OHDSI MEETINGS THIS WEEK
OHDSI STATEMENT ON COVID-19: OHDSI is committed to doing what it can to support and inform the COVID-19 pandemic response, and will prioritize activities aligned to this effort. As we adjust to the current global situation, the OHDSI community will continue to have our regular Tuesday community calls, as well as our various working group calls. Understandably, some calls could be postponed or cancelled due to issues surrounding COVID-19, so we ask that you continuously check the OHDSI forums, weekly digest and social channels for updated information. OHDSI is a virtual community, so we are equipped to continue collaboration during this challenging time.
OMOP CDM Oncology WG - Genomic Subgroup Meeting - Tuesday at 9am ET
OHDSI Community Call - Tuesday at 12pm ET
Zoom Meeting URL: https://columbiacuimc.zoom.us/j/945377669
Meeting ID: 945 377 669
Oncology WG - Development Subgroup Meeting - Wednesday at 10am ET
Natural Language Processing WG - Wednesday at 2pm ET
Psychiatry WG Meeting - Thursday at 8am ET
OMOP CDM Oncology WG - CDM/Vocabulary Subgroup Meeting - Thursday at 10am ET
China WG - Friday at 10am ET
EHR WG - Friday at 10am ET
You can find a full list of upcoming OHDSI meetings here:
New Teleconferencing Details for OHDSI Community Calls - Our weekly OHDSI community calls, which take place every Tuesday at noon will no longer be hosted via webex. For the time being, these calls will be hosted via Zoom. The details to join are:
Zoom Meeting URL: https://columbiacuimc.zoom.us/j/945377669
Meeting ID: 945 377 669
Women of OHDSI mentorship sessions - The Women of OHDSI will be holding ad hoc mentorship meetings, hosted by experts within a variety of areas. These meetings will have the aim of providing a forum for attendees to ask questions and seek guidance. Each meeting will be structured around a given topic, either focused on a specific technical area (ex. how to write a patient-level prediction protocol) or on general career development (ex. improving soft skills). The meetings may include a short presentation on the topic, but will primarily be designed for attendees to ask questions. To help us organize the meetings, please fill in this googleform and outline areas where you feel you could use more support: https://docs.google.com/forms/d/e/1FAIpQLSdXCGVaNZzhqAoVkWXMQOw0jypD6cMuRo658DgpxrkdC7WrBg/viewform
COVID-19 Virtual Study-a-thon - To contribute to the COVID-19 response, the OHDSI community hosted a virtual study-a-thon on March 26-29th. For videos from this event, check out this forum post: FINAL GLOBAL UPDATE: #OHDSICOVID19 Study-A-Thon (video link posted)
And keep up-to-date on all post study-a-thon activities here: https://www.ohdsi.org/covid-19-updates/
If we drive down the cost of transportation in space, we can do anything.
Elon Musk COMMUNITY PUBLICATIONS
A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study.
M Burgermaster, JH Son, PG Davidson, AM Smaldone, G Kuperman, DJ Feller, KG Burt, ME Levine, DJ Albers, C Weng and L Mamykina,
International journal of medical informatics, Apr 30 2020
Self-monitoring technologies produce patient-generated data that could be leveraged to personalize nutritional goal setting to improve population health; however, most computational approaches are limited when applied to individual-level personalization with sparse and irregular self-monitoring data. We applied informatics methods from expert suggestion systems to a challenging clinical problem: generating personalized nutrition goals from patient-generated diet and blood glucose data.We applied qualitative process coding and decision tree modeling to understand how registered dietitians translate patient-generated data into recommendations for dietary self-management of diabetes (i.e., knowledge model). We encoded this process in a set of functions that take diet and blood glucose data as an input and output diet recommendations (i.e., inference engine). Dietitians assessed face validity. Using four patient datasets, we compared our inference engine's output to clinical narratives and gold standards developed by expert clinicians.To dietitians, the knowledge model represented how recommendations from patient data are made. Inference engine recommendations were 63 % consistent with the gold standard (range = 42 %-75 %) and 74 % consistent with narrative clinical observations (range = 63 %-83 %).Qualitative modeling and automating how dietitians reason over patient data resulted in a knowledge model representing clinical knowledge. However, our knowledge model was less consistent with gold standard than narrative clinical recommendations, raising questions about how best to evaluate approaches that integrate patient-generated data with expert knowledge.New informatics approaches that integrate data-driven methods with expert decision making for personalized goal setting, such as the knowledge base and inference engine presented here, demonstrate the potential to extend the reach of patient-generated data by synthesizing it with clinical knowledge. However, important questions remain about the strengths and weaknesses of computer algorithms developed to discern signal from patient-generated data compared to human experts.
Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19.
J Geleris, Y Sun, J Platt, J Zucker, M Baldwin, G Hripcsak, A Labella, D Manson, C Kubin, RG Barr, ME Sobieszczyk and NW Schluger,
The New England journal of medicine, May 2020 07
Hydroxychloroquine has been widely administered to patients with Covid-19 without robust evidence supporting its use.We examined the association between hydroxychloroquine use and intubation or death at a large medical center in New York City. Data were obtained regarding consecutive patients hospitalized with Covid-19, excluding those who were intubated, died, or discharged within 24 hours after presentation to the emergency department (study baseline). The primary end point was a composite of intubation or death in a time-to-event analysis. We compared outcomes in patients who received hydroxychloroquine with those in patients who did not, using a multivariable Cox model with inverse probability weighting according to the propensity score.Of 1446 consecutive patients, 70 patients were intubated, died, or discharged within 24 hours after presentation and were excluded from the analysis. Of the remaining 1376 patients, during a median follow-up of 22.5 days, 811 (58.9%) received hydroxychloroquine (600 mg twice on day 1, then 400 mg daily for a median of 5 days); 45.8% of the patients were treated within 24 hours after presentation to the emergency department, and 85.9% within 48 hours. Hydroxychloroquine-treated patients were more severely ill at baseline than those who did not receive hydroxychloroquine (median ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen, 223 vs. 360). Overall, 346 patients (25.1%) had a primary end-point event (180 patients were intubated, of whom 66 subsequently died, and 166 died without intubation). In the main analysis, there was no significant association between hydroxychloroquine use and intubation or death (hazard ratio, 1.04, 95% confidence interval, 0.82 to 1.32). Results were similar in multiple sensitivity analyses.In this observational study involving patients with Covid-19 who had been admitted to the hospital, hydroxychloroquine administration was not associated with either a greatly lowered or an increased risk of the composite end point of intubation or death. Randomized, controlled trials of hydroxychloroquine in patients with Covid-19 are needed. (Funded by the National Institutes of Health.).
Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.
JM Reps, RD Williams, SC You, T Falconer, E Minty, A Callahan, PB Ryan, RW Park, HS Lim and P Rijnbeek,
BMC medical research methodology, May 2020 06
To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets.Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites.The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation.This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.
Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.
M Kashyap, M Seneviratne, JM Banda, T Falconer, B Ryu, S Yoo, G Hripcsak and NH Shah,
Journal of the American Medical Informatics Association : JAMIA, May 2020 06
Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Data Sciences and Informatics (OHDSI) network.We constructed classifiers using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) R-package, an open-source framework for learning phenotype classifiers using datasets in the Observational Medical Outcomes Partnership Common Data Model. We labeled training data based on the presence of multiple mentions of disease-specific codes. Performance was evaluated on cohorts derived using rule-based definitions and real-world disease prevalence. Classifiers were developed and evaluated across 3 medical centers, including 1 international site.Compared to the multiple mentions labeling heuristic, classifiers showed a mean recall boost of 0.43 with a mean precision loss of 0.17. Performance decreased slightly when classifiers were shared across medical centers, with mean recall and precision decreasing by 0.08 and 0.01, respectively, at a site within the USA, and by 0.18 and 0.10, respectively, at an international site.We demonstrate a high-throughput pipeline for constructing and sharing phenotype classifiers across sites within the OHDSI network using APHRODITE. Classifiers exhibit good portability between sites within the USA, however limited portability internationally, indicating that classifier generalizability may have geographic limitations, and, consequently, sharing the classifier-building recipe, rather than the pretrained classifiers, may be more useful for facilitating collaborative observational research.