Abstract 16922: Extremely Elevated Natriuretic Peptide is Associated With Incident Dialysis in Chronic Heart Failure Patients With Acute Kidney Injury

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Thanat Chaikijurajai ◽  
Sevag Demirjian ◽  
Yuping Wu ◽  
Wai Hong W Tang
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Cleveland Clinic ◽  
Kidney Injury ◽  
Cardiorenal Syndrome ◽  
Filling Pressure ◽  
Increased Risk ◽  
Baseline Creatinine ◽  
Cardiac Filling ◽  
Baseline Characteristics

Introduction: NT-proBNP has been widely used as a diagnostic and prognostic marker for both acute and chronic HF. Previous studies suggested that impaired renal function was the major contributor of an extreme elevation of NT-proBNP levels in patients with chronic HF rather than elevated cardiac filling pressure. Therefore, extremely elevated NT-proBNP levels may provide prognostic value in patients with cardiorenal syndrome. Hypothesis: Extremely elevated NT-proBNP levels are predictive of incident dialysis and dialysis dependence in chronic HF patients developing AKI Methods: Electronic medical records of 478 adult chronic HF patients admitted to the Cleveland Clinic between 2011 and 2016, who developed AKI during the hospital stay and had baseline NT-proBNP before renal consultation were reviewed. We analyzed the association between baseline NT-proBNP and incident dialysis, and dialysis dependence. Dialysis dependence was defined as patients undergoing dialysis within 72 hours of discharge. Results: Mean age was 67.6±13.3 years, 55.9% were male, and 37.7% had CKD. There were no significant differences in baseline characteristics and serum creatinine between patients with and without incident dialysis. Median NT-proBNP was 7,994 pg/mL (IQR; 3,109-19,357 pg/mL). There were 207 (43.3%) patients required dialysis, and 138 patients (66.7%) became dialysis dependent. Higher NT-proBNP was associated with increased risk of incident dialysis (Q 4 vs 1, OR 1.85, 95% C.I. 1.10-3.09, P =0.020), and dialysis dependence (Q4 vs 1, OR 2.96, 95% C.I. 1.25-7.00, P =0.014). However, in multivariate analysis adjusting for age, gender, hypertension, and baseline creatinine, only the association between NT-proBNP and incident dialysis remained statistically significant (Q4 vs 1, OR 1.77, 95% C.I. 1.03-3.02, P =0.038, Figure). Conclusions: Extremely elevated NT-proBNP was independently associated with incident dialysis in chronic HF patients developing AKI.

scholarly journals 58 Overweight and obesity in children with autism spectrum disorder: Findings from primary care electronic medical records

2020 ◽  
Vol 25 (Supplement_2) ◽  
pp. e24-e24
Author(s):  
Laura M Kinlin ◽  
Sarah Carsley ◽  
Charles Keown-Stoneman ◽  
Natasha Saunders ◽  
Karen Tu ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Weight Status ◽  
Severe Obesity ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Overweight And Obesity ◽  
Typically Developing ◽  
Increased Risk

Abstract Introduction/Background Paediatric overweight and obesity are important public health problems worldwide. Children with autism spectrum disorder (ASD) may be at increased risk compared to their typically-developing peers; however, prevalence estimates in ASD have varied widely and existing studies have largely been limited by use of an external comparison group. Objectives To compare prevalence of overweight and obesity in children and youth (<19 years of age) with and without ASD, using electronic medical record data from paediatric primary care visits. Design/Methods This was a cross-sectional analysis of EMRPC (Electronic Medical Records Primary Care) data, representing 385 family physicians in 43 clinics in Ontario, Canada. Age- and sex-standardized body mass index (BMI) z-scores were calculated using abstracted heights and weights from the most recent visit between January 2011 and December 2015. Weight status was determined using World Health Organization growth reference standards. ASD was defined using a previously-validated algorithm in EMRPC, based on an ASD-related term in the ‘Cumulative Patient Profile.’ Chi-square test statistics and multinomial logistic regression were used to compare weight status of those with and without ASD. Results In total, 44,625 children and youth were included, 632 [1.42%] with ASD. Distribution of weight status was significantly different between those with and without ASD (p<0.001) [Table 1]. Compared to their typically-developing peers, children with ASD had significantly higher odds of overweight (unadjusted odds ratio [OR] 1.52; 95% confidence interval [CI] 1.24-1.87), obesity (unadjusted OR 2.55 (2.00-3.26) and severe obesity (unadjusted OR 3.09; 95% CI 2.08-4.60); these associations persisted after adjusting for sex, age, neighborhood income quintile and rural residence (Table 2). Conclusion Data from a large primary care database suggest that children with ASD are at substantially increased risk of overweight, obesity and severe obesity. Findings support the need for anticipatory guidance, prevention and management strategies specific to this clinical population. Future work will aim to better understand at what age differences in weight status emerge, and what nutritional, behavioural, or medical factors differentially affect weight status in the ASD population.

scholarly journals Low ALT values amongst hospitalized patients are associated with increased risk of hypoglycemia and overall mortality: a retrospective, big-data analysis of 51 831 patients

QJM ◽  
2020 ◽  
Author(s):  
E Itelman ◽  
A Segev ◽  
L Ahmead ◽  
E Leibowitz ◽  
M Agbaria ◽  
...  
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Cohort Analysis ◽  
Surrogate Marker ◽  
Tertiary Hospital ◽  
Big Data Analysis ◽  
Hospitalized Patients ◽  
Retrospective Cohort Analysis ◽  
Increased Risk ◽  
Overall Mortality

Summary Background Sarcopenia and frailty influence clinical patients’ outcomes. Low alanine aminotransferase (ALT) serum activity is a surrogate marker for sarcopenia and frailty. In-hospital hypoglycemia is associated, also with worse clinical outcomes. Aim We evaluated the association between low ALT, risk of in-hospital hypoglycemia and subsequent mortality. Design This was a retrospective cohort analysis. Methods We included patients hospitalized in a tertiary hospital between 2007 and 2019. Patients’ data were retrieved from their electronic medical records. Results The cohort included 51 831 patients (average age 70.88). The rate of hypoglycemia was 10.8% (amongst diabetics 19.4% whereas in non-diabetics 8.3%). The rate of hypoglycemia was higher amongst patients with ALT < 10 IU/l in the whole cohort (14.3% vs. 10.4%, P < 0.001) as well as amongst diabetics (24.6% vs. 18.8%, P < 0.001). Both the overall and in-hospital mortality were higher in the low ALT group (57.7% vs. 39.1% P < 0.001 and 4.3% vs. 3.2%, P < 0.001). A propensity score matching, after which a regression model was performed, showed that patients with ALT levels < 10 IU/l had higher risk of overall mortality (HR = 1.21, CI 1.13–1.29, P < 0.001). Conclusions Low ALT values amongst hospitalized patients are associated with increased risk of in-hospital hypoglycemia and overall mortality.

scholarly journals Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers

10.2196/29120 ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. e29120
Author(s):  
Bruna Stella Zanotto ◽  
Ana Paula Beck da Silva Etges ◽  
Avner dal Bosco ◽  
Eduardo Gabriel Cortes ◽  
Renata Ruschel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Medical Records ◽  
Text Classification ◽  
Medical Records ◽  
State Of The Art ◽  
Support Vector ◽  
Data Set ◽  
Automatic Text Classification ◽  
Baseline Characteristics ◽  
Automatic Text

Background With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. Objective This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. Methods Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. Results The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. Conclusions Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations.

Natriuretic peptide as a predictor of adverse renal outcomes in patients with acute kidney injury

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Chaikijurajai ◽  
S Demirjian ◽  
Y Wu ◽  
W Tang
Keyword(s):  
Acute Kidney Injury ◽  
Natriuretic Peptide ◽  
Cleveland Clinic ◽  
Kidney Injury ◽  
Cardiorenal Syndrome ◽  
Funding Source ◽  
Renal Outcomes ◽  
Cardiovascular Stress ◽  
Significant Difference ◽  
Acute Increase

Abstract Background Since cardiorenal syndrome has been increasingly recognised as the intense interaction between the heart and the kidneys, we hypothesised that in patients with acute kidney injury (AKI), a biomarker of cardiovascular stress and heart failure (HF), N-terminal pro-brain natriuretic peptide (NT-proBNP), can predict adverse renal outcomes. Purpose The purpose of this study was to investigate the ability of NT-proBNP to predict need for dialysis and dialysis dependence in patients with AKI. Methods We analysed the association between baseline NT-proBNP measured before renal consultation, and need for dialysis and dialysis dependence, using a cohort of 1,052 AKI patients admitted to the Cleveland Clinic between 2011 and 2016. AKI was defined as acute increase in serum creatinine (Cr) of at least 0.3 mg/dL or 50% from baseline. Dialysis dependence was defined as patients still need dialysis within 72 hours of discharge. Results Mean age was 65.8±13.6 years, 57% were male, 45.4% had chronic HF and 28.2% had chronic kidney disease (CKD). There was no significant difference in chronic HF, CKD, or baseline Cr between AKI patients with and without dialysis. Median NT-proBNP was 6,484.50 pg/mL (interquartile range 2,200.75–15,717.50 pg/mL). We observed that 43.1% had dialysis (among them 67.8% became dialysis dependence). After adjustment for age, gender, hypertension, and baseline Cr, higher NT-proBNP levels were associated with greater likelihood of needing dialysis [quartile (Q) 4 vs. 1, Odd ratio (OR) 1.98, 95% confidence interval (CI) 1.38–2.85, P<0.001] and dialysis dependence (Q 4 vs. 1, OR 2.63, 95% CI 1.41–4.9, P=0.002) (Figure 1). Conclusion Elevated NT-proBNP was independently associated with need for dialysis and dialysis dependence in patients with AKI. Figure 1 Funding Acknowledgement Type of funding source: None

scholarly journals Utility of PCR, Culture, and Antigen Detection Methods for Diagnosis of Legionellosis

2015 ◽  
Vol 53 (11) ◽  
pp. 3474-3477 ◽  
Author(s):  
Derrick J. Chen ◽  
Gary W. Procop ◽  
Sherilynn Vogel ◽  
Belinda Yen-Lieberman ◽  
Sandra S. Richter
Keyword(s):  
Electronic Medical Records ◽  
Legionella Pneumophila ◽  
Medical Records ◽  
Cleveland Clinic ◽  
Test Methods ◽  
Detection Methods ◽  
Content Type ◽  
Epidemiologic Factors ◽  
Legionnaires Disease ◽  
Positive Results

The goal of this retrospective study was to evaluate the performance of different diagnostic tests for Legionnaires' disease in a clinical setting whereLegionella pneumophilaPCR had been introduced. Electronic medical records at the Cleveland Clinic were searched forLegionellaurinary antigen (UAG), culture, and PCR tests ordered from March 2010 through December 2013. For cases where two or more test methods were performed and at least one was positive, the medical record was reviewed for relevant clinical and epidemiologic factors. Excluding repeat testing on a given patient, 19,912 tests were ordered (12,569 UAG, 3,747 cultures, and 3,596 PCR) with 378 positive results. The positivity rate for each method was 0.4% for culture, 0.8% for PCR, and 2.7% for UAG. For 37 patients, at least two test methods were performed with at least one positive result: 10 (27%) cases were positive by all three methods, 16 (43%) were positive by two methods, and 11 (30%) were positive by one method only. For the 32 patients with medical records available, clinical presentation was consistent with proven or probableLegionellainfection in 84% of the cases. For those cases, the sensitivities of culture, PCR, and UAG were 50%, 92%, and 96%, respectively. The specificities were 100% for culture and 99.9% for PCR and UAG.

scholarly journals Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 33 ◽  
Author(s):  
Sheikh S. Abdullah ◽  
Neda Rostamzadeh ◽  
Kamran Sedig ◽  
Amit X. Garg ◽  
Eric McArthur
Keyword(s):  
Multiple Regression ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Visual Analytics ◽  
Clinical Medicine ◽  
Kidney Injury ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Complex Relationships

Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.

scholarly journals Cardiovascular risk factor documentation and management in primary care electronic medical records among people with schizophrenia in Ontario, Canada: retrospective cohort study

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e038013
Author(s):  
Braden O’Neill ◽  
Sumeet Kalia ◽  
Babak Aliarzadeh ◽  
Frank Sullivan ◽  
Rahim Moineddin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Primary Care ◽  
Cohort Study ◽  
Cardiovascular Risk ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Retrospective Cohort ◽  
Increased Risk

ObjectivesIn order to address the substantial increased risk of cardiovascular disease among people with schizophrenia, it is necessary to identify the factors responsible for some of that increased risk. We analysed the extent to which these risk factors were documented in primary care electronic medical records (EMR), and compared their documentation by patient and provider characteristics.DesignRetrospective cohort study.SettingEMR database of the University of Toronto Practice-Based Research Network Data Safe Haven.Participants197 129 adults between 40 and 75 years of age; 4882 with schizophrenia and 192 427 without.Primary and secondary outcome measuresDocumentation of cardiovascular disease risk factors (age, sex, smoking history, presence of diabetes, blood pressure, whether a patient is currently on medication to reduce blood pressure, total cholesterol and high-density lipoprotein cholesterol).ResultsDocumentation of cardiovascular risk factors was more complete among people with schizophrenia (74.5% of whom had blood pressure documented at least once in the last 2 years vs 67.3% of those without, p>0.0001). Smoking status was not documented in 19.8% of those with schizophrenia and 20.8% of those without (p=0.0843). Factors associated with improved documentation included older patients (OR for ages 70–75 vs 45–49=3.51, 95% CI 3.26 to 3.78), male patients (OR=1.39, 95% CI 1.33 to 1.45), patients cared for by a female provider (OR=1.52, 95% CI 1.12 to 2.07) and increased number of encounters (OR for ≥10 visits vs 3–5 visits=1.53, 95% CI 1.46 to 1.60).ConclusionsDocumentation of cardiovascular risk factors was better among people with schizophrenia than without, although overall documentation was inadequate. Efforts to improve documentation of risk factors are warranted in order to facilitate improved management.

Sign in / Sign up

Export Citation Format

Share Document