Observational studyPloS one2020
New predictive models for falls among inpatients using public ADL scale in Japan: A retrospective observational study of 7,858 patients in acute care setting.
Observational study in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 36 citations in OpenAlex.
- Machine Learning-Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter Study.JMIR medical informatics · 2025Observational
- The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment.Journal of medical Internet research · 2025Article
- Cognitive biases and contextual factors explaining variability in nurses' fall risk judgements: a multi-centre cross-sectional study.International journal of nursing studies advances · 2025Article
- Predictive Factors and the Predictive Scoring System for Falls in Acute Care Inpatients: Retrospective Cohort Study.JMIR human factors · 2025Article
- External Validation of the Saga Fall-Related Injury Risk Model and Exploration of Common Factors in Multiple Hospitals: A Retrospective Observational Study.Clinical interventions in aging · 2025Observational
- Validation of a Fall Predictive Model for Inpatients in Japanese Long Term Care Hospitals.International journal of medical sciences · 2025Observational
- Urban-rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.Frontiers in public health · 2025Article
- The Administration of Lemborexant at Admission is Not Associated with Inpatient Falls: A Multicenter Retrospective Observational Study.International journal of general medicine · 2024Article
- Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study.Clinical interventions in aging · 2024Observational
- Validation of the Saga Fall Injury Risk Model.International journal of medical sciences · 2024Observational
- Article
- A Simple and Accurate Model for Predicting Fall Injuries in Hospitalized Patients: Insights from a Retrospective Observational Study in Japan.Medical science monitor : international medical journal of experimental and clinical research · 2023Observational
- Falls as the result of interplay between nurses, patient and the environment: Using text-mining to uncover how and why falls happen.International journal of nursing sciences · 2023Article
- Association of motor index scores with fall incidence among community-dwelling older people.BMC geriatrics · 2022Article
- A Prospective Observational Study Conducted at a Single Center in Japan to Validate a Previously Developed Predictive Formula of In-Hospital Mortality for Patients Aged ≥65 Years with Endogenous Diseases Transported by Ambulance.Medical science monitor : international medical journal of experimental and clinical research · 2022Observational
- External validation of a new predictive model for falls among inpatients using the official Japanese ADL scale, Bedriddenness ranks: a double-centered prospective cohort study.BMC geriatrics · 2022Article
- History of Falls and Bedriddenness Ranks are Useful Predictive Factors for in-Hospital Falls: A Single-Center Retrospective Observational Study Using the Saga Fall Risk Model.International journal of general medicine · 2022Article
- The Relationship Between Knowledge, Attitude, Practice, and Fall Prevention for Childhood in Shanghai, China.Frontiers in public health · 2022Article
- Observational
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimMost predictive models for falls developed previously were awkward to use because of their complexity. We developed and validated a new easier-to-use predictive model for falls of adult inpatients using easily accessible information including the public ADL scale in Japan.
methodsWe retrospectively analyzed data from Japanese adult inpatients in an acute care hospital from 2012 to 2015. Two-thirds of cases were randomly extracted to the test set and one-third to the validation set. Data including age, sex, activity of daily living (ADL), public scales in Japan of ADL "bedriddenness rank," and cognitive function in daily living, hypnotic medications, previous falls, and emergency admission were derived from hospital records. Falls during hospitalization were identified from incident reports. Two predictive models were created by multivariate analysis, each of which was assessed by area under the curve (AUC) from the validation set.
resultsA total of 7,858 adult participants were available. The AUC of model 1, using 13 factors-age, sex (male), emergency admission, use of ambulance, referral letter, admission to Neurosurgery, admission to Internal Medicine, use of hypnotic medication, permanent damage by stroke, history of falls, visual impairment, independence of eating, and bedriddenness rank-with low mutual collinearity and showing significant relationship by multivariate logistic regression analysis, was 0.789 in the validation set. The AUC of parsimonious model 2, using age and seven factors-sex (male), emergency admission, admission to Neurosurgery, use of hypnotic medication, history of falls, independence of eating, and bedriddenness rank-showing statistical significance by multivariate analysis in model 1, was 0.787 in the validation set.
conclusionsWe proposed new predictive models for inpatients' fall using the public ADL scales in Japan, which had a higher degree of usability because of their use of simpler and fewer (8 or 13) predictors, especially parsimonious model 2.
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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.