1. Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children
- Author
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Sameer Thadani, MD, Tzu-Chun Wu, PhD, Danny T. Y. Wu, PhD, Aadil Kakajiwala, MD, Danielle E. Soranno, MD, Gerard Cortina, MD, Rachana Srivastava, MD, Katja M. Gist, DO, Shina Menon, MD, on behalf of the Worldwide Exploration of Renal Replacement Outcomes Collaborative in Kidney Diseases (WE-ROCK) Collaborators, Emily Ahern, CPNP, DNP, Ayse Akcan Arikan, MD, Issa Alhamoud, MD, Rashid Alobaidi, MD, MSc, Pilar Anton-Martin, MD, PhD, Shanthi S. Balani, MD, Matthew Barhight, MD, MS, Abby Basalely, MD, MS, Amee M. Bigelow, MD, MS, Gabriella Bottari, MD, Andrea Cappoli, MD, Abhishek Chakraborty, MD, Eileen A. Ciccia, MD, Michaela Collins, BA, Denise Colosimo, MD, Mihaela A. Damian, MD, MPH, Sara De la Mata Navazo, MD, Gabrielle DeAbreu, MD, Akash Deep, MD, Kathy L. Ding, BS, Kristin J. Dolan, MD, Lama Elbahlawan, MD, Sarah N. Fernandez Lafever, MD, PhD, Dana Y. Fuhrman, DO, MS, Ben Gelbart, MBBS, Katja M. Gist, DO, MSc, Stephen M. Gorga, MD, MSc, Francesco Guzzi, MD, Isabella Guzzo, MD, Taiki Haga, MD, Elizabeth Harvey, MD, Denise C. Hasson, MD, Taylor Hill-Horowitz, BS, Haleigh Inthavong, BS, MS, Catherine Joseph, MD, Ahmad Kaddourah, MD, MS, Aadil Kakajiwala, MD, MSCI, Aaron D. Kessel, MD, MS, Sarah Korn, DO, Kelli A. Krallman, BSN, MS, David M. Kwiatkowski, MD, Msc, Jasmine Lee, MSc, Laurance Lequier, MD, Tina Madani Kia, BS, Kenneth E. Mah, MD, MS, Eleonora Marinari, MD, Susan D. Martin, MD, Tahagod H. Mohamed, MD, Catherine Morgan, MD, MSc, Theresa A. Mottes, APRN, Melissa A. Muff-Luett, MD, Siva Namachivayam, MBBS, Tara M. Neumayr, MD, Jennifer Nhan, MD, MS, Abigail O’Rourke, MD, Nicholas J. Ollberding, PhD, Matthew G. Pinto, MD, Dua Qutob, MD, Valeria Raggi, MD, Stephanie Reynaud, MD, Zaccaria Ricci, MD, Zachary A. Rumlow, DO, María J. Santiago Lozano, MD, PhD, Emily See, MBBS, David T. Selewski, MD, MSCR, Carmela Serpe, MSc, PhD, Alyssa Serratore, RN, MsC, Ananya Shah, BS, Weiwen V. Shih, MD, H. Stella Shin, MD, Cara L. Slagle, MD, Sonia Solomon, DO, Natalja L. Stanski, MD, Michelle C. Starr, MD, MPH, Erin K. Stenson, MD, Amy E. Strong, MD, MSCE, Susan A. Taylor, MSc, Sameer V. Thadani, MD, Amanda M. Uber, DO, Brynna Van Wyk, ARNP, MSN, Tennille N. Webb, MD, MSPH, Huaiyu Zang, PhD, Emily E. Zangla, DO, and Michael Zappitelli, MD, MSc
- Subjects
Medical emergencies. Critical care. Intensive care. First aid ,RC86-88.9 - Abstract
BACKGROUND:. Continuous renal replacement therapy (CRRT) is the favored renal replacement therapy in critically ill patients. Predicting clinical outcomes for CRRT patients is difficult due to population heterogeneity, varying clinical practices, and limited sample sizes. OBJECTIVE:. We aimed to predict survival to ICUs and hospital discharge in children and young adults receiving CRRT using machine learning (ML) techniques. DERIVATION COHORT:. Patients less than 25 years of age receiving CRRT for acute kidney injury and/or volume overload from 2015 to 2021 (80%). VALIDATION COHORT:. Internal validation occurred in a testing group of patients from the dataset (20%). PREDICTION MODEL:. Retrospective international multicenter study utilizing an 80/20 training and testing cohort split, and logistic regression with L2 regularization (LR), decision tree, random forest (RF), gradient boosting machine, and support vector machine with linear kernel to predict ICU and hospital survival. Model performance was determined by the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) due to the imbalance in the dataset. RESULTS:. Of the 933 patients included in this study, 538 (54%) were male with a median age of 8.97 years and interquartile range (1.81–15.0 yr). The ICU mortality was 35% and hospital mortality was 37%. The RF had the best performance for predicting ICU mortality (AUROC, 0.791 and AUPRC, 0.878) and LR for hospital mortality (AUROC, 0.777 and AUPRC, 0.859). The top two predictors of ICU survival were Pediatric Logistic Organ Dysfunction-2 score at CRRT initiation and admission diagnosis of respiratory failure. CONCLUSIONS:. These are the first ML models to predict survival at ICU and hospital discharge in children and young adults receiving CRRT. RF outperformed other models for predicting ICU mortality. Future studies should expand the input variables, conduct a more sophisticated feature selection, and use deep learning algorithms to generate more precise models.
- Published
- 2024
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