The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time.Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak.The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%.The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine. A common readmission timeframe measured by organizations such as the Centers for Medicare and Medicaid Services (CMS) is readmissions within 30 days of the index hospitalization . ReadmisGiven the importance of readmission both clinically and financially, there have been significant efforts to identify individuals at elevated risk of readmission, so that they can be targeted for interventions aimed at reducing readmissions. Recently, predictive analytics has emerged as an effective method for identifying patients at elevated risk of readmissions -17. To iAs with any predictive model, the main indicator of model performance is the c-statistic, which is equivalent to the area under the curve (AUC) of the receiver operating characteristic (ROC) plot. To our knowledge, the highest performing predictive models for all-cause readmissions have associated c-statistic values of 77.1%  and 83.3Predictive models for readmissions are typically developed using clinical and administrative data collected as a part of the care process. Unfortunately, such operational clinical data frequently contain incomplete patient records . This isIn addition to incomplete records, an additional challenge of operational datasets is that such data may change over time, e.g., due to changes in clinical practice or the introduction of a new health information system. Therefore, it is unclear whether selecting a single timeframe for model development is adequate .In recent years, biomedical researchers have investigated the use of voting classifiers -41 and dIn making use of a predictive model, it is often important to know which risk factors are most significant. For example, knowing which clinical factors are most significant for predicting CHF readmissions is important, because it allows for more targeted intervention on those factors. Such risk factor identification is often accomplished using statistical measures . However, it has been suggested that other feature selection and ranking strategies are superior ,20. ThesIn this manuscript, we propose a systematic, three-step approach to healthcare predictive analytics that (i) accounts for the changing and frequently incomplete nature of operational clinical data, (ii) empirically develops optimal predictive models using a combination of various statistical analyses, adjustment methods for handling missing values, feature selection methods, classifiers, and discretization methods, and (iii) empirically categorizes each risk factor as being strong, regular, or weak using a combination of statistical analyses, feature rankers, and a newly developed ranking algorithm.This systematic, three-step approach to predictive analytics was evaluated in the context of predicting CHF readmission within a tertiary academic medical center. To our knowledge, our proposed three-step methodology for optimizing predictive analytics has not been applied to any healthcare domain to date. Here, we summarize the methods and results from this research and discuss the implications, limitations, and future direction of our work.This study was conducted at University of Utah Health Care (UUHC), which is a tertiary academic health system centered in Salt Lake City, Utah. This study was approved by the University of Utah Institutional Review Board (Protocol # 00060215).The subjects evaluated were individuals hospitalized for CHF at UUHC and admitted between January 1st 2003 and June 30th 2013. CHF hospitalizations were defined as those hospitalizations with a primary discharge diagnosis of one of the following ICD9 codes: 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, or 428.XX. This research analyzed 2,787 hospitalizations matching these criteria unless the hospitalization was missing a required explanatory variable as described below. The most frequent primary discharge diagnoses were unspecified CHF , systolic CHF , and diastolic CHF .The data utilized for this analysis was obtained from the UUHC Enterprise Data Warehouse. For each of the 2,787 CHF hospitalizations from January 1st 2003 to June 30th 2013, a number of potential explanatory variables were obtained based on their use in the prior literature in the field ,33 and tVital sign data began to be available regularly from 2008, when a new electronic health record (EHR) system was implemented at UUHC. Given the importance of vital sign data in prior literature in this field, the availability of vital sign data was an important consideration for the definition of the model derivation datasets, as described below. In addition to vital signs, certain laboratory tests of interest were present in only a minority of encounters.In order to account for the significant difference in vital sign availability before and after 2008, three candidate datasets for model derivation were generated: a dataset containing all years except the validation year (2003–2012); a dataset containing the transition year for vital sign data and subsequent years (2008–2012); and a dataset containing years following the transition year (2009–2012). In addition, a validation dataset was generated for hospitalizations from the first six months of 2013. The numbers of records are as follows: 1122 (5.34% complete) for 2003–2007, 227 (42.73% complete) for 2008, 1250 (47.36% complete) for 2009–2012, and 188 (47.34% complete) for the first six months of 2013. The annual readmission rates for 2003 through the first six months of 2013 were 17.06%, 14.50%, 15.38%, 24.11%, 11.81%, 10.57%, 16.78%, 23.20%, 19.94%, 15.50%, and 8.50% respectively. Because there was no clear trend in readmission rates over time, readmission rates were not used to define the temporal derivation datasets.et al.[To handle the missing values, we evaluated the complete-analysis adjustment method and all mean-based imputation algorithms described by Luengo et al.. Howeverdependent variable for the study was readmission  for any cause within 30 days of the index CHF hospitalization. A given hospitalization could serve both as a readmission to an earlier index CHF hospitalization, as well as the index CHF hospitalization for a later readmission. We considered but did not use the CMS definition for CHF readmission [independent explanatory variables and their acronyms are summarized in Table The dmission , becauseDemographic information included gender, race, religion, marital status, insurance/finance class, age, and the zip code of the home address. From the zip code, home proximity and mean household income were estimated as follows: (i) obtain longitude and latitude from the U.S. Census Bureau [s Bureau ; (ii) cas Bureau ; and . Also, each index hospitalization had from 3 to 62 ICD9 billing diagnoses. These comorbidities were abstracted as follows. First, the number of comorbid conditions included in the Charlson index for comorbidities [bidities  was idenbidities .laboratory tests based on their suggestion in the literature as well as the clinical judgment of a practicing cardiologist (BEB). Sixteen tests were excluded because of missing values in > 50% of cases. Only those tests listed in Table Vital signs included 4 variables: last reading systolic blood pressure (LSBP), last heart rate (LHR), first reading weight (FW), and last reading weight (LW). In all vital signs, the first reading refers to the first reading upon hospital admission and the last reading refers to the last reading prior to discharge.We were originally interested in 48 Pre-hospitalization information from the 6 months prior to the index hospitalization included the frequencies of Charlson index comorbidities from any type of encounter , captured as the PriorCharlsonIndexF variable. Also, we included the number of ED encounters (PriorEDF) and the mean of corresponding lengths of stay (PriorEDLOS).Age, LOS, CharlsonIndexF, comorbidities, vital signs, laboratory tests, proximity and income are numeric variables, and the others are categorical.As noted in the introduction, feature selection based on the p-values of statistical tests is common in predictive analytics. Typically, variables with p-values ≤ 0.001 are considered significant risk factors, variables with p-values ≥ 0.1 are considered irrelevant, and variables with intermediate p-values are considered moderately significant. Throughout this research, we used these typical thresholds.pre-processed, with explanatory variables missing in 50% or more of cases being excluded. Also, the changing nature of operational data over time is addressed by generating different derivation datasets based on key time-points during which changes occurred to the underlying data. Then, in the second step, systematic model development is undertaken. Here, the most appropriate classifier, features, discretization algorithm, adjustment method for missing data, and derivation dataset are selected in order to develop the highest performing predictive models. Finally, as the third step, risk factor analysis is conducted on the final predictive model in order to rank the risk factors in terms of their relative significance. Provided below are details of these steps in the proposed approach.Figure The input of this step is the explanatory variables identified by experts and from the literature, as well as the validation and derivation datasets containing both complete and incomplete records. This step analyzes the full dataset (2003 to 2013 in our case study), and variables with ≥ 50% missing information are removed. Also, the distributions of categorical variable values over time are examined, so as to identify changes in data collection practices and to account for such changes. In particular, if there are key points in time when data collection practices or underlying data changes occurred, temporal datasets are generated for model derivation purposes based on these key time points. Furthermore, categorical values are merged if they changed over time to represent the same concept. For example, in this case study, the introduction of a new scheduling and billing system changed the designation of the cardiothoracic surgery service from the “CTI” service to the “CTS” service. Such equivalent categorical values are merged into a single variable . All categorical values are otherwise used without modification. The outputs of this step are the remaining explanatory variables and the candidate temporal datasets.The inputs of this step are the outputs from the first step. This step aims to develop the highest performing models through the selection of the most appropriate classifier, features, discretization algorithm, adjustment method for missing data, and derivation dataset. The outputs from this step are the highest performing predictive model and the associated derivation dataset and methods.2 and t-tests are used for categorical and numeric variables respectively to calculate the related p-values. We use these tests to identify which derivation dataset(s) are strongly correlated  with the validation dataset with regard to readmissions, as such correlation provides an initial indication of which derivation dataset(s) may serve as good candidate(s) for developing the final models. These tests were also used to identify whether any explanatory variable has highly significant p-values across the derivation datasets , which would indicate a potential problem with the explanatory variable in the datasets that would require investigation or exclusion from further analysis.This step begins with preliminary statistical analyses to characterize the data. To study the variable distributions in the derivation datasets compared to the validation dataset, statistical tests are used to identify the closeness of the variable distribution in each derivation dataset compared to the validation dataset. For this analysis, the dependent variable is whether the data is from a derivation dataset  or is from the validation dataset . Then, each candidate derivation dataset is combined with the validation dataset, wherein the independent variables are the independent variables following pre-processing, as well as the outcome of interest . Then, χFollowing the above analysis, the next step is to empirically identify the combination of classifier, features, discretization algorithm, and derivation dataset with the highest predictive ability, as measured by the c-statistic (AUC), accuracy , sensitivity, specificity, PPV, and NPV of the predictive model when tested against the validation dataset. For evaluating these metrics, a cutoff probability of 0.5 is used for predicting the outcome. In other words, if the probability of the outcome  is greater than 0.5, the prediction is for the outcome to occur; if it is less than or equal to 0.5, the prediction is for the outcome to not occur. The dependent variable in this analysis is the outcome of interest . For this analysis, all classifiers reviewed by ,15,33,49This step begins by restricting the analysis to the classifier, discretization algorithm, and derivation dataset identified as leading to the highest performance in the previous step. The objective of this step is to develop the final predictive model and to rank the most significant risk factors. The output of this step is the identification of risk factors in the final predictive model, with each risk factor ranked as strong, regular, or weak.2 and t-tests are used for categorical and numeric variables respectively to identify the degree to which the variable is capable of discriminating between the outcomes of interest.In terms of explanatory variables, in order to avoid omitting any significant variables, all variables resulting from the step one analysis are re-introduced. Then, the final explanatory variables are selected and ranked in the following manner. In this analysis, the dependent variable is whether the outcome of interest  exists  or not . Then, using just the highest-performing derivation dataset, χIn this study, we used four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators . Weka waThe proposed three-step approach to predictive analytics was applied to the prediction of CHF readmission using an operational dataset. Provided below are the results from this sample application of the proposed approach.Of 83 original explanatory variables, 16 laboratory tests were removed due to > 50% missing information, resulting in 67 independent variables for analysis. Figures et al.[In this step, we used two adjustment methods, namely complete-case analysis and mean-based imputation based on algorithms described by Luengo et al.. In compet al., missingDuring the step one analysis Table , all derBased on empirical analysis, the highest performing derivation dataset was from the complete 2008–2012 dataset. The performance characteristics of the best models from each of the candidate derivation datasets with and without discretization are shown in Table As a comparison, we used the LACE index approach to develop predictive models for readmissions  with ourTables Of the 42 risk factors, race, religion, and insurance/finance class variables had weak statistical significance but were included in the step one selection process, and the classifier performed better when they were included as features. Therefore, these three variables were identified as weak risk factors.The remaining 39 variables were statistically significant predictive variables and were included in the step one selection process. Among them, 13 were ranked as the most important classifier variables by the four ranking strategies, where GainRatioAttributeEval and Wrapper rankers acquired equal variable ranks. These strongest identified risk factors were discharge disposition, discretized age, and anemia-related factors . The other strong risk factors were hospital service, CharlsonIndexF, injury and poisoning (CCS diagnosis category 16), and the prior 6-month variables of CharlsonIndexF, FreqED and EDLOS.Here, details are provided for the following risk factors: weak risk factors, strong non-discretized risk factors, and discretized risk factors.Regarding the weak risk factors, the highest readmission rates were 39.9%, 33.3%, and 29.4% for the Asian race, the Protestant religion, and commercial insurance respectively.Regarding strong non-discretized risk factors, discharge disposition was ranked first by two rankers and second by two rankers, indicating it was likely the most important risk factor in this model. Discharge to a rehabilitation facility was associated with the highest readmission rate (61.1%). Five laboratory variables were identified as strong risk factors: RCDW, HGB, RBC, HCT, and BASO%. These risk factors were frequently identified as one of the top five risk factors. Most of the readmitted patients had values within the ranges of 12.53-20.87, 7.19-14.28, 2.92-5.19, 21.77-43.92, and 0–1.21 respectively, and the associated highest readmission rates (20.4%-22.3%) were associated with laboratory values in the range of 14.2-15.87, 8.96-9.85, 3.58-3.90, 27.31-30.08, and 0–0.14 respectively.The other strong risk factors, which ranked frequently from 8 to 13, are summarized as follows. For hospital service, the highest readmission rate (32.7%) was for patients cared for by cardiothoracic surgery. With regard to prior ED utilization, the highest readmission rate (42.9%) was for patients who were seen at the ED at least 6 times in the prior 6 months. The highest readmission rates (range 20.2%-21.8%) for CharlsonIndexF, CCS category 16 (injury and poisoning), and prior 6-month CharlsonIndexF variables were associated with frequencies of 2–5, 0–2, and 0–5 respectively. Among patients with an ICD-9 diagnosis in the injury and poisoning CCS category, the most common reason for that categorization was a diagnosis of hypoxemia (present in 23.4% of patients with the CCS category).Figures Given the relatively small sample size and the large number of features evaluated, we evaluated for the potential for overfitting by testing the model on a separate, unseen validation dataset separate from the derivation and primary validation datasets. This additional validation dataset consisted of 130 CHF admissions from July to October 2013, 19 of which resulted in a readmission (14.5% readmission rate), and 45.73% of which contained complete data. The performance characteristics of the best model remained strong when evaluated against this additional validation dataset, with an AUC of 79.0%, accuracy of 85.4%, sensitivity of 55.3%, specificity of 88.2%, PPV of 44%, and NPV of 90.4%. Moreover, the LACE index predictive model  achievedOf note, as with any predictive model , the perIn this study, a three-step approach to predictive analytics was proposed and piloted on an operational clinical dataset to develop predictive models for CHF readmission. This approach adds to the healthcare literature by acknowledging the changing nature of operational data over time and systematically evaluating various temporal datasets. Moreover, many of the component techniques included in our proposed approach, such as voting classifiers, discretization, wrapper subset feature selection, and various ranking strategies, have not been applied to readmission predictive analytics in the past. Furthermore, to our knowledge, our proposed three-step approach to optimizing predictive analytics has never been applied to date in the healthcare domain. The resulting predictive model had a c-statistic of 86.8%.Our findings are generally consistent with the prior literature in this field, with many of the same risk factors identified for CHF readmission, such as age, LOS, HGB, and HCT . At the An important strength is that the approach is generic and can be generalized to other problem spaces. As a second strength, the model had strong performance, with an 86.8% c-statistic, despite the unseen validation dataset containing all records, including 52.7% of records with missing data. Third, as discussed in the Future Directions section below, the model is designed for operational use, utilizing data that are operationally available, including data that change over time and are frequently missing. Fourth, our approach to risk factor selection utilizes not only statistical methods but also classifier feature selection with ranking strategies. By using multiple independent methods for risk factor selection, our approach increases confidence in the absolute and relative importance of the risk factors that are identified as important by multiple methods. Finally, our approach is based on the use of open-source, readily available tooling and can be replicated by others at minimal cost.With regard to limitations, the study was conducted in a single, relatively small academic health system. Consequently, the results of the study will need to be replicated at other institutions to verify external validity. Second, the approach has not yet been applied to other problem spaces. Therefore, additional studies utilizing this approach will be needed to evaluate its applicability to other domains.In this study, a systematic, three-step approach to predictive analytics has been applied in the domain of CHF readmissions and validated to produce high-performing predictive models. In reducing CHF readmissions specifically, and potentially readmissions in general, our approach could potentially support the development of targeted interventions for addressing this important cause of morbidity, mortality, and excess healthcare costs. The potential for the development of such interventions is discussed below under Future Directions. Moreover, the approach is generic in nature and could potentially be applied in many other areas of health care where predictive analytics could help improve the efficiency and effectiveness of patient care.Based on the results of this study, we are exploring how the predictive model could be used prospectively in an operational clinical setting. Almost all data points used in the model development are available at the time of hospitalization. Moreover, the few data points that would not be available in the EHR system prior to discharge  could either be manually entered or inferred. For example, the final discharge disposition could be inferred from the anticipated discharge disposition as documented by the case manager, length of stay could be inferred using the anticipated discharge date documented by the case manager, and problem list entries could be used as a surrogate for discharge diagnoses.et al.[Using this data, the model can provide a visit-specific probability of readmission that could be used to drive discharge planning and follow-up. For example, patients identified as being at higher risk of readmission could be followed more closely following discharge, for example through daily check-ins by a care manager. The final predictive model is embodied in a Java package that could be readily embedded within an information system to calculate patient-specific readmission probabilities. This information could be made available to care givers through a separate, stand-alone system. Alternatively, this information could potentially be made available as a part of the EHR system using a system integration approach such as that proposed by Zhang et al..All aspects of the three-step model development process could be automated, so that the full development process is automatically repeated periodically. Moreover, to reduce the required computational time, the model re-development process could be configured to use only those methods identified as being most effective in earlier iterations of the full three-step process . We are currently in the process of implementing this automated approach.Moving forward, we believe that the proposed approach to predictive analytics should be applied to other clinical problems and datasets. We are currently in the process of conducting such analyses, and preliminary indications look promising. We also are working on improving our approach, for example by developing systematic approaches for grouping similar values for categorical variables and investigating enhanced methods for imputing missing values.This paper proposed, implemented, and evaluated a systematic, three-step approach to predictive analytics for health care. The approach performs well when applied to the prediction of CHF readmissions and is designed to be generalizable to other problem domains. We anticipate that this approach will contribute to the further use of predictive analytics to improve health and health care.The authors have no competing interests to declare.SEA led the design and conduct of the study, as well as the initial preparation of the manuscript. KK oversaw the design and conduct of the study, as well as the preparation of the manuscript. MZ prepared the analysis datasets, BEB provided clinical guidance on the selection of potential explanatory variables, and both MZ and BB contributed to the review and editing of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/14/41/prepub


The increasing prevalence of obesity in both developed and developing countries is one of the most serious public health problems and has led to a global epidemic. Obesity is one of the greatest risk factors of obstructive sleep apnea (OSA), which is found in 60 to 70% of obese patients mainly due to the buildup of fat tissue in the upper portion of the thorax and neck. The aim of the present randomized clinical trial is to assess daytime sleepiness, sleep architecture and pulmonary function in patients with severe obesity before and after bariatric surgery.JulhoUniversity  and consisted of the collection of clinical data, weight, height, body mass index (BMI), measurements of neck and abdomen circumferences, spirometry, maximum ventilatory pressure measurements, standard overnight polysomnography (PSG) and the administration of the Berlin Questionnaire and Epworth Sleepiness Scale.This randomized, controlled trial, was designed, conducted, and reported in accordance with the standards of The CONSORT  Statement. Patients were divided into a bariatric surgery group and control group. The clinical evaluation was performed at the Sleep Laboratory of the Nove de 2. Significant differences were found between the preoperative and postoperative periods regarding neck (p < 0.001) and waist circumference (p < 0.001), maximum inspiratory pressure (p = 0.002 and p = 0.004) and maximum expiratory pressure (p = 0.001 and p = 0.002) for women and men, respectively, as well as sleep stage N3 (p < 0.001), REM sleep (p = 0.049) and the apnea-hypopnea index (p = 0.008).Fifty-two patients participated in the present study and performed PSG. Out of these, 16 underwent bariatric surgery. After surgery, mean BMI decreased from 48.15 ± 8.58 to 36.91 ± 6.67 Kg/mBariatric surgery effectively reduces neck and waist circumference, increases maximum ventilatory pressures, enhances sleep architecture and reduces respiratory sleep disorders, specifically obstructive sleep apnea, in patients with severe obesity.U1111-1121-8873) and Brazilian Registry of Clinical Trials – ReBEC (RBR-9k9hhv).The protocol for this study was registered with the World Health Organization (Universal Trial Number:  The increasing prevalence of obesity in both developed and developing countries is one of the most serious public health problems and has led to a global epidemic ,2. EpideObesity is one of the greatest risk factors of obstructive sleep apnea (OSA), which is found in 60 to 70% of obese patients mainly due to the buildup of fat tissue in the upper portion of the thorax and neck. The mechanism by which obesity can favor the onset of OSA is not fully elucidated. Increased fat deposits in the neck region cause soft tissue enlargement and contribute to a critical narrowing of the airways. However, the upper airways are not always narrowed by these fat deposits, as evidenced by the fact that a substantial percentage of morbidly obese patients do not present OSA -10. The The aim of a change in diet or surgical intervention in patients with obesity is addressed to improve both health and quality of life through sufficient weight loss in order to reduce or even eliminate comorbidities and promote psychological wellbeing . BariatrThe aim of the present study is to compare daytime sleepiness, respiratory sleep disorders, sleep architecture and pulmonary function variables in subjects with morbid obesity before and after bariatric surgery.A controlled, prospective, randomized clinical trial was carried out based on our previously published study protocol , followiConjunto Hospitalar Mandaqui and addressed to the Sleep Laboratory of the Nove de Julho University were recruited for participation in the study. All institutions are located in the city of Sao Paulo (Brazil). After being informed of the objectives and procedures, those who agreed to participate signed a statement of informed consent.Eighty subjects with morbid obesity treated at the Gastric Surgery Service of the Santa Casa de Misericordia and at the Bariatric Surgery Group of the 2) or BMI between 35 and 39.9 kg/m2 with associated comorbidities. The exclusion criteria were: any active malignancy, active alcohol and/or drug abuse, dementia or treatment-refractory psychiatric diseases leading to an inability to provide informed consent, and use of medication that may interfere with the sleep structure, such as hypnotic drugs or stimulants of the central nervous system.The inclusion criteria were morbid obesity (body mass index [BMI] between 40 and 50 kg/mThe patients were divided into a control group and a bariatric surgery group (2:1). Numbers were generated from a randomization table at a central office, where a set of sealed, opaque, sequentially numbered envelops was used to conceal the allocation. Each envelop contained a card stipulating in which group the individual would participate. The randomization process was adopted due to the very large number of patients to be submitted to bariatric surgery and the inability of the surgical service to meet all the demands. At the end of a mean period of 90 days, the patients in the control group returned to the waiting list to undergo bariatric surgery. All participants were clinically stable.Polysomnography (PSG) was performed before and after bariatric surgery (gastric banding) in the bariatric surgery group with a 90-day interval between evaluations.The clinical evaluation of the subjects, according to pre-specified primary and secondary outcome measures, was performed at the Sleep Laboratory of the Nove de Julho University  and consisted of clinical data collection, that included heart and respiratory rates, peripheral blood pressure, weight, height, BMI, measurements of neck and abdomen circumference, spirometry, respiratory pressure measurements, PSG and the administration of the Berlin Questionnaire and Epworth Sleepiness Scale (ESS).The anatomic references for waist and neck circumference measurements were standardized. Waist circumference was measured at the mid-point between the lower margin of the last rib and the iliac crest, whereas the neck circumference was measured horizontally at the level of the cricoid cartilage .SociedadeBrasileira de Pneumologia e Tisiologia [The pulmonary function test was performed with the patient seated comfortably using the KoKo PFT System spirometer (version 4.11)  following the guidelines for pulmonary function tests determined by the siologia . The measiologia  , involving electroencephalogram, electromyogram, electrooculogram, nasal and oral airflow, thoracic and abdominal respiratory effort, digital pulse oximetry and electrocardiogram. A technician with experience in PSG monitored the patients. All signals were recorded continuously. Sleep stages were visually scored in 30-second epochs. Each PSG recording was analyzed manually, under blind conditions by the same examiner with experience in scoring PSG recordings based on the international standards established by the Academy of Sleep Medicine Manual for Scoring Sleep and Associated Events .Apnea was scored when airflow ceased for 10 s or longer according to usual criteria. Hypopnea was recorded when the airflow was below 50% for at least ten seconds, followed by > 3% oxyhemoglobin desaturation. The apnea/hypopnea index (AHI) was calculated as the number of (apneas + hypopneas)/ hour of sleep time .The ESS is a self-administered, eight-item scale that takes few minutes to respond . These qThe Berlin Questionnaire (BQ) was used in this study. This clinical history questionnaire has recognized efficacy in distinguishing individuals at greater risk for OSA. BQ have ten items organized into three categories: snoring and apnea (5 items), daytime sleepiness (4 items) and systemic arterial hypertension and obesity (1 item). All marked responses are scored as positive. Two or more positive categories indicate high risk .et al. [A previous study published by Lettieriet al.  identifiet al. .t test or Mann–Whitney U test, depending on the type of distribution. For the comparison of variables within the same group, either the paired t-test or Wilcoxon test was used, as appropriate. All tests were 2 tailed and p minor than 0.05 was presumed to represent statistical significance. All analyses were performed using the SPSS version 19.0 .When applicable, descriptive and numerical data were reported as mean ± SD. Comparisons between groups were performed using the Student’s In order to ensure data quality, the physiotherapists and physicians in charge of the data acquisition in this study received specific training. Periodic external monitoring was performed to verify the adequate application of the methodology in collecting information and performing the different evaluations. The results of the pre and postoperative procedures were analyzed by blinded evaluators.Between June 2011 and September 2013, eighty patients were recruited for the study, and eighteen subjects refused to participate and ten were excluded for not meeting the eligibility criteria. Therefore, our final sample consisted of 52 obese patients on the waiting list for bariatric surgery. After randomization, all these patients underwent evaluation protocol, including PSG. Sixteen patients (13 women) who composed the bariatric surgery group were evaluated before and after surgical intervention.The demographic and anthropometric characteristics of all participants were showed in Table The values of pulmonary function, MIP and MEP before and after bariatric surgery can be seen in the Table 2 exhibited OSA. The mean values of BMI, neck and waist circumference and ESS scores can be seen in Figure The sleep physiological variables analyzed, ESS and Berlin questionnaire of patients involved in this study are show in Table - surgery revealed an AHI of 15.65 ± 15.51, which was positively correlated with BMI . The results of PSG after bariatric surgery showed a significant reduction of AHI medium (6.26 ± 7.57). Before surgery, seven patients had a moderate to severe AHI, and after the intervention, only one patient showed a moderate score, as shown in Table The patients undergone to bariatric surgery in the present study exhibited a significant reduction both of BMI (p = 0.004) and of waist circumference of 23.34% and 15.33% (p < 0.001), respectively, at three months following bariatric surgery. A significant reduction of 13.45% (p < 0.001) in neck circumference was found and it was positively correlated with reductions of body weight  and BMI . The PSG preThe Berlin questionnaire revealed a significant improvement between the preoperative (1.75 ± 0.45) and postoperative (1.08 ± 0.28) periods (p = 0.001). This finding was in accordance with the gold standard for the identification of OSA (PSG), for which a significant decrease in the postoperative AHI (p = 0.008) was found.The use of bariatric surgery for weight loss among individuals with morbid obesity has increased since the end of nineties of the last century. Bariatric surgery for individuals considered severely obese is reported to be associated with reductions in comorbidities, obesity-related complications and all-cause mortality . Compari.[Waist circumference allows a practical evaluation of central adiposity, which is best correlated with visceral fat, and its association with BMI is an important marker for predicting the risk of systemic arterial hypertension, dyslipidemia and metabolic syndrome . The pat., who als..et al. [In a study involving obese patients, Stepienet al.  found a et al. . Likewiset al. .et al.[Due to its high prevalence rate, OSA is currently considered a public health problem and a clear association with obesity was observed ,31. Indeet al. found thet al.. In the et al. [In the present study, correlations between the AHI (stratified by severity) and obesity indices revealed a tendency toward a greater frequency of OSA with the increase in BMI. This finding is in agreement with data reported in a study by Raveslootet al. , who fouet al. .Severe obesity is associated with important changes in respiratory pattern, with consequences in lung function that have not been fully clarified yet. The literature reports a reduction of maximum pressure generated by the contraction of the ventilatory muscles due to the biomechanical disadvantage and an increase in respiratory work. The impairment in respiratory mechanics, characterized by a reduction of thoracic compliance due to the buildup of fat, a reduction of tidal volume and a consequent increase in respiratory rate, are positively correlated with the degree of obesity ,35.et al.[et al. [et al. [In a study involving obese individuals between 20 and 64 years of age, Magnaniet al. found th.[et al. . The pre.[et al. ,36. Simi [et al.  referenc [et al. .et al.[1) among individuals with severe obesity prior to bariatric surgery.The assessment of maximal ventilatory muscle strength is of considerable importance in individuals with severe obesity due to the possibility of cardiopulmonary complications stemming from the impairment of these muscles, especially in the postoperative period . In a stet al., obese set al. and our et al., who rep1 (p = 0.003) and FEV1% (p = 0.003) were found after bariatric surgery. These results are in agreement with those reported in a study carried out by Wei et al. [In the present sample, five patients exhibited a restrictive respiratory pattern and the others had values within the normal range when compared to the predicted values. Significant improvements of FVC (p= 0.002), FVC% (p = 0.016), FEVi et al. .et al. [A number of epidemiological studies report a strong association between severe obesity and sleep-related respiratory disorders, especially OSA, with a very high prevalence rate ranging from 50 to 89% ,33,39,40et al. , who repet al. .et al.[Anatomical and physical risk factors for OSA in the general population are well known. Using the Berlin questionnaire on a sample of 744 patients in primary care clinics, Netzeret al. detectedet al.; the queet al.. In the The findings of this study demonstrate that weight loss following bariatric surgery led to a reduction of AHI and enhanced sleep architecture which is in agreement with data reported in previous studies that evaluated respiratory events and sleep architecture using preoperative and postoperative PSG ,40,43. MA weakness of this and other studies in patients following bariatric surgery can be considered the relatively low proportion of patients who agreed to repeat the PSG following significant weight loss and the effect of the first night of the exam in the sleep laboratory, which can compromise sleep efficiency. Another limitation of the present study is the fact that the majority of patients were women, in whom a lesser impact on lung function is expected due to the distribution of fat in the female body.In conclusion, a high frequency of moderate-to-severe OSA was found in the initial sample of 52 patients planning bariatric surgery. The patients undergoing bariatric surgery met the criteria for OSA (75%) and 25% of these patients had moderate-to-severe OSA. The findings demonstrate that bariatric surgery for patients with severe obesity effectively reduces neck and waist circumference, improves pulmonary function, improves sleep architecture and reduces respiratory sleep disorders, especially OSA. The patients are currently in follow up for the determination of the results of bariatric surgery after one year.ICA, WRFJ, IRS, NA, SRN, JJU, NTF, FRT, EJI, PK, FSSLF, RMLN, CAM, GI, CFD, and LVFO declare that there is no association with any commercial enterprise that has interest in the object of this study. No competing of interest.All the authors contributed to the conception and design the study. LVFO, ICA and WRFJ provided the idea of the study, established the hypothesis and wrote the original proposal. CAM, WRFJ, RMLN, FRT, EJI and PK performed the surgical procedures. IRS, SRN and NTF conducted the sleep studies. SRN and NA carried out pulmonary function tests. ICA, FSSLF, GI, CFD, FRT, EJI, PK and LVFO analyzed the data. This research paper was written by ICA, FSSLF, NA, CFD and LVFO, with input from all co-authors. IRS, CAM, WRFJ, RMLN, JJU, NTF, SRN, GI, and CFD were involved in critically revising the manuscript. All authors significantly contributed to this study and read and approved the final manuscript.


Objective. The present study examined mentalizing capacities as well as the relative implication of mentalizing in the comprehension of ironic and sincere assertions among 30 older adults with mild cognitive impairment (MCI) and 30 healthy control (HC) subjects. Method. Subjects were administered a task evaluating mentalizing by means of short stories. A verbal irony comprehension task, in which participants had to identify ironic or sincere statements within short stories, was also administered; the design of the task allowed uniform implication of mentalizing across the conditions. Results. Findings indicated that participants with MCI have second-order mentalizing difficulties compared to HC subjects. Moreover, MCI participants were impaired compared to the HC group in identifying ironic or sincere stories, both requiring mental inference capacities. Conclusion. This study suggests that, in individuals with MCI, difficulties in the comprehension of ironic and sincere assertions are closely related to second-order mentalizing deficits. These findings support previous data suggesting a strong relationship between irony comprehension and mentalizing. Given the aging of the population, the prevalence of dementia such as Alzheimer's disease (AD) is increasing. It is now largely accepted that early identification of AD is crucial. An important scientific and clinical challenge is to clarify the neuropsychological profile of individuals with mild cognitive impairment (MCI) to assist the identification of early AD-related cognitive deficits. The concept of MCI corresponds to the prodromal stage of AD in several, if not most, older adults . OldAmong the neuropsychological characteristics of individuals with MCI, mentalizing deficits have already been documented –5. MentaVerbal irony comprehension is generally defined as a linguistic process used to express information which is directly, or indirectly, opposed to its literal interpretation . Recent The main objectives of this study are twofold: (a) to corroborate previous results indicating the presence of mentalizing deficits in older adults with MCI and (b) to examine the implication of mentalizing in the comprehension of verbal assertions based on mental state inferences  in MCI. To this end, participants were administered two different tasks, one evaluating mentalizing capacities and the other verbal irony comprehension by means of short stories. The mentalizing task assessed the integrity of first-  and second-order  mentalizing, as well as nonsocial reasoning . The verbal irony task assessed the interpretation of ironic and sincere scenarios. Considering existing bodies of evidence of second-order mentalizing deficits in individuals with MCI , 5, 16, Participants included 30 older adults with MCI (age range: 60–83 years) and 30 healthy control (HC) subjects (age range: 55–82 years). Participants with MCI were identified and referred to the research team by experienced geriatricians, neurologists, or general practitioners, or through advertisements displayed in different clinics. All MCI participants met the clinical core criteria of Albert et al. . All MCIFor all participants, exclusion criteria were as follows: (a) history of traumatic brain injury; (b) history of stroke or other cerebrovascular disorders; (c) history of delirium (in the last six months); (d) formal intracranial surgery; (e) history of neurological disorder of cerebral origin or associated with another dementia state ; (f) history of encephalitis or bacterial meningitis; (g) unstable metabolic or medical condition ; (h) history or current diagnosis of a psychiatric illness or dementia according to the DSM-IV ; (i) onc Institut Universitaire en Santé Mentale de Québec.An informed consent form was signed by each participant at the beginning of the first assessment session. This study was approved by the Ethics Research Board of theParticipants were administered a comprehensive battery of clinical and neuropsychological tests in order to verify inclusion/exclusion criteria and to characterize their cognitive and clinical status. The battery included instruments to assess depressive symptoms , functi Combined Stories Test, a task developed and validated by Achim et al. [First- and second-order mentalizing abilities were evaluated using them et al. . A totalm et al. . Short Scenario Irony Comprehension Task (SSICT), a French task of verbal irony comprehension adapted from Eviatar and Just [Theand Just , was useand Just . StimuliThe SSICT consists in written presentation of 14 short stories describing everyday life situations, each including four assertions (for a sample of the task see the following). Sample story in French adapted from Eviatar and Just  is as foLe Placotage (The Chatting) Julie is a talkative person and she cannot help commenting on anything.)Julie est bavarde et elle ne peut s'empêcher de tout commenter. Son ami Robert a l'habitude de l'entendre parler sans cesse et cela le fait bien rire. Un jour, ils se rencontrent pour déjeuner, mais Julie est fatiguée et elle ne parle presque pas. Ironic Statement. Tu es en forme pour un débat, dit Robert. Descriptive Statement. Le déjeuner comporte du lait et des céréales.  sincere and ironic scenarios is considered “social” , while the last statement in descriptive scenarios is “nonsocial” . The presence of descriptive scenarios in the task is aimed at minimizing the response bias that can occur when tasks involve dichotomous answers . To summarize, except for the very last sentence, which varies depending on the story type, all scenarios comprise the interaction between two protagonists and present one triggering event. Moreover, the scenarios are all relatively short (approximately 45 words) in order to minimize the contribution of nonlanguage  cognitive functions.The first sentence always presents the general context of the scenario, whereas the second phrase describes the specific context. The third statement presents a triggering event. Finally, the fourth sentence is the one that has to be evaluated as being either (a) sincere, (b) ironic, or (c) descriptive. Of the 14 scenarios, five describe a sincere situation, five are ironic, and four are descriptive. The fourth statement of theTo correctly interpret both sincere and ironic scenarios, participants must infer or metarepresent mental states. It is assumed that the “mentalizing load” is comparable between sincere and ironic scenarios since, in both cases, the interaction between two protagonists is depicted. Moreover, for both sincere and ironic stories, participants have to infer a mental state in one protagonist by taking into account the context described in the first three literal assertions. The variable of interest in the SSICT is the correct interpretation  of the fourth sentence, which is scored as 1 or 0.Participants were assessed individually over three sessions. The first session included the signature of the informed consent form and the administration of the clinical and neuropsychological tests or questionnaires; this session lasted approximately two hours. Mentalizing abilities (Combined Stories Test) and verbal irony comprehension (SSICT) were assessed during the second and third sessions, in a counterbalanced order. These sessions lasted approximately 30 to 45 minutes each.Participants sat at about 1 m from the computer screen and were asked to read the stories out loud. When finished, the experimenter asked questions  and wrote down the participant's answer on the response sheet.Participants sat at about 1 m from the computer screen. They were asked to read out loud the task instructions, which appeared on the computer screen. They were then asked to read the written stories out loud in a self-paced manner. When the first two sentences of a story were read, participants pressed the “Enter” button in order to see and read sentences 3 and 4 (the first two sentences remained on the screen at this stage). Following reading of the fourth sentence, subjects were asked whether the story was (1) sincere, (2) ironic, or (3) descriptive. They had to answer by pressing the corresponding number on a keypad placed in front of them. Stories were counterbalanced in a random order to control for a possible sequence effect.t-tests were used to compare MCI and HC participants on demographic and clinical data. These analyses were performed (a) to ensure equivalence of the groups with respect to age, sex, and education and (b) to compare the clinical and neuropsychological profile of participants.Except for sex distribution in the groups, which was analysed using Pearson Chi-square test, Student's t-tests were used to compare the groups on first- and second-order mentalizing, as well as nonsocial reasoning. For the SSICT, proportions of correct responses were calculated for each scenario and a mixed ANOVA Group (HC and MCI) X Story type (sincere and ironic) was used in order to compare group performance.Exploratory analyses were first conducted to examine normal distribution assumptions required for inferential analyses. All variables of interest were normally distributed. Regarding the Combined Stories Test, proportions of correct responses were calculated and Student's Z-scores were first computed using the mean and standard deviations of the control group for each measure included in the composite score. Then, the derived Z-scores were averaged in order to quantify the difference in executive functions in MCI participants compared to HC participants. The mean executive composite score for the MCI group was −1.92 (SD = 2.52).Pearson's correlations were used to assess the association between the key variables of the experimental tasks . With regard to the SSICT, the relationship between results for ironic and sincere stories was also analyzed, to examine statistically the extent to which these two conditions rely on similar cognitive processes. Moreover, Pearson's correlations were performed in order to analyse associations between the score on variables of interest in the Combined Stories Test and the SSICT with measures of executive functions and episodic memory . In order to limit the number of analyses, a composite score was computed for executive functions, including measures of manipulation of information in working memory (score on the WAIS-III letter-number sequencing task), controlled inhibition , and mental flexibility . For all analyses, an alpha level of .05 was used.t(57) = −2.74, p = 0.003, and d = .98. However, the groups were comparable regarding their capacity to answer nonsocial reasoning, t(57) = −1.48, p = 0.145, and d = .16, and first-order mentalizing, t(57) = −0.51, p = 0.609, and d = .03, questions.Two participants out of 30 were excluded from the MCI group because of missing data or withdrawal from the study. Thus, 28 MCI participants were included in the final analyses. F = 19.14, p < 0.001, and η2 = .25, revealing that overall performance of MCI was worse than that of HC participants. There was also a main effect of story type, F = 11.96, p < 0.001, and η2 = .17, indicating that ironic assertions were better interpreted by the participants compared to sincere ones. Finally, the interaction was not significant, F = .28, p = 0.756, and η2 = .01.r(59) = 0.40 and p = 0.001) and sincere (r(59) = 0.38 and p = 0.003) stories of the SSICT. A significant relationship was also found between ironic and sincere scores of the SSICT, r(61) = 0.32 and p = 0.011.Overall, the second-order mentalizing score of the Combined Stories Test was significantly related to the scores on both ironic (r(61) = 0.28 and p = 0.027) and total (r(61) = 0.40 and p = 0.001) immediate recall of the RL/RI-16. Significant positive correlations were also found between the composite executive score and sincere stories, r(59) = 0.36 and p = 0.006. A similar pattern of results was found for ironic stories as significant positive relationships were found between this variable and the mean scores of free (r(61) = 0.28 and p = 0.030) and total (r(61) = 0.26 and p = 0.047) immediate recall of the RL/RI-16. A significant positive relationship was also found between score in ironic stories and the composite executive score, r(59) = 0.30 and p = 0.021.With respect to the SSICT, significant positive relationships were found between score in the sincere stories and mean scores of free (r(58) = 0.43 and p = 0.001) and total (r(58) = 0.49 and p < 0.001) immediate recall of the RL/RI-16 and to the composite executive score, r(56) = 0.51 and p < 0.001.Finally, the second-order mentalizing score of the Combined Stories Test was significantly and positively related to the mean scores of free (The main purposes of this study were (1) to confirm second-order mentalizing impairment in MCI and (2) to examine the relative implication of mental state inferences in the understanding of ironic and sincere assertions. With respect to second-order mentalizing abilities, results confirmed that MCI participants had significantly greater difficulties than their healthy counterparts. However, the groups were comparable regarding nonsocial reasoning and first-order mentalizing capacities. Regarding comprehension of ironic and sincere stories involving second-order mentalizing, participants with MCI were impaired relative to the HC group. That is, MCI participants had difficulty in correctly interpreting both ironic and sincere stories.The present findings extend prior results published by Gaudreau et al. . First, Results of the Combined Stories Test  are in l both the sincere and ironic connotation of the stories. This interpretation is supported by the fact that second-order mentalizing abilities, as measured by the Combined Stories Test, which were impaired in our MCI sample compared to the HC group, were also significantly and positively related to the comprehension of sincere and ironic assertions in the SSICT.With respect to the SSICT, the MCI participants' difficulty in interpreting ironic written scenarios is in accord with previous results showing impaired verbal irony comprehension in this population , 15. IntThe significant relationship between second-order mentalizing and irony comprehension can also be explained by other factors. For instance, it could be that these cognitive processes are affected essentially to the same degree by the underlying neuropathology in MCI. Alternatively, the two measures could reflect to some extent a similar cognitive mechanism or process and thus impaired performance on one measure would be accompanied by impaired performance on the other. In fact, it is possible that the positive and significant association between performances in both experimental tasks derives from the underlying abilities in language and executive domains involved in both mental inferences abilities and verbal irony comprehension. Further studies will be necessary to verify these hypotheses.The study of the relationships among the variables of interest brought interesting results, but the authors acknowledge that a better analysis strategy would have been to use linear regression instead of Pearson's correlation. Unfortunately, the sample size in this study was not large enough to use linear regression. That being said, the present results add to the literature by showing that the presence of episodic memory difficulties is significantly linked to poorer comprehension of ironic and sincere assertions as well as poorer second-order mentalizing capacities. Regarding executive functions, results are in line with previous data having demonstrated a relationship between these capacities and irony comprehension , 39, 40.One interesting finding of the present study is the observation of a relationship between measures of episodic memory and the performance on both the SSICT and the Combined Stories Test. These results are consistent with the proposition of Moreau et al.  that epiOne surprising result is that, in both MCI and HC groups, ironic assertions were better interpreted than sincere ones. An analysis of the error patterns indicated that sincere stories were often mistaken as being ironic, and vice versa. In MCI individuals, “ironic” responses consisted of 60% of the total number of errors for sincere scenarios, and this pattern was reversed for ironic scenarios . In the HC group, more than 68% of errors in sincere scenarios consisted of “ironic” responses. Again, the reverse pattern was found in ironic stories, these being identified as “sincere” 64% of the time. One explanation could rely on the fact that stories were read out loud by the participants and were not presented through a prerecorded and controlled voice. Thus, this administration procedure precluded the use of prosody to correctly differentiate sincere from ironic assertions. Prosody is known to be of considerable help when having to differentiate such statements . per se [Prior to this study, the adapted SSICT was validated in 75 young adults  and results indicated that participants better understood sincere compared to ironic scenarios. The discrepancy between findings of the present study and those of the validation study suggests that the differentiation between irony and sincerity gets harder with aging. If so, this interpretation would be consistent with the fact that age impacts second-order mentalizing as measured by short stories, as well as text comprehension per se –49. Agin per se  and the  per se . HencefoThe SSICT has strengths and weaknesses. On one hand, the use of mentalizing as a variable equally distributed in the sincere and ironic stories represents an important improvement in comparison with previous tasks. On the other hand, the standardization of mentalizing between ironic and sincere scenarios may have explained to some extent the overall deficit of MCI participants in the present study. In MCI, verbal irony comprehension could probably be better assessed by means of tasks using different modalities . This question requires further investigation.A limitation of the present study relates to the generalization of the results to all MCI cases, considering the rather small sample size. Because of the known heterogeneity of MCI, the sample in this study may not reflect all the characteristics that can be found in this population. Thus, mentalizing and verbal irony comprehension in MCI should be studied using larger samples. Moreover, longitudinal follow-up is warranted to determine to what extent these cognitive functions can help predict the dementia stage of AD in those individuals having MCI. In all, it is possible that the level of education influenced the performance of our participants, as a link between education and mentalizing was already demonstrated in the aging population . This poOverall, this study provides additional evidence of second-order mentalizing impairment in older adults with MCI and suggests that mental state inferences deficits are associated with difficulties in the comprehension of ironic and sincere assertions in this population. It also brings support to the possible use of mentalization tests in the screening of individuals at risks of evolving towards AD, although this requires further investigation. Further studies should aim at improving the comprehension of the cognitive correlates of ironic and sincere assertions comprehension  and their implication in second-order mentalizing capacities of individuals with MCI or healthy older adults. Such work will not only help identifying early the prodromal stage of AD but also help clarifying the theoretical grounds of the comprehension of verbal irony and sincerity.


Chronic obstructive pulmonary disease (COPD) patients often have dysphagia through age and several co-morbidities, leading to aspiration pneumonia (AsP). COPD patients also have increased risk of developing community-acquired pneumonia (CAP). Using a national inpatient database in Japan, we aimed to compare clinical characteristics and outcomes between AsP and CAP in COPD patients and to verify the factors that affect in-hospital morality.We retrospectively collected data on COPD patients (age ≥40 years) who were admitted for AsP or CAP in 1,165 hospitals across Japan between July 2010 and May 2013. We performed multivariable logistic regression analyses to examine the association of various factors with all-cause in-hospital mortality for AsP and CAP.Of 87,330 eligible patients, AsP patients were more likely to be older, male and have poorer general condition and more severe pneumonia than those with CAP. In-hospital mortality in the AsP group was 22.7 % and 12.2 % in the CAP group. After adjustment for patient background, AsP patients had significantly higher mortality than CAP patients . Subgroup analyses showed higher mortality to be associated with male gender, underweight, dyspnea, physical disability, pneumonia severity, and several co-morbidities. Further, older age and worse level of consciousness were associated with higher mortality in the CAP group, whereas those were not associated in the AsP group.Clinical characteristics differed significantly between AsP and CAP in COPD patients. AsP patients had significantly higher mortality than those with CAP.The online version of this article (doi:10.1186/s12890-015-0064-5) contains supplementary material, which is available to authorized users. Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the world . COPD isAspiration pneumonia (AsP) is assumed to be one of the phenotypes of CAP. AsP demonstrates specific clinical features , 11, sucHowever, there is little information about the clinical features of COPD patients with AsP compared with those with CAP—particularly with respect to in-hospital mortality. Therefore, the purpose of this study was to compare clinical characteristics and outcomes between AsP and CAP in COPD patients using a national inpatient database in Japan. We also aimed to evaluate factors that affect in-hospital mortality in the AsP and CAP patient groups.2; details of BMI categorization appear in Additional file This study used the Diagnosis Procedure Combination database, a nationwide inpatient database in Japan. The database includes administrative claims data and discharge abstract data. With this database, the primary diagnosis on admission, co-morbidities present, and complications during hospitalization are recorded using the International Classification of Disease and Related Health Problems, 10th Revision (ICD-10) codes accompanied by text data in Japanese. The database also contains the following details: patient’s age, sex, body height, and weight; severity of dyspnea based on the Hugh-Jones dyspnea scale  or pneumonia caused by microbes (J10–J18) as the main diagnosis on admission, who had a diagnosis of COPD (J41–J44), and who were discharged between 1 July 2010 and 31 March 2013. To preclude hospital-acquired and ventilator-associated pneumonia, we excluded patients who had pneumonia recorded as a post-admission complication. The presence of COPD was based on physician diagnosis. We divided the patients into those with AsP and CAP.We identified co-morbidities on admission using ICD-10 codes , length of ICU stay (days), requirement of mechanical ventilation, duration of mechanical ventilation (days), and mortality of patients who underwent mechanical ventilation.t test to compare average values, and the Mann–Whitney test to compare the median values between the groups. We performed multivariable logistic regression analyses to assess the relationship between patient-level factors and mortality in the following COPD patients—after adjustment for within-hospital clustering by means of a generalized estimating equation [p < 0.05. We performed all statistical analyses using SPSS Statistics for Windows, Version 22.0 .We used the chi-square test to compare proportions, the two-sample equation : (1) all2, which was significantly lower than that in the CAP group (19.9 kg/m2). The proportion of patients with severe dyspnea grade, low level of consciousness, and low Barthel index on admission was higher in the AsP group than in the CAP group; thus, the general condition on admission was poorer in the AsP group than in the CAP group. Regarding severity of pneumonia, the median A-DROP score in the AsP group was 2 , which was higher than that in the CAP group ; the proportion of severe pneumonia was higher in the AsP group than in the CAP group. Co-morbidities on admission are presented in Table We identified 87,330 patients with COPD (age ≥40 years) who were admitted for pneumonia; 16,388 had AsP, and 70,942 had CAP. The patient characteristics appear in Table The outcomes appear in Table p = 0.001) than CAP. Higher mortality was associated with older age, male gender, lower BMI, more severe dyspnea scale, worse level of consciousness, worse level of activities of daily life, and more severe pneumonia. Higher mortality was also associated with several co-morbidities on admission, including interstitial pneumonia, lung cancer, heart failure, cor pulmonale, chronic renal failure, and bone fracture.The results of the multivariable logistic regression analysis for all-cause in-hospital mortality are presented in Table Subgroup analyses of the AsP and CAP groups (center and right-hand columns, Table Using a national inpatient database in Japan, we compared clinical characteristics and outcomes between AsP and CAP in patients with COPD. The AsP group was more likely to be older, male, have lower BMI, more severe dyspnea, worse levels of consciousness, worse activities of daily life level, and greater severity of pneumonia than the CAP group. Mortality in the AsP group was significantly higher than in the CAP group—even after adjustment for patient backgrounds.It is well known that there is a high incidence of aspiration in the elderly, and it is an important risk factor in CAP . One stuAsP is believed to be one of the phenotypes of CAP. Although there are no definitive diagnostic criteria of AsP, it is generally characterized by aspiration of oropharyngeal contents into the larynx and lower respiratory tract , 28. PreRegarding mortality, it is controversial whether or not the presence of AsP in the general population is an independent short-term prognostic factor. After adjusting for co-morbidities and severity of pneumonia, some studies , 12 haveThe length of hospital stay in the AsP group was significantly greater than in the CAP group, which is consistent with the results of previous studies that focused on AsP. , 12, 30 This study has several limitations. The diagnoses of COPD, AsP, and CAP were based on physician-based assessments. The accuracy of the diagnosis was not certified by specialists. However, in normal clinical settings, these diseases are not always diagnosed by specialists, and so we believe the data relating to physician-based diagnosis to be meaningful. In addition, cases of pneumonia not having been diagnosed on admission but being diagnosed within the first 48 hours after admission may have been excluded in this study: such cases may not have appeared in the category of hospital-acquired pneumonia. Thus, AsP and CAP may have been underestimated in the present study. Further, the degree of airflow limitation was not evaluated in this study: the database does not include the stages of COPD severity or details of pulmonary function tests, including forced expiratory volume in 1 second and other indices. Thus, we cannot evaluate the association between mortality and degree of lung function.In conclusion, this study demonstrates that AsP has different characteristics to CAP and that AsP is associated with higher in-hospital mortality than CAP in COPD patients.


For trauma patients, delays to assessment, resuscitation, and definitive care affect outcomes. We studied the effects of resuscitation area occupancy and trauma team size on trauma team resuscitation speed in an observational study at a tertiary academic institution in Singapore.From January 2014 to January 2015, resuscitation videos of trauma team activated patients with an Injury Severity Score of 9 or more were extracted for review within 14 days by independent reviewers. Exclusion criteria were patients dead on arrival, inter-hospital transfers, and up-triaged patients. Data captured included manpower availability (trauma team size and resuscitation area occupancy), assessment , interventions , and process-of-care time intervals . Clinical data were obtained by chart review and from the trauma registry.Videos of 70 patients were reviewed over a 13-month period. The median time spent in the emergency department was 154.9 minutes (IQR 130.7–207.5) and the median resuscitation team size was 7, with larger team sizes correlating with faster process-of-care time intervals: time to airway assessment (p = 0.08) and time to disposition (p = 0.04). The mean resuscitation area occupancy rate (RAOR) was 1.89±2.49, and the RAOR was positively correlated with time spent in the emergency department (p = 0.009).Our results suggest that adequate staffing for trauma teams and resuscitation room occupancy are correlated with faster trauma resuscitation and reduced time spent in the emergency department. Improvements in hospital trauma systems can lead to better outcomes and survival . Trauma One approach to improving trauma resuscitation outcomes and standards is the auditing of team dynamics  5] as w as w5] aThe use of video recording in trauma as a training technique has been previously described in literature . We usedResuscitation Area Occupancy Rates at t = 0 (RAOR) were extracted from the CCTV footage of all 6 bays at the start of resuscitation. An RAOR of 6 refers to a full resuscitation area with all 6 bays filled, while an RAOR of 1 refers to a resuscitation area with only 1 out of the 6 bays filled (i.e. only the trauma patient in question).The onset of resuscitation (t = 0) was defined as the time at which the patient’s trolley is parked in the resuscitation area, allowing interventions to begin.The end of resuscitation was defined as the time when the patient was wheeled off for final disposition out of the ED.As trauma team size is known to fluctuate in the course of a resuscitation, the trauma team size utilized for this study was defined as the largest size of the team with the trauma team leader present at bedside from the start of resuscitation until eventual disposition out of the ED. Physicians, nurses, and medical students were included only if they actively assisted in resuscitation.Video footage was viewed independently by at least one of the trained co-investigators of the study, with quantitative timings and data distilled for subsequent analysis. Standardization of time-point capture and definitions was performed for the first ten videos with at least two co-investigators viewing the videos independently and recording timings for subsequent inter-rater comparisons and standardization of methods. The observers did not review any videos where they were themselves part of the trauma team activation.Data extracted included: resuscitation manpower characteristics , interventions , and timing of logrolls), and process-of-care time intervals . Interventions were identified and classified by the Advanced Trauma Life Support (ATLS) principles of Airway, Breathing, Circulation, Disability, and Exposure.Additional demographic and clinical data obtained from the hospital trauma registry and chart review included: patient demographics , injury mechanism , ISS, Revised Trauma Score (RTS), and eventual patient outcomes , and in-hospital survival).Ethical approval for this study was granted by the Singapore General Hospital Institutional Review Board. All data was stored on password-protected computers within a locked room (the trauma office) and anonymized prior to statistical analysis for privacy protection. Due to the retrospective observational nature of the study, the study was granted exemption from requiring patient consent by the institutional review board.Out of a total of 185 trauma activations within this 13-month period, 96 met inclusion criteria. Sixty-six patients had an ISS<9, 4 were dead on arrival, and there were 19 patients who were initially under-triaged or transferred from other institutions. Eighteen video-captures were missed due to data overwriting within the system, and 8 videos were corrupted. This is however unlikely to introduce significant bias as the missing and corrupted video files did not occur at any particular time of day or day of week. Videos of 70 patients were analyzed .Of the patients whose videos were captured for analysis, 51 (72.9%) were male, ages ranging from 17 through 77, with a mean age of 41.7 years. Most were blunt injuries . Road traffic injuries (RTI) were the most common mechanism of injury , followed by falls from height , assault , and industrial accidents . The median ISS was 14.00 (interquartile range [IQR] 2.0–22.0) and the median RTS was 7.841 (IQR 6.904–7.841). The median LOS in hospital was 4.75 days (IQR 1.75–18.0). 77.1% of our trauma patients were eventually discharged home and 12.9% were discharged to step-down care, including rehabilitation hospitals.We analyzed the effects of the following resource availability variables on resuscitation timings: Trauma Team size, RAOR, and Frequency of interventions.Team sizes ranged from 4 to 15, with a median team size of 7.00 (IQR 5.75–8.00), out of which 4.79 (SD = 1.55) were physicians.Pearson’s correlation showed that team size was correlated with shorter time to airway assessment and eventual disposition .st Computed Tomography (CT) (p = 0.002).Using Spearman’s correlation, we compared team sizes smaller than the median team size of 7 to team sizes of 7 or more. This also showed longer ATLS primary survey process-of-care time intervals for smaller teams: Time to airway assessment (p = 0.045), Time to start of exposure (p = 0.006), and Time to completion of exposure (p = 0.034), as well as longer Time to 1RAORs at t = 0 were extracted from the CCTV footage. RAORs were not extracted for nine cases due to human error. Repeat measurements of the RAOR were performed over the first 5 minutes for the initial 10 cases, and it was found that the RAOR did not change for most cases. There was a significant positive correlation between RAOR and time to eventual disposition .Potential impediments to assessment, in particular, type of immobilization , and the need for repeat interventions  were also analyzed. These did not affect the process-to-time outcome measures, although repeat FAST was more common in more severely injured patients.In this study, time intervals were mostly viewed as outcomes. Airway assessment coinciding with arrival of the patient in the bay occurred 31.4% of the time (n = 22). Overall, a median time from arrival to airway assessment of 23.00 seconds was observed. Notably, “time between last intervention and disposal” was also found to be the greatest contributor to the total time a patient spent in the ED  .Limitations of our study include the small study sample of 70. Other limitations include the variation of team size over the course of resuscitation. Ideally, every team member should be tagged, their time spent in the resuscitation area tallied up, and the average team size-per-hour calculated. This was deemed unfeasible, and we instead used the team size at the arrival-of-trauma-team-leader as a surrogate for overall team size. While largely accurate, there were times when the team leader arrived only after the majority of procedures had been completed. This parameter may therefore not be fully representative of the actual staffing during the acute early phases of the resuscitation.Furthermore, audio input of the proceedings was unavailable due to the nature of the equipment and the lack of microphones in situ, which would have provided us an insight into the quality of team interactions, rather than just the availability of manpower for the trauma activation. Future studies planned at our institution will analyze the role of teamwork and inter-professional interactions  in traumLastly, there were several other variables unaccounted for in this study of a highly dynamic and multifactorial environment. These include team familiarity, nurse and allied healthcare staff expertise and seniority, and a differentiation of patients by the number of specialties involved.Despite these limitations, our results reveal interesting data on manpower availability and resuscitation timings, serving as a baseline from which we are planning future interventions to speed up resuscitation.In an ideal world, the ATLS approach to trauma resuscitation would be a coordinated, seamless process that flows from one step to the next, with simultaneous interventions where possible . The reaThis study has demonstrated that manpower availability beyond what is conventionally described as the “optimum team size” can result in shorter timings of trauma resuscitation. Firstly, a larger trauma team size results in quicker completion of the primary survey, including airway assessment, initiation, and completion of exposure. Forty-one (58.6%) of our resuscitations were conducted with a team size of seven or more. Other time intervals, although recorded, did not yield statistically significant results as the intervals in question were larger and therefore more susceptible to external variability/influences. Ideally airway is the first step in resuscitation. In our video analyses however, we realized that this did not always happen immediately on patient arrival, although it was the case in the majority of the videos. Correlating patient outcomes with resuscitation timings did not show any statistical significance, partly due to the small numbers, and also because slower resuscitation timings were observed for patients with better RTS and ISS scores where there may have been a reduced sense of urgency.Some authors suggest the presence of an ‘optimum’ number beyond which productivity appears to decrease  18] [19 [1918] . We alsoThe other resuscitation resource explored was RAOR. There was a positive correlation between RAOR and time from resuscitation start to eventual disposition, i.e. the more patients in the resuscitation bay, the slower the trauma resuscitation. Although the mean RAOR was 2.02, almost a quarter of cases (21.3%) experienced full area occupancy (RAOR of 6). We also occasionally noticed the early disposition of patients from the panels in order to free up space for newly arrived patients. These findings have implications for resource management in busy EDs.Lastly, the time between last intervention and disposal, i.e. time in ED spent simply waiting for disposition, is significantly correlated with the total time spent in the ED. In other words, bed waiting time is a statistically significant source of delay, the reduction of which will theoretically lead to improvements in RAOR and overall trauma resuscitation timings. Interestingly, in our study, “time between last intervention and disposal” was the greatest contributor to total time spent in the ED. This suggests that “bed block”, or the lack of in-patient beds, even after efficient trauma resuscitation, can lead to increased ED waiting time . While tIn summary, manpower availability affects trauma resuscitation times, even in a well-staffed tertiary academic hospital with stay-in specialist availability. A larger trauma team size speeds up the completion of ATLS primary survey, and our study does not show any reduction in efficiency from larger team sizes. We also note the significance of resuscitation area occupancy on overall time spent in the ED, indicating a possible need for further resource management. Finally, time from final intervention to disposition is a significant contributor to total ED wait time, the reduction of which will likely lead to improvements in eventual patient outcomes.