Identification of a risk model for prognostic and therapeutic prediction in bladder urothelial carcinoma based on infiltrating CD8+ T cells

Risk prediction models have been developed to identify those at increased risk for emergency admissions, which could facilitate targeted interventions in primary care to prevent these events. Systematic review of validated risk prediction models for predicting emergency hospital admissions in community-dwelling adults. A systematic literature review and narrative analysis was conducted. Inclusion criteria were as follows; community-dwelling adults (aged 18 years and above); Risk: risk prediction models, not contingent on an index hospital admission, with a derivation and ≥1 validation cohort; emergency hospital admission (defined as unplanned overnight stay in hospital); retrospective or prospective cohort studies. Of 18,983 records reviewed, 27 unique risk prediction models met the inclusion criteria. Eleven were developed in the United States, 11 in the United Kingdom, 3 in Italy, 1 in Spain, and 1 in Canada. Nine models were derived using self-report data, and the remainder (n=18) used routine administrative or clinical record data. Total study sample sizes ranged from 96 to 4.7 million participants. Predictor variables most frequently included in models were: (1) named medical diagnoses (n=23); (2) age (n=23); (3) prior emergency admission (n=22); and (4) sex (n=18). Eleven models included nonmedical factors, such as functional status and social supports. Regarding predictive accuracy, models developed using administrative or clinical record data tended to perform better than those developed using self-report data (c statistics 0.63-0.83 vs. 0.61-0.74, respectively). Six models reported c statistics of >0.8, indicating good performance. All 6 included variables for prior health care utilization, multimorbidity or polypharmacy, and named medical diagnoses or prescribed medications. Three predicted admissions regarded as being ambulatory care sensitive. This study suggests that risk models developed using administrative or clinical record data tend to perform better. In applying a risk prediction model to a new population, careful consideration needs to be given to the purpose of its use and local factors.

To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods. In this 10-year population-based retrospective cohort study, 141 516 Chinese T2DM patients aged 18 years or above, without history of CVD or end-stage renal disease and managed in public primary care clinics in 2008, were included and followed up until December 2017. Two-thirds of the patients were randomly selected to develop sex-specific CVD risk prediction models. The remaining one-third of patients were used as the validation sample to evaluate the discrimination and calibration of the models. ML-based methods were applied to missing data imputation, predictor selection, risk prediction modelling, model interpretation, and model evaluation. Cox regression was used to develop the statistical models in parallel for comparison. During a median follow-up of 9.75 years, 32 445 patients (22.9%) developed CVD. Age, T2DM duration, urine albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), systolic blood pressure variability and glycated haemoglobin (HbA1c) variability were the most important predictors. ML models also identified nonlinear effects of several predictors, particularly the U-shaped effects of eGFR and body mass index. The ML models showed a Harrell's C statistic of >0.80 and good calibration. The ML models performed significantly better than the Cox regression models in CVD risk prediction and achieved better risk stratification for individual patients. Using routinely available predictors and ML-based algorithms, this study established 10-year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self-care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care.

Prognostic risk prediction models estimate the probability of developing head and neck cancer (HNC), providing valuable information for managing the disease. While different prognostic HNC risk prediction models have been developed worldwide, a comprehensive evaluation of their methods is lacking. We conducted a scoping review with a critical assessment aiming to identify the methodological strengths and limitations of HNC risk prediction models. We searched Medline, Embase, Scopus, Web of Science, and CAB Abstracts databases and included full-text-available peer-reviewed published papers on developing or validating a prognostic HNC risk prediction model. Study quality was appraised using the PROBAST tool. Nine papers were included. Although all had a high risk of bias, mainly in the analysis domain, only two studies had high concerns about clinical applicability. Currently published studies provide insufficient information on methods, making it difficult to judge the models' quality and applicability. Future investigations should follow the guidelines in reporting the prediction modelling studies.

BackgroundCompelled by the intuitive appeal of predicting each individual patient’s risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics.MethodsData from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke’s r2) for each model.ResultsFindings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity.ConclusionsOur findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0223-2) contains supplementary material, which is available to authorized users.

The tumor microenvironment (TME) comprises immune-infiltrating cells that are closely linked to tumor development. By screening and analyzing genes associated with tumor-infiltrating M0 cells, we developed a risk model to provide therapeutic and prognostic guidance in clear cell renal cell carcinoma (ccRCC). First, the infiltration abundance of each immune cell type and its correlation with patient prognosis were analyzed. After assessing the potential link between the depth of immune cell infiltration and prognosis, we screened the infiltrating M0 cells to establish a risk model centered on three key genes (TMEN174, LRRC19, and SAA1). The correlation analysis indicated a positive correlation between the risk score and various stages of the tumor immune cycle, including B-cell recruitment. Furthermore, the risk score was positively correlated with CD8 expression and several popular immune checkpoints (ICs) (TIGIT, CTLA4, CD274, LAG3, and PDCD1). Additionally, the high-risk group (HRG) had higher scores for tumor immune dysfunction and exclusion (TIDE) and exclusion than the low-risk group (LRG). Importantly, the risk score was negatively correlated with the immunotherapy-related pathway enrichment scores, and the LRG showed a greater therapeutic benefit than the HRG. Differences in sensitivity to targeted drugs between the HRG and LRG were analyzed. For commonly used targeted drugs in RCC, including axitinib, pazopanib, temsirolimus, and sunitinib, LRG had lower IC50 values, indicating increased sensitivity. Finally, immunohistochemistry results of 66 paraffin-embedded specimens indicated that SAA1 was strongly expressed in the tumor samples and was associated with tumor metastasis, stage, and grade. SAA1 was found to have a significant pro-tumorigenic effect by experimental validation. In summary, these data confirmed that tumor-infiltrating M0 cells play a key role in the prognosis and treatment of patients with ccRCC. This discovery offers new insights and directions for the prognostic prediction and treatment of ccRCC.

Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.

Objective: We aimed to screen the genes associated with thyroid cancer (THCA) prognosis, and construct a poly-gene risk prediction model for prognosis prediction and improvement.Methods: The HTSeq-Counts data of THCA were accessed from TCGA database, including 505 cancer samples and 57 normal tissue samples. “edgeR” package was utilized to perform differential analysis, and weighted gene co-expression network analysis (WGCNA) was applied to screen the differential co-expression genes associated with THCA tissue types. Univariant Cox regression analysis was further used for the selection of survival-related genes. Then, LASSO regression model was constructed to analyze the genes, and an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves.Results: Three thousand two hundred seven differentially expressed genes (DEGs) were obtained by differential analysis and 23 co-expression genes (|COR| > 0.5, P < 0.05) were gained after WGCNA analysis. In addition, eight genes significantly related to THCA survival were screened by univariant Cox regression analysis, and an optimal prognostic 3-gene risk prediction model was constructed after genes were analyzed by the LASSO regression model. Based on this model, patients were grouped into the high-risk group and low-risk group. Kaplan-Meier curve showed that patients in the low-risk group had much better survival than those in the high-risk group. Moreover, great accuracy of the 3-gene model was revealed by ROC curve and the remarkable correlation between the model and patients' prognosis was verified using the multivariant Cox regression analysis.Conclusion: The prognostic 3-gene model composed by GHR, GPR125, and ATP2C2 three genes can be used as an independent prognostic factor and has better prediction for the survival of THCA patients.

10-year performance of four models of breast cancer risk: a validation study.

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors and is diagnosed at an advanced stage with a poor prognosis worldwide. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.MethodsTranscriptome data, miRNA-sequencing data, and clinical information were downloaded from the TCGA database. First, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Second, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB)-related genes was analyzed.ResultsA total of 5373 DEmRNAs, 1159 DElncRNAs, and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7 miRNAs, and 5 mRNAs was constructed, and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. The tumor tissues were classified into high- and low-risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis, and nomogram further indicated the risk model was a significant independent prognostic factor what had an excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cell naïve, T cell follicular helper, macrophage M1) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2).ConclusionsThis pyroptosis-related lncRNA risk model possessed good prognostic value, and the ability to predict the outcome of ICB immunotherapy in COAD.

C ardiovascular disease (CVD) is the leading cause of death for US women, and nearly two thirds who died suddenly of CVD had no previous symptoms. 1Therefore, it is of great importance to identify "at-risk" women early, so that effective primary prevention strategies can be instituted.A universal recommendation of prevention guidelines is that all asymptomatic women should undergo a global risk assessment.

To systematically review published studies on fall risk prediction models for inpatients. A systematic review and meta-analysis of prognostic model studies. A literature search was carried out in Web of Science, the Cochrane Library, PubMed, Embase, CINAHL, SinoMed, VIP Database, CNKI and Wanfang Database. The search covered studies on risk prediction models for falls in inpatients from inception to March 9, 2024. The research question was formulated using the PICOTS framework. Data extraction was performed following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). The quality of studies related to risk prediction models was evaluated with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was conducted using STATA 18.0 software. A total of 15 studies were included, with 13 eligible for meta-analysis. Only 2 of these 15 studies had external validation. The reported AUC values ranged from 0.681 to 0.900. The overall risk of bias was high, mainly attributed to inappropriate data sources and improper processing in the analysis domain. The pooled AUC from the meta-analysis was 0.799. After reviewing the predictors included in various models, FRIDs, fall history, age, gait, mental status, gender and incontinence were relatively common. The fall risk prediction model for inpatients performs well overall, but it has a high risk of bias. Future development of risk prediction models should strictly adhere to the PROBAST, combine clinical reality, optimise study design and improve methodological quality. This study provides medical professionals with a clear overview of constructing fall risk prediction models for inpatients. The fall-related predictors in these models help healthcare providers identify high-risk patients and implement preventive strategies. It also offers valuable insights for the development of future prediction models. This study did not include patient or public involvement in its design, conduct, or reporting.

Colorectal Cancer Risk Assessment and Precision Approaches to Screening: Brave New World or Worlds Apart?

Striving towards excellence in research on biomarkers.

A risk prediction model is a mathematical equation that uses patient risk factor data to estimate the probability of a patient experiencing a healthcare outcome. Risk prediction models are used throughout medical practice for a variety of purposes such as predicting the development of a disease, predicting response to treatment, or predicting patient prognosis. Risk prediction modeling is at the forefront of improving the quality of care, reducing costs, and improving population health overall. The authors intend to share a few observations that they had while going through the risk prediction models research articles:[1] 1. Importance of including likelihood ratios (LRs) In studies such as these, it is important to include likelihood ratios (LRs) in addition to sensitivity, specificity, and predictive values. LRs expressed in an easy-to-comprehend language are more useful to clinicians in providing a better interpretation and adoption into operational practice. Eventually, the value of a score/test to influence clinical management decisions will depend upon its ability to alter the pre-test probability of a target condition into, what we call, the post-test probability. A positive LR >10 or a negative LR <0.1 are considered to exert highly significant changes in probability, which is, in turn, sufficient to alter clinical management. 2. The c statistic or receiver operating characteristic curves (ROC) may not be optimal in assessing models that predict future risk or stratify individuals into risk categories ROC curves compare sensitivity versus specificity across a range of values for the ability to predict a dichotomous outcome. The area under the ROC curve (AUROC) is another measure of test performance. However, all these parameters are not intrinsic to the test and are determined by the clinical context in which the test is employed. The c statistic or AUROC uses the test characteristics of sensitivity and specificity to differentiate diseased from healthy patients and is a popular diagnostic test tool. However, it may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. When the goal of a predictive model is to categorize individuals into risk strata, the assessment of such models should be based on how well they achieve this aim. To compare global model fit, use a measure based on the log-likelihood function, such as the Bayes information criterion, in which lower values indicate better fit and a penalty is paid if the number of variables is increased. Cook et al.[2,3] gave four suggestions for comparison of models for risk prediction, which are very apt and valuable for readers working on risk prediction models. 3. Assessing the value of risk predictions using risk stratification tables A novel approach to assessing the value of adding a new marker to a risk prediction model is called the risk stratification approach.[2–4] This involves cross-tabulating risk predictions on the basis of models with and without the new marker, and has been widely adopted in the literature. It is suggested that the readers look into this as well. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

There is a need to identify the best performing risk prediction model for sentinel lymph node biopsy (SLNB) positivity in melanoma. To comprehensively review the characteristics and discriminative performance of existing risk prediction models for SLNB positivity in melanoma. Embase and MEDLINE were searched from inception to May 1, 2024, for English language articles. All studies that either developed or validated a risk prediction model (defined as any calculator that combined more than 1 variable to provide a patient estimate for probability of melanoma SLNB positivity) with a corresponding measure of model discrimination were considered for inclusion by 2 reviewers, with disagreements adjudicated by a third reviewer. Data were extracted in duplicate according to Data Extraction for Systematic Reviews of Prediction Modeling Studies, Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Effects were pooled using random-effects meta-analysis. The primary outcome was the mean pooled C statistic. Heterogeneity was assessed using the I2 statistic. In total, 23 articles describing the development of 21 different risk prediction models for SLNB positivity, 20 external validations of 8 different risk prediction models, and 9 models that included sufficient information to obtain individualized patient risk estimates in routine preprocedural clinical practice were identified. Among all risk prediction models, the pooled weighted C statistic was 0.78 (95% CI, 0.74-0.81) with significant heterogeneity (I2 = 97.4%) that was not explained in meta-regression. The Memorial Sloan Kettering Cancer Center and Melanoma Institute of Australia models were most frequently externally validated with both having strong and comparable discriminative performance (pooled weighted C statistic, 0.73; 95% CI, 0.69-0.78 vs pooled weighted C statistic, 0.70; 95% CI, 0.66-0.74). Discrimination was not significantly different between models that included gene expression profiles (pooled C statistic, 0.83; 95% CI, 0.76-0.90) and those that only used clinicopathologic features (pooled C statistic, 0.77; 95% CI, 0.73-0.81) (P = .11). This systematic review and meta-analysis found several risk prediction models that have been externally validated with strong discriminative performance. Further research is needed to evaluate the associations of their implementation with preprocedural care.