Evidence map›Paper›PMID 33417488›Full record

Observational studyJCO oncology practice2021

Creating and Validating a Predictive Model for Suitability of Hospital at Home for Patients With Solid-Tumor Malignancies.

Kevin Chen, Keval Desai, Soundari Sureshanand, Kerin Adelson, Jeremy I Schwartz, Cary P Gross, Sarwat I Chaudhry

Open access · greenAbstract readObservational Study
In one paragraph

Observational study in JCO oncology practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 58% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

7 authors at 2 institutions in 1 country.

Kevin ChenSection of General Internal Medicine, Yale School of Medicine, New Haven, CT.ORCID 0000-0003-3035-1891
Keval DesaiSection of General Internal Medicine, Yale School of Medicine, New Haven, CT.ORCID 0000-0003-3540-7292
Soundari SureshanandJoint Data Analytics Team, Yale University, New Haven, CT.
Kerin AdelsonSection of General Internal Medicine, Yale School of Medicine, New Haven, CT.
Jeremy I SchwartzSection of General Internal Medicine, Yale School of Medicine, New Haven, CT.ORCID 0000-0003-0139-2600
Cary P GrossSection of General Internal Medicine, Yale School of Medicine, New Haven, CT.
Sarwat I ChaudhrySection of General Internal Medicine, Yale School of Medicine, New Haven, CT.
Yale University · USYale Cancer Center · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

purposeHospital at home (HaH) is a means of providing inpatient-level care at home. Selection of admissions potentially suitable for HaH in oncology is not well studied. We sought to create a predictive model for identifying admissions of patients with cancer, specifically solid-tumor malignancies, potentially suitable for HaH.

methodsIn this observational study, we analyzed admissions of patients with solid-tumor malignancies and unplanned admissions (January 1, 2015, to June 12, 2019) at an academic, urban cancer hospital. Potential suitability for HaH was the primary outcome. Admissions were considered potentially suitable if they did not involve escalation of care, rapid response evaluation, in-hospital death, telemetry, surgical procedure, consultation to a procedural service, advanced imaging, transfusion, restraints, and nasogastric tube placement. Admission source, patient demographics, vital signs, laboratory test results, comorbidities, admission and active cancer diagnoses, and recent hospital utilization were included as candidate variables in a multivariable logistic regression model.

resultsOf 3,322 admissions, 905 (27.2%) patients were potentially suitable for HaH. After variable selection in the derivation cohort (n = 1,097), thirteen factors predicted potential suitability: admission source; temperature and respiratory rate at presentation; hemoglobin; breast cancer, GI cancer, or malignancy of secondary or ill-defined origin; admission for genitourinary, musculoskeletal, or neurologic symptoms, intestinal obstruction or ileus, or evaluation of secondary malignancy; and emergency department visit in prior 90 days. Model c-statistics were 0.71 (95% CI, 0.68 to 0.75) and 0.63 (0.59 to 0.67) in the derivation and validation (n = 1,095) cohorts.

conclusionHospital admissions of patients potentially suitable for HaH may be identifiable using data available at admission.

Indexed as

HospitalizationNeoplasmsEmergency Service, HospitalHospital MortalityHospitalsHumans

Identifiers

PMID33417488
PMCPMC8257922
OpenAlexW3118724433

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.