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Discussion
This article provided the rationale and details of the design and validation process of a novel CDS algorithm designed to help in making RAASi-related prescribing decisions for older adults with diabetes for the prevention and management of DKD. The final version of the algorithm included 9 general recommendations and 36 personalised recommendations considering the variation of life expectancies and functional capacities among older adults.
This pioneering CDS algorithm incorporates personalised care parameters using NNT and TTB values from an MA of RCTs. It leverages a wide range of RCTs as its knowledge base, accommodating various scenarios with differing albuminuria levels, hypertension and kidney disease. Developed by a team of experts in geriatrics, nephrology, pharmacoepidemiology and patient-reported outcomes, it also draws insights from clinicians across disciplines via surveys and Delphi studies. This interdisciplinary approach shaped the algorithm’s design systematically and structurally.
Acknowledging the lack of guidelines or standard methods for CDS algorithm development, referring to prior research offers insights. Lefebvre et al crafted nine deprescribing CDS algorithms for haemodialysis patients employing Lynn’s three-step approach.39 40 They conducted a systematic literature review to address research queries and engaged multidisciplinary experts for content development.39 Farrell et al developed four deprescribing CDS algorithms.41–44 The algorithms were designed using a guidelines development approach through applying the Grading of Recommendations, Assessment, Development and Evaluation process.45 The same research group developed an instruction manual to guide the efforts of future researchers interested in the development of deprescribing CDS algorithms.46
A SR conducted by Souza-Pereira et al provided examples of CDS algorithms used to manage chronic diseases.47 The most addressed chronic diseases were diabetes, cardiovascular diseases and pain. According to their review, most of the algorithms focused on the follow-up management by providing treatment recommendations or guidelines on the management of the disease. The SR showed that most of the algorithms were developed using databases to feed the algorithms, and they were designed with the help of software engineers.47 Yan et al conducted a review on the CDS algorithms that were developed for inpatient pharmacy services.48 The review discussed studies that compared the CDS algorithm interventions to control and showed that around 80% of the evaluated CDS algorithms were associated with statistically significant favourable outcomes compared with control. These outcomes included correct doses based on renal function, the detection of drug–drug interactions and reduced length of stay.48
What distinguishes this CDS algorithm from the previous examples, is that beside providing a structured approach to prescribing, our CDS algorithm will also enable prescribers to incorporate personalised care concepts into their decisions. That is, the algorithm requires the prescriber to consider a variety of patient factors, including goals of therapy, quality of life, remaining life expectancy, and functional capacity. Considering these factors in the care of older adults is crucial, as older adults are heterogeneous in terms of these factors.26
As with other interventions, CDS algorithms aim to improve patients’ care and standardise practice. While algorithms and protocols are helpful, they are not a substitute for clinical expertise.49 It is normal for prescribers to disagree with some of the guidelines or algorithms. Despite this, clinical decision-making should follow a transparent, consistent approach.50 The rationale for deviating from guidelines and algorithms should be well-founded and properly documented. By providing documentation, CDS algorithms could be optimised in the future.50
This algorithm inherits limitations from the MA it was derived from, covering RCTs spanning over 20 years with methodological variations. Inclusion criteria led to diverse participant characteristics, managed through subgroup and sensitivity analysis. Generalisability may be limited as the expert panel were exclusively from Canada. Regular updates and adaptation of the CDS algorithm are essential to ensure relevance.
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