Prediction of death in less than 60 minutes after withdrawal of cardiorespiratory support in potential organ donors after circulatory death.

CLINICAL

AND

TRANSLATIONAL RESEARCH

Prediction of Death in Less Than 60 Minutes After Withdrawal of Cardiorespiratory Support in Potential Organ Donors After Circulatory Death Jorge Brieva,1,4 Nicole Coleman,1 Jeanette Lacey,1 Peter Harrigan,1 Terry J. Lewin,2 and Gregory L. Carter3 Background. Given the stable number of potential organ donors after brain death, donors after circulatory death have been an increasing source of organs procured for transplant. Among the most important considerations for donation after circulatory death (DCD) is the prediction that death will occur within a reasonable period of time after the withdrawal of cardiorespiratory support (WCRS). Accurate prediction of time to death is necessary for the procurement process. We aimed to develop simple predictive rules for death in less than 60 min and test the accuracy of these rules in a pool of potential DCD donors. Methods. A multicenter prospective longitudinal cohort design of DCD eligible patients (n=318), with the primary binary outcome being death in less than 60 min after withdrawal of cardiorespiratory support conducted in 28 accredited intensive care units (ICUs) in Australia. We used a random split-half method to produce two samples, first to develop the predictive classification rules and then to estimate accuracy in an independent sample. Results. The best classification model used only three simple classification rules to produce an overall efficiency of 0.79 (0.72Y0.85), sensitivity of 0.82 (0.73Y0.90), and a positive predictive value of 0.80 (0.70Y0.87) in the independent sample. Using only intensive care unit specialist prediction (a single classification rule) produced comparable efficiency 0.80 (0.73Y0.86), sensitivity 0.87 (0.78Y0.93), and positive predictive value 0.78 (0.68Y0.86). Conclusion. This best predictive model missed only 18% of all potential donors. A positive prediction would be incorrect on only 20% of occasions, meaning there is an acceptable level of lost opportunity costs involved in the unnecessary assembly of transplantation teams and theatres. Keywords: Organ donation, Donation after circulatory death. (Transplantation 2014;98: 1112Y1118)

This study has been approved by the clinical ethical research committee. The study received a Grant from the Australian Organ and Tissue Donation and Transplantation Authority, Australian Government (grant G0910-070). The authors declare no conflicts of interest. 1 Division of Anaesthesia, Intensive Care and Pain Management, John Hunter Hospital, Hunter New England Area Health Service, NSW, Australia. 2 Centre for Translational Neuroscience and Mental Health (CTNMH), University of Newcastle, NSW, Australia. 3 Department of Consultation-Liaison Psychiatry, Calvary Mater Newcastle Hospital, NSW, Australia. 4 Address correspondence to: Jorge Brieva, FCICM, PGDip Echo, Division of Anaesthesia, Intensive Care and Pain Management John Hunter Hospital, Hunter New England Area Health Service, NSW, Australia. E-mail: [email protected] J.B., N.C., J.L., P.H., T.J.L., G.L.C. participated in research design. J.B., N.C., J.L., P.H., T.J.L., G.L.C. participated in the writing of the article. J.B., N.C., J.L., P.H., T.J.L., G.L.C. participated in the performance of the research. J.B., T.J.L., G.L.C. participated in data analysis. Supplemental digital content (SDC) is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal’s Web site (www.transplantjournal.com). Received 15 January 2014. Revision requested 12 February 2014. Accepted 19 March 2014. Copyright * 2014 by Lippincott Williams & Wilkins ISSN: 0041-1337/14/9810-1112 DOI: 10.1097/TP.0000000000000186

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fter overcoming several medical and ethical controversies (1Y4), organ donation after circulatory death (DCD) has experienced a significant expansion over the last decade. Outcome studies of DCD donors especially support renal and hepatic transplantations for end-stage organ failure, with a 5year graft survival for kidney of 87% and 68% for liver (5Y7). In the United Kingdom for the decade 2003 to 2012, organ donation increased from 12.0 per million population (pmp) to 18.3 pmp, with an increase in DCD from 1.1 to 7.9 pmp, whereas donation after brain death remained stable at 10.5 pmp (5). The DCD represented up to 36.9% of the total number of organs procured in the United Kingdom and 22.3% in Australia (8, 9). The increasing practice of DCD has not negatively impacted the number of donors after brain death in Australia and the United States (10, 11). End-of-life practices in Australia are the result of a well-established process involving a progressive medical consensus, family agreement, and palliative care planning, and most patients who die in intensive care will have their treatments limited or withdrawn in the days preceding the death (12Y16). Good end-of-life practices require careful planning of effective palliative therapy and consideration of organ and tissue donation.

A

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& Volume 98, Number 10, November 27, 2014

Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

* 2014 Lippincott Williams & Wilkins

Current practice in DCD requires a time from palliation to the declaration of death from 30, 60, and up to 90 min for liver, kidney, and lung procurement, respectively (17, 18). This variability is mostly because of the impact of the warm ischemic insult on the potential graft. Guidelines and policies for DCD have been developed in accordance with this timing requirement and for the procurement process to be triggered (19, 20). Available tools for predicting time to death after withdrawal of cardiorespiratory support (WCRS) in DCD eligible populations (e.g., United Network for Organ Sharing (UNOS) Donation after Cardiac Death consensus committee criteria and University of Wisconsin DCD Evaluation Tool), require a respiratory assessment performed with a disconnection from mechanical ventilation (21, 22). However, this practice is not widely accepted and there are no clinical guidelines that recommend this form of respiratory assessment in intensive care units. We suspect these predictive instruments (UNOS and University of Wisconsin) are not widely used in clinical practice in Australia and that most Australian units rely on intensive care unit (ICU) specialist opinion or prediction. In previous studies of general ICU populations not restricted to potential DCD patients, we reported that the ICU specialist prediction of death in less than 60 min after WCRS was the best independent predictor of actual death in less than 60 min (23, 24). In these studies, ICU specialist prediction was not made by any explicit or mandated standards but

FIGURE 1.

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represented the clinical opinion of the specialist using whatever method was used by that individual clinician. The ICU specialist opinion was associated with pH, Glasgow Coma Scale, spontaneous respiratory rate, positive end-expiratory pressure, and systolic blood pressure. The aims of the current study were to identify simple clinical predictors of death within 60 min of WCRS in a population of potential DCD patients in Australian ICUs and to develop a set of predictive rules that could be tested in an independent sample.

RESULTS From the PREDICT (24) study, there were only a minority of patients that met the criteria for potential DCD eligibility. There were n=159 DCD eligible participants with primary outcome data in the Development set from an initial sample of n=381 (42%) and n=159 in the Testing set from an initial sample of n=384 (41%). The flow of participants through the study can be seen in Figure 1. Of the continuous variables, only age, weight, height, body mass index, and heart rate were not associated with death in less than 60 min; details of the continuous variables can be seen in Table S1 (Supplemental Digital Content [SDC] http://links.lww.com/TP/A981). For the categorical variables, only gender, admission type, abnormal urea, and sedation were not associated with death in less than 60 min; details of the categorical variables can be seen in Table S2 (SDC, http://links.lww.com/TP/A981).

Flow of participants through the study.


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FIGURE 2.


Classification tree (three levels) using ICU specialist prediction Development set. ICU, intensive care unit.

The first Classification and Regression Tree (CART) model included ICU specialist prediction and ten clinical variables as potential predictors, which yielded a three level solution using 11 nodes with six terminal nodes. The five variables of interest were ICU specialist prediction, total Glasgow Coma Scale (GCS) score (3 vs. Q4), positive end expiratory pressure (PEEP) (0Y5 vs. Q6), systolic blood pressure (BP) (G84 vs. Q85), and spontaneous respiratory rate (SRR) (G10 vs. Q11). Details of the classification tree can be seen in Figure 2. Three classification rules can be expressed for the prediction of death in less than 60 min:

FIGURE 3.

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(1) ICU specialist predicts death less than 60 min and GCS score, 3. (2) ICU specialist predicts death less than 60 min and GCS score, 4 to 15 and systolic BP, 0 to 84. (3) ICU specialist predicts death more than 60 min and PEEP, 6 or higher and SRR, 0 to 10. The second CART model included 10 clinical variables as potential predictors, which yielded a three-level solution using nine nodes with five terminal nodes. The four variables of interest were SRR (0Y10 vs. Q11), total GCS score (3 vs. 4Y15), PEEP (0Y10 vs. Q11), and systolic

Classification tree (three levels) without using ICU specialist prediction Development set. ICU, intensive care unit.


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BP (0Y84 vs. Q85). Details of the classification tree can be seen in Figure 3. Three classification rules can be expressed for the prediction of death in less than 60 min:

efficiency was 0.80 (0.73Y0.86), sensitivity 0.87 (0.78Y0.93), and PPV 0.78 (0.68Y0.86) in the Testing set.

(1) SRR, 0 to 10 and GCS score, 3. (2) SRR, 0 to 10 and GCS score, 4 to 15 and systolic BP, 0 to 84. (3) SRR, 11 or higher and PEEP, 11 or higher. The complete accuracy statistics for the first and second CART models derived from the Development set can be seen in Table 1. The first CART model, using the terminal nodes (level 3), produced an overall efficiency of 0.88 (0.82Y0.93), sensitivity of 0.83 (0.72Y0.90), and a positive predictive value (PPV) of 0.93 (0.84Y0.98). The second CART model, using the terminal nodes (level 3), produced an overall efficiency of 0.83 (0.76Y0.89), 0.78 (0.67Y0.86), and a PPV of 0.87 (0.77Y0.94). These results are not considered statistically different because of the overlap of the confidence intervals; however, there was an absolute difference in misclassification of eight cases (19 vs. 27) in favor of the first CART model, which might be considered as clinically meaningful. The complete accuracy statistics for the first and second CART models when applied to the Testing set can be seen in Table 2. The first CART model, using the terminal nodes (level 3), produced an overall efficiency of 0.79 (0.72Y0.85), sensitivity of 0.82 (0.73Y0.90), and a PPV of 0.80 (0.70Y0.87). The second CART model, using the terminal nodes (level 3), produced an overall efficiency of 0.71 (0.63Y0.78), 0.66 (0.55Y0.76), and a PPV of 0.77 (0.65Y0.86). The results are not considered statistically different; however, there was a difference in misclassification of 13 cases (33 vs. 46) in favor of the first CART model, which might be considered as clinically meaningful. In the first CART model, which uses ICU specialist prediction, the accuracy of ICU specialist prediction alone can be seen by the results for level 1 in the Development and Testing sets. The efficiency was 0.82 (0.76Y0.88), sensitivity 0.81 (0.71Y0.89), and PPV 0.83 (0.73Y0.91) in the Development set;

DISCUSSION Main Findings This a priori planned analysis of potential DCD donors showed that DCD-eligible patients accounted for only 40% of a general ICU population having WCRS and that death within 60 min occurred for only 50% of those. The best classification model for death in less than 60 min after WCRS, derived from CART analysis, used only three predictive ‘‘rules’’ to produce an overall efficiency of 0.79 (0.72Y0.85), sensitivity of 0.82 (0.73Y0.90), and a PPV of 0.80 (0.70Y0.87) in the independent Testing set. Moreover, using ICU specialist prediction alone (a single classification rule) produced comparable results: efficiency 0.80 (0.73Y0.86), sensitivity 0.87 (0.78Y0.93), and PPV 0.78 (0.68Y0.86). Comparison With Other Studies ICU Specialist Prediction Our results highlight the useful role that the ICU specialist can play in predicting death in less than 60 min after WCRS. It is likely that ICU specialist prediction is the prevailing clinical standard for the prediction of time to death after WCRS, and the results of this study strongly support this clinical practice. The ICU specialist prediction of death in less than 60 minutes, with a specificity of 73% and a sensitivity of 56%, has been reported by Wind et al. (25) in the Netherlands. Clinical Predictors and Practice The results of this study indicate that there were two sets of patient characteristics that predicted death within 60 min of WCRS, intensive ventilator support requirements (low SRR and high PEEP) or a combination of impaired respiratory drive (low SRR), coma (low GCS), and high circulatory support requirements (low systolic BP). We

TABLE 1. Accuracy statistics for each level of CART analysis in Development set: specialist prediction included or not included in model

Accuracy statistics

Specialist prediction Development set (n=159) CART level Level 1

Efficiency 0.82 (0.76Y0.88) Sensitivity 0.81 (0.71Y0.89) Specificity 0.84 (0.74Y0.91) PPV (G 60 min) 0.83 (0.73Y0.91) NPV (9 60 min) 0.81 (0.71Y0.89) Kappa 0.65 (0.53Y0.77) 4.94 (2.97Y8.21) LR+ LRj 0.22 (0.24Y0.36) Classification Criterion Predict G60m 960m DeathG60m 65 13 Death960 min 15 66

No specialist prediction Development set (n=159) CART level

Level 2

Level 3

Level 1

Level 2

Level 3

0.84 (0.78Y0.90) 0.74 (0.63Y0.83) 0.95 (0.88Y0.99) 0.94 (0.85Y0.98) 0.78 (0.69Y0.86) 0.69 (0.58Y0.80) 14.57 (5.56Y38.18) 0.28 (0.19Y0.40) Criterion G60m 960m 59 4 21 75





CART, Classification and Regression Tree; PPV, positive predictive value; NPV, negative predictive value; LR+, likelihood ratio positive; LRj, likelihood ratio negative.


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TABLE 2. Accuracy statistics for each level of CART analysis in Testing set: specialist prediction included and not included in model

Accuracy statistics Efficiency Sensitivity Specificity PPV (G60 min) NPV (960 min) Kappa LR+ LRj Classification Predict DeathG60min Death960 min

Specialist prediction testing set (n=159) CART level

No specialist prediction testing set (n=159) CART level

Level 1

Level 2

Level 3

Level 1

Level 2

Level 3







CART, Classification and Regression Tree; PPV, positive predictive value; NPV, negative predictive value; LR+, likelihood ratio positive; LRj, likelihood ratio negative.

consider that these results make physiological sense and have good face validity. Spontaneous respiratory rate, positive expiratory pressure level, and oxygenation in the moments previous to the withdrawal of mechanical ventilation were also strongly associated with the time to death in previous reports (26, 27). Moreover, we have reported similar findings without requiring any disconnection from the ventilator, a practice not widely accepted in Australian ICUs. High circulatory support requirements indicated by hemodynamic variables and vasopressor dose, (25Y28) as well as metabolic failure (systemic acidosis), have been associated with the time to death (26). An association between the level of consciousness and the time to death has been previously established by assessment of the overall GCS or the extension of cranial nerve impairment (25, 27, 28). In the current study, GCS was associated with time to death and was a predictor variable from the CART model for death in less than 60 min. We did not use cranial nerve assessment as a variable in our study. Conflicting results have been reported in regards with the association between other variables, such as potential donor age, medical or surgical admission, and gender (25, 26, 28, 29). In this large prospective multicenter study, we did not find an association between these characteristics and the time to death. Similar to our previous report (24), we found that opioid-based analgesia administration before the WCRS may have an effect in delaying the time to death beyond 60 min. Our study was not designed to capture the cumulative dose and therefore further studies will be required to answer this important question at the time of palliative care provision. Previous reports have described conflicting results in this area of pharmacological treatment at the end of life (30, 31). This area should be further investigated in a prospective pharmacological study with the appropriate methodology.

STUDY STRENGTHS AND LIMITATIONS This study showed few threats to internal validity. We used a multicenter prospective longitudinal cohort design

with consecutive recruitment, which was adequately powered for the multivariate analyses in the original study. The patient population (DCD eligible) and primary outcome (death within 60 min) were selected a priori, with the primary outcome based on clinical utility and the prevailing standard for DCD patients. We used a random split-half technique to produce an independent Development and Testing sets for the predictive models. The CART analyses were appropriate for predicting a binary categorical outcome from a potentially relatively large number of predictive variables. We tested a range of potential predictor variables previously identified in the PREDICT (24) study and from other international studies. Most predictive variables were limited to standardized measures of physiological and clinical parameters, with acceptable measurement biases and with ready availability in ICUs. The continuous predictor variables were converted to meaningful categorical variables in keeping with the UNOS criteria, which also allowed comparison with other studies using these physiological and clinical variables as predictors. We used an independent sample to test the predictive indices with the full range of accuracy statistics necessary to inform clinical decision making. The participants were recruited sequentially from multiple Australian ICUs and so the external validity of the study is expected to be high. The study has some limitations. The predictive variables were largely limited to a single time of collection before WCRS, and the only longitudinal predictors were duration of ICU stay and ventilation. The research coordinators who extracted data and the ICU specialist who determined time of death were not blinded. We did not test any composite predictor, for example, ‘‘oxygen disruption’’ (using oxygen saturation, PEEP, and arterial to inspired oxygen concentration ratio) (22). This study was not designed to evaluate the type and intensity of the treatments given during the palliative care process and how this can impact in the time to death. We have defined ‘‘potential donor’’ by age group and the lack of known malignancy. Other exclusions for consideration for organ donation may be used in particular


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transplantation cases, and therefore, the generalizability to these results to other populations is not known.

CONCLUSION In potential DCD donors, the opinion of the intensive care specialist, the level of respiratory support required, the degree of neurological impairment, and the level of cardiorespiratory support required are useful predictors of death in less than 60 min after the WCRS. These variables should be considered when discussing organ donation with families of patients at the end of life and by organ procurement agents. We know of no studies that have identified what would be considered an effective (or cost-effective) rate of accurately identifying potential DCD patients with death of less than 60 min after WCRS. The usefulness of the best predictive model from this study can be considered for the logistical tasks and organizational costs of using DCD patients for organ procurement. Using this approach would miss only 18% of all potential donors; and a positive prediction would be incorrect on only 20% of occasions, resulting in the unnecessary assembly of organ procurement teams and theatres.

MATERIALS AND METHODS Study Design and Population The PREDICT (24) study used a multicenter prospective longitudinal cohort design, with the primary binary outcome being death in less than 60 min after WCRS, which was conducted in 28 hospitals across Australia with accredited ICUs. The details of the PREDICT study have been previously reported (24). The original population included all mechanically ventilated adults in whom a decision for WCRS had been made. Therapies considered to be ‘‘cardiorespiratory support’’ were invasive mechanical ventilation, vasopressor and inotrope infusions, cardiac pacing, external circulatory support, intraaortic balloon pump, and renal replacement therapies. Patients who had more limited treatments, for example, noninvasive mechanical ventilation, or any alternative treatments to those listed above, were not eligible for inclusion. Patients in whom cardiorespiratory treatments were withheld but not withdrawn and patients with a diagnosis of brain death were excluded. In this article, we report an a priori planned analysis of the DCD eligible subgroup of participants from the PREDICT (24) study. We defined the DCD eligible donor subgroup as any participant younger than 65 years of age with no history of malignancy. We have adopted inclusive criteria, given that the medical suitability for donation decision may vary on a case-to-case basis; however, metastatic malignancy and age continue to be the major variables in donor selection (32, 33).

Measures Age, gender, height, weight, medical or surgical admission, days in ICU, and days on ventilation were extracted from clinical records by a research nurse. The clinical predictor variables were the last recorded measures in the clinical record immediately before WCRS regardless of the treatment. All clinical and drug treatment variables were collected by the research coordinator immediately before WCRS. Spontaneous respiratory rate was assessed by the number of patient-triggered breaths per minute on pressure support by the ventilator. The only longitudinal predictors were days in ICU and days on mechanical ventilation.

Development and Testing Sets We used a random split-half method to produce two samples, the first was the Development set for examination of the potential predictor variables and development of the rules for the prediction algorithms, and the second as a Testing set for the accuracy of the prediction algorithms. We

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maintained the same random split-half samples used in the larger PREDICT (24) study.

Analyses For this study, the primary binary outcome (dependent variable) was death in less than 60 min after WCRS. In the Development set, we examined medians and interquartile ranges for continuous variables and number and percentages for categorical variables, with Wilcoxon rank tests and chisquare tests used, respectively, to test for differences in the strength of association with the primary binary outcome. Continuous predictors were age; weight; height; body mass index, Apache score, total GCS score, days in ICU, days of mechanical ventilation, systolic BP, mean arterial pressure, heart rate, pH, SRR, PEEP, and oxygen saturation. Categorical predictors were gender, medical versus surgical admission, sedation, analgesia vasopressor or inotrope, abnormal liver function tests, urea, creatinine or chest x-ray, and ICU specialist prediction of death less than 60 min. We also transformed several continuous clinical variables into stratified categorical variables using clinically relevant cut points consistent with those used in the UNOS predictive instrument. These were as follows: systolic BP (0Y84, 85Y104, 105+ mm Hg), total GCS score (3, 4Y6, 7+), pH (e7.14, 7.15Y7.24, 7.25Y7.32, 7.33Y7.49, and Q7.50 units), SRR (0Y10, 11Y24, 25+ breaths per minute), and PEEP (0Y5, 6Y10, 11+ cm H2O). We made an a priori decision about the main analysis strategy for development of the predictive rules in the DCD-eligible population. In the Development set, the number of potential predictors was expected to be relatively large (16 univariate predictors in PREDICT), and the number of ‘‘cases’’ of death within 60 min in the DCD population was expected to be relatively small (estimated 50% of 159; n=80). This meant that a logistic regression modeling approach, as used in the PREDICT (24) study, would be potentially inappropriate, and instead, we used a CART analysis to develop the predictive ‘‘rules’’ for evaluation in the Testing set. We used a CART analysis to produce two classification trees with death in less than 60 min after WCRS as the binary-dependent variable in the Development set, and then we reported accuracy statistics for the Development set and the Testing set. Potential predictor variables for inclusion in the CART analysis were selected from those with the strongest univariate associations with death in less than 60 min. The first classification tree included ICU specialist prediction and 10 clinical variables (systolic BP, GCS, oliguria, abnormal liver function tests, abnormal chest x-ray, abnormal creatinine, analgesia, SRR, PEEP); and the second classification tree contained only the 10 clinical variables. The trees were pruned at three levels below the root node for ease of clinical use and because of the minor additional accuracy using more levels. The full range of accuracy statistics (efficiency, sensitivity, specificity, PPV, negative predictive value, likelihood ratio positive and likelihood ratio negative, and the two by two classification tables) was reported for each of the three levels of the two classification trees for both the Development set and the Testing set. The accuracy statistics are reported as proportions or ratios as appropriate, with Confidence Intervals of 95%. We used SPSS 17.0 (SPSS, Chicago, IL) for the descriptive statistics and univariate comparisons, CTree.xls (https://www.sites.google.com/site/ sayhello2angshu/dminexcel) for the CART analyses and DAG Stat (34) for the accuracy statistics.

Power Calculation The power calculations were performed for the larger PREDICT study, which used a logistic regression analysis to determine the independent predictors of death in less than 60 min after WCRS (24). Previous studies indicated that the binary endpoint, death in less than 60 min, was close to 50% of the entire population studied (21, 23, 24). We calculated two minimum sample sizes based on two power levels. A random split-half sample size of 350 subjects (175 with each binary endpoint), with exposure of 40% to the predictor variable in the population, using an alpha of P less than 0.05 and power of 0.90, could detect an odds ratio of 2.0. Similarly, for 350 subjects, with half the rate of exposure (20%) to the predictor variable in


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the population, using an alpha of P less than 0.05 and power of 0.80 could detect an odds ratio of 2.0.

14.

Ethics

15.

Approval from the Hunter New England Clinical Research Ethic Committee was obtained, and specific patient consent was not required. Every participant center obtained local Research Ethics approval.

16.

Participating Centers

17.

Geelong Hospital Barwon Health, Burnie Hospital, Calvary Mater Hospital, Dandenong Hospital, Epworth Freemasons Hospital, Flinders Medical Centre, Frankston Hospital, Fremantle Hospital, Royal Hobart Hospital, John Hunter Hospital, Launceston Hospital, Liverpool Hospital, Lyell McEwin Hospital, Monash Medical Centre, Nepean Hospital, Prince of Wales Hospital, Royal Adelaide Hospital, Royal Darwin Hospital, Royal Melbourne Hospital, Royal Perth Hospital, Royal Prince Alfred Hospital, Sir Charles Gardiner Hospital, Saint George Hospital, Saint Vincent’s Hospital, The Alfred Hospital, The Canberra Hospital, The Northern Hospital, Western Health.

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20. 21.

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Donors after cardiocirculatory death and lung transplantation.

Life Support After Brain Death.

Response to circulatory death determination in uncontrolled organ donors: a panel viewpoint.

Uncontrolled organ donation after circulatory determination of death: US policy failures and call to action.

Legal and ethical implications of NICE guidance aimed at optimising organ transplantation after circulatory death.

Retrograde flush is more protective than heparin in the uncontrolled donation after circulatory death lung donor.

Lung transplantation from donors after circulatory death using portable ex vivo lung perfusion.

Pursuing expanded criteria donors as candidates for kidney donation after circulatory death.

Donation after circulatory determination of death: the university of michigan experience with extracorporeal support.