Devastating Brain Injuries: Assessment and Management Part I: Overview of Brain Death

“To the world you may be one person, but to one person you may be the world.

Active Donor Management During the Hospital Phase of Care Is Associated with More Organs Transplanted per Donor.

BACKGROUND: Meeting donor management goals when caring for potential organ donors has been associated with more organs transplanted per donor (OTPD). Concern persists, however, as to whether this indicates that younger/healthier donors are more likely to meet donor management goals or whether active management affects outcomes. STUDY DESIGN: A prospective observational study of all standard criteria donors was conducted by 10 organ procurement organizations across United Network for Organ Sharing Regions 4, 5, and 6. Donor management goals representing normal critical care end points were measured at 2 time points: when a catastrophic brain injury was recognized and a referral was made to the organ procurement organization by the DH; and after brain death was declared and authorization for organ donation was obtained. Donor management goals Bundle met was defined as achieving any 7 of 9 end points. A positive Bundle status change was defined as not meeting the Bundle at referral and subsequently achieving it at authorization. The primary outcomes measure was having ≥4 OTPD. RESULTS: Data were collected for 1,398 standard criteria donors. Of the 1,166 (83%) who did not meet the Bundle at referral, only 254 (22%) had a positive Bundle status change. On adjusted analysis, positive Bundle status change increased the odds of achieving ≥4 OTPD significantly (odds ratio 2.04; 95% CI 1.49 to 2.81; p \u3c 0.001). CONCLUSIONS: A positive donor management goal Bundle status change during donor hospital management is associated with a 2-fold increase in achieving ≥4 OTPD. Active critical care management of the potential organ donor, as evidenced by improvement in routinely measured critical care end points can be a means by which to substantially increase the number of organs available for transplantation

Risk Factors for Traumatic Injury Findings on Thoracic Computed Tomography Among Patients With Blunt Trauma Having a Normal Chest Radiograph

Hypothesis:Wesought to identify risk factors that might predict acute traumatic injury findings on thoracic computed tomography (TCT) among patients having a normal initial chest radiograph (CR). Design: In this retrospective analysis, Abbreviated Injury Score cutoffs were chosen to correspond with obvious physical examination findings. Multivariate logistic regression analysis was performed to identify risk factors predicting acute traumatic injury findings. Setting: Urban level I trauma center. Patients: All patients with blunt trauma having both CR and TCT between July 1, 2005, and June 30, 2007. Patients with abnormalities on their CR were excluded. Main Outcome Measure: Finding of any acute traumatic abnormality on TCT, despite a normal CR. Results: A total of 2435 patients with blunt trauma were identified; 1744 (71.6%) had a normal initial CR, and 394 (22.6%) of these had acute traumatic findings on TCT. Multivariate logistic regression demonstrated that an abdominal Abbreviated Injury Score of 3 or higher (P=.001; odds ratio, 2.6), a pelvic or extremity Abbreviated Injury Score of 2 or higher (P\u3c.001; odds ratio, 2.0), age older than 30 years (P=.004; odds ratio, 1.4), and male sex (P=.04; odds ratio, 1.3) were significantly associated with traumatic findings on TCT. No aortic injuries were diagnosed in patients with a normal CR. Limiting TCT to patients with 1 or more risk factors predicting acute traumatic injury findings would have resulted in reduced radiation exposure and in a cost savings of almost $250 000 over the 2-year period. Limiting TCT to this degree would not have missed any clinically significant vertebral fractures or vascular injuries. Conclusion: Among patients with a normal screening CR, reserving TCT for older male patients with abdominal or extremity blunt trauma seems safe and costeffective

Impact of Deceased Donor Management on Donor Heart Use and Recipient Graft Survival.

BACKGROUND: Current risk-adjusted models used to predict donor heart use and cardiac graft survival from organ donors after brain death (DBDs) do not include bedside critical care data. We sought to identify novel independent predictors of heart use and graft survival to better understand the relationship between donor management and transplantation outcomes. STUDY DESIGN: We conducted a prospective observational study of DBDs managed from 2008 to 2013 by 10 organ procurement organizations. Demographic data, critical care parameters, and treatments were recorded at 3 standardized time points during donor management. The primary outcomes measures were donor heart use and cardiac graft survival. RESULTS: From 3,433 DBDs, 1,134 hearts (33%) were transplanted and 969 cardiac grafts (85%) survived after 684 ± 392 days of follow-up. After multivariable analysis, independent positive predictors of heart use included standard criteria donor status (odds ratio [OR] 3.93), male sex (OR 1.68), ejection fraction \u3e 50% (OR 1.64), and partial pressure of oxygen to fraction of inspired oxygen ratio \u3e 300 (OR 1.31). Independent negative predictors of heart use included donor age (OR 0.94), BMI \u3e 30 kg/m CONCLUSIONS: Modifiable critical care parameters and treatments predict donor heart use and cardiac graft survival. The discordant relationship between thyroid hormone and donor heart use (negative predictor) vs cardiac graft survival (positive predictor) warrants additional investigation

Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.

Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry.MethodsSeveral machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12-18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index.ResultsOf 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers.ConclusionsThis model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics

Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.

Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry.MethodsSeveral machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12-18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index.ResultsOf 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers.ConclusionsThis model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics