Associations of medication regimen complexity with medication adherence and clinical outcomes in patients with chronic obstructive pulmonary disease: a prospective study

Abstract

Background:

High medication burdens are common in patients with chronic obstructive pulmonary disease (COPD). This study aimed to explore the associations of medication regimen complexity index (MRCI) with medication adherence and clinical outcomes among patients with acute exacerbations of COPD (AECOPD) after hospital discharge.

Methods:

Data were obtained from a nationwide cohort study of inpatients with AECOPD in China. MRCI scores were calculated using the medication list 30 days after discharge and separated into COPD-specific and non-COPD MRCI scores. Medication adherence was measured by the withdrawal rate of COPD or inhaled long-acting bronchodilators 6 months after discharge. Clinical outcomes included re-exacerbations and COPD-related readmissions during the 30-day to 6-month follow-up period. The associations of MRCI with medication withdrawal and clinical outcomes were evaluated using univariate and multivariate logistic regressions. Potential covariates included sociodemographic factors, year of COPD diagnosis, post-bronchodilator percentage predicted forced expiratory volume in 1 s, mMRC score, CAT score, and comorbidities.

Results:

Among the 2853 patients included, the median total MRCI score was 7 [interquartile range (IQR), 7−13]. A high MRCI score (>7) was presented in 1316 patients (46.1%). Of the MRCI score, 91% were COPD specific. The withdrawal rates of the COPD and inhaled long-acting bronchodilators were 24.2% and 24.4%, respectively. Re-exacerbation and COPD-related readmission rates were 10.2% and 7.5%, respectively. After adjusting for covariates, patients with high total MRCI scores were less likely to discontinue COPD drugs [odds ratio (OR), 0.62; 95% confidence interval (CI), 0.52−0.74] and inhaled long-acting bronchodilators (OR, 0.68; 95%CI, 0.57−0.81); conversely, these patients were more likely to experience re-exacerbation (OR, 1.64; 95% CI, 1.27−2.11) and readmission (OR, 1.57; 95% CI, 1.17−2.10).

Conclusion:

MRCI scores were relatively low among post-hospitalized patients with AECOPD in China. Higher MRCI scores were positively associated with adherence to COPD or inhaled medications, and risk of re-exacerbation and readmission.

Registration:

ClinicalTrials.gov identifier: NCT02657525.

Keywords

adherence chronic obstructive pulmonary disease exacerbation medication regimen complexity pharmacotherapy readmission

Introduction

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death worldwide and the seventh leading cause of disability-adjusted life years.¹ Most death and burden originate from acute exacerbations of COPD (AECOPD).² Pharmacotherapy is one of the principal approaches used in the management of COPD.³ Patients with COPD are often prescribed multiple medications for the control of their respiratory disease and comorbidities.⁴ As recorded in the previous study, the mean ± standard deviation (SD) number of medications per patient was 5.0 ± 2.6 at admission due to AECOPD.⁵ A total of 224 (56.3%) patients presented polypharmacy,⁵ defined as chronic concurrent use of five or more drugs for more than 3 months before admission.⁶ The number of comorbidities per participant with COPD was reported ranging from 0 to 11 with a median of 5.⁷ Consistent respiratory symptoms and concomitant chronic diseases (multimorbidity) in patients with COPD can lead to complex medication regimens, possibly resulting in poor adherence⁸ and disease control,⁹ and adverse drug events.¹⁰

The relationship between medication adherence and COPD-related clinical outcomes with medication burden, except for medication counts, in patients with AECOPD is largely unknown. A 65-item medication regimen complexity index (MRCI) is a reliable and valid tool for quantifying medication regimen complexity, including three sections: dosage form, dosage frequency, and administration instruction. It was originally developed and validated in people with COPD.¹¹ As noted to be superior to simple medication counts, MRCI scores could discriminate between regimens with an equal number of medications.¹¹ MRCI has been widely applied to evaluate the medication complexity in various diseases.^12–15 Few previous investigations referred to the correlation of MRCI and adherence and/or clinical outcomes among stable COPD patients; however, none of them focused on inpatients with AECOPD.^10,16,17

The study aimed to explore the associations of MRCI with medication adherence and clinical outcomes among patients with AECOPD after hospital discharge based on nationwide data from China. We are looking for useful insight into the impact of medication burden on AECOPD patients using MRCI to offer the basis for further interventions to promote the simplicity and effectiveness of the medication regime.

Methods

Study design

Data used for these analyses were obtained from the Acute Exacerbations of Chronic Obstructive Pulmonary Disease Inpatient Registry study (ACURE study), an ongoing nationwide multicenter, prospective, observational cohort study of hospitalized AECOPD patients in China (ClinicalTrials.gov identifier: NCT02657525). We collected the data from 1 September 2017 to 5 November 2021 from 173 tertiary or second-class hospitals distributed in 29 provinces in China. The details of the ACURE study have previously been described.¹⁸

The eligibility criteria for the ACURE study were: (1) aged⩾18 years, (2) hospitalization for AECOPD, and (3) signed consent for participation. Patients who lost to follow-up 30 days and 6 months after discharge and those who did not have medication records were excluded. Those patients with a self-reported physician-diagnosed history of COPD were included in our study, although they did not finish spirometry due to their severe condition.¹⁹ Patients without a history of COPD were confirmed by spirometry when a post-bronchodilator ratio forced expiratory volume in 1 s and forced vital capacity (FEV₁/FVC) <0.70 during hospitalization and 30 days after hospitalization.³

Medicine regimen complexity and medication count

The complexity of medication regimens was quantified according to MRCI and the number of medications. MRCI scores were the sum of three sections, including dosage form, dosing frequency, and additional direction. Total MRCI scores for prescription and nonprescription medicines were calculated based on the self-reported home care medication list at 30 days post-discharge rather than the discharge medication list.¹⁰ To be more specific, we divided MRCI scores into COPD-specific, non-COPD parts, and inhaled long-acting bronchodilators.⁷ The total number of COPD-specific, non-COPD parts, and inhaled long-acting bronchodilators were also recorded. COPD medications included pharmacological therapies for COPD as recommended in the guideline,³ such as short-acting/long-acting beta₂-agonists, short-acting/long-acting muscarinic antagonists, inhaled/oral corticosteroids, combination bronchodilator therapy (beta₂-agonists+muscarinic antagonists), triple therapy (bronchodilators plus inhaled corticosteroid), methylxanthines, phosphodiesterase-4 inhibitors, mucolytics, and antioxidant agents. Inhaled long-acting bronchodilators include the single or combination long-acting bronchodilators.³ MRCI scores were calculated separately by two trained pharmacists and the scores were checked by one of the authors to ensure consistency. After rechecking, the values were used as the final MRCI scores.

Outcomes

All participants were expected to complete follow-up 30 days (±2 days) and 6 months (±2 days) after discharge. Any event of interest, date of event occurrence, and other relevant variables aforementioned were recorded. The outcomes of interest were medication adherence and clinical outcome 6 months after discharge. We used the withdrawal rate of COPD medications and inhaled long-acting bronchodilators to assess medication adherence since the discontinuation rate in our study population exceeded 24%.²⁰ We assessed clinical outcomes by re-exacerbations and readmissions due to COPD within a 30-day to 6-month follow-up period after discharge. An exacerbation was defined as an acute episode of intensified symptoms that required additional therapy.³ Readmissions were the severe exacerbations of COPD that required hospitalization after discharge.³

Covariates and definition

The potential covariates for multivariate regression models were taken into account if they were clinically relevant and available, which included sociodemographic factors, year of diagnosis with COPD, post-bronchodilator percentage predicted FEV₁ (FEV₁%pred), baseline Modified British Medical Research Council Questionnaire (mMRC) scale, baseline COPD Assessment Test (CAT) score, and related complications and comorbidities including respiratory, cardiovascular, endocrine and metabolic, digestive, and cerebrovascular diseases.

Because no previous MRCI benchmarks and validated cut-off values were available, we stratified MRCI by median score (high MRCI > 7, low MRCI ⩽ 7).¹⁵ We categorized age by 65 years; body mass index (BMI) into underweight (<18.5 kg/m²), healthy weight (18.5−23.9 kg/m²), overweight (24.0−27.9 kg/m²), and obesity (⩾28.0 kg/m²); year of diagnosis by ⩽1 year, >1 year and ⩽5 years, >5 years and ⩽10 years, and > 10 years; and post-bronchodilator FEV₁%pred by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages (FEV₁%pred ⩾ 80%, 50−79%, 30−49%, <30% represent GOLD 1−4 stage).³ CAT and mMRC are validated questionnaires to evaluate the severity of COPD symptoms and health status.^21,22 Patients with CAT scores ⩾10 and mMRC⩾2 were considered symptomatic.²³

Sensitivity analysis

For a sensitivity analysis, we conducted multivariable analysis for the associations of COPD-specific MRCI scores with withdrawal of medications and disease control, respectively. We also stratified COPD-specific MRCI score by median score (high MRCI > 7, low MRCI ⩽ 7).

Missing data

In the final cohort, there were 1.33% (38/2853), 0.91% (26/2853), and 22% (629/2853) missing values of mMRC, CAT score, and post-bronchodilator FEV₁%pred at baseline, respectively. Missing values were imputed using the multiple imputation method.

Statistical analysis

Continuous variables were presented as median (interquartile range, IQR) and compared between subgroups using Mann–Whitney U-test. Categorical variables were presented as number (percentage, %) and compared using chi-squared test. Univariate and multivariate logistic regressions were used to evaluate the associated factors. Factors with a p-value < 0.10 in the univariate analysis, as well as those with clinical relevance, were included in the multivariate regression models. Odds ratio (OR) and 95% confidence intervals (CI) were calculated to assess the association. All analyses were performed using the SPSS Statistics Software version 24 (IBM Corp, Armonk, NY, USA). A two-sided p-value < 0.05 was considered statistically significant.

Results

Of the 6949 patients eligible in the ACURE database, 2853 patients were finally included in the study. The flow of the study is shown in Figure 1. Of all patients included, 2286 patients (80.1%) were male with a median age of 69 years, as illustrated in Table 1. Over 46.1% of participants (n = 1316) had high total MRCI scores. The median values of CAT and mMRC scores were 12 (IQR, 8−16) and 1.8 (IQR, 1−2), respectively. Most of the patients (66.7%) were diagnosed with GOLD stage 3−4. Among patients with high total MRCI scores, the proportion of male patients, patients who graduated from middle school and college or above, and patients with 2−10 years of COPD history was higher than those with low total MRCI scores. The proportions of severe and symptomatic patients with GOLD stage 4, high mMRC (⩾2), and CAT scores (⩾10) were significantly higher in the high MRCI group than in the low MRCI group. More than half (54.7%) of the participants had non-respiratory diseases. The proportion of patients with non-respiratory comorbid conditions was higher in the high MRCI group than in the low MRCI group. Only coexisting diseases with statistical significance are listed in Table 1.

Figure 1.

Flowchart of patients with AECOPD through the study.

Table 1.

Baseline characteristics of study population.

Characteristics	Total (n = 2853)	High total MRCI^a (>7, n = 1316)	Low total MRCI^a (⩽7, n = 1537)	p-Value
Age, years, median (IQR)	69 (63.75)	69 (63.75)	69 (63.75)	0.764
Age category, n (%)				0.930
⩾65 years	1973 (69.2)	909 (69.1)	1064 (69.2)
<65 years	880 (30.8)	407 (30.9)	473 (30.8)
Sex, n (%)				0.021
Male	2286 (80.1)	1079 (82.0)	1207 (78.5)
Female	567 (19.9)	237 (18.0)	330 (21.5)
BMI, kg/m², median (IQR)	22.0 (19.5, 24.4)	22.0 (19.5, 24.6)	21.9 (19.5, 24.2)	0.267
BMI category, kg/m², n (%)				0.597
<18.5	481 (16.9)	213 (16.2)	268 (17.4)
18.5–23.9	1526 (53.5)	698 (53.0)	828 (53.9)
24–27.9	666 (23.3)	317 (24.1)	349 (22.7)
⩾28	180 (6.3)	88 (6.7)	92 (6.0)
Smoking status, n (%)				<0.001
Current smoking	678 (23.8)	298 (22.6)	380 (24.7)
Ex-smoking	1322 (46.3)	664 (50.5)	658 (42.8)
Non-smoking	853 (29.9)	354 (26.9)	853 (32.5)
Education level, n (%)				0.024
Primary school or lower	1383 (48.4)	602 (45.7)	781 (50.8)
Middle school	910 (31.9)	445 (33.8)	465 (30.3)
High or polytechnic school	421 (14.8)	195 (14.8)	226 (14.7)
College or higher	139 (4.9)	74 (5.6)	65 (4.2)
Years of diagnosis, year, n (%)				<0.001
⩽1 year	779 (27.3)	305 (23.2)	474 (30.8)
>1 year and ⩽5 years	882 (30.9)	434 (33.0)	448 (29.1)
>5 years and ⩽10 years	577 (20.2)	309 (23.5)	268 (17.4)
>10 years	615 (21.6)	268 (20.4)	347 (22.6)
Post-bronchodilator FEV₁%pred at baseline, median (IQR)	42.7 (32.7–54.5)	41.9 (32.0–53.3)	43.4 (33.4–55.7)	0.005
GOLD stage at baseline^b, n (%)				0.026
1 (FEV₁%pred⩾80%)	139 (4.9)	51 (3.9)	88 (5.7)
2 (50%⩽FEV₁%pred < 80%)	811 (28.4)	368 (28.0)	443 (28.8)
3 (30%⩽FEV₁%pred < 50%)	1368 (47.9)	627 (47.6)	741 (48.2)
4 (FEV₁%pred < 30%)	535 (18.8)	270 (20.5)	265 (17.2)
CAT score at baseline, median (IQR)	20 (15.25)	21 (16.25)	19 (15.24)	<0.001
CAT category, n (%)				0.001
⩾10	2638 (92.5)	1241 (94.3)	1397 (90.9)
<10	215 (7.5)	75 (5.7)	140 (9.1)
mMRC score at baseline, median (IQR)	3 (2.3)	3 (2.3)	3 (2.3)	<0.001
mMRC category, n (%)				0.023
⩾2	2429 (85.1)	1142 (86.8)	1287 (83.7)
<2	424 (14.9)	174 (13.2)	250 (16.3)
Bronchiectasis, n (%)	382 (13.4)	205 (15.6)	177 (11.5)	0.001
Pulmonary heart disease, n (%)	612 (21.5)	316 (24.0)	296 (19.3)	0.002
Sleep apnea syndrome, n (%)	46 (1.6)	31 (2.4)	15 (1.0)	0.004
Pulmonary tuberculosis, n (%)	37 (1.3)	23 (1.7)	14 (0.9)	0.049
Non-respiratory comorbidities, n (%)	1561 (54.7)	785 (59.7)	776 (50.5)	<0.001
Hypertension, n (%)	1010 (35.4)	514 (39.1)	496 (32.3)	<0.001
Coronary heart disease, n (%)	494 (17.3)	250 (19.0)	244 (15.9)	0.028
Arrhythmias, n (%)	172 (6.0)	101 (7.7)	71 (4.6)	0.001
Heart failure, n (%)	197 (6.9)	109 (8.3)	88 (5.7)	0.007
Peripheral vascular disease, n (%)	97 (3.4)	63 (4.8)	34 (2.2)	<0.001
Gastresophageal reflux, n (%)	67 (2.3)	48 (3.6)	19 (1.2)	<0.001

BMI, Body Mass Index; CAT score, COPD Assessment Test score; FEV₁%pred, percentage predicted FEV₁; GOLD, Global Initiative for Chronic Obstructive Lung Disease; IQR, interquartile range; MRCI, medication regimen complexity index; mMRC, Modified British Medical Research Council.

MRCI scores were calculated according to the medications 30 days after discharge.

GOLD stage were classified according to the post-bronchodilator FEV₁%pred.

The composition of MRCI scores is shown in Figure 2. Of the total MRCI scores, 91% were COPD-specific, and only 9% were non-COPD MRCI scores. The proportions of three sections of MRCI scores showed variance. In total MRCI scores, the proportion of dosage form, dosage frequency, and additional direction was 40%, 31%, and 29%, respectively. The dosage form sub-score was the biggest contributor to the COPD-specific MRCI scores, accounting for 41%. In contrast, the greatest contributor to the non-COPD MRCI scores was the dosage frequency (43%).

Figure 2.

Donut chart showing the percentages of dosage form, dose frequency, and additional direction to the total MRCI scores of COPD, non-COPD, and all medications.

The median of the total MRCI scores and numbers for all medications was 7 (IQR, 7−13) and 1 (IQR, 1−2), respectively (Table 2). Of the patients enrolled, 689 (24.2%) discontinued COPD medications and 697 (24.4%) discontinued inhaled long-acting bronchodilators during the 30-day to 6-month follow-up period (Table 2). Among patients who discontinued COPD or inhaled medications, MRCI scores and medication numbers were significantly lower than those with adherence to medications. In total, 2502 patients (87.7%) did not have a record of non-COPD medications.

Table 2.

Comparing MRCI scores and medication numbers in patients with and without medication discontinuation.

Scores/Numbers	COPD medication withdrawal^a			Inhaled long-acting bronchodilators withdrawal^b
	Yes (n = 689, 24.2%)	No (n = 2164, 75.8%)	p-Value	Yes (n = 697, 24.4%)	No (n = 2156, 75.6%)	p-Value
Total MRCI scores,^c median (IQR)	7.0 (7.0, 12.0)	7.0 (7.0, 13.0)	<0.001	7.0 (7.0, 13.0)	7.0 (7.0, 13.0)	0.001
COPD-specific MRCI scores, median (IQR)	7.0 (7.0, 11.0)	7.0 (7.0, 13.0)	<0.001	7.0 (7.0, 12.0)	7.0 (7.0, 13.0)	0.043
Non-COPD MRCI scores, n (%)			<0.001			<0.001
0	638 (25.5)	1864(74.5)		641 (25.6)	1861 (74.4)
>0	51 (14.5)	300 (85.5)		56 (16.0)	295 (84.0)
Non-COPD MRCI scores among > 0, median (IQR)	5.0 (3.0, 6.0)	6.0 (3.0, 9.8)		5.0 (3.0, 7.8)	6.0 (3.0, 9.0)
Total number of medications, median (IQR)	1 (1.2)	1 (1.2)	<0.001	1 (1.2)	1 (1.2)	<0.001
COPD-specific medication numbers, median (IQR)	1 (1.2)	1 (1.2)	<0.001	1 (1.2)	1 (1.2)	<0.001
Non-COPD medication numbers, n (%)			<0.001			<0.001
0	638 (25.5)	1864 (74.5)		641 (25.6)	1861 (74.4)
>0	51 (14.5)	300 (85.5)		56 (16.0)	295 (84.0)
Non-COPD medication numbers among > 0, median (IQR)	1 (1.2)	1 (1.3)		1 (1.2)	1 (1.3)

COPD, chronic obstructive pulmonary disease; IQR, interquartile range; MRCI, medication regimen complexity index.

COPD medication withdrawal was defined as the withdrawal of COPD medications 6 months after discharge.

Inhaled long-acting bronchodilators withdrawal was defined as the withdrawal of single or combination long-acting bronchodilators 6 months after discharge.

MRCI scores were calculated according to the medications 30 days after discharge.

The multivariate analysis showed that high total MRCI scores were negatively associated with the withdrawal of COPD medication (OR, 0.62; 95% CI, 0.52−0.74) and inhaled long-acting bronchodilators (OR, 0.68; 95% CI, 0.57−0.81) after adjustment for sociodemographic factors and other covariables (Table 3). In contrast to patients diagnosed with COPD within 1 year, the likelihood of withdrawal of COPD medications was markedly lower among those diagnosed with COPD exceeding 1 year, and the possibility of inhaled long-acting bronchodilators withdrawal was markedly lower in patients with a history of COPD for more than 10 years.

Table 3.

Associations of the total MRCI scores with withdrawal of medication by multivariate analysis.

Predictor	COPD medication withdrawal^a		Inhaled long-acting bronchodilators withdrawal^b
	OR (95% CI)	p-Value	OR (95% CI)	p-Value
Age category
<65 years	1.00 (ref)	–	1.00 (ref)	–
⩾65 years	1.03 (0.85–1.25)	0.753	0.95 (0.78–1.15)	0.568
Sex
Female	1.00 (ref)		1.00 (ref)	–
Male	0.98 (0.75–1.28)	0.889	0.98 (0.75–1.27)	0.861
Smoking status
Non-smoking	1.00 (ref)	–	1.00 (ref)	–
Ex-smoking	1.11(0.89–1.39)	0.352	0.93 (0.73–1.18)	0.548
Current smoking	1.07 (0.84–1.37)	0.565	1.04 (0.80–1.36)	0.748
Education level
Primary school or lower	1.00 (ref)	–	1.00 (ref)	–
Middle school	1.04 (0.85–1.27)	0.718	1.01 (0.83–1.23)	0.924
High or polytechnic school	1.04 (0.80–1.36)	0.761	0.96 (0.74–1.25)	0.777
College or higher	0.97 (0.64–1.49)	0.901	0.81 (0.52–1.25)	0.339
Years of diagnosis, years
⩽1 year	1.00 (ref)	–	1.00 (ref)	–
>1 year and ⩽5 years	0.77 (0.62–0.97)	0.024	0.83 (0.66–1.04)	0.103
>5 years and ⩽10 years	0.76 (0.59–0.98)	0.037	0.78 (0.60–1.00)	0.055
>10 years	0.69 (0.54–0.90)	0.006	0.75 (0.58–0.96)	0.025
GOLD stage at baseline^c
1 (FEV₁%pred⩾80%)	1.00 (ref)	–	1.00 (ref)	–
2 (50%⩽FEV₁%pred < 80%)	0.87 (0.58–1.29)	0.491	0.95 (0.64–1.43)	0.810
3 (30%⩽FEV₁%pred < 50%)	0.78 (0.53–1.15)	0.212	0.89 (0.60–1.32)	0.565
4 (FEV₁%pred < 30%)	0.66 (0.43–1.02)	0.059	0.70 (0.45–1.09)	0.111
CAT category at baseline
<10	1.00 (ref)	–	1.00 (ref)	–
⩾10	1.20 (0.85–1.69)	0.296	1.10 (0.79–1.55)	0.565
mMRC category at baseline
<2	1.00 (ref)	–	1.00 (ref)	–
⩾2	0.88 (0.69–1.13)	0.306	0.92 (0.72–1.18)	0.497
Total MRCI scores^d
Low MRCI scores (⩽7)	1.00 (ref)	–	1.00 (ref)	–
High MRCI scores (>7)	0.62 (0.52–0.74)	<0.001	0.68 (0.57–0.81)	<0.001

CAT score, COPD assessment test score; CI, confidence interval; COPD, chronic obstructive pulmonary disease; GOLD, global initiative for chronic obstructive lung disease; MRCI, medication regimen complexity index; mMRC, modified British medical research council; OR, odds ratio.

COPD medication withdrawal was defined the withdrawal of COPD medications 6 months after discharge.

Inhaled long-acting bronchodilators withdrawal was defined the withdrawal of single or combination long-acting bronchodilators 6 months after discharge.

GOLD stage were classified according to the post-bronchodilator FEV₁%pred.

MRCI scores were calculated according to the medications 30 days after discharge

The re-exacerbation and COPD-related readmission rates of the patients were 10.2% and 7.5%, respectively, during 30-day to 6-month period after discharge (Table 4). Patients with higher MRCI scores and medication numbers from the total patient- and COPD-specific levels were more likely to experience re-exacerbations and readmissions. In contrast, patients with lower non-COPD MRCI scores or those prescribed a higher number of non-COPD drugs were more likely to experience re-exacerbations. After adjusting for relevant covariables, the total MRCI scores were positively associated with re-exacerbations (OR, 1.64; 95% CI, 1.27−2.11) and COPD-related readmissions (OR, 1.57; 95% CI, 1.17−2.10; Table 5). The withdrawal of COPD drugs was not significantly associated with re-exacerbation; however, it was inversely related to COPD-related re-hospitalizations (OR, 0.60; 95% CI, 0.40−0.88). The risks of re-aggravations and readmission were significantly higher in patients with GOLD stage 1 compared to those with GOLD stage 2, in those diagnosed over 5 years compared to those diagnosed within 1 year, and in those complicated with pulmonary heart disease compared to those without the condition. Furthermore, the risk of re-exacerbations was significantly higher in patients who graduated from primary school or lower and those with coronary heart disease. In addition, the likelihood of readmission related to COPD was remarkably higher in nonsmokers compared to current smokers, in underweight patients (BMI < 18.5 kg/m²) compared to those with a healthy weight (18.5−23.9 kg/m²). In sensitivity analysis, similar results were found in the association between the COPD-specific MRCI scores and medication withdrawal or disease control (Supplemental Tables S1 and S2).

Table 4.

MRCI scores and medication numbers in patients with different clinical outcomes.

Scores/Numbers	Re-exacerbation			Readmission
	Yes(n = 292, 10.2%)	No (n = 2561, 89.8%)	p-Value	Yes (n = 215, 7.5%)	No (n = 2638, 92.5%)	p-Value
Total MRCI scores, median (IQR)	11.0 (7.0, 14.4)	7.0 (7.0, 13.0)	<0.001	11.0 (7.0, 13.0)	7.0 (7.0, 13.0)	0.002
COPD-specific MRCI scores, median (IQR)	8.0 (7.0, 13.0)	7.0 (7.0, 13.0)	0.002	9.0 (7.0, 13.0)	7.0 (7.0, 13.0)	0.010
Non-COPD MRCI scores, n (%)			0.001			0.145
0	238 (9.5)	2264 (90.5)		181 (7.2)	2321 (92.8)
>0	54 (15.4)	297 (84.6)		34 (9.7)	317 (90.3)
Non-COPD MRCI scores among > 0, median (IQR)	5.0 (3.0, 8.3)	6.0 (3.0, 9.5)		5.0 (3.0, 8.3)	6.0 (3.0, 9.0)
Total number of medications, median (IQR)	2.0 (1.0, 3.0)	1.0 (1.0, 2.0)	<0.001	2.0 (1.0, 2.0)	1.0 (1.0, 2.0)	0.002
COPD-specific medication numbers, median (IQR)	1.0 (1.0, 2.0)	1.0 (1.0,2.0)	0.003	2.0 (1.0, 2.0)	1.0 (1.0, 2.0)	0.006
Non-COPD medication numbers, n (%)			0.001			0.134
0	238 (9.5)	2264 (90.5)		181 (7.2)	2321 (92.8)
>0	54 (15.4)	297 (84.6)		34 (9.7)	317 (90.3)
Non-COPD number of medications among > 0	1.0 (1.0, 3.0)	1.0 (1.0, 2.0)		1.0 (1.0, 2.0)	1.0 (1.0, 3.0)

The outcomes of re-exacerbations and readmissions were recorded according to the status 6 months after discharge; MRCI scores were calculated according to the medications 30 days after discharge.

CAT score, COPD assessment test score; COPD, chronic obstructive pulmonary disease; IQR, interquartile range; MRCI, medication regimen complexity index; mMRC, modified British medical research council.

Table 5.

Associations of the total MRCI scores with disease control by multivariate analysis.

Predictor	Re-exacerbations^a		Readmissions^a
	OR (95% CI)	p-Value	OR (95% CI)	p-Value
Age category
<65 years	1.00 (ref)	–	1.00 (ref)	–
⩾65 years	0.91 (0.69–1.21)	0.527	0.91 (0.65–1.26)	0.561
BMI category, kg/m², n (%)
<18.5	1.00 (ref)	–	1.00 (ref)	–
18.5–23.9	0.75 (0.54–1.03)	0.077	0.64 (0.45–0.92)	0.016
24–27.9	0.72 (0.46–1.07)	0.100	0.65 (0.42–1.01)	0.055
⩾28	0.90 (0.52–1.57)	0.716	0.68 (0.35–1.32)	0.258
Smoking status
Non-smoking	1.00 (ref)	–	1.00 (ref)	-
Ex-smoking	1.03 (0.77–1.37)	0.858	0.93 (0.67-1.28)	0.659
Current smoking	0.80 (0.55–1.15)	0.226	0.53 (0.34–0.84)	0.007
Education level
Primary school or lower	1.00 (ref)	–	1.00 (ref)	-
Middle school	0.66 (0.49–0.88)	0.005	0.87 (0.63–1.20)	0.400
High or polytechnic school	0.68 (0.46–1.00)	0.048	0.69 (0.44–1.09)	0.110
College or higher	0.44 (0.21–0.93)	0.032	0.61 (0.27–1.35)	0.221
Years of diagnosis, year, n (%)
⩽1 year	1.00 (ref)	–	1.00 (ref)	–
>1 year and ⩽5 years	1.20 (0.83–1.74)	0.330	1.53 (0.98-2.41)	0.063
>5 years and ⩽10 years	1.73 (1.18–2.55)	0.006	2.03 (1.27–3.25)	0.003
>10 years	1.83 (1.25–2.68)	0.002	2.32 (1.47–3.67)	<0.001
GOLD stage at baseline^b
1 (FEV₁%pred ⩾ 80%)	1.00 (ref)	–	1.00 (ref)	–
2 (50% ⩽ FEV₁%pred < 80%)	0.48 (0.27–0.87)	0.015	0.46 (0.23–0.90)	0.024
3 (30% ⩽ FEV₁%pred < 50%)	0.70 (0.40–1.22)	0.204	0.63 (0.33–1.21)	0.163
4 (FEV₁%pred < 30%)	0.63 (0.34–1.14)	0.128	0.73 (0.37–1.46)	0.372
CAT category at baseline
<10	1.00 (ref)	–	1.00 (ref)	–
⩾10	0.97 (0.57–1.64)	0.911	1.05 (0.60–1.98)	0.875
mMRC category at baseline
<2	1.00 (ref)	–	1.00 (ref)	–
⩾2	0.90 (0.62–1.31)	0.580	0.81(0.53–1.25)	0.345
Total MRCI scores^c
Low MRCI scores (⩽7)	1.00 (ref)	–	1.00 (ref)	–
High MRCI scores (>7)	1.64 (1.27–2.11)	<0.001	1.57 (1.17–2.10)	0.002
COPD medication withdrawal^d
No	1.00 (ref)	–	1.00 (ref)	–
Yes	0.75 (0.55–1.04)	0.081	0.60 (0.40–0.88)	0.010
Pulmonary heart disease
No	1.00 (ref)	–	1.00 (ref)	–
Yes	1.37 (1.02–1.84)	0.034	1.51 (1.09–2.10)	0.013
Coronary heart disease
No	1.00 (ref)	–	1.00 (ref)	–
Yes	1.41 (1.03–1.92)	0.031	1.33 (0.93–1.91)	0.118
Heart failure
No	1.00 (ref)	–	1.00 (ref)	–
Yes	1.23 (0.80–1.91)	0.351	1.12 (0.68–1.84)	0.662
Osteoporosis
No	1.00 (ref)	–	1.00 (ref)	–
Yes	2.37 (0.94–5.99)	0.067	2.01 (0.68–5.96)	0.208

The outcomes of re-exacerbation and readmission were recorded according to the status 6 months after discharge.

GOLD stage were classified according to the post-bronchodilator FEV₁%pred.

MRCI scores were calculated according to the medications 30 days after discharge.

COPD medication withdrawal was defined the withdrawal of COPD medications 6 months after discharge.

Discussion

Patients with COPD have high mediation burden and complexity of medication regimen because they tend to be prescribed multiple medications to combat their complicated symptoms and also comorbid conditions.⁴ MRCI is a tool that has been developed and validated to identify and quantify complex medication regimes. To date, only a few studies have applied this instrument to studies referring to patients with COPD.^7,10,17 AECOPD are frequent and significant causes of mortality and heavy disease burden.² Before this study, two studies involving AECOPD patients examined the relationship between MRCI scores and readmissions.^10,17 However, heterogeneity in methodologies and enrolled populations probably led to conflicting results. To the best of our knowledge, the present study is the first to explore the associations of MRCI scores with medication adherence and clinical outcomes among hospitalized patients with AECOPD. This study found that higher MRCI scores were inversely associated with the discontinuation of COPD or inhaled medications and positively associated with the re-exacerbations and COPD-related readmissions after discharge.

Due to the obvious discrepancies between discharge and home care medications lists,¹⁰ we recorded home care MRCI scores at the 30-day follow-up after discharge to reflect the real-world medication regimen complexity of COPD patients in China. A previous study indicated that the median total MRCI scores and prescribed medication numbers in patients with COPD were 24 (IQR, 18.5−31) and 8 (IQR, 6−11), respectively.⁷ The medication burden was greater in those with comorbid hypertension and/or diabetes, with median MRCI scores exceeding 30 and an average of 11.9 prescribed medications.¹⁶ Among patients with AECOPD, the reported MRCI scores were high [mean (SD): 28.72 (4.96)].¹⁰ However, relatively low MRCI scores were observed in our study. The median total MRCI scores in discharged AECOPD patients was 7 (IQR, 7−13), and the number of prescribed medications was 1 (IQR, 1−2). One of the possible reasons for these gaps is poor adherence to medications. Nonadherence to medications, especially inhaled drugs, is a major global problem in patients with COPD.²⁴ As previously reported, self-reported nonadherence to COPD medications ranged between 22% and 93%.²⁵ Poorer medication adherence to COPD medications had been reported in contrast to medications for concomitant hypertension and diabetes, 45.0% of participants had low adherence to COPD medication by self-report and 52.6% by dose count.¹⁶ In our study, 24.2% of patients discontinued COPD medications during the 30-day to 6-month follow-up period. All participants enrolled in the current study were categorized into GOLD group C (mMRC score 0−1 or CAT score < 10) or group D (mMRC score ⩾2 or CAT score⩾10) based on the experience of more than one severe exacerbation (hospital admission) in the past year. Long-acting bronchodilators are the primary maintenance drugs for these patients. However, 24.4% of the patients did not continue to take inhaled drugs 6 months after hospital discharge.

Another reason for the gaps may be the undertreated comorbidities. According to previous study, COPD is a disease with multiple comorbidities; the median number of comorbidities in patients with COPD was 5.⁷ In our study, 1561 patients (54.7%) were diagnosed with more than one type of non-respiratory comorbidity. However, the majority (1285 patients, 82.3%) did not receive non-COPD pharmacotherapy. Only 9% of the total MRCI scores comprised non-COPD MRCI scores. These findings showed that a considerable number of patients in our study were lost to pharmacotherapy care when they were discharged from the hospital. Furthermore, some medications, such as traditional Chinese medicine decoction (15/2853 patients), were not included in the calculation of MRCI scores because the absence of guidance for scoring in the MRCI instrument resulted in the underestimation of MRCI scores.

One of the advantages of MRCI scores over medication counts is in quantifying regimen complexity from three different aspects.¹¹ Consistent with the previous finding, complex dosage formulations primarily drove the complexity of COPD-specific medication regimens, while dosage frequency was the main component in non-COPD medication burden here.⁷ The result indicated that simplifying the dosage formulation of COPD medication would be an effective strategy to minimize the regimen of medications. Inhaler device polypharmacy (defined as the use of three or more different inhaler devices) is common in people with respiratory diseases,^7,26 which could lead to unintentional nonadherence to treatment.²⁷ The concurrent use of multiple different types of inhaler devices was also prevalent in our study, with 657 of 2853 patients (approximately 23%) having two or more inhaler devices. The most frequent untied use was the combination of long-acting beta₂-agonists (LABAs) and inhaled corticosteroids (ICS) with long-acting muscarinic antagonists, followed by LABAs + ICS and short-acting beta₂-agonists. The latest GOLD guideline pointed out that single inhaler therapy may be more convenient and effective than multiple inhalers.²⁸

For ease of interpretation, previous studies have reported turning continuous MRCI scores into categorical variables using different cut-off values.¹⁶ However, no consistent value can be used to categorize MRCI scores due to variances in the population and disease studied.^16,29 We created a two-level categorical variable for MRCI scores using the median based on the distribution of MRCI scores.¹⁵ In the present study, patients with severe impairment of lung function and health status were prone to have high MRCI scores.⁷ A portion of specific respiratory or non-respiratory comorbidities were also related to high MRCI scores, especially cardiovascular diseases.⁷

Additionally, we explored the association between MRCI scores and discontinuation of medications. After adjusting for potential compliance-related factors, total MRCI scores in our study were significantly inversely associated with the withdrawal of medications. Patients who had lower MRCI scores were more likely to stop taking drugs. Since MRCI scores were positively related to CAT and mMRC scores,^7,16 a possible reason is that those with milder symptoms and less impairment of health status were more likely to cease drugs. Similarly, a previous study indicated that a less severe disease characterized by mild airflow limitation was a predictor of discontinuation of COPD drugs.²⁰ However, these findings were inconsistent with other studies in elderly people or patients with diabetes, which reported that higher MRCI was correlated with poor medication adherence.^30,31 In patients with COPD complicated with hypertension and/or diabetes, MRCI scores did not differ significantly between those with and without adequate medication adherence.¹⁶ These aforementioned studies were conducted in a high-income country and did not control covariates when analyzing the associations of MRCI scores with adherence. To our knowledge, this is the first study to examine the associations between MRCI scores and the withdrawal of COPD medication in an upper-middle-income country.

In addition, we found that disease course of COPD was an independent factor associated with medication withdrawal. Compared to patients diagnosed in 1 year, a lower probability of medication withdrawal was shown in those diagnosed in more than 1 year. Most patients had been reported to discontinue the inhaler drug in the first year of treatment within the median observation period of 32 months.²⁰ This finding suggests that the longer a patient has been diagnosed, the more likely he or she is to get access to pharmacological care, and we should pay more attention to medication compliance among recently diagnosed patients.

Two previous studies before this analysis had investigated the relationship between MRCI scores and readmission in patients with COPD.^10,17 One study enrolled not only COPD patients, but also patients with a primary diagnosis of heart failure, acute myocardial infarction, and pneumonia. They concluded that the readmission group had higher discharge MRCI scores than the non-readmission group. However, after controlling for covariates, the discharge MRCI score was no longer a significant predictor of all-cause readmission.¹⁷ Another study compared discharge and home care MRCI scores and analyzed their relationship with unplanned readmission; however, the study did not take other confounding variables into account. In addition, they only included 12 patients with COPD, making the results devoid of generalization.¹⁰ Reduced re-exacerbation and readmission are dispensable goals of care in patients with AECOPD. The current study focused on the evaluation of COPD-specific re-exacerbation and re-hospitalization risk among post-discharge patients with AECOPD. Total home care MRCI scores here were shown to have a significant positive correlation with the risk of re-exacerbation and readmission after adjusting for potentially confounding variables. Other significant factors were associated with re-exacerbations and readmissions, including BMI, smoking status, and comorbidities, as reported previously.^32–34 It should be noted that patients with a long history of COPD (>5 years) were more likely to suffer from exacerbations and readmissions probably due to their worse health status.

Previous studies have indicated that MRCI scores and medication counts are correlated strongly.^7,35 In the present study, the trend for MRCI scores and medication numbers was largely consistent. Considering the possible collinearity with MRCI scores, we did not include medication numbers in the regression analysis.

This study had several strengths. One of the strengths of this study is that the sample size was large and representative. In total, 2853 patients were enrolled in a nationwide cohort study in China, with 173 sites from 29 provinces. Patients were consecutively recruited from target hospitals, including various types (general, specialized, and traditional Chinese medicine hospitals) and grades of hospitals (tertiary care and second-class hospitals) to avoid selection bias. Besides, this is the first study to explore the associations of MRCI scores with medication adherence and clinical outcomes among patients with AECOPD in an upper-middle-income country. Furthermore, we used home care medications rather than discharge medications to calculate MRCI scores, which were more in line with the real-world medication burden.

There are some limitations to this study. First, a potential limitation was the reliance on self-reported medicine lists at home because these were possibly incomplete. In addition, the calculation of MRCI scores was not inadaptable to traditional Chinese medicine decoction. Although the proportion of patients who used the decoction was comparatively low in the present population, the traditional Chinese medicine decoction has been widely applied in some regions of China. Both limitations may lead to an underestimation of the MRCI scores. Furthermore, the thresholds chosen for the MRCI score groups were based on the median rather than an absolute cut-off point due to the unavailability of prior MRCI benchmarks. Although the data were obtained from a prospective cohort study, some variables were inevitably missing in the real-world setting. Due to the aggravation of COPD, some patients were unable to complete spirometry during hospitalization and 30 days after discharge. The impairment of lung function would be underestimated because missing values were imputed using the multiple imputation method.

Conclusion

MRCI scores at the patient, COPD-specific, and non-COPD levels were relatively lower among patients with AECOPD after hospital discharge in China. The probable reasons are that the patients in our study had poor adherence to medications and undertreated combined diseases. These results suggest that we need to pay attention not only to adherence to COPD medications, but also to the pharmacotherapy of comorbidities. Higher MRCI scores were inversely associated with the discontinuation of COPD or inhaled medications. Since the proportion of the severity of symptoms and impairment of health status was higher in patients with higher MRCI scores, mild patients were more likely to have lower MRCI scores and subsequent withdrawal of medications after treatment. Therefore, patients with mild symptoms could be more easily lost in the pharmacological care of COPD and are more likely to benefit from medication adherence interventions. Our findings also indicate that MRCI scores were positively associated with re-exacerbation and COPD-related readmission after discharge. Seriously ill patients tended to be prescribed more medications with high MRCI scores and had higher risks of re-aggravation and re-hospitalization. Therefore, MRCI is a reliable indicator of the severity of the disease. Although the associations between MRCI scores and medication adherence and clinical outcomes may be indirect, MRCI could be used as an assessment tool to predict adherence to pharmacotherapy and clinical outcomes to identify patients who would greatly benefit from additional services, such as pharmacist-led intervention. However, it is difficult to establish fixed cut-off value for MRCI scores due to the great distinction in various populations and different settings, which limits its clinical application. Considering the large amount of work involved in the calculation, the MRCI measure may be more useful than a simple count of medications in intervention studies to evaluate the effect of interventions.

Supplemental Material

sj-docx-1-tar-10.1177_17534666231206249 – Supplemental material for Associations of medication regimen complexity with medication adherence and clinical outcomes in patients with chronic obstructive pulmonary disease: a prospective study

Supplemental material, sj-docx-1-tar-10.1177_17534666231206249 for Associations of medication regimen complexity with medication adherence and clinical outcomes in patients with chronic obstructive pulmonary disease: a prospective study by Ruoxi He, Ye Wang, Xiaoxia Ren, Ke Huang, Jieping Lei, Hongtao Niu, Wei Li, Fen Dong, Baicun Li, Ting Yang and Chen Wang in Therapeutic Advances in Respiratory Disease

Footnotes

Acknowledgements

We are grateful to the study centers which contributed to patient recruitment and data collection, as well as to all patients who participated in this study.

Declarations

ORCID iDs

Ruoxi He

Chen Wang

Supplemental material

Supplemental material for this article is available online.

References

World Health Organization. Global Health Estimates: life expectancy and leading causes of death and disability, https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates (accessed 30 April 2022).

Rehman

Hassali

MAA

Muhammad

, et al. The economic burden of chronic obstructive pulmonary disease (COPD) in Europe: results from a systematic review of the literature. Eur J Health Econ 2020; 21: 181–194.

Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management and prevention of chronic obstructive pulmonary disease 2022 report, https://goldcopd.org/ (accessed 5 May 2022).

Franssen

Spruit

Wouters

. Determinants of polypharmacy and compliance with GOLD guidelines in patients with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2011; 6: 493–501.

Díez-Manglano

Barquero-Romero

Mena

, et al. Polypharmacy in patients hospitalised for acute exacerbation of COPD. Eur Respir J 2014; 44: 791–794.

Hajjar

Cafiero

Hanlon

. Polypharmacy in elderly patients. Am J Geriatr Pharmacother 2007; 5: 345–351.

Negewo

Gibson

Wark

, et al. Treatment burden, clinical outcomes, and comorbidities in COPD: an examination of the utility of medication regimen complexity index in COPD. Int J Chron Obstruct Pulmon Dis 2017; 12: 2929–2942.

Jarab

Mukattash

. Exploring variables associated with medication non-adherence in patients with COPD. Int J Clin Pharm 2019; 41: 1202–1209.

Alves-Conceicao

Rocha

KSS

Silva

FVN

, et al. Are clinical outcomes associated with medication regimen complexity? A systematic review and meta-analysis. Ann Pharmacother 2020; 54: 301–313.

10.

Schoonover

Corbett

Weeks

, et al. Predicting potential postdischarge adverse drug events and 30-day unplanned hospital readmissions from medication regimen complexity. J Patient Saf 2014; 10: 186–191.

11.

George

Phun

Bailey

, et al. Development and validation of the medication regimen complexity index. Ann Pharmacother 2004; 38: 1369–1376.

12.

Marienne

Laville

Caillard

, et al. Evaluation of changes over time in the drug burden and medication regimen complexity in esrd patients before and after renal transplantation. Kidney Int Rep 2021; 6: 128–137.

13.

Ab Rahman

Lim

Thevendran

, et al. Medication regimen complexity and medication burden among patients with type 2 diabetes mellitus: a retrospective analysis. Front Pharmacol 2022; 13: 808190.

14.

Wakai

Ikemura

Kato

, et al. Effect of number of medications and complexity of regimens on medication adherence and blood pressure management in hospitalized patients with hypertension. PLoS One 2021; 16: e0252944.

15.

Feinstein

Friedman

Orth

, et al. Complexity of medication regimens for children with neurological impairment. JAMA Netw Open 2021; 4: e2122818.

16.

Federman

O’Conor

Wolf

, et al. Associations of medication regimen complexity with COPD medication adherence and control. Int J Chron Obstruct Pulmon Dis 2021; 16: 2385–2392.

17.

Abou-Karam

Bradford

Lor

, et al. Medication regimen complexity and readmissions after hospitalization for heart failure, acute myocardial infarction, pneumonia, and chronic obstructive pulmonary disease. SAGE Open Med 2016; 4: 2050312116632426.

18.

Pei

Sun

Wang

, et al. Estimating mortality among inpatients with acute exacerbation of chronic obstructive pulmonary disease using registry data. NPJ Prim Care Respir Med 2020; 30: 28.

19.

Hartl

Lopez-Campos

Pozo-Rodriguez

, et al. Risk of death and readmission of hospital-admitted COPD exacerbations: European COPD Audit. Eur Respir J 2016; 47: 113–121.

20.

Tanaka

Kamiishi

Miyata

, et al. Determinants of long-term persistence with tiotropium bromide for chronic obstructive pulmonary disease. COPD 2015; 12: 233–239.

21.

Fletcher

. Standardised questionnaire on repiratory symptoms: a statement prepared and approved by the MRC Committee on the Aetiology of Chronic Bronchitis (MRC breathlessness score). BMJ 1960; 2: 1665.

22.

Jones

Harding

Berry

, et al. Development and first validation of the COPD assessment test. Eur Respir J 2009; 34: 648–654.

23.

Jones

Adamek

Nadeau

, et al. Comparisons of health status scores with MRC grades in COPD: implications for the GOLD 2011 classification. Eur Respir J 2013; 42: 647–654.

24.

Bengtson

LGS

DePietro

McPheeters

, et al. Real-world outcomes in patients with chronic obstructive pulmonary disease initiating long-acting mono bronchodilator therapy. Ther Adv Respir Dis 2018; 12: 1753466618772750.

25.

Bhattarai

Walpola

Mey

, et al. Barriers and strategies for improving medication adherence among people living with COPD: a systematic review. Respir Care 2020; 65: 1738–1750.

26.

Sterk

. Chronic diseases like asthma and COPD: do they truly exist? Eur Respir J 2016; 47: 359–361.

27.

Bryant

McDonald

Boyes

, et al. Improving medication adherence in chronic obstructive pulmonary disease: a systematic review. Respir Res 2013; 14: 109.

28.

Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management and prevention of chronic obstructive pulmonary disease 2023 report, https://goldcopd.org/ (accessed 15 December 2022).

29.

Pantuzza

LLN

das Gracas Braga Ceccato

Reis

, et al. Factors associated with high medication regimen complexity in primary care older adults in Brazil. Eur Geriatr Med 2020; 11: 279–287.

30.

Mansur

Weiss

Beloosesky

. Looking beyond polypharmacy: quantification of medication regimen complexity in the elderly. Am J Geriatr Pharmacother 2012; 10: 223–229.

31.

Ayele

Tegegn

Ayele

, et al. Medication regimen complexity and its impact on medication adherence and glycemic control among patients with type 2 diabetes mellitus in an Ethiopian general hospital. BMJ Open Diabetes Res Care 2019; 7: e000685.

32.

Celli

Cote

Marin

, et al. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004; 350: 1005–1012.

33.

Welte

. Acute exacerbation in COPD: we must do more. Eur Respir J 2016; 47: 14–15.

34.

Adibi

Sin

Safari

, et al. The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study. Lancet Respir Med 2020; 8: 1013–1021.

35.

Ferreira

Galato

Melo

. Medication regimen complexity in adults and the elderly in a primary healthcare setting: determination of high and low complexities. Pharm Pract (Granada) 2015; 13: 659.

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