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Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly: A STARD compliant study.

ABSTRACT: We aimed to determine the accuracy and failure of OAK device, an automated screening, for the assessment of fall risk in a prospective cohort of healthy adults aged over 65 years. The algorithm for fall risk assessment of the centers for disease control and prevention (CDC) was used as reference standard. Of the 183 individuals recruited, the CDC algorithm classified 80 as being at moderate/high risk and 103 at low risk of falling. OAK device failure incidence was 4.9% (confidence interval [CI] upper limit 7.7%), below the preset threshold for futility-early termination of the study (i.e., not above 15%). The OAK device showed a sensitivity of 84% and a specificity of 67% (receiver operating characteristic [ROC] area 82%; 95% confidence interval [CI] 76-88%), not reaching the preplanned target sensitivity (not lower than 85%). Diagnostic accuracy was not far from the sensitivity levels similar to those obtained with other fall risk assessment. However, some limitations can be considered.ClinicalTrials.gov identifier: NCT02655796.

SUBMITTER: Castellini G

PROVIDER: S-EPMC6775395 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly: A STARD compliant study.

Castellini Greta G Gianola Silvia S Stucovitz Elena E Tramacere Irene I Banfi Giuseppe G Moja Lorenzo L

Medicine 20190901 39

We aimed to determine the accuracy and failure of OAK device, an automated screening, for the assessment of fall risk in a prospective cohort of healthy adults aged over 65 years. The algorithm for fall risk assessment of the centers for disease control and prevention (CDC) was used as reference standard. Of the 183 individuals recruited, the CDC algorithm classified 80 as being at moderate/high risk and 103 at low risk of falling. OAK device failure incidence was 4.9% (confidence interval [CI] ...[more]

PMID: 31574809

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Project description:IntroductionStandards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI.Methods and analysisThe development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group's efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption.Ethics and disseminationEthical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.

Project description:BACKGROUND:Femur fractures are a common orthopedic injury in young children. Falls account for a large portion of accidental femur fractures in young children, but there is also a high prevalence of femur fractures in child abuse, with falls often provided as false histories. Objective information regarding fracture potential in short distance fall scenarios may aid in assessing whether a child's injuries are the result of abuse or an accidental fall. Knowledge of femur loading is the first step towards understanding likelihood of fracture in a fall. OBJECTIVE:Characterize femur loading during feet-first free falls using a surrogate representing a 12-month-old child. METHODS:The femur and hip joint of a surrogate representing a 12-month-old were modified to improve biofidelity and measure femur loading; 6-axis load cells were integrated into the proximal and distal femur. Femur modification was based upon CT imaging of cadaveric femurs in children 10-14 months of age. Using the modified 12-month-old surrogate, feet-first free falls from 69?cm and 119?cm heights onto padded carpet and linoleum were conducted to assess fall dynamics and determine femur loading. Femur compression, bending moment, shear and torsional moment were measured for each fall. RESULTS:Fall dynamics differed across fall heights, but did not substantially differ by impact surface type. Significant differences were found in all loading conditions across fall heights, while only compression and bending loads differed between carpet and linoleum surfaces. Maximum compression, bending, torsion and shear occurred in 119?cm falls and were 572?N, 23?N-m, 11?N-m and 281?N, respectively. CONCLUSIONS:Fall dynamics play an important role in the biomechanical assessment of falls. Fall height was found to influence both fall dynamics and femur loading, while impact surface affected only compression and bending in feet-first falls; fall dynamics did not differ across carpet and linoleum. Improved pediatric thresholds are necessary to predict likelihood of fracture, but morphologically accurate representation of the lower extremity, along with accurate characterization of loading in falls are a crucial first step.

Project description:BackgroundFalls are common adverse events in hospitals, frequently leading to additional health costs due to prolonged stays and extra care. Therefore, reliable fall detection is vital to develop and test fall prevention strategies. However, conventional methods-voluntary incident reports and manual chart reviews-are error-prone and time consuming, respectively. Using a search algorithm to examine patients' electronic health record data and flag fall indicators offers an inexpensive, sensitive, cost-effective alternative.ObjectiveThis study's purpose was to develop a fall detection algorithm for use with electronic health record data, then to evaluate it alongside the Global Trigger Tool, incident reports, a manual chart review, and patient-reported falls.MethodsConducted on 2 campuses of a large hospital system in Switzerland, this retrospective diagnostic accuracy study consisted of 2 substudies: the first, targeting 240 patients, for algorithm development and the second, targeting 298 patients, for validation. In the development study, we compared the new algorithm's in-hospital fall rates with those indicated by the Global Trigger Tool and incident reports; in the validation study, we compared the algorithm's in-hospital fall rates with those from patient-reported falls and manual chart review. We compared the various methods by calculating sensitivity, specificity, and predictive values.ResultsTwenty in-hospital falls were discovered in the development study sample. Of these, the algorithm detected 19 (sensitivity 95%), the Global Trigger Tool detected 18 (90%), and incident reports detected 14 (67%). Of the 15 falls found in the validation sample, the algorithm identified all 15 (100%), the manual chart review identified 14 (93%), and the patient-reported fall measure identified 5 (33%). Owing to relatively high numbers of false positives based on falls present on admission, the algorithm's positive predictive values were 50% (development sample) and 47% (validation sample). Instead of requiring 10 minutes per case for a full manual review or 20 minutes to apply the Global Trigger Tool, the algorithm requires only a few seconds, after which only the positive results (roughly 11% of the full case number) require review.ConclusionsThe newly developed electronic health record algorithm demonstrated very high sensitivity for fall detection. Applied in near real time, the algorithm can record in-hospital falls events effectively and help to develop and test fall prevention measures.

Project description:About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls.The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up.FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators.The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration.FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface.ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR).

Dataset Information

Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly: A STARD compliant study.

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Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly: A STARD compliant study.

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