Table 1.

Characteristics of studies with validated asthma algorithms

Author, year, country, (period)	Data source, population	Sample/case characteristics	Clinical event	Algorithm	Validation
Manual validation
Xi et al,19 2015 Canada	2 large academic primary care clinics Primary care	398 randomly selected patients 16 years and older	Asthma code COPD code Other respiratory condition code Other condition code	Search algorithms: 1. Asthma in disease registry 2. Billing code 3. Asthma in CPP 4. Asthma medications 5. Asthma in chart notes 6. Asthma in CPP OR billing code 493 7. Asthma in CPP OR billing code 493 (exclusion codes 491,492, and 496) 8. (Asthma in chart notes OR asthma medications) AND billing code 493 9. (Billing code 493 OR medications) AND asthma in chart notes 10. Billing diagnostic code 493 AND asthma in chart notes	Manual review
Engelkes et al,20 2014 the Netherlands	ICPI: Dutch GP EHR Primary care	63,518 potential cases identified 22,699 cases after automated text validation Children aged 5–18	Definite, probable, and doubtful cases of asthma	Combination of ICPI communication codes, clinician codes, drug names and free text generated by a machine-learning algorithm (RIPPER)	22,699 cases manually validated, 14,303 asthma cases found
Afzal et al,21 2013 the Netherlands January 2000–January 2012	ICPI: Dutch GP EHR Primary care	63,618 potential asthma cases identified, children aged 5–18	Definite, probable, and doubtful cases of asthma	Combination of ICPI communication codes, clinician codes, drug names and free text generated by a machine-learning algorithm (RIPPER)	5,032 patients manually validated by clinician
Dexheimer et al,22 2013 United States	1 pediatric ED	15,163 assessed, 1,100 asthma patients all asthma patients (2–18 years) in a 3 month time window	Asthma code	Bayesian network system, previously used on claims data (Sanders)	Pediatric asthma/respiratory distress protocol filled in for identified patients
Wu et al,23 2013, 2014 United States	Children enrolled in the Mayo Clinic sick-child daycare program, Secondary care	112 children younger than 4	ICD-9 codes Natural language	Natural language processing (logic) Natural language processing (machine learning)	Manual review by a clinician
Kozyrskyj et al,24 2009 Canada	SAGE: birth cohort of 16,320 children born in 1995 in Manitoba, Canada Questionnaire in 2002 had 3,598 responses Manitoba’s health care registry records	723 children from the group with completed questionnaires 246 cases, 477 controls	Asthma	Database definitions in health care records	Pediatric allergist diagnosis of asthma
Pacheco et al,25 2009 United States	NUgene Project Genome-wide association study	7,970 people with DNA samples, of which 521 had an asthma diagnosis	Asthma diagnosis	Initial asthma cases algorithm: Asthma diagnosis and asthma medication prescription on ≥1 visit AND no other chronic lung disease diagnosis on ≥2 visits AND no reported smoking history ≥10 years Final asthma cases algorithm: Asthma diagnosis on ≥1 visit AND asthma diagnosis or medication presciption on ≥1 other visit AND no other chronic lung disease diagnosis on ≥2 visits AND no reported smoking history ≥10 years Initial asthma controls algorithm: No diagnosis for any respiratory disease or cancer AND no prescription of any astha/COPD/iimmunodepressant medication AND no reported smoking history ≥10 years Final asthma controls algorithm: ≥2 visits with any asthma diagnosis or prescriptions AND no diagnosis for any respiratory disease or listed cancer AND no prescription of any asthma/COPD/immunodepressant medication AND no reported smoking history ≥10 years	Manual review of 100 cases for both algorithms
Vollmer et al,26 2004 United States July 1998 to January 1999	KPNW, Epic, OSCAR, TOPS ED, secondary care	235,000 patients with continuous health plan eligibility aged 15–55 in January 1999 9,723 asthma patients identified	ICD-9 codes	Health care utilization profiles used for validation study 1. Four “high-probable” categories: → Two or more non-urgent care outpatient contacts for asthma → A single non-urgent contact and one or more ED or inpatient contact for asthma → Any Industrial Medicine visit for asthma → Any asthma visit and either of the two medication dispensing criteria 2. Single non-urgent outpatient visit only 3. Four or more β-agonists, with or without a nebulizer treatment order, but no asthma visits of any kind and no ICS dispensings 4. ED or urgent care visit for asthma and nebulizer treatment order, but no other medication criteria met and no other types of asthma visits 5. Hospitalization for asthma, but neither asthma medication criterion met and no outpatient asthma visits of any kind 6. ED or urgent care visit for asthma, but no other types of asthma visits and no asthma medication criteria met 7. Nebulizer treatment but no asthma visits of any kind and no other medication criteria met 8. All other cases	Criteria used in medical records review Probable asthma • Two or more asthma health care visits • A single visit for asthma with a chart notation indicating a prior history of asthma • A single health care visit for active symptoms of asthma (wheeze, cough, shortness of breath) • A single visit for an asthma exacerbation that responds to therapy, even if no prior history Possible asthma • Patient-reported history of asthma noted in chart, but no evidence of active asthma or treatment for asthma • An uncorroborated ED diagnosis of asthma • Diagnosis of “rule out asthma” with no clear resolution
Donahue et al,27 1997 United States	Harvard Pilgrim Health Care (HPHC); Primary, secondary and emergency care	Random sample of 100 patients	Asthma code	Asthma diagnosis and asthma drug dispensing	Manual review by clinicians
Premaratne et al,28 1997 United Kingdom 1994	Accident and EDs of two hospitals	All asthma patients January–March 1994 1,185 records, of which 209 did not have enough data	String containing “asth*”	String containing “asth*” in the free text records	Affirmation of asthma diagnosis: Final diagnosis of asthma by clinical officer OR symptoms of asthma and (history of asthma or bronchodilators given, with improvement) OR known asthmatic presented with symptoms or for medication Rejection of asthma diagnosis: Clear alternative diagnosis Sufficient other information to reject asthma diagnosis
Comparison to an in dependent database
Engeland et al,29 2009 Norway	MBRN: population-based birth registry, all births in Norway since 1967 (more than 2.3 million) NorPD: all dispensed prescriptions from January 2004 in Norway	108,489 pregnancies, of which 4,549 mothers were recorded as having asthma in MBRN	Asthma	Asthma diagnosis in MBRN	NorPD: asthma medication
Coulter et al,30 1989 United Kingdom	7 general practices in the Oxford community health project 2,199 patients on medication Primary care	2,443 on digital register Bronchodilators, inhaled CS, prophylactic drugs	Asthma diagnosis	Asthma diagnosis on register	Manual review against the list of patients on long-term medication
Comparison to a quiestionnaire
Ward et al,31 2004 United Kingdom 1995–2004	GP Practice with 14,830 patients 83 1 controls, 587 responses Primary care	833 asthma patients, 659 responses 16–55 years on 1 October 1997	Asthma in GP database	One of the following criteria: 1. Read coded “asthma” diagnosis, H33 2. Attendances recorded on the asthma care screen 3. An intervention for asthma recorded 4. A textual entry “asthma” or “wheez” in the medical history 5. Inhaled steroids in the repeat prescriptions 6. Inhaled bronchodilators in the repeat prescriptions 7. Cromolyns in the repeat prescriptions	Questionnaire to determine bronchial hyperreactivity Cases: asthma in database Asthma diagnosis and bronchial hyperreactivity: considered positive Asthma diagnosis without bronchial hyperreactivity: further investigated in GP record Controls: bronchial hyperreactivity but no asthma diagnosis

Abbreviations: CPP, cumulative patient profile; ICPI, integrated primary care information database; GP, general practitioner; EHR, electronic health record; SAGE, Study of Asthma, Genes and the Environment; KPNW, Kaiser Permanente Northwest Division; OSCAR, outside claims database; TOPS, The outpatient pharmacy system; ED, emergency department; ICS, inhaled corticosteroids; MBRN, Medical Birth Registry of Norway; CS, corticosteroids; NorPD, Norwegian Prescription Database.