The association of the ankle‐brachial index, the toe‐brachial index, and their difference, with mortality and limb outcomes in dialysis patients
Bibliographic record
Abstract
INTRODUCTION: The ankle-brachial index (ABI) is the most common test to diagnose peripheral artery disease (PAD). In dialysis patients, the ABI may under-diagnose PAD, due to a high prevalence of concomitant medial arterial calcification (MAC). The toe-brachial index (TBI) is not as susceptible to misclassification by MAC. Taking the ABI and TBI together in the form of their difference, the ABI-TBI, may provide a single measure for assessing both atherosclerosis and calcification. The relationship of these variables in dialysis patients has not been well studied. METHODS: We identified 37 dialysis patients referred for vascular studies between 2009 and 2017 in the San Diego Veterans Administration Medical Center (SDVAMC). The ABI and TBI were performed systematically for each patient, and TBI was performed regardless of ABI or waveform. We examined associations between ABI, TBI, and the difference between them (ABI-TBI) with all-cause mortality and major adverse limb events (MALE), which includes revascularizations and amputations. FINDINGS: The mean age was 65 years and 30% were African American. All patients were men, reflecting the Veterans Administration population. There were 26 deaths during follow-up and mortality was highest in patients who had low ABI and low TBI and least in those with high ABI and high TBI. Persons with TBI < 0.7 had an increased risk of all-cause mortality. The ABI-TBI, and the ABI itself, were not significantly associated with all-cause mortality although the patterns were similar. DISCUSSION: Although ABI may be an important initial risk stratification tool, the TBI may be a more informative predictor of mortality in dialysis patients. Strengths of this study include a high rate of MALE and deaths. The TBI, and the difference between ABI and TBI, should be studied further in a larger cohort of persons with advanced kidney disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".