Evidence map›Paper›PMID 36404761›Full record

ArticleJournal of primary care & community health

Real World Evidence and Clinical Utility of KidneyIntelX on Patients With Early-Stage Diabetic Kidney Disease: Interim Results on Decision Impact and Outcomes.

Joji Tokita, Aida Vega, Catherine Sinfield, Nidhi Naik, Shivani Rathi, Sharlene Martin, Stephanie Wang, Leonard Amoruso, Azadeh Zabetian, Steven G Coca and 4 more

Abstract read
In one paragraph

Article in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
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

14 authors.

Joji TokitaThe Barbara T Murphy Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Aida VegaDepartment of General Internal Medicine at Mount Sinai, New York, NY, USA.
Catherine SinfieldIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Nidhi NaikIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Shivani RathiIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Sharlene MartinIcahn School of Medicine at Mount Sinai, New York, NY, USA.
Stephanie WangDepartment of General Internal Medicine at Mount Sinai, New York, NY, USA.
Leonard AmorusoDepartment of General Internal Medicine at Mount Sinai, New York, NY, USA.
Azadeh ZabetianRenalytix AI, Inc, New York, NY, USA.
Steven G CocaThe Barbara T Murphy Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Girish N NadkarniThe Barbara T Murphy Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Fergus FlemingRenalytix AI, Inc, New York, NY, USA.
Michael J DonovanIcahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0003-0772-598X
Robert FieldsMount Sinai Health System, New York, NY, USA.

Funding

COVID and Translational Science supercomputer (CATS)S10OD030463 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2021 to 2021
$2.0M
Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
NIH HHS S10 OD026880NIH HHS S10 OD030463
6 · The paper itself

Abstract

INTRODUCTION AND

objectiveThe lack of precision to identify patients with early-stage diabetic kidney disease (DKD) at near-term risk for progressive decline in kidney function results in poor disease management often leading to kidney failure requiring unplanned dialysis. The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to generate a risk score for progressive decline in kidney function over 5-year in adults with early-stage DKD. Our objective was to assess the impact of KidneyIntelX on management and outcomes in a Health System in the real-world evidence (RWE) study.

methodsKidneyIntelX was introduced into a large metropolitan Health System via a population health-defined approved care pathway for patients with stages 1 to 3 DKD between [November 2020 to March 2022]. Decision impact on visit frequency, medication management, specialist referral, and selected lab values was assessed. We performed an interim analysis in patients through 6-months post-test date to evaluate the impact of risk level with clinical decision-making and outcomes.

resultsA total of 1686 patients were enrolled in the RWE study and underwent KidneyIntelX testing and subsequent care pathway management. The median age was 68 years, 52% were female, 26% self-identified as Black, and 94% had hypertension. The median baseline eGFR was 59 ml/minute/1.73 m

conclusionsThe combination of KidneyIntelX, clinical guidelines and educational support resulted in changes in clinical management by clinicians. After testing, there was an increase in visit frequency, referrals for disease management, and introduction to guideline-recommended medications. These differed by risk category, indicating an impact of KidneyIntelX risk stratification on clinical care.

Indexed as

Diabetes MellitusDiabetic NephropathiesSodium-Glucose Transporter 2 InhibitorsAdultAgedBiomarkersFemaleHumansMaleRenal DialysisRisk FactorsBiomarkersSodium-Glucose Transporter 2 Inhibitorsdecision impact studydiabetic kidney diseaseearly-stageKidneyIntelXReal World Evidence

Identifiers

PMID36404761
PMCPMC9677284

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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 OpenQuestion graph.