Approaching the Hartree-Fock Limit Through the CABS Singles Correction and Auxiliary Basis Sets

Auxiliary basis sets for use in the resolution of the identity (RI) approximation in explicitly correlated methods are presented for the elements H–Ar. These extend the cc-pVnZ-F12/OptRI (n = D–Q) auxiliary basis sets of Peterson and coworkers by the addition of a small number of s- and p-functions, optimized so as to yield the great- est complementary auxiliary basis set (CABS) singles correction to the Hartree-Fock energy. The new sets, denoted OptRI+, also lead to a reduction in errors due to the RI approximation and hence an improvement in correlation energies. The atomization energies and heats of formation for a test set of small molecules, and spectroscopic constants for 27 diatomics, calculated at the CCSD(T)-F12b level, are shown to have improved error distributions for the new auxiliary basis sets with negligible additional effort. The OptRI+ sets retain all of the desirable properties of the original OptRI, including the production of smooth potential energy surfaces, whilst maintaining a compact nature

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 two disease states, Valvular Stenosis and Pulmonary Embolism, 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 minutes) to within a mean value of 4 – 8% 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 5% 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

Computationally efficient velocity profile solutions for cardiac haemodynamics

DOI: 10.1109/IEMBS.2004.1403316This paper reformulates the non-linear differential equations associated with time varying resistance in minimal cardio-vascular system models into a system of linear equations with an analytical solution. The importance of including time varying resistance is shown for a single chamber model where there is a 17.5% difference in cardiac output when compared with a constant resistance model. However, the increased complexity has significant extra computational cost. This new formulation provides a significant computational saving of 15x over the previous method. This improvement enables more physiological accuracy with minimal cost in computational time. As a result, the model can be used in clinical situations to aid diagnosis and therapy selection without compromising on physiological accuracy

Diagnosing cardiac disease states using a minimal cardiovascular model

Cardiovascular disease states are difficult to diagnose due to a variety of underlying dysfunctions combined with reflex mechanisms. To provide more consistent care a cardiovascular system model is combined with an efficient patient-specific parameter identification method. The goal is to identify the patient’s condition and to predict the future patient-specific reaction, making this approach a potential means for model-based guided therapy. The model and parameter-identification method are validated using clinical haemodynamic data measured during drug induced porcine pulmonary embolism experiments (N=6) and PEEP titration experiments (N=6). Identified model parameters are correlated to create predictive measures of haemodynamic changes to clinical therapy or patient condition. Prediction is tested for observed changes in arterial pressure (AP), pulmonary arterial pressure (PAP) and stroke volume (SV) as caused by a clinical change in PEEP. The parameter-identification method tracked pulmonary embolism in porcine data from an initial healthy to the disease state. The full range of haemodynamic responses was captured with mean errors of 4.1% in the pressures and 3.1% in the volumes. Pulmonary resistance increased significantly with the onset of embolism, as expected, with the percentage increase ranging from 89.98% to 261.44% of the initial state. Changes in AP, PAP and SV due to an increase in PEEP were predicted with a mean absolute percentage error less than 10% for 6 data sets. These results provide a first clinical validation of this model-based diagnostic therapeutic decision support approach to haemodynamic management

Dynamic functional residual capacity can be estimated using a stress-strain approach

Invited. Available online 9 June 2010.Background Acute Respiratory Distress Syndrome (ARDS) results in collapse of alveolar units and loss of lung volume at the end of expiration. Mechanical ventilation is used to treat patients with ARDS or Acute Lung Injury (ALI), with the end objective being to increase the dynamic functional residual capacity (dFRC), and thus increasing overall functional residual capacity (FRC). Simple methods to estimate dFRC at a given positive end expiratory pressure (PEEP) level in patients with ARDS/ALI currently does not exist. Current viable methods are time-consuming and relatively invasive. Methods Previous studies have found a constant linear relationship between the global stress and strain in the lung independent of lung condition. This study utilizes the constant stress–strain ratio and an individual patient's volume responsiveness to PEEP to estimate dFRC at any level of PEEP. The estimation model identifies two global parameters to estimate a patient specific dFRC, ß and mß. The parameter ß captures physiological parameters of FRC, lung and respiratory elastance and varies depending on the PEEP level used, and mß is the gradient of ß vs. PEEP. Results dFRC was estimated at different PEEP values and compared to the measured dFRC using retrospective data from 12 different patients with different levels of lung injury. The median percentage error is 18% (IQR: 6.49) for PEEP = 5 cm H2O, 10% (IQR: 9.18) for PEEP = 7 cm H2O, 28% (IQR: 12.33) for PEEP = 10 cm H2O, 3% (IQR: 2.10) for PEEP = 12 cm H2O and 10% (IQR: 9.11) for PEEP = 15 cm H2O. The results were further validated using a cross-correlation (N = 100,000). Linear regression between the estimated and measured dFRC with a median R2 of 0.948 (IQR: 0.915, 0.968; 90% CI: 0.814, 0.984) over the N = 100,000 cross-validation tests. Conclusions The results suggest that a model based approach to estimating dFRC may be viable in a clinical scenario without any interruption to ventilation and can thus provide an alternative to measuring dFRC by disconnecting the patient from the ventilator or by using advanced ventilators. The overall results provide a means of estimating dFRC at any PEEP levels. Although reasonable clinical accuracy is limited to the linear region of the static PV curve, the model can evaluate the impact of changes in PEEP or other mechanical ventilation settings

Glycemic Control Protocol Comparison using Virtual Trials

DTM2011 handbook/programme is given in files and also available as a hard copyBackground: Several accurate glycemic control (AGC) protocols for critical care patients exist but making comparisons is very hard. Objective: This study uses clinically validated virtual patient methods to compare safety and performance for several published AGC protocols. Method: Clinically validated virtual trials were run on 371 patients (39,481 hours, 26,646 measurements) created from the SPRINT AGC cohort. For protocols that do not modulate feed rates enteral nutrition was held at 100% of ACCP goal (25kcal/kg/day) when the patients were clinically fed, and parenteral nutrition rates were matched to clinical data. Performance was defined as %BG within glycemic bands and BG measurement frequency. Safety was defined as the incidence of severe (number patients with BG<40mg/dL) and moderate (%BG<72mg/dL) hypoglycemia. Clinical data from SPRINT is also compared. Results: Clinical SPRINT performance data matched re-simulated SPRINT with 86% vs. 86% BG in 80-145mg/dL, 2.00% vs. 2.07% BG above 180mg/dL and 7.83% vs. 7.29% BG below 72mg/dL, with 14 measurements (over 8 patients) of BG<40mg/dL. Yale results were 83.5%, 3.20%, 5.18%, with 6 severe hypoglycemic patients, using 37,961 measurements (23.0/day). Glucontrol had 75.2%, 3.70%, 9.45%, 52 cases and 26,199 measurements (15.8/day). Braithwaite had 84.2%, 3.00%, 4.22%, 19 cases and 24,396 measurements (14.8/day). The STAR (Stochastic TARgeted) model-based method had 90.6%, 1.67%, 1.33%, 5 cases and 20,591 measurements (12.3/day). Conclusions: Virtual trials provided an effective comparison across protocols with different target bands/values and different clinical cohorts. The model-based STAR protocol provided the best management of patient variability yielding the best performance and safety

A Fast and Accurate Diagnostic Test for Severe Sepsis Using Kernel Classifiers

Severe sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however gold standard blood culture test results may return in up to 48 hours. Insulin sensitivity (SI) is known to decrease with worsening condition and inflammatory response, and could thus be used to aid clinical treatment decisions. Some glycemic control protocols are able to accurately identify SI in real-time. A biomarker for severe sepsis was developed from retrospective SI and concurrent temperature, heart rate, respiratory rate, blood pressure, and SIRS score from 36 adult patients with sepsis. Patients were identified as having sepsis based on a clinically validated sepsis score (ss) of 2 or higher (ss = 0–4 for increasing severity). Kernel density estimates were used for the development of joint probability density profiles for ss = 2 and ss < 2 data hours (213 and 5858 respectively of 6071 total hours) and for classification. From the receiver operator characteristic (ROC) curve, the optimal probability cutoff values for classification were determined for in-sample and out-of-sample estimates. A biomarker including concurrent insulin sensitivity and clinical data for the diagnosis of severe sepsis (ss = 2) achieves 69–94% sensitivity, 75–94% specificity, 0.78–0.99 AUC, 3–17 LHR+, 0.06–0.4 LHR-, 9–38% PPV, 99–100% NPV, and a diagnostic odds ratio of 7–260 for optimal probability cutoff values of 0.32 and 0.27 for in-sample and out-of-sample data, respectively. The overall result lies between these minimum and maximum error bounds. Thus, the clinical biomarker shows good to high accuracy and may provide useful information as a real-time diagnostic test for severe sepsis

Diabetic Retinopathy Screening Using Computer Vision

6-pagesDiabetic Retinopathy (DR) is one of the main causes of blindness and visual impairment in developed countries, stemming solely from diabetes mellitus. Current screening methods using fundus images rely on the experience of the operator as they are manually examined. Automated methods based on neural networks and other approaches have not provided sensitivity or specificity above 85%. This work presents a computer vision based method that directly identifies hard exudates and dot haemorrhages (DH) from 100 digital fundus images from a graded database of images using standard computer vision techniques, and clinical observation and knowledge. Sensitivity and specificity in diagnosis are 95-100% in both cases. Positive and negative prediction values (PPV, NPV) were 95-100% for both cases. The overall method is general, computationally efficient and suitable for further clinical trials to test both accuracy and the ability to the track DR status over time

A linear-scaling method for noncovalent interactions : an efficient combination of absolutely localized molecular orbitals and a local random phase approximation approach

A novel method for the accurate and efficient calculation of interaction energies in weakly bound complexes composed of a large number of molecules is presented. The new ALMO+RPAd method circumvents the prohibitive scaling of coupled cluster singles and doubles while still providing similar accuracy across a diverse range of weakly bound chemical systems. Linear-scaling procedures for the Fock build are given utilizing absolutely localized molecular orbitals (ALMOs), resulting in the a priori exclusion of basis set superposition errors. A bespoke data structure and algorithm using density fitting are described, leading to linear scaling for the storage and computation of the two-electron integrals. Electron correlation is included through a new, linear-scaling pairwise local random phase approximation approach, including exchange interactions, and decomposed into purely dispersive excitations (RPAxd). Collectively, these allow meaningful decomposition of the interaction energy into physically distinct contributions: electrostatic, polarization, charge transfer, and dispersion. Comparison with symmetry-adapted perturbation theory shows good qualitative agreement. Tests on various dimers and the S66 benchmark set demonstrate results within 0.5 kcal mol–1 of coupled cluster singles and doubles results. On a large cluster of water molecules, we achieve calculations involving over 3500 orbital and 12,000 auxiliary basis functions in under 10 min on a single CPU core

Correlation consistent basis sets for explicitly correlated wavefunctions : pseudopotential-based basis sets for the group 11 (Cu, Ag, Au) and 12 (Zn, Cd, Hg) elements

New correlation consistent basis sets for the group 11 (Cu, Ag, Au) and 12 (Zn, Cd, Hg) elements have been developed specifically for use in explicitly correlated F12 calculations. This includes orbital basis sets for valence only (cc-pVnZ-PP-F12, n =D, T, Q) and outer core-valence (cc-pCVnZ-PP-F12) correlation, along with both of these augmented with additional high angular momentum diffuse functions. Matching auxiliary basis sets required for density fitting and resolution-of-the-identity approaches to conventional and F12 integrals have also been optimized. All of the basis sets are to be used in conjunction with small-core relativistic pseudopotentials [Figgen et al., Chem. Phys. 311, 227 (2005)]. The accuracy of the basis sets is determined through benchmark calculation at the explicitly correlated coupled-cluster level of theory for various properties of atoms and diatomic molecules. The convergence of the properties with respect to basis set is dramatically improved compared to conventional coupled-cluster calculations, with cc-pVTZ-PP-F12 results close to conventional estimates of the complete basis set limit. The patterns of convergence are also greatly improved compared to those observed from the use of conventional correlation consistent basis sets in F12 calculations