Allergic Adverse Drug Events After Alert Overrides in Hospitalized Patients.

OBJECTIVES: This study aimed to assess how often overridden drug allergy alerts (ODAAs) lead to allergic adverse drug events (All-ADEs) and to evaluate the frequency with which drug allergy alerts (DAAs) were overridden and the reasons, as well as appropriateness of these overrides. METHODS: A retrospective observational study of DAA generated between 2014 and 2016 was conducted. The corresponding DAA records were reviewed to determine the frequency of alert overrides. A chart review was performed on a subset of 194 ODAA (the first of every 3 chronologically ordered ODAA) to identify All-ADEs and to evaluate the override reasons and the appropriateness of these overrides. RESULTS: A total of 2044 DAAs were overridden (override rate of 44.8%). Most were triggered by a nonexact match (93.81%), when ordering nervous system (21.1%) and cardiovascular system (19.6%) drugs and were generated by physicians (72.7%). The main override reason was that the patient was already taking the drug or had previously tolerated the drug. Only 9.28% of ODAAs were inappropriately overridden. Six All-ADEs (3.09%) were identified and were due to anti-infective (1), antineoplastic (1), and iodinated-contrast (4) drug administration. Most All-ADEs were cutaneous and were mild. None was life-threatening or fatal. The All-ADEs rate was higher among inappropriately ODAA (15.79%, P = 0.013). CONCLUSIONS: Alert overrides are not exempt from clinical consequences, although few are associated with All-ADEs. It is necessary to identify the drugs involved in those reactions and to update allergy lists to generate only specific and important DAA and to avoid the negative consequences of overrides.

Computer-based simulation to reduce EHR-related chemotherapy ordering errors.

BACKGROUND: The electronic health record (EHR) is a contributor to serious patient harm occurring within a sociotechnical system. Chemotherapy ordering is a high-risk task due to the complex nature of ordering workflows and potential detrimental effects if wrong chemotherapeutic doses are administered. Many chemotherapy ordering errors cannot be mitigated through systems-based changes due to the limited extent to which individual institutions are able to customize proprietary EHR software. We hypothesized that simulation-based training could improve providers' ability to identify and mitigate common chemotherapy ordering errors. METHODS: Pediatric hematology/oncology providers voluntarily participated in simulations using an EHR testing ("Playground") environment. The number of safety risks identified and mitigated by each provider at baseline was recorded. Risks were reviewed one-on-one after initial simulations and at a group "lunch-and-learn" session. At three-month follow-up, repeat simulations assessed for improvements in error identification and mitigation, and providers were surveyed about prevention of real-life safety events. RESULTS: The 8 participating providers identified and mitigated an average of 5.5 out of 10 safety risks during the initial simulation, compared 7.4 safety risks at the follow up simulation (p=0.030). Two of the providers (25%) reported preventing at least one real-world patient safety event in the clinical setting as a result of the initial training session. CONCLUSIONS: Simulation-based training may reduce providers' susceptibility to chemotherapy ordering safety vulnerabilities within the EHR. This approach may be used when systems-based EHR improvements are not feasible due to limited ability to customize local instances of proprietary EHR software.

Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders.

Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,300 images; 10 disorders) and SNU datasets (2,201 images; 134 disorders). The algorithm could accurately predict malignancy, suggest primary treatment options, render multi-class classification among 134 disorders, and improve the performance of medical professionals. The area under the curves for malignancy detection were 0.928 ± 0.002 (Edinburgh) and 0.937 ± 0.004 (SNU). The area under the curves of primary treatment suggestion (SNU) were 0.828 ± 0.012, 0.885 ± 0.006, 0.885 ± 0.006, and 0.918 ± 0.006 for steroids, antibiotics, antivirals, and antifungals, respectively. For multi-class classification, the mean top-1 and top-5 accuracies were 56.7 ± 1.6% and 92.0 ± 1.1% (Edinburgh) and 44.8 ± 1.2% and 78.1 ± 0.3% (SNU), respectively. With the assistance of our algorithm, the sensitivity and specificity of 47 clinicians (21 dermatologists and 26 dermatology residents) for malignancy prediction (SNU; 240 images) were improved by 12.1% (P < 0.0001) and 1.1% (P < 0.0001), respectively. The malignancy prediction sensitivity of 23 non-medical professionals was significantly increased by 83.8% (P < 0.0001). The top-1 and top-3 accuracies of four doctors in the multi-class classification of 134 diseases (SNU; 2,201 images) were increased by 7.0% (P = 0.045) and 10.1% (P = 0.0020), respectively. The results suggest that our algorithm may serve as augmented intelligence that can empower medical professionals in diagnostic dermatology.

An optimal interval type-2 fuzzy logic control based closed-loop drug administration to regulate the mean arterial blood pressure.

BACKGROUND AND OBJECTIVE: The main aim of this work is to present an optimal and robust controller design in order to improve the drug infusion to the automatic control of mean arterial blood pressure in conditions like critically-ill or post-operative or anaesthesia administration. The physiological systems also have uncertainty issues such as parameter variations with time or external disturbances and noise. Therefore, a controlled drug administration is necessary to regulate the mean arterial blood pressure of a person during surgery/observation. Over the years, the proportional-integral-derivative (PID) controller is the most commonly used controller in industries due to its easy structure and simplicity. However, this controller does not meet the desired performance with the complex and uncertain plants. Therefore, a robust controller is required to regulate the physiological variables that are uncertain in nature and can affect the human life. METHODS: In this work, a hybrid control scheme consisting of an interval type-2-fuzzy logic controller which acts as pre-compensator to the traditional PID controller is presented, to regulate the mean arterial blood pressure of a patient by administering the drug sodium nitroprusside in a controlled manner. An effective and well-established nature-inspired optimization technique namely cuckoo search algorithm is employed for obtaining the optimal parameters for the presented scheme. RESULTS: Simulation results are presented to show the effectiveness and robustness of proposed interval type-2-fuzzy logic controller based PID controller scheme, for maintaining the mean arterial pressure to 100 mmHg within considerable limit through SNP infusion. The results are further compared with other two controllers namely type-1 fuzzy logic based PID and traditional PID controllers for the parameter variations and external noise. CONCLUSION: In this study, the proposed interval type-2-fuzzy logic controller pre-compensator based PID controller provides an effective control than traditional type-1 fuzzy logic based control scheme and PID controller in terms of overshoot, settling-time and error which are the prime performance objectives of the closed-loop controlled drug delivery of human blood pressure. The presented study provides a firm base for initial design considerations for development of a low-cost closed-loop drug delivery system for blood pressure regulation.

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.

Optimal EPO dosing in hemodialysis patients using a non-linear model predictive control approach.

Anemia management with erythropoiesis stimulating agents is a challenging task in hemodialysis patients since their response to treatment varies highly. In general, it is difficult to achieve and maintain the predefined hemoglobin (Hgb) target levels in clinical practice. The aim of this study is to develop a fully personalizable controller scheme to stabilize Hgb levels within a narrow target window while keeping drug doses low to mitigate side effects. First in-silico results of this framework are presented in this paper. Based on a model of erythropoiesis we formulate a non-linear model predictive control (NMPC) algorithm for the individualized optimization of epoetin alfa (EPO) doses. Previous to this work, model parameters were estimated for individual patients using clinical data. The optimal control problem is formulated for a continuous drug administration. This is currently a hypothetical form of drug administration for EPO as it would require a programmable EPO pump similar to insulin pumps used to treat patients with diabetes mellitus. In each step of the NMPC method the open-loop problem is solved with a projected quasi-Newton method. The controller is successfully tested in-silico on several patient parameter sets. An appropriate control is feasible in the tested patients under the assumption that the controlled quantity is measured regularly and that continuous EPO administration is adjusted on a daily, weekly or monthly basis. Further, the controller satisfactorily handles the following challenging problems in simulations: bleedings, missed administrations and dosing errors.

Designing combination therapies with modeling chaperoned machine learning.

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.

Reduction of inappropriate medication in older populations by electronic decision support (the PRIMA-eDS project): a survey of general practitioners' experiences.

OBJECTIVE: We sought to investigate the experiences of general practitioners (GPs) with an electronic decision support tool to reduce inappropriate polypharmacy in older patients (the PRIMA-eDS [Polypharmacy in chronic diseases: Reduction of Inappropriate Medication and Adverse drug events in older populations by electronic Decision Support] tool) in a multinational sample of GPs and to quantify the findings from a prior qualitative study on the PRIMA-eDS-tool. MATERIALS AND METHODS: Alongside the cluster randomized controlled PRIMA-eDS trial, a survey was conducted in all 5 participating study centers (Bolzano, Italy; Manchester, United Kingdom; Salzburg, Austria; Rostock, Germany; and Witten, Germany) between October 2016 and July 2017. Data were analyzed using descriptive statistics and chi-square tests. RESULTS: Ninety-one (n = 160) percent of the 176 questionnaires were returned. Thirty-two percent of the respondents reported that they did not cease drugs because of the medication check. The 68% who had discontinued drugs comprise 57% who had stopped on average 1 drug and 11% who had stopped 2 drugs or more per patient. The PRIMA-eDS tool was found to be useful (69%) and the recommendations were found to help to increase awareness (86%). The greatest barrier to implementing deprescribing recommendations was the perceived necessity of the medication (69%). The majority of respondents (65%) would use the electronic medication check in routine practice if it was part of the electronic health record. CONCLUSIONS: GPs generally viewed the PRIMA-eDS medication check as useful and as informative. Recommendations were not always followed due to various reasons. Many GPs would use the medication check if integrated into the electronic health record.

Computer-Assisted Propofol Sedation for Esophagogastroduodenoscopy Is Effective, Efficient, and Safe.

BACKGROUND AND AIMS: Computer-assisted propofol sedation (CAPS) allows non-anesthesiologists to administer propofol for gastrointestinal procedures in relatively healthy patients. As the first US medical center to adopt CAPS technology for routine clinical use, we report our 1-year experience with CAPS for esophagogastroduodenoscopy (EGD). METHODS: Between September 2014 and August 2015, 926 outpatients underwent elective EGDs with CAPS at our center. All EGDs were performed by 1 of 17 gastroenterologists certified in the use of CAPS. Procedural success rates, procedure times, and recovery times were compared against corresponding historical controls done with midazolam and fentanyl sedation from September 2013 to August 2014. Adverse events in CAPS patients were recorded. RESULTS: The mean age of the CAPS cohort was 56.7 years (45% male); 16.2% of the EGDs were for variceal screening or Barrett's surveillance and 83.8% for symptoms. The procedural success rates were similar to that of historical controls (99.0% vs. 99.3%; p = 0.532); procedure times were also similar (6.6 vs. 7.4 min; p = 0.280), but recovery time was markedly shorter (31.7 vs. 52.4 min; p < 0.001). There were 11 (1.2%) cases of mild transient oxygen desaturation (< 90%), 15 (1.6%) cases of marked agitation due to undersedation, and 1 case of asymptomatic hypotension. In addition, there were six (0.6%) patients with more pronounced desaturation episodes that required brief (< 1 min) mask ventilation. There were no other serious adverse events. CONCLUSIONS: CAPS appears to be a safe, effective, and efficient means of providing sedation for EGD in healthy patients. Recovery times were much shorter than historical controls.

PET-adapted treatment for newly diagnosed advanced Hodgkin lymphoma (AHL2011): a randomised, multicentre, non-inferiority, phase 3 study.

BACKGROUND: Increased-dose bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone (BEACOPPescalated) improves progression-free survival in patients with advanced Hodgkin lymphoma compared with doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD), but is associated with increased risks of haematological toxicity, secondary myelodysplasia or leukaemia, and infertility. We investigated whether PET monitoring during treatment could allow dose de-escalation by switching regimen (BEACOPPescalated to ABVD) in early responders without loss of disease control compared with standard treatment without PET monitoring. METHODS: AHL2011 is a randomised, non-inferiority, phase 3 study done in 90 centres across Belgium and France. Eligible patients were aged 16-60 years and had newly diagnosed Hodgkin lymphoma, excluding nodular lymphocyte predominant subtype, an Eastern Cooperative Oncology Group performance status score less than 3, a life expectancy of at least 3 months, an Ann Arbor disease stage III, IV, or IIB with mediastinum-to-thorax ratio of 0·33 or greater than or extranodal localisation, and had received no previous treatment for Hodgkin lymphoma. Randomisation was unmasked and done centrally by the permuted block method. Patients were randomly assigned to standard treatment (BEACOPPescalated given every 21 days for six cycles) or PET-driven treatment. All patients received two cycles of upfront BEACOPPescalated, after which PET assessment was done (PET2). In the standard treatment group, PET2 patients completed two additional cycles of BEACOPPescalated induction therapy irrespective of PET2 findings. In the PET-driven treatment group, patients with positive PET2 scans received the further two cycles of BEACOPPescalated and those with a negative PET2 scan switched to two cycles of ABVD for the remaining induction therapy. In both treatment groups, PET at the end of induction therapy was used to decide whether to continue with consolidation therapy in those with negative scans or start salvage therapy in patients with positive scans (either two cycles of ABVD in PET2-negative patients in the PET-driven arm or two cycles of BEACOPPescalated). BEACOPPescalated consisted of bleomycin 10 mg/m2 and vincristine 1·4 mg/m2 intravenously on day 8, etoposide 200 mg/m2 intravenously on days 1-3, doxorubicin 35 mg/m2 and cyclophosphamide 1250 mg/m2 intravenously on day 1, 100 mg/m2 oral procarbazine on days 1-7, and 40 mg/m2 oral prednisone on days 1-14. ABVD was given every 28 days (doxorubicin 25 mg/m2, bleomycin 10 mg/m2, vinblastine 6 mg/m2, and dacarbazine 375 mg/m2 intravenously on days 1 and 15). The primary endpoint was investigator-assessed progression-free survival. Non-inferiority analyses were done by intention to treat and per protocol. The study had a non-inferiority margin of 10%, to show non-inferiority of PET-guided treatment versus standard care with 80% power and an alpha of 2·5% (one-sided). This study is registered with ClinicalTrials.gov, number NCT01358747. FINDINGS: From May 19, 2011, to April 29, 2014, 823 patients were enrolled-413 in the standard care group and 410 in the PET-driven group. 346 (84%) of 410 patients in the PET-driven treatment group were assigned to receive ABVD and 51 (12%) to continue receiving BEACOPPescalated after PET2. With a median follow-up of 50·4 months (IQR 42·9-59·3), 5-year progression-free survival by intention to treat was 86·2%, 95% CI 81·6-89·8 in the standard treatment group versus 85·7%, 81·4-89·1 in the PET-driven treatment group (hazard ratio [HR] 1·084, 95% CI 0·737-1·596; p=0·65) and per protocol the values were 86·7%, 95% CI 81·9-90·3 and 85·4%, 80·7-89·0, respectively (HR 1·144, 0·758-1·726; p=0·74). The most common grade 3-4 adverse events were leucopenia (381 [92%] in the standard treatment group and 387 [95%] in the PET-driven treatment group), neutropenia (359 [87%] and 366 [90%]), anaemia (286 [69%] vs 114 [28%]), thrombocytopenia (271 [66%] and 163 [40%]), febrile neutropenia (145 [35%] and 93 [23%]), infections (88 [22%] and 47 [11%]), and gastrointestinal disorders (49 [11%] and 48 [11%]). Serious adverse events related to treatment were reported in 192 (47%) patients in the standard treatment group and 114 (28%) in the PET-driven treatment group, including infections (84 [20%] of 412 vs 50 [12%] of 407) and febrile neutropenia (21 [5%] vs 23 [6%]). Six (1%) patients in the standard care group died from treatment-related causes (two from septic shock, two from pneumopathy, one from heart failure, and one from acute myeloblastic leukaemia), as did two (<1%) in the PET-driven treatment group (one from septic shock and one from acute myeloblastic leukaemia). INTERPRETATION: PET after two cycles of induction BEACOPPescalated chemotherapy safely guided treatment in patients with advanced Hodgkin lymphoma and allowed the use of ABVD in early responders without impairing disease control and reduced toxicities. PET staging allowed accurate monitoring of treatment in this trial and could be considered as a strategy for the routine management of patients with advanced Hodgkin lymphoma. FUNDING: Programme Hospitalier de Recherche Clinique.

Computerized hybrid decision-making system for hormone replacement therapy in menopausal women.

BACKGROUND: The diversity of the results of different hormone replacement therapy (HRT) protocols and the fuzziness of the conclusions have caused problems in routine clinical practice. OBJECTIVE: To develop an intelligent decision-making system for HRT specifically is appropriate as we use the abbrevation HRT in the background section in menopausal women in order to assist physicians. METHODS: This study consisted of 179 peri- and post-menopausal patients who were admitted to Hacettepe University Hospital (between 1996 and 2001) with various menopausal complaints. Database variables used in this study were age, height, weight, menopause duration, clinical condition, HRT duration, and the laboratory test results. Our newly developed multiple-centered fuzzy clustering (MCFC) algorithm was applied to the medical data set to differentiate patient groups. Finally, a hybrid intelligent decision-making system was developed by combining knowledge-based algorithms and the MCFC algorithm results. RESULTS: We have used Fuzzy C-means, K-means, Hard C-means, similarity based clustering, and MCFC algorithms on the medical data set and have determined that the MCFC algorithm is the most appropriate algorithm for our medical model. We have defined 5 clusters and 16 cluster centers. A diagnostic phrase was assigned to each cluster center by the physician and these clusters together with knowledge-based algorithms were used for the decision-making system. CONCLUSIONS: We have developed a computerized hybrid decision-making system recommending HRT to peri- and post-menopausal women in order to assist and protect physicians.

On medical application of neural networks trained with various types of data.

Neural networks have garnered attention over the past few years. A neural network is a typical model of machine learning that is used to identify visual patterns. Neural networks are used to solve a wide variety of problems, including image recognition problems and time series prediction problems. In addition, neural networks have been applied to medicine over the past few years. This paper classifies the ways in which neural networks have been applied to medicine based on the type of data used to train those networks. Applications of neural networks to medicine can be categorized two types: automated diagnosis and physician aids. Considering the number of patients per physician, neural networks could be used to diagnose diseases related to the vascular system, heart, brain, spinal column, head, neck, and tumors/cancer in three fields: vascular and interventional radiology, interventional cardiology, and neuroradiology. Lastly, this paper also considers areas of medicine where neural networks can be effectively applied in the future.

Dental anaesthesia for children - effects of a computer-controlled delivery system on pain and heart rate: a randomised clinical trial.

Local anaesthesia in dentistry is usually given by conventional injection through a syringe. In this randomised, single-blind, split-mouth clinical study we evaluated the perception of pain and changes in heart rate in children being given dental local anaesthesia using a computer-controlled device compared with that given using a traditional syringe. Participants were in good general health with no contraindications to local anaesthetics. One half of each maxilla was anaesthetised using each technique, the order having been randomly selected according to a computer-generated sequence. The hypothesis was that the controlled anaesthetic flow rate results in virtually imperceptible injections. The outcomes were the perception of pain and the heart rate. Seventy-six children aged from 5-12 years old participated in this study. The mean (SD) pain score of the conventional injection was 5.51 (2.46) and the mean (SD) heart rate was 2.72 (6.76), which were significantly higher than those of the computerised delivery system, which were 4.74 (2.8) and 0.34 (7.3) (p=0.04). More patients anaesthetised with the traditional syringe technique required a second injection (n=21). These results suggest that dental anaesthesia given to children with a computer-controlled delivery system reduced pain better than that given with a conventional syringe.

Individualized anemia management in a dialysis facility - long-term utility as a single-center quality improvement experience
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BACKGROUND: Standard protocol-based approaches to erythropoiesis stimulating agent (ESA) dosing in anemia management of end-stage renal disease (ESRD) fail to address the inter-individual variability in patient's response to ESA. We conducted a single-center quality improvement project to investigate the long-term performance of a computer-designed dosing system. MATERIALS AND METHODS: The study was a retrospective case-control study with long-term follow-up. All hemodialysis patients who received treatment at University Kidney Center (Louisville, KY, USA) between September 1, 2009, and March 31, 2017, were included. We implemented an individualized ESA dosing algorithm into an electronic health records database software to provide patient-specific ESA dose recommendations to anemia managers at monthly intervals. The primary outcome was the percentage of hemoglobin (Hb) concentrations between 10 and 12 g/dL during the case-control study and 9 and 11 g/dL during follow-up. Secondary outcomes were intra- and inter-individual Hb variability. For the case-control study, we compared outcomes over 12 months before and after implementation of the algorithm. Subjects served as their own controls. We used the last Hb concentration of the month and ESA dose per week. Long-term follow-up examined trends in proportion within the target range, Hb, and ESA dose. RESULTS: Individualized ESA dosing in 56 subjects was associated with a moderate (6.6%) increase of mean Hb maintenance within target over the 12-month observation period (62.7% before vs. 69.3% after, p = 0.063). Intra-individual mean Hb variability decreased (1.1 g/dL before vs. 0.8 g/dL after, p < 0.001), so did inter-individual mean Hb variability (1.2 g/dL before vs. 1.0 g/dL after, p = 0.010). Long-term follow-up in 233 subjects for 42 months demonstrated stability of the achieved Hb despite an increasing ESA resistance in the patient population. CONCLUSION: Implementation of the individualized ESA dosing algorithm facilitates improvement in Hb maintenance within target, decreases Hb variability and reduces the dose of ESA required to achieve Hb target.
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Image-guided chemotherapy with specifically tuned blood brain barrier permeability in glioma margins.

Blood-brain barrier (BBB) disruption is frequently observed in the glioma region. However, the tumor uptake of drugs is still too low to meet the threshold of therapeutic purpose. Method: A tumor vasculature-targeted nanoagonist was developed. Glioma targeting specificity of the nanoagonist was evaluated by in vivo optical imaging. BBB permeability at the glioma margin was quantitatively measured by dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Single-photon emission computed tomography imaging/computed tomography (SPECT/CT) quantitatively determined the glioma uptake of the radiolabeled model drug. T2-weighted MRI monitored the tumor volume. Results: Immunostaining studies demonstrated that the BBB remained partially intact in the invasive margin of patients' gliomas regardless of their malignancies. DCE-MRI showed that vascular permeability in the glioma margin reached its maximum at 45 min post nanoagonist administration. In vivo optical imaging indicated the high glioma targeting specificity of the nanoagonist. SPECT/CT showed the significantly enhanced glioma uptake of the model drug after pre-treatment with the nanoagonist. Image-guided paclitaxel injection after nanoagonist-mediated BBB modulation more efficiently attenuated tumor growth and extended survival than in animal models treated with paclitaxel or temozolomide alone. Conclusion: Thus, image-guided drug delivery following BBB permeability modulation holds promise to enhance the efficacy of chemotherapeutics to glioma.

Polimedication: applicability of a computer tool to reduce polypharmacy in nursing homes.

ABSTRACTBackground:The risks of polypharmacy can be far greater than the benefits, especially in the elderly. Comorbidity makes polypharmacy very prevalent in this population; thus, increasing the occurrence of adverse effects. To solve this problem, the most common strategy is to use lists of potentially inappropriate medications. However, this strategy is time consuming. METHODS: In order to minimize the expenditure of time, our group devised a pilot computer tool (Polimedication) that automatically processes lists of medication providing the corresponding Screening Tool of Older Persons' potentially inappropriate Prescriptions alerts and facilitating standardized reports. The drug lists for 115 residents in Santa Marta Nursing Home (Fundación San Rosendo, Ourense, Spain) were processed. RESULTS: The program detected 10.04 alerts/patient, of which 74.29% were not repeated. After reviewing these alerts, 12.12% of the total (1.30 alerts/patient) were considered relevant. The largest number of alerts (41.48%) involved neuroleptic drugs. Finally, the patient's family physician or psychiatrist accepted the alert and made medication changes in 62.86% of the relevant alerts. The largest number of changes (38.64%) also involved neuroleptic drugs. The mean time spent in the generation and review of the warnings was 6.26 minute/patient. Total changes represented a saving of 32.77 € per resident/year in medication. CONCLUSIONS: The application of Polimedication tool detected a high proportion of potentially inappropriate prescriptions in institutionalized elderly patients. The use of the computerized tool achieved significant savings in pharmaceutical expenditure, as well as a reduction in the time taken for medication review.

Assessment of singlet oxygen dosimetry concepts in photodynamic therapy through computational modeling.

BACKGROUND: In photodynamic therapy (PDT) oxygen plays a vital role in killing tumor cells. Therefore oxygen dosimetry is being thoroughly studied. METHODS: Light distribution into tissue is modelled for radiation-induced fibrosarcoma (RIF) and nodular basal cell carcinoma (nBCC), in order to study the influence of blood flow on singlet oxygen concentration effectively leading to cell death ([1O2]rx) from PDT, within this light distribution. This is achieved through initial oxygen supply rate (g0) and initial molecular oxygen concentration ([3O2]0) calculations. Monte Carlo simulations and mathematical models are used for spatial and temporal distributions of [1O2]rx. Hypoxia conditions are simulated by minimizing [3O2]0 and g0. Furthermore, an optimization algorithm is developed to calculate minimum initial molecular oxygen concentration needed ([3O2]0,min) for constant [1O2]rx, when blood flow changes. RESULTS: Our results validate that in initially well-oxygenated scenarios with normal blood flow maximum [1O2]rx values are significantly higher than corresponding values of hypoxic scenarios both for RIF and nBCC models, with maximum oxygen supply rate percentage variations being independent from g0. Moreover, [1O2]rx appears to be more affected by an increase of g0 than of [3O2]0 values. For low blood flow there is a linear relationship between [3O2]0,min and g0, while for better oxygenated areas high blood flow reduces [3O2]0,min needed in exponential manner. CONCLUSIONS: Blood flow appears to be able to compensate for oxygen consumption. The developed optimization protocol on oxygen dosimetry offers a suitable combination of [3O2]0,min and g0 to achieve constant [1O2]rx, despite possible blood flow variations.

A Robust Optimization Approach to Cancer Treatment under Toxicity Uncertainty.

The design of optimal protocols plays an important role in cancer treatment. However, in clinical applications, the outcomes under the optimal protocols are sensitive to variations of parameter settings such as drug effects and the attributes of age, weight, and health conditions in human subjects. One approach to overcoming this challenge is to formulate the problem of finding an optimal treatment protocol as a robust optimization problem (ROP) that takes parameter uncertainty into account. In this chapter, we describe a method to model toxicity uncertainty. We then apply a mixed integer ROP to derive the optimal protocols that minimize the cumulative tumor size. While our method may be applied to other cancers, in this work we focus on the treatment of chronic myeloid leukemia (CML) with tyrosine kinase inhibitors (TKI). For simplicity, we focus on one particular mode of toxicity arising from TKI therapy, low blood cell counts, in particular low absolute neutrophil count (ANC). We develop optimization methods for locating optimal treatment protocols assuming that the rate of decrease of ANC varies within a given interval. We further investigated the relationship between parameter uncertainty and optimal protocols. Our results suggest that the dosing schedule can significantly reduce tumor size without recurrence in 360 weeks while insuring that toxicity constraints are satisfied for all realizations of uncertain parameters.

USE OF FEMORAL ARTERY ULTRASOUND DURING INTRAARTERIAL CHEMOTHERAPY FOR CHILDREN UNDER 10 KG WITH RETINOBLASTOMA.

PURPOSE: To demonstrate the safety and efficacy of intraarterial chemotherapy (IAC) in small infants (<10 kg) with retinoblastoma. METHODS: Retrospective, consecutive, observational case series of patients treated with IAC. Femoral arterial access was obtained using a micropuncture kit and ultrasound guidance, which enabled direct visualization. Melphalan (1.5-5.0 mg), topotecan (0.3-2.0 mg), and/or carboplatin (30-40 mg) were used. Patients underwent adjuvant therapies including laser, cryotherapy, and intravitreal melphalan if persistent disease or recurrence was observed. RESULTS: Fifty-nine injections were administered to 11 eyes of 6 patients. All eyes but one were classified as International Classification Groups C or D. Median patient weight at first IAC cycle was 9.2 kg (mean, 8.9 kg). Median diameter of the femoral artery at the catheterization site was 3.74 mm, measured by two independent observers. Median follow-up was 21.4 months (range 13.1-34.5 months). All eyes were salvaged. CONCLUSION: This study confirmed the safety and efficacy of IAC in infants under 10 kg. Ultrasound guidance enabled successful catheterization of femoral arteries as small as 2.7 mm in diameter. Patients in this study appeared to require fewer injections and lower total doses of chemotherapy compared with previously reported series of comparably advanced disease in larger infants.

Dacryoendoscopy-guided transcanalicular intralesional interferon alpha 2b for canalicular squamous papillomas.

Canalicular papillomatosis is a rare disorder characterized by a mass lesion arising from the epithelium as a stalk from one of the canalicular walls. Traditionally, they have been treated with an open canaliculotomy and excision biopsy with or without additional cryotherapy. A patient with upper canalicular squamous papillomas treated with dacryoendoscopy-guided transcanalicular intralesional and topical interferon alpha 2b is presented, and the ineffectiveness of interferons in this case is discussed.