The kSORT assay to detect renal transplant patients at risk for acute rejecti...Kevin Jaglinski
Development of noninvasive molecular assays to improve disease diagnosis and patient monitoring is a critical need. In renal transplantation, acute rejection (AR) increases the risk for chronic graft injury and failure. Noninvasive diagnostic assays to improve current late and nonspecific diagnosis of rejection are needed. We sought to develop a test using a simple blood gene expression assay to detect patients at high risk for AR.
The kSORT Assay to Detect Renal Transplant Patients at High RiskKevin Jaglinski
This document describes a study that developed a blood gene expression assay called kSORT to detect renal transplant patients at high risk for acute rejection. Researchers analyzed gene expression data from over 500 blood samples across eight transplant centers. They selected a set of 17 genes that could detect acute rejection with high accuracy based on a training set of samples. This gene set was then validated on two independent sets of samples and could predict acute rejection up to 3 months before standard detection methods. A reference-based algorithm using the 17 genes was developed to provide a numerical risk score classifying patients as high or low risk for acute rejection. Overall, the kSORT assay provides a noninvasive tool for detecting acute rejection risk in renal transplant patients.
Genomic medicine is being applied clinically through sequencing technologies that have reduced costs and increased knowledge. Primary applications include diagnosing rare genetic disorders, cancer treatment guidance, and infectious disease monitoring. However, continued innovation depends on lowering costs, expanding knowledge of genetic variation and gene function through large population studies, and developing new technologies.
Ohio States 2016 ASH Review Blood and Marrow TransplantationOSUCCC - James
Ohio State’s 2016 ASH Review
Blood and Marrow Transplantation
Basem M. William, MD, MRCP(UK), FACP
Assistant Professor of Internal Medicine
Blood and Marrow Transplant Program
The Saudi Human Genome Program (SHGP) aims to sequence 100,000 human genomes over five years to study genetic diseases in Saudi Arabia. Genetic diseases have a high burden in Saudi Arabia due to high rates of marriage between relatives. The SHGP was established to identify disease-causing genes and variants specific to the Saudi population using next-generation sequencing across genome centers. It has sequenced over 5,000 samples so far and aims to establish a genomic knowledge database to enable personalized medicine and screening efforts in Saudi Arabia.
This document summarizes guidelines for evaluating kidney function in potential living donors. It recommends estimating glomerular filtration rate (GFR) using serum creatinine and/or cystatin C to assess kidney function. An initial GFR of 90 mL/min/1.73m2 or greater is acceptable for donation, while GFR between 60-89 mL/min/1.73m2 requires individual assessment. Donors with less than 60 mL/min/1.73m2 are not eligible. The guidelines provide criteria for measurement and interpretation of GFR to safely evaluate and select living kidney donors.
Register log in home data how to use pro act als prize als DIPESH30
PRO-ACT is a database that contains clinical trial data from over 8,500 ALS patients. It includes various types of patient information such as demographics, ALS history, symptoms, vital signs, and lab data. The data was de-identified to protect patient privacy. The document provides information on the ethical use of the data and describes the different files contained in PRO-ACT that hold assessment data for subjects. It also gives a brief overview of ALS and states that the database was created by merging data from multiple clinical trials in order to have a large dataset for research purposes.
1) The study analyzed urinary donor-derived cell-free DNA (dd-cfDNA) in 63 kidney transplant patients to determine if it could serve as a biomarker for detecting transplant injury.
2) They found that urinary dd-cfDNA levels were significantly higher in patients experiencing acute rejection or BK virus nephropathy compared to stable transplant patients or those with chronic allograft injury.
3) While urinary dd-cfDNA shows promise as a noninvasive biomarker for detecting acute transplant injury, it lacks specificity to distinguish between different causes of injury such as acute rejection versus BK virus nephropathy.
The kSORT assay to detect renal transplant patients at risk for acute rejecti...Kevin Jaglinski
Development of noninvasive molecular assays to improve disease diagnosis and patient monitoring is a critical need. In renal transplantation, acute rejection (AR) increases the risk for chronic graft injury and failure. Noninvasive diagnostic assays to improve current late and nonspecific diagnosis of rejection are needed. We sought to develop a test using a simple blood gene expression assay to detect patients at high risk for AR.
The kSORT Assay to Detect Renal Transplant Patients at High RiskKevin Jaglinski
This document describes a study that developed a blood gene expression assay called kSORT to detect renal transplant patients at high risk for acute rejection. Researchers analyzed gene expression data from over 500 blood samples across eight transplant centers. They selected a set of 17 genes that could detect acute rejection with high accuracy based on a training set of samples. This gene set was then validated on two independent sets of samples and could predict acute rejection up to 3 months before standard detection methods. A reference-based algorithm using the 17 genes was developed to provide a numerical risk score classifying patients as high or low risk for acute rejection. Overall, the kSORT assay provides a noninvasive tool for detecting acute rejection risk in renal transplant patients.
Genomic medicine is being applied clinically through sequencing technologies that have reduced costs and increased knowledge. Primary applications include diagnosing rare genetic disorders, cancer treatment guidance, and infectious disease monitoring. However, continued innovation depends on lowering costs, expanding knowledge of genetic variation and gene function through large population studies, and developing new technologies.
Ohio States 2016 ASH Review Blood and Marrow TransplantationOSUCCC - James
Ohio State’s 2016 ASH Review
Blood and Marrow Transplantation
Basem M. William, MD, MRCP(UK), FACP
Assistant Professor of Internal Medicine
Blood and Marrow Transplant Program
The Saudi Human Genome Program (SHGP) aims to sequence 100,000 human genomes over five years to study genetic diseases in Saudi Arabia. Genetic diseases have a high burden in Saudi Arabia due to high rates of marriage between relatives. The SHGP was established to identify disease-causing genes and variants specific to the Saudi population using next-generation sequencing across genome centers. It has sequenced over 5,000 samples so far and aims to establish a genomic knowledge database to enable personalized medicine and screening efforts in Saudi Arabia.
This document summarizes guidelines for evaluating kidney function in potential living donors. It recommends estimating glomerular filtration rate (GFR) using serum creatinine and/or cystatin C to assess kidney function. An initial GFR of 90 mL/min/1.73m2 or greater is acceptable for donation, while GFR between 60-89 mL/min/1.73m2 requires individual assessment. Donors with less than 60 mL/min/1.73m2 are not eligible. The guidelines provide criteria for measurement and interpretation of GFR to safely evaluate and select living kidney donors.
Register log in home data how to use pro act als prize als DIPESH30
PRO-ACT is a database that contains clinical trial data from over 8,500 ALS patients. It includes various types of patient information such as demographics, ALS history, symptoms, vital signs, and lab data. The data was de-identified to protect patient privacy. The document provides information on the ethical use of the data and describes the different files contained in PRO-ACT that hold assessment data for subjects. It also gives a brief overview of ALS and states that the database was created by merging data from multiple clinical trials in order to have a large dataset for research purposes.
1) The study analyzed urinary donor-derived cell-free DNA (dd-cfDNA) in 63 kidney transplant patients to determine if it could serve as a biomarker for detecting transplant injury.
2) They found that urinary dd-cfDNA levels were significantly higher in patients experiencing acute rejection or BK virus nephropathy compared to stable transplant patients or those with chronic allograft injury.
3) While urinary dd-cfDNA shows promise as a noninvasive biomarker for detecting acute transplant injury, it lacks specificity to distinguish between different causes of injury such as acute rejection versus BK virus nephropathy.
This document discusses hematopoietic stem cell transplantation (HSCT) and its current state and future opportunities. HSCT is an effective treatment for hematologic, immune, metabolic and cancerous diseases. It replaces defective cells and offers high-dose therapy and graft-versus-tumor effects. While HSCT faces challenges like toxicity and costs, advances in unrelated donor matching, cord blood use, and reduced intensity regimens have expanded its applications. Standards and accreditation are needed to ensure quality, and further growth depends on expanded donor availability and combining HSCT with new cellular immunotherapies.
The document discusses recent advances in myelodysplastic syndromes (MDS), including new risk stratification models, prognostic factors, and therapeutic options for lower-risk and higher-risk MDS such as lenalidomide for lower-risk MDS and azacitidine or allogeneic stem cell transplantation for higher-risk MDS. Clinical trials have shown that lenalidomide can induce transfusion independence in patients with MDS and del(5q) abnormality and azacitidine improves overall survival compared to
A Peripheral Blood Diagnostic Test for Acute Rejection in renal transplantati...Kevin Jaglinski
This study aimed to develop a non-invasive blood test to diagnose acute renal allograft rejection (AR) after kidney transplantation. Researchers analyzed gene expression in peripheral blood samples from kidney transplant recipients paired with contemporary kidney biopsy results. They identified a set of 5 genes that accurately classified AR in two independent validation sets with over 90% sensitivity and specificity. This 5-gene signature differentiated AR from stable transplant function and other non-AR conditions. The results support further prospective validation of this blood-based diagnostic tool to potentially avoid invasive kidney biopsies for rejection monitoring.
This document discusses immunosuppressive therapy for renal transplantation. It covers various types of immunosuppressive drugs used for induction and maintenance, including calcineurin inhibitors (CNIs), mTOR inhibitors, steroids, and antiproliferatives. It provides information on monitoring drug levels, drug toxicities, and strategies to improve graft survival like avoiding high intrapatient drug level variability. It also addresses the impact of immunosuppressive drugs on male reproduction and pregnancy.
Shorter corpus callosum length and smaller cross-sectional area correlated with more severe developmental delay and higher serum 7-dehydrocholesterol levels in individuals with Smith-Lemli-Opitz syndrome. A study of 36 individuals with SLOS found that callosal length and area negatively correlated with developmental quotients in gross motor and language domains. Callosal measurements also negatively correlated with serum 7-dehydrocholesterol levels and positively correlated with total cholesterol levels. The findings suggest callosal development is associated with biochemical abnormalities in SLOS and imaging biomarkers may help evaluate disease severity and outcomes.
Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Mo...Jerry Lee
Special Seminar at the 8th Taiwan Biosignatures Workshop to share overall work of NCI's Center for Strategic Scientific Initiatives since 2003 as well as CSSI's influence on select projects initiated by the 2016 WH Cancer Moonshot Task Force that include Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network, International Cancer Proteogenome Consortium, and the Blood Profiling Atlas in Cancer (BloodPAC) commons.
Treatment landscape of alk+ nsclc 12 novemberssuser4c22ca
This document provides an overview of treatments for ALK+ non-small cell lung cancer (NSCLC). It discusses the epidemiology of ALK+ NSCLC, occurring in 3-5% of NSCLC cases worldwide. First generation ALK inhibitors like crizotinib provided significant benefits for patients. Current NCCN guidelines recommend first-line treatment with newer ALK inhibitors such as alectinib, brigatinib, or lorlatinib which have led to median overall survival rates of over 5 years for some patients with metastatic ALK+ NSCLC. The document reviews the classes of ALK inhibitors including first, second, and third generation treatments and their effectiveness against ALK+ NSCLC.
Transplant Nephrectomy Improves Survival following a Failed Renal Allograft (...Raj Kiran Medapalli
This document summarizes a study examining the impact of transplant nephrectomy on mortality rates following kidney allograft failure. The study used data from the United States Renal Data System on over 19,000 patients who returned to dialysis between 1994-2004 after allograft failure. It found that patients who underwent nephrectomy after late graft failure (>1 year) had a 12% lower risk of death compared to those who did not undergo nephrectomy. However, nephrectomy after early graft failure (<1 year) was associated with a 13% higher risk of death.
This presentation summarizes Advanced Cell Technology's (ACT) business, including its stem cell therapy programs and clinical trials. ACT has two years of cash available and is fully funded for its Phase I/II retinal pigment epithelium (RPE) cell therapy trial. Key programs include RPE cells for age-related macular degeneration, myoblast cells for heart disease, and hemangioblast cells. ACT has a portfolio of over 150 stem cell patents and collaborates with major research institutions. Upcoming milestones include treating the first patient with a human embryonic stem cell therapy in late 2010.
This presentation summarizes Advanced Cell Technology's (ACT) business, including its stem cell therapy programs and clinical trials. ACT has two years of cash available and is fully funded for its Phase I/II retinal pigment epithelium (RPE) cell therapy trial. Key programs include RPE cells for age-related macular degeneration, myoblast cells for heart disease, and hemangioblast cells. ACT has a world-class scientific team and intellectual property portfolio covering over 150 patents. Upcoming milestones include treating the first patient with a human embryonic stem cell therapy in Q4 2010.
This study examined the association between the ACE gene polymorphism and breast cancer risk among Bengalee Hindu females in West Bengal, India. The researchers analyzed samples from 108 breast cancer patients and 128 controls without cancer. They found that the frequency of the DD genotype was higher in patients (57.4%) than controls (25.0%), with a significant association between the DD genotype and increased breast cancer risk. Binary logistic analysis also found the DD genotype was associated with family history of breast cancer, fewer children, and longer-term contraceptive pill use among patients. The results suggest the DD genotype may be useful for determining breast cancer occurrence and prognosis in this population.
This study aims to search for genetic and proteomic risk factors and protective factors associated with coronary heart disease (CHD) in order to develop new diagnostic techniques and therapies. The study will analyze gene expression patterns in peripheral blood monocytes and perform proteomics analysis of blood serum from five patient groups: 1) those with heart attack and risk factors, 2) those with heart attack without risk factors, 3) young individuals with risk factors but no heart attack, 4) elderly individuals with risk factors but no heart attack, and 5) healthy elderly individuals without risk factors. Gene expression profiles will be obtained using microarray analysis and validated with real-time PCR. Differentially expressed genes and proteins may help identify new targets for preventing and
H2O World - H2O for Genomics with Hussam Al-Deen AshabSri Ambati
GenomeDx Biosciences is a clinical genomics company that uses machine learning on genomic data to develop clinical tests for cancer. They developed a genomic classifier to predict prostate tumor Gleason grade using RNA expression data from over 7,000 patients. This Gleason grade classifier was tested on a separate dataset and achieved an AUC of 0.77, outperforming other clinical predictors. The classifier also predicted metastatic outcomes with an AUC of 0.73, demonstrating its ability to predict patient risk. GenomeDx uses the H2O platform for its machine learning work due to its ability to handle high-dimensional genomic data and its deep learning algorithms, which can model complex nonlinear relationships between genes.
This study examined the relationship between arsenic exposure, oxidative stress, and hematological parameters in cervical cancer patients in India. The study found that cervical cancer patients had significantly higher levels of malondialdehyde (MDA), a marker of lipid peroxidation and oxidative stress, and significantly lower hemoglobin levels and red blood cell counts compared to healthy controls. Among cervical cancer patients, those with higher levels of arsenic exposure had even higher MDA levels and lower hemoglobin and red blood cell counts. The results suggest that arsenic exposure may be contributing to increased oxidative stress in cervical cancer patients, which is associated with anemia. However, more research is needed to fully understand the potential role of arsenic as a risk factor for
1) The document describes a quality improvement technique used at a medical center to enhance detection of ASC-H diagnoses through blinded rescreening of Pap tests.
2) It presents data on the usefulness of HPV testing for women diagnosed with ASC-H, showing higher rates of CIN among HPV-positive women compared to HPV-negative women.
3) The study found the highest CIN2/3 detection rate in women aged 30-39 with ASC-H and positive HPV tests, and that negative HPV tests had a 100% negative predictive value for ruling out CIN2/3 in women over 40.
This document discusses a study comparing the implications of using the CKD-EPI equation versus the MDRD equation to estimate GFR and diagnose chronic kidney disease (CKD) in a large healthcare system. The study found:
1) The number of patients identified with CKD stages 3-5 decreased by 10% when using the CKD-EPI equation compared to the MDRD equation.
2) Changes in CKD identification varied by patient characteristics, with a 35% decrease in patients under 60 years old and a 10% increase in patients over 90 years old.
3) Only 14% of patients identified with CKD based on either equation also had a related ICD-9 diagnosis
Kidney transplantation has evolved significantly since the first attempts in the 1930s. Live donor kidney transplantation provides advantages over deceased donor transplants including better outcomes. A comprehensive evaluation of potential live kidney donors is essential and includes medical, surgical, immunological, and psychosocial assessments. Careful evaluation aims to minimize risks to donor health while maximizing the benefits of transplantation for recipients. Ongoing research continues to further develop and improve the live donor evaluation and transplantation process.
The document summarizes highlights from the 2013 Conference on Retroviruses and Opportunistic Infections held in Atlanta, Georgia from March 3-6, 2013. It includes a report on a child who achieved a "functional cure" after receiving very early triple-drug ART for HIV infection. It also discusses results from the SAILING trial showing higher rates of virologic suppression with dolutegravir compared to raltegravir in treatment-experienced patients at 24 weeks. Additional topics covered include updates to DHHS HIV treatment guidelines, research on HIV cure, PrEP trials, and new data on antiretroviral therapy agents.
This study analyzed 231 patients with aneurysmal subarachnoid hemorrhage (SAH) from 25 Mexican hospitals to describe clinical characteristics, risk factors, and outcomes. Hypertension was the main risk factor associated with SAH. Most aneurysms (92%) were located in the anterior circulation and 15% of patients had multiple aneurysms. The median hospital stay was 23 days. Invasive treatments like clipping or coiling were performed in 69% of patients. The in-hospital mortality rate was 20% due to neurological causes. 25% of patients were discharged with significant neurological impairment.
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...Seeling Cheung
The document summarizes the experience of Fiducia & GAD IT AG in bringing Hadoop to their enterprise for fraud detection purposes. They faced challenges of handling high volumes of transaction data in real-time for model-based fraud evaluation. Their solution was to implement an Apache Hadoop platform to address the velocity, variety and volume of transaction data. Key lessons learned included that Hadoop is a complex platform requiring new skills, ongoing support is critical, and standard tasks can generate significant effort. Their blueprint recommends starting with a simple use case, few components, agile development, and budgeting time for training and bug fixing when establishing a big data platform.
CSNI: How State Medicaid Agencies Can Use Analytics to Predict Opioid Abuse a...Seeling Cheung
This document discusses how state Medicaid agencies can use analytics to improve outcomes. It describes CNSI, a company that provides cloud platforms and analytics solutions for Medicaid. CNSI uses IBM technologies like Watson Explorer and Cognos to help clients with predictive modeling, claims analysis, and consolidating member data from multiple sources. Examples of CNSI projects include using text analytics to automate medical record reviews, building models to predict at-risk members for opioid abuse, and creating 360-degree views of member data. The presentation outlines CNSI's approach and provides a roadmap for continued use of analytics.
This document discusses hematopoietic stem cell transplantation (HSCT) and its current state and future opportunities. HSCT is an effective treatment for hematologic, immune, metabolic and cancerous diseases. It replaces defective cells and offers high-dose therapy and graft-versus-tumor effects. While HSCT faces challenges like toxicity and costs, advances in unrelated donor matching, cord blood use, and reduced intensity regimens have expanded its applications. Standards and accreditation are needed to ensure quality, and further growth depends on expanded donor availability and combining HSCT with new cellular immunotherapies.
The document discusses recent advances in myelodysplastic syndromes (MDS), including new risk stratification models, prognostic factors, and therapeutic options for lower-risk and higher-risk MDS such as lenalidomide for lower-risk MDS and azacitidine or allogeneic stem cell transplantation for higher-risk MDS. Clinical trials have shown that lenalidomide can induce transfusion independence in patients with MDS and del(5q) abnormality and azacitidine improves overall survival compared to
A Peripheral Blood Diagnostic Test for Acute Rejection in renal transplantati...Kevin Jaglinski
This study aimed to develop a non-invasive blood test to diagnose acute renal allograft rejection (AR) after kidney transplantation. Researchers analyzed gene expression in peripheral blood samples from kidney transplant recipients paired with contemporary kidney biopsy results. They identified a set of 5 genes that accurately classified AR in two independent validation sets with over 90% sensitivity and specificity. This 5-gene signature differentiated AR from stable transplant function and other non-AR conditions. The results support further prospective validation of this blood-based diagnostic tool to potentially avoid invasive kidney biopsies for rejection monitoring.
This document discusses immunosuppressive therapy for renal transplantation. It covers various types of immunosuppressive drugs used for induction and maintenance, including calcineurin inhibitors (CNIs), mTOR inhibitors, steroids, and antiproliferatives. It provides information on monitoring drug levels, drug toxicities, and strategies to improve graft survival like avoiding high intrapatient drug level variability. It also addresses the impact of immunosuppressive drugs on male reproduction and pregnancy.
Shorter corpus callosum length and smaller cross-sectional area correlated with more severe developmental delay and higher serum 7-dehydrocholesterol levels in individuals with Smith-Lemli-Opitz syndrome. A study of 36 individuals with SLOS found that callosal length and area negatively correlated with developmental quotients in gross motor and language domains. Callosal measurements also negatively correlated with serum 7-dehydrocholesterol levels and positively correlated with total cholesterol levels. The findings suggest callosal development is associated with biochemical abnormalities in SLOS and imaging biomarkers may help evaluate disease severity and outcomes.
Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Mo...Jerry Lee
Special Seminar at the 8th Taiwan Biosignatures Workshop to share overall work of NCI's Center for Strategic Scientific Initiatives since 2003 as well as CSSI's influence on select projects initiated by the 2016 WH Cancer Moonshot Task Force that include Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network, International Cancer Proteogenome Consortium, and the Blood Profiling Atlas in Cancer (BloodPAC) commons.
Treatment landscape of alk+ nsclc 12 novemberssuser4c22ca
This document provides an overview of treatments for ALK+ non-small cell lung cancer (NSCLC). It discusses the epidemiology of ALK+ NSCLC, occurring in 3-5% of NSCLC cases worldwide. First generation ALK inhibitors like crizotinib provided significant benefits for patients. Current NCCN guidelines recommend first-line treatment with newer ALK inhibitors such as alectinib, brigatinib, or lorlatinib which have led to median overall survival rates of over 5 years for some patients with metastatic ALK+ NSCLC. The document reviews the classes of ALK inhibitors including first, second, and third generation treatments and their effectiveness against ALK+ NSCLC.
Transplant Nephrectomy Improves Survival following a Failed Renal Allograft (...Raj Kiran Medapalli
This document summarizes a study examining the impact of transplant nephrectomy on mortality rates following kidney allograft failure. The study used data from the United States Renal Data System on over 19,000 patients who returned to dialysis between 1994-2004 after allograft failure. It found that patients who underwent nephrectomy after late graft failure (>1 year) had a 12% lower risk of death compared to those who did not undergo nephrectomy. However, nephrectomy after early graft failure (<1 year) was associated with a 13% higher risk of death.
This presentation summarizes Advanced Cell Technology's (ACT) business, including its stem cell therapy programs and clinical trials. ACT has two years of cash available and is fully funded for its Phase I/II retinal pigment epithelium (RPE) cell therapy trial. Key programs include RPE cells for age-related macular degeneration, myoblast cells for heart disease, and hemangioblast cells. ACT has a portfolio of over 150 stem cell patents and collaborates with major research institutions. Upcoming milestones include treating the first patient with a human embryonic stem cell therapy in late 2010.
This presentation summarizes Advanced Cell Technology's (ACT) business, including its stem cell therapy programs and clinical trials. ACT has two years of cash available and is fully funded for its Phase I/II retinal pigment epithelium (RPE) cell therapy trial. Key programs include RPE cells for age-related macular degeneration, myoblast cells for heart disease, and hemangioblast cells. ACT has a world-class scientific team and intellectual property portfolio covering over 150 patents. Upcoming milestones include treating the first patient with a human embryonic stem cell therapy in Q4 2010.
This study examined the association between the ACE gene polymorphism and breast cancer risk among Bengalee Hindu females in West Bengal, India. The researchers analyzed samples from 108 breast cancer patients and 128 controls without cancer. They found that the frequency of the DD genotype was higher in patients (57.4%) than controls (25.0%), with a significant association between the DD genotype and increased breast cancer risk. Binary logistic analysis also found the DD genotype was associated with family history of breast cancer, fewer children, and longer-term contraceptive pill use among patients. The results suggest the DD genotype may be useful for determining breast cancer occurrence and prognosis in this population.
This study aims to search for genetic and proteomic risk factors and protective factors associated with coronary heart disease (CHD) in order to develop new diagnostic techniques and therapies. The study will analyze gene expression patterns in peripheral blood monocytes and perform proteomics analysis of blood serum from five patient groups: 1) those with heart attack and risk factors, 2) those with heart attack without risk factors, 3) young individuals with risk factors but no heart attack, 4) elderly individuals with risk factors but no heart attack, and 5) healthy elderly individuals without risk factors. Gene expression profiles will be obtained using microarray analysis and validated with real-time PCR. Differentially expressed genes and proteins may help identify new targets for preventing and
H2O World - H2O for Genomics with Hussam Al-Deen AshabSri Ambati
GenomeDx Biosciences is a clinical genomics company that uses machine learning on genomic data to develop clinical tests for cancer. They developed a genomic classifier to predict prostate tumor Gleason grade using RNA expression data from over 7,000 patients. This Gleason grade classifier was tested on a separate dataset and achieved an AUC of 0.77, outperforming other clinical predictors. The classifier also predicted metastatic outcomes with an AUC of 0.73, demonstrating its ability to predict patient risk. GenomeDx uses the H2O platform for its machine learning work due to its ability to handle high-dimensional genomic data and its deep learning algorithms, which can model complex nonlinear relationships between genes.
This study examined the relationship between arsenic exposure, oxidative stress, and hematological parameters in cervical cancer patients in India. The study found that cervical cancer patients had significantly higher levels of malondialdehyde (MDA), a marker of lipid peroxidation and oxidative stress, and significantly lower hemoglobin levels and red blood cell counts compared to healthy controls. Among cervical cancer patients, those with higher levels of arsenic exposure had even higher MDA levels and lower hemoglobin and red blood cell counts. The results suggest that arsenic exposure may be contributing to increased oxidative stress in cervical cancer patients, which is associated with anemia. However, more research is needed to fully understand the potential role of arsenic as a risk factor for
1) The document describes a quality improvement technique used at a medical center to enhance detection of ASC-H diagnoses through blinded rescreening of Pap tests.
2) It presents data on the usefulness of HPV testing for women diagnosed with ASC-H, showing higher rates of CIN among HPV-positive women compared to HPV-negative women.
3) The study found the highest CIN2/3 detection rate in women aged 30-39 with ASC-H and positive HPV tests, and that negative HPV tests had a 100% negative predictive value for ruling out CIN2/3 in women over 40.
This document discusses a study comparing the implications of using the CKD-EPI equation versus the MDRD equation to estimate GFR and diagnose chronic kidney disease (CKD) in a large healthcare system. The study found:
1) The number of patients identified with CKD stages 3-5 decreased by 10% when using the CKD-EPI equation compared to the MDRD equation.
2) Changes in CKD identification varied by patient characteristics, with a 35% decrease in patients under 60 years old and a 10% increase in patients over 90 years old.
3) Only 14% of patients identified with CKD based on either equation also had a related ICD-9 diagnosis
Kidney transplantation has evolved significantly since the first attempts in the 1930s. Live donor kidney transplantation provides advantages over deceased donor transplants including better outcomes. A comprehensive evaluation of potential live kidney donors is essential and includes medical, surgical, immunological, and psychosocial assessments. Careful evaluation aims to minimize risks to donor health while maximizing the benefits of transplantation for recipients. Ongoing research continues to further develop and improve the live donor evaluation and transplantation process.
The document summarizes highlights from the 2013 Conference on Retroviruses and Opportunistic Infections held in Atlanta, Georgia from March 3-6, 2013. It includes a report on a child who achieved a "functional cure" after receiving very early triple-drug ART for HIV infection. It also discusses results from the SAILING trial showing higher rates of virologic suppression with dolutegravir compared to raltegravir in treatment-experienced patients at 24 weeks. Additional topics covered include updates to DHHS HIV treatment guidelines, research on HIV cure, PrEP trials, and new data on antiretroviral therapy agents.
This study analyzed 231 patients with aneurysmal subarachnoid hemorrhage (SAH) from 25 Mexican hospitals to describe clinical characteristics, risk factors, and outcomes. Hypertension was the main risk factor associated with SAH. Most aneurysms (92%) were located in the anterior circulation and 15% of patients had multiple aneurysms. The median hospital stay was 23 days. Invasive treatments like clipping or coiling were performed in 69% of patients. The in-hospital mortality rate was 20% due to neurological causes. 25% of patients were discharged with significant neurological impairment.
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...Seeling Cheung
The document summarizes the experience of Fiducia & GAD IT AG in bringing Hadoop to their enterprise for fraud detection purposes. They faced challenges of handling high volumes of transaction data in real-time for model-based fraud evaluation. Their solution was to implement an Apache Hadoop platform to address the velocity, variety and volume of transaction data. Key lessons learned included that Hadoop is a complex platform requiring new skills, ongoing support is critical, and standard tasks can generate significant effort. Their blueprint recommends starting with a simple use case, few components, agile development, and budgeting time for training and bug fixing when establishing a big data platform.
CSNI: How State Medicaid Agencies Can Use Analytics to Predict Opioid Abuse a...Seeling Cheung
This document discusses how state Medicaid agencies can use analytics to improve outcomes. It describes CNSI, a company that provides cloud platforms and analytics solutions for Medicaid. CNSI uses IBM technologies like Watson Explorer and Cognos to help clients with predictive modeling, claims analysis, and consolidating member data from multiple sources. Examples of CNSI projects include using text analytics to automate medical record reviews, building models to predict at-risk members for opioid abuse, and creating 360-degree views of member data. The presentation outlines CNSI's approach and provides a roadmap for continued use of analytics.
The document tells a story about the BI team at Big Fish Games who embarked on a quest to democratize data access in their company. They discovered gaps in their current systems and tools and set out to build a lightweight ETL solution called ETL-Lite. Their solution automated data import, export, and merging across systems while including features like scheduling, retries, logging, and handling dependencies between tasks. After battling challenges, their efforts helped more user groups like executives, marketers, analysts and data scientists access and use company data, allowing all to live happily ever after.
The document discusses Southwest Power Pool's initial steps towards creating a data lake. It describes:
- Storing historical and real-time data that exceeded initial expectations, with around 50% being less frequently used
- Conducting a proof-of-concept evaluation of three vendors to offload less frequently used data and allow SQL query access with minimal changes to existing queries
- Choosing BigInsights based on its ability to do this along with supporting existing Netezza functions and allowing federated queries between Netezza and BigInsights
- The multi-phase vision to eventually incorporate more data types and workloads while improving performance, security, and governance
Constant Contact: An Online Marketing Leader’s Data Lake JourneySeeling Cheung
Constant Contact is an online marketing company that handles large amounts of customer data and wants to enable more advanced analytics. They implemented a data lake architecture to centralize data from various sources and make it accessible for different users like data scientists, analysts, and business leaders. The key aspects of their data lake include a flexible reference architecture, identifying different types of users and tools, documenting metadata through an asset inventory, implementing security controls, and establishing lightweight governance practices over the data domains. The data lake approach allows Constant Contact to more easily perform analytics on large, diverse datasets and gain insights.
Seagate: Sensor Overload! Taming The Raging Manufacturing Big Data TorrentSeeling Cheung
Nicholas Berg presented on Seagate's use of big data analytics to manage the large amount of manufacturing data generated from its hard drive production. Seagate collects terabytes of data per day from testing its drives, which it analyzes using Hadoop to improve quality, predict failures, and gain other insights. It faces challenges in integrating this emerging platform due to the rapid evolution of Hadoop and lack of tools to fully leverage large datasets. Seagate is developing its data lake and data science capabilities on Hadoop to better optimize manufacturing and drive design.
Citizens Bank: Data Lake Implementation – Selecting BigInsights ViON Spark/Ha...Seeling Cheung
Citizens Bank was implementing a BigInsights Hadoop Data Lake with PureData System for Analytics to support all internal data initiatives and improve the customer experience. Testing BigInsights on the ViON Hadoop Appliance yielded the productivity, maintenance, and performance Citizens was looking for. Citizens Bank moved some analytics processing from Teradata to Netezza for better cost and performance, implemented BigInsights Hadoop for a data lake, and avoided large capital expenditures for additional Teradata capacity.
Cloud Based Data Warehousing and AnalyticsSeeling Cheung
This document discusses Marriott International's journey to implementing a cloud-based data warehouse and analytics platform using IBM BigSQL on Softlayer cloud infrastructure. It describes the limitations of their existing on-premises system, challenges faced in migrating data and queries to the cloud, lessons learned, and next steps to further improve the platform. The system is now in production use by an initial group of users at Marriott.
This document discusses three use cases for migrating and improving analytics capabilities for a telecom compliance application called Vigilance. The first use case involves migrating the Vigilance audit and search application to IBM's BigInsights platform to improve scalability, performance, and reduce storage costs. The second use case develops IVR SLA reports using BigInsights and Cognos BI. The third use case analyzes call center agent comments and customer data to identify opportunities to increase customer use of self-service channels. Lessons learned focus on the need for an agile development approach and frequent upgrades given the rapid evolution of big data technologies.
Hadoop and SQL: Delivery Analytics Across the OrganizationSeeling Cheung
This document summarizes a presentation given by Nicholas Berg of Seagate and Adriana Zubiri of IBM on delivering analytics across organizations using Hadoop and SQL. Some key points discussed include Seagate's plans to use Hadoop to enable deeper analysis of factory and field data, the evolving Hadoop landscape and rise of SQL, and a performance comparison showing IBM's Big SQL outperforming Spark SQL, especially at scale. The document provides an overview of Seagate and IBM's strategies and experiences with Hadoop.
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Original article:
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Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
4. Joint Principles of the Patient-Centered
Medical Home
AAFP,ACP, AOA, AAP March, 2007
n Personal physician
n Practice in teams
n Whole person orientation
n Care is coordinated and integrated
n Quality and safety are hallmarks
n Enhanced access
5. Background
• Predictive models in kidney transplantation derived from national data
(UNOS, SRTR) lack longitudinal patient level data, thereby limiting
accuracy.
• Adding patient level data capturing dynamic post-transplant clinical
evolution to predictive models, may improve predictive accuracy for graft
loss (GL) risk.
• Complete capture of patient level clinical data in real time would require an
approach that extracts, collates and curates both structured and
unstructured data from electronic health records (EHR)
• These large amounts of data are notable for volume, velocity, variety and,
verified veracity; An operational definition of Big Data
• As such analytic techniques would also need to handle such data
6. Predictive Diagnostics in Different Medical Contexts
1 - Specificity
Sensitivity
0
0.5
1
0 0.5 1
C=0.5Stroke: c = 0.87
AMI: c = 0.85
CABG: c = 0.74
Transplant: c = 0.653
• Less acuity
• Longer follow up window
• Competing Risk
• CURRENT MODELS DO NOT CAPTURE
LONGITUDINAL CLINICAL EVOLUTION
Why are Transplant Models Inferior?
Courtesy: JD Schold
(modified with
permission)
7. Attributes of the Ideal Predictive
Model for Graft Loss
• Appropriate to Center’s Population and customizable
• Ability to discriminate across levels of risk
• Feasibility of build around clinically actionable variables
• Uses data available within the EMR that are collected in the
context of standard patient care
• Biologically relevant to the extent of current understanding
including social determinants and care processes
• Ability to inform on individual patient trajectories and capture
dynamic longitudinal clinical evolution in the temporal context
of routine clinical care
8. Objectives
• Articulate an approach to capture longitudinal post-transplant
clinical evolution among kidney transplant recipients by
capturing structured and unstructured elements from the EHR
• Build predictive models for graft loss and mortality using
patient level data
• Compare model performance with those derived of national
data
• Deploy predictive models in a clinician facing interface
through the electronic medical record to drive post transplant
clinical care
9. Workflow of Data Extraction,
Storage, Analysis and Deployment
Watson
Natural
Language
Processing
Hadoop data
storage
Predictive analytic, Data
Processing and Scoring
10. IBM SPSS Modeler & C&DS
• Predictive modeling.
• Data integration and
processing
• Creation of dynamic
variables (e.g. eGFR
trajectory)
• Predictive model
building.
• Model deployment
and scoring
Automation. (C&DS:
Collaboration &
Deployment Services)
12. eGFR: TRAJECTORY
Similar approaches can be used to incorporate hemoglobin, blood pressure and heart
rates into longitudinal models
Time to first Max
Standard Deviation
Slope from last Max
To day 90 (forced at Max)
13. Statistical Analysis
§ Risk models were developed for 1 & 3 year GL and 3 year
Mortality)
Each of these risk models incorporated variables as follows:
§ Model 1: OPTN/UNOS/SRTR variables
§ Model 2: UNOS + Tx Database Variables
§ Model 3: UNOS + Tx Database + EHR Comorbidities
§ Model 4: UNOS + Tx Database + NLP variables + Trajectory
variables
14. Statistical Analysis
• Backward selection in the Multivariable Firth
logistic model using an exit p-value at the 20
percent level for variable selection
• For verification of selection,
o Step-wise AIC variable selection criteria
o Lasso variable selection technique
• Clinical adjudication if discrepancies of variables
were revealed between methods
• Methods to account for over-fitting were utilized
15. Rationale for Variable Inclusion
Variable Source; Category
KDRI UNOS; Kidney Quality
Caregiver Status Transplant Database; Social
Determinant
Education Status Transplant Database; Social
Determinant
ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic
risk
Banff Lesion Scores EHR, NLP; Immunologic Risk
CMV, BKV PCRs EHR: Immunologic Risk
Cardiovascular events EHR; CV Risk; Access to care
Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic
risk
Hemoglobin and eGFR
Slopes/trajectories
EHR; Biology of Kidney Function
Readmit Counts EHR; Processes of care, Access to
care
16. Rationale for Variable Inclusion
Variable Source; Category
KDRI UNOS; Kidney Quality
Caregiver Status Transplant Database; Social
Determinant
Education Status Transplant Database; Social
Determinant
ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic
risk
Banff Lesion Scores EHR, NLP; Immunologic Risk
CMV, BKV PCRs EHR: Immunologic Risk
Cardiovascular events EHR; Immunologic Risk
Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic
risk
Hemoglobin and eGFR
Slopes/trajectories
EHR; Biology of Kidney Function
Readmit Counts EHR; Processes of care, Access to
care
17. Rationale for Variable Inclusion
Variable Source; Category
KDRI UNOS; Kidney Quality
Caregiver Status Transplant Database; Social
Determinant
Education Status Transplant Database; Social
Determinant
ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic
risk
Banff Lesion Scores EHR, NLP; Immunologic Risk
CMV, BKV PCRs EHR: Immunologic Risk
Cardiovascular events EHR; CV Risk, Access to care
Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic
risk
Hemoglobin and eGFR
Slopes/trajectories
EHR; Biology of Kidney Function
Readmit Counts EHR; Processes of care, Access to
care
18. Rationale for Variable Inclusion
Variable Source; Category
KDRI UNOS; Kidney Quality
Caregiver Status Transplant Database; Social
Determinant
Education Status Transplant Database; Social
Determinant
ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic
risk
Banff Lesion Scores EHR, NLP; Immunologic Risk
CMV, BKV PCRs EHR: Immunologic Risk
Cardiovascular events EHR; CV Risk, Access to care
Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic
risk
Hemoglobin and eGFR
Slopes/trajectories
EHR; Biology of Kidney Function
Readmit Counts EHR; Processes of care, Access to
care
19. Rationale for Variable Inclusion
Variable Source; Category
KDRI UNOS; Kidney Quality
Caregiver Status Transplant Database; Social
Determinant
Education Status Transplant Database; Social
Determinant
ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic
risk
Banff Lesion Scores EHR, NLP; Immunologic Risk
CMV, BKV PCRs EHR: Immunologic Risk
Cardiovascular events EHR; CV Risk, Access to care
Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic
risk
Hemoglobin and eGFR
Slopes/trajectories
EHR; Biology of Kidney Function
Readmit Counts EHR; Processes of care, Access to
care
20. Rationale for Variable Inclusion
Variable Source; Category
KDRI UNOS; Kidney Quality
Caregiver Status Transplant Database; Social
Determinant
Education Status Transplant Database; Social
Determinant
ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic
risk
Banff Lesion Scores EHR, NLP; Immunologic Risk
CMV, BKV PCRs EHR: Immunologic Risk
Cardiovascular events EHR; CV Risk, Access to care
Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic
risk
Hemoglobin and eGFR
Slopes/trajectories
EHR; Biology of Kidney Function
Readmit Counts EHR; Processes of care, Access to
care
21. Rationale for Variable Inclusion
Variable Source; Category
KDRI UNOS; Kidney Quality
Caregiver Status Transplant Database; Social
Determinant
Education Status Transplant Database; Social
Determinant
ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic
risk
Banff Lesion Scores EHR, NLP; Immunologic Risk
CMV, BKV PCRs EHR: Immunologic Risk
Cardiovascular events EHR; CV Risk, access to care
Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic
risk
Hemoglobin and eGFR
Slopes/trajectories
EHR; Biology of Kidney Function
Readmit Counts EHR; Processes of care, Access to
care
22. Rationale for Variable Inclusion
Variable Source; Category
KDRI UNOS; Kidney Quality
Caregiver Status Transplant Database; Social
Determinant
Education Status Transplant Database; Social
Determinant
ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic
risk
Banff Lesion Scores EHR, NLP; Immunologic Risk
CMV, BKV PCRs EHR: Immunologic Risk
Cardiovascular events EHR; CV Risk, access to care
Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic
risk
Hemoglobin and eGFR
Slopes/trajectories
EHR; Biology of Kidney Function
Readmit Counts EHR; Processes of care, Access to
care
23.
24. 3 Year Graft Loss Risk and Data Sources
Odds Ratios; Logistic (Firth)
Odds
Ratio
95% Profile-Likelihood
Confidence Limits p≤0.05
Model 1: UNOS
KDRI 3.975 2.197 7.194 *
Age at Transplant 0.987 0.970 1.004
Female 0.588 0.360 0.936 *
Blood Type B 1.542 0.883 2.596
Model 2: UNOS + Transplant
Database
KDRI 4.160 2.290 7.566 *
Age at Transplant 0.985 0.968 1.002
Female 0.624 0.381 0.997 *
Primary Care Giver 0.469 0.295 0.756 *
25. 3 Year Graft Loss Risk and Data Sources
Odds Ratios; Logistic (Firth)
Odds
Ratio
95% Profile-Likelihood
Confidence Limits p≤0.05
Model 1: UNOS
KDRI 3.975 2.197 7.194 *
Age at Transplant 0.987 0.970 1.004
Female 0.588 0.360 0.936 *
Blood Type B 1.542 0.883 2.596
Model 2: UNOS + Transplant
Database
KDRI 4.160 2.290 7.566 *
Age at Transplant 0.985 0.968 1.002
Female 0.624 0.381 0.997 *
Primary Care Giver 0.469 0.295 0.756 *
26. 3 Year Graft Loss Risk and Data Sources
Model 3: UNOS + Transplant
Database + Comorbidity OR 95% CI p≤0.05
KDRI 4.239 2.330 7.734 *
Age at Transplant 0.985 0.968 1.002
Female 0.623 0.375 1.012
Primary Care Giver 0.492 0.307 0.797 *
Cerebrovascular Disease 0.248 0.027 0.978 *
Cardiac Arrhythmias 1.712 1.031 2.788 *
Alcohol Abuse 2.446 0.793 6.463
Depression 1.829 0.928 3.419
27. 3 Year Graft Loss Risk and Data Sources
Model 3: UNOS + Transplant
Database + Comorbidity OR 95% CI p≤0.05
KDRI 4.239 2.330 7.734 *
Age at Transplant 0.985 0.968 1.002
Female 0.623 0.375 1.012
Primary Care Giver 0.492 0.307 0.797 *
Cerebrovascular Disease 0.248 0.027 0.978 *
Cardiac Arrhythmias 1.712 1.031 2.788 *
Alcohol Abuse 2.446 0.793 6.463
Depression 1.829 0.928 3.419
33. Pairwise Model Comparison
Model 1: UNOS only
Model 2: UNOS + Transplant Database
Model 3: UNOS + Transplant Databases + EHR Comorbidity
Model 4: UNOS + Transplant database + NLP + EHR Comorbidity + EHR Post-
Transplant Trajectory
1 Year Graft Loss 3 Year Graft Loss 3 Year Mortality
Data Model A vs Data Model B P-value Data Model A vs Data Model B P-value Data Model A vs Data Model B P-value
2 (AUC=0.741) vs 1 (AUC=0.716) 0.416 2 (AUC=0.665) vs 1 (AUC=0.661) 0.838 2 (AUC=0.765) vs 1 (AUC=0.765) 1.000
3 (AUC=0.769) vs 1 (AUC=0.716) 0.170 3 (AUC=0.712) vs 1 (AUC=0.661) 0.021 3 (AUC=0.768) vs 1 (AUC=0.765) 0.476
4 (AUC=0.873) vs 1 (AUC=0.716) <.001 4 (AUC=0.846) vs 1 (AUC=0.661) <.001 4 (AUC=0.838) vs 1 (AUC=0.765) 0.004
3 (AUC=0.769) vs 2 (AUC=0.741) 0.119 3 (AUC=0.712) vs 2 (AUC=0.665) 0.006 3 (AUC=0.768) vs 2 (AUC=0.765) 0.476
4 (AUC=0.873) vs 2 (AUC=0.741) <.001 4 (AUC=0.846) vs 2 (AUC=0.665) <.001 4 (AUC=0.838) vs 2 (AUC=0.765) 0.004
4 (AUC=0.873) vs 3 (AUC=0.769) 0.001 4 (AUC=0.846) vs 3 (AUC=0.712) <.001 4 (AUC=0.838) vs 3 (AUC=0.768) 0.007
34. Pairwise Model Comparison
Model 1: UNOS only
Model 2: UNOS + Transplant Database
Model 3: UNOS + Transplant Databases + EHR Comorbidity
Model 4: UNOS + Transplant database + NLP + EHR Comorbidity + EHR Post-
Transplant Trajectory
1 Year Graft Loss 3 Year Graft Loss 3 Year Mortality
Data Model A vs Data Model B P-value Data Model A vs Data Model B P-value Data Model A vs Data Model B P-value
2 (AUC=0.741) vs 1 (AUC=0.716) 0.416 2 (AUC=0.665) vs 1 (AUC=0.661) 0.838 2 (AUC=0.765) vs 1 (AUC=0.765) 1.000
3 (AUC=0.769) vs 1 (AUC=0.716) 0.170 3 (AUC=0.712) vs 1 (AUC=0.661) 0.021 3 (AUC=0.768) vs 1 (AUC=0.765) 0.476
4 (AUC=0.873) vs 1 (AUC=0.716) <.001 4 (AUC=0.846) vs 1 (AUC=0.661) <.001 4 (AUC=0.838) vs 1 (AUC=0.765) 0.004
3 (AUC=0.769) vs 2 (AUC=0.741) 0.119 3 (AUC=0.712) vs 2 (AUC=0.665) 0.006 3 (AUC=0.768) vs 2 (AUC=0.765) 0.476
4 (AUC=0.873) vs 2 (AUC=0.741) <.001 4 (AUC=0.846) vs 2 (AUC=0.665) <.001 4 (AUC=0.838) vs 2 (AUC=0.765) 0.004
4 (AUC=0.873) vs 3 (AUC=0.769) 0.001 4 (AUC=0.846) vs 3 (AUC=0.712) <.001 4 (AUC=0.838) vs 3 (AUC=0.768) 0.007
39. Limitations
• This is a proof of concept which needs to
be evaluated in the clinic
• Single center study that needs to be
replicated in other centers and across
EMR and analytic platforms
• Rules of engagement relevant to real time
deployment in the clinical setting are not
defined
41. Team
MUSC
• Titte Srinivas, MD
• David Taber, Pharm D
• Patrick Mauldin, PhD
• Jingwen Zhang, MS
• Zemin Su, MS
• Justin Marsden, MS
• William Moran, MD, MS
• David Northrup, MS
• Mark Daniels, MS
• Karthick Gourisankaran, MS
• Leslie Lenert, MD, MS
• Katie Reilly, BA
• Martha Sylvia, RN,MSN
IBM
• Haroon Anwar, MS
• Arun Tripathi, Ph.D.
• Salvatore Galascio, MS
45. MUSC Kidney Transplant Modeling
One year Graft Loss (GL)
(Transplant dates between 1/2007 and 6/2015)
• 1,176 patients analyzed
• 45 had 1yr GL (3.8%)
• Logistic Firth
• 90 days exposure period
• Backward Selection + Stepwise AIC
• AUC = 0.873 (0.807 – 0.939)
• Harrell’s Adjusted AUC = 0.829
• BCA (Bias Corrected and Accelerated)
Bootstrap AUC = 0.764 – 0.913
46. MUSC Kidney Transplant Modeling
Three year Graft Loss (GL)
(Transplant dates between 1/2007 and 6/2015)
• 891 patients analyzed
• 89 had 3yr GL (10.0%)
• Logistic Firth
• 1 year exposure
• Backward Selection + Stepwise AIC
• AUC = 0.846
• Harrell’s Adjusted AUC = 0.811
• BCA (Bias Corrected and Accelerated)
Bootstrap AUC = 0.791 – 0.869
47. MUSC Kidney Transplant Modeling
Three year Mortality (ML)
(Transplant dates between 1/2007 and 6/2015)
• 880 patients analyzed
• 76 had 3yr ML (8.9%)
• Logistic Firth
• 1 year exposure
• Backward Selection + Stepwise AIC
• AUC = 0.838
• Harrell’s Adjusted AUC = 0.800
• BCA (Bias Corrected and Accelerated)
Bootstrap AUC = 0.766 – 0.868
48. MUSC Kidney Transplant Modeling
(Transplant dates between 1/2007 and 6/2015)
• Firth Logistic Regression
• 90 days exposure period for 1 year
survival models and 1 year exposure for 3
year models
• Backward Selection + Stepwise AIC
49. Logistic (Firth) regression odds ratio and 95% confidence intervals (Model 4: UNOS + Velos + EHR Comorbidity +
EHR Post-Transplant Trajectory)
Odds Ratio (95% CI)
1 Year Graft Loss 3 Year Graft Loss 3 Year Mortality
UNOS
Age at Transplant 0.98 (0.96, 1.00)* 1.03 (1.00, 1.05)*
Female 0.57 (0.32, 0.99)*
African American 0.46 (0.26, 0.81)*
KDRI 2.48 (0.95, 6.43) 3.06 (1.49, 6.28)* 1.81 (0.88, 3.69)
Blood Type B 3.41 (1.55, 7.35)*
Waitlisting Time (Years) 0.76 (0.56, 0.98)*
First Week Dialysis 2.48 (1.20, 5.00)*
Diabetes 1.67 (0.93, 3.02)
Obesity 0.50 (0.23, 1.06) 0.66 (0.38, 1.11) 0.65 (0.36, 1.14)
Private Insurance 0.46 (0.21, 0.91)*
Previous Kidney Transplant 2.31 (0.99, 5.05)
Graft Loss by 1 Year 2.77 (1.13, 6.54)*
Transplant Database
Finish High School 0.47 (0.23, 0.97)*
Smoker 2.61 (0.87, 6.91)
Primary Care Giver Identified at Transplant 0.40 (0.23, 0.69)*
EHR***
Cerebrovascular Disease 0.07 (<0.01, 0.65)* 0.23 (0.02, 1.13)
Cardiac Arrhythmias 2.16 (1.01, 4.52)*
Alcohol Abuse 3.71 (0.87, 12.73) 3.22 (0.89, 9.78)
Drug Abuse 3.55 (0.63, 15.50)
Depression 1.91 (0.86, 3.98) 0.44 (0.14, 1.13)
Transplant LOS (Days) 1.08 (1.01, 1.26)*
Acute MI during Exposure** 11.14 (2.15, 54.30)*
Cardiac or Vascular Event during Exposure 2.48 (1.06, 5.66)* 2.98 (1.74, 5.10)* 2.23 (1.21, 4.08)*
BK>500 during Exposure 1.93 (0.90, 3.88)
CMV>500 during Exposure 0.20 (0.02, 0.88)*
Pulse Mean during Exposure 1.03 (1.00, 1.07)* 1.04 (1.00, 1.07)*
SBP Mean during Exposure 0.97 (0.94, 1.00)*
Pulse Pressure SDev during Exposure 1.14 (1.04, 1.25)* 1.14 (1.06, 1.22)*
Glucose Mean during Exposure 0.99 (0.98, 1.00)
HGB Slope perMonth - Day7 until End of Exposure 0.78 (0.58, 0.93)* 0.72 (0.56, 0.87)*
Max eGFR during Exposure 0.97 (0.95, 0.99)* 0.99 (0.98, 1.00)*
eGFR Slope from Max Value until End of Exposure 0.83 (0.74, 0.99)* 0.87 (0.73, 0.98)*
Tacrolimus_SDev_during Exposure 1.18 (0.97, 1.42)
Inpatient Readm Count during Exposure 1.42 (1.03, 1.93)* 1.16 (0.98, 1.37)
Emergency Department Visit Count during
Exposure 1.31 (0.93, 1.74)
NLP
Max Acute Banff Score during Exposure 1.37 (1.22, 1.54)*
50. Statistical Analysis
• Assess Overfit: To assess and adjust for overfitting, results
were validated using the Harrell Optimism Correction; The
Harrell optimism corrected c statistic is an “honest” estimate
of internal validity, penalizing for overfitting
• Internal Validation
o Bootstrapping with Bias Correction methodology were used for
model internal validation and AUCs for the ROC curve were
used to determine and compare model accuracy
o Empirical cross check between actual observed events and
model predicted risk scores was conducted by clinicians
• Assess Methodology: To assess the “robustness” of the
logistic methodology, models were also created using survival
analysis (Cox Proportional Hazard)
51. Supplemental Material: Cox proportional Hazard Model Results
Supplemental Table 1: One Year Graft Loss
Green: represents variables in Logistic Model but not in Cox Model
Yellow: represents variables in Cox Model but not in Logistic Model
Cox Proportional Hazard Model
(Concordance (C-index) = 0.873, se = 0.043)
Effect Hazard Ratio LHR ULR p-value
KDRI
Blood Type B 3.080 1.496 6.343 0.002
Obesity 0.588 0.288 1.202 0.145
Waiting Time (Years) 0.776 0.608 0.990 0.042
Finish High School 0.589 0.297 1.166 0.129
Smoker 2.688 1.086 6.655 0.033
Cerebrovascular Disease
Cardiac Arrhythmias 2.206 1.113 4.372 0.023
Alcohol Abuse 2.809 0.845 9.332 0.092
Drug Abuse
Cardiac or Vascular Event_90days 2.539 1.206 5.345 0.014
CMV>500_90days 0.128 0.017 0.959 0.046
SBP Mean_90days 0.974 0.949 1.000 0.048
Pulse Pressure SDev_90days 1.134 1.043 1.234 0.003
Glucose Mean_90days 0.985 0.972 0.997 0.018
HGB Slope perMonth from Day7 until 90days 0.814 0.753 0.879 <.0001
eGFR Slope from Max Value until 90days 0.820 0.724 0.932 0.002
Max eGFR_90days 0.965 0.946 0.985 0.001
Inpatient Readm Count_90days 1.282 0.977 1.682 0.073
Female 0.486 0.233 1.013 0.054
Age At Transplant 1.027 0.998 1.056 0.068
Married 0.501 0.243 1.032 0.061
Depression 0.382 0.101 1.444 0.156
HGB Mean from Day7 until 90days 0.738 0.542 1.004 0.053
Max Acute Banff Score_90days 1.151 0.992 1.334 0.063
Tacrolimus Mean_90days 0.827 0.656 1.042 0.107
52. Supplemental Table 2: Three Year Graft Loss
Cox Proportional Model
(Concordance (C-index) = 0.843, se = 0.031)
Effect Hazard Ratio LHR ULR p-value
KDRI 2.739 1.480 5.069 0.001
Age at Transplant 0.983 0.964 1.002 0.081
Female 0.546 0.333 0.896 0.017
Obesity 0.646 0.401 1.039 0.071
Primary Care Giver 0.581 0.361 0.935 0.025
Cerebrovascular Disease 0.081 0.008 0.798 0.031
Alcohol Abuse
Depression
Acute MI_1yr 4.746 1.537 14.652 0.007
Cardiac or Vascular Event_1yr 2.411 1.461 3.979 0.001
Pulse Pressure SDev_1yr 1.147 1.078 1.221 <.0001
HGB Slope perMonth from Day7 until 1yr 0.817 0.758 0.881 <.0001
Pulse Mean_1yr 1.027 0.998 1.056 0.067
Max eGFR_1yr 0.985 0.975 0.995 0.004
Max Acute Banff Score_1yr 1.296 1.194 1.407 <.0001
ED Readm Count_1yr 1.260 0.944 1.682 0.117
Diabetes 0.647 0.375 1.116 0.118
Receive Disability 0.676 0.413 1.108 0.120
Smoker 1.733 0.837 3.592 0.139
Cardiac Arrhythmias 1.764 1.076 2.894 0.025
BK>500_1yr 0.522 0.252 1.078 0.079
CMV>500_1yr 0.648 0.357 1.177 0.154
eGFR Slope from Max Value until 1yr 0.862 0.762 0.972 0.017
Inpatient Readm Count_1yr 1.152 1.000 1.326 0.049
Tacrolimus Mean_1yr 0.865 0.725 1.033 0.109
Tacrolimus SDev_1yr 1.144 0.956 1.368 0.141
Green: represents variables in Logistic Model but not in Cox Model
Yellow: represents variables in Cox Model but not in Logistic Model
53. Supplemental Table 3: Three Year Mortality
Cox Proportional Model
(Concordance (C-index) = 0.815, se = 0.033)
Effect Hazard Ratio LHR ULR p-value
KDRI
Age at Transplant 1.029 1.007 1.050 0.008
African American 0.649 0.393 1.074 0.093
Private Insurance 0.491 0.249 0.965 0.039
Obesity
First Week Dialysis
Diabetes 1.567 0.930 2.638 0.091
Previous Kidney Transplant 2.481 1.244 4.949 0.010
Graft Loss_1yr 2.363 1.126 4.957 0.023
Depression 0.447 0.177 1.125 0.087
Transplant LOS (Days) 1.049 1.014 1.086 0.006
Cardiac or Vascular Event_1yr 1.990 1.164 3.401 0.012
BK>500_1yr
Pulse Mean_1yr
eGFR Slope from Max Value until 1yr 0.850 0.774 0.932 0.001
Inpatient Readm Count_1yr 1.113 0.968 1.280 0.133
Tacrolimus SDev_1yr 1.166 0.990 1.373 0.066
Distance to MUSC (Miles) 1.001 1.000 1.003 0.155
Receive Disability 0.687 0.408 1.157 0.158
Smoker 1.832 0.808 4.151 0.147
Peripheral Vascular Disorders 1.521 0.784 2.950 0.215
CMV>500_1yr 1.673 0.948 2.951 0.076
SBP Mean_1yr 0.980 0.962 0.999 0.040
HGB Mean from Day7 until 1yr 0.783 0.643 0.954 0.015
HGB Slope perMonth from Day7 until 1yr 0.796 0.565 1.123 0.194
Green: represents variables in Logistic Model but not in Cox Model
Yellow: represents variables in Cox Model but not in Logistic Model