An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation
<h3>Background</h3><p dir="ltr">Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategi...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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| _version_ | 1864513543152336896 |
|---|---|
| author | Rex Parsons (12493642) |
| author2 | Robin Blythe (16681837) Susanna Cramb (3543749) Ahmad Abdel-Hafez (17289934) Steven McPhail (3755290) |
| author2_role | author author author author |
| author_facet | Rex Parsons (12493642) Robin Blythe (16681837) Susanna Cramb (3543749) Ahmad Abdel-Hafez (17289934) Steven McPhail (3755290) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rex Parsons (12493642) Robin Blythe (16681837) Susanna Cramb (3543749) Ahmad Abdel-Hafez (17289934) Steven McPhail (3755290) |
| dc.date.none.fl_str_mv | 2024-11-13T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.2196/59634 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_Electronic_Medical_Record_Based_Prognostic_Model_for_Inpatient_Falls_Development_and_Internal-External_Cross-Validation/29605169 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Epidemiology Health services and systems Information and computing sciences Applied computing Data management and data science Machine learning Mathematical sciences Statistics Clinical prediction model Patient safety prognostic Electronic medical record EMR intervention Risk assessment Clinical decision Support system Inpatient falls |
| dc.title.none.fl_str_mv | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete. Accurate, dynamic, individualized estimates of fall risk for admitted patients using routinely recorded data may assist in prioritizing fall prevention efforts.</p><h3>Objective</h3><p dir="ltr">The objective of this study was to develop and validate an accurate and dynamic prognostic model for inpatient falls among a cohort of patients using routinely recorded electronic medical record data.</p><h3>Methods</h3><p dir="ltr">We used routinely recorded data from 5 Australian hospitals to develop and internally-externally validate a prediction model for inpatient falls using a Cox proportional hazards model with time-varying covariates. The study cohort included patients admitted during 2018-2021 to any ward, with no age restriction. Predictors used in the model included admission-related administrative data, length of stay, and number of previous falls during the admission (updated every 12 hours up to 14 days after admission). Model calibration was assessed using Poisson regression and discrimination using the area under the time-dependent receiver operating characteristic curve.</p><h3>Results</h3><p dir="ltr">There were 1,107,556 inpatient admissions, 6004 falls, and 5341 unique fallers. The area under the time-dependent receiver operating characteristic curve was 0.899 (95% CI 0.88-0.91) at 24 hours after admission and declined throughout admission (eg, 0.765, 95% CI 0.75-0.78 on the seventh day after admission). Site-dependent overestimation and underestimation of risk was observed on the calibration plots.</p><h3>Conclusions</h3><p dir="ltr">Using a large dataset from multiple hospitals and robust methods to model development and validation, we developed a prognostic model for inpatient falls. It had high discrimination, suggesting the model has the potential for operationalization in clinical decision support for prioritizing inpatients for fall prevention. Performance was site dependent, and model recalibration may lead to improved performance.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Medical Internet Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/59634" target="_blank">https://dx.doi.org/10.2196/59634</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_554c79cb048905ce75a26915b2a7dbbf |
| identifier_str_mv | 10.2196/59634 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605169 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-ValidationRex Parsons (12493642)Robin Blythe (16681837)Susanna Cramb (3543749)Ahmad Abdel-Hafez (17289934)Steven McPhail (3755290)Health sciencesEpidemiologyHealth services and systemsInformation and computing sciencesApplied computingData management and data scienceMachine learningMathematical sciencesStatisticsClinical prediction modelPatient safety prognosticElectronic medical recordEMRinterventionRisk assessmentClinical decision Support systemInpatient falls<h3>Background</h3><p dir="ltr">Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete. Accurate, dynamic, individualized estimates of fall risk for admitted patients using routinely recorded data may assist in prioritizing fall prevention efforts.</p><h3>Objective</h3><p dir="ltr">The objective of this study was to develop and validate an accurate and dynamic prognostic model for inpatient falls among a cohort of patients using routinely recorded electronic medical record data.</p><h3>Methods</h3><p dir="ltr">We used routinely recorded data from 5 Australian hospitals to develop and internally-externally validate a prediction model for inpatient falls using a Cox proportional hazards model with time-varying covariates. The study cohort included patients admitted during 2018-2021 to any ward, with no age restriction. Predictors used in the model included admission-related administrative data, length of stay, and number of previous falls during the admission (updated every 12 hours up to 14 days after admission). Model calibration was assessed using Poisson regression and discrimination using the area under the time-dependent receiver operating characteristic curve.</p><h3>Results</h3><p dir="ltr">There were 1,107,556 inpatient admissions, 6004 falls, and 5341 unique fallers. The area under the time-dependent receiver operating characteristic curve was 0.899 (95% CI 0.88-0.91) at 24 hours after admission and declined throughout admission (eg, 0.765, 95% CI 0.75-0.78 on the seventh day after admission). Site-dependent overestimation and underestimation of risk was observed on the calibration plots.</p><h3>Conclusions</h3><p dir="ltr">Using a large dataset from multiple hospitals and robust methods to model development and validation, we developed a prognostic model for inpatient falls. It had high discrimination, suggesting the model has the potential for operationalization in clinical decision support for prioritizing inpatients for fall prevention. Performance was site dependent, and model recalibration may lead to improved performance.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Medical Internet Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/59634" target="_blank">https://dx.doi.org/10.2196/59634</a></p>2024-11-13T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.2196/59634https://figshare.com/articles/journal_contribution/An_Electronic_Medical_Record_Based_Prognostic_Model_for_Inpatient_Falls_Development_and_Internal-External_Cross-Validation/29605169CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296051692024-11-13T03:00:00Z |
| spellingShingle | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation Rex Parsons (12493642) Health sciences Epidemiology Health services and systems Information and computing sciences Applied computing Data management and data science Machine learning Mathematical sciences Statistics Clinical prediction model Patient safety prognostic Electronic medical record EMR intervention Risk assessment Clinical decision Support system Inpatient falls |
| status_str | publishedVersion |
| title | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation |
| title_full | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation |
| title_fullStr | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation |
| title_full_unstemmed | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation |
| title_short | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation |
| title_sort | An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation |
| topic | Health sciences Epidemiology Health services and systems Information and computing sciences Applied computing Data management and data science Machine learning Mathematical sciences Statistics Clinical prediction model Patient safety prognostic Electronic medical record EMR intervention Risk assessment Clinical decision Support system Inpatient falls |