Background Patients with acute exacerbation of chronic obstructive pulmonary disease (COPD) commonly require hospitalization and admission to intensive care unit (ICU). It is useful to identify patients at the time of admission who are likely to have poor outcome. This study was carried out to define the predictors of mortality in patients with acute exacerbation of COPD and to device a scoring system using the baseline physiological variables for prognosticating these patients.
Background
Patients with acute exacerbation of chronic obstructive pulmonary disease (COPD) commonly require hospitalization and admission to intensive care unit (ICU). It is useful to identify patients at the time of admission who are likely to have poor outcome. This study was carried out to define the predictors of mortality in patients with acute exacerbation of COPD and to device a scoring system using the baseline physiological variables for prognosticating these patients.Methods Eighty-two patients with acute respiratory failure secondary to COPD admitted to medical ICU over a one-year period were included. Clinical and demographic profile at the time of admission to ICU including APACHE II score and Glasgow coma scale were recorded at the time of admission to ICU. In addition, acid base disorders, renal functions, liver functions and serum albumin, were recorded at the time of presentation. Primary outcome measure was hospital mortality.
Methods
Eighty-two patients with acute respiratory failure secondary to COPD admitted to medical ICU over a one-year period were included. Clinical and demographic profile at the time of admission to ICU including APACHE II score and Glasgow coma scale were recorded at the time of admission to ICU. In addition, acid base disorders, renal functions, liver functions and serum albumin, were recorded at the time of presentation. Primary outcome measure was hospital mortality.Results Invasive ventilation was required in 69 patients (84.1%). Fifty-two patients survived to hospital discharge (63.4%). APACHE II score at the time of admission to ICU {odds ratio (95 % CI): 1.32 (1.138–1.532); p < 0.001} and serum albumin (done within 24 hours of admission) {odds ratio (95 % CI): 0.114 (0.03-0.432); p = 0.001}. An equation, constructed using the adjusted odds ratio for the two parameters, had an area under the ROC curve of 91.3%. For the choice of cut-off, sensitivity, specificity, positive and negative predictive value for predicting outcome was 90%, 86.5%, 79.4% and 93.7%.
Results
Invasive ventilation was required in 69 patients (84.1%). Fifty-two patients survived to hospital discharge (63.4%). APACHE II score at the time of admission to ICU {odds ratio (95 % CI): 1.32 (1.138–1.532); p < 0.001} and serum albumin (done within 24 hours of admission) {odds ratio (95 % CI): 0.114 (0.03-0.432); p = 0.001}. An equation, constructed using the adjusted odds ratio for the two parameters, had an area under the ROC curve of 91.3%. For the choice of cut-off, sensitivity, specificity, positive and negative predictive value for predicting outcome was 90%, 86.5%, 79.4% and 93.7%.Conclusion APACHE II score at admission and SA levels with in 24 hrs after admission are independent predictors of mortality for patients with COPD admitted to ICU. The equation derived from these two parameters is useful for predicting outcome of these patients.
Conclusion
APACHE II score at admission and SA levels with in 24 hrs after admission are independent predictors of mortality for patients with COPD admitted to ICU. The equation derived from these two parameters is useful for predicting outcome of these patients.

Background Chronic obstructive pulmonary disease (COPD) is characterized by irreversible airway obstruction that leads to chronic disability. Patients with COPD have a longstanding downhill course that is interspersed with episodes of exacerbations requiring hospitalization. COPD is known to be a common disease. There is lack of recent data regarding the burden of this disease from India, with only study on prevalence of COPD published in 1981 [ 1 ]. Data from United States indicate that incidence of disease is on the rise [ 2 ]. During the year 2000, approximately 24 million adults in United States had evidence of obstructive airway disease. COPD was responsible for 1.5 million emergency department visits, 726,000 hospitalizations, and 119,000 deaths [ 2 ]. It is obvious that this disease puts an enormous economic burden on the society. Andersson and coworkers estimated that almost 35-45% of the total per capita health-care costs for COPD are account for by exacerbations alone [ 3 ]. Severe exacerbations requiring hospitalizations are responsible for a large share of these costs and among these, treatment cost for those who require intensive care unit (ICU) admission is highest. In most of the third world countries, large number of ICU beds are occupied by patients with critical illnesses secondary to various infectious diseases, most of which are reversible. It is important to identify patients at the time of admission who are likely to have poor outcome, so that such patients can be managed aggressively. Many prognostic scoring systems have been devised for the same purpose. These scoring systems help to segregate patients who are the sickest and are likely to die from those who are expected to have better outcome and survive. Most of these scoring systems have been devised for a broad range of critically ill patients. The present study was planned to determine the predictors of mortality in patients with exacerbation of COPD admitted to ICU over a one-year period. An attempt was made to develop a scoring system using the predictors of mortality that would help to identify patients at high risk of dying.
Background
Chronic obstructive pulmonary disease (COPD) is characterized by irreversible airway obstruction that leads to chronic disability. Patients with COPD have a longstanding downhill course that is interspersed with episodes of exacerbations requiring hospitalization. COPD is known to be a common disease. There is lack of recent data regarding the burden of this disease from India, with only study on prevalence of COPD published in 1981 [ 1 ]. Data from United States indicate that incidence of disease is on the rise [ 2 ]. During the year 2000, approximately 24 million adults in United States had evidence of obstructive airway disease. COPD was responsible for 1.5 million emergency department visits, 726,000 hospitalizations, and 119,000 deaths [ 2 ]. It is obvious that this disease puts an enormous economic burden on the society. Andersson and coworkers estimated that almost 35-45% of the total per capita health-care costs for COPD are account for by exacerbations alone [ 3 ]. Severe exacerbations requiring hospitalizations are responsible for a large share of these costs and among these, treatment cost for those who require intensive care unit (ICU) admission is highest. In most of the third world countries, large number of ICU beds are occupied by patients with critical illnesses secondary to various infectious diseases, most of which are reversible.It is important to identify patients at the time of admission who are likely to have poor outcome, so that such patients can be managed aggressively. Many prognostic scoring systems have been devised for the same purpose. These scoring systems help to segregate patients who are the sickest and are likely to die from those who are expected to have better outcome and survive. Most of these scoring systems have been devised for a broad range of critically ill patients. The present study was planned to determine the predictors of mortality in patients with exacerbation of COPD admitted to ICU over a one-year period. An attempt was made to develop a scoring system using the predictors of mortality that would help to identify patients at high risk of dying.Methods Prospectively collected data of patients with acute respiratory failure secondary to COPD admitted to medical ICU of All India Institute of Medical sciences, New Delhi, India (a tertiary care center in north India) over a one-year period (January 2002 to December 2002) was reviewed. Diagnosis of COPD was based upon the characteristic findings on history and examination with typical radiographic abnormalities [ 4 ]. Patients admitted to the ICU with COPD but due to any other primary reason such as those with poisoning or acute coronary event were excluded. Similarly, patients in whom the primary cause of respiratory failure was bronchiectasis, bronchial asthma, pulmonary edema or pulmonary embolism were not included. Finally, 82 patients with a primary admission diagnosis of acute respiratory failure secondary to COPD were included. All patients were documented cases with prior pulmonary function test confirmation of irreversible airway obstruction and had been receiving a combination of various bronchodilators. Management of the patients was the primary responsibility of the ICU team. A treatment strategy was individualized for each patient and was the sole prerogative of the treating physician. All patients received regular nebulized bronchodilators including salbutamol (as frequently as 5 mg every 15 minutes to every 8 hours), ipratropium bromide (as frequently as 0.5 mg every 15 minutes to 0.25 mg every 8 hours), and intravenous corticosteroids. Most patients also received antibiotics (n = 75, 91.5%). Oxygen therapy (2-3 lt/min) was administered to spontaneously breathing patients. The decision to institute ventilatory support was taken by the treating physician. Wherever feasible non-invasive ventilation (NIV) was used as the initial strategy. Endotracheal intubation was done for usual indications such as respiratory arrest, deteriorating level of consciousness, rising PaCO 2  despite maximal pharmacological treatment and deteriorating acidemia. Initiation of weaning from mechanical ventilation was considered as soon as the patients were considered capable of breathing spontaneously. Method of weaning trials included t-piece trials, gradual reduction of synchronized intermittent mandatory ventilation (SIMV) breaths and pressure support ventilation (PSV). Clinical and demographic profile at the time of admission to ICU including age, sex, smoking status, history of previous hospital admissions, history of previous intubation and/or ventilatory support, prior evidence of cor pulmonale with or without congestive heart failure were recorded. Findings on clinical examination including heart rate, respiratory rate and mean blood pressure were recorded. Acute physiology and chronic health evaluation II (APACHE II) score and Glasgow coma scale (GCS) were recorded at the time of admission to the ICU. Acid-base abnormalities at the time of presentation were analyzed by recording the arterial blood gas analysis and serum electrolytes (estimations done on AVL 995S). Renal functions, liver functions and serum albumin (SA) done at the time of admission were also recorded. Development of complications during mechanical ventilator such as pneumothorax and ventilator associated pneumonia (VAP) were recorded. Development of acute respiratory distress syndrome (ARDS), sepsis and multi-organ failure was also documented. ARDS was defined as presence of bilateral pulmonary infiltrates on chest radiograph in presence of hypoxemia with PaO 2  / FiO 2  ratio less than 200 without any evidence of left atrial hypertension (American-European Consensus Conference) [ 5 ]. Sepsis was defined as the presence of a clinically identified site of infection ( eg , pneumonia) and two or more of the following: temperature > 38°C or < 36°C; heart rate > 90 beats/min; respiratory rate > 20 breaths/min or PaCO 2  < 32 mm Hg; and WBC count > 12 × 10 9 /L, < 4.0 × 10 9 /L, or > 0.10 immature forms ( ie , bands) (American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference) [ 6 ]. Days on ventilator, days of ICU stay and days of hospital stay were recorded for all the patients. Primary outcome measure was hospital mortality. Statistical analysis Data were double entered to minimize errors and managed on an 'Excel' master sheet. Analysis was done using the statistical software ' SPSS version 10.0 ' (SPPS Corp, Chicago, IL, USA). Descriptive analysis consisted of mean with standard deviation and range for various parameters. Study group was split on the basis of final outcome. Various parameters were compared between the two groups to identify the predictors of mortality. Continuous variables were analyzed using student's t-test whereas Fisher's exact test was used to compare the ordinal variables. Baseline parameters significant on univariate analysis at p < 0.1 were identified as potential predictor variables. These parameters were evaluated using multivariate logistic regression analysis (backward stepwise method) to determine independent predictors of mortality. An equation was constructed using the independent predictors based on the adjusted odds ratios and a diagnostic rule was defined. To evaluate the predictive capability of the variables and the equation, receiver-operator characteristic (ROC) curves were constructed with sensitivity (on X-axis) and 1-specificity (on Y-axis) for various cut-offs. Significance was considered at p < 0.05 (only two tailed) for the present study.
Methods
Prospectively collected data of patients with acute respiratory failure secondary to COPD admitted to medical ICU of All India Institute of Medical sciences, New Delhi, India (a tertiary care center in north India) over a one-year period (January 2002 to December 2002) was reviewed. Diagnosis of COPD was based upon the characteristic findings on history and examination with typical radiographic abnormalities [ 4 ]. Patients admitted to the ICU with COPD but due to any other primary reason such as those with poisoning or acute coronary event were excluded. Similarly, patients in whom the primary cause of respiratory failure was bronchiectasis, bronchial asthma, pulmonary edema or pulmonary embolism were not included. Finally, 82 patients with a primary admission diagnosis of acute respiratory failure secondary to COPD were included. All patients were documented cases with prior pulmonary function test confirmation of irreversible airway obstruction and had been receiving a combination of various bronchodilators.Management of the patients was the primary responsibility of the ICU team. A treatment strategy was individualized for each patient and was the sole prerogative of the treating physician. All patients received regular nebulized bronchodilators including salbutamol (as frequently as 5 mg every 15 minutes to every 8 hours), ipratropium bromide (as frequently as 0.5 mg every 15 minutes to 0.25 mg every 8 hours), and intravenous corticosteroids. Most patients also received antibiotics (n = 75, 91.5%). Oxygen therapy (2-3 lt/min) was administered to spontaneously breathing patients. The decision to institute ventilatory support was taken by the treating physician. Wherever feasible non-invasive ventilation (NIV) was used as the initial strategy. Endotracheal intubation was done for usual indications such as respiratory arrest, deteriorating level of consciousness, rising PaCO 2  despite maximal pharmacological treatment and deteriorating acidemia. Initiation of weaning from mechanical ventilation was considered as soon as the patients were considered capable of breathing spontaneously. Method of weaning trials included t-piece trials, gradual reduction of synchronized intermittent mandatory ventilation (SIMV) breaths and pressure support ventilation (PSV).Clinical and demographic profile at the time of admission to ICU including age, sex, smoking status, history of previous hospital admissions, history of previous intubation and/or ventilatory support, prior evidence of cor pulmonale with or without congestive heart failure were recorded. Findings on clinical examination including heart rate, respiratory rate and mean blood pressure were recorded. Acute physiology and chronic health evaluation II (APACHE II) score and Glasgow coma scale (GCS) were recorded at the time of admission to the ICU. Acid-base abnormalities at the time of presentation were analyzed by recording the arterial blood gas analysis and serum electrolytes (estimations done on AVL 995S). Renal functions, liver functions and serum albumin (SA) done at the time of admission were also recorded. Development of complications during mechanical ventilator such as pneumothorax and ventilator associated pneumonia (VAP) were recorded. Development of acute respiratory distress syndrome (ARDS), sepsis and multi-organ failure was also documented. ARDS was defined as presence of bilateral pulmonary infiltrates on chest radiograph in presence of hypoxemia with PaO 2  / FiO 2  ratio less than 200 without any evidence of left atrial hypertension (American-European Consensus Conference) [ 5 ]. Sepsis was defined as the presence of a clinically identified site of infection ( eg , pneumonia) and two or more of the following: temperature > 38°C or < 36°C; heart rate > 90 beats/min; respiratory rate > 20 breaths/min or PaCO 2  < 32 mm Hg; and WBC count > 12 × 10 9 /L, < 4.0 × 10 9 /L, or > 0.10 immature forms ( ie , bands) (American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference) [ 6 ]. Days on ventilator, days of ICU stay and days of hospital stay were recorded for all the patients. Primary outcome measure was hospital mortality.egieStatistical analysis Data were double entered to minimize errors and managed on an 'Excel' master sheet. Analysis was done using the statistical software ' SPSS version 10.0 ' (SPPS Corp, Chicago, IL, USA). Descriptive analysis consisted of mean with standard deviation and range for various parameters. Study group was split on the basis of final outcome. Various parameters were compared between the two groups to identify the predictors of mortality. Continuous variables were analyzed using student's t-test whereas Fisher's exact test was used to compare the ordinal variables. Baseline parameters significant on univariate analysis at p < 0.1 were identified as potential predictor variables. These parameters were evaluated using multivariate logistic regression analysis (backward stepwise method) to determine independent predictors of mortality. An equation was constructed using the independent predictors based on the adjusted odds ratios and a diagnostic rule was defined. To evaluate the predictive capability of the variables and the equation, receiver-operator characteristic (ROC) curves were constructed with sensitivity (on X-axis) and 1-specificity (on Y-axis) for various cut-offs. Significance was considered at p < 0.05 (only two tailed) for the present study.
Statistical analysis
Data were double entered to minimize errors and managed on an 'Excel' master sheet. Analysis was done using the statistical software ' SPSS version 10.0 ' (SPPS Corp, Chicago, IL, USA). Descriptive analysis consisted of mean with standard deviation and range for various parameters. Study group was split on the basis of final outcome. Various parameters were compared between the two groups to identify the predictors of mortality. Continuous variables were analyzed using student's t-test whereas Fisher's exact test was used to compare the ordinal variables. Baseline parameters significant on univariate analysis at p < 0.1 were identified as potential predictor variables. These parameters were evaluated using multivariate logistic regression analysis (backward stepwise method) to determine independent predictors of mortality. An equation was constructed using the independent predictors based on the adjusted odds ratios and a diagnostic rule was defined. To evaluate the predictive capability of the variables and the equation, receiver-operator characteristic (ROC) curves were constructed with sensitivity (on X-axis) and 1-specificity (on Y-axis) for various cut-offs. Significance was considered at p < 0.05 (only two tailed) for the present study.SPSS version 10.0Results Baseline characteristics Demographic and baseline clinical and laboratory profile of the study group are presented in Table  1 . Almost all patients had type II respiratory failure (n = 74, 90.2%) and showed acute on chronic respiratory acidosis. Study cohort mostly consisted of critically ill patients as suggested by a high mean APACHE II score. History of smoking could be elicited in 65 patients (79.3%). A significant number of patients had history of previous hospitalization as well as intubation (39% and 18.3% respectively). Almost 55% of the patients (n = 45) had evidence of underlying cor pulmonale. Fifteen patients (18.3%) had underlying diabetes mellitus whereas 12 patients (14.6%) were on treatment for hypertension. None of the patients suffered from any other co-morbid condition. An attempt was made to define the cause of exacerbation for all patients. There was evidence of pneumonia in 67% (n = 55) of patients whereas pneumothorax was responsible for decompensation in 3 patients (3.7%). No obvious cause could be found in 24 patients (29%). Only one patient had evidence of sepsis, but none had ARDS at the time of admission to the ICU. Table 1 Descriptive profile of the study group (n = 82) Minimum Maximum Mean ± Std. Deviation Age (years) 35 85 60 ± 10 APACHE II score 3 33 13 ± 6 PR (per minute) 46 166 105 ± 19 RR (per minute) 10 46 27 ± 10 MBP (mmHg) 20 126 89 ± 19 GCS 3 15 12.1 ± 3 pH 6.87 7.44 7.25 ± 0.19 PaCO 2  (mmHg) 40.7 130.7 76.6 ± 23.5 PO 2  (mmHg) 31.5 142.3 83.9 ± 41.7 HCO 3  (mmHg) 5.4 55.4 32.3 ± 8.7 Serum Na (mEq/L) 115 152 136 ± 7 Serum K (mEq/L) 2.00 6.80 4.2 ± 0.9 Serum Albumin (gm%) 1.7 4.4 3.2 ± 0.7 Days on ventilator 1 33 8.7 ± 4.6 Days of ICU stay 1 35 9.6 ± 6.2 Days of hospital stay 1 63 16.3 ± 10.4 RR: Respiratory rate, PR: Pulse rate, MBP: Mean blood pressure, GCS: Glasgow coma scale, Serum Na: Serum sodium, Serum K: Serum potassium. Hospital course Non Invasive Ventilation (NIV) was used as initial strategy in 17 patients (20.7%). This strategy had a success rate of 59% (n = 10). Sixty-nine patients (84.1%) received invasive ventilation (including seven patients who failed NIV and had to be intubated). Sepsis developed in 11 patients (13.4%) and all these patients eventually died. Parameters associated with development of sepsis were high APACHE II score (18 vs. 12, p = 0.005) and low SA (2.6 gm/dL% vs. 3.3 gm/dL, p < 0.001). VAP developed in 6 patients (8.7%) and was associated with an increased stay in the ICU (18 days vs. 10 days, p = 0.021) as well as increased stay in the hospital (30 days vs. 15 days, p = 0.005). Outcome was not significantly affected by development of VAP (50% versus 42.8%). Outcome Hospital mortality was 36.6% (n = 30). Various parameters were compared for survivors and non-survivors (table  2 ). In addition to demographic characteristics (age and sex), presence of cor pulmonale and cause of exacerbation of COPD, baseline parameters significantly different between the two groups on univariate analysis were included in a multivariate equation. APACHE II score at admission to the ICU {odds ratio (95 % CI): 1.32 (1.138-1.532); p < 0.001} and SA (done within 24 hours of admission) {odds ratio (95 % CI): 0.114 (0.03-0.432); p = 0.001} emerged as the independent predictors of mortality. ROC curve showed that both these variables have good predictive capability with area under the ROC curve (AUC) of 86.9% for APACHE II score (Figure  1 ) and 82.2% for SA (Figure  2 ). Best cut-off, taken as the value on the ROC curve at the point where curve sharply angulated, was 13.5 for APACHE II score and that for SA was 3.05 gm/dL. Following equation was determined by combining the two variables using the adjusted odd ratio: Score = (0.278 × APACHE II score) - (2.17 × SA), where APACHE II score is the score at the time of admission and SA (gm/dL) is the level with in the first 24 hours. ROC curve for this equation showed an AUC value of 91.2% (Figure  3 ). We chose a cut-off of -2.97 for the equation. That is, a patient with a score above -2.97 is likely to die whereas the one with below -2.97 likely to survive. This diagnostic rule had a specificity of 86.5% with a sensitivity of 90%. Positive predictive value for this variable was 79.4% whereas negative predictive value was 93.7%. A cut-off of -0.45 was 100% specific for hospital mortality but sensitivity was only 40%. On the other hand a cut-off of -5.5 gave a sensitivity of 100% with specificity of 33%. Table 2 Predictors of mortality for patients with exacerbation of COPD Parameter Survivors (n = 52) Non-survivors (n = 30) p value Mean ± SD Mean ± SD APACHE II score 10.6 ± 4.3 17.5 ± 5.7 0.001 GCS 12.8 ± 2.1 10.8 ± 3.7 0.003 MBP (mmHg) 93 ± 13.6 82.5 ± 24.7 0.015 PR (per minute) 110.7 ± 16.9 102.2 ± 21.1 0.049 Serum Albumin (gm%) 3.5 ± 0.5 2.7 ± 0.6 0.001 PaCO 2  (mmHg) 81.2 ± 20.8 68.7 ± 25.8 0.018 HCO 3  (mmol/L) 33.8 ± 8.1 29.6 ± 9.1 0.035 Need of reintubation 35.3% 4.4% 0.001 Renal Failure Nil 16.7% 0.002 Sepsis Nil 36.7% <0.001 GCS: Glasgow coma scale, MBP: Mean blood pressure, PR: Pulse rate. Figure 1 Receiver operator characteristic (ROC) curve plotted for studying the diagnostic utility of Serum Albumin in predicting outcome of patients. The choice of cut-off is shown by an arrow (3.05 g/dL). Figure 2 Receiver operator characteristic (ROC) curve plotted for studying the diagnostic utility of APACHE II score in predicting outcome of patients. The choice of cut-off is shown by an arrow (13.5). Figure 3 Receiver operator characteristic (ROC) curve plotted for studying the diagnostic utility of score derived form equation in predicting outcome of patients. The choice of cut-off is shown by an arrow (-2.97).
Results
Baseline characteristics Demographic and baseline clinical and laboratory profile of the study group are presented in Table  1 . Almost all patients had type II respiratory failure (n = 74, 90.2%) and showed acute on chronic respiratory acidosis. Study cohort mostly consisted of critically ill patients as suggested by a high mean APACHE II score. History of smoking could be elicited in 65 patients (79.3%). A significant number of patients had history of previous hospitalization as well as intubation (39% and 18.3% respectively). Almost 55% of the patients (n = 45) had evidence of underlying cor pulmonale. Fifteen patients (18.3%) had underlying diabetes mellitus whereas 12 patients (14.6%) were on treatment for hypertension. None of the patients suffered from any other co-morbid condition. An attempt was made to define the cause of exacerbation for all patients. There was evidence of pneumonia in 67% (n = 55) of patients whereas pneumothorax was responsible for decompensation in 3 patients (3.7%). No obvious cause could be found in 24 patients (29%). Only one patient had evidence of sepsis, but none had ARDS at the time of admission to the ICU. Table 1 Descriptive profile of the study group (n = 82) Minimum Maximum Mean ± Std. Deviation Age (years) 35 85 60 ± 10 APACHE II score 3 33 13 ± 6 PR (per minute) 46 166 105 ± 19 RR (per minute) 10 46 27 ± 10 MBP (mmHg) 20 126 89 ± 19 GCS 3 15 12.1 ± 3 pH 6.87 7.44 7.25 ± 0.19 PaCO 2  (mmHg) 40.7 130.7 76.6 ± 23.5 PO 2  (mmHg) 31.5 142.3 83.9 ± 41.7 HCO 3  (mmHg) 5.4 55.4 32.3 ± 8.7 Serum Na (mEq/L) 115 152 136 ± 7 Serum K (mEq/L) 2.00 6.80 4.2 ± 0.9 Serum Albumin (gm%) 1.7 4.4 3.2 ± 0.7 Days on ventilator 1 33 8.7 ± 4.6 Days of ICU stay 1 35 9.6 ± 6.2 Days of hospital stay 1 63 16.3 ± 10.4 RR: Respiratory rate, PR: Pulse rate, MBP: Mean blood pressure, GCS: Glasgow coma scale, Serum Na: Serum sodium, Serum K: Serum potassium.
Baseline characteristics
Demographic and baseline clinical and laboratory profile of the study group are presented in Table  1 . Almost all patients had type II respiratory failure (n = 74, 90.2%) and showed acute on chronic respiratory acidosis. Study cohort mostly consisted of critically ill patients as suggested by a high mean APACHE II score. History of smoking could be elicited in 65 patients (79.3%). A significant number of patients had history of previous hospitalization as well as intubation (39% and 18.3% respectively). Almost 55% of the patients (n = 45) had evidence of underlying cor pulmonale. Fifteen patients (18.3%) had underlying diabetes mellitus whereas 12 patients (14.6%) were on treatment for hypertension. None of the patients suffered from any other co-morbid condition. An attempt was made to define the cause of exacerbation for all patients. There was evidence of pneumonia in 67% (n = 55) of patients whereas pneumothorax was responsible for decompensation in 3 patients (3.7%). No obvious cause could be found in 24 patients (29%). Only one patient had evidence of sepsis, but none had ARDS at the time of admission to the ICU.Descriptive profile of the study group (n = 82)MinimumMaximumMean ± Std. DeviationRR: Respiratory rate, PR: Pulse rate, MBP: Mean blood pressure, GCS: Glasgow coma scale, Serum Na: Serum sodium, Serum K: Serum potassium.Hospital course Non Invasive Ventilation (NIV) was used as initial strategy in 17 patients (20.7%). This strategy had a success rate of 59% (n = 10). Sixty-nine patients (84.1%) received invasive ventilation (including seven patients who failed NIV and had to be intubated). Sepsis developed in 11 patients (13.4%) and all these patients eventually died. Parameters associated with development of sepsis were high APACHE II score (18 vs. 12, p = 0.005) and low SA (2.6 gm/dL% vs. 3.3 gm/dL, p < 0.001). VAP developed in 6 patients (8.7%) and was associated with an increased stay in the ICU (18 days vs. 10 days, p = 0.021) as well as increased stay in the hospital (30 days vs. 15 days, p = 0.005). Outcome was not significantly affected by development of VAP (50% versus 42.8%).
Hospital course
Non Invasive Ventilation (NIV) was used as initial strategy in 17 patients (20.7%). This strategy had a success rate of 59% (n = 10). Sixty-nine patients (84.1%) received invasive ventilation (including seven patients who failed NIV and had to be intubated). Sepsis developed in 11 patients (13.4%) and all these patients eventually died. Parameters associated with development of sepsis were high APACHE II score (18 vs. 12, p = 0.005) and low SA (2.6 gm/dL% vs. 3.3 gm/dL, p < 0.001). VAP developed in 6 patients (8.7%) and was associated with an increased stay in the ICU (18 days vs. 10 days, p = 0.021) as well as increased stay in the hospital (30 days vs. 15 days, p = 0.005). Outcome was not significantly affected by development of VAP (50% versus 42.8%).Outcome Hospital mortality was 36.6% (n = 30). Various parameters were compared for survivors and non-survivors (table  2 ). In addition to demographic characteristics (age and sex), presence of cor pulmonale and cause of exacerbation of COPD, baseline parameters significantly different between the two groups on univariate analysis were included in a multivariate equation. APACHE II score at admission to the ICU {odds ratio (95 % CI): 1.32 (1.138-1.532); p < 0.001} and SA (done within 24 hours of admission) {odds ratio (95 % CI): 0.114 (0.03-0.432); p = 0.001} emerged as the independent predictors of mortality. ROC curve showed that both these variables have good predictive capability with area under the ROC curve (AUC) of 86.9% for APACHE II score (Figure  1 ) and 82.2% for SA (Figure  2 ). Best cut-off, taken as the value on the ROC curve at the point where curve sharply angulated, was 13.5 for APACHE II score and that for SA was 3.05 gm/dL. Following equation was determined by combining the two variables using the adjusted odd ratio: Score = (0.278 × APACHE II score) - (2.17 × SA), where APACHE II score is the score at the time of admission and SA (gm/dL) is the level with in the first 24 hours. ROC curve for this equation showed an AUC value of 91.2% (Figure  3 ). We chose a cut-off of -2.97 for the equation. That is, a patient with a score above -2.97 is likely to die whereas the one with below -2.97 likely to survive. This diagnostic rule had a specificity of 86.5% with a sensitivity of 90%. Positive predictive value for this variable was 79.4% whereas negative predictive value was 93.7%. A cut-off of -0.45 was 100% specific for hospital mortality but sensitivity was only 40%. On the other hand a cut-off of -5.5 gave a sensitivity of 100% with specificity of 33%. Table 2 Predictors of mortality for patients with exacerbation of COPD Parameter Survivors (n = 52) Non-survivors (n = 30) p value Mean ± SD Mean ± SD APACHE II score 10.6 ± 4.3 17.5 ± 5.7 0.001 GCS 12.8 ± 2.1 10.8 ± 3.7 0.003 MBP (mmHg) 93 ± 13.6 82.5 ± 24.7 0.015 PR (per minute) 110.7 ± 16.9 102.2 ± 21.1 0.049 Serum Albumin (gm%) 3.5 ± 0.5 2.7 ± 0.6 0.001 PaCO 2  (mmHg) 81.2 ± 20.8 68.7 ± 25.8 0.018 HCO 3  (mmol/L) 33.8 ± 8.1 29.6 ± 9.1 0.035 Need of reintubation 35.3% 4.4% 0.001 Renal Failure Nil 16.7% 0.002 Sepsis Nil 36.7% <0.001 GCS: Glasgow coma scale, MBP: Mean blood pressure, PR: Pulse rate. Figure 1 Receiver operator characteristic (ROC) curve plotted for studying the diagnostic utility of Serum Albumin in predicting outcome of patients. The choice of cut-off is shown by an arrow (3.05 g/dL). Figure 2 Receiver operator characteristic (ROC) curve plotted for studying the diagnostic utility of APACHE II score in predicting outcome of patients. The choice of cut-off is shown by an arrow (13.5). Figure 3 Receiver operator characteristic (ROC) curve plotted for studying the diagnostic utility of score derived form equation in predicting outcome of patients. The choice of cut-off is shown by an arrow (-2.97).
Outcome
Hospital mortality was 36.6% (n = 30). Various parameters were compared for survivors and non-survivors (table  2 ). In addition to demographic characteristics (age and sex), presence of cor pulmonale and cause of exacerbation of COPD, baseline parameters significantly different between the two groups on univariate analysis were included in a multivariate equation. APACHE II score at admission to the ICU {odds ratio (95 % CI): 1.32 (1.138-1.532); p < 0.001} and SA (done within 24 hours of admission) {odds ratio (95 % CI): 0.114 (0.03-0.432); p = 0.001} emerged as the independent predictors of mortality. ROC curve showed that both these variables have good predictive capability with area under the ROC curve (AUC) of 86.9% for APACHE II score (Figure  1 ) and 82.2% for SA (Figure  2 ). Best cut-off, taken as the value on the ROC curve at the point where curve sharply angulated, was 13.5 for APACHE II score and that for SA was 3.05 gm/dL. Following equation was determined by combining the two variables using the adjusted odd ratio: Score = (0.278 × APACHE II score) - (2.17 × SA), where APACHE II score is the score at the time of admission and SA (gm/dL) is the level with in the first 24 hours. ROC curve for this equation showed an AUC value of 91.2% (Figure  3 ). We chose a cut-off of -2.97 for the equation. That is, a patient with a score above -2.97 is likely to die whereas the one with below -2.97 likely to survive. This diagnostic rule had a specificity of 86.5% with a sensitivity of 90%. Positive predictive value for this variable was 79.4% whereas negative predictive value was 93.7%. A cut-off of -0.45 was 100% specific for hospital mortality but sensitivity was only 40%. On the other hand a cut-off of -5.5 gave a sensitivity of 100% with specificity of 33%.Predictors of mortality for patients with exacerbation of COPDParameterSurvivors (n = 52)Non-survivors (n = 30)p valueMean ± SDMean ± SDGCS: Glasgow coma scale, MBP: Mean blood pressure, PR: Pulse rate.Discussion Primary outcome measure of the present study was hospital mortality. Overall mortality rate was 36.6%. There was a high incidence of need of MV (84.1%). In studies that have taken into account all the patients with COPD requiring hospitalization, mortality rate has been to the tune of 6-42% [ 7 - 10 ]. Weiss & Hudson [ 11 ] reviewed 11 studies carried out to study outcome of patients with exacerbation of COPD and found the combined mortality rate to be 20.3%. Selection bias in the inclusion of patients for the present study precludes the generalization of these figures for patients with exacerbation of COPD requiring hospitalization from India. Only a fraction of all the patients with exacerbation of COPD admitted to our hospital are managed in ICU. Many other patients with acute exacerbation of COPD, especially those who do not require ventilatory support, are managed in the wards only. Because of this fact, by including patients who were admitted to ICU the sickest group of patient with exacerbation of COPD was selected. Various physiological parameters estimated at the time of presentation were analyzed to find predictors of mortality. Only two parameters, namely APACHE II score at admission to ICU and SA in the first 24 hours of admission, were found to be independent predictors of hospital mortality. The same two parameters also predicted development of sepsis on bivariate analysis. Some of the earlier studies have found blood gas parameters like pH [ 12 ] and PaCO 2  [ 13 ] to be useful in predicting outcome in COPD patients, whereas others [ 14 - 16 ] did not. In the present study, although PaCO 2  and HCO 3  were not independent predictors of mortality they tended to be lower in patients who died and the difference was statistically significant on bivariate analysis. Also, mean pH was similar for the two groups. This has not been reported in the earlier studies and investigators in the past have mostly found high PaCO 2  levels to be associated with worse outcome. A possible reason for this finding is that patients with hypercapnia with concordantly high HCO 3  are usually taken care of by mechanical ventilation. On the other hand, low mean PaCO 2  and HCO 3  levels in non-survivors probably reflected underlying metabolic acidosis. It has been reported earlier also that, for similar level of acidosis, patients with respiratory failure resulting in respiratory acidosis have better outcome as compared to patients with metabolic acidosis, that is commonly secondary to associated non-pulmonary organ failure [ 17 ]. Mean pH was similar for both survivors and non-survivors but survivors comprised predominantly of patients with respiratory acidosis (higher PaCO 2  as well as HCO 3 ) whereas non-survivors consisted of patients with metabolic acidosis (lower PaCO 2  and HCO 3  but similar pH). Another finding that corroborates the same fact is that all patients, who had associated renal failure and/or sepsis, died. The incidence of these two complications was significantly higher in non-survivors (renal failure 16.7% vs nil, p = 0.002; sepsis 36.7% vs nil, p < 0.001). Patients with both these complications commonly have associated metabolic acidosis. Prognostic utility of APACHE II score has been extensively investigated. It has been found useful for prognosticating critically ill patients across a wide array of diagnostic categories. Earlier studies have also found APACHE II score to be useful in predicting mortality in COPD patients with acute exacerbation [ 18 - 21 ] although the timing of scoring after admission has varied in different studies. For example Nevins & Epstein [ 18 ] found APACHE II score at 6 hrs after initiation of ventilation to be a useful predictor of mortality. In the present study, APACHE II scoring done at the time of admission to medical ICU was analyzed. SA estimated with in first 24 hrs of admission was also found to be a strong predictor of mortality. SA has also been reported to be of good prognostic value in the past [ 21 - 23 ]. Utility of prognostic value of SA in patients with COPD is interesting. Albumin has a long half-life of approximately 18 days and because of this fact it is unlikely to change with development of acute respiratory failure in patients with COPD. On the other hand SA is known to reflect the underlying nutritional status and to be affected by the severity of chronic illness. These factors are of obvious significance in deciding the outcome of these patients. An important purpose of the present study was to define predictors, which could help to identify patients that are likely to have worse outcome. This would help us to segregate patients who need to be managed aggressively from the very beginning. We looked at individual predictive utility of the parameters (SA and APACHE II score) that were found to be independent predictors of mortality. Both these parameters had good predictive value as evidenced by high AUC values. To improve the predictive utility, an equation was constructed using the adjusted odds ratio of the two parameters. ROC curve for this equation had a superior AUC value of 0.912. A good prognostic marker needs to be highly specific so that false positives remain low. On the other hand, good sensitivity is also desirable so that false negatives are not too high. A cutoff value of -2.97 has been suggested, which is associated with good specificity (86.5%) as well as sensitivity (90%) for predicting mortality of these patients. Cut-off that were associated with 100% specificity and sensitivity were also determined as different ICU's across the globe may have different priorities at different times. Although prospective studies are required to validate the findings of present study, an equation devised by combination of APACHE II score and SA appears to make sense. Estimation of APACHE II score makes use of various physiological variables but does not include SA levels. Also, the chronic physiology score in APACHE II fails to stratify patients according to varying severity of chronic illnesses. This tends to happen in patients with COPD as well. Use of SA, which predominantly reflects the severity of chronic illness, in the equation seems to complement the predictive capability of APACHE II score. The results of the present study reflect the complex interplay of factors that occurs in patients with exacerbation of COPD. In these patients, an acute insult in the form of exacerbating illness develops on top of a chronic smoldering illness. Severity of both acute insult as well as the underlying disease in the background of the level of nutritional status tends to determine the outcome of these patients. Although the equation is useful in to identifying patients with exacerbation of COPD who are likely to have poor outcome, it cannot be looked at in isolation. Other particulars of these patients such as associated illnesses and co-morbidities must be kept in mind before taking a final decision. It cannot be overemphasized that given the sensitivity and specificity of the equation, certain patients with a score below the suggested cut-off may also be sick. Also, the state of patients with exacerbation of COPD tends to remain in a constant flux and need constant monitoring. In spite of having a low score at presentation many of these patients may deteriorate during hospital stay. It is concluded that APACHE II score at admission and SA levels with in first 24 hrs after admission are independent predictors of mortality for patients with exacerbation of COPD. The equation derived by combining these two parameters is useful for identifying patients that are likely to have poor outcome.
Discussion
Primary outcome measure of the present study was hospital mortality. Overall mortality rate was 36.6%. There was a high incidence of need of MV (84.1%). In studies that have taken into account all the patients with COPD requiring hospitalization, mortality rate has been to the tune of 6-42% [ 7 - 10 ]. Weiss & Hudson [ 11 ] reviewed 11 studies carried out to study outcome of patients with exacerbation of COPD and found the combined mortality rate to be 20.3%. Selection bias in the inclusion of patients for the present study precludes the generalization of these figures for patients with exacerbation of COPD requiring hospitalization from India. Only a fraction of all the patients with exacerbation of COPD admitted to our hospital are managed in ICU. Many other patients with acute exacerbation of COPD, especially those who do not require ventilatory support, are managed in the wards only. Because of this fact, by including patients who were admitted to ICU the sickest group of patient with exacerbation of COPD was selected.Various physiological parameters estimated at the time of presentation were analyzed to find predictors of mortality. Only two parameters, namely APACHE II score at admission to ICU and SA in the first 24 hours of admission, were found to be independent predictors of hospital mortality. The same two parameters also predicted development of sepsis on bivariate analysis. Some of the earlier studies have found blood gas parameters like pH [ 12 ] and PaCO 2  [ 13 ] to be useful in predicting outcome in COPD patients, whereas others [ 14 - 16 ] did not. In the present study, although PaCO 2  and HCO 3  were not independent predictors of mortality they tended to be lower in patients who died and the difference was statistically significant on bivariate analysis. Also, mean pH was similar for the two groups. This has not been reported in the earlier studies and investigators in the past have mostly found high PaCO 2  levels to be associated with worse outcome. A possible reason for this finding is that patients with hypercapnia with concordantly high HCO 3  are usually taken care of by mechanical ventilation. On the other hand, low mean PaCO 2  and HCO 3  levels in non-survivors probably reflected underlying metabolic acidosis. It has been reported earlier also that, for similar level of acidosis, patients with respiratory failure resulting in respiratory acidosis have better outcome as compared to patients with metabolic acidosis, that is commonly secondary to associated non-pulmonary organ failure [ 17 ]. Mean pH was similar for both survivors and non-survivors but survivors comprised predominantly of patients with respiratory acidosis (higher PaCO 2  as well as HCO 3 ) whereas non-survivors consisted of patients with metabolic acidosis (lower PaCO 2  and HCO 3  but similar pH). Another finding that corroborates the same fact is that all patients, who had associated renal failure and/or sepsis, died. The incidence of these two complications was significantly higher in non-survivors (renal failure 16.7% vs nil, p = 0.002; sepsis 36.7% vs nil, p < 0.001). Patients with both these complications commonly have associated metabolic acidosis.Prognostic utility of APACHE II score has been extensively investigated. It has been found useful for prognosticating critically ill patients across a wide array of diagnostic categories. Earlier studies have also found APACHE II score to be useful in predicting mortality in COPD patients with acute exacerbation [ 18 - 21 ] although the timing of scoring after admission has varied in different studies. For example Nevins & Epstein [ 18 ] found APACHE II score at 6 hrs after initiation of ventilation to be a useful predictor of mortality. In the present study, APACHE II scoring done at the time of admission to medical ICU was analyzed. SA estimated with in first 24 hrs of admission was also found to be a strong predictor of mortality. SA has also been reported to be of good prognostic value in the past [ 21 - 23 ]. Utility of prognostic value of SA in patients with COPD is interesting. Albumin has a long half-life of approximately 18 days and because of this fact it is unlikely to change with development of acute respiratory failure in patients with COPD. On the other hand SA is known to reflect the underlying nutritional status and to be affected by the severity of chronic illness. These factors are of obvious significance in deciding the outcome of these patients.An important purpose of the present study was to define predictors, which could help to identify patients that are likely to have worse outcome. This would help us to segregate patients who need to be managed aggressively from the very beginning. We looked at individual predictive utility of the parameters (SA and APACHE II score) that were found to be independent predictors of mortality. Both these parameters had good predictive value as evidenced by high AUC values. To improve the predictive utility, an equation was constructed using the adjusted odds ratio of the two parameters. ROC curve for this equation had a superior AUC value of 0.912. A good prognostic marker needs to be highly specific so that false positives remain low. On the other hand, good sensitivity is also desirable so that false negatives are not too high. A cutoff value of -2.97 has been suggested, which is associated with good specificity (86.5%) as well as sensitivity (90%) for predicting mortality of these patients. Cut-off that were associated with 100% specificity and sensitivity were also determined as different ICU's across the globe may have different priorities at different times.Although prospective studies are required to validate the findings of present study, an equation devised by combination of APACHE II score and SA appears to make sense. Estimation of APACHE II score makes use of various physiological variables but does not include SA levels. Also, the chronic physiology score in APACHE II fails to stratify patients according to varying severity of chronic illnesses. This tends to happen in patients with COPD as well. Use of SA, which predominantly reflects the severity of chronic illness, in the equation seems to complement the predictive capability of APACHE II score. The results of the present study reflect the complex interplay of factors that occurs in patients with exacerbation of COPD. In these patients, an acute insult in the form of exacerbating illness develops on top of a chronic smoldering illness. Severity of both acute insult as well as the underlying disease in the background of the level of nutritional status tends to determine the outcome of these patients.Although the equation is useful in to identifying patients with exacerbation of COPD who are likely to have poor outcome, it cannot be looked at in isolation. Other particulars of these patients such as associated illnesses and co-morbidities must be kept in mind before taking a final decision. It cannot be overemphasized that given the sensitivity and specificity of the equation, certain patients with a score below the suggested cut-off may also be sick. Also, the state of patients with exacerbation of COPD tends to remain in a constant flux and need constant monitoring. In spite of having a low score at presentation many of these patients may deteriorate during hospital stay.It is concluded that APACHE II score at admission and SA levels with in first 24 hrs after admission are independent predictors of mortality for patients with exacerbation of COPD. The equation derived by combining these two parameters is useful for identifying patients that are likely to have poor outcome.Competing interests The author(s) declare that they have no competing interests.
Competing interests
The author(s) declare that they have no competing interests.Authors' contributions GCK: concept and design of study, management of patients, preparation of the manuscript. AB: concept of the study, management of patients, statistical analysis, preparation of the manuscript. SKS: management of patients and critical review of the manuscript.
Authors' contributions
GCK: concept and design of study, management of patients, preparation of the manuscript. AB: concept of the study, management of patients, statistical analysis, preparation of the manuscript. SKS: management of patients and critical review of the manuscript.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


Long-range communication requires special technology. Although we usually think of nerve cells communicating over distances measured in millimeters, they must also stretch over centimeters and even meters to enable movement and sensation throughout our bodies. In a disorder known as spastic paraplegia, people experience stiffness and loss of function in their legs because the far ends of the longest nerves in the spinal cord degenerate. One form of this disease, autosomal dominant hereditary spastic paraplegia (AD-HSP), is most frequently caused by mutations in a gene that codes for the enzyme Spastin.Until recently, the function of Spastin has only been inferred by its similarity to another protein, Katanin, which chops up microtubules. The microtubules in neurons, as in all types of cells, provide a network to transport materials from one place in the cell to another. For neurons to communicate with each other and with muscles, neurotransmitters packaged at one end of the cell make the journey through the cell's long, narrow axon on the backs of microtubules. At the other end of the cell, the neurotransmitters reach small cellular projections (boutons) where they are released as a signal to the receiving cell. Since long spinal cord axons are most often affected by AD-HSP, researchers have suggested that transport through axons might be culpable.Is there a link between Spastin, microtubule severing, and AD-HSP? To address this question, Nina Tang Sherwood and colleagues studied the function of Spastin in the fruitfly  Drosophila  by identifying the  spastin  gene in this species and manipulating its expression.spastinThey found that indeed  Drosophila  Spastin in neurons regulates microtubule networks. Overexpressing  spastin  caused collapse of the embryonic central nervous system and also eliminated the microtubule network, as expected based on the related Katanin protein's microtubule-severing activity. But—surprisingly—knocking out  spastin  did not yield the opposite result.  spastin -null flies had fewer microtubule bundles, particularly at the far ends of the neurons. They also exhibited smaller and more numerous boutons that were unusually clustered together.spastinspastinspastinOn the basis of their results, the authors speculate that Spastin cuts microtubules to a manageable size. Too much Spastin chops the microtubules into useless fragments, but too little Spastin may leave microtubule polymers too large to be efficiently moved into newly forming boutons. With an intermediate amount, microtubule pieces are the right size for transport throughout the neuron.Without Spastin, normal motor function ceases. In  spastin -null flies, neurotransmitter release is impaired and flying is impossible. Flies even tend to drag their hind legs. This weakness in the legs is just one compelling parallel to human AD-HSP. The severity of symptoms in people is highly variable, similar to the variability in phenotypes exhibited by  Drosophila  with intermediate  spastin  gene mutations. The authors caution, however, that they do not prove that the fly phenotypes observed arise through the same mechanisms that cause human AD-HSP. The utility of  Drosophila  as a model for human AD-HSP has yet to be demonstrated, but the importance of Spastin in regulating neuronal microtubule networks in vivo is no longer in doubt.spastinspastin


Mental chronometry, which has origins dating back over a century, seeks to measure the time course of mental operations in the human nervous system

How do we relate human thought processes to measurable events in the brain? Mental chronometry, which has origins that date back more than a century, seeks to measure the time course of mental operations in the human nervous system [ 1 ]. From the late 1800s until 1950, the field was built almost entirely around a single method: measuring and comparing people's reaction times during simple cognitive tasks. As far back as 1868, Franciscus Donders [ 2 ] subtracted the time taken to make a single response to an unvarying stimulus—what he called an instructed reflex—from the time it took to make the same response to one of two events, obtaining the time required to discriminate between the two stimuli. Further, he subtracted the time to discriminate two stimuli from a situation in which there were also two possible responses in order to obtain the time required for choice.In the 1950s, studies of reaction time were combined with the then-developing mathematical theory of information [ 3 ] to address issues such as the maximum transmission rate of the human nervous system [ 4 ] and how coding in the brain of stimuli and their responses could influence these limits [ 5 ]. These studies revealed that reaction time alone was not sufficient to elucidate the exact processes by which the brain achieved the human ability to process information. However, when combined with other methods, the latency of responding can help connect brain studies to the behavior of humans in real situations.Recording the average event-related electrical potentials from scalp electrodes became a research tool in the 1960s, with the advent of analog and then digital computers to accomplish the recording and averaging. It became clear that components of the event-related potential could be systematically related to sensory and motor stages of information processing. For instance, a visual stimulus was found to evoke a short-latency scalp response from the primary visual cortex at about 60 milliseconds, followed by positive and negative voltage changes in neighboring visual areas. Similarly, scalp recorded potentials from the frontal cortex could be recorded in relation to motor activity. It was now possible to observe some of the sensory and motor stages that were inferred from Donders's subtractive method (see [ 6 ] for a review).Saul Sternberg [ 7 ] developed a much-improved method for dividing reaction time into successive or serial stages, called the additive factors method. Subjects were asked to determine whether or not a probe digit had been present in a just previously presented series of digits. Sternberg argued that the time to complete the task could be divided into a sensory stage, dependent on stimulus parameters such as the intensity or clarity of the probe; a comparison stage, dependent only on the number of items in memory; and a response stage that reflected the difficulty of the specified response. Factors that influenced one stage (e.g., stimulus intensity) would be additive with those that influenced another stage (e.g., motor output). With this simple framework, it was now possible to determine at which stage(s) a new factor (e.g., nicotine, sleep deprivation, or Parkinson's disease) had its influence.In the 1950s, the advent of microelectrode recordings of single neurons from anesthetized monkeys allowed for an even finer resolution of neurophysiological processes and seemed to provide support for the view that the brain does indeed process information in serial stages. Hubel and Wiesel [ 8 ] argued that successive levels of the visual system could be seen as accomplishing successive analyses of input. The microelectrode strategy was quickly adopted to alert animals, making it apparent that higher level brain areas involved in operations upon input might feedback their influences on earlier processing stages [ 9 , 10 ]. These control systems, often called attention, posed something of a problem for completely serial views of information processing. However, they also provided evidence of localized brain areas within the parietal lobe of the monkey that could be systematically related to processing operations involved in attention—which were then being investigated by mental chronometry in patients with parietal and other lesions [ 11 ].In the late 1980s, neuroimaging experiments made possible the examination of activity in localized brain areas, first through the use of injected radionuclides detected by positron emission tomography (PET) [ 12 ] and later through the use of an externally imposed magnetic field in functional magnetic resonance imaging (fMRI) [ 13 ]. Over the last ten years, fMRI has improved in spatial and temporal resolution and can now provide evidence of quite specific brain areas, in the millimeter range, that are involved in cognitive tasks. Most studies have shown a small number of widely distributed brain areas that must be orchestrated to carry out a cognitive task. Although, as in all sciences, there are disagreements, the convergence of results in areas of attention and language seem to me particularly impressive.When the fMRI method for localization is brought together with methods that can accurately measure timing of the same activity (i.e., electrical or magnetic event-related potentials) they can provide considerable insight into the nature of thought. Consider the simple task of deciding whether a presented digit is above or below five [ 14 ]. Dehaene argued that the task could be divided into four stages. The first involves obtaining the identity of the probe input (encoding), the second making a comparison against the stored representation of the digit five, the third selecting a response, and lastly, checking the output for error. According to additive factor theory, a variable that effects overall reaction time by varying the time to complete one stage will be additive with the effects of variables that affect other stages. The input or encoding stage was varied by using either Arabic or spelled digits (e.g., “3” or “three”). The comparison stage was varied by comparing digits close to five (e.g., six) with those far from five (e.g., nine). The response stage was varied by specifying a response from either the dominant or non-dominant hand and error monitoring was examined by comparing error with correct trials. Each of these variables influenced only the appropriate stage and was additive in its effect with each of the other variables (see  Figure 1 ).Moreover, scalp-recorded, event-related potentials showed a separate scalp distribution and latency for each variable [ 14 ]. Subsequent fMRI data has confirmed and increased the precision of the anatomy proposed for each of these stages.Of course, not all human activities involve a set of exhaustive and independent serial stages that can be shown to add up to the overall reaction time. While tasks like the number comparison discussed above can be usefully divided into stages, some components may deal with simultaneous operations and may be limited only by a total capacity of central mechanisms. We know that many situations involve parallel processing and feedback loops at many levels. Sternberg has attempted to apply a modified version of additive factor theory to brain systems using neuroimaging that allows for some of these possibilities [ 15 , 16 ].Laboratory studies often use the simultaneous execution of two different tasks (dual tasks) to simulate the more realistic situations where humans time-share activities. In this issue of  PLoS Biology , Sigman and Dehaene [ 17 ] provide a model that further extends the additive factor logic to the dual task situation. They propose that for tasks that can be broken down into three consecutive stages—perception, decision based on noisy integration of evidence, and response—the perceptual and motor stages can operate both simultaneously with and independently of stages of another task and are thus easily amenable to additive factor analysis. The decision stage, however, appears to represent a kind of “cognitive bottleneck” for which the reaction times of the two tasks become interdependent. The model adds considerably to the range of situations to which an additive factor approach can be applied, allowing investigators to seek more information about how new variables influence hidden processing stages.PLoS BiologyMany cognitive and emotional tasks studied with neuroimaging have implicated a small number of brain areas that are consistently active. Mental chronometry plays a role in suggesting the cognitive operations that each of these areas performs and how they are organized in real time. The toolkit of new techniques provides the basis for further tests to evaluate whether a chronometric model reveals a crucial set of connected computations (circuit) for carrying out the task. For example, using a magnetic pulse delivered outside the skull, it is now possible to induce a reversible lesion at the time of a particular computation to determine whether the specific computation assigned to a given area is truly needed to carry out the task. Studies using diffusion tensor imaging can examine whether there are large-scale connections between neural areas posited by a particular model. In describing the links between brain and behavior, mental chronometry is still a cornerstone that binds psychology to the techniques of neuroscience.


Nobody should do a new research study, says Clarke, without first systematically reviewing the literature. And journal editors should insist that all research papers are accompanied by an up-to-date systematic review

On May 2, 1898, George Gould used his address to the founding meeting of the Association of Medical Librarians in Philadelphia to present a vision of the future of health information. ‘I look forward,’ he said, ‘to such an organisation of the literary records of medicine that a puzzled worker in any part of the civilised world shall in an hour be able to gain a knowledge pertaining to a subject of the experience of every other man in the world’ [ 1 ]. Has his vision been realised?Information Overload In these early years of the 21st century, with tens of thousands, if not hundreds of thousands, of new research articles being published every year, people who need to make decisions about health care are much more overwhelmed with information than they were in 1898. Some of this information is of good quality, but some of it is not. Thus, anyone wishing to use the health literature to make well-informed decisions must both identify the relevant research from amidst this vast amount of information and then appraise it. This is an impossible task for many. Even though making access to the literature easier and cheaper will increase the ability of people to find research, it will also reveal just how much information there is out there and how daunting is the task of making sense of it. You can get a good idea of the size of the task—and of how electronic publishing and the Internet have transformed the situation—by imagining the following four steps. How long would it take to find articles in your area of interest by paging through back copies of a relevant journal? What about by using its index? Now imagine finding articles of interest to you by going to the Internet and searching PubMed ( www.ncbi.nlm.nih.gov/PubMed ). What about if you searched the whole of the Internet with one or more search engines? Almost certainly, as the speed of the search increased through these four approaches, so would the number of articles retrieved—and also the time that it would take to read through them, appraise them, and decide if they were relevant to whatever decision you were trying to make.
 Doctors are overloaded with information, much of it irrelevant to their practice (Illustration: Rusty Howson, sososo design) Should you just take a shortcut by relying on a single study in a high-profile journal? No. Sampling in this way might lead you to research whose findings are the most striking or atypical, but you would miss similar research that was ‘less fortunate’ with its results. However good the conduct of a piece of research, chance effects mean that some studies will produce an overestimate and some an underestimate of the true effects, but the easy-to-find literature is likely to be dominated by the former [ 2 ]. In addition, using a sample rather than the whole body of relevant research will have less statistical and evidential power to answer the question of interest.
Information Overload
In these early years of the 21st century, with tens of thousands, if not hundreds of thousands, of new research articles being published every year, people who need to make decisions about health care are much more overwhelmed with information than they were in 1898. Some of this information is of good quality, but some of it is not. Thus, anyone wishing to use the health literature to make well-informed decisions must both identify the relevant research from amidst this vast amount of information and then appraise it. This is an impossible task for many. Even though making access to the literature easier and cheaper will increase the ability of people to find research, it will also reveal just how much information there is out there and how daunting is the task of making sense of it.You can get a good idea of the size of the task—and of how electronic publishing and the Internet have transformed the situation—by imagining the following four steps. How long would it take to find articles in your area of interest by paging through back copies of a relevant journal? What about by using its index? Now imagine finding articles of interest to you by going to the Internet and searching PubMed ( www.ncbi.nlm.nih.gov/PubMed ). What about if you searched the whole of the Internet with one or more search engines? Almost certainly, as the speed of the search increased through these four approaches, so would the number of articles retrieved—and also the time that it would take to read through them, appraise them, and decide if they were relevant to whatever decision you were trying to make.Should you just take a shortcut by relying on a single study in a high-profile journal? No. Sampling in this way might lead you to research whose findings are the most striking or atypical, but you would miss similar research that was ‘less fortunate’ with its results. However good the conduct of a piece of research, chance effects mean that some studies will produce an overestimate and some an underestimate of the true effects, but the easy-to-find literature is likely to be dominated by the former [ 2 ]. In addition, using a sample rather than the whole body of relevant research will have less statistical and evidential power to answer the question of interest.The Value of Systematic Reviews Systematic reviews provide a means to minimise these problems. In systematic reviews, the methods to be followed are stated and an attempt is made to identify, appraise, and where appropriate, statistically combine, all relevant research. In fact, a decade before George Gould's address, Lord Rayleigh, at the meeting of the British Association for the Advancement of Science in Montreal, had described such a process for scientific research in general. ‘If, as is sometimes supposed,’ he said, ‘science consisted in nothing but the laborious accumulation of facts, it would soon come to a standstill, crushed, as it were, under its own weight. The suggestion of a new idea, or the detection of a law, supersedes much that has previously been a burden on the memory, and by introducing order and coherence facilitates the retention of the remainder in an available form. Two processes are thus at work side by side, the reception of new material and the digestion and assimilation of the old. One remark, however, should be made. The work which deserves, but I am afraid does not always receive, the most credit is that in which discovery and explanation go hand in hand, in which not only are new facts presented, but their relation to old ones is pointed out’ [ 3 ].
The Value of Systematic Reviews
Systematic reviews provide a means to minimise these problems. In systematic reviews, the methods to be followed are stated and an attempt is made to identify, appraise, and where appropriate, statistically combine, all relevant research. In fact, a decade before George Gould's address, Lord Rayleigh, at the meeting of the British Association for the Advancement of Science in Montreal, had described such a process for scientific research in general.‘If, as is sometimes supposed,’ he said, ‘science consisted in nothing but the laborious accumulation of facts, it would soon come to a standstill, crushed, as it were, under its own weight. The suggestion of a new idea, or the detection of a law, supersedes much that has previously been a burden on the memory, and by introducing order and coherence facilitates the retention of the remainder in an available form. Two processes are thus at work side by side, the reception of new material and the digestion and assimilation of the old. One remark, however, should be made. The work which deserves, but I am afraid does not always receive, the most credit is that in which discovery and explanation go hand in hand, in which not only are new facts presented, but their relation to old ones is pointed out’ [ 3 ].Relating the New to the Old If today's health researchers discussed their findings in the context of relevant, already-existing research, many of the problems of information overload would be eased. You would only need to find the most recent report of a relevant study; its discussion section would place that study within the context of an updated systematic review. This was suggested by the original CONSORT statement on the reporting of randomised trials in 1996 [ 4 ]. However, studies of five of the major general medical journals ( Annals of Internal Medicine, BMJ, Lancet, JAMA,  and the  New England Journal of Medicine ) in 1997 and 2001 found that this was not the case, at least for these journals. Only two of more than 50 reports of randomised trials in these journals in May of those two years included an updated systematic review [ 5 , 6 ]. Including an updated systematic review along with a report of a randomised trial (or any other piece of research) might seem too much to expect of researchers, who might not feel able or willing to do the additional work required. However, the absence of a review should raise the question: on what did the researchers base the design of their new study? To embark on a new study without first systematically reviewing what has been done before is to risk doing research for which the answer is already known. It would also mean that the researchers had denied themselves the opportunity to learn from the successes and failures of others when designing their own study. In addition, researchers have a responsibility to the participants in their research to make sure that the study is of the most appropriate design possible. To help make sure that this is the case, when designing a new study researchers should ensure that they have been adequately informed about what research has been done previously.  Box 1  lists practical suggestions to researchers for making sure that their new study builds on prior knowledge. Box 1. Practical Suggestions for Researchers
 Conduct a systematic review of your research question before embarking on a new study, or identify a relevant review done by someone else. Design your study to take account of the relevant successes and failures of the prior studies, and of the evidence within them. Discuss the findings of your study in the context of an updated systematic review of relevant research. Publish the systematic review within, alongside, or shortly after the report of your study. Provide information from your study to others doing systematic reviews of similar topics.
 It might even be the case that the researchers are able to draw on the work of others, who already have done a systematic review of the relevant topic. Over the last decade, The Cochrane Collaboration ( Box 2 ) has produced more than 2,000 Cochrane systematic reviews ( www.cochrane.org ) [ 7 ]. There are thousands more reviews scattered throughout the literature ( www.york.ac.uk/inst/crd/darefaq.htm ). And with the ability to publish longer versions of articles on the Internet than are practical in print, concerns about article length should no longer be a barrier to the inclusion of a systematic review. Box 2. The Cochrane Collaboration The Cochrane Collaboration ( www.cochrane.org ) is an international, nonprofit, and independent organisation dedicated to helping people make well-informed decisions about health care by preparing, maintaining, and promoting the accessibility of systematic reviews. These reviews are published electronically in The Cochrane Library, which is available on the Internet and CD-ROM. The Cochrane Collaboration was established in 1993 and is named after the British epidemiologist, Archie Cochrane.
Relating the New to the Old
If today's health researchers discussed their findings in the context of relevant, already-existing research, many of the problems of information overload would be eased. You would only need to find the most recent report of a relevant study; its discussion section would place that study within the context of an updated systematic review. This was suggested by the original CONSORT statement on the reporting of randomised trials in 1996 [ 4 ]. However, studies of five of the major general medical journals ( Annals of Internal Medicine, BMJ, Lancet, JAMA,  and the  New England Journal of Medicine ) in 1997 and 2001 found that this was not the case, at least for these journals. Only two of more than 50 reports of randomised trials in these journals in May of those two years included an updated systematic review [ 5 , 6 ].Annals of Internal Medicine, BMJ, Lancet, JAMA,New England Journal of MedicineIncluding an updated systematic review along with a report of a randomised trial (or any other piece of research) might seem too much to expect of researchers, who might not feel able or willing to do the additional work required. However, the absence of a review should raise the question: on what did the researchers base the design of their new study? To embark on a new study without first systematically reviewing what has been done before is to risk doing research for which the answer is already known. It would also mean that the researchers had denied themselves the opportunity to learn from the successes and failures of others when designing their own study. In addition, researchers have a responsibility to the participants in their research to make sure that the study is of the most appropriate design possible. To help make sure that this is the case, when designing a new study researchers should ensure that they have been adequately informed about what research has been done previously.  Box 1  lists practical suggestions to researchers for making sure that their new study builds on prior knowledge.Box 1. Practical Suggestions for Researchers
Conduct a systematic review of your research question before embarking on a new study, or identify a relevant review done by someone else. Design your study to take account of the relevant successes and failures of the prior studies, and of the evidence within them. Discuss the findings of your study in the context of an updated systematic review of relevant research. Publish the systematic review within, alongside, or shortly after the report of your study. Provide information from your study to others doing systematic reviews of similar topics.Conduct a systematic review of your research question before embarking on a new study, or identify a relevant review done by someone else.Design your study to take account of the relevant successes and failures of the prior studies, and of the evidence within them.Discuss the findings of your study in the context of an updated systematic review of relevant research.Publish the systematic review within, alongside, or shortly after the report of your study.Provide information from your study to others doing systematic reviews of similar topics.It might even be the case that the researchers are able to draw on the work of others, who already have done a systematic review of the relevant topic. Over the last decade, The Cochrane Collaboration ( Box 2 ) has produced more than 2,000 Cochrane systematic reviews ( www.cochrane.org ) [ 7 ]. There are thousands more reviews scattered throughout the literature ( www.york.ac.uk/inst/crd/darefaq.htm ). And with the ability to publish longer versions of articles on the Internet than are practical in print, concerns about article length should no longer be a barrier to the inclusion of a systematic review.Box 2. The Cochrane Collaboration
The Cochrane Collaboration ( www.cochrane.org ) is an international, nonprofit, and independent organisation dedicated to helping people make well-informed decisions about health care by preparing, maintaining, and promoting the accessibility of systematic reviews. These reviews are published electronically in The Cochrane Library, which is available on the Internet and CD-ROM. The Cochrane Collaboration was established in 1993 and is named after the British epidemiologist, Archie Cochrane.Updating Gould's Vision As we progress through the 21st century, and health care information continues to become ever more plentiful, there are tremendous opportunities to make knowledge about health care more accessible. However, for this to happen without overwhelming the people who are trying to make health care decisions—for themselves or for someone else—the need for new research to be designed and reported using systematic reviews becomes ever more pressing. Returning to George Gould's vision, but bringing it into the modern era, I hope for a system in which everyone making a decision about health care in any part of the world would be able, in 15 minutes, to obtain up-to-date, reliable evidence of the effects of interventions they might choose, based on all the relevant research. Journals, especially new ones such as  PLoS Medicine , will help achieve this by only publishing a report of a new research study under the following conditions. First, the researchers must justify their study on the basis of a previous systematic review. Second, the journal should publish an updated systematic review (which incorporates the new study) within the new study, alongside it, or shortly thereafter.
Updating Gould's Vision
As we progress through the 21st century, and health care information continues to become ever more plentiful, there are tremendous opportunities to make knowledge about health care more accessible. However, for this to happen without overwhelming the people who are trying to make health care decisions—for themselves or for someone else—the need for new research to be designed and reported using systematic reviews becomes ever more pressing.Returning to George Gould's vision, but bringing it into the modern era, I hope for a system in which everyone making a decision about health care in any part of the world would be able, in 15 minutes, to obtain up-to-date, reliable evidence of the effects of interventions they might choose, based on all the relevant research. Journals, especially new ones such as  PLoS Medicine , will help achieve this by only publishing a report of a new research study under the following conditions. First, the researchers must justify their study on the basis of a previous systematic review. Second, the journal should publish an updated systematic review (which incorporates the new study) within the new study, alongside it, or shortly thereafter.PLoS Medicine