Tadanaga Shimada
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Explore the profile of Tadanaga Shimada including associated specialties, affiliations and a list of published articles.
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24
Citations
426
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Recent Articles
1.
Shime N, Nakada T, Yatabe T, Yamakawa K, Aoki Y, Inoue S, et al.
J Intensive Care
. 2025 Mar;
13(1):15.
PMID: 40087807
The 2024 revised edition of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock (J-SSCG 2024) is published by the Japanese Society of Intensive Care Medicine and...
2.
Shime N, Nakada T, Yatabe T, Yamakawa K, Aoki Y, Inoue S, et al.
Acute Med Surg
. 2025 Feb;
12(1):e70037.
PMID: 39996161
The 2024 revised edition of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock (J-SSCG 2024) is published by the Japanese Society of Intensive Care Medicine and...
3.
Takahashi N, Campbell K, Shimada T, Nakada T, Russell J, Walley K
J Intensive Care
. 2025 Feb;
13(1):10.
PMID: 39980010
Background: Lipoproteins and their component apolipoproteins play an important role in sepsis. However, little is known with regard to the association and causal contribution of these proteins to mortality in...
4.
Shimazui T, Oami T, Shimada T, Tomita K, Nakada T
J Intensive Care
. 2025 Jan;
13(1):3.
PMID: 39800741
Background: Interleukin-6 (IL-6) is a cytokine that predicts clinical outcomes in critically ill patients, including those with sepsis. Elderly patients have blunted and easily dysregulated host responses to infection, which...
5.
Aoki A, Iwamura C, Kiuchi M, Tsuji K, Sasaki A, Hishiya T, et al.
J Clin Immunol
. 2024 Apr;
44(4):104.
PMID: 38647550
Purpose: Auto-antibodies (auto-abs) to type I interferons (IFNs) have been identified in patients with life-threatening coronavirus disease 2019 (COVID-19), suggesting that the presence of auto-abs may be a risk factor...
6.
Hayashi Y, Shimazui T, Tomita K, Shimada T, Miura R, Nakada T
Sci Rep
. 2023 Oct;
13(1):17410.
PMID: 37833430
Increased fluid overload (FO) is associated with poor outcomes in critically ill patients, especially in acute kidney injury (AKI). However, the exact timing from when FO influences outcomes remains unclear....
7.
Machine learning algorithms for predicting days of high incidence for out-of-hospital cardiac arrest
Shimada-Sammori K, Shimada T, Miura R, Kawaguchi R, Yamao Y, Oshima T, et al.
Sci Rep
. 2023 Jun;
13(1):9950.
PMID: 37336904
Predicting out-of-hospital cardiac arrest (OHCA) events might improve outcomes of OHCA patients. We hypothesized that machine learning algorithms using meteorological information would predict OHCA incidences. We used the Japanese population-based...
8.
Yoshida Y, Hayashi Y, Shimada T, Hattori N, Tomita K, Miura R, et al.
Sci Rep
. 2023 Jun;
13(1):9135.
PMID: 37277424
While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study,...
9.
Higashi A, Abe R, Oshima T, Shimada T, Hattori N, Oami T, et al.
Am J Emerg Med
. 2023 Jan;
65:216-217.
PMID: 36588063
No abstract available.
10.
Takeda M, Oami T, Hayashi Y, Shimada T, Hattori N, Tateishi K, et al.
Sci Rep
. 2022 Aug;
12(1):14593.
PMID: 36028534
Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS...