Health Outcome Algorithm Inventory

Project Title Health Outcome Algorithm Inventory
Date Posted
Monday, May 23, 2016
Status
Complete
Deliverables
Description

This file contains a summary of literature reviews conducted in 2012-2013 by the Mini-Sentinel Applied Surveillance Core on International Classification of Diseases, Ninth Revision (ICD-9) algorithms to identify health outcomes of interest in administrative data, focusing on algorithms that have undergone validation through medical records review. The Mini-Sentinel Applied Surveillance Core examined the available literature and consulted with experts in an attempt to locate algorithms to identify specific HOIs. This inventory was produced to assist investigators in selecting algorithms to identify health outcomes of interest in administrative data. 

It should be noted that not all algorithms identified are of sufficient quality for all purposes. Not every algorithm studied by every source was documented. Search strategies to identify algorithms were developed to maximize efficiency. Some relevant studies may have been missed.

DISCLAIMER: The FDA does not specifically endorse use of any of these algorithms, and cannot guarantee the accuracy of the information in this library. The inclusion of an algorithm in this inventory does not imply that its performance is considered adequate for use. Not all data have been cross-validated by multiple investigators, interpretations of investigators may differ, and this was not peer-reviewed in the traditional sense. Algorithms may need to be customized for some purposes, or updated since codes and definitions can change over time. The use of an algorithm in a prior protocol should not be taken to infer that the algorithm has sufficient performance to be used without validation. Expanding knowledge of Sentinel data sources may also lead to changes in which algorithms might be considered optimal from those used in prior protocols. Some protocol algorithms were also used to maximize sensitivity with anticipated validation through medical record reviews, so the algorithms may not be optimal for investigations with no anticipated outcome validation. In addition, good performance of an algorithm in a prior study does not necessarily mean that the algorithm will perform as well in a new population. The potential need for validation of outcomes should always be considered. Investigators must take responsibility for choosing and justifying algorithms used in their work, and determining whether outcome validation is necessary. Further guidance for conducting and reporting on pharmacoepidemiologic safety studies that use electronic healthcare data, including validating health outcomes, may be found within the Best Practices for Conducting and Reporting Pharmacoepidemiologic Safety Studies Using Electronic Healthcare Data guidance document provided by the FDA.

Health Outcome
Achilles tendon rupture (ATR)
Bell’s palsy
Guillain-Barré syndrome
Henoch Schönlein purpura (HSP)
Severe acute liver injury
Spontaneous abortion (SAB)
acute intracranial hemorrhage
acute ischemic stroke
acute pancreatitis
acute respiratory failure
acute stroke (ischemic or hemorrhagic)
agranulocytosis
aplastic anemia
asthma exacerbation
erythema multiforme major (including Stevens-Johnson syndrome and toxic epidermal necrolysis)
febrile seizures
gastrointestinal bleeding
hip fracture
hypertensive emergency
idiopathic thrombocytopenic purpura
inflammatory bowel disease (IBD)
juvenile rheumatoid arthritis
neutropenia
peripheral neuropathy
premature delivery
pulmonary fibrosis
pulmonary hypertension
rhabdomyolysis
sepsis
stillbirth
sudden cardiac death
sudden or rapidly-progressing hearing loss
suicide (including attempted suicide)
systemic lupus erythematosus (SLE)
thrombocytopenia
thrombotic thrombocytopenic purpura
tuberculosis (TB)
type I diabetes
venous thromboembolism (VTE)
Workgroup Leader(s)

Mini-Sentinel Applied Surveillance Core 

Workgroup Members

Patrick Archdeacon MD; Office of Medical Policy, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD

Alfonza Brown MS; Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA

Ryan Carnahan PharmD, MS, BCPP; Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA

Margaret Chorazy PhD; Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA

Elizabeth Chrischilles MS, PhD; Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA

Cristin Freeman MPH, MBE; Center for Clinical Epidemiology and Biostatistics and Center for Pharmacoepidemiology Research and Training, Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA

Candace Fuller PhD and Darren Toh ScD; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

Sean Hennessy PharmD, PhD; Center for Clinical Epidemiology and Biostatistics and Center for Pharmacoepidemiology Research and Training, Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA

Charles E. Leonard PharmD, MSCE; Center for Clinical Epidemiology and Biostatistics, and Center for Pharmacoepidemiology Research and Training, Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA

Daniela Moga MD, PhD; Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA

Hanieh Razzaghi MPH; Center for Clinical Epidemiology and Biostatistics and Center for Pharmacoepidemiology Research and Training, Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA

Yan Zhang PhD; Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA 

Study Type
Literature Reviews