ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on X

Kidney Week

ASN / Education & Meetings / Kidney Week /

Please note that you are viewing an archived section from 2020 and some content may be unavailable. To unlock all content for 2020, please visit the archives.

Abstract: PO0008

Predicting Intra-ICU Mortality Using Machine Learning Algorithms in Patients Who Require Acute Renal Replacement Therapy in a Critical Care Unit

Session Information

Category: Acute Kidney Injury

  • 101 AKI: Epidemiology, Risk Factors, and Prevention

Authors

  • Chang, Hsin-Hsiung, National Cheng kung College of Computer Science and Information Engineering, Tainan, Taiwan
  • Chen, Huiwen, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • Jhamb, Manisha, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
Background

The Acute Physiology, Age, Chronic Health Evaluation (APACHE) and Simplified Acute Physiology Score(SAPS) are not a fair mortality prediction tools for patients receiving acute renal replacement therapy (RRT) in the ICU. Our objective was to develop a series of machine learning models to predict intra-ICU mortality for patients requiring acute dialysis therapy using data obtained one day prior to RRT initiation.

Methods

We extracted data on patients who commenced acute RRT captured in eICU and MIMIC databases. We trained machine learning models by using the eICU and MIMIC dataset. The validity of those models was then evaluated by using MIMIC and eICU.

Results

The eICU cohort included 2,360 patients and MIMIC included 1,274 patients who met our eligibility criteria.Thediscrimination power of the model was evaluatedby calculating the area under the receiver operating characteristic curve (AUC). The intra-ICU mortality AUC of the Sequential Organ Failure Assessment (SOFA) score using data collected one day before RRT initiation was 0.803 (95% CI 0.777-0.829) in eICU and 0.683 (95% CI 0.636-0.729) in MIMIC. The intra-ICU mortality AUC of machine learning models using logistic regression (LR), XGBoost, random forest (RF) and multilayer perceptron (MLP) trained in eICU cohort were 0.858 (95% CI 0.850-0.867), 0.858 (95% CI 0.850-0.866), 0.859 (95% CI 0.848-0.870), 0.864 (95% CI 0.851-0.876) and validated in MIMIC were 0.799 (95% CI:0.775-0.824), 0.809 (95% CI:0.786-0.833), 0.814 (95% CI 0.791-0.837), 0.800 (95% CI 0.776-0.825), respectively. When training the models using MIMIC dataset, the intra-ICU mortality AUCof LR, XGBoost, RF and MLP were 0.818 95% CI (0.786-0.852], 0.821 (95% CI 0.787-0.856) 0.822 (95% CI 0.791-0.854), 0.827 (95% CI 0.795-0.858), respectively. Validating these models using eICU dataset, the AUC of LR, XGBoost, RF and MLP were 0.846 95% CI:0.828-0.864, 0.847 (95% CI 0.829-0.865), 0.853 (95% CI 0.835-0.870), 0.846 (95% CI 0.828-0.865), respectively.

Conclusion

In this study, we designed machine learning models to make intra-ICU mortality prediction for patients who required RRT. Our models correlated better than SOFA score in predicting the mortality of patients requiring RRT in ICU. All of the models almost had excellent performance in both databases.