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Predictive Time-to-Event Model for Major Medical Complications After Colectomy

ABSTRACT: Purpose: The purpose of this study is to create prediction models for when major complications occur after elective colectomy surgery. Justification: After surgery, patients can have multiple complications. Accurate risk prediction after surgery is important for determining an appropriate level of monitoring and facilitating patient recovery at home. Objectives: Investigators aim to develop and internally validate prediction models to predict time-to-complication for each individual major medical complications (pneumonia, myocardial infarction (MI) (i.e. heart attacks), cerebral vascular event (CVA) (i.e. stroke), venous thromboembolism (VTE) (i.e. clots), acute renal failure (ARF) (i.e. kidney failure), and sepsis (i.e. severe infections)) or adverse outcomes (mortality, readmission) within 30-days after elective colectomy. Data analysis: Investigators will be analyzing a data set provided by the National Surgical Quality Improvement Program (NSQIP). Descriptive statistics will be performed. Cox proportional hazard and machine learning models will be created for each complication and outcome outlined in "Objectives". The performances of the models will be assessed and compared to each other.

DISEASE(S): Predictive Model,Diverticulitis,Inflammatory Bowel Diseases,Colorectal Cancer,Colectomy,Complications, Postoperative,Postoperative Complications

PROVIDER: 2397814 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:Coordinated multi-center project with several complementary goals in each one of the sub-projects included. Objectives: 1.To determine risk factors of death, or major complications in the short term; 2.To determine risk factors of death, tumor recurrence, major complications, readmission or deterioration of quality of life in the mid term; 3.Evaluation of patient reported outcomes from before the intervention to the end of the follow-up. 4.To study the role of immunohisto-chemistry markers in the prediction of similar adverse results. This sub-project (coordinator) will approach the determination of risk factors of death, tumor recurrence, major complications, readmission and deterioration of quality of medium term life (1-2 years). Methodology. Design: prospective cohort study with 2 years follow-up after the surgical intervention. Participant centers: 18 hospitals of 6 Autonomous Communities of all Spain. Patients diagnosed of colorectal cancer surgically intervened. Variables: pre-intervention, those of the hospital admission, sociodemographic, immunohisto-chemistry and clinical parameters, that could be in relation to the outcomes to study. Statistic analysis: a derivation sample will be created where the possible predicting parameters will be identified, by cancer of colon or rectum. Predictive models will be created with a good discriminative capacity. A validation of those models will be performed in a validation sub-sample. Logistic and Cox regression models will be used. Simulation models for the prediction of discreet events in the long term will be used.

| 2193392 | ecrin-mdr-crc

Potential Influence of Staphylococcus aureus Clonal Complex 30 Genotype and Transcriptome on Hematogenous Infections

Project description:We report the application of RNA-Seq to assess genomic transcriptional profiles of S. aureus clinical isolates from Clonal Complex 30 and related genetic backgrounds. In this study, CC30 isolates were shown to be significantly less virulent than other CCs in two in vivo sepsis models. The association of CC30 with adhesin-based complications in humans but not sepsis-induced mortality in animal models may be due in part to its unique genomic transcriptional profile and suggests that specific genotypes of S. aureus may be more likely to elicit certain complication types. Staphylococcus aureus transcriptome profiles of exponential growth phase in vitro cultures were generated by RNA-Seq in duplicate, using Illumina HiSeq2500.

2014-07-29 | E-GEOD-59851 | biostudies-arrayexpress

Cardiac organoid model of human myocardial infarction (batch2)

Project description:While human organoid systems have provided a powerful platform in modeling diseases caused by genetic disorders, non-genetic factors, such as lifestyle and environment, are the largest attributable factors to devastating diseases like cardiovascular disease (CVD), the leading cause of death worldwide. Specifically, myocardial infarction (MI) (i.e., heart attack) makes up ~8.5% of CVD and is a common cause of heart failure with a 40% five-year mortality after the first MI. This highlights an urgent need to develop relevant human heart failure models for drug development. This is further evidenced by the disappointing performance of heart failure drugs in clinical trials during the last decade, which has been partially attributed to the distinct differences between human patient hearts and animal heart failure models. Here, we combined major non-genetic causal factors of MI with our previously established cardiac organoids to create the first human organoid model of cardiac infarction. In particular, we leveraged the diffusion limitation in 3D microtissues to recreate the nutrient diffusion gradient across infarcted hearts (i.e., infarct-border-remote zones) in human cardiac organoids to induce cardiac organotypic response to infarction. This enabled the recapitulation of major MI hallmarks in human cardiac organoids at the transcriptomic, structural and functional level.

2020-04-14 | GSE115031 | GEO

Cardiac organoid model of human myocardial infarction

2020-04-14 | GSE113871 | GEO

Risk assessment with gene expression markers in sepsis development

Project description:We investigated the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. We built machine learning classification models on preoperative transcriptomic signatures to predict postoperative outcomes including sepsis. To test the predictive capability of these models for ongoing infection, whole blood RNA sequencing analysis on 61 independent patients with COVID-19 (10 mild, 51 severe cases) was performed.

2024-08-30 | GSE208587 | GEO

Microarray analysis of surgical liver biopsies from 13 obese (nine with type 2 diabetes) and five control subjects

Project description:Hepatic lipid accumulation is an important complication of obesity linked to risk for type 2 diabetes. To identify novel transcriptional changes in human liver which could contribute to hepatic lipid accumulation and associated insulin resistance and type 2 diabetes (DM2), we evaluated gene expression and gene set enrichment in surgical liver biopsies from 13 obese (9 with DM2) and 5 control subjects, obtained in the fasting state at the time of elective abdominal surgery for obesity or cholecystectomy. RNA was isolated for cRNA preparation and hybridized to Affymetrix U133A microarrays. Human liver samples were obtained from 5 lean control subjects undergoing elective cholecystectomy and 13 obese subjects (with or without Type 2 diabetes) undergoing gastric bypass surgery. Subjects with diabetes were classified as either well-controlled or poorly-controlled.

2009-06-01 | E-GEOD-15653 | biostudies-arrayexpress

Potential Influence of Staphylococcus aureus Clonal Complex 30 Genotype and Transcriptome on Hematogenous Infections

2014-07-29 | GSE59851 | GEO

MPI Predicts Outcome of CRC Elderly Patients (MPI=MULTIDIMENSIONAL PROGNOSTIC INDEX; CRC=COLORECTAL CANCER)

Project description:Patients aged ≥75 year scheduled for CRC surgery were studied (104 cases) and variables associated with major postoperative complications / mortality were evaluated. The importance of this report is that MPI-score resulted strongly associated with major complications and it was a primary component of an individual prediction model.

| 2355077 | ecrin-mdr-crc

Longitudinal Profiling in Patients Undergoing Cardiac Surgery Reveals Postoperative Changes in DNA Methylation

Project description:Cardiac surgery and cardiopulmonary bypass induce a substantial immune and inflammatory response, the overactivation of which is associated with significant complications. Longitudinal DNA methylation profiling allows the potential to identify changes in gene regulatory mechanisms that are secondary to surgery and to identify molecular processes that predict and/or cause postoperative complications. In this study, we measure DNA methylation in preoperative and postoperative whole blood samples from 96 patients undergoing cardiac surgery on cardiopulmonary bypass. We identify several loci with statistically significant postoperative changes in methylation. Additionally, two of these loci are associated with new-onset postoperative atrial fibrillation, a significant complication after cardiac surgery. This research establishes that there are statistically significant changes in DNA methylation that occur immediately after cardiac surgery and that these acute alterations in DNA methylation have the granularity to identify processes associated with major postoperative complications.

2023-01-09 | GSE215937 | GEO

Major surgery induces acute changes in DNA methylation associated with immune response pathways

Project description:Using a longitudinal study design, we quantified DNA methylation across the genome in a cohort of 30 elderly patients (average age = 77.9 years (range 62-91)) undergoing major surgery (either elective colorectal surgery, elective hip replacement surgery, and emergency hip surgery following fracture). From each individual, detailed clinical blood measures were collected and DNA methylation was profiled using the Illumina Human Methylation 450 array (g450K arrayh) (Illumina Inc. San Diego, California) in DNA samples isolated from purified PBMCs collected at three time-points: i) immediately before surgery (baseline (BL)), ii) in the morning of post-operative day 1 (POD1) and iii) between post-operative day 4 and 7 (POD4/7) prior to discharge from hospital.

2019-12-24 | GSE142536 | GEO

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