Safety and Feasibility of the PEPPER Adaptive Bolus Advisor and Safety System: A Randomized Control Study

Avari, Parizad; Leal, Yenny; Herrero, Pau; Wos, Marzena; Jugnee, Narvada; Arnoriaga-Rodríguez, María; Thomas, María Gabriela; Liu, Chengyuan; Massana, Quim; López, Beatriz; Niţă, Lucian; Martin, Clare; Fernández‐Real, José Manuel; Oliver, Nick; Fernández-Balsells, Mercè; Reddy, Monika

doi:10.1089/dia.2020.0301

Cited by 25 publications

(91 citation statements)

References 23 publications

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“…Overall, 41 RCTs involving a median of 294 participants (range, 17-2488 participants) met inclusion criteria. 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 …”

Section: Resultsmentioning

confidence: 99%

“…Regarding RCT inclusivity, among the trials selected based on our search criteria, we found that only 20 (49%) were conducted at more than 1 site. 26,[28][29][30][32][33][34]39,42,44,47,50,51,54,56,57,59,[62][63][64] In addition, we found a lack of reporting of demographic information across studies, with only 11 RCTs (27%) reporting participant race or ethnicity. [30][31][32][33]36,44,49,55,59,61,63 Within this subset, studies had a median of 21% of enrolled participants belonging to underrepresented minority groups, a number concordant or slightly higher than proportions reported in prior systematic reviews analyzing medical RCTs.…”

Section: Low Risk Of Biasmentioning

confidence: 99%

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Randomized Clinical Trials of Machine Learning Interventions in Health Care

et al. 2022

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ImportanceDespite the potential of machine learning to improve multiple aspects of patient care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a prerequisite to large-scale clinical adoption of an intervention, and important questions remain regarding how machine learning interventions are being incorporated into clinical trials in health care.ObjectiveTo systematically examine the design, reporting standards, risk of bias, and inclusivity of RCTs for medical machine learning interventions.Evidence ReviewIn this systematic review, the Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection online databases were searched and citation chasing was done to find relevant articles published from the inception of each database to October 15, 2021. Search terms for machine learning, clinical decision-making, and RCTs were used. Exclusion criteria included implementation of a non-RCT design, absence of original data, and evaluation of nonclinical interventions. Data were extracted from published articles. Trial characteristics, including primary intervention, demographics, adherence to the CONSORT-AI reporting guideline, and Cochrane risk of bias were analyzed.FindingsLiterature search yielded 19 737 articles, of which 41 RCTs involved a median of 294 participants (range, 17-2488 participants). A total of 16 RCTS (39%) were published in 2021, 21 (51%) were conducted at single sites, and 15 (37%) involved endoscopy. No trials adhered to all CONSORT-AI standards. Common reasons for nonadherence were not assessing poor-quality or unavailable input data (38 trials [93%]), not analyzing performance errors (38 [93%]), and not including a statement regarding code or algorithm availability (37 [90%]). Overall risk of bias was high in 7 trials (17%). Of 11 trials (27%) that reported race and ethnicity data, the median proportion of participants from underrepresented minority groups was 21% (range, 0%-51%).Conclusions and RelevanceThis systematic review found that despite the large number of medical machine learning–based algorithms in development, few RCTs for these technologies have been conducted. Among published RCTs, there was high variability in adherence to reporting standards and risk of bias and a lack of participants from underrepresented minority groups. These findings merit attention and should be considered in future RCT design and reporting.

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Section: Resultsmentioning

confidence: 99%

Section: Low Risk Of Biasmentioning

confidence: 99%

Randomized Clinical Trials of Machine Learning Interventions in Health Care

et al. 2022

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“…The United Kingdom and Spain Safety and feasibility of the PEPPER adaptive bolus advisor and safety system; a randomized control study Avari et al [33], 2020…”

Section: Original Articlementioning

confidence: 99%

“…Percentage time spent in euglycemia, hypoglycemia, and hyperglycemia; number of episodes of serious hypoglycemia; episodes of hypoglycemia within 5 hours postprandially; severe hypoglycemia (defined as a hypoglycemia event requiring third party assistance); postprandial mean area under the curve at 5 hours (expressed as mmol/L min); glycemia risk and variability measures Difference in change in percentage time in range (3.9-10.0 mmol/L; 70-180 mg/dL) between the intervention arm that receives the PEPPER safety system with adaptive bolus advice and the control arm To evaluate the safety and efficacy of the PEPPER system compared with a standard bolus calculator Avari et al [33], 2020…”

Section: Change Of Treatmentmentioning

confidence: 99%

Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review

Lam¹,

Cheung²,

Munro³

et al. 2022

J Med Internet Res

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Background The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care. Objective This study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice. Methods CINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Results Among the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies. Conclusions There is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine. Trial Registration PROSPERO CRD42021286539; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286539

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“…Continuous glucose monitoring (CGM) devices offer the possibility to record glucose levels almost continuously (e.g., 5-minute sampling time), for the whole day. Algorithms using CGM data have been proposed to adapt therapy parameters as part of decision support systems (19)(20)(21)(22). If the CGM system is combined with a SIP and a smartphone application, this integrated technology allows to collect a complete data record which enables algorithmic adjustments of the basal insulin dose to accommodate the daily insulin needs of an individual following MDI therapy.…”

Section: Introductionmentioning

confidence: 99%

Titration of Long-Acting Insulin Using Continuous Glucose Monitoring and Smart Insulin Pens in Type 1 Diabetes: A Model-Based Carbohydrate-Free Approach

2022

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ObjectiveMultiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D), consisting of long-acting insulin to cover fasting conditions and rapid-acting insulin to cover meals. Titration of long-acting insulin is needed to achieve satisfactory glycemia but is challenging due to inter-and intra-individual metabolic variability. In this work, a novel titration algorithm for long-acting insulin leveraging continuous glucose monitoring (CGM) and smart insulin pens (SIP) data is proposed.MethodsThe algorithm is based on a glucoregulatory model that describes insulin and meal effects on blood glucose fluctuations. The model is individualized on patient’s data and used to extract the theoretical glucose curve in fasting conditions; the individualization step does not require any carbohydrate records. A cost function is employed to search for the optimal long-acting insulin dose to achieve the desired glycemic target in the fasting state. The algorithm was tested in two virtual studies performed within a validated T1D simulation platform, deploying different levels of metabolic variability (nominal and variance). The performance of the method was compared to that achieved with two published titration algorithms based on self-measured blood glucose (SMBG) records. The sensitivity of the algorithm to carbohydrate records was also analyzed.ResultsThe proposed method outperformed SMBG-based methods in terms of reduction of exposure to hypoglycemia, especially during the night period (0 am–6 am). In the variance scenario, during the night, an improvement in the time in the target glycemic range (70–180 mg/dL) from 69.0% to 86.4% and a decrease in the time in hypoglycemia (<70 mg/dL) from 10.7% to 2.6% was observed. Robustness analysis showed that the method performance is non-sensitive to carbohydrate records.ConclusionThe use of CGM and SIP in people with T1D using MDI therapy has the potential to inform smart insulin titration algorithms that improve glycemic control. Clinical studies in real-world settings are warranted to further test the proposed titration algorithm.SignificanceThis algorithm is a step towards a decision support system that improves glycemic control and potentially the quality of life, in a population of individuals with T1D who cannot benefit from the artificial pancreas system.

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Safety and Feasibility of the PEPPER Adaptive Bolus Advisor and Safety System: A Randomized Control Study

Cited by 25 publications

References 23 publications

Randomized Clinical Trials of Machine Learning Interventions in Health Care

Randomized Clinical Trials of Machine Learning Interventions in Health Care

Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review

Titration of Long-Acting Insulin Using Continuous Glucose Monitoring and Smart Insulin Pens in Type 1 Diabetes: A Model-Based Carbohydrate-Free Approach

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