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9 results for “virtual patient model”
Aggregated Virtual Patient Model Dataset
<p>The dataset is a collection of aggregated clinical parameters for the participants (such as clinical scores), parameters extracted from the utilized devices (such as average heart rate per day, average gait speed etc.), and coupled events about them (such as falls, loss of orientation etc.). It contains information which was collected during the clinical evaluation of the older people from medical experts.This information represents the clinical status of the older person across different domains, e.g. physical, psychological, cognitive etc.</p> <p>The dataset contains several medical features which are used by clinicians to assess the overall state of the older people.</p> <p>The purpose of the Virtual Patient Model is to assess the overall state of the older people based on their medical parameters, and to find associations between these parameters and frailty status.</p> <p>A list of the recorded clinical parameters and their description is shown below:</p> <p>- <strong> part_id</strong>: The user ID, which should be a 4-digit number</p> <p>-<strong> q_date</strong>: The recording timestamp, which follows the “YYYY-MM-DDTHH:mm:ss.fffZ” format (eg. 14 September 2017 12:23:34.567, is formatted as 2019-09-14T12:23:34.567Z)</p> <p>-<strong> clinical_visit</strong>: As several clinical evaluations were performed to each older adult, this number shows for which clinical evaluation these measurements refer to</p> <p>-<strong> fried</strong>: Ordinal categorization of frailty level according to Fried operational definition of frailty</p> <p>-<strong> hospitalization_one_year</strong>: Number of nonscheduled hospitalizations in the last year</p> <p>- <strong>hospitalization_three_years</strong>: Number of nonscheduled hospitalizations in the last three years</p> <p>- <strong>ortho_hypotension</strong>: Presence of orthostatic hypotension</p> <p>- <strong>vision</strong>: Visual difficulty (qualitative ordinal evaluation)</p> <p>- <strong>audition</strong>: Hearing difficulty (qualitative ordinal evaluation)</p> <p>- <strong>weight_loss</strong>: Unintentional weight loss >4.5 kg in the past year (categorical answer)</p> <p>- <strong>exhaustion_score</strong>: Self-reported exhaustion (categorical answer)</p> <p>- <strong>raise_chair_time</strong>: Time in seconds to perform a lower limb strength clinical test</p> <p>- <strong>balance_single</strong>: Single foot station (Balance) (categorical answer)</p> <p>- <strong>gait_get_up</strong>: Time in seconds to perform the 3meters’ Timed Get Up And Go Test</p> <p>- <strong>gait_speed_4m</strong>: Speed for 4 meters’ straight walk</p> <p>- <strong>gait_optional_binary</strong>: Gait optional evaluation (qualitative evaluation by the investigator)</p> <p>- <strong>gait_speed_slower</strong>: Slowed walking speed (categorical answer)</p> <p>- <strong>grip_strength_abnormal</strong>: Grip strength outside the norms (categorical answer)</p> <p>- <strong>low_physical_activity</strong>: Low physical activity (categorical answer)</p> <p>- <strong>falls_one_year</strong>: Number of falls in the last year</p> <p>- <strong>fractures_three_years</strong>: Number of fractures during the last 3 years</p> <p>- <strong>fried_clinician</strong>: Fried’s categorization according to clinician’s estimation (when missing data for answering the Fried’s operational frailty definition questionnaire)</p> <p>- <strong>bmi_score</strong>: Body Mass Index (in Kg/m²)</p> <p>- <strong>bmi_body_fat</strong>: Body Fat (%)</p> <p>- <strong>waist</strong>: Waist circumference (in cm)</p> <p>- <strong>lean_body_mass</strong>: Lean Body Mass (%)</p> <p>- <strong>screening_score</strong>: Mini Nutritional Assessment (MNA) screening score</p> <p>- <strong>cognitive_total_score</strong>: Montreal Cognitive Assessment (MoCA) test score</p> <p>- <strong>memory_complain</strong>: Memory complain (categorical answer)</p> <p>- <strong>mmse_total_score</strong>: Folstein Mini-Mental State Exam score</p> <p>- <strong>sleep</strong>: Reported sleeping problems (qualitative ordinal evaluation)</p> <p>- <strong>depression_total_score</strong>: 15-item Geriatric Depression Scale (GDS-15)</p> <p>- <strong>anxiety_perception</strong>: Anxiety auto-evaluation (visual analogue scale 0-10)</p> <p>- <strong>living_alone</strong>: Living Conditions (categorical answer)</p> <p>- <strong>leisure_out</strong>: Leisure activities (number of leisure activities per week)</p> <p>- <strong>leisure_club</strong>: Membership of a club (categorical answer)</p> <p>- <strong>social_visits</strong>: Number of visits and social interactions per week</p> <p>- <strong>social_calls</strong>: Number of telephone calls exchanged per week</p> <p>- <strong>social_phone</strong>: Approximate time spent on phone per week</p> <p>- <strong>social_skype</strong>: Approximate time spent on videoconference per week</p> <p>- <strong>social_text</strong>: Number of written messages (SMS and emails) sent by the participant per week</p> <p>- <strong>house_suitable_participant</strong>: Subjective suitability of the housing environment according to participant’s evaluation (categorical answer)</p> <p>- <strong>house_suitable_professional</strong>: Subjective suitability of the housing environment according to investigator’s evaluation (categorical answer)</p> <p>- <strong>stairs_number</strong>: Number of steps to access house (without possibility to use elevator)</p> <p>- <strong>life_quality</strong>: Quality of life self-rating (visual analogue scale 0-10)</p> <p>- <strong>health_rate</strong>: Self-rated health status (qualitative ordinal evaluation)</p> <p>- <strong>health_rate_comparison</strong>: Self-assessed change since last year (qualitative ordinal evaluation)</p> <p>- <strong>pain_perception</strong>: Self-rated pain (visual analogue scale 0-10)</p> <p>- <strong>activity_regular</strong>: Regular physical activity (ordinal answer)</p> <p>- <strong>smoking</strong>: Smoking (categorical answer)</p> <p>- <strong>alcohol_units</strong>: Alcohol Use (average alcohol units consumption per week)</p> <p>- <strong>katz_index</strong>: Katz Index of ADL score</p> <p>- <strong>iadl_grade</strong>: Instrumental Activities of Daily Living score</p> <p>- <strong>comorbidities_count</strong>: Number of comorbidities</p> <p>- <strong>comorbidities_significant_count</strong>: Number of comorbidities which affect significantly the person’s functional status</p> <p>- <strong>medication_count</strong>: Number of active substances taken on a regular basis</p>
Early Transfer of Hospitalized Patients Incl. COVID-19 to a Virtual Hospital at Home Model - a Clinical Feasibility Study
ClinicalTrials.gov study NCT05087082. IPD Sharing: NO. Countries: 1. Publications: 1.
Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Current ablation therapy for atrial fibrillation is sub-optimal and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more inter-individual variability.</p> <p><strong>Methods: </strong>Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent), undergoing first ablation. Patients were followed for 1-year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fibre orientation maps, electrical properties and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were post-processed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging and atrial fibrillation simulation metrics.</p> <p><strong>Results: </strong>We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging and simulation stress tests (average ten-fold cross-validation area under the curve 0.85 ± 0.09, recall 0.80 ± 0.13, precision 0.74 ± 0.13) outperformed those trained to history and imaging (area under the curve 0.66 ± 0.17), or history alone (area under the curve 0.61 ± 0.14). </p> <p><strong>Conclusion: </strong>A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalise selection for atrial fibrillation ablation.</p> <p><strong>Dataset Description: </strong>We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. </p> <p>We also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA. More details on the coordinate construction are given in our manuscript and <a href="https://www.ncbi.nlm.nih.gov/pubmed/31026761">https://www.ncbi.nlm.nih.gov/pubmed/31026761</a>. These coordinates can be used for registering datasets. </p> <p><strong>Publication</strong>: https://pubmed.ncbi.nlm.nih.gov/35089057/</p>
A Prospective Randomised Control Trial to Study the Effectiveness of a Health Service Innovation Using a Modified Virtual Ward Model to Prevent Unscheduled Readmission of High Risk Patients
ClinicalTrials.gov study NCT02325752. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Innovative Patient-partner-guided Virtual Group Speech Pathology Intervention Model in Head and Neck Cancer
ClinicalTrials.gov study NCT05621889. IPD Sharing: NO. Countries: 1. Publications: 0.
Contribution of Virtual Reality and Modelling in Falling Risk Assessment in Elderly and Parkinson's Disease Patients
ClinicalTrials.gov study NCT03848897. IPD Sharing: NO. Countries: 1. Publications: 0.
Establishing a Virtual Health Community Management Model for Pre-Diabetes Patients
ClinicalTrials.gov study NCT07354997. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Data from: Virtual patients and sensitivity analysis of the Guyton model of blood pressure regulation: towards individualized models of whole-body physiology
Open the record for dataset details and reuse information.
Dataset related to article "Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases "
<p>This record contains raw data related to article "Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases "</p> <p>Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2-3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.</p>
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