OHDSI MEETINGS THIS WEEK
The Book of OHDSI working group meeting - Tuesday at 11am ET
Zoom URL: https://columbiauniversity.zoom.us/j/258043190
OHDSI Community Call - Tuesday at 12pm ET
CDM-Genomics WG meeting - Wednesday at 0am KST
ATLAS workgroup meeting - Wednesday at 10am ET
Patient-Level Prediction WG meeting - Wednesday at 12pm ET
NLP Work Group Meeting - Wednesday at 2pm ET
Population-Level Estimation WG (Western Hemisphere) - Thursday at 12pm ET
China WG meeting - Friday at 10am ET
You can find a full list of upcoming OHDSI meetings here:
Looking for presenters for upcoming OHDSI community calls We are looking for collaborators to share their work on upcoming OHDSI calls. If you are interested in presenting on an upcoming OHDSI call please email me at email@example.com
OHDSI Symposium Page - A full recap of the 2019 OHDSI U.S. Symposium, including photos, videos, collaborator showcase posters, and more, is available here https://www.ohdsi.org/2019-us-symposium-page/. You can find it on the front page under the Symposium recap video, or as the first dropdown under the “2019 OHDSI Symposium” menu. Presentation slides are available there, and Symposium talks will begin to be posted this week, starting with the morning plenary session.
OHDSI Social Showcase We are in the second week of posting one poster/demo/lightning talk from the Collaborator Showcase ( #OHDSISocialShowcase) each weekday on both our Twitter and LinkedIn pages. Please check these out if you are on either, and share to promote all the great work OHDSI is doing. If you took part in the Collaborator Showcase, you’ll get an email on the Monday of the week you will be included to help you promote it.
2019 OHDSI Symposium - Videos Videos from the 2019 OHDSI symposium, including presentations from the main symposium, tutorials and the Women in Real-World Analytics leadership forum are current in post-production and will be made available shortly.
Grit is living life like it’s a marathon, not a sprint.
Angela Duckworth COMMUNITY PUBLICATIONS
Does long-term use of antidiabetic drugs changes cancer risk?
YC Liu, PA Nguyen, A Humayun, SC Chien, HC Yang, RN Asdary, S Syed-Abdul, MH Hsu, M Moldovan, Y Yen, YJ Li, WS Jian and U Iqbal,
Medicine, Oct 2019
Antidiabetic medications are commonly used around the world, but their safety is still unclear. The aim of this study was to investigate whether long-term use of insulin and oral antidiabetic medications is associated with cancer risk.We conducted a well-designed case-control study using 12 years of data from Taiwan's National Health Insurance Research Database and investigated the association between antidiabetic medication use and cancer risk over 20 years. We identified 42,500 patients diagnosed with cancer and calculated each patient's exposure to antidiabetic drugs during the study period. We matched cancer and noncancer subjects matched 1:6 by age, gender, and index date, and used Cox proportional hazard regression and conditional logistic regression, adjusted for potential confounding factors, that is, medications and comorbid diseases that could influence cancer risk during study period.Pioglitazone (adjusted odds ratio [AOR], 1.20; 95% confidence interval [CI], 1.05-1.38); and insulin and its analogs for injection, intermediate or long acting combined with fast acting (AOR, 1.22; 95% CI, 1.05-1.43) were significantly associated with a higher cancer risk. However, metformin (AOR, 1.00; 95% CI, 0.93-1.07), glibenclamide (AOR, 0.98; 95% CI, 0.92-1.05), acarbose (AOR, 1.06; 95% CI, 0.96-1.16), and others do not show evidence of association with cancer risk. Moreover, the risk for specific cancers among antidiabetic users as compared with nonantidiabetic medication users was significantly increased for pancreas cancer (by 45%), liver cancer (by 32%), and lung cancer (by 18%).Antidiabetic drugs do not seem to be associated with an increased cancer risk incidence except for pioglitazone, insulin and its analogs for injection, intermediate or long acting combined with fast acting.
Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital.
MA Dziadzko, PJ Novotny, J Sloan, O Gajic, V Herasevich, P Mirhaji, Y Wu and MN Gong,
Critical care (London, England), Oct 30 2018
Acute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model-Accurate Prediction of Prolonged Ventilation (APPROVE)-to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.This was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort.There were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85-0.88) in 2013 and 0.90 (95% CI 0.84-0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%).An automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification.ClinicalTrials.gov, NCT02488174 . Registered on 18 March 2015.
LPA Variants Are Associated With Residual Cardiovascular Risk in Patients Receiving Statins.
WQ Wei, X Li, Q Feng, M Kubo, IJ Kullo, PL Peissig, EW Karlson, GP Jarvik, MTM Lee, N Shang, EA Larson, T Edwards, CM Shaffer, JD Mosley, S Maeda, M Horikoshi, M Ritchie, MS Williams, EB Larson, DR Crosslin, ST Bland, JA Pacheco, LJ Rasmussen-Torvik, D Cronkite, G Hripcsak, NJ Cox, RA Wilke, CM Stein, JI Rotter, Y Momozawa, DM Roden, RM Krauss and JC Denny,
Circulation, 23 2018 10
Coronary heart disease (CHD) is a leading cause of death globally. Although therapy with statins decreases circulating levels of low-density lipoprotein cholesterol and the incidence of CHD, additional events occur despite statin therapy in some individuals. The genetic determinants of this residual cardiovascular risk remain unknown.We performed a 2-stage genome-wide association study of CHD events during statin therapy. We first identified 3099 cases who experienced CHD events (defined as acute myocardial infarction or the need for coronary revascularization) during statin therapy and 7681 controls without CHD events during comparable intensity and duration of statin therapy from 4 sites in the Electronic Medical Records and Genomics Network. We then sought replication of candidate variants in another 160 cases and 1112 controls from a fifth Electronic Medical Records and Genomics site, which joined the network after the initial genome-wide association study. Finally, we performed a phenome-wide association study for other traits linked to the most significant locus.The meta-analysis identified 7 single nucleotide polymorphisms at a genome-wide level of significance within the LPA/PLG locus associated with CHD events on statin treatment. The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35-1.86; P=2.6×10-10). In the replication cohort, rs10455872 was also associated with CHD events (odds ratio, 1.71; 95% confidence interval, 1.14-2.57; P=0.009). The association of this single nucleotide polymorphism with CHD events was independent of statin-induced change in low-density lipoprotein cholesterol (odds ratio, 1.62; 95% confidence interval, 1.17-2.24; P=0.004) and persisted in individuals with low-density lipoprotein cholesterol ≤70 mg/dL (odds ratio, 2.43; 95% confidence interval, 1.18-4.75; P=0.015). A phenome-wide association study supported the effect of this region on coronary heart disease and did not identify noncardiovascular phenotypes.Genetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering. This finding provides support for exploring strategies targeting circulating concentrations of lipoprotein(a) to reduce CHD events in patients receiving statins.
Lung dose and the potential risk of death in postoperative radiation therapy for non-small cell lung cancer: A study using the method of stratified grouping.
J Heo, OK Noh, HI Kim, M Chun, O Cho, RW Park, D Yoon and YT Oh,
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2018 10
Postoperative radiation therapy may have a detrimental effect on survival in patients with non-small cell lung cancer. We investigated the association of the lung radiation dose with the risk of death in patients treated with postoperative radiation therapy.We analyzed 178 patients with non-small cell lung cancer who received postoperative radiation therapy. The mean lung dose was calculated from dose-volume data, and we categorized patients into the high and low lung dose groups using 2 different methods; (1) simple grouping using the median lung dose of all patients, and (2) stratified grouping using the median lung dose of each subgroup sharing the same confounders. We compared clinical variables, and survival between the high and low lung dose groups.In the simple grouping, there were no significant differences in survivals between the high and low lung dose groups. After stratification, the overall survival of low lung dose group was significantly longer than that of high lung dose group (5-year survival, 60.1% vs. 35.3%, p = 0.039). On multivariable analyses, the lung dose remained a significant prognostic factor for overall survival (hazard ratio, HR = 2.08, p = 0.019).The lung dose was associated with the risk of death in patients with non-small cell lung cancer having the same confounders. Further studies evaluating the risk of death according to the lung dose will be helpful to administer more precise and individualized postoperative radiation therapy.