Left ventricle ejection fraction may predict mortality in renal transplant patients

Özkul, Faruk; Arik, Muhammmet Kasim; Erbiş, Halil; Akbaş, Alpaslan; Yılmaz, Vural Taner; Barutçu, Ahmet; Osmanoğlu, Ibrahim Ali; Koçak, Hüseyin

doi:10.1080/0886022x.2016.1194162

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Many variables included in the SHFM have proven to have significant impact on mortality among patients with ESRD and kidney transplant recipients, including age, coronary artery disease, low ejection fraction, hypertension, hyponatremia, anemia, and hyperuricemia [11][12][13][14][15][16][17]. In our cohort, we found that age, sodium, cholesterol, and lymphocyte count had significant univariate associations with mortality.…”

Section: Discussionmentioning

confidence: 50%

“…The SHFM has also been tested in patients with heart failure and preserved EF, although its predictive ability is slightly less in these patients [10]. Due to shared risk factors between heart and renal disease, the SHFM incorporates several known risk factors for mortality in patients with end-stage renal disease (ESRD) or kidney transplants [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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The Seattle Heart Failure Model in Kidney Transplant Recipients

Perez-Gutierrez,

McGill,

Juengel

et al. 2023

JCM

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Cardiovascular disease is the leading cause of mortality following kidney transplantation. Heart failure affects 17–21% of patients with chronic kidney disease and increases along with time receiving dialysis. The Seattle Heart Failure Model (SHFM) is a validated mortality risk model for heart failure patients that incorporates clinical, therapeutic, and laboratory parameters but does not include measures of kidney function. We applied the SHFM to patients with end-stage renal disease (ESRD) who were being evaluated for kidney transplantation to determine if the model was associated with post-transplant mortality. This retrospective single-center study analyzed survival among 360 adult deceased-donor kidney transplant recipients. Cox regression was used to model post-transplant patient survival. Our findings indicated that a 1.0-point increase in the adapted SHFM score was significantly associated with post-transplant mortality (HR 1.76, 95% CI = 1.10–2.83, p = 0.02), independently of the Kidney Donor Profile Index and Estimated Post-Transplant Survival. Individual covariates of the SHFM were evaluated in univariate analyses, and age, sodium, cholesterol, and lymphocyte count were significantly related to mortality. This study provides preliminary evidence that an adapted SHFM score could be a useful tool in evaluating mortality risk post-transplant in patients with ESRD.

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Section: Discussionmentioning

confidence: 50%

Section: Introductionmentioning

confidence: 99%

The Seattle Heart Failure Model in Kidney Transplant Recipients

Perez-Gutierrez,

McGill,

Juengel

et al. 2023

JCM

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“…Given the high prevalence of cardiovascular disease in patients on HD and its association with morbidity and mortality (2), early identification of patients with low left ventricular EF could facilitate close monitoring and the institution of disease-modifying treatments (6,7). Traditional approaches for evaluation of left ventricular systolic function, such as echocardiography, tend to be time and labor intensive (35).…”

Section: Discussionmentioning

confidence: 99%

“…Left ventricular ejection fraction (EF) is the primary metric of left ventricular systolic function. Deterioration of left ventricular EF in patients on HD has been independently associated with greater risk of cardiovascular and all-cause mortality, highlighting the importance of regular cardiac monitoring (3,6,7). Although traditionally used to quantify left ventricular EF, transthoracic echocardiography remains challenging to implement in outpatient or resource-limited settings due to personnel training requirements in addition to time and cost constraints.…”

Section: Introductionmentioning

confidence: 99%

Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis

Vaid

Jiang

Sawant

et al. 2022

CJASN

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Background and objectivesLeft ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning.Design, setting, participants, & measurementsWe identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic/procedure codes. We developed four models: (1) trained from scratch in patients on hemodialysis, (2) pretrained on a publicly available set of natural images (ImageNet), (3) pretrained on all patients not on hemodialysis, and (4) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve.ResultsWe extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and n=18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of ≤40% (n=461), 41%–50% (n=398), and >50% (n=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59).ConclusionA deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3

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Perioperative Assessment and Intraoperative Core Concepts in the Complex Kidney Patient

Efune

Wang

Mandell³

2018

Curr Transpl Rep

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Left ventricle ejection fraction may predict mortality in renal transplant patients

Abstract: LVEF may predict mortality in renal transplant patients. LVEF is known to be lower in patients with high cardiac mortality, who may require greater modifications of the postoperative risks.

Cited by 4 publications

References 15 publications

The Seattle Heart Failure Model in Kidney Transplant Recipients

The Seattle Heart Failure Model in Kidney Transplant Recipients

Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis

Perioperative Assessment and Intraoperative Core Concepts in the Complex Kidney Patient

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