title: Predictors of a fall event in hospitalized patients with cancer.
abstract: OBJECTIVE
  To determine predictors of fall events in hospitalized patients with cancer and develop a scoring system to predict fall events.
  
  
  METHODS
  Retrospective medical record review.
  
  
  METHODS
  A 1,200-bed tertiary care hospital in northeastern Ohio.
  
  
  METHODS
  145 patients with cancer who did not have a fall event were randomly selected from all oncology admissions from February 2006-January 2007 and compared to 143 hospitalized patients with cancer who had a fall event during the same period.
  
  
  METHODS
  Multivariable logistic regression models predicting falls were fit. Risk score analysis was completed using bootstrap samples to evaluate discrimination between patients who did or did not fall and agreement between predicted and actual fall status. A nomogram of risk scores was created.
  
  
  METHODS
  Fall episodes during hospitalization and patient characteristics that predict falls.
  
  
  RESULTS
  While patients were hospitalized for cancer care, their predictors of a fall episode were low pain level, abnormal gait, cancer type, presence of metastasis, antidepressant and antipsychotic medication use, and blood product use (all p < 0.02); risk model c-statistic was 0.89.
  
  
  CONCLUSIONS
  For hospitalized patients with cancer, predictors reflecting greater fall episode risk can be assessed easily by nursing staff and acted on when the risk is sufficiently high.
  
  
  CONCLUSIONS
  Understanding specific risk factors of falls in an adult oncology population may lead to interventions that reduce fall risk.
authors.affiliation: Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Ohio, USA. caponel@ccf.org
authors.forename: Luann J
authors.initials: LJ
authors.lastname: Capone
authors.forename: Nancy M
authors.initials: NM
authors.lastname: Albert
authors.forename: James F
authors.initials: JF
authors.lastname: Bena
authors.forename: Anne S
authors.initials: AS
authors.lastname: Tang
mesh_terms: D000056:Accident Prevention; D000058:Accidental Falls / Q000517:prevention & control / Q000706:statistics & numerical data*; D000367:Age Factors; D000368:Aged; D000740:Anemia / Q000453:epidemiology; D016022:Case-Control Studies; D015897:Comorbidity; D003704:Dementia / Q000453:epidemiology; D004244:Dizziness / Q000453:epidemiology; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology; D005260:Female; D020233:Gait Disorders, Neurologic / Q000453:epidemiology; D006749:Hospital Design and Construction; D006753:Hospital Records; D006801:Humans; D007297:Inpatients; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000097:blood / Q000451:nursing* / Q000628:therapy; D009730:Nursing Assessment / Q000379:methods*; D009820:Ohio; D010146:Pain / Q000453:epidemiology; D012008:Recurrence; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D062606:Tertiary Care Centers