Continuously Updated Estimation of Heart Transplant Waitlist Mortality

Blackstone, Eugene H.; Rajeswaran, Jeevanantham; Cruz, V.; Hsich, Eileen; Koprivanac, Marijan; Smedira, Nicholas G.; Hoercher, Katherine J.; Thuita, Lucy; Starling, Randall C.

doi:10.1016/j.jacc.2018.05.045

Cited by 30 publications

(23 citation statements)

References 28 publications

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“…Nevertheless, dynamic changes in renal function substantially alters instantaneous estimated mortality on the waitlist. 25 Serum albumin has been shown in several studies to be an independent predictor of mortality. 24,[26][27][28][29][30] In a single center study involving 438 patients admitted for acute decompensated heart failure, serum albumin <3.4 g/dL was one of the strongest predictors of 1 year mortality (aHR=2.05, 95% CI 1.10-3.81, P=0.001).…”

Section: Discussionmentioning

confidence: 99%

Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality

Hsich

Thuita

McNamara

et al. 2019

American Journal of Transplantation

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The pre-listing variables essential for creating an accurate heart transplant allocation score based on survival are unknown. To identify these we studied mortality of adults on the active heart transplant waiting list in the Scientific Registry of Transplant Recipients database from January 1, 2004-August 31, 2015. There were 33,069 candidates awaiting heart transplantation: 7,681 UNOS Status 1A, 13,027 Status 1B, and 12,361 Status 2. During a median waitlist follow-up of 4.3 months, 5514 candidates died. Variables of importance for waitlist mortality were identified by machine learning using Random Survival Forests. Strong correlates predicting survival were estimated glomerular filtration rate (eGFR), serum albumin, extracorporeal membrane oxygenation, ventricular assist device, mechanical ventilation, peak oxygen capacity, hemodynamics, inotrope support, and type of heart disease with less predictive variables including antiarrhythmic agents, history of stroke, vascular disease, prior malignancy, and prior tobacco use.

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Section: Discussionmentioning

confidence: 99%

Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality

Hsich

Thuita

McNamara

et al. 2019

American Journal of Transplantation

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“…Similar to the Cox proportional hazards model, PEMs model the conditional hazard function using aproportional hazard framework with a constant but different baseline hazard within a priori de ned intervals. The time-varying effects weaken the proportional hazards assumption from ( ) "same effect over entire follow-up" to "same effect within an interval of follow-up," which should better approximate the non-proportional hazards in patient mortality after listing (Blackstone et al, 2018). We used time points at 7, 90 and 180 days to split the overall time period in to four episodes.…”

Section: Descriptive Non-parametric Survival Analysis and Piecewise mentioning

confidence: 99%

“…"same effect over entire follow-up" to "same effect within an interval of follow-up," which should better approximate the non-proportional hazards in patient mortality after listing (Blackstone et al, 2018). We used time points at 7, 90 and 180 days to split the overall time period in to four episodes.…”

Section: Descriptive Non-parametric Survival Analysis and Piecewise mentioning

confidence: 99%

Morbidity and Mortality Rates of Boer and Central Highland Goats at Ataye Crossbreeding Program Research Site: Non-parametric Survival Analysis and Piecewise Exponential Model

Yitagesu

Alemnew

2021

Preprint

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Non-parametric survival analysis and piecewise exponential model (PEM) was used to estimate prevalence and incidence of goat mortality, to identify major clinical causes of morbidity and mortality related disease, and to investigate animal and environmental related risk factors affecting goat mortality at Ataye boer goat breeding and evaluation research site. A total of 671 kids and 347 adult (yearling) age goats were used for the analysis of non-parametric survival and piecewise exponential model for survival, mortality incidence rate and causes of morbidity and mortality analysis. The mortality incidence rate of kids and adult goats were 0.638 and 0.302 per animal year respectively. The 25th, 50th and 75th percentile of survival time of kids were 5, 157 and 1,274 days respectively and of adult goats were 280, 828 and 1,557 days respectively. The present mortality rate is relatively larger than reports of boer cross breeding and evaluation research sites in Ethiopia as well as goat mortality prevalence abroad. Gastro-intestinal related diseases, pneumonia, weak kid, agalactia, mismothering and hear water (cowdriosis) were most important causes of mortality. Constant piecewise exponential regression analysis of risk factors indicates that breed, kid birth weight (BWT), doe post-partum weight (PPWT), birth type, birth year and precipitation variables were associated with (p-value < 0.05) kid mortality rate. Pure boer kids compared with CHG cross boer goat, are 2.505 times at higher probability of mortality (p ≤ 0.001). A 1 kilo gram increase of kid birth weight and dam PPWT reduces mortality probability by 32.5% (p-value ≤ 0.001) and 6.4% (p-value ≤ 0.001) respectively. Twin birth kids are1.512 times higher rate of mortality (p-value = 0.001) compared with single born kids. A one-millilitre increment of 15 days average precipitation significantly reduces kid mortality by 7.8% (p-value ≤ 0.001). Fleshing of does during early meeting to improve the post-partum weight of does and kids is also important to reduce both kid and doe mortality at and after kidding. Immunization of new introduced and kids to common endemic diseases in the area, extensive control of ticks to breakdown heart water transmission and use of proper comfortable housing to reduce stress of goats is recommended. Improving nutrition particularly during scarce grazing and browsing feed availability is important to improving the health and reducing mortality of goats in intensively managed goat farms.

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“…Blackstone et al 129 sought to develop such a decision aid by analyzing 414 patients listed for heart transplantation at Cleveland Clinic from 2008 to 2013. Using complex statistical techniques, the authors developed a model for time-related waitlist mortality that aggregates baseline characteristics, clinical events after listing, and time-varying covariables such as laboratory measurements of end-organ function.…”

Section: Cardiac Transplantationmentioning

confidence: 99%

Noteworthy Literature Published in 2018 for Thoracic Organ Transplantation

Tawil

Adams

Nicoara

et al. 2019

Semin Cardiothorac Vasc Anesth

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Publications of note from 2018 are reviewed for the cardiothoracic transplant anesthesiologist. Strategies to expand the availability of donor organs were highlighted, including improved donor management, accumulating experience with increased-risk donors, ex vivo perfusion techniques, and donation after cardiac death. A number of reports examined posttransplant outcomes, including outcomes other than mortality, with new data-driven risk models. Use of extracorporeal support in cardiothoracic transplantation was a prominent theme. Major changes in adult heart allocation criteria were implemented, aiming to improve objectivity and transparency in the listing process. Frailty and prehabilitation emerged as targets of comprehensive perioperative risk mitigation programs.

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Continuously Updated Estimation of Heart Transplant Waitlist Mortality

Cited by 30 publications

References 28 publications

Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality

Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality

Morbidity and Mortality Rates of Boer and Central Highland Goats at Ataye Crossbreeding Program Research Site: Non-parametric Survival Analysis and Piecewise Exponential Model

Noteworthy Literature Published in 2018 for Thoracic Organ Transplantation

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