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1.
R Soc Open Sci ; 11(5): 240126, 2024 May.
Article in English | MEDLINE | ID: mdl-39076824

ABSTRACT

Mathematical models describing the spatial spreading and invasion of populations of biological cells are often developed in a continuum modelling framework using reaction-diffusion equations. While continuum models based on linear diffusion are routinely employed and known to capture key experimental observations, linear diffusion fails to predict well-defined sharp fronts that are often observed experimentally. This observation has motivated the use of nonlinear degenerate diffusion; however, these nonlinear models and the associated parameters lack a clear biological motivation and interpretation. Here, we take a different approach by developing a stochastic discrete lattice-based model incorporating biologically inspired mechanisms and then deriving the reaction-diffusion continuum limit. Inspired by experimental observations, agents in the simulation deposit extracellular material, which we call a substrate, locally onto the lattice, and the motility of agents is taken to be proportional to the substrate density. Discrete simulations that mimic a two-dimensional circular barrier assay illustrate how the discrete model supports both smooth and sharp-fronted density profiles depending on the rate of substrate deposition. Coarse-graining the discrete model leads to a novel partial differential equation (PDE) model whose solution accurately approximates averaged data from the discrete model. The new discrete model and PDE approximation provide a simple, biologically motivated framework for modelling the spreading, growth and invasion of cell populations with well-defined sharp fronts. Open-source Julia code to replicate all results in this work is available on GitHub.

2.
J Am Vet Med Assoc ; : 1-7, 2024 Jul 24.
Article in English | MEDLINE | ID: mdl-39047788

ABSTRACT

OBJECTIVE: To describe and evaluate the use of preoperative percutaneous ultrasound-guided anchor wire placement to aid intraoperative localization of superficial foreign bodies and abscesses in dogs. ANIMALS: 11 dogs. CLINICAL PRESENTATION: In a retrospective observational study, the medical records of dogs that underwent surgical exploration of superficial abscesses, guided by anchor wire, between 2018 and 2023 were reviewed for clinical and histopathological findings and complications. Owners or veterinarians were contacted to collect long-term follow-up information. RESULTS: 11 dogs were included. Superficial swelling was the most common clinical presentation. Computed tomography and ultrasound revealed an abscess cavity and suspected foreign body in 9 dogs and an abscess cavity without evidence of a foreign body in 2 dogs. Anchor wires were placed in close proximity to the foreign body or inside the abscess. All documented foreign bodies were successfully located and retrieved. Two dogs suffered minor postoperative complications. No major intra- or postoperative complications were documented. One dog displayed recurrence of clinical signs, but no further surgical management was required. CLINICAL RELEVANCE: Preoperative percutaneous placement of an anchor wire via ultrasound guidance was successful in aiding intraoperative localization of nonpalpable abscesses and retrieval of foreign bodies. This technique may decrease surgical time, minimize the surgical approach required, and increase the likelihood of successful localization.

3.
Article in English | MEDLINE | ID: mdl-38881383

ABSTRACT

OBJECTIVE: (1) Describe short and long-term opioid prescribing patterns and variation after common otolaryngologic procedures and (2) assess risk factors for chronic opioid use in this cohort. STUDY DESIGN: Retrospective cohort. SETTING: Optum's deidentified Integrated Claims-Clinical data set. METHODS: An adult cohort of patients undergoing common otolaryngology procedures from 2010 to 2017 was identified. Associations between procedure and other covariates with any initial opioid prescription and continuous opioid prescriptions were assessed with multivariable modeling. Opioid use was defined as continuous if a new prescription was filled within 30 days of the previous prescription. A time-to-event analysis assessed continuous prescriptions from the index procedure to end of the last continuous opioid prescription. RESULTS: Among a cohort of 19,819 patients undergoing predominately laryngoscopy procedures (12,721, 64.2%), 2585 (13.0%) received an opioid prescription with variation in receiving a prescription, daily dose, and total initially prescribed dose varying by procedure, patient demographics, provider characteristics, and facility type. Opioids were prescribed most frequently after tonsillectomy (45.4%) and least frequently after laryngoscopy with interventions (3.9%), which persisted in the multivariable models. Overall rates of continuous use at 180 and 360 days were 0.48% and 0.27%, respectively. Among patients receiving an initial opioid prescription, maintaining continuous prescriptions was associated with tonsillectomy procedures, age (adjusted hazard ratio [aHR]: 0.997 per year, 95% confidence interval [CI]: 0.993-0.999), opioid prescriptions 6 months preprocedure (aHR: 0.42, 95% CI: 0.37-0.47), and nonotolaryngology initial prescribers (aHRs: <1, P < .05). CONCLUSION: There is substantial variation in initial prescribing practices and continuous opioid prescriptions after common Otolaryngology procedures, but the overall rate of maintaining a continuous prescription starting after these procedures is very low. LEVEL OF EVIDENCE: Level 3.

4.
Bull Math Biol ; 86(7): 80, 2024 May 27.
Article in English | MEDLINE | ID: mdl-38801489

ABSTRACT

Many commonly used mathematical models in the field of mathematical biology involve challenges of parameter non-identifiability. Practical non-identifiability, where the quality and quantity of data does not provide sufficiently precise parameter estimates is often encountered, even with relatively simple models. In particular, the situation where some parameters are identifiable and others are not is often encountered. In this work we apply a recent likelihood-based workflow, called Profile-Wise Analysis (PWA), to non-identifiable models for the first time. The PWA workflow addresses identifiability, parameter estimation, and prediction in a unified framework that is simple to implement and interpret. Previous implementations of the workflow have dealt with idealised identifiable problems only. In this study we illustrate how the PWA workflow can be applied to both structurally non-identifiable and practically non-identifiable models in the context of simple population growth models. Dealing with simple mathematical models allows us to present the PWA workflow in a didactic, self-contained document that can be studied together with relatively straightforward Julia code provided on GitHub . Working with simple mathematical models allows the PWA workflow prediction intervals to be compared with gold standard full likelihood prediction intervals. Together, our examples illustrate how the PWA workflow provides us with a systematic way of dealing with non-identifiability, especially compared to other approaches, such as seeking ad hoc parameter combinations, or simply setting parameter values to some arbitrary default value. Importantly, we show that the PWA workflow provides insight into the commonly-encountered situation where some parameters are identifiable and others are not, allowing us to explore how uncertainty in some parameters, and combinations of parameters, regardless of their identifiability status, influences model predictions in a way that is insightful and interpretable.


Subject(s)
Mathematical Concepts , Models, Biological , Humans , Likelihood Functions , Computer Simulation , Population Dynamics/statistics & numerical data , Workflow , Algorithms
5.
BMC Prim Care ; 25(1): 135, 2024 Apr 25.
Article in English | MEDLINE | ID: mdl-38664665

ABSTRACT

BACKGROUND: Engaging patients and community members in healthcare implementation, research and evaluation has become more popular over the past two decades. Despite the growing interest in patient engagement, there is scant evidence of its impact and importance. Boot Camp Translation (BCT) is one evidence-based method of engaging communities in research. The purpose of this report is to describe the uptake by primary care practices of cardiovascular disease prevention materials produced through four different local community engagement efforts using BCT. METHODS: EvidenceNOW Southwest (ENSW) was a randomized trial to increase cardiovascular disease (CVD) prevention in primary care practices. Because of its study design, Four BCTs were conducted, and the materials created were made available to participating practices in the "enhanced" study arm. As a result, ENSW offered one of the first opportunities to explore the impact of the BCT method by describing the uptake by primary care practices of health messages and materials created locally using the BCT process. Analysis compared uptake of locally translated BCT products vs. all other products among practices based on geography, type of practice, and local BCT. RESULTS: Within the enhanced arm of the study that included BCT, 69 urban and 13 rural practices participated with 9 being federally qualified community health centers, 14 hospital owned and 59 clinician owned. Sixty-three practices had 5 or fewer clinicians. Two hundred and ten separate orders for materials were placed by 43 of the 82 practices. While practices ordered a wide variety of BCT products, they were more likely to order materials developed by their local BCT. CONCLUSIONS: In this study, patients and community members generated common and unique messages and materials for cardiovascular disease prevention relevant to their regional and community culture. Primary care practices preferred the materials created in their region. The greater uptake of locally created materials over non-local materials supports the use of patient engagement methods such as BCT to increase the implementation and delivery of guideline-based care. Yes, patient and community engagement matters. TRIAL REGISTRATION AND IRB: Trial registration was prospectively registered on July 31, 2015 at ClinicalTrials.gov (NCT02515578, protocol identifier 15-0403). The project was approved by the Colorado Multiple Institutional Review Board and the University of New Mexico Human Research Protections Office.


Subject(s)
Cardiovascular Diseases , Primary Health Care , Humans , Cardiovascular Diseases/prevention & control , Patient Participation/methods , Community Participation , Health Promotion/methods
6.
Bone ; 180: 116998, 2024 03.
Article in English | MEDLINE | ID: mdl-38184100

ABSTRACT

Osteon morphology provides valuable information about the interplay between different processes involved in bone remodelling. The correct quantitative interpretation of these morphological features is challenging due to the complexity of interactions between osteoblast behaviour, and the evolving geometry of cortical pores during pore closing. We present a combined experimental and mathematical modelling study to provide insights into bone formation mechanisms during cortical bone remodelling based on histological cross-sections of quiescent human osteons and hypothesis-testing analyses. We introduce wall thickness asymmetry as a measure of the local asymmetry of bone formation within an osteon and examine the frequency distribution of wall thickness asymmetry in cortical osteons from human iliac crest bone samples from women 16-78 years old. Our measurements show that most osteons possess some degree of asymmetry, and that the average degree of osteon asymmetry in cortical bone evolves with age. We then propose a comprehensive mathematical model of cortical pore filling that includes osteoblast secretory activity, osteoblast elimination, osteoblast embedment as osteocytes, and osteoblast crowding and redistribution along the bone surface. The mathematical model is first calibrated to symmetric osteon data, and then used to test three mechanisms of asymmetric wall formation against osteon data: (i) delays in the onset of infilling around the cement line; (ii) heterogeneous osteoblastogenesis around the bone perimeter; and (iii) heterogeneous osteoblast secretory rate around the bone perimeter. Our results suggest that wall thickness asymmetry due to off-centred Haversian pores within osteons, and that nonuniform lamellar thicknesses within osteons are important morphological features that can indicate the prevalence of specific asymmetry-generating mechanisms. This has significant implications for the study of disruptions of bone formation as it could indicate what biological bone formation processes may become disrupted with age or disease.


Subject(s)
Haversian System , Osteoblasts , Humans , Female , Adolescent , Young Adult , Adult , Middle Aged , Aged , Haversian System/anatomy & histology , Bone and Bones , Osteocytes , Cortical Bone
7.
J R Soc Interface ; 21(210): 20230402, 2024 01.
Article in English | MEDLINE | ID: mdl-38290560

ABSTRACT

Throughout the life sciences, we routinely seek to interpret measurements and observations using parametrized mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achieved by assuming that the data are noisy measurements of the solution of a deterministic mathematical model, and that measurement errors are additive and normally distributed. While this assumption of additive Gaussian noise is extremely common and simple to implement and interpret, it is often unjustified and can lead to poor parameter estimates and non-physical predictions. One way to overcome this challenge is to implement a different measurement error model. In this review, we demonstrate how to implement a range of measurement error models in a likelihood-based framework for estimation, identifiability analysis and prediction, called profile-wise analysis. This frequentist approach to uncertainty quantification for mechanistic models leverages the profile likelihood for targeting parameters and understanding their influence on predictions. Case studies, motivated by simple caricature models routinely used in systems biology and mathematical biology literature, illustrate how the same ideas apply to different types of mathematical models. Open-source Julia code to reproduce results is available on GitHub.


Subject(s)
Models, Biological , Systems Biology , Likelihood Functions , Systems Biology/methods , Uncertainty
8.
J Theor Biol ; 580: 111732, 2024 03 07.
Article in English | MEDLINE | ID: mdl-38218530

ABSTRACT

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth-death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction-diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.


Subject(s)
Models, Statistical , Models, Theoretical , Software , Linear Models , Biology , Models, Biological
9.
Bull Math Biol ; 86(1): 8, 2023 12 13.
Article in English | MEDLINE | ID: mdl-38091169

ABSTRACT

Co-culture tumour spheroid experiments are routinely performed to investigate cancer progression and test anti-cancer therapies. Therefore, methods to quantitatively characterise and interpret co-culture spheroid growth are of great interest. However, co-culture spheroid growth is complex. Multiple biological processes occur on overlapping timescales and different cell types within the spheroid may have different characteristics, such as differing proliferation rates or responses to nutrient availability. At present there is no standard, widely-accepted mathematical model of such complex spatio-temporal growth processes. Typical approaches to analyse these experiments focus on the late-time temporal evolution of spheroid size and overlook early-time spheroid formation, spheroid structure and geometry. Here, using a range of ordinary differential equation-based mathematical models and parameter estimation, we interpret new co-culture experimental data. We provide new biological insights about spheroid formation, growth, and structure. As part of this analysis we connect Greenspan's seminal mathematical model to co-culture data for the first time. Furthermore, we generalise a class of compartment-based spheroid mathematical models that have previously been restricted to one population so they can be applied to multiple populations. As special cases of the general model, we explore multiple natural two population extensions to Greenspan's seminal model and reveal biological mechanisms that can describe the internal dynamics of growing co-culture spheroids and those that cannot. This mathematical and statistical modelling-based framework is well-suited to analyse spheroids grown with multiple different cell types and the new class of mathematical models provide opportunities for further mathematical and biological insights.


Subject(s)
Neoplasms , Spheroids, Cellular , Humans , Coculture Techniques , Spheroids, Cellular/pathology , Models, Biological , Mathematical Concepts , Neoplasms/pathology , Models, Theoretical
10.
PLoS Comput Biol ; 19(9): e1011515, 2023 09.
Article in English | MEDLINE | ID: mdl-37773942

ABSTRACT

Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Developing mechanistic insight by combining mathematical models and experimental data is especially critical in mathematical biology as new data and new types of data are collected and reported. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic, computationally-efficient workflow we call Profile-Wise Analysis (PWA) that addresses all three steps in a unified way. Recently-developed methods for constructing 'profile-wise' prediction intervals enable this workflow and provide the central linkage between different workflow components. These methods propagate profile-likelihood-based confidence sets for model parameters to predictions in a way that isolates how different parameter combinations affect model predictions. We show how to extend these profile-wise prediction intervals to two-dimensional interest parameters. We then demonstrate how to combine profile-wise prediction confidence sets to give an overall prediction confidence set that approximates the full likelihood-based prediction confidence set well. Our three case studies illustrate practical aspects of the workflow, focusing on ordinary differential equation (ODE) mechanistic models with both Gaussian and non-Gaussian noise models. While the case studies focus on ODE-based models, the workflow applies to other classes of mathematical models, including partial differential equations and simulation-based stochastic models. Open-source software on GitHub can be used to replicate the case studies.


Subject(s)
Models, Biological , Models, Theoretical , Likelihood Functions , Workflow , Software
11.
JAMA Otolaryngol Head Neck Surg ; 149(10): 919-928, 2023 Oct 01.
Article in English | MEDLINE | ID: mdl-37615970

ABSTRACT

Importance: Diagnostic delay can negatively affect patient outcomes in head and neck cancer (HNC). Neck mass and other symptoms of undiagnosed HNC may be treated with antibiotics, delaying diagnosis and treatment, despite current clinical practice guidelines. Objective: To investigate temporal trends, associated factors, and time from symptom onset to antibiotic prescribing before an HNC diagnosis. Design, Setting, and Participants: A retrospective cohort study was conducted using data obtained from a deidentified electronic health records data set from January 1, 2011, to December 31, 2018. Patients with HNC enrolled in the data set for at least 1 year before diagnosis date determined by either 1 inpatient encounter or first of 2 outpatient encounters within 6 months were included. Data analysis was conducted from May 1 to November 9, 2022. Exposure: Antibiotic prescription within 3 months before HNC diagnosis date. Main Outcomes and Measures: The primary outcome was days from the first documented symptom to HNC diagnosis. Results: The cohort included 7811 patients with HNC (4151 [53.1%] men, mean [SD] age, 60.2 [15.8] years). At least 1 antibiotic was prescribed for 1219 patients (15.6%) within 3 months before HNC diagnosis. This represented an increase over the 8.9% prescribing rate during the baseline period 12 to 9 months before diagnosis. The rate of antibiotic prescribing within 3 months before diagnosis did not change significantly over time (quarterly percent change, 0.49%; 95% CI, -3.06% to 4.16%). Patients receiving an antibiotic prescription within 3 months of an HNC diagnosis had a 21.1% longer time between symptom onset and HNC diagnoses (adjusted rate ratio [ARR], 1.21; 95% CI, 1.14-1.29). Compared with diagnosis by otolaryngologists, primary care/internal medicine physicians were most likely to prescribe antibiotics for patients who were diagnosed with a presenting symptom (adjusted prevalence ratio, 1.60; 95% CI, 1.27-2.02). In patients presenting with neck mass/swelling, those presenting with other symptoms were more likely to have longer intervals from symptom onset to diagnosis (ARR, 1.31; 95% CI, 1.08-1.59). Conclusions and Relevance: The findings of this cohort study suggest there is an increased rate of antibiotic prescription in the 3 months before HNC diagnosis, which is associated with an increased time to diagnosis. These findings identify an area for improvement in HNC care and guidelines.

12.
Can Fam Physician ; 69(8): 531-536, 2023 08.
Article in English | MEDLINE | ID: mdl-37582587

ABSTRACT

OBJECTIVE: To provide family physicians with a practical evidence-based approach to the management of patients with sialadenitis. SOURCES OF INFORMATION: MEDLINE and PubMed databases were searched for English-language research on sialadenitis and other salivary gland disorders, as well as for relevant review articles and guidelines published between 1981 and 2021. MAIN MESSAGE: Sialadenitis refers to inflammation or infection of the salivary glands and is a condition that can be caused by a broad range of processes including infectious, obstructive, and autoimmune. History and physical examination play important roles in directing management, while imaging is often useful to establish a diagnosis. Red flags such as suspected abscess formation, signs of respiratory obstruction, facial paresis, and fixation of a mass to underlying tissue should prompt urgent referral to head and neck surgery or a visit to the emergency department. CONCLUSION: Family physicians can play an important role in the diagnosis and management of sialadenitis. Prompt recognition and treatment of the condition can prevent the development of complications.


Subject(s)
Sialadenitis , Humans , Sialadenitis/diagnosis , Sialadenitis/therapy , Sialadenitis/etiology , Diagnostic Imaging/adverse effects , Physical Examination
13.
Can Fam Physician ; 69(8): e159-e164, 2023 08.
Article in French | MEDLINE | ID: mdl-37582592

ABSTRACT

OBJECTIF: Proposer aux médecins de famille une approche pratique fondée sur des données probantes pour la prise en charge de patients souffrant de sialadénite. SOURCES DE L'INFORMATION: Une recension a été effectuée dans les bases de données MEDLINE et PubMed pour trouver des recherches publiées en anglais sur la sialadénite et d'autres troubles des glandes salivaires, ainsi que des revues et des lignes directrices pertinentes, publiées entre 1981 et 2021. MESSAGE PRINCIPAL: La sialadénite désigne une inflammation ou une infection des glandes salivaires; elle peut être causée par un large éventail de processus de nature infectieuse, obstructive et auto-immune. L'anamnèse et l'examen physique jouent un rôle important pour orienter la prise en charge, tandis que l'imagerie est souvent utile pour établir un diagnostic. Des signaux d'alerte comme la formation suspectée d'un abcès, des signes d'obstruction respiratoire, une parésie faciale et la fixation d'une masse aux tissus sous-jacents devraient inciter à faire une demande de consultation urgente en chirurgie de la tête et du cou, ou à recommander une visite au service d'urgence. CONCLUSION: Les médecins de famille peuvent jouer un rôle important dans le diagnostic et la prise en charge de la sialadénite. Une reconnaissance et un traitement rapides du problème peuvent prévenir la survenance de complications.

14.
Implement Sci Commun ; 4(1): 41, 2023 Apr 20.
Article in English | MEDLINE | ID: mdl-37081581

ABSTRACT

BACKGROUND: Untreated opioid use disorder (OUD) is a significant public health problem. Buprenorphine is an evidence-based treatment for OUD that can be initiated in and prescribed from emergency departments (EDs) and office settings. Adoption of buprenorphine initiation among ED clinicians is low. The EMBED pragmatic clinical trial investigated the effectiveness of a clinical decision support (CDS) tool to promote ED clinicians' behavior related to buprenorphine initiation in the ED. While the CDS intervention was not associated with increased rates of buprenorphine treatment for patients with OUD at intervention ED sites, attending physicians at intervention EDs were more likely to initiate buprenorphine at least once over the duration of the study compared to those in the usual care arms (44.4% vs 34.0%, P = 0.01). This suggests the CDS intervention may be associated with increased adoption of buprenorphine initiation. As a secondary aim, we sought to identify the determinants of CDS adoption, implementation, and maintenance in a variety of ED settings and geographic locations. METHODS: We purposively sampled and conducted semi-structured, in-depth interviews with clinicians across EMBED trial sites randomized to the intervention arm from five healthcare systems. Interviews elicited clinician experiences regarding buprenorphine initiation and CDS use. Interviews were analyzed using directed content analysis informed by the Practical, Robust Implementation and Sustainability Model (PRISM). We used a hybrid approach (a priori codes informed by PRISM and emergent codes) for codebook development. ATLAS.ti (version 9.0) was used for data management. Coded data were analyzed within individual interview transcripts and across all interviews to identify major themes. This process involved (1) combining, comparing, and making connections between codes; (2) writing analytic memos about observed patterns; and (3) frequent team meetings to discuss emerging patterns. RESULTS: Twenty-eight interviews were conducted. Major themes that influenced the successful adoption, implementation, and maintenance of the EMBED intervention and ED-initiated BUP were organizational culture and commitment, clinician training and support, the ability to connect patients to ongoing treatment, and the ability to tailor implementation to each ED. These findings informed the identification of implementation strategies (framed using PRISM domains) to enhance the ED initiation of buprenorphine. CONCLUSION: The findings from this qualitative analysis can provide guidance to build better systems to promote the adoption of ED-initiated buprenorphine.

16.
Head Neck ; 45(7): 1663-1675, 2023 07.
Article in English | MEDLINE | ID: mdl-37096786

ABSTRACT

BACKGROUND: Uninsured individuals age 55-64 experience disproportionately poor outcomes compared to their insured counterparts. Adequate coverage may prevent these delays. This study investigates a "Medicare-effect" on head and neck squamous cell carcinoma (HNSCC) diagnosis and treatment. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for persons ages 60-70 years in the United States from 2000 to 2016 with HNSCC. A "Medicare effect" was defined as an increase in incidence, reduction in advanced stage presentation, and/or decrease in cancer-specific mortality (CSM). RESULTS: Compared to their Medicaid or uninsured counterparts, patients age 65 have an increased incidence of HNSCC diagnosis, reduction in advanced stage presentation, decrease in cancer-specific mortality, and higher likelihood of receiving cancer-specific surgery. CONCLUSIONS: Patients age 65 with Medicare have decreased incidence of HNSCC, less hazard of late-stage diagnosis, and lower cancer-specific mortality than their Medicaid or uninsured counterparts, supporting the idea of a "Medicare effect" in HNSCC.


Subject(s)
Head and Neck Neoplasms , Medicare , Humans , Aged , United States/epidemiology , Middle Aged , Squamous Cell Carcinoma of Head and Neck , SEER Program , Head and Neck Neoplasms/diagnosis , Head and Neck Neoplasms/therapy , Medicaid
17.
Cancer ; 129(9): 1372-1383, 2023 05 01.
Article in English | MEDLINE | ID: mdl-36808090

ABSTRACT

BACKGROUND: There has been conflicting evidence on the independent prognostic role of human papillomavirus (HPV) status in sinonasal cancer. The objective of this study was to assess whether the survival of patients with sinonasal cancer differs based on various HPV statuses, including HPV-negative, positive for the high-risk HPV-16 and HPV-18 (HPV16/18) subtypes, and positive for other high-risk and low-risk HPV subtypes. METHODS: In this retrospective cohort study, data from the National Cancer Database were extracted from the years 2010-2017 for patients who had primary sinonasal cancer (N = 12,009). The outcome of interest was overall survival based on HPV tumor status. RESULTS: Study included an analytic cohort of 1070 patients with sinonasal cancer who had confirmed HPV tumor status (732 [68.4%] HPV-negative; 280 [26.2%] HPV16/18-positive; 40 [3.7%] positive for other high-risk HPV; and 18 [1.7%] positive for low-risk HPV). HPV-negative patients had the lowest all-cause survival probability at 5 years postdiagnosis (0.50). After controlling for covariates, HPV16/18-positive patients had a 37% lower mortality hazard than HPV-negative patients (adjusted hazard ratio, 0.63; 95% confidence interval [CI], 0.48-0.82). Patients aged 64-72 years (crude prevalence ratio, 0.66; 95% CI, 0.51-0.86) and 73 years and older (crude prevalence ratio, 0.43; 95% CI, 0.31-0.59) presented with lower rates of HPV16/18-positive sinonasal cancer than those aged 40-54 years. In addition, Hispanic patients had a 2.36 times higher prevalence of non-HPV16/18 sinonasal cancer than non-Hispanic White patients. CONCLUSIONS: These data suggest that, for patients with sinonasal cancer, HPV16/18-positive disease may confer a significant survival advantage compared with HPV-negative disease. Other high-risk and low-risk HPV subtypes have survival rates similar to the rates for HPV-negative disease. HPV status might be an important independent prognostic factor in sinonasal cancer that could be used in patient selection and clinical decisions.


Subject(s)
Carcinoma, Squamous Cell , Papillomavirus Infections , Paranasal Sinus Neoplasms , Humans , Human Papillomavirus Viruses , Carcinoma, Squamous Cell/pathology , Retrospective Studies , Human papillomavirus 16/genetics , Squamous Cell Carcinoma of Head and Neck/complications , Paranasal Sinus Neoplasms/pathology
18.
PLoS Comput Biol ; 19(1): e1010844, 2023 01.
Article in English | MEDLINE | ID: mdl-36662831

ABSTRACT

An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have been developed to harmonise this juxtaposition, yet cannot always resolve the mismatch between available data and the granularity required in mathematical models to answer important biological questions. Often, it is only simple phenomenological models, such as the logistic and Gompertz growth models, that are identifiable from standard experimental measurements. To draw insights from complex, non-identifiable models that incorporate key biological mechanisms of interest, we study the geometry of a map in parameter space from the complex model to a simple, identifiable, surrogate model. By studying how non-identifiable parameters in the complex model quantitatively relate to identifiable parameters in surrogate, we introduce and exploit a layer of interpretation between the set of non-identifiable parameters and the goodness-of-fit metric or likelihood studied in typical identifiability analysis. We demonstrate our approach by analysing a hierarchy of mathematical models for multicellular tumour spheroid growth experiments. Typical data from tumour spheroid experiments are limited and noisy, and corresponding mathematical models are very often made arbitrarily complex. Our geometric approach is able to predict non-identifiabilities, classify non-identifiable parameter spaces into identifiable parameter combinations that relate to features in the data characterised by parameters in a surrogate model, and overall provide additional biological insight from complex non-identifiable models.


Subject(s)
Models, Biological , Neoplasms , Humans , Models, Theoretical , Computational Biology , Probability
19.
PLoS Comput Biol ; 19(1): e1010833, 2023 01.
Article in English | MEDLINE | ID: mdl-36634128

ABSTRACT

Tumours are subject to external environmental variability. However, in vitro tumour spheroid experiments, used to understand cancer progression and develop cancer therapies, have been routinely performed for the past fifty years in constant external environments. Furthermore, spheroids are typically grown in ambient atmospheric oxygen (normoxia), whereas most in vivo tumours exist in hypoxic environments. Therefore, there are clear discrepancies between in vitro and in vivo conditions. We explore these discrepancies by combining tools from experimental biology, mathematical modelling, and statistical uncertainty quantification. Focusing on oxygen variability to develop our framework, we reveal key biological mechanisms governing tumour spheroid growth. Growing spheroids in time-dependent conditions, we identify and quantify novel biological adaptation mechanisms, including unexpected necrotic core removal, and transient reversal of the tumour spheroid growth phases.


Subject(s)
Neoplasms , Spheroids, Cellular , Humans , Spheroids, Cellular/pathology , Oxygen , Models, Biological , Neoplasms/pathology , Models, Theoretical
20.
Math Biosci ; 355: 108950, 2023 01.
Article in English | MEDLINE | ID: mdl-36463960

ABSTRACT

Calibrating mathematical models to describe ecological data provides important insight via parameter estimation that is not possible from analysing data alone. When we undertake a mathematical modelling study of ecological or biological data, we must deal with the trade-off between data availability and model complexity. Dealing with the nexus between data availability and model complexity is an ongoing challenge in mathematical modelling, particularly in mathematical biology and mathematical ecology where data collection is often not standardised, and more broad questions about model selection remain relatively open. Therefore, choosing an appropriate model almost always requires case-by-case consideration. In this work we present a straightforward approach to quantitatively explore this trade-off using a case study exploring mathematical models of coral reef regrowth after some ecological disturbance, such as damage caused by a tropical cyclone. In particular, we compare a simple single species ordinary differential equation (ODE) model approach with a more complicated two-species coupled ODE model. Univariate profile likelihood analysis suggests that the both models are practically identifiable. To provide additional insight we construct and compare approximate prediction intervals using a new parameter-wise prediction approximation, confirming both the simple and complex models perform similarly with regard to making predictions. Our approximate parameter-wise prediction interval analysis provides explicit information about how each parameter affects the predictions of each model. Comparing our approximate prediction intervals with a more rigorous and computationally expensive evaluation of the full likelihood shows that the new approximations are reasonable in this case. All algorithms and software to support this work are freely available as jupyter notebooks on GitHub so that they can be adapted to deal with any other ODE-based models.


Subject(s)
Models, Biological , Software , Likelihood Functions , Models, Theoretical , Algorithms
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