Using ordinal logistic regression to evaluate the performance of laser-Doppler predictions of burn-healing time

Background Laser-Doppler imaging (LDI) of cutaneous blood flow is beginning to be used by burn surgeons to predict the healing time of burn wounds; predicted healing time is used to determine wound treatment as either dressings or surgery. In this paper, we do a statistical analysis of the performance of the technique. Methods We used data from a study carried out by five burn centers: LDI was done once between days 2 to 5 post burn, and healing was assessed at both 14 days and 21 days post burn. Random-effects ordinal logistic regression and other models such as the continuation ratio model were used to model healing-time as a function of the LDI data, and of demographic and wound history variables. Statistical methods were also used to study the false-color palette, which enables the laser-Doppler imager to be used by clinicians as a decision-support tool. Results Overall performance is that diagnoses are over 90% correct. Related questions addressed were what was the best blood flow summary statistic and whether, given the blood flow measurements, demographic and observational variables had any additional predictive power (age, sex, race, % total body surface area burned (%TBSA), site and cause of burn, day of LDI scan, burn center). It was found that mean laser-Doppler flux over a wound area was the best statistic, and that, given the same mean flux, women recover slightly more slowly than men. Further, the likely degradation in predictive performance on moving to a patient group with larger %TBSA than those in the data sample was studied, and shown to be small. Conclusion Modeling healing time is a complex statistical problem, with random effects due to multiple burn areas per individual, and censoring caused by patients missing hospital visits and undergoing surgery. This analysis applies state-of-the art statistical methods such as the bootstrap and permutation tests to a medical problem of topical interest. New medical findings are that age and %TBSA are not important predictors of healing time when the LDI results are known, whereas gender does influence recovery time, even when blood flow is controlled for. The conclusion regarding the palette is that an optimum three-color palette can be chosen 'automatically', but the optimum choice of a 5-color palette cannot be made solely by optimizing the percentage of correct diagnoses

Preventing Nerve Function Impairment in Leprosy: Validation and Updating of a Prediction Rule

Leprosy is caused by a bacterium that attacks the peripheral nerves. This may cause nerve function impairment (NFI), resulting in handicaps and disabilities. Therefore, prediction and prevention of NFI is extremely important in the management of leprosy. In 2000, a prediction rule for NFI was published, but circumstances have changed since the study was performed in the 1990s: the leprosy detection delay has shortened and the definition of NFI has changed. The original rule used ‘leprosy classification’ and ‘NFI present at diagnosis’ to predict future NFI. In the current patient population we studied an adjusted rule based on ‘leprosy classification’ and ‘presence of antibodies’. This adjusted rule predicted NFI more often than the original rule. With the adjusted rule it is now also possible to assess NFI risk before the first nerve damage event takes place. This may help doctors and health workers to improve surveillance for people at high risk. Early detection and treatment can then prevent permanent disabilities

Correlating changes in lung function with patient outcomes in chronic obstructive pulmonary disease: a pooled analysis

Background Relationships between improvements in lung function and other clinical outcomes in chronic obstructive pulmonary disease (COPD) are not documented extensively. We examined whether changes in trough forced expiratory volume in 1 second (FEV1) are correlated with changes in patient-reported outcomes. Methods Pooled data from three indacaterol studies (n = 3313) were analysed. Means and responder rates for outcomes including change from baseline in Transition Dyspnoea Index (TDI), St. George's Respiratory Questionnaire (SGRQ) scores (at 12, 26 and 52 weeks), and COPD exacerbation frequency (rate/year) were tabulated across categories of ΔFEV1. Also, generalised linear modelling was performed adjusting for covariates such as baseline severity and inhaled corticosteroid use. Results With increasing positive ΔFEV1, TDI and ΔSGRQ improved at all timepoints, exacerbation rate over the study duration declined (P < 0.001). Individual-level correlations were 0.03-0.18, but cohort-level correlations were 0.79-0.95. At 26 weeks, a 100 ml increase in FEV1 was associated with improved TDI (0.46 units), ΔSGRQ (1.3-1.9 points) and exacerbation rate (12% decrease). Overall, adjustments for baseline covariates had little impact on the relationship between ΔFEV1 and outcomes. Conclusions These results suggest that larger improvements in FEV1 are likely to be associated with larger patient-reported benefits across a range of clinical outcomes

A New Calibrated Bayesian Internal Goodness-of-Fit Method: Sampled Posterior p-Values as Simple and General p-Values That Allow Double Use of the Data

Background: Recent approaches mixing frequentist principles with Bayesian inference propose internal goodness-of-fit (GOF) p-values that might be valuable for critical analysis of Bayesian statistical models. However, GOF p-values developed to date only have known probability distributions under restrictive conditions. As a result, no known GOF p-value has a known probability distribution for any discrepancy function. Methodology/Principal Findings: We show mathematically that a new GOF p-value, called the sampled posterior p-value (SPP), asymptotically has a uniform probability distribution whatever the discrepancy function. In a moderate finite sample context, simulations also showed that the SPP appears stable to relatively uninformative misspecifications of the prior distribution. Conclusions/Significance: These reasons, together with its numerical simplicity, make the SPP a better canonical GOF p-value than existing GOF p-values

An Assessment of the Effectiveness of High Definition Cameras as Remote Monitoring Tools for Dolphin Ecology Studies.

Research involving marine mammals often requires costly field programs. This paper assessed whether the benefits of using cameras outweighs the implications of having personnel performing marine mammal detection in the field. The efficacy of video and still cameras to detect Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the Fremantle Harbour (Western Australia) was evaluated, with consideration on how environmental conditions affect detectability. The cameras were set on a tower in the Fremantle Port channel and videos were perused at 1.75 times the normal speed. Images from the cameras were used to estimate position of dolphins at the water’s surface. Dolphin detections ranged from 5.6 m to 463.3 m for the video camera, and from 10.8 m to 347.8 m for the still camera. Detection range showed to be satisfactory when compared to distances at which dolphins would be detected by field observers. The relative effect of environmental conditions on detectability was considered by fitting a Generalised Estimation Equations (GEEs) model with Beaufort, level of glare and their interactions as predictors and a temporal auto-correlation structure. The best fit model indicated level of glare had an effect, with more intense periods of glare corresponding to lower occurrences of observed dolphins. However this effect was not large (-0.264) and the parameter estimate was associated with a large standard error (0.113).The limited field of view was the main restraint in that cameras can be only applied to detections of animals observed rather than counts of individuals. However, the use of cameras was effective for long term monitoring of occurrence of dolphins, outweighing the costs and reducing the health and safety risks to field personal. This study showed that cameras could be effectively implemented onshore for research such as studying changes in habitat use in response to development and construction activities

Incidence of cardiovascular events after kidney transplantation and cardiovascular risk scores: study protocol

<p>Abstract</p> <p>Background</p> <p>Cardiovascular disease (CVD) is the major cause of death after renal transplantation. Not only conventional CVD risk factors, but also transplant-specific risk factors can influence the development of CVD in kidney transplant recipients.</p> <p>The main objective of this study will be to determine the incidence of post-transplant CVD after renal transplantation and related factors. A secondary objective will be to examine the ability of standard cardiovascular risk scores (Framingham, Regicor, SCORE, and DORICA) to predict post-transplantation cardiovascular events in renal transplant recipients, and to develop a new score for predicting the risk of CVD after kidney transplantation.</p> <p>Methods/Design</p> <p>Observational prospective cohort study of all kidney transplant recipients in the A Coruña Hospital (Spain) in the period 1981-2008 (2059 transplants corresponding to 1794 patients).</p> <p>The variables included will be: donor and recipient characteristics, chronic kidney disease-related risk factors, pre-transplant and post-transplant cardiovascular risk factors, routine biochemistry, and immunosuppressive, antihypertensive and lipid-lowering treatment. The events studied in the follow-up will be: patient and graft survival, acute rejection episodes and cardiovascular events (myocardial infarction, invasive coronary artery therapy, cerebral vascular events, new-onset angina, congestive heart failure, rhythm disturbances and peripheral vascular disease).</p> <p>Four cardiovascular risk scores were calculated at the time of transplantation: the Framingham score, the European Systematic Coronary Risk Evaluation (SCORE) equation, and the REGICOR (Registre Gironí del COR (Gerona Heart Registry)), and DORICA (Dyslipidemia, Obesity, and Cardiovascular Risk) functions.</p> <p>The cumulative incidence of cardiovascular events will be analyzed by competing risk survival methods. The clinical relevance of different variables will be calculated using the ARR (Absolute Risk Reduction), RRR (Relative Risk Reduction) and NNT (Number Needed to Treat).</p> <p>The ability of different cardiovascular risk scores to predict cardiovascular events will be analyzed by using the c index and the area under ROC curves. Based on the competing risks analysis, a nomogram to predict the probability of cardiovascular events after kidney transplantation will be developed.</p> <p>Discussion</p> <p>This study will make it possible to determine the post-transplant incidence of cardiovascular events in a large cohort of renal transplant recipients in Spain, to confirm the relationship between traditional and transplant-specific cardiovascular risk factors and CVD, and to develop a score to predict the risk of CVD in these patients.</p

Food web persistence is enhanced by non-trophic interactions.

The strength of interspecific interactions is often proposed to affect food web stability, with weaker interactions increasing the persistence of species, and food webs as a whole. However, the mechanisms that modify interaction strengths, and their effects on food web persistence are not fully understood. Using food webs containing different combinations of predator, prey, and nonprey species, we investigated how predation risk of susceptible prey is affected by the presence of species not directly trophically linked to either predators or prey. We predicted that indirect alterations to the strength of trophic interactions translate to changes in persistence time of extinction-prone species. We assembled interaction webs of protist consumers and turbellarian predators with eight different combinations of prey, predators and nonprey species, and recorded abundances for over 130 prey generations. Persistence of predation-susceptible species was increased by the presence of nonprey. Furthermore, multiple nonprey species acted synergistically to increase prey persistence, such that persistence was greater than would be predicted from the dynamics of simpler food webs. We also found evidence suggesting increased food web complexity may weaken interspecific competition, increasing persistence of poorer competitors. Our results demonstrate that persistence times in complex food webs cannot be predicted from the dynamics of simplified systems, and that species not directly involved in consumptive interactions likely play key roles in maintaining persistence. Global species diversity is currently declining at an unprecedented rate and our findings reveal that concurrent loss of species that modify trophic interactions may have unpredictable consequences for food web stability

Exploring the association of dual use of the VHA and Medicare with mortality: separating the contributions of inpatient and outpatient services

<p>Abstract</p> <p>Background</p> <p>Older veterans may use both the Veterans Health Administration (VHA) and Medicare, but the association of dual use with health outcomes is unclear. We examined the association of indirect measures of dual use with mortality.</p> <p>Methods</p> <p>Our secondary analysis used survey, claims, and National Death Index data from the Survey on Assets and Health Dynamics among the Oldest Old. The analytic sample included 1,521 men who were Medicare beneficiaries. Veterans were classified as dual users when their self-reported number of hospital episodes or physician visits exceeded that in their Medicare claims. Veterans reporting inpatient or outpatient visits but having no Medicare claims were classified as VHA-only users. Proportional hazards regression was used.</p> <p>Results</p> <p>897 (59%) of the men were veterans, of whom 134 (15%) were dual users. Among dual users, 60 (45%) met the criterion based on inpatient services, 54 (40%) based on outpatient services, and 20 (15%) based on both. 766 men (50%) died. Adjusting for covariates, the independent effect of any dual use was a 38% increased mortality risk (AHR = 1.38; p = .02). Dual use based on outpatient services marginally increased mortality risk by 45% (AHR = 1.45; p = .06), and dual use based on both inpatient and outpatient services increased the risk by 98% (AHR = 1.98; p = .02).</p> <p>Conclusion</p> <p>Indirect measures of dual use were associated with increased mortality risk. New strategies to better coordinate care, such as shared medical records, should be considered.</p

Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study

Background: Traditional methods for analyzing clinical and epidemiological cohort study data have been focused on the first occurrence of a health outcome. However, in many situations, recurrent event data are frequently observed. It is inefficient to use methods for the analysis of first events to analyse recurrent event data

Overview of data-synthesis in systematic reviews of studies on outcome prediction models

Background: Many prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of reviews synthesize and report the results of primary outcome prediction studies. Methods: Outcome prediction reviews published in MEDLINE between October 2005 and March 2011 were eligible and 127 Systematic reviews with the aim to summarize outcome prediction studies written in English were identified for inclusion. Characteristics of the reviews and the primary studies that were included were independently assessed by 2 review authors, using standardized forms. Results: After consensus meetings a total of 50 systematic reviews that met the inclusion criteria were included. The type of primary studies included (prognostic factor or outcome prediction) was unclear in two-thirds of the reviews. A minority of the reviews reported univariable or multivariable point estimates and measures of dispersion from the primary studies. Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear. In most reviews there was no information about model performance. Quantitative analysis was performed in 10 reviews, and 49 reviews assessed the primary studies qualitatively. In both analyses types a range of different methods was used to present the results of the outcome prediction studies. Conclusions: Different methods are applied to synthesize primary study results but quantitative analysis is rarely performed. The description of its objectives and of the primary studies is suboptimal and performance parameters of the outcome prediction models are rarely mentioned. The poor reporting and the wide variety of data synthesis strategies are prone to influence the conclusions of outcome prediction reviews. Therefore, there is much room for improvement in reviews of outcome prediction studies. (aut.ref.