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Falls in People With Indication for Antihypertensive Therapy: A Risk Prediction Model

A risk-prediction model using common clinical measures can categorize a person’s chances of having a serious fall as high or low after at least 1 measurement of elevated blood pressure, researchers report. A retrospective cohort study used primary care data from the U.K. Clinical Practice Research Datalink (CPRD) GOLD to develop the STRAtifying Treatments In the multi-morbid Frail elderlY (STRATIFY)-Falls clinical prediction model. The data covered 1.8 million people aged 40 years or older with BP readings of 130–179 mm Hg; nearly 63,000 serious falls occurred in the group, providing a basis for risk estimation.

Data from the CPRD Aurum for 3.8 million people and 207,000 serious falls were used for external validation of the model, with these results: “The final model consisted of 24 predictors, including age, sex, ethnicity, alcohol consumption, living in an area of high social deprivation, a history of falls, multiple sclerosis, and prescriptions of antihypertensives, antidepressants, hypnotics, and anxiolytics. Upon external validation, the recalibrated model showed good discrimination, with pooled C statistics of 0.833 (95% confidence interval 0.831 to 0.835) and 0.843 (0.841 to 0.844) at five and 10 years, respectively. Original model calibration was poor on visual inspection and although this was improved with recalibration, under-prediction of risk remained (observed to expected ratio at 10 years 1.839, 95% confidence interval 1.811 to 1.865). Nevertheless, decision curve analysis suggests potential clinical utility, with net benefit larger than other strategies.”

The authors conclude, “Although miscalibration was evident on external validation, the model still had potential clinical utility around risk thresholds of 10% and so could be useful in routine clinical practice to help identify those at high risk of falls who might benefit from closer monitoring or early intervention to prevent future falls. Further studies are needed to explore the appropriate thresholds that maximise the model’s clinical utility and cost effectiveness.”

Source: BMJ