Evidence map›Paper›PMID 34862099›Full record

ArticleEuropean urology2022

A Clinical Decision Aid to Support Personalized Treatment Selection for Patients with Clinical T1 Renal Masses: Results from a Multi-institutional Competing-risks Analysis.

Sarah P Psutka, Roman Gulati, Michael A S Jewett, Kamel Fadaak, Antonio Finelli, Laura Legere, Todd M Morgan, Phillip M Pierorazio, Mohamad E Allaf, Jeph Herrin and 8 more

Open access · greenAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European urology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 5% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors at 5 institutions in 2 countries.

Sarah P PsutkaDepartment of Urology, University of Washington, Seattle, WA, USA. Electronic address: spsutka@gmail.com.
Roman GulatiDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Michael A S JewettDepartments of Surgery (Urology) and Surgical Oncology, Princess Margaret Cancer Center and University Health Network, University of Toronto, Toronto, Canada.
Kamel FadaakDepartment of Urology, King Fahd Hospital of the University, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Antonio FinelliDepartments of Surgery (Urology) and Surgical Oncology, Princess Margaret Cancer Center and University Health Network, University of Toronto, Toronto, Canada.
Laura LegereDepartments of Surgery (Urology) and Surgical Oncology, Princess Margaret Cancer Center and University Health Network, University of Toronto, Toronto, Canada.
Todd M MorganDepartment of Urology, University of Michigan, Ann Arbor, MI, USA.
Phillip M PierorazioDepartment of Urology, Brady Urological Institute, Department of Urology at Johns Hopkins, Baltimore, MD, USA.
Mohamad E AllafDepartment of Urology, Brady Urological Institute, Department of Urology at Johns Hopkins, Baltimore, MD, USA.
Jeph HerrinDivision of Cardiology, Yale School of Medicine, New Haven, CT, USA; Health Research & Educational Trust, Chicago, IL, USA.
Christine M LohseDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
R Houston ThompsonDepartment of Urology, Mayo Clinic, Rochester, MN, USA.
Stephen A BoorjianDepartment of Urology, Mayo Clinic, Rochester, MN, USA.
Thomas D AtwellDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Grant D SchmitDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Brian A CostelloDivision of Medical Oncology, Mayo Clinic, Rochester, MN, USA.
Nilay D ShahDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Bradley C LeibovichDepartment of Urology, Mayo Clinic, Rochester, MN, USA.
Mayo Clinic in Arizona · USMayo Clinic in Florida · USFred Hutch Cancer Center · USKing Fahd Hospital of the University · SAUniversity of Michigan–Ann Arbor · US

Funding

Statistical modeling to support population and translational cancer researchR50CA221836 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Roman Gulati · 2017 to 2026
$2.0M
NCI NIH HHS R50 CA221836
6 · The paper itself

Abstract

backgroundPersonalized treatment for clinical T1 renal cortical masses (RCMs) should take into account competing risks related to tumor and patient characteristics.

objectiveTo develop treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-d Clavien grade ≥3 complications across radical nephrectomy (RN), partial nephrectomy (PN), thermal ablation (TA), and active surveillance (AS). DESIGN, SETTING, AND

participantsPretreatment clinical and radiological features were collected for consecutive adult patients treated with initial RN, PN, TA, or AS for RCMs at four high-volume referral centers (2000-2019). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Prediction models used competing-risks regression for CSM and OCM and logistic regression for 90-d Clavien grade ≥3 complications. Performance was assessed using bootstrap validation. RESULTS AND LIMITATIONS: The cohort comprised 5300 patients treated with RN (n = 1277), PN (n = 2967), TA (n = 476), or AS (n = 580). Over median follow-up of 5.2 yr (interquartile range 2.5-8.7), there were 117 CSM, 607 OCM, and 198 complication events. The C index for the predictive models was 0.80 for CSM, 0.77 for OCM, and 0.64 for complications. Predictions from the fitted models are provided in an online calculator (https://small-renal-mass-risk-calculator.fredhutch.org). To illustrate, a hypothetical 74-yr-old male with a 4.5-cm RCM, body mass index of 32 kg/m

conclusionsWe present a risk calculator incorporating pretreatment features to estimate treatment-specific competing risks of mortality and complications for use during shared decision-making and personalized treatment selection for RCMs. PATIENT SUMMARY: We present a risk calculator that generates personalized estimates of the risks of death from cancer or other causes and of complications for surgical, ablation, and surveillance treatment options for patients with stage 1 kidney tumors.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsAdultDecision Support TechniquesHumansMaleNephrectomyPrecision MedicineRetrospective StudiesTreatment OutcomeAblationComorbidityCompeting risksDecision aidNephrectomyPerformance statusRenal cell carcinomaShared decision-makingSurveillanceTreatment

Identifiers

PMID34862099
PMCPMC10351331
OpenAlexW3217344408

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.