Patient Harm Associated with Flexible Ureteroscopes: A Cross-Sectional Analysis of FDA MAUDE Reports with Reliability-Tested Harm Classification

Abstract

Background:

Adverse events associated with flexible ureteroscopes are increasingly reported to postmarket surveillance systems; however, the lack of standardized harm classification limits reproducibility and actionable insights.

Objectives:

To develop and reliability-test a reproducible taxonomy for classifying patient harm and death in Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) reports (2020–2025) and to characterize mechanisms and severity profiles.

Methods:

We conducted a retrospective cross-sectional analysis of 532 MAUDE reports (product class: ureteroscopes; event: injury/death). Following the exclusion of nontarget devices (n = 212), literature-only records (n = 129), no-harm reports (n = 5), and duplicates (n = 39), 148 unique reports (including 1 death) were analyzed. Two independent clinicians coded reports using a prespecified taxonomy for (1) primary harm category, (2) harm mechanism, and (3) severity grade. Inter-rater reliability was quantified using unweighted and weighted Cohen’s kappa (κ).

Results:

Inter-rater reliability was substantial for the primary harm category (κ = 0.82; 95% confidence interval [CI] 0.76–0.88; 86% agreement) and severity grade (weighted κ = 0.79; 95% CI 0.71–0.87) but moderate for mechanism (κ = 0.61). Traumatic (31.1%) and infectious (30.4%) categories predominated. Operational harm (clinical consequences without tissue injury) accounted for 20.3%. Most events (69.4%) were moderate or severe. While breakage/detachment was the most common mechanism (27.0%), 48.0% of reports lacked sufficient detail for classification. Reports involving reprocessing/contamination were disproportionately severe (63.6%). The single death involved septic shock with findings suggestive of device bioburden.

Conclusions:

This reliability-tested taxonomy enables reproducible harm phenotyping in passive surveillance. However, nearly half of MAUDE narratives lack sufficient detail for mechanism coding. As a passive system lacking exposure denominators, MAUDE supports analysis of reporting composition but not incidence estimation or valid between-device comparisons.