Blood–urea–nitrogen-to-serum–albumin ratio in predicting the value of patients with contrast-induced nephropathy for coronary heart disease

Pan, Qiong; Peng, Yanchun; Ni, Hong; Lin, Lingyu; Luo, Baolin; Huang, Xizhen; Chen, Liangwan; Lin, Yanjuan

doi:10.1007/s11255-023-03915-4

Cited by 4 publications

(1 citation statement)

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“…Several studies have reported nonlinearity in clinical medicine 39 , 40 . As indicated by the above evidence, even nonlinear databases in clinical medicine can be adapted to graph theory using several methods: (1) adjust the weights of edges using indicators other than correlation coefficients (e.g., mutual information) to reflect nonlinear relationships 41 .…”

Section: Discussionmentioning

confidence: 99%

Innovative decision making tools using discrete mathematics for stress urinary incontinence treatment

Okui

2024

Sci Rep

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In this study, we applied graph theory to clinical decision-making for Stress Urinary Incontinence (SUI) treatment. Utilizing discrete mathematics, we developed a system to visually understand the shortest path to the desired treatment outcomes by considering various patient variables. Focusing on women aged 35–50, we examined the effectiveness of Tension-free Vaginal Tape (TVT) surgery and Vaginal Erbium Laser (VEL) treatment for over 15 years. The TVT group consisted of 102 patients who underwent surgery using either the Advantage Fit mid-urethral sling system (Boston Scientific Co., MA, USA) or the GYNECARE TVT retropubic system (Ethicon Inc., NJ, USA). The VEL group included 113 patients treated with a non-ablative Erbium: YAG laser (FotonaSmooth™ XS; Fotona d.o.o., Ljubljana, Slovenia), and there were 112 patients in the control group. We constructed a network diagram analyzing the correlations between health, demographic factors, treatment methods, and patient outcomes. By calculating the shortest path using heuristic functions, we identified significant correlations and treatment effects. This approach supports patient decision making by choosing between TVT and VEL treatments based on individual objectives. Our findings provide new insights into SUI treatment, highlighting the value of a data-driven personalized approach for clinical decision-making. This interdisciplinary study bridges the gap between mathematics and medicine, demonstrating the importance of a data-centric approach in clinical decisions.

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Section: Discussionmentioning

confidence: 99%