Prepivotal Study Exploring Safety, Efficacy, and Usability of the Automated Wearable Artificial Kidney (AWAK) PD Device
Bibliographic record
Abstract
Background: The AWAK PD is an innovative, wearable sorbent-based device providing tidal PD. The device, weighing about 3kg, automates fills and drains, with adjustable glucose for customized ultrafiltration (UF) requirements. Methods: 14 subjects underwent screening (30 days), titration period (up to 21 days), washout (2 days), and a treatment period of at-home use (7 days) with an optional extension (23 days), at a single Singapore hospital. A nomogram and PD prescribing titration guide were developed to determine cartridge type, therapy duration, initial fill tonicity and glucose (absorbed/added) to achieve target UF. The primary aim was to complete at least 70% of sorbent dialysis in each therapy. Other aims included maintaining body weight within 5% of target weight, having stable plasma biochemistry and electrolytes, and assessing patients’ experience using symptom and device usability questionnaires. Illness Intrusiveness Rating Scale (IIRS) was assessed in the 3 subjects using AWAK for 30 days. Results: 11 subjects (10 male, mean±SD age 60±7 years and dialysis vintage 21±9 months) entered the 7-day treatment period, with 1 withdrawal. 3 of these subjects completed 30 days. No device-related serious adverse events occurred. 95% of therapies conducted (76/80) were completed. Optimal UF was achieved by adjusting the dextrose concentrations. All subjects remained within 5% of their target weight. Table 1 depicts plasma biochemistry and UF results. Usability scores were high; 10/11 patients agreed they were confident using the device and 8 felt the device was useful, 3 were neutral. IIRS scores improved in the 3 patients on AWAK for 30 days (average score: 38 at baseline; 20 after AWAK). Conclusion: AWAK PD is safe to use and a reduction in the IIRS is noted after 30 days of use; reasons could range from ease of use of the device to increased flexibility to suit their lifestyle. Minor device modifications can be done to optimize solute clearance. Funding: Commercial Support - AWAK Technologies Pte Ltd Table 1: Summary of analytes and UF - median (IQR). Interim data - database lock in Aug - Measure UF (mL) Weight (kg) Urea (mmol/L) Creatinine (μmol/L) β2-microglobulin (µg/L) Sodium (mmol/L) Potassium (mmol/L) Bicarbonate (mmol/L) Baseline 340 (158-669) 73.7 (64.9 - 88.0) 20.8 (19.9 - 22.9) 866 (761 - 1008) 23787 (20397 - 29819) 138 (136 - 141) 4.2 (3.8 - 4.5) 26.0 (25.0 - 28.1) AWAK 599 (288-829) 73.8 (63.9 - 87.0) 24.3 (21.2 - 28.0)* 987 (835 - 1066)* 23707 (20816 - 33471) 138 (136 - 139) 4.3 (4.1 - 4.5) 24.6 (23.8 - 26.2) *p-value <0.05
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".