scholarly journals Chemotherapy safety and severe adverse events in cancer patients: Strategies to efficiently avoid chemotherapy errors in in- and outpatient treatment

2009 ◽  
Vol 124 (3) ◽  
pp. 722-728 ◽  
Author(s):  
Anna Markert ◽  
Véronique Thierry ◽  
Martina Kleber ◽  
Michael Behrens ◽  
Monika Engelhardt
Keyword(s):  
Adverse Events ◽  
Cancer Patients ◽  
Outpatient Treatment ◽  
Severe Adverse Events

scholarly journals Alert Regarding Cisplatin-induced Severe Adverse Events in Cancer Patients with Xeroderma Pigmentosum

2017 ◽  
Vol 56 (8) ◽  
pp. 979-982 ◽  
Author(s):  
Makoto Sumiyoshi ◽  
Hiroshi Soda ◽  
Noriaki Sadanaga ◽  
Hirokazu Taniguchi ◽  
Takaya Ikeda ◽  
...  
Keyword(s):  
Adverse Events ◽  
Cancer Patients ◽  
Xeroderma Pigmentosum ◽  
Severe Adverse Events

Improving FOLFIRINOX safety in pancreatic cancer patients through multidimensional remote monitoring and proactive care using a domomedecine mobile platform.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. TPS4673-TPS4673
Author(s):  
Mohamed Bouchahda ◽  
Sandra Komarzynski ◽  
Ayhan Ulusakarya ◽  
Amal Attari ◽  
Alban Duprès ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Adverse Events ◽  
Real Time ◽  
Cancer Patients ◽  
Hospital Admissions ◽  
Performance Status ◽  
Advanced Pancreatic Cancer ◽  
Treatment Compliance ◽  
Critical Parameters ◽  
Severe Adverse Events

TPS4673 Background: Pancreatic cancer is a poor prognosis and fast-growing cancer, whose five-year survival is 6% in Europe and the US. FOLFIRINOX has been established as the reference medical treatment for this disease worldwide, yet it also causes leuko-neutropenia, thrombocytopenia, diarrhea, anorexia, asthenia, weight loss, and peripheral sensory neuropathy. Its indication is usually limited to patients having a WHO performance status of 0 or 1. This treatment is often interrupted once Grade 3-4 clinical or hematological toxicities occur, resulting in poor patient performance status and quality of life. Presently, no prospective study monitor and evaluate the qualitative and quantitative effects of FOLFIRINOX on the daily life of pancreatic cancer patients in real-time. Such monitoring would provide early warning signals for the identification of any improvement or deterioration of the patient condition. Whenever necessary, proactive interventions would be triggered to avoid emergency hospitalization for severe adverse events and to enhance treatment compliance. Methods: Our study involves the use of the mobile e-Health platform PiCADo (JMIR 2018) to track and analyse circadian rhythms, symptoms, and body weight in real time in 45 advanced pancreatic cancer patients at 4 centres. The patients are continuously telemonitored for rest-activity, temperature and 3D-orientation via a BLE sensor during the six weeks following the first FOLFIRINOX course. Patients weigh themselves daily on a BLE scale and self-rate their symptoms using a touchscreen on GPRS tablet. Alerts are generated according to preset yet modifiable thresholds of automatically computed critical parameters. From these data, we will evaluate the rate of emergency hospital admissions and the admission-free survival, the rates of severe adverse events, patients’ symptoms dynamics, and their relations with the disruption of the patients’ circadian rhythm. Patient satisfaction and research experience will also be assessed, since engagement is at the core of the success of the approach. The results will guide a future randomized trial comparing standard pancreatic cancer patient care with a personalized FOLFIRINOX approach, including chronotherapy delivery. Support: Ramsay-Sante, Altran.

scholarly journals The Benefit of Enhanced Daycare of Traditional Chinese Medicine for Cancer Treatment Related Adverse Events: A Retrospective Study of Medical Records

2021 ◽  
Vol 20 ◽  
pp. 153473542110256
Author(s):  
Hsiu-An Wu ◽  
Chien-Hung Chen ◽  
Ming-Hsien Hsieh ◽  
Yung-Chang Wu ◽  
Jung-Peng Chiu ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Retrospective Study ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Adverse Events ◽  
Cancer Patients ◽  
Medical Records ◽  
Self Evaluation ◽  
Severe Adverse Events

Objective: Cancer patients undergo therapies that might lead to severe adverse events. The enhanced daycare of Traditional Chinese medicine (TCM) we describe was intended to help cancer patients suffering from severe adverse events to obtain relief. We used the Taiwan brief version of the Common Terminology Criteria for Adverse Events Version 4.0 (Taiwan brief version questionnaire of CTCAE) as a primary measurement to evaluate the efficacy of the enhanced day care of TCM. The secondary measurements were the Taiwanese version of the Brief Fatigue Inventory (BFI-T) questionnaire and the World Health Organization Quality of Life-BREF (WHOQOL-BREF) questionnaire, which were used to quantify fatigue and quality of life (QOL), respectively. Methods/Design: This is a retrospective study of medical records. There were 401 patients treated with enhanced daycare of TCM from June 2017 to November 2019. Results: Among 22 common adverse symptoms in the Taiwan brief version questionnaire of CTCAE4.0, 14 symptoms achieved a significant improvement, and the change of the total scores was also statistically significant ( P < .001). Cancer stages II to IV showed significant improvement on the CTCAE and BFI-T; stage I only showed improvement on the BFI-T. On the WHOQOL questionnaire, there was a statistically significant difference in self-evaluation of the quality of life ( P = .001) and self-evaluation of the total health condition aspect ( P < .001). Conclusions: The enhanced TCM daycare program helped cancer patients decrease the severity of their adverse events and improve their fatigue and QOL. ClinicalTrials.gov identifier: NCT04606121.

scholarly journals Olaparib dose re‐escalation in ovarian cancer patients who experienced severe and/or uncommon adverse events: A case series

2021 ◽  
Vol 17 (S3) ◽  
pp. 3-11
Author(s):  
Siew‐Fei Ngu ◽  
Ka‐Yu Tse ◽  
Mandy M. Y. Chu ◽  
Hextan Y. S. Ngan ◽  
Karen K. L. Chan
Keyword(s):  
Ovarian Cancer ◽  
Adverse Events ◽  
Cancer Patients ◽  
Case Series

scholarly journals Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanna Iivanainen ◽  
Jussi Ekstrom ◽  
Henri Virtanen ◽  
Vesa V. Kataja ◽  
Jussi P. Koivunen
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Adverse Events ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Patient Reported Outcomes ◽  
Prediction Models ◽  
Accuracy Score ◽  
Patient Reported ◽  
Auc Value

Abstract Background Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. Methods The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. Results The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. Conclusion The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset. Trial registration Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019

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