Development and external validation of a risk prediction model for falls in patients with an indication for antihypertensive treatment: retrospective cohort study

Abstract

Objective To develop and externally validate the STRAtifying Treatments In the multi-morbid Frail elderlY (STRATIFY)-Falls clinical prediction model to identify the risk of hospital admission or death from a fall in patients with an indication for antihypertensive treatment. Design Retrospective cohort study. Setting Primary care data from electronic health records contained within the UK Clinical Practice Research Datalink (CPRD). Participants Patients aged 40 years or older with at least one blood pressure measurement between 130 mm Hg and 179 mm Hg. Main outcome measure First serious fall, defined as hospital admission or death with a primary diagnosis of a fall within 10 years of the index date (12 months after cohort entry). Model development was conducted using a Fine-Gray approach in data from CPRD GOLD, accounting for the competing risk of death from other causes, with subsequent recalibration at one, five, and 10 years using pseudo values. External validation was conducted using data from CPRD Aurum, with performance assessed through calibration curves and the observed to expected ratio, C statistic, and D statistic, pooled across general practices, and clinical utility using decision curve analysis at thresholds around 10%. Results Analysis included 1 772 600 patients (experiencing 62 691 serious falls) from CPRD GOLD used in model development, and 3 805 366 (experiencing 206 956 serious falls) from CPRD Aurum in the external validation. 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. Conclusions This prediction model uses commonly recorded clinical characteristics and distinguishes well between patients at high and low risk of falls in the next 1-10 years. 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.

Risk of Falls and Fractures in Individuals With Cataract, Age-Related Macular Degeneration, or Glaucoma

“…Previous studies have reported good validity in the reporting of fractures, but there remains potential biases for our outcomes . As we captured all-cause traumatic fractures, a proportion may have not related to falls, but we were unable to ascertain whether this was the case.…”

“…Previous studies have reported good validity in the reporting of fractures, but there remains potential biases for our outcomes. [43][44][45][46] As we captured all-cause traumatic fractures, a proportion may have not related to falls, but we were unable to ascertain whether this was the case. Despite this, our analysis for fractures is likely to be more accurate than for falls because of the multifactorial nature of falls.…”

Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI

“…These individuals were more likely to be obese, be from an area of high deprivation, or be prescribed multiple antihypertensive medications, but using the present tool alongside existing CVD prediction tools 4 would provide the most personalised risk estimates. Such tools could also be enhanced by incorporating similar evidence regarding the risk of falls 31 to develop a multidimensional antihypertensive harm tool.…”

“…In conclusion, the present study developed and validated a clinical prediction model for admission to hospital or death with AKI, and found most patients with an indication for antihypertensives had a very-low risk of AKI. This model could be used to reassure patients starting or up-titrating antihypertensive treatment, and should be used alongside other prediction models for adverse events related to antihypertensive therapy 31 to allow GPs and patients to better understand the full spectrum of benefits and harms from such treatment.…”

The association between antihypertensive treatment and serious adverse events by age and frailty: A cohort study

“…This approach is common place in the context of anticoagulation for patients with atrial fibrillation [ 31 ], where prediction models are used to estimate an individual’s risk of stroke (and likelihood of benefiting from treatment) [ 32 ] and weigh this against their risk of a serious bleed (which may be exacerbated by treatment) [ 33 ]. Such models are now being developed in the context of antihypertensive treatment prescription [ 34 , 35 ], and these are needed to facilitate personalised treatment decisions based on an individual’s risk and personal preferences. It is likely to be some time before these are available in routine clinical practice, so in the meantime, the results from our study offer an important insight as to when one might want to consider intervening to prevent an individual from suffering adverse events from antihypertensive treatment.…”