Evidence map›Paper›PMID 35023835›Full record

ArticleJMIR rehabilitation and assistive technologies2022

Patient Outcomes and Lessons Learned From Treating Patients With Severe COVID-19 at a Long-term Acute Care Hospital: Single-Center Retrospective Study.

Pete Grevelding, Henry Charles Hrdlicka, Steve Holland, Lorraine Cullen, Amanda Meyer, Catherine Connors, Darielle Cooper, Allison Greco

Open access · goldAbstract read
In one paragraph

Article in JMIR rehabilitation and assistive technologies, 2022. 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
0.5field-weighted citation impact, top 36% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Observational
  2. 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

8 authors at 1 institution in 1 country.

Pete GreveldingMilne Institute for Healthcare Innovation, Gaylord Specialty Healthcare, Wallingford, CT, United States.ORCID https://orcid.org/0000-0001-7734-3608
Henry Charles HrdlickaMilne Institute for Healthcare Innovation, Gaylord Specialty Healthcare, Wallingford, CT, United States.ORCID https://orcid.org/0000-0002-7014-7919
Steve HollandClinical and Medical Services, Gaylord Specialty Healthcare, Wallingford, CT, United States.ORCID https://orcid.org/0000-0003-4400-7362
Lorraine CullenMilne Institute for Healthcare Innovation, Gaylord Specialty Healthcare, Wallingford, CT, United States.ORCID https://orcid.org/0000-0003-2905-5657
Amanda MeyerMilne Institute for Healthcare Innovation, Gaylord Specialty Healthcare, Wallingford, CT, United States.ORCID https://orcid.org/0000-0001-9878-8040
Catherine ConnorsDepartment of Inpatient Physical Therapy, Gaylord Specialty Healthcare, Wallingford, CT, United States.ORCID https://orcid.org/0000-0003-2645-7992
Darielle CooperDepartment of Inpatient Speech Language Pathology, Gaylord Specialty Healthcare, Wallingford, CT, United States.ORCID https://orcid.org/0000-0002-7615-4562
Allison GrecoDepartment of Inpatient Speech Language Pathology, Gaylord Specialty Healthcare, Wallingford, CT, United States.ORCID https://orcid.org/0000-0001-8825-5072
Gaylord Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the continuation of the COVID-19 pandemic, shifting active COVID-19 care from short-term acute care hospitals (STACHs) to long-term acute care hospitals (LTACHs) could decrease STACH census during critical stages of the pandemic and maximize limited resources.

objectiveThis study aimed to describe the characteristics, clinical management, and patient outcomes during and after the acute COVID-19 phase in an LTACH in the Northeastern United States.

methodsThis was a single-center group comparative retrospective analysis of the electronic medical records of patients treated for COVID-19-related impairments from March 19, 2020, through August 14, 2020, and a reference population of medically complex patients discharged between December 1, 2019, and February 29, 2020. This study was conducted to evaluate patient outcomes in response to the holistic treatment approach of the facility.

resultsOf the 127 total COVID-19 admissions, 118 patients were discharged by the data cutoff. At admission, 29.9% (38/127) of patients tested positive for SARS-CoV-2 infection. The mean age of the COVID-19 cohort was lower than that of the reference cohort (63.3, 95% CI 61.1-65.4 vs 65.5, 95% CI 63.2-67.8 years; P=.04). There were similar proportions of males and females between cohorts (P=.38); however, the proportion of non-White/non-Caucasian patients was higher in the COVID-19 cohort than in the reference cohort (odds ratio 2.79, 95% CI 1.5-5.2; P=.001). The mean length of stay in the COVID-19 cohort was similar to that in the reference cohort (25.5, 95% CI 23.2-27.9 vs 29.9, 95% CI 24.7-35.2 days; P=.84). Interestingly, a positive correlation between patient age and length of stay was observed in the COVID-19 cohort (r

conclusionsThe majority of patients treated at the LTACH for severe COVID-19 and related complications benefited from coordinated care and rehabilitation. In comparison to the reference cohort, patients treated for COVID-19 were discharged with greater improvements in ambulation distance and assistance needs during a similar length of stay. These findings indicate that other patients with COVID-19 would benefit from care in an LTACH.

Indexed as

COVID-19long-term acute care hospitaloccupational therapyphysical therapypostacute carepost–COVID-19pulmonaryrehabilitationrespiratory therapySARS-CoV-2speech-language pathologyspeech therapysubacute COVID-19

Identifiers

PMID35023835
PMCPMC8834875
OpenAlexW4205993661

What OpenQuestion holds

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Registered trials

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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.