A clinical tool to risk stratify potential kidney transplant recipients and predict severe adverse events

Nygaard, Rachel M; Sirany, A.; Wyman, Elizabeth A.; Bodner, J.; Richardson, Chad J; Ney, Arthur L.; Odland, Mark D.; Hill, Mark J.

doi:10.1111/ctr.12848

Cited by 10 publications

(9 citation statements)

References 22 publications

(51 reference statements)

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“…Our findings highlight the important value of incorporating cTnT measurements in addition to cardiac and vascular disease in predicting mortality after kidney transplant. The C-statistic of the ACTV score (0.64) was similar to that of other predictive scores using pre-transplant data [10,24]. Specifically, the clinical tool developed by Grams et al [10] included 19 pretransplant variables and had a C-statistic of 0.65 in the original cohort and 0.68 by cross-validation.…”

Section: Discussionmentioning

confidence: 71%

“…Specifically, the clinical tool developed by Grams et al [10] included 19 pretransplant variables and had a C-statistic of 0.65 in the original cohort and 0.68 by cross-validation. In the model by Nygaard et al [24], there were 5 pre-transplant variables (age, functional performance, dialysis with or without diabetes, and CV factors which were composed of 8 additional different variables). The C-statistic of the development and validation cohort for the raw score was 0.73.…”

Section: Discussionmentioning

confidence: 99%

“…Several mortality risk prediction models for kidney transplant recipients exist, which rely on a variety and often large number of clinical and laboratory variables both pre and post transplant and have not incorporated cTnT [5,8,10,14,15,[22][23][24]. The purpose of this study is to examine the role of pre-transplant clinical variables and cTnT to predict 5-and 10-year mortality in a cohort of kidney transplant recipients at a tertiary center.…”

Section: Introductionmentioning

confidence: 99%

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Cardiac Troponin T Risk Stratification Model Predicts All-Cause Mortality Following Kidney Transplant

et al. 2018

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Background: We evaluated the role of increased cardiac troponin T (cTnT), vascular, and cardiac diseases in predicting 5 and 10-year all-cause mortality after kidney transplantation. Methods: We reviewed a cohort of 764 kidney transplant recipients and analyzed pertinent cardiovascular risk factors at the time of transplant evaluation. Proportional hazards regression analysis with bootstrapping method was utilized to provide a risk stratification score for mortality. Results: Mean age was 58.8 years (SD 12.1) and median follow-up was 7.0 years (range 1 day to 18.0 years). Fifty-four percent of patients (n = 415) had cTnT measured (median 0.02 ng/mL, range 0.01–4.91). Fifty-three percent (n = 407) had vascular disease, 59% (n = 448) had diabetes, and 44% (n = 336) had cardiac disease pre-transplant. Sixty percent (n = 460) required dialysis. Older age, increased cTnT, pre-transplant vascular and cardiac diseases predicted mortality in multivariate analysis. We derived 2 scoring systems with and without cTnT – the ACV and ACTV scores (age, cardiac disease, elevated cTnT, and vascular disease) – as predictors of mortality after kidney transplant. Point assignments were: age 60–69 years (1), age ≥70 years (2), cardiac disease (1), cTnT ≥0.04 ng/mL (1), and vascular disease (1). Both scoring systems significantly predicted mortality. The ACTV score better delineated risk stratification across score levels (0–2, 3–4, and 5 points). Conclusions: We developed a risk schema predictive of all-cause mortality after kidney transplant in a derivation cohort. The ACTV score, including an elevated cTnT, provided superior prediction compared to a scoring system without cTnT. Further studies to validate these findings are needed.

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Section: Discussionmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cardiac Troponin T Risk Stratification Model Predicts All-Cause Mortality Following Kidney Transplant

et al. 2018

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“…As a practical and simple tool, the risk score is an effective evaluation strategy for many diseases. 19 , 20 This approach is very practical for predicting depressive symptoms in China’s growing population of menopausal women. The aim of the present study was to develop a risk assessment model based on hot flashes and sweating symptoms.…”

Section: Introductionmentioning

confidence: 99%

Prediction of risk of depressive symptoms in menopausal women based on hot flash and sweating symptoms: a multicentre study

Zheng¹,

Zhou²,

Hu³

et al. 2017

CIA

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ObjectiveThe present study aimed to develop a symptom-based (namely, hot flashes and sweating) scoring system for predicting the risk of depressive symptoms in menopausal women via a multicentre cross-sectional survey.MethodsThe data examined in the present study were obtained from 1,004 women aged 40–60 years who underwent physical examination at A Hospital. The basic information was obtained using a questionnaire-based survey. A self-rating depression scale was used to obtain the depressive symptom scores, while the Kupperman Menopausal Index was used to obtain the scores for the frequency of hot flashes and sweating. A logistic regression model was also established. The resulting β coefficient was employed to calculate and predict the risk of depressive symptoms in these women and a risk scoring system was established. The scoring system was validated using samples from 2 other centers (validation sample 1: B Hospital, 440 women; validation sample 2: C Hospital, 247 women).ResultsThe scoring system developed to predict the risk of depressive symptoms in menopausal women was based on hot flash and sweating symptoms and associated with menopausal status, hot flash scores, education level (high school education and below) and being diabetic. The scoring system yielded a total score of 0–54 points. For women in the study sample, the area under the curve (AUC) of depressive symptom risk score was 0.750 (95% CI, 0.708–0.793). Validation sample 1 had an AUC of 0.731 (95% CI, 0.667–0.794), while validation sample 2 had an AUC of 0.744 (95% CI, 0.669–0.820). The optimal cut-off score to assess depressive symptoms in women participating in the present study was 31 points. The sensitivity and specificity for predicting depressive symptoms in the study sample were 0.667 and 0.701, respectively. In contrast, the sensitivity was 0.840 in validation sample 1 and 0.879 in validation sample 2.ConclusionThe hot flash and sweating symptom-based scoring system developed to predict the risk of depressive symptoms in menopausal women relies on non-laboratory survey data. The system is simple, practical, and convenient to use. For Chinese huge population of menopausal women, the scoring system should be considered a reliable screening tool for depressive symptoms.

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“…However, the complex, polygenic, multidimensional properties of the data available for a given patient have exceeded the capacity for human intuitive integration. Currently used models are limited in their predictive ability, and there is no model that formally organizes and integrates the incomprehensible amount of data available on each patient into an aggregate, individual risk assessment 1‐5 . The fundamental problem is not a lack of awareness that risks have relationships with one another, but an inability to integrate the data describing social determinants of health and patient satisfaction with mechanistic and point‐of ‐care data such that they can inform clinical practice.…”

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confidence: 99%

Eudaimonia: An Aristotelian approach to transplantation

McElroy

Kirk

2021

American Journal of Transplantation

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Despite extraordinary achievements in over the past 20 years, the field of transplantation remains hindered by relatively narrow metrics for success. Eudaimonia is an Aristotelian concept that refers to flourishing, or achieving the best conditions possible, in every sense. The vast amounts of patient data that are collected throughout the transplant care continuum, ranging from social determinants of health to genomic profiles and patient‐reported outcomes, afford us unprecedented opportunity to enhance our definition of success for our transplant patients. We must engage the technologies available for data integration and analysis and apply them in an insightful way, such that our clinical practice evolves beyond patient and graft survival and toward a more comprehensive state of wellness.

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A clinical tool to risk stratify potential kidney transplant recipients and predict severe adverse events

Cited by 10 publications

References 22 publications

Cardiac Troponin T Risk Stratification Model Predicts All-Cause Mortality Following Kidney Transplant

Cardiac Troponin T Risk Stratification Model Predicts All-Cause Mortality Following Kidney Transplant

Prediction of risk of depressive symptoms in menopausal women based on hot flash and sweating symptoms: a multicentre study

Eudaimonia: An Aristotelian approach to transplantation

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