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Aims To develop and validate a user‐friendly scale for predicting acute‐phase adverse outcomes in acute ischemic stroke (AIS), thereby optimizing clinical management. Methods This retrospective study enrolled AIS patients within 72 h of onset (excluding thrombectomy), stratified according to thrombolysis status to develop treatment‐specific prognostic models. The prognostic scale of AIS acute stage based on treatment stratification (PAIST) was developed using clinical variables, with discharge mRS as the primary endpoint, followed by external validation. Results A total of 1971 AIS patients (437 thrombolyzed) were included. Both thrombolysis‐specific and non‐thrombolysis‐specific models incorporated core predictors (baseline NIHSS, deep vein thrombosis, neuron specific enolase, neutrophil percentage) but differed in cut‐off values and weightings. Additionally, the non‐thrombolysis‐specific model integrated three extra variables: age, fasting blood glucose, and serum potassium. External validation demonstrated PAIST outperformed the benchmark model (AUCs: thrombolysis group 0.759 vs. 0.698; non‐thrombolysis group 0.850 vs. 0.801; all p ≤ 0.05). PAIST‐based risk stratification effectively identified high‐risk patients, with poor prognosis rates of 76.92% (thrombolysis group) and 61.11% (non‐thrombolysis group). Conclusion The PAIST scale is an effective and practical tool for acute‐phase prognostic risk stratification in AIS. Its treatment‐stratified design enables accurate risk assessment, thereby supporting individualized clinical decision‐making. The PAIST prognostic tool, developed through thrombolysis‐based stratification and externally validated, effectively identifies AIS patients at high risk of acute‐phase adverse outcomes by integrating multidimensional variables, demonstrating superior predictive performance to support clinical management.

Immune checkpoint inhibitors, particularly PD-1 inhibitors, are widely used in hepatocellular carcinoma therapy, many received PD-1 inhibitors beyond first-line, but heterogeneous treatment responses require reliable biomarkers. The interaction of immune function, nutritional status, and inflammatory responses affects tumor progression and survival, yet their prognostic value in PD-1 inhibitor-treated HCC patients remains unclear. This study developed a novel nutritional-immune-inflammatory score (NIIS) to evaluate its prognostic value in HCC patients receiving PD-1 inhibitors. We analyzed 355 HCC patients treated with PD-1 inhibitors (training: n=249; validation: n=106), the cohort included 18.6% Child-Pugh B patients. Fourteen nutritional, immune, and inflammatory biomarkers were evaluated. Prognostic indicators were selected via univariate and LASSO Cox regression. The NIIS was constructed and validated for OS prediction. A nomogram integrating the NIIS with clinical variables was developed and validated based on calibration curves, AUC, and DCA, and compared with the BCLC staging system. The primary outcome assessed was OS from the initiation of PD-1 inhibitor therapy in HCC patients. The NIIS (ALRI, APRI, PALBI, AAPR) showed strong prognostic stratification. High-risk patients had shorter OS (training: P =1.764×10^-8; verification: P=2.775×10^-6). Higher NIIS were significantly associated with advanced tumor stage, poor liver function grade, multiple and larger tumors, tumor thrombus, vascular invasion, and elevated AFP levels (P<0.05). Multivariate Cox analysis confirmed the NIIS as an independent prognostic factor for OS (training: HR=1.565, 95% CI: 1.273-1.925; verification: HR=1.341, 95% CI: 1.065-1.687). A nomogram integrating the NIIS with clinical variables was constructed for individualized prognosis prediction, demonstrating superior predictive performance compared to the conventional BCLC staging system. The NIIS and nomogram provide a clinically useful tool for risk stratification in HCC immunotherapy, this model outperforming conventional staging systems and may optimize patient selection for PD-1 inhibitor therapy. Prospective multicenter studies are warranted to validate its generalizability.

Background: Treatment strategies have changed dramatically in recent years with the development of a variety of agents for metastatic hormone-naïve prostate cancer (mHNPC). There is a need to identify prognostic factors for the appropriate choice of treatment for patients with mHNPC, and we retrospectively examined these factors. Methods: Patients with mHNPC treated at our institution from 2000 to 2019 were included in this study. Overall survival (OS) was estimated retrospectively using the Kaplan–Meier method, and factors associated with OS were identified using univariate and multivariate analyses. A prognostic model was then developed based on the factors identified. Follow-up was terminated on 24 October 2021. Results: The median follow-up duration was 44.2 months, whereas the median OS was 85.2 months, with 88 patients succumbing to their disease. Multivariate analysis identified Gleason pattern (GP) 5 content, bone scan index (BSI) ≥ 1.5, and lactate dehydrogenase (LDH) levels ≥ 300 IU/L as prognostic factors associated with OS. We also developed a prognostic model that classified patients with mHNPC as low risk with no factor, intermediate risk with one factor, and high risk with two or three factors. Conclusions: Three prognostic factors for OS were identified in patients with mHNPC, namely GP5 inclusion, BSI ≥ 1.5, and LDH ≥ 300. Using these three factors, we developed a new prognostic model for OS that can more objectively predict patient prognosis.

The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements. We aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies. We conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for reporting ML studies. The articles were screened by their title and abstract against a set of 9 inclusion and exclusion criteria. Articles that were filtered through had their quality evaluated by 2 raters using a 9-point checklist constructed from guideline development good practices. The average κ was 0.71 across all quality criteria. The resulting 17 high-quality source papers were defined as having a quality score equal to or higher than the median. The reporting items in these 17 articles were consolidated and screened against a set of 6 inclusion and exclusion criteria. The resulting reporting items were sent to an external group of 11 ML experts for review and updated accordingly. The updated checklist was used to assess the reporting in 6 recent modeling papers in JMIR AI. Feedback from the external review and initial validation efforts was used to improve the reporting items. In total, 37 reporting items were identified and grouped into 5 categories based on the stage of the ML project: defining the study details, defining and collecting the data, modeling methodology, model evaluation, and explainability. None of the 17 source articles covered all the reporting items. The study details and data description reporting items were the most common in the source literature, with explainability and methodology guidance (ie, data preparation and model training) having the least coverage. For instance, a median of 75% of the data description reporting items appeared in each of the 17 high-quality source guidelines, but only a median of 33% of the data explainability reporting items appeared. The highest-quality source articles tended to have more items on reporting study details. Other categories of reporting items were not related to the source article quality. We converted the reporting items into a checklist to support more complete reporting. Our findings supported the need for a set of consolidated reporting items, given that existing high-quality guidelines and checklists do not individually provide complete coverage. The consolidated set of reporting items is expected to improve the quality and reproducibility of ML modeling studies.

Tumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).BACKGROUNDTumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).\"GEO2R,\" \"limma\" R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. \"Survival,\" \"survminer,\" \"rms\" R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.METHODS\"GEO2R,\" \"limma\" R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. \"Survival,\" \"survminer,\" \"rms\" R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.A total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = -0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By \"survival ROC\" R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.RESULTSA total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = -0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By \"survival ROC\" R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.In conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.CONCLUSIONIn conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.

To evaluate how often newly developed risk prediction models undergo external validation and how well they perform in such validations. We reviewed derivation studies of newly proposed risk models and their subsequent external validations. Study characteristics, outcome(s), and models' discriminatory performance [area under the curve, (AUC)] in derivation and validation studies were extracted. We estimated the probability of having a validation, change in discriminatory performance with more stringent external validation by overlapping or different authors compared to the derivation estimates. We evaluated 127 new prediction models. Of those, for 32 models (25%), at least an external validation study was identified; in 22 models (17%), the validation had been done by entirely different authors. The probability of having an external validation by different authors within 5 years was 16%. AUC estimates significantly decreased during external validation vs. the derivation study [median AUC change: −0.05 (P < 0.001) overall; −0.04 (P = 0.009) for validation by overlapping authors; −0.05 (P < 0.001) for validation by different authors]. On external validation, AUC decreased by at least 0.03 in 19 models and never increased by at least 0.03 (P < 0.001). External independent validation of predictive models in different studies is uncommon. Predictive performance may worsen substantially on external validation.

BACKGROUND:Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate. METHODS:We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations. RESULTS:We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding. CONCLUSIONS:Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.

Background Breast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer. Methods We conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients. Results From the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality ( n  = 28), recurrence ( n  = 23), or both ( n  = 7). The most frequently used predictors were nodal status ( n  = 49), tumour size ( n  = 42), tumour grade ( n  = 29), age at diagnosis ( n  = 24), and oestrogen receptor status ( n  = 21). Models were developed in Europe ( n  = 25), Asia ( n  = 13), North America ( n  = 12), and Australia ( n  = 1) between 1982 and 2016. Models were validated in the development cohorts ( n  = 43) and/or independent populations ( n  = 17), by comparing the predicted outcomes with the observed outcomes ( n  = 55) and/or with the outcomes estimated by other models ( n  = 32), or the outcomes estimated by individual prognostic factors ( n  = 8). The most commonly used methods were: Cox proportional hazards regression for model development ( n  = 32); the absolute differences between the predicted and observed outcomes ( n  = 30) for calibration; and C-index/AUC ( n  = 44) for discrimination. Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations. Conclusions Many prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.

Background A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. Methods We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. Results We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Conclusions Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model.

BackgroundHepatocellular carcinoma (HCC) remains a highly lethal malignancy due to tumor heterogeneity and treatment resistance. This study characterized E3 ubiquitin ligase RNF157 expression patterns in HCC through integrated multi-omics and single-cell analysis, developed an RNF157-associated prognostic signature, and explored its relationship with tumor microenvironment (TME) populations.MethodsClinical and RNA expression data were obtained from TCGA, GEO databases, and scRNA-seq datasets (GSE149614). Protein-protein interaction networks were constructed via STRING database. Based on single-cell analysis revealing RNF157’s heterogeneous expression in cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs), we performed validation experiments using lentiviral shRNAs targeting FAP in CAFs and CD11b in TAMs. All experiments included appropriate controls with three independent biological replicates.ResultsSingle-cell analysis identified significant heterogeneity in HCC samples, with RNF157 showing variable expression across cell types within the TME. The prognostic model demonstrated moderate predictive performance (AUC: 0.65–0.78 for 1–5 years survival). Flow cytometry confirmed successful experimental manipulation of TME populations, with reduced FAP + CAFs (45.54%→22.01%) and CD11b+ TAMs (35.03%→24.18%) following respective gene depletion.ConclusionWe characterized RNF157 expression patterns in HCC at single-cell resolution and established a prognostic signature with moderate predictive performance requiring independent validation before clinical application.