Dataset Information

Prediction of poor outcome in patients with acute liver failure-systematic review of prediction models.

ABSTRACT:

Introduction

Acute liver failure is a rare disease with high mortality and liver transplantation is the only life saving therapy. Accurate prognosis of ALF is crucial for proper intervention.

Aim

To identify and characterize newly developed prognostic models of mortality for ALF patients, assess study quality, identify important variables and provide recommendations for the development of improved models in the future.

Methods

The online databases MEDLINE® (1950-2012) and EMBASE® (1980-2012) were searched for English-language articles that reported original data from clinical trials or observational studies on prognostic models in ALF patients. Studies were included if they developed a new model or modified existing prognostic models. The studies were evaluated based on an existing framework for scoring the methodological and reporting quality of prognostic models.

Results

Twenty studies were included, of which 18 reported on newly developed models, 1 on modification of the Kings College Criteria (KCC) and 1 on the Model for End-Stage Liver Disease (MELD). Ten studies compared the newly developed models to previously existing models (e.g. KCC); they all reported that the new models were superior. In the 12-point methodological quality score, only one study scored full points. On the 38-point reporting score, no study scored full points. There was a general lack of reporting on missing values. In addition, none of the studies used performance measures for calibration and accuracy (e.g. Hosmer-Lemeshow statistics, Brier score), and only 5 studies used the AUC as a measure of discrimination.

Conclusions

There are many studies on prognostic models for ALF but they show methodological and reporting limitations. Future studies could be improved by better reporting and handling of missing data, the inclusion of model calibration aspects, use of absolute risk measures, explicit considerations for variable selection, the use of a more extensive set of reference models and more thorough validation.

SUBMITTER: Wlodzimirow KA

PROVIDER: S-EPMC3522683 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Prediction of poor outcome in patients with acute liver failure-systematic review of prediction models.

Wlodzimirow Kama A KA Eslami Saeid S Chamuleau Robert A F M RA Nieuwoudt Martin M Abu-Hanna Ameen A

PloS one 20121214 12

<h4>Introduction</h4>Acute liver failure is a rare disease with high mortality and liver transplantation is the only life saving therapy. Accurate prognosis of ALF is crucial for proper intervention.<h4>Aim</h4>To identify and characterize newly developed prognostic models of mortality for ALF patients, assess study quality, identify important variables and provide recommendations for the development of improved models in the future.<h4>Methods</h4>The online databases MEDLINE® (1950-2012) and E ...[more]

PMID: 23272081

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Project description:ObjectiveTo map and assess prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease (COPD).DesignSystematic review.Data sourcesPubMed until November 2018 and hand searched references from eligible articles.Eligibility criteria for study selectionStudies developing, validating, or updating a prediction model in COPD patients and focusing on any potential clinical outcome.ResultsThe systematic search yielded 228 eligible articles, describing the development of 408 prognostic models, the external validation of 38 models, and the validation of 20 prognostic models derived for diseases other than COPD. The 408 prognostic models were developed in three clinical settings: outpatients (n=239; 59%), patients admitted to hospital (n=155; 38%), and patients attending the emergency department (n=14; 3%). Among the 408 prognostic models, the most prevalent endpoints were mortality (n=209; 51%), risk for acute exacerbation of COPD (n=42; 10%), and risk for readmission after the index hospital admission (n=36; 9%). Overall, the most commonly used predictors were age (n=166; 41%), forced expiratory volume in one second (n=85; 21%), sex (n=74; 18%), body mass index (n=66; 16%), and smoking (n=65; 16%). Of the 408 prognostic models, 100 (25%) were internally validated and 91 (23%) examined the calibration of the developed model. For 286 (70%) models a model presentation was not available, and only 56 (14%) models were presented through the full equation. Model discrimination using the C statistic was available for 311 (76%) models. 38 models were externally validated, but in only 12 of these was the validation performed by a fully independent team. Only seven prognostic models with an overall low risk of bias according to PROBAST were identified. These models were ADO, B-AE-D, B-AE-D-C, extended ADO, updated ADO, updated BODE, and a model developed by Bertens et al. A meta-analysis of C statistics was performed for 12 prognostic models, and the summary estimates ranged from 0.611 to 0.769.ConclusionsThis study constitutes a detailed mapping and assessment of the prognostic models for outcome prediction in COPD patients. The findings indicate several methodological pitfalls in their development and a low rate of external validation. Future research should focus on the improvement of existing models through update and external validation, as well as the assessment of the safety, clinical effectiveness, and cost effectiveness of the application of these prognostic models in clinical practice through impact studies.Systematic review registrationPROSPERO CRD42017069247.

Project description:IntroductionPrediction models for clinical outcome after carotid artery stenting or carotid endarterectomy could aid physicians in estimating peri- and postprocedural risks in individual patients. We aimed to identify existing prediction models for short- and long-term outcome after carotid artery stenting or carotid endarterectomy in patients with symptomatic or asymptomatic carotid stenosis, and to summarise their most important predictors and predictive performance.Patients and methodsWe performed a systematic literature search for studies that developed a prediction model or risk score published until 22 December 2016. Eligible prediction models had to predict the risk of vascular events with at least one patient characteristic.ResultsWe identified 37 studies that developed 46 prediction models. Thirty-four (74%) models were developed in carotid endarterectomy patients; 27 of these (59%) predicted short-term (in-hospital or within 30 days) risk. Most commonly predicted outcome was stroke or death (n = 12; 26%). Age (n = 31; 67%), diabetes mellitus (n = 21; 46%), heart failure (n = 16; 35%), and contralateral carotid stenosis ≥50% or occlusion (n = 16; 35%) were most commonly used as predictors. For 25 models (54%), it was unclear how missing data were handled; a complete case analysis was performed in 15 (33%) of the remaining 21 models. Twenty-eight (61%) models reported the full regression formula or risk score with risk classification. Twenty-one (46%) models were validated internally and 12 (26%) externally. Discriminative performance (c-statistic) ranged from 0.66 to 0.94 for models after carotid artery stenting and from 0.58 to 0.74 for models after carotid endarterectomy. The c-statistic ranged from 0.55 to 0.72 for the external validations.DiscussionAge, diabetes mellitus, heart failure, and contralateral carotid stenosis ≥50% or occlusion were most often used as predictors in all models. Discriminative performance (c-statistic) was higher for prediction models after carotid artery stenting than after carotid endarterectomy.ConclusionThe clinical usefulness of most prediction models for short- or long-term outcome after carotid artery stenting or carotid endarterectomy remains unclear because of incomplete reporting, methodological limitations, and lack of external validation.

Project description:BACKGROUND:The ability to predict risk allows healthcare providers to propose which patients might benefit most from certain therapies, and is relevant to payers' demands to justify clinical and economic value. To understand the robustness of risk prediction models for heart failure (HF), we conducted a systematic literature review to (1) identify HF risk-prediction models, (2) assess statistical approach and extent of validation, (3) identify common variables, and (4) assess risk of bias (ROB). METHODS:Literature databases were searched from March 2013 to May 2018 to identify risk prediction models conducted in an out-of-hospital setting in adults with HF. Distinct risk prediction variables were ranked according to outcomes assessed and incorporation into the studies. ROB was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS:Of 4720 non-duplicated citations, 40 risk-prediction publications were deemed relevant. Within the 40 publications, 58 models assessed 55 (co)primary outcomes, including all-cause mortality (n = 17), cardiovascular death (n = 9), HF hospitalizations (n = 15), and composite endpoints (n = 14). Few publications reported detail on handling missing data (n = 11; 28%). The discriminatory ability for predicting all-cause mortality, cardiovascular death, and composite endpoints was generally better than for HF hospitalization. 105 distinct predictor variables were identified. Predictors included in >5 publications were: N-terminal prohormone brain-natriuretic peptide, creatinine, blood urea nitrogen, systolic blood pressure, sodium, NYHA class, left ventricular ejection fraction, heart rate, and characteristics including male sex, diabetes, age, and BMI. Only 11/58 (19%) models had overall low ROB, based on our application of PROBAST. In total, 26/58 (45%) models discussed internal validation, and 14/58 (24%) external validation. CONCLUSIONS:The majority of the 58 identified risk-prediction models for HF present particular concerns according to ROB assessment, mainly due to lack of validation and calibration. The potential utility of novel approaches such as machine learning tools is yet to be determined. REGISTRATION NUMBER:The SLR was registered in Prospero (ID: CRD42018100709).

Project description:InductionLiver transplantation (LT) is the only final therapy for patients with acute liver failure (ALF) that cannot be controlled by conservative treatment. Acute pancreatitis (AP) is a recognized complication of ALF. The pathogenesis of AP in ALF patients has not yet been elucidated. The appearance of AP complicates the patients condition and causes a significantly increased risk of mortality.Patient concernsWe report 2 fatal cases who were both admitted with yellowing of skin and sclera with general weakness lasting for 2 weeks.DiagnosisAfter admission, the laboratory examination of case 1 showed liver dysfunction with serum levels of total bilirubin (TB) 270 μmol/l, alanine aminotransferase (ALT) 106 U/l. Abdominal computed tomography (CT) showed pelvic and peritoneal cavity fluids, occupation of left lateral lobe of liver and unclear margin of pancreas. The clinical laboratory findings of case 2 revealed TB 351.1 μmol/l, ALT 252 U/l, blood lactic acid 18 mmol/l, ammonia 209 μmol/l. And abdominal CT showed pancreatic exudation. They were both diagnosed with acute liver failure, hepatic encephalopathy and AP which was confirmed during the operation.InterventionsThey were both received a routine orthotopic LT.OutcomesAfter the surgery, their liver functions recovered well, and they received conventional conservative treatment for pancreatitis. However, the treatment was not adequately effective, and the infection was too serious and both died of multiple organ failure despite emergency rescue efforts on day 21 and day 19 after LT.ConclusionAP is a serious complication that can contribute to prohibitive morbidity and mortality in LT patients. For this reason, the vulnerable state of the pancreas and the scoring system must be defined to help clinicians decide whether a patient is suitable for liver transplantation, and the clinical experience in the treatment of pancreatitis after LT needs to be summarized as an optimal treatment guideline to facilitate better treatment.

Dataset Information

Prediction of poor outcome in patients with acute liver failure-systematic review of prediction models.

Introduction

Aim

Methods

Results

Conclusions

Publications

Prediction of poor outcome in patients with acute liver failure-systematic review of prediction models.

Similar Datasets

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