Serum lipid levels predict mortality of aged patients in acute intracerebral hemorrhage

Serum lipid levels predict mortality of aged patients in acute intracerebral hemorrhage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serum lipid levels predict mortality of aged patients in acute intracerebral hemorrhage Jun-Meng Huang, Yong-Bo Ma, Shu-Qiang Zhang, Chao-Yi Huang, Rui Feng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7615725/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Intracerebral hemorrhage (ICH) carries high early mortality. Admission lipids have been linked to short-term outcomes, yet prior studies focused on conventional markers and rarely assessed age-specific effects. Because older adults often have lower nutritional reserve and higher medical complication rates, the prognostic value of lipids may be age dependent. We therefore evaluated a broader lipid profile to test their associations with 90-day mortality across different age group. Methods We conducted a single-center cohort at a tertiary academic hospital. Serum lipids, including total cholesterol(TC), triglycerides(TG), high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), apolipoprotein A1(Apo A1), and apolipoprotein B(Apo B), were measured upon hospital admission. Patients were stratified as < 45 years and ≥ 45 years. Multivariate logistic regression was applied to investigate the associations between lipid levels, lipid ratios, and 90-day mortality. Discrimination was assessed using stratified 5-fold cross-validated single-marker receiver operating characteristic (ROC) analyses. Results We included 503 patients with acute ICH. In those older than 45 years, lower nonHDL-C (OR 0.898; 95%CI 0.814–0.990), Apo B (OR 0.861; 95%CI 0.748–0.990), nonHDL-C/HDL-C (OR 0.964; 95%CI 0.930–0.999), and Apo B/Apo A1 (OR 0.816; 95%CI 0.687–0.968) were independently associated with higher 90-day mortality. In ROC analyses, Apo B (AUC = 0.671) and Apo B/Apo A1 (AUC = 0.676) showed the highest discrimination among single markers, indicating modest predictive performance. Conclusion Lipid levels and ratios, particularly Apo B and Apo B/Apo A1, are independent predictors of 90-day mortality in middle-aged and older ICH patients, aiding clinical risk stratification. intracerebral hemorrhage lipid lipid ratio apolipoprotein B mortality Figures Figure 1 Figure 2 1. Introduction Intracerebral hemorrhage (ICH) accounts for 20–30% of all stroke cases, with an estimated 3.5 million new cases annually 1 , 2 . As a severe and critical neurological condition, many ICH patients have poor outcomes, with a 1-year mortality rate of up to 50%, and over 60% of survivors experiencing severe functional disability. Despite numerous clinical trials aimed at improving ICH prognosis, a specific disease-modifying treatment remains lacking 3 – 5 . Further exploration of modifiable predictors related to the ICH outcome is important. Previous studies have evaluated the strong correlation between various lipid levels [total cholesterol(TC), triglyceride(TG), high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), non-high-density lipoprotein cholesterol (nonHDL-C)] and the risks of ICH 6 – 12 , microhemorrhages foci 13 , hematoma growth 14 , 15 and ICH-related mortality 16 – 18 . Specifically, higher admission TC, LDL-C and LDL-C/HDL-C ratios have been linked to lower early ICH mortality. Early ICH mortality reflects diverse pathways, including mass effect and hematoma expansion at any age. In older adults, additional vulnerability to infection, cardiopulmonary complications, and treatment limitations further contributes. Lower lipid levels have been associated with hematoma expansion 14 , infection susceptibility 19 , and markers of malnutrition. These suggest that the lipid levels in ICH patients may vary across different age groups. Therefore, this study aimed to investigate the relationship between lipid profiles, lipid ratios, and 90-day mortality in ICH across different age groups, to assist clinicians in risk identification for clinical practice. 2. Method 2.1 Study population Patients diagnosed with ICH were admitted to the First Affiliated Hospital of Chongqing Medical University, a tertiary academic hospital, and enrolled in our study. Our study size was determined by data availability. We retrospectively analyzed data from patients with acute ICH recorded in our clinical research database, covering the period from May 2017 to June 2022. Patients, aged ≥ 18 years, with primary acute ICH within 72 hours after ICH ictus were enrolled. We excluded patients with primary intraventricular hemorrhage (IVH), secondary ICH, anticoagulant-associated bleeding, leukemia-associated cerebral hemorrhage, multiple intracranial hemorrhages, and cases lacking lipid data on admission or complete follow-up data at 90 days. The Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Chongqing, China) approved our interviews (approval: 2017-075) on September 25, 2017. All participants provided written informed consent before the examination, permitting data analysis. 2.2 Data acquisition At enrollment, we recorded each patient’s age, sex, medical history (including hypertension, ischemic stroke, and diabetes), as well as smoking and alcohol-consumption habits. Blood pressure (BP) in the prone position was measured upon admission. ICH severity was assessed using the Glasgow Coma Scale (GCS). All patients underwent computed tomography (CT) scans upon admission. The CT images were stored in Digital Imaging and Communications in Medicine (DICOM) format and independently reviewed by two experienced radiologists, both blinded to clinical and outcome data. Hematoma volume measurements and region-of-interest delineation were carried out using 3D Slicer (version 5.2.2, www.slicer.org ), an open-source, semi-automated software platform that has been validated for three-dimensional medical image processing, including visualization, segmentation, registration, and quantitative volumetric analysis. All CT scans were independently reviewed by two blinded raters: one senior neurologist (W.S.Y.) and one neuroradiologist (S.Q.Z.). Fasting blood samples were collected and analyzed at the Clinical Laboratory Department of the First Affiliated Hospital of Chongqing Medical University. A fully automated biochemical analyzer (Cobas c701; Roche, Basel, Switzerland) with original laboratory reagents was used to measure lipid levels.TC, TG, HDL-C, and LDL-C levels were measured using an enzymatic assay. Apolipoprotein A1 (Apo A1) and apolipoprotein B (Apo B) levels were measured using immunoturbidimetric assays. NonHDL-C was defined as TC minus HDL-C. The ratios of TC/TG, TC/nonHDL-C, HDL-C/TC, HDL-C/nonHDL-C, HDL-C/Apo A1, LDL-C/TC, LDL-C/Apo B, nonHDL-C/TC, nonHDL-C/HDL-C, nonHDL-C/Apo B, Apo A1/Apo B and Apo B/Apo A1 were calculated. Functional outcome at 3 months was assessed by telephone interviews with patients or their relatives, using the modified Rankin Scale (mRS). A score of 6 denotes death. . 2.3 Statistical Analysis Data were analyzed with R (version 4.2.3). Baseline characteristics are presented as mean ± standard deviation (SD) for normally distributed continuous variables, median [interquartile range (IQR)] for non-normal continuous variables, and number (percentage) for categorical variables. Baseline balance between groups was assessed using standardized mean differences (SMDs). Values < 0.10 were considered negligible. Multivariable logistic regression was used to examine the independent association of admission lipid levels and lipid ratios with 90-day mortality. Variables showing p < 0.10 in univariable analyses were entered into the multivariable models. To examine whether the association between lipid markers and 90-day mortality varied with age, we fitted multivariable logistic regression models including a product (interaction) term between age and each lipid variable. For interpretability, lipid concentrations were scaled to represent an OR per 10-unit increase, whereas lipid ratios were scaled per 0.1-unit increase. The global significance of each interaction was assessed using likelihood ratio tests. Discriminatory performance of each lipid marker across age groups was evaluated by receiver-operating-characteristic (ROC) analysis. We report the area under the ROC curve (AUC) together with sensitivity and specificity at the optimal cutoff. All tests were two-tailed, and statistical significance was set at p < 0.05. 3. Result A total of 503 participants with complete data were involved in the analysis (Fig. 1 ). The mean age of the overall population was 65.00 years. A total of 76 patients (15.11%) died within 90 days of onset. The baseline characteristics, clinical scales, radiological findings, and laboratory data are summarized in Table 1 . Patients were categorized into two groups based on 90-day survival status. Patients who died were older (SMD = 0.48), had worse neurologic severity (higher GCS, SMD = 1.38), larger hematomas (SMD = 0.77), and more IVH (SMD = 0.83). They also had higher SBP (SMD = 0.31) and lower DBP (SMD = 0.23), were less often smokers (SMD = 0.34), and had small-to-moderate differences in sex and diabetes (SMD = 0.21). For lipids, mortality group showed lower atherogenic cholesterol:TC, LDL-C, nonHDL-C, and Apo B (SMD = 0.20–0.40). Ratio patterns were concordant: higher Apo A1-preponderant ratios (HDL-C/TC, HDL-C/nonHDL-C, Apo A1/Apo B, TC/nonHDL-C; SMD = 0.38–0.46) and lower Apo B-centric ratios (LDL-C/TC, nonHDL-C/TC, nonHDL-C/HDL-C, Apo B/Apo A1; SMD = 0.21–0.44). Differences were negligible for TG, TC/TG, LDL-C/apoB, surgery, and hemorrhage location (all SMD < 0.10). Table 1 Baseline Characteristics, Risk Factors, Clinical Scales, Radiology, and Laboratory Findings. Variables 90day-Mortality SMD Overall(n = 503) Alive(n = 427) Dead(n = 76) Age (median, [IQR]), y 65.00 [53.00, 73.00] 63.00 [52.50, 72.00] 70.50 [61.75, 78.00] 0.479 Sex(male), % 359 (71.4) 311 (72.8) 48 (63.2) 0.209 Pre-ICH history Diabetes, % 113 (22.5) 90 (21.1) 23 (30.3) 0.211 Hypertension, % 458 (91.1) 389 (91.1) 69 (90.8) 0.011 Ischemic stroke, % 45 (8.9) 35 (8.2) 10 (13.2) 0.161 Smoking, % 236 (46.9) 211 (49.4) 25 (32.9) 0.341 Drinking, % 171 (34.0) 149 (34.9) 22 (28.9) 0.128 SBP (median [IQR]) 167.00 [149.00, 183.00] 165.00 [147.00, 182.00] 173.50 [160.75, 189.00] 0.307 DBP (median [IQR]) 95.00 [84.00, 107.00] 96.00 [85.00, 108.00] 92.00 [81.00, 104.00] 0.230 GCS on Admission (median, [IQR]) 15.00 [13.00, 15.00] 15.00 [13.00, 15.00] 9.00 [5.75, 13.00] 1.375 Surgery, % 34 (6.8) 30 (7.0) 4 (5.3) 0.073 ICH volume (median, [IQR]) 9.74 [4.06, 20.46] 8.83 [3.57, 18.67] 19.11 [7.50, 53.00] 0.768 Location, % 0.067 Deep 356 (70.8) 304 (71.2) 52 (68.4) Lobe 89 (17.7) 75 (17.6) 14 (18.4) Infratentorial 58 (11.5) 48 (11.2) 10 (13.2) IVH, % 137 (27.2) 92 (21.5) 45 (59.2) 0.831 Alb (median, IQR), g/L 41.00 [38.00, 44.00] 41.00 [38.00, 44.00] 40.00 [37.00, 44.00] 0.165 Lipids TC (median, IQR), mg/dl 172.85 [150.23, 200.89] 175.56 [151.97, 201.66] 162.61 [140.37, 192.87] 0.200 TG (median, IQR), mg/dl 104.51 [71.74, 155.44] 106.28 [73.07, 159.87] 98.76 [68.64, 136.62] 0.004 HDL-C (median, IQR), mg/dl 48.72 [39.83, 59.94] 48.72 [39.44, 59.55] 51.43 [44.76, 65.16] 0.284 LDL-C (median, IQR), mg/dl 106.73 [84.11, 132.44] 107.89 [87.01, 133.02] 93.19 [69.70, 118.23] 0.288 Apo A1 (median, IQR), mg/dl 134.00 [118.00, 155.50] 134.00 [117.00, 154.00] 139.00 [122.00, 163.50] 0.185 Apo B (median, IQR), mg/dl 91.00 [75.50, 111.00] 93.00 [78.00, 111.00] 78.00 [63.50, 95.75] 0.400 nonHDL-C (median, IQR) 124.90 [98.42, 149.65] 126.45 [102.86, 151.01] 110.02 [83.72, 138.15] 0.306 Lipid Ratios TC/TG (median, IQR) 1.64 [1.12, 2.36] 1.64 [1.11, 2.35] 1.64 [1.21, 2.39] 0.019 TC/nonHDL-C (median, IQR) 1.40 [1.30, 1.54] 1.38 [1.29, 1.53] 1.48 [1.37, 1.68] 0.398 HDL-C/TC (median, IQR) 0.29 [0.23, 0.35] 0.28 [0.23, 0.34] 0.33 [0.27, 0.40] 0.464 HDL-C/nonHDL-C (median, IQR) 0.40 [0.30, 0.54] 0.38 [0.29, 0.53] 0.48 [0.37, 0.68] 0.398 HDL-C/Apo A1 (median, IQR) 0.37 [0.32, 0.41] 0.37 [0.32, 0.41] 0.38 [0.34, 0.42] 0.255 LDL-C/TC (median, IQR) 0.63 [0.56, 0.67] 0.63 [0.56, 0.68] 0.59 [0.51, 0.65] 0.349 LDL-C/Apo B (median, IQR) 1.18 [1.08, 1.27] 1.17 [1.08, 1.26] 1.20 [1.07, 1.27] 0.047 nonHDL-C/TC (median, IQR) 0.71 [0.65, 0.77] 0.72 [0.66, 0.77] 0.67 [0.60, 0.73] 0.464 nonHDL-C/HDL-C (median, IQR) 2.49 [1.84, 3.29] 2.60 [1.90, 3.40] 2.08 [1.47, 2.67] 0.212 nonHDL-C/Apo B (median, IQR) 1.33 [1.26, 1.40] 1.33 [1.26, 1.40] 1.33 [1.26, 1.44] 0.060 Apo A1/Apo B (median, IQR) 1.48 [1.19, 1.89] 1.45 [1.17, 1.82] 1.74 [1.43, 2.19] 0.380 Apo B/Apo A1 (median, IQR) 0.67 [0.53, 0.84] 0.69 [0.55, 0.85] 0.57 [0.46, 0.70] 0.439 Abbreviation:Alb, albumin; Apo A1, Apolipoprotein A1; Apo B, apolipoprotein B; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; HDL-C, high-density lipoprotein-cholesterol; ICH, intracerebral hemorrhage; IQR, interquartile range; LDL-C, low-density lipoprotein-cholesterol; nonHDL-C, non-high-density lipoprotein-cholesterol; SBP, systolic blood pressure; SMD, standardized mean difference;TC, total cholesterol; TG, triglyceride. In the overall cohort, univariable logistic regression showed that lipid levels and ratios, age, sex, diabetes status, smoking history, SBP, DBP, admission hematoma volume, IVH, and GCS score were each associated with 90-day mortality ( p < 0.10). (Table 2 ). However, in multivariable logistic regression, adjusting for the covariates identified in the univariable analysis (age, sex, diabetes, smoking, SBP/DBP, admission hematoma volume, hemorrhage location, intraventricular hemorrhage, albumin and GCS), the associations between all lipid levels and 90-day mortality were not statistically significant(Fig. 2 ). To assess whether the prognostic value of lipids differed by age, we incorporated interaction terms between age and each lipid species in the multivariable model. The age interactions were significant for Apo B (OR = 0.992;95% CI, 0.985–1.000; P = 0.049), nonHDL-C (OR = 0.997; 95% CI, 0.993–1.000; P = 0.056), the nonHDL-C/HDL-C ratio (OR = 0.999; 95% CI, 0.998–1.000; P = 0.042),and the Apo B/Apo A1 ratio (OR = 0.993; 95% CI, 0.987–1.000; P = 0.043). In view of these age-lipid interactions, we subsequently conducted an age-stratified subgroup analysis. Table 2 Univariable logistic regression analyses of 90-day mortality after ICH by age group. Variable overall(n = 503) Age > = 45(n = 457) P Value OR (95% CI) P Value OR (95% CI) Age <0.001 1.040 (1.019–1.061) <0.001 1.057 (1.032–1.083) Sex 0.087 0.639 (0.383–1.068) 0.034 0.566 (0.334–0.959) Pre-ICH history Hypertension 0.930 0.963 (0.413–2.244) 0.953 1.027 (0.414–2.547) Diabetes 0.079 1.625 (0.945–2.794) 0.087 1.628 (0.931–2.848) Ischemic stroke 0.167 1.697 (0.802–3.591) 0.322 1.484 (0.679–3.241) Smoking 0.009 0.502 (0.300–0.840) 0.013 0.507 (0.297–0.868) Drinking 0.314 0.760 (0.446–1.297) 0.171 0.669 (0.376–1.189) SBP on Admission 0.013 1.012 (1.002–1.021) 0.019 1.012 (1.002–1.022) DBP on Admission 0.080 0.987 (0.973–1.002) 0.043 0.984 (0.968–1.000) ICH volume on Admission <0.001 1.035 (1.024–1.046) <0.001 1.034 (1.023–1.046) Location Deep Ref Ref Ref Ref Lobe 0.790 1.091 (0.574–2.074) 0.899 1.044 (0.537–2.029) Infratentorial 0.603 1.218 (0.580–2.558) 0.899 0.946 (0.402–2.227) IVH <0.001 5.286 (3.167–8.823) <0.001 4.737 (2.785–8.057) GCS on Admission <0.001 0.678 (0.626–0.735) <0.001 0.682 (0.627–0.741) Surgery 0.574 0.735 (0.251–2.150) 0.694 0.780 (0.226–2.690) Alb 0.171 0.964 (0.915–1.016) 0.258 0.967 (0.914–1.025) Lipids TC 0.096 0.995 (0.988–1.001) 0.004 0.989 (0.982–0.997) TG 0.970 1.000 (0.998–1.002) 0.137 0.998 (0.994–1.001) HDL-C 0.018 1.018 (1.003–1.034) 0.029 1.018 (1.002–1.034) LDL-C 0.013 0.991 (0.983–0.998) 0.002 0.987 (0.979–0.995) nonHDL-C 0.008 0.991 (0.984–0.998) <0.001 0.984 (0.976–0.992) Apo A1 0.128 1.006 (0.998–1.014) 0.123 1.007 (0.998–1.015) Apo B 0.001 0.983 (0.973–0.993) 0.000 0.976 (0.965–0.987) Lipids ratio TC/TG 0.881 1.002 (0.980–1.024) 0.908 1.001 (0.979–1.025) TC/nonHDL-C 0.010 1.133 (1.031–1.246) 0.007 1.148 (1.039–1.269) HDL-C/TC <0.001 1.607 (1.250–2.066) <0.001 1.738 (1.332–2.267) HDL-C/nonHDL-C 0.010 1.133 (1.031–1.246) 0.007 1.148 (1.039–1.269) HDL-C/Apo A1 0.033 1.551 (1.037–2.320) 0.034 1.578 (1.036–2.405) LDL-C/TC 0.003 0.718 (0.577–0.894) 0.006 0.710 (0.555–0.907) LDL-C/Apo B 0.688 1.030 (0.890–1.192) 0.350 1.079 (0.920–1.267) nonHDL-C/TC <0.001 0.622 (0.484–0.800) <0.001 0.575 (0.441–0.751) nonHDL-C/HDL-C 0.020 0.973 (0.950–0.996) <0.001 0.940 (0.913–0.968) nonHDL-C/Apo B 0.660 1.015 (0.950–1.084) 0.637 1.035 (0.897–1.195) Apo A1/Apo B 0.038 1.036 (1.002–1.071) <0.001 1.041 (1.004–1.079) Apo B/Apo A1 <0.001 0.806 (0.715–0.908) <0.001 0.736 (0.642–0.844) Abbreviation: Alb, albumin; Apo A1, apolipoprotein A1; Apo B, apolipoprotein B; CI, confidence interval; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; HDL-C, high-density lipoprotein-cholesterol; IQR, interquartile range; LDL-C, low-density lipoprotein-cholesterol; nonHDL-C, non-high-density lipoprotein-cholesterol; OR, odds ratio; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride. Note: Odds ratios for lipids ratios are reported per 0.1-unit increase. 3.1. Subgroup analysis After stratifying patients by age, we found diabetes (SMD = 0.271) and prior ischemic stroke (SMD = 0.320) were more prevalent, whereas drinking (SMD = 0.265) was less frequent in patients aged ≥ 45 years. Admission DBP was lower in the ≥ 45 group (SMD = 0.740), while systolic pressure was similar (SMD = 0.148). Fewer older patients underwent surgery (SMD = 0.497). Albumin was slightly lower (SMD = 0.181). Lipid profiles in the ≥ 45 group were characterized by higher HDL-C (SMD = 0.572) and Apo A1 (SMD = 0.382), but lower TG (SMD = 0.367), LDL-C (SMD = 0.277), nonHDL-C (SMD = 0.463), and Apo B (SMD = 0.568). Specifically, hypertension history (SMD = 0.071), smoking (SMD = 0.068), GCS on admission (SMD = 0.093), and IVH (SMD = 0.029) showed negligible between-group differences. Detailed results are presented in Table 3 . Table 3 Baseline Characteristics, Risk Factors, Clinical Scales, Radiology, and Laboratory Findings between Age groups. Variables Age ≤ 44(n = 46) Age ≥ 45(n = 457) SMD Age (median, [IQR]), y 38.50 [34.25, 41.00] 66.00 [55.00, 74.00] 3.186 Sex(male), % 36 (78.3) 323 (70.7) 0.175 Pre-ICH history Diabetes, % 6 (13.0) 107 (23.4) 0.271 Hypertension, % 41 (89.1) 417 (91.2) 0.071 Ischemic stroke, % 1 (2.2) 44 (9.6) 0.320 Smoking, % 23 (50.0) 213 (46.6) 0.068 Drinking, % 21 (45.7) 150 (32.8) 0.265 SBP (median [IQR]) DBP (median [IQR]) 171.00 [148.00, 184.75] 166.00 [149.00, 183.00] 0.148 DBP (median [IQR]) 113.50 [92.75, 125.00] 94.00 [83.00, 105.00] 0.740 GCS on Admission (median, [IQR]) 15.00 [12.00, 15.00] 15.00 [13.00, 15.00] 0.093 Surgery, % 10 (21.7) 24 (5.3) 0.497 ICH volume on Admission (median [IQR]) 13.45 [5.35, 29.23] 9.49 [4.03, 19.91] 0.148 Location, % 0.310 Deep 29 (63.0) 327 (71.6) Lobe 7 (15.2) 82 (17.9) Infratentorial 10 (21.7) 48 (10.5) IVH, % 12 (26.1) 125 (27.4) 0.029 Alb (median, IQR), g/L 42.00 [39.00, 46.00] 41.00 [38.00, 44.00] 0.181 Lipids TC (median, IQR), mg/dl 180.01 [149.27, 224.19] 172.85 [150.43, 199.15] 0.357 TG (median, IQR), mg/dl 127.10 [91.45, 200.17] 101.86 [69.97, 147.91] 0.367 HDL-C (median, IQR), mg/dl 40.80 [34.51, 51.91] 49.11 [41.38, 61.10] 0.572 LDL-C (median, IQR), mg/dl 113.11 [93.68, 149.36] 105.96 [83.91, 131.86] 0.277 Apo A1 (median, IQR), mg/dl 121.00 [106.25, 141.25] 135.00 [119.00, 157.00] 0.382 Apo B (median, IQR), mg/dl 106.00 [81.75, 129.75] 91.00 [75.00, 109.00] 0.568 nonHDL-C (median, IQR) 137.86 [108.95, 179.43] 124.52 [97.45, 146.95] 0.463 Lipid Ratios TC/TG (median, IQR) 1.39 [0.82, 1.98] 1.66 [1.15, 2.39] 0.295 TC/nonHDL-C (median, IQR) 1.32 [1.23, 1.40] 1.41 [1.31, 1.56] 0.692 HDL-C/TC (median, IQR) 0.24 [0.19, 0.29] 0.29 [0.24, 0.36] 0.783 HDL-C/nonHDL-C (median, IQR) 0.32 [0.23, 0.40] 0.41 [0.31, 0.56] 0.692 HDL-C/Apo A1 (median, IQR) 0.34 [0.31, 0.37] 0.37 [0.33, 0.41] 0.539 LDL-C/TC (median, IQR) 0.65 [0.60, 0.69] 0.62 [0.56, 0.67] 0.088 LDL-C/Apo B (median, IQR) 1.12 [1.02, 1.23] 1.18 [1.09, 1.27] 0.414 nonHDL-C/TC (median, IQR) 0.76 [0.71, 0.81] 0.71 [0.64, 0.76] 0.783 nonHDL-C/HDL-C (median, IQR) 3.17 [2.47, 4.26] 2.43 [1.79, 3.20] 0.400 nonHDL-C/Apo B (median, IQR) 1.31 [1.27, 1.39] 1.34 [1.26, 1.41] 0.204 Apo A1/Apo B (median, IQR) 1.20 [0.95, 1.56] 1.51 [1.22, 1.92] 0.604 Apo B/Apo A1 (median, IQR) 0.83 [0.64, 1.05] 0.66 [0.52, 0.82] 0.684 Abbreviation: Alb, albumin; Apo A1, Apolipoprotein A1; Apo B, apolipoprotein B; GCS, Glasgow Coma Scale; HDL-C, high-density lipoprotein-cholesterol; ICH, intracerebral hemorrhage; IQR, interquartile range; LDL-C, low-density lipoprotein-cholesterol; nonHDL-C, non-high-density lipoprotein-cholesterol; TC, total cholesterol; TG, triglyceride. Results of multivariate logistic regression show that lipid levels and ratios were not significantly associated with 90-day mortality in patients younger than 45 years. In contrast, in patients older than 45 years, low levels of nonHDL-C (OR, 0.898; 95% CI, 0.814–0.990), Apo B (OR, 0.861; 95% CI, 0.748–0.990), nonHDL-C/HDL-C (OR, 0.964; 95% CI, 0.930–0.999), and Apo B/Apo A1 (OR, 0.816; 95% CI, 0.687–0.968) remained significantly associated with higher 90-day mortality (p < 0.05)(Fig. 2 ). Across stratified 5-fold cross-validated single-marker ROC analyses (N = 457; events = 15.3%), discrimination was modest. The best marker was Apo B/Apo A1 (AUC 0.676, 95% CI 0.611–0.741). At the Youden-optimal cut-off, sensitivity was 0.857 and specificity 0.442, with positive predictive value (PPV) 0.217, negative predictive value (NPV) 0.945. PPV was low, whereas NPV was high, indicating limited rule-in value but some rule-out value.(Table 4 ) Table 4 cross-validated single-marker ROC analyses and threshold-based diagnostic indices Variables AUC 95%CI cut-off Sensitivity Specificity PPV NPV LR+ LR− Apo A/Apo B 0.676 0.611–0.741 1.385 0.857 0.442 0.217 0.945 1.536 0.323 Apo B/Apo A 0.676 0.611–0.741 0.722 0.857 0.442 0.217 0.945 1.536 0.323 Apo B 0.671 0.601–0.741 90.500 0.743 0.548 0.229 0.922 1.643 0.469 HDL-C/TC 0.663 0.598–0.728 0.268 0.829 0.444 0.212 0.935 1.491 0.386 HDL-C/nonHDL-C 0.663 0.598–0.728 0.366 0.829 0.444 0.212 0.935 1.491 0.386 nonHDL-C/HDL-C 0.663 0.598–0.728 2.731 0.829 0.444 0.212 0.935 1.491 0.386 TC/nonHDL-C 0.663 0.598–0.728 1.366 0.829 0.444 0.212 0.935 1.491 0.386 nonHDL-C/TC 0.663 0.598–0.728 0.732 0.829 0.444 0.212 0.935 1.491 0.386 nonHDL-C 0.653 0.582–0.724 130.000 0.743 0.444 0.195 0.905 1.337 0.579 LDL-C 0.627 0.551–0.702 100.000 0.571 0.602 0.206 0.886 1.436 0.712 LDL-C/TC 0.621 0.548–0.693 0.618 0.671 0.561 0.217 0.904 1.528 0.586 TC 0.615 0.540–0.689 165.701 0.629 0.615 0.228 0.902 1.633 0.604 HDL-C 0.579 0.508–0.650 44.277 0.786 0.375 0.185 0.906 1.256 0.572 HDL-C/Apo A1 0.571 0.499–0.643 0.349 0.757 0.382 0.182 0.897 1.226 0.635 Apo A1 0.561 0.488–0.634 128.500 0.700 0.413 0.178 0.884 1.193 0.726 TG 0.555 0.483–0.628 122.669 0.743 0.375 0.177 0.890 1.188 0.686 LDL-C/Apo B 0.530 0.455–0.604 1.176 0.600 0.506 0.180 0.875 1.216 0.790 nonHDL-C/Apo B 0.494 0.416–0.573 1.298 0.443 0.649 0.186 0.866 1.260 0.859 TC/TG 0.485 0.412–0.558 0.828 0.143 0.894 0.196 0.852 1.348 0.959 Abbrevation: AUC, area under the receiver operating characteristic curve;CI, confidence interval; LR+, positive likelihood ratio; LR-, negative likelihood ratio; NPV, negative predictive value; PPV, positive predictive value. 4. Discussion In middle-aged and older patients with ICH, lower admission nonHDL-C and Apo B were independently associated with increased 90-day mortality. Among lipid ratios, nonHDL-C/HDL-C and Apo B/Apo A1 were significant, with Apo B/Apo A1 demonstrating the greatest discriminative ability. Previous studies indicate that lower admission TC, TG, and LDL-C levels, as well as a lower LDL-C/HDL-C ratio, are independently associated with higher mortality after ICH 16 – 18 . After adjusting for age, hematoma volume, IVH, albumin, and blood glucose levels, each 1-mmol/L decrease in TC was associated with approximately a three-fold increase in in-hospital mortality (OR = 3.136) 18 . Additionally, a study by Feng et al. suggested that nonHDL-C levels have a higher predictive value for poor prognosis in female ICH patients compared to LDL-C 20 . These findings are consistent with our study. In our analysis, adding Apo A1 and Apo B to the lipid panel showed that Apo B and the Apo B/Apo A1 ratio was independently associated with 90-day mortality after ICH. Apo B, nonHDL-C, and LDL-C are highly correlated but not identical, Apo B quantifies the number of atherogenic particles (LDL, Intermediate-Density Lipoprotein(IDL), Very-Low-Density Lipoprotein(VLDL) and remnants) and therefore captures risk not fully reflected by LDL-C concentration alone 21 . Compared to LDL-C or nonHDL-C, Apo B can be measured more cheaply, accurately, and precisely 21 . Although the absolute gains in AUC were small, Apo B related metrics remained significant after multivariable adjustment, supporting their incremental prognostic value beyond standard lipids. Therefore, the predictive value of Apo B and nonHDL-C for mortality from acute ICH, along with the potential mechanisms, warrants further exploration. Importantly, the predictive value appeared to be age dependent, with stronger associations in middle-aged and older patients. Several mechanisms may underlie this pattern. First, older adults have higher risks of pneumonia and cardiopulmonary complications after ICH and are more likely to experience treatment limitations, making early systemic vulnerability a key driver of mortality 22 , 23 .Second, low lipid levels can reflect frailty, malnutrition. Patients with limited nutritional reserves are less able to tolerate the inflammatory catabolic stress and therefore face higher mortality. Several studies have identified malnutrition, including low weight, low BMI, hypoalbuminemia, and other indicators, as independent predictors of mortality in hospitalized patients 24 , 25 . Third, circulating lipoproteins, including Apo B containing particles, bind and neutralize Lipopolysaccharide(LBS), aided by LPS-binding protein and phospholipid transfer protein, thereby contributing to innate immune buffering 26 , 27 . Consequently, very low lipid levels may diminish this buffering capacity. A meta-analysis revealed a negative correlation between admission lipid levels (including TC, HDL-C, and LDL-C) and mortality, based on data from 24 studies comprising 2542 ICU inpatients 28 . Thus, low lipid levels in ICH patients, may contribute to the increased risk of mortality, particularly in middle-aged and elderly individuals. In our cohort, cross-validated single-marker ROC analyses showed modest discrimination, with low PPV and high NPV at Youden cut-offs. Thus, lipid markers are better suited for ruling out very high short-term risk than for ruling in and should be embedded within multivariable tools rather than used as stand-alone triggers. Clinically, ischemic events after ICH are common and contribute to mortality 29 , 30 . Statins are known to prevent ischemic events, but the benefit of using statins after ICH remains unclear. Observational study has demonstrated that statin use during the acute phase of ICH may be associated with more favorable outcomes 31 – 33 . Meanwhile, statin use in the chronic phase was not associated with an increased risk of recurrent hemorrhage and also reduced the risk of ischemic stroke 34 . Conversely, randomized controlled trials indicate that while statins diminish the likelihood of ischemic events, they may elevate the risk of ICH 35 . Another study suggests that initiating statin therapy following an ICH does not increase mortality but is associated with an increase in peak PHE 36 . Given our age-dependent signal, older adults with lower lipid reserve carry higher early risk, statin use should be individualized, prioritizing stabilization, nutrition, and infection prevention in high-risk older patients while considering continuation in lower-risk patients. Notably, the ongoing NCT03936361 trial is recruiting patients aged 50 years and above who have experienced ICH within seven days of onset ( www.clinicaltrials.com ). This clinical trial aims to explore the prognostic impact of continuing or discontinuing statin therapy after ICH onset. The results of this study are eagerly awaited. The findings have several implications for clinical practice. Firstly, this study found that lipid profiles and ratios, particularly Apo B and Apo B/Apo A1, have greater predictive value for 90-day mortality in ICH patients. These findings offer a new perspective on the early identification of mortality risk in ICH. Secondly, our findings indicated a significant correlation between lipid levels and age stratification, with a notable predictive capacity for ICH outcome. This highlights the necessity for the development of tailored treatment plans for diverse populations. Additionally, these findings suggest that the use of lipid-lowering medications in the acute stages of ICH should carefully weigh the prognostic benefits and risks for each patient, thereby assisting clinicians in decision-making. This study has several limitations. First, this study did not include patients’ BMI, waist circumference, or abdominal circumference, which could represent their nutritional status and might affect the analysis between lipid levels and 90-day mortality. Second, the small sample size of this study limits its capacity to reliably evaluate the causal relationship between lipid profiles and ICH mortality. Furthermore, this study was conducted at a single center, so the external validity of the results needs to be verified in large-scale, multicenter prospective studies. 5. Conclusion In our cohort of patients with ICH, admission lipid levels and ratios were independently associated with 90-day mortality in middle-aged and elderly ICH patients. Cross-validated single-marker ROC analyses showed modest discrimination overall. Apo B and Apo B/Apo A1 performed best, yielding low PPV but high NPV at Youden cut-offs. These markers are therefore more useful for ruling out very high early risk and are best applied within multivariable risk models to developing individualized treatment plans. Abbreviations ICH intracerebral hemorrhage IVH intraventricular hemorrhage. Declarations 6. Acknowledgements We would like to thank every patient who participated in our project. 7. Funding Dr Wen-Song Yang was supported by grants from the National Natural Science Foundation of Chongqing (No. CSTB2022NSCQ-MSX0800), and the China Postdoctoral Science Foundation (No. 2022MD723747). Joint project of Chongqing Health Commission and Science and Technology Bureau (NO. 2024QNXM022). 8. Disclosure 8.1 Competing interests Authors declare no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 8.2 Authors' contributions W.S.Y. was responsible for the study conceptualization and data curation. J.M.H., S.Q.Z., W.S.Y., Y.B.M., C.Y.H., R.F. and Y.Q.S. did investigation. J.M.H. drafted the manuscript. W.S.Y. reviewed and edit the manuscript. J.M.H. and W.S.Y. did the formal analysis. W.S.Y. did funding acquisition. W.S.Y. was responsible for administrative, technical, or material support. All authors read and approved the final manuscript. 8.3 Use of AI and AI-assisted Technologies Statement During the preparation of this work, we used ChatGPT (OpenAI) to improve the clarity and readability of the English text. After using this tool, we reviewed and edited the text as needed and take full responsibility for the content of the publication. 8.4 Ethical Statement The Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Chongqing, China) approved our interviews (approval: 2017-075) on September 25, 2017. Respondents gave written consent for review and signature before starting interviews. 8.5 Compliance with instructions to authors This manuscript adheres to all requirements outlined in the Instructions for Authors. 8.6 Originality and exclusivity This work is original, has not been published elsewhere, and is not under consideration by any other journal. 8.7 Reporting checklist We followed the STROBE reporting guideline for observational studies. A completed STROBE checklist has been prepared and submitted with this manuscript. References Intracerebral haemorrhage. Nat Rev Dis Primers. 2023;9(1):15. 10.1038/s41572-023-00428-3 . de Oliveira Manoel AL, Goffi A, Zampieri FG, et al. The critical care management of spontaneous intracranial hemorrhage: a contemporary review. Crit Care. 2016;20:272. 10.1186/s13054-016-1432-0 . Qureshi AI, Palesch YY, Barsan WG, et al. Intensive Blood-Pressure Lowering in Patients with Acute Cerebral Hemorrhage. 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11:31:08","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155247,"visible":true,"origin":"","legend":"","description":"","filename":"NECAD25009990structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7615725/v1/9ccec5bad992cfdd69b55d59.xml"},{"id":92800149,"identity":"31da1323-e278-40c0-bc56-6cd5dd77c479","added_by":"auto","created_at":"2025-10-05 11:31:08","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162361,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7615725/v1/c1bbe7d2d9705c1b9454f387.html"},{"id":92801643,"identity":"4bbf8e9e-b09f-4e27-8c55-20f26ae05ecc","added_by":"auto","created_at":"2025-10-05 11:39:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":557204,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart describing study cohorts.\u003c/p\u003e\n\u003cp\u003eOf 934 patients who met inclusion criteria, 40 were excluded (5 primary intraventricular hemorrhage, 18 secondary intracerebral hemorrhage, 11 anticoagulant-associated bleeding, 1 leukemia-associated cerebral hemorrhage, and 2 multiple intracranial hemorrhages), leaving 894 enrolled. A further 391 were excluded during follow-up (79 lost to follow-up and 312 without baseline lipid measurements), resulting in a final analytic sample of 503 patients.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7615725/v1/854a97952ceab994fce42a7d.png"},{"id":92800134,"identity":"95d7d9b9-cfd8-4b5f-bba0-34ebd7c67f6c","added_by":"auto","created_at":"2025-10-05 11:31:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":951586,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable-adjusted analysis for the general population and the elderly group.\u003c/p\u003e\n\u003cp\u003eMultivariable-adjusted independent effects of each lipid level and lipid ratios on 90-day mortality after ICH in the overall population (A) and in the middle-aged and elderly(age≥45) population (B). Multivariable models were adjusted for age, sex, diabetes, smoking status, IVH, SBP, DBP, GCS, ICH volume, hemorrhage location and albumin.\u003c/p\u003e\n\u003cp\u003eAbbrevations: Apo A1, Apolipoprotein A1; Apo B, apolipoprotein B; CI, confidence interval; OR, odds ratio; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; nonHDL-C, non-high-density lipoprotein-cholesterol; TC, total cholesterol; TG, triglyceride\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7615725/v1/b284b711c8066d7937a8f2d4.jpg"},{"id":93812246,"identity":"308d8cee-7579-4683-8fe2-eb6c3c176dc3","added_by":"auto","created_at":"2025-10-17 20:38:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2511423,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7615725/v1/3ac5e9ee-bfae-46ef-825a-2f67901e284f.pdf"},{"id":92800132,"identity":"2bb93b9a-b265-4c7b-a625-9dd42797a8d9","added_by":"auto","created_at":"2025-10-05 11:31:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32901,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcohort.docx","url":"https://assets-eu.researchsquare.com/files/rs-7615725/v1/1dcfdeede8d8699530accc15.docx"}],"financialInterests":"","formattedTitle":"Serum lipid levels predict mortality of aged patients in acute intracerebral hemorrhage","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIntracerebral hemorrhage (ICH) accounts for 20\u0026ndash;30% of all stroke cases, with an estimated 3.5\u0026nbsp;million new cases annually\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. As a severe and critical neurological condition, many ICH patients have poor outcomes, with a 1-year mortality rate of up to 50%, and over 60% of survivors experiencing severe functional disability. Despite numerous clinical trials aimed at improving ICH prognosis, a specific disease-modifying treatment remains lacking\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Further exploration of modifiable predictors related to the ICH outcome is important.\u003c/p\u003e\u003cp\u003ePrevious studies have evaluated the strong correlation between various lipid levels [total cholesterol(TC), triglyceride(TG), high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), non-high-density lipoprotein cholesterol (nonHDL-C)] and the risks of ICH\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, microhemorrhages foci\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, hematoma growth\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and ICH-related mortality\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Specifically, higher admission TC, LDL-C and LDL-C/HDL-C ratios have been linked to lower early ICH mortality.\u003c/p\u003e\u003cp\u003eEarly ICH mortality reflects diverse pathways, including mass effect and hematoma expansion at any age. In older adults, additional vulnerability to infection, cardiopulmonary complications, and treatment limitations further contributes. Lower lipid levels have been associated with hematoma expansion\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, infection susceptibility\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and markers of malnutrition. These suggest that the lipid levels in ICH patients may vary across different age groups.\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to investigate the relationship between lipid profiles, lipid ratios, and 90-day mortality in ICH across different age groups, to assist clinicians in risk identification for clinical practice.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003ePatients diagnosed with ICH were admitted to the First Affiliated Hospital of Chongqing Medical University, a tertiary academic hospital, and enrolled in our study. Our study size was determined by data availability. We retrospectively analyzed data from patients with acute ICH recorded in our clinical research database, covering the period from May 2017 to June 2022. Patients, aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, with primary acute ICH within 72 hours after ICH ictus were enrolled. We excluded patients with primary intraventricular hemorrhage (IVH), secondary ICH, anticoagulant-associated bleeding, leukemia-associated cerebral hemorrhage, multiple intracranial hemorrhages, and cases lacking lipid data on admission or complete follow-up data at 90 days. The Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Chongqing, China) approved our interviews (approval: 2017-075) on September 25, 2017. All participants provided written informed consent before the examination, permitting data analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data acquisition\u003c/h2\u003e\u003cp\u003eAt enrollment, we recorded each patient\u0026rsquo;s age, sex, medical history (including hypertension, ischemic stroke, and diabetes), as well as smoking and alcohol-consumption habits. Blood pressure (BP) in the prone position was measured upon admission. ICH severity was assessed using the Glasgow Coma Scale (GCS).\u003c/p\u003e\u003cp\u003eAll patients underwent computed tomography (CT) scans upon admission. The CT images were stored in Digital Imaging and Communications in Medicine (DICOM) format and independently reviewed by two experienced radiologists, both blinded to clinical and outcome data. Hematoma volume measurements and region-of-interest delineation were carried out using 3D Slicer (version 5.2.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.slicer.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.slicer.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an open-source, semi-automated software platform that has been validated for three-dimensional medical image processing, including visualization, segmentation, registration, and quantitative volumetric analysis. All CT scans were independently reviewed by two blinded raters: one senior neurologist (W.S.Y.) and one neuroradiologist (S.Q.Z.). Fasting blood samples were collected and analyzed at the Clinical Laboratory Department of the First Affiliated Hospital of Chongqing Medical University. A fully automated biochemical analyzer (Cobas c701; Roche, Basel, Switzerland) with original laboratory reagents was used to measure lipid levels.TC, TG, HDL-C, and LDL-C levels were measured using an enzymatic assay. Apolipoprotein A1 (Apo A1) and apolipoprotein B (Apo B) levels were measured using immunoturbidimetric assays. NonHDL-C was defined as TC minus HDL-C. The ratios of TC/TG, TC/nonHDL-C, HDL-C/TC, HDL-C/nonHDL-C, HDL-C/Apo A1, LDL-C/TC, LDL-C/Apo B, nonHDL-C/TC, nonHDL-C/HDL-C, nonHDL-C/Apo B, Apo A1/Apo B and Apo B/Apo A1 were calculated.\u003c/p\u003e\u003cp\u003eFunctional outcome at 3 months was assessed by telephone interviews with patients or their relatives, using the modified Rankin Scale (mRS). A score of 6 denotes death. .\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed with R (version 4.2.3). Baseline characteristics are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed continuous variables, median [interquartile range (IQR)] for non-normal continuous variables, and number (percentage) for categorical variables. Baseline balance between groups was assessed using standardized mean differences (SMDs). Values\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were considered negligible.\u003c/p\u003e\u003cp\u003eMultivariable logistic regression was used to examine the independent association of admission lipid levels and lipid ratios with 90-day mortality. Variables showing \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariable analyses were entered into the multivariable models. To examine whether the association between lipid markers and 90-day mortality varied with age, we fitted multivariable logistic regression models including a product (interaction) term between age and each lipid variable. For interpretability, lipid concentrations were scaled to represent an OR per 10-unit increase, whereas lipid ratios were scaled per 0.1-unit increase. The global significance of each interaction was assessed using likelihood ratio tests.\u003c/p\u003e\u003cp\u003eDiscriminatory performance of each lipid marker across age groups was evaluated by receiver-operating-characteristic (ROC) analysis. We report the area under the ROC curve (AUC) together with sensitivity and specificity at the optimal cutoff.\u003c/p\u003e\u003cp\u003eAll tests were two-tailed, and statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003eA total of 503 participants with complete data were involved in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of the overall population was 65.00 years. A total of 76 patients (15.11%) died within 90 days of onset.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe baseline characteristics, clinical scales, radiological findings, and laboratory data are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients were categorized into two groups based on 90-day survival status. Patients who died were older (SMD\u0026thinsp;=\u0026thinsp;0.48), had worse neurologic severity (higher GCS, SMD\u0026thinsp;=\u0026thinsp;1.38), larger hematomas (SMD\u0026thinsp;=\u0026thinsp;0.77), and more IVH (SMD\u0026thinsp;=\u0026thinsp;0.83). They also had higher SBP (SMD\u0026thinsp;=\u0026thinsp;0.31) and lower DBP (SMD\u0026thinsp;=\u0026thinsp;0.23), were less often smokers (SMD\u0026thinsp;=\u0026thinsp;0.34), and had small-to-moderate differences in sex and diabetes (SMD\u0026thinsp;=\u0026thinsp;0.21). For lipids, mortality group showed lower atherogenic cholesterol:TC, LDL-C, nonHDL-C, and Apo B (SMD\u0026thinsp;=\u0026thinsp;0.20\u0026ndash;0.40). Ratio patterns were concordant: higher Apo A1-preponderant ratios (HDL-C/TC, HDL-C/nonHDL-C, Apo A1/Apo B, TC/nonHDL-C; SMD\u0026thinsp;=\u0026thinsp;0.38\u0026ndash;0.46) and lower Apo B-centric ratios (LDL-C/TC, nonHDL-C/TC, nonHDL-C/HDL-C, Apo B/Apo A1; SMD\u0026thinsp;=\u0026thinsp;0.21\u0026ndash;0.44). Differences were negligible for TG, TC/TG, LDL-C/apoB, surgery, and hemorrhage location (all SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.10).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics, Risk Factors, Clinical Scales, Radiology, and Laboratory Findings.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e90day-Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSMD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall(n\u0026thinsp;=\u0026thinsp;503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlive(n\u0026thinsp;=\u0026thinsp;427)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDead(n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (median, [IQR]), y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.00 [53.00, 73.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.00 [52.50, 72.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.50 [61.75, 78.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex(male), %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e359 (71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e311 (72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-ICH history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (30.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e458 (91.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e389 (91.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (90.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemic stroke, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e236 (46.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e211 (49.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171 (34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167.00 [149.00, 183.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165.00 [147.00, 182.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e173.50 [160.75, 189.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.00 [84.00, 107.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.00 [85.00, 108.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.00 [81.00, 104.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS on Admission (median, [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 [13.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 [13.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.00 [5.75, 13.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.375\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICH volume (median, [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.74 [4.06, 20.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.83 [3.57, 18.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.11 [7.50, 53.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e356 (70.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e304 (71.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (68.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfratentorial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVH, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (59.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlb (median, IQR), g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.00 [38.00, 44.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.00 [38.00, 44.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.00 [37.00, 44.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172.85 [150.23, 200.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175.56 [151.97, 201.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e162.61 [140.37, 192.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104.51 [71.74, 155.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106.28 [73.07, 159.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.76 [68.64, 136.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.72 [39.83, 59.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.72 [39.44, 59.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.43 [44.76, 65.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106.73 [84.11, 132.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107.89 [87.01, 133.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.19 [69.70, 118.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo A1 (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134.00 [118.00, 155.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.00 [117.00, 154.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139.00 [122.00, 163.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo B (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.00 [75.50, 111.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.00 [78.00, 111.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.00 [63.50, 95.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124.90 [98.42, 149.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126.45 [102.86, 151.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.02 [83.72, 138.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid Ratios\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC/TG (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.64 [1.12, 2.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64 [1.11, 2.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.64 [1.21, 2.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC/nonHDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40 [1.30, 1.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.38 [1.29, 1.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48 [1.37, 1.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/TC (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29 [0.23, 0.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28 [0.23, 0.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33 [0.27, 0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/nonHDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.40 [0.30, 0.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38 [0.29, 0.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48 [0.37, 0.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/Apo A1 (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37 [0.32, 0.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37 [0.32, 0.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38 [0.34, 0.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C/TC (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.63 [0.56, 0.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63 [0.56, 0.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59 [0.51, 0.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C/Apo B (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18 [1.08, 1.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17 [1.08, 1.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 [1.07, 1.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/TC (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.71 [0.65, 0.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72 [0.66, 0.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67 [0.60, 0.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/HDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.49 [1.84, 3.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.60 [1.90, 3.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.08 [1.47, 2.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/Apo B (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33 [1.26, 1.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33 [1.26, 1.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.33 [1.26, 1.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo A1/Apo B (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48 [1.19, 1.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45 [1.17, 1.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.74 [1.43, 2.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo B/Apo A1 (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67 [0.53, 0.84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.69 [0.55, 0.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.57 [0.46, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAbbreviation:Alb, albumin; Apo A1, Apolipoprotein A1; Apo B, apolipoprotein B; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; HDL-C, high-density lipoprotein-cholesterol; ICH, intracerebral hemorrhage; IQR, interquartile range; LDL-C, low-density lipoprotein-cholesterol; nonHDL-C, non-high-density lipoprotein-cholesterol; SBP, systolic blood pressure; SMD, standardized mean difference;TC, total cholesterol; TG, triglyceride.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the overall cohort, univariable logistic regression showed that lipid levels and ratios, age, sex, diabetes status, smoking history, SBP, DBP, admission hematoma volume, IVH, and GCS score were each associated with 90-day mortality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, in multivariable logistic regression, adjusting for the covariates identified in the univariable analysis (age, sex, diabetes, smoking, SBP/DBP, admission hematoma volume, hemorrhage location, intraventricular hemorrhage, albumin and GCS), the associations between all lipid levels and 90-day mortality were not statistically significant(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To assess whether the prognostic value of lipids differed by age, we incorporated interaction terms between age and each lipid species in the multivariable model. The age interactions were significant for Apo B (OR\u0026thinsp;=\u0026thinsp;0.992;95% CI, 0.985\u0026ndash;1.000; P\u0026thinsp;=\u0026thinsp;0.049), nonHDL-C (OR\u0026thinsp;=\u0026thinsp;0.997; 95% CI, 0.993\u0026ndash;1.000; P\u0026thinsp;=\u0026thinsp;0.056), the nonHDL-C/HDL-C ratio (OR\u0026thinsp;=\u0026thinsp;0.999; 95% CI, 0.998\u0026ndash;1.000; P\u0026thinsp;=\u0026thinsp;0.042),and the Apo B/Apo A1 ratio (OR\u0026thinsp;=\u0026thinsp;0.993; 95% CI, 0.987\u0026ndash;1.000; P\u0026thinsp;=\u0026thinsp;0.043). In view of these age-lipid interactions, we subsequently conducted an age-stratified subgroup analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariable logistic regression analyses of 90-day mortality after ICH by age group.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eoverall(n\u0026thinsp;=\u0026thinsp;503)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;45(n\u0026thinsp;=\u0026thinsp;457)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.040 (1.019\u0026ndash;1.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.057 (1.032\u0026ndash;1.083)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.639 (0.383\u0026ndash;1.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.566 (0.334\u0026ndash;0.959)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-ICH history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.963 (0.413\u0026ndash;2.244)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.027 (0.414\u0026ndash;2.547)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.625 (0.945\u0026ndash;2.794)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.628 (0.931\u0026ndash;2.848)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemic stroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.697 (0.802\u0026ndash;3.591)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.484 (0.679\u0026ndash;3.241)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.502 (0.300\u0026ndash;0.840)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.507 (0.297\u0026ndash;0.868)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.760 (0.446\u0026ndash;1.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.669 (0.376\u0026ndash;1.189)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP on Admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.012 (1.002\u0026ndash;1.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.012 (1.002\u0026ndash;1.022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP on Admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.987 (0.973\u0026ndash;1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.984 (0.968\u0026ndash;1.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICH volume on Admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.035 (1.024\u0026ndash;1.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.034 (1.023\u0026ndash;1.046)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.091 (0.574\u0026ndash;2.074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.044 (0.537\u0026ndash;2.029)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfratentorial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.218 (0.580\u0026ndash;2.558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.946 (0.402\u0026ndash;2.227)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.286 (3.167\u0026ndash;8.823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.737 (2.785\u0026ndash;8.057)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS on Admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.678 (0.626\u0026ndash;0.735)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.682 (0.627\u0026ndash;0.741)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.735 (0.251\u0026ndash;2.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.780 (0.226\u0026ndash;2.690)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.964 (0.915\u0026ndash;1.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.967 (0.914\u0026ndash;1.025)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.995 (0.988\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.989 (0.982\u0026ndash;0.997)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000 (0.998\u0026ndash;1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.998 (0.994\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.018 (1.003\u0026ndash;1.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.018 (1.002\u0026ndash;1.034)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.991 (0.983\u0026ndash;0.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.987 (0.979\u0026ndash;0.995)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.991 (0.984\u0026ndash;0.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.984 (0.976\u0026ndash;0.992)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.006 (0.998\u0026ndash;1.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.007 (0.998\u0026ndash;1.015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.983 (0.973\u0026ndash;0.993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.976 (0.965\u0026ndash;0.987)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipids ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC/TG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.002 (0.980\u0026ndash;1.024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.001 (0.979\u0026ndash;1.025)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC/nonHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.133 (1.031\u0026ndash;1.246)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.148 (1.039\u0026ndash;1.269)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/TC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.607 (1.250\u0026ndash;2.066)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.738 (1.332\u0026ndash;2.267)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/nonHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.133 (1.031\u0026ndash;1.246)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.148 (1.039\u0026ndash;1.269)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/Apo A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.551 (1.037\u0026ndash;2.320)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.578 (1.036\u0026ndash;2.405)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C/TC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.718 (0.577\u0026ndash;0.894)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.710 (0.555\u0026ndash;0.907)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C/Apo B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.030 (0.890\u0026ndash;1.192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.079 (0.920\u0026ndash;1.267)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/TC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.622 (0.484\u0026ndash;0.800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.575 (0.441\u0026ndash;0.751)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/HDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.973 (0.950\u0026ndash;0.996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.940 (0.913\u0026ndash;0.968)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/Apo B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.015 (0.950\u0026ndash;1.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.035 (0.897\u0026ndash;1.195)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo A1/Apo B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.036 (1.002\u0026ndash;1.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.041 (1.004\u0026ndash;1.079)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo B/Apo A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.806 (0.715\u0026ndash;0.908)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.736 (0.642\u0026ndash;0.844)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAbbreviation: Alb, albumin; Apo A1, apolipoprotein A1; Apo B, apolipoprotein B; CI, confidence interval; DBP, diastolic blood pressure; GCS, Glasgow Coma Scale; HDL-C, high-density lipoprotein-cholesterol; IQR, interquartile range; LDL-C, low-density lipoprotein-cholesterol; nonHDL-C, non-high-density lipoprotein-cholesterol; OR, odds ratio; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride.\u003c/p\u003e\u003cp\u003eNote: Odds ratios for lipids ratios are reported per 0.1-unit increase.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Subgroup analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAfter stratifying patients by age, we found diabetes (SMD\u0026thinsp;=\u0026thinsp;0.271) and prior ischemic stroke (SMD\u0026thinsp;=\u0026thinsp;0.320) were more prevalent, whereas drinking (SMD\u0026thinsp;=\u0026thinsp;0.265) was less frequent in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years. Admission DBP was lower in the \u0026ge;\u0026thinsp;45 group (SMD\u0026thinsp;=\u0026thinsp;0.740), while systolic pressure was similar (SMD\u0026thinsp;=\u0026thinsp;0.148). Fewer older patients underwent surgery (SMD\u0026thinsp;=\u0026thinsp;0.497). Albumin was slightly lower (SMD\u0026thinsp;=\u0026thinsp;0.181). Lipid profiles in the \u0026ge;\u0026thinsp;45 group were characterized by higher HDL-C (SMD\u0026thinsp;=\u0026thinsp;0.572) and Apo A1 (SMD\u0026thinsp;=\u0026thinsp;0.382), but lower TG (SMD\u0026thinsp;=\u0026thinsp;0.367), LDL-C (SMD\u0026thinsp;=\u0026thinsp;0.277), nonHDL-C (SMD\u0026thinsp;=\u0026thinsp;0.463), and Apo B (SMD\u0026thinsp;=\u0026thinsp;0.568). Specifically, hypertension history (SMD\u0026thinsp;=\u0026thinsp;0.071), smoking (SMD\u0026thinsp;=\u0026thinsp;0.068), GCS on admission (SMD\u0026thinsp;=\u0026thinsp;0.093), and IVH (SMD\u0026thinsp;=\u0026thinsp;0.029) showed negligible between-group differences. Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics, Risk Factors, Clinical Scales, Radiology, and Laboratory Findings between Age groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;44(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;45(n\u0026thinsp;=\u0026thinsp;457)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSMD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (median, [IQR]), y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.50 [34.25, 41.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.00 [55.00, 74.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex(male), %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (78.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e323 (70.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-ICH history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e417 (91.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemic stroke, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213 (46.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (median [IQR])\u003c/p\u003e\u003cp\u003eDBP (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171.00 [148.00, 184.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166.00 [149.00, 183.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.50 [92.75, 125.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.00 [83.00, 105.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS on Admission (median, [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 [12.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 [13.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICH volume on Admission (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.45 [5.35, 29.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.49 [4.03, 19.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e327 (71.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfratentorial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVH, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlb (median, IQR), g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.00 [39.00, 46.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.00 [38.00, 44.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180.01 [149.27, 224.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e172.85 [150.43, 199.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127.10 [91.45, 200.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101.86 [69.97, 147.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.80 [34.51, 51.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.11 [41.38, 61.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.11 [93.68, 149.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105.96 [83.91, 131.86]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo A1 (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121.00 [106.25, 141.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135.00 [119.00, 157.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo B (median, IQR), mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106.00 [81.75, 129.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.00 [75.00, 109.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137.86 [108.95, 179.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124.52 [97.45, 146.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid Ratios\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC/TG (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.39 [0.82, 1.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.66 [1.15, 2.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC/nonHDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32 [1.23, 1.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41 [1.31, 1.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/TC (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.24 [0.19, 0.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29 [0.24, 0.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/nonHDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32 [0.23, 0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.41 [0.31, 0.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/Apo A1 (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34 [0.31, 0.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37 [0.33, 0.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C/TC (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.65 [0.60, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62 [0.56, 0.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C/Apo B (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12 [1.02, 1.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18 [1.09, 1.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/TC (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76 [0.71, 0.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71 [0.64, 0.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/HDL-C (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.17 [2.47, 4.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.43 [1.79, 3.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/Apo B (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.31 [1.27, 1.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.34 [1.26, 1.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo A1/Apo B (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20 [0.95, 1.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51 [1.22, 1.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo B/Apo A1 (median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83 [0.64, 1.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66 [0.52, 0.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAbbreviation: Alb, albumin; Apo A1, Apolipoprotein A1; Apo B, apolipoprotein B; GCS, Glasgow Coma Scale; HDL-C, high-density lipoprotein-cholesterol; ICH, intracerebral hemorrhage; IQR, interquartile range; LDL-C, low-density lipoprotein-cholesterol; nonHDL-C, non-high-density lipoprotein-cholesterol; TC, total cholesterol; TG, triglyceride.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eResults of multivariate logistic regression show that lipid levels and ratios were not significantly associated with 90-day mortality in patients younger than 45 years. In contrast, in patients older than 45 years, low levels of nonHDL-C (OR, 0.898; 95% CI, 0.814\u0026ndash;0.990), Apo B (OR, 0.861; 95% CI, 0.748\u0026ndash;0.990), nonHDL-C/HDL-C (OR, 0.964; 95% CI, 0.930\u0026ndash;0.999), and Apo B/Apo A1 (OR, 0.816; 95% CI, 0.687\u0026ndash;0.968) remained significantly associated with higher 90-day mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Across stratified 5-fold cross-validated single-marker ROC analyses (N\u0026thinsp;=\u0026thinsp;457; events\u0026thinsp;=\u0026thinsp;15.3%), discrimination was modest. The best marker was Apo B/Apo A1 (AUC 0.676, 95% CI 0.611\u0026ndash;0.741). At the Youden-optimal cut-off, sensitivity was 0.857 and specificity 0.442, with positive predictive value (PPV) 0.217, negative\u003c/p\u003e\u003cp\u003epredictive value (NPV) 0.945. PPV was low, whereas NPV was high, indicating limited rule-in value but some rule-out value.(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ecross-validated single-marker ROC analyses and threshold-based diagnostic indices\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecut-off\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLR+\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eLR\u0026minus;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo A/Apo B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.611\u0026ndash;0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo B/Apo A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.611\u0026ndash;0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.601\u0026ndash;0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/TC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.598\u0026ndash;0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/nonHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.598\u0026ndash;0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/HDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.598\u0026ndash;0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC/nonHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.598\u0026ndash;0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/TC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.598\u0026ndash;0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.582\u0026ndash;0.724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.551\u0026ndash;0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C/TC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.548\u0026ndash;0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.540\u0026ndash;0.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e165.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.508\u0026ndash;0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C/Apo A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.499\u0026ndash;0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.488\u0026ndash;0.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.483\u0026ndash;0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C/Apo B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.455\u0026ndash;0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonHDL-C/Apo B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.416\u0026ndash;0.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC/TG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.412\u0026ndash;0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eAbbrevation: AUC, area under the receiver operating characteristic curve;CI, confidence interval; LR+, positive likelihood ratio; LR-, negative likelihood ratio; NPV, negative predictive value; PPV, positive predictive value.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn middle-aged and older patients with ICH, lower admission nonHDL-C and Apo B were independently associated with increased 90-day mortality. Among lipid ratios, nonHDL-C/HDL-C and Apo B/Apo A1 were significant, with Apo B/Apo A1 demonstrating the greatest discriminative ability.\u003c/p\u003e\u003cp\u003ePrevious studies indicate that lower admission TC, TG, and LDL-C levels, as well as a lower LDL-C/HDL-C ratio, are independently associated with higher mortality after ICH\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. After adjusting for age, hematoma volume, IVH, albumin, and blood glucose levels, each 1-mmol/L decrease in TC was associated with approximately a three-fold increase in in-hospital mortality (OR\u0026thinsp;=\u0026thinsp;3.136)\u003csup\u003e18\u003c/sup\u003e. Additionally, a study by Feng et al. suggested that nonHDL-C levels have a higher predictive value for poor prognosis in female ICH patients compared to LDL-C\u003csup\u003e20\u003c/sup\u003e. These findings are consistent with our study. In our analysis, adding Apo A1 and Apo B to the lipid panel showed that Apo B and the Apo B/Apo A1 ratio was independently associated with 90-day mortality after ICH. Apo B, nonHDL-C, and LDL-C are highly correlated but not identical, Apo B quantifies the number of atherogenic particles (LDL, Intermediate-Density Lipoprotein(IDL), Very-Low-Density Lipoprotein(VLDL) and remnants) and therefore captures risk not fully reflected by LDL-C concentration alone\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Compared to LDL-C or nonHDL-C, Apo B can be measured more cheaply, accurately, and precisely\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Although the absolute gains in AUC were small, Apo B related metrics remained significant after multivariable adjustment, supporting their incremental prognostic value beyond standard lipids. Therefore, the predictive value of Apo B and nonHDL-C for mortality from acute ICH, along with the potential mechanisms, warrants further exploration.\u003c/p\u003e\u003cp\u003eImportantly, the predictive value appeared to be age dependent, with stronger associations in middle-aged and older patients. Several mechanisms may underlie this pattern. First, older adults have higher risks of pneumonia and cardiopulmonary complications after ICH and are more likely to experience treatment limitations, making early systemic vulnerability a key driver of mortality\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.Second, low lipid levels can reflect frailty, malnutrition. Patients with limited nutritional reserves are less able to tolerate the inflammatory catabolic stress and therefore face higher mortality. Several studies have identified malnutrition, including low weight, low BMI, hypoalbuminemia, and other indicators, as independent predictors of mortality in hospitalized patients\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Third, circulating lipoproteins, including Apo B containing particles, bind and neutralize Lipopolysaccharide(LBS), aided by LPS-binding protein and phospholipid transfer protein, thereby contributing to innate immune buffering\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Consequently, very low lipid levels may diminish this buffering capacity. A meta-analysis revealed a negative correlation between admission lipid levels (including TC, HDL-C, and LDL-C) and mortality, based on data from 24 studies comprising 2542 ICU inpatients\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Thus, low lipid levels in ICH patients, may contribute to the increased risk of mortality, particularly in middle-aged and elderly individuals.\u003c/p\u003e\u003cp\u003eIn our cohort, cross-validated single-marker ROC analyses showed modest discrimination, with low PPV and high NPV at Youden cut-offs. Thus, lipid markers are better suited for ruling out very high short-term risk than for ruling in and should be embedded within multivariable tools rather than used as stand-alone triggers. Clinically, ischemic events after ICH are common and contribute to mortality\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Statins are known to prevent ischemic events, but the benefit of using statins after ICH remains unclear. Observational study has demonstrated that statin use during the acute phase of ICH may be associated with more favorable outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Meanwhile, statin use in the chronic phase was not associated with an increased risk of recurrent hemorrhage and also reduced the risk of ischemic stroke\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Conversely, randomized controlled trials indicate that while statins diminish the likelihood of ischemic events, they may elevate the risk of ICH\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Another study suggests that initiating statin therapy following an ICH does not increase mortality but is associated with an increase in peak PHE\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Given our age-dependent signal, older adults with lower lipid reserve carry higher early risk, statin use should be individualized, prioritizing stabilization, nutrition, and infection prevention in high-risk older patients while considering continuation in lower-risk patients. Notably, the ongoing NCT03936361 trial is recruiting patients aged 50 years and above who have experienced ICH within seven days of onset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.clinicaltrials.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.clinicaltrials.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This clinical trial aims to explore the prognostic impact of continuing or discontinuing statin therapy after ICH onset. The results of this study are eagerly awaited.\u003c/p\u003e\u003cp\u003eThe findings have several implications for clinical practice. Firstly, this study found that lipid profiles and ratios, particularly Apo B and Apo B/Apo A1, have greater predictive value for 90-day mortality in ICH patients. These findings offer a new perspective on the early identification of mortality risk in ICH. Secondly, our findings indicated a significant correlation between lipid levels and age stratification, with a notable predictive capacity for ICH outcome. This highlights the necessity for the development of tailored treatment plans for diverse populations. Additionally, these findings suggest that the use of lipid-lowering medications in the acute stages of ICH should carefully weigh the prognostic benefits and risks for each patient, thereby assisting clinicians in decision-making.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, this study did not include patients\u0026rsquo; BMI, waist circumference, or abdominal circumference, which could represent their nutritional status and might affect the analysis between lipid levels and 90-day mortality. Second, the small sample size of this study limits its capacity to reliably evaluate the causal relationship between lipid profiles and ICH mortality. Furthermore, this study was conducted at a single center, so the external validity of the results needs to be verified in large-scale, multicenter prospective studies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn our cohort of patients with ICH, admission lipid levels and ratios were independently associated with 90-day mortality in middle-aged and elderly ICH patients. Cross-validated single-marker ROC analyses showed modest discrimination overall. Apo B and Apo B/Apo A1 performed best, yielding low PPV but high NPV at Youden cut-offs. These markers are therefore more useful for ruling out very high early risk and are best applied within multivariable risk models to developing individualized treatment plans.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eintracerebral hemorrhage\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIVH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eintraventricular hemorrhage.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank every patient who participated in our project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr Wen-Song Yang was supported by grants from the National Natural Science Foundation of Chongqing (No. CSTB2022NSCQ-MSX0800), and the China Postdoctoral Science Foundation (No. 2022MD723747). Joint project of Chongqing Health Commission and Science and Technology Bureau (NO. 2024QNXM022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.1 Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.2 Authors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.S.Y. was responsible for the study conceptualization and data curation. J.M.H., S.Q.Z., W.S.Y., Y.B.M., C.Y.H., R.F. and Y.Q.S. did investigation. J.M.H. drafted the manuscript. W.S.Y. reviewed and edit the manuscript. J.M.H. and W.S.Y. did the formal analysis. W.S.Y. did funding acquisition. W.S.Y. was responsible for administrative, technical, or material support. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.3 Use of AI and AI-assisted Technologies Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, we used ChatGPT (OpenAI) to improve the clarity and readability of the English text. After using this tool, we reviewed and edited the text as needed and take full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.4 Ethical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Chongqing, China) approved our interviews (approval: 2017-075) on September 25, 2017. Respondents gave written consent for review and signature before starting interviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.5 Compliance with instructions to authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript adheres to all requirements outlined in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.6\u003c/strong\u003e \u003cstrong\u003eOriginality and exclusivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is original, has not been published elsewhere, and is not under consideration by any other journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.7 Reporting checklist\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe followed the STROBE reporting guideline for observational studies. A completed STROBE checklist has been prepared and submitted with this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIntracerebral haemorrhage. 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Stroke. 2021;52(3):975\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/STROKEAHA.120.029345\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.120.029345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"intracerebral hemorrhage, lipid, lipid ratio, apolipoprotein B, mortality","lastPublishedDoi":"10.21203/rs.3.rs-7615725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7615725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIntracerebral hemorrhage (ICH) carries high early mortality. Admission lipids have been linked to short-term outcomes, yet prior studies focused on conventional markers and rarely assessed age-specific effects. Because older adults often have lower nutritional reserve and higher medical complication rates, the prognostic value of lipids may be age dependent. We therefore evaluated a broader lipid profile to test their associations with 90-day mortality across different age group.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a single-center cohort at a tertiary academic hospital. Serum lipids, including total cholesterol(TC), triglycerides(TG), high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), apolipoprotein A1(Apo A1), and apolipoprotein B(Apo B), were measured upon hospital admission. Patients were stratified as \u0026lt;\u0026thinsp;45 years and \u0026ge;\u0026thinsp;45 years. Multivariate logistic regression was applied to investigate the associations between lipid levels, lipid ratios, and 90-day mortality. Discrimination was assessed using stratified 5-fold cross-validated single-marker receiver operating characteristic (ROC) analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe included 503 patients with acute ICH. In those older than 45 years, lower nonHDL-C (OR 0.898; 95%CI 0.814\u0026ndash;0.990), Apo B (OR 0.861; 95%CI 0.748\u0026ndash;0.990), nonHDL-C/HDL-C (OR 0.964; 95%CI 0.930\u0026ndash;0.999), and Apo B/Apo A1 (OR 0.816; 95%CI 0.687\u0026ndash;0.968) were independently associated with higher 90-day mortality. In ROC analyses, Apo B (AUC\u0026thinsp;=\u0026thinsp;0.671) and Apo B/Apo A1 (AUC\u0026thinsp;=\u0026thinsp;0.676) showed the highest discrimination among single markers, indicating modest predictive performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eLipid levels and ratios, particularly Apo B and Apo B/Apo A1, are independent predictors of 90-day mortality in middle-aged and older ICH patients, aiding clinical risk stratification.\u003c/p\u003e","manuscriptTitle":"Serum lipid levels predict mortality of aged patients in acute intracerebral hemorrhage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-05 11:31:03","doi":"10.21203/rs.3.rs-7615725/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c242ee1-fbbb-45e6-b3eb-b14586af3cfe","owner":[],"postedDate":"October 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T20:30:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-05 11:31:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7615725","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7615725","identity":"rs-7615725","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}