Biomedical subjects

P Bélisle

Publications and source records attributed to P Bélisle.

7 recordsLinked to original sources

PubMed2001

Predictors of quality of life 6 months and 1 year after acute myocardial infarction.

BACKGROUND: Quality of life (QOL) is an increasingly important outcome measure after hospital admission for acute myocardial infarction (AMI). However, the ability to adjust these outcomes for differences between compared groups of patients is limited because the predictors of QOL after AMI are unknown. METHODS: To identify any clinical, demographic, and psychosocial characteristics of patients at admission that were independent predictors of QOL 6 months and 1 year after AMI, we measured physical and mental QOL (Short Form-36 Physical and Mental Component summary scores) and overall QOL (EuroQol health perception scale) in a prospective cohort of 587 patients admitted at 10 hospitals in Quebec. A set of plausible multivariate linear regression models was created for each outcome measure with use of the Bayesian Information Criterion. RESULTS: Mean physical, mental, and overall QOL scores corresponding to the time immediately before admission (baseline) were 45 (95% confidence interval [CI] 44-46), 47 (95% CI 46-48), and 70 (95% CI 68-72), respectively. By 1 year, mean physical, mental, and overall QOL scores were close to baseline (45 [95% CI 44-46], 48 [95% CI 47-49], and 73 [95% CI 71-74], respectively). The predictors of physical, mental, and overall QOL were similar at 6 months and 1 year. Important predictors of physical QOL were the corresponding score at baseline, age, and previous bypass surgery (beta coefficients at 1 year: 5 [per 10-point difference in baseline score], -1 [per 10-year age difference], 5.3; 95% CIs 4 to 5, -2 to -1, -9.2 to -1.3, respectively). Predictors of mental QOL were the corresponding score at baseline and depression (beta coefficients at 1 year: 3 [per 10-point difference in baseline score], -3 [per 10-point difference in depression score]; 95% CIs 2 to 4, -5 to -2, respectively). Predictors of overall QOL included the corresponding score at baseline and age (beta at 1 year: 2 [per 10-point score difference], -3 [per 10-year age difference]; 95% CIs 1 to 3, -4 to -1, respectively). Depression was also a predictor of impaired physical and overall QOL at 6 months (beta at 6 months: -1.6 [per 10-point score difference], -5.4 [per 10-point score difference]; 95% CIs -2.9 to -0.4, -7.7 to -3.2, respectively). No variables related to treatments received in-hospital were found in the most clinically relevant models. CONCLUSIONS: These results suggest that age and psychosocial characteristics at baseline are the most important predictors of QOL after AMI. Other clinical characteristics and treatments received in-hospital do not appear to strongly affect patients' long-term perceptions of QOL.

Activities of Daily Living↗

PubMed2000

Iron stores and coronary artery disease: a clinical application of a method to incorporate measurement error of the exposure in a logistic regression model.

Rates of coronary artery disease (CAD) increase sharply after menopause. We examined the hypotheses that high iron stores, as measured by plasma ferritin levels, are a risk factor for CAD and that the increase in iron stores after menopause is at least in part responsible for the rise in CAD in women. We also investigated measurement error of plasma ferritin using a Bayesian conditional independence model and incorporated it into the estimation of the odds ratio (OR) for males. Cases had >/=1 coronary artery stenosis >/=70%. Controls had no visible coronary lesions on angiography. The median plasma ferritin level was 48 mg/L (interquartile range: 28 to 86) among 244 cases and 45 mg/l (24 to 85) among 140 controls. The multivariate analyses among females, males, and females and males combined did not support an association between plasma ferritin levels and CAD (OR for one unit change in log ferritin 1.01, 95% CI 0.71-1.44, OR 0.95, 95% CI 0.66-1.37 and OR 0.95, 95% CI 0.75-1.21, respectively). Accounting for the measurement error of ferritin in males slightly improved the precision of the estimate of the OR but did not unmask an association (OR: 0.94, 95% CI 0.69-1.30). We conclude that high ferritin levels before or after menopause are not associated with CAD. Measurement error might be considered in situations where a one-time measurement is assumed to be representative of long-term exposure.

Aged↗

PubMed1999

Taking account of between-patient variability when modeling decline in Alzheimer's disease.

The pattern of deterioration in patients with Alzheimer's disease is highly variable within a given population. With recent speculation that the apolipoprotein E allele may influence rate of decline and claims that certain drugs may slow the course of the disease, there is a compelling need for sound statistical methodology to address these questions. Current statistical methods for describing decline do not adequately take into account between-patient variability and possible floor and/or ceiling effects in the scale measuring decline, and they fail to allow for uncertainty in disease onset. In this paper, the authors analyze longitudinal Mini-Mental State Examination scores from two groups of Alzheimer's disease subjects from Palo Alto, California, and Minneapolis, Minnesota, in 1981-1993 and 1986-1988, respectively. A Bayesian hierarchical model is introduced as an elegant means of simultaneously overcoming all of the difficulties referred to above.

Alzheimer Disease↗

PubMed1999

Outcomes of total hip and knee replacement: preoperative functional status predicts outcomes at six months after surgery.

OBJECTIVE: To determine whether patients with knee or hip osteoarthritis (OA) who have worse physical function preoperatively achieve a postoperative status that is similar to that of patients with better preoperative function. METHODS: This study surveyed an observational cohort of 379 consecutive patients with definite OA who were without other inflammatory joint diseases and were undergoing either total hip or knee replacement in a US (Boston) and a Canadian (Montreal) referral center. Questionnaires on health status (the Short Form 36 and Western Ontario and McMaster Universities Osteoarthritis Index) were administered preoperatively and at 3 and 6 months postoperatively. Physical function and pain due to OA were deemed the most significant outcomes to study. RESULTS: Two hundred twenty-two patients returned their questionnaires. Patients in the 2 centers were comparable in age, sex, time to surgery, and proportion of hip/knee surgery. The Boston group had more education, lower comorbidity, and more cemented knee prostheses. Patients undergoing hip or knee replacement in Montreal had lower preoperative physical function and more pain than their Boston counterparts. In patients with lower preoperative physical function, function and pain were not improved postoperatively to the level achieved by those with higher preoperative function. This was most striking in patients undergoing total knee replacement. CONCLUSION: Surgery performed later in the natural history of functional decline due to OA of the knee, and possibly of the hip, results in worse postoperative functional status.

Aged↗

PubMed1999

Can health utility measures be used in lupus research? A comparative validation and reliability study of 4 utility indices.

OBJECTIVE: To assess validity and reliability of 4 utility indices in patients with systemic lupus erythematosus (SLE). METHODS: Twenty-five patients with stable SLE underwent assessment of disease activity [Systemic Lupus Disease Activity Measure (SLAM-R) and SLE Disease Activity Index (SLEDAI)] and damage [Systemic Lupus Collaborating Clinics/American College of Rheumatology Damage Index (SLICC/ACR DI)] and completed a health survey [Medical Outcome Survey Short Form-36 (SF-36)] and 4 utility measures: the visual analog scale (VAS), the time trade-off (TTO), the standard gamble (SG), and the McMaster Health Utilities Index Mark 2 (HUI2). To assess validity, Pearson's correlations were calculated between the SF-36 subscales and the utility measures. To assess reliability, intraclass correlations or kappa coefficients were calculated between first and second assessments, performed from 2 to 4 weeks apart, in patients without important clinical change in disease activity. RESULTS: Disease activity from a SLAM-R varied from 0 to 14 (median = 4) and SLEDAI from 0 to 18 (median = 0). All subscales of the SF-36 correlated well with the VAS [lowest r = 0.56, 95% confidence interval (CI) (0.17, 0.80)] and poorly with the SG [maximum r = 0.41, CI (-0.01, 0.70); minimum r = 0.10, CI (-0.32, 0.50)]. The subscales of bodily pain (r = 0.56), mental health (r = 0.45), physical functioning (r = 0.62), role-emotional (r = 0.47), social functioning (r = 0.49) and vitality (r = 0.44) correlated significantly with TTO. All subscales correlated significantly [lowest r = 0.48, CI (0.09, 0.75)] with the HUI2 index of pain. Intraclass correlations for the VAS (ICC = 0.67) and TTO (ICC = 0.60) were good. They were fair for the SG (ICC = 0.45). The kappa coefficient was poor (0.32) for the HUI attribute of pain, but varied from fair (0.46) to excellent (0.88) for the remaining attributes. Regression analysis showed that a model incorporating the SLAM-R value and SF-36 subset of mental health was a good predictor of VAS and TTO utility measures. CONCLUSION: The VAS, TTO, and to some extent, the HUI2, when compared with the SF-36 health survey, are valid and reliable measures to assess health related quality of life in a group of patients with SLE and may be useful for future research in this population. On the basis of these results the usefulness of the SG is questionable in these patients.

Adult↗

PubMed1998

Change-point analysis of neuron spike train data.

In many medical experiments, data are collected across time, over a number of similar trials, or over a number of experimental units. As is the case of neuron spike train studies, these data may be in the form of counts of events per unit of time. These counts may be correlated within each trial. It is often of interest to know if the introduction of an intervention, such as the application of a stimulus, affects the distribution of the counts over the course of the experiment. In such investigations, each trial generates a sequence of data that may or may not contain a change in distribution at some point in time. Each sequence of integer counts can be viewed as arising from a Poisson process and are therefore independently distributed or as an integer-valued time series that allows for correlations between these counts. The main aim of this paper is to show how the ensemble of sample paths may be used to make inference about the distribution of the instantaneous times of change in a given population. This will be accomplished using a Bayesian hierarchical model for these change-points in time. A bonus of these models is they also allow for inference about the probability of a change in each unit and the magnitude of the effects, if any. The use of such change-point models on integer-valued time series is illustrated on neuron spike train data, although the methods can be applied to other situations where integer-valued processes arise.

Action Potentials↗

PubMed1997

Bayesian and mixed Bayesian/likelihood criteria for sample size determination.

Sample size estimation is a major component of the design of virtually every experiment in medicine. Prudent use of the available prior information is a crucial element of experimental planning. Most sample size formulae in current use employ this information only in the form of point estimates, even though it is usually more accurately expressed as a distribution over a range of values. In this paper, we review several Bayesian and mixed Bayesian/likelihood approaches to sample size calculations based on lengths and coverages of posterior credible intervals. We apply these approaches to the design of an experiment to estimate the difference between two binomial proportions, and we compare results to those derived from standard formulae. Consideration of several criteria can contribute to selection of a final sample size.

Bayes Theorem↗