Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data.
Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000700 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832542623022186496 |
|---|---|
| author | Neda Aminnejad Michelle Greiver Huaxiong Huang |
| author_facet | Neda Aminnejad Michelle Greiver Huaxiong Huang |
| author_sort | Neda Aminnejad |
| collection | DOAJ |
| description | Chronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk. Our model utilizes electronic medical record (EMR) data, specifically by incorporating patient demographics, laboratory results, chronic conditions, risk factors, and medication codes to predict the onset of CKD in diabetic patients six months in advance, achieving an average Area Under the Curve (AUC) of 0.88. We leverage aggregated EMR data to effectively capture relevant information within the observation year instead of using temporal EMR data. Furthermore, we identify the most significant features for predicting CKD onset, including mean, minimum, and first quartile of estimated glomerular filtration rate (eGFR) during the observation year, along with variables such as diagnosis age and duration of hypertension, osteoarthritis, and diabetes, as well as levels of hemoglobin and fasting blood glucose (FBG). We also explored a refined model utilizing only these most significant features, which yields a slightly lower AUC of 0.86. These variables are typically available in primary data, empowering physicians for real-time risk assessment. The proposed model's ability to identify higher-risk patients is essential for timely intervention, personalized care, risk stratification, patient education, and potential cost savings. This research contributes valuable insights for healthcare practitioners seeking efficient tools for early CKD detection in diabetic populations. |
| format | Article |
| id | doaj-art-efe23d0ca49d402f8a893f5de565cfc0 |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-efe23d0ca49d402f8a893f5de565cfc02025-02-03T21:31:35ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-01-0141e000070010.1371/journal.pdig.0000700Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data.Neda AminnejadMichelle GreiverHuaxiong HuangChronic kidney disease (CKD) affects over 13% of the population, totaling more than 800 million individuals worldwide. Timely identification and intervention are crucial to delay CKD progression and improve patient outcomes. This research focuses on developing a predictive model to classify diabetic patients showing signs of kidney function impairment based on their CKD development risk. Our model utilizes electronic medical record (EMR) data, specifically by incorporating patient demographics, laboratory results, chronic conditions, risk factors, and medication codes to predict the onset of CKD in diabetic patients six months in advance, achieving an average Area Under the Curve (AUC) of 0.88. We leverage aggregated EMR data to effectively capture relevant information within the observation year instead of using temporal EMR data. Furthermore, we identify the most significant features for predicting CKD onset, including mean, minimum, and first quartile of estimated glomerular filtration rate (eGFR) during the observation year, along with variables such as diagnosis age and duration of hypertension, osteoarthritis, and diabetes, as well as levels of hemoglobin and fasting blood glucose (FBG). We also explored a refined model utilizing only these most significant features, which yields a slightly lower AUC of 0.86. These variables are typically available in primary data, empowering physicians for real-time risk assessment. The proposed model's ability to identify higher-risk patients is essential for timely intervention, personalized care, risk stratification, patient education, and potential cost savings. This research contributes valuable insights for healthcare practitioners seeking efficient tools for early CKD detection in diabetic populations.https://doi.org/10.1371/journal.pdig.0000700 |
| spellingShingle | Neda Aminnejad Michelle Greiver Huaxiong Huang Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data. PLOS Digital Health |
| title | Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data. |
| title_full | Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data. |
| title_fullStr | Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data. |
| title_full_unstemmed | Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data. |
| title_short | Predicting the onset of chronic kidney disease (CKD) for diabetic patients with aggregated longitudinal EMR data. |
| title_sort | predicting the onset of chronic kidney disease ckd for diabetic patients with aggregated longitudinal emr data |
| url | https://doi.org/10.1371/journal.pdig.0000700 |
| work_keys_str_mv | AT nedaaminnejad predictingtheonsetofchronickidneydiseaseckdfordiabeticpatientswithaggregatedlongitudinalemrdata AT michellegreiver predictingtheonsetofchronickidneydiseaseckdfordiabeticpatientswithaggregatedlongitudinalemrdata AT huaxionghuang predictingtheonsetofchronickidneydiseaseckdfordiabeticpatientswithaggregatedlongitudinalemrdata |