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Min-Hsiang Chuang,Yu-Shuo Tang,Jui-Yi Chen,Heng-Chih Pan,Hung-Wei Liao,Wen-Kai Chu,Chung-Yi Cheng,Vin-Cent Wu,Michael Heung 대한당뇨병학회 2024 Diabetes and Metabolism Journal Vol.48 No.2
Background: The initiation of sodium-glucose cotransporter-2 inhibitors (SGLT2i) typically leads to a reversible initial dip in estimated glomerular filtration rate (eGFR). The implications of this phenomenon on clinical outcomes are not well-defined.Methods: We searched MEDLINE, Embase, and Cochrane Library from inception to March 23, 2023 to identify randomized controlled trials and cohort studies comparing kidney and cardiovascular outcomes in patients with and without initial eGFR dip after initiating SGLT2i. Pooled estimates were calculated using random-effect meta-analysis.Results: We included seven studies in our analysis, which revealed that an initial eGFR dip following the initiation of SGLT2i was associated with less annual eGFR decline (mean difference, 0.64; 95% confidence interval [CI], 0.437 to 0.843) regardless of baseline eGFR. The risk of major adverse kidney events was similar between the non-dipping and dipping groups but reduced in patients with a ≤10% eGFR dip (hazard ratio [HR], 0.915; 95% CI, 0.865 to 0.967). No significant differences were observed in the composite of hospitalized heart failure and cardiovascular death (HR, 0.824; 95% CI, 0.633 to 1.074), hospitalized heart failure (HR, 1.059; 95% CI, 0.574 to 1.952), or all-cause mortality (HR, 0.83; 95% CI, 0.589 to 1.170). The risk of serious adverse events (AEs), discontinuation of SGLT2i due to AEs, kidney-related AEs, and volume depletion were similar between the two groups. Patients with >10% eGFR dip had increased risk of hyperkalemia compared to the non-dipping group.Conclusion: Initial eGFR dip after initiating SGLT2i might be associated with less annual eGFR decline. There were no significant disparities in the risks of adverse cardiovascular outcomes between the dipping and non-dipping groups.
Hsing-Chih Tsai,Min-Chih Liao 대한토목학회 2019 KSCE JOURNAL OF CIVIL ENGINEERING Vol.23 No.8
The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. This paper utilizes a derivative of genetic programming to model the torsional strength of reinforced concrete beams using polynomiallike equations. Furthermore, the calculation results of current building codes are introduced into the learning of input-output functional mapping as potential inputs to improve prediction accuracy and to suggest improvements to these building codes. The results show that introducing European building codes significantly improves the prediction accuracy to a level that is significantly above that achievable using the initial parameters alone. In addition, the results highlight that improvements of particular building codes are relevant to different parameter combinations. Moreover, suggestions for future modifications of European building codes were brought out.
Knowledge-based learning for modeling concrete compressive strength using genetic programming
Hsing-Chih Tsai,Min-Chih Liao 사단법인 한국계산역학회 2019 Computers and Concrete, An International Journal Vol.23 No.4
The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams’ laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams’ laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.