Front. Neurol.Frontiers in NeurologyFront. Neurol.1664-2295Frontiers Media S.A.10.3389/fneur.2023.1268627NeurologyOriginal ResearchRisk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factorsFengHao1WangXin1WangWenjuan1ZhaoXingquan1234*1Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China2China National Clinical Research Center for Neurological Diseases, Beijing, China3Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China4Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China
Edited by: Zhe Kang Law, National University of Malaysia, Malaysia
Reviewed by: Wen-Jun Tu, Chinese Academy of Medical Sciences and Peking Union Medical College, China; Jafri Malin Abdullah, University of Science Malaysia, Malaysia
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Background
This study aimed to identify the risk factors and construct a prediction model for the prognosis of intracerebral hemorrhage (ICH) at discharge, 3 months, and 12 months.
Methods
A total of 269 patients with ICH were retrospectively enrolled at our hospital between January 2014 and August 2016. The prognosis of ICH was assessed using the modified Rankin Scale (mRS); an mRS score > 2 was considered a poor outcome. The primary endpoint was the 3-month mRS, whereas the secondary endpoints included the mRS scores at discharge and 12 months, and mortality.
Results
The Glasgow Coma Scale (GCS), National Institutes of Health (NIH) stroke scale, International Normalized Ratio (INR), blood urea nitrogen (BUN), epencephalon hemorrhage, and primary hematoma volume were significantly associated with a poor mRS score at 3 months. The predictive value of the prediction model based on these factors for a poor mRS score was 87.8%. Furthermore, a poor mRS score at discharge was affected by the GCS, NIH stroke scale, and primary hematoma volume; the constructed model based on these factors had a predictive value of 87.6%. In addition, the GCS, NIH stroke scale, and surgery were significantly related to a poor mRS score at 12 months; the predictive value of the constructed model based on the aforementioned factors for a poor mRS score was 86.5%. Finally, primary hematoma volume is significantly associated with the risk of 12 months mortality.
Conclusions
The study identified risk factors and constructed a prediction model for poor mRS scores and mortality at discharge, 3 and 12 months in patients with ICH. The prediction models for mRS scores showed a relatively high predictive performance.
risk factorsprediction modelprognosisintracerebral hemorrhagecerebral microhemorrhagesection-at-acceptanceEndovascular and Interventional Neurology1 Introduction
Stroke accounted for 12.2 million incident case of stroke in 2019, and 101 million prevalent cases of stroke (1). In China, there was an estimated 17.8 million adults presented a stroke, and 2.3 million of cases dying as a result (2–4). Stroke can be classified as ischemic or hemorrhagic, with the latter including intracerebral hemorrhage (ICH) and subarachnoid hemorrhage. ICH accounts for 15–20% of stroke cases, with an annual incidence of 24.3 per 100,000 population, and the disability rate can reach as high as 75% (5, 6). Currently, there are no effective therapeutic strategies for ICH, and prognostic factors, such as hyperglycemia, hypertension, hematoma volume, and hematoma expansion, need to be identified to improve the prognosis of ICH (7).
The prognostic factors for ICH have already been identified, including older age, higher blood plasma glutamate, tumor necrosis factor alpha (TNF-α), initial ICH volume, Scandinavian Stroke Scale score, dialysis, diabetes, Glasgow Coma Scale (GCS) score, bilateral dilated pupils, higher international normalized ratio (INR), hematoma in the cerebellum or brainstem, hematoma volume, and the presence of intraventricular hematoma (8–10). However, these studies did not assess the role of cerebral microhemorrhages (CMs) in ICH prognosis. CMs have been observed in 5% of healthy elderly individuals; the risk factors for CMs include a history of stroke or dementia, hypertension, and diabetes (11). Studies have illustrated that the number and location of CMs are significantly associated with the progression of ICH (12, 13); however, whether they affect the prognosis of ICH remains unclear.
Therefore, we conducted this study to identify the potential prognostic factors for ICH and constructed a predictive model for functional status based on CMs and clinical factors.
2 Methods2.1 Study design and population
Patients with ICH admitted to our hospital between January 2014 and August 2016 were retrospectively collected. This study was approved by the institutional review board of Beijing Tiantan Hospital, Capital Medical University (KY2014-023-02), and informed consent was signed from all patients after explaining the purpose of the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Patients were included if the following criteria were met: (1) adult patients (age ≥ 18.0 years); (2) patients firstly presenting with the onset of ICH; (3) patients admitted in our department within 24 h; (4) a diagnosis of ICH on the basis of brain computerized tomography; and (5) available information regarding CMs, clinical factors, and modified Rankin Scale (mRS) at discharge, 3 and 12 months. Patients were excluded if they (1) had a primary ventricular hemorrhage; (2) were diagnosed with secondary ICH, including head trauma, brain tumor, aneurysm, cavernous hemangioma, arteriovenous malformations, acute thrombolysis, coagulopathy, or moyamoya disease; or (3) had incomplete information regarding CMs or outcomes.
2.2 Data collection and variable definition
Patients information was collected from electronic medical records, including age, sex, time from onset to admission, smoking, alcohol, body mass index (BMI), hypertension, diabetes mellitus (DM), hyperlipidemia, history of ischemic stroke, history of hemorrhagic stroke, history of subarachnoid hemorrhage, antiplatelet drugs, anticoagulant drugs, antihypertensive drugs, lipid-lowering drugs, antidiabetic drugs, systolic blood pressure (SBP), diastolic blood pressure (DBP), GCS, National Institutes of Health (NIH) stroke scale, white blood cell (WBC), platelet, fasting glucose, INR, creatinine, blood urea nitrogen (BUN), total cholesterol (TC), triglyceride (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), glytamic-pyruvic transaminase (ALT), glutamic oxalacetic transaminase (AST), alkaline phosphatase (ALP), surgical treatment, location, primary hematoma volume, deep CMs, lobar CMs, subtentorial CMs, and total CMs. Blood samples were obtained in all patients within 1 h after admission. The fasting lipid profiles were obtained on the second day of admission. The susceptibility-weighted imaging showed very low signal intensity lesions was defined as CMs. The signal of CMs was similar to the venous on the susceptibility-weighted imaging phase image, and opposite the calcification signal. The inter- and intra-observer reliability for detecting CMs with Cohen's kappa values of 0.96 and 0.98, respectively. Similarly, the Cohen's kappa values of inter- and intra-observer reliability for assessing the location of CMs were 0.96 and 0.98, respectively. The modified Rankin Scale (mRS) was used to assess the prognosis of ICH, and a poor outcome was defined as an mRS score > 2 (14). Functional outcomes (mRS) were assessed after discharge, at 3 months, and at 12 months. The primary endpoint was the 3-months mRS, whereas the secondary endpoints included the mRS scores at discharge and 12 months, and mortality.
2.3 Statistical analysis
The baseline characteristics of the patients with ICH with good or poor functional outcomes were assigned as continuous and categorical variables, respectively. Continuous variables are presented as mean (standard deviation) or median (interquartile range) according to the data distribution, while categorical variables are shown as number and frequency. Differences between good and poor functional outcomes were analyzed using the independent t test, Kruskal–Wallis test, or chi-square test. Univariate logistic regression analysis was performed to identify potential risk factors, and the factors were subjected to the multivariate logistic regression analysis using α = 0.05 and β = 0.10. The deep CMs, lobar CMs, subtentorial CMs, and total CMs were mandatory inclusion model, and the prediction model for functional outcomes at discharge, at 3 months, and 12 months was assessed using the receiver operating characteristic (ROC) curve with the area under the curve (AUC). All tests were two-sided, and P < 0.05 was regarded as statistically significant. SPSS version 18 for Windows (SPSS, Chicago, IL, USA) was used to perform statistical analyses.
3 Results3.1 Baseline characteristics
Of 269 included patients, the mean age was 56.05 years, and 71% of included patients were male. The median time from onset to admitted was 5.16 h. Thirteen patients (4.83%) received surgical treatment, while the remaining patients treated with conservative therapy. The baseline characteristics according to the 3-month functional outcomes are shown in Table 1. There were significant differences between mRS ≤ 2 and mRS > 2 for age (P = 0.002), time from onset to admission (P = 0.029), the use of antidiabetic drugs (P = 0.015), GCS (P < 0.001), NIH stroke scale (P < 0.001), WBC (P = 0.026), fasting glucose (P < 0.001), INR (P = 0.009), BUN (P = 0.041), LDL (P = 0.002), and primary hematoma volume (P < 0.001). However, we did not find significant differences between the groups regarding sex, smoking, alcohol consumption, BMI, hypertension, DM, hyperlipidemia, history of ischemic stroke, history of hemorrhagic stroke, history of subarachnoid hemorrhage, antiplatelet drugs, anticoagulant drugs, antihypertensive drugs, lipid-lowering drugs, SBP, DBP, platelet, creatinine, TC, TG, HDL, ALT, AST, ALP, surgical treatment, location, deep CMs, lobar CMs, subtentorial CMs, and total CMs.
The baseline characteristics of collected patients.
Variable
Overall (n = 269)
3 months mRS
Good ( ≤ 2) (n = 175)
Poor (>2) (n = 94)
P-value
Age (years)
56.05 (13.09)
54.21 (12.79)
59.47 (13.03)
0.002
Sex
0.942
Female
78 (29.00)
51 (29.14)
27 (28.72)
Male
191 (71.00)
124 (70.86)
67 (71.28)
Time from onset to admitted (hours)
5.16 (2.52, 15.33)
5.80 (2.65, 16.73)
4.50 (2.18, 7.85)
0.029
Smoking
0.990
Never
134 (49.81)
87 (49.71)
47 (50.00)
Current
94 (34.94)
62 (35.43)
32 (34.04)
Ever
36 (13.38)
23 (13.14)
13 (13.83)
Unclear
5 (1.86)
3 (1.71)
2 (2.13)
Alcohol
0.325
Yes
138 (51.30)
94 (53.71)
44 (46.81)
No
126 (46.84)
79 (45.14)
47 (50.00)
Unclear
5 (1.86)
2 (1.14)
3 (3.19)
BMI (kg/m2)
25.24 (22.86, 27.68)
25.24 (23.03, 27.76)
25.21 (22.60, 27.44)
0.683
Hypertension
191 (71.27)
124 (71.26)
67 (71.28)
0.998
Diabetes mellitus
38 (14.13)
28 (16.00)
10 (10.64)
0.229
Hyperlipidemia
30 (11.15)
22 (12.57)
8 (8.51)
0.313
History of ischemic stroke
38 (14.13)
22 (12.57)
16 (17.02)
0.318
History of hemorrhagic stroke
6 (2.23)
2 (1.14)
4 (4.26)
0.224
History of subarachnoid hemorrhage
1 (0.37)
1 (0.57)
0 (0.00)
1.000
Antiplatelet drugs
0.613
Yes
37 (13.75)
23 (13.14)
14 (14.89)
No
219 (81.41)
145 (82.86)
74 (78.72)
Unclear
13 (4.83)
7 (4.00)
6 (6.38)
Anticoagulant drugs
0.100
Yes
0 (0.00)
0 (0.00)
0 (0.00)
No
263 (97.77)
173 (98.86)
90 (95.74)
Unclear
6 (2.23)
2 (1.14)
4 (4.26)
Antihypertensive drugs
0.882
Yes
93 (34.57)
61 (34.86)
32 (34.04)
No
164 (60.97)
107 (61.14)
57 (60.64)
Unclear
12 (4.46)
7 (4.00)
5 (5.32)
Lipid-lowering drugs
0.204
Yes
21 (7.81)
16 (9.14)
5 (5.32)
No
235 (87.36)
153 (87.43)
82 (87.23)
Unclear
13 (4.83)
6 (3.43)
7 (7.45)
Antidiabetic drug
0.015
Yes
22 (8.18)
19 (10.86)
3 (3.19)
No
238 (88.48)
153 (87.43)
85 (90.43)
Unclear
9 (3.35)
3 (1.71)
6 (6.38)
SBP (mmHg)
162.99 (24.81)
161.50 (25.07)
165.79 (24.19)
0.188
DBP (mmHg)
95.00 (80.00, 108.00)
95.00 (80.00, 109.00)
96.00 (80.00, 106.00)
0.903
GCS
15.00 (13.00, 15.00)
15.00 (14.00, 15.00)
14.00 (10.00, 15.00)
< 0.001
NIH stroke scale
7.00 (3.00, 13.00)
4.00 (1.00, 9.00)
13.00 (9.00, 18.00)
< 0.001
WBC (*109/L)
9.46 (7.53, 11.67)
9.14 (7.35, 11.24)
10.02 (7.73, 13.11)
0.026
Platelet (*109/L)
215.50 (181.00, 257.00)
218.00 (188.00, 254.00)
209.00 (162.00, 260.00)
0.236
Fasting glucose (mmol/L)
5.21 (4.47, 6.04)
4.93 (4.31, 5.80)
5.80 (4.86, 6.44)
< 0.001
INR
0.95 (0.91, 1.00)
0.94 (0.90, 1.00)
0.96 (0.92, 1.02)
0.009
Creatinine (μmoI/L)
61.54 (51.38, 72.40)
63.00 (51.79, 72.40)
60.57 (50.27, 72.25)
0.525
BUN (mmol/L)
5.10 (4.15, 6.20)
5.00 (4.10, 6.00)
5.20 (4.30, 6.50)
0.041
TC (mmol/L)
4.70 (4.07, 5.43)
4.80 (4.09, 5.56)
4.55 (3.95, 5.21)
0.054
TG (mmol/L)
1.33 (1.00, 1.75)
1.42 (1.04, 1.83)
1.21 (0.93, 1.63)
0.060
HDL (mmol/L)
1.15 (1.00, 1.42)
1.15 (0.98, 1.40)
1.19 (1.02, 1.51)
0.103
LDL (mmol/L)
3.05 (2.43, 3.63)
3.16 (2.54, 3.77)
2.86 (2.15, 3.33)
0.002
ALT (U/L)
27.05 (20.00, 34.20)
26.70 (19.60, 34.10)
27.80 (21.70, 34.20)
0.511
AST (U/L)
21.35 (17.90, 26.90)
21.10 (17.90, 25.10)
22.40 (17.90, 28.60)
0.107
ALP (U/L)
82.90 (65.40, 98.20)
83.00 (64.30, 97.10)
81.75 (65.90, 105.30)
0.778
Surgical treatment
0.078
No
256 (95.17)
170 (97.14)
86 (91.49)
Yes
13 (4.83)
5 (2.86)
8 (8.51)
Location
0.194
Lobar
80 (30.89)
60 (35.50)
20 (22.22)
Basal ganglia
111 (42.86)
66 (39.05)
45 (50.00)
Thalamus
44 (16.99)
26 (15.38)
18 (20.00)
Brainstem
13 (5.02)
9 (5.33)
4 (4.44)
Epencephalon
11 (4.25)
8 (4.73)
3 (3.33)
Primary hematoma volume
12.50 (4.90, 28.10)
9.60 (3.80, 26.50)
20.43 (8.90, 40.10)
< 0.001
Deep CMs
0.134
No
161 (59.85)
99 (56.57)
62 (65.96)
Yes
108 (40.15)
76 (43.43)
32 (34.04)
Lobar CMs
0.940
No
221 (82.16)
144 (82.29)
77 (81.91)
Yes
48 (17.84)
31 (17.71)
17 (18.09)
Subtentorial CMs
0.705
No
217 (80.67)
140 (80.00)
77 (81.91)
Yes
52 (19.33)
35 (20.00)
17 (18.09)
Total CMs
0.519
No
133 (49.44)
84 (48.00)
49 (52.13)
Yes
136 (50.56)
91 (52.00)
45 (47.87)
ALP, alkaline phosphatase; ALT, glytamic-pyruvic transaminase; AST, glutamic oxalacetic transaminase; BMI, body mass index; BUN, blood urea nitrogen; CMs, cerebral microhemorrhages; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; HDL, high density lipoprotein; INR, international normalized ratio; LDL, low density lipoprotein; mRS, Modified Rankin Scale; NIH, National Institutes of Health; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; WBC, white blood cell.
3.2 Three-month mRS
The identified risk factors for the 3-month mRS are shown in Table 2. The univariate analysis found that poor functional outcomes could be affected by age [odds ratio (OR): 1.03; P = 0.002], time from onset to admission (OR: 0.97; P = 0.011), no use of antidiabetic drugs (P = 0.048) or unclear use of antidiabetic drugs (P = 0.007), GCS (P < 0.001), NIH stroke scale (P < 0.001), WBC (P = 0.005), INR (P = 0.023), BUN (P = 0.046), LDL (P = 0.005), AST (P = 0.031), surgical treatment (P = 0.049), basal ganglia hemorrhage (P = 0.027), and primary hematoma volume (P < 0.001). After adjusting for potential confounders, we noted that the GCS (OR: 1.93; 95% CI: 1.33–2.79; P < 0.001), NIH stroke scale (OR: 1.53; 95% CI: 1.32–1.78; P < 0.001), INR (OR: 1804.29; 95% CI: 6.44–505855.5; P = 0.009), BUN (OR: 1.47; 95% CI: 1.08–2.01; P = 0.014), epencephalon hemorrhage (OR: 115.80; 95% CI: 9.89–1356.22; P < 0.001), and primary hematoma volume (OR: 1.05; 95% CI: 1.01–1.08; P = 0.007) were significantly associated with a poor 3-month mRS score. The predictive value of the prediction model based on these factors was 87.8% (95% CI: 83.4–92.2%; Figure 1).
The risk factors for 3-months mRS in ICH patients.
Variable
Univariate logistic analysis
Multivariate logistic analysis
β value
OR (95%CI)
P-value
β value
OR (95%CI)
P-value
Age
0.032
1.03 (1.01–1.05)
0.002
Sex
−0.020
0.98 (0.56–1.70)
0.942
Time from onset to admitted
−0.029
0.97 (0.95–0.99)
0.011
Smoking
Never
Ref
-
-
Current
−0.046
0.96 (0.55–1.66)
0.872
Ever
0.045
1.05 (0.49–2.25)
0.908
Unclear
0.210
1.23 (0.20–7.65)
0.821
Alcohol
Yes
Ref
-
-
No
0.240
1.27 (0.76–2.11)
0.355
Unclear
1.164
3.20 (0.52–19.86)
0.211
BMI (kg/m2)
−0.015
0.99 (0.92–1.05)
0.658
Hypertension
0.001
1.00 (0.57–1.74)
0.998
Diabetes mellitus
−0.470
0.63 (0.29–1.35)
0.232
Hyperlipidemia
−0.435
0.65 (0.28–1.52)
0.316
History of ischemic stroke
0.355
1.43 (0.71–2.87)
0.319
History of hemorrhagic stroke
1.347
3.84 (0.69–21.39)
0.124
History of subarachnoid hemorrhage
−12.717
0.00 (0.00)
0.987
Antiplatelet drugs
Yes
Ref
-
-
No
−0.176
0.84 (0.41–1.72)
0.632
Unclear
0.342
1.41 (0.39–5.05)
0.599
Anticoagulant drugs (unclear vs. no)
1.347
3.84 (0.69–21.39)
0.124
Antihypertensive drugs
Yes
Ref
-
-
No
0.015
1.02 (0.59–1.73)
0.955
Unclear
0.309
1.36 (0.40–4.63)
0.621
Lipid-lowering drugs
Yes
Ref
-
-
No
0.539
1.72 (0.61–4.85)
0.309
Unclear
1.317
3.73 (0.85–16.44)
0.082
Antidiabetic drug
Yes
Ref
-
-
No
1.258
3.52 (1.01–12.23)
0.048
Unclear
2.539
12.66 (2.00–80.12)
0.007
SBP (mmHg)
0.007
1.01 (1.00–1.02)
0.188
DBP (mmHg)
−0.002
1.00 (0.98–1.01)
0.815
GCS
−0.241
0.79 (0.71–0.87)
< 0.001
0.656
1.93 (1.33–2.79)
< 0.001
NIH stroke scale
0.175
1.19 (1.14–1.25)
< 0.001
0.428
1.53 (1.32–1.78)
< 0.001
WBC (*109/L)
0.107
1.11 (1.03–1.20)
0.005
Platelet (*109/L)
−0.002
1.00 (0.99–1.00)
0.332
Fasting glucose (mmol/L)
0.077
1.08 (0.96–1.22)
0.204
INR
3.580
35.87 (1.65–779.98)
0.023
7.498
1804.29 (6.44–505855.5)
0.009
Creatinine (μmoI/L)
−0.005
0.99 (0.98–1.01)
0.405
BUN (mmol/L)
0.155
1.17 (1.00–1.36)
0.046
0.387
1.47 (1.08–2.01)
0.014
TC (mmol/L)
−0.224
0.80 (0.61–1.05)
0.108
TG (mmol/L)
−0.372
0.69 (0.44–1.08)
0.107
HDL (mmol/L)
0.454
1.57 (0.89–2.79)
0.121
LDL (mmol/L)
−0.468
0.63 (0.45–0.87)
0.005
ALT (U/L)
0.003
1.00 (0.99–1.01)
0.563
AST (U/L)
0.024
1.02 (1.00–1.05)
0.031
ALP (U/L)
0.001
1.00 (0.99–1.01)
0.716
Surgical treatment
−1.151
0.32 (0.10–1.00)
0.049
Location
Lobar
Ref
-
-
Basal ganglia
0.716
2.05 (1.09–3.85)
0.027
1.045
2.84 (0.73–11.13)
0.134
Thalamus
0.731
2.08 (0.95–4.56)
0.068
1.678
5.35 (0.94–30.42)
0.058
Brainstem
0.288
1.33 (0.37–4.80)
0.660
1.129
3.09 (0.10–92.56)
0.515
Epencephalon
0.118
1.12 (0.27–4.65)
0.871
4.752
115.80 (9.89–1356.22)
< 0.001
Primary hematoma volume
0.021
1.02 (1.01–1.03)
< 0.001
0.045
1.05 (1.01–1.08)
0.007
Deep CMs
−0.397
0.67 (0.40–1.13)
0.135
−0.391
0.68 (0.12–3.82)
0.658
Lobar CMs
0.025
1.03 (0.53–1.97)
0.939
1.292
3.64 (0.83–15.98)
0.087
Subtentorial CMs
−0.124
0.88 (0.46–1.68)
0.705
1.217
3.38 (0.74–15.32)
0.115
Total CMs
−0.165
0.85 (0.51–1.40)
0.519
−1.416
0.24 (0.03–1.83)
0.170
Receiver operating characteristic curve for the risk of a 3-month modified Rankin Scale (mRS) score > 2 in patients with intracerebral hemorrhage (ICH), including the six-component risk factor model.
3.3 mRS at discharge
The identified risk factors for the mRS score at discharge are presented in Table 3. The univariate analyses revealed that the time from onset to admission (P = 0.018), GCS score (P < 0.001), NIH stroke scale score (P < 0.001), AST level (P = 0.002), surgical treatment (P = 0.016), basal ganglia hemorrhage (P = 0.022), and primary hematoma volume (P < 0.001) were significantly associated with poor functional outcomes. After adjusting for potential confounders, the mRS at discharge could be affected by the GCS (OR: 1.48; 95% CI: 1.08–2.03; P = 0.015), NIH stroke scale (OR: 1.47; 95% CI: 1.30–1.66; P < 0.001), and primary hematoma volume (OR: 1.04; 95% CI: 1.01–1.06; P = 0.002). The model constructed based on these factors had a predictive value of 87.6% (95% CI: 83.0–92.2%; Figure 2).
The risk factors for mRS at discharge in ICH patients.
Variable
Univariate logistic analysis
Multivariate logistic analysis
β value
OR (95%CI)
P-value
β value
OR (95%CI)
P-value
Age
0.012
1.01 (0.99–1.03)
0.221
Sex
−0.110
0.90 (0.52–1.53)
0.688
Time from onset to admitted
−0.024
0.98 (0.96–1.00)
0.018
Smoking
Never
Ref
-
-
Current
0.382
1.46 (0.86–2.49)
0.160
Ever
−0.269
0.76 (0.35–1.66)
0.496
Unclear
−0.962
0.38 (0.04–3.51)
0.395
Alcohol
Yes
Ref
-
-
No
0.219
1.24 (0.76–2.03)
0.382
Unclear
0.036
1.04 (0.17–6.41)
0.969
BMI (kg/m2)
−0.023
0.98 (0.92–1.04)
0.487
Hypertension
0.164
1.18 (0.69–2.02)
0.551
Diabetes mellitus
−0.104
0.90 (0.45–1.82)
0.771
Hyperlipidemia
−0.569
0.57 (0.25–1.29)
0.175
History of ischemic stroke
−0.104
0.90 (0.45–1.82)
0.771
History of hemorrhagic stroke
0.346
1.41 (0.28–7.13)
0.676
History of subarachnoid hemorrhage
−12.913
0.00 (0.00)
0.986
Antiplatelet drugs
Yes
Ref
-
-
No
0.042
1.04 (0.51–2.12)
0.908
Unclear
0.229
1.26 (0.35–4.49)
0.725
Anticoagulant drugs (unclear vs. no)
1.986
7.29 (0.84–63.27)
0.072
Antihypertensive drugs
Yes
Ref
-
-
No
0.120
1.13 (0.67–1.89)
0.651
Unclear
0.078
1.08 (0.32–3.66)
0.900
Lipid-lowering drugs
Yes
Ref
-
-
No
0.598
1.82 (0.68–4.85)
0.232
Unclear
1.070
2.92 (0.69–12.36)
0.146
Antidiabetic drug
Yes
Ref
-
-
No
0.011
1.01 (0.42–2.46)
0.981
Unclear
0.591
1.81 (0.38–8.64)
0.459
SBP (mmHg)
0.002
1.00 (0.99–1.01)
0.681
DBP (mmHg)
−0.006
0.99 (0.98–1.01)
0.408
GCS
−0.335
0.72 (0.63–0.81)
< 0.001
0.393
1.48 (1.08–2.03)
0.015
NIH stroke scale
0.234
1.26 (1.19–1.34)
< 0.001
0.384
1.47 (1.30–1.66)
< 0.001
WBC (*109/L)
0.058
1.06 (0.99–1.14)
0.111
Platelet (*109/L)
−0.001
1.00 (1.00–1.00)
0.756
Fasting glucose (mmol/L)
0.085
1.09 (0.97–1.23)
0.163
INR
0.813
2.25 (0.14–36.05)
0.565
Creatinine (μmoI/L)
−0.007
0.99 (0.98–1.01)
0.273
BUN (mmol/L)
0.024
1.02 (0.89–1.18)
0.745
TC (mmol/L)
0.025
1.03 (0.80–1.32)
0.844
TG (mmol/L)
−0.393
0.68 (0.44–1.04)
0.074
HDL (mmol/L)
0.235
1.26 (0.84–1.90)
0.259
LDL (mmol/L)
−0.062
0.94 (0.71–1.25)
0.674
ALT (U/L)
0.010
1.01 (1.00–1.02)
0.118
AST (U/L)
0.042
1.04 (1.02–1.07)
0.002
ALP (U/L)
0.001
1.00 (0.99–1.01)
0.824
Surgical treatment
−1.616
0.20 (0.05–0.74)
0.016
Location
Lobar
Ref
-
-
Basal ganglia
0.692
2.00 (1.10–3.62)
0.022
Thalamus
0.492
1.64 (0.77–3.47)
0.200
Brainstem
0.204
1.23 (0.37–4.11)
0.740
Epencephalon
−13.569
0.00 (0.00)
0.971
Primary hematoma volume
0.033
1.03 (1.02–1.05)
< 0.001
0.036
1.04 (1.01–1.06)
0.002
Deep CMs
−0.126
0.88 (0.54–1.45)
0.620
0.105
1.11 (0.20–6.22)
0.905
Lobar CMs
−0.429
0.65 (0.34–1.26)
0.200
−0.049
0.95 (0.20–4.53)
0.951
Subtentorial CMs
−0.472
0.62 (0.33–1.18)
0.147
0.276
1.32 (0.30–5.72)
0.713
Total CMs
−0.283
0.75 (0.46–1.22)
0.253
−1.163
0.31 (0.04–2.25)
0.248
Receiver operating characteristic curve for the risk of a modified Rankin Scale (mRS) score > 2 at discharge in patients with intracerebral hemorrhage (ICH), including the three-component risk factor model.
3.4 Twelve-month mRS
The identified risk factors for the mRS score at 12 months are presented in Table 4. The univariate analyses found that the 12-month mRS score was affected by age (P < 0.001), history of ischemic stroke (P = 0.008), unclear information regarding the use of antidiabetic drugs (P = 0.012), GCS (P < 0.001), NIH stroke scale (P < 0.001), TC (P = 0.006), LDL (P = 0.002), surgical treatment (P = 0.004), and primary hematoma volume (P < 0.001). After adjusting for potential confounders, we noted that the GCS (OR: 2.18; 95% CI: 1.41–3.38; P < 0.001), NIH stroke scale (OR: 1.54; 95% CI: 1.30–1.81; P < 0.001), and treated with surgery (OR: 0.04; 95% CI: 0.00–0.29; P = 0.002) were significantly associated with a poor mRS after 12 months. The prediction model based on the above factors had a predictive value of 86.5% (95% CI: 81.8–91.1%; Figure 3).
The risk factors for 12-months mRS in ICH patients.
Variable
Univariate logistic analysis
Multivariate logistic analysis
β value
OR (95%CI)
P-value
β value
OR (95%CI)
P-value
Age
0.055
1.06 (1.03–1.08)
< 0.001
0.103
1.11 (1.05–1.17)
< 0.001
Sex
−0.537
0.58 (0.30–1.13)
0.111
Time from onset to admitted
−0.022
0.98 (0.95–1.00)
0.073
Smoking
Never
Ref
-
-
Current
0.370
1.45 (0.78–2.68)
0.238
Ever
0.376
1.46 (0.63–3.37)
0.381
Unclear
0.926
2.52 (0.40–15.84)
0.323
Alcohol
Yes
Ref
-
-
No
−0.173
0.84 (0.48–1.48)
0.550
Unclear
1.485
4.41 (0.71–27.51)
0.112
BMI (kg/m2)
−0.052
0.95 (0.88–1.03)
0.184
Hypertension
0.409
1.51 (0.79–2.88)
0.216
Diabetes mellitus
−0.229
0.80 (0.35–1.83)
0.591
Hyperlipidemia
−0.293
0.75 (0.29–1.91)
0.542
History of ischemic stroke
0.968
2.63 (1.29–5.39)
0.008
History of hemorrhagic stroke
1.155
3.17 (0.63–16.12)
0.164
History of subarachnoid hemorrhage
−12.462
0.00 (0.00)
0.989
Antiplatelet drugs
Yes
Ref
-
-
No
−0.358
0.70 (0.32–1.51)
0.364
Unclear
0.390
1.48 (0.39–5.54)
0.563
Anticoagulant drugs (unclear vs. no)
1.155
3.17 (0.63–16.12)
0.164
Antihypertensive drugs
Yes
Ref
-
-
No
−0.233
0.79 (0.44–1.42)
0.437
Unclear
0.308
1.36 (0.38–4.92)
0.639
Lipid-lowering drugs
Yes
Ref
-
-
No
−0.022
0.98 (0.34–2.79)
0.966
Unclear
1.009
2.74 (0.62–12.08)
0.182
Antidiabetic drug
Yes
Ref
-
-
No
1.192
3.29 (0.75–14.51)
0.115
Unclear
2.525
12.49 (1.76–88.65)
0.012
SBP (mmHg)
0.006
1.01 (0.99–1.02)
0.280
DBP (mmHg)
−0.007
0.99 (0.98–1.01)
0.387
GCS
−0.237
0.79 (0.71–0.87)
< 0.001
0.781
2.18 (1.41–3.38)
< 0.001
NIH stroke scale
0.143
1.15 (1.10–1.21)
< 0.001
0.431
1.54 (1.30–1.81)
< 0.001
WBC (*109/L)
0.008
1.01 (0.93–1.09)
0.842
Platelet (*109/L)
−0.005
1.00 (0.99–1.00)
0.073
Fasting glucose (mmol/L)
0.122
1.13 (1.00–1.28)
0.055
INR
1.915
6.78 (0.25–185.68)
0.257
Creatinine (μmoI/L)
−0.001
1.00 (0.99–1.01)
0.860
BUN (mmol/L)
0.137
1.15 (0.98–1.35)
0.095
TC (mmol/L)
−0.481
0.62 (0.44–0.87)
0.006
TG (mmol/L)
−0.430
0.65 (0.38–1.11)
0.116
HDL (mmol/L)
0.177
1.19 (0.86–1.66)
0.290
LDL (mmol/L)
−0.621
0.54 (0.36–0.80)
0.002
ALT (U/L)
−0.001
1.00 (0.99–1.01)
0.923
AST (U/L)
0.017
1.02 (1.00–1.04)
0.115
ALP (U/L)
0.005
1.00 (1.00–1.01)
0.219
Surgical treatment
−1.698
0.18 (0.06–0.58)
0.004
−3.321
0.04 (0.00–0.29)
0.002
Location
Lobar
Ref
-
-
Basal ganglia
0.300
1.35 (0.67–2.70)
0.398
Thalamus
0.517
1.68 (0.72–3.92)
0.232
Brainstem
0.575
1.78 (0.49–6.52)
0.385
Epencephalon
−0.916
0.40 (0.05–3.36)
0.399
Primary hematoma volume
0.025
1.03 (1.01–1.04)
< 0.001
Deep CMs
−0.042
0.96 (0.54–1.69)
0.886
−1.516
0.22 (0.04–1.37)
0.104
Lobar CMs
0.655
1.92 (0.98–3.77)
0.056
1.152
3.16 (0.62–16.10)
0.165
Subtentorial CMs
0.031
1.03 (0.51–2.08)
0.931
0.360
1.43 (0.31–6.71)
0.647
Total CMs
0.293
1.34 (0.77–2.34)
0.304
0.842
2.32 (0.29–18.37)
0.425
Receiver operating characteristic curve for the risk of a 12-month modified Rankin Scale (mRS) score > 2 in patients with intracerebral hemorrhage (ICH), including the three-component risk factor model.
3.5 Mortality
The number of death occurred at discharge, 3 and 12-months were 1, 5, and 13, respectively. Considering lower incidences of mortality at discharge, and 3-months, the risk factors for 12-months were identified and shown in Table 5. The univariate analyses found that the 12-month mortality was affected by age (P = 0.002), history of ischemic stroke (P < 0.001), GCS (P = 0.046), platelet (P = 0.011), LDL (P = 0.049), and primary hematoma volume (P = 0.024). After adjusting for potential confounders, we noted primary hematoma volume was associated with an increased risk of 12-months mortality (OR: 1.04; 95% CI: 1.00–1.08; P = 0.029).
The risk factors for 12-months mortality in ICH patients.
Variable
Univariate logistic analysis
Multivariate logistic analysis
β value
OR (95%CI)
P-value
β value
OR (95%CI)
P-value
Age
0.083
1.09 (1.03–1.14)
0.002
0.046
1.05 (0.94–1.17)
0.405
Sex
−1.641
0.19 (0.03–1.52)
0.118
Time from onset to admitted
−0.006
0.99 (0.96–1.03)
0.750
Smoking
Never
Ref
-
-
Current
−2.095
0.12 (0.01–1.37)
0.088
Ever
−1.493
0.23 (0.02–2.40)
0.217
Unclear
−1.012
0.36 (0.03–4.39)
0.426
Alcohol
Yes
Ref
-
-
No
−1.402
0.25 (0.03–2.47)
0.233
Unclear
−2.031
0.13 (0.01–1.46)
0.098
BMI (kg/m2)
−0.016
0.98 (0.90–1.08)
0.728
Hypertension
0.103
1.11 (0.33–3.71)
0.868
Diabetes mellitus
−0.707
0.49 (0.06–3.91)
0.503
Hyperlipidemia
−0.427
0.65 (0.08–5.20)
0.687
History of ischemic stroke
2.136
8.47 (2.67–26.83)
< 0.001
1.677
5.35 (0.57–49.79)
0.141
History of hemorrhagic stroke
-
-
0.999
History of subarachnoid hemorrhage
-
-
1.000
Antiplatelet drugs
Yes
Ref
-
-
No
0.843
2.32 (0.25–21.37)
0.457
Unclear
−1.085
0.34 (0.04–3.04)
0.333
Anticoagulant drugs (unclear vs. no)
1.431
4.18 (0.45–38.66)
0.207
Antihypertensive drugs
Yes
Ref
-
-
No
−0.704
0.49 (0.05–4.83)
0.545
Unclear
−0.573
0.56 (0.07–4.93)
0.605
Lipid-lowering drugs
Yes
Ref
-
-
No
0.234
1.26 (0.10–15.50)
0.855
Unclear
−0.629
0.53 (0.06–4.52)
0.564
Antidiabetic drug
Yes
Ref
-
-
No
-
-
0.998
Unclear
−0.856
0.43 (0.05–3.68)
0.437
SBP (mmHg)
−0.011
0.99 (0.97–1.01)
0.360
DBP (mmHg)
−0.026
0.98 (0.94–1.01)
0.167
GCS
−0.168
0.85 (0.72–1.00)
0.046
0.539
1.72 (0.72–4.07)
0.221
NIH stroke scale
0.054
1.06 (0.99–1.12)
0.092
0.144
1.15 (0.89–1.49)
0.271
WBC (*109/L)
−0.137
0.87 (0.71–1.06)
0.177
Platelet (*109/L)
−0.014
0.99 (0.98–1.00)
0.011
−0.009
0.99 (0.97–1.01)
0.350
Fasting glucose (mmol/L)
−0.275
0.76 (0.52–1.12)
0.165
INR
−0.081
0.92 (0.23–3.78)
0.911
Creatinine (μmoI/L)
0.005
1.01 (0.98–1.03)
0.651
BUN (mmol/L)
0.098
1.10 (0.81–1.50)
0.526
TC (mmol/L)
−0.588
0.56 (0.26–1.21)
0.137
TG (mmol/L)
−0.325
0.72 (0.23–2.30)
0.582
HDL (mmol/L)
−0.077
0.93 (0.32–2.65)
0.886
LDL (mmol/L)
−0.949
0.39 (0.15–1.00)
0.049
−0.916
0.40 (0.10–1.55)
0.185
ALT (U/L)
−0.011
0.99 (0.95–1.03)
0.581
AST (U/L)
0.007
1.01 (0.97–1.05)
0.741
ALP (U/L)
−0.016
0.98 (0.96–1.01)
0.255
Surgical treatment
−0.527
0.59 (0.07–4.92)
0.626
Location
Lobar
Ref
-
-
Basal ganglia
-
-
0.999
Thalamus
-
-
0.999
Brainstem
-
-
0.999
Epencephalon
-
-
0.999
Primary hematoma volume
0.022
1.02 (1.00–1.04)
0.024
0.042
1.04 (1.00–1.08)
0.029
Deep CMs
−1.358
0.26 (0.06–1.19)
0.081
-
-
0.996
Lobar CMs
−0.186
0.83 (0.18–3.87)
0.813
-
-
0.997
Subtentorial CMs
0.237
1.27 (0.34–4.78)
0.726
-
-
0.996
Total CMs
−0.517
0.60 (0.19–1.87)
0.376
-
-
0.997
4 Discussion
This study first identified the risk factors for functional outcomes and mortality at discharge, at 3 months, and at 12-months in patients with ICH. A multifactorial predictive model for the risk of poor functional outcomes was constructed, which could be used to screen high-risk patients, and effective treatments could be applied to improve the prognosis of ICH patients. Our study recruited 269 patients with ICH, of which 94 patients reported a 3-month mRS > 2. The 3-month mRS score was affected by the GCS, NIH stroke scale, INR, BUN, epencephalon hemorrhage, and primary hematoma volume. Moreover, the GCS, NIH stroke scale, and primary hematoma volume could affect the mRS at discharge, while the mRS at 12 months could be affected by the GCS, NIH stroke scale, and surgical treatment. Furthermore, the risk of 12-months mortality could affected by primary hematoma volume. The constructed prediction model for mRS scores at discharge, at 3 months, and at 12 months showed a relatively high predictive performance.
Several studies have constructed prediction models for ICH patients (15–19). Fukuda et al. identified 187 patients with aneurysmal subarachnoid hemorrhage and found that a constructed model containing D-dimer was associated with a better discrimination ability for poor outcomes (15). Wang et al. (16) applied an automated machine learning-based approach to construct a prognostic model for patients with ICH and pointed out that the random forest provides the best predictive performance. Katsuki et al. (17) used data from 140 patients with hypertensive ICH and found that the prediction model constructed using a deep learning framework was superior to the model derived from the ICH score, ICH Grading Scale, and FUNC score. Trevisi et al. (18) found that the important features for functional outcomes included the GCS, Charlson Comorbidity Index, ICH score, ICH volume, pupillary status, brainstem location, age, anticoagulant/antiplatelet agents, intraventricular hemorrhage, and cerebellar location, and the discriminative ability was high. Wu et al. (19) identified 83 patients with ICH and found that the combined model applied radiomic scores obtained from intraparenchymal hemorrhage, intraventricular hemorrhage, and clinical characteristics with high accuracy in predicting poor functional outcomes. However, these studies did not systematically identify the risk factors for poor functional outcomes in patients with ICH, and the characteristics of CMs have not been addressed. Thus, the current study was performed to identify risk factors and construct a prediction model for functional outcomes at discharge, at 3 months, and at 12-months in patients with ICH.
Our study found that the risk factors at various time points differed; similar risk factors included the GCS and NIH stroke scale. A potential reason for this could be that these two scales reflect the neurological status of patients with ICH (20, 21). Additional factors potentially influencing functional outcomes included INR, BUN, epencephalon hemorrhage, surgery, and primary hematoma volume. Several reasons could explain these results: (1) elevated INR has already been demonstrated to be associated with a poor prognosis in patients with ICH, especially in patients treated with warfarin (22); (2) BUN is an important index to assess renal function, which could reflect the severity of disease status in patients with ICH (23); (3) hemorrhage location is significantly related to the prognosis of ICH because the invasion sites are associated with the severity of disease (24); and (4) patients treated with surgery are significantly related to hematoma volume, which could affect the prognosis of ICH (25, 26). Therefore, early intervention strategies should be implemented for identified modifiable risk factors in order to improve patient prognosis.
Our study reported the risk of 12-months mortality could affected by primary hematoma volume, which could explained by the primary hematoma volume are significantly related to the severity of disease and subsequent treatments. Moreover, considering the incidences of mortality at discharge and 3-months were lower than expected, thus the power was not enough to detect potential associations. In addition, although our study found that functional outcomes were not affected by CMs, especially deep, lobar, subtentorial, and total CMs, the prediction model based on CMs and clinical factors for mRS > 2 at discharge, 3 and 12 months had a relatively high predictive performance. Considering that the risk factors for poor functional outcomes have already been identified, effective strategies should be applied to patients at a high risk of poor functional outcomes.
This study has some limitations. First, the analysis was based on retrospective data, and the results of our study could have been affected by selection and recall biases. Second, our study was restricted by single-center study with a small sample size, thus the conclusions of this study should be recommended cautiously. Third, the severity of ICH was not restricted as an inclusion criterion, and the prognosis of ICH could be affected by the presence of more severe or mild symptoms. Fourth, the background therapies for ICH differed among the included patients, which could have affected the prognosis of ICH. Fifth, biomarkers levels change over the course of follow-up was not addressed, which needed further explored. Sixth, the predictive model was not verified using an external cohort.
This study identified the risk factors for functional outcomes and mortality at discharge, at 3 months, and at 12 months in patients with ICH, and the CMs were addressed. Moreover, a prediction model was constructed based on the identified risk factors with relatively high predictive performance, which could be applied in clinical practice to identify high-risk patients. Further large-scale prospective studies are required to validate the constructed model.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by the Institutional Review Board of Beijing Tiantan Hospital, Capital Medical University (KY2014-023-02). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by National Science and Technology Major Project (2017ZX09304018) and National Key Research and Development Program of China (Grant No. 2018YFC1312200/2018YFC1312204).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
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ReferencesCollaboratorsGBDS. 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) 20:795–820. 10.1016/S1474-4422(21)00252-034487721TuWJZhaoZYinPCaoLZengJChenH. Estimated burden of stroke in China in 2020. JAMA Netw Open. (2023) 6:e231455. 10.1001/jamanetworkopen.2023.145536862407TuWJWangLD. Special writing group of China stroke surveillance report. China stroke surveillance report 2021. Mil Med Res. (2023) 10:33. 10.1186/s40779-023-00463-x37468952WangWJiangBSunHRuXSunDWangL. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults. Circulation. (2017) 135:759–71. 10.1161/CIRCULATIONAHA.116.02525028052979van AschCJLuitseMJRinkelGJvan der TweelIAlgraAKlijnCJ. Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol. (2010) 9:167–76. 10.1016/S1474-4422(09)70340-020056489IkramMAWieberdinkRGKoudstaalPJ. International epidemiology of intracerebral hemorrhage. Curr Atheroscler Rep. (2012) 14:300–6. 10.1007/s11883-012-0252-1GongYWangYChenDTengYXuFYangP. Predictive value of hyperglycemia on prognosis in spontaneous intracerebral hemorrhage patients. Heliyon. (2023) 9:e14290. 10.1016/j.heliyon.2023.e1429036925553On SPohRSalorRSPhilipRGChekkattuRHLimMA. The burden and risks factors for intracerebral hemorrhage in a Southeast Asian population. Clin Neurol Neurosurg. (2022) 214:107145. 10.1016/j.clineuro.2022.10714535114482OvervadTFAndersenSDLarsenTBLipGYHSogaardMSkjothF. Incidence and prognostic factors for recurrence of intracerebral hemorrhage in patients with and without atrial fibrillation: a cohort study. Thromb Res. (2020) 191:1–8. 10.1016/j.thromres.2020.03.02432339765RendevskiVAleksovskiBStojanovDRendevskaAMAleksovskiVGjorgoskiI. Modeling prognostic factors for poor neurological outcome in conservatively treated patients with intracerebral hemorrhage: a focus on TNF-alpha. Clin Neurol Neurosurg. (2018) 172:51–8. 10.1016/j.clineuro.2018.06.02729975876CordonnierCAl-Shahi SalmanRWardlawJ. Spontaneous brain microbleeds: systematic review, subgroup analyses and standards for study design and reporting. Brain. (2007) 130:1988–2003. 10.1093/brain/awl38717322562JeonSBKangDWChoAHLeeEMChoiCGKwonSU. Initial microbleeds at MR imaging can predict recurrent intracerebral hemorrhage. J Neurol. (2007) 254:508–12. 10.1007/s00415-006-0406-617401517JouventEPuyLChabriatH. Cerebral microhemorrhages: significance, associations, diagnosis, and treatment. Curr Treat Options Neurol. (2016) 18:35. 10.1007/s11940-016-0418-127314427van SwietenJCKoudstaalPJVisserMCSchoutenHJvan GijnJ. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. (1988) 19:604–7. 10.1161/01.STR.19.5.604FukudaHLoBYamamotoYHandaAYamamotoYKurosakiY. Plasma D-dimer may predict poor functional outcomes through systemic complications after aneurysmal subarachnoid hemorrhage. J Neurosurg. (2017) 127:284–90. 10.3171/2016.5.JNS1676727518526WangHLHsuWYLeeMHWengHHChangSWYangJT. Automatic machine-learning-based outcome prediction in patients with primary intracerebral hemorrhage. Front Neurol. (2019) 10:910. 10.3389/fneur.2019.0091031496988KatsukiMKakizawaYNishikawaAYamamotoYUchiyamaT. Postsurgical functional outcome prediction model using deep learning framework (prediction one, sony network communications Inc.) for hypertensive intracerebral hemorrhage. Surg Neurol Int. (2021) 12:203. 10.25259/SNI_222_202134084630TrevisiGCaccavellaVMScerratiASignorelliFSalamoneGGOrsiniK. Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage. Neurosurg Rev. (2022) 45:2857–67. 10.1007/s10143-022-01802-735522333WuTCLiuYLChenJHHoCHZhangYSuMY. Prediction of poor outcome in stroke patients using radiomics analysis of intraparenchymal and intraventricular hemorrhage and clinical factors. Neurol Sci. (2023) 44:1289–300. 10.1007/s10072-022-06528-436445541WangWJLuJJWangYJWangCXWangYLHoffK. Clinical characteristics, management, and functional outcomes in Chinese patients within the first year after intracerebral hemorrhage: analysis from China national stroke registry. CNS Neurosci Ther. (2012) 18:773–80. 10.1111/j.1755-5949.2012.00367.x22943144FinocchiCBalestrinoMMalfattoLMancardiGSerratiCGandolfoC. National Institutes of Health Stroke Scale in patients with primary intracerebral hemorrhage. Neurol Sci. (2018) 39:1751–5. 10.1007/s10072-018-3495-y30009334CurtzeSStrbianDMeretojaAPutaalaJErikssonHHaapaniemiE. Higher baseline international normalized ratio value correlates with higher mortality in intracerebral hemorrhage during warfarin use. Eur J Neurol. (2014) 21:616–22. 10.1111/ene.1235224447727GaoBGuHYuWLiuSZhouQKangK. Admission dehydration is associated with significantly lower in-hospital mortality after intracerebral hemorrhage. Front Neurol. (2021) 12:637001. 10.3389/fneur.2021.63700133763017EslamiVTahsili-FahadanPRivera-LaraLGandhiDAliHParry-JonesA. Influence of intracerebral hemorrhage location on outcomes in patients with severe intraventricular hemorrhage. Stroke. (2019) 50:1688–95. 10.1161/STROKEAHA.118.02418731177984WangNLinWZhuXTuQZhuDQuS. Conventional craniotomy versus conservative treatment in patients with minor spontaneous intracerebral hemorrhage in the basal ganglia. Chin Neurosurg J. (2022) 8:26. 10.1186/s41016-022-00288-y35986426WangLLiuCLuEZhangDZhangHXuX. Total intracranial volume as a covariate for predicting prognosis in patients with primary intracerebral hemorrhage. Clin Neurol Neurosurg. (2022) 214:107135. 10.1016/j.clineuro.2022.10713535121234