Early Detection Of Adverse Events Research Articles

Side effects and efficacy of low-dose amiodarone in rhythm disorders

Amiodarone is one of the most widely used antiarrhythmic drugs, which is the most effective drug for maintaining sinus rhythm.   Taking this drug correlates with side effects such as pulmonary toxicity, thyroid dysfunction, neurotoxicity, hepatotoxicity, and skin manifestations. In addition, in some cases, amiodarone remains as a first-line therapy to maintain sinus rhythm. The side effects of amiodarone depend on the dose and duration of the drug. Systematic reviews and meta-analyses have shown the safety profile of a low dose of amiodarone, defined as 200 mg and a very low dose of amiodarone 100 mg. Due to the use of catheter ablation, the use of a low dose of amiodarone is sufficient. In this literature review, we have cited the side effects of a low dose of amiodarone. In addition, although there is evidence of a safer low-dose spectrum, it is not free from side effects and needs to develop an algorithm for early detection of adverse events, as well as studying the effectiveness according to modern research methods, such as an implantable heart monitor, which in turn is of undoubted interest.

Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.

Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed. Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. "Pain or numbness" (n=57, 36.3%), "fever" (n=46, 29.3%), and "nausea" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives. Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work.

Empowering Pharmacovigilance: Unleashing the Potential of Generative AI in Drug Safety Monitoring

Pharmacovigilance plays a crucial role in ensuring drug safety and promoting patient well-being throughout the life cycle of medicinal products. However, this field faces several challenges, including underreporting of adverse events, data quality issues, and the complexity of signal detection in large datasets. To address these challenges and enhance drug safety monitoring, there is a growing interest in harnessing the potential of generative artificial intelligence (AI) techniques. This article explores the applications and implications of generative AI in pharmacovigilance. It provides an overview of popular generative models and their working principles, highlighting their ability to analyse drug databases, medical literature, and real-world data sources to identify drug-drug interactions, adverse events, and potential safety signals. Moreover, it emphasizes the importance of human validation and expert oversight in interpreting and acting on the insights generated by generative AI algorithms. The integration of generative AI with traditional pharmacovigilance methods creates a synergistic approach, combining the computational power of AI with human expertise. This integration can lead to improved signal detection, efficient case report generation, proactive risk assessment, and optimized resource allocation. Additionally, the article addresses challenges related to data quality, interpretability, and model validation in generative AI applications, emphasizing the need for standardized protocols and collaborative efforts among stakeholders. Overall, the potential of generative AI in pharmacovigilance is vast. By leveraging its capabilities, we can enhance drug safety monitoring, facilitate early detection of adverse events, and improve patient outcomes. However, it is crucial to address ethical considerations, ensure data privacy, and maintain human oversight to foster responsible and effective implementation of generative AI in pharmacovigilance practices.

Active Pharmacovigilance Study: A Follow-Up Model of Oral Anti-Cancer Drugs under Additional Monitoring.

Adverse drug reactions (ADRs) are responsible for almost 5% of hospital admissions, making it necessary to implement different pharmacovigilance strategies. The additional monitoring (AM) concept has been highlighted and intended to increase the number of suspected ADRs reported, namely in medicines with limited safety data. A prospective, descriptive study of active pharmacovigilance (AP) was conducted between 2019 and 2021 in the Local Health Unit of Matosinhos (LHUM) (Porto, Portugal). A model of AP for medicines under AM, namely oral antineoplastic agents, was designed. Follow-up consultations were performed, and adverse events (AEs) data were collected. The overall response to the treatment was evaluated through the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria. A total of 52 patients were included in the study, and 14 antineoplastic drugs under AM were analyzed. Of the total number of patients included, only 29 developed at least one type of toxicity. Hematological disorders were the most reported suspected ADR. However, only four patients interrupted their treatment due to toxicity. After 12 months of treatment, most patients had disease progression, which was the main reason for therapy discontinuation. This AP model played an important role in the early detection of AEs and, consequently, contributed to better management of them. Increasing the number of suspected ADR reports is crucial for drugs with limited safety data.

Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0).

Surgical workflow analysis involves systematically breaking down operations into key phases and steps. Automatic analysis of this workflow has potential uses for surgical training, preoperative planning, and outcome prediction. Recent advances in machine learning (ML) and computer vision have allowed accurate automated workflow analysis of operative videos. In this Idea, Development, Exploration, Assessment, Long-term study (IDEAL) stage 0 study, the authors sought to use Touch Surgery for the development and validation of an ML-powered analysis of phases and steps in the endoscopic transsphenoidal approach (eTSA) for pituitary adenoma resection, a first for neurosurgery. The surgical phases and steps of 50 anonymized eTSA operative videos were labeled by expert surgeons. Forty videos were used to train a combined convolutional and recurrent neural network model by Touch Surgery. Ten videos were used for model evaluation (accuracy, F1 score), comparing the phase and step recognition of surgeons to the automatic detection of the ML model. The longest phase was the sellar phase (median 28 minutes), followed by the nasal phase (median 22 minutes) and the closure phase (median 14 minutes). The longest steps were step 5 (tumor identification and excision, median 17 minutes); step 3 (posterior septectomy and removal of sphenoid septations, median 14 minutes); and step 4 (anterior sellar wall removal, median 10 minutes). There were substantial variations within the recorded procedures in terms of video appearances, step duration, and step order, with only 50% of videos containing all 7 steps performed sequentially in numerical order. Despite this, the model was able to output accurate recognition of surgical phases (91% accuracy, 90% F1 score) and steps (76% accuracy, 75% F1 score). In this IDEAL stage 0 study, ML techniques have been developed to automatically analyze operative videos of eTSA pituitary surgery. This technology has previously been shown to be acceptable to neurosurgical teams and patients. ML-based surgical workflow analysis has numerous potential uses-such as education (e.g., automatic indexing of contemporary operative videos for teaching), improved operative efficiency (e.g., orchestrating the entire surgical team to a common workflow), and improved patient outcomes (e.g., comparison of surgical techniques or early detection of adverse events). Future directions include the real-time integration of Touch Surgery into the live operative environment as an IDEAL stage 1 (first-in-human) study, and further development of underpinning ML models using larger data sets.

Management of Treatment-Related Infectious Complications in High-Risk Hemato-Oncological Patients via Telemedicine.

BackgroundInfectious complications, especially febrile neutropenia, in hemato-oncological patients are associated with considerable morbidity, mortality and expenses. Remote monitoring of physiological functions and thus early detection of adverse events via telemedicine could improve the safety of these high-risk patients and save financial resources by shortening the time-to-antibiotics.MethodsPatients undergoing active cancer treatment in high risk of acquiring severe infection are selected and enrolled in this project. Each patient receives a digital blood pressure monitor, an infrared thermometer and a mobile hub (cell phone). In the comfort of their homes, patients measure their blood pressure/pulse and body temperature regularly or whenever they feel unwell. The obtained data are encrypted and forwarded via the mobile hub to the password-protected portal. The values registered outside the set-up range trigger the alarms, which are immediately sent to the designated physician who can check the portal in real-time from any device with an Internet connection, contact the patient, if need be, and initiate the anti-infective therapy almost instantly after the first symptoms occur.ResultsFifty hemato-oncological patients were recruited between March 1, 2018 and August 1, 2020. Two hundred ninety-seven alarms of body temperature were registered and checked by the physician and patients were contacted in 18.5% of the cases (55/297). Among these 55 events, 13 required medical assistance, which makes it approximately one-quarter of all conducted telephone interventions (23.4%) and neither septic shock nor death due to treatment-related toxicity occurred.ConclusionTelemedicine seems like a useful tool to improve the safety of high risk hemato-oncological patients when treatment-related infectious complications are concerned.

Modern methods of remote monitoring and rehabilitation of patients with cardiovascular diseases

Highlights. The review summarizes the latest achievements in the field of remote patient monitoring, highlights the main advantages and limitations of modern methods of remote monitoring and rehabilitation of patients with cardiovascular diseases.Abstract. Devices for remote health monitoring and rehabilitation that use modern telemedicine technologies are becoming more and more widespread in cardiovascular disease management. Modern technologies are affordable and reliable assistants for healthcare professionals, they assist in monitoring patients “at home”, early detection of adverse events, and timely notification of the need for medical help. The review summarizes the latest achievements in the field of remote patient monitoring for ambulatory management, diagnostics, dynamic monitoring, and cardiac rehabilitation of patients with cardiovascular diseases. The main advantages and limitations of remote patient monitoring technologies are presented, and methods for the implementation of these technologies are discussed.

Abstract OT1-12-08: Randomized study comparing electronic patient reported outcomes (ePROs) monitoring with routine follow up during trastuzumab deruxtecan treatment in patients with inoperable or metastatic breast cancer (PRO-DUCE study)

Abstract Background: Trastuzumab Deruxtecan (T-DXd) is a novel antibody-drug conjugate that targets human epidermal growth factor receptor 2 (HER2) with a DNA topoisomerase I inhibitor payload and has demonstrated antitumor activity in clinical trials. Based on the results of a global Phase II clinical trial (DESTINY-Breast01, NCT03248492), T-DXd was conditionally approved for the treatment of patients with HER2+ unresectable or metastatic breast cancer who had received ≥2 prior anti-HER2 based therapies (US, EU, UK, etc) or after prior chemotherapy (Japan). With regards to the safety profile, the incidence of cumulative toxicities such as peripheral neuropathy and edema was lower with T-DXd as compared to what has been observed with taxanes. However, chemotherapy-induced nausea and vomiting (CINV), fatigue, and neutrophil count decrease were observed as treatment emergent adverse events (TEAE). Interstitial lung disease (ILD)/pneumonitis was especially observed in 15.2% of patients treated with T-DXd, leading to death in 2.7% of patients. These adverse events need appropriate monitoring and management in daily clinical practice. We hypothesized that electronic patient-reported outcomes (ePROs) monitoring would allow early detection of adverse events due to T-DXd and thus improve patient safety and quality of life (QoL). Study design: This is an open-label, multicenter, randomized controlled study comparing ePRO monitoring with routine follow-up of patients with HER2+ advanced or metastatic breast cancer who receive T-DXd in routine clinical practice. Patients randomized to the ePRO group are monitored through weekly PRO-CTCAE assessment as well as daily temperature and SpO2 measurement using the ePRO application on the patient's mobile device. Changes in symptoms are monitored by the attending physician using an electronic chart. In addition, an e-mail alert is sent to the site medical staff if any symptoms exceed a predetermined threshold. The primary endpoint of the study is the change from baseline in the EORTC QLQ-C30 (Global QoL) score at 24 weeks. Secondary endpoints include time to deterioration of Global QoL, EORTC FA12 score change, overall survival, progression-free survival, and adherence to the ePRO monitoring as assessed by the proportion of patients completing the questionnaires. Analysis of primary endpoint will be done using mixed effects models for repeated measures (MMRM) and sample size will be estimated based on the Wald test for differences between groups in mean parameters at 24 weeks. To detect 10-points score difference between the groups with a standard deviation of 24, with a two-sided alpha level of 10%, and a power of 87%, 55 patients would be required in each group. Enrollment started in March 2021. Clinical trial identification: jRCTs031200387 Citation Format: Takafumi Sangai, Yuichiro Kikawa, Mitsuchika Hosoda, Yohei Hamanaka, Yuko Tanabe, Tatsuya Yoshida, Kaori Tane, Daisuke Takabatake, Tetsuhiko Taira, Kazuhiro Araki, Takayuki Iwamoto, Mamoru Takada, Kazutaka Narui, Takeshi Yamaguchi, Akimitsu Yamada, Tomoko Miura, Yukari Uemura, Tomohiko Aihara, Hirofumi Mukai, Naruto Taira. Randomized study comparing electronic patient reported outcomes (ePROs) monitoring with routine follow up during trastuzumab deruxtecan treatment in patients with inoperable or metastatic breast cancer (PRO-DUCE study) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT1-12-08.

Адаптация и валидация шкалы непереносимости метотрексата MISS у детей с ювенильным идиопатическим артритом: одномоментное поперечное исследование

Background. Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in children. First-line therapy for JIA includes methotrexate (MTX); however, it can cause adverse events, which necessitates early diagnosis of MTX intolerance. The Methotrexate Intolerance Severity Score (MISS) is one of diagnostic instruments that can be used for this purpose. Objective. To validate, customize, and implement the MISS questionnaire into clinical practice. Patients and methods. This cross-sectional study was conducted between January and May 2022 and included 100 JIA patients (32 boys and 68 girls) aged ≤17 years receiving MTX. We validated the MISS questionnaire to evaluate possible adverse events associated with MTX. We performed linguistic and cultural customization of the MISS questionnaire. To assess the reliability of the questionnaire, we calculated the Cronbach's alpha coefficient. Results. Cronbach's alpha coefficient showed that MISS is a reliable tool to detect MTX intolerance. We found that more than a quarter of patients had MTX intolerance with nausea being the most common complaint among the participants. Conclusion. Despite high MTX efficacy in clinical practice, patients often demonstrated poor treatment adherence due to MTXrelated adverse events. It is important to continue the implementation of the Russian version of MISS to ensure early detection of adverse events and timely initiation of adequate treatment. Key words: children, juvenile idiopathic arthritis, methotrexate, Methotrexate Intolerance Severity Score, drug intolerance

Perceptions of Mobile Health Technology in Elective Surgery: A Qualitative Study of North American Surgeons.

To explore the surgeon-perceived added value of mobile health technologies (mHealth), and determine facilitators of and barriers to implementing mHealth. Despite the growing popularity of mHealth and evidence of meaningful use of patient-generated health data in surgery, implementation remains limited. This was an exploratory qualitative study following the Consolidated Criteria for Reporting Qualitative Research. Purposive sampling was used to identify surgeons across the United States and Canada. The Consolidated Framework for Implementation Research informed development of a semistructured interview guide. Video-based interviews were conducted (September-November 2020) and interview transcripts were thematically analyzed. Thirty surgeons from 8 specialties and 6 North American regions were interviewed. Surgeons identified opportunities to integrate mHealth data pre- operatively (eg, expectation-setting, decision-making) and during recovery (eg, remote monitoring, earlier detection of adverse events) among higher risk patients. Perceived advantages of mHealth data compared with surgical and patient-reported outcomes included easier data collection, higher interpretability and objectivity of mHealth data, and the potential to develop more patientcentered and functional measures of health. Surgeons identified a variety of implementation facilitators and barriers around surgeon- and patient buy-in, integration with electronic medical records, regulatory/reimbursement concerns, and personnel responsible for mHealth data. Surgeons described similar considerations regarding perceptions of mHealth among patients, including the potential to address or worsen existing disparities in surgical care. These findings have the potential to inform the effective and equitable implementation of mHealth for the purposes of supporting patients and surgical care teams throughout the delivery of surgical care.

Patient adherence to statin therapy within 12 months after coronary stenting depending on regular or remote monitoring

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Russian Ministry of Health Background. Low adherence to statins remains a challenge in the treatment of patients with cardiovascular diseases. Some patients who underwent coronary stenting (CS) are unavailable for regular follow-up with outpatient visits. The ability to remotely monitor patients after CS may facilitate adherence to treatment, achieve target low density lipoprotein (LDL) cholesterol levels and early detection of adverse events. We aimed to evaluate the adherence to statin therapy in patients after CS receiving remote monitoring or care with outpatient visits. Methods. We enrolled 279 consecutive stable CAD/silent myocardial ischemia patients (61.5 ± 9.5 years) who underwent CS. The patients were randomized into groups of regular outpatient visits (group 1, n = 96), remote monitoring (group 2, n = 95) and control group (group 3, n = 88). The visits (cardio exam and blood testing) and remote monitoring (videoconference, telephone care and blood tests interpretation) were performed at 1, 3, 6 and 12 months after CS for groups 1 and 2. Patients in the control group were cared by a physician at the residence place, the contact with the study coordinator was performed at baseline and 12 months after CS. Adherence to the prescribed medical therapy based on the four-item Morisky Green Levine Medication Adherence Scale was assessed at each contact with the study coordinator. Results. Patient adherence to statin therapy 12 months after CS was 53.6% for group 1, 55.8% for group 2 and 24.4% for group 3 (p < 0.05 for group 3 versus groups 1 and 2). In group 1 26.9/36.5/31.7/37.4*/41.3*% of patients achieved target LDL level at baseline/1mo/3mo/6mo/12mo, respectively (р<0.05 vs. baseline). In group 2 - 35.8/36.8/40.0/51.6*/57.9*% of patients (р<0.05 vs. baseline). In group 3 25.5/28.2% of patients achieved target LDL level at baseline/12mo, respectively. The significant decrease in LDL cholesterol levels between baseline and 12mo values was observed in groups 1 and 2 (p < 0.05). No differences were observed in group 3. Conclusion. The groups of patients receiving remote monitoring or care with outpatient visits demonstrate the same increase in the proportion of patients that achieved target LDL cholesterol levels within 12 months after CS. The remote monitoring is a safe strategy for improving and maintaining the adherence to statins in patients after CS.

Modeling Diagnostic Strategies to Manage Toxic Adverse Events following Cancer Immunotherapy

BackgroundAlthough immunotherapy (IMT) provides significant survival benefits in selected patients, approximately 10% of patients experience (serious) immune-related adverse events (irAEs). The early detection of adverse events will prevent irAEs from progressing to severe stages, and routine testing for irAEs has become common practice. Because a positive test outcome might indicate a clinically manifesting irAE that requires treatment to (temporarily) discontinue, the occurrence of false-positive test outcomes is expected to negatively affect treatment outcomes. This study explores how the UPPAAL modeling environment can be used to assess the impact of test accuracy (i.e., test sensitivity and specificity), on the probability of patients entering palliative care within 11 IMT cycles.MethodsA timed automata-based model was constructed using real-world data and expert consultation. Model calibration was performed using data from 248 non–small-cell lung cancer patients treated with nivolumab. A scenario analysis was performed to evaluate the effect of changes in test accuracy on the probability of patients transitioning to palliative care.ResultsThe constructed model was used to estimate the cumulative probabilities for the patients’ transition to palliative care, which were found to match real-world clinical observations after model calibration. The scenario analysis showed that the specificity of laboratory tests for routine monitoring has a strong effect on the probability of patients transitioning to palliative care, whereas the effect of test sensitivity was limited.ConclusionWe have obtained interesting insights by simulating a care pathway and disease progression using UPPAAL. The scenario analysis indicates that an increase in test specificity results in decreased discontinuation of treatment due to suspicion of irAEs, through a reduction of false-positive test outcomes.

The effects of cyclophosphamide, adriamycin and 5-fluorouracil chemotherapy on blood cells and cardiac hemodynamics in breast carcinoma patients: a case study at Dr. Kariadi General Hospital, Semarang, Indonesia

Background: One of the treatment modalities for breast malignancy is chemotherapy. Chemotherapy could kill cancer cells as well as normal cells, including blood cells and cardiac cells. Early detection of adverse events includes myelosuppression and cardiotoxicity could reduce morbidity and mortality rates. This study aims to determine a decrease in blood cells and cardiac hemodynamics in breast carcinoma patients who underwent Cyclophosphamide, Adriamycin, and 5-Fluorouracil (CAF) chemotherapy.Methods: Analytical prospective observational study with breast carcinoma. The measurements were Haemoglobin (Hb), Hematocrit (Ht), Erythrocyte (Ery), Leukocyte (Leu), Platelet (Plt), Left Ventricle Ejection Fraction (LVEF), Stroke Volume (SV), and Cardiac Output (CO) until 3rd cycles of chemotherapy. Data were analyzed using SPSS version 17 for Windows.Results: There were 35 subjects enrolled in this study. Decreases in Hb, Ht, Ery, Leu, and Plt after the 1st, 2nd, and 3rd cycles of chemotherapy were statistically significant (p<0.05). There was a statistically significant decrease in LVEF, SV, and CO after the 1st, 2rd and 3rd cycles of chemotherapy (p<0.05). Besides, the SV and CO after the 2nd and 3rd cycles of chemotherapy were also decreased significantly (p<0.05).Conclusions: There is a decrease in blood cells (Hb, Ht, Ery, Leu, Plt) and cardiac hemodynamics (LVEF, SV, and CO) in breast carcinoma patients who underwent CAF chemotherapy.

The left ventricular assist device as a patient monitoring system.

Technological progress of left ventricular assist devices (LVADs) towards rotary blood pumps and the optimization of medical management contributed to the significant improvements in patient survival as well as LVAD support duration. Even though LVAD therapy is now well-established for end-stage heart failure patients, the long-term occurrence of adverse events (AE) such as bleeding, infection or stroke, still represent a relevant burden. An early detection of AE, before onset of major symptoms, can lead to further optimization of patient treatment and thus mitigate the burden of AE. Continuous patient monitoring facilitates identification of pathophysiological states and allows anticipation of AE to improve patient management. In this paper, methods, algorithms and possibilities for continuous patient monitoring based on LVAD data are reviewed. While experience with continuous LVAD monitoring is currently limited to a few centers worldwide, the pace of developments in this field is fast and we expect these technologies to have a global impact on the well-being of LVAD patients.

Adaptive threshold-based alarm strategies for continuous vital signs monitoring

Continuous vital signs monitoring in post-surgical ward patients may support early detection of clinical deterioration, but novel alarm approaches are required to ensure timely notification of abnormalities and prevent alarm-fatigue. The current study explored the performance of classical and various adaptive threshold-based alarm strategies to warn for vital sign abnormalities observed during development of an adverse event. A classical threshold-based alarm strategy used for continuous vital signs monitoring in surgical ward patients was evaluated retrospectively. Next, (combinations of) six methods to adapt alarm thresholds to personal or situational factors were simulated in the same dataset. Alarm performance was assessed using the overall alarm rate and sensitivity to detect adverse events. Using a wireless patch-based monitoring system, 3999 h of vital signs data was obtained in 39 patients. The clinically used classical alarm system produced 0.49 alarms/patient/day, and alarms were generated for 11 out of 18 observed adverse events. Each of the tested adaptive strategies either increased sensitivity to detect adverse events or reduced overall alarm rate. Combining specific strategies improved overall performance most and resulted in earlier presentation of alarms in case of adverse events. Strategies that adapt vital sign alarm thresholds to personal or situational factors may improve early detection of adverse events or reduce alarm rates as compared to classical alarm strategies. Accordingly, further investigation of the potential of adaptive alarms for continuous vital signs monitoring in ward patients is warranted.

Assessing the Feasibility of a Novel mHealth App in Hematopoietic Stem Cell Transplant Patients

Hematopoietic stem cell transplantation (HCT) is a curative treatment option for patients with hematologic conditions but presents many complications that must be managed as a complex, chronic condition. Mobile health applications (mHealth apps) may permit tracking of symptoms in HCT. In seeking strategies to manage the complexities of HCT, our team collaborated with Sicklesoft, Inc., to develop an mHealth app specifically for HCT patients to allow for daily evaluation of patient health, Technology Recordings to better Understand Bone Marrow Transplantation (TRU-BMT). The primary value of this application is that of potentially enhancing the monitoring of symptoms and general health of patients undergoing HCT, with the ultimate goal of allowing earlier detection of adverse events, earlier intervention, and improving outcomes. To first evaluate patient interest in mHealth apps, we designed and administered an interest survey to patients at the 2017 BMT-InfoNet reunion. As a follow-up to the positive feedback received, we began testing the TRU-BMT app in a Phase 1 pilot study. Thirty patients were enrolled in this single-arm study and were given the TRU-BMT mHealth app on a smartphone device in addition to a wearable activity tracker. Patients were followed for up to 180 days, all the while receiving daily app monitoring. Adherence to TRU-BMT was approximately 30% daily and 44% weekly, and greater adherence was associated with increased meal completion, decreased heart rate, and shorter hospital stay. TRU-BMT assessments of symptom severity were significantly associated with duration of hospital stay and development of chronic graft-versus-host disease. Our findings suggest that using TRU-BMT throughout HCT is feasible for patients and established a proof-of-concept for a future randomized control trial of the TRU-BMT application in HCT.© 2021 American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Hospitalizations and Clinical Outcome in Metastatic Colorectal Cancer During Regorafenib or TAS-102 Therapy.

Simple SummaryRegorafenib and TAS-102 showed a survival benefit against placebo, and both drugs are approved for the treatment of metastatic colorectal cancer (mCRC) beyond second-line. The highly differential toxicity profile of both substances has led to a potentially biased perception of drug tolerability and complications—such as hospitalization—in the oncologic community. The aim of this retrospective analysis was to investigate hospitalization frequency during regorafenib and TAS-102 treatment and the impact of hospitalizations on survival. Treatment with regorafenib as well as a low Eastern Cooperative Oncology Group (ECOG) performance status turned out to be independent risk factors for hospitalization. Hospitalizations due to gastrointestinal toxicity were only seen with regorafenib. However, hospitalizations during regorafenib or TAS-102 treatment did not impact survival. In light of increased gastrointestinal toxicity leading to hospitalization during regorafenib treatment, we call for increased awareness to drug-specific toxicities, in order to prevent unnecessary complications by the early detection of adverse events and prompt counteraction.Current National Comprehensive Cancer Network (NCCN) and European Society of Medical Oncology (ESMO) guidelines recommend regorafenib or trifluridine/tipiracil (TAS-102) for the third-line therapy of metastatic colorectal cancer (mCRC). In this analysis, we evaluated hospitalizations during regorafenib or TAS-102 treatment and the impact of hospitalizations on overall survival (OS). This retrospective analysis was based on unselected, consecutive mCRC patients treated with regorafenib and/or TAS-102 at the tertiary cancer centers in Salzburg and Wels-Grieskirchen, Austria. Between January 2013 and May 2019, 93 patients started third- or fourth-line therapy with regorafenib or TAS-102. Tumor therapy (regorafenib versus TAS-102, HR: 1.95 [95% CI: 1.07–3.54], p = 0.03) and the Eastern Cooperative Oncology Group (ECOG) performance status (2–3 versus 0–1, HR: 4.04 [95% CI: 2.11–7.71], p < 0.001) showed a statistically significant association with hospitalization risk in multivariate analysis. The corresponding hospitalization probability from initiation of third- or fourth-line was 30% with regorafenib versus 18% with TAS-102 at five weeks and 41% versus 28% at ten weeks, respectively. Hospitalizations irrespective of cause during regorafenib or TAS-102 therapy did neither impact median survival in patients undergoing only third-line therapy (never-hospitalized: 5.7 months [95% CI: 3.9–10.5] versus hospitalized: 5.4 months [95% CI: 2.8–9.6], p = 0.45), nor in patients receiving third- and fourth-line therapy (12.2 months [95% CI: 10.6–28.8] versus 18.6 months [95% CI: 6.3-not reached], p = 0.90). In conclusion, apart from poor ECOG performance status, regorafenib therapy was associated with an increased hospitalization probability during palliative systemic third- and fourth-line therapy in mCRC. However, hospitalizations during regorafenib or TAS-102 therapy did not impact OS beyond second-line therapy.

Subset Analysis for Screening Drug-Drug Interaction Signal Using Pharmacovigilance Database.

Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.

Identification of target small molecule tyrosine kinase inhibitors that need monitoring and clinical application of protocol for early detection of cancer therapeutics-related cardiac dysfunction using signal detection: An investigation of real world data.

In order to detect cancer therapeutics-related cardiac dysfunction (CTRCD) early, we identified which drugs were to be monitored using signal detection and the package insert, and created and applied a protocol to address this. Adverse event data recorded in the Japanese Adverse Drug Event Report (JADER) database between April 2004 and January 2018 were used. Among small molecule tyrosine kinase inhibitors that are not described in the serious side-effects section of the package insert despite signal detection, tyrosine kinase inhibitors with severe side-effects in the background of cases reported by JADER database were selected to be monitored in clinical practice. We applied our findings clinically by creating a protocol to detect CTRCD early. All cases at Tosei General Hospital where the target tyrosine kinase inhibitors were administered from when they were first released in November 2019 were included. We compared the results from before and after we began the protocol to clarify its effects. We found that CTRCD was not described in the serious side-effect section of the package inserts for Bosutinib, Alectinib, and Osimertinib even though CTRCD signals were detected for them. Therefore, it is possible that we may have previously overlooked CTRCD. When we applied our protocol using Osimertinib as the target drug, we were able to detect CTRCD early in 5/21 (24%) patients. It was clarified that the drug identification method used in this study for early detection of adverse events leads to early detection of adverse events when applied clinically.

Signal detection of chemotherapy-related cardiac dysfunction on small molecule tyrosine kinase inhibitors

Abstract Background Treatment results of small molecule tyrosine kinase inhibitors (TKIs) have been improved, but cancer treatment-related cardiac dysfunction (CTRCD) has become a problem. However, the CTRCD risk of TKIs has not been fully clarified, and monitoring indicators have not been established. Therefore, in order to detect CTRCD early, we aimed to pinpoint the TKIs that needs attention. Method Data from the Japanese Adverse Drug Event Report database from April 2004 through January 2018 was used. In this study, a total of 25 TKIs in monotherapy were studied in principle to avoid the presence of concomitant medication. We examined the relationship between heart failure and TKIs with a reporting odds ratio (ROR) which is a signal detection by data mining Method. The 95% confidence interval lower limit value > 1 was taken as a signal. We also confirmed the target kinase and heart failure alert in the package insert of the TKIs. Result Signals of heart failure were detected in 10 (Alectinib, Axitinib, Bosutinib, Dasatinib, Imatinib, Osimertinib, Pazopanib, Ponatinib, Ruxolitinib and Sunitinib) of the 25 TKIs, and ABL-1, VEGFR and PDGFR were common in the target kinases. Signals were also detected in Alectinib, Bosutinib and Osimertinib, but their package inserts did not alert for heart failure. Death cases were also included in reported heart failure cases. Conclusion Knowledge of the ten TKIs or drugs having ABL-1, VEGFR, PDGFR as the target kinase that signal for a risk of heart failure may lead to the early detection of adverse events by monitoring. There are also serious cases for three drugs for which the risk of heart failure is unknown, so we suggest that it is necessary to conduct periodic monitoring.