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--- research:project_proposal_template_3 [2016/09/20 14:34]
schillil
+++ research:project_proposal_template_3 [2018/04/20 20:34]
evan [Discussion]
@@ Line 1: / Line 1: @@
-====== Concordance with AAD guidelines for the treatment of acne vulgaris======
+======  OHDSI Perioperative Prediction: Development and Validation of Prognostic Prediction models for Post-Operative Outcomes of Interest ======
 <WRAP box justify round>
-**Objective:** //The objective of this study is to evaluate physician concordance with AAD guidelines for the treatment of acne vulgaris.//
+**Objective:** //To create a set of patient level prediction models for patients undergoing non-maternal, non-cardiac surgeries, examining a set of post-operative outcomes of interest//
-**Rationale:** //The American Academy of Dermatology (AAD) has published guidelines for the use of systemic antibiotics for the management of acne. These include: 1) minimizing the duration of therapy (ideally to 3-4 months and no longer than 6 months) to decrease risk of resistance and 2) prescribing concomitant treatment with a topical retinoid or topical retinoid/benzoyl peroxide to increase efficacy. A large retrospective cohort study of teenagers in the United Kingdom revealed that of antibiotic courses prescribed by general practitioners, 29% exceeded 6 months in duration, and 62% were not associated with a topical retinoid. Our study aims to determine adherence with AAD guidelines for oral antibiotic use in the management of patients with acne among general practitioners and dermatologists in the United States and other countries. The evidence through the OHDSI network may help identify a need to improve concordance to achieve the best treatment efficacy for patients.//
+**Rationale:** //Surgical procedures are frequently performed in large health care systems, with over 15 million invasive surgeries per year in the United States (1).  Serious complication rates arise in this population (2).  In an effort to counsel patients and reduce their cardiac and non-cardiac surgical risks, the field of perioperative medicine often looks to multivariate prediction models across outcomes of interest.   Point of care deployments of these often favour parsimonious models (e.g. the 6 point Revised Cardiac Risk Index (3)).  These could potentially be outperformed or complemented by machine learning approaches to prediction that utilize a comprehensive representation of the patient record as a feature source, especially as point of care application becomes automated in the era of the electronic medical record.  //
-**Project Lead(s):** //Stephanie Chapman, Renee Domozych, Jessica Mounessa, Robert P. Dellavalle, Lisa Schilling//
+**Project Lead(s):** //Evan Minty, Lichy Han, Nigam Shah//
+**Coordinating Institution(s):** //Stanford University//
+** Additional Participants:**  //Collaborators Welcome//
+**Full Protocol:** //in development//
+**Initial Proposal Date:**  April 20 2018
+**Launch Date:**  //TBA//
+**Study Closure Date:  //TBA//**
+**Results Submission:** //TBA//
+</WRAP>
+===== Requirements =====
+**CDM:** //V5, uses Feature Extraction 2.0//
+**Table Accessed:**  //TBA//
+**Database Dialects:** SQL Server, Postgres, Oracle
+**Software:**  //R//
 ===== Code =====
 [[https://github.com/OHDSI/StudyProtocols]]
+===== Discussion =====
+//http://forums.ohdsi.org/t/perioperative-prediction-suite/3996 //
+===== Datasets Run =====
+  * <list your own datasets or leave blank>
+~~NOTOC~~

Observational Health Data Sciences and Informatics

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