Performance of StatSensor Point-of-Care Device for Measuring Creatinine in Patients With Chronic Kidney Disease and Postkidney Transplantation
Bibliographic record
Abstract
BACKGROUND: The StatSensor is a point-of-care device which measures creatinine in capillary whole blood. Previous studies reported an underestimation of the creatinine measurements at high creatinine concentrations and were performed in the prestandardization era for creatinine. OBJECTIVE: This accuracy-based study evaluates the use of this device in kidney-transplanted patients and those with chronic kidney disease (CKD). DESIGN: Cross-sectional diagnostic accuracy study. SETTING: Nephrology outpatient clinic in an urban tertiary center. PARTICIPANTS: Adults with CKD or a functioning kidney transplant. MEASUREMENTS: Duplicate StatSensor creatinine measurements were performed on capillary whole blood samples collected by direct fingerstick and SAFE-T-FILL collection device. Results were compared with simultaneous venous blood sampling for serum and plasma creatinine measured by an enzymatic method on the Roche Integra 400 mainframe analyzer with traceability to the ID-GC-MS (isotope dilution gas chromatography mass spectrometry) reference method. METHODS: Deming regression, Pearson correlation coefficient, and Bland-Altman analysis were used to assess accuracy and comparability between capillary whole blood measured by StatSensor and plasma creatinine measured by routine analyzer with traceability to the reference method. Estimated glomerular filtration (eGFR) rates were calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and concordance with Kidney Disease Improving Global Outcomes (KDIGO) CKD stage classification was evaluated. RESULTS: There were 60 participants (mean age = 61.9 ± 15.0 years, 55% men, 33% transplant, mean plasma creatinine = 137 ± 59 µmol/L). Bland-Altman analysis indicated a positive mean bias of 12.7 µmol/L between StatSensor fingerstick creatinine measurement and plasma creatinine. Comparison of eGFR (CKD-EPI) calculated from the StatSensor fingerstick creatinine versus plasma creatinine showed misclassification across all KDIGO CKD stages. Postanalytical correction of the bias did not improve misclassifications. The use of mean of duplicate StatSensor creatinine results did not improve performance compared with the use of singlet results. LIMITATIONS: Single center, limited participant numbers. CONCLUSIONS: The results of our study suggest that the limiting characteristics of the StatSensor device are not only bias, but also imprecision. The level of imprecision observed may influence clinical decision-making and limit the usefulness of StatSensor as a CKD screening tool. If choosing to utilize it for either screening for or monitoring CKD, it is essential that clinicians understand the limitations of point-of-care devices and apply this knowledge to test interpretation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".