CANKADO PRO-React: E-health solution with dynamic symptom questionnaires and automated recommendations for cancer patients.

2018 ◽  
Vol 36 (7_suppl) ◽  
pp. 7-7
Author(s):  
Timo Schinkothe ◽  
Susan Praveen ◽  
Abhishek Lal ◽  
Baiju Rahman
Keyword(s):  
Cancer Patients ◽  
Patient Reported Outcomes ◽  
Automated Analysis ◽  
Routine Care ◽  
Patient Relationship ◽  
Conventional Chemotherapy ◽  
Online Documentation ◽  
Patient Reported ◽  
Automated Feedback ◽  
Dynamic Recognition

7 Background: It is proven that the online documentation and transmission of symptoms can prolong the overall survival of cancer patients. Recent studies have shown that this advantage exists both in conventional chemotherapy and in oral therapy.Current implementations require a healthcare professional who monitors the data online. In most centers, such kind of human resources is not available except in clinical trials. It was therefore the goal of this project to develop an eHealth procedure, in which no permanent online observation is necessary. Methods: PRO-React, a symptom-based automated feedback or advice feature is integrated into the eHealth platform CANKADO. In the online settings, physician activates medication plans for his patients, documents drug-taking and sets reminders for the patient of future intake times. PRO-React assists the patients daily in global health documentation and dynamic symptom questionnaires. A built-in automated analysis brings forth suitable recommendations for patients like discuss with the physician, visit the physician immediately, visit directly or go to ER. The patient reported outcomes are shared in real-time with the physicians. Results: Trials are ongoing. Interim analysis is planed for January 2018. Results will be presented at the conference. Conclusions: PRO-React helps in the dynamic recognition of events with minimal daily documentation from the patients as well as assist in reacting to the needs of the patients. It also circumvents the need of a mediator group like nurses and thereby increase compliance and improve the doctor-patient-relationship leading to decreased mortality and higher patient satisfaction. PRO-React is available for routine care but also included into several multi-center trials.

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

scholarly journals Linguistic validation of the simplified Chinese version of the US National Cancer Institute’s patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE™)

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Cheng KKF ◽  
S. A. Mitchell ◽  
N. Chan ◽  
E. Ang ◽  
W. Tam ◽  
...  
Keyword(s):  
Adverse Events ◽  
Cancer Patients ◽  
Patient Reported Outcomes ◽  
A Priori ◽  
Cognitive Interviews ◽  
Simplified Chinese ◽  
Patient Reported ◽  
The Us ◽  
The Common ◽  
Response Choices

Abstract Background The aim of this study was to translate and linguistically validate the U.S. National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE™) into Simplified Chinese for use in Singapore. Methods All 124 items of the English source PRO-CTCAE item library were translated into Simplified Chinese using internationally established translation procedures. Two rounds of cognitive interviews were conducted with 96 cancer patients undergoing adjuvant treatment to determine if the translations adequately captured the PRO-CTCAE source concepts, and to evaluate comprehension, clarity and ease of judgement. Interview probes addressed the 78 PRO-CTCAE symptom terms (e.g. fatigue), as well as the attributes (e.g. severity), response choices, and phrasing of ‘at its worst’. Items that met the a priori threshold of ≥20% of participants with comprehension difficulties were considered for rephrasing and retesting. Items where < 20% of the sample experienced comprehension difficulties were also considered for rephrasing if better phrasing options were available. Results A majority of PRO-CTCAE-Simplified Chinese items were well comprehended by participants in Round 1. One item posed difficulties in ≥20% and was revised. Two items presented difficulties in < 20% but were revised as there were preferred alternative phrasings. Twenty-four items presented difficulties in < 10% of respondents. Of these, eleven items were revised to an alternative preferred phrasing, four items were revised to include synonyms. Revised items were tested in Round 2 and demonstrated satisfactory comprehension. Conclusions PRO-CTCAE-Simplified Chinese has been successfully developed and linguistically validated in a sample of cancer patients residing in Singapore.

scholarly journals Patient-Reported Outcomes, Health-Related Quality of Life, and Clinical Outcomes for Urothelial Cancer Patients Receiving Chemo- or Immunotherapy: A Real-Life Experience

2021 ◽  
Vol 10 (9) ◽  
pp. 1852
Author(s):  
Gry Assam Taarnhøj ◽  
Henriette Lindberg ◽  
Christoffer Johansen ◽  
Helle Pappot
Keyword(s):  
Quality Of Life ◽  
Cancer Patients ◽  
Patient Reported Outcomes ◽  
Life Experience ◽  
Locally Advanced ◽  
Neoadjuvant Treatment ◽  
Real Life ◽  
Oncological Treatment ◽  
Patient Reported

Patients with urothelial cell carcinoma (UCC) often have comorbidities, which cause trouble for the completion of oncological treatment, and little is known about their quality of life (QoL). The aim of the present study was to obtain and describe patient-reported outcomes (PRO) and QoL data from UCC patients in the treatment for locally advanced muscle-invasive or metastatic UCC. A total of 79 patients with UCC completed four questionnaires (EORTC QLQ-C30, QLQ-BLM30, HADS, and select PRO-CTCAE™ questions) once weekly during their treatment. From those, 26 patients (33%) underwent neoadjuvant treatment for local disease while 53 patients (67%) were treated for metastatic disease. Of all patients, 54% did not complete the planned treatment due to progression, nephrotoxicity, death, or intolerable symptoms during treatment. The five most prevalent PRO-CTCAE grade ≥ 2 symptoms were frequent urination (37%), fatigue (35%), pain (31%), dry mouth (23%), and swelling of the arms or legs (23%). The baseline mean overall QoL was 61 (±SD 24) for all patients (neoadjuvant (73, ±SD 19) and metastatic (54, ±SD 24)) and remained stable over the course of treatment for both groups. A stable overall QoL was observed for the patients in this study. More than half of the patients did not, however, complete the planned treatment. Further supportive care is warranted for bladder cancer patients.

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