Fall incidents in nursing home residents: development of a predictive clinical rule (FINDER)

Abstract

ObjectivesTo develop (part I) and validate (part II) an electronic fall risk clinical rule (CR) to identify nursing home residents (NH-residents) at risk for a fall incident.DesignObservational, retrospective case–control study.SettingNursing homes.ParticipantsA total of 1668 (824 in part I, 844 in part II) NH-residents from the Netherlands were included. Data of participants from part I were excluded in part II.Primary and secondary outcome measuresDevelopment and validation of a fall risk CR in NH-residents. Logistic regression analysis was conducted to identify the fall risk-variables in part I. With these, three CRs were developed (ie, at the day of the fall incident and 3 days and 5 days prior to the fall incident). The overall prediction quality of the CRs were assessed using the area under the receiver operating characteristics (AUROC), and a cut-off value was determined for the predicted risk ensuring a sensitivity ≥0.85. Finally, one CR was chosen and validated in part II using a new retrospective data set.ResultsEleven fall risk-variables were identified in part I. The AUROCs of the three CRs form part I were similar: the AUROC for models I, II and III were 0.714 (95% CI: 0.679 to 0.748), 0.715 (95% CI: 0.680 to 0.750) and 0.709 (95% CI: 0.674 to 0.744), respectively. Model III (ie, 5 days prior to the fall incident) was chosen for validation in part II. The validated AUROC of the CR, obtained in part II, was 0.603 (95% CI: 0.565 to 0.641) with a sensitivity of 83.41% (95% CI: 79.44% to 86.76%) and a specificity of 27.25% (95% CI 23.11% to 31.81%).ConclusionMedication data and resident characteristics alone are not sufficient enough to develop a successful CR with a high sensitivity and specificity to predict fall risk in NH-residents.Trial registration numberNot available.

Identification of North American Nursing Diagnosis Association—Nursing Interventions Classification—Nursing Outcomes Classification of nursing home residents using on‐time data by android smartphone application by registered nurses

“…When NH RNs assess the risk of falls for residents, it should be based on observations and questions about risk factors. Age is a key variable when assessing fall risk in NH residents (Milosevic et al., 2021; Schoberer et al., 2022). The identified specific risk factors related to falls by RNs were “Self‐care deficit:bathing/hygiene” (8.1%), related factor with cognitive dysfunction (14.1%), and difficulty washing the body (15.5%).…”

A Fall Risk Assessment Model for Community-Dwelling Elderly Individuals Based on Gait Parameters

“…Clinical fall risk assessment for older adults typically comprises questionnaires or functional assessments of posture and gait, such as the St. Thomas's Risk Assessment Tool, the Morse Fall Scale, and the Timed Up and Go Test [9], [10], [11], [12]. However, these methods have low sensitivity and specificity because falls are caused by multiple and complex factors [13], [14]. Furthermore, these methods are subjective and their applicability is often limited to a specific setting or population [14], [15].…”