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

W. Zane Billings, Annika Cleven, Jacqueline Dworaczyk, Ariella Perry Dale, Mark Ebell, Brian McKay, Andreas Handel

Project Description

We analyzed the predictive power of patient-reported symptom data in comparison to clinician reported symptom data to predict clinical diagnosis with influenza and lab-confirmed diagnosis. We also analyzed the agreement between patient and clinician symptoms, as well as the agreement between PCR and RADT diagnostic methods.

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

Data

The data used in this analysis was collected from a university health center from December 2016 through February 2017. Patients with an upper respiratory complaint were required to fill out a questionnaire before their visit, which included several questions about the presence or absence of symptoms. Patients were required to answer all questions on the survey. At the time of the visit, a clinician was required to mark the same symptoms as present or absent. The duration of illness and prescence of 18 symptoms were assessed by both the clinician and patient. The data is saved as Data.csv and Data.Rda. More details of the study design and data collection have been published previously. (Dale AP, Ebell M, McKay B, Handel A, Forehand R, Dobbin K. Impact of a Rapid Point of Care Test for Influenza on Guideline Consistent Care and Antibiotic Use. The Journal of the American Board of Family Medicine. 2019;32(2):226-233. doi:10.3122/jabfm.2019.02.180183)

Files

Data

The anonymized and cleaned data are both included in this folder of this project.

Products

The manuscript and any presentations that have been created for the project are included.

R

This file contains all of the data cleaning and analysis code. There is an additional README in that file with more detailed information.

Results

The figures, tables, and data used for or created by data analysis are documented in this folder.

Cope reproducibility

To re-run the code in this repository, you should:

• Install R 4.2.2 (newer versions may not work with renv).
• Open the R project -- renv should activate and install the correct version of itself.
• Run renv::restore() to install the correct package versions.
• You can then run the code as described in the R folder. The script wrapper.R is a convenience utility for rerunning all of the code at once (this will take a long time).