Evidence map›Paper›PMID 30909904›Full record

ArticleBMC health services research2019

Association of in-hospital resource utilization with post-acute spending in Medicare beneficiaries hospitalized for acute myocardial infarction: a cross-sectional study.

Sudhakar V Nuti, Shu-Xia Li, Xiao Xu, Lesli S Ott, Tara Lagu, Nihar R Desai, Karthik Murugiah, Michael Duan, John Martin, Nancy Kim and 1 more

Abstract read
In one paragraph

Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Sudhakar V NutiYale School of Medicine, 333 Cedar Street, New Haven, CT, 06516, USA.
Shu-Xia LiCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT, 06510, USA.
Xiao XuCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT, 06510, USA.
Lesli S OttCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT, 06510, USA.
Tara LaguTufts University School of Medicine, 145 Harrison Ave, Boston, MA, USA.
Nihar R DesaiCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT, 06510, USA.
Karthik MurugiahCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT, 06510, USA.
Michael DuanPremier, Inc, 13034 Ballantyne Corporate Place, Charlotte, NC, 28277, USA.
John MartinPremier, Inc, 13034 Ballantyne Corporate Place, Charlotte, NC, 28277, USA.
Nancy KimSection of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
Harlan M KrumholzCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, 1 Church Street, Suite 200, New Haven, CT, 06510, USA. harlan.krumholz@yale.edu.ORCID http://orcid.org/0000-0003-2046-127X

Funding

Yale-CORE Career Development Program in Patient-Centered Outcomes ResearchK12HS023000 · AHRQ · YALE UNIVERSITY · PI KRUMHOLZ, HARLAN MARC · 2014 to 2018
$3.4M
Defining Optimal Clinical Strategies for Critically Ill Heart Failure PatientsK01HL114745 · NHLBI · BAYSTATE MEDICAL CENTER, INC. · PI LAGU, TARA C · 2013 to 2017
$721k
AHRQ HHS K12 HS023000NHLBI NIH HHS K01 HL114745NHLBI NIH HHS K01HL114745
6 · The paper itself

Abstract

backgroundEfforts to decrease hospitalization costs could increase post-acute care costs. This effect could undermine initiatives to reduce overall episode costs and have implications for the design of health care under alternative payment models.

methodsAmong Medicare fee-for-service beneficiaries aged ≥65 years hospitalized with acute myocardial infarction (AMI) between July 2010 and June 2013 in the Premier Healthcare Database, we studied the association of in-hospital and post-acute care resource utilization and outcomes by in-hospital cost tertiles.

resultsAmong patients with AMI at 326 hospitals, the median (range) of each hospital's mean per-patient in-hospital risk-standardized cost (RSC) for the low, medium, and high cost tertiles were $16,257 ($13,097-$17,648), $18,544 ($17,663-$19,875), and $21,831 ($19,923-$31,296), respectively. There was no difference in the median (IQR) of risk-standardized post-acute payments across cost-tertiles: $5014 (4295-6051), $4980 (4349-5931) and $4922 (4056-5457) for the low (n = 90), medium (n = 98), and high (n = 86) in-hospital RSC tertiles (p = 0.21), respectively. In-hospital and 30-day mortality rates did not differ significantly across the in-hospital RSC tertiles; however, 30-day readmission rates were higher at hospitals with higher in-hospital RSCs: median = 17.5, 17.8, and 18.0% at low, medium, and high in-hospital RSC tertiles, respectively (p = 0.005 for test of trend across tertiles).

conclusionsIn our study of patients hospitalized with AMI, greater resource utilization during the hospitalization was not associated with meaningful differences in costs or mortality during the post-acute period. These findings suggest that it may be possible for higher cost hospitals to improve efficiency in care without increasing post-acute care utilization or worsening outcomes.

Indexed as

AgedCross-Sectional StudiesEconomics, HospitalFee-for-Service PlansHealth ExpendituresHealth ResourcesHospitalizationHumansMedicareMyocardial InfarctionPatient Acceptance of Health CarePatient ReadmissionUnited StatesBundled paymentsCostsHealth policyMedicarePost-acute

Identifiers

PMID30909904
PMCPMC6432744

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.