Development of a pediatric end-stage liver disease score to predict poor outcome in children awaiting liver transplantation1

McDiarmid, Sue V.; Anand, Ravinder; Lindblad, Anne S.

doi:10.1097/00007890-200207270-00006

Cited by 348 publications

(243 citation statements)

References 15 publications

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“…PELDS provide a useful model consisting of five objective parameters: total serum bilirubin, INR, albumin, growth failure and age. PELDS can predict death or deterioration in the condition of the patient that may require transfer to the intensive care unit while the patient awaits liver transplantation [16] . The PELDS and the Z-score, an anthropometric parameter for malnutrition [17] , indicated that the medical conditions of the G1 patients were indeed worse and that their indications for liver transplantation were relatively urgent compared with those for the G2 patients.…”

Section: Discussionmentioning

confidence: 99%

Living donor liver transplantation with body-weight more or less than 10 kilograms

Yang

Huang

Chen

et al. 2015

WJG

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AIM:To compare the outcomes of pediatric patients weighing less than or more than 10 kg who underwent liver transplantation. METHODS: Data for 196 pediatric patients who underwent living donor liver transplantation betweenJune 1994 and February 2011 were reviewed retrospectively. The information for each patient was anonymized and de-identified before analysis. The data included information regarding the pre-transplant conditions, intraoperative fluid replacement and outcomes for each patient. The 196 patients were divided into two groups: those with body weights of less than 10 kg were included in group 1 (G1; n = 101), while those with body weights of more than 10 kg were included in group 2 (G2; n = 95). For each group, the patients' ages, body weights, heights, pediatric end stage liver disease scores, anesthesia times, and warm and cold ischemic times were analyzed. In addition, between-group comparisons were also made. Mann-Whitney U tests were used to compare all the variables except for complications and survival rates, which were analyzed using χ 2 tests and Kaplan-Meier tests, respectively. RESULTS:The general medical conditions of the G1 Retrospective StudyLiving donor liver transplantation with body-weight more or less than 10 kilograms patients were worse than those of the G2 patients, as shown by the higher pediatric end stage liver disease scores and poorer Z -scores. In addition, the preoperative Hb and serum albumin levels were all lower for the G1 patients than for the G2 patients. The G1 patients also had significantly more intraoperative blood loss than the G2 patients. In addition, the intraoperative fluid requirements for the G1 patients, including leukocyte poor red blood cell transfusions, 5% albumin infusions and crystalloid infusions, were significantly higher than those for the G2 patients. The risk of intraoperative portal vein thrombosis was higher for the patients in G1 than for those in G2. However, the one-year survival rates (95.9% and 96.8% for G1 and G2, respectively) and three-year survival rates (94.9% and 94.6% for G1 and G2, respectively) for both groups were similar. CONCLUSION:Patients weighing less than 10 kg typically have poorer conditions, but their survival rates are comparable to those of children weighing more than 10 kg. Core tip: Although pediatric patients weighing less than 10 kg who underwent living donor liver transplantation usually had worse pre-transplant conditions, more intraoperative bleeding and more portal vein complications, their three-year survival rate was 94%, a rate comparable to that achieved by children weighing more than 10 kg. A prerequisite for achieving these excellent results is good and effective teamwork. Cooperation between surgeons, plastic surgeons, and anesthesiologists in the operating theater is fundamental, but the contributions of radiologists are also critical. Radiologists should be available in the operating room to perform Doppler ultrasounds or place vascular stents, as required.Yang SC, Huang CJ, Chen CL, Wang CH,...

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Section: Discussionmentioning

confidence: 99%

Living donor liver transplantation with body-weight more or less than 10 kilograms

Yang

Huang

Chen

et al. 2015

WJG

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“…Clinical records were consulted for clinical and laboratory data, such as age, gender, weight, preoperative medication, and relevant liver function tests. Part of this information was used to calculate the pediatric end-stage liver disease (PELD) score (McDiarmid et al, 2002).…”

Section: Methodsmentioning

confidence: 99%

In Vitro Cytochrome P450 Activity Decreases in Children with High Pediatric End-Stage Liver Disease Scores

et al. 2012

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To improve the modeling and simulation of pharmacokinetics in pediatric patients, research into developmental and disease-specific determinants is needed. This article describes the evaluation of the activity of in vitro cytochrome P450 (P450), an important enzyme family in drug metabolism, in children with hepatic dysfunction. The activity of six P450 isoforms (CYP1A2, 2C9, 2C19, 2D6, 2E1, and 3A4) was evaluated in 31 patients with different pathologies, predominantly biliary atresia (n = 23). Hypervariable activity was observed for all the isoforms. Compared with average adult activity, low activity levels were seen for CYP1A2, 2C19, 2E1, and 3A4. For CYP2E1 and 3A4, a positive correlation between activity and abundance was observed. Age, comedication, and genotype could not be used as predictors for P450 activity in this patient population. In contrast, the pediatric end-stage liver disease score was negatively correlated with the ln(activity). This finding suggests a decrease in P450 activity with deteriorating hepatic function. Moreover, the activity of all isoforms was correlated, demonstrating a concomitant decrease of all isoforms in young patients with liver disease. To our knowledge, this is the first study to evaluate P450 activity in children with hepatic impairment. The presented data may provide support in the further optimization of a diseasespecific model in this patient population.

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“…Hazelbaker Assessment 93 Evaluates lingual frenulum function PRISM Score (Pediatric Risk of Mortality) 94 Assesses risk of mortality in PICU patients Pediatric appendicitis score 95 Assesses an acute abdomen and diagnoses appendicitis in children SNAP (Score for Neonatal Acute Physiology) 96 Scores neonatal acute physiology and predicts mortality SNAP-II and SNAPPE-II (Score for Neonatal Acute Physiology II and Score for Neonatal Acute Physiology Perinatal Extension II) 97 Scores neonatal acute physiology and predicts mortality P-MODS (Pediatric Multiple Organ Dysfunction Score) 98 Grades multiple organ system dysfunction in critically ill children PELOD Score (Pediatric Logistic Organ Dysfunction) 99 Scores the severity of multiple organ dysfunction syndrome in pediatric intensive care units PIM (Pediatric Index of Mortality) and PIM-2 100 Predicts the risk of death for children ,16 y of age in intensive care PELD (Pediatric End-stage Liver Disease score) 101 Predicts death or death-moved to ICU in children awaiting liver transplantation PALF (Pediatric Acute Liver Failure) 102 Predicts outcome in pediatric acute liver failure pRIFLE (Pediatric Risk, Injury, Failure, Loss, and End-Stage Renal Disease) 103 Characterizes the pattern of acute kidney injury in critically ill children WAT-1 (Withdrawal Assessment Tool-1) 104 Assess clinically important withdrawal symptoms in the PICU a Sample of available pediatric-specific measures. The list of measures is not exclusive and should not be interpreted as a product endorsement by the AAP.…”

Section: Access To Informationmentioning

confidence: 99%

Pediatric Aspects of Inpatient Health Information Technology Systems

Lehmann¹,

Weinberg²,

Alexander³

et al. 2015

Pediatrics

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Clinical Health Act accelerated the adoption of electronic health records (EHRs) with providers and hospitals, who can claim incentive monies related to meaningful use. Despite the increase in adoption of commercial EHRs in pediatric settings, there has been little support for EHR tools and functionalities that promote pediatric quality improvement and patient safety, and children remain at higher risk than adults for medical errors in inpatient environments. Health information technology (HIT) tailored to the needs of pediatric health care providers can improve care by reducing the likelihood of errors through information assurance and minimizing the harm that results from errors. This technical report outlines pediatric-specific concepts, child health needs and their data elements, and required functionalities in inpatient clinical information systems that may be missing in adult-oriented HIT systems with negative consequences for pediatric inpatient care. It is imperative that inpatient (and outpatient) HIT systems be adapted to improve their ability to properly support safe health care delivery for children.

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Development of a pediatric end-stage liver disease score to predict poor outcome in children awaiting liver transplantation1

Abstract: A model using five objective parameters can accurately predict death or death-moved to ICU in children awaiting liver transplantation.

Cited by 348 publications

References 15 publications

Living donor liver transplantation with body-weight more or less than 10 kilograms

Living donor liver transplantation with body-weight more or less than 10 kilograms

In Vitro Cytochrome P450 Activity Decreases in Children with High Pediatric End-Stage Liver Disease Scores

Pediatric Aspects of Inpatient Health Information Technology Systems

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