The Effect of Collaborative Reviews of Electronic Patient-Reported Outcomes on the Congruence of Patient- and Clinician-Reported Toxicity in Cancer Patients Receiving Systemic Therapy: Prospective, Multicenter, Observational Clinical Trial

Mobile Electronic Patient-Reported Outcomes and Interactive Support During Breast and Prostate Cancer Treatment: Health Economic Evaluation From Two Randomized Controlled Trials

BACKGROUND

Digital interventions for supportive care during cancer treatment incorporating electronic patient-reported outcomes (ePROs) can enhance early detection of symptoms and facilitate timely symptom management. However, economic evaluations are needed.

OBJECTIVE

This study aims to conduct a cost-utility analysis of an app for ePRO and interactive support from the perspective of the payer (Region Stockholm Health Care Organization) and to explore its impact on patient health care utilization and costs.

METHODS

Two open-label randomized controlled trials (RCTs) were conducted, including patients undergoing neoadjuvant chemotherapy for breast cancer (B-RCT; N=149) and radiotherapy for prostate cancer (P-RCT; N=150), recruited from oncology clinics at 2 university hospitals in Stockholm, Sweden. EORTC QLQ-C30 scores were mapped to EQ-5D-3L to calculate quality-adjusted life years (QALYs). Intervention and implementation costs and health care costs, obtained from an administrative database, were used to calculate incremental cost-effectiveness ratios (ICERs) in 3 ways: including all health care costs (ICERa), excluding nonacute health care costs (ICERb), and excluding health care costs altogether (ICERc). Nonparametric bootstrapping was used to explore ICER uncertainty. Health care costs were analyzed by classifying them as disease-related or acute.

RESULTS

In both RCT intervention groups, fewer QALYs were lost compared with the control group (P<.001). In the B-RCT, the mean intervention cost was €92 (SD €2; €1=US $1.03). The mean cost for the intervention and all health care was €36,882 (SD €1032) in the intervention group and €35,427 (SD €959) in the control group (P<.001), with an ICERa of €202,368 (95% CI €152,008-€252,728). The mean cost for the intervention and acute health care was €3585 (SD €480) in the intervention group and €3235 (SD €494) in the control group (P<.001). ICERb was €49,903 (95% CI €37,049-€62,758) and ICERc was €13,213 (95% CI €11,145-€15,281); 22 out of 74 (30%) intervention group patients and 24 out of 75 (32%) of the control group patients required acute inpatient care for fever. In the P-RCT, the mean intervention cost was €43 (SD €0.2). The mean cost for the intervention and all health care was €3419 (SD €739) in the intervention group and €3537 (SD €689) in the control group (P<.001), with an ICERa of -€1,092,136 (95% CI -€3,274,774 to €1,090,502). The mean cost for the intervention and acute health care was €1219 (SD €593) in the intervention group and €802 (SD €281) in the control group (P<.001). ICERb was €745,987 (95% CI -€247,317 to €1,739,292) and ICERc was €13,118 (95% CI -68,468 to €94,704). As many as 10 out of the 75 (13%) intervention group patients had acute inpatient care, with the most common symptom being dyspnea, while 9 out of the 75 (12%) control group patients had acute inpatient care, with the most common symptom being urinary tract infection.

CONCLUSIONS

ePRO and interactive support via an app generated a small improvement in QALYs at a low intervention cost and may be cost-effective, depending on the costs considered. Considerable variability in patient health care costs introduced uncertainty around the estimates, preventing a robust determination of cost-effectiveness. Larger studies examining cost-effectiveness from a societal perspective are needed. The study provides valuable insights into acute health care utilization during cancer treatment.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02479607; https://clinicaltrials.gov/ct2/show/NCT02479607, ClinicalTrials.gov NCT02477137; https://clinicaltrials.gov/ct2/show/NCT02477137.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s12885-017-3450-y.

Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning

Background

The use of smartphone apps in cancer patients undergoing systemic treatment can promote the early detection of symptoms and therapy side effects and may be supported by machine learning (ML) for timely adaptation of therapies and reduction of adverse events and unplanned admissions.

Objective

We aimed to create an Early Warning System (EWS) to predict situations where supportive interventions become necessary to prevent unplanned visits. For this, dynamically collected standardized electronic patient reported outcome (ePRO) data were analyzed in context with the patient's individual journey. Information on well-being, vital parameters, medication, and free text were also considered for establishing a hybrid ML model. The goal was to integrate both the strengths of ML in sifting through large amounts of data and the long-standing experience of human experts. Given the limitations of highly imbalanced datasets (where only very few adverse events are present) and the limitations of humans in overseeing all possible cause of such events, we hypothesize that it should be possible to combine both in order to partially overcome these limitations.

Methods

The prediction of unplanned visits was achieved by employing a white-box ML algorithm (i.e., rule learner), which learned rules from patient data (i.e., ePROs, vital parameters, free text) that were captured via a medical device smartphone app. Those rules indicated situations where patients experienced unplanned visits and, hence, were captured as alert triggers in the EWS. Each rule was evaluated based on a cost matrix, where false negatives (FNs) have higher costs than false positives (FPs, i.e., false alarms). Rules were then ranked according to the costs and priority was given to the least expensive ones. Finally, the rules with higher priority were reviewed by two oncological experts for plausibility check and for extending them with additional conditions. This hybrid approach comprised the application of a sensitive ML algorithm producing several potentially unreliable, but fully human-interpretable and -modifiable rules, which could then be adjusted by human experts.

Results

From a cohort of 214 patients and more than 16'000 available data entries, the machine-learned rule set achieved a recall of 19% on the entire dataset and a precision of 5%. We compared this performance to a set of conditions that a human expert had defined to predict adverse events. This "human baseline" did not discover any of the adverse events recorded in our dataset, i.e., it came with a recall and precision of 0%. Despite more plentiful results were expected by our machine learning approach, the involved medical experts a) had understood and were able to make sense of the rules and b) felt capable to suggest modification to the rules, some of which could potentially increase their precision. Suggested modifications of rules included e.g., adding or tightening certain conditions to make them less sensitive or changing the rule consequences: sometimes further monitoring the situation, applying certain test (such as a CRP test) or applying some simple pain-relieving measures was deemed sufficient, making a costly consultation with the physician unnecessary. We can thus conclude that it is possible to apply machine learning as an inspirational tool that can help human experts to formulate rules for an EWS. While humans seem to lack the ability to define such rules without such support, they are capable of modifying the rules to increase their precision and generalizability.

Conclusions

Learning rules from dynamic ePRO datasets may be used to assist human experts in establishing an early warning system for cancer patients in outpatient settings.

Improving Practice in a Head and Neck Oncology Clinic Using the PRO-CTCAE Tool

Background

Patients with head and neck cancer undergoing treatment report many side effects. Using patient-reported outcomes can assist with care management.

Objectives

The purpose of this quality improvement project was to implement the patient-reported outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) measurement system, reduce patient hydration visits, and measure provider satisfaction with the PRO-CTCAE survey.

Methods

Statistical analysis was conducted using IBM SPSS software. Descriptive statistics for means were used to summarize the data for survey completion rate and for the provider satisfaction questionnaire. A Fisher's exact test was used to compare hydration visits before and after implementation of the PRO-CTCAE survey.

Findings

The PRO-CTCAE surveys had a response rate of 91.2% (323/354) when telehealth visits were omitted. Hydration in the presurvey group was 23.5% (150/637) and in the postsurvey group was 38.5% (165/429), a 15% absolute percentage increase (Fisher's exact p < .001). Among providers, the positive response rate was 100% for five questions and 88.9% for two questions.

Implications

The PRO-CTCAE survey allowed the patient to report their symptoms prior to discussing them with their provider. Providers were able to expedite symptom management and get information to patients in a timely manner. The PRO-CTCAE survey should be considered a part of a multidisciplinary approach to caring for patients.

Impact of Electronic Patient-Reported Outcomes on Unplanned Consultations and Hospitalizations in Patients With Cancer Undergoing Systemic Therapy: Results of a Patient-Reported Outcome Study Compared With Matched Retrospective Data

BACKGROUND

The evaluation of electronic patient-reported outcomes (ePROs) is increasingly being used in clinical studies of patients with cancer and enables structured and standardized data collection in patients' everyday lives. So far, few studies or analyses have focused on the medical benefit of ePROs for patients.

OBJECTIVE

The current exploratory analysis aimed to obtain an initial indication of whether the use of the Consilium Care app (recently renamed medidux; mobile Health AG) for structured and regular self-assessment of side effects by ePROs had a recognizable effect on incidences of unplanned consultations and hospitalizations of patients with cancer compared to a control group in a real-world care setting without app use. To analyze this, the incidences of unplanned consultations and hospitalizations of patients with cancer using the Consilium Care app that were recorded by the treating physicians as part of the patient reported outcome (PRO) study were compared retrospectively to corresponding data from a comparable population of patients with cancer collected at 2 Swiss oncology centers during standard-of-care treatment.

METHODS

Patients with cancer in the PRO study (178 included in this analysis) receiving systemic therapy in a neoadjuvant or noncurative setting performed a self-assessment of side effects via the Consilium Care app over an observational period of 90 days. In this period, unplanned (emergency) consultations and hospitalizations were documented by the participating physicians. The incidence of these events was compared with retrospective data obtained from 2 Swiss tumor centers for a matched cohort of patients with cancer.

RESULTS

Both patient groups were comparable in terms of age and gender ratio, as well as the distribution of cancer entities and Joint Committee on Cancer stages. In total, 139 patients from each group were treated with chemotherapy and 39 with other therapies. Looking at all patients, no significant difference in events per patient was found between the Consilium group and the control group (odds ratio 0.742, 90% CI 0.455-1.206). However, a multivariate regression model revealed that the interaction term between the Consilium group and the factor "chemotherapy" was significant at the 5% level (P=.048). This motivated a corresponding subgroup analysis that indicated a relevant reduction of the risk for the intervention group in the subgroup of patients who underwent chemotherapy. The corresponding odds ratio of 0.53, 90% CI 0.288-0.957 is equivalent to a halving of the risk for patients in the Consilium group and suggests a clinically relevant effect that is significant at a 2-sided 10% level (P=.08, Fisher exact test).

CONCLUSIONS

A comparison of unplanned consultations and hospitalizations from the PRO study with retrospective data from a comparable cohort of patients with cancer suggests a positive effect of regular app-based ePROs for patients receiving chemotherapy. These data are to be verified in the ongoing randomized PRO2 study (registered on ClinicalTrials.gov; NCT05425550).

TRIAL REGISTRATION

ClinicalTrials.gov NCT03578731; https://www.clinicaltrials.gov/ct2/show/NCT03578731.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/29271.

Comparison of the Real-World Reporting of Symptoms and Well-Being for the HER2-Directed Trastuzumab Biosimilar Ogivri With Registry Data for Herceptin in the Treatment of Breast Cancer: Prospective Observational Study (OGIPRO) of Electronic Patient-Reported Outcomes

BACKGROUND

Trastuzumab has had a major impact on the treatment of human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). Anti-HER2 biosimilars such as Ogivri have demonstrated safety and clinical equivalence to trastuzumab (using Herceptin as the reference product) in clinical trials. To our knowledge, there has been no real-world report of the side effects and quality of life (QoL) in patients treated with biosimilars using electronic patient-reported outcomes (ePROs).

OBJECTIVE

The primary objective of this prospective observational study (OGIPRO study) was to compare the ePRO data related to treatment side effects collected with the medidux app in patients with HER2-positive BC treated with the trastuzumab biosimilar Ogivri (prospective cohort) to those obtained from historical cohorts treated with Herceptin alone or combined with pertuzumab and/or chemotherapy (ClinicalTrials.gov NCT02004496 and NCT03578731).

METHODS

Patients were treated with Ogivri alone or combined with pertuzumab and/or chemotherapy and hormone therapy in (neo)adjuvant and palliative settings. Patients used the medidux app to dynamically record symptoms (according to the Common Terminology Criteria for Adverse Events [CTCAE]), well-being (according to the Eastern Cooperative Oncology Group Performance Status scale), QoL (using the EQ-5D-5L questionnaire), cognitive capabilities, and vital parameters over 6 weeks. The primary endpoint was the mean CTCAE score. Key secondary endpoints included the mean well-being score. Data of this prospective cohort were compared with those of the historical cohorts (n=38 patients; median age 51, range 31-78 years).

RESULTS

Overall, 53 female patients with a median age of 54 years (range 31-87 years) were enrolled in the OGIPRO study. The mean CTCAE score was analyzed in 50 patients with available data on symptoms, while the mean well-being score was evaluated in 52 patients with available data. The most common symptoms reported in both cohorts included fatigue, taste disorder, nausea, diarrhea, dry mucosa, joint discomfort, tingling, sleep disorder, headache, and appetite loss. Most patients experienced minimal (grade 0) or mild (grade 1) toxicities in both cohorts. The mean CTCAE score was comparable between the prospective and historical cohorts (29.0 and 30.3, respectively; mean difference -1.27, 95% CI -7.24 to 4.70; P=.68). Similarly, no significant difference was found for the mean well-being score between the groups treated with the trastuzumab biosimilar Ogivri and Herceptin (74.3 and 69.8, respectively; mean difference 4.45, 95% CI -3.53 to 12.44; P=.28).

CONCLUSIONS

Treatment of patients with HER2-positive BC with the trastuzumab biosimilar Ogivri resulted in equivalent symptoms, adverse events, and well-being as found for patients treated with Herceptin as determined by ePRO data. Hence, integration of an ePRO system into research and clinical practice can provide reliable information when investigating the real-world tolerability and outcomes of similar therapeutic compounds.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05234021; https://clinicaltrials.gov/study/NCT05234021.

Interference of Herbal Medicine with Axitinib in Metastatic Renal Cell Cancer Treatment: A Case Study

Introduction

The awareness and the clinical relevance of the potential interactions between standard and complementary medicine are increasing in medical oncology. Nonetheless, the research and experience of the efficacy, safety, and toxicity of herbal substances are poorly documented.

Case Presentation

Here, we report the case of a 68-year-old female patient who had been diagnosed with advanced renal cell cancer with metastasis in the liver and pancreas and had undergone surgical resection with hemi-hepatectomy and resection of metastasis in the pancreas in November 2021. Thereafter, chemotherapy was immediately initiated with three-weekly infusions of pembrolizumab and daily intake of the tyrosine kinase inhibitor axitinib. Surprisingly, 3 months after initiation of systemic treatment, the patient developed early progression and metastasis in the liver, which was then treated with selective internal radiotherapy. Despite continued axitinib and pembrolizumab treatment, a short-term follow-up in November 2022 revealed another metastatic lesion in her pancreas. Due to the presumed lack of response to treatment, the plasma concentration of axitinib was measured and found to demonstrate subtherapeutic levels of exposure. Upon extended anamnesis, the patient reported regular intake of herbal substances prescribed by her oncology acupuncturist for gastrointestinal complaints associated with the primary operation.

Conclusion

Further clinical-pharmacological workup strikingly demonstrated a reduction of the therapeutic concentration of axitinib of about 90%, likely caused by herbal drugs such as Dang gui and Bai zhu.

Ovarian Cancer Symptom Clusters: Use of the NIH Symptom Science Model for Precision in Symptom Recognition and Management

BACKGROUND

In the United States, ovarian cancer remains the deadliest gynecologic cancer because most women are diagnosed with advanced disease. Although early-stage ovarian tumors are considered asymptomatic, women experience symptoms throughout disease.

OBJECTIVES

This review identifies ovarian cancer symptom clusters and explores the applicability of the National Institutes of Health Symptom Science Model (NIH-SSM) for prompt symptom recognition and clinical intervention.

METHODS

A focused CINAHL® and PubMed® database search was conducted for studies published from January 2000 to May 2022 using combinations of key terms.

FINDINGS

The NIH-SSM can guide the delivery of precision-focused interventions that address racial disparities and foster equity in symptom- focused care. Enhanced understanding of symptom biology can support clinical oncology nurses in ambulatory and inpatient settings.

Needs of Lung Cancer Patients Receiving Immunotherapy and Acceptance of Digital and Sensor-Based Scenarios for Monitoring Symptoms at Home—A Qualitative-Explorative Study

Background: The development of immunotherapy in the treatment for lung cancer has changed the outlook for both patients and health care practitioners. However, reporting and management of side effects are crucial to ensure effectiveness and safety of treatment. The aim of this study was to learn about the subjective experiences of patients with lung cancer receiving immunotherapy and to explore their potential acceptance of digital and sensor-based systems for monitoring treatment-related symptoms at home. Methods: A qualitative-explorative interview study with patients with lung cancer (n = 21) applying qualitative content analysis. Results: Participants had trouble to classify and differentiate between symptoms they experienced and it seemed challenging to assess whether symptoms are serious enough to be reported and to figure out the right time to report symptoms to health care practitioners. We identified four basic needs: (1) the need to be informed, (2) the need for a trustful relationship, (3) the need to be taken seriously, and (4) the need for needs-oriented treatment concepts. The idea of digital and sensor-based monitoring initially provoked rejection, but participants expressed more differentiated attitudes during the interviews, which could be integrated into a preliminary model to explain the acceptance of digital and sensor-based monitoring scenarios. Conclusions: Supporting lung cancer patients and their health care providers in communicating about treatment-related symptoms is important. Technology-based monitoring systems are considered to be potentially beneficial. However, in view of the many unfulfilled information needs and the unsatisfactory reporting of symptoms, it must be critically questioned what these systems can and should compensate for, and where the limits of such monitoring lie.