Childbirth Computational Models: Characteristics and Applications

2020 ◽  
Author(s):  
Sheng Chen ◽  
Michele J. Grimm
Keyword(s):  
Computational Models ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Patient Specific ◽  
Clinical Observations ◽  
Current State ◽  
Specific Assessment ◽  
Maternal Injuries ◽  
Complex Phenomena ◽  
Birth Processes

Abstract The biomechanical process of childbirth is necessary to usher in new lives – but it can also result in trauma. This physically intense process can put both the mother and the child at risk of injuries and complications that have life-long impact. Computational models, as a powerful tool to simulate and explore complex phenomena, have been used to improve our understanding of childbirth processes and related injuries since the 1990s. The goal of this paper is to review and summarize the breadth and current state of the computational models of childbirth in the literature – focusing on those that investigate the mechanical process and effects. We first summarize the state of critical characteristics that have been included in computational models of childbirth (i.e., maternal anatomy, fetal anatomy, cardinal movements, and maternal soft tissue mechanical behavior). We then delve into the findings of the past studies of birth processes and mechanical injuries in an effort to bridge the gap between the theoretical, numerical assessment and the empirical, clinical observations and practices. These findings are from applications of childbirth computational models in four areas: (1) the process of childbirth itself, (2) maternal injuries, (3) fetal injuries, and (4) protective measures employed by clinicians during delivery. Finally, we identify some of the challenges that computational models still face and suggest future directions through which more biofidelic simulations of childbirth might be achieved, with the goal that advancing models may provide more efficient and accurate, patient-specific assessment to support future clinical decision-making.

A Public Repository of Image-Based Computational Models for Patient-Specific Blood Flow Simulation

2012 ◽  
Author(s):  
Nathan M. Wilson ◽  
Ana K. Ortiz ◽  
Allison B. Johnson ◽  
Frank R. Arko ◽  
Jeffrey A. Feinstein ◽  
...  
Keyword(s):  
Blood Flow ◽  
Computational Models ◽  
Clinical Decision Making ◽  
Flow Simulation ◽  
Clinical Decision ◽  
Arterial Disease ◽  
Patient Specific ◽  
Blood Flow Simulation ◽  
Coronary Arterial Disease ◽  
Public Data

Over the past two decades, significant progress has been made on increasing the realism and fidelity of image-based patient-specific blood flow simulation. A clear example of this progress is the first-of-a-kind multi-center clinical trial under way by Heartflow, Inc. (Redwood City, CA) attempting to utilize blood flow simulation in clinical decision making for coronary arterial disease. While recent applications of patient-specific blood flow simulation are impressive, numerous opportunities still exist for its application in advanced research in disease progression, design of better medical devices, and additional clinical applications for patient-specific interventional planning. Three core challenges face researchers in this space. First, state-of-the art techniques for patient-specific anatomic model construction and hemodynamic simulation require specialized, complex software. In recent years, open-source initiatives such as SimVascular and VMTK have addressed this need. Second, the access to clinical data has traditionally been limited to those with strong ties to research hospitals. Finally, public data for verification and validation of computational models for blood flow has also been limited.

Transitioning towards official clinical guidelines for the diagnostics and treatment of severe behavioural problems in children across Europe: current state and future directions.

2017 ◽  
Author(s):  
Alexandra-Raluca Gatej ◽  
Audri Lamers ◽  
Robert Vermeiren ◽  
Lieke van Domburgh
Keyword(s):  
Mental Health ◽  
Clinical Guidelines ◽  
Clinical Decision Making ◽  
Daily Practice ◽  
Clinical Decision ◽  
European Guidelines ◽  
Current State ◽  
Assessment And Treatment ◽  
Evidence Based Treatments ◽  
Multifactorial Approach

Severe behaviour problems (SBPs) in early childhood include oppositional and aggressive behaviours and predict negative mental health outcomes later in life. Although effective treatments for this group are available and numerous clinical practice guidelines have been developed to facilitate the incorporation of evidence-based treatments in clinical decision-making (NICE, 2013), many children with SBPs remain unresponsive to treatment (Lahey & Waldman, 2012). At present, it is unknown how many countries in Europe possess official clinical guidelines for SBPs diagnosis and treatment and what is their perceived utility. The aim was to create an inventory of clinical guidelines (and associated critical needs) for the diagnostics and treatment of SBPs in youth mental health across Europe according to academic experts and mental health clinicians’ opinions. To investigate the aim, two separate online semi-structured questionnaires were used, one directed at academics (N=28 academic experts; 23 countries), and the other at clinicians (N=124 clinicians; 24 countries). Three key results were highlighted. First, guidelines for SBPs are perceived as beneficial by both experts and clinicians. However, their implementation needs to be reinforced and content better adapted to daily practice. Improvements may include taking a multifactorial approach to assessment and treatment, involving the systems around the child, and multidisciplinary collaboration. Second, academic experts and clinicians support the need for further developing national / European guidelines. Finally, future guidelines should address current challenges identified by clinicians to be more applicable to daily practice.

scholarly journals Quantifying the Impact of Patient-Specific Factors and Disease Severity on Clinical Decision Making in Cuff Tear Arthropathy: A Case-Based Survey

2019 ◽  
Vol 15 (3) ◽  
pp. 276-285
Author(s):  
Adam P. Schumaier ◽  
Yehia H. Bedeir ◽  
Joshua S. Dines ◽  
Keith Kenter ◽  
Lawrence V. Gulotta ◽  
...  
Keyword(s):  
Decision Making ◽  
Disease Severity ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Patient Specific ◽  
Cuff Tear Arthropathy ◽  
Cuff Tear ◽  
Specific Factors ◽  
The Impact ◽  
Case Based

scholarly journals Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190347 ◽  
Author(s):  
Nick van Osta ◽  
Aurore Lyon ◽  
Feddo Kirkels ◽  
Tijmen Koopsen ◽  
Tim van Loon ◽  
...  
Keyword(s):  
Right Ventricular ◽  
Computational Models ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Patient Specific ◽  
Model Parameters ◽  
Arrhythmogenic Cardiomyopathy ◽  
Tissue Properties ◽  
Myocardial Disease ◽  
Deformation Patterns

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters ( n  = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

Complementing sparse vascular imaging data by physiological adaptation rules

2020 ◽  
Author(s):  
Maarten H.G. Heusinkveld ◽  
Robert J. Holtackers ◽  
Bouke P. Adriaans ◽  
Jos Op't Roodt ◽  
Theo Arts ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Mechanical Load ◽  
Wall Stress ◽  
Clinical Decision ◽  
Physiological Adaptation ◽  
Patient Specific ◽  
Data Sets ◽  
Imaging Data ◽  
Artery Segment ◽  
Flow Waveforms

Introduction:Mathematical modeling of pressure and flow waveforms in blood vessels using pulse wave propagation (PWP)-models has tremendous potential to support clinical decision-making. For a personalized model outcome, measurements of all modeled vessel radii and wall thicknesses are required. In clinical practice, however, data sets are often incomplete. To overcome this problem, we hypothesized that the adaptive capacity of vessels in response to mechanical load could be utilized to fill in the gaps of incomplete patient-specific data sets. Methods:We implemented homeostatic feedback loops in a validated PWP model to allow adaptation of vessel geometry to maintain physiological values of wall stress and wall shear stress. To evaluate our approach, we gathered vascular MRI and ultrasound data sets of wall thicknesses and radii of central and arm arterial segments of ten healthy subjects. Reference models (i.e. termed RefModel, n=10) were simulated using complete data, whereas adapted models (AdaptModel, n=10) used data of one carotid artery segment only while the remaining geometries in this model were estimated using adaptation. We evaluated agreement between RefModel and AdaptModel geometries, as well as between pressure and flow waveforms of both models. Results:Limits of agreement (bias±2SD of difference) between AdaptModel and RefModel radii and wall thicknesses were 0.2±2.6 mm and -140±557 μm, respectively. Pressure and flow waveform characteristics of the AdaptModel better resembled those of the RefModels as compared to the model in which the vessels were not adapted.Conclusions:Our adaptation-based PWP-model enables personalization of vascular geometries even when not all required data is available.

Combination chemotherapy versus temozolomide (TMZ) for patients with MGMT methylated (m-MGMT) glioblastoma (GBM): Results of cellworks omics biosimulation—MyCare-015.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14501-e14501
Author(s):  
Michael Castro ◽  
Nirjhar Mundkur ◽  
Anusha Pampana ◽  
Aftab Alam ◽  
Aktar Alam ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Clinical Decision Making ◽  
Drug Effects ◽  
Standard Of Care ◽  
Clinical Decision ◽  
Phase Iii ◽  
Patient Specific ◽  
Experimental Therapy ◽  
Newly Diagnosed ◽  
Current Standard

e14501 Background: UKT-03 evaluated TMZ plus Lomustine in a single arm phase II trial in newly diagnosed GBM patients. An overall survival of 23 months was a substantial improvement over historical experience. Patients with m-MGMT v. unmethylated tumors had a 2-yr survival of 75% and median survival not reached compared to 20% and 12.5 months, respectively. These data formed the basis for NOA-9, a randomized phase III trial in newly diagnosed, m-MGMT GBM which randomized 141 patients to standard therapy or experimental therapy with Lomustine and TMZ every 6 weeks. A superiority for the combination was observed: 48.1 v. 31.4 months for the standard arm in the ITT analysis. Nevertheless, many neurooncologists are reluctant to adopt this approach. The current standard of care uses single biomarker, m-MGMT, in contrast to comprehensive pathway analysis (CPA). We sought to determine if CPA could discriminate more effectively among each patient’s likelihood of benefiting from combination treatment. Methods: Cellworks Singula employs a novel Cellworks Omics Biology Model (CBM) to predict patient-specific biomarker and phenotype response of personalized GBM avatars to drug agents, radiation, and targeted therapies. The CBM was developed and validated using PubMed to generate protein network maps of patient-specific activated and inactivated disease pathways. CBM was used to simulate the TMZ and TMZ-Lomustine therapies for each patient in a TCGA cohort of 368 GBM patients. Omics data including methylation, whole exome sequencing, and copy number alterations were input into CBM. The Singula Composite Inhibition Score (CIS) was calculated based on the measured quantitative drug effects. Results: Though incremental gain from the combination was seen in all patients, CIS varied across the population with relative scores ranging from 32-82, with best responders have more than twice the benefit. Conclusions: CPA shows that m-MGMT is an excellent biomarker for determining the likelihood of benefit from TMZ and lomustine, with the caveat that CBM identifies 18% could be spared from TMZ exposure and would benefit from Lomustine alone. Otherwise, these data lend support for evolving the standard of care with combination therapy for patients with m-MGMT GBM and should help overcome a reluctance to employing combination therapy. Additionally, CBM has utility to individualize clinical decision making. [Table: see text]

scholarly journals Artificial intelligence in arthroplasty

Arthroplasty ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Glen Purnomo ◽  
Seng-Jin Yeo ◽  
Ming Han Lincoln Liow
Keyword(s):  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Recent Literature ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Patient Specific ◽  
Allocation Decision ◽  
Medical Specialties ◽  
Future Direction ◽  
Improve Patient

AbstractArtificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

Clinical Reasoning: A State of the Science Report

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Amy Golden Holder
Keyword(s):  
Clinical Reasoning ◽  
Cognitive Process ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Future Research ◽  
Client Care ◽  
Current State ◽  
Process Understanding ◽  
Information Center ◽  
State Of The Science

AbstractClinical reasoning is the cognitive process that nurses use to gather and incorporate information into a larger bank of personal knowledge. This incorporated information guides therapeutic actions, and helps determine client care. Since the process guides therapeutic actions regarding client care, failure to use the process effectively leads to poor clinical decision-making, inappropriate actions, or inaction. Because of the criticality of this process, this paper presents an analysis of the literature that reveals the current state of the science of clinical reasoning, identifies gaps in knowledge, and elucidates areas for future research. A systematic review of the databases the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Educational Resources Information Center (ERIC), PsychInfo, the Education Full Text (H.W. Wilson), and PubMed revealed 873 articles on the topic of clinical reasoning. Quality appraisal narrowed the field to 27 pieces of literature. Appendix A gives the State of the Science Coding Sheet used to identify the selections used in this research. Appendix B contains a summary of this literature. Although analysis of this literature shows that three theories exist on how to utilize most effectively the clinical reasoning process presently; a clear consistent definition is lacking. Additional research should focus on closing gaps that exist in defining the process, understanding the process, establishing linkages to non-clinical reasoning processes, and developing measures to both develop and accurately measure clinical reasoning.

scholarly journals Inpatient Palliative Care Utilization for Patients with Brain Metastases

2021 ◽  
Author(s):  
Meghan Price ◽  
Elizabeth P Howell ◽  
Tara Dalton ◽  
Luis Ramirez ◽  
Claire Howell ◽  
...  
Keyword(s):  
Palliative Care ◽  
Brain Metastases ◽  
Clinical Decision Making ◽  
Symptom Burden ◽  
Retrospective Chart Review ◽  
Clinical Decision ◽  
Patient Specific ◽  
Care Utilization ◽  
Black African ◽  
Patient Race

Abstract Introduction Given the high symptom burden and complex clinical decision making associated with a diagnosis of brain metastases (BM), specialty Palliative Care (PC) can meaningfully improve patient quality of life. However, no prior study has formally evaluated patient-specific factors associated with PC consultation among BM patients. Methods We examined the rates of PC consults in a cohort of 1303 patients with brain metastases admitted to three tertiary medical centers from October 2015 to December 2018. Patient demographics, surgical status, 30-day readmission, and death data were collected via retrospective chart review. PC utilization was assessed by identifying encounters for which an inpatient consult to PC was placed. Statistical analyses were performed to compare characteristics and outcomes between patients who did and did not receive PC consults. Results We analyzed 1303 patients admitted to the hospital with brain metastases. The average overall rate of inpatient PC consultation was 19.6%. Rates of PC utilization differed significantly by patient race (17.5% in White/Caucasian vs. 26.0% in Black/African American patients, p = 0.0014). Patients who received surgery during their admission had significantly lower rates of PC consultation (3.9% vs 22.4%, p < 0.0001). Patients who either died during their admission or were discharged to hospice had significantly higher rates of PC than those who were discharged home or to rehabilitation (p < 0.0001). Conclusions In our dataset, PC consultation rates varied by patient demographic, surgical status, discharging service and practice setting. Further work is needed to identify the specific barriers to optimally utilizing specialty PC in this population.

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