Data Science and Real World Health Data Improve Precision for Diagnosis: Acute Kidney Injury, Acute-on-chronic and Chronic Kidney Disease (Preprint)

BACKGROUND The criteria for the diagnosis of kidney disease outlined in “The Kidney Disease: Improving Global Outcomes (KDIGO)” are based on a patient’s current, historical and baseline data. The diagnosis of acute (AKI), chronic (CKD) and acute-on-chronic kidney disease requires past measurements of creatinine and back-calculation and the interpretation of several laboratory values over a certain period. Diagnosis may be hindered by unclear definition of the individual creatinine baseline and rough ranges of norm values set without adjustment for age, ethnicity, comorbidities and treatment. Classification of the correct diagnosis and the sufficient staging improves coding, data quality, reimbursement, the choice of therapeutic approach and the patient’s outcome. OBJECTIVE With the help of a complex rule-engine a data-driven approach to assign the diagnoses acute, chronic and acute-on-chronic kidney disease is applied. METHODS Real-time and retrospective data from the hospital’s Clinical Data Warehouse of in- and outpatient cases treated between 2014 – 2019 is used. Delta serum creatinine, baseline values and admission and discharge data are analyzed. A KDIGO based standard query language (SQL) algorithm applies specific diagnosis (ICD) codes to inpatient stays. To measure the effect on diagnosis, Text Mining on discharge documentation is conducted. RESULTS We show that this approach yields an increased number of diagnoses as well as higher precision in documentation and coding (unspecific diagnosis ICD N19* coded in % of N19 generated 17.8 in 2016, 3.3 in 2019). CONCLUSIONS Our data-driven method supports the process and reliability of diagnosis and staging and improves the quality of documentation and data. Measuring patients’ outcome will be the next step of the project.