Seasonal changes in proportion of cardiac surgeries associated with diabetes, smoking and elderly age.

BACKGROUND: Seasonal variations in the ambient temperature may affect the exacerbation of cardiovascular diseases. Our primary objective was to evaluate the seasonality of the monthly proportion of cardiac surgeries associated with diabetes, smoking and/or elderly age at a tertiary-care university hospital in East-Central Europe with a temperate climate zone. As a secondary objective, we also assessed whether additional factors affecting small blood vessels (smoking, aging, obesity) modulate the seasonal variability of diabetes.
METHODS: Medical records were analyzed for 9838 consecutive adult patients who underwent cardiac surgery in 2007-2018. Individual seasonal variations of diabetes, smoking, and elderly patients were analyzed monthly, along with the potential risk factors for cardiovascular complication. We also characterized whether pairwise coexistence of diabetes, smoking, and elderly age augments or blunts the seasonal variations.
RESULTS: Seasonal variations in the monthly proportion of cardiac surgeries associated with diabetes, smoking and/or elderly age were observed. The proportion of cardiac surgeries of non-elderly and smoking patients with diabetes peaked in winter (amplitude of change as [peak-nadir]/nadir: 19.2%, p<0.02), which was associated with increases in systolic (6.1%, p<0.001) and diastolic blood pressures (4.4%, p<0.05) and serum triglyceride levels (27.1%, p<0.005). However, heart surgery in elderly patients without diabetes and smoking was most frequently required in summer (52.1%, p<0.001). Concomitant occurrence of diabetes and smoking had an additive effect on the requirement for cardiac surgery (107%, p<0.001), while the simultaneous presence of older age and diabetes or smoking eliminated seasonal variations.
CONCLUSIONS: Scheduling regular cardiovascular control in accordance with periodicities in diabetes, elderly, and smoking patients more than once a year may improve patient health and social consequences. TRIAL REGISTRATION: NCT03967639.

Background

Diabetes mellitus is a complex metabolic disease that can affect vital functions [1]. Cardiovascular complications, such as atherosclerotic cardiovascular and heart failure, are the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM), which is the most common form of the disease [2, 3]. Treatment of cardiovascular comorbidities comprises drug therapy; however, progression and/or exacerbation of these diabetes-related circulatory complications often require surgical intervention.

It is well established that the incidence of severe cardiovascular diseases exhibits a seasonal pattern, with more frequent relative occurrence during winter [4-9]. Several factors may contribute to these seasonal variations, such as activation of the sympathetic nervous system and increased cathecholamines [10], elevated serum cholesterol levels [11], prothrombotic shift in the hemostatic system via elevated fibrinogen levels [12, 13], decreased physical activity [4], and vitamin D deficiency [14]. Most of these pathological processes are influenced by diabetes [2]. Consequently, seasonal augmentation of clinical signs and symptoms can be anticipated in patients with T2DM, which may require alterations in treatment strategy involving the requirement for cardiovascular surgery.

Therefore, the primary objective of the present study was to reveal whether the proportion of cardiac surgeries associated with diabetes requiring heart surgery exhibits seasonal variations, peaking during winter. To address this goal, we evaluated the monthly proportion of cardiac surgeries for patients with diabetes within a 12-year period at the cardiac surgery unit of a tertiary-care university hospital. As a secondary objective, we also evaluated whether factors affecting small blood vessels (smoking, aging, and obesity) modulate the seasonal variability of T2DM along with the potential risk factors for cardiovascular complications (blood pressure, serum triglyceride, cholesterol and glucose levels). The rationale of the study is related to the fact that worsening and/or exacerbation of cardiovascular complications necessitating surgery can often be prevented with appropriate medical treatments in patients with diabetes. Thus, exploration of this cold-related seasonal phenomenon may elucidate the need for a more frequent patient follow-up to avoid progression with appropriately timed preventive measures.

Methods

Study design and population

Ethical approval for this study (no. 274/2018/a) was provided by the Human Research Ethics Committee of Szeged University, Hungary (chairperson, Prof. T. Wittmann) on January 21, 2019. The study was registered at clinicaltrials.gov (NCT03967639).

Medical records were retrospectively analyzed for all 9838 consecutive adult patients who underwent surgery at our institution (Cardiac Surgery Unit, Second Department of Internal Medicine and Cardiology Center at the University Hospitals of Szeged, Hungary) from January 1, 2007 to December 31, 2018. Patients underwent the entire spectra of cardiac surgeries. Our clinical practice avoided a waiting list; therefore, all operations were performed within five days after establishing the requirement for surgical intervention. Patient records were discarded in case of emergency reoperations as a consequence of tamponade or acute bleeding, since these events are not related to exacerbation of cardiovascular disorders. Accordingly, patients were included in the analyses only after a primary or redo open heart surgeries.

Cardiac surgery patients were assigned to the following groups, or combinations of groups, based on hospital medical records. Patients were defined as having T2DM if their medical history included a diagnosis of T2DM and/or hemoglobin A1c (HbA1c) > 6.5%, in accordance with the diagnostic criteria of the American Diabetes Association [15]. Since almost all (99.6%) patients with diabetes had T2DM, and the etiology and pathophysiological characteristics of type 1 diabetes mellitus differs from that of T2DM, only T2DM patients were included in the analyses, with an average of 8.6 years diagnosed disease period. Among T2DM patients, 25.8% were treated with insulin. T2DM patients treated with insulin or oral antidiabeticss were pooled in the final analyses. Patients were assigned to the smoking group based on the definitions of the National Center for Health Statistics [16]: current smokers (smoked 100 cigarettes in his or her lifetime and who currently smokes cigarettes); everyday smokers (smoked at least 100 cigarettes in his or her lifetime and currently smokes every day); or ex-smokers (ceased tobacco use <12 months ago). Patient were considered to be elderly if they were older than the average life expectancy age in southern Hungary published by the Hungarian Central Statistical Office during the study period: ≥72 years for males and ≥79 years for females [17]. Obesity was classed according to the definition of the World Health Organization as body mass index (BMI) ≥30 kg/m2 [18]. Individual seasonal effects of these factors were analyzed on a monthly basis. To identify the coexistence of these factors with potential additive or regressive effects on seasonal changes, the combined occurrence of statistically significant factors were also examined.

Noninvasive systolic and diastolic blood pressure values were registered at admission, and serum triglyceride, cholesterol and glucose levels were measured from venous blood samples collected from the first blood samples after arrival to the hospital.

Monthly average temperature data

Average monthly temperature data for the study period were obtained from the database of the Hungarian Meteorological Service.

Data processing and statistical analyses

Statistically significant differences between the study groups for continuous variables were assessed by one-way analysis of variance followed by followed by Bonferroni’s post-hoc tests. Pearson’s Chi-squared tests were used to evaluate differences in categorical variables. Monthly proportion of surgeries associated with the different disorders were calculated as the number of new patients for cardiac surgeries with a given risk factor (independently of the other two risk factors, alone, and in combination; e.g., T2DM alone; T2DM and smoking; T2DM, smoking, and elderly) divided by the total number of cardiac surgery patients in the same month. Seasonality of the monthly aggregated proportion of surgeries associated with the observed disorders and values for blood pressure, triglyceride, cholesterol and glucose during the study period was assessed using Walter–Elwood and negative binomial regression methods [19], assuming that data followed a sinusoidal curve with a periodicity of one year. Geometric models were used to investigate seasonality by assuming that seasonal fluctuations of an event occur on a fixed date every year and might be described using cyclic patterns over a period of time. The power of the Walter-Elwood test is 100% [20], and the percentage of change (variation) is the main effect size for the association between seasonal variation and health parameters. The deviance statistic was used to check a goodness of fit for negative binomial regression models. Similarly, Walter and Elwood also described a goodness of fit calculation for their methods [19], which was also taken into account.

Diabetes, aging, smoking, obesity, and gender were considered as possible risk factors for cardiac surgeries. Relative change (peak–mean)/mean was calculated to quantify the severity of seasonality and two compare seasonal amplitudes. Statistical analyses were performed using Stata software package (version 17, Statacorp, College Station, Texas) and p-values < 0.05 were considered statistically significant. The charts were prepared by using SigmaPlot software package (Version 13, Systat Software, Inc. Chicago, IL, USA).

Results

The involvement of patients in the retrospective data analyses and their group allocation is demonstrated on a CONSORT flow chart (Fig 1). The total of 9881 patients were enrolled in the study period. Twenty-seven patients were excluded from the data analyses due to incomplete data set in anthropometrical data and/or blood pressure and/or blood sample analyses. Furthermore, type 1 diabetes was diagnosed in 16 patients; they were also excluded from the analyses due to their fundamentally different diabetes phenotype than in the main population including T2DM. These considerations resulted in classifications of 9838 patients.

Fig 1

Consort flowchart.

Group allocation ana analyses of cardiac surgery patients with diabetes mellitus only (T2DM alone), smoking (SM alone), and aging (Elderly alone). Groups containing pairwise (T2DM + SM, T2DM + Elderly, and SM + Elderly) and concomitant combination (“All”) significant factors were also separated. “None” denotes no occurrence of these risk factors. The total of 9881 patients were enrolled in the study period. Forty-three patients were excluded from the data set due to incomplete registration of the anthropometric outcomes and/or blood sample analyses (n = 27), or subsequent to the diagnosis of type 1 diabetes (n = 16). As a result, 9838 cardiac surgery patients were included in the analyses.

Anthropometric data and clinical characteristics

The anthropometric data and main clinical characteristics of the study groups according to significant factors exhibiting seasonal variations in the overall proportion of surgeries are summarized in S1 Table in S1 File. In agreement with the worldwide proportion of patients undergoing cardiac surgery who had T2DM (30%-40%) [21], 38.4% of patients were diagnosed T2DM in the present study. In accordance with the diagnostic criteria, HbA1c was significantly higher in patients with diabetes (7.75±1.17) than in those without metabolic disorders (5.69±0.4). The preponderance of males observed in the whole study population (63.1%) was also present in each subgroup, with the exception of patients with T2DM alone (51.4%) and those without examined risk factors (52.4%). T2DM was significantly associated with higher body weight (84.4 vs. 77.6 kg, p<0.001) and BMI (31.0 vs. 28.1 kg/m2, p<0.001), whereas smoking was associated with lower BMI (27.1 kg/m2; p<0.001). Compared with patients without risk factors, aortic diseases were more frequent in elderly patients (p<0.05), whereas the prevalence of grown-up congenital heart diseases was lower in T2DM, smoking, and elderly patients (p<0.05). The proportion of coronary disease was generally greater in T2DM and smoking patients (p<0.05).

The total number of cardiac surgery patients included in the data analyses in each month is demonstrated on S2 Table in S1 File. These data show no significant seasonality; only the holiday seasons show a decrease in the number of patients due the limited availability of human resources available at the university hospital, however the relative frequency of the various diagnoses was not different between months.

Seasonal variabilities: Significant risk factors

There were no statistically significant seasonal variations for gender (p = 0.81) and BMI (p = 0.75); therefore, these variables were not included in further analyses. The main types of heart disease with sufficient numbers of patients available to analyze seasonal changes revealed no statistically significant periodicity (p = 0.30, p = 0.58, p = 0.51, and p = 0.75 for aortic stenosis, mitral insufficiency, coronary artery disease, and coronary artery disease with mitral insufficiency, respectively). Conversely, statistically significant seasonal variations for the monthly aggregated data were observed for T2DM (p<0.02), smoking (p<0.001), and elderly (p<0.001) patients alone. Therefore, further analyses were based on these significant variables alone, and their pairwise and combined coexistence were also examined.

Seasonal variabilities in cardiac surgery patients

Seasonal patterns of statistically significant factors (T2DM alone, smoking alone, and aging alone) for the monthly aggregated data over the 12-year study period are shown in Fig 2. The proportion of cardiac surgeries in patients who had T2DM or smoking peaked during the winter months and decreased in the summer. Conversely, the seasonal peak for elderly patients was observed in the summer and was lowest in the winter months.

Fig 2

Seasonal changes in the proportion of surgeries associated with type 2 diabetes mellitus (T2DM only), smoking (SM only), and aging (Elderly only) for the monthly aggregated data over the 12-year study period (January 1, 2007 to December 31, 2018).

Seasonal variations in the proportion of cardiac surgeries for patients with paired combinations of the significant factors are shown in Fig 3. No statistically significant seasonal variations were observed in elderly patients with T2DM (p = 0.66) or smoking (p = 0.46). However, the apparent seasonal variations of T2DM and smoking were additive, resulting a marked and statistically significant effect with peak occurrences of these patients in winter and lower in the summer months.

Fig 3

Seasonal changes in the proportion of surgeries associated with combined smoking and aging (SM + Elderly), type 2 diabetes mellitus and aging (T2DM + Elderly), and type 2 diabetes mellitus and smoking (T2DM + SM) for the monthly aggregated data over the 12-year study period (January 1, 2007 to December 31, 2018).

Table 1 summarizes the main parameters of the seasonal variations observed for the statistically significant individual factors (T2DM, smoking, and aging) and their combination (T2DM and smoking) that demonstrated statistically significant seasonality (p<0.001). The goodness of fit of a simple harmonic trend to the data was excellent (>0.9) for the seasonality in T2DM, elderly, and smoking T2DM patients who underwent cardiac surgery. The model fit was worse for seasonal change for smoking only patients; however, a highly significant periodic trend was still observed. The proportion of cardiac surgery patients with T2DM and smoking peaked in January and December, respectively, whereas elderly patients most frequently underwent cardiac surgeries in June. To express the magnitude of seasonal differences in the observed risk factors for cardiac surgery, the amplitudes of each factor relative to the mean rate ([peak—mean]/mean) and to the nadir ([peak—nadir]/nadir) were also calculated. The greatest seasonal variability was observed for the relative proportion of smoking patients with T2DM, with values indicating that the rate of such patients at the cardiac surgery unit was more than double in November compared with that in May. The magnitude of seasonal variations in the proportion of cardiac surgeries associated with elderly, smoking, and T2DM patients were lower, but still demonstrated a markedly increased relative risk for cardiac surgeries in the corresponding peak periods.

Table 1

Characteristic parameters of seasonality for statistically significant variables.

	T2DM alone	SM alone	Elderly alone	SM + T2DM
Significance	p = 0.0184	p<0.001	p<0.001	p<0.001
Goodness of fit	0.92	0.51	0.95	0.97
Peak (month)	January	December	June	November
Nadir (month)	July	May	December	May
(peak–mean)/mean (%)	9.6	16.4	20.3	42.6
(peak–nadir)/nadir (%)	19.2	34.3	52.1	107.0
Maximum increase in proportion of cardiac surgeries (month)	October	August	March	September

Maximum increase in the proportion of cardiac surgeries associated with the different pathologies refers to the peak of the first derivative of the fitted seasonality curves. T2DM: type 2 diabetes mellitus; SM: smoking.

Subgroup analyses: Risk factors for cardiovascular complications

S1 Fig in S1 File demonstrates the seasonal variations in the potential risk factors for cardiovascular complications, such as the systolic and diastolic arterial blood pressure, and serum levels of triglyceride, total cholesterol and glucose in group of patients with significant factors for seasonal changes (T2DM alone, smoking alone, and aging alone). Averaging data over the 12-year study period revealed significant seasonal changes in systolic ([peak-nadir]/nadir: 6.1%, p<0.001, with peak in February-March) and diastolic blood pressures (4.4%, p<0.05, with peak in January) and serum triglyceride in diabetic patients (17.1%, p<0.005, with peak in December). Significant seasonal variations were also observed in systolic and diastolic blood pressures in smoking patients (6.1% and 6.5%, respectively, p<0.001 for both, with peaks in February), and serum cholesterol in elderly patients (9.1%, p<0.001, with peak in February).

Discussion

The present study analyzed the medical records of all adult consecutive patients in the past 12-year period at the cardiac surgery unit in our tertiary-care university hospital. Our analyses revealed that the monthly proportion of patients undergoing cardiac surgery with diabetes, smoking, and elderly age exhibited seasonal variation. Non-elderly patients with diabetes and/or smoking showed a peak proportion rate during the winter, whereas heart surgery in elderly patients without diabetes and smoking was most frequently required in the summer. Concomitant occurrence of diabetes and smoking had an additive effect on the proportion of cardiac surgeries associated with the observed pathologies, while the simultaneous presence of older age and diabetes or smoking eliminated the seasonal variation.

Emphases were made on the accuracy and adequacy of data registration. The risk factors examined for seasonal changes (diabetes, smoking and elderly) were identified based on objective, and internationally well-defined diagnostic criteria. Data registration was performed by a stable staff of clinicians including five specialized anesthesiologists during the whole study period on the day prior to the surgery. Since the standard practice at our institution is to avoid waiting lists longer than five days, the seasonal trends observed in the present study accurately reflect the worsening and exacerbation of cardiovascular diseases requiring surgical interventions.

One of the main findings of the present study was a significant elevation in the proportion of cardiac surgeries associated with diabetes during the coldest months of the year. The sinusoidal seasonal trend suggested that the relative risk for patients with diabetes undergoing cardiac surgery during the winter period is almost 20% higher than that in the summer (Fig 2 and Table 1). In patients with diabetes, hyperglycemia leads to endothelial dysfunction, resulting in low-grade inflammatory, prothrombotic, proliferative, and vasoconstrictive processes [22]. These mechanisms may converge and lead to hypertension, atherosclerotic cardiovascular disease, and heart failure [2, 3, 23]. Hypertension may be worsened in a cold environment [4, 24, 25], elevating the myocardial workload and myocardial oxygen demand, or exacerbating functional valve insufficiencies. In addition to these mechanisms, viral infections [26] and/or vitamin deficiency [27] may also be involved. This seasonality is reflected in the high seasonal variation in serum glucose level [28] and the incidence rate of type 1 [26] and T2DM [29, 30] during the coldest months. The systolic and diastolic blood pressures at admission along with the serum triglyceride was significantly higher the present T2DM cohort in January than July over the 12-year study period (S1 Fig in S1 File). Severe manifestation of all these pathologies requires surgical intervention more frequently during winter for coronary and aortic valve diseases (S1 Table in S1 File).

The proportion of smoking patients undergoing cardiac surgeries without T2DM or old age was lower than those with T2DM alone in our population. Since the seasonal changes were similar in smoking patients and patients with T2DM only, the relative peak-to-peak seasonal variability reached 34% (Fig 1 and Table 1). Similar to T2DM, smoking is also characterized by a blunted response to endothelium-dependent vasodilators due to a diminished bioavailability of nitric oxide [31]. Therefore, the mechanisms also triggered by endothelial dysfunction and subsequent elevated vascular tone may be responsible for the seasonal variations of smoking patients in the cardiac surgery population. Common pathophysiological processes in T2DM and smoking responsible for the seasonality were confirmed by the additive effect of these factors, as it is also reflected in the systolic and diastolic blood pressures in the coldest season (S1 Fig in S1 File). Therefore, the relative peak-to-peak seasonal variability reached more than 100% in smoking patients with diabetes (Fig 3 and Table 1).

A further significant seasonal variability in the cardiac surgery cohort was observed for elderly patients without T2DM or smoking with peak-to-peak seasonal variability >50% (Fig 1 and Table 1). In contrast with smoking and T2DM patients, the proportion of elderly patients undergoing cardiac surgeries peaked in summer. This opposite trend in morbidity may be attributed to compromised elasticity of large conductive arteries [32]. Stiffening of the large arteries makes elderly people susceptible to hypovolemia and hypotension [33]. While seasonal variations were masked by the multifactorial comorbidities of this elderly cohort, the significantly lower diastolic blood pressure observed in the summer months (73.1 mmHg in June-August) compared with winter season (76.5 mmHg in December-February, p<0.05) is in agreement with previous findings, demonstrating that exacerbation of symptoms is expected to be more frequent during the warmest season.

Interestingly, seasonal variability disappeared if aging was associated with diabetes or smoking (Fig 3). The lack of seasonality in these comorbidities may be attributed to the superposition of two sinusoidal waves of aging and diabetes or smoking. Since these waves have similar a period but opposite phases, the periodicity is eliminated. While the lack of season-dependent periodicity in these patients mimics an invariable monthly proportion, these patients are still exposed to both individual risk factors of aging, diabetes, or smoking.

There was no evidence for seasonal changes in the proportion of patients undergoing surgeries for their specific heart diseases (i.e., aortic stenosis, mitral insufficiency, or coronary artery disease) for the whole population. Seasonal variation may be related to peripheral vasculature sensitivity to temperature changes rather than the type of cardiac disease. This suggests that diabetes, smoking, and aging are the primary season-dependent factors regardless of the nature of heart pathology. Gender and BMI do not directly affect the peripheral vasculature, as these factors exhibited no seasonal appearance.

An important feature of our findings is related to local climate. Hungary is situated in East-Central Europe and has four seasons with a continental climate. The Hungarian Meteorological Service calculated the average monthly temperature from daily averages, which varied between 0.7°C (33.3°F) in January and 23.1°C (73.6°F) in July during the 12-year study period in our region (Fig 4). Our findings may represent the seasonal changes of cardiovascular comorbidities of diabetes, smoking, and aging in the temperate climate zone of the world where the majority of the human population resides.

Fig 4

Monthly temperature (mean and SD) calculated from the daily averages according to the Hungarian Meteorological Service for the 12-year study period (January 1, 2007 to December 31, 2018) in South-East Hungary.

Some limitation related to the present findings warrant consideration. Hungarian health insurance system provides benefit coverage to all citizens. In addition, free medication is available for low-income patients. These factors blunt the influence of different financial background on patient care. However, the continuous health control may be looser in some patients with social negligence that may be a biasing factor in the cardiovascular effects of diabetes. This biasing effect is expected to play a minor role in our findings due to involvement of large cohort for an extended period of time. However, generalization of our findings to other regions with other social and health care systems requires the consideration of local socioeconomic factors, national and institutional scheduling policies.

Conclusions

In conclusion, analyses of the monthly proportion of cardiac surgeries associated with diabetes, smoking, and elderly age showed seasonal variations, demonstrating a periodicity in exacerbation of cardiovascular diseases requiring surgical intervention. Matching the intensity and control rate of care to these periodicities could prevent worsening of cardiovascular status in diabetes, elderly, and smoking patients. Cardiovascular risk factors should be systematically assessed at least twice per year in diabetes, smoking, and elderly patients. Cardiovascular assessments should primarily occur during the fall–winter period in diabetes and smoking patients, and the spring–summer season in elderly people. Optimal management of patients with diabetes require more frequent cardiovascular risk assessment than the recommended annual control [2, 15], at least three times a year with a focus on the blood pressure and triglyceride assessments. Considering the seasonal trends for risk factors affecting at least half whole population may improve patient and social health care.

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26 Apr 2022

SEASONAL CHANGES IN INCIDENCE OF PATIENTS WITH DIABETES UNDERGOING CARDIAC SURGERY

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Reviewer #1: The author indicated the seasonal chaneges in the incidence of cardiac surgery in the patients with diabetes, smoking and/or aging. One of the main findings of the study was a significant elevation of the incidence of patients with diabetes during the coldest months of the year. They speculated the mechanism using several references, and indicated the possible concerning about high blood pressure and triglyceride.

The authors state that there was no seasonal change due to the difference in heart surgery, but isn't there seasonality in the amount of heart surgery? That point should be stated.

The authors should state what they should be especially careful about when conducting seasonal examinations for DMs, smokers, and the elderly.

Reviewer #2: As stated in the title and elsewhere in the manuscript the goal of the authors was to analyze the seasonal variation in the incidence of patients with diabetes undergoing cardiac surgery.

I have several comments to make about this manuscript but the major one is about the definition the authors provide for incidence rate (methods page 6): “ incidence rates were calculated as the number of patients with a given risk factor … divided by the total number of patients in the same month.” The authors use diabetes, advanced age and smoking as outcomes and consider the entire population of hospitalized patients in a given month as the population exposed, the population at risk of developing such outcomes. This is plainly wrong. A more precise description of the authors’ work is a description of seasonal changes in the distribution of risk factors. Only percentages are provided therefore we do not know if the actual number of patients, those with DM for example, increases or decreases in a given month.

Second, the authors provide only p values both in the abstract and the results. As if p values were the only numerical measure worth reporting. An actual numerical measure should be provided for each one of the results, not just their p values.

Methods are insufficiently described. For example the last sentence in the abstract method section belong to the discussion. The authors should clarify the regression methods they used and the results they are referring to. Please clarify exclusion criteria both in the flow chart and the methods. Please explain the following sentence in Methods: “Since patient records were discarded in case of emergency reoperations as a consequence of tamponade or acute bleeding, patients were included in the analyses only after a primary or redo open heart surgeries”.

“Since there was no difference in cardiovascular status between T2DM patients treated with insulin or oral antidiabetics…” How was this assessed?

Results: “In accordance with the diagnostic criteria, HbA1c was significantly higher in patients with diabetes…” This is not a result. Since higher A1c was used to define patients with DM, by definition it is going to be higher in these patients. Why the group None from the flow chart was not included in the figures. I am unclear about which results were achieved from regression vs. stratification.

Reviewer #3: Thank you for inviting me to review this manuscript. This study aims to determine seasonal trends of aging, diabetes, and smoking in a representative sample size from a single center in Hungary. The results showed higher incidence of non-elderly patients with diabetes and smoking during winter periods, while elderly population was predominant in summer periods. This study is interesting and novel, however there are several methodological flaws that reduces its utility in clinical practice. Although personally I am not convinced that season influences the volume of perioperative comorbidities, this study might elucidate further ideas/research in this area. Below I have made some comments that would improve the quality of the manuscript.

Major comments

(1) Abstract: Primary objective should be explicitly written in the abstract and introduction. Please mention what your primary outcome was? I am confused with this statement “Potential additive or subtractive effects of the coexistence of these factors” please re-write and clarify what effects were measured. Authors should provide more details in methods: what statistical technique did they use to measure seasonal effect? Authors should give more than p values.. what effect size did they use?

(2) Introduction: authors should specify the rationale of this study. What is the primary intention of discovering seasonal changes of commorbidities among cardiac surgery patients?

(3) Statistical analysis: the description is incomplete and inconsistent. Please explain how did you measure “significant seasonal change” and “season variability”, provide the effect size that you used, and what method was used to estimate P values. All this information should be clearly stated in the manuscript. Be aware that P values do not give a complete picture and have several limitations. Additionally, the authors did not explain what technique was used for the charts (figures - modeling).

(4) The authors mentioned “goodness of fit” method in results, but there is no explanation of this in methods.

(5) Several limitations should be discussed more broadly in the discussion. For instance, the lack of adjustment for socioeconomic factors, national policies, and institutional scheduling policies.

(6) The seasonal changes of systolic/diastolic pressures in diabetic patients and smokers is interesting and should be described in more detail. I would encourage the authors to present these results in a separate paragraph “Subgroup Analysis”.

(7) Discussion: “This opposite trend in mortality may be attributed to compromised elasticity” Authors did NOT present any data of mortality in this manuscript. Please be consistent.

**********

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14 Jun 2022

Reviewer #1:

“The author indicated the seasonal chaneges in the incidence of cardiac surgery in the patients with diabetes, smoking and/or aging. One of the main findings of the study was a significant elevation of the incidence of patients with diabetes during the coldest months of the year. They speculated the mechanism using several references, and indicated the possible concerning about high blood pressure and triglyceride.”

Reply 1: We thank the Reviewer for the thoughtful comments contributing to the clarification of these important methodological details.

“The authors state that there was no seasonal change due to the difference in heart surgery, but isn't there seasonality in the amount of heart surgery? That point should be stated.”

Reply 2: This methodological detail is addressed in detail by examining thoroughly the monthly changes in the amount of heart surgery. As the figure demonstrates, there is no seasonal trend in the data in this respect. Only the holyday seasons (July-August and December) show a decrease in the number of patients due to the scheduling the cardiac surgeries by taking into account the availability of human resources available at the university hospital. Please consider that seasonality was demonstrated in the relative incidence rate, which is independent from the total number of patients and thus, the drops in the holyday seasons have no impact on the conclusions of the paper. It is also important to note that this decrease in the total number of patients was not associated with statistically significant differences (p=0.21 by chi-square test) in the relative number of cardiac surgery patients with different diagnoses (see last columns of the table). This conclusion is further confirmed by the sensitivity analyses asked by Reviewer 2. The revised manuscript includes this point (page 7 bottom, page 8 top) in the main manuscript and Table S2 in the online data supplement).

“The authors should state what they should be especially careful about when conducting seasonal examinations for DMs, smokers, and the elderly.”

Reply 3: The manuscript has been extended by this suggestion of the Reviewer by stating the critical factors when conducting seasonal examinations. Emphases were made on the accuracy and adequacy of data registration. The risk factors examined for seasonal changes (diabetes, smoking and elderly) were identified based on objective, and internationally well-defined diagnostic criteria. Data registration was performed by a stable staff of clinicians including five specialized anesthesiologists during the whole study period on the day prior to the surgery. A further important factor is the lack of waiting list in our institution, since patient flow was not limited by infrastructural available from the human of other resource side. These considerations has been added to the revised manuscript (page 10, bottom).

Reviewer #2:

“As stated in the title and elsewhere in the manuscript the goal of the authors was to analyze the seasonal variation in the incidence of patients with diabetes undergoing cardiac surgery.”

Reply 1: We thank the Reviewer for the thorough revision of our paper and for the pertinent comments contributing to the improvement of our manuscript. Please find below our replies and clarification in a point-by-point fashion.

“I have several comments to make about this manuscript but the major one is about the definition the authors provide for incidence rate (methods page 6): “ incidence rates were calculated as the number of patients with a given risk factor … divided by the total number of patients in the same month.” The authors use diabetes, advanced age and smoking as outcomes and consider the entire population of hospitalized patients in a given month as the population exposed, the population at risk of developing such outcomes. This is plainly wrong. A more precise description of the authors’ work is a description of seasonal changes in the distribution of risk factors. Only percentages are provided therefore we do not know if the actual number of patients, those with DM for example, increases or decreases in a given month.”

Reply 2: We thank the Reviewer for noting the need for clarification of the population description. It was indeed not clearly stated in the original manuscript that our seasonality analyses were performed only on new patients for cardiac surgeries in each month. Therefore, we have used incidence rather than prevalence in the manuscript, as the latter would have suggested the total number of cases in a given timeframe. This consideration was elaborated by adding more details to the description of the data analyses (page 6).

In agreement with the Reviewer’s suggestion, we have re-analyzed the results by modifying the population to carry out sensitivity analyses. First, we have constructed a base population in which all patients with “T2DM alone”, “T2DM and smoking” and “smoking alone” were excluded. Then we have added “T2DM alone”, “T2DM and smoking” and “smoking alone” as single factor to the population in the new seasonality model. Secondly, we have constructed another base population in which all patients with “T2DM and smoking”, “T2DM alone”, “smoking alone” and “elderly age” were excluded. The seasonality analyses were then carried out, where we have added to the base population only a single variable among those which were excluded. That is, “smoking alone”, “T2DM and smoking”, “T2DM alone” and “elderly age” variables were also analyzed using this reconstructed population. These models have revealed similar significant peaks for seasonality as compared to those reported in the original manuscript for “smoking alone”, “T2DM and smoking”, “T2DM alone” variables. However, there is a slight shift in peak of seasonality for the “elderly age” variable. Thus, the sensitivity analysis is in agreement with our reported results and confirms our main messages. We attach the statistical output of these additional analyses for the interest of the Reviewer (results of the Walter-Elwood method). These outputs also include the actual number of patients for a given month.

“Second, the authors provide only p values both in the abstract and the results. As if p values were the only numerical measure worth reporting. An actual numerical measure should be provided for each one of the results, not just their p values.”

Reply 3: Based on the Reviewer’s comment, numerical values have been added to the Abstract. We were aiming at avoiding the presentation of the same data in both a table/figure and in the textual Results section of the manuscript, since this may be generally considered as redundant data report. In agreement with the Reviewer’s comment, the entire Results section was revised to include the most important numerical outcomes, where relevant. Please note that where the statement is related to the presence or absence of a statistical significance, reporting a p value summarizes the findings appropriately.

“Methods are insufficiently described. For example the last sentence in the abstract method section belong to the discussion.“

Reply 4: The quoted sentence describes an important methodological detail relevant to our data analyses, and we sought to clarify this point in the Abstract, not only in the manuscript body. In the presence of a waiting list, the relative incidence of the different disorders (e.g., T2DM, smoking and elderly) in the cardiac surgery population would not reflect exacerbation of cardiovascular disorders and would have biased our results accordingly. Based on the Reviewers comment, this sentence was removed from the Abstract.

“The authors should clarify the regression methods they used and the results they are referring to. “

Reply 5: We thank the Reviewer to point out the need for a more detailed description of the statistical method. This part of the manuscript (page 6) has been expanded substantially to clarify that geometric models were used to investigate seasonality by assuming that seasonal fluctuations of an event occur on a fixed date every year and might be described using cyclic patterns over a period of time. The deviance statistic was used to check a goodness of fit for negative binomial regression models.

“Please clarify exclusion criteria both in the flow chart and the methods.“

Reply 6: The exclusion criteria were clarified both in the main text of the manuscript (page 4 and 7) and in the legend of Fig. 1 (page 17).

“Please explain the following sentence in Methods: “Since patient records were discarded in case of emergency reoperations as a consequence of tamponade or acute bleeding, patients were included in the analyses only after a primary or redo open heart surgeries”.”

Reply 7: The rationale for this methodological statement was indeed not clear in the original manuscript. Taking into account surgical procedures related to tamponade and/or bleeding in the relative incidence of the different disorders (e.g., T2DM, smoking and elderly) in the cardiac surgery population would not reflect exacerbation of cardiovascular disorders, as it is the case for other type of primary or redo open heart surgeries. Accordingly, these cases were not assessed for eligibility to avoid the potential bias of these correction surgeries on the definition of exact incidence. Based on the Reviewers comment, the relevant part of the manuscript has been expanded to clarify this methodological consideration (page 4).

“ “Since there was no difference in cardiovascular status between T2DM patients treated with insulin or oral antidiabetics…” How was this assessed?”

Reply 8: We thank the Reviewer for raising the need for evidence in this statement. Systematic assessment of the cardiovascular status of T2DM patients treated with insulin or oral antidiabetics would have required prospective analyses with a need of large cohort due to the clinician- and patient-related variabilities. Thus, such measurements were not in the focus of the present study. This sentence has been moderated accordingly (page 5, top).

“Results: “In accordance with the diagnostic criteria, HbA1c was significantly higher in patients with diabetes…” This is not a result. Since higher A1c was used to define patients with DM, by definition it is going to be higher in these patients. Why the group None from the flow chart was not included in the figures. I am unclear about which results were achieved from regression vs. stratification.”

Reply 9: We fully agree with the Reviewer on the questionable mixture of the diagnostic criteria and the Results. We thought that reporting the exact HbA1c is informative in assessing the difference between the patients with or without diabetes, however it makes no sense to perform a statistical analysis on this outcome. Accordingly, the questioned statistical outcome was omitted from the revised Results section (page 7).

As concerns the question of inclusion of data on the charts, please consider that only those variables were presented on the figures where significant seasonal variations for the monthly aggregated data were observed, i.e., T2DM (p<0.02), smoking (p<0.001), and elderly (p<0.001) patients alone and their combined coexistence. This is clarified in the Methods section of the paper (page 8, paragraph 2). Including other groups of patients on the graphs with no seasonal variation (e.g., group “None”) would somewhat overload the figures and thereby divert the focus of the readers from the main message.

Reviewer #3:

“Thank you for inviting me to review this manuscript. This study aims to determine seasonal trends of aging, diabetes, and smoking in a representative sample size from a single center in Hungary. The results showed higher incidence of non-elderly patients with diabetes and smoking during winter periods, while elderly population was predominant in summer periods. This study is interesting and novel, however there are several methodological flaws that reduces its utility in clinical practice. Although personally I am not convinced that season influences the volume of perioperative comorbidities, this study might elucidate further ideas/research in this area. Below I have made some comments that would improve the quality of the manuscript.”

Reply: We thank the Reviewer for the thorough revision of the manuscript and for the thoughtful comments contributing greatly to the improvement of the paper.

“Major comments

“(1) Abstract: Primary objective should be explicitly written in the abstract and introduction. Please mention what your primary outcome was? I am confused with this statement “Potential additive or subtractive effects of the coexistence of these factors” please re-write and clarify what effects were measured. Authors should provide more details in methods: what statistical technique did they use to measure seasonal effect? Authors should give more than p values.. what effect size did they use?” “

Reply 1: We thank the Reviewer for raising the need for this important clarification. The primary objective of the present study is expressed more explicitly in the revised version of the Abstract (page 2) and the Background (page 3 bottom and page 4 top), in agreement with the Reviewer’s request. The examination of the additive and subtractive factors was also clarified in the Abstract by rewording this part of the abstract with avoiding these ambiguous terminologies (page 3). Further details are included in the revised manuscript about the statistical technique to measure seasonal effect (page 6). Furthermore, the entire Abstract and Results sections were revised to report our findings in a more detailed manner than giving p values only. In accordance with the Reviewer’s comment, the statistical technique to measure seasonal effects is described in a more detailed manner in the revised manuscript with citing more relevant literature (page 6, paragraph 1), and the effect size is also specified (page 6).

“(2) Introduction: authors should specify the rationale of this study. What is the primary intention of discovering seasonal changes of commorbidities among cardiac surgery patients?”

Reply 2: The Introduction has been rewritten to state clearly the rationale of the study (page 3 bottom and page 4 top).

“(3) Statistical analysis: the description is incomplete and inconsistent. Please explain how did you measure “significant seasonal change” and “season variability”, provide the effect size that you used, and what method was used to estimate P values. All this information should be clearly stated in the manuscript. Be aware that P values do not give a complete picture and have several limitations. Additionally, the authors did not explain what technique was used for the charts (figures - modeling).”

Reply 3: We thank the Reviewer for noting the need for a more detailed description of the statistical analyses. We fully agree that p values do not give a complete picture and have several limitations and thus, we added the key figures to the Abstract and the Results section.

In our study, the geometric models were used to investigate seasonality, which was introduced by Edwards in 1961 [1]. Here, we assume that seasonal fluctuations of an event occur on a fixed date every year and might be described using cyclic patterns over a period of time. Walter and Elwood developed this model by adding the population at risk [2]. Stolwijk et al described the use of this method for general linear models [3]. We have used in our analyzes both Walter-Elwood test and negative binomial regression methods. The power of the Walter-Elwood test is 100% [4-5]. These details have been added to the revised manuscript (page 6).

As concerns the Reviewer’s comment on the effect size, please consider that if statistical power is high, the likelihood of deciding there is an effect, when one does exist, is high [6].

The charts were prepared by using SigmaPlot software package (Version 13, Systat Software, Inc. Chicago, IL, USA); this is now specified in the revised manuscript (page 6).

[1] Edwards, J. H. (1961): The recognition and estimation of cyclic trends. – Ann Hum Genet 25: 83-87.

[2] Walter, S. D., Elwood, J. M. (1975): A test for seasonality of events with a variable population at risk. – Br J PrevSoc Med 29: 18-21.

[3] Stolwijk, A.M., Straatman, H., Zielhuis, G. A. (1999): Studying seasonality by using sine and cosine functions in regression analysis. – J Epidemiol Community Health 53: 235-238.

[4] Walter, S. D. (1977): The power of a test for seasonality. – Br J PrevSoc Med 31: 137-140.

[5] Barnett, A. G., Dobson, A. J. (2010): Analysing Seasonal Health Data. – Springer, ISBN 978-3-642-10748-1.

[6] Gail M. Sullivan, MD, MPH and Richard Feinn, PhD. Using Effect Size—or Why the P Value Is Not Enough. Journal of Graduate Medical Education, September 2012

“(4) The authors mentioned “goodness of fit” method in results, but there is no explanation of this in methods.”

Reply 4: We thank the Reviewer for noting this lack of this information in the Methods. In generally, a chi-square test was applied to check a goodness of fit. The deviance statistic was used to check a goodness of fit for negative binomial regression models. Similarly, Walter and Elwood described a goodness of fit calculation [2] which was also applied. All analyses were carried out using STATA version 17 statistical software to confirm the goodness of fit of models. This methodological detail has been added to the revised manuscript (page 6).

“(5) Several limitations should be discussed more broadly in the discussion. For instance, the lack of adjustment for socioeconomic factors, national policies, and institutional scheduling policies.”

Reply 5: Discussion of the limitation of our findings is an important point. Accordingly, the manuscript has been expanded to include this aspect (page 13 bottom, page 14 top).

“(6) The seasonal changes of systolic/diastolic pressures in diabetic patients and smokers is interesting and should be described in more detail. I would encourage the authors to present these results in a separate paragraph “Subgroup Analysis”.”

Reply 6: We thank for this suggestion, these risk factors for cardiovascular complications are emphasized more in the revised manuscript by including these results in a separate paragraph titled “Subgroup analyses: risk factors for cardiovascular complications” (page 9, bottom).

“(7) Discussion: “This opposite trend in mortality may be attributed to compromised elasticity” Authors did NOT present any data of mortality in this manuscript. Please be consistent.”

Reply 7: We thank for picking up this mistake. We meant to write here “morbidity” instead of mortality, as the latter was indeed not assessed. The sentence has been corrected accordingly (page 12).

Submitted filename: Seasonality-Reply-to-Reviewers-v2.docx

Click here for additional data file.

5 Jul 2022

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

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Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: I Don't Know

Reviewer #3: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The author indicated the seasonal changes in the incidence of cardiac surgery in the patients with diabetes, smoking and/or aging. One of the main findings of the study was a significant elevation of the incidence of patients with diabetes during the coldest months of the year. They speculated the mechanism using several references, and indicated the possible concerning about high blood pressure and triglyceride.

The authors are well responding to the reviewer's questions and comments. The manuscript is well revised and suitable for the publication.

Reviewer #2: The authors have greatly improved the manuscript. However an important point in my opinion was not addressed. I appreciate the fact that only new surgeries were taken into account was highlighted and the sensitivity analysis using different base populations, with and w/o the risk factors, but I believe that what the authors describe is not incidence. The denominator for the incidence is the population exposed to the risk of an event. If the event is cardiac surgery and the exposure is type 2 diabetes or smoking, then the population exposed would be all people with diabetes or all smokers. Certainly not all people undergoing cardiac surgery. If the authors consider as the event “a person having diabetes AND undergoing cardiac surgery” then the population at risk would be all people with cardiac disease, with and w/o diabetes.

Even if we make several reasonable assumptions: the need for cardiac surgeries among people without diabetes remains constant; the number of people at risk, with DM or smokers remain constant throughout the change of season, it is unclear why the authors present their results as incidence variations.

For example, if among 100 people undergoing cardiac surgeries in one month there are 30 with diabetes, and the following month the number of people with diabetes who have surgery doubles because of temperature variation, then we would have a proportion of surgeries associated with diabetes, given the above assumptions, from 30% to 46% which is not the measure of incidence variation.

Reviewer #3: Thank you for addressing most of the reviewers' comments. However, the explanation about the effect size still remains unclear. I understand that the percentage of change (variation) is the main effect size for the association between seasonal variation and health parameters. Is that right? Please be more concise and explicit in this regard.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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13 Jul 2022

Reviewer #2:

“The authors have greatly improved the manuscript. However an important point in my opinion was not addressed. I appreciate the fact that only new surgeries were taken into account was highlighted and the sensitivity analysis using different base populations, with and w/o the risk factors, but I believe that what the authors describe is not incidence. The denominator for the incidence is the population exposed to the risk of an event. If the event is cardiac surgery and the exposure is type 2 diabetes or smoking, then the population exposed would be all people with diabetes or all smokers. Certainly not all people undergoing cardiac surgery. If the authors consider as the event “a person having diabetes AND undergoing cardiac surgery” then the population at risk would be all people with cardiac disease, with and w/o diabetes.

Even if we make several reasonable assumptions: the need for cardiac surgeries among people without diabetes remains constant; the number of people at risk, with DM or smokers remain constant throughout the change of season, it is unclear why the authors present their results as incidence variations.

For example, if among 100 people undergoing cardiac surgeries in one month there are 30 with diabetes, and the following month the number of people with diabetes who have surgery doubles because of temperature variation, then we would have a proportion of surgeries associated with diabetes, given the above assumptions, from 30% to 46% which is not the measure of incidence variation.”

Reply: We thank the Reviewer for appreciating our efforts to revise our paper based on the previous highly pertinent comments. We are also indebted for highlighting the remaining concern with the use of “incidence” as a potentially misleading terminology. We fully accept the detailed explanation of the correct interpretation of our data and changed the term “incidence” into “proportion of surgeries associated with diabetes/smoking/elderly” throughout in the revised manuscript.

Reviewer #3:

“Thank you for addressing most of the reviewers' comments. However, the explanation about the effect size still remains unclear. I understand that the percentage of change (variation) is the main effect size for the association between seasonal variation and health parameters. Is that right? Please be more concise and explicit in this regard.”

Reply: We are grateful to the Reviewer for acknowledging our revision and we thank for noting for the need of clarifying this methodological detail. In agreement with the Reviewer’s statement, the percentage of change (variation) is the main effect size for the association between seasonal variation and health parameters. This is now specified explicitly in the revised manuscript (page 6, paragraph 1).

Submitted filename: PLOS_2nd_Reviewers_QA.docx

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23 Aug 2022

SEASONAL CHANGES IN PROPORTION OF CARDIAC SURGERIES ASOCIATED WITH DIABETES, SMOKING AND ELDERLY AGE

PONE-D-21-33749R2

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: I Don't Know

Reviewer #3: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: All my comments have been addressed satisfactorily by the authors. I have no further recommendations.

Reviewer #3: Thank you for addressing all my comments. This study highlights the importance of seasonal changes of comorbidities. Congratulation on a nice paper.

**********

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Reviewer #2: No

Reviewer #3: No

**********

26 Aug 2022

PONE-D-21-33749R2

SEASONAL CHANGES IN PROPORTION OF CARDIAC SURGERIES ASOCIATED WITH DIABETES, SMOKING AND ELDERLY AGE

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