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Acute Kidney Injury and Recovery Predictions of Hospitalized Patients with COVID-19.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Acute Kidney Injury and Recovery Predictions of Hospitalized Patients with COVID-19./
Author:	Annadi, Raji Reddy.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	31 p.
Notes:	Source: Masters Abstracts International, Volume: 82-12.
Contained By:	Masters Abstracts International82-12.
Subject:	Physiology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28541135
ISBN:	9798516056444

Acute Kidney Injury and Recovery Predictions of Hospitalized Patients with COVID-19.
Annadi, Raji Reddy.

Acute Kidney Injury and Recovery Predictions of Hospitalized Patients with COVID-19. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 31 p.

Source: Masters Abstracts International, Volume: 82-12.

Thesis (M.S.)--State University of New York at Stony Brook, 2021.

This item must not be sold to any third party vendors.

COVID-19 is an infectious disease caused by a novel human coronavirus, SARS-CoV-2. Patients with pre-existing COVID-19 infection are vulnerable and more proned to other health disorders like respiratory failure, multiple organ dysfunction etc. Acute kidney injury (AKI) is also strongly associated with hospitalized COVID-19 patients, having poor health outcomes. Hence it would be beneficial to predict AKI and analyze the key predictors among the COVID-19 patients. Although several research studies have been conducted in predicting the diseases based on different machine learning algorithms, but very few have been reported to predict the AKI and recovery of the AKI patients. Hence to address the problem of predicting the above-mentioned outcomes, an XGBoost machine learning model was developed for each prediction task that takes admission values recorded within 48 hours of the patient's admission. A dataset of 3043 hospitalized patients was considered which was collected at Stony Brook University Hospital during the current COVID-19 pandemic.Additionally, studies predicting whether the patient with and without COVID-19 undergoes renal replacement therapy (RRT) or dies in the hospital were also conducted. Our machine learning models were able to predict COVID-19-associated AKI, RRT, death with an average precision (AP) of 0.36, 0.54 and 0.66 respectively while their baselines were 0.18, 0.03 and0.10. In those without COVID-19 the APs were 0.23, 0.24, 0.35 while their baselines were 0.09, 0.004 and 0.05. To have enhanced APs, the hyper-parameters of the models were tuned and their interpretability is reported using SHAP. Among COVID-19 patients, the key predictors for AKI include serum creatinine (Scr), age, urine RBCs, for RRT they were Ionized Ca, IL-6, Na urine, Scr and for death they were arterial O2, fraction of inspired oxygen, lymphocyte count. Similar key predictors can be defined for COVID-19 negative patients.

ISBN: 9798516056444Subjects--Topical Terms:

518431
Physiology.
Subjects--Index Terms:

Acute kidney injury

Acute Kidney Injury and Recovery Predictions of Hospitalized Patients with COVID-19.
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520 $a COVID-19 is an infectious disease caused by a novel human coronavirus, SARS-CoV-2. Patients with pre-existing COVID-19 infection are vulnerable and more proned to other health disorders like respiratory failure, multiple organ dysfunction etc. Acute kidney injury (AKI) is also strongly associated with hospitalized COVID-19 patients, having poor health outcomes. Hence it would be beneficial to predict AKI and analyze the key predictors among the COVID-19 patients. Although several research studies have been conducted in predicting the diseases based on different machine learning algorithms, but very few have been reported to predict the AKI and recovery of the AKI patients. Hence to address the problem of predicting the above-mentioned outcomes, an XGBoost machine learning model was developed for each prediction task that takes admission values recorded within 48 hours of the patient's admission. A dataset of 3043 hospitalized patients was considered which was collected at Stony Brook University Hospital during the current COVID-19 pandemic.Additionally, studies predicting whether the patient with and without COVID-19 undergoes renal replacement therapy (RRT) or dies in the hospital were also conducted. Our machine learning models were able to predict COVID-19-associated AKI, RRT, death with an average precision (AP) of 0.36, 0.54 and 0.66 respectively while their baselines were 0.18, 0.03 and0.10. In those without COVID-19 the APs were 0.23, 0.24, 0.35 while their baselines were 0.09, 0.004 and 0.05. To have enhanced APs, the hyper-parameters of the models were tuned and their interpretability is reported using SHAP. Among COVID-19 patients, the key predictors for AKI include serum creatinine (Scr), age, urine RBCs, for RRT they were Ionized Ca, IL-6, Na urine, Scr and for death they were arterial O2, fraction of inspired oxygen, lymphocyte count. Similar key predictors can be defined for COVID-19 negative patients.
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