2023
DOI: 10.3399/bjgp.2022.0389
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Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI

Abstract: Background: Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks. Aim: To develop a prediction model estimating the risk of AKI in people potentially indicated for antihypertensive treatment. Design and setting: This observational cohort study used routine Primary Care data from the Clinical Practice Research Datalink (CPRD) in England. Methods: People aged 40+ y… Show more

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Cited by 4 publications
(4 citation statements)
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“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…The model has been externally validated, showing excellent discrimination and good calibration in most patients, except those at very high risk [ 38 •]. Similarly, the STRATIFY-AKI tool uses commonly available information within an individual’s electronic health record to estimate the risk of hospitalisation or death from acute kidney injury within the next 1, 5, or 10 years [ 39 ]. Upon external validation, this model also showed excellent discrimination and reasonable calibration across the vast majority of patients attending primary care, potentially eligible for blood pressure lowering treatment [ 39 ].…”
Section: Measuring the Risk Of Adverse Eventsmentioning
confidence: 99%
“…It is therefore important that deprescribing decisions are made in collaboration between the primary care clinician, geriatrician, cardiologist and pharmacist, using as much information about an individual’s health status and underlying risk as possible. Here, the STRATIFY tools [ 38 •, 39 ] described above may be useful in providing insights as to which patients are most likely to be at non-cardiovascular-related harm.…”
Section: Understanding the Potential Benefits And Harms Of Treatmentmentioning
confidence: 99%
“…6 Additionally, as individuals at the highest risk of CVD may be those most likely to experience SAEs, alternative hypertension management strategies to the intensive protocol and SBP goals of SPRINT may need to be considered in SPRINT‐eligible US adults. 7 , 8 …”
mentioning
confidence: 99%