Evidence map›Paper›PMID 26342217›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2016

Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation.

Matthew F Toerper, Eleni Flanagan, Sauleh Siddiqui, Jeff Appelbaum, Edward K Kasper, Scott Levin

Open access · bronzeAbstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
13.7field-weighted citation impact, top 2% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Merging Data Diversity of Clinical Medical Records to Improve Effectiveness.International journal of environmental research and public health · 2019
    Article
  4. Article
  5. Review
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 2 institutions in 1 country.

Matthew F ToerperJohns Hopkins Department of Emergency Medicine, 1830 East Monument Street, Suite 6-100, Baltimore, MD 21287, USA Johns Hopkins Health System Operations Integration, 600 N. Wolfe Street, Administration Bldg. Suite 420, Baltimore, MD 21287, USA mtoerper@jhu.edu.
Eleni FlanaganJohns Hopkins Heart and Vascular Institute, 600 N. Wolfe Street, The Johns Hopkins Hospital, Baltimore, MD 21287, USA.
Sauleh SiddiquiDepartment of Civil Engineering, Johns Hopkins Systems Institute, Johns Hopkins University, 3400 N Charles Street, Baltimore, MD 21218, USA Department of Applied Mathematics and Statistics, Johns Hopkins Systems Institute, Johns Hopkins University, 3400 N Charles Street, Baltimore, MD 21218, USA.
Jeff AppelbaumJohns Hopkins Health System Operations Integration, 600 N. Wolfe Street, Administration Bldg. Suite 420, Baltimore, MD 21287, USA.
Edward K KasperDivision of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland.
Scott LevinJohns Hopkins Department of Emergency Medicine, 1830 East Monument Street, Suite 6-100, Baltimore, MD 21287, USA Johns Hopkins Health System Operations Integration, 600 N. Wolfe Street, Administration Bldg. Suite 420, Baltimore, MD 21287, USA.
Johns Hopkins University · USJohns Hopkins Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs).

methodsThe forecast model was derived using a 13-month retrospective cohort of 6384 catheterization patients. Predictor variables such as demographics, scheduled procedures, and clinical indicators mined from free-text notes were input to a multivariable logistic regression model that predicted the probability of inpatient admission. The model was embedded into a web-based application connected to the local EMR system and used to support bed management decisions. After implementation, the tool was prospectively evaluated for accuracy on a 13-month test cohort of 7029 catheterization patients.

resultsThe forecast model predicted admission with an area under the receiver operating characteristic curve of 0.722. Daily aggregate forecasts were accurate to within one bed for 70.3% of days and within three beds for 97.5% of days during the prospective evaluation period. The web-based application housing the forecast model was used by cardiology providers in practice to estimate daily admissions from the catheterization laboratory. DISCUSSION: The forecast model identified older age, male gender, invasive procedures, coronary artery bypass grafts, and a history of congestive heart failure as qualities indicating a patient was at increased risk for admission. Diagnostic procedures and less acute clinical indicators decreased patients' risk of admission. Despite the site-specific limitations of the model, these findings were supported by the literature.

conclusionData-driven predictive analytics may be used to accurately forecast daily demand for inpatient beds for cardiac catheterization patients. Connecting these analytics to EMR data sources has the potential to provide advanced operational decision support.

Indexed as

AlgorithmsCardiac CatheterizationElectronic Health RecordsPatient AdmissionAdultAgedAged, 80 and overAge FactorsFemaleForecastingHeart FailureHospital AdministrationHumansInternetLogistic ModelsMalecardiac care unitcardiac catheterizationforecastinghospitalpatient admission

Identifiers

PMID26342217
PMCPMC4954614
OpenAlexW1229688857

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.