الفهرس 
Anatomy of the liver Gross AnatomyOnly 14 pages are availabe for public view
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Abstract
Cirrhosis of liver is a common health problem and results in significant morbidity and mortality. In the past, treatment of cirrhosis was mostly supportive and directed toward management of complications of this disease. Liver transplantation has changed the management and outcome of patients with cirrhosis of liver.
All patients with end-stage cirrhosis should be considered for liver transplantation in good time and for many patients in a life-threatening situation, liver transplantation remains the only chance for survival.
Living-donor liver transplantation (LDLT) has been refined and accepted as a valuable treatment for patients with end-stage liver disease in order to overcome the shortage of organs and mortality on the waiting list.
Given the potential risks to the living donor, only recipients with a reasonably favorable post-transplant outcome should be considered for LDLT. Thus, before proceeding to work up any potential donor, the recipient candidate should first be deemed suitable for the LDLT operation both medically as well as surgically.
Developing a prognostic model that can accurately predict mortality of patients after liver transplantation has been the focus of much research. Such models are important for many reasons. First, it can be used to identify patients with end-stage liver disease who may benefit from liver transplantation. Second, prediction of mortality after liver transplantation can provide patients with information about their prognosis that will support informed decision making about their treatment. Third, prognostic information is an essential component of the construction of risk-adjusted comparisons of outcomes between transplant units.
However, we might have to accept that the ability to predict post-transplant outcome based on pretransplant characteristics will always be inadequate given the influence of chance events that occur in the perioperative period have on post-transplant survival.
The MELD score has been proven to be an accurate predictor of waitlist mortality, as demonstrated in the pioneering study by Wiesner et al., with a c-statistic of 0.83 when used to predict 3-month mortality of candidates on the waitlist.
The MELD score is a poor predictor of mortality following transplantation. This observation was confirmed by Desai et al. in their analysis, which reports a c-statistic of only 0.54 with the use of the MELD to predict 3-month recipient mortality following liver transplantation.
Methods other than the MELD score, such as the Child–Turcotte–Pugh score, also had a poor ability to predict post transplant survival.
The preallocation score to predict survival outcomes following liver transplantation (P-SOFT) was designed to evaluate a candidate prior to liver allograft allocation and results in a c-statistic of 0.69 (CI 0.67–0.70) as a predictor of 3-month recipient survival following liver transplantation.
The aim of our study was to highlight and evaluate the P-SOFT score as a novel method to predict patient survival 3 months following living donor liver transplantation in Egypt.
The current cross-sectional study was conducted on 50 Egyptian candidates and recipients of LDLT who have met our criteria. All patients had undergone LDLT at either Liver Transplantation Center of Maadi Armed Forces Medical Compound or Liver Transplantation Unit of Ain Shams University Specialized Hospital.
All patients were subjected to a thorough pretransplant evaluation on 3 subsequent phases:
Phase 1: full history taking, careful clinical examination, lab investigations and imaging studies.
Phase 2: viral and tumour markers, and medical consultations.
Phase 3: psychiatry and anaesthesia consultations and GIT endoscopies.
For the purpose of our study, our patients’ data were collected and the 14 risk factors utilized by the score were identified. The recipients’ pretransplant P-SOFT scores were calculated according to the points assigned for each risk factor, and accordingly patients were stratified into the available 3 groups:
1- The low risk group: points range 0-5.
2- The low-moderate risk group: points range 6-15.
3- The high-moderate risk group: points range 16-35.
Our main outcome measure was patient survival after transplantation and our plan was to follow up the recipient 3 months post LDLT to assess the ability of the P-SOFT score to predict the recipient survival at 3 months following living donor liver transplantation.
We made comparisons between the risk groups as regard recipient mortality odds within 3 months post LDLT, and regarding recipient survival so as to identify the significant statistical correlation between survival and risk based on P-SOFT score. Means for the survival times also compared among groups and statistically analysed. Then we tested the accuracy and predictive capacity of the score by comparing the total scores for survivors versus those died and with ROC curve analysis. Finally, we statistically analysed the available risk factors, in addition to recipient gender, and its correlation with patient survival.
The mean post-transplant follow-up time of the surviving patients was 24 months. By the end of the 3-months posttransplant, the overall survival rate for the recipients post LDLT was 80%. Patient survival of recipients with <5 points (low risk) was 81.8%, 6-15 points (low-moderate risk) was 80.8% and 16-35 points (high-moderate risk) was 50%.
By 3-months post LDLT, the odds of patient mortality for the low-moderate and high-moderate risk groups were respectively 1.07 (95%CI 0.249-4.603) and 4.50 (95% CI 0.229-88.248) times higher compared to the odds of patient mortality for the low risk group, with non-significant p-values for both of them.
We did not find a significant correlation between patient survival and the risk identified by the P-SOFT score based groups (p-value = 0.554).
The Kaplan–Meier survival curve and overall comparisons of the means for survival times post LDLT based on risk point totals from P-SOFT score did not show a significance (p-value = 0.072).
The mean (average) P-SOFT score was 6.22. Mann-Whitney U test yielded non-statistically significant differences between the means of the total P-SOFT scores for survivors and those who died at the 3-months post LDLT with p-value = 0.319.
The correlation between P-SOFT score and patient survival 3 months post-transplant appeared to be weak with p-value = 0.314, and c-statistic (area under the curve) was 0.604, 95% CI 0.398 to 0.809.
To gain insight into why the predictive value of P-SOFT score in post-transplant survival was so poor, we conducted a Cox analysis using the P-SOFT score variables. In this analysis, we found that only the ICU pretransplant was significant in predicting post-transplant recipient survival (p-value = 0.041) and other patient variables including gender, medical status including pre-transplant MELD score and comorbidities were not significantly correlated with post-transplant survival.