Extreme temperature increases the severity of intracerebral hemorrhage: An analysis based on the cold region of China

Xun Xu; Chunyang Liu; Rui Liu; Qiuyi Jiang; Enzhou Lu; Chao Yuan; Yanchao Liang; Huan Xiang; Boxian Zhao; Xin Chen; Ailing Lian; Qi Zhou; Guang Yang

doi:10.2478/fzm-2022-0024

Extreme temperature increases the severity of intracerebral hemorrhage: An analysis based on the cold region of China

doi: 10.2478/fzm-2022-0024

Xun Xu^{1, 2, #},
Chunyang Liu^{1, 2, #},
Rui Liu^{1, 2, #},
Qiuyi Jiang^{1, 2},
Enzhou Lu^{1, 2},
Chao Yuan^{1, 2},
Yanchao Liang^{1, 2},
Huan Xiang^{1, 2},
Boxian Zhao^{1, 2},
Xin Chen^{1, 2},
Ailing Lian³,
Qi Zhou⁴,
Guang Yang^{1, 2
, ,}

1.
Department of Neurosurgery, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
2.
Institute of Brain Science, Harbin Medical University, Harbin 150081, China
3.
Department of Nursing, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
4.
Research Administration Office, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, China

More Information

Corresponding author: Guang Yang, E-mail: yangguang1227@163.com

摘要

Abstract: Objective The purpose of this study was to find a suitable model to evaluate the relationship between temperature and intracerebral hemorrhage (ICH) and explore the effects of cold spells and heat waves on the clinicopathological parameters of ICH patients. Methods We conducted a retrospective study based on the ICH admission in the First Affiliated Hospital of Harbin Medical University from 2015 to 2020 (N = 11 124). The relationship between different seasons and the number of patients with ICH was explored. Poisson Akaike information criterion (AIC) was used to select the optimal model for temperature and ICH. Binary logistic regression analysis was used to investigate the association between extreme temperatures and clinicopathological features. Results Hospital admissions for patients with ICH showed monthly changes. The optimal cold spell was defined as the daily average temperature < 3rd percentile, lasting for five days, while the optimal heat wave was defined as the daily average temperature > 97th percentile, lasting for three days. Based on the generalized extreme weather model, cold climate significantly increased the risk of hematoma volume expansion (OR 1.003; 95% CI: 1.000-1.005, P = 0.047). In the optimal model, the occurrence of cold spells and heat waves increased the risk of midline shift in both conditions (OR 1.067; 95% CI: 1.021-1.115, P = 0.004; OR 1.077; 95% CI: 1.030-1.127, P = 0.001). Conclusion Our study shows that seasonal cold spells and heat waves are essential factors affecting ICH severity, and targeted preventive measures should be taken to minimize the pathological impacts.
- ambient temperature /
- intracerebral hemorrhage /
- hematoma volume /
- midline shift

HTML全文

Table 1. Demographic and clinical characteristics of ICH patients enrolled in the present study during the period from 2015 to 2020

Items	Total (N = 11 124)
Age, years	58.28 ± 12.76
Gender, N (%)
Male	7 435 (66.8)
Female	3 689 (33.2)
Drinking, N (%)	5 310 (50.3)
Smoking, N (%)	5 293 (50.1)
History of diabetes, N (%)	1 258 (11.5)
History of hypertension, N (%)	7 432 (67.9)
History of ischemic stroke, N (%)	1 493 (13.6)
HLOS, days	9.00 (6.00-12.00)^*
Body temperature, ℃	36.56 ± 0.43^#
Heart rate, beats per min	81.42 ± 17.81^#
SBP, mm Hg	169.24 ± 31.13^#
DBP, mm Hg	98.07 ± 18.99^#
Initial cerebral hemorrhage volume, mL	13.55 (4.77-32.36)^*
Midline shift, mm	2.49 (0-4.92)^*
SBP, systolic blood pressure; DBP, diastolic blood pressure; HLOS, hospital length of stay; ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage on presentation; ^*, values are median (interquartile range, IQR); ^#, values are mean ± SD.

下载: 导出CSV

Table 2. Month-dependent changes of 6-year averaged values (from 2015 to 2020) of number, proportion, vital signs and imaging data in patients with ICH

Month	N	%	SBP, mm Hg ^#	DBP, mm Hg ^#	Initial cerebral hemorrhage volume, mL ^*	Midline shift, mm ^*	Mean temperature, ℃ ^#
Jan	998	9.0	172.39 ± 33.43	99.01 ± 19.17	26.38 (24.15-28.60)	2.34 (0-4.88)	-16.77 ± 4.64
Feb	898	8.1	170.59 ± 29.16	98.46 ± 18.43	23.41 (21.14-25.68)	2.05 (0-5.00)	-11.94 ± 6.11
Mar	1 023	9.2	169.02 ± 32.11	98.43 ± 19.12	24.89 (22.45-27.32)	2.37 (0-4.51)	-0.24 ± 6.74
Apr	956	8.6	171.77 ± 32.34	98.18 ± 18.83	26.31 (23.89-28.72)	3.10 (0-4.83)	8.40 ± 5.46
May	920	8.3	169.98 ± 30.43	97.32 ± 18.36	25.33 (22.80-27.85)	2.85 (0-5.17)	15.40 ± 4.67
Jun	767	6.9	168.84 ± 30.78	97.46 ± 17.75	24.08 (21.45-26.71)	2.56 (0-5.12)	20.33 ± 2.99
Jul	734	6.6	166.51 ± 32.02	98.78 ± 22.19	25.95 (22.78-29.12)	3.57(1.90-5.72)	23.83 ± 2.64
Aug	798	7.2	165.45 ± 30.73	99.26 ± 22.67	25.07 (21.99-28.15)	3.00(0.69-4.85)	21.61 ± 2.89
Sep	953	8.6	168.05 ± 29.13	98.37 ± 18.19	22.61 (19.58-25.64)	2.41 (0-4.41)	16.00 ± 3.84
Oct	1 125	10.1	167.99 ± 30.68	97.16 ± 17.69	24.43 (21.92-26.94)	3.06 (0-5.41)	6.32 ± 4.64
Nov	980	8.8	169.60 ± 30.32	97.59 ± 18.18	24.56 (21.80-27.32)	0(0-4.90)	-6.05 ± 6.11
Dec	892	8.0	168.05 ± 31.14	96.79 ± 18.13	25.08 (22.41-27.74)	0(0-4.32)	-14.66 ± 5.54
Lost	80	0.7
ICH, intracerebral hemorrhage; SBP, systolic blood pressure; DBP, diastolic blood pressure; ^*, values are median (interquartile range, IQR); ^#, values are mean ± SD.

下载: 导出CSV

Table 3. Comparison of the baseline demographic and clinical characteristics of patients with ICH stratified by heat wave or cold spell.

Group	Age, y ^#	Sex (male), N (%)	Drinking, N (%)		Smoking, N (%)	History of Diabetes, N (%)
Control group (N = 8 752)	58.33 ± 12.579	5 837 (66.7)	4 179 (50.0)		4 140 (49.5)	992 (11.5)
Cold-spell group (N = 1 030)	58.24 ± 12.931	720 (69.9)^*	409 (42.1)^***		421 (43.2)^***	117 (11.6)
Hot-wave group (N = 992)	58.16 ± 13.560	647 (65.2)	520 (56.9)^***		526 (57.5)^***	116 (11.9)

Group	History of Hypertension, N (%)	History of Ischemic stroke, N (%)	SBP, mm Hg^#		DBP, mm Hg^#	Heart Rate, beats per min ^#
Control group (N = 8 752)	5 872 (68.1)	1 182 (13.7)	169.36 ± 30.787		97.94 ± 18.638	81.33 ± 17.818
Cold-spell group (N = 1 030)	637 (63.2)^**	141 (13.9)	170.67 ± 32.988		98.23 ± 18.752	81.53 ± 18.300
Hot-wave group (N = 992)	681 (70.2)	126 (12.9)	166.76 ± 32.366^*		98.59 ± 22.175	81.67 ± 17.375

Group	Body Temperature, ℃	Initial hematoma volume, mL ^##	Location, N (%)
Group	Body Temperature, ℃	Initial hematoma volume, mL ^##	Lobar	Deep	IVH	Infratentorial
Control group (N = 8 752)	36.59 ± 0.42	2.48 (0-17.816)	982 (18.4)	3 762 (70.5)	158 (3.0)	437 (8.2)
Cold-spell group (N = 1 030)	36.58 ± 0.45	14.56 (5.020-36.030)^*	149 (19.9)	501 (66.9)	22 (2.9)	77 (10.3)
Hot-wave group (N = 992)	36.60 ± 0.42	13.4921 (4.267-32.719)	105 (17.1)	436 (71.1)	18 (2.9)	54 (8.8)

Group	Midline shift binary, N (%)	Midline shift, mm ^##	Mean temperature, ℃ ^##	Surgery, N (%)
Control group (N = 8 752)	2 589 (64.4)	2.52 (0-4.840)	6.20 (-5.20-15.60)	2 047 (23.4)
Cold-spell group (N = 1 030)	327 (67.4)	2.64 (0-5.354)	-19.00 (-20.90--17.60)^***	236 (23.0)
Hot-wave group (N = 992)	360 (81.1)^***	3.3155 (1.551-5.520)^***	24.9000 (23.70-26.30)^***	243 (24.5)
SBP, systolic blood pressure; DBP, diastolic blood pressure; ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage on presentation; ^, P < 0.05 (vs. control.); ^, P < 0.01 (vs. control.); ^**, P < 0.001 (vs. control.); ^#, values are mean ± SD; ^##, values are median (interquartile range, IQR).

下载: 导出CSV

Table 4. Cold spell days and heat-wave days discovered by different intensities and frequencies

Models	Definition	Number of days	N	AIC value
1	< 10th percentile with ≥ 2 days duration	200	1030	1772.168
2	< 10th percentile with ≥ 3 days duration	167	843	1546.364
3	< 10th percentile with ≥ 5 days duration	118	576	1136.208
4	< 5th percentile with ≥ 2 days duration	98	485	698.945
5	< 5th percentile with ≥ 3 days duration	76	382	603.058
6	< 5th percentile with ≥ 5 days duration	29	138	269.518
7	< 3rd percentile with ≥ 2 days duration	57	285	429.656
8	< 3rd percentile with ≥ 3 days duration	49	235	363.799
9	< 3rd percentile with ≥ 5 days duration	21	104	198.704
10	> 90th percentile with ≥ 2 days duration	256	992	898.831
11	> 90th percentile with ≥ 3 days duration	244	938	834.535
12	> 90th percentile with ≥ 5 days duration	191	766	639.896
13	> 95th percentile with ≥ 2 days duration	131	487	177.928
14	> 95th percentile with ≥ 3 days duration	108	417	172.234
15	> 95th percentile with ≥ 5 days duration	67	272	115.869
16	> 97th percentile with ≥ 2 days duration	78	284	24.453
17	> 97th percentile with ≥ 3 days duration	48	193	0.421
18	> 97th percentile with ≥ 5 days duration	17	63	25.945
Dependent variable: average temperature; Independent variables: onset month, alcohol consumption, smoking, length of hospital stay, systolic blood pressure, history of hypertension, intracerebral hemorrhage volume, midline displacement.

下载: 导出CSV

Table 5. Predicted effects of cold wave and heat wave on blood pressure, bleeding volume, midline shift and other related variables in the Logistic regression

Logistic regression		Sex (male)	Drinking	Smoking	SBP	Initial hematoma volume	Midline shift binary	Midline shift
Model 1	OR (95% CI)	1.160 (1.008-1.335)	0.727 (0.636-0.832)	0.776 (0.679-0.887)	1.001 (0.999-1.003)	1.003 (1.000-1.005)	1.144 (0.936 -1.398)	1.002 (0.980 -1.023)
	P value	0.038	< 0.001	< 0.001	0.214	0.047	0.189	0.879
Model 9	OR (95% CI)	0.667 (0.427-1.043)	0.132 (0.072-0.241)	0.201 (0.120-0.340)	1.001 (0.994-1.007)	1.002 (0.994-1.009)	2.625 (1.525-4.519)	1.067 (1.021-1.115)
	P value	0.076	< 0.001	< 0.001	0.814	0.671	< 0.001	0.004
Model 10	OR (95% CI)	0.937 (0.816-1.075)	1.321 (1.151-1.156)	1.378 (1.200-1.582)	0.997 (0.995-1.000)	1.002 (0.999-1.005)	1.058 (1.035-1.082)	2.369 (1.852-3.030)
	P value	0.352	< 0.001	< 0.001	0.019	0.292	< 0.001	0.001
Model 17	OR (95% CI)	0.961 (0.711-1.298)	1.289 (0.947-1.755)	1.247 (0.918-1.694)	1.002 (0.997-1.006)	0.998 (0.990 -1.005)	3.618 (1.912-6.846)	1.077 (1.030-1.127)
	P value	0.795	0.107	0.157	0.516	0.578	< 0.001	0.001
SBP, systolic blood pressure.

下载: 导出CSV

参考文献(30)

[1]	GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019; Lancet, 2020; 396(10258): 1204-1222. doi: 10.1016/S0140-6736(20)30925-9
[2]	Wang W Z, Jiang B, Sun H X, et al. Prevalence, incidence, and mortality of stroke in China: results from a Nationwide Population-based survey of 480 687 adults. Circulation, 2017; 135(8): 759-771. doi: 10.1161/CIRCULATIONAHA.116.025250
[3]	Ma Q, Li R, Wang L J, P., et al. Temporal trend and attributable risk factors of stroke burden in China, 1990-2019: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health, 2021; 6(12): e897-e906.
[4]	GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol, 2021; 20(10): 795-820. doi: 10.1016/S1474-4422(21)00252-0
[5]	Fang C W, Ma M C, Lin H J, et al. Ambient temperature and spontaneous intracerebral haemorrhage: a cross-sectional analysis in Tainan, Taiwan. BMJ Open, 2012; 2(3): e000842. doi: 10.1136/bmjopen-2012-000842
[6]	Gomes J, Damasceno A, Carrilho V, et al. Triggering of stroke by ambient temperature variation: a case-crossover study in Maputo, Mozambique. Clin Neurol Neurosurg, 2015; 129: 72-77. doi: 10.1016/j.clineuro.2014.12.002
[7]	Polcaro-Pichet S, Kosatsky B J, Potter M, et al. Bilodeau-Bertrand, and N. Auger, Effects of cold temperature and snowfall on stroke mortality: A case-crossover analysis. Environ Int, 2019; 126: 89-95. doi: 10.1016/j.envint.2019.02.031
[8]	Li T, Horton R M, Bader D A, et al. Long-term projections of temperature-related mortality risks for ischemic stroke, hemorrhagic stroke, and acute ischemic heart disease under changing climate in Beijing, China. Environ Int, 2018; 112: 1-9. doi: 10.1016/j.envint.2017.12.006
[9]	Zheng D H, Arima E, Heeley A, et al. Ambient temperature and severity of intracerebral haemorrhage: the INTERACT1 study. Neuroepidemiology, 2014; 42(3): 169-173. doi: 10.1159/000358304
[10]	Wang W D, Wang H, Liu H, et al. Rend of declining stroke mortality in China: reasons and analysis. Stroke Vasc Neurol, 2017; 2(3): 132-139. doi: 10.1136/svn-2017-000098
[11]	Guo Y A, Gasparrini B G, Armstrong B, et al. Heat wave and mortality: a multicountry, multicommunity study. Environ Health Perspect, 2017; 125(8): 087006. doi: 10.1289/EHP1026
[12]	Gao J F, Yu Z, Xu J, et al. The association between cold spells and admissions of ischemic stroke in Hefei, China: modified by gender and age. Sci Total Environ, 2019; 669: 140-147. doi: 10.1016/j.scitotenv.2019.02.452
[13]	Monteiro A V, Carvalho J, Góis C, et al. Use of "Cold Spell" indices to quantify excess Chronic Obstructive Pulmonary Disease (COPD) morbidity during winter (November to March 2000-2007): case study in Porto. Int J Biometeorol, 2013; 57(6): 857-870. doi: 10.1007/s00484-012-0613-z
[14]	Qi X Z, Wang X, Xia J, et al. The effects of heatwaves and cold spells on patients admitted with acute ischemic stroke. Ann Transl Med, 2021; 9(4): 309. doi: 10.21037/atm-20-4256
[15]	Ponjoan A J, Blanch L, Alves-Cabratosa R, et al. Effects of extreme temperatures on cardiovascular emergency hospitalizations in a Mediterranean region: a self-controlled case series study. Environ Health, 2017; 16(1): 32. doi: 10.1186/s12940-017-0238-0
[16]	Song X S, Wang, Hu Y L, et al. Impact of ambient temperature on morbidity and mortality: an overview of reviews. Sci Total Environ, 2017; 586: 241-254. doi: 10.1016/j.scitotenv.2017.01.212
[17]	Han J S, Liu J, Zhang L et al. The impact of temperature extremes on mortality: a time-series study in Jinan, China. BMJ Open, 2017; 7(4): e014741. doi: 10.1136/bmjopen-2016-014741
[18]	Yamamoto S M, Koh K, Matsumura K, et al. Impact of low ambient temperature on the occurrence of spontaneous intracerebral hemorrhage-analysis of population-based stroke registry in Toyama, Japan. J Stroke Cerebrovasc Dis, 2022; 31(1): 106156. doi: 10.1016/j.jstrokecerebrovasdis.2021.106156
[19]	Luo Y H, Li F, Huang N, et al. The cold effect of ambient temperature on ischemic and hemorrhagic stroke hospital admissions: a large database study in Beijing, China between years 2013 and 2014-utilizing a distributed lag non-linear analysis. Environ Pollut, 2018; 232: 90-96. doi: 10.1016/j.envpol.2017.09.021
[20]	Chen J H, Jiang L, Wu X, et al. Association of ischemic and hemorrhagic strokes hospital admission with extreme temperature in Nanchang, China-a case-crossover study. J Clin Neurosci, 2017; 43: 89-93. doi: 10.1016/j.jocn.2017.04.044
[21]	Wang Q C, Gao H, Liu W, et al. Hypertension modifies the short-term effects of temperature on morbidity of hemorrhagic stroke. Sci Total Environ, 2017; 598: 198-203. doi: 10.1016/j.scitotenv.2017.04.159
[22]	Guo P M, Zheng Y, Wang W, et al. Effects of ambient temperature on stroke hospital admissions: Results from a time-series analysis of 104, 432 strokes in Guangzhou, China. Sci Total Environ, 2017; 580: 307-315. doi: 10.1016/j.scitotenv.2016.11.093
[23]	Cuspidi C, Ochoa J E, Parati G. Seasonal variations in blood pressure: a complex phenomenon. J Hypertens, 2012; 30(7): 1315-1320. doi: 10.1097/HJH.0b013e328355d7f9
[24]	McArthur K, Dawson J, Walters D, What is it with the weather and stroke? Expert Rev Neurother, 2010; 10(2): 243-249. doi: 10.1586/ern.09.154
[25]	Valeri C R, MacGregor H, Cassidy G, et al. Effects of temperature on bleeding time and clotting time in normal male and female volunteers. Crit Care Med, 1995; 23(4): 698-704. doi: 10.1097/00003246-199504000-00019
[26]	Takumi I, Mishina M, Kominami S, et al. Ambient temperature change increases in stroke onset: analyses based on the Japanese regional metrological measurements. J Nippon Med Sch, 2015; 82(6): 281-286. doi: 10.1272/jnms.82.281
[27]	Wang X Y, Cao D, Hong D, et al. Ambient temperature and stroke occurrence: a systematic review and meta-analysis. Int J Environ Res Public Health, 2016; 13(7): 698. doi: 10.3390/ijerph13070698
[28]	Li L, Huang S L, Duan Y R, et al. Effect of ambient temperature on stroke onset: a time-series analysis between 2003 and 2014 in Shenzhen, China. Occup Environ Med, 2021. Epub ahead of print.
[29]	Shah A S, Lee K K, McAllister D A, et al. Short term exposure to air pollution and stroke: systematic review and meta-analysis. BMJ, 2015; 350: h1295.
[30]	Xu Z, Tong S, Pan H, et al. Associations of extreme temperatures with hospitalizations and post-discharge deaths for stroke: what is the role of pre-existing hyperlipidemia? Environ Res, 2021; 193: 110391. doi: 10.1016/j.envres.2020.110391