OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm ET
US TOLL: +1-415-655-0001
Meeting Number: 199 982 907
Population-Level Estimation (Eastern hemisphere) Workgroup Call - Wednesday at 3pm Hong Kong time
OHDSI Atlas/WebAPI Working Group - Wednesday’s at 10am EST
GIS Working Group Meeting - Next Monday (November 5th) at 10am ET
Meeting Number: 735 317 239
Simple, modern video meetings for the global workforce. Join from anywhere, including your desktop, browser, mobile device, or video room device.
2018 OHDSI Symposium Materials - All slides, handouts and abstracts from this year’s symposium have been uploaded here: https://www.ohdsi.org/past-events/2018-ohdsi-symposium-materials/
2018 OHDSI Symposium Recordings - Video records from the main symposium will be available online by Monday, November 5th. Recordings from this year’s tutorials will be posted online by Wednesday, Novemeber 14th.
Epically OHDSI - Episode 2 is available here: https://youtu.be/2R5dWNH4HxA COMMUNITY PUBLICATIONS
Predicting the need for a reduced drug dose, at first prescription.
A Coulet, NH Shah, M Wack, MB Chawki, N Jay and M Dumontier,
Scientific reports, Oct 22 2018
Prescribing the right drug with the right dose is a central tenet of precision medicine. We examined the use of patients' prior Electronic Health Records to predict a reduction in drug dosage. We focus on drugs that interact with the P450 enzyme family, because their dosage is known to be sensitive and variable. We extracted diagnostic codes, conditions reported in clinical notes, and laboratory orders from Stanford's clinical data warehouse to construct cohorts of patients that either did or did not need a dose change. After feature selection, we trained models to predict the patients who will (or will not) require a dose change after being prescribed one of 34 drugs across 23 drug classes. Overall, we can predict (AUC ≥ 0.70-0.95) a dose reduction for 23 drugs and 22 drug classes. Several of these drugs are associated with clinical guidelines that recommend dose reduction exclusively in the case of adverse reaction. For these cases, a reduction in dosage may be considered as a surrogate for an adverse reaction, which our system could indirectly help predict and prevent. Our study illustrates the role machine learning may take in providing guidance in setting the starting dose for drugs associated with response variability.
Supervised signal detection for adverse drug reactions in medication dispensing data.
T Hoang, J Liu, E Roughead, N Pratt and J Li,
Computer methods and programs in biomedicine, Jul 2018
Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality and thus should be detected early to reduce consequences on health outcomes. Medication dispensing data are comprehensive sources of information about medicine uses that can be utilized for the signal detection of ADRs. Sequence symmetry analysis (SSA) has been employed in previous studies to detect signals of ADRs from medication dispensing data, but it has a moderate sensitivity and tends to miss some ADR signals. With successful applications in various areas, supervised machine learning (SML) methods are promising in detecting ADR signals. Gold standards of known ADRs and non- ADRs from previous studies create opportunities to take into account additional domain knowledge to improve ADR signal detection with SML.We assess the utility of SML as a signal detection tool for ADRs in medication dispensing data with the consideration of domain knowledge from DrugBank and MedDRA. We compare the best performing SML method with SSA.We model the ADR signal detection problem as a supervised machine learning problem by linking medication dispensing data with domain knowledge bases. Suspected ADR signals are extracted from the Australian Pharmaceutical Benefit Scheme (PBS) medication dispensing data from 2013 to 2016. We construct predictive features for each signal candidate based on its occurrences in medication dispensing data as well as its pharmacological properties. Pharmaceutical knowledge bases including DrugBank and MedDRA are employed to provide pharmacological features for a signal candidate. Given a gold standard of known ADRs and non-ADRs, SML learns to differentiate between known ADRs and non-ADRs based on their combined predictive features from linked sources, and then predicts whether a new case is a potential ADR signal.We evaluate the performance of six widely used SML methods with two gold standards of known ADRs and non-ADRs from previous studies. On average, gradient boosting classifier achieves the sensitivity of 77%, specificity of 81%, positive predictive value of 76%, negative predictive value of 82%, area under precision-recall curve of 81%, and area under receiver operating characteristic curve of 82%, most of which are higher than in other SML methods. In particular, gradient boosting classifier has 21% higher sensitivity than and comparable specificity with SSA. Furthermore, gradient boosting classifier detects 10% more unknown potential ADR signals than SSA.Our study demonstrates that gradient boosting classifier is a promising supervised signal detection tool for ADRs in medication dispensing data to complement SSA.
Real-world data reveal a diagnostic gap in non-alcoholic fatty liver disease.
M Alexander, AK Loomis, J Fairburn-Beech, J van der Lei, T Duarte-Salles, D Prieto-Alhambra, D Ansell, A Pasqua, F Lapi, P Rijnbeek, M Mosseveld, P Avillach, P Egger, S Kendrick, DM Waterworth, N Sattar and W Alazawi,
BMC medicine, 2018 13 08
Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease worldwide. It affects an estimated 20% of the general population, based on cohort studies of varying size and heterogeneous selection. However, the prevalence and incidence of recorded NAFLD diagnoses in unselected real-world health-care records is unknown. We harmonised health records from four major European territories and assessed age- and sex-specific point prevalence and incidence of NAFLD over the past decade.Data were extracted from The Health Improvement Network (UK), Health Search Database (Italy), Information System for Research in Primary Care (Spain) and Integrated Primary Care Information (Netherlands). Each database uses a different coding system. Prevalence and incidence estimates were pooled across databases by random-effects meta-analysis after a log-transformation.Data were available for 17,669,973 adults, of which 176,114 had a recorded diagnosis of NAFLD. Pooled prevalence trebled from 0.60% in 2007 (95% confidence interval: 0.41-0.79) to 1.85% (0.91-2.79) in 2014. Incidence doubled from 1.32 (0.83-1.82) to 2.35 (1.29-3.40) per 1000 person-years. The FIB-4 non-invasive estimate of liver fibrosis could be calculated in 40.6% of patients, of whom 29.6-35.7% had indeterminate or high-risk scores.In the largest primary-care record study of its kind to date, rates of recorded NAFLD are much lower than expected suggesting under-diagnosis and under-recording. Despite this, we have identified rising incidence and prevalence of the diagnosis. Improved recognition of NAFLD may identify people who will benefit from risk factor modification or emerging therapies to prevent progression to cardiometabolic and hepatic complications.
Database Studies of Treatment-Resistant Depression Should Take Account of Adequate Dosing.
D Fife, C Blacketer, JM Reps and P Ryan,
The primary care companion for CNS disorders, Jul 26 2018
The objective of this study was to estimate how commonly patients with pharmacologically treated depression (PTD) do not receive adequate doses of antidepressant (AD) medications. Such prescribing would have epidemiologic and clinical implications. Patients with PTD have treatment-resistant depression (TRD) if they do not benefit from ≥ 2 AD medications taken with reasonable compliance for adequate durations at adequate doses. Some database studies of TRD do not assess AD medication dose and would, therefore, overestimate TRD incidence unless physicians treating PTD patients routinely prescribe AD medications at adequate doses before changing medications.Using data from 3 US health services databases from September 1, 2010, through December 31, 2014, we created PTD cohorts and defined an AD medication era as a sequence of dispensings with ≤ 30 days between the end of the days' supply of each dispensing and the start of the next. We classified AD medication eras according to whether they had ≥ 1 dispensing at or above the minimum therapeutic dose.The proportion of AD medication eras with ≥ 1 dose at or above the minimum therapeutic dose varied from 59.6% in the Medicaid database to 66.0% in a database of privately insured patients.In the population at risk for TRD, a substantial proportion of AD medication dispensing eras do not reach the minimum therapeutic dose. TRD incidence is likely to be overestimated in database studies that do not take account of dose. Clinicians should be aware that AD medication regimens are often stopped without reaching the minimum therapeutic dose, which may cause unnecessary switching.