During the last decade, 10 drugs have been approved by the Food and Drug Administration (FDA) for the treatment of metastatic renal cell carcinoma (mRCC). Many of these treatments are oral tyrosine kinase inhibitors (TKI’s) targeting the vascular endothelial growth factor (VEGF) receptor. These TKI’s are known to cause a variety of side effects including fatigue, hypertension, nausea, diarrhea, weight loss, palmar-plantar erythrodysesthesia, rash, and endocrine side effects. The exact timing and degree of side effects are difficult to predict but can be serious in 50% or more of cases. Given that these drugs are generally dosed at near maximum tolerated doses in all patients (i.e. flat dosing) and that the toxicities can be rapid onset in some cases within days or weeks of starting therapy, prompt recognition and management of toxicities are crucial to ensure safe management that will still lead to clinical efficacy.1-3 Other targeted therapies used in the treatment of mRCC include mammalian target of rapamycin (mTOR) inhibitors and immune checkpoint inhibitors, both of which can also have a wide variety of toxicities requiring close monitoring. Current and future indications may include combinations of these agents that further enhance their risks. Therefore, as new cancer therapies and indications for treatment are developed, it is imperative we maximize the ability to monitor patients in real time to assess rapid physiologic changes that could be harbingers of more serious safety concerns.
The mobile health (mHealth) industry is one of the largest growing business sectors in the world.4 In recent years, smart phone technology has advanced considerably, and patients are able to use a variety of devices to monitor health related parameters including physical activity, diet, blood pressure, heart rate, weight, blood sugar and many other important variables.4,5 Even prior to the rise of mHealth, telemedicine has been shown to be an effective tool to help manage many chronic medical problems including diabetes, heart failure, COPD and mental health.6-9 Although mHealth applications are being explored in cancer care,10,11 there are no published clinical trials evaluating the use of mHealth technology in clinical monitoring of patients with mRCC.
Side effects are quite common with VEGF inhibitor therapy. For instance a pooled analysis of clinical studies of patients treated with the now FDA approved VEGF inhibitors sunitinib, sorafenib, pazopanib, axitinib, and bevacizumab, showed dose reductions in 13-52% of pa- tients, dose interruptions 21-72% of patients, and discontinuations due to adverse events in 4-28% of patients.12 This is important as failure to maintain dose intensity may lead to decrease in survival in patients with mRCC.13 Hypertension, a side effect of these drugs was found to occur in 20-40% of patients. Gastrointestinal side effects such as anorexia, nausea, vomiting, and diarrhea were commonly reported in approximately 30-60% of patients and fatigue was reported in 50-60% of cases.12 Physical activity monitoring may be a valuable tool in assessing functionality in cancer patients and also a potential tool to encourage exercise, which may help combat symptoms like fatigue.14 As a result, in a very small study, we sought to investigate how mHealth technology could be used to monitor important clinical parameters including blood pressure, weight, and physical activity in patients initiating VEGF targeted therapy for metastatic renal cell carcinoma.
Two patients starting oral VEGF receptor TKI therapy for mRCC consented to enroll in a pilot clinical research trial. Patients were provided a wireless blood pressure monitor (iHealth Feel, iHealth Labs Inc.), a wireless weight scale (Smart body analyzer, Withings Inc.) and a physical activity tracker (UP24, Jawbone Co.). Patients were advised to use these monitors on a daily basis for ninety days at the beginning of starting therapy. Patients were taught how to transmit health data from these monitors to the Health App (Apple Inc.) on his or her smart phone. At our academic institution, patients can use the Duke MyChart App (patient portal) to send health data stored from the Health App directly into the electronic medical record called Maestro Care (Epic Systems, Co.). This data flow is shown in Figure 1. English speaking patients with home wireless internet access and an Apple mobile smartphone with the Health App (Apple Inc.) already installed were eligible to participate. The objectives were to demonstrate feasibility of data collection for this novel method of remotely monitoring mHealth data and also to compare electronically obtained mHealth data to information collected at interval clinic visits.
An 81-year-old female with a past medical history notable for hypertension, was diagnosed with metastatic renal cell carcinoma with metastases to the right lower lobe of the lung with associated large pleural effusion. At diagnosis, she underwent a right radical cytoreductive nephrectomy with pathology showing grade IV clear cell renal cell carcinoma. The patient started pazopanib 800 mg daily post-nephrectomy, and developed worsening treatment-related hypertension in the subsequent weeks. Five weeks after initiating pazopanib, she awoke from sleep with acute onset of shortness of breath and presented to the emergency department where her ejection fraction was 35%, her cardiac enzymes were negative, and her B-type natriuretic peptide (BNP) was elevated. She was admitted to the hospital, and initiated on heart failure therapy for what was presumed to be pazopanib induced cardiomyopathy. With aggressive medical management including diuretics, and antihypertensive therapy, her ejection fraction normalized and she had a repeat echocardiogram showing EF of 59%. The patient had a very nice response to pazopanib with a resolved pleural effusion and decreased size of her right lower lobe pleural effusion, but was switched to everolimus due to cardiotoxicity from pazopanib. The patient progressed on everolimus, and for third line systemic therapy, she was started on axitinib (another VEGF receptor-TKI) at a low dose of 3 mg twice daily. Of clinical concern was that the patient could be at high risk for repeat cardiotoxicity when starting axitinib therapy and needed close monitoring of her cardiovascular status including blood pressure.
Prior to starting on axitinib the patient was enrolled in our clinical study, and we initiated daily home blood pressure, weight, and activity monitoring with mHealth technology. To minimize her risk of cardiotoxicity, we aimed to keep her blood pressure less than 140/90. Figure 2A shows her blood pressure at four interval clinic visits during the first 90 days compared to the data from home blood pressure monitoring. In this particular case, home blood pressure monitoring detected blood pressure rises above SBP > 140 more quickly than monitoring only during clinic visits. Anti-hypertensive medications were up-titrated at the first interval clinic visit, which was approximately 2 weeks after the initiation of axitinib therapy. Significant weight loss was detected sooner with mHealth monitoring compared to interval clinic visits. Also of interest, her performance status was measured at KPS 90 and ECOG of 1 during each clinic visit during the 90-day interval. Her steps/day are shown during this same time period. The patient had re-staging scans three months into treatment with axitinib, which had shown stable disease at the time.
A 70-year-old gentleman with a history of hypertension, sleep apnea, diabetes, and obesity presented with painless gross hematuria. The patient’s imaging at presentation showed a left lower pole renal mass, enlarged periaortic and retroperitoneal lymph nodes, and bilateral pulmonary nodules. The patient underwent a cytoreductive nephrectomy, and pathology was consistent with clear cell RCC. The patient also had an endo-bronchial metastasis blocking 90% of the right main-stem bronchus and partially obstructing the left main-stem bronchus requiring a laser tumor ablation. Subsequent to this ablation, he was started on pazopanib post operatively.
Prior to starting on pazopanib he was enrolled onto our clinical study. Mobile health technology was used to monitor the patient’s blood pressure, physical activity, and weight changes after initiating treatment (Figure 2B). Due to this patient’s cardiovascular co-morbidities, blood pressure monitoring was important. Blood pressure readings at home and during 5 interval clinic visits are shown. Weight changes are picked up earlier through home monitoring as shown. The patient’s performance status was assessed as ECOG 1 during each clinic visit, KPS was not assessed. Steps per day are shown as well. Unfortunately, this patient had progressive disease after his first re-staging CT scan 3 months after initiating pazopanib therapy. Subsequently, he was taken off of pazopanib and started on everolimus, which was the standard second line therapy at the time.
The concept of home health monitoring is not new as telehealth applications have been well established in the care of chronic diseases such as hypertension, diabetes and COPD.6-9 However, as commercially available mHealth technology continues to become more sophisticated, medical professionals are slow to incorporate and validate the usefulness of new methods of home monitoring.4,5 Providing an efficient, secure method to transmit mHealth data from patients to providers is necessary. One major limitation to the use of mHealth in clinical care is the lack of methods to incorporate mHealth data easily into our health information systems.15 In this initial pilot study, we are able to demonstrate the feasibility of successfully transferring mHealth blood pressure, weight, and physical activity data by two patients electronically into the medical record easily available for review by the investigators. It should also be noted that both patients were fairly compliant with mHealth monitoring. No formal reminders were provided to patients to continue using their devices. The fact that the data from the devices flows directly into the electronic medical record is important to note as it allows a mechanism for health providers and nurses to monitor this data. Furthermore, the technology used in this study involves an electronic platform by Epic Systems Co., which is an electronic medical record system used widely, and as a result may have generalizability for larger scale use. Having an efficient mechanism for patients to share data is an essential first step to being able to validate and incorporate mHealth into clinical care.
We believe there is a role to expand the use of mHealth applications on a larger scale as well. Detecting, and intervening on real-time clinical changes based on mHealth monitoring may have potential to help improve treatment efficacy, prevent hospitalizations, prevent hospital resource utilization, and improve survival in patients with renal cell carcinoma. In the case studies presented, patients had metastatic renal cell carcinoma and were treated with VEGF receptor TKIs, a class of oral medications that have different dosing levels and a wide variety of common side effects.1-3 Detection and management of these side effects quickly is essential, given that maintaining dose intensity of VEGF receptor TKIs improves patient survival.13 Blood pressure increases are thought to be a direct on-target side effect of inhibiting the VEGF receptor. Recognizing and treating hypertension may be important to prevent morbidity such as heart failure, PRESS syndrome, and cardiovascular events in patients. For instance, in Case 1, blood pressure increases were detected through mHealth monitoring even prior to the first interval clinic visit, which may have provided an earlier opportunity for clinical intervention in this case. Previous studies have shown that home BP monitoring in patients receiving VEGF receptor TKIs is important to detect as occult rises in blood pressure can be missed by measuring only at clinic visits.16 Weight loss in patients with cancer is common, especially in patients being treated with medications that cause gastrointestinal side effects. In both cases presented, weight loss was more evident even during the first few weeks of therapy through home monitoring. This may serve as a way to detect potential gastrointestinal intolerability as well as design symptom management to combat weight loss sooner. It should be noted that with respect to home monitoring of weight and blood pressure, measurements between home and clinic monitoring were fairly similar from an accuracy standpoint (Figure 2); although some health monitoring devices such as the blood pressure cuff used in this study are FDA approved, many are not. While much still needs to be done in terms of validating the accuracy of particular health monitors, the precision and benefits of real time monitoring in the cases presented here appears to give valuable clinical information.
Both patients in this case study presented were shown to have a robust performance status of ECOG of 1. This generally indicates patients who are fairly active with normal activity and minimal symptoms. Both patients consistently took less than 5000 steps/day, which is often considered to be sedentary behavior.17 Home monitoring of physical activity could provide a better assessment of performance status. Furthermore, being able to detect real time changes in activity patterns may provide a more objective manner to quantify changes especially when it comes to subjective clinical assessments such as fatigue, functionality, and quality of life.
Finally, it is worth mentioning that two recent studies in cancer patients also emphasize the importance of prompt symptom detection in patients with cancer. A phase III clinical trial (NCT02361099) in 121 patients with metastatic lung cancer showed how a web application based surveillance approach to capture symptoms improved patient survival compared to standard of care interval clinic based symptom monitoring (19 months vs. 11.8 months).11 Just recently, another clinical trial (NCT00578006) showed how using a web-based symptom monitoring patient reported outcomes (PROs) tool, which automatically alerted health care providers to severe or worsening patient symptoms, improved survival compared to usual care in outpatient cancer patients receiving chemotherapy (31.2 vs 26.0 months).18
It is important to acknowledge that there are many hurdles to consider in regards to expanding mHealth applications in cancer care. These include the potential for breach of privacy of patient health information, validation of the accuracy of mHealth sensor technology, health care cost and reimbursement, as well as the issue of determining how to triage clinical responses to real time monitoring of health information. Our current report shows how mHealth can be used to remotely monitor clinical parameters such as blood pressure, weight, and physical activity, which are important for patients with mRCC treated with VEGF inhibitors. In an era where newer treatments for renal cancer including targeted agents, immunotherapies, and combination approaches continue to expand rapidly, we believe this feasibility study is an important first step in a continuum of research to eventually design larger interventional trials, which will validate and better define how mHealth can help improve clinical outcomes in this patient population.
BP – Blood pressure
FDA – Food and Drug Administration
mRCC – metastatic renal cell carcinoma
VEGF – vascular endothelial growth factor
TKI’s – tyrosine kinase inhibitors
mTOR – mammalian target of rapamycin
App – Application
BNP – B-type natriuretic peptide
Conflict of Interest
Richard A. Bloomfield Jr was Director of Mobile Technology Strategy for Duke University Health System at the time this clinical trial was designed and completed. He currently works for Apple, Inc.
1. Cohen RB, Oudard S. Antiangiogenic therapy for advanced renal cell carcinoma: management of treatment-related toxicities. Invest New Drugs. 2012;30(5):2066-2079.
2. Hudes GR, Carducci MA, Choueiri TK, et al. NCCN Task Force report: optimizing treatment of advanced renal cell carcinoma with molecular targeted therapy. Journal of the National Comprehensive Cancer Network: JNCCN. 2011;9 Suppl 1:S1-29.
3. Hall PS, Harshman LC, Srinivas S, Witteles RM. The frequency and severity of cardiovascular toxicity from targeted therapy in advanced renal cell carcinoma patients. JACC Heart Fail. 2013;1(1):72-78.
4. Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Transl Med. 2015;7(283):283rv283.
5. Odeh B, Kayyali R, Nabhani-Gebara S, Philip N. Optimizing cancer care through mobile health. Support Care Cancer. 2015;23(7):2183-2188.
6. Bonoto BC, de Araujo VE, Godoi IP, et al. Efficacy of Mobile Apps to Support the Care of Patients With Diabetes Mellitus: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. JMIR Mhealth Uhealth. 2017;5(3):e4.
7. Goldberg LR, Piette JD, Walsh MN, et al. Randomized trial of a daily electronic home monitoring system in patients with advanced heart failure: the Weight Monitoring in Heart Failure (WHARF) trial. Am Heart J. 2003;146(4):705-712.
8. Alwashmi M, Hawboldt J, Davis E, Marra C, Gamble JM, Abu Ashour W. The Effect of Smartphone Interventions on Patients With Chronic Obstructive Pulmonary Disease Exacerbations: A Systematic Review and Meta-Analysis. JMIR Mhealth Uhealth. 2016;4(3):e105.
9. Agboola SO, Ju W, Elfiky A, Kvedar JC, Jethwani K. The effect of technology-based interventions on pain, depression, and quality of life in patients with cancer: a systematic review of randomized controlled trials. J Med Internet Res. 2015;17(3):e65.
10. Agboola S, Flanagan C, Searl M, Elfiky A, Kvedar J, Jethwani K. Improving outcomes in cancer patients on oral anti-cancer medications using a novel mobile phone-based intervention: study design of a randomized controlled trial. JMIR research protocols. 2014;3(4):e79.
11. Denis F, Lethrosne C, Pourel N, et al. Overall survival in patients with lung cancer using a web-application-guided follow-up compared to standard modalities: Results of phase III randomized trial. Journal of Clinical Oncology. 2016;34(18_suppl):LBA9006-LBA9006.
12. Schmidinger M. Understanding and managing toxicities of vascular endothelial growth factor (VEGF) inhibitors. EJC supplements : EJC : official journal of EORTC, European Organization for Research and Treatment of Cancer … [et al.]. 2013;11(2):172-191.
13. Houk BE, Bello CL, Poland B, Rosen LS, Demetri GD, Motzer RJ. Relationship between exposure to sunitinib and efficacy and tolerability endpoints in patients with cancer: results of a pharmacokinetic/pharmacodynamic meta-analysis. Cancer Chemother Pharmacol. 2010;66(2): 357-371.
14. Beg MS, Gupta A, Stewart T, Rethorst CD. Promise of Wearable Physical Activity Monitors in Oncology Practice. J Oncol Pract. 2017;13(2):82-89.
15. Nasi G, Cucciniello M, Guerrazzi C. The role of mobile technologies in health care processes: the case of cancer supportive care. J Med Internet Res. 2015;17(2):e26.
16. Azizi M, Chedid A, Oudard S. Home blood-pressure monitoring in patients receiving sunitinib. N Engl J Med. 2008;358(1):95-97.
17. Tudor-Locke C, Bassett DR, Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34(1):1-8.
18. Basch EM, Deal AM, Dueck AC, et al. Overall survival results of a randomized trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. Journal of Clinical Oncology. 2017;35(15_suppl):LBA2-LBA2. KCJ
Corresponding Author: Sundhar Ramalingam, MD, Division of Medical Oncology, Duke University Department of Medicine, Durham, North Carolina Address: 2301 Erwin Road/Durham, NC 27710 e-mail: email@example.com Phone: 919 244 7237 Fax: 919 854 6969