Data Science and real world health data improve precision for diagnosis: Acute Kidney Injury, acute-on-chronic and Chronic Kidney Disease (Preprint)

2021 ◽  
Author(s):  
Karen Triep ◽  
Alexander Benedikt Leichtle ◽  
Martin Meister ◽  
Georg Martin Fiedler ◽  
Olga Endrich
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Data Science ◽  
Query Language ◽  
Correct Diagnosis ◽  
Kidney Injury ◽  
Data Driven ◽  
World Health ◽  
Discharge Data ◽  
The Individual

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.

Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

2020 ◽  
Vol 13 ◽  
Author(s):  
Pramila Arulanthu ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Field ◽  
Data Set ◽  
Medical Issues ◽  
Health Organization

: The medical data has an enormous quantity of information. This data set requires effective classification for accurate prediction. Predicting medical issues is an extremely difficult task in which Chronic Kidney Disease (CKD) is one of the major unpredictable diseases in medical field. Perhaps certain medical experts do not have identical awareness and skill to solve the issues of their patients. Most of the medical experts may have underprivileged results on disease diagnosis of their patients. Sometimes patients may lose their life in nature. As per the Global Burden of Disease (GBD-2015) study, death by CKD was ranked 17th place and GBD-2010 report 27th among the causes of death globally. Death by CKD is constituted 2·9% of all death between the year 2010 and 2013 among people from 15 to 69 age. As per World Health Organization (WHO-2005) report, 58 million people expired by CKD. Hence, this article presents the state of art review on Chronic Kidney Disease (CKD) classification and prediction. Normally, advanced data mining techniques, fuzzy and machine learning algorithms are used to classify medical data and disease diagnosis. This study reviews and summarizes many classification techniques and disease diagnosis methods presented earlier. The main intention of this review is to point out and address some of the issues and complications of the existing methods. It is also attempts to discuss the limitations and accuracy level of the existing CKD classification and disease diagnosis methods.

scholarly journals Postoperative acute kidney injury in adult non-cardiac surgery: joint consensus report of the Acute Disease Quality Initiative and PeriOperative Quality Initiative

2021 ◽  
Author(s):  
John R. Prowle ◽  
Lui G. Forni ◽  
Max Bell ◽  
Michelle S. Chew ◽  
Mark Edwards ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Cardiac Surgery ◽  
Kidney Disease ◽  
Kidney Injury ◽  
Adverse Outcomes ◽  
Baseline Risk ◽  
Major Surgery ◽  
Increased Risk

AbstractPostoperative acute kidney injury (PO-AKI) is a common complication of major surgery that is strongly associated with short-term surgical complications and long-term adverse outcomes, including increased risk of chronic kidney disease, cardiovascular events and death. Risk factors for PO-AKI include older age and comorbid diseases such as chronic kidney disease and diabetes mellitus. PO-AKI is best defined as AKI occurring within 7 days of an operative intervention using the Kidney Disease Improving Global Outcomes (KDIGO) definition of AKI; however, additional prognostic information may be gained from detailed clinical assessment and other diagnostic investigations in the form of a focused kidney health assessment (KHA). Prevention of PO-AKI is largely based on identification of high baseline risk, monitoring and reduction of nephrotoxic insults, whereas treatment involves the application of a bundle of interventions to avoid secondary kidney injury and mitigate the severity of AKI. As PO-AKI is strongly associated with long-term adverse outcomes, some form of follow-up KHA is essential; however, the form and location of this will be dictated by the nature and severity of the AKI. In this Consensus Statement, we provide graded recommendations for AKI after non-cardiac surgery and highlight priorities for future research.

scholarly journals P0296LAST HIT ‘' INFECTIONS'’ BROUGHT THE END: CHRONIC KIDNEY DISEASE TO RENAL REPLACEMENT TREATMENT

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Feyza Bora ◽  
Emine Asar ◽  
Fatih Yılmaz ◽  
Ümit Çakmak ◽  
Fevzi F Ersoy ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Outpatient Clinic ◽  
Artery Bypass ◽  
Kidney Injury ◽  
The Elderly ◽  
Severe Form ◽  
Vaccination Programs ◽  
Cardiac Decompensation ◽  
Replacement Treatment

Abstract Background and Aims It is evident that Chronic Kidney Disease (CKD) influences the risk of developing AKI (Acute Kidney Injury) and recent studies suggest that CKD patients who experienced an episode of AKI are more likely to progress to end stage renal disease (ESRD) than patients without CKD. AKI-CKD association might originate from common comorbidities associated with both AKI and CKD, such as diabetes and/or hypertension, and concurrent increase in interventions leading to frequent exposure to various nephrotoxins. AKI in the elderly has been shown to increase the risk of progression to CKD to ESRD. AKI is common in critically ill patients, and those patients with the most severe form of AKI, requiring RRT, have a mortality rate of 50–80 %. Patients with an eGFR <45 ml/min per 1.73m2 who experienced an episode of dialysis-requiring AKI were at very high risk for impaired recovery of renal function. Our aim was to determine the reasons that initiate hemodialysis (renal decompensation) in patients with regular follow-up in the low clearance polyclinic without renal replacement treatment (RRT). Method The retrospective study included predialysis CKD patients who had followed up regularly and had undergone RRT in recent 4 years. Data on baseline characteristics and medical history were obtained from patient hospital records. Results Of the 228 patients, 155 (68%) were male and 73 (32%) were female. The mean age was 58 years (45-66). Diabetes Mellitus was the first in the etiology of CKD (26,3 %), the second was unknown (12,7 %), the third was hypertension (11,8 %). 145 patients (63,6%) underwent regular hemodialysis (HD) (62 years, 55-69), 25 patients (11%) began peritoneal dialysis (PD), 58 patients (25%) had renal transplantation. 52 patients underwent HD with renal decompensation, 22 (%42,3) had working arteriovenous fistula (AVF). There was no decompensation in patients with PD or transplantation plan. 34 patients started HD because of infections (65%), 8 patients (15%) after operations (4 was Coronary Artery Bypass Grafting-CABG), 6 patients (%11,5) after coronary angiography, 4 patients (7,5%) with cardiac decompensation. 2 patients died during the hospitalisation for infections. Of 145 HD patients, 89 (%61,4) had AVF. The patients who had renal decompensation were more older 63 (58-70), have lower Hgb 9,7 g/L (9,1-10,7) and albumin 3,5 g/L (3,2-3,9) level (p<0,05). There was no difference in eGFR at the beginning of HD between renal decompensation and other HD patients. 42 patients did not undergo HD at the time we suggested during visits. Of them 9 patients (%21) had renal decompensation (6 infections,3 CABG), 17 patients (%40) had AVF. 3 of them died. The others underwent HD for uremic complications. Conclusion We have shown that infections are as the leading cause of renal decompensation. Most of our patients who started to RRT from our low clearance outpatient clinic have chosen HD for RRT. Prevention of infections via vaccination programs or early diagnosis at regular policlinic or telephone visits, and informing patients adequately about nephrotoxic drugs or the conditions that may cause renal decompensation are among the first tasks of the predialysis outpatient clinic. Transition of CKD patients to RRTs, with proper preparation, neither late nor early- at the most appropriate time- should be among in our goals. This may reduce the cost of ESRD patients.

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