PONE-D-20-20822

Employment status before and after open heart valve surgery: A cohort study

PLOS ONE

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Reviewer #1: Partly

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Reviewer #1: No

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Reviewer #1: Yes

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Reviewer #1: Yes

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5. Review Comments to the Author

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Reviewer #1: Thank you for the opportunity to review this interesting manuscript. The research described in this paper seems to be a retrospective cohort study looking at the clinical predictors associated with returning to work after heart valve surgery in patients aged 18 to 63 years. The data used for the research come from hospital records and registry data (secondary data sources). The manuscript is written in a clear English that is easy to understand.

Overall, the study finds that poorer cardiovascular health (based on EuroScore-II score) and a longer hospital stay after the heart valve operation lowers the chance of returning to work. The authors claim that this is the first study to focus on clinical characteristics and return to work. I can believe this, because it is hard to see how this information, which is rather apparent and often a secondary finding of papers looking at rehabilitation interventions, merits research. Nevertheless, I have some comments and questions regarding the manuscript itself and the methods chosen that I hope will be helpful.

Abstract

Line 40: Maybe you could add a very brief description mention EuroScore as clinical characteristic in parentheses. I think a brief description of EuroScore would be helpful as well.

Line 47-48: I think it would be easier to read if the OR for of longer length of hospital stay was written in descending order.

Background

Lines 61-62: Other factors that might impact the ability to return to work after heart surgery are mentioned, without listing them. This is fine, but one factor that seems to be neglected in this paper is the type of work a patient will be returning to. Someone with a physical job may find it harder to return to work than someone with a desk job after heart surgery. This could be mentioned here and must be considered later in the statistical analysis.

Line 79: The second objective of the study is rather vague. The factors being investigated should be named. This seems to be an explorative study, as the objectives are formulated in a way that does not sound as if a concrete hypothesis is being tested. The fact that this is an explorative study should be stated in the paper.

Lines 91-97: Reading the list of exclusions, I got the impression these exclusions were decided while reviewing the medical records and not based on an a priori list of exclusions. Is this the case? Was an a priori study protocol prepared for this research (even one that was not published)?

Line 103: EuroScore is mentioned here very briefly. Later the tables state EuroScore-II. Please describe the EuroScore used, how it is calculated and the meaning of the index values here. Was the log of EuroScore-II really included in the regression models or the binary categories ≥2.3 vs <2.3?

As I understand it, the EuroScore is calculated prior to the operation. Are there any other clinical values, beside atrial fibrillation that describe the health of patients directly after the operation and can be considered?

Lines 116-121: This was the one part of the text I did not understand. I do not see how the three items listed in lines 116-118 line up with the descriptions provided in 119-120. Do items 1 and 2 correspond to the workforce described in lines 119? I think this needs to be rewritten to be clearer.

Who determined if someone was “still capable of working” for the DREAM registry? Is there a legal definition of “capable of working” that can be described here?

Lines 134-135: I do not see the use of doing testing for normality with the Shapiro-Wilks test. The regression model being used, does not require normality.

What is the time scale unit for return to work? Days? Weeks?

Logistic regression would not be my first or second choice for this type of data. Ideally this should be evaluated as time-to-event with either a Cox regression model. This allows for censoring of data, which means people dying or having a perioperative stroke after hospital discharge can be included into the analysis until the time of their death/stroke (at which time they are censored). If the time scale for return to work is not that exact, an alternative would be to calculated incidence rate ratios (IRR) using Poisson-regression and person-months as a time scale.

The longer one is away from work, the harder it may be to return. I think it would be interesting if the time on sick-leave (in days or weeks) just prior to the OP could be included in the analysis as a variable. While this might be a proxy for the severity of the underlying illness, prolonged time away from work before the OP may make it harder to imagine going back afterwards.

How was the model selection conducted?

Results

Line 152: Early retirement: I do not understand why the patients who were already retired before their operation are just now being excluded. Even though they are described as being excluded, they are included in table 2, which is confusing. I do not understand why their exclusion cannot be mentioned already in the methods and why they are not consistently excluded in the results. If you want to see if someone comes out of early retirement post-OP, this should be considered a new research question and considered separately in only the subgroup that was in early retirement pre-OP.

Table 1:

Was there any data on arterial blood pressure/ hypertension for the population that could be included in this table?

Did any of the patients take part in post-OP rehabilitation programs? If so, the prevalence of such rehabilitation should be described, and perhaps considered in the regression models as this might also impact return to work.

The type or field of work conducted pre-OP should be described. At least the proportion working in physically strenuous fields of work should be mentioned. Job strain would also be an interesting factor to consider. High job strain (e.g. demand control model) may also prevent return to work.

Were any of the patients self-employed? Could this partially explain the number of patients not on sick leave at the time of their operation? People who are self-employed tend to return to work sooner. This might also need to be considered in the regression model.

The youngest age range is wide: 18-45 years. I suspect the indications for the OP were different for the youngest patients, while coronary artery disease was probably more common in older patients. Can some information regarding the indications in the different age groups be provided?

Younger patients who were also students seem make it difficult to interpret the results for this youngest age group, as they would not be categorized as being on sick leave. Could students or the youngest patients (<30 years) be excluded from the analysis to check this? This could either replace the main analysis or be a sensitivity analysis.

Figures 2: This is an interesting depiction of the proportion on sick leave or working. Would it also be possible to depict the proportion of patients who go into early retirement post-OP?

Figure 3: The stratification of Figure 2 into the different age groups takes up a lot of space and adds little to the paper. I find it hard to compare the groups. think it might be nicer if the curves could be included in one single graphic. Maybe as Kaplan-Meier curves post-OP.

Figure 4: Although age is otherwise considered in age-groups, age seems to have been included as a continuous variable in the regression model. While this is legitimate, I think it would make it add to the interpretation of the descriptive analysis of return-to-work if the same age-categories were used in the regression model. I think suspect the chance of returning to work might also be significant for some age-groups, while the increased chance of returning to work is not significant for an increase in a single year of age. Also, increased chance of returning to work might not be linear for age. It might be lower for younger age groups, higher in the middle and lower again for the oldest age group. Using the age categories will show if this is the case.

Line 244 (and Figure 4): I do not think the term “co-morbidity” is the best descriptor to describe the EuroScore and post-OP atrial fibrillation. A better term for these indicators of cardiovascular health is needed. A co-morbidity would be additional diseases, such as diabetes or kidney disorders. Why were these not considered in the model?

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Reviewer #1: No

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Employment status before and after open heart valve surgery: A cohort study

PONE-D-20-20822R1

Dear Dr. Borregaard,

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Kind regards,

Corstiaan den Uil

Academic Editor

PLOS ONE

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