Layperson Perception of Reflux-Related Symptoms

How Do Patients and Otolaryngologists Define Dizziness?

OBJECTIVE

To assess for differences in how patients and otolaryngologists define the term dizziness.

METHODS

Between June 2020 and December 2022, otolaryngology clinicians and consecutive patients at 5 academic otolaryngology institutions across the United States were asked to define the term "dizziness" by completing a semantics-based questionnaire containing 20 common descriptors of the term within 5 symptom domains (imbalance-related, lightheadedness-related, motion-related, vision-related, and pain-related). The primary outcome was differences between patient and clinician perceptions of dizzy-related symptoms. Secondary outcomes included differences among patient populations by geographic location.

RESULTS

Responses were obtained from 271 patients and 31 otolaryngologists. Patients and otolaryngologists selected 7.7 ± 3.5 and 7.1 ± 4.3 symptoms, respectively. Most patients (266, 98.2%) selected from more than 1 domain and 17 (6.3%) patients identified symptoms from all 5 domains. Patients and clinicians were equally likely to define dizziness using terms from the imbalance (difference, -2.3%; 95% CI, -13.2%, 8.6%), lightheadedness (-14.1%; -29.2%, 1.0%), and motion-related (9.4; -0.3, 19.1) domains. Patients were more likely to include terms from the vision-related (23.6%; 10.5, 36.8) and pain-related (18.2%; 10.3%, 26.1%) domains. There were minor variations in how patients defined dizziness based on geographic location.

CONCLUSIONS

Patients and otolaryngologists commonly described dizziness using symptoms related to imbalance, lightheadedness, and motion. Patients were more likely to use vision or pain-related terms. Understanding of these semantic differences may enable more effective patient-clinician communication.

Use of Patient-Reported Symptom Data in Clinical Decision Rules for Predicting Influenza in a Telemedicine Setting

INTRODUCTION

Increased use of telemedicine could potentially streamline influenza diagnosis and reduce transmission. However, telemedicine diagnoses are dependent on accurate symptom reporting by patients. If patients disagree with clinicians on symptoms, previously derived diagnostic rules may be inaccurate.

METHODS

We performed a secondary data analysis of a prospective, nonrandomized cohort study at a university student health center. Patients who reported an upper respiratory complaint were required to report symptoms, and their clinician was required to report the same list of symptoms. We examined the performance of 5 previously developed clinical decision rules (CDRs) for influenza on both symptom reports. These predictions were compared against PCR diagnoses. We analyzed the agreement between symptom reports, and we built new predictive models using both sets of data.

RESULTS

CDR performance was always lower for the patient-reported symptom data, compared with clinician-reported symptom data. CDRs often resulted in different predictions for the same individual, driven by disagreement in symptom reporting. We were able to fit new models to the patient-reported data, which performed slightly worse than previously derived CDRs. These models and models built on clinician-reported data both suffered from calibration issues.

DISCUSSION

Patients and clinicians frequently disagree about symptom presence, which leads to reduced accuracy when CDRs built with clinician data are applied to patient-reported symptoms. Predictive models using patient-reported symptom data performed worse than models using clinician-reported data and prior results in the literature. However, the differences are minor, and developing new models with more data may be possible.