Predicting in-patient falls in a geriatric clinic

Marschollek, Michael; Nemitz, G.; Gietzelt, Matthias; Wolf, Klaus-Hendrik; Schwabedissen, H. Meyer zu; Haux, Reinhold

doi:10.1007/s00391-009-0035-7

Cited by 58 publications

(40 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The negative predictive value, in turn, is high in both models (93.5%), so that patients who will not fall - and therefore do not need specific preventive measures - can be identified correctly. Overall, the results are similar to those obtained in a previous smaller study conducted by some of the authors [21], and they seem disappointing, especially as the test battery contains established and validated tests often used for assessing fall risk, such as the Timed Up & Go [22] or the POMA [6]. On the other hand, a high fall risk is not necessarily associated with an actual fall event which to some extent is random in a short and variable in-patient period of time, even more so if a special environment such as a geriatric ward is the setting.…”

Section: Discussionsupporting

confidence: 92%

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

Marschollek

Gövercin

Rust

et al. 2012

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

BackgroundHospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).MethodsA data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.ResultsThe classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.ConclusionsOur results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.

show abstract

Section: Discussionsupporting

confidence: 92%

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

Marschollek

Gövercin

Rust

et al. 2012

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…The lower back, including the pelvis, sacrum, and the L3 to L5 vertebrae, is the most common sensor location and was the only location in 65% of the studies. This site approximates the center of mass location [46,49,60,61,74] and is acceptable for long-term at-home use [49,61]. Other sensor locations include the head [18,55,64], upper back [44,73,75], sternum [17,42,45], shoulder [41], elbow [41], wrist [41], hip [18,56], thigh [42], knee [41,56], shank [50], ankle [41,56], and foot [69,71].…”

Section: Resultsmentioning

confidence: 99%

“…Several papers emphasized the importance of using prospective fall risk occurrence in their future research [51,54,60,62,72-74], thereby avoiding the two biggest limitations of retrospective fall assessment: inaccurate recollection of fall history and gait changes due to past falls.…”

Section: Discussionmentioning

confidence: 99%

Review of fall risk assessment in geriatric populations using inertial sensors

Howcroft

Kofman

Lemaire

2013

J NeuroEngineering Rehabil

303

209

View full text Add to dashboard Cite

BackgroundFalls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.MethodsForty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.ResultsInertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).ConclusionsInertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

show abstract

“…A number of additional parameters can be derived that can better indicate gait and balance impairments, including Sit-to-Stand (S2ST) duration, Stand-to-Sit (ST2S) duration, and the amplitude range of anterior-posterior, to name just a few. The iTUG has proven to be sensitive to pathologies [11], [12] and useful in fall risk prediction [15]. In our prior work [16] we introduced a smartphone application called sTUG (smartphone-enabled Timed-Up-and-Go).…”

Section: Introductionmentioning

confidence: 99%

Smart Button: A wearable system for assessing mobility in elderly

Dzhagaryan

Milenkovic

Jovanov

et al. 2015

2015 17th International Conference on E-Health Networking, Application &Amp; Services (HealthCom)

View full text Add to dashboard Cite

Abstract-Continuous advances in sensors, semiconductors, wireless networks, mobile and cloud computing enable the development of integrated wearable computing systems for continuous health monitoring. These systems can be used as a part of diagnostic procedures, in the optimal maintenance of chronic conditions, in the monitoring of adherence to treatment guidelines, and for supervised recovery. In this paper, we describe a wearable system called Smart Button designed to assess mobility of elderly. The Smart Button is easily mounted on the chest of an individual and currently quantifies the Timed-Upand-Go and 30-Second Chair Stand tests. These two tests are routinely used to assess mobility, balance, strength of the lower extremities, and fall risk of elderly and people with Parkinson's disease. The paper describes the design of the Smart Button, parameters used to quantify the tests, signal processing used to extract the parameters, and integration of the Smart Button into a broader mHealth system.

show abstract

Predicting in-patient falls in a geriatric clinic

Cited by 58 publications

References 23 publications

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

Review of fall risk assessment in geriatric populations using inertial sensors

Smart Button: A wearable system for assessing mobility in elderly

Contact Info

Product

Resources

About