Effect of second booster vaccinations and prior infection against SARS-CoV-2 in the UK SIREN healthcare worker cohort

Background

The protection of fourth dose mRNA vaccination against SARS-CoV-2 is relevant to current global policy decisions regarding ongoing booster roll-out. We aimed to estimate the effect of fourth dose vaccination, prior infection, and duration of PCR positivity in a highly-vaccinated and largely prior-COVID-19 infected cohort of UK healthcare workers.

Methods

Participants underwent fortnightly PCR and regular antibody testing for SARS-CoV-2 and completed symptoms questionnaires. A multi-state model was used to estimate vaccine effectiveness (VE) against infection from a fourth dose compared to a waned third dose, with protection from prior infection and duration of PCR positivity jointly estimated.

Findings

1298 infections were detected among 9560 individuals under active follow-up between September 2022 and March 2023. Compared to a waned third dose, fourth dose VE was 13.1% (95% CI 0.9 to 23.8) overall; 24.0% (95% CI 8.5 to 36.8) in the first 2 months post-vaccination, reducing to 10.3% (95% CI -11.4 to 27.8) and 1.7% (95% CI -17.0 to 17.4) at 2-4 and 4-6 months, respectively. Relative to an infection >2 years ago and controlling for vaccination, 63.6% (95% CI 46.9 to 75.0) and 29.1% (95% CI 3.8 to 43.1) greater protection against infection was estimated for an infection within the past 0-6, and 6-12 months, respectively. A fourth dose was associated with greater protection against asymptomatic infection than symptomatic infection, whilst prior infection independently provided more protection against symptomatic infection, particularly if the infection had occurred within the previous 6 months. Duration of PCR positivity was significantly lower for asymptomatic compared to symptomatic infection.

Interpretation

Despite rapid waning of protection, vaccine boosters remain an important tool in responding to the dynamic COVID-19 landscape; boosting population immunity in advance of periods of anticipated pressure, such as surging infection rates or emerging variants of concern.

Funding

UK Health Security Agency, Medical Research Council, NIHR HPRU Oxford, Bristol, and others.

survextrap: a package for flexible and transparent survival extrapolation

BACKGROUND

Health policy decisions are often informed by estimates of long-term survival based primarily on short-term data. A range of methods are available to include longer-term information, but there has previously been no comprehensive and accessible tool for implementing these.

RESULTS

This paper introduces a novel model and software package for parametric survival modelling of individual-level, right-censored data, optionally combined with summary survival data on one or more time periods. It could be used to estimate long-term survival based on short-term data from a clinical trial, combined with longer-term disease registry or population data, or elicited judgements. All data sources are represented jointly in a Bayesian model. The hazard is modelled as an M-spline function, which can represent potential changes in the hazard trajectory at any time. Through Bayesian estimation, the model automatically adapts to fit the available data, and acknowledges uncertainty where the data are weak. Therefore long-term estimates are only confident if there are strong long-term data, and inferences do not rely on extrapolating parametric functions learned from short-term data. The effects of treatment or other explanatory variables can be estimated through proportional hazards or with a flexible non-proportional hazards model. Some commonly-used mechanisms for survival can also be assumed: cure models, additive hazards models with known background mortality, and models where the effect of a treatment wanes over time. All of these features are provided for the first time in an R package, survextrap, in which models can be fitted using standard R survival modelling syntax. This paper explains the model, and demonstrates the use of the package to fit a range of models to common forms of survival data used in health technology assessments.

CONCLUSIONS

This paper has provided a tool that makes comprehensive and principled methods for survival extrapolation easily usable.

Guidance on the use of complex systems models for economic evaluations of public health interventions

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19

We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.

Trends in outcomes following COVID-19 symptom onset in Milan: a cohort study

BACKGROUND

For people with symptomatic COVID-19, the relative risks of hospital admission, death without hospital admission and recovery without admission, and the times to those events, are not well understood. We describe how these quantities varied with individual characteristics, and through the first wave of the pandemic, in Milan, Italy.

METHODS

A cohort study of 27 598 people with known COVID-19 symptom onset date in Milan, Italy, testing positive between February and June 2020 and followed up until 17 July 2020. The probabilities of different events, and the times to events, were estimated using a mixture multistate model.

RESULTS

The risk of death without hospital admission was higher in March and April (for non-care home residents, 6%-8% compared with 2%-3% in other months) and substantially higher for care home residents (22%-29% in March). For all groups, the probabilities of hospitalisation decreased from February to June. The probabilities of hospitalisation also increased with age, and were higher for men, substantially lower for healthcare workers and care home residents, and higher for people with comorbidities. Times to hospitalisation and confirmed recovery also decreased throughout the first wave. Combining these results with our previously developed model for events following hospitalisation, the overall symptomatic case fatality risk was 15.8% (15.4%-16.2%).

CONCLUSIONS

The highest risks of death before hospital admission coincided with periods of severe burden on the healthcare system in Lombardy. Outcomes for care home residents were particularly poor. Outcomes improved as the first wave waned, community healthcare resources were reinforced and testing became more widely available.

Value of Information Analysis in Models to Inform Health Policy

Value of information (VoI) is a decision-theoretic approach to estimating the expected benefits from collecting further information of different kinds, in scientific problems based on combining one or more sources of data. VoI methods can assess the sensitivity of models to different sources of uncertainty and help to set priorities for further data collection. They have been widely applied in healthcare policy making, but the ideas are general to a range of evidence synthesis and decision problems. This article gives a broad overview of VoI methods, explaining the principles behind them, the range of problems that can be tackled with them, and how they can be implemented, and discusses the ongoing challenges in the area.

Risk factors associated with severe hospital burden of COVID-19 disease in Regione Lombardia: a cohort study

BACKGROUND

Understanding the risk factors associated with hospital burden of COVID-19 is crucial for healthcare planning for any future waves of infection.

METHODS

An observational cohort study is performed, using data on all PCR-confirmed cases of COVID-19 in Regione Lombardia, Italy, during the first wave of infection from February-June 2020. A multi-state modelling approach is used to simultaneously estimate risks of progression through hospital to final outcomes of either death or discharge, by pathway (via critical care or not) and the times to final events (lengths of stay). Logistic and time-to-event regressions are used to quantify the association of patient and population characteristics with the risks of hospital outcomes and lengths of stay respectively.

RESULTS

Risks of severe outcomes such as ICU admission and mortality have decreased with month of admission (for example, the odds ratio of ICU admission in June vs March is 0.247 [0.120-0.508]) and increased with age (odds ratio of ICU admission in 45-65 vs 65 + age group is 0.286 [0.201-0.406]). Care home residents aged 65 + are associated with increased risk of hospital mortality and decreased risk of ICU admission. Being a healthcare worker appears to have a protective association with mortality risk (odds ratio of ICU mortality is 0.254 [0.143-0.453] relative to non-healthcare workers) and length of stay. Lengths of stay decrease with month of admission for survivors, but do not appear to vary with month for non-survivors.

CONCLUSIONS

Improvements in clinical knowledge, treatment, patient and hospital management and public health surveillance, together with the waning of the first wave after the first lockdown, are hypothesised to have contributed to the reduced risks and lengths of stay over time.

Trends in undiagnosed HIV prevalence in England and implications for eliminating HIV transmission by 2030: an evidence synthesis model

BACKGROUND

A target to eliminate HIV transmission in England by 2030 was set in early 2019. This study aimed to estimate trends from 2013 to 2019 in HIV prevalence, particularly the number of people living with undiagnosed HIV, by exposure group, ethnicity, gender, age group, and region. These estimates are essential to monitor progress towards elimination.

METHODS

A Bayesian synthesis of evidence from multiple surveillance, demographic, and survey datasets relevant to HIV in England was used to estimate trends in the number of people living with HIV, the proportion of people unaware of their HIV infection, and the corresponding prevalence of undiagnosed HIV. All estimates were stratified by exposure group, ethnicity, gender, age group (15-34, 35-44, 45-59, or 60-74 years), region (London, or outside of London) and year (2013-19).

FINDINGS

The total number of people living with HIV aged 15-74 years in England increased from 83 500 (95% credible interval 80 200-89 600) in 2013 to 92 800 (91 000-95 600) in 2019. The proportion diagnosed steadily increased from 86% (80-90%) to 94% (91-95%) during the same time period, corresponding to a halving in the number of undiagnosed infections from 11 600 (8300-17 700) to 5900 (4400-8700) and in undiagnosed prevalence from 0·29 (0·21-0·44) to 0·14 (0·11-0·21) per 1000 population. Similar steep declines were estimated in all subgroups of gay, bisexual, and other men who have sex with men and in most subgroups of Black African heterosexuals. The pace of reduction was less pronounced for heterosexuals in other ethnic groups and people who inject drugs, particularly outside London; however, undiagnosed prevalence in these groups has remained very low.

INTERPRETATION

The UNAIDS target of diagnosing 90% of people living with HIV by 2020 was reached by 2016 in England, with the country on track to achieve the new target of 95% diagnosed by 2025. Reductions in transmission and undiagnosed prevalence have corresponded to large scale-up of testing in key populations and early diagnosis and treatment. Additional and intensified prevention measures are required to eliminate transmission of HIV among the communities that have experienced slower declines than other subgroups, despite having very low prevalences of HIV.

FUNDING

UK Medical Research Council and Public Health England.

Non-parametric frailty Cox models for hierarchical time-to-event data

We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation-Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.

Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model

Background

Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology.

Methods

Motivated by analysis of a clinical administrative database of 42,871 Heart Failure patients, we develop a semi-Markov, illness-death, multi-state model of repeated admissions to hospital, subsequent discharge and death. Transition times between these health states each have a flexible baseline hazard, with proportional hazards for patient characteristics (case-mix adjustment) and a discrete distribution for frailty terms representing clusters of providers. Models were estimated using an Expectation-Maximization algorithm and the number of clusters was based on the Bayesian Information Criterion.

Results

We are able to identify clusters of providers for each transition, via the inclusion of a nonparametric discrete frailty. Specifically, we detect 5 latent populations (clusters of providers) for the discharge transition, 3 for the in-hospital to death transition and 4 for the readmission transition. Out of hospital death rates are similar across all providers in this dataset. Adjusting for case-mix, we could detect those providers that show extreme behaviour patterns across different transitions (readmission, discharge and death).

Conclusions

The proposed statistical method incorporates both multiple time-to-event outcomes and identification of clusters of providers with extreme behaviour simultaneously. In this way, the whole patient pathway can be considered, which should help healthcare managers to make a more comprehensive assessment of performance.

Development and External Validation of Prediction Models for 10-Year Survival of Invasive Breast Cancer. Comparison with PREDICT and CancerMath

Purpose: To compare PREDICT and CancerMath, two widely used prognostic models for invasive breast cancer, taking into account their clinical utility. Furthermore, it is unclear whether these models could be improved.Experimental Design: A dataset of 5,729 women was used for model development. A Bayesian variable selection algorithm was implemented to stochastically search for important interaction terms among the predictors. The derived models were then compared in three independent datasets (n = 5,534). We examined calibration, discrimination, and performed decision curve analysis.Results: CancerMath demonstrated worse calibration performance compared with PREDICT in estrogen receptor (ER)-positive and ER-negative tumors. The decline in discrimination performance was -4.27% (-6.39 to -2.03) and -3.21% (-5.9 to -0.48) for ER-positive and ER-negative tumors, respectively. Our new models matched the performance of PREDICT in terms of calibration and discrimination, but offered no improvement. Decision curve analysis showed predictions for all models were clinically useful for treatment decisions made at risk thresholds between 5% and 55% for ER-positive tumors and at thresholds of 15% to 60% for ER-negative tumors. Within these threshold ranges, CancerMath provided the lowest clinical utility among all the models.Conclusions: Survival probabilities from PREDICT offer both improved accuracy and discrimination over CancerMath. Using PREDICT to make treatment decisions offers greater clinical utility than CancerMath over a range of risk thresholds. Our new models performed as well as PREDICT, but no better, suggesting that, in this setting, including further interaction terms offers no predictive benefit. Clin Cancer Res; 24(9); 2110-5. ©2018 AACR.

Multi-state modelling of repeated hospitalisation and death in patients with heart failure: The use of large administrative databases in clinical epidemiology

In chronic diseases like heart failure (HF), the disease course and associated clinical event histories for the patient population vary widely. To improve understanding of the prognosis of patients and enable health care providers to assess and manage resources, we wish to jointly model disease progression, mortality and their relation with patient characteristics. We show how episodes of hospitalisation for disease-related events, obtained from administrative data, can be used as a surrogate for disease status. We propose flexible multi-state models for serial hospital admissions and death in HF patients, that are able to accommodate important features of disease progression, such as multiple ordered events and competing risks. Fully parametric and semi-parametric semi-Markov models are implemented using freely available software in R. The models were applied to a dataset from the administrative data bank of the Lombardia region in Northern Italy, which included 15,298 patients who had a first hospitalisation ending in 2006 and 4 years of follow-up thereafter. This provided estimates of the associations of age and gender with rates of hospital admission and length of stay in hospital, and estimates of the expected total time spent in hospital over five years. For example, older patients and men were readmitted more frequently, though the total time in hospital was roughly constant with age. We also discuss the relative merits of parametric and semi-parametric multi-state models, and model assessment and comparison.

On Modelling Minimal Disease Activity

Objective

To explore methods for statistical modelling of minimal disease activity (MDA) based on data from intermittent clinic visits.

Methods

The analysis was based on a 2‐state model. Comparisons were made between analyses based on “complete case” data from visits at which MDA status was known, and the use of hidden model methodology that incorporated information from visits at which only some MDA defining criteria could be established. Analyses were based on an observational psoriatic arthritis cohort.

Results

With data from 856 patients and 7,024 clinic visits, analysis was based on virtually all visits, although only 62.6% provided enough information to determine MDA status. Estimated mean times for an episode of MDA varied from 4.18 years to 3.10 years, with smaller estimates derived from the hidden 2‐state model analysis. Over a 10‐year period, the estimated expected times spent in MDA episodes of longer than 1 year was 3.90 to 4.22, and the probability of having such an MDA episode was estimated to be 0.85 to 0.91, with longer times and greater probabilities seen with the hidden 2‐state model analysis.

Conclusion

A 2‐state model provides a useful framework for the analysis of MDA. Use of data from visits at which MDA status can not be determined provide more precision, and notable differences are seen in estimated quantities related to MDA episodes based on complete case and hidden 2‐state model analyses. The possibility of bias, as well as loss of precision, should be recognized when complete case analyses are used.

flexsurv: A Platform for Parametric Survival Modeling in R

flexsurv is an R package for fully-parametric modeling of survival data. Any parametric time-to-event distribution may be fitted if the user supplies a probability density or hazard function, and ideally also their cumulative versions. Standard survival distributions are built in, including the three and four-parameter generalized gamma and F distributions. Any parameter of any distribution can be modeled as a linear or log-linear function of covariates. The package also includes the spline model of Royston and Parmar (2002), in which both baseline survival and covariate effects can be arbitrarily flexible parametric functions of time. The main model-fitting function, flexsurvreg, uses the familiar syntax of survreg from the standard survival package (Therneau 2016). Censoring or left-truncation are specified in 'Surv' objects. The models are fitted by maximizing the full log-likelihood, and estimates and confidence intervals for any function of the model parameters can be printed or plotted. flexsurv also provides functions for fitting and predicting from fully-parametric multi-state models, and connects with the mstate package (de Wreede, Fiocco, and Putter 2011). This article explains the methods and design principles of the package, giving several worked examples of its use.

flexsurv: A Platform for Parametric Survival Modeling in R

flexsurv is an R package for fully-parametric modeling of survival data. Any parametric time-to-event distribution may be fitted if the user supplies a probability density or hazard function, and ideally also their cumulative versions. Standard survival distributions are built in, including the three and four-parameter generalized gamma and F distributions. Any parameter of any distribution can be modeled as a linear or log-linear function of covariates. The package also includes the spline model of Royston and Parmar (2002), in which both baseline survival and covariate effects can be arbitrarily flexible parametric functions of time. The main model-fitting function, flexsurvreg, uses the familiar syntax of survreg from the standard survival package (Therneau 2016). Censoring or left-truncation are specified in 'Surv' objects. The models are fitted by maximizing the full log-likelihood, and estimates and confidence intervals for any function of the model parameters can be printed or plotted. flexsurv also provides functions for fitting and predicting from fully-parametric multi-state models, and connects with the mstate package (de Wreede, Fiocco, and Putter 2011). This article explains the methods and design principles of the package, giving several worked examples of its use.

State selection in Markov models for panel data with application to psoriatic arthritis

Markov multistate models in continuous-time are commonly used to understand the progression over time of disease or the effect of treatments and covariates on patient outcomes. The states in multistate models are related to categorisations of the disease status, but there is often uncertainty about the number of categories to use and how to define them. Many categorisations, and therefore multistate models with different states, may be possible. Different multistate models can show differences in the effects of covariates or in the time to events, such as death, hospitalisation, or disease progression. Furthermore, different categorisations contain different quantities of information, so that the corresponding likelihoods are on different scales, and standard, likelihood-based model comparison is not applicable. We adapt a recently developed modification of Akaike's criterion, and a cross-validatory criterion, to compare the predictive ability of multistate models on the information which they share. All the models we consider are fitted to data consisting of observations of the process at arbitrary times, often called 'panel' data. We develop an implementation of these criteria through Hidden Markov models and apply them to the comparison of multistate models for the Health Assessment Questionnaire score in psoriatic arthritis. This procedure is straightforward to implement in the R package 'msm'.

Survival extrapolation in the presence of cause specific hazards

Health economic evaluations require estimates of expected survival from patients receiving different interventions, often over a lifetime. However, data on the patients of interest are typically only available for a much shorter follow‐up time, from randomised trials or cohorts. Previous work showed how to use general population mortality to improve extrapolations of the short‐term data, assuming a constant additive or multiplicative effect on the hazards for all‐cause mortality for study patients relative to the general population. A more plausible assumption may be a constant effect on the hazard for the specific cause of death targeted by the treatments. To address this problem, we use independent parametric survival models for cause‐specific mortality among the general population. Because causes of death are unobserved for the patients of interest, a polyhazard model is used to express their all‐cause mortality as a sum of latent cause‐specific hazards. Assuming proportional cause‐specific hazards between the general and study populations then allows us to extrapolate mortality of the patients of interest to the long term. A Bayesian framework is used to jointly model all sources of data. By simulation, we show that ignoring cause‐specific hazards leads to biased estimates of mean survival when the proportion of deaths due to the cause of interest changes through time. The methods are applied to an evaluation of implantable cardioverter defibrillators for the prevention of sudden cardiac death among patients with cardiac arrhythmia. After accounting for cause‐specific mortality, substantial differences are seen in estimates of life years gained from implantable cardioverter defibrillators. © 2014 The Authors Statistics in Medicine Published by John Wiley & Sons Ltd.

Cost-effectiveness of initial stress cardiovascular MR, stress SPECT or stress echocardiography as a gate-keeper test, compared with upfront invasive coronary angiography in the investigation and management of patients with stable chest pain: mid-term outcomes from the CECaT randomised controlled trial

OBJECTIVES

To compare outcomes and cost-effectiveness of various initial imaging strategies in the management of stable chest pain in a long-term prospective randomised trial.

SETTING

Regional cardiothoracic referral centre in the east of England.

PARTICIPANTS

898 patients (69% man) entered the study with 869 alive at 2 years of follow-up. Patients were included if they presented for assessment of stable chest pain with a positive exercise test and no prior history of ischaemic heart disease. Exclusion criteria were recent infarction, unstable symptoms or any contraindication to stress MRI.

PRIMARY OUTCOME MEASURES

The primary outcomes of this follow-up study were survival up to a minimum of 2 years post-treatment, quality-adjusted survival and cost-utility of each strategy.

RESULTS

898 patients were randomised. Compared with angiography, mortality was marginally higher in the groups randomised to cardiac MR (HR 2.6, 95% CI 1.1 to 6.2), but similar in the single photon emission CT-methoxyisobutylisonitrile (SPECT-MIBI; HR 1.0, 95% CI 0.4 to 2.9) and ECHO groups (HR 1.6, 95% CI 0.6 to 4.0). Although SPECT-MIBI was marginally superior to other non-invasive tests there were no other significant differences between the groups in mortality, quality-adjusted survival or costs.

CONCLUSIONS

Non-invasive cardiac imaging can be used safely as the initial diagnostic test to diagnose coronary artery disease without adverse effects on patient outcomes or increased costs, relative to angiography. These results should be interpreted in the context of recent advances in imaging technology.

TRIAL REGISTRATION

ISRCTN 47108462, UKCRN 3696.

Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial

Decision-analytic models must often be informed using data that are only indirectly related to the main model parameters. The authors outline how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. A graphical model is built to represent how observed data are generated from statistical models with unknown parameters and how those parameters are related to quantities of interest for decision making. This forms the basis of an algorithm to estimate a posterior probability distribution, which represents the updated state of evidence for all unknowns given all data and prior beliefs. This process calibrates the quantities of interest against data and, at the same time, propagates all parameter uncertainties to the results used for decision making. To illustrate these methods, the authors demonstrate how a previously developed Markov model for the progression of human papillomavirus (HPV-16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV-16 and HPV-16-related disease by age, cervical cancer incidence, and other published information. Previously, a discrete collection of plausible scenarios was identified but with no further indication of which of these are more plausible. Instead, the authors derive a Bayesian posterior distribution, in which scenarios are implicitly weighted according to how well they are supported by the data. In particular, we emphasize the appropriate choice of prior distributions and checking and comparison of fitted models.

Optimal timing of allogeneic hematopoietic stem cell transplantation in patients with myelodysplastic syndrome

Allogeneic hematopoietic stem cell transplantation (HSCT) represents the only curative treatment for patients with myelodysplastic syndrome (MDS), but involves non-negligible morbidity and mortality. Registry studies have shown that advanced disease stage at transplantation is associated with inferior overall survival. To define the optimal timing of allogeneic HSCT, we carried out a decision analysis by studying 660 patients who received best supportive care and 449 subjects who underwent transplantation. Risk assessment was based on both the International Prognostic Scoring System (IPSS) and the World Health Organization classification-based Prognostic Scoring System (WPSS). We used a continuous-time multistate Markov model to describe the natural history of disease and evaluate the effect of allogeneic HSCT on survival. This model estimated life expectancy from diagnosis according to treatment policy at different risk stages. Relative to supportive care, estimated life expectancy increased when transplantation was delayed from the initial stages until progression to intermediate-1 IPSS-risk or to intermediate WPSS-risk stage, and then decreased for higher risks. Modeling decision analysis on WPSS versus IPSS allowed better estimation of the optimal timing of transplantation. These observations indicate that allogeneic HSCT offers optimal survival benefits when the procedure is performed before MDS patients progress to advanced disease stages.

Predictors of outcome for anal fistula surgery

OBJECTIVES

To review our experience with patients treated for anal fistula secondary to cryptoglandular disease and to determine factors that influence postoperative outcome.

DESIGN

Retrospective review.

SETTING

A regional tertiary referral center.

PATIENTS

Adult patients with anal fistula secondary to cryptoglandular disease.

INTERVENTIONS

Fistulotomy, advancement flap, and fistula plugging.

MAIN OUTCOME MEASURES

Rates of operative failure (persistent fistula), incontinence, and septic complications. We evaluated age, sex, previous operation, fistula type, number of fistula tracts, horseshoe fistula, and intervention type to determine their independent influence on outcomes.

RESULTS

One hundred seventy-nine patients (79.3% male) underwent fistula operation from October 1, 2003, through December 31, 2008. Median age was 45 years. Fistulotomy was undertaken in 82.7% of patients, advancement flap in 10.6%, and plugging in 6.7%. The rates of operative failure, postoperative incontinence, and septic complications were 15.6%, 15.6%, and 7.3%, respectively. Plugging carried the highest failure rate (83.3%) compared with fistulotomy (10.1%) (odds ratio [OR], 44.3 [95% confidence interval (CI), 8.9-221.0; P < .001]) and was the only independent predictor for failure after adjusting for all variables. Being older than 45 years was associated with a higher postoperative incontinence rate compared with the younger group (adjusted OR, 2.8 [95% CI, 1.0-7.7; P = .04]). High transsphincteric and suprasphincteric fistulas were predictors of incontinence compared with subcutaneous fistulas (adjusted OR, 22.9 [95% CI, 2.2-242.0; P = .009] and 61.5 [4.5-844.0; P = .002], respectively). The only predictor of septic complications was plugging compared with fistulotomy (adjusted OR, 15.1 [95% CI, 2.3-97.7; P = .004]).

CONCLUSIONS

Fistulotomy is the preferred operation for anal fistula. Plugging is associated with the highest operative failure and septic complication rates. Incontinence was influenced more by fistula type and age rather than procedure.

A Framework for Addressing Structural Uncertainty in Decision Models

Decision analytic models used for health technology assessment are subject to uncertainties. These uncertainties can be quantified probabilistically, by placing distributions on model parameters and simulating from these to generate estimates of cost-effectiveness. However, many uncertain model choices, often termed structural assumptions, are usually only explored informally by presenting estimates of cost-effectiveness under alternative scenarios. The authors show how 2 recent research proposals represent parts of a framework to formally account for all common structural uncertainties. First, the model is expanded to include parameters that encompass all possible structural choices. Uncertainty can then arise because these parameters are estimated imprecisely from data, for example, a treatment effect of doubtful significance. Uncertainty can also arise if there are no relevant data. If there are relevant data, uncertainty can be addressed by averaging expected costs and effects generated from probabilistic analysis of the models with and without the parameter. The weights used for averaging are related to the predictive ability of each model, assessed against the data. If there are no data, additional parameters can often be informed by eliciting expert beliefs as probability distributions. These ideas are illustrated in decision models for antiplatelet therapies for vascular disease and new biologic drugs for the treatment of active psoriatic arthritis.

Expansion of the kidney donor pool by using cardiac death donors with prolonged time to cardiorespiratory arrest

Donation after Cardiac Death (DCD) is an increasingly important source of kidney transplants, but because of concerns of ischemic injury during the agonal phase, many centers abandon donation if cardiorespiratory arrest has not occurred within 1 h of controlled withdrawal of life-supporting treatment (WLST). We report the impact on donor numbers and transplant function using instead a minimum 'cut-off' time of 4 h. The agonal phase of 173 potential DCD donors was characterized according to the presence or absence of: acidemia; lactic acidosis; prolonged (>30 min) hypotension, hypoxia or oliguria, and the impact of these characteristics on 3- and 12-month transplant outcome evaluated by multivariable regression analysis. Of the 117 referrals who became donors, 27 (23.1%) arrested more than 1 h after WLST. Longer agonal-phase times were associated with greater donor instability, but surprisingly neither agonal-phase instability nor its duration influenced transplant outcome. In contrast, 3- and 12-month eGFR in the 190 transplanted kidneys was influenced independently by donor age, and 3-month eGFR by cold ischemic time. DCD kidney numbers are increased by 30%, without compromising transplant outcome, by lengthening the minimum waiting time after WLST from 1 to 4 h.

Expansion of the kidney donor pool by using cardiac death donors with prolonged time to cardiorespiratory arrest

Donation after Cardiac Death (DCD) is an increasingly important source of kidney transplants, but because of concerns of ischemic injury during the agonal phase, many centers abandon donation if cardiorespiratory arrest has not occurred within 1 h of controlled withdrawal of life-supporting treatment (WLST). We report the impact on donor numbers and transplant function using instead a minimum 'cut-off' time of 4 h. The agonal phase of 173 potential DCD donors was characterized according to the presence or absence of: acidemia; lactic acidosis; prolonged (>30 min) hypotension, hypoxia or oliguria, and the impact of these characteristics on 3- and 12-month transplant outcome evaluated by multivariable regression analysis. Of the 117 referrals who became donors, 27 (23.1%) arrested more than 1 h after WLST. Longer agonal-phase times were associated with greater donor instability, but surprisingly neither agonal-phase instability nor its duration influenced transplant outcome. In contrast, 3- and 12-month eGFR in the 190 transplanted kidneys was influenced independently by donor age, and 3-month eGFR by cold ischemic time. DCD kidney numbers are increased by 30%, without compromising transplant outcome, by lengthening the minimum waiting time after WLST from 1 to 4 h.

Structural and parameter uncertainty in Bayesian cost-effectiveness models

Health economic decision models are subject to various forms of uncertainty, including uncertainty about the parameters of the model and about the model structure. These uncertainties can be handled within a Bayesian framework, which also allows evidence from previous studies to be combined with the data. As an example, we consider a Markov model for assessing the cost-effectiveness of implantable cardioverter defibrillators. Using Markov chain Monte Carlo posterior simulation, uncertainty about the parameters of the model is formally incorporated in the estimates of expected cost and effectiveness. We extend these methods to include uncertainty about the choice between plausible model structures. This is accounted for by averaging the posterior distributions from the competing models using weights that are derived from the pseudo-marginal-likelihood and the deviance information criterion, which are measures of expected predictive utility. We also show how these cost-effectiveness calculations can be performed efficiently in the widely used software WinBUGS.

Accounting for uncertainty in health economic decision models by using model averaging

Health economic decision models are subject to considerable uncertainty, much of which arises from choices between several plausible model structures, e.g. choices of covariates in a regression model. Such structural uncertainty is rarely accounted for formally in decision models but can be addressed by model averaging. We discuss the most common methods of averaging models and the principles underlying them. We apply them to a comparison of two surgical techniques for repairing abdominal aortic aneurysms. In model averaging, competing models are usually either weighted by using an asymptotically consistent model assessment criterion, such as the Bayesian information criterion, or a measure of predictive ability, such as Akaike's information criterion. We argue that the predictive approach is more suitable when modelling the complex underlying processes of interest in health economics, such as individual disease progression and response to treatment.

Bayesian graphical models for regression on multiple data sets with different variables

Routinely collected administrative data sets, such as national registers, aim to collect information on a limited number of variables for the whole population. In contrast, survey and cohort studies contain more detailed data from a sample of the population. This paper describes Bayesian graphical models for fitting a common regression model to a combination of data sets with different sets of covariates. The methods are applied to a study of low birth weight and air pollution in England and Wales using a combination of register, survey, and small-area aggregate data. We discuss issues such as multiple imputation of confounding variables missing in one data set, survey selection bias, and appropriate propagation of information between model components. From the register data, there appears to be an association between low birth weight and environmental exposure to NO(2), but after adjusting for confounding by ethnicity and maternal smoking by combining the register and survey data under our models, we find there is no significant association. However, NO(2) was associated with a small but significant reduction in birth weight, modeled as a continuous variable.

Studying place effects on health by synthesising individual and area-level outcomes

It is well established that there exist substantial area-level socio-demographic variations in population health. However, area-level associations between deprivation and health cannot necessarily be interpreted as place effects on individual health. We demonstrate how recently developed statistical models for combining individual and aggregate data can help to separate the effects of place of residence and personal circumstances. We apply these to two health outcomes: risk of hospitalisation for cardiovascular disease (CVD) and risk of self-reported limiting long-term illness (LLTI). A combination of small-area data from UK hospital episode statistics and the UK census and individual data from the Health Survey for England are analysed, using a new multilevel modelling method termed hierarchical related regression (HRR). The standard multilevel model for place and health explains outcomes from individual data in terms of individual and area-level characteristics. HRR models increase precision by also explaining population aggregate outcomes, in terms of the same predictors. Aggregate outcomes are modelled by averaging the individual-level exposure-outcome relationship over the area, which can alleviate the ecological bias associated with interpreting the relationship between aggregate quantities as an individual-level relationship. We find that there are associations between area-level deprivation indicators and both area-level rates of hospital admission for CVD and area-level rates of LLTI. Multilevel models fitted to the individual data alone had insufficient power to determine whether these associations were due to compositional or contextual effects. Using HRR models which incorporate area-level outcomes in addition to individual outcomes, we found that for CVD, the area-level differences were mostly explained by individual-level effects, in particular the increased risk for individuals from non-white ethnic backgrounds. In contrast, there remained a significant association between LLTI and area-level deprivation even after adjusting for the significant increased risk associated with individual-level ethnicity and income. Our study illustrates that extending multilevel models to incorporate both individual and area-level outcomes increases power to distinguish between contextual and compositional effects.

Diagnostic accuracy of coronary angiography and risk factors for post-heart-transplant cardiac allograft vasculopathy

Cardiac allograft vasculopathy (CAV) is a common cause of death after heart transplantation. Coronary angiography is used to monitor the progress of recipients. Diagnostic accuracy of angiography and risk factors for CAV have not been clearly established. Between August 1979 and January 2002, 566 1-year survivors of heart transplantation underwent 2168 angiograms and were classified as having no CAV (0% stenosis), mild-moderate CAV (up to 70% stenosis), or severe CAV (>70% stenosis). We used serial measurements of stenosis to estimate the diagnostic accuracy of angiography and to assess the following risk factors for CAV onset, progression, and survival: recipient and donor age and sex, preoperative ischemic heart disease (IHD), acute rejection rates, cytomegalovirus (CMV) infection, and serologic status. CAV was diagnosed by angiography in 248 of 556 (45%) 1-year survivors, with a mean onset time of 8.6 years. Patients spent a mean of 3.4 years with mild-moderate disease and 3.4 years with severe disease before death. Angiography specificity was 97.8%, and sensitivity was 79.3%. The following variables were found to significantly increase the risk of CAV onset: recipient age relative rate (95% confidence interval) 1.16 (1.01-1.34), donor age by 1.27 (1.13-1.43), male recipient by 2.00 (1.11-2.57), pretransplant IHD by 1.75 (1.30-2.36), cumulative rejection by 1.13 (1.05-1.21), and CMV infection by 1.42 (1.06-1.92). Acute rejection increased risk of death by 1.48 (1.19-1.85). Angiography is highly specific and moderately sensitive for diagnosis of CAV. Risk of CAV onset is related to donor age and recipient history of pretransplant IHD and is further increased by immune-related insults of acute rejection and CMV infection.

A simple model to predict coronary disease in patients undergoing operation for mitral regurgitation

BACKGROUND

Coexistent coronary disease can be identified in a third of patients with mitral valve disease. This study aims to evaluate candidate selection strategy using risk factor identification and logistic regression and to develop an additive model for the prediction of coexistent coronary disease.

METHODS

The sample is a consecutive series of patients who had mitral repair from 1987 to 1999. Sensitivities and specificities were calculated for each risk factor. Variables for prediction of coronary disease were entered into a univariate analysis, and predictors were entered into a forward and backward stepwise multivariate logistic regression model to form a predictive score. An additive model was derived from transformation of the logistic model. Receiver operating characteristic curves were used to compare discrimination and precision quantified by the Hosmer-Lemeshow statistic.

RESULTS

The American Heart Association and American College of Cardiology risk factor identification selection criteria for the 359 patients who had screening coronary angiography yielded 100% sensitivity and 1% specificity. Risk prediction with our logistic model produced a receiver operating characteristic curve area of 0.91 and Hosmer-Lemeshow score of 3.4 (p = 0.9). Similar discriminating ability for our patients was achieved by the Cleveland Clinic logistic model (receiver operator characteristic curve area of 0.79; Hosmer-Lemeshow score of 12; p = 0.1). Our five-item additive model produced receiver operating characteristic curve area of 0.91 and Hosmer-Lemeshow score of 3.81 (p = 0.80).

CONCLUSIONS

Simple risk factor identification has excellent sensitivity but is limited by specificity. Logistic regression modeling is an accurate risk prediction method but is difficult to apply at the bedside. Simplicity and accuracy may be achieved by the logistic regression-derived simple additive model.

Acute and chronic onset of bronchiolitis obliterans syndrome (BOS): are they different entities?

BACKGROUND

Bronchiolitis obliterans syndrome (BOS), defined as an irreversible, staged decline in forced expiratory volume in 1 second (FEV(1)), is an established marker of obliterative bronchiolitis. Potential causes of BOS include sub-clinical chronic rejection and/or exaggerated healing response following acute injury. BOS may thus result from two or more distinct processes, both acute and chronic.

METHODS

A total of 5,916 measurements of FEV(1) from 204 lung transplant recipients surviving at least 6 months after transplantation were analyzed. Follow-up ranged from 6 months to 13 years. By adjusting for the acute effects of rejection, pulmonary infection and measurement variation on FEV(1) trace, patients either had a linear decline characterized by a single acute drop in FEV(1) of >15% at BOS onset, or a chronic linear decline in FEV(1). The fraction having acute onset was estimated. Acute events occurring within the first 6 months were assessed as risk factors for acute onset BOS.

RESULTS

Of the 204 patients, 8% died before BOS onset and 18% were BOS-free at analysis. For 18% of patients, BOS onset followed a chronic linear decline in FEV(1) of 3.7% per year, with a median time of BOS onset >99 months. For 56% of patients, BOS onset followed an acute drop in FEV(1) of median 33.8% (95% CI 19.1% to 39.7%), with median onset time of 52 months. During the first 6 months, acute rejection was significantly and independently associated with acute onset of BOS (relative risk = 1.15 per episode, 95% CI [1.03 to 1.29], p = 0.01), whereas pulmonary infection and cytomegalovirus (CMV) infection were not. Acute BOS onset followed a documented acute event in the previous 6 months in 38 of 114 (33%) of cases.

CONCLUSIONS

BOS likely reflects more than one process. Compared with those who had a slow linear decline in lung function, acute BOS onset was associated with acute rejection in the first 6 months, was often triggered by an acute event and had poor prognosis, with obliterative bronchiolitis (OB) the main cause of death.

Risk factors for the development and progression of dyslipidemia after heart transplantation

BACKGROUND

Hyperlipidemia is an important complication after organ transplantation and contributes to the development of posttransplant accelerated coronary artery diseases.

METHODS

We have retrospectively evaluated the relative contribution of various risk factors associated with the development and progression of hyperlipidemia in 194 heart transplant recipients by the use of mixed effects multiple linear regression analysis. The demographic characteristics evaluated were primary diagnosis of ischemic heart disease (IHD), gender, and age. Postoperative characteristics included number of treated rejections, dosage of cyclosporine (CYA), tacrolimus (TAC), prednisolone and azathioprine, and concentration of serum creatinine and glucose. The effects of administration of antihypertensive agents, diuretics, and lipid lowering agents were also studied.

RESULTS

The total cholesterol concentration increased significantly in the first 3 months posttransplant but gradually decreased thereafter. Total cholesterol and the ratio of low density lipoprotein (LDL) cholesterol to high density lipoprotein (HDL) cholesterol (LDL-C/HDL-C) increased to a greater extent in patients with IHD although female transplant recipients had a greater increase in the total cholesterol concentration. Each episode of rejection increased serum cholesterol by 0.306 mmol/liter (0.258, 0.355) [mean (95% C.I.)] and serum triglyceride by 0.164 mmol/liter (0.12, 0.209) although switching to TAC improved total cholesterol and LDL-C/HDL-C. Administration of frusemide, increased the total cholesterol and LDL-C/HDL-C whereas administration of bumetanide or metolazone increased the concentration of serum triglyceride. Serum glucose was associated with hypertriglyceridemia whereas serum creatinine was associated with increases in the total cholesterol, LDL-C/HDL-C and triglyceride.

CONCLUSIONS

We have identified demographic and postoperative covariables that predispose heart transplant recipients to hyperlipidemia. Some of these risk factors, such as the effect of diuretics, have not been identified before in this group of patients and may be amenable to modification or closer control. TAC rather than CYA may be the immunosuppressive of choice for patients who are at greater risk of developing hyperlipidemia.

Hidden Markov models for the onset and progression of bronchiolitis obliterans syndrome in lung transplant recipients

Chronic rejection in lung transplant recipients is monitored by repeated measurement of forced expiratory volume in one second (FEV1). This marker is measured at irregular intervals and is also affected by covariates and short-term fluctuation. This paper describes the use of hidden Markov models for the underlying staged functional decline. Maximum likelihood methods are used to simultaneously estimate disease progression rates and the effects of mismeasurement and covariates.

The complete set of predicted genes from Saccharomyces cerevisiae in a readily usable form

Nearly all of the open reading frames (ORFs) of the yeast Saccharomyces cerevisiae have been synthesized by PCR using a set of approximately 6000 primer pairs. Each of the forward primers has a common 22-base sequence at its 5' end, and each of the back primers has a common 20-base sequence at its 5' end. These common termini allow reamplification of the entire set of original PCR products using a single pair of longer primers-in our case, 70 bases. The resulting 70-base elements that flank each ORF can be used for rapid and efficient cloning into a linearized yeast vector that contains these same elements at its termini. This cloning by genetic recombination obviates the need for ligations or bacterial manipulations and should permit convenient global approaches to gene function that require the assay of each putative yeast gene.

Studying the 'Grammar of Psychotherapy' Course using a student and control population. Some results, trends and disappointments

In a study of the effects of the teaching of the 'Grammar of Psychotherapy' Course, 26 subjects were matched with 27 controls. The students were found to have significantly better interviews than the controls as rated by their patients and by independent blind assessment of their audiotapes six months later. There are some trends which were of interest, although not statistically significant.

Acetaminophen: a practical pharmacologic overview

Acetaminophen is an effective analgesic and antipyretic agent with few adverse effects when used in recommended dosages. The drug is metabolized mainly in the liver, and the several end products have no harmful effects. An intermediate compound in a minor metabolic pathway, however, is toxic; it is normally inactivated by glutathione. In the case of an acetaminophen overdose the hepatic stores of glutathione seem to become depleted, leaving the toxic intermediate free to damage liver tissue. Such damage is unlikely to occur unless the plasma concentration of acetaminophen peaks above 150 micrograms/mL--a level far in excess of the 5 to 20 micrograms/mL achieved with therapeutic doses of the drug. Long-term therapeutic use of acetaminophen does not appear to be associated with liver damage, although some case reports suggest the possibility. Acetaminophen poisoning follows an acute overdose and, if untreated, is manifested clinically by an initial phase of nonspecific signs and symptoms, a latent period in which the liver transaminase levels rise and then, 3 to 5 days after the ingestion, signs of more serious hepatic dysfunction. Most patients do not progress beyond the first or second phase. They and those who survive the third phase recover with no residual injury to the liver. Appropriate antidotal therapy markedly reduces the severity of the initial damage.

The conversion of methyl-2-acetoxyethyl-2'-chloroethylamine to an acetylcholine-like aziridinium ion and its action on the isolated guinea pig ileum

Methyl-2-acetoxyethyl-2′-chloroethylamine (acetyicholine-mustard) isomerizes in aqueous solution to form a cyclic ion, N-methyl-N-(2-acetoxyethyl)aziridinium, which structurally resembles acetylcholine. It is a potent stimulant of the guinea pig ileum, being approximately one-sixth as potent as acetylcholine at pH 7.4 and one-third as potent at pH 8.4. The agonist activity is inhibited by atropine, by preincubation with acetylcholinesterase, and pretreatment with thiosulfate ion. Mepyramine does not inhibit the stimulant action.One hour exposures of ileum segments to concentrations of acetylcholine-mustard in excess of those producing maximal responses, followed by a 1 h recovery period, did not produce evidence of postsynaptic receptor alkylation. Post-treatment responses to acetylcholine were slightly depressed, but these reductions were not related to the incubation concentrations of the agonist haloalkylamine. Pilocarpine-induced responses were unaltered by this treatment whereas 5-hydroxytryptamine responses were slightly potentiated and histamine responses were slightly and inconsistently modified.These treatments produced persistent, dose-related increases in muscle tone, an effect consistent with accumulations of spontaneously liberated acetylcholine and possibly caused by inhibition of in situ acetylcholinesterase.Ostensibly, the evidence suggests that the acetylcholine-like aziridinium ion can stimulate, but not inhibit, the muscarinic receptors of the guinea pig ileum.