Disease Awareness in Patients With Type 2 Diabetes: Analysis of Baseline Data From the SMART-Finder Observational Study
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is a common comorbidity of type 2 diabetes mellitus (T2DM). Data on the determination of CKD-related biomarkers among patients with T2DM in a real-life setting within Germany are limited. Objective: We aimed to determine the prevalence of CKD and risk factors, availability of urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) values, treatment satisfaction, and quality of life among patients with T2DM in Germany. Methods: SMART-Finder is a retrospective and prospective, observational, digital, patient-centered cohort study being performed as part of the routine use of an adherence-supporting app. This baseline analysis' observation period was from August to November 2023. Patients with T2DM in Germany who actively used the MyTherapy app; allowed push notifications; and documented use of diabetes medications, renin-angiotensin system inhibitors, finerenone, and/or blood glucose test strips were eligible for inclusion. Study materials (background information, electronic consent form, and laboratory and electronic questionnaires) were provided to eligible patients via app push notifications. Participants completed an electronic case report form that included questions on their blood pressure; their most recent UACR, eGFR, and glycated hemoglobin (HbA1c) values in the past 12 months; the EQ-5D-5L; and the Diabetes Treatment Satisfaction Questionnaire. The primary outcome was the proportion of patients with a UACR of ≥30 mg/g. Results: Of 9527 invited eligible patients, 101 completed the electronic case report form (male: n=61; female: n=40; age: mean 54.2, SD 11.4 y). Of these, 1 female patient and 5 male patients reported their UACR values; 3 (all male) had a UACR of ≥30 mg/g. The remaining 95 patients reported that their health care professionals had not provided UACR measurements. Only 9 (8.9%) patients were aware of their latest eGFR values (3 patients: 15-44 mL/min/1.73 m2; 6 patients: 45-89 mL/min/1.73 m2), 90 provided HbA1c values (80 patients: ≥6.0%), 46 had a systolic blood pressure of ≥130 mm Hg, and 83 reported former or current nephrotoxic medication intake. The mean EQ-5D-5L index score was 0.7 (SD 0.3; range -0.1 to 1.0; 50 patients). The mean Diabetes Treatment Satisfaction Questionnaire score was 28.8 (SD 6.8; range 9.0-36.0; 49 patients). Conclusions: Patients with T2DM who were using an adherence-supporting app in Germany lacked awareness of CKD-related biomarkers but had high knowledge of self-manageable biomarkers (eg, blood pressure, serum fasting glucose, and HbA1c values). Our results suggest that treating physicians either do not test for UACRs and eGFRs or do not inform patients about the results. Nonadherence to diagnostic testing guidelines and a lack of physician-patient communication put patients at risk. Another reason for this health literacy imbalance may be the focus on HbA1c instead of kidney comorbidity in patient education material. Future goals for diabetes management must include guideline-compliant testing of CKD-related biomarkers and open physician-patient communication.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".