scholarly journals Prediction of Postoperative Infection for Patients Undergoing Gastrointestinal Surgery: Findings from Electronic Health Records

2020 ◽  
Vol 11 (2) ◽  
pp. 36-46
Author(s):  
Kyoko Nakazawa ◽  
Takashi Ishikawa ◽  
Akira Toyama ◽  
Toshifumi Wakai ◽  
Kohei Akazawa
Keyword(s):  
Risk Factors ◽  
Electronic Health Records ◽  
Surgical Procedures ◽  
Postoperative Infection ◽  
Gastrointestinal Surgery ◽  
Significant Risk ◽  
Estimating Equation ◽  
Endoscopic Surgical Procedures ◽  
Health Records ◽  
Electronic Health

Introduction: Postoperative infection is a major cause of morbidity and prolonged hospitalization in patients undergoing gastrointestinal surgery. This observational study aimed to investigate the risk factors associated with postoperative infection and to develop a prediction model for postoperative infections that occur after gastrointestinal surgery. Methods: The study population comprised 1637 patients who underwent gastrointestinal surgery at Niigata University Medical and Dental Hospital between June 2013 and May 2017. Observational data from 1883 surgical procedures were used in the statistical analyses (including 198 patients who underwent several operations). Results: The generalized estimating equation (GEE) was used to detect significant risk factors, including older age, history of smoking, body temperature greater than 38 °C, non-endoscopic surgical procedures, surgery in the thoracic or lower gastrointestinal tract, and use of medical nutritional products during surgery. The sensitivity and specificity of the GEE model were 88.2% and 55.1%, respectively. Conclusion: This study established a predictable GEE model, incorporating the data of patients who were hospitalized several times into a prediction analysis, even though the sensitivity was not sufficiently high. The GEE model, which is considered clinically useful, can be constructed using a variety of variables, including those obtained from electronic health records.

scholarly journals Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e043487
Author(s):  
Hao Luo ◽  
Kui Kai Lau ◽  
Gloria H Y Wong ◽  
Wai-Chi Chan ◽  
Henry K F Mak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Hong Kong ◽  
Electronic Health Records ◽  
Case Control Study ◽  
Case Control ◽  
Dementia Diagnosis ◽  
Health Records ◽  
Electronic Health ◽  
Control Study

IntroductionDementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case–control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history.Methods and analysisWe will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared.Ethics and disseminationThis study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients’ records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities’ Action in Response to Dementia project (https://www.tip-card.hku.hk/).

scholarly journals 1665. Using Electronic Health Records to Describe TB in Community Health Settings: a Cohort Analysis in a Large Safety-Net Population

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Electronic Health Records ◽  
Safety Net ◽  
The United States ◽  
Research Network ◽  
Health Records ◽  
Treatment Regimens ◽  
Data Source ◽  
Electronic Health

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures

scholarly journals Prevalence, incidence, and outcomes across cardiovascular diseases in homeless individuals using national linked electronic health records

2020 ◽  
Vol 41 (41) ◽  
pp. 4011-4020
Author(s):  
Atsunori Nanjo ◽  
Hannah Evans ◽  
Kenan Direk ◽  
Andrew C Hayward ◽  
Alistair Story ◽  
...  
Keyword(s):  
Risk Factors ◽  
Electronic Health Records ◽  
Mortality Risk ◽  
Homeless People ◽  
Arterial Disease ◽  
Population Based ◽  
Health Records ◽  
Coronary Death ◽  
Health And Social Care ◽  
Electronic Health

Abstract Aims The risk and burden of cardiovascular disease (CVD) are higher in homeless than in housed individuals but population-based analyses are lacking. The aim of this study was to investigate prevalence, incidence and outcomes across a range of specific CVDs among homeless individuals. Methods and results  Using linked UK primary care electronic health records (EHRs) and validated phenotypes, we identified homeless individuals aged ≥16 years between 1998 and 2019, and age- and sex-matched housed controls in a 1:5 ratio. For 12 CVDs (stable angina; unstable angina; myocardial infarction; sudden cardiac death or cardiac arrest; unheralded coronary death; heart failure; transient ischaemic attack; ischaemic stroke; subarachnoid haemorrhage; intracerebral haemorrhage; peripheral arterial disease; abdominal aortic aneurysm), we estimated prevalence, incidence, and 1-year mortality post-diagnosis, comparing homeless and housed groups. We identified 8492 homeless individuals (32 134 matched housed individuals). Comorbidities and risk factors were more prevalent in homeless people, e.g. smoking: 78.1% vs. 48.3% and atrial fibrillation: 9.9% vs. 8.6%, P < 0.001. CVD prevalence (11.6% vs. 6.5%), incidence (14.7 vs. 8.1 per 1000 person-years), and 1-year mortality risk [adjusted hazard ratio 1.64, 95% confidence interval (CI) 1.29–2.08, P < 0.001] were higher, and onset was earlier (difference 4.6, 95% CI 2.8–6.3 years, P < 0.001), in homeless, compared with housed people. Homeless individuals had higher CVD incidence in all three arterial territories than housed people. Conclusion  CVD in homeless individuals has high prevalence, incidence, and 1-year mortality risk post-diagnosis with earlier onset, and high burden of risk factors. Inclusion health and social care strategies should reflect this high preventable and treatable burden, which is increasingly important in the current COVID-19 context.

scholarly journals Analysis of Risk Factors Progression of Preterm Delivery Using Electronic Health Records

2020 ◽  
Author(s):  
Zeineb Safi ◽  
Neethu Venugopal ◽  
Haytham Ali ◽  
Michel Makhlouf ◽  
Sabri Boughorbel
Keyword(s):  
Risk Factors ◽  
Electronic Health Records ◽  
Preterm Delivery ◽  
Control Group ◽  
Study Cohort ◽  
Patient History ◽  
Health Records ◽  
Time Points ◽  
Electronic Health ◽  
The Impact

Abstract Background: Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and reducing the frequency of occurrence of preterm deliveries. The purpose of this work is to identify preterm delivery risk factors and their progression throughout the pregnancy from a large collection of Electronic Health Records (EHR). Results: The study cohort includes more than 60,000 deliveries in the USA with the complete medical history from EHR for diagnoses, medications, procedures and demographics. We propose a temporal analysis of risk factors by estimating and comparing risk ratios at different time points prior to the delivery event. We selected the following time points before delivery: 9, 6, 3 and 1 month(s). We did so by conducting a retrospective cohort study of patient history for a selected set of mothers who delivered preterm and a control group of mothers that delivered full-term. We analyzed the extracted data using a logistic regression model. The results of our analyses showed that the highest risk ratio corresponds to history of previous preterm delivery. Other risk factors were identified, some of which are consistent with those that are reported in the literature, others need further investigation. Conclusions: The comparative analysis of the risk factors at different time points showed that risk factors in the early pregnancy related to patient history and chronic condition, while the risk factors in late pregnancy are specific to the current pregnancy. Our analysis unifies several previously reported studies on preterm risk factors. It also gives important insights on the changes of risk factors in the course of pregnancy.

scholarly journals An exploratory study of the use of the electronic health records of hypertensive patients to support the primary prevention of stroke in Shanghai

2019 ◽  
Author(s):  
Tingting Yang ◽  
Fen Li ◽  
Bifan Zhu ◽  
Yuqian Chen ◽  
Duo Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Electronic Health Records ◽  
Physical Inactivity ◽  
High Risk Population ◽  
Health Records ◽  
Hypertensive Patients ◽  
Stroke Risk Factors ◽  
Baseline Information ◽  
Electronic Health

Abstract Background The value of targeting people with a high risk of stroke based on electronic health records (EHRs) in Shanghai is largely undiscovered.Aim To test the hypothesis that EHRs might be developed into an evidence-based support system to identify people who are at high risk of stroke.Methods We performed a screen analysis utilizing EHRs to target the population with stroke risk factors, such as hypertension, diabetes mellitus, obesity, smoking and physical inactivity. We calculated the distribution of each risk factor and the combinations of risk factors.Results In the Jiading District of Shanghai, 46,580 hypertensive patients with complete baseline information joined the hypertensive patient management system from 2014 to 2017. The majority of the patients were older than 60 years. Physical inactivity (83.24%), smoking (24.07%), diabetes (16.87%), and obesity (12.23%) were highly prevalent in hypertension participants. Approximately 4377 patients had hypertension only, accounting for 9.70% of the total patients in this study. Approximately 52.47% of the patients had two risk factors at the same time, and 38.13% of the patients had hypertension, which means that 17,762 patients could be identified as a high-risk population for stroke according to the criteria established by the National Stroke Screening Survey.Conclusion Our exploratory findings suggest the feasibility of targeting populations with a high risk of stroke using the EHRs of hypertensive patients.

Sign in / Sign up

Export Citation Format

Share Document