AimsThe objective of the present study is to assess the prognostic value of acute kidney injury (AKI) in the evolution of patients with heart failure (HF) using real-world data. Methods and results Patients with a diagnosis of HF and with serial measurements of renal function collected throughout the study period were included. Estimated glomerular filtration rate (GFR) was calculated with the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). AKI was defined when a sudden drop in creatinine with posterior recovery was recorded. According to the Risk, Injury, Failure, Loss, and End-Stage Renal Disease (RIFLE) scale, AKI severity was graded in three categories: risk [1.5-fold increase in serum creatinine (sCr)], injury (2.0-fold increase in sCr), and failure (3.0-fold increase in sCr or sCr > 4.0 mg/dL). AKI incidence and risk of hospitalization and mortality after the first episode were calculated by adjusting for potential confounders. A total of 30 529 patients with HF were included. During an average follow-up of 3.2 years, 5294 AKI episodes in 3970 patients (13.0%) and incidence of 3.3/100 HF patients/year were recorded. One episode was observed in 3161 (10.4%), two in 537 (1.8%), and three or more in 272 (0.9%). They were more frequent in women with diabetes and hypertension. The incidence increases across the GFR levels (Stages 1 to 4: risk 7.6%, 6.8%, 11.3%, and 12.5%; injury 2.1%, 2.0%, 3.3%, and 5.5%; and failure 0.9%, 0.6%. 1.4%, and 8.0%). A total of 3817 patients with acute HF admission were recorded during the follow-up, with incidence of 38.4/100 HF patients/year, 3101 (81.2%) patients without AKI, 545 (14.3%) patients with one episode, and 171 (4.5%) patients with two or more. The number of AKI episodes [one hazard ratio (HR) 1.05 (0.98-1.13); two or more HR 2.01 (1.79-2.25)] and severity [risk HR 1.05 (0.97-1.04); injury HR 1.41 (1.24-1.60); and failure HR 1.90 (1.64-2.20)] increases the risk of hospitalization. A total of 10 560 deaths were recorded, with incidence of 9.3/100 HF patients/year, 8951 (33.7%) of subjects without AKI episodes, 1180 (11.17%) of subjects with one episode, and 429 (4.06%) with two or more episodes. The number of episodes [one HR 1.05 (0.98-1.13); two or more HR 2.01 (1.79-2.25)] and severity [risk 1.05 confidence interval (CI) (0.97-1.14), injury 1.41 (CI 1.24-1.60), and failure 1.90 (CI 1.64-2.20)] increases mortality risk. Conclusions The study demonstrated the worse prognostic value of sudden renal function decline in HF patients and pointed to those with more future risk who require review of treatment and closer follow-up.
Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR). Subjects and methods: 2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the most influentual variables, the LASSO algorithm setting was used, and to tackle the issue of one class exceeding the other one by a large amount, we used the Synthetic Minority Oversampling Technique (SMOTE). Finally, Random Forest (RF) and XGBoost models were constructed. Results: The full XGBoost model obtained the maximum accuracy, a high negative predictive value, and the highest positive predictive value. Gender, age, unstable angina, atrial fibrillation and acute myocardial infarct are the variables that most influence EF value. Applied in the EHR dataset, with a total of 25,594 patients with an ICD-code of HF and no regular follow-up in cardiology clinics, 6170 (21.1%) were identified as pertaining to the reduced EF group. Conclusion: The obtained algorithm was able to identify a number of HF patients with reduced ejection fraction, who could benefit from a protocol with a strong possibility of success. Furthermore, the methodology can be used for studies using data extracted from the Electronic Health Records.
Objective: To assess the impact of hypertension in the risk of cardiovascular events and mortality in a population of diabetic patients from a Mediterranean population based in Real World Evidence. Design and method: The sample was recruited from beneficiaries of the Valencian Health Agency's health care system, with a population of 3799884 people older than 25 years in 2012. The observational study was undertaken as part of routine clinical practice from January 2012 to December 2016. Diabetes was defined as a non-fasting glucose higher or equal to 200 mg/dl, a recorded physician diagnosis, medication use or an HbA1c higher or equal to 6.5%. Hypertension was defined by a recorded physician diagnosis or antihypertensive medication use. Estimated glomerular filtration rate (eGFR) was calculated from calibrated creatinine, age and sex using the CKD-EPI and CKD was defined when eGFR < 60 ml/min/1.73m2. Vital status was determined by matching records and death certificates from the Spanish National Death Index. Results: Among the total population of 3799884, DM was present in 510922 (13%) patients (12% in women and 15% in men), Average of HbA1c was 6.9% + 1.4%. Hypertension was recorded in 387590 (75%) with a rate of BP < 140/90 mmHg of 45% and CKD in 125441 (24%). The incidence rates for DM, with and without diagnostic of HTN, of acute myocardial infarction, heart failure and stroke, as well all-cause mortality by age and sex rates are in the figure, in which the times of increment of risk due to the presence of HTN is presented. Conclusions: Presence of HTN in patients of diabetes largely increases the risk of MI, stroke, HF and all-cause mortality. The impact even it is higher in more younger patients, it still until the last decade of life.
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