Clinical features and outcomes of advanced HER2+ esophageal/GEJ cancer with brain metastasis.

BACKGROUND: Brain metastasis (BRM) is uncommon in gastroesophageal cancer. As such, clinicopathologic and molecular determinants of BRM and impact on clinical outcome remain incompletely understood. METHODS: We retrospectively analyzed clinicopathologic data from advanced esophageal/gastroesophageal junction (E/GEJ) patients at Johns Hopkins from 2003 to 2021. We investigated the association between several clinical and molecular features and the occurrence of BRM, with particular focus on human epidermal growth factor receptor 2 (HER2) overexpression. Survival outcomes and time to BRM onset were also evaluated. RESULTS: We included 515 patients with advanced E/GEJ cancer. Tumors were 78.3% esophageal primary, 82.9% adenocarcinoma, 31.0% HER2 positive. Cumulative incidence of BRM in the overall cohort and within HER2+ subgroup was 13.8% and 24.3%, respectively. HER2 overexpression was associated with increased risk of BRM [odds ratio 2.45; 95% confidence interval (CI) 1.10-5.46]. On initial presentation with BRM, 50.7% had a solitary brain lesion and 11.3% were asymptomatic. HER2+ status was associated with longer median time to onset of BRM (14.0 versus 6.3 months, P < 0.01), improved median progression free survival on first-line systemic therapy (hazard ratio 0.35, 95% CI 0.16-0.80), and improved median overall survival (hazard ratio 0.20, 95% CI 0.08-0.54) in patients with BRM. CONCLUSION: HER2 overexpression identifies a gastroesophageal cancer molecular subtype that is significantly associated with increased risk of BRM, though with later onset of BRM and improved survival likely reflecting the impact of central nervous system-penetrant HER2-directed therapy. The prevalence of asymptomatic and solitary brain lesions suggests that brain surveillance for HER2+ patients warrants prospective investigation.

DNA methylation in small cell lung cancer defines distinct disease subtypes and correlates with high expression of EZH2.

Small cell lung cancer (SCLC) is an aggressive malignancy characterized by early metastasis, rapid development of resistance to chemotherapy and genetic instability. This study profiles DNA methylation in SCLC, patient-derived xenografts (PDX) and cell lines at single-nucleotide resolution. DNA methylation patterns of primary samples are distinct from those of cell lines, whereas PDX maintain a pattern closely consistent with primary samples. Clustering of DNA methylation and gene expression of primary SCLC revealed distinct disease subtypes among histologically indistinguishable primary patient samples with similar genetic alterations. SCLC is notable for dense clustering of high-level methylation in discrete promoter CpG islands, in a pattern clearly distinct from other lung cancers and strongly correlated with high expression of the E2F target and histone methyltransferase gene EZH2. Pharmacologic inhibition of EZH2 in a SCLC PDX markedly inhibited tumor growth.

Robust global identifiability theory using potentials--Application to compartmental models.

This paper presents a global practical identifiability theory for analyzing and identifying linear and nonlinear compartmental models. The compartmental system is prolonged onto the potential jet space to formulate a set of input-output equations that are integrals in terms of the measured data, which allows for robust identification of parameters without requiring any simulation of the model differential equations. Two classes of linear and non-linear compartmental models are considered. The theory is first applied to analyze the linear nitrous oxide (N2O) uptake model. The fitting accuracy of the identified models from differential jet space and potential jet space identifiability theories is compared with a realistic noise level of 3% which is derived from sensor noise data in the literature. The potential jet space approach gave a match that was well within the coefficient of variation. The differential jet space formulation was unstable and not suitable for parameter identification. The proposed theory is then applied to a nonlinear immunological model for mastitis in cows. In addition, the model formulation is extended to include an iterative method which allows initial conditions to be accurately identified. With up to 10% noise, the potential jet space theory predicts the normalized population concentration infected with pathogens, to within 9% of the true curve.

A simplified model for mitral valve dynamics.

Located between the left atrium and the left ventricle, the mitral valve controls flow between these two cardiac chambers. Mitral valve dysfunction is a major cause of cardiac dysfunction and its dynamics are little known. A simple non-linear rotational spring model is developed and implemented to capture the dynamics of the mitral valve. A measured pressure difference curve was used as the input into the model, which represents an applied torque to the anatomical valve chords. A range of mechanical model hysteresis states were investigated to find a model that best matches reported animal data of chord movement during a heartbeat. The study is limited by the use of one dataset found in the literature due to the highly invasive nature of getting this data. However, results clearly highlight fundamental physiological issues, such as the damping and chord stiffness changing within one cardiac cycle, that would be directly represented in any mitral valve model and affect behaviour in dysfunction. Very good correlation was achieved between modeled and experimental valve angle with 1-10% absolute error in the best case, indicating good promise for future simulation of cardiac valvular dysfunction, such as mitral regurgitation or stenosis. In particular, the model provides a pathway to capturing these dysfunctions in terms of modeled stiffness or elastance that can be directly related to anatomical, structural defects and dysfunction.

Patient specific identification of the cardiac driver function in a cardiovascular system model.

The cardiac muscle activation or driver function, is a major determinant of cardiovascular dynamics, and is often approximated by the ratio of the left ventricle pressure to the left ventricle volume. In an intensive care unit, the left ventricle pressure is usually never measured, and the left ventricle volume is only measured occasionally by echocardiography, so is not available real-time. This paper develops a method for identifying the driver function based on correlates with geometrical features in the aortic pressure waveform. The method is included in an overall cardiovascular modelling approach, and is clinically validated on a porcine model of pulmonary embolism. For validation a comparison is done between the optimized parameters for a baseline model, which uses the direct measurements of the left ventricle pressure and volume, and the optimized parameters from the approximated driver function. The parameters do not significantly change between the two approaches thus showing that the patient specific approach to identifying the driver function is valid, and has potential clinically.

A fast generalizable solution method for glucose control algorithms.

In critical care tight control of blood glucose levels has been shown to lead to better clinical outcomes. The need to develop new protocols for tight glucose control, as well as the opportunity to optimize a variety of other drug therapies, has led to resurgence in model-based medical decision support in this area. One still valid hindrance to developing new model-based protocols using so-called virtual patients, retrospective clinical data, and Monte Carlo methods is the large amount of computational time and resources needed. This paper develops fast analytical-based methods for insulin-glucose system model that are generalizable to other similar systems. Exploiting the structure and partial solutions in a subset of the model is the key in finding accurate fast solutions to the full model. This approach successfully reduced computing time by factors of 5600-144000 depending on the numerical error management method, for large (50-164 patients) virtual trials and Monte Carlo analysis. It thus allows new model-based or model-derived protocols to be rapidly developed via extensive simulation. The new method is rigorously compared to existing standard numerical solutions and is found to be highly accurate to within 0.2%.

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets.

Lumped parameter approaches for modelling the cardiovascular system typically have many parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found. By utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care.

Review of fundus autofluorescence in choroidal melanocytic lesions.

Fundus autofluorescence (FAF) imaging takes advantage of the fluorescent properties of some molecules, especially lipofuscin. FAF derives mainly from retinal pigment epithelium (RPE) and Bruch's membrane. Using confocal scanning laser ophthalmoscope (cSLO) we have previously shown that FAF associated with pigmented choroidal lesions can be attributed to mainly lipofuscin (orange pigment) within the RPE. Other causes of FAF include hyperpigmentation, drusen, or fibrous metaplasia probably because they also cause lipofuscin accumulation in the overlying RPE. There is a total or partial correlation between FAF and the foci of lipofuscin and hyperpigmentation in about 90% of the cases. The FAF patterns of choroidal melanocytic lesions were classified as patchy or diffuse. The patchy pattern was defined as the presence of distinct areas of increased FAF between areas of normal autofluorescence. The diffuse pattern was characterized by the presence of increased FAF with indistinct borders over a larger part (>50%) of the tumour in the absence of such intervening areas. Choroidal melanomas presented with either a diffuse or patchy pattern, whereas choroidal naevi demonstrated only the patchy pattern. Diffuse FAF pattern was more often associated with larger choroidal melanomas as well as with early venous and late hyperfluorescence on fluorescein angiography. Limitations of these observations depend on the field of depth of cSLO; thus, FAF from other planes could not be detected. Increased retinal thickness, intraretinal oedema, or presence of subretinal fluid may also affect the FAF signal.

Clopidogrel attenuates coated-platelet production in patients undergoing elective coronary catheterization.

INTRODUCTION: Coated-platelets are a subclass of highly thrombotic activated platelets with an enhanced ability to generate thrombin. Excessive numbers of coated-platelets are believed to increase thrombotic risk. A previous report demonstrated that P2Y12 inhibition in vitro attenuates coated-platelet formation. The aim of this study was to determine the effect clopidogrel has on coated-platelet formation. METHODS AND RESULTS: We enrolled 27 patients undergoing elective coronary angiography. A total of 3 blood samples were taken from eligible patients: baseline, 24-hour postclopidogrel (preangiography), and 6-hour postangiography. Coated-platelet levels, expressed as percentage of total platelets, were determined with convulxin and thrombin with or without 1.5 or 6 microM adenosine diphosphate (ADP). Baseline levels of coated-platelets were 40.0% +/- 14.3% (mean +/- 1 SD). After clopidogrel exposure, the coated-platelet level was 32.8% +/- 13.6%, representing a significant 7.2% absolute reduction (AR) (17.8% relative reduction (RR); P < 0.0001). Clopidogrel significantly lowered the convulxin, thrombin plus ADP coated-platelet production (11.0% AR; 20.1% RR for 1.5 microM and 11.2% AR; 19.1% RR for 6 microM). CONCLUSIONS: This is the first report on the impact of in vivo administration of a P2Y12 antagonist on coated-platelet formation. The significance of a partial attenuation in coated-platelet potential has yet to be determined, but this could represent a new antithrombotic mechanism of clopidogrel beyond inhibition of ADP-induced aggregation.

Model-based identification and diagnosis of a porcine model of induced endotoxic shock with hemofiltration.

A previously validated cardiovascular system (CVS) model and parameter identification method for cardiac and circulatory disease states are extended and further validated in a porcine model (N=6) of induced endotoxic shock with hemofiltration. Errors for the identified model are within 10% when the model is re-simulated and compared to the clinical data. All identified parameter trends over time in the experiments match clinically expected changes both individually and over the cohort. This work represents a further clinical validation of these model-based cardiovascular diagnosis and therapy guidance methods for use with monitoring endotoxic disease states.

Immune cells in the human choroid.

AIM: To characterise the leucocytes in human macular choroid with and without drusen, and in eyes with advanced age-related macular degeneration (AMD) with fibrovascular scarring (FVS). METHODS: Ten eyes from nine donors (range 55-91 years of age) were obtained from an eye bank within 38 h post mortem. Fixed macular biopsies were sectioned, stained immunochemically and examined for the presence of leucocyte antigens CD45, CD4, CD8, CD14 and CD83. RESULTS: Four eyes without drusen, four eyes with drusen and two eyes with FVS contained 23.9 (SD 6.2)%, 27.5 (7.2)%, and 19.3 (11.3)% CD45-positive cells, respectively. The corresponding percentages for CD4-positive cells were 5.4 (4.3), 8.9 (3.0) and 7.5 (8.1); for CD8-positive cells, 3.8 (0.7), 6.8 (2.2) and 6.3 (2.1); and for CD14-positive cells, 3.7 (3.7), 3.6 (1.6) and 2.6 (3.6), respectively. The authors found CD83-positive cells solely in one of the two FVS eyes examined that had the more severe form of scarring. CONCLUSION: Human choroid contains similar amounts of CD4-positive cells and monocytes irrespective of the presence of drusen, but CD8-positive cells are more abundant in macular choroid with drusen. The presence of haematopoietic cells in the macular choroid provides further evidence for the possible participation of inflammatory cells in pathogenesis of AMD.

Model-based identification of PEEP titrations during different volemic levels.

A cardiovascular system (CVS) model has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture all main hemodynamic trends in a porcine model of pulmonary embolism. In this research, a slightly extended CVS model and parameter identification process are presented and validated in a porcine experiment of positive end-expiratory pressure (PEEP) titrations at different volemic levels. The model is extended to more physiologically represent the separation of venous and arterial circulation. Errors for the identified model are within 5% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents another clinical validation of the underlying fundamental CVS model, and the methods and approach of using them for cardiovascular diagnosis in critical care.

Prediction of hemodynamic changes towards PEEP titrations at different volemic levels using a minimal cardiovascular model.

A cardiovascular system model and parameter identification method have previously been validated for porcine experiments of induced pulmonary embolism and positive end-expiratory pressure (PEEP) titrations, accurately tracking all the main hemodynamic trends. In this research, the model and parameter identification process are further validated by predicting the effect of intervention. An overall population-specific rule linking specific model parameters to increases in PEEP is formulated to predict the hemodynamic effects on arterial pressure, pulmonary artery pressure and stroke volume. Hemodynamic changes are predicted for an increase from 0 to 10 cm H(2)O with median absolute percentage errors less than 7% (systolic pressures) and 13% (stroke volume). For an increase from 10 to 20 cm H(2)O median absolute percentage errors are less than 11% (systolic pressures) and 17% (stroke volume). These results validate the general applicability of such a rule, which is not pig-specific, but holds over for all analyzed pigs. This rule enables physiological simulation and prediction of patient response. Overall, the prediction accuracy achieved represents a further clinical validation of these models, methods and overall approach to cardiovascular diagnosis and therapy guidance.

Model-based cardiac diagnosis of pulmonary embolism.

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification methods for this model are highly non-linear and non-convex, hindering real-time clinical application. An integral-based identification method that transforms the problem into a linear, convex problem, has been previously developed, but was only applied on continuous simulated data with random noise. This paper extends the method to handle discrete sets of clinical data, unmodelled dynamics, a significantly reduced data set theta requires only the minimum and maximum values of the pressure in the aorta, pulmonary artery and the volumes in the ventricles. The importance of integrals in the formulation for noise reduction is illustrated by demonstrating instability in the identification using simple derivative-based approaches. The cardiovascular system (CVS) model and parameter identification method are then clinically validated on porcine data for pulmonary embolism. Errors for the identified model are within 10% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents the first clinical validation of these models, methods and approach to cardiovascular diagnosis in critical care.

A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients.

BACKGROUND: Critically ill patients are often hyperglycemic and insulin resistant, as well as highly dynamic. Tight glucose control has been shown to significantly reduce mortality in critical care. A physiological model of the glucose-insulin regulatory system is improved and used to develop an adaptive control protocol utilizing both nutritional and insulin inputs to control hyperglycemia. The approach is clinically verified in a critical care patient cohort. METHODS: A simple two-compartment model for glucose rate of appearance in plasma due to stepwise enteral glucose fluxes is developed and incorporated into a previously validated system model. A control protocol modulating intravenous insulin infusion and bolus, with an enteral feed rate, is developed, enabling tight and predictive glycemic regulation to preset targets. The control protocol is adaptive to patient time-variant effective insulin resistance. The model and protocol are verified in seven 10-h and one 24-h proof-of-concept clinical trials. Ethics approval was granted by the Canterbury Ethics Committee. RESULTS: Insulin requirements varied widely following acute changes in patient physiology. The algorithm developed successfully adapted to patient metabolic status and insulin sensitivity, achieving an average target acquisition error of 9.3% with 90.7% of all targets achieved within +/-20%. Prediction errors may not be distinguishable from sensor measurement errors. Large errors (>20%) are attributable to highly dynamic and unpredictable changes in patient condition. CONCLUSIONS: Tight, targeted stepwise regulation was exhibited in all trials. Overall, tight glycemic regulation is achieved in a broad critical care cohort with optimized insulin and nutrition delivery, effectively managing glycemia even with high effective insulin resistance.

Fast normalized cross correlation for motion tracking using basis functions.

Digital image-based elasto-tomography (DIET) is an emerging method for non-invasive breast cancer screening. Effective clinical application of the DIET system requires highly accurate motion tracking of the surface of an actuated breast with minimal computation. Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate foundation for motion tracking. However, even using fast Fourier transform (FFT) methods, it is too computationally intense for rapidly managing several large images. A significantly faster method of calculating the NCC is presented that uses rectangular approximations in place of randomly placed landmark points or the natural marks on the breast. These approximations serve as an optimal set of basis functions that are automatically detected, dramatically reducing computational requirements. To prove the concept, the method is shown to be 37-150 times faster than the FFT-based NCC with the same accuracy for simulated data, a visco-elastic breast phantom experiment and human skin. Clinically, this approach enables thousands of randomly placed points to be rapidly and accurately tracked providing high resolution for the DIET system.

Integral-based identification of patient specific parameters for a minimal cardiac model.

A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 min) to within a mean value of 4-10% in the presence of 5-15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.

Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study.

Stress-induced hyperglycaemia is prevalent in intensive care, impairing the immune response. Nutritional support regimes with high glucose content further exacerbate the problem. Tight glucose control has been shown to reduce mortality by up to 43% if levels are kept below 6.1 mmol/L. This research develops a control algorithm with insulin and nutritional inputs for targeted glucose control in the critically ill. Ethics approval for this research was granted by the Canterbury Ethics Committee. Proof-of-concept clinical pilot trials were conducted on intubated, insulin-dependent Christchurch ICU patients (n=7) on constant nutritional support. A target 10-15% reduction in glucose level per hour for a desired glucose level of 4-6 mmol/L was set. 43% and 91% of glucose targets were achieved within +/-5 and +/-20%, respectively. The mean error was 8.9% (0.5 mmol/L), with an absolute range [0, 2.9] mmol/L. End glucose levels were 40% lower compared to initial values. All large target errors are attributable to sudden changes in patient physiology at low glucose values, rather than systemic deficiencies. Target errors are consistent with and explainable by published sensor error distributions. The results show that intensive model-based glucose management with nutrition control reduced absolute glucose levels progressively while reducing the severity of glycaemic fluctuation even with significant inter-patient variability and time-varying physiological condition. Trials spanning longer periods of time are in development to verify the short-term pilot studies performed and to test the adaptability of the controller. Clinically, these results indicate potential in clinical use to reduce ICU mortality as well as reduce risk of severe complications.

Efficient implementation of non-linear valve law and ventricular interaction dynamics in the minimal cardiac model.

A minimal model of the cardio-vascular system (CVS) with ventricular interaction and inertial effects that accurately captures the physiological trends of a variety of disease states has been developed. However, the physiologically accurate open on pressure, close on flow valve law is computationally heavy to implement, reducing the model's potential clinical benefit. A significantly simpler representation of the valve law using Heaviside functions is derived and the ventricular interaction equations are reformulated to obtain a unique closed form analytical solution. The new formulation is tested and compared with the previous formulation for a healthy human and four clinically significant disease states: mitral and aortic stenosis, pulmonary embolism and septic shock. The new model formulation matches the previous model definition, differing by a mean model response error of no more than 0.2%. Computationally, it is 24 x faster than the previous method. More specifically, a short 20-beat simulation that took 102 s now requires 4.3 s, significantly improving the model's potential for practical use in a diagnostic and/or decision support role in the intensive care unit.

Adaptive bolus-based set-point regulation of hyperglycemia in critical care.

Critically ill patients are often hyperglycemic and extremely diverse in their dynamics. Consequently, fixed protocols and sliding scales can result in error and poor control. A two-compartment glucose-insulin system model that accounts for time-varying insulin sensitivity and endogenous glucose removal, along with two different saturation kinetics is developed and verified in proof-of-concept clinical trials for adaptive control of hyperglycemia. The adaptive control algorithm monitors the physiological status of a critically ill patient, allowing real-time tight glycemic regulation. The bolus-based insulin administration approach is shown to result in safe, targeted stepwise glycemic reduction for three critically ill patients.