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Assessing Cardiovascular Risk through Retinal Fundus Imaging

Posted on May 1, 2018 in News | 0 comments

WITHOUT SOURCES
Feburary 2018 saw the release of the exciting research paper ‘Prediction of cardiovascular risk factor from retinal fundus photographs via deep learning’ in Nature Biomedical Engineering. The Google Team had developed a new artificial intelligence technology using deep convolutional neural networks to assess both individual cardiovascular risk factors and the risk of a cardiac event through retinal fundus imaging.

The algorithm assesses individual risk factors (including smoking, blood pressure) by generating a heat map so the algorithm assesses the anatomical regions most relevant to the particular cardiovascular risk factor. It is particularly impressive that many of the risk factors which the algorithm predicted were risk factors previous not believed to be present in retinal images, including age, gender, smoking status and systolic blood pressure. Alongside assessing individual risk factors the team also developed a model to predict major adverse cardiovascular event onset within 5 years.

Current means of cardiovascular disease risk calculation include the Framingham CVD Risk Prediction Score, Pooled Cohort Equations, and Systematic Coronary Risk Evaluation. There are continued efforts to improve the quality of the risk prediction calculators, as there are limitations associated with some of them. For example the Framingham CVD Risk Prediction Score, which is the most commonly used risk estimation system worldwide, has been found to overestimate risk for women and has been less effective in predicting risk for elderly people.

This development has exciting implications in improving identification of individuals at risk for cardiovascular disease. Cardiovascular disease poses a significant global burden as the leading global cause of death. In 2015 Cardiovascular disease represented 31% of all global deaths of which three quarters occurred in low and middle income countries.

The algorithm not only has the ability to improve outcomes of patients at risk for developing cardiovascular disease through early identification, but also other diseases as the individual risk factors identified (including blood pressure, age, smoking, gender, race) can potentially be used to calculate risk for other diseases such as Chronic Kidney Disease and Diabetes.

WITH SOURCES
Feburary 2018 saw the release of the exciting research paper ‘Prediction of cardiovascular risk factor from retinal fundus photographs via deep learning’ in Nature Biomedical Engineering. The Google Team had developed a new artificial intelligence technology using deep convolutional neural networks to assess both individual cardiovascular risk factors and the risk of a cardiac event through retinal fundus imaging.

The algorithm assesses individual risk factors (including smoking, blood pressure) by generating a heat map so the algorithm assesses the anatomical regions most relevant to the particular cardiovascular risk factor. It is particularly impressive that many of the risk factors which the algorithm predicted were risk factors previous not believed to be present in retinal images, including age, gender, smoking status and systolic blood pressure. Alongside assessing individual risk factors the team also developed a model to predict major adverse cardiovascular event onset within 5 years.
(1).

Current means of cardiovascular disease risk calculation include the Framingham CVD Risk Prediction Score, Pooled Cohort Equations, and Systematic Coronary Risk Evaluation. There are continued efforts to improve the quality of the risk prediction calculators, as there are limitations associated with some of them. For example the Framingham CVD Risk Prediction Score, which is the most commonly used risk estimation system worldwide, has been found to overestimate risk for women(2) and has been less effective in predicting risk for elderly people (3).

This development has exciting implications in improving identification of individuals at risk for cardiovascular disease. Cardiovascular disease poses a significant global burden as the leading global cause of death. In 2015 Cardiovascular disease represented 31% of all global deaths of which three quarters occurred in low and middle income countries (4).

The algorithm not only has the ability to improve outcomes of patients at risk for developing cardiovascular disease through early identification, but also other diseases as the individual risk factors identified (including blood pressure, age, smoking, gender, race) can potentially be used to calculate risk for other diseases such as Chronic Kidney Disease and Diabetes.

Sources
1. Prediction of cardiovascular risk factors form retinal fundus photographs via deep learning. Nature biomedical engineering. https://www.nature.com/articles/s41551-018-0195-0

2. Validation of the Framingham general cardiovascular risk score in a multiethnic Asian population: Retrospective cohort study http://bmjopen.bmj.com/content/5/5/e007324?utm_source=trendmd&utm_medium=cpc&utm_campaign=bmjopen&trendmd-shared=1&utm_content=Journalcontent&utm_term=TrendMDPhase4
3. Value and limitations of existing scores for the Assessment of Cardiovascular Risk: A Review for Clinicians. Journal of the American College of Cardiology. https://www.sciencedirect.com/science/article/pii/S0735109709025029
4. WHO Cardiovascular Diseases Fact Sheet May 2017 http://www.who.int/mediacentre/factsheets/fs317/en/

Effectiveness of a Countdown Timer in Reducing or Turnover Time

Posted on Dec 28, 2017 in Original Article | 0 comments

Effectiveness of a Countdown Timer in Reducing or Turnover Time

Majbah Uddin, MS1, Robert Allen, PhD2, Nathan Huynh, PhD1, Jose M. Vidal, PhD3, Kevin M. Taaffe, PhD4, Lawrence D. Fredendall, PhD5, Joel S. Greenstein, PhD4

1Department of Civil and Environmental Engineering, University of South Carolina; 2School of Health Research, Clemson University; 3Department of Computer Science and Engineering, University of South Carolina; 4Department of Industrial Engineering, Clemson University; 5Department of Management, Clemson University.

Corresponding Author: nathan.huynh@sc.edu

Journal MTM 6:3:25–33, 2017

doi:10.7309/jmtm.6.3.5


Background: In production environments, a countdown timer is used to report the status of the planned start time and to provide both a communication mechanism and an accountability aid.1 It has been used in the airline industry to remind all personnel of the remaining time until when the aircraft door should be closed. This study explored the effectiveness of a countdown timer in the operating room (OR).

Aims: This study was designed to assess the effectiveness of a countdown timer in the OR setting and to determine the factors that contribute to prolonged OR turnover time (TOT) (defined to be from the “procedure finish” time of the preceding case to the “procedure start” time of the following case), as well as the impact each of the significant factors has on TOT. In this study, the term case denotes a surgical procedure.

Method: An Android app named ORTimer was developed for the study. The app was installed on Android tablets that were placed at the Certified Registered Nurse Anesthetist (CRNA) workstations in the OR at Greenville Memorial Hospital (GMH) in South Carolina. The CRNAs helped collect the event milestones and record the delay reasons (if applicable). Additional OR case information was extracted from GMH’s electronic medical record. Regression analysis was used to identify significant factors that contribute to prolonged OR TOT and to estimate their impacts. A t-test was conducted to test the hypothesis that the use of a countdown timer is effective in an OR environment.

Results: The data from a total of 232 cases where the ORTimer app was used were examined. Among the factors (i.e., delay reasons and case information) considered, an outpatient from a following case had the highest correlation with excessive room idle time, which is the difference between the actual TOT and the allotted TOT. Delays due to patient-related issues added about 12.7 minutes to the turnover time (90% CI: 7.2, 18.3) when other factors were fixed. Delays due to preoperative-related issues added about 27.4 minutes to the turnover time (90% CI: 20.0, 34.7) when other factors were fixed.

Conclusions: As is the case with most production environments,1 the use of a visual management tool such as the countdown timer in the OR is found to be effective. Additional research is needed to determine whether this finding is applicable to other hospitals.

Keywords: Information Technology (IT) intervention, operating room efficiency, operating room process improvement.


Clinical Application of a Smartphone-Based Ophthalmic Camera Adapter in Under-Resourced Settings in Nepal

Posted on Dec 28, 2017 in Original Article | 0 comments

Clinical Application of a Smartphone-Based Ophthalmic Camera Adapter in Under-Resourced Settings in Nepal

Carmel Mercado, MD1, John Welling, MD2,3,4, Matthew Oliva, MD3,4,5, Jack Li, MD2, Reeta Gurung, MD6, Sanduk Ruit, MD6, Geoff Tabin, MD1,3,8, David Chang, MD7, Suman Thapa, MD, PhD6, David Myung, MD, PhD1,8

1Byers Eye Institute, Stanford University, Palo Alto, CA, USA; 2John A Moran Eye Center, University of Utah, Salt Lake City, Utah, USA; 3Himalayan Cataract Project, Waterbury, VT, USA; 4Medical Eye Center, Medford, OR, USA; 5Casey Eye Institute, Oregon Health Sciences University, Portland, OR, USA; 6Tilganga Institute of Ophthalmology, Kathmandu, Nepal; 7Los Altos Eye Physicians, Los Altos, CA, USA; 8VA Palo Alto Health Care System, Palo Alto, CA, USA

Correspondence Author: david.myung@stanford.edu

Journal MTM 6:3:34–42, 2017

doi:10.7309/jmtm.6.3.6


Background: The ability to obtain high quality ocular images utilizing smartphone technology is of special interest in under-resourced parts of the world where traditional ocular imaging devices are cost-prohibitive, difficult to transport, and require a trained technician for operation.

Purpose: The purpose of this study was to explore potential anterior and posterior segment ocular imaging use cases for a smartphone-based ophthalmic camera adapter (Paxos Scope, Digisight Technologies, San Francisco, CA, USA) in under-resourced settings in Nepal.

Methods: From September to November of 2015 we utilized the Paxos Scope smartphone camera adapter coupled with an iPhone 5 to explore anterior and posterior segment clinical applications for this mobile technology. We used the device at a tertiary eye care facility, a rural eye hospital and a rural cataract outreach camp. We tested the device’s capability for high quality photo-documentation in clinic, in the operating room, and in the outreach camp setting. Images were automatically uploaded to a secure, cloud-based electronic medical record system that facilitated sharing of images with other providers for telemedicine purposes.

Results: Herein we present 17 ocular images documenting a wide variety of anterior and posterior segment pathology using the Paxos Scope from clinical cases seen in a variety of settings in Nepal.

Conclusions: We found the quality of both the anterior and posterior segment images to be excellent in the clinic, the operating room, and the outreach camp settings. We found the device to be versatile and user-friendly, with a short learning curve. The Paxos Scope smartphone camera adapter may provide an affordable, high-quality, mobile ocular imaging option for under-resourced parts of the world.

Keywords: Paxos Scope, mobile health, smartphone ophthalmic imaging, teleophthalmology, triage


Medical Students’ Perception on the Use of QR Code Versus Traditional Pen-and-Paper as an Attendance Record Tool in Medical School

Posted on Dec 28, 2017 in Original Article | 0 comments

Medical Students’ Perception on the Use of QR Code Versus Traditional Pen-and-Paper as an Attendance Record Tool in Medical School

Kwee Choy Koh, MBBS, MMed, Pilane Liyanage Ariyananda, MD, FRCP, Esha Das Gupta, MRCP, FRCP, Rumi R. Khajotia, MD, MD, Sethuraman Nagappan, MRCP, FRCP, Vaani Valerie Visuvanathan, MBBS, MRCP, Mina Mustafa Mahmood, MD, MRCP, Siew Huoy Chua, MB BCh BAO, MRCP, Poh Sim Chung, Dipl. BA

Assoc. Prof. Kwee Choy Koh, Postal address: Head & Infectious Diseases Consultant, Department of Medicine, Clinical Campus, International Medical University, Jalan Rasah, 70400 Seremban, Negeri Sembilan, Malaysia

Corresponding Author: kweechoy_koh@imu.edu.my

Journal MTM 6:3:2–6, 2017

doi:10.7309/jmtm.6.3.2


Background: Student attendance at teaching-learning sessions is traditionally registered using pen-and-paper. This method has many weaknesses: lost, hard to verify, attendance-by-proxy, late submission. The Quick-Response Code is a two-dimensional barcode that can be read with a QR reader on a smartphone that captures and instantly transmits information to a cloud storage. We describe the use of a QRC method to register medical students’ attendance and assessed their perception of this method compared to PAP.

Method: The attendance of 112 medical students in all teaching and learning sessions in internal medicine posting was registered either with QRC or PAP. A static QR code was generated using open source software online and linked to Google Document for storage of data registered. At the end of the posting, the students’ perception was assessed using a 4-point Likert scale satisfaction survey.

Results: 83 students participated in the survey (74% response rate). 100% owned a smart device and 91.6% had data connectivity. Compared to PAP, the QRC method was perceived to be more convenient, more accurate, more secure and more environment-friendly and preferred (all p < 0.05). The QRC method was not significantly faster than the PAP (p = 0.361).

Conclusion: The QRC method was the preferred attendance record tool compared to the traditional PAP method. Its adoption in the closed and secure environment of a medical campus and hospital is feasible and should be explored.

Keywords: QR code, attendance record tool, medical school