Renal (Kidney) Disease

Machine-Learning Prediction Model for Personalized Urinary Tract Infection Care in Children

Description

The study will develop and implement a validated machine learning model to optimize voiding cystourethrogram timing and use for diagnosing vesicoureteral reflux (VUR) in children, aiming to reduce the significant health and economic impacts of VUR and recurrent febrile urinary tract infections (fUTIs) by standardizing practices, minimizing unnecessary procedures, and ensuring timely diagnosis for those at highest risk, ultimately seeking to prevent renal injury from fUTIs.

Grant Number
K08 HS029526
Principal Investigator(s)
Wang, Hsin-Hsiao Scott

Renal outcomes following intravenous contrast administration in patients with acute kidney injury: A multi-site retrospective propensity-adjusted analysis.

Citation
Ehmann MR, Mitchell J, Levin S, Smith A, Menez S, Hinson JS, Klein EY. Renal outcomes following intravenous contrast administration in patients with acute kidney injury: a multi-site retrospective propensity-adjusted analysis. Intensive Care Med. 2023 Jan 30. doi: 10.1007/s00134-022-06966-w. Epub ahead of print. PMID: 36715705.
Principal Investigator
Hinson, Jeremiah S.
Levin, Scott R.
Project Name
Transforming Kidney Care in the Emergency Department Using Artificial Intelligence-Driven Clinical Decision Support

Optimal acute kidney injury algorithm for detecting acute kidney injury at emergency department presentation.

Citation
Ehmann MR, Hinson JS, Menez S, Smith A, Klein EY, Levin S. Optimal acute kidney injury algorithm for detecting acute kidney injury at emergency department presentation. Kidney Medicine. 2023 Feb: 5(2): 100588.
Principal Investigator
Hinson, Jeremiah S.
Levin, Scott R.
Project Name
Transforming Kidney Care in the Emergency Department Using Artificial Intelligence-Driven Clinical Decision Support

Implementation of an Electronic Care Plan for People With Multiple Chronic Conditions - Final Report

Citation
RTI International. Implementation of an Electronic Care Plan for People With Chronic Conditions - Final Report. Prepared under Contract No. 75P00120F37004. AHRQ Publication No. 23-0010. Rockville, MD: Agency for Healthcare Research and Quality; November 2022.
Principal Investigator
Marcial, Laura
Project Name
Implementation of an Electronic Care Plan for People with Multiple Chronic Conditions

Preventing adverse events in cataract surgery: recommendations from a Massachusetts expert panel.

Citation
Nanji KC, Roberto SA, Morley MG, et al. Preventing adverse events in cataract surgery: recommendations from a Massachusetts expert panel. Anesth Analg 2017 Oct 4. doi: 10.1213/ANE.0000000000002529. [Epub ahead of print]. PMID: 28991115.
Principal Investigator
Nanji, Karen C.
Project Name
Preventing Perioperative Medication Errors and Adverse Drug Events Through the Use of Clinical Decision Support