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The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

ABSTRACT: To evaluate relative accuracy of a newly developed Stroke Assessment of Fall Risk (SAFR) for classifying fallers and non-fallers, compared with a health system fall risk screening tool, the Fall Harm Risk Screen.Prospective quality improvement study conducted at an inpatient stroke rehabilitation unit at a large urban university hospital.Patients admitted for inpatient stroke rehabilitation (N = 419) with imaging or clinical evidence of ischemic or hemorrhagic stroke, between 1 August 2009 and 31 July 2010.Not applicable.Sensitivity, specificity, and area under the curve for Receiver Operating Characteristic Curves of both scales' classifications, based on fall risk score completed upon admission to inpatient stroke rehabilitation.A total of 68 (16%) participants fell at least once. The SAFR was significantly more accurate than the Fall Harm Risk Screen (p < 0.001), with area under the curve of 0.73, positive predictive value of 0.29, and negative predictive value of 0.94. For the Fall Harm Risk Screen, area under the curve was 0.56, positive predictive value was 0.19, and negative predictive value was 0.86. Sensitivity and specificity of the SAFR (0.78 and 0.63, respectively) was higher than the Fall Harm Risk Screen (0.57 and 0.48, respectively).An evidence-derived, population-specific fall risk assessment may more accurately predict fallers than a general fall risk screen for stroke rehabilitation patients. While the SAFR improves upon the accuracy of a general assessment tool, additional refinement may be warranted.

SUBMITTER: Breisinger TP

PROVIDER: S-EPMC4229409 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

Breisinger Terry P TP Skidmore Elizabeth R ER Niyonkuru Christian C Terhorst Lauren L Campbell Grace B GB

Clinical rehabilitation 20140521 12

<h4>Objective</h4>To evaluate relative accuracy of a newly developed Stroke Assessment of Fall Risk (SAFR) for classifying fallers and non-fallers, compared with a health system fall risk screening tool, the Fall Harm Risk Screen.<h4>Design and setting</h4>Prospective quality improvement study conducted at an inpatient stroke rehabilitation unit at a large urban university hospital.<h4>Participants</h4>Patients admitted for inpatient stroke rehabilitation (N = 419) with imaging or clinical evide ...[more]

PMID: 24849795

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Project description:ImportanceThe ability of present-day etiologic stroke classification systems to generate subtypes with discrete stroke characteristics is not known.ObjectiveTo test the hypothesis that etiologic stroke subtyping identifies different disease processes that can be recognized through their different clinical courses.Design, setting, and participantsWe performed a head-to-head evaluation of the ability of the Causative Classification of Stroke (CCS), Trial of Org 10172 in Acute Stroke Treatment (TOAST), and ASCO (A for atherosclerosis, S for small-vessel disease, C for cardiac source, and O for other cause) classification systems to generate etiologic subtypes with different clinical, imaging, and prognostic characteristics in 1816 patients with ischemic stroke. This study included 2 cohorts recruited at separate periods; the first cohort was recruited between April 2003 and June 2006 and the second between June 2009 and December 2011. Data analysis was performed between June 2014 and May 2016.Main outcomes and measuresSeparate teams of stroke-trained neurologists performed CCS, TOAST, and ASCO classifications based on information available at the time of hospital discharge. We assessed the association between etiologic subtypes and stroke characteristics by computing receiver operating characteristic curves for binary variables (90-day stroke recurrence and 90-day mortality) and by calculating the ratio of between-category to within-category variability from the analysis of variance for continuous variables (admission National Institutes of Health Stroke Scale score and acute infarct volume).ResultsAmong the 1816 patients included, the median age was 70 years (interquartile range, 58-80 years) (830 women [46%]). The classification systems differed in their ability to assign stroke etiologies into known subtypes; the size of the undetermined category was 33% by CCS, 53% by TOAST, and 42% by ASCO (P < .001 for all binary comparisons). All systems provided significant discrimination for the validation variables tested. For the primary validation variable (90-day recurrence), the area under the receiver operating characteristic curve was 0.71 (95% CI, 0.66-0.75) for CCS, 0.61 (95% CI, 0.56-0.67) for TOAST, and 0.66 (95% CI, 0.60-0.71) for ASCO (P = .01 for CCS vs ASCO; P < .001 for CCS vs TOAST; P = .13 for ASCO vs TOAST). The classification systems exhibited similar discrimination for 90-day mortality. For admission National Institutes of Health Stroke Scale score and acute infarct volume, CCS generated more distinct subtypes with higher between-category to within-category variability than TOAST and ASCO.Conclusions and relevanceOur findings suggest that the major etiologic stroke subtypes are distinct categories with different stroke characteristics irrespective of the classification system used to identify them. We further show that CCS generates discrete etiologic categories with more diverse clinical, imaging, and prognostic characteristics than either TOAST or ASCO.

Project description:This study aimed to examine the predictive validity of two internationally well-known instruments, the Modified Home Falls and Accidents Screening Tool (Modified HOME FAST) and the Modified Home Falls and Accidents Screening Tool-Self Report (Modified HOME FAST-SR), and the newly developed Thai Home Falls Hazard Assessment Tool (Thai-HFHAT) (69 items) in predicting falls among older Thai adults. It also aimed to examine the predictive validity of the two abbreviated versions (44 and 27 items) of the Thai-HFHAT, which were developed post hoc to accommodate older adults' limited literacy and poor vision and to facilitate the identification of high-impact home fall hazards that are prevalent in the Thailand context. A prospective cohort study was conducted among 450 participants aged 60 years and above who were assessed by the aforementioned tools at baseline, for which data on fall incidence were then collected during the one-year follow-up. The Cox proportional hazard model was applied to estimate hazard ratios (HRs); then, Harrell's C-statistics and receiver operating characteristic (ROC) analyses were conducted to identify the best cutoff point, sensitivity and specificity for each instrument. The results showed that the fall hazard rate was 2.04 times per 1,000 person-days. Taking into account both the predictive validity and applicability, the Thai-HFHAT (44 items) was found to be the most suitable screening tool due to its highest sensitivity and specificity (93% and 72%) at the cutoff score of 18. In conclusion, our study showed that these internationally validated home fall hazard assessment tools were quite applicable for Thailand, but further tailoring the tools into a specific local context yielded even more highly valid tools in predicting fall risk among older Thai adults. Although these findings were well reproducible by inferring from the internal validation results, further external validation in the independent population is necessary.

Project description:OBJECTIVE: We conducted a study to develop an assessment sheet for fall prediction in stroke inpatients that is handy and reliable to help ward staff to devise a fall prevention strategy for each inpatient immediately upon admission. METHODS: The study consisted of three steps: (1) developing a data sampling form to record variables related to risk of falls in stroke inpatients and conducting a follow-up survey for stroke inpatients from their admission to discharge by using the form; (2) carrying out analyses of characteristics of the present subjects and selecting variables showing a high hazard ratio (HR) for falls using the Cox regression analysis; (3) developing an assessment sheet for fall prediction involving variables giving the integral coefficient for each variable in accordance with the HR determined in the second step. RESULTS AND DISCUSSION: (1) Subjects of the present survey were 704 inpatients from 17 hospitals including 270 fallers. (2) We selected seven variables as predictors of the first fall: central paralysis, history of previous falls, use of psychotropic medicines, visual impairment, urinary incontinence, mode of locomotion and cognitive impairment. (3) We made 960 trial models in combination with possible coefficients for each variable, and among them we finally selected the most suitable model giving coefficient number 1 to each variable except mode of locomotion, which was given 1 or 2. The area under the ROC curve of the selected model was 0.73, and sensitivity and specificity were 0.70 and 0.69, respectively (4/5 at the cut-off point). Scores calculated from the assessment sheets of the present subjects by adding coefficients of each variable showed normal distribution and a significantly higher mean score in fallers (4.94 +/- 1.29) than in non-fallers (3.65 +/- 1.58) (P = 0.001). The value of the Barthel Index as the index of ADL of each subject was indicated to be in proportion to the assessment score of each subject. CONCLUSION: We developed an assessment sheet for fall prediction in stroke inpatients that was shown to be available and valid to screen inpatients with risk of falls immediately upon admission.

Project description:INTRODUCTION:The aftermath of stroke leaves many consequences including cognitive deficits and falls due to imbalance. Stroke survivors and families struggle to navigate the complex healthcare system with little assistance posthospital discharge, often leading to early hospital readmission and worse stroke outcomes. Telemedicine Guided Education on Secondary Stroke and Fall Prevention Following Inpatient Rehabilitation feasibility study examines whether stroke survivors and their caregivers find value in telerehabilitation (TR) home visits that provide individualised care and education by a multidisciplinary team after discharge from inpatient rehabilitation. METHODS AND ANALYSIS:A prospective, single arm, pilot study is designed to evaluate the feasibility of weekly TR home visits initiated postdischarge from inpatient rehabilitation. Newly diagnosed patients with stroke are recruited from a Houston-based comprehensive stroke centre inpatient rehabilitation unit, loaned an iPad with data plan and trained to use information technology security-approved videoconferencing application. After hospital discharge, six weekly TR home visits are led by rotating specialists (pharmacist, physical/occupational therapist, speech therapist, rehabilitation physician, social worker, geriatrician specialised in fracture prevention) followed by satisfaction survey on week 7. Specialists visually assess patients in real time, educate them on secondary stroke and fall prevention and suggest ways to improve function including direct medical interventions when indicated. Primary outcomes are proportion of eligible patients consenting to the study, participation rate in all six TR home visits and satisfaction score. The study started 31 December 2015 with plan to enrol up to 50 patients over 24 months. Feasibility study results will inform us as to whether a randomised controlled trial is warranted to determine efficacy of TR home visit intervention in improving stroke outcomes. ETHICS AND DISSEMINATION:Ethics approval obtained by the Institutional Review Board (IRB), Committee for the Protection of Human Subjects, IRB number: HSC-MS-14-0994. Study results will be submitted for publication in a peer-reviewed journal.

Project description:ImportanceSeizure risk stratification is needed to boost inpatient seizure detection and to improve continuous electroencephalogram (cEEG) cost-effectiveness. 2HELPS2B can address this need but requires validation.ObjectiveTo use an independent cohort to validate the 2HELPS2B score and develop a practical guide for its use.Design, setting, and participantsThis multicenter retrospective medical record review analyzed clinical and EEG data from patients 18 years or older with a clinical indication for cEEG and an EEG duration of 12 hours or longer who were receiving consecutive cEEG at 6 centers from January 2012 to January 2019. 2HELPS2B was evaluated with the validation cohort using the mean calibration error (CAL), a measure of the difference between prediction and actual results. A Kaplan-Meier survival analysis was used to determine the duration of EEG monitoring to achieve a seizure risk of less than 5% based on the 2HELPS2B score calculated on first- hour (screening) EEG. Participants undergoing elective epilepsy monitoring and those who had experienced cardiac arrest were excluded. No participants who met the inclusion criteria were excluded.Main outcomes and measuresThe main outcome was a CAL error of less than 5% in the validation cohort.ResultsThe study included 2111 participants (median age, 51 years; 1113 men [52.7%]; median EEG duration, 48 hours) and the primary outcome was met with a validation cohort CAL error of 4.0% compared with a CAL of 2.7% in the foundational cohort (P = .13). For the 2HELPS2B score calculated on only the first hour of EEG in those without seizures during that hour, the CAL error remained at less than 5.0% at 4.2% and allowed for stratifying patients into low- (2HELPS2B = 0; <5% risk of seizures), medium- (2HELPS2B = 1; 12% risk of seizures), and high-risk (2HELPS2B, ≥2; risk of seizures, >25%) groups. Each of the categories had an associated minimum recommended duration of EEG monitoring to achieve at least a less than 5% risk of seizures, a 2HELPS2B score of 0 at 1-hour screening EEG, a 2HELPS2B score of 1 at 12 hours, and a 2HELPS2B score of 2 or greater at 24 hours.Conclusions and relevanceIn this study, 2HELPS2B was validated as a clinical tool to aid in seizure detection, clinical communication, and cEEG use in hospitalized patients. In patients without prior clinical seizures, a screening 1-hour EEG that showed no epileptiform findings was an adequate screen. In patients with any highly epileptiform EEG patterns during the first hour of EEG (ie, a 2HELPS2B score of ≥2), at least 24 hours of recording is recommended.

Project description:Background and purposeStroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity.MethodsData were harmonized from 4 stroke studies (follow-up range, ≈12-18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests.ResultsThe predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53).ConclusionsDementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

Dataset Information

The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

Publications

The Stroke Assessment of Fall Risk (SAFR): predictive validity in inpatient stroke rehabilitation.

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