MPI Predicts Outcome of CRC Elderly Patients (MPI=MULTIDIMENSIONAL PROGNOSTIC INDEX; CRC=COLORECTAL CANCER)
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Cancer,Colorectal Neoplasms,Elderly,Fragility
PROVIDER: 2355077 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:Backgroundprediction of length of stay (LOS) may be useful to optimise care plans to reduce the negative outcomes related to hospitalisation.Objectiveto evaluate whether the Multidimensional Prognostic Index (MPI), based on a Comprehensive Geriatric Assessment (CGA), may predict LOS in hospitalised older patients.Designprospective multicentre cohort study.Settingtwenty Geriatrics Units.Participantspatients aged 65 and older consecutively admitted to Geriatrics Units.Measurementat admission, the CGA-based MPI was calculated by using a validated algorithm that included information on basal and instrumental activities of daily living, cognitive status, nutritional status, the risk of pressures sores, co-morbidity, number of drugs and co-habitation status. According to validated cut-offs, subjects were divided into three groups of risk, i.e. MPI-1 low risk (value ≤0.33), MPI-2 moderate risk (value 0.34-0.66) and MPI-3 severe risk of mortality (value ≥0.67).Resultstwo thousand and thirty-three patients were included; 1,159 were women (57.0%). Age- and sex-adjusted mean LOS in patients divided according to the MPI grade was MPI-1 = 10.1 (95% CI 8.6-11.8), MPI-2 = 12.47 (95% CI 10.7-14.68) and MPI-3 = 13.41 (95% CI 11.5-15.7) days (P for trend <0.001). The overall accuracy of the MPI to predict LOS was good (C-statistic 0.74, 95% CI 0.72-0.76). Moreover, a statistically significant trend of LOS means was found even in patients stratified according to their International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) main diagnosis.Conclusionsthe MPI is an accurate predictor of LOS in older patients hospitalised with the most frequent diseases.
| S-EPMC4711657 | biostudies-literature
Project description:BackgroundThe Multidimensional Prognostic Index (MPI) is a validated predictive tool for long-term mortality based on information collected in a standardized Comprehensive Geriatric Assessment. We investigated whether the MPI is an effective predictor of intrahospital mortality and length of hospital stay after admission to acute geriatric wards.MethodsProspective study of 1,178 older patients (702 women and 476 men, 85.0±6.8 years) admitted to 20 geriatrics units. Within 48 hours from admission, the MPI, according to an earlier validated algorithm, was calculated. Subjects were divided into three groups of MPI score, low-risk (MPI-1 value ? 0.33), moderate-risk (MPI-2 value 0.34-0.66), and severe-risk of mortality (MPI-3 value ? 0.67), on the basis of earlier established cut-offs. Associations with in-hospital mortality and length of stay were examined using multivariable Cox regression models and adjusted Poisson linear mixed-effects models, respectively.ResultsAt admission, 23.6% subjects had a MPI-1 score, 33.8% had a MPI-2 score, and 42.6% had a MPI-3 score. Subjects with higher MPI score at admission were older (p < .001), more frequently women (p < .001) and had higher prevalence of common chronic conditions. After adjustment for age, gender, and diseases, patients included in the MPI-2 and MPI-3 groups had a significantly higher risk for intrahospital mortality (hazard ratio: 3.48, 95% confidence intervals: 1.02-11.88, p = .047; hazard ratio: 8.31, 95% confidence intervals: 2.54-27.19, p < .001) than patients included in the MPI-1 group, respectively. In multivariable model, length of stay significantly increased across the three MPI groups (11.29 [0.5], 13.73 [1.3], and 15.30 [1.4] days, respectively [p < .0001]).ConclusionsIn older acute care inpatients, MPI score assessed at hospital admission is an independent predictor of in-hospital mortality and the length of hospital stay.
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Project description:Interventions: Control Intervention1: Nil: Nil
Control Intervention2: Nil: Nil
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2019-06-03 | GSE125273 | GEO
Project description:The variability of suicidal thoughts and other clinical factors during follow-up has emerged as a promising phenotype to identify vulnerable patients through Ecological Momentary Assessment (EMA). In this study, we aimed to (1) identify clusters of clinical variability, and (2) examine the features associated with high variability. We studied a set of 275 adult patients treated for a suicidal crisis in the outpatient and emergency psychiatric departments of five clinical centers across Spain and France. Data included a total of 48,489 answers to 32 EMA questions, as well as baseline and follow-up validated data from clinical assessments. A Gaussian Mixture Model (GMM) was used to cluster the patients according to EMA variability during follow-up along six clinical domains. We then used a random forest algorithm to identify the clinical features that can be used to predict the level of variability. The GMM confirmed that suicidal patients are best clustered in two groups with EMA data: low- and high-variability. The high-variability group showed more instability in all dimensions, particularly in social withdrawal, sleep measures, wish to live, and social support. Both clusters were separated by ten clinical features (AUC = 0.74), including depressive symptoms, cognitive instability, the intensity and frequency of passive suicidal ideation, and the occurrence of clinical events, such as suicide attempts or emergency visits during follow-up. Initiatives to follow up suicidal patients with ecological measures should take into account the existence of a high variability cluster, which could be identified before the follow-up begins.
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Project description:A common feature in patients with abdominal aortic aneurysms (AAA) is the formation of a nonocclusive intraluminal thrombus (ILT) in regions of aortic dilation. Platelets are known to maintain hemostasis and propagate thrombosis through several redundant activation mechanisms, yet the role of platelet activation in the pathogenesis of AAA associated ILT is still poorly understood. Thus, we sought to investigate how platelet activation impacts the pathogenesis of AAA. Using RNA-sequencing, we identify that the platelet-associated transcripts are significantly enriched in the ILT compared to the adjacent aneurysm wall and healthy control aortas. We found that the platelet specific receptor glycoprotein VI (GPVI) is among the top enriched genes in AAA ILT and is increased on the platelet surface of AAA patients. Examination of a specific indicator of platelet activity, soluble GPVI (sGPVI), in two independent AAA patient cohorts is highly predictive of a AAA diagnosis and associates more strongly with aneurysm growth rate when compared to D-dimer in humans. Finally, intervention with the anti-GPVI antibody (JAQ1) in mice with established aneurysms blunted the progression of AAA in two independent mouse models. In conclusion, we show that levels of sGPVI in humans can predict a diagnosis of AAA and AAA growth rate, which may be critical in the identification of high-risk patients. We also identify GPVI as a novel platelet-specific AAA therapeutic target, with minimal risk of adverse bleeding complications, where none currently exist.
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Project description:N7-methyladenosine (m7G) modifications have been the subject of growing research interest with respect to their relationship with the progression and treatment of various cancers. This analysis was designed to examine the association between m7G-related gene expression and colorectal cancer (CRC) patient outcomes. Initial training analyses were performed using the TCGA dataset, with the GSE28722 dataset then being used to validate these results. Univariate Cox analyses were initially conducted to screen out prognostic m7G-related genes, after which a LASSO approach was used to construct an m7G risk score (MRS) model. Kaplan-Meier curves, ROC curves, and Cox analyses were subsequently used to validate the prognostic utility of this model in CRC patients. The R maftools package was further employed to assess mutational characteristics in CRC patients in different MRS subgroups, while the ESTIMATE, CIBERSORT, and ssGSEA tools were used to conduct immune infiltration analyses. A WGCNA was then performed to identify key immune-associated hub genes. The EIF4E3, GEMIN5, and NCBP2 genes were used to establish the MRS model. Patients with high MRS scores exhibited worse overall survival than patients with low scores. In Cox analyses, MRS scores were independently associated with CRC patient prognosis. Patients with low MRS scores exhibited a higher tumor mutational burden and higher levels of microsatellite instability. In immune infiltration analyses, higher immune checkpoint expression and greater immune cell infiltration were also observed in patients with low MRS scores. WGCNA analyses further identified 25 CD8+ T cell infiltration-associated genes. These findings suggest that MRS values represent a useful biomarker capable of differentiating among CRC patients with different immunological features and prognostic outcomes, offering an opportunity to better determine which patients are likely to benefit from immune checkpoint inhibitor treatment.
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