a nomogram was created to delineate the predictive model. Results: After consolidating irAEs from the skin and endocrine systems into a single protective irAE category and applying landmark analysis, variable selection was conducted for the prognostic prediction model along with other candidate variables. The finalized model comprised seven variables: ECOG PS score, tumor stage, HER2 expression status in tumor tissue, first-line anti-PD-1 treatment, peritoneal metastasis, CONUT score, and protective irAE. The overall concordance index for the model was 0.66. Calibration analysis verified the model's accuracy in aligning predicted outcomes with actual results. Clinical decision curve analysis indicated that utilizing this model for treatment decisions could enhance the net benefit regarding 1- and 2-year survival rates for patients. Conclusion: This study developed a prognostic prediction model by integrating common clinical characteristics of irAEs and G/GEJ adenocarcinoma. This model exhibits good clinical practicality and possesses accurate predictive ability for overall survival OS in patients with advanced G/GEJ adenocarcinoma.