A validation of the GeRi-Score on 120-day mortality, the impact of a pre-operative visit by a geriatrician, and timing of surgery on the outcome was conducted. The score has predictive value for 120-day mortality. No advantage was found for surgery within 24 h or a preoperative geriatric visit.
OBJECTIVE: Numerous tools predict mortality among patients with hip fractures, but they include many variables, require time-consuming assessment, and are difficult to calculate. The GeRi-Score provides a quick method of pre-operative assessment. The aim of this study is to validate the score in the 120-day follow-up and determine the impact of a pre-operative visit by a geriatrician and timing of surgery on the patient outcome.
METHODS: A retrospective analysis of the AltersTraumaRegister DGU® from 2017 to 2021 was conducted, including all proximal femur fractures. The patients were divided into low-, moderate-, and high-risk groups based on the GeRi-Score. Mortality was analyzed using logistic regression. To determine the influence of the time to surgery and the preoperative visit by a geriatrician, matching was performed using the exact GeRi-Score, preoperative walking ability, type of fracture, and the time to surgery.
RESULTS: The study included 38,570 patients, divided into 12,673 low-risk, 18,338 moderate-risk, and 7,559 high-risk patients. The moderate-risk group had three times the mortality risk of the low-risk group (OR 3.19 (95% CI 2.68-3.79; p<0.001)), while the high-risk group had almost eight times the mortality risk than the low-risk group (OR 7.82 (95% CI 6.51-9.93; p<0.001)). No advantage was found for surgery within the first 24 h across all groups. There was a correlation of a preoperative geriatric visit and mortality showing an increase in the moderate and high-risk group on in-house mortality.
CONCLUSIONS: The GeRi-Score has predictive value for 120-day mortality. No advantage was found for surgery within 24 h. The analysis did not demonstrate a benefit of the preoperative geriatric visit, but more data are needed.

UNASSIGNED: Immune-checkpoint inhibitors (ICI) are associated with life-threatening myocarditis but milder presentations are increasingly recognized. The same autoimmune process that causes ICI-myocarditis can manifest concurrent generalized myositis, myasthenia-like syndrome, and respiratory muscle failure. Prognostic factors for this \"cardiomyotoxicity\" are lacking.
UNASSIGNED: A multicenter registry collected data retrospectively from 17 countries between 2014-2023. A multivariable cox regression model (hazard-ratio(HR), [95%confidence-interval]) was used to determine risk factors for the primary composite outcome: severe arrhythmia, heart failure, respiratory muscle failure, and/or cardiomyotoxicity-related death. Covariates included demographics, comorbidities, cardio-muscular symptoms, diagnostics, and treatments. Time-dependent covariates were used and missing data were imputed. A point-based prognostic risk score was derived and externally validated.
UNASSIGNED: In 748 patients (67% male, age 23-94), 30-days incidence of the primary composite outcome, cardiomyotoxic death, and overall death were 33%, 13%, and 17% respectively. By multivariable analysis, the primary composite outcome was associated with active thymoma (HR=3.60[1.93-6.72]), presence of cardio-muscular symptoms (HR=2.60 [1.58-4.28]), low QRS-voltage on presenting electrocardiogram (HR for ≤0.5mV versus >1mV=2.08[1.31-3.30]), left ventricular ejection fraction (LVEF) <50% (HR=1.78[1.22-2.60]), and incremental troponin elevation (HR=1.86 [1.44-2.39], 2.99[1.91-4.65], 4.80[2.54-9.08], for 20, 200 and 2000-fold above upper reference limit, respectively). A prognostic risk score developed using these parameters showed good performance; 30-days primary outcome incidence increased gradually from 3.9%(risk-score=0) to 81.3%(risk-score≥4). This risk-score was externally validated in two independent French and US cohorts. This risk score was used prospectively in the external French cohort to identify low risk patients who were managed with no immunosuppression resulting in no cardio-myotoxic events.
UNASSIGNED: ICI-myocarditis can manifest with high morbidity and mortality. Myocarditis severity is associated with magnitude of troponin, thymoma, low-QRS voltage, depressed LVEF, and cardio-muscular symptoms. A risk-score incorporating these features performed well.
UNASSIGNED: NCT04294771 and NCT05454527.

BACKGROUND: Terminally ill patients benefit from earlier engagement in palliative care. However, this does not always occur. This project assessed if an already available risk score, the Care Assessment Needs (CAN) score, would be able to identify patients at greatest risk for mortality within 30 days of hospital admission within the Veterans Health Administration (VHA).
METHODS: The cohort of this retrospective analysis included all VA acute are patients over 18 years of age with a recent CAN score. The CAN score is an automatically calculated VA risk score that was repurposed to determine if it could predict risk of mortality after acute care admission. Univariate logistic regression was performed to obtain the probability of mortality within 30 days of admission, based on their CAN score.
RESULTS: 298,467 patient records were assessed from January 1, 2019, to December 31, 2019. There was 6% mortality after 30 days of admissions, and 17% mortality within 1-year post-admission. Mean CAN score was 65 (SD: 29). On average, each incremental increase in the CAN score increased the probability of mortality by 7%. Patients with a CAN score of 90 had a 10% probability of 30-day post-admission mortality.
CONCLUSIONS: A readily available risk score, automatically calculated from EHR data, was able to identify patients at high risk for 30-day mortality in the acute care setting. Next steps will be to assess how the CAN score can be utilized to in improve end of life care for high-risk hospitalized Veterans.

BACKGROUND: The hepatocellular carcinoma (HCC) incident rate is gradually increasing yearly despite all the research and efforts taken by scientific communities and governing bodies. Approximately 90% of all liver cancer cases belong to HCC. Usually, HCC patients approach the treatment in the late stages of this malignancy which becomes the primary cause of high mortality rate. The knowledge about molecular pathogenesis of HCC is limited and needs more attention from researchers to identify the driver genes and miRNAs, which causes to translate this information into clinical practice. Therefore, the key regulators identification of miRNA-mRNA regulatory network is essential to identify HCC-associated genes.
METHODS: We extracted microRNA (miRNA) and messenger RNA (mRNA) expression datasets of normal and tumor HCC patient samples from UCSC Xena followed by identifying differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Univariate and multivariate cox-proportional hazard models were utilized to identify DEMs having significant association with overall survival (OS). Kaplan-Meier (KM) plotter was used to validate the presence of prognostic DEMs. A risk-score model was used to evaluate the effectiveness of KM-plotter validated DEMs combination on risk of samples. Target DEGs of prognostic miRNAs were identified via sources such as miRTargetLink and miRWalk followed by their validation in an external microarray cohort and enrichment analysis.
RESULTS: 562 DEGs and 388 DEMs were identified followed by seven prognostic miRNAs (i.e., miR-19a, miR-19b, miR-30d-5p, miR-424-5p, miR-3677-5p, miR-3913-5p, miR-7705) post univariate, multivariate, risk-score model evaluation and KM-plotter analyses. ANLN, MRO, CPEB3 were their targets and were also validated in GSE84005 dataset.
CONCLUSIONS: The findings of this study decipher that most significant miRNAs and their identified target genes have association with apoptosis, inflammation, cell cycle regulation and cancer-related pathways, which appear to contribute to HCC pathogenesis and therefore, the discovery of new targets.

Liver cancer is a prevalent disease with a dismal prognosis. The aim of the research is to identify subgroups based on malignant cell receptor ligand gene from single-cell RNA, which might lead to customized immunotherapy for patients with liver cancer.
Based on scRNA-seq data, we identified the receptor-ligand genes associated with prognosis and classify patients into molecular subtypes by univariate Cox regression and consensus clustering. LASSO regression was performed to construct a prognostic model, which was validated in TCGA and ICGC datasets. Immune infiltration and prediction of immunotherapy response were analyzed using ssGSEA, ESTIMATE, TIDE, and TRS score calculation. Finally, qPCR and Western blot validation of key genes and protein levels in cell lines.
A risk model using 16-gene expression levels predicted liver cancer patients\' prognosis. The RiskScore associated significantly with tumor clinical characteristics and immunity, integrated with clinicopathological features for survival prediction. Differential expression of SRXN1 was verified in hepatocellular carcinoma and normal liver cells.
Our study utilizes single-cell analysis to investigate the communication between malignant cells and other cell types, identifying molecular subtypes based on malignant cell receptor ligand genes, offering new insights for the development of personalized immunotherapy and prognostic prediction models.

BACKGROUND: The aim of this study was to develop a new risk prediction score (NH Score) for patients undergoing coronary artery bypass grafting (CABG) specific to the Indian population and compare it to the Society of Thoracic Surgeon (STS) Score and the EuroSCORE II.
METHODS: The baseline features of adult patients who underwent CABG between the years 2015 and 2021 (n = 6703) were taken and split into training data (2015-2020; n = 5561) and validation data (2020-2021; n = 1142). The CatBoost algorithm was trained to predict risk scores (NH score), and the performance was tested on the validation set by Precision-Recall Curve and F1 Score. Model calibration was measured by the Brier Score, Expected Calibration Error and Maximum Calibration Error.
RESULTS: The NH score outperformed both the STS and EuroSCORE II for all outcomes. For mortality, the PR AUC for NH Score was (0.463 [95% confidence interval [CI], 0.28-0.64]) compared to 0.113 [95% CI, 0.04-0.22] for the STS score and 0.146 [95% CI, 0.06-0.31] for the EuroSCORE II (p ≪ 0.0001). With respect to morbidity NH Score was superior to the STS score (0.43 [95% CI, 0.33-0.50]) vs. (0.229 [95% CI, 0.18-0.3, p < 0.0001). The observed to the predicted ratio for NH score was superior to the STS Score and similar to EuroSCORE II. NH Score was also more accurate at predicting the risk of prolonged ventilation compared to the STS Score.
CONCLUSIONS: NH score shows an excellent improvement over the performance of STS score and EuroSCORE II for modelling risk predictions for patients undergoing CABG in Indian population. It warrants further validation for larger datasets.

Inflammation is an important pathogenic factor of most malignant tumors. It is essential to understand mechanism underlying inflammation and cancer development, so as to formulate and develop anti-cancer treatment strategies. However, inflammatory-related gene characterization as well as risk model construction in prognosis and response chemotherapy or immunotherapy in NSCLC are still remain unclear.
A total of 1014 lung cancer samples with RNA-seqencing results were download from The Cancer Genome Atlas (TCGA) database. The patient cohort was randomized as a training and test cohorts, and 200 inflammatory-related genes were selected based on previously published data. Consensus clustering and Enrichment and immune function analyses base on Differential expression genes (DEGs) were performed. Prognosis Prediction Model were Constructed and Chemotherapy and immunotherapy sensitivity base on this model were performed. At last, H1299 and HCC827 cells were used to tested the mitoxantrone and oxal iplatin sensitivity after KRT6A knockdown.
We identified the inflammatory-related genes from NSCLC datasets to build one prognosis prediction signature based on cluster inflammatory-related genes to lay a certain foundation for distinguishing high-risk NSCLC cases with dismal prognostic outcome. The nomogram provides the AUC values for 1-, 3-, and 5-year overall survival were 0.831, 0.853, and 0.86 in validation cohort. Morover, different sensitivity of immunotherapy or chemotherapy also were classified base on the different risk groups in NSCLC patients, which provided potent clinical reference. At last, targeting KRT6A sensitive to mitoxantrone and oxaliplatin in H1299 and HCC827 cells.
Inflammatory-related gene risk-score is the potential chemotherapeutic and immunotherapeutic biomarker for NSCLC, and targeting KRT6A sensitive to mitoxantrone and oxaliplatin in NSCLC.HighlightsInflammatory-related genes can lay a certain foundation for distinguishing high-risk NSCLC cases with dismal prognostic outcome.Risk-score base on inflammatory-related genes is positive correlated with CD274, TGFBR1 and TGFB1 expression.Targeting KRT6A sensitive to mitoxantrone and oxaliplatin in H1299 and HCC827 cells.

This study developed an easy-to-use mortality prediction tool, which showed an acceptable discrimination and no significant lack of fit. The GeRi-Score was able to predict mortality and could distinguish between mild, moderate and high risk groups. Therefore, the GeRi-Score might have the potential to distribute the intensity of medical care.
OBJECTIVE: Several mortality-predicting tools for hip fracture patients are available, but all consist of a high number of variables, require a time-consuming evaluation and/or are difficult to calculate. The aim of this study was to develop and validate an easy-to-use score, which depends mostly on routine data.
METHODS: Patients from the Registry for Geriatric Trauma were divided into a development and a validation group. Logistic regression models were used to build a model for in-house mortality and to obtain a score. Candidate models were compared using Akaike information criteria (AIC) and likelihood ratio tests. The quality of the model was tested using the area under the curve (AUC) and the Hosmer-Lemeshow test.
RESULTS: 38,570 patients were included, almost equal distributed to the development and to the validation dataset. The AUC was 0.727 (95% CI 0.711 - 0.742) for the final model, AIC resulted in a significant reduction in deviance compared to the basic model, and the Hosmer-Lemeshow test showed no significant lack of fit (p = 0.07). The GeRi-Score predicted an in-house mortality of 5.3% vs. 5.3% observed mortality in the development dataset and 5.4% vs. 5.7% in the validation dataset. The GeRi-Score was able to distinguish between mild, moderate and high risk groups.
CONCLUSIONS: The GeRi-Score is an easy-to-use mortality-predicting tool with an acceptable discrimination and no significant lack of fit. The GeRi-Score might have the potential to distribute the intensity of perioperative medical care in hip fracture surgery and can be used in quality management programs as benchmark tool.

The necessity to accurately predict recurrence and clinical outcome in early stage colorectal cancer (CRC) is critical to identify those patients who may benefit from adjuvant chemotherapy. Here, we developed and validated a gene-based risk-score algorithm for patient stratification and personalised treatment in early stage disease based on alterations in the secretion of metastasis-related proteins. A quantitative label-free proteomic analysis of the secretome of highly and poorly metastatic CRC cell lines with different genetic backgrounds revealed 153 differentially secreted proteins (fold-change >5). These changes in the secretome were validated at the transcriptomic level. Starting from 119 up-regulated proteins, a six-gene/protein-based prognostic signature composed of IGFBP3, CD109, LTBP1, PSAP, BMP1, and NPC2 was identified after sequential discovery, training, and validation in four different cohorts. This signature was used to develop a risk-score algorithm, named SEC6, for patient stratification. SEC6 risk-score components showed higher expression in the poor prognosis CRC subtypes: consensus molecular subtype 4 (CMS4), CRIS-B, and stem-like. High expression of the signature was also associated with patients showing dMMR, CIMP+ status, and BRAF mutations. In addition, the SEC6 signature was associated with lower overall survival, progression-free interval, and disease-specific survival in stage II and III patients. SEC6-based risk stratification indicated that 5-FU treatment was beneficial for low-risk patients, whereas only aggressive treatments (FOLFOX and FOLFIRI) provided benefits to high-risk patients in stages II and III. In summary, this novel risk-score demonstrates the value of the secretome compartment as a reliable source for the retrieval of biomarkers with high prognostic and chemotherapy-predictive capacity, providing a potential new tool for tailoring decision-making in patient care.

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated. Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G-related prognostic gene signature with immune infiltration in HCC. Methods: The TCGA datasets were used as a training and GEO dataset \"GSE76427\" for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2. Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment. Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.