OHDSI MEETINGS THIS WEEK
The Book of OHDSI Work Group Meeting - Tuesday at 11am ET
Zoom URL: https://columbiauniversity.zoom.us/j/258043190
OHDSI Community Call - Tuesday at 12pm ET
US TOLL: +1-415-655-0001
Meeting Number: 199 982 907
Population-Level Estimation (Western hemisphere) Work Group Call - Thursday at 12pm ET
You can find a full list of upcoming OHDSI meetings here:
2019 European Symposium - Registration is open for second annual European Symposium, set to take place March 29th, 2019 in Rotterdam, Netherlands. For more details, including where to register, check out Peter’s post: European OHDSI Symposium Registration now open!
2019 OHDSI F2F - SAVE THE DATE! The 2019 OHDSI F2F will take place on June 3-4th 2019 at Case Western Reserve University in Cleveland, OH. For more details check out the event page: https://www.ohdsi.org/events/2019-ohdsi-face-to-face/
2019 OHDSI Symposium - SAVE THE DATE! - It’s official! The 2019 OHDSI Symposium will take place on September 15-17th 2019 at the Bethesda North Marriott & Conference Center. The main symposium will be Monday, September 16th with tutorials on September 15th and 17th. For more information, check out the 2019 OHDSI Symposium event page: https://www.ohdsi.org/events/2019-ohdsi-symposium/
2018 OHDSI Symposium Recording - Video records from the main symposium are available here: https://www.ohdsi.org/2018-ohdsi-symposium-videos/
2018 Tutorial Recordings - Intro tutorial videos are online!
CDM Tutorial: https://www.ohdsi.org/past-events/2018-tutorials-omop-common-data-model-and-standardized-vocabularies/
OHDSI Ecosystem: https://www.ohdsi.org/past-events/2018-tutorials-overview-of-the-ohdsi-analysis-ecosystem/
Advanced tutorials will be posted shortly.
The best cure for one’s bad tendencies is to see them fully developed in someone else.
Alain de Botton COMMUNITY PUBLICATIONS
Healthcare utilization and management of actinic keratosis in primary and secondary care: a complementary database analysis.
EC Noels, LM Hollestein, S van Egmond, M Lugtenberg, LPJ van Nistelrooij, P Bindels, J van der Lei, RS Stern, T Nijsten and M Wakkee,
The British journal of dermatology, Jan 2019 12
The high prevalence of actinic keratosis (AK) requires optimal use of healthcare resources.To gain insight in health care utilization and management of AK, by describing the healthcare utilization of people with AK in a population-based cohort and in a primary and secondary care setting.A retrospective cohort study using three complementary data sources was conducted to describe the use of care, diagnosis, treatment, and follow-up of AK patients in the Netherlands. Data sources consisted of a population-based cohort study (Rotterdam Study, RS), routine general practitioner (GP) records (Integrated Primary Care Information, IPCI), and nationwide claims data (DBC Information System, DIS).In the population-based cohort (RS), 69% (918/1,322) of participants diagnosed with AK during a skin screening visit had no prior AK-related visit in their GP record. This proportion was 50% for participants with extensive AK (i.e.,≥10 AKs; n=270). Cryotherapy was the most used AK treatment by both GPs (78%) and dermatologists (41-56%). Topical agents were the second most used treatment by dermatologists (13-21%) but was rarely applied in primary care (2%). During the first AK related GP visit, 31% (171/554) was referred to a dermatologist, with likelihood of being referred comparable between low and high-risk patients, which is inconsistent with the guidelines. Annually, 40·000 new claims representing 13% of all dermatology claims were labelled as cutaneous premalignancy. Extensive follow-up rates (56%) in secondary care were registered, while only 18% received a claim for a subsequent cutaneous malignancy in 5 years.AK management seems to diverge from guidelines in both primary and secondary care. Underutilization of field treatments, inappropriate treatments and high referral rates without proper risk stratification in primary care, combined with extensive follow-up in secondary care result in inefficient use of healthcare resources and overburdening in secondary care. Efforts directed to better risk differentiation and guideline adherence may prove useful in increasing the efficiency in AK management. This article is protected by copyright. All rights reserved.
Identifying Data Elements to Measure Frailty in a Dutch Nationwide Electronic Medical Record Database for Use in Postmarketing Safety Evaluation: An Exploratory Study.
J Sultana, I Leal, M de Wilde, M de Ridder, J van der Lei, M Sturkenboom and G Trifiro',
Drug safety, Jan 2019 08
The role of frailty in postmarketing drug safety is increasingly acknowledged. Few European electronic medical records (EMRs) have been used to explore frailty in observational drug safety research.The aim of this study was to identify data elements, beyond multimorbidity and polypharmacy, that could potentially contribute to measuring frailty among older adults in the Dutch nationwide Integrated Primary Care Information (IPCI) database.Persons aged between 65 and 90 years in the IPCI database were identified from 2008 to 2013. Clinical non-disease, non-drug measurements that could potentially contribute to measuring frailty were identified and selected if they were recorded in > 0.005% of patients and could be included in at least one of three definitions of frailty: the frailty phenotype model, the cumulative deficit model, and direct evaluations of frailty through standardized frailty scores. The frequency of these measures was calculated.Overall, 314,191 (17% of the source population) elderly persons were identified. Of these, 7948 (2.53%) had one or more of 12 clinical measurements identified that could potentially contribute to measuring frailty, such as clinical evaluations of cognition, mobility, and cachexia, as well as direct measures of frailty, such as the Groningen Frailty Index. Three of five measurements required for the frailty phenotype were identified in < 0.5% of the population: cachexia, reduced walking speed, and reduced physical activity; weakness and fatigue were not identified. The measurements outlined above may be appropriate for the cumulative deficit definition of frailty, provided that at least 30 deficits, including comorbidities and drug utilization, are evaluated in total. The most commonly recorded item identified that could potentially be used in a cumulative frailty model was the Mini-Mental State Examination score (N= 2850; 0.91%); the only recorded direct measurement of frailty was the Groningen Frailty Index (N = 2382; 0.76%).Non-disease, non-drug clinical data that could potentially contribute to a frailty model was not commonly recorded in the IPCI; less than 3% of a cohort of elderly persons had these data recorded, suggesting that the use of these data in postmarketing drug safety evaluation may be limited.
Incorporating Observed Physiological Data to Personalize Pediatric Vital Sign Alarm Thresholds.
S Poole and N Shah,
Biomedical informatics insights, 2019
Bedside monitors are intended as a safety net in patient care, but their management in the inpatient setting is a significant patient safety concern. The low precision of vital sign alarm systems leads to clinical staff becoming desensitized to the sound of the alarm, a phenomenon known as alarm fatigue. Alarm fatigue has been shown to increase response time to alarms or result in alarms being ignored altogether and has negative consequences for patient safety. We present methods to establish personalized thresholds for heart rate and respiratory rate alarms. These thresholds are first chosen based on patient characteristics available at the time of admission and are then adapted to incorporate vital signs observed in the first 2 hours of monitoring. We demonstrate that the adapted thresholds are similar to those chosen by clinicians for individual patients and would result in fewer alarms than the currently used age-based thresholds. Personalized vital sign alarm thresholds can help to alleviate the problem of alarm fatigue in an inpatient setting while ensuring that all critical vital signs are detected.
From Data Silos to Standardized, Linked, and FAIR Data for Pharmacovigilance: Current Advances and Challenges with Observational Healthcare Data.
Quantitative Evaluation of the Relationship between T-Wave-Based Features and Serum Potassium Level in Real-World Clinical Practice.
D Yoon, HS Lim, JC Jeong, TY Kim, JG Choi, JH Jang, E Jeong and CM Park,
BioMed research international, 2018
Proper management of hyperkalemia that leads to fatal cardiac arrhythmia has become more important because of the increased prevalence of hyperkalemia-prone diseases. Although T-wave changes in hyperkalemia are well known, their usefulness is debatable. We evaluated how well T-wave-based features of electrocardiograms (ECGs) are correlated with estimated serum potassium levels using ECG data from real-world clinical practice.We collected ECGs from a local ECG repository (MUSE™) from 1994 to 2017 and extracted the ECG waveforms. Of about 1 million reports, 124,238 were conducted within 5 minutes before or after blood collection for serum potassium estimation. We randomly selected 500 ECGs and two evaluators measured the amplitude (T-amp) and right slope of the T-wave (T-right slope) on five lead waveforms (V3, V4, V5, V6, and II). Linear correlations of T-amp, T-right slope, and their normalized feature (T-norm) with serum potassium levels were evaluated using Pearson correlation coefficient analysis.Pearson correlation coefficients for T-wave-based features with serum potassium between the two evaluators were 0.99 for T-amp and 0.97 for T-right slope. The coefficient for the association between T-amp, T-right slope, and T-norm, and serum potassium ranged from -0.22 to 0.02. In the normal ECG subgroup (normal ECG or otherwise normal ECG), there was no correlation between T-wave-based features and serum potassium level.T-wave-based features were not correlated with serum potassium level, and their use in real clinical practice is currently limited.
Technology Access, Technical Assistance, and Disparities in Inpatient Portal Use
Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance.
S Lee, J Han, RW Park, GJ Kim, JH Rim, J Cho, KH Lee, J Lee, S Kim and JH Kim,
Drug safety, Jan 2019 16
Integration of controlled vocabulary-based electronic health record (EHR) observational data is essential for real-time large-scale pharmacovigilance studies.To provide a semantically enriched adverse drug reaction (ADR) dictionary for post-market drug safety research and enable multicenter EHR-based extensive ADR signal detection and evaluation, we developed a comprehensive controlled vocabulary-based ADR signal dictionary (CVAD) for pharmacovigilance.A CVAD consists of (1) administrative disease classifications of the International Classification of Diseases (ICD) codes mapped to the Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA® PTs); (2) two teaching hospitals' codes for laboratory test results mapped to the Logical Observation Identifiers Names and Codes (LOINC) terms and MedDRA® PTs; and (3) clinical narratives and ADRs encoded by standard nursing statements (encoded by the International Classification for Nursing Practice [ICNP]) mapped to the World Health Organization-Adverse Reaction Terminology (WHO-ART) terms and MedDRA® PTs.Of the standard 4514 MedDRA® PTs from Side Effect Resources (SIDER) 4.1, 1130 (25.03%), 942 (20.86%), and 83 (1.83%) terms were systematically mapped to clinical narratives, laboratory test results, and disease classifications, respectively. For the evaluation, we loaded multi-source EHR data. We first performed a clinical expert review of the CVAD clinical relevance and a three-drug ADR case analyses consisting of linezolid-induced thrombocytopenia, warfarin-induced bleeding tendency, and vancomycin-induced acute kidney injury.CVAD had a high coverage of ADRs and integrated standard controlled vocabularies to the EHR data sources, and researchers can take advantage of these features for EHR observational data-based extensive pharmacovigilance studies to improve sensitivity and specificity.
Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin: Analysis From the Observational Health Data Sciences and Informatics Initiative.
R Vashisht, K Jung, A Schuler, JM Banda, RW Park, S Jin, L Li, JT Dudley, KW Johnson, MM Shervey, H Xu, Y Wu, K Natrajan, G Hripcsak, P Jin, M Van Zandt, A Reckard, CG Reich, J Weaver, MJ Schuemie, PB Ryan, A Callahan and NH Shah,
JAMA network open, Aug 2018 03
Consensus around an efficient second-line treatment option for type 2 diabetes (T2D) remains ambiguous. The availability of electronic medical records and insurance claims data, which capture routine medical practice, accessed via the Observational Health Data Sciences and Informatics network presents an opportunity to generate evidence for the effectiveness of second-line treatments.To identify which drug classes among sulfonylureas, dipeptidyl peptidase 4 (DPP-4) inhibitors, and thiazolidinediones are associated with reduced hemoglobin A1c (HbA1c) levels and lower risk of myocardial infarction, kidney disorders, and eye disorders in patients with T2D treated with metformin as a first-line therapy.Three retrospective, propensity-matched, new-user cohort studies with replication across 8 sites were performed from 1975 to 2017. Medical data of 246 558 805 patients from multiple countries from the Observational Health Data Sciences and Informatics (OHDSI) initiative were included and medical data sets were transformed into a unified common data model, with analysis done using open-source analytical tools. Participants included patients with T2D receiving metformin with at least 1 prior HbA1c laboratory test who were then prescribed either sulfonylureas, DPP-4 inhibitors, or thiazolidinediones. Data analysis was conducted from 2015 to 2018.Treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones starting at least 90 days after the initial prescription of metformin.The primary outcome is the first observation of the reduction of HbA1c level to 7% of total hemoglobin or less after prescription of a second-line drug. Secondary outcomes are myocardial infarction, kidney disorder, and eye disorder after prescription of a second-line drug.A total of 246 558 805 patients (126 977 785 women [51.5%]) were analyzed. Effectiveness of sulfonylureas, DPP-4 inhibitors, and thiazolidinediones prescribed after metformin to lower HbA1c level to 7% or less of total hemoglobin remained indistinguishable in patients with T2D. Patients treated with sulfonylureas compared with DPP-4 inhibitors had a small increased consensus hazard ratio of myocardial infarction (1.12; 95% CI, 1.02-1.24) and eye disorders (1.15; 95% CI, 1.11-1.19) in the meta-analysis. Hazard of observing kidney disorders after treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones was equally likely.The examined drug classes did not differ in lowering HbA1c and in hazards of kidney disorders in patients with T2D treated with metformin as a first-line therapy. Sulfonylureas had a small, higher observed hazard of myocardial infarction and eye disorders compared with DPP-4 inhibitors in the meta-analysis. The OHDSI collaborative network can be used to conduct a large international study examining the effectiveness of second-line treatment choices made in clinical management of T2D.
Temporal Biomedical Data Analytics.
R Moskovitch, Y Shahar, F Wang and G Hripcsak,
Journal of biomedical informatics, Jan 2019 14