100 research outputs found
Neutrophil gelatinase-associated lipocalin in kidney transplantation is an early marker of graft dysfunction and is associated with one-year renal function
Urinary neutrophil gelatinase-associated lipocalin (uNGAL) has been suggested as potential early marker of delayed graft function (DGF) following kidney transplantation (KTx). We conducted a prospective study in 40 consecutive KTx recipients to evaluate serial changes of uNGAL within the first week after KTx and assess its performance in predicting DGF (dialysis requirement during initial posttransplant week) and graft function throughout first year. Urine samples were collected on post-KTx days 0, 1, 2, 4, and 7. Linear mixed and multivariable regression models, receiver-operating characteristic (ROC), and areas under ROC curves were used. At all-time points, mean uNGAL levels were significantly higher in patients developing DGF (n = 18). Shortly after KTx (3-6 h), uNGAL values were higher in DGF recipients (on average +242 ng/mL, considering mean dialysis time of 4.1 years) and rose further in following days, contrasting with prompt function recipients. Day-1 uNGAL levels accurately predicted DGF (AUC-ROC = 0.93), with a performance higher than serum creatinine (AUC-ROC = 0.76), and similar to cystatin C (AUC-ROC = 0.95). Multivariable analyses revealed that uNGAL levels at days 4 and 7 were strongly associated with one-year serum creatinine. Urinary NGAL is an early marker of graft injury and is independently associated with dialysis requirement within one week after KTx and one-year graft function.The authors recognize and thank Abbott Laboratories for their valuable contribution for donating kits used for testing almost 200 samples. The remaining kits were financed by funds of Unit for Multidisciplinary Investigation in Biomedicine, Porto, Portuga
Predictive factors of graft dysfunction and long-term kidney allograft failure
Picard Elizabeth. Séminaire sur le Golfe Persique et les changements structurels du système international -Téhéran -17-18 décembre 1995 . In: CEMOTI, n°22, 1996. Arabes et Iraniens. pp. 347-352
Fatores preditivos de disfunção e perda do enxerto renal a longo prazo
Kidney transplantation is considered the treatment of choice for many patients with endstage chronic kidney disease; however, despite advancements in short-term allograft survival, long-term survival has not paralleled this improvement. Due to the inevitable ischemic damage and associated reperfusion injury, delayed graft function (DGF) is a common complication after kidney transplantation, which may negatively affect graft survival. Because serum creatinine (SCr) and other traditional markers of kidney injury are insensitive and delayed in the detection of the early stages of kidney damage and DGF, there has been a keen interest in the identification of novel biomarkers for the early detection of allograft dysfunction that could expedite treatment and improve long-term patient and graft survival. Biomarkers are characteristics that can be objectively measured in a biological sample. In clinical settings, biomarkers enable the diagnosis of a dysfunction or disease and, in some cases, they are used to monitor a treatment or to make a prognosis regarding the future outcome of a patient. The analysis of predictive factors of graft dysfunction and long-term kidney allograft failure focusing on novel biomarkers was the major motivation for this work. Thus, the general aim of this thesis was to investigate the potential of different biomarkers to reliably diagnose and predict early graft dysfunction and their effect on long-term kidney allograft failure as well as to gain insight into the underlying mechanisms of graft dysfunction.
Patients and Methods:The study involved three cohorts of patients: two retrospective
cohorts that included kidney transplant recipients selected from a database that contained transplant and follow-up information on kidney transplants performed between 1983 and 2008 (for the first retrospective cohort) or 2012 (for the second retrospective cohort); and one prospective cohort that included 40 patients undergoing kidney transplantation between December 2009 and June 2010. The first retrospective cohort was used to validate the one-year SCr as a surrogate endpoint of long-term graft survival, and the second retrospective cohort was considered to analyze the impact of DGF (defined by the need for dialysis during the first week after kidney transplantation) on graft and patient survival using a competing risks approach. The studies based on the prospective cohort had a longitudinal observational design, which was initiated at the time of transplantation; this cohort was used to examine nine potential candidate biomarkers for the early diagnosis of DGF (one biomarker in urine and eight biomarkers in blood): cystatin C (CysC), neutrophil gelatinase-associated lipocalin (N.GAL), leptin and adiponectin, malondialdehyde (M.D.A), superoxide dismutase (S.OD), glutathione reductases (GR), peroxidases (GPx) and total antioxidant status (TAS). Five samples per patient were collected within the first week: 3 to 6 h prior to transplant surgery (pre-transplant); on the subsequent morning at approximately 8 to 12 h after graft reperfusion (day-1); and then on the second (day-2), fourth (day-4) and seventh (day-7) days after transplant, which resulted in five samples per patient.
A linear mixed effects model was used to evaluate the longitudinal changes of the potential new biomarkers of early graft dysfunction over the first week after kidney transplantation and to identify the factors associated with these changes. The performance of the candidate biomarkers in the prediction of DGF was examined using
receiver-operating characteristic (R.OC) curves. Survival analysis methods, including a
survival analysis that accounted for competing risks were used to identify the predictive
factors of long-term graft survival.
Results: Of the large number of variables that were considered, the SCr levels at 1, 6 and 12 months following kidney transplantation, as well as the changes between 1 and 6 months and between 6 and 12 months were independently associated with late graft failure.
The R.OC curves identified urinary NGAL, MDA and CysC on the first postoperative day as moderately (NGAL) and highly (MDA and CysC) accurate in the prediction of DGF. Both urinary NGAL (at days 4 and 7) and MDA (day-7) were independently associated with one-year graft function, adjusting for variables that typically affect graft function, including acute rejection episodes and re-admissions during the first post-transplant year.
Leptin at day-1 was slightly better than SCr in the prediction of the need for dialysis within the first week post-transplant, whereas adiponectin, SOD, GR, GPx and TAS were not. A triple-biomarker approach that used SCr, CysC, and MDA measured 8 to 12 h after kidney transplantation, was the most informative combination, which resulted in an increased ability (AUC=0.96) to distinguish patients with graft damage who would require dialysis within the first week. The application of a subdistribution regression model for competing risks indicated that DGF by itself and independent of acute rejection had a detrimental effect on long-term graft survival, but not on patient survival.
Conclusions: Independent of acute rejection, DGF per se was significantly associated
with poor-graft survival, but not with patient survival. Urinary NGAL and serum CysC and MDA were early, noninvasive, and accurate predictors of both the need for dialysis within the first week of kidney transplantation and one-year graft function. A triple-biomarker approach using SCr, CysC and MDA were highly predictive of DGF. Combining biomarkers from different pathophysiologic pathways appears to be a rational and reliable strategy to optimize sensitivity and specificity and obtain additive diagnostic and prognostic information.Financial support for this thesis was kindly provided by:
Centro Hospital do Porto
Unit for Multidisciplinary Research in Biomedicine, Porto
Abbott Laboratories (for providing the kits for measuring urinary NGAL in almost 200 samples)
Astellas Pharma (for supporting the acquisition of reagents for measuring oxidative markers
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Developing Predictive Models for Risk of Postoperative Complications and Hemodynamic Instability in Patients Undergoing Surgery
Patients undergoing high-risk surgeries are often at higher risk of developing hemodynamic instability during surgery resulting in poor postoperative outcomes. This is usually associated with significantly increased postoperative morbidity and mortality, which therefore makes the early identification of these critical events and those patients at risk of postoperative complications crucial. With these motivations in mind, we first created a large deidentified research dataset of surgical case medical records from University of California, Irvine Medical Center (UCIMC) matched with physiological waveforms as well as intermittent vital sign values, lab values, and ventilator settings. To our knowledge, such a dataset does not currently exist for the intraoperative environment. We hope that creating a such a dataset will allow for advances in machine learning for intraoperative care. Using medical data from UCLA, we have developed deep neural network models to classify the risks of postoperative mortality, acute kidney injury, and reintubation utilizing readily available intraoperative information. Our risk scores were compared to currently commonly used risk indices ASA and Surgical Apgar as well as logistic regression. While the deep neural network models performed better than the risk scores and logistic regression, clinicians require additional information to assess what led to increased risk of complications. To address this, we also assessed the use of generalized additive neural networks (GANNs) to create a graphical look at how different features contributed to the risk of in hospital mortality. Finally, we were also interested in predicting critical intraoperative events to allow for time for the clinician to avoid such events. We focused on intraoperative hypotension as it is easier to define and has been shown to lead to increased risk of acute kidney injury, stroke, and myocardial injury. For the hypotension prediction models, we looked at the arterial pressure waveform and EMR data as inputs. Overall, these aims address a gap in current clinical decision guidance and support to reduce adverse events during surgery as well complications after
Profiling Patients With Heart Failure and Testing a Motivational Interviewing Intervention to Improve Heart Failure Self-Care
Background: Heart failure (HF) is the fastest growing cardiovascular syndrome in the United States and the most common reason for hospitalization of Medicare recipients. HF is prevalent, costly to society and complex to manage. The purpose of this body of work was to strengthen the evidence base for self-care by studying understudied aspects of HF self-care maintenance.
Methods/Results: This body of work entails four discrete studies. The first study identified modifiable predictors of patients who are at risk of consuming a diet higher in sodium than recommended by the 2010 Heart Failure Society of America guidelines. The second study identified two unique patterns of sodium intake, very high (mean 4.5 g/day) and generally adherent (mean 2.4 g/day). Predictors of the very high sodium intake group were being obese, having diabetes mellitus and less than 65 years old. The third study identified unique patterns of inflammation and myocardial stress in a sample of patients with HF from the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise TraiNing (HF-ACTION) clinical trial. Predictors of the worst biomarker pattern were identified and exercise was protective for being in the worst biomarker pattern. In response to these studies, the Motivational Interviewing Tailored Interventions for Heart Failure patients (MITI-HF) randomized controlled trial was designed and conducted to test the efficacy of a tailored motivational interviewing approach to improve self-care, physical HF symptoms and quality of life in patients with HF. Motivational interviewing was a successful approach for improving self-care maintenance, but there were no differences between groups for self-care management, self-care confidence, physical HF symptoms or quality of life.
Conclusions: In the context of the rising prevalence of HF within an environment of increasing cost-conscious appropriation of healthcare resources, this body of work provides evidence for targeting self-care interventions to patients who are at highest risk of poor outcomes. It also provides evidence that motivational interviewing is a successful approach for improving self-care maintenance behaviors, specifically eating a lower sodium diet and exercising
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