Evidence map›Paper›PMID 37101728›Full record

ArticleCancer informatics2023

Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis.

Nazneen Fatima Shaikh, Chan Shen, Traci LeMasters, Nilanjana Dwibedi, Amit Ladani, Usha Sambamoorthi

Open access · goldAbstract read
In one paragraph

Article in Cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 31% of its field
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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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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

6 authors at 4 institutions in 1 country.

Nazneen Fatima ShaikhDepartment of Pharmaceutical Systems and Policy, West Virginia University School of Pharmacy, Morgantown, WV, USA.
Chan ShenDepartment of Surgery, College of Medicine, The Pennsylvania State University, Hershey, PA, USA.
Traci LeMastersDepartment of Pharmaceutical Systems and Policy, West Virginia University School of Pharmacy, Morgantown, WV, USA.
Nilanjana DwibediOutcomes Science and Services, ConcertAI, CT, USA.
Amit LadaniDepartment of Medicine, Section of Rheumatology, West Virginia University School of Medicine, Morgantown, WV, USA.
Usha SambamoorthiPharmacotherapy Department College of Pharmacy, "Vashisht" Professor of Health Disparities, HEARD Scholar, Institute for Health Disparities, University of North Texas Health Sciences Center, Fort Worth, TX, USA.
West Virginia University · USHealth Outcomes Solutions (United States) · USPennsylvania State University · USUniversity of North Texas Health Science Center · US

Funding

AIM-AHEAD Coordinating Center - All Four CoresOT2OD032581 · OD · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Paul Avillach, Bettina M. Beech · 2021 to 2026
$168.7M
Texas Center for Minority Health, Education, Research and OutreachU54MD006882 · NIMHD · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI VISHWANATHA, JAMBOOR K. · 2017 to 2022
$6.0M
NIH HHS OT2 OD032581NIMHD NIH HHS U54 MD006882
6 · The paper itself

Abstract

ObjectiveS: This study examined prescription NSAIDs as one of the leading predictors of incident depression and assessed the direction of the association among older cancer survivors with osteoarthritis. Methods: This study used a retrospective cohort (N = 14, 992) of older adults with incident cancer (breast, prostate, colorectal cancers, or non-Hodgkin's lymphoma) and osteoarthritis. We used the longitudinal data from the linked Surveillance, Epidemiology, and End Results -Medicare data for the study period from 2006 through 2016, with a 12-month baseline and 12-month follow-up period. Cumulative NSAIDs days was assessed during the baseline period and incident depression was assessed during the follow-up period. An eXtreme Gradient Boosting (XGBoost) model was built with 10-fold repeated stratified cross-validation and hyperparameter tuning using the training dataset. The final model selected from the training data demonstrated high performance (Accuracy: 0.82, Recall: 0.75, Precision: 0.75) when applied to the test data. SHapley Additive exPlanations (SHAP) was used to interpret the output from the XGBoost model. Results: Over 50% of the study cohort had at least one prescption of NSAIDs. Nearly 13% of the cohort were diagnosed with incident depression, with the rates ranging between 7.4% for prostate cancer and 17.0% for colorectal cancer. The highest incident depression rate of 25% was observed at 90 and 120 cumulative NSAIDs days thresholds. Cumulative NSAIDs days was the sixth leading predictor of incident depression among older adults with OA and cancer. Age, education, care fragmentation, polypharmacy, and zip code level poverty were the top 5 predictors of incident depression. Conclusion: Overall, 1 in 8 older adults with cancer and OA were diagnosed with incident depression. Cumulative NSAIDs days was the sixth leading predictor with an overall positive association with incident depression. However, the association was complex and varied by the cumulative NSAIDs days.

Indexed as

cancerdepressioninflammationmachine learningNSAIDsolder adultsOsteoarthritis

Identifiers

PMID37101728
PMCPMC10123903
OpenAlexW4366431540

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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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 Socratic graph.