scholarly journals Personalized Dosing of Intravenous Vancomycin Among Critically Ill Neonates in Hong Kong: Harnessing Electronic Health Records to Develop a Web-Based Dosing Interface (Preprint)

10.2196/29458 ◽  
2021 ◽  
Author(s):  
Ka Ho Matthew Hui ◽  
Hugh Simon Lam ◽  
Cheuk Hin Twinny Chow ◽  
Yuen Shun Janice Li ◽  
Pok Him Tom Leung ◽  
...  
Keyword(s):  
Hong Kong ◽  
Electronic Health Records ◽  
Critically Ill ◽  
Health Records ◽  
Web Based ◽  
Critically Ill Neonates ◽  
Electronic Health

Personalized Dosing of Intravenous Vancomycin Among Critically Ill Neonates in Hong Kong: Harnessing Electronic Health Records to Develop a Web-Based Dosing Interface (Preprint)

2021 ◽  
Author(s):  
Ka Ho Matthew Hui ◽  
Hugh Simon Lam ◽  
Cheuk Hin Twinny Chow ◽  
Yuen Shun Janice Li ◽  
Pok Him Tom Leung ◽  
...  
Keyword(s):  
Hong Kong ◽  
Electronic Health Records ◽  
Bayesian Approach ◽  
Dose Optimization ◽  
Individual Dose ◽  
Health Records ◽  
Web Based ◽  
Model Based ◽  
Poppk Model ◽  
Critically Ill Neonates

BACKGROUND Intravenous (IV) vancomycin is used in the treatment of severe infection in neonates. It is efficacious but also associated with elevated risks of developing acute kidney injury. The risk is even higher in neonates admitted to the neonatal intensive care unit (NICU) because the pharmacokinetics of vancomycin in neonates vary widely. Therapeutic drug monitoring has been an integral part of the management to guide individual dose adjustments based on observed serum vancomycin concentrations (Cs) to balance efficacy against toxicity. However, the existing trough-based approach shows poor evidence for improved clinical outcomes. The updated clinical practice guideline recommends population pharmacokinetic (popPK) model-based approaches, targeting area under curve preferably through the Bayesian approach. Since Bayesian methods cannot be performed manually and require specialized computer programs, there is an urgent need to provide the clinicians with a user-friendly interface to facilitate accurate, personalized dosing recommendations for vancomycin in critically ill neonates. OBJECTIVE To utilize medical data from electronic health records (EHRs) to develop a popPK model and subsequently a web-based interface to perform model-based approaches to individual dose optimization of IV vancomycin for NICU patients in local medical institutions. METHODS Data were collected from EHR sources, namely Clinical Information System, In-Patient Medication Order Entry, and electronic Patient Record for subjects prescribed IV vancomycin in the NICU of Prince of Wales Hospital and Queen Elizabeth Hospital in Hong Kong. Patient demographics, serum creatinine (SCr), vancomycin administration records and Cs were collected. The popPK model used comprises a two-compartment infusion model, and various covariate models were tested against body weight, postmenstrual age (PMA), and SCr for the best goodness-of-fit. A previously published web-based dosing interface was replicated and adapted to the needs in this study. RESULTS The final dataset consisted of EHR data extracted from 207 subjects, obtaining a total of 689 Cs measurements. The final model chosen explains 82% of the variability in vancomycin clearance. All parameter estimates are within the bootstrapping confidence intervals. Predictive plots, residual plots, and visual predictive checks demonstrate good model predictability. Model approximations show that the model-based Bayesian approach consistently promotes the probability of target attainment (PTA) above 75%, while only half of the subjects can achieve PTA over 50% with the trough-based approach. The dosing interface was developed with the capability to optimize individual doses with the model-based empirical or Bayesian approach. CONCLUSIONS Utilizing EHRs, a satisfactory popPK model has been verified and used to develop the web-based individual dose optimization interface. The interface is expected to improve treatment outcomes of IV vancomycin in the treatment of severe infections among neonates in local NICUs. This study provides the foundation upon which to conduct a cohort study to demonstrate the utility of the new approach compared with previous dosing methods.

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 Linking cohort-based data with electronic health records: a proof-of-concept methodological study in Hong Kong

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e045868
Author(s):  
Le Gao ◽  
Miriam T Y Leung ◽  
Xue Li ◽  
Celine S L Chui ◽  
Rosa S M Wong ◽  
...  
Keyword(s):  
Hong Kong ◽  
Electronic Health Records ◽  
Birth Cohort ◽  
Descriptive Analysis ◽  
Exact Matching ◽  
Normal Behaviour ◽  
Health Records ◽  
Cohort Data ◽  
Identity Card ◽  
Electronic Health

ObjectivesData linkage of cohort-based data and electronic health records (EHRs) has been practised in many countries, but in Hong Kong there is still a lack of such research. To expand the use of multisource data, we aimed to identify a feasible way of linking two cohorts with EHRs in Hong Kong.MethodsParticipants in the ‘Children of 1997’ birth cohort and the Chinese Early Development Instrument (CEDI) cohort were separated into several batches. The Hong Kong Identity Card Numbers (HKIDs) of each batch were then uploaded to the Hong Kong Clinical Data Analysis and Reporting System (CDARS) to retrieve EHRs. Within the same batch, each participant has a unique combination of date of birth and sex which can then be used for exact matching, as no HKID will be returned from CDARS. Raw data collected for the two cohorts were checked for the mismatched cases. After the matching, we conducted a simple descriptive analysis of attention deficit hyperactivity disorder (ADHD) information collected in the CEDI cohort via the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) and EHRs.ResultsIn total, 3473 and 910 HKIDs in the birth cohort and CEDI cohort were separated into 44 and 5 batches, respectively, and then submitted to the CDARS, with 100% and 97% being valid HKIDs respectively. The match rates were confirmed to be 100% and 99.75% after checking the cohort data. From our illustration using the ADHD information in the CEDI cohort, 36 (4.47%) individuals had ADHD–Combined score over the clinical cut-off in the SWAN survey, and 68 (8.31%) individuals had ADHD records in EHRs.ConclusionsUsing date of birth and sex as identifiable variables, we were able to link the cohort data and EHRs with high match rates. This method will assist in the generation of databases for future multidisciplinary research using both cohort data and EHRs.

A Web-Based Application to Exchange Electronic Health Records and Medical Images in Ophthalmology

2009 ◽  
pp. 152-164
Author(s):  
Isabel de la Torre Díez
Keyword(s):  
Electronic Health Records ◽  
Medical Images ◽  
Universal Access ◽  
Diabetic Patients ◽  
Health Records ◽  
Web Based ◽  
Clinical Document ◽  
Extensible Markup ◽  
Electronic Health ◽  
Web Based System

This chapter describes a Web -based application to store and exchange Electronic Health Records (EHR) and medical images in Ophthalmology: TeleOftalWeb 3.2. The Web -based system has been built on Java Servlet and Java Server Pages (JSP) technologies. Its architecture is a typical three-layered with two databases. The user and authentication information is stored in a relational database: MySQL 5.0. The patient records and fundus images are achieved in an Extensible Markup Language (XML) native database: dbXML 2.0. The application uses XML-based technologies and Health Level Seven/Clinical Document Architecture (HL7/CDA) specifications. The EHR standardization is carried out. The main application object is the universal access to the diabetic patients EHR by physicians wherever they are.

Performance Evaluation of a Web-Based System to Exchange Electronic Health Records Using Queueing Model (M/M/1)

2010 ◽  
Vol 36 (2) ◽  
pp. 915-924 ◽  
Author(s):  
Isabel de la Torre ◽  
Francisco Javier Díaz ◽  
Míriam Antón ◽  
Mario Martínez ◽  
José Fernando Díez ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Electronic Health Records ◽  
Queueing Model ◽  
Health Records ◽  
Web Based ◽  
Electronic Health ◽  
Web Based System

scholarly journals Professional and interprofessional differences in electronic health records use and recognition of safety issues in critically ill patients

2016 ◽  
Vol 30 (5) ◽  
pp. 636-642 ◽  
Author(s):  
Knewton K. Sakata ◽  
Laurel S. Stephenson ◽  
Ashley Mulanax ◽  
Jesse Bierman ◽  
Karess Mcgrath ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Health Records ◽  
Safety Issues ◽  
Electronic Health

Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records

2021 ◽  
Author(s):  
Sreekar Mantena ◽  
Aldo Robles Arévalo ◽  
Jason H. Maley ◽  
Susana M. da Silva Vieira ◽  
Roselyn Mateo-Collado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Health Records ◽  
Electronic Health
Sign in / Sign up

Export Citation Format

Share Document