Cargando…
Stratification of risk for hospital admissions for injury related to fall: cohort study
Objective To determine whether the ability to stratify an individual patient’s hazard for falling could facilitate development of focused interventions aimed at reducing these adverse outcomes. Design Clinical and sociodemographic data from electronic health records were utilized to derive multiple...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group Ltd.
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208628/ https://www.ncbi.nlm.nih.gov/pubmed/25954985 http://dx.doi.org/10.1136/bmj.g5863 |
_version_ | 1782341151719686144 |
---|---|
author | Castro, Victor M McCoy, Thomas H Cagan, Andrew Rosenfield, Hannah R Murphy, Shawn N Churchill, Susanne E Kohane, Isaac S Perlis, Roy H |
author_facet | Castro, Victor M McCoy, Thomas H Cagan, Andrew Rosenfield, Hannah R Murphy, Shawn N Churchill, Susanne E Kohane, Isaac S Perlis, Roy H |
author_sort | Castro, Victor M |
collection | PubMed |
description | Objective To determine whether the ability to stratify an individual patient’s hazard for falling could facilitate development of focused interventions aimed at reducing these adverse outcomes. Design Clinical and sociodemographic data from electronic health records were utilized to derive multiple logistic regression models of hospital readmissions for injuries related to falls. Drugs used at admission were summarized based on reported adverse effect frequencies in published drug labeling. Setting Two large academic medical centers in New England, United States. Participants The model was developed with 25 924 individuals age ≥40 with an initial hospital discharge. The resulting model was then tested in an independent set of 13 032 inpatients drawn from the same hospital and 36 588 individuals discharged from a second large hospital during the same period. Main outcome measure Hospital readmissions for injury related to falls. Results Among 25 924 discharged individuals, 680 (2.6%) were evaluated in the emergency department or admitted to hospital for a fall within 30 days of discharge, 1635 (6.3%) within 180 days of discharge, 2360 (9.1%) within one year, and 3465 (13.4%) within two years. Older age, female sex, white or African-American race, public insurance, greater number of drugs taken on discharge, and score for burden of adverse effects were each independently associated with hazard for fall. For drug burden, presence of a drug with a frequency of adverse effects related to fall of 10% was associated with 3.5% increase in odds of falling over the next two years (odds ratio 1.04, 95% confidence interval 1.02 to 1.05). In an independent testing set, the area under the receiver operating characteristics curve was 0.65 for a fall within two years based on cross sectional data and 0.72 with the addition of prior utilization data including age adjusted Charlson comorbidity index. Portability was promising, with area under the curve of 0.71 for the longitudinal model in a second hospital system. Conclusions It is potentially useful to stratify risk of falls based on clinical features available as artifacts of routine clinical care. A web based tool can be used to calculate and visualize risk associated with drug treatment to facilitate further investigation and application. |
format | Online Article Text |
id | pubmed-4208628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BMJ Publishing Group Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-42086282014-10-29 Stratification of risk for hospital admissions for injury related to fall: cohort study Castro, Victor M McCoy, Thomas H Cagan, Andrew Rosenfield, Hannah R Murphy, Shawn N Churchill, Susanne E Kohane, Isaac S Perlis, Roy H BMJ Research Objective To determine whether the ability to stratify an individual patient’s hazard for falling could facilitate development of focused interventions aimed at reducing these adverse outcomes. Design Clinical and sociodemographic data from electronic health records were utilized to derive multiple logistic regression models of hospital readmissions for injuries related to falls. Drugs used at admission were summarized based on reported adverse effect frequencies in published drug labeling. Setting Two large academic medical centers in New England, United States. Participants The model was developed with 25 924 individuals age ≥40 with an initial hospital discharge. The resulting model was then tested in an independent set of 13 032 inpatients drawn from the same hospital and 36 588 individuals discharged from a second large hospital during the same period. Main outcome measure Hospital readmissions for injury related to falls. Results Among 25 924 discharged individuals, 680 (2.6%) were evaluated in the emergency department or admitted to hospital for a fall within 30 days of discharge, 1635 (6.3%) within 180 days of discharge, 2360 (9.1%) within one year, and 3465 (13.4%) within two years. Older age, female sex, white or African-American race, public insurance, greater number of drugs taken on discharge, and score for burden of adverse effects were each independently associated with hazard for fall. For drug burden, presence of a drug with a frequency of adverse effects related to fall of 10% was associated with 3.5% increase in odds of falling over the next two years (odds ratio 1.04, 95% confidence interval 1.02 to 1.05). In an independent testing set, the area under the receiver operating characteristics curve was 0.65 for a fall within two years based on cross sectional data and 0.72 with the addition of prior utilization data including age adjusted Charlson comorbidity index. Portability was promising, with area under the curve of 0.71 for the longitudinal model in a second hospital system. Conclusions It is potentially useful to stratify risk of falls based on clinical features available as artifacts of routine clinical care. A web based tool can be used to calculate and visualize risk associated with drug treatment to facilitate further investigation and application. BMJ Publishing Group Ltd. 2014-10-24 /pmc/articles/PMC4208628/ /pubmed/25954985 http://dx.doi.org/10.1136/bmj.g5863 Text en © Castro et al 2014 http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Research Castro, Victor M McCoy, Thomas H Cagan, Andrew Rosenfield, Hannah R Murphy, Shawn N Churchill, Susanne E Kohane, Isaac S Perlis, Roy H Stratification of risk for hospital admissions for injury related to fall: cohort study |
title | Stratification of risk for hospital admissions for injury related to fall: cohort study |
title_full | Stratification of risk for hospital admissions for injury related to fall: cohort study |
title_fullStr | Stratification of risk for hospital admissions for injury related to fall: cohort study |
title_full_unstemmed | Stratification of risk for hospital admissions for injury related to fall: cohort study |
title_short | Stratification of risk for hospital admissions for injury related to fall: cohort study |
title_sort | stratification of risk for hospital admissions for injury related to fall: cohort study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208628/ https://www.ncbi.nlm.nih.gov/pubmed/25954985 http://dx.doi.org/10.1136/bmj.g5863 |
work_keys_str_mv | AT castrovictorm stratificationofriskforhospitaladmissionsforinjuryrelatedtofallcohortstudy AT mccoythomash stratificationofriskforhospitaladmissionsforinjuryrelatedtofallcohortstudy AT caganandrew stratificationofriskforhospitaladmissionsforinjuryrelatedtofallcohortstudy AT rosenfieldhannahr stratificationofriskforhospitaladmissionsforinjuryrelatedtofallcohortstudy AT murphyshawnn stratificationofriskforhospitaladmissionsforinjuryrelatedtofallcohortstudy AT churchillsusannee stratificationofriskforhospitaladmissionsforinjuryrelatedtofallcohortstudy AT kohaneisaacs stratificationofriskforhospitaladmissionsforinjuryrelatedtofallcohortstudy AT perlisroyh stratificationofriskforhospitaladmissionsforinjuryrelatedtofallcohortstudy |