Identification of Patients With Elevated Urine Albumin–to-Creatinine Ratio Levels in a Type 2 Diabetes Mellitus Cohort Based on Data Submitted by Patients via a Smartphone App (SMART-Finder): Protocol for an Observational Study
Bibliographic record
Abstract
BACKGROUND: Despite effective treatment options, chronic kidney disease (CKD) has become a major cause of mortality worldwide due to the ever-increasing number of patients with type 2 diabetes mellitus (T2DM). Guideline-compliant, at least, annual screening of patients with T2DM is crucial to prevent renal disease progression. However, data on the prevalence of CKD in patients with T2DM and on screening frequency are limited. SMART-Finder is the first study to exclusively use data provided directly by patients via an adherence app to collect information on the prevalence of CKD, risk factors, disease management, and quality of life of patients with T2DM in Germany. OBJECTIVE: The primary objective of this study is to determine the proportion of patients with T2DM and an elevated urine albumin-to-creatinine ratio (UACR; albumin-to-creatinine ratio stage A2 and A3) at baseline and after 12 (±3) months. Secondary objectives include the proportion of patients who remain in or switch to another albumin-to-creatinine ratio classification category after 12 months, information on quality of life, disease awareness, and adherence rates, as well as the proportion of patients without any UACR-screening data. Recruitment occurs via push notification among MyTherapy app users with T2DM. METHODS: This is a single-arm, retrospective/prospective, observational, digital, patient-centered cohort study, with recruitment and data documentation via a health app. Required routine laboratory data are provided by treating physicians to their patients for data entry. The study population includes adult patients with T2DM documenting their data in the MyTherapy app using their own smartphone or tablet. Study participants are provided with a specifically developed electronic case report form containing questions on demographic and general data, quality of life, disease awareness, and laboratory values including estimated glomerular filtration rate, UACR, hemoglobin 1Ac, and blood pressure. Apart from demographic and general data, all data are collected at baseline and 12 months after the last UACR assessment. An automatically generated push notification reminds participants of the second data entry. The extracted and pseudonymized data are analyzed descriptively. RESULTS: The enrollment period for this study started in February 2023 and shall end after 12 months or after the enrollment of 5000 patients. An interim analysis is planned 3 months after the inclusion of the first patient and the final analysis after 12 months of follow-up. CONCLUSIONS: Overall, the study will contribute to minimizing the existing data gap on the prevalence of CKD in patients with T2DM in Germany, provide important insights into the current disease management of patients with T2DM in everyday clinical practice in Germany, and support guideline-based care for the participating patients. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44996.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".