Bayesian predictors of very poor health related quality of life and mortality in patients with COPD

Background: Chronic obstructive pulmonary disease (COPD) is associated with increased mortality and poor health-related quality of life (HRQoL) compared with the general population. The objective of this study was to identify clinical characteristics which predict mortality and very poor HRQoL among the COPD population and to develop a Bayesian prediction model. Methods: The data consisted of 738 patients with COPD who had visited the Pulmonary Clinic of the Helsinki and Turku University Hospitals during 1995-2006. The data set contained 49 potential predictor variables and two outcome variables: survival (dead/alive) and HRQoL measured with a 15D instrument (very poor HRQoL = 0.70). In the first phase of model validation we randomly divided the material into a training set (n = 538), and a test set (n = 200). This procedure was repeated ten times in random fashion to obtain independently created training sets and corresponding test sets. Modeling was performed by using the training set, and each model was tested by using the corresponding test set, repeated in each training set. In the second phase the final model was created by using the total material and eighteen most predictive variables. The performance of six logistic regressions approaches were shown for comparison purposes. Results: In the final model, the following variables were associated with mortality or very poor HRQoL: age at onset, cerebrovascular disease, diabetes, alcohol abuse, cancer, psychiatric disease, body mass index, Forced Expiratory Volume (FEV1) % of predicted, atrial fibrillation, and prolonged QT time in ECG. The prediction accuracy of the model was 77%, sensitivity 0.30, specificity 0.95, positive predictive value 0.68, negative predictive value 0.78, and area under the ROC curve 0.69. While the sensitivity of the model reminded limited, good specificity, moderate accuracy, comparable or better performance in classification and better performance in variable selection and data usage in comparison to the logistic regression approaches, and positive and negative predictive values indicate that the model has potential in predicting mortality and very poor HRQoL in COPD patients. Conclusion: We developed a Bayesian prediction model which is potentially useful in predicting mortality and very poor HRQoL in patients with COPD.Peer reviewe

Prediction models for the development of COPD: A systematic review

Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts

Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures

Background and Objective: Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements. Methods: Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax ) after the walking, and the HR decay 3 min after (HRR3 ). The use of BN allows the assessment of the patients’ status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient’s 6MWT outcomes. Results: Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax ) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3 ), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. Conclusions: We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care

Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures

Background and Objective Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements. Methods Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax) after the walking, and the HR decay 3 min after (HRR3). The use of BN allows the assessment of the patients’ status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient's 6MWT outcomes. Results Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. Conclusions We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care.Peer ReviewedPostprint (published version

Spatial epidemiology of lung-cancer mortality : geographical heterogeneity and risk-factors assessment

PhD ThesisCancer is the leading cause of mortality in Andalucía (southern Spain) for both men and women, and lung cancer is the main cause of cancer mortality for men. Radon-gas exposure is the second most important cause of lung-cancer after tobacco-smoking, which also causes larynx cancer, and Chronic Obstructive Pulmonary Disease (COPD). Radon-gas is a radioactive decay element which originates from radium. Consequently, presence in the soil varies according to lithology (rock composition) which is a surrogate measure for potential radon-gas exposure. Lithology can explain some lung-cancer deaths, but not deaths due to either larynx cancer or COPD. A small-area analysis was implemented for the period 1986-1995. Fully-Bayesian regression analysis was used to assess the association between lithology and the spatial distribution of lung-cancer deaths (25,006 cases). Area-level deprivation, a surrogate measure for tobacco-smoking, was accounted for. The number of deaths due to larynx cancer (3,653 cases) and COPD (5,143 cases) were also modelled for comparison purposes. Computation was accomplished via Markov Chain Monte Carlo methods, using WinBUGS software. The spatial distribution of lung-cancer deaths (but neither larynx cancer, nor COPD) was positively associated with lithology, which is consistent with current epidemiological knowledge. These results remained after adjusting for area-level deprivation. The model used allows for separate estimation of risk due to both lithology (RR = 1.02; 95% Credible Interval (CI) = 1.015 – 1.031) and deprivation (RR = 1.04; 95% CI = 1.033 – 1.048). This lithology score overcomes the difficulties in obtaining actual radon-gas measurements, and can be further improved. The results go some way to explaining the regional variability in lung cancer mortality in Southern Spain.Fellowship granted by the Spanish body Instituto de Salud Carlos III (ref BAE06/90003

Estimating the prevalence of chronic medical co-morbidities in the seriously mentally ill in primary care: a modelling framework

An increasing body of evidence suggests that, in comparison to the general population, patients with severe mental illnesses such as schizophrenia or bipolar disorder have worse physical health and a far shorter life expectancy, due primarily to co-morbid chronic diseases. The standardised mortality ratio for all forms of mental disorder is at least 1.5 and varies with the type and severity of the disorder. Whilst data on the prevalence of chronic diseases in primary care is available nationally, there is a lack of health intelligence on medical co-morbidities associated with chronic mental illnesses. The aim of this PhD was to develop and validate epidemiological models for predicting expected prevalence of two major chronic medical conditions namely, coronary heart disease (CHD) and chronic obstructive pulmonary disease (COPD), on general practice data for people with concurrent serious mental illness (SMI) group. The study probed the national epidemiological synthetic estimation of the two physical disorders to determine their prevalence within a local primary care setting and their co-existence within the serious mentally ill (SMI) group identified through the Quality Framework dataset (QOF) within GP practices and their localities. The expected prevalence was compared with recorded cases

COPD Clinical Control : predictors and long-term follow-up of the CHAIN cohort

Control in COPD is a dynamic concept that can reflect changes in patients' clinical status that may have prognostic implications, but there is no information about changes in control status and its long-term consequences. We classified 798 patients with COPD from the CHAIN cohort as controlled/uncontrolled at baseline and over 5 years. We describe the changes in control status in patients over long-term follow-up and analyze the factors that were associated with longitudinal control patterns and related survival using the Cox hazard analysis. 134 patients (16.8%) were considered persistently controlled, 248 (31.1%) persistently uncontrolled and 416 (52.1%) changed control status during follow-up. The variables significantly associated with persistent control were not requiring triple therapy at baseline and having a better quality of life. Annual changes in outcomes (health status, psychological status, airflow limitation) did not differ in patients, regardless of clinical control status. All-cause mortality was lower in persistently controlled patients (5.5% versus 19.1%, p = 0.001). The hazard ratio for all-cause mortality was 2.274 (95% CI 1.394-3.708; p = 0.001). Regarding pharmacological treatment, triple inhaled therapy was the most common option in persistently uncontrolled patients (72.2%). Patients with persistent disease control more frequently used bronchodilators for monotherapy (53%) at recruitment, although by the end of the follow-up period, 20% had scaled up their treatment, with triple therapy being the most frequent therapeutic pattern. The evaluation of COPD control status provides relevant prognostic information on survival. There is important variability in clinical control status and only a small proportion of the patients had persistently good control. Changes in the treatment pattern may be relevant in the longitudinal pattern of COPD clinical control. Further studies in other populations should validate our results. Trial registration: Clinical Trials.gov: identifier NCT01122758