Positive Alerts Research Articles

Analysis of medication adherence-related notes from a service-oriented community pharmacy.

Medication nonadherence is a significant public health problem. Community pharmacists are positioned to intervene, however, the process is not well understood. To classify and quantify the reasons for nonadherence documented by community pharmacists. A retrospective content analysis of pharmacist notes related to nonadherence at a service oriented community pharmacy in the Midwest United States. Notes from the site's dispensing custom documentation software were obtained from September 1, 2014 through February 28, 2015 that were labeled "compliance", either prompted by proportion of days covered calculations or entered as a drug therapy problem. A code list was iterated for the notes based on the literature and by reading the notes and generating descriptive codes. A reliability analysis was calculated for two coders. Notes were coded, check-coded, and discrepancies were resolved using a consensus process. Frequencies were calculated for each code and representative text was selected. Pharmacists documented 3491 notes as part of their continuous medication monitoring process. Nineteen codes were developed. The reliability for the coders had a Cohen's Kappa of 0.749. The majority of notes (61.4%) documented the pharmacist evaluated the refill and had no concerns or would continue to follow. Also documented were specific reasons for out of range PDCs not indicative of a nonadherence problem. Only 2.2% of notes specifically documented a nonadherence problem, such as forgetfulness or cost. While pharmacists encountered many false positive nonadherence alerts, following up with patients led to hundreds of discussions and clarifications about how patients use their medications at home. These results suggest a small minority of late refills are judged by pharmacists as indicative of an adherence problem, contrary to the prevailing literature. Pharmacists may benefit from modifying their approach to nonadherence interviewing and documentation as they seek to address nonadherence in practice. This study descriptively coded community pharmacist notes related to medication nonadherence. Most notes were prompted by automatic calculations by the documentation software of the proportion of days covered. This prompted regular conversations about non-adherence issues and clarifications on how medications are being used at home. Documentation suggested most adherence problems were technical in nature rather than stemming from patient factors like medication beliefs. Pharmacists could be more intentional and descriptive when documenting nonadherence issues so that more targeted follow up can be made, including a greater investigation of medication beliefs.

Improving Recognition of Pediatric Severe Sepsis in the Emergency Department: Contributions of a Vital Sign–Based Electronic Alert and Bedside Clinician Identification

Recognition of pediatric sepsis is a key clinical challenge. We evaluate the performance of a sepsisrecognition process including an electronic sepsis alert and bedside assessment in a pediatric emergency department (ED). This was a cohort study with quality improvement intervention in a pediatric ED. Exposure was a positive electronic sepsis alert, defined as elevated pulse rate or hypotension, concern for infection, and at least one of the following: abnormal capillary refill, abnormal mental status, or high-risk condition. A positive electronic sepsis alert prompted team assessment or huddle to determine need for sepsis protocol. Clinicians could initiate team assessment or huddle according to clinical concern without positive electronic sepsis alert. Severe sepsis outcome defined as activation of the sepsis protocol in the ED or development of severe sepsis requiring ICU admission within 24 hours. There were 182,509 ED visits during the study period, with 86,037 before electronic sepsis alert implementation and 96,472 afterward, and 1,112 (1.2%) positive electronic sepsis alerts. Overall, 326 patients (0.3%) were treated for severe sepsis within 24 hours. Test characteristics of the electronic sepsis alert alone to detect severe sepsis were sensitivity 86.2% (95% confidence interval [CI] 82.0% to 89.5%), specificity 99.1% (95% CI 99.0% to 99.2%), positive predictive value 25.4% (95% CI 22.8% to 28.0%), and negative predictive value 100% (95% CI 99.9% to 100%). Inclusion of the clinician screen identified 43 additional electronic sepsis alert-negative children, with severe sepsis sensitivity 99.4% (95% CI 97.8% to 99.8%) and specificity 99.1% (95% CI 99.1% to 99.2%). Electronic sepsis alert implementation increased ED sepsis detection from 83% to 96%. Electronic sepsis alert for severe sepsis demonstrated good sensitivity and high specificity. Addition of clinician identification of electronic sepsis alert-negative patients further improved sensitivity. Implementation of the electronic sepsis alert was associated with improved recognition of severe sepsis.

Real-time surveillance and response system for Ebola and other emerging infections

ObjectiveWe will describe a real-time mobile surveillance and casemanagement system designed to organize data collected bymultiple officers about cases and their contacts. We will discuss thissurveillance system and its application for Ebola and other infectiousdiseases in the Democratic Republic of the Congo (DRC) and othersimilar settings. We will review the technology, results, challenges,lessons-learned, and applicability to other contexts.IntroductionImproving surveillance and response is a critical component ofthe Global Health Security Agenda. While it is impossible to predictwhere the next Ebola outbreak will occur, it is very likely that anotheroutbreak will occur in the DRC. Of the 20 known outbreaks, 7 haveoccurred in the DRC, one as recently as 2014. To rapidly detectand respond to an Ebola outbreak, we sought to develop a real-timesurveillance and response system for use in DRC and similar settings.RTI International developed Coconut Surveillance mobile software,which is currently used for real-time malaria surveillance andresponse in Zanzibar, Africa, where malaria elimination efforts areunderway. We took this system and adapted it for Ebola as a possibletool for surveillance and response to Ebola and other (re)emergingdiseases. Plans include pilot testing functionality at clinical sites inDRC, where surveillance infrastructure is limited at the local level.Coconut Surveillance is a mobile disease surveillance and rapidresponse system currently used for malaria elimination activities.It receives suspected positive case alerts from the field via mobilephones and uses mobile software to guide surveillance officersthrough a follow-up process. Coconut Surveillance runs on Androidmobile devices that are used to coordinate work in the field as well asprovide decision support during data collection and case management.In addition to standard case information, the GPS coordinates ofthe case’s household are captured as well as malaria status of allhousehold members. Data are collected and accessed off-line, and aresynchronized with a shared database when Internet connectivity isavailable. This tool has been used successfully in Zanzibar for morethan three years and has been recognized as one of the most advancedapplications of its kind.MethodsWe adapted the Coconut Surveillance system for Ebolasurveillance and response, and expanded the system for use with othercommunicable diseases. With a near real-time outbreak detectionsystem for Ebola, we may reduce the response time and contain anoutbreak faster. Using a cloud-based data repository, the modifiedCoconut System, known as Coconut Plus, also has the added value ofcase and case-contacts specific information sharing in real-time withthe national, provincial, and district level public health authorities,who would have convenient and secure access to case and contactinformation via the Internet. The software modifications to theCoconut System have been informed by testing and stakeholderfeedback.ResultsWe have developed Coconut Plus around the Coconut softwarearchitecture, which allows the team to quickly develop specificworkflows and applications, such as contact tracing, on top of a solidand well-supported base. Additionally, the adaptation was structuredto accommodate the build-out of multiple diseases, and is uniquelyhelpful for diseases that require tracking many contacts. We weregranted access in DRC to test interoperability with DHIS 2, the mostwidely used health information system software in Ebola effectedcountries. Coconut Plus is now using the DHIS 2 organizationalhierarchy definition, which means that organizational hierarchy(including information on administrative units and health carefacilities) can be exported directly from DHIS 2 to Coconut Plus.Stakeholder feedback on the usability and feasibility of the adaptedsystem has been enthusiastic, and stressed the need for additionalresources to make a pilot successful, including mobile phones andimproved mobility of surveillance staff in the field. The followingscreencast provides an overview of the application: https://www.youtube.com/watch?v=jjLT3pLLW-UConclusionsCoconut Surveillance Plus solves an absence of a real-time mobiledecision support disease surveillance and response system that can beused for Ebola and other infectious diseases in countries with limitedsurveillance infrastructure. More broadly, this system could also beused for many communicable diseases that require contact tracing andan urgent outbreak response in environments that require rapid scaleup of a distributed surveillance, rapid response, and case managementsystem.

Automation bias in electronic prescribing

BackgroundClinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This study tests for the presence of AB in e-prescribing and the impact of task complexity and interruptions on AB.MethodsOne hundred and twenty students in the final two years of a medical degree prescribed medicines for nine clinical scenarios using a simulated e-prescribing system. Quality of CDS (correct, incorrect and no CDS) and task complexity (low, low + interruption and high) were varied between conditions. Omission errors (failure to detect prescribing errors) and commission errors (acceptance of false positive alerts) were measured.ResultsCompared to scenarios with no CDS, correct CDS reduced omission errors by 38.3% (p < .0001, n = 120), 46.6% (p < .0001, n = 70), and 39.2% (p < .0001, n = 120) for low, low + interrupt and high complexity scenarios respectively. Incorrect CDS increased omission errors by 33.3% (p < .0001, n = 120), 24.5% (p < .009, n = 82), and 26.7% (p < .0001, n = 120). Participants made commission errors, 65.8% (p < .0001, n = 120), 53.5% (p < .0001, n = 82), and 51.7% (p < .0001, n = 120). Task complexity and interruptions had no impact on AB.ConclusionsThis study found evidence of AB omission and commission errors in e-prescribing. Verification of CDS alerts is key to avoiding AB errors. However, interventions focused on this have had limited success to date. Clinicians should remain vigilant to the risks of CDS failures and verify CDS.

Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures.

Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS.

Transcranial Motor Evoked Potentials during Spinal Deformity Corrections-Safety, Efficacy, Limitations, and the Role of a Checklist.

Intraoperative neuromonitoring (IONM) has become a standard of care in spinal deformity surgeries to minimize the incidence of new onset neurological deficit. Stagnara wake up test and ankle clonus test are the oldest techniques described for spinal cord monitoring, but they cannot be solely relied upon as a neuromonitoring modality. Somatosensory evoked potentials monitor only dorsal tracts and give high false positive and negative alerts. Transcranial motor evoked potentials (TcMEPs) monitor the more useful motor pathways. The purpose of our study was to report the safety, efficacy, limitations of TcMEPs in spine deformity surgeries, and the role of a checklist. Retrospective review of all spinal deformity surgeries performed with TcMEPs from 2011 to 2015. All patients were subjected to IONM by TcMEPs during the spinal deformity surgery. Patients were included in the study only if complete operative reports and neuromonitoring data and postoperative neurological data were available for review. An alert was defined as 80% or more decrement in the motor evoked potential amplitude, or increase in threshold of 100 V or more from baseline. The systemic and surgical causes of IONM alerts and the postoperative neurological status were recorded. In total, 61 patients underwent surgery for spinal deformities with TcMEPs. The average age was 12.6 years (6-36 years) and male:female ratio was 1:1.3. Diagnoses included idiopathic scoliosis (n = 35), congenital scoliosis (n = 13), congenital kyphosis (n = 7), congenital kyphoscoliosis (n = 4), post-infectious kyphosis (n = 1), and post-traumatic kyphosis (n = 1). The average kyphosis was 72° (45°-101°) and the average scoliosis was 84° (62°-128°). There were in total 33 alerts in 22 patients (36%). The most common causes were hypotension (n = 7), drug induced (n = 5), deformity correction (n = 5), osteotomies (n = 3), tachycardia (n = 1), screw placement (n = 2), and electrodes disconnection (n = 1). Reversal of the inciting event cause resulted in complete reversal of the alert in 90% of the times. Three patients showed persistent alerts, out of whom one had a positive wake up test and woke up with neurodeficit, which recovered over few weeks, while the other patients showed persistent alerts but woke up without any deficit. Sensitivity and specificity of TcMEP in deformity correction surgery were 100 and 96.6%, respectively, in our study. IONM alerts are frequent during spinal deformity surgery. In our study, more than 50% of the alerts were associated with anesthetic management. IONM with TcMEPs is a safe and effective monitoring technique and wake up test still remains a valuable tool in cases of a persistent alert.

Towards a system for complex analysis of security events in large-scale networks

After almost two decades of development, modern Security Information and Event Management (SIEM) systems still face issues with normalisation of heterogeneous data sources, high number of false positive alerts and long analysis times, especially in large-scale networks with high volumes of security events. In this paper, we present our own prototype of SIEM system, which is capable of dealing with these issues. For efficient data processing, our system employs in-memory data storage (SAP HANA) and our own technologies from the previous work, such as the Object Log Format (OLF) and high-speed event normalisation. We analyse normalised data using a combination of three different approaches for security analysis: misuse detection, query-based analytics, and anomaly detection. Compared to the previous work, we have significantly improved our unsupervised anomaly detection algorithms. Most importantly, we have developed a novel hybrid outlier detection algorithm that returns ranked clusters of anomalies. It lets an operator of a SIEM system to concentrate on the several top-ranked anomalies, instead of digging through an unsorted bundle of suspicious events. We propose to use anomaly detection in a combination with signatures and queries, applied on the same data, rather than as a full replacement for misuse detection. In this case, the majority of attacks will be captured with misuse detection, whereas anomaly detection will highlight previously unknown behaviour or attacks. We also propose that only the most suspicious event clusters need to be checked by an operator, whereas other anomalies, including false positive alerts, do not need to be explicitly checked if they have a lower ranking. We have proved our concepts and algorithms on a dataset of 160 million events from a network segment of a big multinational company and suggest that our approach and methods are highly relevant for modern SIEM systems.

PREDICTION OF HUMAN BEHAVIOR BY MEANS OF THE APPLICATION OF FUZZY REASONING IN SECURITY VIDEOS

Ponte Academic JournalMar 2017, Volume 73, Issue 3 PREDICTION OF HUMAN BEHAVIOR BY MEANS OF THE APPLICATION OF FUZZY REASONING IN SECURITY VIDEOSAuthor(s): Susana A. Arias T. ,Caroline Galarza G, Judith Nuñez-R, Carlos Melendez T, Cristian Fernando Galarza, Elsa Hernandez, Roberto Jacome GalarzaJ. Ponte - Mar 2017 - Volume 73 - Issue 3 doi: 10.21506/j.ponte.2017.3.5 Abstract:Today, working with human behavior is vitally important, especially if we consider the impact neuroscience and security systems. We propose the idea to discover the alarm behavior in different contexts. We center our study on movement expression and writing as they are depositories of positive and negative alerts values from fuzzy and Bayesian algorithms. We show examples of how easily and reuse is possible, however we are working on improving our framework, in order to make it much more manageable for the fuzzy use. Conclusions and future work of our research were generated to visualize what the new tools to be developed Download full text:Check if you have access through your login credentials or your institution Username Password

Clinical Practice: Using a Best Evidence Sepsis Scoring Tool to Identify and Manage Pediatric Patients With Severe Sepsis in the Emergency Department

Severe sepsis and septic shock are leading causes of pediatric morbidity and mortality, resulting in prolonged hospitalization and increased healthcare costs. 1,2 Delays in recognition of sepsis, vascular access, and administration of fluids and antibiotics are major barriers within pediatric emergency departments (ED). 3,4 Severe sepsis is defined as symptoms suspicious of infection plus signs of organ dysfunction or tissue hypoperfusion. 5 A sepsis trigger tool at triage can identify vital sign abnormalities of severe sepsis, alert ED resources, and rapidly begin the sepsis protocol. 3 Annually, almost 100,000 pediatric patients present to the ED with signs of severe sepsis. 6. Using the concept of “PIRO” (predisposition, infection, response, and organ dysfunction), the sepsis tool was adapted to identify pediatric patients at risk for sepsis with signs of infection, age-related abnormal vital signs, and signs of organ dysfunction. With 5 or greater score (maximum score of 16), a “sepsis alert” was paged. A multidisciplinary team was mobilized: ED nurse, ED paramedic, physician, respiratory therapist, and child life specialist. Using a nurse-initiated pathway, patient was placed on cardiac apnea monitor, pulse oximeter, and oxygen, vital sign monitoring was begun, intravenous (IV) line insertion with lab work was obtained, and weight-based IV fluid bolus was initiated with antibiotics anticipated. Sepsis scores were repeated after interventions or with status changes. An ED sepsis committee was formed to audit charts and educate staff on the sepsis tool. From January 2014 through April 2015, median times for triage-to-IV fluid bolus improved from 65 to 51 min and triage-to-antibiotic times improved from 137 to 80 min. With early recognition and treatment of sepsis, ED experienced improved patient mortality rates, shorter hospital stays, and decreased hospital costs. The successes of multidisciplinary interventions, effective communication, increased awareness, and staff compliance have led to decreases in triage-to-bolus and triage-to-antibiotic times. The tool was accurate in identifying severe sepsis; the admission rate for positive sepsis alerts was 60%. View a PDF of this poster presentation.

Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department.

BackgroundSepsis is an often-fatal syndrome resulting from severe infection. Rapid identification and treatment are critical for septic patients. We therefore developed a probabilistic model to identify septic patients in the emergency department (ED). We aimed to produce a model that identifies 80 % of sepsis patients, with no more than 15 false positive alerts per day, within one hour of ED admission, using routine clinical data.MethodsWe developed the model using retrospective data for 132,748 ED encounters (549 septic), with manual chart review to confirm cases of severe sepsis or septic shock from January 2006 through December 2008. A naïve Bayes model was used to select model features, starting with clinician-proposed candidate variables, which were then used to calculate the probability of sepsis. We evaluated the accuracy of the resulting model in 93,733 ED encounters from April 2009 through June 2010.ResultsThe final model included mean blood pressure, temperature, age, heart rate, and white blood cell count. The area under the receiver operating characteristic curve (AUC) for the continuous predictor model was 0.953. The binary alert achieved 76.4 % sensitivity with a false positive rate of 4.7 %.ConclusionsWe developed and validated a probabilistic model to identify sepsis early in an ED encounter. Despite changes in process, organizational focus, and the H1N1 influenza pandemic, our model performed adequately in our validation cohort, suggesting that it will be generalizable.

How Regrouping Alerts in Computerized Physician Order Entry Layout Influences Physicians' Prescription Behavior: Results of a Crossover Randomized Trial.

BackgroundAs demonstrated in several publications, low positive predictive value alerts in computerized physician order entry (CPOE) induce fatigue and may interrupt physicians unnecessarily during prescription of medication. Although it is difficult to increase the consideration of medical alerts by physician through an improvement of their predictive value, another approach consists to act on the way they are presented. The interruption management model inspired us to propose an alternative alert display strategy of regrouping the alerts in the screen layout, as a possible solution for reducing the interruption in physicians’ workflow.ObjectiveIn this study, we compared 2 CPOE designs based on a particular alert presentation strategy: one design involved regrouping the alerts in a single place on the screen, and in the other, the alerts were located next to the triggering information. Our objective was to evaluate experimentally whether the new design led to fewer interruptions in workflow and if it affected alert handling.MethodsThe 2 CPOE designs were compared in a controlled crossover randomized trial. All interactions with the system and eye movements were stored for quantitative analysis.ResultsThe study involved a group of 22 users consisting of physicians and medical students who solved medical scenarios containing prescription tasks. Scenario completion time was shorter when the alerts were regrouped (mean 117.29 seconds, SD 36.68) than when disseminated on the screen (mean 145.58 seconds, SD 75.07; P=.045). Eye tracking revealed that physicians fixated longer on alerts in the classic design (mean 119.71 seconds, SD 76.77) than in the centralized alert design (mean 70.58 seconds, SD 33.53; P=.001). Visual switches between prescription and alert areas, indicating interruption, were reduced with centralized alerts (mean 41.29, SD 21.26) compared with the classic design (mean 57.81, SD 35.97; P=.04). Prescription behavior (ie, prescription changes after alerting), however, did not change significantly between the 2 strategies of display. The After-Scenario Questionnaire (ASQ) that was filled out after each scenario showed that overall satisfaction was significantly rated lower when alerts were regrouped (mean 4.37, SD 1.23) than when displayed next to the triggering information (mean 5.32, SD 0.94; P=.02).ConclusionsCentralization of alerts in a table might be a way to motivate physicians to manage alerts more actively, in a meaningful way, rather than just being interrupted by them. Our study could not provide clear recommendations yet, but provides objective data through a cognitive psychological approach. Future tests should work on standardized scenarios that would enable to not only measure physicians’ behavior (visual fixations and handling of alerts) but also validate those actions using clinical criteria.

Do false positive alerts in naïve clinical decision support system lead to false adoption by physicians? A randomized controlled trial

ObjectivesFalse positive alerts in patient-safety-related clinical decision support systems (CDSS) are defined as alerts which incorrectly prompt when no-risk patients are encountered. It is an unfavorable condition which may potentially mislead physicians. The aim is to investigate physician responses toward false positive (FP) and true positive (TP) alerts in CDSS for the prevention of contrast-induced nephropathy (CIN). MethodsA two-arm cluster randomized controlled trial was conducted in university hospitals. Eligible physicians were randomized to receive alert intervention or no intervention (groups 1 and 2, respectively). The alert system was embedded with a deliberately non-specific risk detection tool in order to generate TP and FP alerts. The naïve alert system would alert the physician to cancel the order regardless of the patient being at-risk or not at-risk. CIN risk was stratified as at-risk and no-risk according to a patient's pre-existing renal function. Contrast imaging order-cancellation rate was measured as primary outcome. Results3802 contrast-enhanced examination orders from 66 physicians were analyzed. Demographic data and risk distributions of patients were similar and well-balanced between two groups. In the intervention group, a total of 1892 alerts were generated (332 TP alerts and 1560 FP alerts). Order-cancellation rates were 5.1% versus 1.4% in groups 1 and 2 for at-risk patients (relative risk [RR] = 3.69) from TP alerts, and 1.0% versus 1.4% for no-risk patients (RR = 0.71) from FP alerts. Using generalized linear model with generalized estimating equation, the FP alerts had no order-cancellation effect when compared to the control arm (adjusted RR = 0.69; 95%CI, 0.36–1.32). The TP alerts had a larger order-cancellation effect than that of the control arm (adjusted RR = 2.95; 95%CI, 0.94–9.27), which revealed a marginal trend toward significance. However, the effect was not statistically significant (adjusted RR = 1.24; 95%CI, 0.71–2.18) if TP and FP alerts were mixed. ConclusionsPhysicians are not likely to adopt recommendations provided by false positive alerts in patient-safety-related CDSS. If reporting only the adoption rate of CDSS as a whole without differentiating between TP and FP alerts, the effects of TP and FP alerts will be mixed, and thus, will lead to an underestimation of system effectiveness.

Environmental fate and effect assessment of thioridazine and its transformation products formed by photodegradation

An experimental and in silico quantitative structure-activity relationship (QSAR) approach was applied to assess the environmental fate and effects of the antipsychotic drug Thioridazine (THI). The sunlight-driven attenuation of THI was simulated using a Xenon arc lamp. The photodegradation reached the complete primary elimination, whereas 97% of primary elimination and 11% of mineralization was achieved after 256 min of irradiation for the initial concentrations of 500 μg L−1 and 50 mg L−1, respectively. A non-target approach for the identification and monitoring of transformation products (TPs) was adopted. The structure of the TPs was further elucidated using liquid chromatography–high resolution mass spectrometry (LC–HRMS). The proposed photodegradation pathway included sulfoxidation, hydroxylation, dehydroxylation, and S- and N-dealkylation, taking into account direct and indirect photolysis through a self-sensitizing process in the higher concentration studied. The biodegradability of THI and photolytic samples of THI was tested according to OECD 301D and 301F, showing that THI and the mixture of TPs were not readily biodegradable. Furthermore, THI was shown to be highly toxic to environmental bacteria using a modified luminescent bacteria test with Vibrio fischeri. This bacteriotoxic activity of THI was significantly reduced by phototransformation and individual concentration-response analysis confirmed a lowered bacterial toxicity for the sulfoxidation products Thioridazine-2-sulfoxide and Thioridazine-5-sulfoxide. Additionally, the applied QSAR models predicted statistical and rule-based positive alerts of mutagenic activities for carbazole derivative TPs (TP 355 and TP 339) formed through sulfoxide elimination, which would require further confirmatory in vitro validation tests.

Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications.

In 2014/2015, Public Health Ontario developed disease-specific, cumulative sum (CUSUM)-based statistical algorithms for detecting aberrant increases in reportable infectious disease incidence in Ontario. The objective of this study was to determine whether the prospective application of these CUSUM algorithms, based on historical patterns, have improved specificity and sensitivity compared to the currently used Early Aberration Reporting System (EARS) algorithm, developed by the US Centers for Disease Control and Prevention. A total of seven algorithms were developed for the following diseases: cyclosporiasis, giardiasis, influenza (one each for type A and type B), mumps, pertussis, invasive pneumococcal disease. Historical data were used as baseline to assess known outbreaks. Regression models were used to model seasonality and CUSUM was applied to the difference between observed and expected counts. An interactive web application was developed allowing program staff to directly interact with data and tune the parameters of CUSUM algorithms using their expertise on the epidemiology of each disease. Using these parameters, a CUSUM detection system was applied prospectively and the results were compared to the outputs generated by EARS. The outcome was the detection of outbreaks, or the start of a known seasonal increase and predicting the peak in activity. The CUSUM algorithms detected provincial outbreaks earlier than the EARS algorithm, identified the start of the influenza season in advance of traditional methods, and had fewer false positive alerts. Additionally, having staff involved in the creation of the algorithms improved their understanding of the algorithms and improved use in practice. Using interactive web-based technology to tune CUSUM improved the sensitivity and specificity of detection algorithms.

Degradation of cyclophosphamide and 5-fluorouracil by UV and simulated sunlight treatments: Assessment of the enhancement of the biodegradability and toxicity

The presence of pharmaceuticals in the environment has triggered concern among the general population and received considerable attention from the scientific community in recent years. However, only a few publications have focused on anticancer drugs, a class of pharmaceuticals that can exhibit cytotoxic, genotoxic, mutagenic, carcinogenic and teratogenic effects. The present study investigated the photodegradation, biodegradation, bacterial toxicity, mutagenicity and genotoxicity of cyclophosphamide (CP) and 5-fluorouracil (5-FU). The photodegradation experiments were performed at a neutral to slight pH range (7–7.8) using two different lamps (medium-pressure mercury lamp and a xenon lamp). The primary elimination of the parent compounds was monitored by means of liquid chromatography tandem mass spectrometry (LC-IT-MS/MS). NPOC (non-purgeable organic carbon) analyses were carried out in order to assess mineralization rates. The Closed Bottle Test (CBT) was used to assess ready biodegradability. A new method using Vibrio fischeri was adopted to evaluate toxicity. CP was not degraded by any lamp, whereas 5-FU was completely eliminated by irradiation with the mercury lamp but only partially by the Xe lamp. No mineralization was observed for the experiments performed with the Xe lamp, and a NPOC removal of only 18% was registered for 5-FU after 256 min using the UV lamp. Not one of the parent compounds was readily biodegradable in the CBT. Photo transformation products (PTPs) resulting from photolysis were neither better biodegradable nor less toxic than the parent compound 5-FU. In contrast, the results of the tests carried out with the UV lamp indicated that more biodegradable and non-toxic PTPs of 5-FU were generated. Three PTPs were formed during the photodegradation experiments and were identified. The results of the in silico QSAR predictions showed positive mutagenic and genotoxic alerts for 5-FU, whereas only one of the formed PTPs presented positive alerts for the genotoxicity endpoint.

A Comparison of One-Dimensional and Comprehensive Two-Dimensional Gas Chromatography for Decomposition Odour Profiling Using Inter-Year Replicate Field Trials

Decomposition odour analysis involves the chemical profiling of volatile organic compounds produced by decomposing remains. This is important for areas of forensic science that rely on the detection of decomposition odour such as insect attraction to carrion, positive alerts of cadaver dogs to decomposing remains, and the development of field instrumentation for search and recovery procedures. Traditionally decomposition odour analysis has been performed using gas chromatography–quadrupole mass spectrometry (GC–qMS); however, the use of comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC×GC–TOFMS) is rapidly becoming more prevalent. The objective of this study was to compare GC–qMS and GC×GC–TOFMS for decomposition odour profiling based on inter-year replicate field studies using decomposing porcine remains. The increased peak capacity, sensitivity and selectivity afforded by GC×GC–TOFMS allowed peak co-elutions, chromatographic artefacts, and dynamic range to be more easily addressed and managed. Furthermore, the software associated with GC×GC–TOFMS provided several additional benefits including improved peak alignment between samples and increased consistency of reported results, overall allowing for additional statistical tests to be applied following data processing. Future GC–qMS results could be improved by implementing some of these software-associated procedures, potentially reducing the magnitude of variation observed between GC–qMS and GC×GC–TOFMS studies. One-dimensional GC analysis may also benefit substantially from coupling with TOFMS detection to provide an indirect increase in peak capacity using deconvolution. However, the wealth of information gained by using GC×GC–TOFMS in decomposition odour profiling is undoubtedly an asset in this field of research.

Assessing the Risk of Formation of Potential Genotoxic Degradation Products in a Small-Molecule Kinase Inhibitor Drug Substance and Drug Product

Galunisertib is a kinase inhibitor designed to selectively inhibit TGF-β signaling. Drug substance stress degradation studies performed during clinical development demonstrated two degradation products via oxidation of the nitrogen(s) of the pyridine moieties in the presence of dilute hydrogen peroxide. These “N-oxide” potential degradation products generated positive alerts for mutagenicity by in silico structure–activity relationship-based genotoxicity assessment, and both tested positive in the Ames bacterial mutagenicity test. These compounds were also identified as potential process impurities that could originate through the use of hydrogen peroxide, and this reagent was subsequently removed from the synthetic route. A toxicology limit of not more than 166 ppm (w/w relative to the drug substance) combined for the two N-oxides was assigned on the basis of clinical dosing. An LC–MS method was developed to test for the N-oxides with a limit of quantitation set at <10% of the toxicology limit in both th...

Degradation of 5-FU by means of advanced (photo)oxidation processes: UV/H2O2, UV/Fe2 +/H2O2 and UV/TiO2 — Comparison of transformation products, ready biodegradability and toxicity

The present study investigates the degradation of the antimetabolite 5-fluorouracil (5-FU) by three different advanced photo oxidation processes: UV/H2O2, UV/Fe2+/H2O2 and UV/TiO2. Prescreening experiments varying the H2O2 and TiO2 concentrations were performed in order to set the best catalyst concentrations in the UV/H2O2 and UV/TiO2 experiments, whereas the UV/Fe2+/H2O2 process was optimized varying the pH, Fe2+ and H2O2 concentrations by means of the Box–Behnken design (BBD). 5-FU was quickly removed in all the irradiation experiments. The UV/Fe2+/H2O2 and UV/TiO2 processes achieved the highest degree of mineralization, whereas the lowest one resulted from the UV/H2O2 treatment. Six transformation products were formed during the advanced (photo)oxidation processes and identified using low and high resolution mass spectrometry. Most of them were formed and further eliminated during the reactions. The parent compound of 5-FU was not biodegraded, whereas the photolytic mixture formed in the UV/H2O2 treatment after 256min showed a noticeable improvement of the biodegradability in the closed bottle test (CBT) and was nontoxic towards Vibrio fischeri. In silico predictions showed positive alerts for mutagenic and genotoxic effects of 5-FU. In contrast, several of the transformation products (TPs) generated along the processes did not provide indications for mutagenic or genotoxic activity. One exception was TP with m/z 146 with positive alerts in several models of bacterial mutagenicity which could demand further experimental testing. Results demonstrate that advanced treatment can eliminate parent compounds and its toxicity. However, transformation products formed can still be toxic. Therefore toxicity screening after advanced treatment is recommendable.

Alert Management System using K-means Based Genetic for IDS

One of the most important tools in security field is Intrusion Detection System. The aim of the IDS is to monitor suspicious network traffic and generate alerts. These systems are known to generate numerousfalse positive alerts. Analyzing the alerts manually by security expert need more time and could be error prone.Another problem with IDS is Identifying attack types and generating correct alerts related to attacks.we introducenew alert management systems to overcome mentioned problems. Alert management systems help security experts to manage alerts and produce a high level view of alerts. In this paper a new alert clustering algorithm for IDS Alert Management System proposed that uses the K-mean Based Genetic (KBG). The proposed algorithm reduces alerts and detects false positive alerts. By the experimental results on DARPA KDD cup 98 the system is able to cluster and classify alerts and causes reducing false positive alerts considerably.

Customized reference ranges for laboratory values decrease false positive alerts in intensive care unit patients.

BackgroundTraditional electronic medical record (EMR) interfaces mark laboratory tests as abnormal based on standard reference ranges derived from healthy, middle-aged adults. This yields many false positive alerts with subsequent alert-fatigue when applied to complex populations like hospitalized, critically ill patients. Novel EMR interfaces using adjusted reference ranges customized for specific patient populations may ameliorate this problem.ObjectiveTo compare accuracy of abnormal laboratory value indicators in a novel vs traditional EMR interface.MethodsLaboratory data from intensive care unit (ICU) patients consecutively admitted during a two-day period were recorded. For each patient, available laboratory results and the problem list were sent to two mutually blinded critical care experts, who marked the values about which they would like to be alerted. All disagreements were resolved by an independent super-reviewer. Based on this gold standard, we calculated and compared the sensitivity, specificity, positive and negative predictive values (PPV, NPV) of customized vs traditional abnormal value indicators.ResultsThirty seven patients with a total of 1341 laboratory results were included. Experts’ agreement was fair (kappa = 0.39). Compared to the traditional EMR, custom abnormal laboratory value indicators had similar sensitivity (77% vs 85%, P = 0.22) and NPV (97.1% vs 98.6%, P = 0.06) but higher specificity (79% vs 61%, P<0.001) and PPV (28% vs 11%, P<0.001).ConclusionsReference ranges for laboratory values customized for an ICU population decrease false positive alerts. Disagreement among clinicians about which laboratory values should be indicated as abnormal limits the development of customized reference ranges.