Identification of Patient-Reported Outcome Phenotypes Among Oncology Patients With Palliative Care Needs
PURPOSE: Despite evidence-based guidelines recommending early palliative care, it remains unclear how to identify and refer oncology patients, particularly in settings with constrained access to palliative care. We hypothesize that patient-reported outcome (PRO) data can be used to characterize patients with palliative care needs. To determine if PRO data can identify latent phenotypes that characterize indications for specialty palliative care referral.
METHODS: We conducted a retrospective study of self-reported symptoms on the Edmonton Symptom Assessment System collected from solid tumor oncology patients (n = 745) referred to outpatient palliative care. Data were collected as part of routine clinical care from October 2012 to March 2018 at eight community and academic sites. We applied latent profile analysis to identify PRO phenotypes and examined the association of phenotypes with clinical and demographic characteristics using multinomial logistic regression.
RESULTS: We identified four PRO phenotypes: (1) Low Symptoms (n = 295, 39.6%), (2) Moderate Pain/Fatigue + Mood (n = 180, 24.2%), (3) Moderate Pain/Fatigue + Appetite + Dyspnea (n = 201, 27.0%), and (4) High Symptoms (n = 69, 9.3%). In a secondary analysis of 421 patients, we found that two brief items assessing social and existential needs aligned with higher severity symptom and psychological distress phenotypes.
CONCLUSION: Oncology patients referred to outpatient palliative care in a real-world setting can be differentiated into clinically meaningful phenotypes using brief, routinely collected PRO measures. Latent modeling provides a mechanism to use patient-reported data on a population level to identify distinct subgroups of patients with unmet palliative needs.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
|---|---|
| Enthalten in: |
JCO oncology practice - 17(2021), 10 vom: 23. Okt., Seite e1473-e1488 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Kaufmann, Tara L [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Journal Article |
|---|
| Anmerkungen: |
Date Completed 02.11.2021 Date Revised 06.01.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1200/OP.20.00849 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM323166598 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM323166598 | ||
| 003 | DE-627 | ||
| 005 | 20231225183607.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1200/OP.20.00849 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1077.xml |
| 035 | |a (DE-627)NLM323166598 | ||
| 035 | |a (NLM)33760637 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Kaufmann, Tara L |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Identification of Patient-Reported Outcome Phenotypes Among Oncology Patients With Palliative Care Needs |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 02.11.2021 | ||
| 500 | |a Date Revised 06.01.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a PURPOSE: Despite evidence-based guidelines recommending early palliative care, it remains unclear how to identify and refer oncology patients, particularly in settings with constrained access to palliative care. We hypothesize that patient-reported outcome (PRO) data can be used to characterize patients with palliative care needs. To determine if PRO data can identify latent phenotypes that characterize indications for specialty palliative care referral | ||
| 520 | |a METHODS: We conducted a retrospective study of self-reported symptoms on the Edmonton Symptom Assessment System collected from solid tumor oncology patients (n = 745) referred to outpatient palliative care. Data were collected as part of routine clinical care from October 2012 to March 2018 at eight community and academic sites. We applied latent profile analysis to identify PRO phenotypes and examined the association of phenotypes with clinical and demographic characteristics using multinomial logistic regression | ||
| 520 | |a RESULTS: We identified four PRO phenotypes: (1) Low Symptoms (n = 295, 39.6%), (2) Moderate Pain/Fatigue + Mood (n = 180, 24.2%), (3) Moderate Pain/Fatigue + Appetite + Dyspnea (n = 201, 27.0%), and (4) High Symptoms (n = 69, 9.3%). In a secondary analysis of 421 patients, we found that two brief items assessing social and existential needs aligned with higher severity symptom and psychological distress phenotypes | ||
| 520 | |a CONCLUSION: Oncology patients referred to outpatient palliative care in a real-world setting can be differentiated into clinically meaningful phenotypes using brief, routinely collected PRO measures. Latent modeling provides a mechanism to use patient-reported data on a population level to identify distinct subgroups of patients with unmet palliative needs | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Getz, Kelly D |e verfasserin |4 aut | |
| 700 | 1 | |a Hsu, Jesse Y |e verfasserin |4 aut | |
| 700 | 1 | |a Bennett, Antonia V |e verfasserin |4 aut | |
| 700 | 1 | |a Takvorian, Samuel U |e verfasserin |4 aut | |
| 700 | 1 | |a Kamal, Arif H |e verfasserin |4 aut | |
| 700 | 1 | |a DeMichele, Angela |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t JCO oncology practice |d 2020 |g 17(2021), 10 vom: 23. Okt., Seite e1473-e1488 |w (DE-627)NLM301053103 |x 2688-1535 |7 nnns |
| 773 | 1 | 8 | |g volume:17 |g year:2021 |g number:10 |g day:23 |g month:10 |g pages:e1473-e1488 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1200/OP.20.00849 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 17 |j 2021 |e 10 |b 23 |c 10 |h e1473-e1488 | ||