***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: OBJECTIVE To determine the prevalence , correlates and recognition of  depression among in patients of general hospitals in Wuhan , China . METHOD A total of 513 patients were r and omly selected from 1923 in patients from three general hospitals  and evaluated with a Chinese version of the Structured Clinical Interview for Diagnostic and Statistical Manual-IV Axis I disorders by eight psychiatrists . Logistic regression was used to identify factors that were associated with depression . RESULTS The prevalence ( 95 % confidence interval ) of all current depressive disorders and major depressive disorder ( MDD ) was found to be 16.2 % ( 13.0 - 19.4 % ) and 9.4 % ( 6.8 - 11.9 % ) , respectively . The correlates for depression include higher hospital class , divorce/being widowed/separation , low family income , chronic diseases , lack of medical insurance , dwelling in rural area , suffering from severe illness and multiple hospitalization history . None of the patients with current MDD were detected , treated or referred to psychiatric consultation . CONCLUSIONS The prevalence of  depression among in patients of general hospitals in Wuhan , China , was high . None of the depressive patients were recognized or treated for depression , indicating a serious neglect of depression in general hospitals . Our studies suggest an urgent need to improve clinicians ' ability to detect and treat depression BACKGROUND  Rehospitalization  occurs in approximately 20 % of medical in patients within 90 days of discharge . Rehospitalization accounts for considerable morbidity , mortality , and costs . Identification of risk factors could lead to interventions to reduce rehospitalization . The objective of the study was to determine if physical and mental health , substance abuse , and social support are risk factors for rehospitalization . METHOD This was a prospect i ve cohort study in an innercity population conducted from September 2002 to September 2004 . Participants included 144 adult in patients with at least 1 hospital admission in the past 6 months . Measurements included age , length of stay , number of admissions in the past year , and medical comorbidity as well as measures of depression , alcohol and drug abuse , social support , and health-related quality of life . The outcome studied was the rehospitalization status of participants within 90 days of the index hospitalization . RESULTS The mean age of the subjects was 54.8 years ; 48 % were black and 78 % spoke English as a primary language . Subjects were admitted a mean of 2.5 times in the year before the index admission . Sixty-four patients ( 44 % ) were subsequently rehospitalized within 90 days after the index admission . In bivariate analysis , rehospitalized patients had more prior admissions ( median of 3.0 vs. 2.0 admissions , p = .002 ) , greater medical comorbidity ( mean Charlson Comorbidity Index score of 2.6 vs. 2.0 , p = .04 ) , and poorer physical functional status ( mean SF-12 physical component score of 31.5 vs. 36.2 , p = .03 ) . A logistic regression model , including prior admissions in the last year , comorbidity , physical functional status , and depression , showed that depression tripled the odds of rehospitalization ( odds ratio = 3.3 , 95 % CI = 1.2 to 9.3 ) . This model had fair accuracy in identifying patients at greatest risk for rehospitalization ( c statistic = 0.72 ) . CONCLUSIONS Hospitalized patients with a history of prior hospitalization within 6 months who screen positive for depression are 3 times more likely to be rehospitalized within 90 days in this relatively high-risk population . Screening during hospitalization for depressive symptoms may identify those at risk for rehospitalization OBJECTIVES To identify variables associated with mortality in patients admitted to the hospital for acute exacerbation of COPD . DESIGN Prospect i ve cohort study . SETTING Acute-care hospital in Barcelona ( Spain ) . PATIENTS One hundred thirty-five consecutive patients hospitalized for acute exacerbation of COPD , between October 1996 and May 1997 . MEASUREMENTS AND RESULTS Clinical , spirometric , and gasometric variables were evaluated at the time of inclusion in the study . Socioeconomic characteristics , comorbidity , dyspnea , functional status , depression , and quality of life were analyzed . Mortality at 180 days , 1 year , and 2 years was 13.4 % , 22 % , and 35.6 % , respectively . Sixty-four patients ( 47.4 % ) were dead at the end of the study ( median follow-up duration , 838 days ) . Greater mortality was observed in the bivariate analysis among the oldest patients ( p < 0.0001 ) , women ( p < 0.01 ) , and unmarried patients ( p < 0.002 ) . Hospital admission during the previous year ( p < 0.001 ) , functional dependence ( Katz index ) [ p < 0.0004 ] , greater comorbidity ( Charlson index ) [ p < 0.0006 ] , depression ( Yesavage Scale ) [ p < 0.00001 ] ) , quality of life ( St. George 's Respiratory Question naire [ SGRQ ] ) [ p < 0.01 ] , and PCO(2 ) at discharge ( p < 0.03 ) were also among the significant predictors of mortality . In the multivariate analysis , the activity SGRQ subscale ( p < 0.001 ; odds ratio [ OR ] , 2.62 ; confidence interval [ CI ] , 1.43 to 4.78 ) , comorbidity ( p < 0.005 ; OR , 2.2 ; CI , 1.26 to 3.84 ) , depression ( p < 0.004 ; OR , 3.6 ; CI , 1.5 to 8.65 ) , hospital readmission ( p < 0.03 ; OR , 1.85 ; CI , 1.26 to 3.84 ) , and marital status ( p < 0.0002 ; OR , 3.12 ; CI , 1.73 to 5.63 ) were independent predictors of mortality . CONCLUSIONS Quality of life , marital status , depressive symptoms , comorbidity , and prior hospital admission provide relevant information of prognosis in this group of COPD patients Background The results of R and omized Controlled Trials ( RCTs ) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio . These do not constitute the sufficient statistics required for meta- analysis or cost-effectiveness analysis , and their use in secondary analyses requires strong assumptions that may not have been adequately tested . In order to enhance the quality of secondary data analyses , we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated . Methods We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations , using where available information on number of events and numbers at risk . The reproducibility and accuracy of survival probabilities , median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics ( survival probabilities , medians and hazard ratios ) with statistics based on repeated reconstructions by multiple observers . Results The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics . Accuracy was excellent for survival probabilities and medians , for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported . Conclusion The algorithm is a reliable tool for meta- analysis and cost-effectiveness analyses of RCTs reporting time-to-event data . It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves Hospitalization is associated with a long-term increased risk for death , especially in older persons ( 1 - 4 ) . Although the mediators for this increased risk have not been fully eluci date d , depression may play an important role . Because depression is common in hospitalized older patients , an association between depression and mortality in this population would be of significant clinical importance ( 5 - 13 ) . The hypothesis that depression may be a mediator of death in hospitalized patients is supported by studies demonstrating that depressive symptoms are associated with increased mortality in community-dwelling patients and in highly selected groups of hospitalized older patients , such as those with acute myocardial infa rct ion ( 14 - 18 ) . These studies have often been limited by inadequate accounting for the complex interrelations between depressive symptoms and other predictors of death , such as acute physiologic impairment , chronic comorbid illness , functional impairment , and cognitive impairment . Because depressive symptoms are clearly correlated with and may partly be the result of these other factors ( 5 , 19 - 20 ) , improving our underst and ing of the relation between depression and death requires use of st and ardized methods to measure and adjust for confounders . We tested the hypothesis that depressive symptoms are associated with long-term mortality in hospitalized older patients . We demonstrated previously that depressive symptoms are strongly associated with adverse health status outcomes in hospitalized medical patients through 90 days after admission ( 21 ) . However , our initial study found no association between depressive symptoms and mortality during the first 90 days after admission ( 21 ) . In this report , we extend mortality follow-up to 3 years by merging our data with a national mortality data base . Furthermore , we adjusted for st and ard measures of physiologic impairment , comorbid illness , and functional impairment at hospital admission to control for the possibility that higher levels of these confounders in patients with more depressive symptoms affect the association between depressive symptoms and death . Methods Patients Patients were drawn from serial , prospect i ve longitudinal studies of functional change in older hospitalized patients on the general medical service of University Hospitals of Clevel and . The inclusion and exclusion criteria for these studies are described elsewhere ( 3 , 21 , 22 ) . The first study enrolled 206 patients 75 years of age or older who were admitted between March 1990 and July 1990 . The second study , a controlled trial of an intervention to improve functional outcomes , enrolled 651 patients 70 years of age or older who were admitted between November 1990 and March 1992 . The first study , which was a pilot study for the second study , enrolled consecutive patients . The second study r and omly assigned patients to an intervention design ed to improve functional outcomes in older persons or to usual care ( 22 ) . In each study , patients admitted to the intensive care unit , telemetry service , or oncology service were excluded . Data collection procedures in both studies were almost identical . Other than a slightly higher mean patient age in the first study cohort , the demographic , clinical , and functional characteristics of patients in the first study , the control group of the second study , and the intervention group of the second study were similar . Additional analyses that adjusted for whether patients were in the first study cohort or the control group compared with the intervention group of the second study cohort yielded results that were almost identical to the results reported here . Of 857 older patients enrolled in the two studies , 284 were excluded from the current study because they were too ill or confused to be interviewed about depressive symptoms at the time of admission ( n=164 ) , were admitted from nursing homes ( n=38 ) , were not available for interview ( n=37 ) , declined interview ( n=27 ) , or died before being approached ( n=18 ) . We excluded patients admitted from nursing homes because interview data were less consistently obtained from these patients . Thus , the analytic sample for this study comprised 573 patients . Assessment of Depressive Symptoms Within 48 hours of admission , patients were interviewed by using the  15-item Geriatric Depression Scale to assess depressive symptoms over the past week ( 23 , 24 ) . The Geriatric Depression Scale is well suited for use in acutely ill older persons because it focuses on symptoms of depression that are less likely to be directly influenced by somatic illness . Examples of items on this scale include feeling bored , dropping activities and interests , feeling helpless , feeling worthless , feeling that life is empty , feeling that others are better off , preferring to stay at home instead of doing new things , and feeling hopeless . We divided patients into those reporting five or fewer symptoms and those reporting six or more symptoms ; these are commonly recommended cutoffs on the 15-item Geriatric Depression Scale ( 24 - 26 ) . Measurement of Mortality We determined mortality and date of death during the 3 years after hospitalization by merging our files with the National Death Index , a data base of all deaths in the United States generated from state death certificates . Its sensitivity and specificity have been reported to be 98 % and 100 % , respectively ( 27 ) . Measurement of Potential Confounders Shortly after admission , we surveyed each patient 's primary nurse about the patient 's independence in six activities of daily living ( dressing , bathing , grooming , toileting , transferring , and eating ) based on the scale of Katz ( 28 ) . Within 48 hours of admission , we administered to patients the first 21 items of the 30-item Folstein Mini-Mental State Examination ( 29 ) . To minimize respondent burden , we used only the first 21 items . Scores on the 21-item instrument have previously been shown to correlate highly ( r=0.9 ) with scores on the 30-item instrument and to have construct validity on the basis of their strong association with functional outcomes ( 30 ) . Data gathered from medical records included the reason for admission , the components of the Acute Physiology and Chronic Health Evaluation ( APACHE ) II score ( 31 ) , and the components of the weight 
    Output:
    
Depressive symptoms were common in medical in patients and are associated with an increased risk of adverse events postdischarge .