In OHDSI/Hades: Health Analytics Data-to-Evidence Suite

These papers were written using HADES.

Clinical application

• Anand TV, Bu F, Schuemie MJ, et al.Comparative safety and effectiveness of angiotensin converting enzyme inhibitors and thiazides and thiazide-like diuretics under strict monotherapy J Clin Hypertens (Greenwich). 2024 Apr;26(4):425-430.

• Cai CX, Nishimura A, Bowring MG, et al. https://www.sciencedirect.com/science/article/pii/S2468653024001180 Ophthalmol Retina. 2024 Mar 20:S2468-6530(24)00118-0.

• Khera R, Dhingra LS, Aminorroaya A, et al. Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM BMJ Med. 2023 Oct 6;2(1):e000651.

• Voss EA, Shoaibi A, Yin Hui Lai L, et al. Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study EClinicalMedicine. 2023 Apr;58:101932.

• Chandran U, Reps J, Yang R, et al. Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care Cancer Epidemiol Biomarkers Prev. 2023 Mar 6;32(3):337-343.

• John LH, Kors JA, Fridgeirsson EA, et al. External validation of existing dementia prediction models on observational health data BMC Med Res Methodol. 2022 Dec 5;22(1):311.

• Reps JM, Wilcox M, McGee BA, et al. Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy BMC Pregnancy Childbirth. 2022 May 26;22(1):442.

• Li X, Burn E, Duarte-Salles T, Yin C, et al. Comparative risk of thrombosis with thrombocytopenia syndrome or thromboembolic events associated with different covid-19 vaccines: international network cohort study from five European countries and the US BMJ. 2022 Oct 26;379:e071594.

• Yang C, Williams RD, Swerdel JN, et al. Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis Semin Arthritis Rheum. 2022 Oct;56:152050.

• Nishimura A, Xie J, Kostka K, Duarte-Salles T, et al. International cohort study indicates no association between alpha-1 blockers and susceptibility to COVID-19 in benign prostatic hyperplasia patients Front Pharmacol. 2022 Sep 14;13:945592.

• Londhe AA, Holy CE, Weaver J, et al. Risk of retinal detachment and exposure to fluoroquinolones, common antibiotics, and febrile illness using a self-controlled case series study design: Retrospective analyses of three large healthcare databases in the US PLoS One. 2022 Oct 6;17(10):e0275796.

• Voss EA, Ali SR, Singh A, et al. Hip Fracture Risk After Treatment with Tramadol or Codeine: An Observational Study Drug Saf. 2022 Jul;45(7):791-807.

• Recalde M, Roel E, Pistillo A, et al. Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom nt J Obes (Lond). 2021 Nov;45(11):2347-2357.

• Li X, Ostropolets A, Makadia R, Shoaibi A, et al. Characterising the background incidence rates of adverse events of special interest for covid-19 vaccines in eight countries: multinational network cohort study BMJ. 2021 Jun 14;373:n1435.

• Shoaibi A, Rao GA, Voss EA, et al. Phenotype Algorithms for the Identification and Characterization of Vaccine-Induced Thrombotic Thrombocytopenia in Real World Data: A Multinational Network Cohort Study Drug Saf. 2022 Jun;45(6):685-698.

• Reps JM, Wilcox M, McGee BA, et al. Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy BMC Pregnancy Childbirth. 2022 May 26;22(1):442. doi: 10.1186/s12884-022-04741-9.

• Prats-Uribe A, Sena AG, Lai LYH, et al. Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study BMJ. 2021 May 11;373:n1038.

• Kostka K, Duarte-Salles T, Prats-Uribe A, et al. Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS Clin Epidemiol. 2022 Mar 22;14:369-384.

• Williams RD, Markus AF, Yang C, et al. Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network BMC Med Res Methodol. 2022 Jan 30;22(1):35. doi: 10.1186/s12874-022-01505-z.

• Nestsiarovich A, Reps JM, Matheny ME, et al. Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study Transl Psychiatry. 2021 Dec 20;11(1):642.

• Williams RD, Reps JM; OHDSI/EHDEN Knee Arthroplasty Group, et al. 90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model Knee Surg Sports Traumatol Arthrosc. 2021 Dec 6. doi: 10.1007/s00167-021-06799-y.

• Chen R, Suchard MA, Krumholz HM, et al. Comparative First-Line Effectiveness and Safety of ACE (Angiotensin-Converting Enzyme) Inhibitors and Angiotensin Receptor Blockers: A Multinational Cohort Study Hypertension. 2021 Sep;78(3):591-603. doi: 10.1161/HYPERTENSIONAHA.120.16667.

• Duarte-Salles T, Vizcaya D, Pistillo A, Casajust P, et al. Thirty-Day Outcomes of Children and Adolescents With COVID-19: An International Experience Pediatrics. 2021 Sep;148(3):e2020042929.

• Chan You S, Krumholz HM, Suchard MA, et al. Comprehensive Comparative Effectiveness and Safety of First-Line β-Blocker Monotherapy in Hypertensive Patients: A Large-Scale Multicenter Observational Study Hypertension. 2021 May 5;77(5):1528-1538. doi: 10.1161/HYPERTENSIONAHA.120.16402.

• Morales DR, Conover MM, You SC, et al. Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis. Lancet Digit Health. 2020 Dec 17:S2589-7500(20)30289-2. doi: 10.1016/S2589-7500(20)30289-2.

• You SC, Rho Y, Bikdeli B, et al. Association of Ticagrelor vs Clopidogrel With Net Adverse Clinical Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention. JAMA. 2020 Oct 27;324(16):1640-1650. doi: 10.1001/jama.2020.16167.

• Burn E, You SC, Sena AG, et al. Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study. Nat Commun. 2020 Oct 6;11(1):5009. doi: 10.1038/s41467-020-18849-z.

• Lane JCE, Weaver J, Kostka K, et al. Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study. Lancet Rheumatology. 2020 Aug 21.

• Kim Y, Tian Y, Yang J, et al. Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis. Sci Rep. 2020 Jul 6;10(1):11115.

• Hripcsak G, Suchard MA, Shea S, et al. Comparison of Cardiovascular and Safety Outcomes of Chlorthalidone vs Hydrochlorothiazide to Treat Hypertension. JAMA Intern Med. 2020 Feb 17.

• Reps JM, Cepeda MS, Ryan PB. Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data. PLoS One. 2020 Feb 13;15(2):e0228632.

• Wang Q, Reps JM, Kostka KF, et al. Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network. PLoS One. 2020 Jan 7;15(1):e0226718.

• Suchard MA, Schuemie MJ, Krumholz HM, et al. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet. 2019 Nov 16;394(10211):1816-1826.

• You SC, Jung S, Swerdel JN, Ryan PB, et al. Comparison of First-Line Dual Combination Treatments in Hypertension: Real-World Evidence from Multinational Heterogeneous Cohorts. Korean Circ J. 2020 Jan;50(1):52-68.

• Johnston SS, Morton JM, Kalsekar I, et al. Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery. Value Health. 2019 May;22(5):580-586.

• Weinstein RB, Ryan PB, Berlin JA, et al. Channeling Bias in the Analysis of Risk of Myocardial Infarction, Stroke, Gastrointestinal Bleeding, and Acute Renal Failure with the Use of Paracetamol Compared with Ibuprofen. Drug Saf. 2020 Sep;43(9):927-942.

• Vashisht R, Jung K, Schuler A, et al. 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. JAMA Network Open. 2018; 1: e181755.

• Ryan PB, Buse JB, Schuemie MJ, et al. Comparative effectiveness of canagliflozin, SGLT2 inhibitors and non-SGLT2 inhibitors on the risk of hospitalization for heart failure and amputation in patients with type 2 diabetes mellitus: A real-world meta-analysis of 4 observational databases (OBSERVE-4D). Diabetes, obesity & metabolism. 2018; 20: 2585-97.

• Yuan Z, DeFalco FJ, Ryan PB, et al. Risk of lower extremity amputations in people with type 2 diabetes mellitus treated with sodium-glucose co-transporter-2 inhibitors in the USA: A retrospective cohort study. Diabetes, obesity & metabolism. 2018; 20: 582-9.

• Weinstein RB, Ryan P, Berlin JA, et al. Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications. Drug safety. 2017; 40: 1279-92.

• Wang Y, Desai M, Ryan PB, et al. Incidence of diabetic ketoacidosis among patients with type 2 diabetes mellitus treated with SGLT2 inhibitors and other antihyperglycemic agents. Diabetes Res Clin Pract. 2017; 128: 83-90.

• Ryan PB, Schuemie MJ, Ramcharran D and Stang PE. Atypical Antipsychotics and the Risks of Acute Kidney Injury and Related Outcomes Among Older Adults: A Replication Analysis and an Evaluation of Adapted Confounding Control Strategies. Drugs & aging. 2017; 34: 211-9.

• Ramcharran D, Qiu H, Schuemie MJ, et al. Atypical Antipsychotics and the Risk of Falls and Fractures Among Older Adults: An Emulation Analysis and an Evaluation of Additional Confounding Control Strategies. J Clin Psychopharmacol. 2017; 37: 162-8.

• Boland MR, Parhi P, Li L, et al. Uncovering exposures responsible for birth season - disease effects: a global study. J Am Med Inform Assoc. 2017 Sep 28.

• Duke JD, Ryan PB, Suchard MA, et al. Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network. Epilepsia. 2017; 58: e101-e6.

Methods research

• Fridgeirsson EA, Williams R, Rijnbeek P, et al. https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocae109/7676584?login=false Am Med Inform Assoc. 2024 May 20:ocae109.

• Schuemie M, Reps J, Black A, et al. Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research Stud Health Technol Inform. 2024 Jan 25:310:966-970.

• Bu F, Schuemie MJ, Nishimura A, et al. Bayesian safety surveillance with adaptive bias correction Stat Med. 2024 Jan 30;43(2):395-418.

• Voss EA, Blacketer C, van Sandijk S, et al. European Health Data & Evidence Network-learnings from building out a standardized international health data network J Am Med Inform Assoc. 2023 Dec 22;31(1):209-219.

• Gauffin O, Brand JS, Vidlin SH, et al. Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study Drug Saf. 2023 Dec;46(12):1335-1352.

• Wu Q, Schuemie MJ, Suchard MA, et al. Pade approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes J Biomed Inform. 2023 Aug 18:104476.

• Arshad F, Schuemie MJ, Bu F, et al. Serially Combining Epidemiological Designs Does Not Improve Overall Signal Detection in Vaccine Safety Surveillance Drug Saf. 2023 Aug;46(8):797-807.

• Lee DY, Choi B, Kim C, et al. Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study J Med Internet Res. 2023 Jul 20;25:e46165.

• Williams RD, den Otter S, Reps JM, et al. The DELPHI Library: Improving Model Validation, Transparency and Dissemination Through a Centralised Library of Prediction Models Stud Health Technol Inform. 2023 May 18;302:139-140.

• Yang C, Fridgeirsson EA, Kors JA, et al. Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability? Stud Health Technol Inform. 2023 May 18;302:129-130.

• Schuemie MJ, Bu F, Nishimura A, et al. Adjusting for both sequential testing and systematic error in safety surveillance using observational data: Empirical calibration and MaxSPRT Stat Med. 2023 Feb 28;42(5):619-631.

• Zhang L, Wang Y, Schuemie MJ, et al. Adjusting for indirectly measured confounding using large-scale propensity score J Biomed Inform. 2022 Oct;134:104204.

• Swerdel JN, Schuemie M, Murray G, et al. PheValuator 2.0: Methodological improvements for the PheValuator approach to semi-automated phenotype algorithm evaluation J Biomed Inform. 2022 Aug 19;104177.

• Schuemie MJ, Arshad F, Pratt N, et al. Vaccine Safety Surveillance Using Routinely Collected Healthcare Data-An Empirical Evaluation of Epidemiological Designs Front Pharmacol. 2022 Jul 6;13:893484.

• Ostropolets A, Ryan PB, Schuemie MJ, et al. Characterizing Anchoring Bias in Vaccine Comparator Selection Due to Health Care Utilization With COVID-19 and Influenza: Observational Cohort Study JMIR Public Health Surveill. 2022 Jun 17;8(6):e33099.

• Reps JM, Williams RD, Schuemie MJ, et al. Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability BMC Med Inform Decis Mak. 2022 May 25;22(1):142. doi: 10.1186/s12911-022-01879-6.

• Williams RD, Reps JM, Kors JA, et al. Using Iterative Pairwise External Validation to Contextualize Prediction Model Performance: A Use Case Predicting 1-Year Heart Failure Risk in Patients with Diabetes Across Five Data Sources Drug Saf. 2022 May;45(5):563-570.

• Ostropolets A, Ryan P, Schuemie M, et al. Differential anchoring effects of vaccination comparator selection: characterizing a potential bias due to healthcare utilization in COVID-19 versus influenza JMIR Public Health Surveill. 2022 Apr 26. doi: 10.2196/33099

• Ostropolets A, Li X, Makadia R, et al., Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases Front Pharmacol. 2022 Apr 26;13:814198.

• Li X, Lai LY, Ostropolets A, et al. Bias, Precision and Timeliness of Historical (Background) Rate Comparison Methods for Vaccine Safety Monitoring: An Empirical Multi-Database Analysis Front Pharmacol. 2021 Nov 24;12:773875. doi: 10.3389/fphar.2021.773875.

• Schuemie MJ, Chen Y, Madigan D, et al. Combining cox regressions across a heterogeneous distributed research network facing small and zero counts Stat Methods Med Res. 2021 Nov 29. doi: 10.1177/09622802211060518.

• Reps JM, Ryan P, Rijnbeek PR. Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data BMJ Open. 2021 Dec 24;11(12):e050146. doi: 10.1136/bmjopen-2021-050146.

• Khalid S, Yang C, Blacketer C, et al. A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data Comput Methods Programs Biomed. 2021 Nov;211:106394. doi: 10.1016/j.cmpb.2021.106394.

• Reps JM, Kim C, Williams RD, et al. Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR Med Inform. 2021 Apr 5;9(4):e21547. doi: 10.2196/21547.

• Fortin SP, Johnston SS, Schuemie MJ. Applied comparison of large-scale propensity score matching and cardinality matching for causal inference in observational research. BMC Med Res Methodol. 2021 May 24;21(1):109. doi: 10.1186/s12874-021-01282-1.

• Reps JM, Rijnbeek P, Cuthbert A, et al. An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort design BMC Med Inform Decis Mak. 2021 Feb 6;21(1):43. doi: 10.1186/s12911-021-01408-x.

• Schuemie MJ, Weinstein R, Ryan PB, et al. Quantifying bias in epidemiologic studies evaluating the association between acetaminophen use and cancer Regul Toxicol Pharmacol. 2021 Mar;120:104866. doi: 10.1016/j.yrtph.2021.104866.

• Thurin NH, Lassalle R, Schuemie M, et al. Empirical assessment of case-based methods for identification of drugs associated with acute liver injury in the French National Healthcare System database (SNDS). Pharmacoepidemiol Drug Saf. 2020 Oct 25. doi: 10.1002/pds.5161.

• Schuemie MJ, Ryan PB, Pratt N, et al. Large-scale evidence generation and evaluation across a network of databases (LEGEND): assessing validity using hypertension as a case study. J Am Med Inform Assoc. 2020 Aug 1;27(8):1268-1277.

• Thurin NH, Lassalle R, Schuemie M, et al. Empirical assessment of case-based methods for identification of drugs associated with upper gastrointestinal bleeding in the French National Healthcare System database (SNDS). Pharmacoepidemiol Drug Saf. 2020 Aug;29(8):890-903. doi: 10.1002/pds.5038.

• Reps JM, Williams RD, You SC, et al. 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. BMC Med Res Methodol. 2020 May 6;20(1):102.

• Schuemie MJ, Cepeda MS, Suchard MA, et al. How Confident Are We About Observational Findings in Health Care: A Benchmark Study Harvard Data Science Review, 2(1).

• Schuemie MJ, Ryan PB, Man KKC, et al. A plea to stop using the case-control design in retrospective database studies. Stat Med. 2019 Sep 30;38(22):4199-4208.

• Reps JM, Rijnbeek PR, Ryan PB. Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data. Drug Saf. 2019 May 3.

• Reps JM, Schuemie MJ, Suchard MA, et al. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc. 2018;25(8):969-975.

• Schuemie MJ, Ryan PB, Hripcsak G, et al. Improving reproducibility by using high-throughput observational studies with empirical calibration. Philosophical transactions Series A, Mathematical, physical, and engineering sciences. 2018; 376.

• Tian Y, Schuemie MJ and Suchard MA. Evaluating large-scale propensity score performance through real-world and synthetic data experiments. International journal of epidemiology. 2018.

• Schuemie MJ, Hripcsak G, Ryan PB, et al. Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data. Proceedings of the National Academy of Sciences of the United States of America. 2018; 115: 2571-7.

• Schuemie MJ, Hripcsak G, Ryan PB, et al. Robust empirical calibration of p-values using observational data. Statistics in medicine. 2016; 35: 3883-8.

• Schuemie MJ, Ryan PB, DuMouchel W, et al. Interpreting observational studies: why empirical calibration is needed to correct p-values. Statistics in medicine. 2014; 33: 209-18.

OHDSI/Hades documentation built on April 3, 2025, 3:06 p.m.