Dataset Information

Predicting in-hospital mortality among critically ill patients with end-stage liver disease.

ABSTRACT: Critically-ill patients with end-stage liver disease (ESLD) are at high risk for death during intensive care unit hospitalization, and currently available prognostic models have limited accuracy in this population. We aimed to identify variables associated with in-hospital mortality among critically ill ESLD patients and to develop and validate a simple, parsimonious model for bedside use.We performed a retrospective chart review of 653 intensive care unit admissions for ESLD patients; modeled in-hospital mortality using multivariable logistic regression; and compared the predictive ability of several different models using the area under receiver operating characteristic (AU-ROC) curves.Multivariable predictors of in-hospital mortality included Model for End-stage Liver Disease (MELD) score, Acute Physiology and Chronic Health Evaluation (APACHE) II score, mechanical ventilation, and gender; there was also an interaction between MELD score and gender (P < .02). MELD alone had better discrimination (AU-ROC 0.83) than APACHE II alone (AU-ROC 0.76), and adding mechanical ventilation to MELD achieved the single largest increase in model discrimination (AU-ROC 0.85; P < .01). In a parsimonious, 2-predictor model, higher MELD scores (OR 1.14 per 1-point increase; 95% CI 1.11-1.16), and mechanical ventilation (OR 6.20; 95% CI 3.05-12.58) were associated with increased odds of death. Model discrimination was also excellent in the validation cohort (AU-ROC 0.90).In critically ill ESLD patients, a parsimonious model including only MELD and mechanical ventilation is more accurate than APACHE II alone for predicting in-hospital mortality. This simple bedside model can provide clinicians and patients with valuable prognostic information for medical decision-making.

SUBMITTER: Balekian AA

PROVIDER: S-EPMC4405501 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting in-hospital mortality among critically ill patients with end-stage liver disease.

Balekian Alex A AA Gould Michael K MK

Journal of critical care 20121009 6

<h4>Purpose</h4>Critically-ill patients with end-stage liver disease (ESLD) are at high risk for death during intensive care unit hospitalization, and currently available prognostic models have limited accuracy in this population. We aimed to identify variables associated with in-hospital mortality among critically ill ESLD patients and to develop and validate a simple, parsimonious model for bedside use.<h4>Materials and methods</h4>We performed a retrospective chart review of 653 intensive car ...[more]

PMID: 23059012

Similar Datasets

Project description:PurposeEnd-stage kidney disease (ESKD) is a major worldwide health problem. Patients with ESKD are thought to have a significant risk for development of complications following an operation. However, the study of ESKD and its outcomes following major operations remains rare, particularly in critical illness. Therefore, this study aimed to demonstrate how the outcomes of ESKD patients were affected when they underwent a major operation and were admitted to the intensive care unit (ICU), compared with non-ESKD patients.MethodsA retrospective matched case cohort study was conducted in 122 critically ill surgical patients who underwent a major operation and were admitted to the ICU, during 2013 and 2016. Sixty-one ESKD patients who required long-term dialysis were enrolled and compared with 61 matched non-ESKD patients. The matching criteria were the same age interval (±5 years), gender, and type of operation. The ICU mortality was compared to the primary outcome of the study.ResultsPatients' baseline characteristics between ESKD and non-ESKD were similar to a priori matching criteria and other demographics, except for pre-existing diabetes mellitus and hypertension, which were found significantly more in ESKD (p = 0.03 and 0.04, respectively). For operations, ESKD showed a higher grade of the American Society of Anesthesiologist (ASA) physical status (p < 0.001), but there were no differences for emergency surgery (p = 0.71) and duration of operation (p = 0.34). At ICU admission, the severity of illness measured by the Sequential Organ Failure Assessment (SOFA) score was greater in ESKD (8.9 ± 2.6 vs 5.6 ± 2.5; p < 0.001). However, after eliminating renal domain, SOFA non-renal score was equivalent (5.7 ± 2.2 vs 5.2 ± 2.3, p = 0.16). The ICU mortality was significantly higher in critically-ill surgical patients with ESKD than non-ESKD (23% vs 5%, p=0.007), along with hospital mortality rates (34% vs 10%, p = 0.002). The multivariable logistic regression analyses adjusted for age and SOFA non-renal score demonstrated that ESKD had a significant association with ICU and hospital mortality (adjOR = 5.59; 95%CI [1.49-20.88], p = 0.01 and adjOR = 4.55; 95%CI[1.67-12.44], p = 0.003, respectively).ConclusionPatients who underwent a major operation and needed intensive care admission with pre-existing ESKD requiring long-term dialysis were associated with greater mortality than patients without ESKD. More careful assessment before, during, and after major surgical procedures should be performed in this group of patients to improve post-operative outcomes.

Project description:Prevalence of chronic kidney disease (CKD) amongst intensive care unit (ICU) admissions is rising. How mortality and risk of end-stage renal disease (ESRD) differs between those with and without CKD and with acute kidney injury (AKI) is unclear. Determining factors that increase the risk of ESRD is essential to optimise treatment, identify patients requiring nephrological surveillance and for quantification of dialysis provision.This cohort study used the Swedish intensive care register 2005-2011 consisting of 130,134 adult patients. Incomplete cases were excluded (26,771). Patients were classified (using diagnostic and intervention codes as well as admission creatinine values) into the following groups: ESRD, CKD, AKI, acute-on-chronic disease (AoC) or no renal dysfunction (control). Primary outcome was all-cause mortality. Secondary outcome was ESRD incidence.Of 103,363 patients 4,192 had pre-existing CKD; 1389 had ESRD; 5273 developed AKI and 998 CKD patients developed AoC. One-year mortality was greatest in AoC patients (54 %) followed by AKI (48.7 %), CKD (47.6 %) and ESRD (40.3 %) (P?<?0.001). Five-year mortality was highest for the CKD and AoC groups (71.3 % and 68.2 %, respectively) followed by AKI (61.8 %) and ESRD (62.9 %) (P?<?0.001). ESRD incidence was greatest in the AoC and CKD groups (adjusted incidence rate ratio (IRR) 259 (95 % confidence interval (CI) 156.9-429.1) and 96.4, (95 % CI 59.7-155.6) respectively) and elevated in AKI patients compared with controls (adjusted IRR 24 (95 % CI 3.9-42.0); P?<?0.001). Risk factors independently associated with ESRD in 1-year survivors were, according to relative risk ratio, AoC (356; 95 % CI 69.9-1811), CKD (267; 95 % CI 55.1-1280), AKI (30; 95 % CI 5.98-154) and presence of elevated admission serum potassium (4.6; 95 % CI 1.30-16.40) (P?<?0.001).Pre-ICU renal disease significantly increases risk of death compared with controls. Subjects with AoC disease had extreme risk of developing ESRD. All patients with CKD who survive critical care should receive a nephrology referral.NCT02424747 April 20th 2015.

Project description:PurposeMELD-XI, an adapted version of Model for End-stage Liver Disease (MELD) score excluding INR, was reported to predict outcomes e.g. in patients with acute heart failure. We aimed to evaluate MELD-XI in critically ill patients admitted to an intensive care unit (ICU) for prognostic relevance.MethodsA total of 4381 medical patients (66±14 years, 2862 male) admitted to a German ICU between 2004 and 2009 were included and retrospectively investigated. Admission diagnoses were e.g. myocardial infarction (n = 2034), sepsis (n = 694) and heart failure (n = 688). We divided our patients in two cohorts basing on their MELD-XI score and evaluated the MELD-XI score for its prognostic relevance regarding short-term and long-term survival. Optimal cut-offs were calculated by means of the Youden-Index.ResultsPatients with a MELD-XI score >12 had pronounced laboratory signs of organ failure and more comorbidities. MELD-XI >12 was associated with an increase in short-term (27% vs 6%; HR 4.82, 95%CI 3.93-5.93; p<0.001) and long-term (HR 3.69, 95%CI 3.20-4.25; p<0.001) mortality. In a univariate Cox regression analysis for all patients MELD-XI was associated with increased long-term mortality (changes per score point: HR 1.06, 95%CI 1.05-1.07; p<0.001) and remained to be associated with increased mortality after correction in a multivariate regression analysis for renal failure, liver failure, lactate concentration, blood glucose concentration, oxygenation and white blood count (HR 1.04, 95%CI 1.03-1.06; p<0.001). Optimal cut-off for the overall cohort was 11 and varied remarkably depending on the admission diagnosis: myocardial infarction (9), pulmonary embolism (9), cardiopulmonary resuscitation (17) and pneumonia (17). We performed ROC-analysis and compared the AUC: SAPS2 (0.78, 95%CI 0.76-0.80; p<0.0001) and APACHE (0.76, 95%CI 0.74-0.78; p<0.003) score were superior to MELD-XI (0.71, 95%CI 0.68-0.73) for prediction of mortality.ConclusionsThe easily calculable MELD-XI score is a robust and reliable tool to predict both intra-ICU and long-term mortality in critically ill medical patients admitted to an ICU. Optimal cut-off values for MELD-XI scores seem to depend on the primary disease and need to be validated in future prospective studies. Compared to SAPS2 and APACHE score, MELD-XI lacks precision but might have comparable and even additive value, as it is easily available and independent of subjective values.

Dataset Information

Predicting in-hospital mortality among critically ill patients with end-stage liver disease.

Publications

Predicting in-hospital mortality among critically ill patients with end-stage liver disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets