Does adding risk-trends to survival models improve in-hospital mortality predictions? A cohort study

<p>Abstract</p> <p>Background</p> <p>Clinicians informally assess changes in patients' status over time to prognosticate their outcomes. The incorporation of trends in patient status into regression models could improve their ability to predict outcomes. In this study, we used a unique approach to measure trends in patient hospital death risk and determined whether the incorporation of these trend measures into a survival model improved the accuracy of its risk predictions.</p> <p>Methods</p> <p>We included all adult inpatient hospitalizations between 1 April 2004 and 31 March 2009 at our institution. We used the daily mortality risk scores from an existing time-dependent survival model to create five trend indicators: absolute and relative percent change in the risk score from the previous day; absolute and relative percent change in the risk score from the start of the trend; and number of days with a trend in the risk score. In the derivation set, we determined which trend indicators were associated with time to death in hospital, independent of the existing covariates. In the validation set, we compared the predictive performance of the existing model with and without the trend indicators.</p> <p>Results</p> <p>Three trend indicators were independently associated with time to hospital mortality: the absolute change in the risk score from the previous day; the absolute change in the risk score from the start of the trend; and the number of consecutive days with a trend in the risk score. However, adding these trend indicators to the existing model resulted in only small improvements in model discrimination and calibration.</p> <p>Conclusions</p> <p>We produced several indicators of trend in patient risk that were significantly associated with time to hospital death independent of the model used to create them. In other survival models, our approach of incorporating risk trends could be explored to improve their performance without the collection of additional data.</p

Plasma and CSF neurofilament light: Relation to longitudinal neuroimaging and cognitive measures

OBJECTIVE: We aimed to (1) assess and compare baseline plasma and CSF neurofilament light (NfL) for cross-sectional and longitudinal associations with neuroimaging or cognition and (2) determine whether change in plasma NfL corresponded with change in these outcomes. // METHODS: Seventy-nine participants without dementia, median age 76 years, had plasma and CSF NfL, neuropsychological testing, and neuroimaging (MRI, amyloid PET, FDG-PET) at the same study visit, and a repeat visit (15 or 30 months later) with both plasma NfL and neuroimaging. Plasma NfL was measured on the Simoa-HD1 Platform and CSF NfL with an in-house ELISA. Linear mixed effects models were used to examine the associations between baseline plasma or CSF NfL and cognitive and neuroimaging outcomes adjusting for age, sex, and education. The relationship between change in plasma NfL and change in the outcomes was assessed using linear regression. // RESULTS: There were no cross-sectional associations between CSF or plasma NfL and any neuroimaging or cognitive measure. Longitudinally, higher baseline plasma NfL was associated with worsening in all neuroimaging measures, except amyloid PET, and global cognition. Higher baseline CSF NfL was associated with worsening in cortical thickness and diffusion MRI. The beta estimates for CSF NfL were similar to those for plasma NfL. Change in plasma NfL was associated with change in global cognition, attention, and amyloid PET. // CONCLUSION: Elevated baseline plasma NfL is a prognostic marker of cognitive decline and neuroimaging measures of neurodegeneration, and has similar effect sizes to baseline CSF NfL. Change in plasma NfL also tracked with short-term cognitive change

Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys

<p>Abstract</p> <p>Background</p> <p>The weighted estimators generally used for analyzing case-cohort studies are not fully efficient and naive estimates of the predictive ability of a model from case-cohort data depend on the subcohort size. However, case-cohort studies represent a special type of incomplete data, and methods for analyzing incomplete data should be appropriate, in particular multiple imputation (MI).</p> <p>Methods</p> <p>We performed simulations to validate the MI approach for estimating hazard ratios and the predictive ability of a model or of an additional variable in case-cohort surveys. As an illustration, we analyzed a case-cohort survey from the Three-City study to estimate the predictive ability of D-dimer plasma concentration on coronary heart disease (CHD) and on vascular dementia (VaD) risks.</p> <p>Results</p> <p>When the imputation model of the phase-2 variable was correctly specified, MI estimates of hazard ratios and predictive abilities were similar to those obtained with full data. When the imputation model was misspecified, MI could provide biased estimates of hazard ratios and predictive abilities. In the Three-City case-cohort study, elevated D-dimer levels increased the risk of VaD (hazard ratio for two consecutive tertiles = 1.69, 95%CI: 1.63-1.74). However, D-dimer levels did not improve the predictive ability of the model.</p> <p>Conclusions</p> <p>MI is a simple approach for analyzing case-cohort data and provides an easy evaluation of the predictive ability of a model or of an additional variable.</p

Clinical research without consent in adults in the emergency setting: a review of patient and public views

<p>Abstract</p> <p>Background</p> <p>In emergency research, obtaining informed consent can be problematic. Research to develop and improve treatments for patients admitted to hospital with life-threatening and debilitating conditions is much needed yet the issue of research without consent (RWC) raises concerns about unethical practices and the loss of individual autonomy. Consistent with the policy and practice turn towards greater patient and public involvement in health care decisions, in the US, Canada and EU, guidelines and legislation implemented to protect patients and facilitate acute research with adults who are unable to give consent have been developed with little involvement of the lay public. This paper reviews research examining public opinion regarding RWC for research in emergency situations, and whether the rules and regulations permitting research of this kind are in accordance with the views of those who ultimately may be the most affected.</p> <p>Methods</p> <p>Seven electronic databases were searched: Medline, Embase, CINAHL, Cochrane Database of Systematic Reviews, Philosopher's Index, Age Info, PsychInfo, Sociological Abstracts and Web of Science. Only those articles pertaining to the views of the public in the US, Canada and EU member states were included. Opinion pieces and those not published in English were excluded.</p> <p>Results</p> <p>Considering the wealth of literature on the perspectives of professionals, there was relatively little information about public attitudes. Twelve studies employing a range of research methods were identified. In five of the six questionnaire surveys around half the sample did <it>not </it>agree generally with RWC, though paradoxically, a higher percentage would <it>personally </it>take part in such a study. Unfortunately most of the studies were not designed to investigate individuals' views in any depth. There also appears to be a level of mistrust of medical research and some patients were more likely to accept an experimental treatment 'outside' of a research protocol.</p> <p>Conclusion</p> <p>There are too few data to evaluate whether the rules and regulations permitting RWC protects – or is acceptable to – the public. However, any attempts to engage the public should take place in the context of findings from further basic research to attend to the apparently paradoxical findings of some of the current surveys.</p

Estimating demographic parameters for capture-recapture data in the presence of multiple mark types

In mark-recapture studies, various techniques can be used to uniquely identify individual animals, such as ringing, tagging or photo-identification using natural markings. In some long-term studies more than one type of marking procedure may be implemented during the study period. In these circumstances, ignoring the different mark types can produce biased survival estimates since the assumption that the different mark types are equally catchable (homogeneous capture probability across mark types) may be incorrect.We implement an integrated approach where we simultaneously analyse data obtained using three different marking techniques, assuming that animals can be cross-classified across the different mark types. We discriminate between competing models using the AIC statistic. This technique also allows us to estimate both relative mark-loss probabilities and relative recapture efficiency rates for the different marking methods.We initially perform a simulation study to explore the different biases that can be introduced if we assume a homogeneous recapture probability over mark type, before applying the method to a real dataset. We make use of data obtained from an intensive long-term observational study of UK female grey seals (Halichoerus grypus) at a single breeding colony, where three different methods are used to identify individuals within a single study: branding, tagging and photo-identification based on seal coat pattern or pelage.PostprintPeer reviewe