Development and validation of a clinical prediction model for concurrent pulmonary infection in convalescent patients with intracerebral hemorrhage

Development and validation of a clinical prediction model for concurrent pulmonary infection in convalescent patients with 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 Development and validation of a clinical prediction model for concurrent pulmonary infection in convalescent patients with intracerebral hemorrhage Jixiang Xu, Yan Li, Shaomin Li, Xiaoxiao Han, Ynliang QI, Xiaomei Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3981136/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 Objectives : This study aims to develop and validate a clinical prediction model for assessing the risk of concurrent pulmonary infection（PI）in patients recovering from intracerebral hemorrhage（ICH）. Methods : In this retrospective study, we compiled clinical data from 761 patients in the recovery phase of intracerebral hemorrhage, with 504 cases included in the PI group and 254 in the no PI group. Initially, univariate logistic regression was used to screen predictive factors. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to optimize these predictors. Variables identified from LASSO regression were included in a multivariable logistic regression analysis, incorporating variables with P < 0.05 into the final model. A nomogram was constructed, and its discriminative ability was evaluated using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC). Model performance was assessed using calibration plots and the Hosmer-Lemeshow goodness-of-fit test (HL test). Additionally, the net clinical benefit was evaluated through clinical decision curve （DOC）analysis. Results Key predictors of PI included age, antibiotic use, consciousness disturbances, tracheotomy, dysphagia, bed rest duration, nasal feeding, and procalcitonin levels. The model demonstrated strong discrimination (C-index: 0.901, 95%CI: 0.878~0.924) and fit (Hosmer-Lemeshow test P=0.982), with significant clinical utility as per DCA. Conclusion This study constructed a nomogram prediction model based on the demographic and clinical characteristics of convalescent patients with intracerebral hemorrhage. Further studies showed that this model is of great value in the prediction of pulmonary infection in convalescent patients with intracerebral hemorrhage. The recovery period of intracerebral hemorrhage Pulmonary infection Clinical prediction model Nomogram Rehabilitation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background The World Stroke Organization's 2022 Global Stroke Fact Sheet identifies stroke as the second leading global cause of death, including in China [1,2] . Intracerebral hemorrhage (ICH), a critical stroke subtype, has a one-year mortality rate of approximately 20% [3] and contributes significantly to global health burdens [4] . Although great progresses have been made in surgical techniques and intensive care management, up to half of the patients die within 30 days of intracerebral hemorrhage [5] , the surviving patients may remain in intensive care for a long time, mostly with severe disturbance of consciousness [6] . The recovery period of intracerebral hemorrhage is considered to last from 2 to 6 months after the onset of the hemorrhage [7] . During recovery, many ICH patients, often bedridden and immunocompromised, are susceptible to complications, notably pulmonary infections, which affect 10% according to a meta-analysis of 130,000 post-stroke cases [8] . Studies indicate pulmonary infections can increase ICH patient mortality by about 30% [9,10] . Therefore, analyzing early-stage pulmonary infection risk factors in ICH convalescents is crucial for prevention and prognosis improvement. Literature on pulmonary infection prevention in ICH recovery is sparse. Existing studies suggest age, d-dimer levels, and Glasgow Coma Score (GCS) as potential risk factors, but comprehensive analyses including factors like tracheostomy and dysphagia are lacking. Most current research relies on statistical analysis without creating practical clinical models. These studies often yield non-intuitive results with limited clinical application. A timely and effective assessment model is crucial for identifying intervention opportunities for pulmonary infection in ICH convalescents. Nomogram-based models, which score multiple risk factors for a clear, aggregate prediction, are not yet established for this purpose. Our study addresses this gap by developing a nomogram model for pulmonary infection in ICH convalescents, offering healthcare professionals a practical tool to identify high-risk patients." Methods Study Design and Patients This study is a retrospective observational research. The clinical data was collected from September 1, 2020, to December 31, 2022, with all patients sourced from the Hyperbaric Oxygen Medicine Department of the Second People's Hospital of Hefei City, China. Inclusion criteria :(1) meeting the diagnostic criteria of the “Guidelines for the Management of Spontaneous Intracerebral Hemorrhage” issued by the American Heart Association/American Stroke Association in 2022 [11] ; ༈2༉age ≥ 18 years;༈3༉ diagnosis of pulmonary infection on the third day of admission based on physical examination, biochemical tests, and chest X-ray. The following patients were excluded from the study: ༈1༉patients who or whose family members were unwilling to sign the informed consent form; ༈2༉age < 18 years; ༈3༉those who suffered from pulmonary infection within 48 hours of admission; ༈4༉those with trauma-induced cerebral hemorrhage or brain tumors; ༈5༉those with incomplete clinical data. The flow chart of the selection process is shown in Fig. 1 . Date Collection The data of patient medical records were collected and analyzed. A total of 35 influencing factors were selected in this study based on our previous literature accumulation and clinical experience, including patient's age (1 = ≥ 60 years, 0 = < 60 years), gender (1 = male, 0 = female), cerebral hemorrhage site (1 = basal ganglia hemorrhage, 2 = brainstem hemorrhage, 3 = ventricular hemorrhage, 4 = cerebellar hemorrhage, and 5 = thalamic hemorrhage), prophylactic antibiotic use (1 = used, 0 = not used), mechanical ventilation (1 = yes, 0 = no), hyperbaric oxygen therapy (1 = yes, 0 = no ), underlying disease (1 = hypertension, 2 = hyperlipidemia, 3 = diabetes, 4 = cerebrovascular malformation, and 5 = arteriosclerosis), invasive operation (1 = yes, 0 = no), disturbance of consciousness (GCS score, 1 = < 12, 0 = ≥ 12), smoking (1 = yes, 0 = none), drinking (1 = yes, 0 = none), tracheostomy (1 = yes, 0 = none), dysphagia (1 = yes, 0 = no), length of bed rest (1 = ≥ 2 months, 0 = < 2 months), number of hospitalizations (1 = ≥ 3, 0 = < 3), nasal feeding (1 = yes, 0 = none), nutritional status (1 = normal, 0 = abnormal), and indicators related to liver function, kidney function, blood lipids, immunity, or electrolytes (1 = normal, 0 = abnormal), such as procalcitonin (PCT), C-reactive protein (CRP), white blood cells (WBC), percentage of neutrophils (NE), lymphocyte count (LY), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (Cr), blood urea nitrogen (BUN), lactate dehydrogenase (LDH), triglycerides (TG), cholesterol (CHOL), blood potassium (K ), and prealbumin (PAB). Statistical analysis In this retrospective study, patient baseline and clinical data were analyzed, categorizing variables as frequencies and percentages. Group comparisons (PI vs. non-PI) employed unpaired t-tests/Wilcoxon rank sum tests and Pearson chi-square/Fisher exact tests. Initial risk factor screening was conducted using univariate logistic regression, followed by variable optimization via LASSO (Least Absolute Shrinkage and Selection Operator) regression, with the optimal λ value determined through 5-fold cross-validation. Subsequent multivariate logistic regression on LASSO-optimized factors identified significant predictors, used to develop the nomogram model. The model's discriminatory power was evaluated by the area under the curve (AUC), with internal validation through 1000 Bootstrap resamples [12] . Decision curve analysis (DCA)was employed to assess the nomogram's clinical utility, measuring net benefits at varying threshold probabilities. [13] . All statistical analyses were performed using R software (version 4.1.2), considering P < 0.05 as statistically significant. Results Baseline characteristics This study enrolled a total of 761 convalescent patients with intracerebral hemorrhage, of which 504 developed pulmonary infections, and 257 did not. The detailed data are shown in Table 1 . Table 1 baseline and clinical data of the study population Characteristic [ALL] (N = 761) Non-PI (N = 257) PI( N = 504) P-value Age, n (%) < 0.001 0 280 (36.8%) 125 (48.6%) 155 (30.8%) 1 481 (63.2%) 132 (51.4%) 349 (69.2%) Sex, n (%) 0.249 0 320 (42.0%) 116 (45.1%) 204 (40.5%) 1 441 (58.0%) 141 (54.9%) 300 (59.5%) Type of intracerebral hemorrhage, n (%) 0.619 1 206 (27.1%) 74 (28.8%) 132 (26.2%) 2 134 (17.6%) 46 (17.9%) 88 (17.5%) 3 245 (32.2%) 74 (28.8%) 171 (33.9%) 4 147 (19.3%) 51 (19.8%) 96 (19.0%) 5 29 (3.81%) 12 (4.67%) 17 (3.37%) Antibacterial, n (%) < 0.001 0 238 (31.3%) 149 (58.0%) 89 (17.7%) 1 523 (68.7%) 108 (42.0%) 415 (82.3%) Hyperbaric oxygen, n (%) 0.455 0 269 (35.3%) 96 (37.4%) 173 (34.3%) 1 492 (64.7%) 161 (62.6%) 331 (65.7%) Mechanical ventilation, n (%) 0.543 0 635 (83.4%) 211 (82.1%) 424 (84.1%) 1 126 (16.6%) 46 (17.9%) 80 (15.9%) Nutritional status, n (%) 0.608 0 344 (45.2%) 120 (46.7%) 224 (44.4%) 1 417 (54.8%) 137 (53.3%) 280 (55.6%) Underlying disease, n (%) 0.481 1 300 (39.4%) 111 (43.2%) 189 (37.5%) 2 243 (31.9%) 74 (28.8%) 169 (33.5%) 3 121 (15.9%) 39 (15.2%) 82 (16.3%) 4 70 (9.20%) 22 (8.56%) 48 (9.52%) 5 27 (3.55%) 11 (4.28%) 16 (3.17%) Invasive Operations, n (%) 0.675 0 234 (30.7%) 76 (29.6%) 158 (31.3%) 1 527 (69.3%) 181 (70.4%) 346 (68.7%) Disturbance of consciousness, n (%) < 0.001 0 102 (13.4%) 79 (30.7%) 23 (4.56%) 1 659 (86.6%) 178 (69.3%) 481 (95.4%) drink, n (%) 0.404 0 621 (81.6%) 205 (79.8%) 416 (82.5%) 1 140 (18.4%) 52 (20.2%) 88 (17.5%) smoke, n (%) 0.756 0 339 (44.5%) 117 (45.5%) 222 (44.0%) 1 422 (55.5%) 140 (54.5%) 282 (56.0%) Tracheotomy, n (%) < 0.001 0 218 (28.6%) 148 (57.6%) 70 (13.9%) 1 543 (71.4%) 109 (42.4%) 434 (86.1%) Deglutition disorders, n (%) < 0.001 0 188 (24.7%) 88 (34.2%) 100 (19.8%) 1 573 (75.3%) 169 (65.8%) 404 (80.2%) bed time, n (%) < 0.001 0 293 (38.5%) 144 (56.0%) 149 (29.6%) 1 468 (61.5%) 113 (44.0%) 355 (70.4%) Number of hospitalizations, n (%) 0.482 0 302 (39.7%) 97 (37.7%) 205 (40.7%) 1 459 (60.3%) 160 (62.3%) 299 (59.3%) Nasal feed, n (%) < 0.001 0 217 (28.5%) 110 (42.8%) 107 (21.2%) 1 544 (71.5%) 147 (57.2%) 397 (78.8%) PCT, n (%) < 0.001 0 342 (44.9%) 152 (59.1%) 190 (37.7%) 1 419 (55.1%) 105 (40.9%) 314 (62.3%) ALB, n (%) 0.003 0 352 (46.3%) 99 (38.5%) 253 (50.2%) 1 409 (53.7%) 158 (61.5%) 251 (49.8%) WBC, n (%) 0.038 0 283 (37.2%) 82 (31.9%) 201 (39.9%) 1 478 (62.8%) 175 (68.1%) 303 (60.1%) NE, n (%) 0.553 0 479 (62.9%) 166 (64.6%) 313 (62.1%) 1 282 (37.1%) 91 (35.4%) 191 (37.9%) LY, n (%) 0.831 0 653 (85.8%) 222 (86.4%) 431 (85.5%) 1 108 (14.2%) 35 (13.6%) 73 (14.5%) CRP, n (%) 0.925 0 515 (67.7%) 175 (68.1%) 340 (67.5%) 1 246 (32.3%) 82 (31.9%) 164 (32.5%) TBIL, n (%) 0.077 0 643 (84.5%) 226 (87.9%) 417 (82.7%) 1 118 (15.5%) 31 (12.1%) 87 (17.3%) DBIL, n (%) 0.621 0 584 (76.7%) 194 (75.5%) 390 (77.4%) 1 177 (23.3%) 63 (24.5%) 114 (22.6%) IBIL, n (%) 0.021 0 448 (58.9%) 136 (52.9%) 312 (61.9%) 1 313 (41.1%) 121 (47.1%) 192 (38.1%) K, n (%) 0.983 0 386 (50.7%) 131 (51.0%) 255 (50.6%) 1 375 (49.3%) 126 (49.0%) 249 (49.4%) ALT, n (%) 0.965 0 553 (72.7%) 186 (72.4%) 367 (72.8%) 1 208 (27.3%) 71 (27.6%) 137 (27.2%) AST, n (%) 0.460 0 524 (68.9%) 172 (66.9%) 352 (69.8%) 1 237 (31.1%) 85 (33.1%) 152 (30.2%) Cr, n (%) 0.418 0 722 (94.9%) 241 (93.8%) 481 (95.4%) 1 39 (5.12%) 16 (6.23%) 23 (4.56%) BUN, n (%) 0.390 0 607 (79.8%) 210 (81.7%) 397 (78.8%) 1 154 (20.2%) 47 (18.3%) 107 (21.2%) LDH, n (%) 0.588 0 679 (89.2%) 232 (90.3%) 447 (88.7%) 1 82 (10.8%) 25 (9.73%) 57 (11.3%) TG, n (%) 0.505 0 604 (79.4%) 208 (80.9%) 396 (78.6%) 1 157 (20.6%) 49 (19.1%) 108 (21.4%) CHOL, n (%) 1.000 0 210 (27.6%) 71 (27.6%) 139 (27.6%) 1 551 (72.4%) 186 (72.4%) 365 (72.4%) PAB, n (%) 0.755 0 15 (1.97%) 4 (1.56%) 11 (2.18%) 1 746 (98.0%) 253 (98.4%) 493 (97.8%) Univariate logistic regression Univariate logistic analysis revealed that age, antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, length of bed rest, number of hospitalizations, nasal feeding, PCT, ALB, WBC, and NE were significantly correlated with concurrent pulmonary infection in the recovery period of intracerebral hemorrhage(p < 0.05, Table 2 ). Table 2 Univariant analysis on the impact of pulmonary infection during the recovery period of intracerebral hemorrhage characteristics β S.E Wald OR 95%CI P Age 0.757 0.15777 4.799 2.132 2.132(1.566–2.907) 0.000 Sex 0.19 0.15475 1.231 1.21 1.21(0.893–1.638) 0.218 Type of intracerebral hemorrhage5 -0.23 0.40404 -0.57 0.794 0.794(0.362–1.79) 0.568 Type of intracerebral hemorrhage4 0.054 0.22608 0.238 1.055 1.055(0.678–1.648) 0.812 Type of intracerebral hemorrhage3 0.259 0.20112 1.287 1.295 1.295(0.873–1.923) 0.198 Type of intracerebral hemorrhage2 0.07 0.23279 0.301 1.072 1.072(0.681–1.698) 0.764 Antibacterial 1.861 0.17209 10.817 6.433 6.433(4.606–9.047) 0.000 Hyperbaric oxygen 0.132 0.15947 0.826 1.141 1.141(0.833–1.558) 0.409 Mechanical ventilation -0.144 0.20331 -0.711 0.865 0.865(0.583–1.296) 0.477 Nutritional status 0.091 0.15385 0.589 1.095 1.095(0.81–1.48) 0.556 Underlying disease5 -0.158 0.40952 -0.385 0.854 0.854(0.386–1.955) 0.700 Underlying disease4 0.248 0.28388 0.873 1.281 1.281(0.742–2.269) 0.382 Underlying disease3 0.211 0.22833 0.924 1.235 1.235(0.793–1.945) 0.356 Underlying disease2 0.294 0.18366 1.599 1.341 1.341(0.937–1.927) 0.110 Invasive Operations -0.084 0.16704 -0.502 0.92 0.92(0.661–1.273) 0.615 Disturbance of consciousness 2.228 0.25265 8.819 9.282 9.282(5.746–15.53) 0.000 drink -0.182 0.19462 -0.933 0.834 0.834(0.571–1.227) 0.351 smoke 0.06 0.15408 0.388 1.062 1.062(0.784–1.436) 0.698 Tracheotomy 2.13 0.18034 11.814 8.418 8.418(5.938–12.04) 0.000 Deglutition disorders 0.744 0.1725 4.311 2.104 2.104(1.5-2.951) 0.000 bed time 1.111 0.15913 6.979 3.036 3.036(2.226–4.155) 0.000 Number of hospitalizations -0.123 0.15742 -0.782 0.884 0.884(0.648–1.202) 0.434 Nasal feed 1.021 0.16661 6.129 2.776 2.776(2.004–3.854) 0.000 PCT 0.872 0.15669 5.567 2.392 2.392(1.762–3.259) 0.000 ALB -0.475 0.1561 -3.046 0.622 0.622(0.457–0.843) 0.002 WBC -0.348 0.16182 -2.148 0.706 0.706(0.513–0.968) 0.032 NE -0.369 0.15502 -2.378 0.692 0.692(0.51–0.937) 0.017 LY 0.072 0.22157 0.324 1.074 1.074(0.701–1.674) 0.746 CRP 0.029 0.16416 0.177 1.029 1.029(0.748–1.424) 0.860 TBIL 0.419 0.22489 1.865 1.521 1.521(0.989–2.394) 0.062 DBIL -0.105 0.1799 -0.585 0.9 0.9(0.634–1.285) 0.559 IBIL 0.107 0.15951 0.672 1.113 1.113(0.815–1.525) 0.502 K 0.015 0.15332 0.099 1.015 1.015(0.752–1.372) 0.922 ALT -0.022 0.17171 -0.13 0.978 0.978(0.7-1.373) 0.897 AST -0.135 0.16431 -0.821 0.874 0.874(0.634–1.208) 0.412 Cr -0.328 0.33497 -0.98 0.72 0.72(0.376–1.412) 0.327 BUN 0.186 0.19469 0.955 1.204 1.204(0.827–1.776) 0.340 LDH 0.168 0.25316 0.665 1.183 1.183(0.728–1.973) 0.506 TG 0.146 0.19235 0.761 1.158 1.158(0.798–1.698) 0.446 CHOL 0.002 0.17145 0.014 1.002 1.002(0.714–1.399) 0.989 PAB -0.344 0.58897 -0.585 0.709 0.709(0.195–2.095) 0.559 Optimization of risk factor screening by LASSO regression LASSO regression analysis was performed using the glmnet package in R language, with the occurrence of pulmonary infection during the recovery period of intracerebral hemorrhage as the dependent variable and all influencing factors in the aforementioned univariate analysis as independent variables. As shown in Fig. 1 , the coefficients of the influencing factors initially included in the model decreased with the change of the penalty coefficient λ. Ultimately, some of the coefficients decreased to zero. In this case, the over-fitting of the model can be avoided to the greatest extent and the best selection effect can be obtained. We next sought to determine the best penalty coefficient λ for a well-performed model with the fewest influencing factors. For this purpose, the mean square error variation with the parameter Log λ was plotted following 5-fold cross-validation. Totally, 8 predictor variables were identified, including age, prophylactic antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, length of bed rest, nasal feeding, and PCT, with a regression coefficient of 0.102, 1.177, 1.377, 1.518, 0.357, 0.636, 0.413, and 0.360, respectively(Fig. 2 and Fig. 3 ). multivariate logistic regression analysis Multivariate logistic regression analysis was performed on the above screened risk factors. Age, prophylactic antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, length of bed rest, nasal feeding, and PCT were identified as independent risk factors for concurrent pulmonary infection in the recovery period of intracerebral hemorrhage (P < 0.05, Table 3 ). Table 3 Multivariate logistic regression analysis of the influencing factors of pulmonary infection during the recovery period of intracerebral hemorrhage characteristics B SE Z OR 95%CI P (Intercept) -6.557 0.55832 -11.745 0.001 0.001(0-0.004) 0.000 Age 0.521 0.21858 2.381 1.683 1.683(1.096–2.586) 0.017 Antibacterial 1.706 0.2214 7.706 5.507 5.507(3.587–8.556) 0.000 Disturbance of consciousness 2.184 0.33656 6.488 8.878 8.878(4.672–17.53) 0.000 Tracheotomy 2.19 0.23797 9.201 8.931 8.931(5.651–14.38) 0.000 Deglutition disorders 0.997 0.25466 3.914 2.709 2.709(1.649–4.485) 0.000 bed time 1.224 0.21823 5.61 3.402 3.402(2.228–5.25) 0.000 Nasal feed 0.927 0.22483 4.125 2.528 2.528(1.63–3.941) 0.000 PCT 0.878 0.21573 4.071 2.406 2.406(1.582–3.69) 0.000 Construction and validation of the nomogram prediction model for concurrent pulmonary infection in the recovery period of intracerebral hemorrhage A nomogram was created using the rms package in R language based on the results of the aforementioned multivariate logistic regression analysis (Fig. 4 ). Then, the assigned values of the risk factors in the nomogram were added together to obtain a specific total score, which corresponds to the predicted value for the incidence of pulmonary infection in convalescent patients with intracerebral hemorrhage. Next, we performed model validation based on discrimination, calibration and clinical utility. The ROC curve showed that the AUC of concurrent pulmonary infection in the recovery period of intracerebral hemorrhage was 0.901 (95% CI: 0.878–0.924). The repeated sampling of the original data by Bootstrap for 1000 times yielded an AUC of 0.900 (95% CI: 0.877–0.923) (Fig. 5 ). As shown in Fig. 6 , the calibration curve of the prediction model displayed a high degree of agreement between the predicted and actual probabilities, which was almost close to 1 (Hosmer-Lemeshow test, χ 2 = 4.284, p = 0.982), indicating that the model has a good goodness of fit. We further evaluated the clinical effectiveness of the model using the DCA decision curve. As depicted in Fig. 7 , selection of this model for predicting concurrent pulmonary infections in convalescent patients with intracerebral hemorrhage at the threshold probability between 10% and 92% led to an increase in the net clinical benefit. Taken together, these data indicated that the clinical prediction model established in this study has strong discriminative power, calibration ability and clinical validity for the occurrence of pulmonary infection in convalescent patients with ICH. Discussion Effect of pulmonary infection on convalescent patients with intracerebral hemorrhage The progression of intracerebral hemorrhage (ICH) encompasses hyperacute, acute, and recovery phases. Notably, up to 40% of ICH patients suffer from pulmonary infections during recovery, significantly impacting their rehabilitation and long-term outcomes [14–16] . Factors like age, consciousness disturbances, tracheotomy, and nasal feeding contribute to these infections by lowering immunity and facilitating bacterial growth [17–20] . In our study, the incidence of pulmonary infection in ICH convalescents was 33.77%, higher than previously reported rates [20,21] . This disparity could be linked to factors such as older age, prolonged bed rest, and a high proportion of patients in a comatose state. Pulmonary infections, while not significantly affecting acute phase mortality, elevate the risk of death and lead to longer hospital stays during the recovery phase, potentially exposing patients to more pathogens [22,23] . Moreover, these infections can lead to severe complications like sepsis and respiratory failure [21,24,25] . highlighting the critical need for early detection and management of high-risk ICH patients. Analysis of risk factors for concurrent pulmonary infection in convalescent patients with intracerebral hemorrhage Advanced age is almost the most frequent risk factor for nosocomial infections [26] . It has been shown that increasing age is highly correlated with the incidence of pulmonary infection in ICH patients [27] . Sui et al. found that for every one-year increase in age, the risk of stroke-related pulmonary infection increases by 1.113 times [28] . In this study, we observed that the risk of developing pulmonary infection was 1.683 times higher in the patients over 60 years old than in those under 60 years old. This observation may be explained by the fact that compared with the younger patients, the elderly ones have a significantly weaker immune system and a greatly reduced ability to eliminate pathogens for an effective control the infections. In addition, the decline in the ciliary motility of the bronchial mucosa, cough response, and lung tissue elasticity in the elderly patients weakens the expectoration function, thereby increasing the chance of pulmonary infection after illness. A meta-analysis by Westendorp et al. in 2012 found that prophylactic antibiotic use reduced infection rates (hazard ratio = 0.58) [29] . On the contrary, another meta-analysis by Yuan et al. revealed a 7.10-fold increase in the risk of developing pulmonary infection in patients undergoing prophylactic antibiotic use compared to those who did not receive prophylactic antibiotics [30] . Similar to the research of Yuan et al, the present study found that the risk of pulmonary infection in patients undergoing prophylactic antibiotic use was 5.507 times as high as that in patients who did not receive prophylactic antibiotics. The opposite results between the above two meta-analyses may be attributed to the difference in the type of literature involved in the analyses (randomized controlled trial vs case-control and cohort study). Additionally, the study population and the disease severity differed in the two studies. In the present study, convalescent patients with intracerebral hemorrhage were selected as the study subjects. Thus, the overall condition of patients was relatively stable. In the analysis conducted by Westendorp et al., while prophylactic antibiotic use displayed a positive significance for patients with severe stroke, its significance for the mild patients may be exaggerated. Convalescent patients with intracerebral hemorrhage with lower GCS scores have more severe disturbance of consciousness, and are more likely to die from ischemia and hypoxia in brain tissue due to cerebral edema and intracranial pressure. In turn, these complications may exacerbate damage to the central nervous system and impair the patient's cough reflex. In this study, we observed that the incidence of pulmonary infection in patients with moderate or higher disturbance of consciousness was 8.879 times as high as that in patients with mild or less consciousness, showing that the lower the score of disturbance of consciousness, the higher the incidence of lung infection. This observation is consistent with the results of the previous studies [28,31] . In terms of tracheotomy, increased exposure to the external environment stimulates the production of more secretions from the airways, which compromises local defenses and provides conditions for bacterial colonization, thus increasing the incidence of pulmonary infection [32] . In this study, we found that the incidence of pulmonary infection in patients undergoing tracheotomy was 8.931 times as high as that in patients who did not receive tracheotomy. This finding was similar to the results of the previous researches [19,33] . Numerous studies have identified dysphagia and bed rest time as the most important factors causing pulmonary infection in ICH patients [30,34] . It has been demonstrated that the presence of normal chewing and swallowing not only prevents the accumulation of bacteria in the throat, but also ensures proper secretion of secretions. Therefore, patients with reduced or absent cough and swallowing reflexes are more prone to pulmonary infection. A study conducted by Walter et al clearly showed that dysphagia promotes an increased probability of aspiration and increases the risk of pulmonary infection by 10 times [35] . Brogan et al also found that about half of ICH patients had dysphagia, while 48% of them developed pulmonary infection [36] . It has not yet been determined whether the length of bed rest acts as an independent risk factor for pulmonary infection. Ward et al showed that prolonged bed rest led to the accumulation of secretions in the lower part of the trachea, which makes the patients be more prone to difficulty in expectoration, thus resulting in an increase in the incidence of pulmonary infection [37] . Mao et al found that prolonged bed rest had the highest prognostic value for pulmonary infection, with an AUC value of 0.908 [38] . In this study, we observed that the incidence of pulmonary infection in patients with dysphagia was 2.709 times as high as that in patients without dysphagia, while the incidence of pulmonary infection in patients who had been bedridden for more than 2 months was 3.402 times as high as that in patients who had been bedridden for no more than 2 months. This observation was similar to the findings of Mao et al [38] . The present study identified nasal feeding as another important risk factor for the pulmonary infection. A meta-analysis showed a 9.87-fold increase in the risk of pulmonary infection in ICH patients with nasal feeding [30] . Here, we found that the incidence of pulmonary infection was increased by 2.528 times in patients with nasal feeding compared to those without nasal feeding. This finding could be explained by the fact that nasal feeding leads to oral flora dysbiosis, making oral care difficult. Besides, nasal feeding may impair gag reflex and cough function of the patients. Studies have shown that the placement of a nasogastric tube allows for a direct connection between the respiratory tract and the external environment, which can serve as an important entry point for pathogenic microorganisms. Serum biomarkers such as PCT have been widely used to predict bacterial infection and severity [39] . A growing number of studies have clearly indicated that elevated serum PCT levels are highly correlated with pulmonary infections following intracerebral hemorrhage [40] . A study by LU et al suggested a high predictive efficacy of PCT for pulmonary infections after intracerebral hemorrhage based on the ROC curve area [41] . Similarly, the present study showed that the probability of pulmonary infection in patients with abnormal PCT was 2.406 times as high as that in patients with normal PCT. The nomogram model has predictive ability for pulmonary infection in convalescence patients with intracerebral hemorrhage The present study not only described the independent risk factors for pulmonary infection in convalescence patients with intracerebral hemorrhage, but also created a clinical predictive nomogram for early identification of high-risk patients through data analysis. In this study, both univariate logistic regression and Lasso regression were used to perform screening, and the screened risk factors were included in multivariate logistic regression analysis to build a prediction model. This study identified age, prophylactic antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, length of bed rest, nasal feeding, and PCT as independent risk factors for convalescent patients with intracerebral hemorrhage. The analysis showed that the AUC of the prediction model for concurrent pulmonary infection in the recovery period of intracerebral hemorrhage was 0.901 with 95% CI of 0.878 to 0.924. The repeated sampling by Bootstrap for 1000 times yielded an AUC of 0.900 with 95% CI of 0.877 to 0.923. In this case, the AUC value decreased by only 0.001, indicating that the model has an excellent discrimination. The Hosmer-Lemeshow test revealed a high degree of agreement between the predicted and actual probabilities, which is almost close to 1 (P = 0.982), showing that the model has a good goodness of fit. The prediction model constructed in this study based on the DCA decision curve can help clinical staff to make better clinical decisions. Given that a relatively complicated calculation is required for equation-based prediction model construction, the present study used the nomogram to visualize the prediction model. With this approach, clinical staff can perform dynamic risk assessment based on the identified risk factors for each individual patient, screen patients at high risk of pulmonary infection, and accurately predict the risk of pulmonary infection in convalescent patients with intracerebral hemorrhage. Therefore, this study can help clinical staff to target the risk factors and improve the precise prevention of concurrent pulmonary infection in convalescent patients with intracerebral hemorrhage. In summary, ICH convalescents face a heightened risk of concurrent pulmonary infection. While complete prevention of these infections is currently unfeasible, predictive interventions based on identified risk factors are essential. Our focus on ICH patients in recovery, a period with high pulmonary infection incidence, aims to improve their prognosis. By identifying key risk factors and developing a clinical prediction model, we enable early assessment and intervention. However, our study has limitations: it is a retrospective, single-center analysis, necessitating multicenter, prospective trials for model validation. Additionally, not all potential risk factors, like infection parameters, were examined and warrant future research. Finally, external validation is necessary to confirm the nomogram model's efficacy. Conclusions This research successfully developed and validated a tailored nomogram for predicting pulmonary infections in ICH convalescents, demonstrating notable accuracy and effectiveness. This model equips clinicians with a powerful tool for precise risk assessment and targeted medical intervention. Future efforts should concentrate on external validation and continuous refinement of the model to further enhance its applicability and reliability in clinical settings. Abbreviations PI Pulmonary infection ICH Intracerebral hemorrhage PCT procalcitonin CRP C-reactive protein WBC white blood cells NE percentage of neutrophils LY lymphocyte count TBIL total bilirubin DBIL direct bilirubin IBIL indirect bilirubin ALB albumin ALT alanine aminotransferase AST aspartate aminotransferase Cr creatinine BUN blood urea nitrogen LDH lactate dehydrogenase TG triglycerides CHOL cholesterol K blood potassium PAB prealbumin Declarations Acknowledgements We would like to express our gratitude to the nurses and physicians in our Hyperbaric Medicine department for their continuous efforts in improving patient care. Additionally, we would like to extend our appreciation to Professor Xiaomei Zhou for her valuable suggestions regarding clinical statistics. Authors' Contributions ZXM conceptualized and designed the study, while XJX performed the data analysis and drafted the initial manuscript. YL 、QYLwere responsible for the collection of clinical data. HXX and LSM scored the chest X-rays. ZXM reviewed and revised the manuscript. All authors read and approved the final version of the manuscript. All authors take full responsibility for the content of this manuscript and have approved its submission. Funding Anhui Medical University 2022 Research Fund (2022xkj109). Availability of Data and Materials The raw data supporting the conclusions of this article will be made available by the corresponding authors to qualified researchers. Ethics approval and consent to participate Any involvement of human participants, materials, or data complied with the Declaration of Helsinki. This study was conducted with approval from the Ethics Committee of the Second People’s Hospital of Hefei（(Ethics Number: 2022-Scientific Research-091）. In addition, we have received written informed consent from the patient’s family. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Feigin VL, Brainin M, Norrving B, et al. World Stroke Organization (WSO): Global Stroke Fact Sheet 2022[J]. Int J Stroke, 2022, 17(1): 18–29. Ma Q, Li R, Wang L, et al. Temporal trend and attributable risk factors of stroke burden in China, 1990–2019: an analysis for the Global Burden of Disease Study 2019[J]. Lancet Public Health, 2021, 6(12): e897-e906. Liljehult J, Christensen T, Christensen KB. Early Prediction of One-Year Mortality in Ischemic and Haemorrhagic Stroke[J]. J Stroke Cerebrovasc Dis, 2020, 29(4): 104667. Krishnamurthi RV, Ikeda T, Feigin VL. Global, Regional and Country-Specific Burden of Ischaemic Stroke, Intracerebral Haemorrhage and Subarachnoid Haemorrhage: A Systematic Analysis of the Global Burden of Disease Study 2017[J]. Neuroepidemiology, 2020, 54(2): 171–179. 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Herb Formula (GCis) Prevents Pulmonary Infection Secondary to Intracerebral Hemorrhage by Enhancing Peripheral Immunity and Intestinal Mucosal Immune Barrier[J]. Front Pharmacol, 2022, 13: 888684. Wang K-W, Chen H-J, Lu K, et al. Pneumonia in patients with severe head injury: incidence, risk factors, and outcomes[J]. Journal of Neurosurgery, 2013, 118(2): 358–363. Wang Q, Liu Y, Han L, et al. Risk factors for acute stroke-associated pneumonia and prediction of neutrophil-to-lymphocyte ratios[J]. Am J Emerg Med, 2021, 41: 55–59. Xu S, Du B, Shan A, et al. The risk factors for the postoperative pulmonary infection in patients with hypertensive cerebral hemorrhage: A retrospective analysis[J]. Medicine (Baltimore), 2020, 99(51): e23544. Li W, Xu L, Zhao H, et al. Analysis of clinical distribution and drug resistance of klebsiella pneumoniae pulmonary infection in patients with hypertensive intra cerebral hemorrhage after minimally invasive surgery[J]. Pak J Med Sci, 2022, 38(1): 237–242. 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Int J Ophthalmol, 2018, 11(1): 43–47. Zhao S, Zhang T, Zhao J, et al. A Retrospective Analysis of Factors Impacting Rehabilitation Outcomes in Patients With Spontaneous Intracerebral Hemorrhage[J]. Am J Phys Med Rehabil, 2020, 99(11): 1004–1011. Sui R, Zhang L. Risk factors of stroke-associated pneumonia in Chinese patients[J]. Neurological Research, 2013, 33(5): 508–513. WF; W, JD༛ V, F V. Antibiotic therapy for preventing infections in patients with acute stroke[J]. Cochrane Database Syst Rev, 2012: 2012;2011:CD008530. Yuan MZ, Li F, Tian X, et al. Risk factors for lung infection in stroke patients: a meta-analysis of observational studies[J]. Expert Rev Anti Infect Ther, 2015, 13(10): 1289–1298. Hilker R, Poetter C, Findeisen N, et al. Nosocomial pneumonia after acute stroke: implications for neurological intensive care medicine[J]. Stroke, 2003, 34(4): 975–981. Selvaraj SA, Lee KE, Harrell M, et al. Infection Rates and Risk Factors for Infection Among Health Workers During Ebola and Marburg Virus Outbreaks: A Systematic Review[J]. J Infect Dis, 2018, 218(suppl_5): S679-S689. Harms H, Grittner U, Droge H, et al. Predicting post-stroke pneumonia: the PANTHERIS score[J]. Acta Neurol Scand, 2013, 128(3): 178–184. Grossmann I, Rodriguez K, Soni M, et al. Stroke and Pneumonia: Mechanisms, Risk Factors, Management, and Prevention[J]. Cureus, 2021, 13(11): e19912. Walter U, Knoblich R, Steinhagen V, et al. Predictors of pneumonia in acute stroke patients admitted to a neurological intensive care unit[J]. J Neurol, 2007, 254(10): 1323–1329. Brogan E, Langdon C, Brookes K, et al. Dysphagia and factors associated with respiratory infections in the first week post stroke[J]. Neuroepidemiology, 2014, 43(2): 140–144. Ward K, Seymour J, Steier J, et al. Acute ischaemic hemispheric stroke is associated with impairment of reflex in addition to voluntary cough[J]. Eur Respir J, 2010, 36(6): 1383–1390. Mao L, Liu X, Zheng P, et al. Epidemiologic Features, Risk Factors, and Outcomes of Respiratory Infection in Patients with Acute Stroke[J]. Ann Indian Acad Neurol, 2019, 22(4): 395–400. Hug A, Murle B, Dalpke A, et al. Usefulness of serum procalcitonin levels for the early diagnosis of stroke-associated respiratory tract infections[J]. Neurocrit Care, 2011, 14(3): 416–422. Hotter B, Hoffmann S, Ulm L, et al. Inflammatory and stress markers predicting pneumonia, outcome, and etiology in patients with stroke: Biomarkers for predicting pneumonia, functional outcome, and death after stroke[J]. Neurol Neuroimmunol Neuroinflamm, 2020, 7(3). Lu Y LX, Chen YJ, Yu J, Yin SJ. Serum iron and A(2)DS(2) score in stroke-associated pneumonia.[J]. Int J Clin Exp Med: 2015;2018(2014):6163–6170. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3981136","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274628970,"identity":"09634033-a87c-4ad1-97be-7368635465fa","order_by":0,"name":"Jixiang Xu","email":"","orcid":"","institution":"The Second People's Hospital of Hefei, Hefei Hospital Afliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jixiang","middleName":"","lastName":"Xu","suffix":""},{"id":274628971,"identity":"774c5fc1-0d47-435b-919f-632005498ed9","order_by":1,"name":"Yan Li","email":"","orcid":"","institution":"Hefei Hospital 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20:25:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":455032,"visible":true,"origin":"","legend":"\u003cp\u003eSelection process of prognostic variables of severe multiple trauma by LASSO regression\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3981136/v1/ea9a2a842075a3b3b9dceeae.jpeg"},{"id":51775173,"identity":"3d8b60d7-5f08-403f-b9fe-c4c320d2690d","added_by":"auto","created_at":"2024-02-28 20:33:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":227784,"visible":true,"origin":"","legend":"\u003cp\u003eSelection process of the value of lambda by cross validation\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3981136/v1/d65d3bac31270a87cd3d4416.jpeg"},{"id":51774180,"identity":"88dad1c9-dfc0-4d7d-99a4-7b7d6436351c","added_by":"auto","created_at":"2024-02-28 20:25:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30563,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram risk prediction model for recovery period of intracerebral hemorrhage\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3981136/v1/3ae45f68fae04794910e6f0c.png"},{"id":51774178,"identity":"4ff4b18d-f754-4d48-868b-4ee5f0a6c8e9","added_by":"auto","created_at":"2024-02-28 20:25:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22724,"visible":true,"origin":"","legend":"\u003cp\u003eThe AUC (representative the discriminatory ability of the model) of the model and the internal validation.((A) shows the AUC of the predictive model, and (B) shows the AUC of the internal validation using the bootstrap method (resampling = 1000))\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3981136/v1/e728e1a9dbc5b8c0a6ad8b91.png"},{"id":51774177,"identity":"2ad0b631-05ce-4ab4-b324-43d2305efc97","added_by":"auto","created_at":"2024-02-28 20:25:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9617,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the prediction model\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3981136/v1/2da115bea09647077360c391.png"},{"id":51774179,"identity":"d6ed004f-8acc-424b-b6c9-4181bce2293c","added_by":"auto","created_at":"2024-02-28 20:25:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":65696,"visible":true,"origin":"","legend":"\u003cp\u003eDCA of the nomogram\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3981136/v1/5061598dadb708f6fbc0afc4.png"},{"id":52262766,"identity":"6e7bed56-92c9-4d1f-b354-93865aae3bdc","added_by":"auto","created_at":"2024-03-08 11:20:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":982150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3981136/v1/6fac0251-c3e8-4227-8436-a2645c8ba313.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDevelopment and validation of a clinical prediction model for concurrent pulmonary infection in convalescent patients with intracerebral hemorrhage\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe World Stroke Organization's 2022 Global Stroke Fact Sheet identifies stroke as the second leading global cause of death, including in China\u003csup\u003e[1,2]\u003c/sup\u003e. Intracerebral hemorrhage (ICH), a critical stroke subtype, has a one-year mortality rate of approximately 20%\u003csup\u003e[3]\u003c/sup\u003e and contributes significantly to global health burdens\u003csup\u003e[4]\u003c/sup\u003e. Although great progresses have been made in surgical techniques and intensive care management, up to half of the patients die within 30 days of intracerebral hemorrhage \u003csup\u003e[5]\u003c/sup\u003e, the surviving patients may remain in intensive care for a long time, mostly with severe disturbance of consciousness\u003csup\u003e[6]\u003c/sup\u003e. The recovery period of intracerebral hemorrhage is considered to last from 2 to 6 months after the onset of the hemorrhage\u003csup\u003e[7]\u003c/sup\u003e. During recovery, many ICH patients, often bedridden and immunocompromised, are susceptible to complications, notably pulmonary infections, which affect 10% according to a meta-analysis of 130,000 post-stroke cases\u003csup\u003e[8]\u003c/sup\u003e. Studies indicate pulmonary infections can increase ICH patient mortality by about 30% \u003csup\u003e[9,10]\u003c/sup\u003e. Therefore, analyzing early-stage pulmonary infection risk factors in ICH convalescents is crucial for prevention and prognosis improvement.\u003c/p\u003e \u003cp\u003eLiterature on pulmonary infection prevention in ICH recovery is sparse. Existing studies suggest age, d-dimer levels, and Glasgow Coma Score (GCS) as potential risk factors, but comprehensive analyses including factors like tracheostomy and dysphagia are lacking. Most current research relies on statistical analysis without creating practical clinical models. These studies often yield non-intuitive results with limited clinical application. A timely and effective assessment model is crucial for identifying intervention opportunities for pulmonary infection in ICH convalescents. Nomogram-based models, which score multiple risk factors for a clear, aggregate prediction, are not yet established for this purpose. Our study addresses this gap by developing a nomogram model for pulmonary infection in ICH convalescents, offering healthcare professionals a practical tool to identify high-risk patients.\"\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patients\u003c/h2\u003e \u003cp\u003eThis study is a retrospective observational research. The clinical data was collected from September 1, 2020, to December 31, 2022, with all patients sourced from the Hyperbaric Oxygen Medicine Department of the Second People's Hospital of Hefei City, China. Inclusion criteria :(1) meeting the diagnostic criteria of the \u0026ldquo;Guidelines for the Management of Spontaneous Intracerebral Hemorrhage\u0026rdquo; issued by the American Heart Association/American Stroke Association in 2022\u003csup\u003e[11]\u003c/sup\u003e; ༈2༉age\u0026thinsp;\u0026ge;\u0026thinsp;18 years;༈3༉ diagnosis of pulmonary infection on the third day of admission based on physical examination, biochemical tests, and chest X-ray. The following patients were excluded from the study: ༈1༉patients who or whose family members were unwilling to sign the informed consent form; ༈2༉age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; ༈3༉those who suffered from pulmonary infection within 48 hours of admission; ༈4༉those with trauma-induced cerebral hemorrhage or brain tumors; ༈5༉those with incomplete clinical data. The flow chart of the selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDate Collection\u003c/h2\u003e \u003cp\u003eThe data of patient medical records were collected and analyzed. A total of 35 influencing factors were selected in this study based on our previous literature accumulation and clinical experience, including patient's age (1\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;60 years, 0\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;60 years), gender (1\u0026thinsp;=\u0026thinsp;male, 0\u0026thinsp;=\u0026thinsp;female), cerebral hemorrhage site (1\u0026thinsp;=\u0026thinsp;basal ganglia hemorrhage, 2\u0026thinsp;=\u0026thinsp;brainstem hemorrhage, 3\u0026thinsp;=\u0026thinsp;ventricular hemorrhage, 4\u0026thinsp;=\u0026thinsp;cerebellar hemorrhage, and 5\u0026thinsp;=\u0026thinsp;thalamic hemorrhage), prophylactic antibiotic use (1\u0026thinsp;=\u0026thinsp;used, 0\u0026thinsp;=\u0026thinsp;not used), mechanical ventilation (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no), hyperbaric oxygen therapy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no ), underlying disease (1\u0026thinsp;=\u0026thinsp;hypertension, 2\u0026thinsp;=\u0026thinsp;hyperlipidemia, 3\u0026thinsp;=\u0026thinsp;diabetes, 4\u0026thinsp;=\u0026thinsp;cerebrovascular malformation, and 5\u0026thinsp;=\u0026thinsp;arteriosclerosis), invasive operation (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no), disturbance of consciousness (GCS score, 1\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;12, 0\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;12), smoking (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;none), drinking (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;none), tracheostomy (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;none), dysphagia (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no), length of bed rest (1\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;2 months, 0\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;2 months), number of hospitalizations (1\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;3, 0\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;3), nasal feeding (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;none), nutritional status (1\u0026thinsp;=\u0026thinsp;normal, 0\u0026thinsp;=\u0026thinsp;abnormal), and indicators related to liver function, kidney function, blood lipids, immunity, or electrolytes (1\u0026thinsp;=\u0026thinsp;normal, 0\u0026thinsp;=\u0026thinsp;abnormal), such as procalcitonin (PCT), C-reactive protein (CRP), white blood cells (WBC), percentage of neutrophils (NE), lymphocyte count (LY), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (Cr), blood urea nitrogen (BUN), lactate dehydrogenase (LDH), triglycerides (TG), cholesterol (CHOL), blood potassium (K ), and prealbumin (PAB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this retrospective study, patient baseline and clinical data were analyzed, categorizing variables as frequencies and percentages. Group comparisons (PI vs. non-PI) employed unpaired t-tests/Wilcoxon rank sum tests and Pearson chi-square/Fisher exact tests. Initial risk factor screening was conducted using univariate logistic regression, followed by variable optimization via LASSO (Least Absolute Shrinkage and Selection Operator) regression, with the optimal λ value determined through 5-fold cross-validation. Subsequent multivariate logistic regression on LASSO-optimized factors identified significant predictors, used to develop the nomogram model. The model's discriminatory power was evaluated by the area under the curve (AUC), with internal validation through 1000 Bootstrap resamples\u003csup\u003e[12]\u003c/sup\u003e. Decision curve analysis (DCA)was employed to assess the nomogram's clinical utility, measuring net benefits at varying threshold probabilities.\u003csup\u003e[13]\u003c/sup\u003e. All statistical analyses were performed using R software (version 4.1.2), considering P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThis study enrolled a total of 761 convalescent patients with intracerebral hemorrhage, of which 504 developed pulmonary infections, and 257 did not. The detailed data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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 and clinical data of the study population\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[ALL] (N\u0026thinsp;=\u0026thinsp;761)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PI (N\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePI( N\u0026thinsp;=\u0026thinsp;504)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155 (30.8%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e481 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e349 (69.2%)\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\u003eSex, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e320 (42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204 (40.5%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e441 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141 (54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300 (59.5%)\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\u003eType of intracerebral\u003c/p\u003e \u003cp\u003ehemorrhage, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e206 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132 (26.2%)\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (17.5%)\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171 (33.9%)\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\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96 (19.0%)\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\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (3.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (4.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (3.37%)\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\u003eAntibacterial, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e238 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89 (17.7%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e523 (68.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108 (42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e415 (82.3%)\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\u003eHyperbaric oxygen, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e269 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173 (34.3%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e492 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161 (62.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331 (65.7%)\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\u003eMechanical ventilation, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e635 (83.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211 (82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e424 (84.1%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80 (15.9%)\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\u003eNutritional status, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224 (44.4%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e417 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e280 (55.6%)\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\u003eUnderlying disease, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189 (37.5%)\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e169 (33.5%)\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82 (16.3%)\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\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (9.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (8.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (9.52%)\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\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (3.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (4.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (3.17%)\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\u003eInvasive Operations, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158 (31.3%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e527 (69.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e346 (68.7%)\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\u003eDisturbance of consciousness, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (4.56%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e659 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178 (69.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e481 (95.4%)\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\u003edrink, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e621 (81.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205 (79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e416 (82.5%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (17.5%)\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\u003esmoke, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e339 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e222 (44.0%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e422 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e282 (56.0%)\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\u003eTracheotomy, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e218 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70 (13.9%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e543 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e434 (86.1%)\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\u003eDeglutition disorders, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100 (19.8%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e573 (75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169 (65.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e404 (80.2%)\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\u003ebed time, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e293 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149 (29.6%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e468 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e355 (70.4%)\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\u003eNumber of hospitalizations, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e302 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e205 (40.7%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e459 (60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299 (59.3%)\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\u003eNasal feed, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e217 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (21.2%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e544 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147 (57.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e397 (78.8%)\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\u003ePCT, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190 (37.7%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e419 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e314 (62.3%)\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\u003eALB, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e352 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e253 (50.2%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e409 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e251 (49.8%)\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\u003eWBC, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e283 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201 (39.9%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e478 (62.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e303 (60.1%)\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\u003eNE, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e479 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e313 (62.1%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e282 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91 (35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e191 (37.9%)\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\u003eLY, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e653 (85.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e222 (86.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e431 (85.5%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73 (14.5%)\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\u003eCRP, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e515 (67.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e340 (67.5%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e246 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164 (32.5%)\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\u003eTBIL, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e643 (84.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e226 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e417 (82.7%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (17.3%)\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\u003eDBIL, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e584 (76.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194 (75.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390 (77.4%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114 (22.6%)\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\u003eIBIL, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e448 (58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e312 (61.9%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e313 (41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192 (38.1%)\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\u003eK, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e386 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131 (51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255 (50.6%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e375 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126 (49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249 (49.4%)\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\u003eALT, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e553 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e367 (72.8%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137 (27.2%)\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\u003eAST, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e524 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172 (66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e352 (69.8%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152 (30.2%)\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\u003eCr, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e722 (94.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241 (93.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e481 (95.4%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (5.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (6.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (4.56%)\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\u003eBUN, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e607 (79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210 (81.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e397 (78.8%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (21.2%)\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\u003eLDH, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e679 (89.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232 (90.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e447 (88.7%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (9.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (11.3%)\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\u003eTG, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e604 (79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208 (80.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e396 (78.6%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108 (21.4%)\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\u003eCHOL, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139 (27.6%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e551 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186 (72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e365 (72.4%)\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\u003ePAB, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (1.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (1.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (2.18%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e746 (98.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e253 (98.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e493 (97.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate logistic regression\u003c/h2\u003e \u003cp\u003eUnivariate logistic analysis revealed that age, antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, length of bed rest, number of hospitalizations, nasal feeding, PCT, ALB, WBC, and NE were significantly correlated with concurrent pulmonary infection in the recovery period of intracerebral hemorrhage(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eUnivariant analysis on the impact of pulmonary infection during the recovery period of intracerebral hemorrhage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.132(1.566\u0026ndash;2.907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.21(0.893\u0026ndash;1.638)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of intracerebral hemorrhage5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.794(0.362\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of intracerebral hemorrhage4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.055(0.678\u0026ndash;1.648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of intracerebral hemorrhage3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.295(0.873\u0026ndash;1.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of intracerebral hemorrhage2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.072(0.681\u0026ndash;1.698)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntibacterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.433(4.606\u0026ndash;9.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperbaric oxygen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.141(0.833\u0026ndash;1.558)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.865(0.583\u0026ndash;1.296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutritional status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.095(0.81\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying disease5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.854(0.386\u0026ndash;1.955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying disease4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.281(0.742\u0026ndash;2.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying disease3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.235(0.793\u0026ndash;1.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying disease2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.341(0.937\u0026ndash;1.927)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive Operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92(0.661\u0026ndash;1.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisturbance of consciousness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.282(5.746\u0026ndash;15.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.834(0.571\u0026ndash;1.227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.062(0.784\u0026ndash;1.436)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTracheotomy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.418(5.938\u0026ndash;12.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeglutition disorders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.104(1.5-2.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ebed time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.036(2.226\u0026ndash;4.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of hospitalizations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.884(0.648\u0026ndash;1.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.434\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNasal feed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.776(2.004\u0026ndash;3.854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.392(1.762\u0026ndash;3.259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.622(0.457\u0026ndash;0.843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.706(0.513\u0026ndash;0.968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.692(0.51\u0026ndash;0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.074(0.701\u0026ndash;1.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.029(0.748\u0026ndash;1.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.521(0.989\u0026ndash;2.394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9(0.634\u0026ndash;1.285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.113(0.815\u0026ndash;1.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.015(0.752\u0026ndash;1.372)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.978(0.7-1.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.874(0.634\u0026ndash;1.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72(0.376\u0026ndash;1.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.204(0.827\u0026ndash;1.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.183(0.728\u0026ndash;1.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.506\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.158(0.798\u0026ndash;1.698)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.002(0.714\u0026ndash;1.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.709(0.195\u0026ndash;2.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.559\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 \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOptimization of risk factor screening by LASSO regression\u003c/h2\u003e \u003cp\u003eLASSO regression analysis was performed using the glmnet package in R language, with the occurrence of pulmonary infection during the recovery period of intracerebral hemorrhage as the dependent variable and all influencing factors in the aforementioned univariate analysis as independent variables. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the coefficients of the influencing factors initially included in the model decreased with the change of the penalty coefficient λ. Ultimately, some of the coefficients decreased to zero. In this case, the over-fitting of the model can be avoided to the greatest extent and the best selection effect can be obtained. We next sought to determine the best penalty coefficient λ for a well-performed model with the fewest influencing factors. For this purpose, the mean square error variation with the parameter Log λ was plotted following 5-fold cross-validation. Totally, 8 predictor variables were identified, including age, prophylactic antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, length of bed rest, nasal feeding, and PCT, with a regression coefficient of 0.102, 1.177, 1.377, 1.518, 0.357, 0.636, 0.413, and 0.360, respectively(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003emultivariate logistic regression analysis\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression analysis was performed on the above screened risk factors. Age, prophylactic antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, length of bed rest, nasal feeding, and PCT were identified as independent risk factors for concurrent pulmonary infection in the recovery period of intracerebral hemorrhage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eMultivariate logistic regression analysis of the influencing factors of pulmonary infection during the recovery period of intracerebral hemorrhage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001(0-0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.683(1.096\u0026ndash;2.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibacterial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.507(3.587\u0026ndash;8.556)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisturbance of consciousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.878(4.672\u0026ndash;17.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTracheotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.931(5.651\u0026ndash;14.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeglutition disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.709(1.649\u0026ndash;4.485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebed time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.402(2.228\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNasal feed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.528(1.63\u0026ndash;3.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.406(1.582\u0026ndash;3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\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 \u003cb\u003eConstruction and validation of the nomogram prediction model for concurrent pulmonary infection in the recovery period of intracerebral hemorrhage\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA nomogram was created using the rms package in R language based on the results of the aforementioned multivariate logistic regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Then, the assigned values of the risk factors in the nomogram were added together to obtain a specific total score, which corresponds to the predicted value for the incidence of pulmonary infection in convalescent patients with intracerebral hemorrhage.\u003c/p\u003e \u003cp\u003eNext, we performed model validation based on discrimination, calibration and clinical utility. The ROC curve showed that the AUC of concurrent pulmonary infection in the recovery period of intracerebral hemorrhage was 0.901 (95% CI: 0.878\u0026ndash;0.924). The repeated sampling of the original data by Bootstrap for 1000 times yielded an AUC of 0.900 (95% CI: 0.877\u0026ndash;0.923) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the calibration curve of the prediction model displayed a high degree of agreement between the predicted and actual probabilities, which was almost close to 1 (Hosmer-Lemeshow test, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.284, p\u0026thinsp;=\u0026thinsp;0.982), indicating that the model has a good goodness of fit. We further evaluated the clinical effectiveness of the model using the DCA decision curve. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003e, selection of this model for predicting concurrent pulmonary infections in convalescent patients with intracerebral hemorrhage at the threshold probability between 10% and 92% led to an increase in the net clinical benefit. Taken together, these data indicated that the clinical prediction model established in this study has strong discriminative power, calibration ability and clinical validity for the occurrence of pulmonary infection in convalescent patients with ICH.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEffect of pulmonary infection on convalescent patients with intracerebral hemorrhage\u003c/h2\u003e \u003cp\u003eThe progression of intracerebral hemorrhage (ICH) encompasses hyperacute, acute, and recovery phases. Notably, up to 40% of ICH patients suffer from pulmonary infections during recovery, significantly impacting their rehabilitation and long-term outcomes\u003csup\u003e[14\u0026ndash;16]\u003c/sup\u003e. Factors like age, consciousness disturbances, tracheotomy, and nasal feeding contribute to these infections by lowering immunity and facilitating bacterial growth \u003csup\u003e[17\u0026ndash;20]\u003c/sup\u003e. In our study, the incidence of pulmonary infection in ICH convalescents was 33.77%, higher than previously reported rates \u003csup\u003e[20,21]\u003c/sup\u003e. This disparity could be linked to factors such as older age, prolonged bed rest, and a high proportion of patients in a comatose state. Pulmonary infections, while not significantly affecting acute phase mortality, elevate the risk of death and lead to longer hospital stays during the recovery phase, potentially exposing patients to more pathogens \u003csup\u003e[22,23]\u003c/sup\u003e. Moreover, these infections can lead to severe complications like sepsis and respiratory failure \u003csup\u003e[21,24,25]\u003c/sup\u003e. highlighting the critical need for early detection and management of high-risk ICH patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of risk factors for concurrent pulmonary infection in convalescent patients with intracerebral hemorrhage\u003c/h2\u003e \u003cp\u003eAdvanced age is almost the most frequent risk factor for nosocomial infections\u003csup\u003e[26]\u003c/sup\u003e. It has been shown that increasing age is highly correlated with the incidence of pulmonary infection in ICH patients\u003csup\u003e[27]\u003c/sup\u003e. Sui et al. found that for every one-year increase in age, the risk of stroke-related pulmonary infection increases by 1.113 times\u003csup\u003e[28]\u003c/sup\u003e. In this study, we observed that the risk of developing pulmonary infection was 1.683 times higher in the patients over 60 years old than in those under 60 years old. This observation may be explained by the fact that compared with the younger patients, the elderly ones have a significantly weaker immune system and a greatly reduced ability to eliminate pathogens for an effective control the infections. In addition, the decline in the ciliary motility of the bronchial mucosa, cough response, and lung tissue elasticity in the elderly patients weakens the expectoration function, thereby increasing the chance of pulmonary infection after illness.\u003c/p\u003e \u003cp\u003eA meta-analysis by Westendorp et al. in 2012 found that prophylactic antibiotic use reduced infection rates (hazard ratio\u0026thinsp;=\u0026thinsp;0.58)\u003csup\u003e[29]\u003c/sup\u003e. On the contrary, another meta-analysis by Yuan et al. revealed a 7.10-fold increase in the risk of developing pulmonary infection in patients undergoing prophylactic antibiotic use compared to those who did not receive prophylactic antibiotics\u003csup\u003e[30]\u003c/sup\u003e. Similar to the research of Yuan et al, the present study found that the risk of pulmonary infection in patients undergoing prophylactic antibiotic use was 5.507 times as high as that in patients who did not receive prophylactic antibiotics. The opposite results between the above two meta-analyses may be attributed to the difference in the type of literature involved in the analyses (randomized controlled trial vs case-control and cohort study). Additionally, the study population and the disease severity differed in the two studies. In the present study, convalescent patients with intracerebral hemorrhage were selected as the study subjects. Thus, the overall condition of patients was relatively stable. In the analysis conducted by Westendorp et al., while prophylactic antibiotic use displayed a positive significance for patients with severe stroke, its significance for the mild patients may be exaggerated.\u003c/p\u003e \u003cp\u003eConvalescent patients with intracerebral hemorrhage with lower GCS scores have more severe disturbance of consciousness, and are more likely to die from ischemia and hypoxia in brain tissue due to cerebral edema and intracranial pressure. In turn, these complications may exacerbate damage to the central nervous system and impair the patient's cough reflex. In this study, we observed that the incidence of pulmonary infection in patients with moderate or higher disturbance of consciousness was 8.879 times as high as that in patients with mild or less consciousness, showing that the lower the score of disturbance of consciousness, the higher the incidence of lung infection. This observation is consistent with the results of the previous studies\u003csup\u003e[28,31]\u003c/sup\u003e. In terms of tracheotomy, increased exposure to the external environment stimulates the production of more secretions from the airways, which compromises local defenses and provides conditions for bacterial colonization, thus increasing the incidence of pulmonary infection\u003csup\u003e[32]\u003c/sup\u003e. In this study, we found that the incidence of pulmonary infection in patients undergoing tracheotomy was 8.931 times as high as that in patients who did not receive tracheotomy. This finding was similar to the results of the previous researches\u003csup\u003e[19,33]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumerous studies have identified dysphagia and bed rest time as the most important factors causing pulmonary infection in ICH patients\u003csup\u003e[30,34]\u003c/sup\u003e. It has been demonstrated that the presence of normal chewing and swallowing not only prevents the accumulation of bacteria in the throat, but also ensures proper secretion of secretions. Therefore, patients with reduced or absent cough and swallowing reflexes are more prone to pulmonary infection. A study conducted by Walter et al clearly showed that dysphagia promotes an increased probability of aspiration and increases the risk of pulmonary infection by 10 times\u003csup\u003e[35]\u003c/sup\u003e. Brogan et al also found that about half of ICH patients had dysphagia, while 48% of them developed pulmonary infection\u003csup\u003e[36]\u003c/sup\u003e. It has not yet been determined whether the length of bed rest acts as an independent risk factor for pulmonary infection. Ward et al showed that prolonged bed rest led to the accumulation of secretions in the lower part of the trachea, which makes the patients be more prone to difficulty in expectoration, thus resulting in an increase in the incidence of pulmonary infection\u003csup\u003e[37]\u003c/sup\u003e. Mao et al found that prolonged bed rest had the highest prognostic value for pulmonary infection, with an AUC value of 0.908\u003csup\u003e[38]\u003c/sup\u003e. In this study, we observed that the incidence of pulmonary infection in patients with dysphagia was 2.709 times as high as that in patients without dysphagia, while the incidence of pulmonary infection in patients who had been bedridden for more than 2 months was 3.402 times as high as that in patients who had been bedridden for no more than 2 months. This observation was similar to the findings of Mao et al \u003csup\u003e[38]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present study identified nasal feeding as another important risk factor for the pulmonary infection. A meta-analysis showed a 9.87-fold increase in the risk of pulmonary infection in ICH patients with nasal feeding\u003csup\u003e[30]\u003c/sup\u003e. Here, we found that the incidence of pulmonary infection was increased by 2.528 times in patients with nasal feeding compared to those without nasal feeding. This finding could be explained by the fact that nasal feeding leads to oral flora dysbiosis, making oral care difficult. Besides, nasal feeding may impair gag reflex and cough function of the patients. Studies have shown that the placement of a nasogastric tube allows for a direct connection between the respiratory tract and the external environment, which can serve as an important entry point for pathogenic microorganisms. Serum biomarkers such as PCT have been widely used to predict bacterial infection and severity\u003csup\u003e[39]\u003c/sup\u003e. A growing number of studies have clearly indicated that elevated serum PCT levels are highly correlated with pulmonary infections following intracerebral hemorrhage\u003csup\u003e[40]\u003c/sup\u003e. A study by LU et al suggested a high predictive efficacy of PCT for pulmonary infections after intracerebral hemorrhage based on the ROC curve area\u003csup\u003e[41]\u003c/sup\u003e. Similarly, the present study showed that the probability of pulmonary infection in patients with abnormal PCT was 2.406 times as high as that in patients with normal PCT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe nomogram model has predictive ability for pulmonary infection in convalescence patients with intracerebral hemorrhage\u003c/h2\u003e \u003cp\u003eThe present study not only described the independent risk factors for pulmonary infection in convalescence patients with intracerebral hemorrhage, but also created a clinical predictive nomogram for early identification of high-risk patients through data analysis. In this study, both univariate logistic regression and Lasso regression were used to perform screening, and the screened risk factors were included in multivariate logistic regression analysis to build a prediction model. This study identified age, prophylactic antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, length of bed rest, nasal feeding, and PCT as independent risk factors for convalescent patients with intracerebral hemorrhage. The analysis showed that the AUC of the prediction model for concurrent pulmonary infection in the recovery period of intracerebral hemorrhage was 0.901 with 95% CI of 0.878 to 0.924. The repeated sampling by Bootstrap for 1000 times yielded an AUC of 0.900 with 95% CI of 0.877 to 0.923. In this case, the AUC value decreased by only 0.001, indicating that the model has an excellent discrimination. The Hosmer-Lemeshow test revealed a high degree of agreement between the predicted and actual probabilities, which is almost close to 1 (P\u0026thinsp;=\u0026thinsp;0.982), showing that the model has a good goodness of fit. The prediction model constructed in this study based on the DCA decision curve can help clinical staff to make better clinical decisions. Given that a relatively complicated calculation is required for equation-based prediction model construction, the present study used the nomogram to visualize the prediction model. With this approach, clinical staff can perform dynamic risk assessment based on the identified risk factors for each individual patient, screen patients at high risk of pulmonary infection, and accurately predict the risk of pulmonary infection in convalescent patients with intracerebral hemorrhage. Therefore, this study can help clinical staff to target the risk factors and improve the precise prevention of concurrent pulmonary infection in convalescent patients with intracerebral hemorrhage.\u003c/p\u003e \u003cp\u003eIn summary, ICH convalescents face a heightened risk of concurrent pulmonary infection. While complete prevention of these infections is currently unfeasible, predictive interventions based on identified risk factors are essential. Our focus on ICH patients in recovery, a period with high pulmonary infection incidence, aims to improve their prognosis. By identifying key risk factors and developing a clinical prediction model, we enable early assessment and intervention. However, our study has limitations: it is a retrospective, single-center analysis, necessitating multicenter, prospective trials for model validation. Additionally, not all potential risk factors, like infection parameters, were examined and warrant future research. Finally, external validation is necessary to confirm the nomogram model's efficacy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis research successfully developed and validated a tailored nomogram for predicting pulmonary infections in ICH convalescents, demonstrating notable accuracy and effectiveness. This model equips clinicians with a powerful tool for precise risk assessment and targeted medical intervention. Future efforts should concentrate on external validation and continuous refinement of the model to further enhance its applicability and reliability in clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePI Pulmonary infection\u003c/p\u003e \u003cp\u003eICH Intracerebral hemorrhage\u003c/p\u003e \u003cp\u003ePCT procalcitonin\u003c/p\u003e \u003cp\u003eCRP C-reactive protein\u003c/p\u003e \u003cp\u003eWBC white blood cells\u003c/p\u003e \u003cp\u003eNE percentage of neutrophils\u003c/p\u003e \u003cp\u003eLY lymphocyte count\u003c/p\u003e \u003cp\u003eTBIL total bilirubin\u003c/p\u003e \u003cp\u003eDBIL direct bilirubin\u003c/p\u003e \u003cp\u003eIBIL indirect bilirubin\u003c/p\u003e \u003cp\u003eALB albumin\u003c/p\u003e \u003cp\u003eALT alanine aminotransferase\u003c/p\u003e \u003cp\u003eAST aspartate aminotransferase\u003c/p\u003e \u003cp\u003eCr creatinine\u003c/p\u003e \u003cp\u003eBUN blood urea nitrogen\u003c/p\u003e \u003cp\u003eLDH lactate dehydrogenase\u003c/p\u003e \u003cp\u003eTG triglycerides\u003c/p\u003e \u003cp\u003eCHOL cholesterol\u003c/p\u003e \u003cp\u003eK blood potassium\u003c/p\u003e \u003cp\u003ePAB prealbumin\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the nurses and physicians in our Hyperbaric Medicine department for their continuous efforts in improving patient care. Additionally, we would like to extend our appreciation to Professor Xiaomei Zhou for her valuable suggestions regarding clinical statistics.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZXM conceptualized and designed the study, while XJX performed the data analysis and drafted the initial manuscript. YL 、QYLwere responsible for the collection of clinical data. HXX and LSM scored the chest X-rays. ZXM reviewed and revised the manuscript. All authors read and approved the final version of the manuscript. All authors take full responsibility for the content of this manuscript and have approved its submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnhui Medical University 2022 Research Fund (2022xkj109).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the corresponding authors to qualified researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAny involvement of human participants, materials, or data complied with the Declaration of Helsinki. This study was conducted with approval from the Ethics Committee of the Second People\u0026rsquo;s Hospital of Hefei（(Ethics Number: 2022-Scientific Research-091）. In addition, we have received written informed consent from the patient\u0026rsquo;s family.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eFeigin VL, Brainin M, Norrving B, et al. World Stroke Organization (WSO): Global Stroke Fact Sheet 2022[J]. Int J Stroke, 2022, 17(1): 18\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMa Q, Li R, Wang L, et al. Temporal trend and attributable risk factors of stroke burden in China, 1990\u0026ndash;2019: an analysis for the Global Burden of Disease Study 2019[J]. 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Dysphagia and factors associated with respiratory infections in the first week post stroke[J]. Neuroepidemiology, 2014, 43(2): 140\u0026ndash;144.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWard K, Seymour J, Steier J, et al. Acute ischaemic hemispheric stroke is associated with impairment of reflex in addition to voluntary cough[J]. Eur Respir J, 2010, 36(6): 1383\u0026ndash;1390.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMao L, Liu X, Zheng P, et al. Epidemiologic Features, Risk Factors, and Outcomes of Respiratory Infection in Patients with Acute Stroke[J]. Ann Indian Acad Neurol, 2019, 22(4): 395\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHug A, Murle B, Dalpke A, et al. Usefulness of serum procalcitonin levels for the early diagnosis of stroke-associated respiratory tract infections[J]. Neurocrit Care, 2011, 14(3): 416\u0026ndash;422.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHotter B, Hoffmann S, Ulm L, et al. Inflammatory and stress markers predicting pneumonia, outcome, and etiology in patients with stroke: Biomarkers for predicting pneumonia, functional outcome, and death after stroke[J]. Neurol Neuroimmunol Neuroinflamm, 2020, 7(3).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLu Y LX, Chen YJ, Yu J, Yin SJ. Serum iron and A(2)DS(2) score in stroke-associated pneumonia.[J]. Int J Clin Exp Med: 2015;2018(2014):6163\u0026ndash;6170.\u003c/span\u003e\u003c/li\u003e\n\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":"The recovery period of intracerebral hemorrhage, Pulmonary infection, Clinical prediction model, Nomogram, Rehabilitation","lastPublishedDoi":"10.21203/rs.3.rs-3981136/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3981136/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: This study aims to develop and validate a clinical prediction model for assessing the risk of concurrent pulmonary infection（PI）in patients recovering from intracerebral hemorrhage（ICH）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: In this retrospective study, we compiled clinical data from 761 patients in the recovery phase of intracerebral hemorrhage, with 504 cases included in the PI group and 254 in the no PI group. Initially, univariate logistic regression was used to screen predictive factors. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to optimize these predictors. Variables identified from LASSO regression were included in a multivariable logistic regression analysis, incorporating variables with P \u0026lt; 0.05 into the final model. A nomogram was constructed, and its discriminative ability was evaluated using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC). Model performance was assessed using calibration plots and the Hosmer-Lemeshow goodness-of-fit test (HL test). Additionally, the net clinical benefit was evaluated through clinical decision curve （DOC）analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eKey predictors of PI included age, antibiotic use, consciousness disturbances, tracheotomy, dysphagia, bed rest duration, nasal feeding, and procalcitonin levels. The model demonstrated strong discrimination (C-index: 0.901, 95%CI: 0.878~0.924) and fit (Hosmer-Lemeshow test P=0.982), with significant clinical utility as per DCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThis study constructed a nomogram prediction model based on the demographic and clinical characteristics of convalescent patients with intracerebral hemorrhage. Further studies showed that this model is of great value in the prediction of pulmonary infection in convalescent patients with intracerebral hemorrhage.\u003c/p\u003e","manuscriptTitle":"Development and validation of a clinical prediction model for concurrent pulmonary infection in convalescent patients with intracerebral hemorrhage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 20:25:35","doi":"10.21203/rs.3.rs-3981136/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":"45ee9031-0d1d-47ff-9cfb-047bf6b06f4a","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-08T11:16:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 20:25:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3981136","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3981136","identity":"rs-3981136","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}