Repair of DNA Breaks by Break-Induced Replication.

Double-strand DNA breaks (DSBs) are the most lethal type of DNA damage, making DSB repair critical for cell survival. However, some DSB repair pathways are mutagenic and promote genome rearrangements, leading to genome destabilization. One such pathway is break-induced replication (BIR), which repairs primarily one-ended DSBs, similar to those formed by collapsed replication forks or telomere erosion. BIR is initiated by the invasion of a broken DNA end into a homologous template, synthesizes new DNA within the context of a migrating bubble, and is associated with conservative inheritance of new genetic material. This mode of synthesis is responsible for a high level of genetic instability associated with BIR. Eukaryotic BIR was initially investigated in yeast, but now it is also actively studied in mammalian systems. Additionally, a significant breakthrough has been made regarding the role of microhomology-mediated BIR in the formation of complex genomic rearrangements that underly various human pathologies.

Cuproptosis in lung cancer: therapeutic options and prognostic models.

Lung cancer (LC) is a serious threat to mankind. The survival of LC patients is still poor despite the enormous efforts that have been made to develop novel treatments. A copper-dependent cell death termed cuproptosis is distinct from known programmed cell death (PCD). Cuproptosis is induced by the disruption of the binding of copper to lipoylated tricarboxylic acid (TCA) cycle proteins of mitochondrial respiratory chains. Potential approaches for treating LC are inducing cell cuproptosis and targeting cell copper death mechanisms. Thus, in this review, we summarize the systemic and cellular metabolic processes of copper. We highlight the possible therapeutic options of employing copper ionophores and chelators for inducing cuproptosis. Moreover, we summarize the prognostic models based on cuproptosis-related genes (CRGs) to identify promising biomarkers for tumor diagnosis and therapy. This review aims to provide a comprehensive summary of CRGs-based prognostic models and promising therapeutic options for cuproptosis induction in LC.

Surgery in elderly patients with intracranial meningioma: neuropsychological functioning during a long term follow-up.

Surgical treatment of elderly patients with meningioma is has proved to be safe, especially when patients are selected using dedicated surgical scores. These scores take into account tumor size, edema, location and patient's co-morbidities. Neuropsychological functioning (NPF) of this kind of patients has been poorly studied in literature and it is not taken into account by these scores. Aim of our study was to describe the long-term outcome in terms of NPF of elderly patients undergoing surgery. Patients older than 70 years of age affected by intracranial meningioma and selected with the Clinical-Radiological Grading Score were included in our study. Neuropsychological testing was performed using a dedicated battery of tests before surgery, 3 and 12 months after surgery. Clinical, neurological and radiological outcomes were studied as well. Forty-one patients with a median age of 74 years were included in this study. Preoperatively only 1/41 patients showed a normal NPF with all tests scoring normally. Four out of 39 patients showed a complete neuropsychological recovery after 3 months; while 10/37 patients had a complete recovery after 12 months. NPF showed a trend of progressive improvement after surgery. Our study is the first experience reported in literature describing a long term follow-up in elderly patients after surgery for intracranial meningioma. In our series, surgery determined an improvement of NPF over time; especially with a low complication rate related to the selection of patients obtained through the CRGS. Further studies need to be performed in order to understand how brain edema, tumor size, volume and tumor location affect NPF in both short and long term.

Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model.

Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored. Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis. Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS. Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.

Prognostic value and immunological function of cuproptosis-related genes in lung adenocarcinoma.

Background: Lung cancer is one of the malignant tumors with the highest morbidity and fatality rates worldwide. The overall survival (OS) of lung adenocarcinoma (LUAD) is poor. Cuproptosis is a copper-triggered modality of mitochondrial cell death, however, its contribution to the emergence of lung cancer is unknown. The clinical implication and immunological function of cuproptosis-related genes (CRGs) in LUAD has yet to be established. Methods: TCGA, HPA, GEPIA, Kaplan-Meier, TIMER and CancerSEA database were used to explore the prognostic value and biological function of CRGs in LUAD. Results: CRGs are primarily involved in copper ion transport, the citrate cycle (TCA cycle) and central carbon metabolism in LUAD. The mRNA expression of COA6, UBE2D1, DLAT, SLC25A3, and DBH was significantly increased. The expression of COA6, DLAT, SLC25A3, DBH, and LOXL2 were all strongly associated with the clinicopathological stages in LUAD. Moreover, high expression of COA6, UBE2D1, DLAT, SLC25A3 and LOXL2 was related to poor OS. The expression of SLC25A3 and LOXL2 showed different association with immune cell infiltration. The single cell sequencing demonstrated that CRGs play important roles in the regulation of DNA damage response, inflammation and metastasis in LUAD. Conclusions: In summary, this study comprehensively uncovered that CRGs could be identified as potential prognostic and immunological biomarkers in LUAD. Our current research could provide a solid theoretical basis for LUAD survival research and clinical decision-making.

Multi-omics analysis of the prognostic and biological role of cuproptosis-related gene in gastric cancer.

Background: A considerable number of gastric cancer (GC) patients cannot receive benefits from current treatments. We aimed to identify possible biomarkers of cuproptosis-related genes (CRGs) in GC patients, which may help guide precision medicine-based decision-making. Methods: RNA sequencing data, copy number variations (CNVs) data, and single nucleotide variant (SNV) data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Set Cancer Analysis (GSCA) database. Chi-squared test was adopted to screen differentially expressed CRGs (DE-CRGs) between samples from 14 kinds of carcinoma and adjacent tissue samples. Then, GC samples were divided into high- and low-expressed groups based on DE-CRGs for further survival analyses and the selection of biomarkers. Methylation sites related with biomarkers were acquired. The correlation between immune cells and biomarkers was verified. Finally, miRNA-mRNA, TFs-mRNA, and co-expression networks were established to detect factors with regulating effects on biomarkers. Results: Three CRGs including LIAS, GLS, and CDKN2A were identified as biomarkers in GC patients. Three methylation sites with a significant survival effect including cg13601799, 07562918, and 07253264 were acquired. Then, we found that B cells native was significantly correlated with CDKN2A, four immune cells such as T cells regulatory are significantly correlated with GLS, and two immune cells such as T cells CD4 memory activated were significantly correlated with LIAS. Moreover, 10 miRNAs in the miRNA-mRNA network and three transcription factors (TFs) in the TFs-mRNA network had a significant correlation with overall survival (OS). Finally, 20 enrichment functions were obtained on the basis of the co-expression network. Conclusions: Three biomarkers with a prognosis prediction value of GC were found, and multi-factor regulatory networks were constructed to screen out 13 factors with regulating influences of biomarkers.

Characterization of cuproptosis in gastric cancer and relationship with clinical and drug reactions.

Gastric cancer (GC) is the fifth most common cancer worldwide. Cuproptosis is associated with cell growth and death as well as tumorigenesis. Aiming to lucubrate the potential influence of CRGs in gastric cancer, we acquired datasets of gastric cancer patients from TCGA and GEO. The identification of molecular subtypes with CRGs expression was achieved through unsupervised learning-cluster analysis. To evaluate the application value of subtypes, the K-M survival analysis was conducted to evaluate the clinical prognostic characteristics. Subsequently, we performed Gene Set Variation Analysis (GSVA) and utilized ssGSEA to quantify the extent of immune infiltration. Further, the K-M survival analysis was used to identify the prognosis-related CRGs. Next, signature genes of diagnostic predictive value were screened using the least absolute shrinkage and selection operator (LASSO) algorithm from the expression matrix for TCGA, as well as the signature gene-related subtype was clustered by the "ConsensusClusterPlus" package. Finally, the immunological and drug sensitivity assessments of the signature gene-related subtypes were conducted. A total of 173 CRGs were identified, most of the CRGs undergo copy number variation in gastric cancer. Under different patient subtypes, immune cell levels differed significantly, and the subtype exhibiting high expression of the CRGs had a better prognosis. Furthermore, we selected 34 CRGs that were highly correlated with the prognosis of gastric cancer. By constructing a multivariate Cox proportional-hazards model and a hazard scoring system, we were able to categorize patients into high- and low-risk groups based on their hazard score. K-M analysis demonstrated a significant survival disadvantage in the high-risk group. Based on Lasso regression analysis, we screened 16 signature genes, a multivariate logistic regression model [cutoff: 0.149 (0.000, 0.974), AUC:0.987] and a prognosis network diagram was constructed and their prediction efficiency for gastric cancer prognostic diagnosis was well validated. According to the signature genes, the patients were separated to two signature subtypes. We found that patients with higher CRGs expression and better prognosis had lower levels of immune infiltration. Finally, according to the results of drug susceptibility analysis, docetaxel, 5-Fluorouracil, gemcitabin, and paclitaxel were found to be more sensitive to gastric cancer.

A novel prognostic prediction model of cuprotosis-related genes signature in hepatocellular carcinoma.

Background: Cuprotosis is a recently discovered copper-dependent cell death mechanism that relies on mitochondrial respiration. However, the role of cuprotosis-related genes (CRGs) in hepatocellular carcinoma (HCC) and their prognostic significances remain unknown. Methods: Based on the recently published CRGs, the LASSO Cox regression analysis was applied to construct a CRGs risk model using the gene expression data from the International Cancer Genome Consortium as a training set, followed by validation with datasets from The Cancer Genome Atlas and the Gene Expression Omnibus (GSE14520). Functional enrichment analysis of the CRGs was performed by single-sample gene set enrichment analysis. Results: Five of the 13 previously published CRGs were identified to be associated with prognosis in HCC. Kaplan-Meier analysis suggested that patients with high-risk scores have a shorter overall survival time than patients with low-risk scores. ROC curves indicated that the average AUC was more than 0.7, even at 4 years, and at least 0.5 at 5 years. Moreover, addition of this CRG risk score can significantly improve the efficiency of predicting overall survival compared to using traditional factors alone. Functional analysis demonstrated increased presence of Treg cells in patients with high-risk scores, suggesting a suppressed immune state in these patients. Finally, we point to the possibility that novel immunotherapies such as inhibitors of PDCD1, TIGIT, IDO1, CD274, CTLA4, and LAG3 may have potential benefits in high-risk patients. Conclusion: We constructed a better prognostic model for liver cancer by using CRGs. The CRG risk score established in this study can serve as a potentially valuable tool for predicting clinical outcome of patients with HCC.

Analysis of cuproptosis-related genes in Ulcerative colitis and immunological characterization based on machine learning.

Cuproptosis is a novel form of cell death, mediated by protein lipid acylation and highly associated with mitochondrial metabolism, which is regulated in the cell. Ulcerative colitis (UC) is a chronic inflammatory bowel disease that recurs frequently, and its incidence is increasing worldwide every year. Currently, a growing number of studies have shown that cuproptosis-related genes (CRGs) play a crucial role in the development and progression of a variety of tumors. However, the regulatory role of CRGs in UC has not been fully elucidated. Firstly, we identified differentially expressed genes in UC, Likewise, CRGs expression profiles and immunological profiles were evaluated. Using 75 UC samples, we typed UC based on the expression profiles of CRGs, followed by correlative immune cell infiltration analysis. Using the weighted gene co-expression network analysis (WGCNA) methodology, the cluster's differentially expressed genes (DEGs) were produced. Then, the performances of extreme gradient boosting models (XGB), support vector machine models (SVM), random forest models (RF), and generalized linear models (GLM) were constructed and predicted. Finally, the effectiveness of the best machine learning model was evaluated using five external datasets, receiver operating characteristic curve (ROC), the area under the curve of ROC (AUC), a calibration curve, a nomogram, and a decision curve analysis (DCA). A total of 13 CRGs were identified as significantly different in UC and control samples. Two subtypes were identified in UC based on CRGs expression profiles. Immune cell infiltration analysis of subtypes showed significant differences between immune cells of different subtypes. WGCNA results showed a total of 8 modules with significant differences between subtypes, with the turquoise module being the most specific. The machine learning results showed satisfactory performance of the XGB model (AUC = 0.981). Finally, the construction of the final 5-gene-based XGB model, validated by the calibration curve, nomogram, decision curve analysis, and five external datasets (GSE11223: AUC = 0.987; GSE38713: AUC = 0.815; GSE53306: AUC = 0.946; GSE94648: AUC = 0.809; GSE87466: AUC = 0.981), also proved to predict subtypes of UC with accuracy. Our research presents a trustworthy model that can predict the likelihood of developing UC and methodically outlines the complex relationship between CRGs and UC.

Cuproptosis-related risk score predicts prognosis and characterizes the tumor microenvironment in colon adenocarcinoma.

Introduction: Cuproptosis is a novel copper-dependent regulatory cell death (RCD), which is closely related to the occurrence and development of multiple cancers. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon adenocarcinoma (COAD) remains unclear. Methods: Transcriptome, somatic mutation, somatic copy number alteration and the corresponding clinicopathological data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). Difference, survival and correlation analyses were conducted to evaluate the characteristics of CRGs in COAD patients. Consensus unsupervised clustering analysis of CRGs expression profile was used to classify patients into different cuproptosis molecular and gene subtypes. TME characteristics of different molecular subtypes were investigated by using Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA). Next, CRG Risk scoring system was constructed by applying logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis and multivariate cox analysis. Real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) were used to exam the expression of key Risk scoring genes. Results: Our study indicated that CRGs had relatively common genetic and transcriptional variations in COAD tissues. We identified three cuproptosis molecular subtypes and three gene subtypes based on CRGs expression profile and prognostic differentially expressed genes (DEGs) expression profile, and found that changes in multilayer CRGs were closely related to the clinical characteristics, overall survival (OS), different signaling pathways, and immune cell infiltration of TME. CRG Risk scoring system was constructed according to the expression of 7 key cuproptosis-related risk genes (GLS, NOX1, HOXC6, TNNT1, GLS, HOXC6 and PLA2G12B). RT-qPCR and IHC indicated that the expression of GLS, NOX1, HOXC6, TNNT1 and PLA2G12B were up-regulated in tumor tissues, compared with those in normal tissues, and all of GLS, HOXC6, NOX1 and PLA2G12B were closely related with patient survival. In addition, high CRG risk scores were significantly associated with high microsatellite instability (MSI-H), tumor mutation burden (TMB), cancer stem cell (CSC) indices, stromal and immune scores in TME, drug susceptibility, as well as patient survival. Finally, a highly accurate nomogram was constructed to promote the clinical application of the CRG Risk scoring system. Discussion: Our comprehensive analysis showed that CRGs were greatly associated with TME, clinicopathological characteristics, and prognosis of patient with COAD. These findings may promote our understanding of CRGs in COAD, providing new insights for physicians to predict prognosis and develop more precise and individualized therapy strategies.

A new prognostic model for glioblastoma multiforme based on coagulation-related genes.

Background: Glioblastoma multiforme (GBM) is the most aggressive, common, and lethal type of primary brain tumor. Multiple cancers have been associated with abnormalities in the coagulation system that facilitate tumor invasion and metastasis. In GBM, the prognostic value and underlying mechanism of coagulation-related genes (CRGs) have not been explored. Methods: RNA sequencing (RNA-seq) and clinical information on GBM were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. Following the identification of differentially expressed CRGs (DECRGs) between GBM and control samples, the survival-related DECRGs were selected via univariate and multivariate Cox regression analyses to establish a prognostic signature. The prognostic performance and clinical utility of the prognostic signature were assessed by the Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve analysis, and a nomogram was constructed. The signature genes-related underlying mechanisms were analyzed according to gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-cell analysis. Finally, the difference in immune cell infiltration, stromal score, immune score, and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) score were compared between different risk groups. Results: A 5-gene prognostic signature (PLAUR, GP6, C5AR1, SERPINA5, F2RL2) was established for overall survival (OS) prediction of GBM patients. The predicted efficiency of the prognostic signature was confirmed in TGGA-GBM dataset and validated in the CGGA-GBM dataset, revealing that it could differentiate GBM patients from controls well, and high risk score was accompanied with poor prognosis. Moreover, biological process (BP) and signaling pathway analyses showed that signature genes were mainly enriched in the functions of blood coagulation and tumor invasion and metastasis. Moreover, high-risk patients exhibited higher levels of immune cell infiltration, stromal score, immune score, and ESTIMATE score than that of low-risk patients. Conclusions: An analysis of coagulation-related prognostic signatures was conducted in this study, as well as how signature genes may affect GBM progress, providing information that might provide new ideas for the development of GBM-related molecular targeted therapies.

Identification of cuproptosis-related subtypes, construction of a prognosis model, and tumor microenvironment landscape in gastric cancer.

Introduction: Cuproptosis is a novel identified regulated cell death (RCD), which is correlated with the development, treatment response and prognosis of cancer. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of gastric cancer (GC) remains unknown. Methods: Transcriptome profiling, somatic mutation, somatic copy number alteration and clinical data of GC samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database to describe the alterations of CRGs from genetic and transcriptional fields. Differential, survival and univariate cox regression analyses of CRGs were carried out to investigate the role of CRGs in GC. Cuproptosis molecular subtypes were identified by using consensus unsupervised clustering analysis based on the expression profiles of CRGs, and further analyzed by GO and KEGG gene set variation analyses (GSVA). Genes in distinct molecular subtypes were also analyzed by GO and KEGG gene enrichment analyses (GSEA). Differentially expressed genes (DEGs) were screened out from distinct molecular subtypes and further analyzed by GO enrichment analysis and univariate cox regression analysis. Consensus clustering analysis of prognostic DEGs was performed to identify genomic subtypes. Next, patients were randomly categorized into the training and testing group at a ratio of 1:1. CRG Risk scoring system was constructed through logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis, univariate and multivariate cox analyses in the training group and validated in the testing and combined groups. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to evaluate the expression of key Risk scoring genes. Sensitivity and specificity of Risk scoring system were examined by using receiver operating characteristic (ROC) curves. pRRophetic package in R was used to investigate the therapeutic effects of drugs in high- and low- risk score group. Finally, the nomogram scoring system was developed to predict patients' survival through incorporating the clinicopathological features and CRG Risk score. Results: Most CRGs were up-regulated in tumor tissues and showed a relatively high mutation frequency. Survival and univariate cox regression analysis revealed that LIAS and FDX1 were significantly associated with GC patients' survival. After consensus unsupervised clustering analysis, GC patients were classified into two cuproptosis molecular subtypes, which were significantly associated with clinical features (gender, age, grade and TNM stage), prognosis, metabolic related pathways and immune cell infiltration in TME of GC. GO enrichment analyses of 84 DEGs, obtained from distinct molecular subtypes, revealed that DEGs primarily enriched in the regulation of metabolism and intracellular/extracellular structure in GC. Univariate cox regression analysis of 84 DEGs further screened out 32 prognostic DEGs. According to the expression profiles of 32 prognostic DEGs, patients were re-classified into two gene subtypes, which were significantly associated with patients' age, grade, T and N stage, and survival of patients. Nest, the Risk score system was constructed with moderate sensitivity and specificity. A high CRG Risk score, characterized by decreased microsatellite instability-high (MSI-H), tumor mutation burden (TMB) and cancer stem cell (CSC) index, and high stromal and immune score in TME, indicated poor survival. Four of five key Risk scoring genes expression were dysregulated in tumor compared with normal samples. Moreover, CRG Risk score was greatly related with sensitivity of multiple drugs. Finally, we established a highly accurate nomogram for promoting the clinical applicability of the CRG Risk scoring system. Discussion: Our comprehensive analysis of CRGs in GC demonstrated their potential roles in TME, clinicopathological features, and prognosis. These findings may improve our understanding of CRGs in GC and provide new perceptions for doctors to predict prognosis and develop more effective and personalized therapy strategies.

Ca2+ as a coordinator of skeletal muscle differentiation, fusion and contraction.

Muscle regeneration is essential for vertebrate muscle homeostasis and recovery after injury. During regeneration, muscle stem cells differentiate into myocytes, which then fuse with pre-existing muscle fibres. Hence, differentiation, fusion and contraction must be tightly regulated during regeneration to avoid the disastrous consequences of premature fusion of myocytes to actively contracting fibres. Cytosolic calcium (Ca2+ ), which is coupled to both induction of myogenic differentiation and contraction, has more recently been implicated in the regulation of myocyte-to-myotube fusion. In this viewpoint, we propose that Ca2+ -mediated coordination of differentiation, fusion and contraction is a feature selected in the amniotes to facilitate muscle regeneration.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia.

Background: Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML). Methods: RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated. Results: Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents. Conclusion: A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.

Bundling post-acute care services into MS-DRG payments.

OBJECTIVE: A bundled hospital payment system that encompasses both acute and post-acute care has been proposed as a means of creating financial incentives in the Medicare fee-for-service system to foster care coordination and to improve the current disorganized system of post care. The objective of this study was to evaluate the statistical stability of alternative designs of a hospital payment system that includes post-acute care services to determine the feasibility of using a combined hospital and post-acute care bundle as a unit of payment. METHODS: The Medicare Severity-Diagnosis Related Groups (MS-DRGs) were subdivided into clinical subclasses that measured a patient's chronic illness burden to test whether a patient's chronic illness burden had a substantial impact on post-acute care expenditures. Using Medicare data the statistical performance of the MS-DRGs with and without the chronic illness subclasses was evaluated across a wide range of post-acute care windows and combinations of post-acute care service bundles using both submitted charges and Medicare payments. RESULTS: The statistical performance of the MS-DRGs as measured by R(2) was consistently better when the chronic illness subclasses are included indicating that MS-DRGs by themselves are an inadequate unit of payment for post-acute care payment bundles. In general, R(2) values increased as the post-acute care window length increased and decreased as more services were added to the post-acute care bundle. DISCUSSION: The study results suggest that it is feasible to develop a payment system that incorporates significant post-acute care services into the MS-DRG inpatient payment bundle. This expansion of the basic DRG payment approach can provide a strong financial incentive for providers to better coordinate care potentially leading to improved efficiency and outcome quality.

Therapeutic Strategies of the Intracranial Meningioma in Elderly Patients

OBJECTIVE: The apparent increase in the incidence of the intracranial meningiomas in the elderly is due in part to improved diagnostic tools and improved span of life. The authors carried out a retrospective study to validate the use of the Clinical-Radiological Grading System (CRGS) as a clinical tool to orientate surgical decision making in elderly patients and to explore prognostic factors of survival. METHODS: From January 1997 to January 2006, the authors consecutively recruited and surgically treated 20 patients older than 65 years of age with radiologic findings of intracranial meningiomas and a preoperative evaluation based on the CRGS. RESULTS: High CRGS score was associated with a higher probability of good outcome (p=0.004) and a lower probability of postoperative complications (p=0.049). Among the different subset items of the CRGS score, larger maximum tumor diameters (D> or =4cm) and the presence of a severe peritumoral edema were associated with incidence rate of postoperative poor outcome and complications (p<0.05). Additionally, the critical location of the tumor was also correlated with poor outcome (p<0.05). CONCLUSION: A CRGS score higher than 13 is a good prognostic indication of survival. The CRGS score is a useful and practical tool for the selection of elderly patients affected by intracranial meningiomas as surgical candidates.