Cost-effectiveness and value of information analysis of NephroCheck and NGAL tests compared to standard care for the diagnosis of acute kidney injury

Background Early and accurate acute kidney injury (AKI) detection may improve patient outcomes and reduce health service costs. This study evaluates the diagnostic accuracy and cost-effectiveness of NephroCheck and NGAL (urine and plasma) biomarker tests used alongside standard care, compared with standard care to detect AKI in hospitalised UK adults. Methods A 90-day decision tree and lifetime Markov cohort model predicted costs, quality adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) from a UK NHS perspective. Test accuracy was informed by a meta-analysis of diagnostic accuracy studies. Clinical trial and observational data informed the link between AKI and health outcomes, health state probabilities, costs and utilities. Value of information (VOI) analysis informed future research priorities. Results Under base case assumptions, the biomarker tests were not cost-effective with ICERs of £105,965 (NephroCheck), £539,041 (NGAL urine BioPorto), £633,846 (NGAL plasma BioPorto) and £725,061 (NGAL urine ARCHITECT) per QALY gained compared to standard care. Results were uncertain, due to limited trial data, with probabilities of cost-effectiveness at £20,000 per QALY ranging from 0 to 99% and 0 to 56% for NephroCheck and NGAL tests respectively. The expected value of perfect information (EVPI) was £66 M, which demonstrated that additional research to resolve decision uncertainty is worthwhile. Conclusions Current evidence is inadequate to support the cost-effectiveness of general use of biomarker tests. Future research evaluating the clinical and cost-effectiveness of test guided implementation of protective care bundles is necessary. Improving the evidence base around the impact of tests on AKI staging, and of AKI staging on clinical outcomes would have the greatest impact on reducing decision uncertainty.

Explaining distortions in metacognition with an attractor network model of decision uncertainty

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.

Prioritizing Research Informing Antibiotic Prophylaxis Guidelines for Knee Arthroplasty Patients

Introduction: Guidelines for routine antibiotic prophylaxis (AP) before dental procedures to prevent periprosthetic joint infection (PJI) have been hampered by the lack of prospective clinical trials. Objectives: To apply value-of-information (VOI) analysis to quantify the value of conducting further clinical research to reduce decision uncertainty regarding the cost-effectiveness of AP strategies for dental patients undergoing total knee arthroplasty (TKA). Methods: An updated decision model and probabilistic sensitivity analysis (PSA) evaluated the cost-effectiveness of AP and decision uncertainty for 3 AP strategies: no AP, 2-y AP, and lifetime AP. VOI analyses estimated the value and cost of conducting a randomized controlled trial (RCT) or observational study. We used a linear regression meta-modeling approach to calculate the population expected value of partial perfect information and a Gaussian approximation to calculate population expected value of sample information, and we subtracted the cost for research to obtain the expected net benefit of sampling (ENBS). We determined the optimal trial sample sizes that maximized ENBS. Results: Using a willingness-to-pay threshold of $100,000 per quality-adjusted life-year, the PSA found that a no-AP strategy had the highest expected net benefit, with a 60% probability of being cost-effective, and 2-y AP had a 37% probability. The optimal sample size for an RCT to determine AP efficacy and dental-related PJI risk would require approximately 421 patients per arm with an estimated cost of $14.7 million. The optimal sample size for an observational study to inform quality-of-life parameters would require 2,211 patients with an estimated cost of $1.2 million. The 2 trial designs had an ENBS of approximately $25 to $26 million. Conclusion: Given the uncertainties associated with AP guidelines for dental patients after TKA, we conclude there is value in conducting further research to inform the risk of PJI, effectiveness of AP, and quality-of-life values. Knowledge Transfer Statement: The results of this value-of-information analysis demonstrate that there is substantial uncertainty around clinical, health status, and economic parameters that may influence the antibiotic prophylaxis guidance for dental patients with total knee arthroplasty. The analysis supports the contention that conducting additional clinical research to reduce decision uncertainty is worth pursuing and will inform the antibiotic prophylaxis debate for clinicians and dental patients with prosthetic joints.

Uncertainty Analysis in Intervention Impact on Health Inequality for Resource Allocation Decisions

Cost-effectiveness analysis, routinely used in health care to inform funding decisions, can be extended to consider impact on health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates socioeconomic differences in model parameters to capture how an intervention would affect both overall population health and differences in health between population groups. In DCEA, uncertainty analysis can consider the decision uncertainty around on both impacts (i.e., the probability that an intervention will increase overall health and the probability that it will reduce inequality). Using an illustrative example assessing smoking cessation interventions (2 active interventions and a “no-intervention” arm), we demonstrate how the uncertainty analysis could be conducted in DCEA to inform policy recommendations. We perform value of information (VOI) analysis and analysis of covariance (ANCOVA) to identify what additional evidence would add most value to the level of confidence in the DCEA results. The analyses were conducted for both national and local authority-level decisions to explore whether the conclusions about decision uncertainty based on the national-level estimates could inform local policy. For the comparisons between active interventions and “no intervention,” there was no uncertainty that providing the smoking cessation intervention would increase overall health but increase inequality. However, there was uncertainty in the direction of both impacts when comparing between the 2 active interventions. VOI and ANCOVA show that uncertainty in socioeconomic differences in intervention effectiveness and uptake contributes most to the uncertainty in the DCEA results. This suggests potential value of collecting additional evidence on intervention-related inequalities for this evaluation. We also found different levels of decision uncertainty between settings, implying that different types and levels of additional evidence are required for decisions in different localities.

Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2–3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

Distinct Neural Representations of Decision Uncertainty in Metacognition and Mentalizing

Metacognition and mentalizing are both associated with meta-level mental state representations. Specifically, metacognition refers to monitoring one’s own cognitive processes, while mentalizing refers to monitoring others’ cognitive processes. However, this self-other dichotomy is insufficient to delineate the two high-level mental processes. We here used functional magnetic resonance imaging (fMRI) to systematically investigate the neural representations of different levels of decision uncertainty in monitoring different targets (the current self, the past self, and others) performing a perceptual decision-making task. Our results reveal diverse formats of intrinsic mental state representations of decision uncertainty in mentalizing, separate from the associations with external information. External information was commonly represented in the right inferior parietal lobe (IPL) across the mentalizing tasks. However, the meta-level mental states of decision uncertainty attributed to others were uniquely represented in the dorsomedial prefrontal cortex (dmPFC), rather than the temporoparietal junction (TPJ) that also equivalently represented the object-level mental states of decision inaccuracy attributed to others. Further, the object-level and meta-level mental states of decision uncertainty, when attributed to the past self, were represented in the precuneus and the lateral frontopolar cortex (lFPC), respectively. In contrast, the dorsal anterior cingulate cortex (dACC) consistently represented both decision uncertainty in metacognition and estimate uncertainty during monitoring the different mentalizing processes, but not the inferred decision uncertainty in mentalizing. Hence, our findings identify neural signatures to clearly delineate metacognition and mentalizing and further imply distinct neural computations on the mental states of decision uncertainty during metacognition and mentalizing.

Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model

Modeling human ratings data subject to raters’ decision uncertainty is an attractive problem in applied statistics. In view of the complex interplay between emotion and decision making in rating processes, final raters’ choices seldom reflect the true underlying raters’ responses. Rather, they are imprecisely observed in the sense that they are subject to a non-random component of uncertainty, namely the decision uncertainty. The purpose of this article is to illustrate a statistical approach to analyse ratings data which integrates both random and non-random components of the rating process. In particular, beta fuzzy numbers are used to model raters’ non-random decision uncertainty and a variable dispersion beta linear model is instead adopted to model the random counterpart of rating responses. The main idea is to quantify characteristics of latent and non-fuzzy rating responses by means of random observations subject to fuzziness. To do so, a fuzzy version of the Expectation–Maximization algorithm is adopted to both estimate model’s parameters and compute their standard errors. Finally, the characteristics of the proposed fuzzy beta model are investigated by means of a simulation study as well as two case studies from behavioral and social contexts.

Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes vs. reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. Here we analyzed 1-year follow-up data from a subset of the same participants (N=325) to assess parameter stability and relationships to other clinical and task measures. We also assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N=48), SUD (N=29), and DEP/ANX (N=121). Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46). Similar to previous baseline findings, these parameters correlated with multiple response time measures (ps&lt;.001) and self-reported anxiety (r=.30, p&lt;.001) and decision difficulty (r=.44, p&lt;.001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.