Evidence map›Paper›PMID 30016125›Full record

ArticleJournal of oncology practice2018

Misery Loves Company: Presenting Symptom Clusters to Urgent Care by Patients Receiving Antineoplastic Therapy.

Bobby Daly, Kevin Nicholas, Dmitriy Gorenshteyn, Stefania Sokolowski, Lior Gazit, Lynn Adams, Jennie Matays, Lauren L Katzen, Yeneat O Chiu, Han Xiao and 11 more

Abstract read
In one paragraph

Article in Journal of oncology practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  15. From metrics to practice: identifying preventable emergency department visits for patients with cancer.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2021
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  16. Neuro-oncology practice · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Bobby DalyMemorial Sloan Kettering Cancer Center, New York, NY.
Kevin NicholasMemorial Sloan Kettering Cancer Center, New York, NY.
Dmitriy GorenshteynMemorial Sloan Kettering Cancer Center, New York, NY.
Stefania SokolowskiMemorial Sloan Kettering Cancer Center, New York, NY.
Lior GazitMemorial Sloan Kettering Cancer Center, New York, NY.
Lynn AdamsMemorial Sloan Kettering Cancer Center, New York, NY.
Jennie MataysMemorial Sloan Kettering Cancer Center, New York, NY.
Lauren L KatzenMemorial Sloan Kettering Cancer Center, New York, NY.
Yeneat O ChiuMemorial Sloan Kettering Cancer Center, New York, NY.
Han XiaoMemorial Sloan Kettering Cancer Center, New York, NY.
Rori SalvaggioMemorial Sloan Kettering Cancer Center, New York, NY.
Abigail Baldwin-MedskerMemorial Sloan Kettering Cancer Center, New York, NY.
Kimberly ChowMemorial Sloan Kettering Cancer Center, New York, NY.
Judith NelsonMemorial Sloan Kettering Cancer Center, New York, NY.
Mikel RossMemorial Sloan Kettering Cancer Center, New York, NY.
Kenneth K NgMemorial Sloan Kettering Cancer Center, New York, NY.
Alice ZervoudakisMemorial Sloan Kettering Cancer Center, New York, NY.
Wendy PerchickMemorial Sloan Kettering Cancer Center, New York, NY.
Diane L ReidyMemorial Sloan Kettering Cancer Center, New York, NY.
Brett A SimonMemorial Sloan Kettering Cancer Center, New York, NY.
Isaac WagnerMemorial Sloan Kettering Cancer Center, New York, NY.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

purposeThe Centers for Medicare & Medicaid Services (CMS) identifies suboptimal management of treatment toxicities as a care gap and proposes the measurement of hospital performance on the basis of emergency department visits for 10 common symptoms. Current management strategies do not address symptom co-occurrence.

methodsWe evaluated symptom co-occurrence in three patient cohorts that presented to a cancer hospital urgent care center in 2016. We examined both the CMS-identified symptoms and an expanded clinician-identified set defined as symptoms that could be safely managed in the outpatient setting if identified early and managed proactively. The cohorts included patients who presented with a CMS-defined symptom within 30 days of treatment, patients who presented within 30 days of treatment with a symptom from the expanded set, and patients who presented with a symptom from the expanded set within 30 days of treatment start. Symptom co-occurrence was measured by Jaccard index. A community detection algorithm was used to identify symptom clusters on the basis of a random walk process, and network visualizations were used to illustrate symptom dynamics.

resultsThere were 6,429 presentations in the CMS symptom-defined cohort. The network analysis identified two distinct symptom clusters centered around pain and fever. In the expanded symptom cohort, there were 5,731 visits and six symptom clusters centered around fever, emesis/nausea, fatigue, deep vein thrombosis, pain, and ascites. For patients who newly initiated treatment, there were 1,154 visits and four symptom clusters centered around fever, nausea/emesis, fatigue, and deep vein thrombosis.

conclusionUncontrolled symptoms are associated with unplanned acute care. Recognition of the complexity of symptom co-occurrence can drive improved management strategies.

Indexed as

Ambulatory CareAntineoplastic AgentsAscitesCancer Care FacilitiesCluster AnalysisFatigueFemaleFeverHumansMaleMiddle AgedNauseaNeoplasmsPainVenous ThrombosisVomitingAntineoplastic Agents

Identifiers

PMID30016125
PMCPMC6804915

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.