Influence of angle Kappa on the optimal intraocular orientation of asymmetric multifocal intraocular lenses

PURPOSE: to evaluate the effects of kappa angle and intraocular orientation on the theoretical performance of asymmetric multifocal intraocular lenses (MIOL). METHODS: For a total of 21 corneal aberrations, a computational analysis simulated the implantation of a computationally designed MIOL. An image quality parameter (IQ) (visually modulated transfer function metric) was calculated for a 5.0-mm pupil and for three conditions: distance, intermediate, and near vision. The procedure was repeated for each eye after a rotation of the MIOL with respect to the cornea from 0º to 360º in 5º steps. Kappa angles from 0 to 900 microns, in 150 microns steps, combined with two two variants of MIOL centration were tested: in the corneal apex or in the center of the entrance pupil. A p-value ≤ 0.05 was considered significant. RESULTS: There were statistically significant differences of the IQ depending of the intraocular orientation of the MIOL. If kappa angle was increased, there was a statistically significant decrease of the IQ. The IQ maintained stable when the optimal intraocular orientation was re-calculated for each kappa angle. In general, the inter-variability of the results between subjects was very high. There were no strong evidences supporting that there exists a preferable centration point. CONCLUSIONS: Our results suggest that kappa angle theoretically affects significantly the performance of asymmetric MIOL implantation. However, its negative effect can be compensated if a customized intraocular orientation is calculated taking into account the presence of the kappa angle

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Executable cancer models: successes and challenges.

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.

A Fast Parameter Identification Framework for Personalized Pharmacokinetics.

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algorithm based on the Cluster Newton method, namely the constrained Cluster Newton method, where the initial points of the parameters are constrained by the database. The algorithm was tested with the compartmental model of propofol on a database of 59 subjects. The average overall absolute percentage error based on constrained Cluster Newton method is 12.10% with the threshold approach, and 13.42% with the nearest-neighbor approach. The average computation time of one estimation is 13.10 seconds. Using parallel computing, the average computation time is reduced to 1.54 seconds, achieved with 12 parallel workers. The results suggest that the proposed framework can effectively improve the prediction accuracy of the pharmacokinetic parameters with limited observations in comparison to the conventional methods. Computation cost analyses indicate that the proposed framework can take advantage of parallel computing and provide solutions within practical response times, leading to fast and accurate parameter identification of pharmacokinetic problems.

The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study.

Background: False-positive drug-drug interaction alerts are frequent and result in alert fatigue that can result in prescribers bypassing important alerts. Development of a method to present patient-appropriate alerts is needed to help restore alert relevance. Objective: The purpose of this study was to assess the potential for patient-specific drug-drug interaction (DDI) alerts to reduce alert burden. Methods: This project was conducted at a tertiary care medical center. Seven of the most frequently encountered DDI alerts were chosen for developing patient-specific, algorithm-based DDI alerts. For each of the DDI pairs, 2 algorithms featuring different values for modifying factors were made. DDI alerts from the 7 drug pairs were collected over 30 days. Outcome measures included the number of DDI alerts generated before and after patient-specific algorithm application to the same patients over the same time period. Results: A total of 14 algorithms were generated, and each was evaluated by comparing the number of alerts generated by our existing, customized clinical decision support (CDS) software and the patient-specific algorithms. The CDS DDI alerting software generated an average of 185.3 alerts per drug pair over the 30-day study period. Patient-specific algorithms reduced the number of alerts resulting from the algorithms by 11.3% to 93.5%. Conclusion and Relevance: Patient-specific DDI alerting is an innovative and effective approach to reduce the number of DDI alerts, may potentially increase the appropriateness of alerts, and may decrease the potential for alert fatigue.

Quantifying the risk of local Zika virus transmission in the contiguous US during the 2015-2016 ZIKV epidemic.

BACKGROUND: Local mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties in the contiguous United States (US), prompting the issuance of travel, prevention, and testing guidance across the contiguous US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US. METHODS: We present a framework for the projection of ZIKV autochthonous transmission in the contiguous US during the 2015-2016 epidemic using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental, and detailed population data. The model generates an ensemble of travel-related case counts and simulates their potential to have triggered local transmission at the individual level in the 2015-2016 ZIKV epidemic. RESULTS: We estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025° × 0.025° cell level across the contiguous US. We provide a risk measure based on the probability of observing local transmission in a specific location during a ZIKV epidemic modeled after the epidemic observed during the years 2015-2016. The high spatial and temporal resolution of the model allows us to generate statistical estimates of the number of ZIKV introductions leading to local transmission in each location. We find that the risk was spatially heterogeneously distributed and concentrated in a few specific areas that account for less than 1% of the contiguous US population. Locations in Texas and Florida that have actually experienced local ZIKV transmission were among the places at highest risk according to our results. We also provide an analysis of the key determinants for local transmission and identify the key introduction routes and their contributions to ZIKV transmission in the contiguous US. CONCLUSIONS: This framework provides quantitative risk estimates, fully captures the stochasticity of ZIKV introduction events, and is not biased by the under-ascertainment of cases due to asymptomatic cases. It provides general information on key risk determinants and data with potential uses in defining public health recommendations and guidance about ZIKV risk in the US.

A Novel Derivation Predicting Survival After Primary Tumor Resection in Stage IV Colorectal Cancer: Validation of a Prognostic Scoring Model and an Online Calculator to Provide Individualized Survival Estimation.

BACKGROUND: A prognostic scoring model has been devised previously to predict survival following primary tumor resection in patients with metastatic colorectal cancer and unresectable metastases. This has yet to be validated. OBJECTIVE: The main objectives of this study are to validate the proposed prognostic scoring model and create an interactive online calculator to estimate an individual's survival after primary tumor resection. DESIGN: Clinical data and survival outcomes of patients were extracted from a prospectively maintained database. Patients were categorized into good, moderate, or poor survivor groups based on the previously proposed scoring algorithm. Discrimination was assessed and recalibration was performed, with the recalibrated model implemented as an interactive Web application to provide individualized survival probability. SETTINGS: This study was conducted at a tertiary referral center. PATIENTS: The study included 324 consecutive patients with metastatic colorectal carcinoma and unresectable metastases who underwent primary tumor resection between January 2008 and December 2013. MAIN OUTCOME MEASURES: The primary outcome measured was overall survival. RESULTS: Three hundred twenty-four patients were included in the study. Median survival in the good, moderate, and poor prognostic groups was 56.8, 25.7, and 19.9 months (log rank test, p = 0.003). The κ statistic was 0.638 and RD was 0.101. Significant differences in survival were found between the moderate and good prognostic groups (HR, 2.79; 95% CI, 1.51-5.15; p = 0.001) and between poor and good prognostic groups (HR, 4.12; 95% CI, 1.98-8.55; p < 0.001). The model was implemented as an interactive online calculator to provide individualized survival estimation after primary tumor resection (http://bit.ly/Stage4PrognosticScore). LIMITATIONS: Selection bias and single-center data preclude the generalizability of the proposed model. Information regarding the severity or likelihood of developing symptoms from the primary tumor were also not accounted for in the prognostic scoring model proposed. CONCLUSIONS: The prognostic scoring model provides good prognostic stratification of survival after primary tumor resection and may be a useful tool to predict survival after primary tumor resection. See Video Abstract at http://links.lww.com/DCR/A330.

Three-dimensional printing in cardiology: Current applications and future challenges.

Three-dimensional (3D) printing has attracted a huge interest in recent years. Broadly speaking, it refers to the technology which converts a predesigned virtual model to a touchable object. In clinical medicine, it usually converts a series of two-dimensional medical images acquired through computed tomography, magnetic resonance imaging or 3D echocardiography into a physical model. Medical 3D printing consists of three main steps: image acquisition, virtual reconstruction and 3D manufacturing. It is a promising tool for preoperative evaluation, medical device design, hemodynamic simulation and medical education, it is also likely to reduce operative risk and increase operative success. However, the most relevant studies are case reports or series which are underpowered in testing its actual effect on patient outcomes. The decision of making a 3D cardiac model may seem arbitrary since it is mostly based on a cardiologist's perceived difficulty in performing an interventional procedure. A uniform consensus is urgently necessary to standardize the key steps of 3D printing from imaging acquisition to final production. In the future, more clinical trials of rigorous design are possible to further validate the effect of 3D printing on the treatment of cardiovascular diseases. (Cardiol J 2017; 24, 4: 436-444).

Medicina de precisión: un viaje a Ítaca / Precision medicine: A modern odyssey

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Natural Language Understanding Performance & Use Considerations in Virtual Medical Encounters.

A virtual standardized patient (VSP) prototype was tested for natural language understanding (NLU) performance. The conversational VSP was evaluated in a controlled 61 subject study over four repetitions of a patient case. The prototype achieved more than 92% appropriate response rate from naïve users on their first attempt and results were stable by their fourth case repetition. This level of performance exceeds prior efforts and is at a level comparable of accuracy as seen in human conversational patient training, with caveats. This level of performance was possible due to the use of a unified medical taxonomy underpinning that allows virtual patient language training to be applied to all cases in our system as opposed to benefiting a single patient case.

Grupos de alta para pacientes con distimia crónica: una propuesta psicoterapéutica orientada al fin de tratamiento / Groups of discharge for patients with chronic distimia: a psychotherapeutic proposal orientated to the end of treatment

La intención del autor en este artículo es reflexionar desde la teoría psicoanalítica y desde la experiencia clínica y proponer un planteamiento terapéutico para los pacientes con distimia crónica resistente a los tratamientos. Este trabajo incluye las siguientes formulaciones 1- Los pacientes tratados durante mucho tiempo en las Unidades de Salud Mental Comunitarias y que se cronifican en sus mecanismos patogénicos han de tener una ineludible fecha final de tratamiento 2- Se justifica la psicoterapia de grupo como una posible última fase de su tratamiento en salud mental 3- Se hace la hipótesis de que el alta mejorará la posición de resistencia al cambio psicológico. En este recorrido se revisan los textos freudianos Análisis Terminable e Interminable, Más Allá del Principio de Placer y el concepto de Técnica Activa propuesto por Ferenczi. A lo largo del artículo se presenta el diseño de los tres grupos terapéuticos realizados, la metodología seguida, los resultados obtenidos, las conclusiones y las preguntas finales (AU)

The intention of the author in this article is to think from the psychoanalytic theory and from the clinical experience. and propose a therapeutic plan for patients with distimia chronicle resistants to the treatments. This work includes the following formulations: 1-The patients treated for a long time in the Community Mental Health Units who become chronic in their pathogenic mechanisms have to have an unavoidable final date of treatment 2-we justify the group psychotherapy itself as a last possible phase of the treatment 3- it’s done the hypothesis that the discharge will improve the position of resistance to a psychological change. We review the Freudian texts: Analysis Terminable and Interminable, Beyond the Pleasure Principle and the concept 'active technique' proposed by Ferenczi. Along the article it is presented the design of the three group psychotherapies done, the followed methodology, the obtained results, the conclusions and the final questions (AU)

Evaluation of Dosimetry Check software for IMRT patient-specific quality assurance.

The purpose of this study is to evaluate the use of the Dosimetry Check system for patient-specific IMRT QA. Typical QA methods measure the dose in an array dosimeter surrounded by homogenous medium for which the treatment plan has been recomputed. With the Dosimetry Check system, fluence measurements acquired on a portal dosimeter is applied to the patient's CT scans. Instead of making dose comparisons in a plane, Dosimetry Check system produces isodose lines and dose-volume histograms based on the planning CT images. By exporting the dose distribution from the treatment planning system into the Dosimetry Check system, one is able to make a direct comparison between the calculated dose and the planned dose. The versatility of the software is evaluated with respect to the two IMRT techniques - step and shoot and volumetric arc therapy. The system analyzed measurements made using EPID, PTW seven29, and IBA MatriXX, and an intercomparison study was performed. Plans from patients previously treated at our institution with treated anatomical site on brain, head & neck, liver, lung, and prostate were analyzed using Dosimetry Check system for any anatomical site dependence. We have recommendations and possible precautions that may be necessary to ensure proper QA with the Dosimetry Check system.

Optimizing computational feature sets for subthalamic nucleus localization in DBS surgery with feature selection.

OBJECTIVE: Microelectrode recording (MER) is used to identify the subthalamic nucleus (STN) during deep brain stimulation (DBS) surgery. Automated STN detection typically involves extracting quantitative features from MERs for classifier training. This study evaluates the ability of feature selection to identify optimal feature combinations for automated STN localization. METHODS: We extracted 13 features from 65 MERs for classifier training. For logistic regression (LR) classification, we compared classifiers identified by feature selection to those containing all possible feature combinations. We used classification error as our metric with hold-one-patient-out cross-validation. We also compared patient-specific vs. independent normalization on classifier performance. RESULTS: Feature selection and patient-specific normalization were superior to non-optimized, patient-independent classifiers. Feature selection, patient-specific normalization, and both produced relative error reductions of 4.95%, 31.36%, and 38.92%, respectively. Three of four feature-selected LR classifiers performed better than 99% of classifiers with all possible feature combinations. Optimal feature combinations were not predictable from individual feature performance. CONCLUSIONS: Feature selection reduces classification error in automated STN localization from MERs. Additional improvement from patient-specific normalization suggests these approaches are necessary for clinically reliable automation of MER interpretation. SIGNIFICANCE: These findings represent an incremental advance in automated functional localization of STN from MER in DBS surgery.

Reproducibility in patient-specific IMRT QA.

The purpose of this study was to determine the reproducibility of patient-specific, intensity-modulated radiation therapy (IMRT) quality assurance (QA) results in a clinical setting. Six clinical patient plans were delivered to a variety of devices and analyses, including 1) radiographic film; 2) ion chamber; 3) 2D diode array delivered and analyzed in three different configurations (AP delivery with field-by-field analysis, AP delivery with composite analysis, and planned gantry angle delivery); 4) helical diode array; and 5) in-house-designed multiple ion chamber phantom. The six clinical plans were selected from a range of treatment sites and were of various levels of complexity. Of note, three of the plans had failed at least preliminary evaluation with our in-house IMRT QA; the other three plans had passed QA. These plans were delivered three times sequentially without changing the setup, and then delivered two more times after breaking down and rebuilding the setup between each. This allowed for an investigation of reproducibility (in terms of dose, dose difference or percent of pixels passing gamma) of both the delivery and the physical setup. This study showed that the variability introduced from the setup was generally higher than the variability from redelivering the plan. Radiographic film showed the poorest reproducibility of the dosimeters investigated. In conclusion, the various IMRT QA systems demonstrated varying abilities to reproduce QA results consistently. All dosimetric devices demonstrated a reproducibility (coefficient of variation) of less than 4% in their QA results for all plans, with an average reproducibility of less than 2%. This work provides some quantification for the variability that may be seen for IMRT QA dosimeters.