Effect of a Resilience Model-Based Care Plan in Newly Diagnosed Colorectal Cancer Patients
Ontology highlight
ABSTRACT:
DISEASE(S): Colon Cancer,Fatigue,Colonic Neoplasms
PROVIDER: 2253905 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:Analysis of transcript abundance estimates as a function of child soldier status, PTSD symptoms, and psychological resilience. Gene expression profiling was conducted on dried blood spot (DBS) samples collected from community dwelling adolescents and young adults in Nepal. Approximatley half of the sample were former child soldiers in the Nepal People's War and the other half were demographically similiar civilian non-combatants. In addition to basic demographic characteristics (age, sex, ethnic minority status, social caste status, education level), participants were also assessed on syptoms of post-traumatic stress (PTS, assessed by a culturally adapted version of The Child PTSD Symptom Scale; Kohrt BA, et al. (2011) Validation of cross-cultural child mental health and psychosocial research instruments: adapting the Depression Self-Rating Scale and Child PTSD Symptom Scale in Nepal. BMC Psychiatry 11(1):e127, with higher values indicating greater PTSD symptoms) and psychological resilience (assessed by a culturally adapted version of the Resilience Scale; Wagnild GM & Young HM (1993) Development and psychometric evaluation of the Resilience Scale. Journal of Nursing Measurement, with higher values indicating greater resilience). Dichotomous variables were coded 0=no/absent and 1=yes/present. Valid gene expression data are available for 254 samples.
2016-07-27 | E-GEOD-77164 | biostudies-arrayexpress
Project description:We recently defined a gene expression-based signature of high-risk multiple myeloma; this predictive signature was developed with and independently validated for newly diagnosed patients treated with high dose therapy and stem cell rescue. Here we use Phase 3 clinical trial data to show that this signature also predicts short survival in relapsed disease treated with single agent bortezomib or high dose dexamethasone. In addition, a survival signature derived with relapsed myeloma samples identified newly diagnosed patients with short survival. Taken together these data suggest that a similar biology underlies poor outcome in both newly diagnosed and relapsed myeloma and provide strong evidence that the high-risk signature is a powerful tool to identify patients who are candidates for new therapeutic regimens. Keywords: Model validation
2011-08-20 | GSE8991 | GEO
Project description:We recently defined a gene expression-based signature of high-risk multiple myeloma; this predictive signature was developed with and independently validated for newly diagnosed patients treated with high dose therapy and stem cell rescue. Here we use Phase 3 clinical trial data to show that this signature also predicts short survival in relapsed disease treated with single agent bortezomib or high dose dexamethasone. In addition, a survival signature derived with relapsed myeloma samples identified newly diagnosed patients with short survival. Taken together these data suggest that a similar biology underlies poor outcome in both newly diagnosed and relapsed myeloma and provide strong evidence that the high-risk signature is a powerful tool to identify patients who are candidates for new therapeutic regimens. Keywords: Model validation See above (Series_summary)
2011-08-19 | E-GEOD-8991 | biostudies-arrayexpress
Project description:The purpose of this study is to explore the efficacy and safety of everolimus administered as a first-line treatment in newly-diagnosed patients with advanced or inoperable Gastrointestinal (GI) or pancreatic neuroendocrine tumors.
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Project description:BackgroundHypertension and diabetes mellitus are often jointly present, especially in early onset cases of either disease. We investigated clinical characteristics of hypertensive patients with newly diagnosed diabetes and diabetic patients with newly diagnosed hypertension.MethodsOur study subjects were recruited in a China nationwide multicenter registry of hypertension and diabetes (n = 2510). We performed logistic regression to compare patients seen for hypertension in cardiology, with newly diagnosed diabetes (n = 137) and patients seen for diabetes mellitus in endocrinology, with newly diagnosed hypertension (n = 155). Albuminuria was defined as a urinary albumin-to-creatinine ratio of ≥ 30 mg/g, and left ventricular hypertrophy according to the Cornell product index.ResultsThese two groups of patients with both hypertension and diabetes mellitus were similar in most of the characteristics (P ≥ 0.06). However, hypertensive patients with newly diagnosed diabetes, compared to diabetic patients with newly diagnosed hypertension, had a significantly greater body mass index (26.3 vs. 25.4 kg/m2, P = 0.03) and slower heart rate (73.7 vs. 78.1 beats/min, P = 0.01). In logistic regression analyses adjusted for sex (48.3% women) and age (mean 60.0 ± 11.5 years), the odds ratio for newly diagnosed diabetes mellitus versus newly diagnosed hypertension was 1.27 (95% CI 1.03-1.56) and 0.80 (95% CI 0.66-0.96) for body mass index (+ 3 kg/m2) and heart rate (+ 10 beat/min), respectively. Hypertensive patients with newly diagnosed diabetes also had a lower prevalence of albuminuria (16.0% vs. 30.1%, P = 0.02) and slightly and non-significantly higher prevalence of left ventricular hypertrophy (5.1% vs. 1.9%, P = 0.14) than diabetic patients with newly diagnosed hypertension.ConclusionsEarlier or later onset of hypertension than diabetes mellitus may have different risk factors and organ damage.
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Project description:ObjectiveTo understand the infection characteristics and risk factors for infection by analyzing multicenter clinical data of newly diagnosed multiple myeloma (NDMM) patients.MethodsThis study reviewed 564 NDMM patients from 2 large tertiary hospitals from January 2018 to December 2021, of whom 395 comprised the training set and 169 comprised the validation set. Thirty-eight variables from first admission records were collected, including patient demographic characteristics, clinical scores and characteristics, laboratory indicators, complications, and medication history, and key variables were screened using the Lasso method. Multiple machine learning algorithms were compared, and the best performing algorithm was used to build a machine learning prediction model. The model performance was evaluated using the AUC, accuracy, and Youden's index. Finally, the SHAP package was used to assess two cases and demonstrate the application of the model.ResultsIn this study, 15 important key variables were selected, namely, age, ECOG, osteolytic disruption, VCD, neutrophils, lymphocytes, monocytes, hemoglobin, platelets, albumin, creatinine, lactate dehydrogenase, affected globulin, β2 microglobulin, and preventive medicine. The predictive performance of the XGBoost model was significantly better than that of the other models (AUROC: 0.8664), and it also performed well for the expected dataset (accuracy: 68.64%).ConclusionA machine learning algorithm was used to establish an infection prediction model for NDMM patients that was simple, convenient, validated, and performed well in reducing the incidence of infection and improving the prognosis of patients.
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Project description:Acute Myeloid Leukemia (AML) is a heterogenous disease characterized by immature blasts at different maturation stage. We used single cell sequencing technique to analyze newly diagnosed AML patients.
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Project description:ObjectiveTo develop a prognostic model to predict disease outcomes in individual patients with Parkinson disease (PD) and perform an external validation study in an independent cohort.MethodsModel development was done in the Comorbidity and Aging in Rehabilitation Patients: The Influence on Activities (CARPA) cohort (Netherlands). External validation was performed using the Cambridgeshire Parkinson's Incidence from GP to Neurologist (CamPaIGN) cohort (UK). Both are longitudinal incident cohort studies that prospectively followed up patients with PD from the time of diagnosis. A composite outcome measure was made in which patients were classified as having an unfavorable prognosis when they had postural instability or dementia at the 5-year assessment (or at the last assessment before loss to follow-up), or had died before this time. The final model was derived with a backward selection strategy from candidate predictor variables that were measured at baseline.ResultsIn the resulting model, higher patient age, higher Unified Parkinson's Disease Rating Scale motor examination axial score, and a lower animal fluency score were all associated with a higher probability of an unfavorable outcome. External validation confirmed good discriminative ability between favorable and unfavorable outcomes with an area under the receiver operating characteristic curve of 0.85 (95% confidence interval 0.77-0.93) and a well-calibrated model with a calibration slope of 1.13 and no significant lack of fit (Hosmer-Lemeshow test: p = 0.39).ConclusionWe constructed a model that allows individual patient prognostication at 5 years from diagnosis, using a small set of predictor variables that can easily be obtained by clinicians or research nurses.
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Project description:Chronic social defeat (CSD) in mice has been increasingly employed in experimental resilience research. Particularly, the degree of CSD-induced social avoidance is used to classify animals into resilient (socially non-avoidant) versus susceptible (avoidant). Inspired by human data pointing to threat-safety discrimination and responsiveness to extinction training of aversive memories as characteristics of resilient individuals, we here describe a translationally informed stratification which identified three phenotypic subgroups of mice following CSD: the Discriminating-avoiders, characterised by successful social threat-safety discrimination and successful extinction of social avoidance; the Indiscriminate-avoiders, showing fear generalisation, and the Non-avoiders (absence of social avoidance) displaying impaired conditioned learning. Furthermore, and supporting the biological validity of our approach, we uncovered subgroup-specific transcriptional signatures in classical fear conditioning and anxiety-related brain regions. Our reconceptualisation of resilience in mice refines the currently used dichotomous classification and contributes to advancing future translational approaches.
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Project description:DNA Hydroxymethylation is Associated with Disease Severity and Persists at Enhancers of Oncogenic Regions in Multiple Myeloma
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