TY - GEN
T1 - A novel model for predicting hospitalization risk among hemodialysis patients based on blood test variables
AU - Al-Ani, Fatimah M.
AU - Khandoker, Ahsan H.
AU - Corridon, Peter R.
AU - Holt, Stephen G.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Hemodialysis patients are at high risk of hospitalization. Predicting such risk in dialysis patients may be critical to maintaining quality of life and reducing costs to the healthcare system. In this paper, we present and fractional polynomial stepwise logistic regression model to specify how routinely collected blood test variables could be linked to a significant increase in hospitalization risk. We found that eight of nineteen variables were significantly able to predict hospitalization risk; albumin (p<0.05), creatinine (p<0.05), calcium (p<0.01), bicarbonate (p<0.01), hemoglobin (p<0.05), mean cell hemoglobin concentration (MCHC) (p<0.0001), mean corpuscular volume (MCV) (p<0.0001), and potassium (p<0.01). The model achieved accuracy, sensitivity, and specificity of 77.31%, 83.03%, and 69.05%, respectively.
AB - Hemodialysis patients are at high risk of hospitalization. Predicting such risk in dialysis patients may be critical to maintaining quality of life and reducing costs to the healthcare system. In this paper, we present and fractional polynomial stepwise logistic regression model to specify how routinely collected blood test variables could be linked to a significant increase in hospitalization risk. We found that eight of nineteen variables were significantly able to predict hospitalization risk; albumin (p<0.05), creatinine (p<0.05), calcium (p<0.01), bicarbonate (p<0.01), hemoglobin (p<0.05), mean cell hemoglobin concentration (MCHC) (p<0.0001), mean corpuscular volume (MCV) (p<0.0001), and potassium (p<0.01). The model achieved accuracy, sensitivity, and specificity of 77.31%, 83.03%, and 69.05%, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85179641786&partnerID=8YFLogxK
U2 - 10.1109/EMBC40787.2023.10340227
DO - 10.1109/EMBC40787.2023.10340227
M3 - Conference contribution
C2 - 38083570
AN - SCOPUS:85179641786
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Y2 - 24 July 2023 through 27 July 2023
ER -