human
***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: Thirty-four otherwise healthy patients having to undergo elective upper abdominal surgery were r and omly assigned to two equal groups . In the treatment group , constant positive airway pressure ( CPAP ) with an expiratory pressure of 12 cm H2O was applied at one hour following extubation , and at daily intervals for the first five days following surgery for a continuous period of three hours . The control group received no CPAP treatment . All patients were given postoperative physiotherapy . In patients who received postoperative CPAP with an end-expiratory pressure of 12 cm H2O , marked normalization of pulmonary function was noted Postoperative pulmonary complications are associated with substantial morbidity and mortality . It has been estimated that nearly one fourth of deaths occurring within 6 days of surgery are related to postoperative pulmonary complications ( 1 ) . Postoperative infections are also a major source of the morbidity and mortality associated with undergoing surgery . Pneumonia is the most serious postoperative complication that is included in both of these categories . Pneumonia ranks as the third most common postoperative infection , behind urinary tract and wound infection ( 2 ) . According to the National Nosocomial Infection Surveillance system , pneumonia occurred in 18 % of patients after surgery ( 3 ) . Postoperative pneumonia occurs in 9 % to 40 % of patients , and the associated mortality rate is 30 % to 46 % , depending on the type of surgery ( 1 , 4 ) . Previous studies of risk factors used various definitions of postoperative pulmonary complications . Atelectasis ( 1 , 47 ) , postoperative pneumonia ( 12 , 46 , 811 ) , the acute respiratory distress syndrome ( 9 , 12 ) , and postoperative respiratory failure ( 6 , 9 , 11 , 13 ) have been classified as postoperative pulmonary complications . Although the clinical significance of each of these complications varies greatly , they were grouped together as a single outcome in previous studies ( 6 ) . Some studies were limited to examination of risk factors in patients undergoing abdominal or thoracic procedures or in patients with specific medical conditions , such as chronic obstructive pulmonary disease ( 2 , 4 , 6 , 1012 , 14 ) . These studies were often based on a small sample from one institution , and studies of independent sample s did not vali date their findings ( 15 , 16 ) . Although clinicians have used preoperative cardiac risk indexes for more than 20 years ( 17 ) , previous studies have not produced a vali date d risk index that predicts risk for pneumonia ( 18 ) . Our main objective was to develop and vali date a multifactorial risk index for predicting postoperative pneumonia . Preoperative risk factors for postoperative pneumonia were determined in a large multicenter observational cohort of patients undergoing various major noncardiac operations . Specific goals included identification of predictors that are easily obtained and commonly accessible to care providers before surgery . Using these predictors , we developed and vali date d a risk assessment model and scoring system for predicting postoperative pneumonia , analogous to risk assessment models for predicting cardiac complications ( 17 , 19 , 20 ) . Methods Patients were selected from the Department of Veterans Affairs National Surgical Quality Improvement Program ( NSQIP ) . A detailed description of the study methods is published elsewhere and is summarized briefly here ( 13 , 2123 ) . Participating Hospitals Our study includes patients enrolled between 1 September 1995 and 31 August 1999 at 100 Veterans Affairs medical centers that perform major surgery . Patients enrolled at 97 medical centers between 1 September 1997 and 31 August 1999 were used to develop the model , and patients enrolled at 100 medical centers between 1 September 1995 and 31 August 1997 were used to vali date the model . Selection of Patients All noncardiac operations performed under general , spinal , epidural , local , or monitored anesthesia were eligible for inclusion as index operations . All eligible operations were included at low-volume hospitals ( < 140 eligible operations per month ) . At 5 high-volume hospitals ( 140 eligible operations per month ) , the first 36 consecutive eligible operations were entered in each consecutive 8-day period , beginning with a different day each period . On the basis of a review of operative mortality rates from the Veterans Affairs administrative discharge data base for 1988 and 1989 , selected operations with very low mortality rates were excluded from the NSQIP . Major transplantation procedures and patients entered into the study within the previous 30 days were also excluded . Patients were defined as having postoperative pneumonia if they met the Centers for Disease Control and Prevention definition of nosocomial pneumonia after surgery ( Table 1 ) ( 24 ) . We excluded patients who were ventilator dependent before surgery and those who had preoperative pneumonia . Patients who experienced postoperative respiratory failure or unplanned intubation before postoperative pneumonia was diagnosed were also excluded . Table 1 . Definition of Postoperative Pneumonia Data Collection A surgical risk- assessment nurse was assigned at each center to collect the data . All variables were defined before data collection ( 21 ) . The nurses completed in-depth training on all study definitions and conduct of the protocol . Annual meetings and regular conference calls were conducted , and two traveling nurse coordinators performed site visits to maintain data reliability . Generic preoperative , intraoperative , and postoperative variables were chosen on the basis of clinical relevance , reliability of data collection , and availability of data . Preoperative data were obtained by the study nurse from the medical chart or a surgical risk- assessment profile that was completed by the surgical resident caring for the patient and later verified by the study nurse from the medical record . Preoperative laboratory values within 30 days of the index operation and closest to the time of operation were acquired automatically from the laboratory software in each center 's computer system . Intraoperative variables were collected from the operative log and anesthesia record for the index operation . The index operation was defined as the first eligible operation performed on the patient . Other operations performed under the same anesthetic by the same or different surgical team were also recorded . The type of surgery was primarily classified by the anatomic location of the surgical incision ( 13 ) . On the 30th postoperative day , the study nurse obtained outcome information by chart review , interviews with care providers , reports from morbidity and mortality conferences , and communication with each patient by letter or by telephone .  Postoperative mortality was defined as death from any cause inside or outside the hospital within 30 days of surgery . The nurse entered all data into a surgical risk assessment module in each center 's computer system . A summary of each patient data record was forwarded to the chief of the surgical service 30 days after surgery for inspection . No later than 45 days after surgery , the patient records were transmitted automatically to the statistical coordinating center for editing and analysis . Missing Data Data were more than 92 % complete , except for preoperative laboratory variables ; the completeness of these depended on whether the specific laboratory tests had been ordered . Patients with missing data for tests that were not ordered more than 8 % of the time were excluded from the development and validation data sets . Twelve percent of blood urea nitrogen and 9 % of creatinine values were missing ; thus , patients for whom these values were missing were excluded from the development and validation data sets . Statistical Analysis Data were analyzed by using two software packages ( SAS for UNIX [ SAS Institute , Inc. , Cary , North Carolina ] and Stata statistical software , version 7.0 for Windows [ Stata Corp. , College Station , Texas ] ) . Using patients from the development set ( enrolled between 1 September 1997 and 31 August 1999 ) , we used the chi-square test for categorical variables and the Student t-test for continuous variables to test the relationship between each potential risk factor and postoperative pneumonia . All potential risk factors were entered into a logistic regression model with postoperative pneumonia as the dependent variable and the potential risk factors as the independent variables . Blood urea nitrogen was categorized into ranges suggested by its U-shaped relationship to postoperative pneumonia in univariate analyses . Indicator variables created for each range were included in the logistic regression model . Potential risk factors that were not statistically significant ( P < 0.05 ) were sequentially deleted from the full model . Excluded variables were reintroduced at various stages of model development until only statistically significant risk factors remained . Two-way interaction between recent smoking and history of chronic obstructive pulmonary disease was analyzed and found to be statistically nonsignificant . Robust variance estimates were calculated by using the HuberWhite method to account for the effect of clustering of patients within hospitals ( 25 ) . Development of a Scoring System By using the methods of Le Gall and colleagues ( 26 ) , point values were assigned to each risk factor by multiplying the -coefficients from the logistic regression model by 10 and rounding off to the nearest integer . The point total for each patient was design ated the postoperative pneumonia risk index and was used in a multiple logistic regression equation design ed to convert the risk index to the probability of developing postoperative pneumonia . Because the risk index scores were highly skewed , a shrinking power transformation , ln ( postoperative pneumonia risk index score + 25 ) , where ln indicates the natural logarithm , was incorporated into the model for improved calibration ( 26 ) . Evaluation of Model Performance Formal goodness-of-fit ( HosmerLemeshow ) tests ( 27 ) were performed by using data on patients from the development cohort , the validation cohort , and 10 r and om sets of five hospitals to assess model calibration . The C-statistic was used to evaluate discrimination in model development and validation ( 28 ) . Results Patient Characteristics and Postoperative Pneumonia Rates Of the 206 434 patients enrolled between 1 September 1997 and 31 August 1999 , 5395 patients ( 2.6 % ) with preoperative pneumonia or dependence on a ventilator were excluded . An additional 729 patients were excluded because STUDY OBJECTIVE To determine how risk factors could be combined to best predict the development of a  postoperative pulmonary complication ( PPC ) following abdominal surgery .  DESIGN Prospect i ve model-building study . Logistic regression models were developed using significant risk factors identified in the univariate analysis . SETTING Four midwestern hospitals . PATIENTS Convenience sample of 400 patients who underwent abdominal surgical procedures between January 1993 and August 1995 . MEASUREMENTS AND RESULTS Multi criteria outcome for postoperative pulmonary complication used to collectively assess atelectasis and pneumonia . Twenty-three risk factors were assessed . Six risk factors were identified as independent by logistic regression : age > or = 60 years ( adjusted odds ratio [ Adj OR ] , 1.89 ) ; impaired preoperative cognitive function ( Adj OR , 5.93 ) ; smoking history within the past 8 weeks ( Adj OR , 2.27 ) ; body mass index > or = 27 ( Adj OR , 2.82 ) ; history of cancer ( Adj OR , 2.23 ) ; and incision site-upper abdominal or both upper/lower abdominal incision ( Adj OR 2.30 ) . CONCLUSIONS These results provide a framework for identifying patients at risk of developing a  PPC following abdominal surgery .  A reliable and valid risk index could be used clinical ly to guide preoperative and postoperative pulmonary care and target limited re sources for patients at risk OBJECTIVE : Internists frequently evaluate preoperative cardiopulmonary risk and comanage cardiac and pulmonary complications , but the comparative incidence and clinical importance of these complications are not clearly delineated . This study evaluated incidence and length of stay for both cardiac and pulmonary complications after  elective laparotomy .  DESIGN : Nested case-control . SETTING : University-affiliated Department of Veterans Affairs Hospital . PATIENTS : Computerized registry of all 2,291 patients undergoing elective abdominal operations from 1982 to 1991.MEASUREMENT AND MAIN RESULTS : Strategy for ascertainment and verification of complications was systematic and explicit . The charts of all 116 patients identified by the registry as having complications and 412 ( 19 % ) r and omly selected from 2,175 remaining patients were review ed to verify presence or absence of cardiac or pulmonary complications , using explicit criteria and independent abstract ion of pre- and postoperative components of charts . From these 528 vali date d cases and controls ( 23 % of the cohort ) , 96 cases and 96 controls were matched by operation type and age within ten years . Hospital and intensive care unit stays were significantly longer ( p<0.0001 ) for the cases than for the controls ( 24.1 vs 10.3 and 5.8 vs 1.5 days , respectively ) . All 19 deaths occurred among the cases . Among the cases , pulmonary complications occurred significantly more often than cardiac complications ( p<0.00001 ) and were associated with significantly longer hospital stays ( 22.7 vs 10.4 days , p=0.001 ) . Combined cardiopulmonary complications occurred among 26 % of the cases . Misclassification-corrected incidence rates for the entire cohort were 9.6 % ( 95 % CI 7.2–12.0 ) for pulmonary and 5.7 % ( 95 % CI 3.8–7.7 ) for cardiac complications . CONCLUSIONS : For noncardiac surgery , previous research has focused on cardiac risk . In this study , pulmonary complications were more frequent , were associated with longer hospital stay , and occurred in combination with cardiac complications in a substantial proportion of cases . These results suggest that further research is needed to fully characterize the clinical epidemiology of postoperative card 
    Output:
    
agent
***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: Thirty-four otherwise healthy patients having to undergo elective upper abdominal surgery were r and omly assigned to two equal groups . In the treatment group , constant positive airway pressure ( CPAP ) with an expiratory pressure of 12 cm H2O was applied at one hour following extubation , and at daily intervals for the first five days following surgery for a continuous period of three hours . The control group received no CPAP treatment . All patients were given postoperative physiotherapy . In patients who received postoperative CPAP with an end-expiratory pressure of 12 cm H2O , marked normalization of pulmonary function was noted Postoperative pulmonary complications are associated with substantial morbidity and mortality . It has been estimated that nearly one fourth of deaths occurring within 6 days of surgery are related to postoperative pulmonary complications ( 1 ) . Postoperative infections are also a major source of the morbidity and mortality associated with undergoing surgery . Pneumonia is the most serious postoperative complication that is included in both of these categories . Pneumonia ranks as the third most common postoperative infection , behind urinary tract and wound infection ( 2 ) . According to the National Nosocomial Infection Surveillance system , pneumonia occurred in 18 % of patients after surgery ( 3 ) . Postoperative pneumonia occurs in 9 % to 40 % of patients , and the associated mortality rate is 30 % to 46 % , depending on the type of surgery ( 1 , 4 ) . Previous studies of risk factors used various definitions of postoperative pulmonary complications . Atelectasis ( 1 , 47 ) , postoperative pneumonia ( 12 , 46 , 811 ) , the acute respiratory distress syndrome ( 9 , 12 ) , and postoperative respiratory failure ( 6 , 9 , 11 , 13 ) have been classified as postoperative pulmonary complications . Although the clinical significance of each of these complications varies greatly , they were grouped together as a single outcome in previous studies ( 6 ) . Some studies were limited to examination of risk factors in patients undergoing abdominal or thoracic procedures or in patients with specific medical conditions , such as chronic obstructive pulmonary disease ( 2 , 4 , 6 , 1012 , 14 ) . These studies were often based on a small sample from one institution , and studies of independent sample s did not vali date their findings ( 15 , 16 ) . Although clinicians have used preoperative cardiac risk indexes for more than 20 years ( 17 ) , previous studies have not produced a vali date d risk index that predicts risk for pneumonia ( 18 ) . Our main objective was to develop and vali date a multifactorial risk index for predicting postoperative pneumonia . Preoperative risk factors for postoperative pneumonia were determined in a large multicenter observational cohort of patients undergoing various major noncardiac operations . Specific goals included identification of predictors that are easily obtained and commonly accessible to care providers before surgery . Using these predictors , we developed and vali date d a risk assessment model and scoring system for predicting postoperative pneumonia , analogous to risk assessment models for predicting cardiac complications ( 17 , 19 , 20 ) . Methods Patients were selected from the Department of Veterans Affairs National Surgical Quality Improvement Program ( NSQIP ) . A detailed description of the study methods is published elsewhere and is summarized briefly here ( 13 , 2123 ) . Participating Hospitals Our study includes patients enrolled between 1 September 1995 and 31 August 1999 at 100 Veterans Affairs medical centers that perform major surgery . Patients enrolled at 97 medical centers between 1 September 1997 and 31 August 1999 were used to develop the model , and patients enrolled at 100 medical centers between 1 September 1995 and 31 August 1997 were used to vali date the model . Selection of Patients All noncardiac operations performed under general , spinal , epidural , local , or monitored anesthesia were eligible for inclusion as index operations . All eligible operations were included at low-volume hospitals ( < 140 eligible operations per month ) . At 5 high-volume hospitals ( 140 eligible operations per month ) , the first 36 consecutive eligible operations were entered in each consecutive 8-day period , beginning with a different day each period . On the basis of a review of operative mortality rates from the Veterans Affairs administrative discharge data base for 1988 and 1989 , selected operations with very low mortality rates were excluded from the NSQIP . Major transplantation procedures and patients entered into the study within the previous 30 days were also excluded . Patients were defined as having postoperative pneumonia if they met the Centers for Disease Control and Prevention definition of nosocomial pneumonia after surgery ( Table 1 ) ( 24 ) . We excluded patients who were ventilator dependent before surgery and those who had preoperative pneumonia . Patients who experienced postoperative respiratory failure or unplanned intubation before postoperative pneumonia was diagnosed were also excluded . Table 1 . Definition of Postoperative Pneumonia Data Collection A surgical risk- assessment nurse was assigned at each center to collect the data . All variables were defined before data collection ( 21 ) . The nurses completed in-depth training on all study definitions and conduct of the protocol . Annual meetings and regular conference calls were conducted , and two traveling nurse coordinators performed site visits to maintain data reliability . Generic preoperative , intraoperative , and postoperative variables were chosen on the basis of clinical relevance , reliability of data collection , and availability of data . Preoperative data were obtained by the study nurse from the medical chart or a surgical risk- assessment profile that was completed by the surgical resident caring for the patient and later verified by the study nurse from the medical record . Preoperative laboratory values within 30 days of the index operation and closest to the time of operation were acquired automatically from the laboratory software in each center 's computer system . Intraoperative variables were collected from the operative log and anesthesia record for the index operation . The index operation was defined as the first eligible operation performed on the patient . Other operations performed under the same anesthetic by the same or different surgical team were also recorded . The type of surgery was primarily classified by the anatomic location of the surgical incision ( 13 ) . On the 30th postoperative day , the study nurse obtained outcome information by chart review , interviews with care providers , reports from morbidity and mortality conferences , and communication with each patient by letter or by telephone .  Postoperative mortality was defined as death from any cause inside or outside the hospital within 30 days of surgery . The nurse entered all data into a surgical risk assessment module in each center 's computer system . A summary of each patient data record was forwarded to the chief of the surgical service 30 days after surgery for inspection . No later than 45 days after surgery , the patient records were transmitted automatically to the statistical coordinating center for editing and analysis . Missing Data Data were more than 92 % complete , except for preoperative laboratory variables ; the completeness of these depended on whether the specific laboratory tests had been ordered . Patients with missing data for tests that were not ordered more than 8 % of the time were excluded from the development and validation data sets . Twelve percent of blood urea nitrogen and 9 % of creatinine values were missing ; thus , patients for whom these values were missing were excluded from the development and validation data sets . Statistical Analysis Data were analyzed by using two software packages ( SAS for UNIX [ SAS Institute , Inc. , Cary , North Carolina ] and Stata statistical software , version 7.0 for Windows [ Stata Corp. , College Station , Texas ] ) . Using patients from the development set ( enrolled between 1 September 1997 and 31 August 1999 ) , we used the chi-square test for categorical variables and the Student t-test for continuous variables to test the relationship between each potential risk factor and postoperative pneumonia . All potential risk factors were entered into a logistic regression model with postoperative pneumonia as the dependent variable and the potential risk factors as the independent variables . Blood urea nitrogen was categorized into ranges suggested by its U-shaped relationship to postoperative pneumonia in univariate analyses . Indicator variables created for each range were included in the logistic regression model . Potential risk factors that were not statistically significant ( P < 0.05 ) were sequentially deleted from the full model . Excluded variables were reintroduced at various stages of model development until only statistically significant risk factors remained . Two-way interaction between recent smoking and history of chronic obstructive pulmonary disease was analyzed and found to be statistically nonsignificant . Robust variance estimates were calculated by using the HuberWhite method to account for the effect of clustering of patients within hospitals ( 25 ) . Development of a Scoring System By using the methods of Le Gall and colleagues ( 26 ) , point values were assigned to each risk factor by multiplying the -coefficients from the logistic regression model by 10 and rounding off to the nearest integer . The point total for each patient was design ated the postoperative pneumonia risk index and was used in a multiple logistic regression equation design ed to convert the risk index to the probability of developing postoperative pneumonia . Because the risk index scores were highly skewed , a shrinking power transformation , ln ( postoperative pneumonia risk index score + 25 ) , where ln indicates the natural logarithm , was incorporated into the model for improved calibration ( 26 ) . Evaluation of Model Performance Formal goodness-of-fit ( HosmerLemeshow ) tests ( 27 ) were performed by using data on patients from the development cohort , the validation cohort , and 10 r and om sets of five hospitals to assess model calibration . The C-statistic was used to evaluate discrimination in model development and validation ( 28 ) . Results Patient Characteristics and Postoperative Pneumonia Rates Of the 206 434 patients enrolled between 1 September 1997 and 31 August 1999 , 5395 patients ( 2.6 % ) with preoperative pneumonia or dependence on a ventilator were excluded . An additional 729 patients were excluded because STUDY OBJECTIVE To determine how risk factors could be combined to best predict the development of a  postoperative pulmonary complication ( PPC ) following abdominal surgery .  DESIGN Prospect i ve model-building study . Logistic regression models were developed using significant risk factors identified in the univariate analysis . SETTING Four midwestern hospitals . PATIENTS Convenience sample of 400 patients who underwent abdominal surgical procedures between January 1993 and August 1995 . MEASUREMENTS AND RESULTS Multi criteria outcome for postoperative pulmonary complication used to collectively assess atelectasis and pneumonia . Twenty-three risk factors were assessed . Six risk factors were identified as independent by logistic regression : age > or = 60 years ( adjusted odds ratio [ Adj OR ] , 1.89 ) ; impaired preoperative cognitive function ( Adj OR , 5.93 ) ; smoking history within the past 8 weeks ( Adj OR , 2.27 ) ; body mass index > or = 27 ( Adj OR , 2.82 ) ; history of cancer ( Adj OR , 2.23 ) ; and incision site-upper abdominal or both upper/lower abdominal incision ( Adj OR 2.30 ) . CONCLUSIONS These results provide a framework for identifying patients at risk of developing a  PPC following abdominal surgery .  A reliable and valid risk index could be used clinical ly to guide preoperative and postoperative pulmonary care and target limited re sources for patients at risk OBJECTIVE : Internists frequently evaluate preoperative cardiopulmonary risk and comanage cardiac and pulmonary complications , but the comparative incidence and clinical importance of these complications are not clearly delineated . This study evaluated incidence and length of stay for both cardiac and pulmonary complications after  elective laparotomy .  DESIGN : Nested case-control . SETTING : University-affiliated Department of Veterans Affairs Hospital . PATIENTS : Computerized registry of all 2,291 patients undergoing elective abdominal operations from 1982 to 1991.MEASUREMENT AND MAIN RESULTS : Strategy for ascertainment and verification of complications was systematic and explicit . The charts of all 116 patients identified by the registry as having complications and 412 ( 19 % ) r and omly selected from 2,175 remaining patients were review ed to verify presence or absence of cardiac or pulmonary complications , using explicit criteria and independent abstract ion of pre- and postoperative components of charts . From these 528 vali date d cases and controls ( 23 % of the cohort ) , 96 cases and 96 controls were matched by operation type and age within ten years . Hospital and intensive care unit stays were significantly longer ( p<0.0001 ) for the cases than for the controls ( 24.1 vs 10.3 and 5.8 vs 1.5 days , respectively ) . All 19 deaths occurred among the cases . Among the cases , pulmonary complications occurred significantly more often than cardiac complications ( p<0.00001 ) and were associated with significantly longer hospital stays ( 22.7 vs 10.4 days , p=0.001 ) . Combined cardiopulmonary complications occurred among 26 % of the cases . Misclassification-corrected incidence rates for the entire cohort were 9.6 % ( 95 % CI 7.2–12.0 ) for pulmonary and 5.7 % ( 95 % CI 3.8–7.7 ) for cardiac complications . CONCLUSIONS : For noncardiac surgery , previous research has focused on cardiac risk . In this study , pulmonary complications were more frequent , were associated with longer hospital stay , and occurred in combination with cardiac complications in a substantial proportion of cases . These results suggest that further research is needed to fully characterize the clinical epidemiology of postoperative card 
    Output:
    