Development of an algorithm for assessing fall risk in a Japanese inpatient population.
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ABSTRACT:
SUBMITTER: Nakanishi T
PROVIDER: S-EPMC8429765 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionThe burden of disease from dementia is a growing global concern as incidence increases dramatically with age, and average life expectancy has been increasing around the world. Planning for an ageing population requires reliable projections of dementia prevalence; however, existing population projections are simple and have poor predictive accuracy. The Dementia Population Risk Tool (DemPoRT) will predict incidence of dementia in the population setting using multivariable modelling techniques and will be used to project dementia prevalence.Methods and analysisThe derivation cohort will consist of elderly Ontario respondents of the Canadian Community Health Survey (CCHS) (2001, 2003, 2005 and 2007; 18 764 males and 25 288 females). Prespecified predictors include sociodemographic, general health, behavioural, functional and health condition variables. Incident dementia will be identified through individual linkage of survey respondents to population-level administrative healthcare databases (1797 and 3281 events, and 117 795 and 166 573 person-years of follow-up, for males and females, respectively, until 31 March 2014). Using time of first dementia capture as the primary outcome and death as a competing risk, sex-specific proportional hazards regression models will be estimated. The 2008/2009 CCHS survey will be used for validation (approximately 4600 males and 6300 females). Overall calibration and discrimination will be assessed as well as calibration within predefined subgroups of importance to clinicians and policy makers.Ethics and disseminationResearch ethics approval has been granted by the Ottawa Health Science Network Research Ethics Board. DemPoRT results will be submitted for publication in peer-review journals and presented at scientific meetings. The algorithm will be assessable online for both population and individual uses.Trial registration numberClinicalTrials.gov NCT03155815, pre-results.
| S-EPMC5665213 | biostudies-literature
Project description: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.
| S-EPMC4229409 | biostudies-literature
Project description:Given the dramatic rise in the prevalence of obesity, greater focus on prevention is necessary. We sought to develop and validate a population risk tool for obesity to inform prevention efforts.We developed the Obesity Population Risk Tool (OPoRT) using the longitudinal National Population Health Survey and sex-specific Generalized Estimating Equations to predict the 10-year risk of obesity among adults 18 and older. The model was validated using a bootstrap approach accounting for the survey design. Model performance was measured by the Brier statistic, discrimination was measured by the C-statistic, and calibration was assessed using the Hosmer-Lemeshow Goodness of Fit Chi Square (HL ?2).Predictive factors included baseline body mass index, age, time and their interactions, smoking status, living arrangements, education, alcohol consumption, physical activity, and ethnicity. OPoRT showed good performance for males and females (Brier 0.118 and 0.095, respectively), excellent discrimination (C statistic ? 0.89) and achieved calibration (HL ?2 <20).OPoRT is a valid and reliable algorithm that can be applied to routinely collected survey data to estimate the risk of obesity and identify groups at increased risk of obesity. These results can guide prevention efforts aimed at reducing the population burden of obesity.
| S-EPMC5773177 | biostudies-literature
Project description:INTRODUCTION:Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. METHODS AND ANALYSIS:The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. ETHICS AND DISSEMINATION:This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible electronically for population and individual uses. TRIAL REGISTRATION NUMBER:ClinicalTrials.gov NCT02267447.
| S-EPMC4208046 | biostudies-literature
Project description:OBJECTIVES:To evaluate the utility of routinely collected Hendrich II fall scores in predicting returns to the emergency department (ED) for falls within 6 months. DESIGN:Retrospective electronic record review. SETTING:Academic medical center ED. PARTICIPANTS:Individuals aged 65 and older seen in the ED from January 1, 2013, through September 30, 2015. MEASUREMENTS:We evaluated the utility of routinely collected Hendrich II fall risk scores in predicting ED visits for a fall within 6 months of an all-cause index ED visit. RESULTS:For in-network patient visits resulting in discharge with a completed Hendrich II score (N = 4,366), the return rate for a fall within 6 months was 8.3%. When applying the score alone to predict revisit for falls among the study population the resultant receiver operating characteristic (ROC) plot had an area under the curve (AUC) of 0.64. In a univariate model, the odds of returning to the ED for a fall in 6 months were 1.23 times as high for every 1-point increase in Hendrich II score (odds ratio (OR)=1.23 (95% confidence interval (CI)=1.19-1.28). When included in a model with other potential confounders or predictors of falls, the Hendrich II score is a significant predictor of a return ED visit for fall (adjusted OR=1.15, 95% CI=1.10-1.20, AUC=0.75). CONCLUSION:Routinely collected Hendrich II scores were correlated with outpatient falls, but it is likely that they would have little utility as a stand-alone fall risk screen. When combined with easily extractable covariates, the screen performs much better. These results highlight the potential for secondary use of electronic health record data for risk stratification of individuals in the ED. Using data already routinely collected, individuals at high risk of falls after discharge could be identified for referral without requiring additional screening resources.
| S-EPMC5937931 | biostudies-literature
Project description:BackgroundDiet is considered an important factor for bone health, but is composed of a wide variety of foods containing complex combinations of nutrients. Therefore we investigated the relationship between dietary patterns and fall-related fractures in the elderly.MethodsWe designed a population-based prospective survey of 1178 elderly people in Japan in 2002. Dietary intake was assessed with a 75-item food frequency questionnaire (FFQ), from which dietary patterns were created by factor analysis from 27 food groups. The frequency of fall-related fracture was investigated based on insurance claim records from 2002 until 2006. The relationship between the incidence of fall-related fracture and modifiable factors, including dietary patterns, were examined. The Cox proportional hazards regression model was used to examine the relationships between dietary patterns and incidence of fall-related fracture with adjustment for age, gender, Body Mass Index (BMI) and energy intake.ResultsAmong 877 participants who agreed to a 4 year follow-up, 28 suffered from a fall-related fracture. Three dietary patterns were identified: mainly vegetable, mainly meat and mainly traditional Japanese. The moderately confirmed (see statistical methods) groups with a Meat pattern showed a reduced risk of fall-related fracture (Hazard ratio = 0.36, 95% CI = 0.13 - 0.94) after adjustment for age, gender, BMI and energy intake. The Vegetable pattern showed a significant risk increase (Hazard ratio = 2.67, 95% CI = 1.03 - 6.90) after adjustment for age, gender and BMI. The Traditional Japanese pattern had no relationship to the risk of fall-related fracture.ConclusionsThe results of this study have the potential to reduce fall-related fracture risk in elderly Japanese. The results should be interpreted in light of the overall low meat intake of the Japanese population.
| S-EPMC2895588 | biostudies-literature
Project description:Subspecialty consultation in inpatient medicine is increasing, and enhancing performance of consultation services may have a broad-reaching impact. Multisource feedback is an important tool in assessing competence and improving performance. A mechanism for primary team resident feedback on performance of consult services has not been described.We developed and evaluated an instrument designed to assess internal medicine (IM) subspecialty inpatient consult service performance. We hypothesized that the instrument would be feasible to administer and provide important information to fellowship directors.The instrument was administered in 2015 and 2016 at a single academic center. All IM residents were invited to evaluate 10 IM subspecialty consult services on 4 items and an overall satisfaction rating. The instrument allowed for free-text feedback to fellows. Program directors completed another survey assessing the impact of the consult service evaluation.A total of 113 residents responded (47 in 2015 and 66 in 2016, for a combined response rate of 35%). Each of the 4 items measured (communication, professionalism, teaching, and pushback) correlated significantly with the overall satisfaction rating in univariate and multivariate analyses. There were no differences in ratings across postgraduate year or year of administration. There was considerable variation in ratings among the services evaluated. The 7 program directors who provided feedback found the survey useful and made programmatic changes following evaluation implementation.A primary team resident evaluation of inpatient medicine subspecialty consult services is feasible, provides valuable information, and is associated with changes in consult service structure and curricula.
| S-EPMC5734333 | biostudies-literature
Project description:ImportanceEvaluating the association of social determinants of health with chronic diseases at the population level requires access to individual-level factors associated with disease, which are rarely available for large populations. Synthetic populations are a possible alternative for this purpose.ObjectiveTo construct and validate a synthetic population that statistically mimics the characteristics and spatial disease distribution of a real population, using real and synthetic data.Design, setting, and participantsThis population-based decision analytical model used data for Allegheny County, Pennsylvania, collected from January 2015 to December 2016, to build a semisynthetic population based on the synthetic population used by the modeling and simulation platform FRED (A Framework for Reconstructing Epidemiological Dynamics). Disease status was assigned to this population using health insurer claims data from the 3 major insurance providers in the county or from the National Health and Nutrition Examination Survey. Biological, social, and other variables were also obtained from the National Health Interview Survey, Allegheny County, and public databases. Data analysis was performed from November 2016 to February 2020.ExposuresRisk of cardiovascular disease (CVD) death.Main outcomes and measuresDifference between expected and observed CVD death risk. A validated risk equation was used to estimate CVD death risk.ResultsThe synthetic population comprised 1 188 112 individuals with demographic characteristics similar to those of the 2010 census population in the same county. In the synthetic population, the mean (SD) age was 40.6 (23.3) years, and 622 997 were female individuals (52.4%). Mean (SD) observed 4-year rate of excess CVD death risk at the census tract level was -40 (523) per 100 000 persons. The correlation of social determinant data with difference between expected and observed CVD death risk indicated that income- and education-based social determinants were associated with risk. Estimating improved social determinants of health and biological factors associated with disease did not entirely remove the excess in CVD death rates. That is, a 20% improvement in the most significant determinants still resulted in 105 census tracts with excess CVD death risk, which represented 24% of the county population.Conclusions and relevanceThe results of this study suggest that creating a geographically explicit synthetic population from real and synthetic data is feasible and that synthetic populations are useful for modeling disease in large populations and for estimating the outcome of interventions.
| S-EPMC7489828 | biostudies-literature
Project description:IntroductionWe aimed to assess the current scope of handoff education and practice among resident physicians in academic centers and to propose a standardized handoff algorithm for the transition of care from the emergency department (ED) to an inpatient setting.MethodsThis was a cross-sectional survey targeted at the program directors, associate or assistant program directors, and faculty members of emergency medicine (EM) residency programs in the United States (U.S.). The web-based survey was distributed to potential subjects through a listserv. A panel of experts used a modified Delphi approach to develop a standardized algorithm for ED to inpatient handoff.Results121 of 172 programs responded to the survey for an overall response rate of 70.3%. Our survey showed that most EM programs in the U.S. have some form of handoff training, and the majority of them occur either during orientation or in the clinical setting. The handoff structure from ED to inpatient is not well standardized, and in those places with a formalized handoff system, over 70% of residents do not uniformly follow it. Approximately half of responding programs felt that their current handoff system was safe and effective. About half of the programs did not formally assess the handoff proficiency of trainees. Handoffs most commonly take place over the phone, though respondents disagree about the ideal place for a handoff to occur, with nearly equivalent responses between programs favoring the bedside over the phone or face-to-face on a computer. Approximately two-thirds of responding programs reported that their residents were competent in performing ED to inpatient handoffs. Based on this survey and on the review of the literature, we developed a five-step algorithm for the transition of care from the ED to the inpatient setting.ConclusionOur results identified the current trends of education and practice in transitions of care, from the ED to the inpatient setting in U.S. academic medical centers. An algorithm, which guides this process, is proposed to address the current gap in the standardized approach to ED to inpatient handoffs that were identified in the survey's assessment of needs.
| S-EPMC5226771 | biostudies-literature
Project description:PurposeTo facilitate surveillance and evaluate interventions addressing opioid-related overdoses, algorithms are needed for use in large health care databases to identify and differentiate community-occurring opioid-related overdoses from inpatient-occurring opioid-related overdose/oversedation.MethodsData were from Kaiser Permanente Northwest (KPNW), a large integrated health plan. We iteratively developed and evaluated an algorithm for electronically identifying inpatient overdose/oversedation in KPNW hospitals from 1 January 2008 to 31 December 2014. Chart audits assessed accuracy; data sources included administrative and clinical records.ResultsThe best-performing algorithm used these rules: (1) Include events with opioids administered in an inpatient setting (including emergency department/urgent care) followed by naloxone administration within 275 hours of continuous inpatient stay; (2) exclude events with electroconvulsive therapy procedure codes; and (3) exclude events in which an opioid was administered prior to hospital discharge and followed by readmission with subsequent naloxone administration. Using this algorithm, we identified 870 suspect inpatient overdose/oversedation events and chart audited a random sample of 235. Of the random sample, 185 (78.7%) were deemed overdoses/oversedation, 37 (15.5%) were not, and 13 (5.5%) were possible cases. The number of hours between time of opioid and naloxone administration did not affect algorithm accuracy. When "possible" overdoses/oversedations were included with confirmed events, overall positive predictive value (PPV) was very good (PPV = 84.0%). Additionally, PPV was reasonable when evaluated specifically for hospital stays with emergency/urgent care admissions (PPV = 77.0%) and excellent for elective surgery admissions (PPV = 97.0%).ConclusionsAlgorithm performance was reasonable for identifying inpatient overdose/oversedation with best performance among elective surgery patients.
| S-EPMC6767384 | biostudies-literature