# External Validation of a Multivariable Diagnostic Prediction Model for Acute Invasive Fungal Rhinosinusitis in Tertiary Care Settings

## TL;DR

This study validates a diagnostic model for a serious fungal sinus infection in a new patient group, showing it works well despite differences in health conditions.

## Contribution

The paper externally validates an existing AIFS diagnostic model in a new tertiary care cohort with distinct comorbidity profiles.

## Key findings

- Both the three-variable and four-variable models showed strong discrimination (C-indexes of 0.844 and 0.963, respectively).
- The models slightly overpredicted risk but maintained acceptable calibration in a different patient population.
- The model's performance suggests it can be generalized to patients with varying underlying health conditions.

## Abstract

Prompt detection and intervention are crucial for improving outcomes in acute invasive fungal rhinosinusitis (AIFS). Diagnostic prediction models assist in risk‐stratification, but their accuracy requires testing through external validation. This study aims to validate a previously published diagnostic prediction model for AIFS in an independent cohort.

A retrospective chart review was conducted at a tertiary care center (2008–2023) to identify patients with an otolaryngology consult for suspected AIFS. Of 65 patients identified, 11 (16.9%) were diagnosed with AIFS based on histopathology. Risk was calculated using Yin et al.’s predictive model. Predictive performance was assessed by calibration and discrimination.

Patients had significantly higher rates of diabetes (46.2% vs. 26.1%, p = 0.002), long‐term steroid use (60% vs. 28.2%, p < 0.0001), and solid organ transplantation (38.5% vs. 8.5%, p < 0.001), compared with the development cohort, with conversely lower rates of hematologic malignancy (29.2% vs. 58.7, p < 0.001) and neutropenia (19.4% vs. 41%, p = 0.001). Despite these differences, both the three‐variable (C‐index: 0.844; 95% CI, 0.736–0.952) and four‐variable models (C‐index: 0.963; 95% CI, 0.919–1) showed adequate discrimination. Both models exhibited slight overprediction of risk, with a calibration‐in‐the‐large predicted risk of 24.1% (95% CI, 13.68–34.46) for the three‐variable model and 24.2% (95% CI, 13.76–34.57) for the four‐variable model. Calibration plots confirmed overprediction.

The AIFS diagnostic model demonstrates acceptable discrimination and calibration on external validation, with generalizability to patients with different comorbidities. Larger studies are recommended to further test the model's predictive performance and clinical applicability.