Follow-Up of Cancer Patients Receiving Anti-PD-(L)1 Therapy Using an Electronic Patient-Reported Outcomes Tool (KISS): Prospective Feasibility Cohort Study

FASTOCH: Feasibility of Electronic Patient-Reported Outcomes in Older Patients With Cancer-A Multicenter Prospective Study

PURPOSE

Multiple studies have demonstrated that electronic patient-reported outcomes (ePROs) improve overall survival and quality of life in cancer care. However, there are no specific prospective data on remote ePRO monitoring in the older population, although they represent a significant proportion of patients with cancer.

PATIENTS AND METHODS

From February 2021 to April 2022, patients age 75 years and older under active anticancer treatment were consecutively recruited in six institutions. Remote ePRO feasibility was determined in intention-to-test (ITT) on the basis of the number of active users in the overall population. Primary failure applied to patients who had no Internet access or declined to test ePROs, while the other patients were assigned to the ITT population. Feasibility was also determined per-protocol on the basis of the number of active patients in the ITT population.

RESULTS

Of the 473 patients included, primary failure applied to 288 patients (233 of whom had no Internet access). Among the 185 patients in ITT, 122 used ePROs, leading to a 26% feasibility in ITT and a 66% feasibility per protocol. In a multivariate analysis, the intent to test population was from a higher socioprofessional category (P = .009) and felt in better general condition in the Geriatric 8-score evaluation (P = .002). Active patients significantly differed from the inactive on their self-assessment of a better general condition (P < .001) only.

CONCLUSION

Our multicenter study showed a limited feasibility rate (26%) of remote ePROs monitoring for older patients with cancer, mainly because of technology barriers. Yet, among the patients who did have Internet access, most of them indeed used ePROs (66%). Given the expected benefit of ePROs, the technology barriers therefore need to be lifted to improve cancer care in older patients.

Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases

Limitations of pharmaceutical drugs and biologics for chronic diseases (e.g., medication non-adherence, adverse effects, toxicity, or inadequate efficacy) can be mitigated by mobile medical apps, known as digital therapeutics (DTx). Authorization of adjunct DTx by the US Food and Drug Administration and draft guidelines on "prescription drug use-related software" illustrate opportunities to create drug + digital combination therapies, ultimately leading towards drug-device combination products (DTx has a status of medical devices). Digital interventions (mobile, web-based, virtual reality, and video game applications) demonstrate clinically meaningful benefits for people living with Alzheimer's disease, dementia, rheumatoid arthritis, cancer, chronic pain, epilepsy, depression, and anxiety. In the respective animal disease models, preclinical studies on environmental enrichment and other non-pharmacological modalities (physical activity, social interactions, learning, and music) as surrogates for DTx "active ingredients" also show improved outcomes. In this narrative review, we discuss how drug + digital combination therapies can impact translational research, drug discovery and development, generic drug repurposing, and gene therapies. Market-driven incentives to create drug-device combination products are illustrated by Humira® (adalimumab) facing a "patent-cliff" competition with cheaper and more effective biosimilars seamlessly integrated with DTx. In conclusion, pharma and biotech companies, patients, and healthcare professionals will benefit from accelerating integration of digital interventions with pharmacotherapies.

Exploring the potential of digital therapeutics: An assessment of progress and promise

Digital therapeutics (DTx), a burgeoning subset of digital health solutions, has garnered considerable attention in recent times. These cutting-edge therapeutic interventions employ diverse technologies, powered by software algorithms, to treat, manage, and prevent a wide array of diseases and disorders. Although DTx shows significant promise as an integral component of medical care, its widespread integration is still in the preliminary stages. This limited adoption can be largely attributed to the scarcity of comprehensive research that delves into DTx's scope, including its technological underpinnings, potential application areas, and challenges-namely, regulatory hurdles and modest physician uptake. This review aims to bridge this knowledge gap by offering an in-depth overview of DTx products' value to both patients and clinicians. It evaluates the current state of maturity of DTx applications driven by digital technologies and investigates the obstacles that developers and regulators encounter in the market introduction phase.

An eHealth App (CAPABLE) Providing Symptom Monitoring, Well-Being Interventions, and Educational Material for Patients With Melanoma Treated With Immune Checkpoint Inhibitors: Protocol for an Exploratory Intervention Trial

BACKGROUND

Since treatment with immune checkpoint inhibitors (ICIs) is becoming standard therapy for patients with high-risk and advanced melanoma, an increasing number of patients experience treatment-related adverse events such as fatigue. Until now, studies have demonstrated the benefits of using eHealth tools to provide either symptom monitoring or interventions to reduce treatment-related symptoms such as fatigue. However, an eHealth tool that facilitates the combination of both symptom monitoring and symptom management in patients with melanoma treated with ICIs is still needed.

OBJECTIVE

In this pilot study, we will explore the use of the CAPABLE (Cancer Patients Better Life Experience) app in providing symptom monitoring, education, and well-being interventions on health-related quality of life (HRQoL) outcomes such as fatigue and physical functioning, as well as patients' acceptance and usability of using CAPABLE.

METHODS

This prospective, exploratory pilot study will examine changes in fatigue over time in 36 patients with stage III or IV melanoma during treatment with ICI using CAPABLE (a smartphone app and multisensory smartwatch). This cohort will be compared to a prospectively collected cohort of patients with melanoma treated with standard ICI therapy. CAPABLE will be used for a minimum of 3 and a maximum of 6 months. The primary endpoint in this study is the change in fatigue between baseline and 3 and 6 months after the start of treatment. Secondary end points include HRQoL outcomes, usability, and feasibility parameters.

RESULTS

Study inclusion started in April 2023 and is currently ongoing.

CONCLUSIONS

This pilot study will explore the effect, usability, and feasibility of CAPABLE in patients with melanoma during treatment with ICI. Adding the CAPABLE system to active treatment is hypothesized to decrease fatigue in patients with high-risk and advanced melanoma during treatment with ICIs compared to a control group receiving standard care. The Medical Ethics Committee NedMec (Amsterdam, The Netherlands) granted ethical approval for this study (reference number 22-981/NL81970.000.22).

TRIAL REGISTRATION

ClinicalTrials.gov NCT05827289; https://clinicaltrials.gov/study/NCT05827289.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/49252.

The Feasibility, Acceptability, and Effectiveness of Electronic Patient-Reported Outcome Symptom Monitoring for Immune Checkpoint Inhibitor Toxicities: A Systematic Review

PURPOSE

Increasing use of immune checkpoint inhibitors (ICIs) in routine cancer care will increase the incidence of immune-related adverse events (irAEs). Systems are needed to support remote monitoring for irAEs. Electronic patient-reported outcome (ePRO) symptom monitoring systems can help monitor and manage symptoms and side effects. We assessed the content and features of ePRO symptom monitoring systems for irAEs, and their feasibility, acceptability, and impact on patient outcomes and health care utilization.

METHODS

A systematic literature search was conducted in May 2022 on MEDLINE, Embase, PsycINFO, and Cochrane Central Register of Controlled Trials. Quantitative and qualitative data relevant to the review questions were extracted and synthesized in tables.

RESULTS

Seven papers describing five ePRO systems were included. All systems collected PROs between clinic visits. Two of five used validated symptom questionnaires, 3/5 provided prompts to complete questionnaires, 4/5 provided reminders to self-report, and 3/5 provided clinician alerts for severe/worsening side effects. Four of five provided coverage of ≥26/30 irAEs in the ASCO irAE guideline. Feasibility and acceptability were demonstrated with consent rates of 54%-100%, 17%-27% of questionnaires generating alerts, and adherence rates of 74%-75%. One paper showed a reduction in grade 3-4 irAEs, treatment discontinuation, clinic visit duration, and emergency department presentations, while another showed no difference in these outcomes or the rate of steroid use.

CONCLUSION

There is preliminary evidence of the feasibility and acceptability of ePRO symptom monitoring for irAEs. However, further studies are needed to confirm the impact on ICI-specific outcomes, such as the frequency of grade 3-4 irAEs and duration of immunosuppression. Suggestions for the content and features of future ePRO systems for irAEs are provided.

Assessing the impact of digital patient monitoring on health outcomes and healthcare resource usage in addition to the feasibility of its combination with at-home treatment, in participants receiving systemic anticancer treatment in clinical practice: protocol for an interventional, open-label, multicountry platform study (ORIGAMA)

INTRODUCTION

Digital patient monitoring (DPM) tools can enable more effective clinical care and improved patient outcomes in cancer. However, their broad adoption requires ease of use and demonstration of real-world clinical utility/impact. ORIGAMA (MO42720) is an interventional, open-label, multicountry platform study investigating the clinical utility of DPM tools and specific treatments. ORIGAMA will begin with two cohorts that aim to assess the impact of the atezolizumab-specific Roche DPM Module (hosted on the Kaiku Health DPM platform (Helsinki, Finland)) on health outcomes and healthcare resource usage, and its feasibility to support at-home treatment administration, in participants receiving systemic anticancer treatment. Other digital health solutions may be added to future cohorts.

METHODS AND ANALYSIS

In Cohort A, participants with metastatic non-small cell lung cancer (NSCLC), extensive-stage SCLC or Child Pugh A unresectable hepatocellular carcinoma will be randomised to a locally approved anticancer regimen containing intravenous atezolizumab (TECENTRIQ, F. Hoffmann-La Roche Ltd/Genentech) and local standard-of-care support, with/without the Roche DPM Module. Cohort B will assess the feasibility of the Roche DPM Module in supporting administration of three cycles of subcutaneous atezolizumab (1875 mg; Day 1 of each 21-day cycle) in the hospital, followed by 13 cycles at home by a healthcare professional (ie, flexible care), in participants with programmed cell-death ligand 1-positive, early-stage NSCLC. The primary endpoints are the mean difference in change of the participant-reported Total Symptom Interference Score at Week 12 from baseline (Cohort A) and flexible care adoption rate at Cycle 6 (Cohort B).

ETHICS AND DISSEMINATION

This study will be conducted according to the Declaration of Helsinki, and/or the applicable laws and regulations of the country in which the research is conducted, whichever affords the greater protection to the individual. The study received its first Ethics Committee approval in Spain in October 2022. Participants will provide written informed consent in a face-to-face setting. The results of this study will be presented at national and/or international congresses and disseminated via publication in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT05694013.

Symptoms, symptom clusters and associated factors among cancer patients receiving immune checkpoint inhibitor therapy: A cross-sectional survey

PURPOSE

The purpose of this study was to explore the symptoms and symptom clusters and analyse associated factors among cancer patients receiving ICI therapy.

METHODS

We analysed the data of 216 cancer patients who received immune checkpoint inhibitor therapy from the internal medicine unit of a university cancer centre in China. Participants were surveyed using the Eastern Cooperative Oncology Group Performance Score (ECOG PS) assessment, the ICI therapy symptom assessment scale, and demographic and disease characteristic questionnaires designed for this study. Exploratory factor analysis and multiple linear regression analysis were performed to analyse the data.

RESULTS

The most common symptoms in patients with grade 1-2 symptom severity were fatigue (57.4%), itching (34.3%) and cough (33.3%), and those with grade 3-4 symptom severity were rash (7.9%), joint pain (6.9%), muscle soreness (6.5%) and fatigue (6.5%). Four symptom clusters were identified: nonspecific, musculoskeletal, respiratory and cutaneous (the cumulative contribution to the variance was 64.070%). ECOG PS, disease course and gender were significantly associated with the nonspecific symptom cluster (Adj R² = 14.3). ECOG PS and disease course were significantly associated with the respiratory symptom cluster (Adj R² = 8.9). ECOG PS, disease course and education level were significantly associated with the musculoskeletal symptom cluster (Adj R² = 20.2).

CONCLUSION

Cancer patients receiving ICI therapy experience various symptoms with apparent clustering. The factors associated with symptom clusters included gender, education level, ECOG PS and disease course. These findings would be useful for medical personnel to provide relevant interventions to promote symptom management of ICI therapy.

Association of Rare Immune-Related Adverse Events to Survival in Advanced Cancer Patients Treated with Immune Checkpoint Inhibitors: A Real-World Single-Center Cohort Study

Immune checkpoint inhibitors (ICIs) are associated with immune-related (ir) adverse events (AEs) resembling autoimmune diseases. In this retrospective cohort study of patients (pts) treated with ICIs at Oulu University Hospital from 2014-2020, we analysed the spectrum of severe irAEs and their prognostic nature, focusing on rare irAEs. Pts (n = 173) with lung cancer (n = 76, 43.9%), melanoma (n = 56, 32.4%), renal and bladder cancers (n = 34, 19.7%), head and neck cancers (n = 4, 2.3%), SCC (n = 2, 1.2%), and CRC (n = 1, 0.6%) receiving single anti-PD-(L)1 (n = 160) or combination (ICI-ICI n = 9, ICI-chemotherapy n = 4) therapy were included. The survival analysis focused on single anti-PD-(L)1-treated patients with melanoma, lung cancer, and renal and bladder cancers (n = 142). Grade ≥ 3 irAEs of multiple aetiology occurred in 29 patients treated with single-PD-L1 therapy (20.4%), which was associated with improved progression-free survival (PFS) (HR 0.50, CI 0.31-0.78) but not overall survival (OS) (HR 0.88, CI 0.52-1.50). Rare grade ≥ 3 events occurred in 10 (7.0%) pts with no association with PFS (HR 0.90, CI 0.42-1.94). Hence, the presence of rare grade ≥ 3 irAEs was associated with a tendency for inferior OS (HR 1.44, CI 0.66-3.11). Pts with rare grade ≥ 3 irAEs had inferior OS, possibly reflecting the delay in diagnostic workflow and the treatment of irAEs. One explanation for the high incidence of irAEs could be the Finnish population-based genetic variation affecting the immune system.

Efficiency of Electronic Health Record Assessment of Patient-Reported Outcomes After Cancer Immunotherapy: A Randomized Clinical Trial

IMPORTANCE

Cancer immunotherapy causes a wide range of immune-related adverse events (irAEs) that require close and timely follow-up.

OBJECTIVES

To compare the efficiency between electronic patient-reported outcomes (ePRO) and traditional follow-up models in cancer immunotherapy.

DESIGN, SETTING, AND PARTICIPANTS

This open-label randomized clinical trial was performed from September 1, 2019, to March 31, 2021. Patients were randomized to the ePRO model intervention or a control group by a computer system. A total of 28 Chinese tertiary care hospitals participated. Patients who were receiving cancer immunotherapy and could use smartphones or computers were eligible. A total of 300 patients were screened and 278 (92.7%) were enrolled.

INTERVENTIONS

The control group was followed up using traditional methods, including clinic visits every 21 days and telephone follow-up every 3 months. In the intervention group, the ePRO follow-up model included a questionnaire of common symptoms and an image recognition function to evaluate grades of typical irAEs. Patients completed questionnaires weekly and uploaded pictures of results between visits. When grade 1 or 2 irAEs occurred, standardized advice was sent automatically. If grade 3 or 4 irAEs were reported, the model alerted the health care team for assessment and intervention immediately. All patients were followed up for 6 months or until treatment completion.

MAIN OUTCOMES AND MEASURES

Incidence of serious (grades 3 to 4) irAEs, emergency department (ED) visits, quality of life (QOL), time spent implementing the ePRO model, rate of treatment discontinuation, and death were compared between groups post intervention.

RESULTS

A total of 278 patients (mean [SD] age, 58.8 [12.7 (range, 27-78)] years; 206 men [74.1%]) were included in the analysis, consisting of 141 in the intervention group and 137 in the control group. At the postintervention evaluation, the intervention group showed a reduced incidence of serious irAEs (29 of 141 [20.6%] vs 46 of 137 [33.6%]; hazard ratio [HR], 0.51 [95% CI, 0.30-0.88]; P = .01), fewer ED visits (23 of 141 [16.3%] vs 41 of 137 [29.9%]; HR, 0.46 [95% CI, 0.26-0.81]; P = .01), a lower rate of treatment discontinuation (5 of 141 [3.6%] vs 15 of 137 [11.0%]; HR, 0.30 [95% CI, 0.11-0.85]; P = .02), a higher QOL level (mean [SD] score, 74.2 [15.1; 95% CI, 71.7-76.9] vs 64.7 [28.5; 95% CI, 61.0-68.4]; P = .001), and less time implementing follow-up (mean [SD], 8.2 [3.9; 95% CI, 5.0-10.6] minutes vs 36.1 [15.3; 95% CI, 33.6-38.8] minutes; P < .001). However, there were no significant differences between groups in death rates (2 of 141 [1.4%] vs 5 of 137 [3.6%]; HR, 0.38 [95% CI, 0.07-1.99]; P = .28).

CONCLUSIONS AND RELEVANCE

This randomized clinical trial found that the ePRO follow-up model can improve safety and QOL of patients receiving cancer immunotherapy as well as reduce time spent monitoring. This model may provide reliable information and management recommendations.

TRIAL REGISTRATION

Chinese Clinical Trial Registry Identifier: ChiCTR2100052819.

Predicting Objective Response Rate (ORR) in Immune Checkpoint Inhibitor (ICI) Therapies with Machine Learning (ML) by Combining Clinical and Patient-Reported Data

ICIs are a standard of care in several malignancies; however, according to overall response rate (ORR), only a subset of eligible patients benefits from ICIs. Thus, an ability to predict ORR could enable more rational use. In this study a ML-based ORR prediction model was built, with patient-reported symptom data and other clinical data as inputs, using the extreme gradient boosting technique (XGBoost). Prediction performance for unseen samples was evaluated using leave-one-out cross-validation (LOOCV), and the performance was evaluated with accuracy, AUC (area under curve), F1 score, and MCC (Matthew’s correlation coefficient). The ORR prediction model had a promising LOOCV performance with all four metrics: accuracy (75%), AUC (0.71), F1 score (0.58), and MCC (0.4). A rather good sensitivity (0.58) and high specificity (0.82) of the model were seen in the confusion matrix for all 63 LOOCV ORR predictions. The two most important symptoms for predicting the ORR were itching and fatigue. The results show that it is possible to predict ORR for patients with multiple advanced cancers undergoing ICI therapies with a ML model combining clinical, routine laboratory, and patient-reported data even with a limited size cohort.

Follow-up Care for Patients Receiving Immune Checkpoint Inhibitors

The rapid advances in cancer immunotherapy using immune checkpoint inhibitors (ICIs) have led to significantly improved survival of patients. But at the same time, it also associates with multiple immune-related adverse events (irAEs). The irAEs can affect a wide range of organs, and induce nonspecific symptoms with delayed onset and prolonged duration that is easily neglected, which may lead to life-threatening disorders. Therefore, follow-up care for patients receiving ICIs for irAEs management has become an essential competency in cancer nursing. There are several guidelines about the management of irAEs, which focused on diagnosis, grading, and treatment. However, studies on relevant follow-up care are rare. Nurses play an important role in follow-up care, whose relevant knowledge and skills are indispensable. Combined with domestic and foreign guidelines and related studies, this paper reviewed the occurrence and characteristics of irAEs and highlighted the contents, timing, models, and effects of follow-up care for patients receiving ICIs, to provide a reference for clinical nursing practice and improve the safety of immunotherapy for patients.

How Should Oncologists Choose an Electronic Patient-Reported Outcome System for Remote Monitoring of Patients With Cancer?

Electronic patient-reported outcome (ePRO) systems for symptom monitoring in patients with cancer have shown quality of life and survival benefits in controlled trials. They are beginning to be used in routine oncology practice. Many software developers provide software solutions for clinicians, but how should clinicians decide which system to use? We propose a synthesis of the main questions regarding the effectiveness, safety, and functionality of an ePRO system that a clinician should ask software providers to assist in the selection of a software product in order to obtain the best value tools for their patients and their practice.

eHealth intervention to manage symptoms for patients with cancer on immunotherapy (SOFIA): a study protocol for a randomised controlled external pilot trial

INTRODUCTION

Immune checkpoint therapy (ICT) is associated with a distinct pattern of immune-related adverse events (irAEs) caused by inadvertently redirecting immune responses to healthy tissues. IrAEs can occur at any time; however, in most cases, they arise during the first 14 weeks of the beginning of immune checkpoint blockade. In many cases, immunotherapy must be discontinued due to irAEs. Early detection of irAEs triggers the temporary withholding of ICT or initiation of short-term immunosuppressive treatment, is crucial in preventing further aggravation of irAEs and enables safe re-exposure to ICT. This prospective study aims to evaluate the feasibility of an eHealth intervention for patients under immunotherapy (managing symptoms of immunotherapy, SOFIA). The SOFIA-App consists of two components: SOFIA-Monitoring, a tool to rate patient-reported outcomes (PROs) including irAEs, and SOFIA-Coaching, which provides important information about cancer-specific and immunotherapy-specific topics and the counselling services of the National Centre for Tumour Diseases (NCT) Heidelberg.

METHODS AND ANALYSIS

We outlined a patient-level two-arm randomised controlled pilot trial of the intervention (SOFIA) versus no-SOFIA for patients with cancer beginning an immunotherapy, aged ≥18 years, recruited from the NCT, Heidelberg. Feasibility outcomes include: recruitment rate; drop-out rate; reasons for refusal and drop-out; willingness to be randomised, utilisation rate of SOFIA-Monitoring and utilisation time of SOFIA-Coaching, physicians utilisation rate of the PROs; feasibility of the proposed outcome measures and optimal sample size estimation. The clinical outcomes are measures of quality of life, psychosocial symptoms, self-efficacy, physician-patient communication and medical process data, which are assessed at the beginning of the intervention, postintervention and at 6-month follow-up.

ETHICS AND DISSEMINATION

This trial protocol was approved by the Ethical Committee of Heidelberg University, Germany (Reference, S-581/2018).

TRIAL REGISTRATION NUMBER

We registered the study in the German Clinical Trial Register (Reference: DRKS00021064). Findings will be disseminated broadly via peer-reviewed empirical journals, articles and conference presentations.

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

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.