Evidence map›Paper›PMID 37753237›Full record

Observational studyCritical care explorations2023

Telemedicine Critical Care-Mediated Mortality Reductions in Lower-Performing Patient Diagnosis Groups: A Prospective, Before and After Study.

Walter A Boyle, Christopher M Palmer, Lisa Konzen, Bradley A Fritz, Jason White, Michelle Simkins, Brian Dieffenderfer, Ayesha Iqbal, Jill Bertrand, Shelley Meyer and 8 more

Abstract readObservational Study
In one paragraph

Observational study in Critical care explorations, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

18 authors.

Walter A BoyleDivision of Critical Care Medicine, Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO.
Christopher M PalmerDivision of Critical Care Medicine, Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO.
Lisa KonzenBarnes Jewish Hospital, St. Louis, MO.
Bradley A FritzDivision of Critical Care Medicine, Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO.
Jason WhiteBarnes Jewish Hospital, St. Louis, MO.
Michelle SimkinsCenter for Clinical Excellence, BJC Healthcare, St. Louis, MO.
Brian DieffenderferBarnes Jewish Hospital, St. Louis, MO.
Ayesha IqbalCenter for Clinical Excellence, BJC Healthcare, St. Louis, MO.
Jill BertrandBarnes Jewish Hospital, St. Louis, MO.
Shelley MeyerBarnes Jewish Hospital, St. Louis, MO.
Paul KerbyDivision of Critical Care Medicine, Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO.
Sara BuckmanSection of Acute and Critical Care Surgery, Department of Surgery, Washington University in St. Louis, St. Louis, MO.
Vladimir DespotovicDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University in St. Louis, St. Louis, MO.
Jim KozlowskiDivision of Critical Care Medicine, Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO.
Patricia Crimmins RedaBarnes Jewish Hospital, St. Louis, MO.
Igor ZwirDepartment of Psychiatry, Washington University in St. Louis, St. Louis, MO.
C Charles GuInstitute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, St. Louis, MO.
Uchenna R OfomaDivision of Critical Care Medicine, Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO.

Funding

Washington University Institute of Clinical and Translational Sciences (KL2) KL2TR002346 · NCATS · WASHINGTON UNIVERSITY · PI Dominic N Reeds · 2017 to 2026
$14.0M
NCATS NIH HHS KL2 TR002346
6 · The paper itself

Abstract

objectivesStudies evaluating telemedicine critical care (TCC) have shown mixed results. We prospectively evaluated the impact of TCC implementation on risk-adjusted mortality among patients stratified by pre-TCC performance.

designProspective, observational, before and after study.

settingThree adult ICUs at an academic medical center. PATIENTS: A total of 2,429 patients in the pre-TCC (January to June 2016) and 12,479 patients in the post-TCC (January 2017 to June 2019) periods.

interventionsTCC implementation which included an acuity-driven workflow targeting an identified "lower-performing" patient group, defined by ICU admission in an Acute Physiology and Chronic Health Evaluation diagnoses category with a pre-TCC standardized mortality ratio (SMR) of greater than 1.5. MEASUREMENTS AND MAIN

resultsThe primary outcome was risk-adjusted hospital mortality. Risk-adjusted hospital length of stay (HLOS) was also studied. The SMR for the overall ICU population was 0.83 pre-TCC and 0.75 post-TCC, with risk-adjusted mortalities of 10.7% and 9.5% (

conclusionsIn well-staffed and overall higher-performing ICUs in an academic medical center, Acute Physiology and Chronic Health Evaluation granularity allowed identification of a historically lower-performing patient group that experienced a striking TCC-associated reduction in SMR and risk-adjusted mortality. This study provides additional evidence for the relationship between pre-TCC performance and post-TCC improvement.

Indexed as

Critical CareDiagnosis-Related GroupsHospital MortalityTelemedicineAcademic Medical CentersAgedFemaleHumansIntensive Care UnitsLength of StayMaleMiddle AgedProspective StudiesAcute Physiology and Chronic Health Evaluationintensive care unit organizationpredictive scoring systemstele-intensive care unittelemedicinetelemedicine critical care

Identifiers

PMID37753237
PMCPMC10519574

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.