Literature DB >> 33963910

Impact of assessment frequency of patient-reported outcomes: an observational study using an eHealth platform in cancer patients.

Pasquale F Innominato1,2,3, Sandra Komarzynski4, Robert Dallmann5, Nicholas I Wreglesworth6,7, Mohamed Bouchahda8,9,10,11, Abdoulaye Karaboué8,12, Ayhan Ulusakarya8,11, Christian P Subbe7,13, David Spiegel14,15, Francis A Lévi5,8,11,16.   

Abstract

BACKGROUND AND AIM: The evaluation of patient-reported outcomes (PRO) in cancer has proven relevant positive clinical impact on patients' communication with healthcare professionals, decision-making for management, well-being, and overall survival. However, the optimal frequency of PRO assessment has yet to be defined. Based on the assumption that more frequent sampling would enhance accuracy, we aimed at identifying the optimal sampling frequency that does not miss clinically relevant insight.
METHODS: We used pilot data from 31 advanced cancer patients who completed once daily the 19-item MD Anderson Symptom Inventory at home. The resulting dataset allowed us to compare different PRO assessment frequencies to daily sampling, i.e., alternate days (q2d), every third day (q3d), or once a week (q1w). We evaluated the sampling frequencies for two main outcomes: average symptom intensity and identification of severe symptoms.
RESULTS: The majority of the differences between corresponding averages of daily data and those for q2d, q3d, and q1w datasets were close to 0, yet the extremes exceeded 5. Clinically meaningful differences, i.e., > 1, were observed in 0.76% of patient items for q2d, in 2.72% for q3d, and in 11.93% for q1w. Moreover, median values of missed instances of a severe symptom (i.e., > 6) were 14.6% for q2d, 27.8% for q3d, and 55.6% for q1w.
CONCLUSIONS: Our analysis suggests that in patients receiving chemotherapy for advanced cancer, increasing the density of PRO collection enhances the accuracy of PRO assessment to a clinically meaningful extent. This is valid for both computations of averages symptom burden and for the recognition of episodes of severe symptom intensity.

Entities:  

Keywords:  Cancer; Digital oncology; Domomedicine; MDASI; MHealth; Patient-reported outcomes; Symptoms

Year:  2021        PMID: 33963910     DOI: 10.1007/s00520-021-06262-1

Source DB:  PubMed          Journal:  Support Care Cancer        ISSN: 0941-4355            Impact factor:   3.603


  3 in total

1.  Randomized Trial of a Smartphone Mobile App to Improve Symptoms and Adherence to Oral Therapy for Cancer.

Authors:  Joseph A Greer; Jamie M Jacobs; Nicole Pensak; Lauren E Nisotel; Joel N Fishbein; James J MacDonald; Molly E Ream; Emily A Walsh; Joanne Buzaglo; Alona Muzikansky; Inga T Lennes; Steven A Safren; William F Pirl; Jennifer S Temel
Journal:  J Natl Compr Canc Netw       Date:  2020-02       Impact factor: 11.908

2.  Assessing symptom distress in cancer patients: the M.D. Anderson Symptom Inventory.

Authors:  C S Cleeland; T R Mendoza; X S Wang; C Chou; M T Harle; M Morrissey; M C Engstrom
Journal:  Cancer       Date:  2000-10-01       Impact factor: 6.860

3.  Lung Cancer App (LuCApp) study protocol: a randomised controlled trial to evaluate a mobile supportive care app for patients with metastatic lung cancer.

Authors:  Oriana Ciani; Maria Cucciniello; Francesco Petracca; Giovanni Apolone; Giampaolo Merlini; Silvia Novello; Paolo Pedrazzoli; Nicoletta Zilembo; Chiara Broglia; Enrica Capelletto; Marina Garassino; Elena Nicod; Rosanna Tarricone
Journal:  BMJ Open       Date:  2019-02-15       Impact factor: 2.692

  3 in total
  1 in total

Review 1.  Holistic Needs Assessment of Cancer Survivors-Supporting the Process Through Digital Monitoring of Circadian Physiology.

Authors:  Max Gibb; Hannah Winter; Sandra Komarzynski; Nicholas I Wreglesworth; Pasquale F Innominato
Journal:  Integr Cancer Ther       Date:  2022 Jan-Dec       Impact factor: 3.077

  1 in total

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