Predictive Variables Affecting Transfusion Requirements in Orthotopic Liver Transplantation

INTRODUCTION AND AIMS: Adult orthotopic liver transplantation (OLT) is associated with considerable blood product requirements. The aim of this study was to assess the ability of preoperative information to predict intraoperative red blood cell (RBC) transfusion requirements among adult liver recipients. METHODS: Preoperative variables with previously demonstrated relationships to intraoperative RBC transfusion were identified from the literature: sex, age, pathology, prothrombin time (PT), factor V, hemoglobin (Hb), and platelet count (plt). These variables were then retrospectively collected from 758 consecutive adult patients undergoing OLT from 1997 to 2007. Relationships between these variables and intraoperative blood transfusion requirements were examined by both univariate analysis and multiple linear regression analysis. RESULTS: Univariate analysis confirmed significant associations between RBC transfusion and PT, factor V, Hb, Plt, pathology, and age (P values all < .001). However, stepwise backward multivariate analysis excluded variables Plt and factor V from the multiple regression linear model. The variables included in the final predictive model were PT, Hb, age, and pathology. Patients suffering from liver carcinoma required more blood products than those suffering from other pathologies. Yet, the overall predictive power of the final model was limited (R(2) = .308; adjusted R(2) = .30). CONCLUSION: Preoperative variables have limited predictive power for intraoperative RBC transfusion requirements even when significant statistical associations exist, identifying only a small portion of the observed total transfusion variability. Preoperative PT, Hb, age, and liver pathology seem to be the most significant predictive factors but other factors like severity of liver disease, surgical technique, medical experience in liver transplantation, and other noncontrollable human variables may play important roles to determine the final transfusion requirements.

Hopelessness and Related Variables among Cancer Patients in the Southern European Psycho-Oncology Study (SEPOS).

BACKGROUND: Although hopelessness has been studied in cancer, no data are available in non-English-speaking countries. OBJECTIVE: The authors sought to amass data from Southern European countries (Italy, Portugal, Spain, and Switzerland) in order to fill this void. METHOD: A group of 312 cancer patients completed the Mini-MAC Hopelessness subscale, the Hospital Anxiety and Depression Scale (HADS), the Cancer Worry Inventory (CWI), and a six-item Visual Analog scale (VAS) to measure intensity of physical symptoms, general well-being, difficulty in coping with cancer, intensity of social support from close relationships, leisure activity, and support from religious beliefs. RESULTS: Regression analysis indicated that HADS-Depression, VAS Maladaptive Coping and Well-Being, and the CWI explained 42% of the variance. CONCLUSION: Hopelessness in cancer patients seems not exclusively to correspond to depression, but is related to various other psychosocial factors, such as maladaptive coping, as well.

Sepsis Mortality Prediction Based on Predisposition, Infection and Response

OBJECTIVE: To empirically test, based on a large multicenter, multinational database, whether a modified PIRO (predisposition, insult, response, and organ dysfunction) concept could be applied to predict mortality in patients with infection and sepsis. DESIGN: Substudy of a multicenter multinational cohort study (SAPS 3). PATIENTS: A total of 2,628 patients with signs of infection or sepsis who stayed in the ICU for >48 h. Three boxes of variables were defined, according to the PIRO concept. Box 1 (Predisposition) contained information about the patient's condition before ICU admission. Box 2 (Injury) contained information about the infection at ICU admission. Box 3 (Response) was defined as the response to the infection, expressed as a Sequential Organ Failure Assessment score after 48 h. INTERVENTIONS: None. MAIN MEASUREMENTS AND RESULTS: Most of the infections were community acquired (59.6%); 32.5% were hospital acquired. The median age of the patients was 65 (50-75) years, and 41.1% were female. About 22% (n=576) of the patients presented with infection only, 36.3% (n=953) with signs of sepsis, 23.6% (n=619) with severe sepsis, and 18.3% (n=480) with septic shock. Hospital mortality was 40.6% overall, greater in those with septic shock (52.5%) than in those with infection (34.7%). Several factors related to predisposition, infection and response were associated with hospital mortality. CONCLUSION: The proposed three-level system, by using objectively defined criteria for risk of mortality in sepsis, could be used by physicians to stratify patients at ICU admission or shortly thereafter, contributing to a better selection of management according to the risk of death.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.

Infections After Liver Transplantation: A Retrospective, Single-Center Study

Objective. To access the incidence of infectious problems after liver transplantation (LT). Design. A retrospective, single-center study. Materials and Methods. Patients undergoing LT from January 2008 to December 2011 were considered. Exclusion criterion was death occurring in the first 48 hours after LT. We determined the site of infection and the bacterial isolates and collected and compared recipient’s variables, graft variables, surgical data, post-LT clinical data. Results. Of the 492 patients who underwent LT and the 463 considered for this study, 190 (Group 1, 41%) developed at least 1 infection, with 298 infections detected. Of these, 189 microorganisms were isolated, 81 (51%) gram-positive bacteria (most frequently Staphylococcus spp). Biliary infections were more frequent (mean time of 160.4 167.7 days after LT); from 3 months after LT, gram-negative bacteria were observed (57%). Patients with infections after LT presented lower aminotransferase levels, but higher requirements in blood transfusions, intraoperative vasopressors, hemodialysis, and hospital stay. Operative and cold ischemia times were similar. Conclusion. We found a 41% incidence of all infections in a 2-year follow-up after LT. Gram-positive bacteria were more frequent isolated; however, negative bacteria were commonly isolated later. Clinical data after LT were more relevant for the development of infections. Donors’ variables should be considered in future analyses.

Clinical Outcome and Morphologic Determinants of Mural Thrombus in Abdominal Aortic Endografts

OBJECTIVE:Endograft mural thrombus has been associated with stent graft or limb thrombosis after endovascular aneurysm repair (EVAR). This study aimed to identify clinical and morphologic determinants of endograft mural thrombus accumulation and its influence on thromboembolic events after EVAR. METHODS: A prospectively maintained database of patients treated by EVAR at a tertiary institution from 2000 to 2012 was analyzed. Patients treated for degenerative infrarenal abdominal aortic aneurysms and with available imaging for thrombus analysis were considered. All measurements were performed on three-dimensional center-lumen line computed tomography angiography (CTA) reconstructions. Patients with thrombus accumulation within the endograft's main body with a thickness >2 mm and an extension >25% of the main body's circumference were included in the study group and compared with a control group that included all remaining patients. Clinical and morphologic variables were assessed for association with significant thrombus accumulation within the endograft's main body by multivariate regression analysis. Estimates for freedom from thromboembolic events were obtained by Kaplan-Meier plots. RESULTS: Sixty-eight patients (16.4%) presented with endograft mural thrombus. Median follow-up time was 3.54 years (interquartile range, 1.99-5.47 years). In-graft mural thrombus was identified on 30-day CTA in 22 patients (32.4% of the study group), on 6-month CTA in 8 patients (11.8%), and on 1-year CTA in 17 patients (25%). Intraprosthetic thrombus progressively accumulated during the study period in 40 patients of the study group (55.8%). Overall, 17 patients (4.1%) presented with endograft or limb occlusions, 3 (4.4%) in the thrombus group and 14 (4.1%) in the control group (P = .89). Thirty-one patients (7.5%) received an aortouni-iliac (AUI) endograft. Two endograft occlusions were identified among AUI devices (6.5%; overall, 0.5%). None of these patients showed thrombotic deposits in the main body, nor were any outflow abnormalities identified on the immediately preceding CTA. Estimated freedom from thromboembolic events at 5 years was 95% in both groups (P = .97). Endograft thrombus accumulation was associated with >25% proximal aneurysm neck thrombus coverage at baseline (odds ratio [OR], 1.9; 95% confidence interval [CI], 1.1-3.3), neck length ≤ 15 mm (OR, 2.4; 95% CI, 1.3-4.2), proximal neck diameter ≥ 30 mm (OR, 2.4; 95% CI, 1.3-4.6), AUI (OR, 2.2; 95% CI, 1.8-5.5), or polyester-covered stent grafts (OR, 4.0; 95% CI, 2.2-7.3) and with main component "barrel-like" configuration (OR, 6.9; 95% CI, 1.7-28.3). CONCLUSIONS: Mural thrombus formation within the main body of the endograft is related to different endograft configurations, main body geometry, and device fabric but appears to have no association with the occurrence of thromboembolic events over time.