Machine learning prediction model of acute kidney injury after percutaneous coronary intervention
Toshiki Kuno; Takahisa Mikami; Yuki Sahashi; Yohei Numasawa; Masahiro Suzuki; Shigetaka Noma; Keiichi Fukuda; Shun Kohsaka · 2022 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Acute kidney injury (AKI) after percutaneous coronary intervention (PCI) is associated with a significant risk of morbidity and mortality. The traditional risk model provided by the National Cardiovascular Data Registry (NCDR) is useful for predicting the preprocedural risk of AKI, although the scoring system requires a number of clinical contents. We sought to examine whether machine learning (ML) techniques could predict AKI with fewer NCDR-AKI risk model variables within a comparable PCI database in Japan. We evaluated 19,222 consecutive patients undergoing PCI between 2008 and 2019 in a Ja
Abstract by Toshiki Kuno; Takahisa Mikami; Yuki Sahashi; Yohei Numasawa; Masahiro Suzuki; Shigetaka Noma; Keiichi Fukuda; Shun Kohsaka, Scientific Reports (2022) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41598-021-04372-8