Evidence map›Paper›PMID 29069090›Full record

ArticlePloS one2017

The MELD-Plus: A generalizable prediction risk score in cirrhosis.

Uri Kartoun, Kathleen E Corey, Tracey G Simon, Hui Zheng, Rahul Aggarwal, Kenney Ng, Stanley Y Shaw

Abstract readValidation Study
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

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  17. MRI-based radiomic feature analysis of end-stage liver disease for severity stratification.International journal of computer assisted radiology and surgery · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Uri KartounHarvard Medical School, Boston, Massachusetts, United States of America.ORCID http://orcid.org/0000-0003-0988-8037
Kathleen E CoreyHarvard Medical School, Boston, Massachusetts, United States of America.
Tracey G SimonHarvard Medical School, Boston, Massachusetts, United States of America.
Hui ZhengCenter for Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Rahul AggarwalHarvard Medical School, Boston, Massachusetts, United States of America.
Kenney NgIBM Research, Cambridge, Massachusetts, United States of America.
Stanley Y ShawHarvard Medical School, Boston, Massachusetts, United States of America.

Funding

Informatics for Integrating Biology/ the Bedside (RMI)U54LM008748 · NLM · BRIGHAM AND WOMEN'S HOSPITAL · PI KOHANE, ISAAC S. · 2004 to 2013
$39.0M
The Impact of Obstructive Sleep Apnea on Non-Alcoholic Fatty Liver DiseaseK23DK099422 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI COREY, KATHLEEN ELIZABETH · 2013 to 2017
$938k
NIDDK NIH HHS K23 DK099422NLM NIH HHS U54 LM008748
6 · The paper itself

Abstract

BACKGROUND AND

aimsAccurate assessment of the risk of mortality following a cirrhosis-related admission can enable health-care providers to identify high-risk patients and modify treatment plans to decrease the risk of mortality.

methodsWe developed a post-discharge mortality prediction model for patients with a cirrhosis-related admission using a population of 314,292 patients who received care either at Massachusetts General Hospital (MGH) or Brigham and Women's Hospital (BWH) between 1992 and 2010. We extracted 68 variables from the electronic medical records (EMRs), including demographics, laboratory values, diagnosis codes, and medications. We then used a regularized logistic regression to select the most informative variables and created a risk score that comprises the selected variables. To evaluate the potential for generalizability of our score, we applied it on all cirrhosis-related admissions between 2010 and 2015 at an independent EMR data source of more than 18 million patients, pooled from different health-care systems with EMRs. We calculated the areas under the receiver operating characteristic curves (AUROCs) to assess prediction performance.

resultsWe identified 4,781 cirrhosis-related admissions at MGH/BWH hospitals, of which 778 resulted in death within 90 days of discharge. Nine variables were the most effective predictors for 90-day mortality, and these included all MELD-Na's components, as well as albumin, total cholesterol, white blood cell count, age, and length of stay. Applying our nine-variable risk score (denoted as "MELD-Plus") resulted in an improvement over MELD and MELD-Na scores in several prediction models. On the MGH/BWH 90-day model, MELD-Plus improved the performance of MELD-Na by 11.4% (0.78 [95% CI, 0.75-0.81] versus 0.70 [95% CI, 0.66-0.73]). In the MGH/BWH approximate 1-year model, MELD-Plus improved the performance of MELD-Na by 8.3% (0.78 [95% CI, 0.76-0.79] versus 0.72 [95% CI, 0.71-0.73]). Performance improvement was similar when the novel MELD-Plus risk score was applied to an independent database; when considering 24,042 cirrhosis-related admissions, MELD-Plus improved the performance of MELD-Na by 16.9% (0.69 [95% CI, 0.69-0.70] versus 0.59 [95% CI, 0.58-0.60]).

conclusionsWe developed a new risk score, MELD-Plus that accurately stratifies the short-term mortality of patients with established cirrhosis, following a hospital admission. Our findings demonstrate that using a small set of easily accessible structured variables can help identify novel predictors of outcomes in cirrhosis patients and improve the performance of widely used traditional risk scores.

Indexed as

AgedCohort StudiesFemaleHumansLiver CirrhosisLogistic ModelsMaleMiddle AgedRisk

Identifiers

PMID29069090
PMCPMC5656314

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.