[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]

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.

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.

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.

[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.

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 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.

Sustained improved survival of patients with metastatic melanoma after the introduction of anti-PD-1-based therapies

BACKGROUND

The introduction of modern therapies improved the median survival of patients with metastatic melanoma (MM). Here, we determined the real-world impact of modern treatments on the long-term survival of MM.

METHODS

In a population-based study, we extracted all cases of MM diagnosed in four non-consecutive years marked by major changes in available 1st line treatments (2012, 2014, 2016, and 2018) from the Danish MM Database. Patients were grouped into "trial-like" and "trial-excluded" based on common trial eligibility criteria.

RESULTS

We observed a sustained improved survival of "trial-like" patients diagnosed in 2016 or in 2018, compared to 2012 or 2014, but no major differences in 2018 versus 2016. In contrast, while survival of "trial-excluded" patients in 2016 was better compared to 2014 and 2012, survival in 2018 was improved over all previous years. We then developed a prognostic model based on multivariable stratified Cox regression, to predict the survival of newly diagnosed MM patients. Internal validation showed excellent discrimination and calibration, with a time-area-under-the-curve above 0.79 at multiple time horizons, for up to four years after diagnosis.

CONCLUSIONS

The introduction of modern treatments such as anti-PD-1 has led to a sustained, improved survival of real-world patients with MM, regardless of their eligibility for clinical trials. We provide an updateable prognostic model that can be used to improve patient information. Overall, these data highlight a positive population-based impact of modern treatments and can help health technology assessment agencies worldwide to evaluate the appropriateness of drug pricing based on known cost-benefit data.

Regression-based prognostic models for functional independence after postacute brain injury rehabilitation are not transportable: a systematic review

BACKGROUND AND OBJECTIVES

To identify and summarize validated multivariable prognostic models for the Functional Independence Measure® (FIM®) at discharge from post-acute inpatient rehabilitation in adults with acquired brain injury (ABI).

METHODS

This review was conducted based on the recommendations of the Cochrane Prognosis Methods Group and adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Three databases were systematically searched in May 2021 and updated in April 2022. Main inclusion criteria were: a) adult patients with ABI, b) validated multivariable prognostic model, c) time of prognostication within 1-week of admission to post-acute rehabilitation, and d) outcome was the FIM® at discharge from post-acute rehabilitation.

RESULTS

The search yielded 3,169 unique articles. Three articles fulfilled the inclusion criteria, accounting for n = 6 internally and n = 2 externally validated prognostic models. Discrimination was estimated as an area under the curve between 0.76 and 0.89. Calibration was deemed to be assessed insufficiently. The included models were judged to be of high risk of bias.

CONCLUSION

Current prognostic models for the FIM® in post-acute rehabilitation for patients with ABI lack the methodological rigor to support clinical use outside the development setting. Future studies addressing functional independence should ensure appropriate model validation and conform to uniform reporting standards for prognosis research.

The reporting of prognostic prediction models for obstetric care was poor: a cross-sectional survey of 10-year publications

BACKGROUND

To investigate the reporting of prognostic prediction model studies in obstetric care through a cross-sectional survey design.

METHODS

PubMed was searched to identify prognostic prediction model studies in obstetric care published from January 2011 to December 2020. The quality of reporting was assessed by the TRIPOD checklist. The overall adherence by study and the adherence by item were calculated separately, and linear regression analysis was conducted to explore the association between overall adherence and prespecified study characteristics.

RESULTS

A total of 121 studies were included, while no study completely adhered to the TRIPOD. The results showed that the overall adherence was poor (median 46.4%), and no significant improvement was observed after the release of the TRIPOD (43.9 to 46.7%). Studies including both model development and external validation had higher reporting quality versus those including model development only (68.1% vs. 44.8%). Among the 37 items required by the TRIPOD, 10 items were reported adequately with an adherence rate over of 80%, and the remaining 27 items had an adherence rate ranging from 2.5 to 79.3%. In addition, 11 items had a report rate lower than 25.0% and even covered key methodological aspects, including blinding assessment of predictors (2.5%), methods for model-building procedures (4.5%) and predictor handling (13.5%), how to use the model (13.5%), and presentation of model performance (14.4%).

CONCLUSIONS

In a 10-year span, prognostic prediction studies in obstetric care continued to be poorly reported and did not improve even after the release of the TRIPOD checklist. Substantial efforts are warranted to improve the reporting of obstetric prognostic prediction models, particularly those that adhere to the TRIPOD checklist are highly desirable.

Novel six-gene prognostic signature based on colon adenocarcinoma immune-related genes

Background

Colon adenocarcinoma (COAD) is one of the most common gastrointestinal tumors worldwide, and immunotherapy is one of the most promising treatments for it. Identifying immune genes involved in the development and maintenance of cancer is key to the use of tumor immunotherapy. This study aimed to determine the prognostic value of immune genes in patients with COAD and to establish an immune-related gene signature. Differentially expressed genes, immune-related genes (DEIGs), and transcription factors (DETFs) were screened using the following databases: Cistrome, The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and InnateDB. We constructed a network showing the regulation of DEIGs by DETFs. Using weighted gene co-expression network analysis, we prepared 5 co-expressed gene modules; 6 hub genes (CD1A, CD1B, FGF9, GRP, SERPINE1, and F2RL2) obtained using univariate and multivariate regression analysis were used to construct a risk model. Patients from TCGA database were divided into high- and low-risk groups based on whether their risk score was greater or less than the mean; the public dataset GSE40967, which contains gene expression profiles of 566 colon cancer patients, was used for validation.

Results

Survival analysis, somatic gene mutations, and tumor-infiltrating immune cells differed significantly between the high- and low-risk groups.

Conclusions

This immune-related gene signature could play an important role in guiding treatment, making prognoses, and potentially developing future clinical applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04909-2.

Comprehensive analysis of emerging flame retardants, a risk factor to prostate cancer?

Among man-made chemicals, flame retardants have caused great environmental concerns. Several studies in recent years have investigated potential sources of flame retardants, environmental distribution, exposure to wild animals and humans and toxicity. However, studies focusing on the prediction of toxicity of flame retardants are limited. Herein, toxicological and tumor databases were applied to evaluate the potential correlation between emerging flame retardants (EFRs) and tumors. Further analysis also showed that EFRs may be associated with prostate cancer (PCa). After constructing an EFR-related prognostic prediction model, it was established that EFR-related genes showed a strong prognostic predictive value among PCa patients. In addition, compared with the clinical characteristics model (including age, Gleason score, prostate-specific antigen level, T stage and N stage), a prognostic predictive model-based risk score demonstrated a better predictive value of PCa. The AUC of the 31-gene prognostic signature at 1, 3 and 5 years was 0.843, 0.824 and 0.819, respectively. In addition, the AUC of the risk score, Gleason score, age, PSA level, T stage and N stage were 0.843, 0.637, 0.414, 0.490, 0.668 and 0.517, respectively. Our analysis provides a comprehensive map of EFR interaction genes and demonstrated a new direction for environmentally hazardous materials and diseases.

A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma

BACKGROUND

In this study, we aimed to mine immune-related RNAs expressed in early cervical squamous cell carcinoma to construct prognostic prediction models.

METHODS

The RNA sequencing data of 309 cervical squamous cell carcinoma (CSCC) cases, including data of individuals with available clinical information, were obtained from The Cancer Genome Atlas (TCGA) database. We included 181 early-stage CSCC tumor samples with clinical survival and prognosis information (training dataset). Then, we downloaded the GSE44001 gene expression profile data from the National Center for Biotechnology Information Gene Expression Omnibus (validation dataset). Gene ontology annotation and the Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to analyze the biological functions of differentially expressed immune-related genes (DEIRGs). We established protein-protein interactions and competing endogenous RNA networks using Cytoscape. Using the Kaplan-Meier method, we evaluated the association between the high- and low-risk groups and the actual survival and prognosis information. Our univariate and multivariate Cox regression analyses screened for independent prognostic factors.

RESULTS

We identified seven prognosis-related signature genes (RBAKDN, CXCL2, ZAP70, CLEC2D, CD27, KLRB1, VCAM1), the expression of which was markedly associated with overall survival (OS) in CSCC patients. Also, the risk score of the seven-gene signature discripted superior ability to categorize CSCC patients into high-risk and low-risk groups, with a observablydifferent OS in the training and validation datasets. We screened two independent prognostic factors (Pathologic N and prognostic score model status) that correlated significantly by univariate and multivariate Cox regression analyses in the TCGA dataset. To further explore the potential mechanism of immune-related genes, we observed associated essential high-risk genes with a cytokine-cytokine receptor interaction.

CONCLUSIONS

This study established an immune-related RNA signature, which provided a reliable prognostic tool and may be of great significance for determining immune-related biomarkers in CSCC.

Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology

BACKGROUND

We developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use by patients and practitioners that is designed to be appropriate for integration into primary care health information technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes significant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses data that are readily available in primary care settings, it supports targeting of interventions delivered as part of clinical practice that are aimed at risk reduction.

METHODS

We used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients who were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a model that is to be integrated into the same context from which the data were derived.

RESULTS

The resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art discrimination (estimated AUROC 0.84) and good calibration (assessed visually.) The model relies only on information that is readily available in Canadian primary care settings, and hence is appropriate for integration into Canadian primary care health information technology.

CONCLUSIONS

If the contextual challenges arising when using primary care electronic medical record data are appropriately addressed, highly discriminative models for osteoarthritis risk may be constructed using only data commonly available in primary care. Because the models are constructed from data in the same setting where the model is to be applied, internal validation provides strong evidence that the resulting model will perform well in its intended application.