Impact of lengthening script duration of inhaled corticosteroid (ICS) therapy on clinical outcomes in asthma

Transcription

1 Study protocol Study protocol Impact of lengthening script duration of inhaled corticosteroid (ICS) therapy on clinical outcomes in asthma A historical, observational study to assess whether increasing the number of days covered by prescriptions for inhaled corticosteroids (ICS) impacts clinical outcomes achieved Date: 23/07/2015 Research in Real-Life Pte Ltd 16 Raffles Quay #33-03 Hong Leong Building Singapore Research in Real-Life Ltd 5a Coles Lane Oakington Cambridge CB24 3BA United Kingdom Phone (+44) Fax (+44) Web site

2 Chief Investigator: Professor David Price, Professor of Primary Care Respiratory Medicine and RiRL Director Mobile: Office number: Skype ID: respiratoryresearch Project coordinator: Dr Arjun Jain Research in Real-Life Ltd 5a Coles Lane, Oakington, Cambridgeshire CB24 3BA, UK Direct number: (+44) Study sponsor: Teva 2

3 TITLE Impact of lengthening script duration of inhaled corticosteroid (ICS) therapy on clinical outcomes in asthma Subtitle A historical, observational study to assess whether increasing the number of days covered by prescriptions for inhaled corticosteroids (ICS) impacts clinical outcomes achieved Protocol version number 2 Medicinal product Product code Marketing authorisation holder Marketing authorisation number Study aims and objectives Country of study Author NA NA NA NA The aim of this study is to determine whether increasing the script duration of inhaled corticosteroid (ICS) therapy leads to improved clinical outcomes for patients with asthma. United Kingdom RiRL UK Ltd 5a Coles Lane Oakington Cambridge CB24 3BA United Kingdom 3

4 Contents 1.0 Background Study aims and objectives Data source Study design Treatment arms Study population Inclusion criteria Exclusion criteria Analyses Software Significance testing Descriptive analyses Patient characterization Outcomes Definitions Body Mass Index (BMI) Charlson Comorbidity Index (CCI) Medication Possession Ratio (MPR) Percentage predicted peak flow readings (PEF) Exacerbations (ATS/ERS Position Statement) SABA usage Drug codes Analyses Software used Significance testing Summary statistics Plots Regulatory and ethical compliance Data dissemination Advisory group Research team Timelines References

5 1.0 Background Maximising patient adherence, defined as the extent to which the patient s behaviour matches agreed recommendations from the prescriber [1], is key to achieving the full clinical benefit of treatment. Unfortunately, poor adherence remains a major issue in the management of many chronic conditions including asthma [2]. Asthma treatments are particularly susceptible to adherence issues due to their long duration, involvement of multiple medications and periods of symptom remission [3]. Research indicates poor adherence is associated with sub-optimal outcomes, with data obtained from the Optimum Patient Care Research Database (OPCRD) indicating a significant association between patient-reported poor adherence and decreased asthma control [4]. Devising and implementing methods that increase adherence medication is therefore a potential method of improving asthma outcomes. One suggested adherence strategy is increasing the period of time covered by treatment prescription. Previous Research in Real Life RiRL) analysis supports this strategy to be implemented in clinical practice [5], with adherence to extrafine hydrofluoroalkane beclometasone dipropionate (inhaler lasting up to 100 days) superior to fluticasone propionate (inhaler lasting up to 60 days). Another US-based study showed that adherence to three long-term medications (cholesterol-lowering, anti-hypertensive or anti-diabetic) was superior among patients prescribed 90 day compared with 30 day packs [6]. 2.0 Study aims and objectives The current study aims at assessing the relation between prescription patterns (unchanged vs increased script duration) and (i) cumulative time covered (MPR, medication possession ratio) and (ii) clinical outcomes over a one-year outcome period, in patients receiving ICS with any script duration during the baseline year. 5

6 3.0 Data source Optimum patient care research database This study used the Optimum Patient Care Research Database (OPCRD) which comprises anonymous data extracted from practices in order to perform reviews of their chronic respiratory services. Two types of anonymised patient data are typically collected: (1) Routine clinical data OPC software interfaces with primary care practice management systems and extracts disease coding and prescribing information. (2) Questionnaires Patients identified as recipients of the respiratory service under review are invited to complete validated disease assessment questionnaires to better understand their current health status (and/or possible reasons for sub-optimal status). Anonymised questionnaires are assigned a unique code to aid matching routine data to questionnaire results. The OPCRD has been approved by Trent Multi Centre Research Ethics Committee for clinical research use. The anonymous, longitudinal patient data offers a high-quality data source for use in clinical, epidemiological and pharmaceutical research. It enables research to be carried out across a broad-range of respiratory areas. Clinical practice research datalink The study also used the UK s Clinical Practice Research Datalink (CPRD), a large computerised primary care database. The CPRD contains de-identified, longitudinal data from 3.6 million active medical records and 13 million records overall from more than 450 subscribing practices throughout the UK. A practice-based quality marker, the up-to-standard date, is generated by the CPRD for each subscribing practice, and data subsequent to the practice up-to-standard date are considered to be acceptable, research quality, prospectively recorded data. The CPRD is well-validated and used frequently for medical and health research. 6

7 4.0 Study design This is a historical cohort database study consisting of a baseline and outcome period. The baseline period is the year before repeat prescription of ICS therapy (index date). At the repeat prescription date (index date), selected patients will meet one of the mutually exclusive following conditions: - ICS prescription with the same ICS strength at the same daily dose o either with the same prescription duration (fully unchanged prescription) o OR with an increased duration, - prescription of the same daily ICS dose but with a higher strength ICS with lower number of doses per day, e.g. FP-250 one dose twice a day instead of FP-125 two doses twice a day; such that the prescription lasted longer with the same daily dose of ICS. The outcome period is the year following repeat prescription date (index date). Figure 1: Study diagram 7

8 4.1 Treatment arms Same script duration cohort: Patients that are issued script with same dose, device and drug, with same number of inhalers on script as baseline. Increased script duration cohort: 1) Patients that are issued a script with multiple number of inhalers with same ICS drug and dose as baseline 2) Patients that move to higher dose of the same ICS drug and device as baseline with lower frequency of use per day (such that inhaler lasts longer) 5.0 Study population 5.1 Inclusion criteria Patients have to meet the following inclusion criteria: Aged: 5 80 years; Evidence of current asthma treatment, defined as 2 asthma prescriptions during the baseline year, 1 of which must have been for any ICS Continuation of asthma therapy, defined as: 2 asthma prescriptions during the outcome year (i.e. 1 in addition to that prescribed at repeat prescription date); Two years of continuous practice data comprising 1 year baseline data and 1 year of outcome data (up-to-standard data for CPRD patients). 5.2 Exclusion criteria The following patients will be excluded from the analysis: Patients with any chronic respiratory disease, except asthma, at any time; Patients receiving maintenance oral steroids during baseline year; Smokers and ex-smokers aged over 60 years 8

9 6.0 Analyses 6.1 Software All analysis will be carried out using IBM SPSS Statistics version 22 [12], SAS version 9.3 [13] and Microsoft Office EXCEL Significance testing Statistically significant results will be defined as p < 0.05 and trends as 0.05 p < Descriptive analyses 7.1 Patient characterization Patients will be characterised according to the following: Age Sex BMI Percent predicted FEV1 Smoking status Co-morbidities Lower respiratory tract infections Hospital admissions Exacerbations (ATS/ERS Joint Taskforce definition) SABA usage Medication possession ratio (MPR) Vaccination rate Non-asthma consultation rates Annual reviews in past 12 months Deprivation index 9

10 7.2 Outcomes The primary outcome is: 1. Severe exacerbation rates (ATS/ERS Joint Position Statement). Defined as an occurrence * of the following: asthma-related hospital admissions or A&E attendance for asthma or an acute course of oral steroids prescribed for asthma exacerbations. The secondary outcomes are: 1. Risk domain asthma control (RDAC) Defined as absence of asthma related hospital admission, A&E attendance, outpatient department attendance, acute use of oral steroids and antibiotics prescribed with evidence of a respiratory consultation. * Where 1 oral steroid course / hospitalisation occurs within 2 weeks of each other, these events will be considered to be the result of the same exacerbation (and will only be counted once). Asthma-related hospitalisations: consists of either a definite asthma emergency attendance or a definite asthma hospital admission; OR a generic hospitalisation read code which has been recorded on the same day as a Lower Respiratory Consultation (see below - excluding where the only lower respiratory code recorded on that day was for a lung function test). Acute oral steroid use associated with asthma exacerbation treatment will be defined as: all courses that are definitely not maintenance therapy, and/or all courses where dosing instructions suggest exacerbation treatment (e.g. 6,5,4,3,2,1 reducing, or 30mg as directed), and/or all courses with no dosing instructions, but unlikely to be maintenance therapy due to prescription strength or frequency of prescriptions. where maintenance therapy is defined as: daily dosing instructions of <=10mg Prednisolone or prescriptions for 1mg or 2.5mg Prednisolone tablets where daily dosing instructions are not available. e Where 1 oral steroid course / hospitalisation / antibiotics prescription occur within 2 weeks of each other, these events will be considered to be the result of the same exacerbation (and will only be counted once). Lower Respiratory Consultations consist of the following:1.lower respiratory Read codes (including Asthma, COPD and LRTI Read codes); 2. Asthma/COPD review codes excluding any monitoring letter codes; 3. Lung function and/or asthma monitoring Note: where 1 oral steroid course / hospitalisation / antibiotics prescription occur within 2 weeks of each other, these events will be considered to be the result of the same exacerbation (and will only be counted once). 10

11 2. Overall asthma control Overall asthma control achieved if risk domain asthma control achieved and average daily dose of: 200µg salbutamol / 500µg terbutaline. 3. Treatment Stability Defined as achieved RDAC and no additional therapy (including increased dose of ICS and/or use of additional therapy including long-acting bronchodilator (LABA), theophylline, leukotreine receptor antagonists (LTRAs). 4. Short-acting beta2 agonist (SABA) use Defined as mean daily SABA dose; calculated as total SABA dose prescribed over the outcome year (based on prescription refills) divided by 365 days. 11

12 8.0 Definitions 8.1 Body Mass Index (BMI) The Body Mass Index is a representative measure of body weight based on the weight and height of the subject. It is defined as the weight (in kg) divided by the square of the height (in m) and is measured in kg/m 2. BMI categories: Underweight <18.5 kg/m 2 Normal 18.5 kg/m kg/m 2 Overweight 25 kg/m kg/m 2 Obese 30 kg/m Charlson Comorbidity Index (CCI) The Charlson Comorbidity Index was developed in the US in 1987 as a method of classifying prognostic comorbidity in longitudinal studies [7]. It predicts the one-year mortality for a patient who may have a range of comorbid conditions such as heart disease, AIDS or cancer. Each condition is assigned a weight depending on the risk of dying associated with the condition; scores are then summed to give a total score predicting mortality. The weights were revised and up-dated (for example, mortality due to HIV has fallen) by Dr. Foster Intelligence (DFI) in their HSMR Methodology documentation [8] and calibrated using UK data (due to differences in coding practice and hospital patient population characteristics from the US), using ICD-10 codes. As a result: DFI have expanded the coding definition of some conditions; Only secondary diagnoses (DIAG02 DIAG14) are now considered; There is greater variation in weights between conditions and the Charlson Index (the sum of the weights) can be treated as a continuous variable (limited to the range 0 50) for the purposes of risk adjustment. 12

13 The weights, codes and conditions used in this study are summarised in the following table: Condition Condition Name ICD 10 codes New weight 1 Acute myocardial I21, I22, I23, I252, I258 5 infarction 2 Cerebral vascular G450, G451, G452, G454, 11 accident G458, G459, G46, I60 I69 3 Congestive heart failure I Connective tissue M05, M060, M063, M069, 4 disorder M32, M332, M34, M353 5 Dementia F00, F01, F02, F03, F Diabetes E101, E105, E106, E108, 3 E109, E111, E115, E116, E118, E119, E131, E131, E136, E138, E139, E141, E145, E146, E148, E149 7 Liver disease K702, K703, K717, K73, K Peptic ulcer K25, K26, K27, K Peripheral vascular I71, I739, I790, R02, Z958, 6 disease Z Pulmonary disease J40 J47, J60 J Cancer C00 C76, C80 C Diabetes complications E102, E103, E104, E107, 1 E112, E113, E114, E117, E132, E133, E134, E137, E142, E143, E144, E Paraplegia G041, G81, G820, G821, 1 G Renal disease I12, I13, N01, N03, N N056, N072 N074, N18, N19, N25 15 Metastatic cancer C77, C78, C Severe liver disease K721, K729, K766, K HIV B20, B21, B22, B23, B24 2 Table 1: Co-morbid conditions and scores used in the Charlson Co-morbidity Index (CCI) 13

14 8.3 Medication Possession Ratio (MPR) The Medication Possession Ratio (MPR) for ICS is defined using the following formula [9]. Medication Possession Ratio = 100% The numerator is truncated at 365 if greater than 365. The MPR is a measure of adherence to therapy and a cut-off of 80% has previously been used [10, 11] in categorising asthma patients as adherent or non-adherent; the MPR has therefore been categorised as a dichotomous variable: < 80% and 80% for this analysis. 8.4 Percentage predicted peak flow readings (PEF) The percent predicted PEF values have been calculated using the following predicted values: For male and female adults aged over 18 years of age, Roberts equations [12] have been used: Predicted PEF (litres/sec) for males = (5.317 * (height in metres)) - (0.062* (age in yrs)) Predicted PEF (litres/sec) for females = (4.087 * (height in metres)) - (0.050* (age in yrs)) For paediatrics aged 4-18 years and > 1.1m tall, Rosenthal s equations [13] have been used: Predicted PEF (litres/sec) for boys 4-18 years < cm tall = (0.073 * (height in cm)) Predicted PEF (litres/sec) for boys 4-18 years >= cm tall = (0.125 * (height in cm)) Predicted PEF (litres/sec) for girls 4-18 years < cm tall = (0.079 * (height in cm)) Predicted PEF (litres/sec) for girls 4-18 years >= cm tall = (0.064 * (height in cm))

15 For paediatrics aged 4-18 years and 1.1m tall, Robinson s equations (from Cotes [14]) have been used: Predicted PEF (litres/sec) = 4.93 * (height in meters) Exacerbations (ATS/ERS Position Statement) An exacerbation is defined as an occurrence * of the following: 1. Asthma-related : a. Hospital admissions OR b. A&E attendance; OR 2. An acute course of oral steroids prescribed for asthma exacerbations 8.6 SABA usage Average daily SABA dosage during outcome year, calculated as average number of puffs per day over the year multiplied by strength (in µg); i.e. strength and categorised as appropriate to the data. SABA Dosage (average daily dose during outcome year) categorised as: 0µg, >0-100µg µg, µg and >401+ µg. * Where 1 oral steroid course / hospitalisation occurs within 2 weeks of each other, these events will be considered to be the result of the same exacerbation (and will only be counted once). Asthma-Related Hospitalisations: consists of either a definite asthma emergency attendance or a definite asthma hospital admission; OR a generic hospitalisation Read code which has been recorded on the same day as a Lower Respiratory Consultation (see below - excluding where the only lower respiratory code recorded on that day was for a lung function test) Acute oral steroid use associated with asthma exacerbation treatment will be defined as: all courses that are definitely not maintenance therapy (defined as: daily dosing instructions of 10mg Prednisolone or prescriptions for 1mg or 2.5mg Prednisolone tablets where daily dosing instructions are not available), AND/OR all courses where dosing instructions suggest exacerbation treatment (e.g. 6,5,4,3,2,1 reducing, or 30mg as directed) AND/OR all courses with no dosing instructions, but unlikely to be maintenance therapy due to prescription strength or frequency of prescriptions 15

16 9.0 Drug codes The drug codes used in this analysis are detailed in the British National Formulary [15] and summarised in Table 2. ANALYSIS DRUGS BNF DRUG CLASS GERD drugs Proton-pump inhibitors Cardiac drugs Beta-blockers NSAIDS Paracetamol SABA ICS LABA LTRA Theophylline Antibiotics Oral steroids Allergy Positive Inotropic drugs Diuretics Anti-arrhythmic drugs Hypertension and heart failure Nitrates, calcium-channel blockers and other antianginal drugs Beta-adrenoceptor blocking drugs Beta-adrenoceptor blocking drugs Non-steroidal anti-inflammatory drugs Paracetamol Short acting beta-agonists Corticosteroids (inhaled for respiratory conditions) Long acting beta-agonists Leukotriene receptor antagonists Theophylline Aminoglycosides Antileprotic drugs Antipseudomonal penicillins Antituberculous drugs Broad-spectrum penicillins Clindamycin Macrolides Mecillinams Metronidazole and tinidzole Penicillinase-resistant penicillins Quinolones Some other antibacterials Tetracyclines Prednisolone Antihistamines Nasal steroids Topical steroids for the skin Nasal cromones Table 2: Summary of BNF drug class 16

17 10.0 Analyses 10.1 Software used All analyses will be carried out using IBM SPSS Statistics version 22 [16], SAS version 9.3 [17] and Microsoft Office Excel Significance testing Statistically significant results will be defined as p < 0.05 and trends as 0.05 p < Summary statistics Summary statistics will be produced for all baseline and outcome variables. For variables measured on the interval or ratio scale, these included: Sample size (n) Percentage non-missing Mean Variance / Standard Deviation Range (Minimum / Maximum) Median Inter-quartile Range (25 th and 75 th percentiles) For categorical variables, the summary statistics included: Sample size (n) Range (if applicable) Count and Percentage by category (distribution). 17

18 10.4 Plots Plots will be produced for all baseline and outcome variables, as a complete dataset and by treatment group. For variables measured on the interval or ratio scale, these will include Frequency plots Box plots Frequency plots will be used to illustrate the distribution of the variable and whether categorisation would be necessary (for example, if heavily skewed). Box plots will be used to illustrate the location and spread of the variable and identify potential outliers. Plots by treatment group will be used to highlight baseline and outcome differences between treatment groups. For categorical variables, mosaic plots will be used to illustrate distributions and highlight baseline and outcome differences between treatment groups 11.0 Regulatory and ethical compliance This study was designed and shall be implemented and reported in accordance with the criteria of the European Network Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) study and follows the ENCePP Code of Conduct (EMA 2014). Once a final version of the protocol has been agreed and reviewed by the advisory group, this study will be registered with Data dissemination Initial results will be presented in poster and/or oral format at appropriate thoracic conferences. At least one manuscript containing more detailed results and methodology will be submitted to a journal specialising in respiratory medicine. Submission for publications will be made as soon as the analyses are completed and the results are verified. 18

19 13.0 Advisory group Nicolas Roche, Dirkje Postma, Richard Martin, Eliot Israel, Gene Colice, Rupert Jones 14.0 Research team Research Organisation: Research in Real-Life (RiRL) Ltd Chief Investigator: David Price, Professor of Primary Care Respiratory Medicine and RiRL Director Mobile: Office number: Skype ID: respiratoryresearch Other RiRL team members: Commercial and Compliance Director: Catherine Hutton Researcher: Dr Arjun Jain Senior statistician: Vicky Thomas Senior data analyst: Derek Skinner Study sponsor: Teva 15.0 Timelines New timelines Due date Task Status Protocol 27 th July Complete Data extraction 4 weeks* On-going Descriptive analysis 4 weeks* Upcoming Report / Slide set 4 weeks* Upcoming Manuscript TBC Upcoming *Time required following confirmation of the revised protocol by the SC Delay in data extraction due to unforeseen circumstances (approval required for new data analyst) 19

20 16.0 References [1] * Horne R, Weinman J, Barber N, Elliott R, Morgan M. Concordance, adherence and compliance in medicine-taking, Report for the National Co-ordinating Centre for NHS Service Delivery and Organisation R & D 2005 [2] Rand C, Wise R. Measuring Adherence to Asthma Medication Regimens. American Journal of Respiratory and Critical Care Medicine, Vol. 149, Supplement: Asthma Outcome Measures (1994), pp. S69-S76 [3] Bender BG, Bender SE. Patient-identified barriers to asthma treatment adherence: responses to interviews, focus groups, and questionnaires. Immunol Allergy Clin North Am. 2005;25(1):107 [4] Clatworthya J, Price D, Ryan D, Haughneyb J, Horne R. The value of self-report assessment of adherence, rhinitis and smoking in relation to asthma control. Primary Care Respiratory Journal (2009); 18(4): [5] Research in Real Life. Qvar vs. FP in smoking asthmatics - sub-analysis [6] Hermes M, Gleason PP, Starner CI. Adherence to chronic medication therapy associated with 90-day supplies compared with 30-day supplies [abstract]. J MANAG CARE PHARM. 2010;16: [7] Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and Validation. J Chronic Dis 1987;40: [8] Doctor Foster Health HSMR mortality indicators. Available online at: methodology Nov 2010.pdf [9] Ivanova JI, Birnbaum HG, Hsieh M, et al. Adherence to Inhaled Corticosteroid Use and Local Adverse Events in Persistent Asthma. Am J Manag Care. 2008;14(12): [10] Erickson SR, Coombs JH, Kirking DM, Azimi AR. Compliance from self-reported versus pharmacy claims data with metered-dose inhalers. Ann Pharmacother. 2001;5(9): [11] Brooks MC, Richards JM, Kohler CL, et al. Assessing adherence to asthma medication and inhaler regimens: a psychometric analysis of adult self-reported scales. Med Care. 1994;32(3): [12] Roberts CM MacRae KD et al. Reference values and prediction equations for normal lung function in a non-smoking white urban population. Thorax. 1991; 46: [13] Rosenthal M Cramer D et al. Lung function in white children aged 4 to 19 years. II. Single breath analysis and plethysmography. Thorax. 1993; 48: [14] Cotes JE. Lung Function. Assessment and Application in Medicine. Fifth Edition. Blackwell Scientific Publications pages 460 and

21 [15] British Medical Association and the Royal Pharmaceutical Society of Great Britain. British National Formulary. Version 56. London; BNF Group; [16] IBM SPSS Statistics Statistics family. Available online at: 01.ibm.com/software/uk/analytics/spss/ [17] SAS Institute Inc Statistical Analysis with SAS/STAT Software. Available online at: 21