Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Instruments for assessing the risk of falls in acute hospitalized patients: a systematic review and meta-analysis

Background Falls are a serious problem for hospitalized patients, reducing the duration and quality of life. It is estimated that over 84% of all adverse events in hospitalized patients are related to falls. Some fall risk assessment tools have been developed and tested in environments other than those for which they were developed with serious validity discrepancies. The aim of this review is to determine the accuracy of instruments for detecting fall risk and predicting falls in acute hospitalized patients. Methods Systematic review and meta-analysis. Main databases, related websites and grey literature were searched. Two blinded reviewers evaluated title and abstracts of the selected articles and, if they met inclusion criteria, methodological quality was assessed in a new blinded process. Meta-analyses of diagnostic ORs (DOR) and likelihood (LH) coefficients were performed with the random effects method. Forest plots were calculated for sensitivity and specificity, DOR and LH. Additionally, summary ROC (SROC) curves were calculated for every analysis. Results Fourteen studies were selected for the review. The meta-analysis was performed with the Morse (MFS), STRATIFY and Hendrich II Fall Risk Model scales. The STRATIFY tool provided greater diagnostic validity, with a DOR value of 7.64 (4.86 - 12.00). A meta-regression was performed to assess the effect of average patient age over 65 years and the performance or otherwise of risk reassessments during the patient’s stay. The reassessment showed a significant reduction in the DOR on the MFS (rDOR 0.75, 95% CI: 0.64 - 0.89, p = 0.017). Conclusions The STRATIFY scale was found to be the best tool for assessing the risk of falls by hospitalized acutely-ill adults. However, the behaviour of these instruments varies considerably depending on the population and the environment, and so their operation should be tested prior to implementation. Further studies are needed to investigate the effect of the reassessment of these instruments with respect to hospitalized adult patients, and to consider the real compliance by healthcare personnel with procedures related to patient safety, and in particular concerning the prevention of falls

Fatores de risco para quedas em pacientes adultos hospitalizados: um estudo caso-controle

Objective: to identify risk factors for falls in hospitalized adult patients. Methods: a matched case-control study (one control for each case). A quantitative study conducted in clinical and surgical units of a teaching hospital in Southern Brazil. The sample comprised 358 patients. Data were collected over 18 months between 2013-2014. Data analysis was performed with descriptive statistics and conditional logistic regression using Microsoft Excel and SPSS version 18.0. Results: risk factors identified were: disorientation/confusion [OR 4.25 (1.99 to 9.08), p<0.001]; frequent urination [OR 4.50 (1.86 to 10.87), p=0.001]; walking limitation [OR 4.34 (2.05 to 9.14), p<0.001]; absence of caregiver [OR 0.37 (0.22 to 0.63), p<0.001]; postoperative period [OR 0.50 (0.26 to 0.94), p=0.03]; and number of medications administered within 72 hours prior the fall [OR 1.20 (1.04 to 1.39) p=0.01]. Conclusion: risk for falls is multifactorial. However, understanding these factors provides support to clinical decision-making and positively influences patient safety.Objetivo: identificar los factores de riesgo para la ocurrencia de caídas en pacientes adultos hospitalizados. Métodos: un estudio caso-control emparejado (un control para cada caso). Investigación cuantitativa llevada a cabo en unidades clínicas y quirúrgicas de un hospital universitario en el Sur de Brasil. La muestra constó de 358 pacientes. Se recopilaron datos durante 18 meses, entre 2013-2014. El análisis de los datos se realizó mediante estadística descriptiva y regresión logística condicional, utilizando el Microsoft Excel y el SPSS versión 18.0. Resultados: los factores de riesgo identificados fueron: desorientación/confusión [OR 4,25 (1,99 a 9,08), p<0,001]; micción frecuente [OR 4,50 (1,86 a 10,87), p=0,001]; limitación para caminar [OR 4,34 (2,05 a 9,14), p<0,001]; ausencia de cuidadores [OR 0,37 (0,22 a 0,63), p<0,001]; período postoperatorio [OR 0,50 (0,26 a 0,94), p=0,03]; y número de medicamentos administrados dentro de las 72 horas previas a la caída [OR 1,20 (1,04 a 1,39) p=0,01]. Conclusión: los riesgos de caídas son multifactoriales. Sin embargo, la comprensión de estos factores respalda la toma de decisiones clínicas y tiene un impacto positivo en la seguridad del paciente.Objetivo: identificar os fatores de risco para a ocorrência de quedas em pacientes adultos hospitalizados. Métodos: estudo do tipo caso-controle pareado (um controle para cada caso). Pesquisa quantitativa realizada em unidades clínicas e cirúrgicas de um hospital universitário da região Sul do Brasil. A amostra incluiu 358 pacientes. Os dados foram coletados durante 18 meses, entre 2013-2014. A análise dos dados foi realizada por meio de estatística descritiva e regressão logística condicional, utilizando o Microsoft Excel e o SPSS versão 18.0. Resultados: os fatores de risco identificados foram: desorientação/confusão [OR 4,25 (1,99 a 9,08), p<0,001]; micção frequente [OR 4,50 (1,86 a 10,87), p=0,001]; limitação para caminhar [OR 4,34 (2,05 a 9,14), p<0,001]; ausência de cuidador [OR 0,37 (0,22 a 0,63), p<0,001]; período pós-operatório [OR 0,50 (0,26 a 0,94), p=0,03]; e o número de medicamentos administrados nas 72 horas anteriores à queda [OR 1,20 (1,04 a 1,39) p=0,01]. Conclusão: os riscos para quedas são multifatoriais. Todavia, conhecê-los dá suporte à decisão clínica do enfermeiro, o que contribui para a busca das melhores intervenções preventivas e impacta positivamente na segurança dos pacientes

Queda e sua associação à síndrome da fragilidade no idoso: revisão sistemática com metanálise

OBJETIVO Analisar a prevalência de quedas e da síndrome da fragilidade e a associação entre essas duas síndromes na população idosa. MÉTODO Revisão sistemática, sem restrição de datas, nos idiomas inglês, português e espanhol, nas bases de dados PubMed, CINAHL, LILACS e na biblioteca virtual SciElo. A associação entre ambas as variáveis foi extraída dos próprios artigos (Odds Ratio e os Intervalos de Confiança de 95%). RESULTADOS Foram incluídos na revisão 19 artigos publicados entre 2001 e 2015. A prevalência de queda no idoso frágil esteve entre 6,7% e 44%; nos pré-frágeis, entre 10,0% e 52,0%, e nos não frágeis, entre 7,6% e 90,4%. A associação entre ambas as variáveis apresentou o valor de OR 1,80 (IC 95% 1,51-2,13). CONCLUSÃO Há evidências de que a queda está associada à fragilidade do idoso. Outros fatores podem influenciar essa associação, como idade, sexo, instrumento de coleta de dados dos estudos, local onde vive e o próprio processo de senescência.OBJETIVO Analizar la prevalencia de caídas y el síndrome de la fragilidad y la asociación entre ambos síndromes en la población mayor. MÉTODO Revisión sistemática, sin restricción de fechas, en los idiomas inglés, portugués y español, en las bases de datos PubMed, CINAHL, LILACS y en la biblioteca virtual SciElo. La asociación entre ambas variables fue extraída de los propios artículos (Odds Ratio y los Intervalos de Confianza del 95%). RESULTADOS Fueron incluidos en la revisión 19 artículos publicados entre 2001 y 2015. La prevalencia de caída en el anciano frágil estuvo entre el 6,7% y el 44%; en los pre frágiles, entre el 10,0% y el 52,0%; y en los no frágiles, entre el 7,6% y el 90,4%. La asociación entre ambas variables presentó el valor de OR 1,80 (IC 95% 1,51-2,13). CONCLUSIÓN Hay evidencias de que la caída está asociada con la fragilidad del anciano. Otros factores pueden influenciar dicha asociación, tales como edad, sexo, instrumento de recolección de datos de los estudios, sitio en donde vive y el proceso mismo de ancianidad.OBJECTIVE To analyze the prevalence of falls and frailty syndrome and the association between these two syndromes in the elderly population. METHOD Systematic review, without restriction of dates, in English, Portuguese and Spanish languages, in the databases PubMed, CINAHL, LILACS and in the SciElo virtual library. The association between both variables was extracted from the studies (Odds Ratio and 95% Confidence Intervals). RESULTS The review included 19 studies published between 2001 and 2015. The prevalence of falls in the frail elderly population was between 6.7% and 44%; in the pre-frail, between 10.0% and 52.0%, and in the non-frail, between 7.6% and 90.4%. The association between both variables presented a value of OR 1.80 (95% CI 1.51-2.13). CONCLUSION There is evidence that falls are associated to the frailty in the elderly. Other factors may influence this association, such as age, sex, data collection instrument of the studies, place where they live and the process of senescence

Hospital to Home: Fall Prevention Interventions for the Discharging Patient

Falls is a major public health problem globally, with an estimated 646,000 fatal falls per year. This makes falls the second leading cause of unintentional injury death. Falls are very costly with non-fatal fall injuries costing about $50 billion per year and fatal falls with an estimated$754 million. Many risk factors contribute to a person’s risk of falling. Risk factors include age, gender, muscle strength, underlying medical or disabling conditions, and unsafe environments. Patients who have been hospitalized are also among those at risk. Most hospitalized patients are assessed frequently to determine their risk of falling so that care plans can be adjusted to implement strategies to avoid a fall. However, nurses frequently discharge patients with little to no education or tools to prevent falls at home. The purpose of this scholarly inquiry project is to explore the best practices for fall prevention after discharging home from the hospital. An extensive integrative literature review highlighted evidence that supports and arms patients, families, and support systems with tools that will help prevent a fall at home after being discharged from the hospital. A conceptual map details the interventions that need to be integrated at discharge to help create a home fall prevention plan of care. Three themes emerged from the literature and include criteria for implementing falls risk discharge interventions, fall risk discharge interventions, and the outcomes from the interventions. Recommendations for nursing are also built into this project that can guide nurses in protecting patients by implementing evidence-based strategies to prevent patients from falling at home after discharge and decrease the risk of reoccurring hospitalizations or fall fatality

Delirium in hip fractured patients

The current clinical case concerns the mixed delirium in a 70-year-old man with hip fracture, following a fall at home. In his medical history, the patient reported several comorbidities, among which also sarcopenia. Delirium was already diagnosed by the geriatrician on hospital admission. The patient underwent hip endoprosthesis surgery after 24 hours without any intra-operative complications. However, in the post-operative period delirium persisted, causing a prolonged hospital stay, a delayed physio-therapy rehabilitation with poor functional recovery, and subsequent insti-tutionalization. The prevalence of delirium in older people with hip fracture is extremely high and it is associated with several negative outcomes. Delirium is considered a multifactorial disorder, and, in particular, sarcopenia appears directly linked to the development of delirium. The systematic assessment of sarcopenia should be performed in hospitalized older patients with hip fracture, together with the other predisposing risk factors for delirium, to timely identify people at higher risk for both delirium and disability

Association between frailty and delirium in older adult patients discharged from hospital.

BACKGROUND: Delirium and frailty - both potentially reversible geriatric syndromes - are seldom studied together, although they often occur jointly in older patients discharged from hospitals. This study aimed to explore the relationship between delirium and frailty in older adults discharged from hospitals. METHODS: Of the 221 patients aged >65 years, who were invited to participate, only 114 gave their consent to participate in this study. Delirium was assessed using the confusion assessment method, in which patients were classified dichotomously as delirious or nondelirious according to its algorithm. Frailty was assessed using the Edmonton Frailty Scale, which classifies patients dichotomously as frail or nonfrail. In addition to the sociodemographic characteristics, covariates such as scores from the Mini-Mental State Examination, Instrumental Activities of Daily Living scale, and Cumulative Illness Rating Scale for Geriatrics and details regarding polymedication were collected. A multidimensional linear regression model was used for analysis. RESULTS: Almost 20% of participants had delirium (n=22), and 76.3% were classified as frail (n=87); 31.5% of the variance in the delirium score was explained by frailty (R (2)=0.315). Age; polymedication; scores of the Confusion Assessment Method (CAM), instrumental activities of daily living, and Cumulative Illness Rating Scale for Geriatrics; and frailty increased the predictability of the variance of delirium by 32% to 64% (R (2)=0.64). CONCLUSION: Frailty is strongly related to delirium in older patients after discharge from the hospital

Our Grandparents, Our Parents, Our Future Selves: Optimizing Function in Old Age. Syracuse Seminar Series on Aging.

Most of my research at Yale University School of Medicine over the past several years has focused on identifying older adults at risk of functional decline and disability, identifying events that may precipitate the transition from functional independence to disability, and developing strategies to postpone or reduce frailty and disability. As a result of the Precipitating Events Project (PEP) and other research conducted by the Yale Center on Aging/Pepper Center, we now realize that age is only a proxy for other factors that lead to disability, and that some of these factors can be modified to reduce the risk of disability. In fact, disability rates have been steadily declining among older adults for decades.geriatrics, aging, gerontology, disability, precipitating event, functional decline, vulnerability, compression of morbidity, reserve organ capacity, exercise, physical activity, falls, Yale PREHAB study, lifestyle interventions, independence, elders, FICSIT trial, frailty

Frailty in hospitalized adults

The purpose of this cross-sectional, retrospective, descriptive study was to characterize frailty in hospitalized adults 55 years of age and older admitted to medical units at one large academic medical center during a 15-month time frame and determine if level of frailty on admission predicted length of stay (LOS) and 30-day readmission. Frailty is a syndrome characterized by multisystem physiologic dysregulation due to intrinsic and extrinsic stressors resulting in decreased compensatory reserve and ability to effectively respond to destabilizing health events. Stressors associated with hospitalization may increase risk for frailty or accelerate its development. Frailty is a significant concern as it is associated with morbidity, functional decline, long LOS, readmission, institutionalization, and mortality. There is scant research on frailty in acutely-ill hospitalized adults, especially those ¡Ý 65 years of age. Understanding frailty in this population is imperative because frailty is potentially preventable, treatable, and reversible. Frailty was operationalized as 14 evidence-based frailty components defined by 26 indicator variables. Frailty components were Nutrition, Weakness, Fatigue, Chronic Pain, Dyspnea, Falls, Vision, Depression, Cognition, Social Support, low Hemoglobin, low Albumin, high C-reactive protein (CRP) or hs-CRP, and abnormal WBC count. Each frailty component was scored as one point if at least one indicator variable was present on admission, and summed to derive a Frailty Score, where a higher Frailty Score suggests greater level of frailty (range, 0 to 14). Sociodemographic, clinical, and laboratory data were retrieved from the electronic medical record through web-based data query tools and record review (N = 278). Mean age was 70.2 (SD = 1.3; range, 55¨C98), slightly over half were female, 64% were White, one-third were Black. The mean comorbidity count was 13 (SD = 4.56; range. 1¨C26) and medication count was 12 (SD = 5.2; range, 0¨C31). The most prevalent frailty components (> 81%) were Fatigue, Weakness, Nutrition, Hemoglobin, Albumin, and CRP or hs-CRP. The mean Frailty Score was 9.03 (SD = 1.98; range, 2¨C13). Multiple linear regression was performed with 20 predictor variables and the Frailty Score and then with 14 of the 20 predictor variables that were significant in bivariate linear regression with the Frailty Score using the ENTER and STEPWISE method. All multiple regression models yielded seven significant predictor variables. Six predictors were common to all models: comorbidity, acute pain, ADL assistance, urinary incontinence, Braden Scale score, current tobacco use. In multiple regression with 20 predictors, age was a significant predictor however in multiple regression using ENTER and STEPWISE for 14 predictors, female gender was significant but not age. Results from STEPWISE regression yielded seven significant predictors that explained 27% of the variance in the Frailty Score (adj. R2 = .266, df (14, 263), F = 8.163, p = .000). Mean LOS was 9.92 days (SD = 9.58; range, 1¨C72; median, 7; mode, 5). Simple linear regression for the Frailty Score and log10 transformed LOS was statistically significant (adj. R2 = .090, df (1, 276), F = 29.293, p = .000). Twelve percent experienced 30-day readmission. Logistic regression conducted for the Frailty Score and 30-day readmission was not statistically significant (X 2 = 4.121, df (5), p = .532; ¦Â coefficient = .100, df (1), 95% CI = .913¨C1.1337, p = .307). The Frailty Score characterized this hospitalized population as acutely ill with high comorbidity, symptom burden, nutrition deficits and evidence of physiologic vulnerability and inflammation. Study findings have implications for nursing practice, interdisciplinary collaboration, education, research, and public policy