Virtual Patient Model Research Articles

A new ‘surface to Cone-beam CT’ registration method to obtain an appropriate 3D virtual patient model for orthognathic surgery planning

A new ‘surface to Cone-beam CT’ registration method to obtain an appropriate 3D virtual patient model for orthognathic surgery planning

1A1-B08 顎顔面への模擬触診システムに関する研究(医療ロボティクス・メカトロニクス(1))

Though maxillofacial palpation is one of the essential diagnosis skills for dentists, students of dental school have no experiences of undergoing maxillofacial palpation against ambulant patients before their graduation due to the concerns about risks to the patients. Hence, a virtual training environment of maxillofacial palpation has been desired from the coauthor dentists. Then this study aims to develop a virtual patient model of a maxillofacial palpation system that is composed of a virtual patient model and a haptic interface. A virtual patient model was firstly built by using CT data of head mannequin and linear finite element method. This paper introduces the virtual patient model and discusses about the future development of this system.

Blood glucose concentration regulation in Type 1 diabetics using multi model multi parametric model predictive control: An empirical approach

A Glucose- Insulin steady state static map is obtained from the Hovorka's 8th order virtual patient model. Three First Order Plus Time Delay (FOPTD) models are derived for the three piecewise linear regions in it. Through polyhedral vector space partitioning based on constraint violation, critical regions in state vector space are identified. A state feedback gain based controller is designed for each critical region. The controller design prevents constraint violations and ensures convexity while regulating the state vector to origin. The solution is also globally minimal. The state vector space of each empirical model is subjected to such analysis, resulting in three different set of critical regions and corresponding controllers. Gain Scheduling (GS) based on the Blood Glucose Concentration (BGC) measurement ensured proper profile selection. Through delay time compensation techniques, the multi model multi-parametric Model Predictive Control (mp-MPC) is designed for pure dynamics of each linear region. It is observed that the gain scheduled controller regulates the BGC within the acceptable range (80mg/dL to 160 mg/dL) during multiple meal disturbances. The explicit state feedback gain nature of the controller implies ease of deployment on memory constrained embedded devices.

A strategy for developing new treatment paradigms for neuropsychiatric and neurocognitive symptoms in Alzheimer’s disease

Successful disease modifying drug development for Alzheimer’s disease (AD) has hit a roadblock with the recent failures of amyloid-based therapies, highlighting the translational disconnect between preclinical animal models and clinical outcome. Although disease modifying therapies are the Holy Grail to pursue, symptomatic therapies addressing cognitive and neuropsychiatric aspects of the disease are also extremely important for the quality of life of patients and caregivers. Despite the fact that neuropsychiatric problems in Alzheimer patients are the major driver for costs associated with institutionalization, no good preclinical animal models with predictive validity have been documented. We propose a combination of quantitative systems pharmacology (QSP), phenotypic screening and preclinical animal models as a novel strategy for addressing the bottleneck in both cognitive and neuropsychiatric drug discovery and development for AD. Preclinical animal models such as transgene rats documenting changes in neurotransmitters with tau and amyloid pathology will provide key information that together with human imaging, pathology and clinical data will inform the virtual patient model. In this way QSP modeling can partially overcome the translational disconnect and reduce the attrition of drug programs in the clinical setting. This approach is different from target driven drug discovery as it aims to restore emergent properties of the networks and therefore likely will identify multitarget drugs. We review examples on how this hybrid humanized QSP approach has been helpful in predicting clinical outcomes in schizophrenia treatment and cognitive impairment in AD and expand on how this strategy could be applied to neuropsychiatric symptoms in dementia. We believe such an innovative approach when used carefully could change the Research and Development paradigm for symptomatic treatment in AD.

Nonparametric Identification of Glucose-Insulin Process in IDDM Patient with Multi-meal Disturbance

Modern close loop control for blood glucose level in a diabetic patient necessarily uses an explicit model of the process. A fixed parameter full order or reduced order model does not characterize the inter-patient and intra-patient parameter variability. This paper deals with a frequency domain nonparametric identification of the nonlinear glucose-insulin process in an insulin dependent diabetes mellitus patient that captures the process dynamics in presence of uncertainties and parameter variations. An online frequency domain kernel estimation method has been proposed that uses the input–output data from the 19th order first principle model of the patient in intravenous route. Volterra equations up to second order kernels with extended input vector for a Hammerstein model are solved online by adaptive recursive least square (ARLS) algorithm. The frequency domain kernels are estimated using the harmonic excitation input data sequence from the virtual patient model. A short filter memory length of M = 2 was found sufficient to yield acceptable accuracy with lesser computation time. The nonparametric models are useful for closed loop control, where the frequency domain kernels can be directly used as the transfer function. The validation results show good fit both in frequency and time domain responses with nominal patient as well as with parameter variations.

The Identifiable Virtual Patient Model: Comparison of Simulation and Clinical Closed-Loop Study Results

Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be obtained from a clinical study evaluating the same modification. We evaluated the ability of a low-order identifiable virtual patient (IVP) model to achieve this goal. Ten adult subjects (42.5 ± 11.5 years of age; 18.0 ± 13.5 years diabetes; 6.9 ± 0.8% hemoglobin A1c) previously characterized with the IVP model were studied following the procedures independently reported in a pediatric study assessing proportional-integral-derivative control with and without a 50% meal insulin bolus. Peak postprandial glucose levels with and without the meal bolus and use of supplemental carbohydrate to treat hypoglycemia were compared using two-way analysis of variance and chi-square tests, respectively. The meal bolus decreased the peak postprandial glucose levels in both the adult-simulation and pediatricclinical study (231 ± 38 standard deviation to 205 ± 33 mg/dl and 226 ± 51 to 194 ± 47 mg/dl, respectively; p = .0472). No differences were observed between the peak postprandial levels obtained in the two studies (clinical and simulation study not different, p = .57; interaction p = .83) or in the use of supplemental carbohydrate (3 occurrences in 17 patient days of closed-loop control in the clinical-pediatric study; 7 occurrences over 20 patient days in the adult-simulation study, p = .29). Closed-loop simulations using an IVP model can predict clinical study outcomes in patients studied independently from those used to develop the model.

In Silico Evaluation of Glucose Control Protocols for Critically Ill Patients

This letter presents an in silico evaluation method of glucose control protocols for critically ill patients with hyperglycemia. Although various glucose control protocols were introduced and investigated in clinical trials, development and validation of a novel glucose control protocol for critically ill patients require too much time and resources in clinical evaluation. We employed a virtual patient model of the critically ill patient with hyperglycemia and evaluated the clinically investigated glucose control protocols in a computational environment. The three-day simulation results presented the time profiles of glucose and insulin concentrations, the amount of enteral feed and intravenous bolus of glucose, and the intravenous insulin infusion rate. The hyperglycemia and hypoglycemia index, blood glucose concentrations, insulin doses, intravenous glucose infusion rates, and glucose feed rates were compared between different protocols. It is shown that a similar hypoglycemia incidence exists in simulation and clinical results. We concluded that this in silico simulation method using a virtual patient model could be useful for predicting hypoglycemic incidence of novel glucose control protocols for critically ill patients, prior to clinical trials.

TU‐G‐BRC‐02: Predictive Tracking for Real‐Time Deformable Motion Using a Dynamic Virtual Patient Model

Purpose: To develop a novel tracking technique for intensity modulated radiation therapy (IMRT) based on dynamic virtual patient modeling. Method and Materials: Predictive tracking uses a dynamic virtual patient model to account for the three‐dimensional deformable motion of the target during realistic IMRT deliveries. The virtual patient model was developed based on the 4DCT images and the structure contours delineated on different respiratory phases. With a real‐time breathing curve, the virtual patient model could be updated to a real‐time model which would then be used to predict the motion of the target, to modify the IMRT leaf sequence on‐line to account for the target motion, and to calculate the delivery dose of the IMRT plan with consideration of the interplay effect. A lung case was chosen to demonstrate predictive tracking. The quality of the predictive tracking plan was compared with two commonly used 4D IMRT treatment planning techniques: maximum intensity projection (MIP) and planning on individual phases (IP). Results: For a realistic breathing motion, predictive tracking offered better plan quality than MIP and IP. PTV V97 was 90.4% for a MIP plan, 88.6% for an IP plan and 94.1% for a predictive tracking plan. Lung V20 was 20.1% for the MIP plan, 17.8% for the IP plan and 17.5% for the predictive tracking plan. Conclusion: Predictive tracking based on dynamic virtual patient modeling is a novel 4D planning/delivery technique which is capable of managing the real time 3D deformable motion of target without increasing the workload of treatment planning or time of delivery. For a realistic breathing motion with consideration of the interplay effect in beam delivery, predictive tracking gave better plan quality than IP and MIP.Conflict of interest: Research supported by grant R01 LM009362.

Real time 4D IMRT treatment planning based on a dynamic virtual patient model: Proof of concept

To develop a novel four-dimensional (4D) intensity modulated radiation therapy (IMRT) treatment planning methodology based on dynamic virtual patient models. The 4D model-based planning (4DMP) is a predictive tracking method which consists of two main steps: (1) predicting the 3D deformable motion of the target and critical structures as a function of time during treatment delivery; (2) adjusting the delivery beam apertures formed by the dynamic multi-leaf collimators (DMLC) to account for the motion. The key feature of 4DMP is the application of a dynamic virtual patient model in motion prediction, treatment beam adjustment, and dose calculation. A lung case was chosen to demonstrate the feasibility of the 4DMP. For the lung case, a dynamic virtual patient model (4D model) was first developed based on the patient's 4DCT images. The 4D model was capable of simulating respiratory motion of different patterns. A model-based registration method was then applied to convert the 4D model into a set of deformation maps and 4DCT images for dosimetric purposes. Based on the 4D model, 4DMP treatment plans with different respiratory motion scenarios were developed. The quality of 4DMP plans was then compared with two other commonly used 4D planning methods: maximum intensity projection (MIP) and planning on individual phases (IP). Under regular periodic motion, 4DMP offered similar target coverage as MIP with much better normal tissue sparing. At breathing amplitude of 2 cm, the lung V20 was 23.9% for a MIP plan and 16.7% for a 4DMP plan. The plan quality was comparable between 4DMP and IP: PTV V97 was 93.8% for the IP plan and 93.6% for the 4DMP plan. Lung V20 of the 4DMP plan was 2.1% lower than that of the IP plan and Dmax to cord was 2.2 Gy higher. Under a real time irregular breathing pattern, 4DMP had the best plan quality. PTV V97 was 90.4% for a MIP plan, 88.6% for an IP plan and 94.1% for a 4DMP plan. Lung V20 was 20.1% for the MIP plan, 17.8% for the IP plan and 17.5% for the 4DMP plan. The deliverability of the real time 4DMP plan was proved by calculating the maximum leaf speed of the DMLC. The 4D model-based planning, which applies dynamic virtual patient models in IMRT treatment planning, can account for the real time deformable motion of the tumor under different breathing conditions. Under regular motion, the quality of 4DMP plans was comparable with IP and superior to MIP. Under realistic motion in which breathing amplitude and period change, 4DMP gave the best plan quality of the three 4D treatment planning techniques.

Validation of a virtual patient and virtual trials method for accurate prediction of tight glycemic control protocol performance

Effective tight glycemic control (TGC) can improve outcomes, but is difficult to achieve. In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective TGC protocols. However, no such method has been fully validated. This study tests two matched cohorts from the Glucontrol trial treated with different protocols. The goal is to validate the ability of in-silico virtual patient models and methods to accurately predict patient-specific and clinical trial glycemic outcomes.

SU‐GG‐J‐45: Application of Virtual Patient Model in 4D IMRT Monte Carlo Treatment Planning

Purpose: 4D IMRT planning using 4D CT images has uncertainties such as image artifacts, image sorting uncertainty and deformable image registration uncertainty. On the other hand, using virtual reality to create patient‐specific virtual model can represent the patient more accurately. Therefore, in this study, we are aiming to investigate the feasibility of 4D IMRT planning using virtual patient model. Method and Materials: The input contours for virtual model was from Pinnacle treatment planning system. The polygon of a structure formed from sampled control points, were used to reconstruct the NURBS surface of this structure. A 4D virtual patient was initially a sequence of 3D NURBS models at different phases. To be applied in real‐time breathing management, a few methods were used to adjust the model to account for the variation of breathing and to give model the ability to predict motion. Kaiman filter was used to predict the breathing curve. Two Monte Carlo codes, MCSIM and EGS4‐VLSI were used to simulate the treatment plan using the opening density matrix exported from Pinnacle optimization results. MCsim used patient's CT in dose calculation while EGS4‐VLSI used a phantom converted from patient's model. DVHS were compared for end‐exhale phase among the results from Pinnacle and the two Monte Carlo codes. Results: Monte Carlo codes benchmark showed good agreement for clinical measured data for 10×10 cm2 PDD and profiles. DVHs showed differences mainly due to the inhomogeneity effects for dose calculation. Conclusion: A virtual model based 4D IMRT method was proposed and preliminary results show the feasibility of this methodology. Varity exits among different calculation method, real clinical benefit of model based planning needs further investigation.Conflict of interest: Research supported by grant R0I LM009362.

Individual radiation therapy patient whole-body phantoms for peripheral dose evaluations: method and specific software

This study presents a method aimed at creating radiotherapy (RT) patient-adjustable whole-body phantoms to permit retrospective and prospective peripheral dose evaluations for enhanced patient radioprotection. Our strategy involves virtual whole-body patient models (WBPM) in different RT treatment positions for both genders and for different age groups. It includes a software tool designed to match the anatomy of the phantoms with the anatomy of the actual patients, based on the quality of patient data available. The procedure for adjusting a WBPM to patient morphology includes typical dimensions available in basic auxological tables for the French population. Adjustment is semi-automatic. Because of the complexity of the human anatomy, skilled personnel are required to validate changes made in the phantom anatomy. This research is part of a global project aimed at proposing appropriate methods and software tools capable of reconstituting the anatomy and dose evaluations in the entire body of RT patients in an adapted treatment planning system (TPS). The graphic user interface is that of a TPS adapted to obtain a comfortable working process. Such WBPM have been used to supplement patient therapy planning images, usually restricted to regions involved in treatment. Here we report, as an example, the case of a patient treated for prostate cancer whose therapy planning images were complemented by an anatomy model. Although present results are preliminary and our research is ongoing, they appear encouraging, since such patient-adjusted phantoms are crucial in the optimization of radiation protection of patients and for follow-up studies.

SU‐GG‐T‐347: Methods to Calculate Organ Doses Due to Scattered Radiation From External Beam Radiation Treatment Using Various Patient Phantoms and Monte Carlo Simulations

Purpose: It has been long known that patients treated with radiation carry a risk of developing second cancers in their lifetimes as a result of their treatments. In addition to hosting a symposium on “Secondary Cancer Risks for Emerging Radiation Treatments,” the AAPM has recently formed Task Group 158 that will provide recommendations on the dosimetry protocols for quantifying scattered radiation dose outside the treatment volume. Measurements are useful in providing a relative comparison of different treatment modalities or beam characteristics. On the other hand, more precise organ dose equivalent values need to be calculated from Monte Carlo simulations and anatomically realistic computational phantoms. This study outlines a computational platform that integrates anatomically‐refined virtual patient models with a detailed model of a medical accelerator head to calculate scattered doses to patients treated with radiation. Method and Materials: A detailed model of a 6‐MV Varian Clinac 2100C was developed in MCNPX. Several virtual patient models have been integrated with the accelerator head model in MCNPX. Various conventional and IMRT treatment plans have been considered. Results: Average organ doses are significantly different than point‐wise measurement values. The differences can be attributed to various sources of scattered radiation that deposits dose in the organ. Large dose gradients will be present throughout organs at short and intermediate distances from the tumor volume, while organs at longer distances will have a more uniform dose distribution. Conclusion: A computational platform to calculate organ dose from scattered radiation during radiation treatments has been demonstrated. Results indicate that average organ/tissue doses are significantly different than point‐wise measurements. Organ doses calculated in this manner will likely provide more accurate organ dose information for epidemiologic studies and for assessing the risk during the treatment planning.

Run-to-run control of blood glucose concentrations for people with type 1 diabetes mellitus

Run-to-run control has been applied to several traditional batch processes in the chemical industry. The 24-h cycle of eating meals, measuring blood glucose concentrations, and delivering the correct insulin bolus, with the goal of achieving the optimal blood glucose profile, can be viewed in the same spirit as traditional batch processes such as emulsion polymerization. In this paper, we aim to exploit the "repetitive" nature of the insulin therapy of people with Type 1 diabetes. A run-to-run algorithm is used on a virtual diabetic patient model to control blood glucose concentrations. The insulin input is parameterized into the timing and amount of the dose while the glucose output is parameterized into the maximum and minimum glucose concentrations. Robustness of the algorithm to variations in the meal amount, meal timing, and insulin sensitivity parameter is addressed. In general, the algorithm is able to converge when the meal timing is varied within +/- 40 min. If the meal size is underestimated by approximately 10 grams (g), the algorithm is able to converge within a reasonable time frame for breakfast, lunch, and dinner. If the meal size is overestimated by 20-25 g, the algorithm is able to converge. When random variations in the meal timing and the meal amount are introduced, the variation on the output variables, Gmax and Gmin, scales according to the amount of variation allowed. Along with this, the insulin sensitivity of the virtual patient model is varied. The algorithm is robust for differences in insulin sensitivity less than +/- 50% of the nominal value.

WE‐C‐224A‐05: Patient Modeling and Organ Dose Calculations Using Monte Carlo Methods

The risk for a patient to develop secondary cancers from non‐target exposures can be assessed from the equivalent doses to various radiosensitive organs. To determine organ doses, a computational model or physical phantom that represents the whole patient anatomy must be used. Physical phantoms (such as the RANDO and ATOM phantoms) have tiny cavities on each of the tissue‐equivalent slices for inserting TL and MOSFET dosimeters. When beam is delivered according to a patient's treatment plan, the dosimeters are processed to provide organ dose information. The experimental procedures can be time‐consuming and expensive, especially when various patients and treatment plans are studies. Virtual patient models, on the other hand, can be combined with a Monte Carlo code to simulate the transport of radiation in the body. These models cover the entire body and typically contain a large number of defined organs. Coupled with a model of the accelerator, one can calculate detailed information about secondary dose distributions in the patient body. Whole‐body models are classified into three types: 1) Stylized models that are based on surface equations, 2) Tomographic models that are derived from medical images, and 3) Hybrid equation‐voxel models that describe organ boundaries using advanced primitives such as the NURBS for realtime deformation (4D simulations). To date, more than 20 tomographic models have been developed for radiation protection and nuclear medicine applications. An international consortium on computational human models (www.virtualphantoms.org) has been recently formed to promote research in this area. A team of researchers from Rensselaer, Vanderbilt, University of Florida, Massachusetts General Hospital and Johns Hopkins University is working on a project to standardize a library of age‐ and gender‐specific models.This lecture presents the current status of patient modeling and the application of various tools to study secondary doses from radiation treatment involving both photons and protons. Detailed information on the VIP‐Man model developed from the Visible Human Project is presented. Segmentation of more than 80 organs, including the red bone marrow, and the implementation of the VIP‐Man model into EGS, MCNP/X and GEANT4 are discussed. Finally, organ doses and effective doses for proton treatment plans using various adult and pediatric patient models are presented.Educational Objectives of this symposium are:1. Understanding new tools of Monte Carlo‐based patient and accelerator modeling for secondary dose studies.2. Understanding the effective dose data for various modalities.

TH‐E‐224C‐07: Monte Carlo Dose Calculation Procedure Using a 4D Motion Simulating Chest Model

Purpose: To apply an anatomically detailed motion‐simulating virtual patient model to the study of external beam treatment planning. Method and Materials: Breathing induced organ motion modeling may be classified broadly into (a) geometry‐based and (b) physics‐based methods. We have been developing a 4D motion‐simulating chest model from a 3D tomographic model of the Visible Human images by varying the shape, size and location of the organs. Anatomical features where from the VIP‐Man model that contains 80 segmented organs and tissues. The Non‐Uniform Rational B‐Splines (NURBS) surfaces of the organs were reconstructed to deform the organs by changing the control points. Clinically measured motion patterns were used to guide the deformation and motion. Four‐field conformal photon beams were simulated for the treatment of a lung tumor case. The energy of the beam was assumed to be 6 MeV and the 4‐field irradiation geometry was assumed to be AP, PA, RLAT, and LLAT. The lesion was simulated in the left lung and the PTV is designed as a sphere of 5‐mm radius. The motion‐simulating model is implemented into the EGSnrc code to calculate the absorbed doses using various non‐registration registration methods. Results: The results showed that the dose to tumor could be up to 40% differences from phase to phase. DVH for this calculation showed less homogeneity for the whole breathing cycle. However, if the beam was gated to one phase, the result showed better homogeneity for target. Conclusion: A 4D chest motion‐simulating model has been developed using the segmented Visible Human images. This study summarized procedures to develop a 4D motion‐simulating chest model and demonstrated the usefulness of the model for Monte Carlo calculations. Possible ways to improve the motion simulation using physics‐based tissue properties available from surgical simulation community are discussed.

THE INTERACTION BETWEEN CAPSULE ENDOSCOPY AND IMPLANTABLEPACEMAKERS/DEFIBRILLATORS

Purpose: The M2A Capsule Endoscopy (Given Imaging, Ltd., Yoqneam, Israel) is a valuable tool for detecting small bowel disorders. The capsule transmits digital images to an external detector via radiofrequency energy (100–472 kHz). Approximately 2.4 mil Americans have implantable pacemakers, and 460,000 have implantable cardioverter defibrillators (ICDs), which also utilize radiofrequency energy (100–175 kHz) to communicate with programmers. Concerns about serious interactions between the M2A capsule and implantable pacemakers and defibrillators and potential interaction with radiofrequency overlap have been raised. We performed in-vitro experiments to determine the interactions between M2A capsule endoscopy and pacemakers and ICDs. Methods: We tested 3 current technology pacemaker pulse generators: models AT501 and KDR901 (Medtronic, Minneapolis, MN) and model 1296 Insignia (Guidant, St. Paul, MN); and 2 ICDs: model A155 Vitality AVT (Guidant) and model 7274 Marquis DR (Medtronic). Each of these was placed in an electrode gel bath along with the M2A capsule, while varying the distance of the capsule at 2, 6, 12, and 18 cm from the pacemakers and ICDs. We used a Virtual Interactive Patient model 9595 (Medtronic) to analyze pulse generator performance while simulating normal sinus rhythm. Atrial and ventricular electrograms and marker channels were observed for 30 seconds at each of the 4 distances and repeated in random sequence 3 times with the observer blinded to distance. This was repeated with the devices programmed to both nominal and most sensitive settings (0.15-2.8 mV). The pacemakers and ICDs were then attached to standard pacing and defibrillation leads, and the experiment was repeated at nominal and most sensitive settings. Results: All tracings were read in blinded fashion and in random order by a boarded cardiac electrophysiologist. There was no abnormal sensing on the atrial or ventricular electrograms or the marker channels in any device at any distance from the M2A capsule, even at the most sensitive programmable values. Similarly, there was no inappropriate pacemaker or ICD performance or spurious programming. Conclusions: Based on the extensive testing at variable distances of exposure and potential overlap of frequency, the M2A capsule endoscopy can be used safely with implantable cardiac rhythm control devices. Pacemakers and ICDs should no longer be considered exclusion criteria to patients who have need of this technology.

How We Do It: Using a surgical navigation system in the management of the ossified cochlea

The ossified cochlea poses both concerns and challenges to any implant team. One of the surgical aims is to implant as many of an implant system's electrodes into the cochlea as close to the modiolus as possible, thus facilitating stimulation of the surviving cell bodies in the spiral ganglion. Within the field of otorhinolaryngology various surgical navigation systems have been introduced to facilitate surgical orientation during endoscopic sinus and skull-base surgery. The principle behind such systems is that specialized software uses high-resolution computerized tomography and magnetic resonance imaging scans to create a 3D image of the patient's anatomy. This ‘virtual patient model’ is then ‘registered’ with the patient's anatomical landmarks. Surgical instruments registered into, or linked with, the system can then be tracked within the 3D model. This is the first time this system has been described for surgery of the inner ear, and we describe how the technique allows more precise control of the bur tip while drilling tunnels that approximate with the apical and distal parts of the basal coil of the ossified cochlea.

Syndrome dysimmunitaire, vascularite et pancytopénie induits par le vaccin contre l’hépatite B

Recruitment maneuvers (RMs) following with positive-end-expiratory-pressure (PEEP) have proved effective in recruiting lung volume and preventing alveoli collapse. To date, standards for optimal patient-specific PEEP are unknown, resulting in variability in care and reduced outcomes, both indicating the need for personalized care. This research extends a well-validated virtual patient model by adding novel elements to model, which is able to utilize bedside available respiratory data, without increasing modelling complexity, to predict patient-specific lung distension and thus to minimise barotrauma risk. Prediction accuracy and robustness are validated against clinical data from 18 volume controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12cmH2O), where predictions were made up to 12cmH2O of PEEP ahead. Using an exponential basis function set for prediction yields an absolute median peak inspiratory pressure prediction error of 1.50cmH2O for 623 prediction cases. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2=0.90 (623 predictions), which is a measurable improvement in prediction error compared to predictions without using the proposed distension function (R2=0.82). Moreover, the R2 value increases to 0.93-0.95 if only clinically relevant ΔPEEP steps (2-6cmH2O) are considered with an overall median absolute error in peak pressure prediction of 1.04cmH2O. Overall, the results demonstrate the potential and significance for accurately capturing distension mechanics, allowing better risk assessment, as well as extending and more fully validating this virtual mechanical ventilation patient model.