survivors are often 
 chronically impaired in their movements and are left with long - term disabilities1 . understanding factors present at baseline 
 that affect outcome and healthcare utilization post - stroke is important . the most useful 
 tool for the indirect evaluation of the functional outcome of and service quality variation 
 for stroke is the average length of stay2 . some of the main variables that can predict the average length of stay are stroke severity , 
 age , comorbidity , education , and depression3 . medical comorbidities are common in patients with spontaneous 
 intracerebral hemorrhage and ischemic stroke4 , 5 . a comorbidity is defined as a clinical 
 condition that exists at the time of the onset of the event which is likely to influence the 
 outcome under study6 . the charlson 
 comorbidity index ( cci ) accounts for multiple comorbidities by creating a sum score , 
 weighted according to the presence of various conditions , and is commonly used in outcome 
 and mortality studies . although it may seem intuitive that comorbid conditions would 
 influence outcome and mortality after stroke , few studies have , in fact , evaluated the 
 independent impact of such comorbidities7 , 8 . thus , this study was carried out to 
 investigate the association between cci score and functional outcome ( length of stay ) after 
 hemorrhagic and ischemic stroke . data from the korean national hospital discharge in - depth injury survey for 6  years , from 
 2005 to 2010 , were used . the korean national hospital discharge in - depth injury survey was 
 initiated in 2004 by the korean centers for disease control based on the u.s data from ~170 general hospitals with 100 or more 
 beds were surveyed , based on discharge summary data from discharged patients medical 
 records . survey items included information on medical institutions , patient admissions , 
 diseases , and treatments , demographic and geographic information . stroke patient cases were defined as the 21,494 cases with principal diagnosis codes i60 , 
 i61 , i62 , and i63 , according to the international classification of disease , and they were 
 analyzed after dividing the cases into hemorrhagic ( i60 , i61 , and i62 ) and ischemic ( i63 ) 
 stroke cases . the length of stay , a dependent variable , was calculated using the dates of hospitalization 
 and discharge recorded in the korean national hospital discharge in - depth injury survey . independent variables included demographic characteristics , characteristics of medical 
 institutions , medical service utilization characteristics , and cci . medical service 
 utilization characteristics included admission route , physical therapy , brain surgery , and 
 cci , and they were used to reflect the severity of disease . evaluation and comparison of medical effects and medical care outcomes should adjust 
 patients comorbidity risks systematically together with the severity of the main diagnosis 
 for qualitative differences in medical care and proper outcome evaluation . a weight , from one to six 
 points , is provided for 19 diseases , and the sum of the weights is adjusted . generally , the 
 method provides evaluations by categorizing the score into zero , one , two , and  3 . for statistical analyses , the sas software ( ver . frequency analysis was 
 conducted to examine comorbidity distribution in the subjects , and the t - test and analysis 
 of variance ( anova ) were carried out to compare average differences in the lengths of stay 
 by general characteristics , in accordance with cci . multiple regression analysis was carried 
 out using dummy variables to assess factors affecting stroke patients length of stay . to 
 examine the effects of cci in more detail , model 1 , including variables of general 
 characteristic factors only , and model 2 , including cci and general characteristic factors , a multiple regression analysis using dummy variables was carried out to 
 examine factors affecting stroke patients length of stay . equal variance and the normality 
 of the length of stay , the dependent variable , were reviewed and found to be non - normally 
 distributed . thus , the lengths of stay were log - transformed before being used in the 
 analysis . the distribution of each comorbidity in 6,925 hemorrhagic stroke patients and 14,569 
 ischemic stroke patients was investigated . according to the analysis of hemorrhagic stroke 
 patients , cerebrovascular disease accounted for the largest proportion among the 
 comorbidities , followed by diabetes with no complications at 10.1% and hemiplegia at 9.1% . 
 in ischemic stroke patients , diabetes with no complications comprised 24.2% of 
 comorbidities , followed by cerebrovascular disease at 14.3% and hemiplegia at 8.2% ( table n , % ) conditioncci weighthemorrhagic ( i60-i62 ) ( n=6,925)ischemic ( i63 ) ( n=14,569)yesnoyesnomyocardial infarction130 ( 0.4)6,895 ( 99.6)102 ( 0.7)14,467 ( 99.3)congestive heart failure183 ( 1.2)6,842 ( 98.8)510 ( 3.5)14,059 ( 96.5)peripheral vascular disease144 ( 0.6)6,881 ( 99.4)144 ( 1.0)14,425 ( 99.0)cerebrovascular disease12,166 ( 31.3)4,759 ( 68.7)2,089 ( 14.3)12,480 ( 85.7)dementia184 ( 1.2)6,841 ( 98.8)333 ( 2.3)14,236 ( 97.7)chronic pulmonary disease160 ( 0.9)6,865 ( 99.1)240 ( 1.6)14,329 ( 98.4)connective tissue disease113 ( 0.2)6,912 ( 99.8)37 ( 0.3)14,532 ( 99.7)ulcer176 ( 1.1)6,849 ( 98.9)199 ( 1.4)14,370 ( 98.6)mild liver disease1186 ( 2.7)6,739 ( 97.3)262 ( 1.8)14,307 ( 98.2)diabetes1700 ( 10.1)6,225 ( 89.9)3,530 ( 24.2)11,039 ( 75.8)diabetes with end - organ damage263 ( 0.9)6,862 ( 99.1)391 ( 2.7)14,178 ( 97.3)hemiplegia2632 ( 9.1)6,293 ( 90.9)1,192 ( 8.2)13,377 ( 91.8)moderate or severe renal disease297 ( 1.4)6,828 ( 98.6)215 ( 1.5)14,354 ( 98.5)leukemia , lymphoma , any tumor293 ( 1.3)6,832 ( 98.7)250 ( 1.7)14,319 ( 98.3)moderate or severe liver disease315 ( 0.2)6,910 ( 99.8)13 ( 0.1)14,556 ( 99.9)metastatic solid tumor618 ( 0.3)6,907 ( 99.7)59 ( 0.4)14,510 ( 99.6)acquired immune deficiency syndrome60 ( 0.0)6,925 ( 100.0)1 ( 0.0)14,568 ( 100.0)cci : charlson comorbidity index ) . cci : charlson comorbidity index to examine differences in the lengths of stay by general characteristics according to 
 hemorrhagic stroke patients cci , the t - test and anova were performed . the length of stay of 
 those aged between 50 and 64  years among hemorrhagic stroke patients was longest , and the 
 type of insurance with the longest length of stay was other insurance , followed by 
 medicaid and national health insurance . regarding the number of beds , when physical therapy was conducted , brain 
 surgery was performed , or when patients died , the lengths of stay were longer . the lengths 
 of stay were statistically significantly different according to hemorrhagic stroke patients 
 age , type of insurance , number of hospital beds , physical therapy , brain surgery , and death 
 ( table 2table 2.differences in the lengths of stay by general characteristics according to 
 hemorrhagic stroke patients ccivariablescci=0meansdcci=1meansdcci=2meansdcci3meansdtotalmeansdgendermale24.439.728.447.846.373.665.381.932.354.1female25.364.330.852.947.978.554.858.732.464.0age<5023.641.327.941.551.265.274.476.232.050.7506427.259.336.672.444.962.256.772.735.265.4657424.553.726.930.349.691.861.478.432.459.67523.059.123.531.041.692.045.048.727.058.1insurance typehealth insurance22.647.226.638.042.864.954.666.629.150.9medicaid37.571.050.0101.058.477.987.877.650.084.2others61.8124.650.867.7109.4183.6115.6128.774.9132.9residence urban sizemetropolitan24.744.231.358.346.768.156.474.332.456.1urban25.156.330.448.151.992.662.466.233.462.5rural24.566.623.328.835.439.364.882.229.058.4bed size10029931.163.129.451.652.480.359.968.834.863.830049935.385.132.748.061.3111.075.392.142.683.750099922.846.830.553.944.770.160.269.631.556.21,00017.923.423.432.937.644.347.459.024.534.9hospital locationseoul23.549.032.371.546.074.865.472.133.463.1metropolitan24.035.030.748.342.152.647.467.830.145.3gyeonggi23.947.826.732.950.590.060.353.931.754.5others27.674.328.141.152.692.067.983.734.672.3admission routeemergency25.252.329.8849.6747.3571.4960.8569.8932.8457.66ambulatory23.158.528.3456.1145.67101.0556.7082.6129.6067.36physical therapyno22.547.226.947.042.878.455.872.128.855.2yes53.098.160.873.565.762.476.868.562.080.9brain surgeryno22.753.425.247.637.466.047.673.427.056.0yes29.853.537.554.462.087.474.967.242.564.0deathno25.948.532.847.252.572.466.473.234.756.0yes17.182.217.160.421.887.528.954.319.073.7total24.853.529.750.547.176.160.371.832.359.3p<0.05 , p<0.01 ) . to examine differences in ischemic stroke patients lengths of stay by general 
 characteristics according to cci , the t - test and anova were carried out . the type of 
 insurance with the longest length of stay among ischemic stroke patients was the lengths of stay were 
 longest when the patients lived in a large city and when they were admitted to a hospital 
 with 300499 beds . the lengths of stay according to admission route were longer when 
 patients were admitted from an emergency room . the lengths of stay were also longer when 
 patients received physical therapy or brain surgery . the lengths of stay according to 
 ischemic stroke patients type of insurance , size of the city where they resided , the number 
 of beds at the hospital , and receipt of physical therapy and brain surgery were 
 statistically significantly different ( table 
 3table 3.differences in the lengths of stay by general characteristics according to 
 ischemic stroke patients ccivariablescci=0meansdcci=1meansdcci=2meansdcci3meansdtotalmeansdgendermale13.422.915.724.324.0940.4331.0846.3617.0729.46female14.323.616.827.724.12362632.5338.8117.5728.39age<5012.725.414.620.426.744.834.142.616.229.4506413.927.016.227.623.630.734.556.717.531.8657413.117.516.028.323.336.131.238.117.026.87515.024.317.121.724.846.328.634.117.928.6insurance typehealth insurance13.021.015.322.222.832.930.743.416.326.2medicaid19.828.723.444.533.367.834.433.723.941.3others25.178.925.643.231.332.3135.2124.031.671.2residence urban sizemetropolitan14.526.316.525.024.439.130.741.518.130.4urban13.722.916.024.123.431.233.649.817.027.8rural12.816.616.132.025.251.929.027.116.228.6bed size10029917.832.520.128.333.967.435.552.520.838.030049917.428.223.444.321.725.238.371.221.438.650099911.917.514.220.522.229.530.137.615.623.41,00010.814.013.117.323.337.329.525.615.322.1hospital locationseoul12.322.216.123.223.031.534.043.717.528.2metropolitan15.024.815.823.824.147.126.032.617.329.5gyeonggi13.025.615.724.922.733.334.037.416.628.2others14.021.317.130.425.936.133.655.117.429.5admission routeemergency13.821.316.527.025.142.232.045.117.729.6ambulatory13.827.115.622.821.426.630.738.316.427.3physical therapyno12.620.814.924.920.730.729.244.215.426.0yes26.037.728.131.440.161.539.240.731.542.9brain surgeryno13.622.515.724.123.638.229.940.316.827.7yes40.761.466.183.665.549.191.490.161.474.6deathno13.621.716.124.924.539.231.642.217.228.0yes19.649.820.044.316.626.133.264.620.747.4total13.823.216.225.924.138.731.743.517.329.0p<0.05 , p<0.01 ) . to examine factors affecting stroke patients lengths of stay , a multiple regression 
 analysis using dummy variables was conducted . for model 1 , which included general 
 characteristics variables only , significance was accepted for p values < 0.001 , a much 
 lower level than a significance level of 0.05 ; thus , model 1 was a significant regression 
 model . independent variables with significant relationships 
 with the log - transformed lengths of stay in model 1 included the type of insurance , the size 
 of city in which patients resided , the number of beds at the hospital , the location of the 
 medical institution , admission route , receipt of physical therapy , brain surgery , death , and 
 the type of stroke . for model 2 , which included patients general characteristics and cci variables , significance was accepted for p values < 0.001 , a much lower level than a significance 
 level of 0.05 ; thus , this was also a significant regression model . the explanatory power of 
 the model was 21.4% , higher than that of model 1 . independent variables with significant 
 relationships with the log - transformed lengths of stay in model 2 included gender , the type 
 of insurance , the size of city in which the patients resided , the number of beds at the 
 hospital , the location of the medical institution , admission route , receipt of physical 
 therapy , brain surgery , death , the type of stroke , and cci . the length of stay 
 was longest for those residing in large cities , when the number of beds in the hospital was 
 300499 , and when the medical institution was located in a metropolitan city . the lengths of 
 stay were longer when patients were admitted to a hospital from an emergency room than 
 hospitalization through outpatient care . the length of stay was longer when patients 
 received physical therapy or brain surgery , when the patient died , and when the stroke type 
 was hemorrhagic , rather than ischemic . knowledge of variables , including cci , 
 that affect functional outcome after intracerebral hemorrhage and ischemic stroke assist the 
 targeting of programs that can help reduce risk factors and distribute resources for stroke 
 management9 . although using the cci may be questionable because it was developed to predict mortality 
 and morbidity , high comorbidity , as measured by the cci , has also been associated with 
 poorer outcomes after acute stroke7 , 10 . in this study , the cci score was 
 independently associated with the lengths of stay after hemorrhagic and ischemic stroke . the 
 mean length of stay for stroke patients using medical facilities was 5.3 days in the united 
 states , 7 days in canada , and 12.7 days in france11,12,13 . however , the average length of stay in korea is longer than in 
 these other countries . this indicates that our country does not effectively manage the 
 length of stay of stroke patients in medical institutions . reduction of the length of stay 
 is being encouraged at acute hospitals , so physical therapists must provide patients with 
 rehabilitation programs that achieve the desired functional outcome14 , 15 . thus , to develop national standard clinical guidelines and medical care for stroke 
 patients , variables with significant relationships with the lengths of stay , including 
 physical therapy , need to be continuously managed .
[ purpose ] the purpose of this study was to understand factors present at baseline that 
 affect outcome and healthcare utilization post - stroke . 
 we investigated the association 
 between the charlson comorbidity index ( cci ) score and functional outcome ( length of stay ) 
 after hemorrhagic and ischemic stroke . 
 [ subjects and methods ] data from the korean 
 national hospital discharge in - depth injury survey for 6  years , from 2005 to 2010 , were 
 used . the t - test and analysis of variance 
 were carried out to compare average differences 
 in the length of stay with the general characteristics in accordance with cci . 
 multiple 
 regression analysis was carried out using dummy variables to look at factors affecting 
 stroke patients length of stay . 
 [ results ] independent variables with significant 
 relationships with the log - transformed length of stay included gender , type of insurance , 
 the size of city of residence , the number of beds in the hospital , the location of the 
 medical institution , hospitalization path , receipt of physical therapy , treatment 
 involving brain surgery , death , the type of stroke , and cci . 
 [ conclusion ] the results of 
 the present study suggests that cci independently influences the length of stay after 
 ischemic and hemorrhagic stroke and that variables with significant relationships with the 
 log - transformed length of stay need to be continuously managed .