Evidence map›Paper›PMID 42312221›Full record

ArticleSAGE open medicine2026

Using latent class analysis to identify subgroups of postoperative older adults.

Kevin H McLaughlin, Amie Bettencourt, Daniel Young, Erik Hoyer, Michael Friedman, Elizabeth Colantuoni, Lee A Goeddel, Pedro Gozalo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in SAGE open medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Kevin H McLaughlinDepartment of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID https://orcid.org/0000-0002-5957-0935
Amie BettencourtDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Daniel YoungDepartment of Physical Therapy, University of Nevada Las Vegas, Baltimore, MD, USA.
Erik HoyerDepartment of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Michael FriedmanDepartment of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Elizabeth ColantuoniDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Lee A GoeddelDepartment of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Pedro GozaloDepartment of Health Services, Policy and Practice, Brown University School of Public Health, Providence, RI, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Identify subgroups of postoperative older adults using a latent class analysis (LCA) of routinely collected patient data, including preoperative frailty and postoperative mobility. Methods: Retrospective analysis of routinely collected data for adults 65 years and older (N=2,036) admitted to the hospital following surgery at our institution. We utilized LCA to identify patient subgroups based on routinely collected measures of frailty, mobility, activities of daily living, and general health status. We compared hospital outcomes between the identified groups, including extended length of stay (LOS) (>0.5 SD beyond mean LOS by surgical category), discharge disposition (i.e., home versus non-home discharge), and utilization (weekly visit frequency) of physical therapy (PT) and occupational therapy (OT). Results: We identified 3 subgroups of patients that we labeled Low Frailty-High Mobility (LF-HM), High Frailty-Low Mobility (HF-LM), and Low Frailty-Low Mobility (LF-LM), representing 15.3%, 27.6%, and 57.1% of the cohort, respectively. Discharge to home was highest among the LF-HM group (99%), followed by LF-LM (96%), and HF-LM (77%). Extended LOS was most common among the HF-LM group (27%), followed by LF-LM (18%), and LF-HM (6%). PT and OT visit frequencies were highest in the HF-LM group followed by the LF-LM and LF-HM groups. Conclusions: This study identified 3 subgroups of postoperative older adults using routinely collected patient data. These groups may help to explain variations in hospital outcomes that have not been adequately explained using measures of preoperative or postoperative measures alone. These results may be useful in developing tailored postoperative care for older adults.

Indexed as

electronic health record datageriatrichealth services researchprecision medicinesurgery

Identifiers

PMID42312221
PMCPMC13269985

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.