Trial reportPloS one2025
Non-linear association between AKI alert detection rate by physicians and medical costs.
Trial report in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Transparent AI-driven personalized risk prediction system for acute kidney injury after total hip arthroplasty.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney injury (AKI) is associated with high mortality rates and long-term adverse outcomes and significantly increases medical costs. The AKI electronic alert system built the AKI diagnostic algorithm into the medical system, along with automated collection of key indications and generation of alerts. However, the relationship between the AKI electronic alert system and medical costs is still unknown.
methodsAn exploratory secondary analysis of data from a double-blinded, multicenter, parallel, randomized controlled trial to investigate the association between the AKI electronic alert system and medical costs.
resultsFinally, a total of 6030 patients were enrolled in this study. Multivariate logistic regression analysis revealed that the alert group was not significantly associated with medical costs (all p-values > 0.05). However, the rate of alert detection by an attending physician demonstrated a notable negative correlation with medical costs; adjusted effects for direct and total costs were -126.78$ and -236.82$, respectively. The curve fitting and threshold effect analysis revealed that when the rate of alert detection by an attending physician was between 18% and 59%, each unit increase in the rate corresponded to decreases in direct cost by 363.94 (-463.34, -264.55) $ and in total cost by 698.93 (-885.78, -512.07) $. Our subgroup analysis also found a significant relationship between the rate and medical costs.
conclusionThe alert group did not significantly reduce medical costs compared to the usual care group. However, the rate of alert detection by an attending physician had a significant negative association with medical costs, and there was a threshold effect between them. When the rate was between 18% and 59%, medical costs decreased as the rate increased, and when the rate was < 18% or ≥ 59%, medical costs did not decrease as the rate increased.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.