A Clinical Decision Tool to Calculate Pretest Probability of Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma.

BACKGROUND: Although sentinel lymph node biopsy (SLNB) status is a strong prognostic indicator for cutaneous melanoma, unnecessary SLNBs have substantial cost and morbidity burden. OBJECTIVE: This study was designed to develop, validate, and present a personalized, clinical, decision-making tool using nationally representative data with clinically actionable probability thresholds (Expected Lymphatic Metastasis Outcome [ELMO]). METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2017 and the National Cancer Database (NCDB) from 2004 to 2015 were used to develop and internally validate a logistic ridge regression predictive model for SLNB positivity. External validation was done with 1568 patients at a large tertiary referral center. RESULTS: The development cohort included 134,809 patients, and the internal validation cohort included 38,518 patients. ELMO (AUC 0.85) resulted in a 29.54% SLNB reduction rate and greater sensitivity in predicting SLNB status for T1b, T2a, and T2b tumors than previous models. In external validation, ELMO had an accuracy of 0.7586 and AUC of 0.7218. Limitations of this study are potential miscoding, unaccounted confounders, and effect modification. CONCLUSIONS: ELMO ( https://melanoma-sentinel.herokuapp.com/ ) has been developed and validated (internally and externally) by using the largest publicly available dataset of melanoma patients and was found to have high accuracy compared with other published models and gene expression tests. Individualized risk estimates for SLNB positivity are critical in facilitating thorough decision-making for healthcare providers and patients with melanoma.

Loss to Follow-Up and the Care Cascade for Cervical Cancer Care in Rural Guatemala: A Cross-Sectional Study.

PURPOSE: More than 80% of cervical cancer cases and deaths occur in low- and middle-income countries. Here, we analyze a large geographically extensive cross-sectional data set from the Western rural highlands of Guatemala. Our objective is to better characterize weak points in care along the cervical cancer care continuum and investigate sociodemographic and clinical correlates of loss to follow-up. METHODS: We conducted a retrospective review of electronic health records data from July 21, 2015, through December 10, 2020 for a cytology-based screening and cervical cancer treatment program. We used a care cascade analysis to characterize the progression of individuals through screening, confirmatory testing, and treatment. We examined demographic and clinical factors correlated with screening and loss to follow-up using multivariate logistic regression. RESULTS: A total of 8,872 individuals were included in the analysis. Five thousand nine hundred thirteen cervical cancer screenings were conducted. 4.1% of all screening tests were abnormal, including 0.61% cervical intraepithelial neoplasia or overt cervical cancer. Care cascade analysis showed that 67% of eligible women accepted screening. Of those requiring confirmatory testing or treatment, 73% completed recommended follow-up. In adjusted multivariable analysis, prior history of sexual transmitted infection, prior experience with cervical cancer screening, older age, and current contraceptive use were associated with accepting screening. Age and contraceptive use were also associated with retention in care after a positive first screen. CONCLUSION: In a large rural Guatemalan retrospective cohort, a care continuum analysis showed that both declining the opportunity to receive cervical cancer screening as well as declining confirmatory testing after a first positive screen were both important weak points along the care continuum. These data support the need for comprehensive and culturally appropriate initiatives to improve screening uptake and retention in care.

Data-driven prediction and origin identification of epidemics in population networks.

Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics.

A Bayesian Framework to Estimate Fluid and Material Parameters in Micro-swimmer Models.

To advance our understanding of the movement of elastic microstructures in a viscous fluid, techniques that utilize available data to estimate model parameters are necessary. Here, we describe a Bayesian uncertainty quantification framework that is highly parallelizable, making parameter estimation tractable for complex fluid-structure interaction models. Using noisy in silico data for swimmers, we demonstrate the methodology's robustness in estimating fluid and elastic swimmer parameters, along with their uncertainties. We identify correlations between model parameters and gain insight into emergent swimming trajectories of a single swimmer or a pair of swimmers. Our proposed framework can handle data with a spatiotemporal resolution representative of experiments, showing that this framework can be used to aid in the development of artificial micro-swimmers for biomedical applications, as well as gain a fundamental understanding of the range of parameters that allow for certain motility patterns.

Detection of arterial wall abnormalities via Bayesian model selection.

Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for haemodynamical flow models.

Data-driven selection and parameter estimation for DNA methylation mathematical models.

Epigenetics is coming to the fore as a key process which underpins health. In particular emerging experimental evidence has associated alterations to DNA methylation status with healthspan and aging. Mammalian DNA methylation status is maintained by an intricate array of biochemical and molecular processes. It can be argued changes to these fundamental cellular processes ultimately drive the formation of aberrant DNA methylation patterns, which are a hallmark of diseases, such as cancer, Alzheimer's disease and cardiovascular disease. In recent years mathematical models have been used as effective tools to help advance our understanding of the dynamics which underpin DNA methylation. In this paper we present linear and nonlinear models which encapsulate the dynamics of the molecular mechanisms which define DNA methylation. Applying a recently developed Bayesian algorithm for parameter estimation and model selection, we are able to estimate distributions of parameters which include nominal parameter values. Using limited noisy observations, the method also identified which methylation model the observations originated from, signaling that our method has practical applications in identifying what models best match the biological data for DNA methylation.

Expanding access to primary healthcare for women through a microfinance institution: A case study from rural Guatemala.

The utilization of existing social networks is increasingly being recognized as a powerful strategy for delivering healthcare services to underserved populations in low- and middle-income countries. In Guatemala, multiple barriers prevent access to healthcare services for rural and indigenous populations, and strategies for delivering healthcare in more efficient ways are needed. The case study we describe here is a unique collaboration between a microfinance institution (Friendship Bridge) and a primary care organization (Wuqu' Kawoq | Maya Health Alliance) to scale up healthcare through an existing lending-borrowing social network. The program provides primary care services to female clients of Friendship Bridge in rural areas of Guatemala, with nurses working as frontline primary care providers, providing door-to-door healthcare services. Over the first 22 months of the project, we have reached over 3500 of Friendship Bridge's clients, with overall high acceptance of services. All clinical documentation and program monitoring and evaluation are done through audit trails within an electronical medical record system, which improves efficiency and lowers the associated time and resources costs. We utilize quality improvement methodologies to aid in decision making and programmatic adjustments scale up. These strategies have allowed us to expand services rapidly under challenging geographic and logistical constraints, while concurrently iteratively improving staff training and supervision, clinical care, and client engagement processes.