An Agent-Based Model of the Response to Angioplasty and Bare-Metal Stent Deployment in an Atherosclerotic Blood Vessel

Purpose

While animal models are widely used to investigate the development of restenosis in blood vessels following an intervention, computational models offer another means for investigating this phenomenon. A computational model of the response of a treated vessel would allow investigators to assess the effects of altering certain vessel- and stent-related variables. The authors aimed to develop a novel computational model of restenosis development following an angioplasty and bare-metal stent implantation in an atherosclerotic vessel using agent-based modeling techniques. The presented model is intended to demonstrate the body’s response to the intervention and to explore how different vessel geometries or stent arrangements may affect restenosis development.

Methods

The model was created on a two-dimensional grid space. It utilizes the post-procedural vessel lumen diameter and stent information as its input parameters. The simulation starting point of the model is an atherosclerotic vessel after an angioplasty and stent implantation procedure. The model subsequently generates the final lumen diameter, percent change in lumen cross-sectional area, time to lumen diameter stabilization, and local concentrations of inflammatory cytokines upon simulation completion. Simulation results were directly compared with the results from serial imaging studies and cytokine levels studies in atherosclerotic patients from the relevant literature.

Results

The final lumen diameter results were all within one standard deviation of the mean lumen diameters reported in the comparison studies. The overlapping-stent simulations yielded results that matched published trends. The cytokine levels remained within the range of physiological levels throughout the simulations.

Conclusion

We developed a novel computational model that successfully simulated the development of restenosis in a blood vessel following an angioplasty and bare-metal stent deployment based on the characteristics of the vessel cross-section and stent. A further development of this model could ultimately be used as a predictive tool to depict patient outcomes and inform treatment options.     

Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis

Background

The persistent spread of Rhodesian human African trypanosomiasis (HAT) in Uganda in recent years has increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial distribution of the disease.

Objectives

One recent study used simple logistic regression methods to explore the relationship between prevalence of Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock. Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future.

Materials and Methods

Using a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects.

Results

Analysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial distribution of Rhodesian HAT and the linkages between the disease''s distribution and minimum land surface temperature have also been confirmed via the application of these methods.

Conclusions

Predictive mapping indicates an increased risk of high HAT prevalence in the future in areas surrounding livestock markets, demonstrating the importance of livestock trading for continuing disease spread. Adherence to government policy to treat livestock at the point of sale is essential to prevent the spread of sleeping sickness in Uganda.     

Cohen’s h for detection of disease association with rare genetic variants

Background

The power of the genome wide association studies starts to go down when the minor allele frequency (MAF) is below 0.05. Here, we proposed the use of Cohen’s h in detecting disease associated rare variants. The variance stabilizing effect based on the arcsine square root transformation of MAFs to generate Cohen’s h contributed to the statistical power for rare variants analysis. We re-analyzed published datasets, one microarray and one sequencing based, and used simulation to compare the performance of Cohen’s h with the risk difference (RD) and odds ratio (OR).

Results

The analysis showed that the type 1 error rate of Cohen’s h was as expected and Cohen’s h and RD were both less biased and had higher power than OR. The advantage of Cohen’s h was more obvious when MAF was less than 0.01.

Conclusions

Cohen’s h can increase the power to find genetic association of rare variants and diseases, especially when MAF is less than 0.01.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-875) contains supplementary material, which is available to authorized users.     

Prioritizing genes responsible for host resistance to influenza using network approaches

Background

The genetic make-up of humans and other mammals (such as mice) affects their resistance to influenza virus infection. Considering the complexity and moral issues associated with experiments on human subjects, we have only acquired partial knowledge regarding the underlying molecular mechanisms. Although influenza resistance in inbred mice has been mapped to several quantitative trait loci (QTLs), which have greatly narrowed down the search for host resistance genes, only few underlying genes have been identified.

Results

To prioritize a list of promising candidates for future functional investigation, we applied network-based approaches to leverage the information of known resistance genes and the expression profiles contrasting susceptible and resistant mouse strains. The significance of top-ranked genes was supported by different lines of evidence from independent genetic associations, QTL studies, RNA interference (RNAi) screenings, and gene expression analysis. Further data mining on the prioritized genes revealed the functions of two pathways mediated by tumor necrosis factor (TNF): apoptosis and TNF receptor-2 signaling pathways. We suggested that the delicate balance between TNF’s pro-survival and apoptotic effects may affect hosts’ conditions after influenza virus infection.

Conclusions

This study considerably cuts down the list of candidate genes responsible for host resistance to influenza and proposed novel pathways and mechanisms. Our study also demonstrated the efficacy of network-based methods in prioritizing genes for complex traits.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-816) contains supplementary material, which is available to authorized users.     

Assessing Risk Factors for Migraine: Differences in Gender Transmission

Aim

Our aim was to assess which specific factors are contributing to an increased risk of migraine in a group of 131 Portuguese families.

Methods

We studied 319 first-degree relatives, using a multilevel approach to account for the dependency among members from the same family. We included in the model relative’s gender, the proband’s gender and age-at-onset, to evaluate if any of these variables were associated with relative’s affection status. We also included in the model proband’s migraine subtype. We further assessed female and male transmissions within the proband nuclear family.

Results

Relatives’ gender was found to be a risk factor for migraine (Odds Ratio = 2.86; 95% CI = 1.75–4.67), with females at a higher risk. When splitting probands according to their migraine subtype, we found that none of the variables studied contributed to relatives of MA-probands affection-status. Our results also show a significant difference between proband’s transmission and the gender of the parents and offspring.

Conclusions

With this study, we showed that gender is truly a risk factor for migraine and that a gender-biased transmission is also observed. This reinforce the importance of identifying genes associated with migraine that are modulated by genes located in the sex chromosomes and the study of mitochondrial DNA or X-chromosome and hormonal-related effects associated with migraine susceptibility.     

Using Highly Detailed Administrative Data to Predict Pneumonia Mortality

Background

Mortality prediction models generally require clinical data or are derived from information coded at discharge, limiting adjustment for presenting severity of illness in observational studies using administrative data.

Objectives

To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days.

Research Design

After dividing the dataset into derivation and validation sets, we created a hierarchical generalized linear mortality model that included patient demographics, comorbidities, medications, therapies, and diagnostic tests administered in the first 2 hospital days. We then applied the model to the validation set.

Subjects

Patients aged ≥18 years admitted with pneumonia between July 2007 and June 2010 to 347 hospitals in Premier, Inc.’s Perspective database.

Measures

In hospital mortality.

Results

The derivation cohort included 200,870 patients and the validation cohort had 50,037. Mortality was 7.2%. In the multivariable model, 3 demographic factors, 25 comorbidities, 41 medications, 7 diagnostic tests, and 9 treatments were associated with mortality. Factors that were most strongly associated with mortality included receipt of vasopressors, non-invasive ventilation, and bicarbonate. The model had a c-statistic of 0.85 in both cohorts. In the validation cohort, deciles of predicted risk ranged from 0.3% to 34.3% with observed risk over the same deciles from 0.1% to 33.7%.

Conclusions

A mortality model based on detailed administrative data available in the first 2 hospital days had good discrimination and calibration. The model compares favorably to clinically based prediction models and may be useful in observational studies when clinical data are not available.     

Combining Epidemiologic and Biostatistical Tools to Enhance Variable Selection in HIV Cohort Analyses

Background

Variable selection is an important step in building a multivariate regression model for which several methods and statistical packages are available. A comprehensive approach for variable selection in complex multivariate regression analyses within HIV cohorts is explored by utilizing both epidemiological and biostatistical procedures.

Methods

Three different methods for variable selection were illustrated in a study comparing survival time between subjects in the Department of Defense’s National History Study and the Atlanta Veterans Affairs Medical Center’s HIV Atlanta VA Cohort Study. The first two methods were stepwise selection procedures, based either on significance tests (Score test), or on information theory (Akaike Information Criterion), while the third method employed a Bayesian argument (Bayesian Model Averaging).

Results

All three methods resulted in a similar parsimonious survival model. Three of the covariates previously used in the multivariate model were not included in the final model suggested by the three approaches. When comparing the parsimonious model to the previously published model, there was evidence of less variance in the main survival estimates.

Conclusions

The variable selection approaches considered in this study allowed building a model based on significance tests, on an information criterion, and on averaging models using their posterior probabilities. A parsimonious model that balanced these three approaches was found to provide a better fit than the previously reported model.     

The Quality of Spouses’ Social Networks Contributes to Each Other’s Cardiovascular Risk

Objectives

Although the quality of one’s own social relationships has been related to cardiovascular morbidity and mortality, whether a partner’s social network quality can similarly influence one’s cardiovascular risk is unknown. In this study we tested whether the quality of a partner’s social networks influenced one’s own ambulatory blood pressure (ABP).

Methods

The quality of 94 couples’ social networks was determined using a comprehensive model of relationships that separates out social ties that are sources of positivity(supportive), negativity (aversive), and both positivity and negativity (ambivalent). We then utilized statistical models (actor-partner analyses) that allowed us to separate out the links between one’s own social network quality on ABP (actor influences), a partner’s social network quality on ABP (partner influences), and a couple’s network quality combined on ABP (actor X partner interactions).

Results

Independent of one’s own relationship quality, results showed that an individual’s ABP was lower if their spouse had more supportive ties, and higher if a spouse had more aversive and ambivalent ties. In addition, couples’ networks in combination were associated with higher ABP but only if both had a low number of supportive ties, or a high number of aversive or ambivalent ties.

Conclusions

These data suggest that the social ties of those we have close relationships with may influence our cardiovascular risk and opens new opportunities to capitalize on untapped social resources or to mitigate hidden sources of social strain.     

Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis

Background

Adverse drug events (ADEs) detection and assessment is at the center of pharmacovigilance. Data mining of systems, such as FDA’s Adverse Event Reporting System (AERS) and more recently, Electronic Health Records (EHRs), can aid in the automatic detection and analysis of ADEs. Although different data mining approaches have been shown to be valuable, it is still crucial to improve the quality of the generated signals.

Objective

To leverage structural similarity by developing molecular fingerprint-based models (MFBMs) to strengthen ADE signals generated from EHR data.

Methods

A reference standard of drugs known to be causally associated with the adverse event pancreatitis was used to create a MFBM. Electronic Health Records (EHRs) from the New York Presbyterian Hospital were mined to generate structured data. Disproportionality Analysis (DPA) was applied to the data, and 278 possible signals related to the ADE pancreatitis were detected. Candidate drugs associated with these signals were then assessed using the MFBM to find the most promising candidates based on structural similarity.

Results

The use of MFBM as a means to strengthen or prioritize signals generated from the EHR significantly improved the detection accuracy of ADEs related to pancreatitis. MFBM also highlights the etiology of the ADE by identifying structurally similar drugs, which could follow a similar mechanism of action.

Conclusion

The method proposed in this paper provides evidence of being a promising adjunct to existing automated ADE detection and analysis approaches.     

Mathematical modeling of malaria infection with innate and adaptive immunity in individuals and agent-based communities

Background

Agent-based modeling of Plasmodium falciparum infection offers an attractive alternative to the conventional Ross-Macdonald methodology, as it allows simulation of heterogeneous communities subjected to realistic transmission (inoculation patterns).

Methodology/Principal Findings

We developed a new, agent based model that accounts for the essential in-host processes: parasite replication and its regulation by innate and adaptive immunity. The model also incorporates a simplified version of antigenic variation by Plasmodium falciparum. We calibrated the model using data from malaria-therapy (MT) studies, and developed a novel calibration procedure that accounts for a deterministic and a pseudo-random component in the observed parasite density patterns. Using the parasite density patterns of 122 MT patients, we generated a large number of calibrated parameters. The resulting data set served as a basis for constructing and simulating heterogeneous agent-based (AB) communities of MT-like hosts. We conducted several numerical experiments subjecting AB communities to realistic inoculation patterns reported from previous field studies, and compared the model output to the observed malaria prevalence in the field. There was overall consistency, supporting the potential of this agent-based methodology to represent transmission in realistic communities.

Conclusions/Significance

Our approach represents a novel, convenient and versatile method to model Plasmodium falciparum infection.     

Graduate entry medicine: selection criteria and student performance

Background

Graduate entry medicine raises new questions about the suitability of students with different backgrounds. We examine this, and the broader issue of effectiveness of selection and assessment procedures.

Methods

The data included background characteristics, academic record, interview score and performance in pre-clinical modular assessment for two years intake of graduate entry medical students. Exploratory factor analysis is a powerful method for reducing a large number of measures to a smaller group of underlying factors. It was used here to identify patterns within and between the selection and performance data.

Principal Findings

Basic background characteristics were of little importance in predicting exam success. However, easily interpreted components were detected within variables comprising the ‘selection’ and ‘assessment’ criteria. Three selection components were identified (‘Academic’, ‘GAMSAT’, ‘Interview’) and four assessment components (‘General Exam’, ‘Oncology’, ‘OSCE’, ‘Family Case Study’). There was a striking lack of relationships between most selection and performance factors. Only ‘General Exam’ and ‘Academic’ showed a correlation (Pearson''s r = 0.55, p<0.001).

Conclusions

This study raises questions about methods of student selection and their effectiveness in predicting performance and assessing suitability for a medical career. Admissions tests and most exams only confirmed previous academic achievement, while interview scores were not correlated with any consequent assessment.     

Primary mucosal sinonasal melanoma—Case report and review of the literature. The role of complex treatment-surgery and adjuvant radiotherapy

Aim

The place of adjuvant radiotherapy in the treatment of sinonasal melanoma.

Background

Sinonasal mucosal melanoma is a rare disease with poor prognosis and requires a complex treatment. Elective neck dissection in patients with N0 and adjuvant radiotherapy has been a source of controversy. High late regional recurrence rates rise questions about elective irradiation of the neck nodes in patients with N0 stage disease.

Methods

We present our two years’ follow up in a case of locally advanced sinonasal melanoma and literature review of the treatment options for mucosal melanoma.

Results

In locally advanced sinonasal melanoma treated with surgical resection, postoperative radiotherapy and chemotherapy we had local tumor control. Two years later, a regional contralateral recurrence without distant metastasis occurred.

Conclusions

Literature data for frequent neck lymph nodes recurrences justify elective neck dissection. Postoperative elective neck radiotherapy for patients with locally advanced sinonasal melanoma and clinically N0 appears to decrease the rate of late regional recurrences.     

Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study

Objectives

To objectively evaluate automatic volumetric breast density assessment in Full-Field Digital Mammograms (FFDM) using measurements obtained from breast Magnetic Resonance Imaging (MRI).

Material and Methods

A commercially available method for volumetric breast density estimation on FFDM is evaluated by comparing volume estimates obtained from 186 FFDM exams including mediolateral oblique (MLO) and cranial-caudal (CC) views to objective reference standard measurements obtained from MRI.

Results

Volumetric measurements obtained from FFDM show high correlation with MRI data. Pearson’s correlation coefficients of 0.93, 0.97 and 0.85 were obtained for volumetric breast density, breast volume and fibroglandular tissue volume, respectively.

Conclusions

Accurate volumetric breast density assessment is feasible in Full-Field Digital Mammograms and has potential to be used in objective breast cancer risk models and personalized screening.     

External Validation of a Nomogram That Predicts the Pathological Diagnosis of Thyroid Nodules in a Chinese Population

Introduction

Nomograms are statistical predictive models that can provide the probability of a clinical event. Nomograms have better performance for the estimation of individual risks because of their increased accuracy and objectivity relative to physicians’ personal experiences. Recently, a nomogram for predicting the likelihood that a thyroid nodule is malignant was introduced by Nixon. The aim of this study was to determine whether Nixon’s nomogram can be validated in a Chinese population.

Materials and Methods

All consecutive patients with thyroid nodules who underwent surgery between January and June 2012 in our hospital were enrolled to validate Nixon’s nomogram. Univariate and multivariate analyses were used to identify the risk factors for thyroid carcinoma. Discrimination and calibration were employed to evaluate the performance of Nixon’s model in our population.

Results

A total of 348 consecutive patients with 409 thyroid nodules were enrolled. Thyroid ultrasonographic characteristics, including shape, echo texture, calcification, margins, vascularity and number (solitary vs. multiple nodules), were associated with malignance in the multivariate analysis. The discrimination of all nodules group, the group with a low risk of malignancy (predictive proportion <50%) and the group with a high risk of malignancy (predictive proportion ≥50%) using Nixon’s nomogram was satisfactory, and the area under the receiver operating characteristic curve of the three groups were 0.87, 0.75 and 0.72, respectively. However, the calibration was significant (p = 0.55) only in the high-risk group.

Conclusion

Nixon’s nomogram is a valuable predictive model for the Chinese population and has been externally validated. It has good performance for patients with a high risk of malignancy and may be more suitable for use with these patients in China.     

Impact on Clinical and Cost Outcomes of a Centralized Approach to Acute Stroke Care in London: A Comparative Effectiveness Before and After Model

Background

In July 2010 a new multiple hub-and-spoke model for acute stroke care was implemented across the whole of London, UK, with continuous specialist care during the first 72 hours provided at 8 hyper-acute stroke units (HASUs) compared to the previous model of 30 local hospitals receiving acute stroke patients. We investigated differences in clinical outcomes and costs between the new and old models.

Methods

We compared outcomes and costs ‘before’ (July 2007–July 2008) vs. ‘after’ (July 2010–June 2011) the introduction of the new model, adjusted for patient characteristics and national time trends in mortality and length of stay. We constructed 90-day and 10-year decision analytic models using data from population based stroke registers, audits and published sources. Mortality and length of stay were modelled using survival analysis.

Findings

In a pooled sample of 307 patients ‘before’ and 3156 patients ‘after’, survival improved in the ‘after’ period (age adjusted hazard ratio 0.54; 95% CI 0.41–0.72). The predicted survival rates at 90 days in the deterministic model adjusted for national trends were 87.2% ‘before’ % (95% CI 86.7%–87.7%) and 88.7% ‘after’ (95% CI 88.6%–88.8%); a relative reduction in deaths of 12% (95% CI 8%–16%). Based on a cohort of 6,438 stroke patients, the model produces a total cost saving of £5.2 million per year at 90 days (95% CI £4.9-£5.5 million; £811 per patient).

Conclusion

A centralized model for acute stroke care across an entire metropolitan city appears to have reduced mortality for a reduced cost per patient, predominately as a result of reduced hospital length of stay.