JFOA Journal

Introduction

The economic burden and prevalence of cardiovascular disease is substantial [1,2]. Current treatment modalities for individuals experiencing myocardial infarction, such as percutaneous coronary intervention, typically prioritize swift revascularization and early mobilization in the initial days following the event [3-5].

Traditionally, there has been a disproportionate research focus on optimizing the immediate treatment of acute myocardial infarctions compared to the subsequent follow-up procedures [6]. Nevertheless, recent investigations have illuminated the potential benefits of structured follow-up in enhancing the quality of life for patients after myocardial infarction [7-9].

In previous papers we have shown that a structured sick-leave program following an uncomplicated myocardial infarction is a cost-effective method to decrease the number of days absent from work, without affecting quality of life negatively [10,11].

In this paper, we aim at exploring which socio-economic factors that favors early return to work for patients after an acute myocardial infarction.

Methods

Participants and Randomization

One hundred and forty-three patients who were admitted to Oslo University Hospital due to an acute myocardial infarction were included in the study. All patients were assessed against the inclusion/exclusion criteria (Table 1). Patients were randomized into either the intervention group or to conventional care group. Randomization was performed by means of simple randomization by random allocation to study groups after each inclusion [12]. The random allocation was performed by drawing a numbered ticket, were the number corresponded to one of the two study groups. The number of tickets that were prepared for the study was set after calculating sample size and ensured balanced randomization between the study groups.

Table 1: Inclusion/Exclusion criteria.

Sample size calculations showed that about 50 patients per study groups would allow 80% power for detecting a clinically significant difference in each of the SF-36 health domains with P = 0.05 [13,14]. A total of 100 patients would also offer greater than 80% power to detect a clinically worthwhile 0.1 ±0.2 SD difference in utility scores on the UBQ-H questionnaire [15]. To cover for patients lost to follow-up it was decided to include about 120 patients in the study.

For all patients we collected a full medical history, demographic data, marital status, education level, professional category, and salary range. ClinicalTrials.gov Identifier: NCT01108653.

Study Groups

Patients randomized to the intervention group were given a structuralized program with full- time sick-leave for 2 weeks after discharge. They were also given a telephone number to a cardiologist at the department of cardiology, available for support and questions during office time. After the initial two-week sick leave, the patients were encouraged to return to work full-time or part-time according to an individual adaptation. The general practitioner of the patients was also instructed to help the patients to go back to work as soon as possible.

All patients were transferred back to their local hospitals after potential PCI therapy at Oslo University Hospital. The conventional group was then sick listed according to the discharging doctor's assessment and received no special follow-up or advice on when to return to work.

Outcome measures

Sick-leave duration

The length of every patient's absence from work was recorded at the 12-month control. The duration of sick leave was calculated from the day of discharge from the hospital to the first day back to paid work.

Quality of life

Quality of life measures were performed at baseline and at 12 months using the Utility- Based Quality of Life – Heart (UBQ-H), and the Medical Outcomes Study Short Form-36 (SF36), and questionnaires.

The SF-36 from the RAND Corporation is a well-established survey of patient health, both physical and mental, and is validated for the use in monitoring and assessing care outcomes in adult patients. The SF-36 guides suggest that a difference of 10 points between groups per domain indicates a clinically significant difference [13,14].

The UBQ-H was developed specifically for use in coronary artery disease. Components of UBQ-H include physical, psychological and social measures. It also includes three summary measures of quality of life: A time trade-off item, a rating scale and an ordinal health assessment item [15].

Statistical analysis

In this study 143 patients were included to cover for patients lost to follow-up. Statistical analysis was performed using the SPSS Statistics 26 software. Data was tested for normality using Kolmogorov–Smirnov test. Unpaired t tests, χ² tests and Mann–Whitney U tests for non-normal data were used for comparisons between groups. Statistical significance was inferred when P<0.05. All results are presented unadjusted for multiple comparisons.

Results

Study Population and Characteristics

The study comprised a total of 143 patients. However, 17 individuals were lost to follow-up, with 13 patients failing to attend scheduled controls despite attempts through phone calls and mail, and four being excluded due to concurrent medical conditions such as cancer and debilitating injuries.

All baseline characteristics were balanced across the study groups, as indicated in Table 2. Among the 143 patients initially enrolled, 98 (68.5%) received an index diagnosis of NSTEMI, while 45 (31.5%) had an index diagnosis of STEMI. Moreover, 140 patients (97.9%) underwent PCI as the primary treatment, while 3 patients (2.1%) received intravenous thrombolytic therapy as the primary mode of treatment. Additionally, 41 patients (28.7%) had a previous history of AMI or PCI.

Table 2: Baseline characteristics.

Absence from work results

The whole study group had a mean of 18.8 (CI 95% 17.9-19.7) days absent from work. The conventional group had a mean of 20.4 (CI 95% 18.9-21.8) days absent from work, while the number for the structuralized group was significantly lower, with a mean of 17.2 (CI 95% 16.2-18.2) days absent from work. A two-sample t-test gives an estimated p-value<0.001, making the difference in absence between the two groups statistically significant.

Predictors of time to return to work

Analysis of qualitative demographic and socioeconomic variables, using the chi-square test, shows a significant correlation between early return to work and non-manual job category (p=0.034), self-employment status (p<0.001), and educational level (p=0.019) (Table 3).

Table 3: Qualitative predictors associated with RTW.

For quantitative variables such as age, BMI, and SF36 and UBQ-H utilities, univariate Cox regression analysis was utilized. The findings indicate that only SF36 (p=0.012) and UBQ-H (p=0.007) utilities reporting higher quality of life exhibited associations with early return to work (Table 4).

Table 4: Quantitative predictors associated with RTW.

Multivariate analysis of qualitative and quantitative variables, indicates that possessing a university degree (p=0.004), being self-employed (p<0.001), and reporting a higher quality of life (p=0.029) were factors significantly associated with early return to work (Table 5).

Table 5: Predictors associated with RTW.

Discussion

Our results fall in line with previously published results, where early return to work after an uncomplicated myocardial infarction was found to be associated with socio-economic factors [16,17].

Stendardo, et al., found in their 2018 paper a similar association between early return to work and socio-economical factors as our study presents [16]. They also conclude that early return to work is more related to socio-economical variables than clinical variables. Interestingly, Sun et al, published in 2022 a prospective longitudinal cohort study that found a significant association between both socio-economical and clinical variables for early return to work [17].

Comparatively, these findings that indicate that early return to work is associated with clinical variables such as comorbidities and post-infarction complications, as well as with socio-economic factors such as employment status and level of educations, have also been found in other studies [18-20]. Furthermore, the amount of time absent from work after a myocardial infarction varies greatly in around the world and is likely also dependent on local societal factors [2,7-9].

In our previous papers we discuss the benefits of a structured sick-leave program following an uncomplicated myocardial infarction [10,11]. Our finding indicate that such a program can help reduce time absent from work, without affecting quality of life negatively, all while being cost-effective from a health-economic perspective. The findings in this paper falls in line with previous research on the subject and can further help tailor a structured sick-leave program for patients following an uncomplicated myocardial infarction.

The major limitations of our study included the relatively small sample size and lack in continuity in the inclusion and follow-up of the patients. The small sample size is likely to have limited the ability to detect reliably smaller, yet possibly still clinically important, changes that may exist in quality of life. It is also important to note that our findings apply to a select group of patients under the age of 65, without any form of post-MI complications.

The findings of our study strengthen the case for a structured sick leave program to all patients after acute coronary syndrome. However, the limitation in the study warrants further investigation into this field, including larger cohorts and longer follow-up.

Acknowledgements

Conflicts of interests: None declared

Funding

The study was supported by the Norwegian Helse South/East

Sample Data Availability Statements

The data underlying this article cannot be shared publicly due to [Data cannot be shared for ethical/privacy reasons.]. The data will be shared on reasonable request to the corresponding author.

References

1. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, et al. (2014) Executive summary: heart disease and stroke statistics--2014 update: a report from the American Heart Association. Circulation 21: 399-410.
2. Isaaz K, Coudrot M, Sarby MH, Cerisier A, Lamaud M, et al. (2010) Return to work after acute ST-segment elevation myocardial infarction in the modern era of reperfusion by direct percutaneous coronary intervention. Arch Cardiovasc Dis 5: 310-316.
3. Kolloch R, Legler UF, Champion A, Cooper-Dehoff RM, Handberg E, et al. (2008) Impact of resting heart rate on outcomes in hypertensive patients with coronary artery disease: findings from the INternational VErapamil-SR/trandolapril STudy (INVEST). Eur Heart J 29: 1327-1334.
4. Girerd N, Magne J, Rabilloud M, Charbonneau E, Mohamadi S, et al. (2012) The impact of complete revascularization on long-term survival is strongly dependent on age. Ann Thorac Surg 94: 1166-1172.
5. Hall TS, von Lueder TG, Zannad F, Rossignol P, Duarte K, et al. (2018) Relationship between left ventricular ejection fraction and mortality after myocardial infarction complicated by heart failure or left ventricular dysfunction. Int J Cardiol 272: 260-266.
6. Edwards K, Jones N, Newton J, Foster C, Judge A, et al. (2017) The cost-effectiveness of exercise-based cardiac rehabilitation: a systematic review of the characteristics and methodological quality of published literature. Health Econ Rev 19: 37.
7. Kavradim ST, Özer ZC (2020) The effect of education and telephone follow-up intervention based on the Roy Adaptation Model after myocardial infarction: randomised controlled trial. Scand J Caring Sci 34: 247-260.
8. Tella NC, Arnaiz CS, Gatius JR, Torres OY, Santiago LG (2017) Assessment of the length of sick leave in patients with ischemic heart disease. BMC Cardiovasc Disord 17: 32.
9. Khot UN, Johnson MJ, Wiggins NB, Lowry AM, Rajeswaran J, et al. (2018) Long-Term Time-Varying Risk of Readmission After Acute Myocardial Infarction. J Am Heart Assoc 7: e009650.
10. Tjessem L, Agewall S (2023) Evaluation of a Structuralized Sick-Leave Programme Compared with usual Care Sick-Leave Management for Patients after an Acute Myocardial Infarction. J Rehabil Med 55: jrm4569.
11. Tjessem L, Agewall S (2023) Sick-leave Program after Myocardial Infarction. J Community Med Public Health 7: 359.
12. Kim J, Shin W (2014) How to do random allocation (randomization). Clin Orthop Surg 6: 103-109.
13. Ware J, Sherbourne C (1992) The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 30: 473-483.
14. Jenkinson C, Coulter A, Wright L (1993) Short form 36 (SF36) health survey questionnaire: normative data for adults of working age. BMJ 306: 1437-1440.
15. Martin AJ, Glasziou PP, Simes RJ (1999) A cardiovascular extension of the Health Measurement Questionnaire. J Epidemiol Community Health 53: 548-557.
16. Stendardo M, Bonci M, Casillo V, Miglio R, Giovannini G, et al. (2018) Predicting return to work after acute myocardial infarction: Socio-occupational factors overcome clinical conditions. PLoS One 13: e0208842.
17. Sun W, Gholizadeh L, Perry L, Kang K (2022) Predicting Return to Work Following Myocardial Infarction: A Prospective Longitudinal Cohort Study. Int J Environ Res Public Health 19: 8032.
18. Jiang Z, Dreyer RP, Spertus JA, Masoudi FA, Li J, et al. (2018) Factors Associated With Return to Work After Acute Myocardial Infarction in China. JAMA Netw Open 1: e184831.
19. Sun W, Gholizadeh L, Perry L, Kang K, Heydari M (2021) Factors associated with return to work following myocardial infarction: A systematic review of observational studies. J Clin Nurs 30: 323-340.