Evidence map›Paper›PMID 30119627›Full record

ArticleBMC medical informatics and decision making2018

Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS).

Zhaoyi Chen, Victoria Y Bird, Rupam Ruchi, Mark S Segal, Jiang Bian, Saeed R Khan, Marie-Carmelle Elie, Mattia Prosperi

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
3.1field-weighted citation impact, top 8% 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, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. 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

8 authors at 2 institutions in 1 country.

Zhaoyi ChenDepartment of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, 2004 Mowry Road, PO Box 100231, Gainesville, Florida, 32610-0231, USA. zhaoyi@ufl.edu.ORCID 0000-0002-4062-0867
Victoria Y BirdDepartment of Urology, University of Florida, Gainesville, Florida, USA.
Rupam RuchiDivision of Nephrology, Hypertension, & Renal Transplantation, University of Florida, Gainesville, Florida, USA.
Mark S SegalDivision of Nephrology, Hypertension, & Renal Transplantation, University of Florida, Gainesville, Florida, USA.
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.
Saeed R KhanDepartment of Pathology, Immunology, and Laboratory Medicine, Gainesville, Florida, USA.
Marie-Carmelle ElieDepartment of Emergency Medicine, University of Florida, Gainesville, Florida, USA.
Mattia ProsperiDepartment of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, 2004 Mowry Road, PO Box 100231, Gainesville, Florida, 32610-0231, USA.
University of Florida · USUniversity of Florida Health · US

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
NCATS NIH HHS UL1 TR001427
6 · The paper itself

Abstract

backgroundKidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate a 'multi-domain' personalized diagnostic acute care algorithm for KS (DACA-KS), merging demographic, vital signs, clinical, and laboratory information.

methodsWe utilized a large, single-center database of patients admitted to acute care units in a large tertiary care hospital. Patients diagnosed with KS were compared to groups of patients with acute abdominal/flank/groin pain, genitourinary diseases, and other conditions. We analyzed multiple information domains (several thousands of variables) using a collection of statistical and machine learning models with feature selectors. We compared sensitivity, specificity and area under the receiver operating characteristic (AUROC) of our approach with the STONE score, using cross-validation.

resultsThirty eight thousand five hundred and ninety-seven distinct adult patients were admitted to critical care between 2001 and 2012, of which 217 were diagnosed with KS, and 7446 with acute pain (non-KS). The multi-domain approach using logistic regression yielded an AUROC of 0.86 and a sensitivity/specificity of 0.81/0.82 in cross-validation. Increase in performance was obtained by fitting a super-learner, at the price of lower interpretability. We discussed in detail comorbidity and lab marker variables independently associated with KS (e.g. blood chloride, candidiasis, sleep disorders).

conclusionsAlthough external validation is warranted, DACA-KS could be integrated into electronic health systems; the algorithm has the potential used as an effective tool to help nurses and healthcare personnel during triage or clinicians making a diagnosis, streamlining patients' management in acute care.

Indexed as

AlgorithmsBig DataPractice Guidelines as TopicAdultAgedCritical CareFemaleHumansKidney CalculiMaleMiddle AgedModels, TheoreticalPrecision MedicineTertiary Care CentersBig data analysisDiagnostic algorithmKidney stones

Identifiers

PMID30119627
PMCPMC6098647
OpenAlexW2887712995

What OpenQuestion holds

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LicenceCC BY
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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.