Evidence map›Paper›PMID 35818288›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2022

Falls prediction using the nursing home minimum dataset.

Richard D Boyce, Olga V Kravchenko, Subashan Perera, Jordan F Karp, Sandra L Kane-Gill, Charles F Reynolds, Steven M Albert, Steven M Handler

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  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

8 authors.

Richard D BoyceDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID 0000-0002-2993-2085
Olga V KravchenkoDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Subashan PereraAging Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.
Jordan F KarpDepartment of Psychiatry, College of Medicine, University of Arizona, Tucson, Arizona, USA.
Sandra L Kane-GillDepartment of Pharmacy and Therapeutics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Charles F ReynoldsAging Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.
Steven M AlbertDepartment of Behavioral and Community Health Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID 0000-0001-6786-9956
Steven M HandlerDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Funding

VARIABILITY, STABILITY & SMOOTHNESS OF WALKINGP30AG024827 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Subashan Perera · 2004 to 2026
$28.6M
The internship in Biomedical Research, Informatics, and Computer Science (iBRIC): Biomedical Informatics and Data Science research experiences for students from Minority Serving InstitutionsT15LM007059 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HARRY S HOCHHEISER · 1987 to 2026
$23.9M
RESEARCH METHODS COREP30MH090333 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REYNOLDS, CHARLES F. · 2011 to 2015
$8.3M
Improving medication safety for nursing home residents prescribed psychotropic drK01AG044433 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BOYCE, RICHARD DAVID · 2014 to 2016
$342k
NIA NIH HHS K01 AG044433NIA NIH HHS P30 AG024827NIMH NIH HHS P30 MH090333NIMH NIH HHS P30 MH90333NLM NIH HHS T15 LM007059
6 · The paper itself

Abstract

objectiveThe purpose of the study was to develop and validate a model to predict the risk of experiencing a fall for nursing home residents utilizing data that are electronically available at the more than 15 000 facilities in the United States. MATERIALS AND

methodsThe fall prediction model was built and tested using 2 extracts of data (2011 through 2013 and 2016 through 2018) from the Long-term Care Minimum Dataset (MDS) combined with drug data from 5 skilled nursing facilities. The model was created using a hybrid Classification and Regression Tree (CART)-logistic approach.

resultsThe combined dataset consisted of 3985 residents with mean age of 77 years and 64% female. The model's area under the ROC curve was 0.668 (95% confidence interval: 0.643-0.693) on the validation subsample of the merged data. DISCUSSION: Inspection of the model showed that antidepressant medications have a significant protective association where the resident has a fall history prior to admission, requires assistance to balance while walking, and some functional range of motion impairment in the lower body; even if the patient exhibits behavioral issues, unstable behaviors, and/or are exposed to multiple psychotropic drugs.

conclusionThe novel hybrid CART-logit algorithm is an advance over the 22 fall risk assessment tools previously evaluated in the nursing home setting because it has a better performance characteristic for the fall prediction window of ≤90 days and it is the only model designed to use features that are easily obtainable at nearly every facility in the United States.

Indexed as

Nursing HomesPsychotropic DrugsAgedHumansRisk AssessmentRisk FactorsUnited StatesPsychotropic Drugsfall prevention interventionfallslong-term care minimum datasetskilled nursing facilities

Identifiers

PMID35818288
PMCPMC9382393

What Socratic 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 Socratic graph.