Dark Mode
UAE Chronic Kidney Disease Risk Factor Data
Patient Health Records & Digital Health
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Electronic health records detail clinical measurements and medical history for 491 patients diagnosed with chronic kidney disease (CKD) who were either living with or deemed at risk of cardiovascular disease. The data was gathered from Tawam Hospital in Al-Ain city, Abu Dhabi, and spans a one-year collection period in 2008. This collection allows for advanced analysis, such as machine learning applications, focusing on risk factors, disease progression, and baseline characteristics of this specific patient cohort. The dataset is particularly valuable for studying the interplay between kidney health, cardiovascular comorbidities, and medication usage within a Middle Eastern population.
Columns
The data includes 22 clinical variables detailing laboratory results, examinations, and medical history. Key columns include:
- Sex: Binary indicator for woman (0) or man (1).
- AgeBaseline: The patient's age at the start of the follow-up period (ranging from 23 to 89 years).
- HistoryDiabetes: Indicator for a history of diabetes.
- HistoryCHD: Indicator for a history of coronary heart disease.
- HistoryVascular: Indicator for a history of vascular disease.
- HistorySmoking: Indicator for a history of smoking.
- HistoryHTN: Indicator for a history of hypertension.
- HistoryDLD: Indicator for a history of dyslipidemia.
- HistoryObesity: Indicator for a history of obesity.
- DLDmeds, DMmeds, HTNmeds: Indicators for current medication usage (dyslipidemia, diabetes, hypertension).
- ACEIARB: Indicator for taking ACEI or ARB medications.
- CholesterolBaseline: Baseline cholesterol level (mean 4.98).
- CreatinineBaseline: Baseline creatinine level (mean 67.9).
- eGFRBaseline: Estimated glomerular filtration rate, a measure of renal function (mean 98.1).
- sBPBaseline / dBPBaseline: Baseline systolic and diastolic blood pressure.
- BMIBaseline: Baseline body-mass index (mean 30.2).
- TimeToEventMonths / TIME_YEAR: Follow-up duration in months or years until a severe CKD event or the last recorded visit (maximum 111 months).
- EventCKD35: The outcome variable indicating the occurrence of a severe chronic kidney disease event.
Distribution
The product is provided as a single tabular data file titled
ChronicKidneyDisease_EHRs_from_AbuDhabi.csv, with a size of approximately 27.96 kB. The data comprises records for 491 unique patients, consisting of 241 women and 250 men. The structure includes 22 distinct columns, all of which are validated, with no identified mismatched or missing values across the 491 records. The expected update frequency for this specific file is never.Usage
Ideal applications involve developing predictive models for severe chronic kidney disease events (EventCKD35) or cardiovascular outcomes in CKD patients. It is highly suitable for tasks such as:
- Machine learning analysis to identify significant risk factors for CKD progression.
- Statistical modelling of the relationship between baseline measurements (e.g., eGFR, BMI, blood pressure) and long-term outcomes.
- Public health research examining the prevalence of comorbidities (e.g., hypertension, diabetes, obesity) within a Middle Eastern CKD population.
- Benchmarking survival analysis related to CKD events.
Coverage
Geographic Scope: Al-Ain city, Abu Dhabi, United Arab Emirates.
Time Range: Data was collected between 1st January and 31st December 2008. Follow-up information extends up to 9 years (111 months).
Demographic Scope: 491 adult patients (average age 53.2 years) diagnosed with chronic kidney disease who were specifically identified as having or being at risk of cardiovascular disease, according to local hospital standards.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Data Scientists and Machine Learning Researchers: To train predictive models for disease progression and risk stratification.
- Clinical Epidemiologists: To study disease prevalence and the impact of baseline clinical factors on patient outcomes over time.
- Healthcare Policy Modellers: To analyse the impact of medication uptake (e.g., ACEI/ARB, HTN meds) on severe CKD events.
- Academics: For educational purposes related to health data analysis and electronic medical records.
Dataset Name Suggestions
- Abu Dhabi CKD and Cardiovascular Risk EHRs
- Tawam Hospital CKD Patient Outcomes (2008 Cohort)
- UAE Chronic Kidney Disease Risk Factor Data
- CKD Patient Health Records: Machine Learning Ready
Attributes
Original Data Source: UAE Chronic Kidney Disease Risk Factor Data
QUALITYLoading...