Electronic Monitoring Data Research Articles

Fishing event detection and species classification using computer vision and artificial intelligence for electronic monitoring

Fisheries regulations require detailed catch reporting on commercial fishing vessels. Vital components for the sustainable management of fish stocks include a robust estimate of the number of fish caught and the species composition. Catch recording is often done manually by human observers on fishing vessels. Human observers are costly, and consistent data streams can be subject to observer availability and the weather. On-vessel cameras (electronic monitoring, EM) are a growing alternative to human observers. However, on-land human auditors are required to review hundreds of hours of videos recorded during fishing trips that can last for weeks. In this paper, a framework is presented to automatically detect fish in EM videos, count the total fishing events, and classify the fish species. For this purpose, a deep learning and computer vision-based model is developed to efficiently detect fish and fishers onboard a vessel. Secondly, a vision-based tracking pipeline tracks the detected fish and counts the total fishing events in the videos. Thirdly, the extracted fishing events are classified through a deep learning-based fish species classifier, to provide the distribution of different fish species caught for a fishing trip. For our experiments, the datasets were prepared using the electronic monitoring data of multiple fishing trips of a fishing vessel. The videos were recorded on Australian longline vessels targeting tunas and billfish. For the fish detection task, video frames were extracted and labelled manually to provide a digital ground-truth. For the fish species classification task, hundreds of fish images of multiple species were cropped to provide a training dataset for the fish classifier. For the fish counting task, manual counts for the fishing events of individual fish species were generated for the test fishing trips. The developed fish and fisher detector achieves a mean Average Precision of 87.0 % for fish and 94.0 % for fishers on test video frames. The fishing event detection pipeline achieves an Average Precision of 81.0 % and an Average Recall of 74.5 % on test videos. The fish species classifier achieves an Accuracy (Top-1) of 91.11 % for the classification of cropped fish images and 89.05 % for the classification of extracted fishing events from the videos. Experimental results show that our proposed computer vision and artificial intelligence-based solution for video analysis has great potential to automate the auditing process from electronic monitoring footage and contribute to the sustainable management of fish stocks.

Concordance between pharmacy dispensing and electronic monitoring data of direct oral anticoagulants - A secondary analysis of the MAAESTRO study

BackgroundDirect oral anticoagulants are the preferred treatment for stroke patients with atrial fibrillation. Pharmacy dispensing data represent a practical method to identify suboptimal medication adherence. ObjectiveThis study investigates whether pharmacy dispensing data are indicative of real-life adherence behavior, using data from 130 patients in the MAAESTRO study (2018–2022) in Basel, Switzerland. MethodsThis secondary data analysis of the MAAESTRO study (Dietrich, 2024) included patients with electronic monitoring (EM) and dispensing data for 12 months. Patients with at least two refills were included in the analysis.We categorized refill series into three adherence patterns using the Delta T method (Baumgartner, 2022): all refills on time, erratic refills, end-gaps ≥10 days. EM-adherence was assessed through “taking adherence” and “missing days” (24h without intake). We analyzed: i) all dispensing data (“all refills”); ii) all data independently of the MAAESTRO phase (“all phases”); iii) the last two dispensing data (“last”), and iv) EM data from the MAAESTRO phase that match the date of the last refill (“matched”). Associations between refill patterns and adherence were examined using Spearman correlation and Fisher's exact test. ResultsData analyzed from 50 patients (mean age 76.4 ± 9.1 years, 56.0 % male) included 252 refills with a median of 4 refills per patient. Refill patterns were: all refills on time (40.0 %), erratic refills (36.0 %), and end-gaps >10 days (24.0 %). Mean taking adherence was 89.3 ± 13.7 %. EM data revealed missing days in 82.0 % of patients, with 61.0 % having irregular refill patterns. Matched taking adherence was moderately associated with Delta T over all refills (p = 0.034) and the last refill (p = 0.013). ConclusionsDispensing data processed with the Delta T method correlate moderately with EM data. The Delta T value for the last two refills shows promise for estimating irregular adherence, suggesting potential for targeted interventions in pharmacy practice.

Design of Power Monitoring System for 2 Solar Panels Based on Thingspeak

Indonesia is one of the countries with large solar energy potential, namely around 3,294.4 GW recorded in 2022. However, solar energy utilization during 2022 is only 0.01%. Based on this problem, a research was carried out which aimed to combine the power produced by solar panels. This research emerged as a response to the need for energy efficiency and desires. With proper monitoring, solar panel owners can optimize their use of solar energy, reduce waste, and ensure that their solar panels are working at maximum capacity. This research is included in engineering research to determine the performance specifications and design specifications of the system that has been designed. Performance specifications cover a series of system electronic components and monitoring data displays displayed on Thingspeak. The design specifications in this research include sensor characterization, accuracy and precision of the Power Monitoring System For 2 Solar Panels Based On Thingspeak. From the results of the design specifications, the sensor used in the system has a high linearity value, good accuracy of 98.735% (voltage), 97.027% (current), and 97.994% (light intensity) and good accuracy of 99.905% (voltage), 99.549% (current), and 99.874% (light intensity).

An overview of the impacts of fishing on seabirds, including identifying future research directions

Abstract Knowledge of fisheries impacts, past and present, is essential for understanding the ecology and conservation of seabirds, but in a rapidly changing world, knowledge and research directions require updating. In this Introduction and in the articles in this Themed Set “Impacts of fishing on seabirds”, we update our understanding of how fishing impacts seabird communities and identify areas for future research. Despite awareness of the problems and mitigation efforts for >20 years, fisheries still negatively impact seabirds via the effects of bycatch, competition, and discards. Bycatch continues to kill hundreds of thousands of seabirds annually, with negative population-level consequences. Fisheries for forage fish (e.g. anchovy, sandeel, and krill) negatively impact seabirds by competing for the same stocks. Historically, discards supplemented seabird diets, benefitting some species but also increasing bycatch rates and altering seabird community composition. However, declining discard production has led to potentially deleterious diet switches, but reduced bycatch rates. To improve research into these problems, we make the following recommendations: (1) improve data collection on seabird–vessel interaction and bycatch rates, on fishing effort and vessel movements (especially small-scale fleets), and on mitigation compliance, (2) counter the current bias towards temperate and high-latitude ecosystems, larger-bodied species and particular life stages or times of year (e.g. adults during breeding), and (3) advance our currently poor understanding of combined effects of fisheries and other threats (e.g. climate change, offshore renewables). In addition, research is required on under-studied aspects of fishing impacts: consequences for depleted sub-surface predators, impacts of illegal, unreported and unregulated fishing, artisanal and emerging fisheries, such as those targeting mesopelagic fish, have received insufficient research attention. Some of these shortfalls can be overcome with new tools (e.g. electronic monitoring, remote sensing, artificial intelligence, and big data) but quantifying and addressing fishing impacts on seabirds requires greater research investment at appropriate spatio-temporal scales, and more inclusive dialogue from grassroots to national and international levels to improve governance as fishing industries continue to evolve.

Intrapartum electronic fetal monitoring features associated with a clinical diagnosis of nonreassuring fetal status

Nonreassuring fetal status detected by continuous electronic fetal monitoring accounts for almost 1 in 4 primary cesarean deliveries. However, given the subjective nature of the diagnosis, there is a need to identify the electronic fetal monitoring patterns that are clinically considered nonreassuring. This study aimed to describe which electronic fetal monitoring features are most commonly associated with first-stage cesarean delivery for nonreassuring fetal status, and to evaluate the risk of neonatal acidemia following cesarean delivery for nonreassuring fetal status. This was a nested case-control study in a prospectively collected cohort of patients with singleton pregnancies at ≥37 weeks' gestation, admitted in spontaneous labor or for induction of labor from 2010 to 2014 at a single tertiary care center. Patients with preterm pregnancies, multiple gestations, planned cesarean delivery, or nonreassuring fetal status in the second stage of labor were excluded. Cases were identified as having nonreassuring fetal status on the basis of what was documented in the operative note by the delivering physician. Controls were patients without nonreassuring fetal status within 1 hour of delivery. Cases were matched to controls in a 1:2 ratio by parity, obesity, and history of cesarean delivery. Electronic fetal monitoring data were abstracted by credentialed obstetrical research nurses for the 60 minutes before delivery. The primary exposure of interest was the incidence of high-risk category II electronic fetal monitoring features in the 60 minutes before delivery; in particular, the incidence of minimal variability, recurrent late decelerations, recurrent variable decelerations, tachycardia, and >1 prolonged deceleration were compared between groups. We also compared neonatal outcomes between cases and controls, including fetal acidemia (umbilical artery pH <7.1), other umbilical artery gas analytes, and neonatal and maternal outcomes. Of the 8580 patients in the parent study, 714 (8.3%) underwent cesarean delivery for nonreassuring fetal status in the first stage of labor. Patients diagnosed with nonreassuring fetal status requiring cesarean delivery were more likely to have recurrent late decelerations, >1 prolonged deceleration, and recurrent variable decelerations compared with controls. More than 1 prolonged deceleration was associated with 6 times increased rate of nonreassuring fetal status diagnosis resulting in cesarean delivery (adjusted odds ratio, 6.73 [95% confidence interval, 2.47-8.33]). Rates of fetal tachycardia were similar between groups. Minimal variability was less common in the nonreassuring fetal status group compared with controls (adjusted odds ratio, 0.36 [95% confidence interval, 0.25-0.54]). Compared with control deliveries, cesarean delivery for nonreassuring fetal status was associated with nearly 7 times higher risk of neonatal acidemia (7.2% vs 1.1%; adjusted odds ratio, 6.93 [95% confidence interval, 3.83-12.54]). Composite neonatal morbidity and composite maternal morbidity were more likely among patients delivered for nonreassuring fetal status in the first stage (3.9% vs 1.1%; adjusted odds ratio, 5.70 [2.60-12.49]; and 13.3% vs 8.0%; adjusted odds ratio, 1.99 [1.41-2.80]). Although multiple category II electronic fetal monitoring features have been traditionally linked to acidemia, the presence of recurrent late decelerations, recurrent variable decelerations, and prolonged decelerations seemed to concern obstetricians enough to surgically intervene for nonreassuring fetal status. A clinical intrapartum diagnosis of nonreassuring fetal status in the setting of these electronic fetal monitoring features is also associated with increased risk of acidemia, suggesting clinical validity to the diagnosis of nonreassuring fetal status.

STORAGE MANAGEMENT OF NARCOTICS AND PSYCHOTROPIC DRUGS

The storage management of narcotics and psychotropic drugs is a critical aspect of pharmaceutical practice, encompassing a set of stringent guidelines and protocols designed to ensure the safety, security, and regulatory compliance of these controlled substances. This abstract provides an overview of the key considerations in the storage of narcotics and psychotropic drugs, focusing on the challenges faced by healthcare professionals, regulatory frameworks governing storage practices, and emerging technologies that contribute to enhanced storage management. The article delves into the specific requirements for storing narcotics and psychotropic drugs, emphasizing the importance of controlled environments, secure storage facilities, and robust inventory control systems. It explores the impact of temperature, humidity, and light on the stability and efficacy of these substances, highlighting the need for precise environmental monitoring and control measures. Regulatory compliance is a cornerstone of storage management, and the abstract outlines the international and national regulations that govern the handling and storage of narcotics and psychotropic drugs. It discusses the role of regulatory bodies in setting standards, conducting inspections, and enforcing compliance to prevent diversion, theft, or misuse of these controlled substances. Furthermore, the abstract addresses the challenges faced by healthcare institutions and pharmacies in balancing the need for accessibility of these medications with the imperative to maintain strict security measures. It explores best practices in designing storage facilities, implementing access controls, and training personnel to ensure a seamless yet secure workflow. As technology continues to evolve, the abstract discusses innovative solutions that contribute to efficient storage management. This includes the integration of electronic monitoring systems, automation, and data analytics to enhance real-time tracking, reduce errors, and improve overall accountability in the storage of narcotics and psychotropic drugs.

Trouble with Big Brother: Counterproductive Consequences of Monitoring through Erosion of LMX

Changing workplace dynamics have led employers to increasingly adopt electronic monitoring technologies so supervisors can observe and ensure employee productivity—an outcome the monitoring literature has long supported. Yet, employee engagement and success depend on strong leader-member exchange (LMX), and the relational consequences of electronic monitoring for supervisor and employee are surprisingly unknown. To help resolve tension within the monitoring literature and add understanding regarding the effects of electronic performance monitoring on employee productivity, we use social exchange theory to examine the implications of electronic monitoring for the supervisor-employee relationship. We theorize that employee electronic monitoring facilitates (rather than inhibits) production deviance and inhibits (rather than facilitates) task performance by eroding the leader-member exchange relationship between supervisor and employee. Acknowledging the potential for electronic monitoring data to be useful to employees, we further argue that supervisors can buffer the negative relational consequences of electronic monitoring by using monitoring data for developmental purposes (i.e., developmental feedback). Across an experimental online study and a field study, we find converging support for our predictions and rule out alternative explanations. This research provides timely insights into how to effectively use electronic performance monitoring without promoting unintended consequences.

1009-P: The Association between Patient Characteristics and the Efficacy of Remote Patient Monitoring and Messaging

In the Pilot 4T study (n=135) , remote patient monitoring (RPM) was associated with improved time in range (TIR) and lower A1c. Measuring differences in how patients respond to messages from the care team is essential for the design of effective, personalized care models. We analyzed electronic health record (EHR) and continuous glucose monitor (CGM) data to estimate the week-over-week impact of RPM messages on patients’ TIR. We applied statistical clustering methods to divide patients who received messages into two groups based on their changes in TIR and compared the characteristics of these groups. Receiving a message was associated with a greater mean week-to-week improvement in TIR after a low-TIR week [4.9 percentage points (pp) after a message vs. 2.5pp without a message; p &amp;lt; 0.001]. A patient’s TIR improvement after receiving a message is greater on average if the same patient’s TIR improved following previous messages. We identified two groups of patients with significantly different responses to messages. The group with greater mean TIR improvement after messages contains a larger proportion of non-white, non-English speaking, and publicly insured patients. We found that messages were associated with different magnitudes of TIR improvement across two clusters of patients. Identifying patients who benefit more from RPM could facilitate the personalization of management strategies. Disclosure J.Ferstad: None. P.Prahalad: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. E.Fox: None. R.Johari: None. D.Scheinker: None. Funding Helmsley Charitable Trust, ISPAD-JDRF Fellowship,NIH R18DK122422,Stanford Diabetes Research Center,LPCH Auxiliaries,Stanford Maternal and Child Health Research Institute, The Stanford REDCap platform (http://redcap.stanford.edu) is developed and operated by Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085.

Adherence to Subcutaneous Interferon Beta-1a in Multiple Sclerosis Patients Receiving Periodic Feedback on Drug Use by Discussion of Readouts of Their Rebismart® Injector: Results of the Prospective Cohort Study REBIFLECT.

IntroductionConsistent treatment adherence is an important determinant of durable response in multiple sclerosis (MS). Published data indicate that adherence to > 80% of prescribed doses may be considered optimal. Feedback of electronic application monitoring data to patients has been considered a promising means to support high adherence.MethodsThe 2-year prospective non-interventional study REBIFLECT conducted at outpatient neurological centers (731 patients at 134 study sites in Germany) investigated whether treatment adherence to subcutaneous (sc) interferon beta-1 injection during a 1-year period is enhanced by regular physician-patient talks reflecting dosing data recorded by the application device in the context of clinical data or disease parameters. Qualitative adherence was defined as number of weeks with properly distributed injections per total number of weeks with prescribed injections. Quantitative adherence was defined as number of administered injections per prescribed injections.ResultsOverall median qualitative adherence was 90.5%. Approximately 70% of patients with adherence data available in the respective periods had a qualitative treatment adherence of 80–100%. With a mean of 97.9% quantitative adherence was very high and remained stable in the 2-year observation period. The stability of this effect is demonstrated by the subgroup with just one reflection talk (≥ 100%) and only a slight decrease in the subgroup with more than five reflection talks (97.9%).ConclusionTreatment adherence with the Rebismart® device was generally very high, consistent with other non-interventional studies. The first reflection talk supported by RebiSmart® induces excellent adherence, stabilized by repetition. Reflection to patients of subcutaneous interferon beta-1a treatment monitored by RebiSmart® is recommended to ensure prolonged strong treatment adherence.

Bycatch Estimates From a Pacific Tuna Longline Fishery Provide a Baseline for Understanding the Long-Term Benefits of a Large, Blue Water Marine Sanctuary

Bycatch on pelagic tuna longlines has contributed to population declines in several far-ranging, oceanic species and presents a conservation challenge that area-based management tools are increasingly promoted to address. In January 2020 the Republic of Palau, concerned about the impacts of longline fishing in its waters, closed 80% of its exclusive economic zone to all extractive activities, reserving the remaining 20% for a domestic fishing zone (DFZ). One of a growing number of very large marine protected areas, the Palau National Marine Sanctuary (PNMS) spans ∼500,000 km2 and was established inter alia to allow for the recovery of fish stocks adversely impacted by tuna longline fleets. Given that the main tuna stocks targeted in the western Pacific are not overexploited, the benefits of protection potentially afforded by the sanctuary are likely greater for vulnerable bycatch species. Evaluations of the sanctuary’s performance require, in part, a baseline of historical catch rates and effort distribution in the distant-water fleet (DWF) and locally based fleet (LBF) operating in Palau prior to sanctuary implementation. We describe the fishing effort, catch rates, catch estimates and fishing mortality in Palau’s longline fishery based on logbook, observer and electronic monitoring data. We defined bycatch as any species, retained or discarded, other than targeted tunas. Between 2010 and 2020, 104.8 million hooks were deployed, catching over 2 million individuals from 117 taxa at an overall target:bycatch ratio of 1:1, with a retention rate of ∼62%. Pronounced differences in fishing strategies and spatial distribution of effort between fleets were associated with large variations in catch rates and composition. The LBF had a larger effect on populations of at-risk species relative to the DWF, with higher catch rates and magnitudes for several vulnerable species and higher observable fishing mortality rates (64% vs 50% in the DWF). The sanctuary reshaped Palau’s longline fishery, contracting the fishery’s area and capacity. The relocation of the DFZ eliminated the LBF and constrained the DWF to an area where the fleet’s total catch rates and those of a number of vulnerable species were historically lower relative to former fishing grounds now closed by the sanctuary. Our results highlight the importance of consistent bycatch monitoring and emphasize the need for regional area-based approaches for managing longline fisheries.

Ecological risks of a data-limited fishery using an ensemble of approaches

Overexploitation is currently the main cause of marine defaunation. Vulnerability to overexploitation varies across populations. Determining which populations are of highest ecological risk from fishing mortality guides management. Because no single approach is optimal across taxonomic groups, a multi-model ensemble of relative risk estimates for a data-poor Pacific Ocean tuna longline fishery was obtained from two semi-quantitative Productivity-Susceptibility Analyses (PSAs) and from a quantitative approach that estimates instantaneous fishing mortality to compare to reference points of yield-per-recruit models. Individual estimates were combined to produce a pooled mean relative risk rank order. The study identified stocks below biological limits for which the contribution from this fishery to cumulative anthropogenic mortality may warrant intervention. Relative risks in descending order were for populations of albatrosses, cetaceans, mesopelagic sharks, rays, marine turtles, epipelagic sharks and teleosts. The fishery’s contribution to cumulative fishing mortality of western central north Pacific Ocean striped marlin warrants a more rigorous assessment to determine absolute risks. The study identified the disparate factors explaining relative risk from an individual fishery versus absolute risk from cumulative anthropogenic mortality sources. Improved risk assessments are possible by addressing identified deficits with PSAs, obtaining information on variables that explain catch and post-capture survival risks that was unavailable for this assessment, improving electronic monitoring data quality and filling gaps in life history traits. Findings support stakeholders to design integrated bycatch management frameworks that mitigate fishing mortality of the most vulnerable taxa and account for multispecies conflicts that result from some bycatch mitigation methods.

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:• To promote trust, the science must be understandable.• To enhance uptake, the workflow should not be impacted greatly.• To maximize buy-in, engagement at all levels is important.• To ensure relevance, the education must be tailored to the clinical role and hospital culture.• To lead to clinical action, the information must integrate into clinical care.• To promote sustainability, there should be periodic support interactions after formal implementation.

Application of Smartphone Technologies in Disease Monitoring: A Systematic Review.

Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies and examine their acceptance by chronic patients. We systematically examined the use of smartphone applications (apps) in chronic disease monitoring and management in databases, namely, Medline, Web of Science, Embase, and Proquest, published from 2010 to 2020. Results showed that app-based weight management programs had a significant effect on healthy eating and physical activity (p = 0.002), eating behaviours (p < 0.001) and dietary intake pattern (p < 0.001), decreased mean body weight (p = 0.008), mean Body Mass Index (BMI) (p = 0.002) and mean waist circumference (p < 0.001). App intervention assisted in decreasing the stress levels (paired t-test = 3.18; p < 0.05). Among cancer patients, we observed a high acceptance of technology (76%) and a moderately positive correlation between non-invasive electronic monitoring data and questionnaire (r = 0.6, p < 0.0001). We found a significant relationship between app use and standard clinical evaluation and high acceptance of the use of apps to monitor the disease. Our findings provide insights into critical issues, including technology acceptance along with regulatory guidelines to be considered when designing, developing, and deploying smartphone solutions targeted for chronic patients.

Electronic monitoring for improved accountability in western Pacific tuna longline fisheries

The collection of accurate fisheries catch data is critical to ensuring sustainable management of tuna fisheries, mitigating their environmental impacts and for managing transboundary fish stocks. These challenges are exemplified by the western Pacific tuna longline fishery, who’s management includes >26 nations, but is informed by critically low coverage of fishing activities by scientific observers. The gap in observer data could be filled by electronic monitoring (EM), but there are few trials that span multiple nations. A large-scale trial of EM systems on tuna longliners based in Palau, Federated States of Micronesia and the Republic of the Marshall Islands, is reported on. Comparisons are made of catch rates of market and bycatch species in corresponding EM, logbook and human observer data. Retained species were under-reported in logbooks by up to three times and discards of many species were not reported in logbooks. Discards identified in the EM data included threatened species such as marine turtles. Catch rate estimates from EM data were comparable to those estimated by human observers. EM data recorded a higher species diversity of catches than logbook data. Analysis of the EM data indicated clusters of bycatch that were associated with specific fishing practices. These results suggest further expansion of EM could inform improved management of both target and bycatch species. Ultimately greater coverage of EM data could contribute to reconciling debates in international stock allocation schemes and support actions to reduce the impacts of the fishery on threatened bycatch species.

Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing

Electronic monitoring (EM) is increasingly used to monitor catch and bycatch in wild capture fisheries. EM video data are still manually reviewed and adds to ongoing management costs. Computer vision, machine learning, and artificial intelligence-based systems are seen to be the next step in automating EM data workflows. Here we show some of the obstacles we have confronted and approaches taken as we develop a system to automatically identify and count target and bycatch species using cameras deployed to an industry vessel. A Convolutional Neural Network was trained to detect and classify target and bycatch species groups, and a visual tracking system was developed to produce counts. The multiclass detector achieved a mean average precision of 53.42%. Based on the detection results, the visual tracking system provided automatic fish counts for the test video data. Automatic counts were within two standard deviations of the manual counts for the target species and most times for the bycatch species. Unlike other recent attempts, weather and lighting conditions were largely controlled by mounting cameras under cover.

Optimizing Oral Targeted Anticancer Therapies Study for Patients With Solid Cancer: Protocol for a Randomized Controlled Medication Adherence Program Along With Systematic Collection and Modeling of Pharmacokinetic and Pharmacodynamic Data.

BackgroundThe strengthening or substitution of intravenous cytotoxic chemotherapy cycles by oral targeted anticancer therapies, such as protein kinase inhibitors (PKIs), has provided impressive clinical benefits and autonomy as well as a better quality of life for patients with cancer. Despite these advances, adverse event management at home and medication adherence remain challenging. In addition, PKI plasma concentrations vary significantly among patients with cancer receiving the same dosage, which could explain part of the observed variability in the therapeutic response.ObjectiveThe aim of this optimizing oral targeted anticancer therapies (OpTAT) study is to optimize and individualize targeted anticancer treatments to improve patient care and self-monitoring through an interprofessional medication adherence program (IMAP) combined with measurement PKI plasma concentrations.MethodsThe OpTAT study has two parts: (1) a 1:1 randomized medication adherence program, in which the intervention consists of regular motivational interviewing sessions between the patient and the pharmacist, along with the delivery of PKIs in electronic monitors, and (2) a systematic collection of blood samples and clinical and biological data for combined pharmacokinetic and pharmacodynamic analysis. On the basis of the electronic monitor data, medication adherence will be compared between groups following the three operational definitions: implementation of treatment during the persistent period, persistence with treatment and longitudinal adherence. The implementation will be described using generalized estimating equation models. The persistence of PKI use will be represented using a Kaplan-Meier survival curve. Longitudinal adherence is defined as the product of persistence and implementation. PKI pharmacokinetics will be studied using a population approach. The relationship between drug exposure and efficacy outcomes will be explored using Cox regression analysis of progression-free survival. The relationship between drug exposure and toxicity will be analyzed using a pharmacokinetic-pharmacodynamic model and by logistic regression analysis. Receiver operating characteristic analyses will be applied to evaluate the best exposure threshold associated with clinical benefits.ResultsThe first patient was included in May 2015. As of June 2021, 262 patients had participated in at least one part of the study: 250 patients gave at least one blood sample, and 130 participated in the adherence study. Data collection is in process, and the final data analysis is planned to be performed in 2022.ConclusionsThe OpTAT study will inform us about the effectiveness of the IMAP program in patients with solid cancers treated with PKIs. It will also shed light on PKI pharmacokinetic and pharmacodynamic properties, with the aim of learning how to adapt the PKI dosage at the individual patient level to increase PKI clinical suitability. The IMAP program will enable interprofessional teams to learn about patients’ needs and to consider their concerns about their PKI self-management, considering the patient as an active partner.Trial RegistrationClinicalTrials.gov NCT04484064; https://clinicaltrials.gov/ct2/show/NCT04484064.International Registered Report Identifier (IRRID)DERR1-10.2196/30090

311 Development of a prediction model for cesarean delivery among term women with presumed intraamniotic infection

To identify factors associated with cesarean delivery (CD) among term laboring women who develop an intrapartum fever due to presumed intraamniotic infection and to develop a predictive model for CD to assist in patient counseling. We performed a multi-center, retrospective cohort study among febrile, term laboring women between January 1, 2015 and December 31, 2017. We excluded women who had a documented non-obstetric source of infection, received antibiotics on admission for other indications, used immunosuppressant medication chronically, or had an intrauterine fetal demise. The medical record was reviewed, and sociodemographic, labor, and clinical characteristics were extracted. We identified the factors associated with CD and built our predictive model using backward stepwise regression. The final model was shown through the area under the receiver operating characteristic curve (AUC). The cohort consisted of 850 febrile laboring women, of whom 295 (34.7%) had a CD. Indications for Cesarean delivery included 34.6% for failure to progress, 33.6% for non-reassuring fetal heart tracing, 29.8% for arrest of descent, and 2.0% for failed operative delivery due to maternal exhaustion. The final model included stage of labor stratified by cervical exam dilation (less than 6 cm of dilation in latent labor, 6 cm to 9.5 cm in active first stage of labor, and 10 cm dilation in second stage of labor), duration of rupture of membranes (in hours), and if labor was induced. Our final model had an AUC of 0.74 (95% CI 0.71-0.78) with a Hosmer-Lemeshow test P=0.07. Using a predicted probability cutoff of 0.47, the model’s sensitivity was 54.7%, specificity was 80.7% with a positive predictive value of 60.1% and negative predictive value of 77.0% to predict CD. Information available at the time of development of an intrapartum fever may be used to counsel patients regarding the likelihood of CD. Our study was limited by unavailable electronic fetal monitoring data which also heavily influences CD risk.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Deep learning methods applied to electronic monitoring data: automated catch event detection for longline fishing

Abstract Electronic monitoring (EM) systems have become functional and cost-effective tools for the conservation and sustainable harvesting of marine resources. EM is an alternative to on-board observers, which produces video segments that can subsequently be reviewed by analysts. It is currently used in a range of fisheries. There are two major challenges to the widespread adoption of EM. One is the large storage requirement for the video footage recorded and the other is the long time required by analysts to review the video footage. We propose an automated catch event detection framework to address these challenges. Our solution, based on deep learning techniques, automatically extracts video segments of catch events, which substantially reduces storage space and review time by analysts. Here, we demonstrate the framework using video footage from three longline fishing trips. The system recalled nearly 100% of the catch events across all trips.

Insights Into Direct Oral Anticoagulant Therapy Implementation of Stroke Survivors with Atrial Fibrillation in an Ambulatory Setting

ObjectivesTo describe how stroke survivors with atrial fibrillation implement direct oral anticoagulant treatment and propose appropriate metrics to describe adherence. Materials and MethodsStroke patients with atrial fibrillation electronically recorded their self-administered direct oral anticoagulants (apixaban, dabigatran, edoxaban, rivaroxaban) during a 6-month observation phase after hospitalisation for ischemic stroke. Taking and timing adherence, correct dosing days, drug holidays, time of the day and day of the week subsets, dose-to-dose intervals and longest intervals between two consecutive doses were calculated from electronic monitoring data to describe and discuss the implementation phase of adherence. ResultsData from 41 patients were analysed. Median age was 77 (IQR = 69–84), 63.4% were male and the majority suffered a mild stroke (median NIHSS: 1). Mean taking and timing adherence exceeded 90%. Correct dosing occurred in 86.6% of the days. Seven patients (17.1%) had intake pauses of three or more consecutive days. Patients with twice-daily regimen (70.7%) had higher taking adherence in the morning than in the evening (94.4% versus 89.9%; p = 0.001). No therapy- or anamneses-related characteristic was associated with taking adherence. ConclusionsAlthough adherence to direct oral anticoagulants of stroke patients with atrial fibrillation exceeded 90%, deviant intake patterns such as drug holidays and missed evening doses were common and raise concerns. Appropriate adherence metrics calculated from electronic monitoring data may guide healthcare professionals elucidating patient-tailored adherence-enhancing interventions.ClinicalTrials.gov registration number: NCT03344146

Adherence to recommendations for ART and targeted PrEP use among HIV serodiscordant couples in East Africa: the \u201cPrEP as a bridge to ART\u201d strategy

BackgroundPrEP use should be aligned with periods of risk for HIV acquisition. For HIV serodiscordant couples, PrEP can be used as a bridge until the partner living with HIV takes antiretroviral therapy (ART) long enough to achieve viral suppression (the “PrEP as a Bridge to ART” strategy). However, adherence to this strategy is unknown.MethodsIn a demonstration project in Kenya and Uganda, HIV-uninfected partners of serodiscordant couples were advised to take PrEP until the partner living with HIV took ART for ≥ 6 months. PrEP discontinuation was then recommended unless there were concerns about ART adherence, immediate fertility intentions, or outside partners with unknown HIV/ART status. Electronic adherence monitoring and socio-behavioral questionnaire data were used in logistic regression models to explore completion of this strategy and continuation of PrEP beyond recommendations to stop its use.ResultsAmong 833 serodiscordant couples, 436 (52%) HIV-uninfected partners completed ≥ 6 months of PrEP as a bridge to ART. Strategy completion was associated with older age (aOR per 5 years = 1.1; p = 0.008) and having fewer children (aOR = 0.9; p = 0.019). Of the 230 participants encouraged to stop PrEP according to strategy recommendations, 170 (74%) did so. PrEP continuation among the remaining 60 participants was associated with more education (aOR = 1.1; p = 0.029), a preference for PrEP over ART (aOR = 3.6; p = 0.026), comfort with managing their serodiscordant relationship (aOR = 0.6; p = 0.046), and believing PrEP makes sex safe (aOR = 0.5; p = 0.026).ConclusionHalf of participants completed the PrEP as a bridge to ART strategy and the majority stopped PrEP as recommended. These findings suggest that targeting PrEP to periods of risk is a promising approach; however, tailoring counseling around aligning PrEP use and HIV risk will be important for optimal strategy implementation.