敗血症の早期発見に広く使用されている AI ツールが医師の疑念を生む可能性がある(Widely used AI tool for early sepsis detection may be cribbing doctors’ suspicions)

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2024-02-14 ミシガン大学

Multidrug resistant bacteria inside a biofilm. Image credit: iStock

ミシガン大学の新しい研究によると、敗血症の早期警告システムとして開発された特許取得済みの人工知能ソフトウェアは、患者の治療開始前に高リスクと低リスクの患者を区別することができない。このツールはEpicの電子医療記録ソフトウェアの一部であり、米国の患者の54%と国際の患者の2.5%を対象にしている。研究者は、AIが患者が敗血症にかかるリスクを正確に特定するのに役立つ可能性があるデータが、臨床医が治療を開始するまでに提供されないことを発見した。

<関連情報>

治療開始前の敗血症予測モデルの評価 Evaluation of Sepsis Prediction Models before Onset of Treatment

Fahad Kamran, Ph.D.; Donna Tjandra, M.S. ; Andrew Heiler, M.B.A. ; Jessica Virzi, M.S.N. ; Karandeep Singh, M.D.; Jessie E. King, M.D., Ph.D.; Thomas S. Valley, M.D.; M.Sc. , and Jenna Wiens, Ph.D
NEJM AI  Published February: 7, 2024
DOI: 10.1056/AIoa2300032

Abstract

BACKGROUND
Timely interventions, such as antibiotics and intravenous fluids, have been associated with reduced mortality in patients with sepsis. Artificial intelligence (AI) models that accurately predict risk of sepsis onset could speed the delivery of these interventions. Although sepsis models generally aim to predict its onset, clinicians might recognize and treat sepsis before the sepsis definition is met. Predictions occurring after sepsis is clinically recognized (i.e., after treatment begins) may be of limited utility. Researchers have not previously investigated the accuracy of sepsis risk predictions that are made before treatment begins. Thus, we evaluate the discriminative performance of AI sepsis predictions made throughout a hospitalization relative to the time of treatment.

METHODS
We used a large retrospective inpatient cohort from the University of Michigan’s academic medical center (2018–2020) to evaluate the Epic sepsis model (ESM). The ability of the model to predict sepsis, both before sepsis criteria are met and before indications of treatment plans for sepsis, was evaluated in terms of the area under the receiver operating characteristic curve (AUROC). Indicators of a treatment plan were identified through electronic data capture and included the receipt of antibiotics, fluids, blood culture, and/or lactate measurement. The definition of sepsis was a composite of the Centers for Disease Control and Prevention’s surveillance criteria and the severe sepsis and septic shock management bundle definition.

RESULTS
The study included 77,582 hospitalizations. Sepsis occurred in 3766 hospitalizations (4.9%). ESM achieved an AUROC of 0.62 (95% confidence interval [CI], 0.61 to 0.63) when including predictions before sepsis criteria were met and in some cases, after clinical recognition. When excluding predictions after clinical recognition, the AUROC dropped to 0.47 (95% CI, 0.46 to 0.48).

CONCLUSIONS
We evaluate a sepsis risk prediction model to measure its ability to predict sepsis before clinical recognition. Our work has important implications for future work in model development and evaluation, with the goal of maximizing the clinical utility of these models. (Funded by Cisco Research and others.)

医療・健康
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