The Journal of Korean Medicine (대한한의학회지)

The Society of Korean Medicine (대한한의학회)
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Development of Standardized Predictive Models for Traditional Korean Medical Diagnostic Pattern Identification in Stroke Subjects: A Hospital-based Multi-center Trial

  • Jung, Woo-Sang (Department of Cardiology and Neurology, College of Korean Medicine, Kyung Hee University) ;
  • Cho, Seung-Yeon (Department of Cardiology and Neurology, College of Korean Medicine, Kyung Hee University) ;
  • Park, Seong-Uk (Department of Cardiology and Neurology, College of Korean Medicine, Kyung Hee University) ;
  • Moon, Sang-Kwan (Department of Cardiology and Neurology, College of Korean Medicine, Kyung Hee University) ;
  • Park, Jung-Mi (Department of Cardiology and Neurology, College of Korean Medicine, Kyung Hee University) ;
  • Ko, Chang-Nam (Department of Cardiology and Neurology, College of Korean Medicine, Kyung Hee University) ;
  • Cho, Ki-Ho (Department of Cardiology and Neurology, College of Korean Medicine, Kyung Hee University) ;
  • Kwon, Seungwon (Department of Cardiology and Neurology, College of Korean Medicine, Kyung Hee University)
  • Received : 2019.10.26
  • Accepted : 2019.11.22
  • Published : 2019.12.01
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Abstract

Objectives: To develop a standardized diagnostic pattern identification equation for stroke patients, our group conducted a study to derive the predictive logistic equations. However, the sample size was relatively small. In the current study, we aimed to derive new predictive logistic equations for each diagnostic pattern using an expanded number of subjects. Methods: This study was a hospital-based multi-center trial recruited stroke patients within 30 days of symptom onset. Patients' general information, and the variables related to diagnostic pattern identification were measured. The diagnostic pattern of each patient was identified independently by two Korean Medicine Doctors. To derive a predictive model for pattern identification, binary logistic regression analysis was applied. Results: Among the 1,251 patients, 385 patients (30.8%) had the Fire Heat Pattern, 460 patients (36.8%) the Phlegm Dampness Pattern, 212 patients (16.9%) the Qi Deficiency Pattern, and 194 patients (15.5%) the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for each pattern were determined. Conclusion: The predictive equations for Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency would be useful to determine individual stroke patients' pattern identification in the clinical setting. However, further studies using objective measurements are necessary to validate these data.

Keywords

References

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