Anticipating impending peripheral intravenous catheter failure: A diagnostic accuracy observational study combining ultrasound and artificial intelligence to improve clinical care

Abstract

Objective:

Peripheral intravenous catheter (PIVC) failure occurs in approximately 50% of insertions. Unexpected PIVC failure leads to treatment delays, longer hospitalizations, and increased risk of patient harm. In current practice there is no method to predict if PIVC failure will occur until it is too late and a grossly obvious complication has occurred. The aim of this study is to demonstrate the diagnostic accuracy of a predictive model for PIVC failure based on artificial intelligence (AI).

Methods:

This study evaluated the capabilities of a novel machine learning algorithm. The algorithm was trained using real-world ultrasound videos of PIVC sites with a goal of predicting which PIVCs would fail within the following day. After training, AI models were validated using another, unseen, collection of real-world ultrasound videos of PIVC sites.

Results:

2133 ultrasound videos (361 failure and 1772 non-failure) were used for algorithm development. When the algorithm was tasked with predicting failure in the unseen collection of videos, the best achieved results were an accuracy of 0.93, sensitivity of 0.77, specificity of 0.98, positive predictive value of 0.91, negative predictive value of 0.93, and area under the curve of 0.87.

Conclusions:

This proprietary and novel machine learning algorithm can accurately and reliably predict PIVC failure 1 day prior to clinically evident failure. Implementation of this technology in the patient care setting would provide timely information for clinicians to plan and manage impending device failure. Future research on the use of AI technology and PIVCs should focus on improving catheter function and longevity, while limiting complication rates.