Predictive Risk Model for Serious Falls Among Older Persons Living With HIV

• Published in: Journal of acquired immune deficiency syndromes (1999) Vol. 91; no. 2; pp. 168 - 174
• Main Authors: Womack, Julie A., Murphy, Terrence E., Leo-Summers, Linda, Bates, Jonathan, Jarad, Samah, Smith, Alexandria C., Gill, Thomas M., Hsieh, Evelyn, Rodriguez-Barradas, Maria C., Tien, Phyllis C., Yin, Michael T., Brandt, Cynthia A., Justice, Amy C.
• Format: Journal Article
• Language: English
• Published: United States JAIDS Journal of Acquired Immune Deficiency Syndromes 01-10-2022; Lippincott Williams & Wilkins Ovid Technologies
• Subjects: Accidental Falls; Aged; Aged, 80 and over; Aging; Algorithms; Antiretroviral agents; Antiretroviral therapy; Body mass; Body mass index; Body size; Calibration; Coefficients; Cohort analysis; Cohort Studies; Demographics; Falls; Health care; HIV; HIV Infections - complications; HIV Infections - drug therapy; Human immunodeficiency virus; Humans; Machine learning; Middle age; Middle Aged; Older people; Polypharmacy; Radiology; Refitting; Regression models; Risk; Statistical analysis; Substance use
• ISSN: 1525-4135; 1944-7884
• DOI: 10.1097/QAI.0000000000003030

Older (older than 50 years) persons living with HIV (PWH) are at elevated risk for falls. We explored how well our algorithm for predicting falls in a general population of middle-aged Veterans (age 45-65 years) worked among older PWH who use antiretroviral therapy (ART) and whether model fit improved with inclusion of specific ART classes.
This analysis included 304,951 six-month person-intervals over a 15-year period (2001-2015) contributed by 26,373 older PWH from the Veterans Aging Cohort Study who were taking ART. Serious falls (those falls warranting a visit to a health care provider) were identified by external cause of injury codes and a machine-learning algorithm applied to radiology reports. Potential predictors included a fall within the past 12 months, demographics, body mass index, Veterans Aging Cohort Study Index 2.0 score, substance use, and measures of multimorbidity and polypharmacy. We assessed discrimination and calibration from application of the original coefficients (model derived from middle-aged Veterans) to older PWH and then reassessed by refitting the model using multivariable logistic regression with generalized estimating equations. We also explored whether model performance improved with indicators of ART classes.
With application of the original coefficients, discrimination was good (C-statistic 0.725; 95% CI: 0.719 to 0.730) but calibration was poor. After refitting the model, both discrimination (C-statistic 0.732; 95% CI: 0.727 to 0.734) and calibration were good. Including ART classes did not improve model performance.
After refitting their coefficients, the same variables predicted risk of serious falls among older PWH nearly and they had among middle-aged Veterans.