TY - JOUR
T1 - Using Patient Health Profile Evaluation for Predicting the Likelihood of Retinopathy in Patients with Type 2 Diabetes
T2 - A Cross-Sectional Study Using Latent Profile Analysis
AU - Chiou, Shang Jyh
AU - Liao, Kuomeng
AU - Lin, Kuan Chia
AU - Lin, Wender
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Background: To determine whether long-term self-management among patients with type 2 diabetes mellitus has the risk of developing complications. Methods: We conducted a survey of self-management behavior using diabetes self-management scales (DMSES-C and TSRQ-d) from November 2019 to May 2020 linked with biomarkers (glucose, lipid profile, blood pressure, and kidney function), and the varying measure values were transformed into normal rate proportions. We performed latent profile analysis (LPA) to categorize the patient into different patient health profiles using five classes (C1–C5), and we predicted the risk of retinopathy after adjusting for covariates. Results: The patients in C1, C2, and C4 had a higher likelihood of retinopathy events than those in C5, with odds ratios (ORs) of 1.655, 2.168, and 1.788, respectively (p = 0.032). In addition, a longer duration of diabetes was correlated with an increased risk of retinopathy events as well as being elderly. Conclusions: Optimal biomarker health profiles and patients with strong motivation pertaining to their T2DM care yielded better outcomes. Health profiles portraying patient control of diabetes over the long term can categorize patients with T2DM into different behavior groups. Customizing diabetes care information into different health profiles raises awareness of control strategies for caregivers and patients.
AB - Background: To determine whether long-term self-management among patients with type 2 diabetes mellitus has the risk of developing complications. Methods: We conducted a survey of self-management behavior using diabetes self-management scales (DMSES-C and TSRQ-d) from November 2019 to May 2020 linked with biomarkers (glucose, lipid profile, blood pressure, and kidney function), and the varying measure values were transformed into normal rate proportions. We performed latent profile analysis (LPA) to categorize the patient into different patient health profiles using five classes (C1–C5), and we predicted the risk of retinopathy after adjusting for covariates. Results: The patients in C1, C2, and C4 had a higher likelihood of retinopathy events than those in C5, with odds ratios (ORs) of 1.655, 2.168, and 1.788, respectively (p = 0.032). In addition, a longer duration of diabetes was correlated with an increased risk of retinopathy events as well as being elderly. Conclusions: Optimal biomarker health profiles and patients with strong motivation pertaining to their T2DM care yielded better outcomes. Health profiles portraying patient control of diabetes over the long term can categorize patients with T2DM into different behavior groups. Customizing diabetes care information into different health profiles raises awareness of control strategies for caregivers and patients.
KW - diabetic retinopathy
KW - latent profile analysis
KW - patient health profiles
KW - self-efficiency
KW - self-management
KW - type 2 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85131018861&partnerID=8YFLogxK
U2 - 10.3390/ijerph19106084
DO - 10.3390/ijerph19106084
M3 - Article
C2 - 35627621
AN - SCOPUS:85131018861
SN - 1661-7827
VL - 19
JO - International journal of environmental research and public health
JF - International journal of environmental research and public health
IS - 10
M1 - 6084
ER -
