Original Contribution

Meteorological parameters and seasonal variations in pulmonary thromboembolism

Funda Oztuna MD^a,?, Savas Ozsu MD^b, Murat Topbas MD^c, Yilmaz Bulbul MD^a,

Polat Kosucu MD^d, Tevfik Ozlu MD^a

^aDepartment of Chest, School of Medicine, Karadeniz Technical University, 61080 Trabzon, Turkey

^bVakfikebir Government Hospital, School of Medicine, Karadeniz Technical University, 61040 Trabzon, Turkey ^cDepartment of Public Health Disease, School of Medicine, Karadeniz Technical University, 61080 Trabzon, Turkey ^dDepartment of Radiology, School of Medicine, Karadeniz Technical University, 61080 Trabzon, Turkey

Received 30 October 2007; revised 18 December 2007; accepted 19 December 2007

Abstract

Background and objectives: In recent years, circannual variations in incidence and mortality for Venous thromboembolic disease have been demonstrated, with a peak in winter. However, several investigators have observed no seasonal variation in these diseases. The aim of our study was to investigate whether a seasonal variation, in terms of atmospheric pressure, humidity, and temperature, exists for pulmonary thromboembolism.

Method: We retrospectively included 206 patients with a diagnosis of pulmonary embolism (PE) between 1 June 2001 and 31 May 2006.

Results: The highest number of cases in the 5 years concerned occurred in May (29 cases). Although PE occurred most commonly in the spring (72 cases) and autumn (51 cases), the difference was statistically significant (P = .003). There were no case correlations with months and pressure, temperature, or humidity. However, there was a statistically significant positive correlation between case incidence and atmospheric pressure (r = 0.53, P b .0005) and humidity (r = 0.57, P b .0005). In terms of risk factors, seasonal distribution was not statistically significant as regards cases of embolism occurring for surgical or nonsurgical reasons (r = 0.588).

Conclusion: In terms of the relationship between seasons and embolism cases, despite the determination of an insignificant positive correlation, a statistically significant positive correlation was determined between air pressure and humidity and case incidence. There is now a need for further wide-ranging prospective studies including various hematological parameters to clarify the correlation between PE and air pressure.

(C) 2008

* Corresponding author. Department of Chest Diseases, Faculty of Medicine, Karadeniz Technical University, 61080 Trabzon, Turkey. Tel.:

+90 462 377 55 34; fax: +90 462 325 70 31.

E-mail addresses: [email protected], [email protected] (F. Oztuna).

Introduction

Pulmonary embolism (PE) is an important health problem. In recent years, circannual variations in incidence and mortality for venous thromboembolic disease have been demonstrated, with a peak in winter [1-4]. Other studies have

0735-6757/$ – see front matter (C) 2008 doi:10.1016/j.ajem.2007.12.010

shown a reduced incidence of PE in the winter, and a peak in spring and autumn, or peaks in summer and winter [5-7]. This may be explained by an increasing risk of thrombosis due to seasonal variations in environmental risk factors, diet, exercise, plasma lipids, hemorheological and coagulation factors, and seasonal variations in Respiratory infections [8]. However, other studies have shown no seasonal variation in PE or deep venous thrombosis [3,9-11].

As there are few data about the seasonal distribution of PE and its relationship to Weather conditions, we would like to submit for your attention the data from our study aimed at analyzing the influence of changes in meteorological parameters and seasonal variations in PE.

Method

Climate

We documented atmospheric pressure, humidity, and temperature changes as recorded by the Trabzon Provincial Department of Meteorology. Atmospheric pressure was expressed as millibars (mb), humidity as percentage, and air temperatures as degree Celsius. Atmospheric pressure, humidity and temperature data, and the average period between onset of symptoms and diagnosis were calculated, and in cases in which this period was unknown it was estimated as 3 days before presentation [12].

Table 1 Baseline characteristics of patients and risk factors for PE according to seasons

Our hospital serves 5 cities in Turkey, which share the same geographical and climatic features. The region has a mild climate. Summer is hot and wet (July mean temperature, 22.89?C; mean relative humidity [RH], 71.18%). Winter is cool and wet (January mean temperature, 4.67?C; mean RH, 68.76%). Spring and autumn have an intermediate average temperature (April mean temperature,

12.86?C; mean RH, 71.68%; October mean temperature,

15.48?C; mean RH, 72.40%).

Patients

We retrospectively included 206 patients with a diagnosis of PE evaluated by the Department of Pulmonary Medicine between 1 June 2001 and 31 May 2006. All cases diagnosed in the ED, other departments, and our own were enrolled in the study. Cases from other provinces with dissimilar climatic features to those of our region were excluded (13 patients). All patients diagnosed with embolism were included in the study, regardless of risk factors or comorbidities.

Diagnosis

The majority of patients were diagnosed using multislice spiral tomography in the angiography protocol, and a minority were diagnosed using isotope lung scanning. File examina- tions showed that clinical diagnosis had been made in 9 patients in whom these 2 techniques had not been used by joint

Characteristics	Winter (n = 51)	Spring (n = 72)	Summer (n = 35)	Autumn (n = 48)	P
Female/male sex	25/26	43/29	19/16	32/16	.32
Age, y	58.20 +- 18.18	61.46 +- 17.34	61.23 +- 15.86	58.56 +- 15.89	.65
Age N65 y	27	41	20	20	.25
Immobilization	21	29	17	20	.87
Trauma	1	7	3	5	.24
Surgery	20	21	12	19	.58
Recurrent PE	2	1	–	–	.33
Deep venous thrombosis	12	23	9	14	.75
Obesity	1	1	–	1	.74
Pregnancy	5	–	–	1	.008 *
Contraceptive drug use	–	3	–	1	.18
Malignancy	4	12	5	5	.47
Cardiovascular disease	9	10	5	10	.76
Chronic obstructive pulmonary disease	2	3	–	1	.43
neurologic disease	5	4	2	2	.70
No risk factor	–	2	1	–	.26
smoking history	18	20	14	11	.38
Diagnosis					.62
CT angiography	44	62	31	36
Isotope lung scan	5	8	2	9
Other	2	2	2	3
Severity
Mortality	7	6	1	2	.19
Massive/nonmassive PE	13/38	16/56	11/24	5/43	.92
* Statistically significant.

Fig. 1 The number of patients was highest in May (P = .01). However, there was no correlation between number of patients and PE (r = 0.44,

P = .150).

analysis of clinical scoring (Wells score), Doppler ultrasono- graphy, and D-dimer levels [13]. Diagnoses were reviewed according to British Thoracic Society guidelines [14].

• • 1. Computed tomography angiography

Multislide CT angiography was performed using a 4- and 16-channel multislice scanner (Somatom Volume Zoom and Sensation 16, Siemens, Erlangen, Germany). A 100-mL dose of iopromide (Ultravist 370, Schering, Berlin, Germany) was then injected intravenously at a rate of 2 mL/s using a power injector via the right antecubital vein. Bolus tracking was used to determine the start of acquisition. Technical parameters were detector collimation 4 x 1 mm and 16 x 0.75, pitch 1.75 and 1.15, reconstruction interval 1 mm and

0.7, slice thickness 1.25 mm and 0.75, table speed 14 mm/s, and gantry rotation time 0.5 s, 100 mA s, 120 kVp. For the diagnosis of acute PE, the following criteria were used: (I) a partial filling defect, defined as central or marginal intraluminal areas of low attenuation surrounded by variable amounts of contrast medium with regular or irregular borders;

(II) a complete filling defect, that is, intraluminal areas of low attenuation that were not surrounded by contrast medium and that occupied the entire arterial section; (III) the “railway

track” sign, thromboembolic masses seen floating freely in the lumen, allowing the flow of blood between the wall of the vessel and the thrombus and/or embolus; and (IV) mural defects, in cases of peripheral areas of low attenuation within arterial sections [15].

• • 1. Isotope lung scanning

Lung perfusion imaging was performed using a large field-of-view gamma camera (Siemens E-cam Dual-Head, Siemens, USA) equipped with a low-energy, parallel-hole, high-resolution collimator. A dose of 80 MBq of Tc99m- macroaggregated albumin (MAA) was administered intra- venously with the patients in a supine position. All planar images were in a 256 x 256 matrix for 500 K. Interpretation criteria for perfusion scanning (without ventilation imaging) were those suggested by the Prospec- tive Investigative Study of Acute Pulmonary Embolism (PISA-PED) study. PISA-PED criteria are defined as normal, near-normal, abnormal compatible with pulmonary embolism (PE+: single or multiple wedge-shaped Perfusion defects), or abnormal not compatible with pulmonary embolism (PE-: perfusion defects other than wedge shaped) [16].

Fig. 2 The number of patients was highest in the spring (P =

.003). However, the positive correlation between number of patients and PE was not significant (r = 0.80, P = .200).

Fig. 3 There was a negative correlation between number of patients and temperature (r = -0.80, P = .20), but no significant correlation between number of patients and humidity (r = 0.20, P =

.800).

Fig. 4 There was a significant positive correlation between number of patients and atmospheric pressure (r = 0.53, P b .0005).

Statistical analysis

Measurement data are expressed as mean +- SD. The ?2 test was used to compare baseline characteristics and the number of patients presenting in each month and season. A P value b .05 was regarded as indicating statistical significance. Pearson’s correlation was used to determine the correlation between the average atmospheric pressure, humidity, air temperature values, and PE incidence for each month and season. Data were analyzed using the SPSS statistical software (version 13.01, serial number 9069728, SPSS, Inc, Chicago, Ill).

Results

The demographic characteristics of the patients are summarized in Table 1. Apart from pregnancy, no statistical significance was determined in terms of demographic

characteristics and risk factors. Spiral CT was used as the diagnostic tool in the angio protocol in 173 patients, whereas 24 patients were diagnosed clinically and using isotope lung scanning, and other tests (echocardiography, electrocardio- graphy, etc) were used in 9 patients. Shortness of breath was the commonest symptom at admission at 74.3%, followed by chest pain at 27.7%, lateral chest pain at 24.7%, and coughing at 19.9%. The highest numbers of cases were seen in May (29), April (27), and October (23), and this difference was statistically significant (P = .01) (Fig. 1). With regard to atmospheric pressure, the lowest level was in July (974 +- 59.07 mb) and the highest in January (1015 +- 5.20 mb).

In seasonal terms, the greatest number of cases occurred in the spring months (72) followed by winter (51), autumn (48), and summer (35), and this distribution was also statistically significant (P = .003) (Fig. 2). There was positive correlation between embolism cases and air pressure (r = 0.80, P = .200), and a negative correlation with temperature (r = -0.80, P = .200), although these were not statistically significant (Figs. 2 and 3). In terms of months, there was no

Fig. 5 There was a significant positive correlation between number of patients and humidity (r = 0.57, P b .0005).

Fig. 6 The number of surgical patients was highest in May, although this was not significant (P = .588). However, there was no correlation between number of surgical patients and atmospheric pressure (r = 0.17, P = .595).

correlation between cases and atmospheric pressure (r = 0.44, P = .150), temperature (r = -0.52, P = .084), or humidity (r = 0.27, P = .396). However, there was a statistically significant positive correlation between case frequency and atmospheric pressure (r = 0.53, P b.0005) and humidity (r = 0.57, P b .0005) (Figs. 4 and 5).

Diagnosis duration (lag period between symptoms to diagnosis of PE) was 5.5 +- 10.6 days in surgical cases and

10.9 +- 12.6 days in nonsurgical cases, and this difference was statistically significant (P = .004). In terms of risk factors, considering embolism cases occurring for surgical and nonsurgical reasons, it was determined that distribution by month was not statistically significant (P = .588) (Fig. 6). In addition, no correlation was determined between surgical cases’ monthly distribution and pressure, humidity, or temperature. However, there was a negative correlation between the monthly distribution of medical cases and atmospheric pressure (r = -0.28, P = .375) and humidity (r =

-0.54, P = .069), although this was not statistically significant (Fig. 6).

Discussion

Previous studies have shown that hyperprothrombinemia may occur at low atmospheric pressure [17]. In addition, as stated above, seasonal factors have been indicated in various publications. The most important factor in the planning of this study was our realization that there is a rise in cases of embolism in our region in the spring months, particularly in April and May. Could the reason for this be dense fog and sudden variations in air temperature in those months? We aimed to find the answer in our study. Although there are views to the contrary, our study shows that PE does display seasonal variations.

In 1940, De Takats et al [18] reported the incidence of PE to be higher during the spring months and during periods of

low atmospheric pressure. In a similar, 91-case, study, Meral et al [19] determined a negative correlation between case frequency and pressure (r = -0.70; P b.0001). No correlation was determined between PE and atmospheric pressure, humidity, and temperature by Clauss et al [20] in a 316-case series. One of the interesting results of our study was a significant, positive correlation between increased atmo- spheric pressure and cases. These different results may be due to climatic differences in the study centers and their geographical structures. In addition to air pressure, when we examined the humidity and temperature data that may affect seasonal variation, although we determined a negative correlation with temperature, this was not statistically significant (r = -0.80, P = .200).

We determined no significant correlation between monthly distribution of cases and atmospheric pressure, humidity, or temperature. However, there were 2 striking points in that distribution (Fig. 1). The first was that, despite high atmospheric pressure in January, the number of cases was lower than in other months when air pressure was also high. We ascribed this to bad weather condition problems affecting patient transfer to our hospital in that month. The second point was that the number of cases was low in August, when air pressure was high. Applications to hospital generally decline in that month, as it coincides with the hazelnut harvest, and many physicians and patients are on their summer holidays. We concluded that outside the parameters affecting case distribution examined by us (such as air pressure, humidity, temperature, risk factors, etc), the social structure in the region may also be significant.

The other important finding of our study was the increased PE incidence in the spring, although there was no correlation between air pressure, humidity, and air temperature in the spring and the number of patients. In contrast to our results was a previous study by Stein et al

[21] considering a period of about 20 years (1979-1999) in which no difference was found in seasonal incidence of PE

in the entire United States. In a study comprising more than 780 patients, Manfredini et al [22] found that PE cases peaked in winter months independently of any risk factor.

Masotti [23] reported in a 457-case study that, although they determined a powerful correlation with atmospheric pressure in surgical cases, there was only a weak correlation between air pressure, humidity, and temperature in medical cases. In contrast, although there was an insignificant negative correlation between cases’ monthly distribution and air pressure (r = -0.28, P = .375) and humidity (r =

-0.54, P = .069) in our study, no correlation was determined

in medical cases. It was also determined in the same study that there was a significant increase in cold months in C- reactive protein (6.6 vs 4.3 mg/dL), D-dimer (1856 vs 1690 ng/mL), and thrombocyte (251.5 vs 189.4 x 10³/L) levels compared to hot months. Masotti emphasized that these results might support studies by Bull et al [24] and Keatinge et al [25], suggesting they might be linked to hemostatic and hemorheological parameters. One of the gaps in our study was the lack of any other Blood parameters, apart from D- dimer, that might support this. Had D-dimer levels been investigated in all cases in this study, it might have been possible to determine whether or not the test’s diagnostic value changed according to the season.

The highest mortality in a 2831-case series studied by Montes et al [26] in 1996 to 2001 occurred in the spring months (OR, 2.18; CI, 1.18-4.05), and 83% of the patients who died were older than 75 years. The highest mortality in our study was also in winter and spring, although this was not statistically significant. The average age of the 16 patients who died was 63.93 +- 13.68, and in contrast to the Montes et al study, 12.5% were older than 74 years. Looking to see whether this could be explained in terms of embolism risk factors, we saw that 8 (50%) of the dead patients had a surgical history and that massive embolism had occurred in all but one. We did not therefore find a satisfactory explanation for our patients being younger than the average.

Although venous thromboembolism was not statistically significant, it was determined as more of a risk factor in the spring. Gallerani et al [27] determined a significant increase in VTE cases in winter and suggested this might be due to increased hypercoagulability. No seasonal distribution of VTE was identified in some other studies, however [10,20]. Therefore, hypercoagulability appears not to be a sufficient explanation for the seasonal distribution of embolism.

In conclusion, in terms of the relationship between seasons and embolism cases, despite the determination of an insignificant positive correlation, a statistically significant positive correlation was determined between air pressure and humidity and case incidence. There is now a need for further wide-ranging prospective studies including various hemato- logical parameters to clarify the correlation between PE and air pressure.

Acknowledgment

The authors thank Numan Cam (director) and staffs of Trabzon Provincial Department of Meteorology, for help in collection of metrological data; Bircan Sonmez, MD for editorial help.

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