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Predicting an Unfavorable Course of Dizziness in Older Patients. | LitMetric

Predicting an Unfavorable Course of Dizziness in Older Patients.

Ann Fam Med

Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Iniversiteit Amsterdam, Amsterdam, The Netherlands.

Published: September 2018

AI Article Synopsis

  • The study aimed to create a prediction model to identify older patients at risk for worsening dizziness, which significantly impacts their daily lives and care management.
  • Researchers used data from two groups of patients aged 65 and older, analyzing the prevalence of severe dizziness after 6 months and identifying key predictors like DHI score, age, arrhythmia history, and certain movements.
  • The resulting easy-to-use risk score successfully helps primary care physicians recognize high-risk patients for serious dizziness effects, with strong statistical validation from the test cohorts.

Article Abstract

Purpose: Because dizziness in older people is often chronic and can substantially affect daily functioning, it is important to identify those at risk for an unfavorable course of dizziness to optimize their care. We aimed to develop and externally validate a prediction model for an unfavorable course of dizziness in older patients in primary care, and to construct an easy-to-use risk prediction tool.

Methods: We used data from 2 prospective cohorts: a development cohort with 203 patients aged 65 years or older who consulted their primary care physician for dizziness and had substantial dizziness-related impairment (Dizziness Handicap Inventory [DHI] ≥30), and a validation cohort with 415 patients aged 65 years or older who consulted their primary care physician for dizziness of any severity. An unfavorable course was defined as presence of substantial dizziness-related impairment (DHI ≥30) after 6 months.

Results: Prevalence of an unfavorable course of dizziness was 73.9% in the development cohort and 43.6% in the validation cohort. Predictors in the final model were the score on the screening version of the DHI, age, history of arrhythmia, and looking up as a provoking factor. The model showed good calibration and fair discrimination (area under the curve = 0.77). On external validation, discriminative ability remained stable (area under the curve = 0.78). The constructed risk score was strongly correlated with the prediction model. Performance measures for risk score cut-off values are presented to determine the optimal cut-off point for clinical practice.

Conclusions: We developed an easy-to-use risk score for dizziness-related impairment in primary care. The risk score, consisting of only 4 predictors, will help primary care physicians identify patients at high risk for an unfavorable course of dizziness.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6131005PMC
http://dx.doi.org/10.1370/afm.2289DOI Listing

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