Short-term prognostic models for severe acute kidney injury patients receiving prolonged intermittent renal replacement therapy based on machine learning.

ABSTRACT:

Background

As an effective measurement for severe acute kidney injury (AKI), the prolonged intermittent renal replacement therapy (PIRRT) received attention. Also, machine learning has advanced and been applied to medicine. This study aimed to establish short-term prognosis prediction models for severe AKI patients who received PIRRT by machine learning.

Methods

The hospitalized AKI patients who received PIRRT were assigned to this retrospective case-control study. They were grouped based on survival situation and renal recovery status. To screen the correlation, Pearson's correlation coefficient, partial ETA square, and chi-square test were applied, eight machine learning models were used for training.

Results

Among 493 subjects, the mortality rate was 51.93% and the

SUBMITTER: Wei W 

PROVIDER: S-EPMC10367369 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature