Create accountLogin

Recent searches

No recent searches

Search options

Only available when logged in.
med-mastodon.com is one of the many independent Mastodon servers you can use to participate in the fediverse.
Medical community on Mastodon

Administered by:

Server stats:

396
active users

med-mastodon.com: AboutProfiles directoryPrivacy policy

Mastodon: AboutGet the appKeyboard shortcutsView source codev4.3.8

AMPEL-CDSS

outperforms classic scores. One of these is the end-stage liver disease prediction model for 90-day mortality ()

- We derived an ML model AMELD which outperforms MELD, MELD-Na, MELD 3.0, and MELD-Plus7

- How? AMELD extends the classic MELD predictors INR, bilirubin, and cystatin C / creatinine to include total protein, cholinesterase and

doi.org/10.1515/labmed-2022-01

Using more accurate AMELD prediction may improve for performing liver transplants

Front page of a paper with the title "A new machine-learning based prediction of survival in patients with end-stage liver disease" Abstract: This study aims to improve the model of end-stage liver disease (MELD) score for 90-day mortality prediction with the help of different ML algorithms. Methods: We retrospectively analyzed the clinical and laboratory data of 654 patients who were recruited during the evaluation process for liver transplantation at University Hospital Leipzig. After comparing 13 different machinelearning algorithms in a nested cross-validation setting and selecting the best performing one, we built a new model to predict 90-day mortality in patients with end-stage liver disease. Results: Penalized regression algorithms yielded the highest prediction performance in our machine-learning algorithm benchmark. In favor of a simpler model, we chose the least absolute shrinkage and selection operator (lasso) regression. Beside the classical MELD international normalized ratio (INR) and bilirubin, the lasso regression selected cystatin C over creatinine, aswell as IL-6, total protein, and cholinesterase. The new model offers improved discrimination and calibration over MELD and MELD with sodium (MELD-Na), MELD 3.0, or the MELD-Plus7 risk score. Conclusions: We provide a new machine-learning-based model of end-stage liver disease that incorporates synthesis and inflammatory markers and may improve the classical MELD score for 90-day survival prediction.
#AMPEL#CDSS#LTx
Feb 13, 2023, 11:57 AM·Public·Web
0boosts·2favorites
ExploreLive feeds
About