Dataset Information

Fracture Risk Assessment in Long-term Care (FRAiL): Development and Validation of a Prediction Model.

ABSTRACT: Background:Strategies used to predict fracture in community-dwellers may not be useful in the nursing home (NH). Our objective was to develop and validate a model (Fracture Risk Assessment in Long-term Care [FRAiL]) to predict the 2-year risk of hip fracture in NH residents using readily available clinical characteristics. Methods:The derivation cohort consisted of 419,668 residents between May 1, 2007 and April 30, 2008 in fee-for service Medicare. Hip fractures were identified using Part A diagnostic codes. Resident characteristics were obtained using the Minimum Data Set and Part D claims. Multivariable competing risk regression was used to model 2-year risk of hip fracture. We validated the model in a remaining 1/3 sample (n = 209,834) and in a separate cohort in 2011 (n = 858,636). Results:Mean age was 84 years (range 65-113 years) and 74.5% were female. During 1.8 years mean follow-up, 14,553 residents (3.5%) experienced a hip fracture. Fifteen characteristics in the final model were associated with an increased risk of hip fracture including dementia severity, ability to transfer and walk independently, prior falls, wandering, and diabetes. In the derivation sample, the concordance index was 0.69 in men and 0.71 in women. Calibration was excellent. Results were similar in the internal and external validation samples. Conclusions:The FRAiL model was developed specifically to identify NH residents at greatest risk for hip fracture, and it identifies a different pattern of risk factors compared with community models. This practical model could be used to screen NH residents for fracture risk and to target intervention strategies.

SUBMITTER: Berry SD

PROVIDER: S-EPMC5946931 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fracture Risk Assessment in Long-term Care (FRAiL): Development and Validation of a Prediction Model.

Berry Sarah D SD Zullo Andrew R AR Lee Yoojin Y Mor Vincent V McConeghy Kevin W KW Banerjee Geetanjoli G D'Agostino Ralph B RB Daiello Lori L Dosa David D Kiel Douglas P DP

The journals of gerontology. Series A, Biological sciences and medical sciences 20180501 6

<h4>Background</h4>Strategies used to predict fracture in community-dwellers may not be useful in the nursing home (NH). Our objective was to develop and validate a model (Fracture Risk Assessment in Long-term Care [FRAiL]) to predict the 2-year risk of hip fracture in NH residents using readily available clinical characteristics.<h4>Methods</h4>The derivation cohort consisted of 419,668 residents between May 1, 2007 and April 30, 2008 in fee-for service Medicare. Hip fractures were identified u ...[more]

PMID: 28958013

Similar Datasets

Project description:BackgroundThe majority of frail elderly who live in long-term care (LTC) are not treated for osteoporosis despite their high risk for fragility fractures. Clinical Practice Guidelines for the diagnosis and management of osteoporosis provide guidance for the management of individuals 50 years and older at risk for fractures, however, they cannot benefit LTC residents if physicians perceive barriers to their application. Our objectives are to explore current practices to fracture risk assessment by LTC physicians and describe barriers to applying the recently published Osteoporosis Canada practice guidelines for fracture assessment and prevention in LTC.MethodsA cross-sectional survey was conducted with the Ontario Long-Term Care Physicians Association using an online questionnaire. The survey included questions that addressed members' attitudes, knowledge, and behaviour with respect to fracture risk assessment in LTC. Closed-ended responses were analyzed using descriptive statistics and thematic framework analysis for open-ended responses.ResultsWe contacted 347 LTC physicians; 25% submitted completed surveys (81% men, mean age 60 (Standard Deviation [SD] 11) years, average 32 [SD 11] years in practice). Of the surveyed physicians, 87% considered prevention of fragility fractures to be important, but a minority (34%) reported using validated fracture risk assessment tools, while 33% did not use any. Clinical risk factors recommended by the OC guidelines for assessing fracture risk considered applicable included; glucocorticoid use (99%), fall history (93%), age (92%), and fracture history (91%). Recommended clinical measurements considered applicable included: weight (84%), thyroid-stimulating hormone (78%) and creatinine (73%) measurements, height (61%), and Get-Up-and-Go test (60%). Perceived barriers to assessing fracture risk included difficulty acquiring necessary information, lack of access to tests (bone mineral density, x-rays) or obtaining medical history; resource constraints, and a sentiment that assessing fracture risk is futile in this population because of short life expectancy and polypharmacy.ConclusionPerceived barriers to fracture risk assessment and osteoporosis management in LTC have not changed recently, contributing in part to the ongoing care gap in osteoporosis management. Our findings highlight the importance to adapt guidelines to be applicable to the LTC environment, and to develop partnerships with stakeholders to facilitate their use in clinical practice.

Project description:ObjectivesTo establish a scoring system combining the ACEF score and the quantitative blood flow ratio (QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention (PCI).MethodsIn this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263 consecutive cases of CAD patients after PCI in PANDA III trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.ResultsIn both the Random Forest Model and the DeepSurv Model, age, renal function (creatinine), cardiac function (LVEF) and post-PCI coronary physiological index (QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age (years)/EF (%) + 1 (if creatinine ≥ 2.0 mg/dL) + 1 (if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination (C-statistic = 0.651; 95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration (Hosmer-Lemeshow χ2 = 7.070; P = 0.529) for predicting 2-year patient-oriented composite endpoint (POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan-Meier analysis (adjusted HR = 1.89; 95% CI: 1.18-3.04; log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.ConclusionsAn improved scoring system combining clinical and coronary lesion-based functional variables (ACEF-QFR) was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.

Project description:ObjectivesICU survivors often suffer from long-lasting physical, mental, and cognitive health problems after hospital discharge. As several interventions that treat or prevent these problems already start during ICU stay, patients at high risk should be identified early. This study aimed to develop a model for early prediction of post-ICU health problems within 48 hours after ICU admission.DesignProspective cohort study in seven Dutch ICUs.Setting/patientsICU patients older than 16 years and admitted for greater than or equal to 12 hours between July 2016 and March 2020.InterventionsNone.Measurements and main resultsOutcomes were physical problems (fatigue or ≥ 3 new physical symptoms), mental problems (anxiety, depression, or post-traumatic stress disorder), and cognitive impairment. Patient record data and questionnaire data were collected at ICU admission, and after 3 and 12 months, of 2,476 patients. Several models predicting physical, mental, or cognitive problems and a composite score at 3 and 12 months were developed using variables collected within 48 hours after ICU admission. Based on performance and clinical feasibility, a model, PROSPECT, predicting post-ICU health problems at 3 months was chosen, including the predictors of chronic obstructive pulmonary disease, admission type, expected length of ICU stay greater than or equal to 2 days, and preadmission anxiety and fatigue. Internal validation using bootstrapping on data of the largest hospital ( n = 1,244) yielded a C -statistic of 0.73 (95% CI, 0.70-0.76). External validation was performed on data ( n = 864) from the other six hospitals with a C -statistic of 0.77 (95% CI, 0.73-0.80).ConclusionsThe developed and externally validated PROSPECT model can be used within 48 hours after ICU admission for identifying patients with an increased risk of post-ICU problems 3 months after ICU admission. Timely preventive interventions starting during ICU admission and follow-up care can prevent or mitigate post-ICU problems in these high-risk patients.

Project description:BackgroundAbout 30% of stroke patients suffer from aphasia. As aphasia strongly affects daily life, most patients request a prediction of outcome of their language function. Prognostic models provide predictions of outcome, but external validation is essential before models can be used in clinical practice. We aim to externally validate the prognostic model from the Sequential Prognostic Evaluation of Aphasia after stroKe (SPEAK-model) for predicting the long-term outcome of aphasia caused by stroke.MethodsWe used data from the Rotterdam Aphasia Therapy Study - 3 (RATS-3), a multicenter RCT with inclusion criteria similar to SPEAK, an observational prospective study. Baseline assessment in SPEAK was four days after stroke and in RATS-3 eight days. Outcome of the SPEAK-model was the Aphasia Severity Rating Scale (ASRS) at 1 year, dichotomized into good (ASRS-score of 4 or 5) and poor outcome (ASRS-score < 4). In RATS-3, ASRS-scores at one year were not available, but we could use six month ASRS-scores as outcome. Model performance was assessed with calibration and discrimination.ResultsWe included 131 stroke patients with first-ever aphasia. At six months, 86 of 124 (68%) had a good outcome, whereas the model predicted 88%. Discrimination of the model was good with an area under the receiver operation characteristic curve of 0.87 (95%CI: 0.81-0.94), but calibration was unsatisfactory. The model overestimated the probability of good outcome (calibration-in-the-large α = - 1.98) and the effect of the predictors was weaker in the validation data than in the derivation data (calibration slope β = 0.88). We therefore recalibrated the model to predict good outcome at six months.ConclusionThe original model, renamed SPEAK-12, has good discriminative properties, but needs further external validation. After additional external validation, the updated SPEAK-model, SPEAK-6, may be used in daily practice to discriminate between patients with good and patients with poor outcome of aphasia at six months after stroke.Trial registrationRATS-3 was registered on January 13th 2012 in the Netherlands Trial Register: NTR3271 . SPEAK was not listed in a trial registry.

Project description:BACKGROUND:Societal expenditures on work-disability benefits is high in most Western countries. As a precursor of long-term work restrictions, long-term sickness absence (LTSA) is under continuous attention of policy makers. Different healthcare professionals can play a role in identification of persons at risk of LTSA but are not well trained. A risk prediction model can support risk stratification to initiate preventative interventions. Unfortunately, current models lack generalizability or do not include a comprehensive set of potential predictors for LTSA. This study is set out to develop and validate a multivariable risk prediction model for LTSA in the coming year in a working population aged 45-64?years. METHODS:Data from 11,221 working persons included in the prospective Study on Transitions in Employment, Ability and Motivation (STREAM) conducted in the Netherlands were used to develop a multivariable risk prediction model for LTSA lasting ?28 accumulated working days in the coming year. Missing data were imputed using multiple imputation. A full statistical model including 27 pre-selected predictors was reduced to a practical model using backward stepwise elimination in a logistic regression analysis across all imputed datasets. Predictive performance of the final model was evaluated using the Area Under the Curve (AUC), calibration plots and the Hosmer-Lemeshow (H&L) test. External validation was performed in a second cohort of 5604 newly recruited working persons. RESULTS:Eleven variables in the final model predicted LTSA: older age, female gender, lower level of education, poor self-rated physical health, low weekly physical activity, high self-rated physical job load, knowledge and skills not matching the job, high number of major life events in the previous year, poor self-rated work ability, high number of sickness absence days in the previous year and being self-employed. The model showed good discrimination (AUC 0.76 (interquartile range 0.75-0.76)) and good calibration in the external validation cohort (H&L test: p =?0.41). CONCLUSIONS:This multivariable risk prediction model distinguishes well between older workers with high- and low-risk for LTSA in the coming year. Being easy to administer, it can support healthcare professionals in determining which persons should be targeted for tailored preventative interventions.

Project description:BackgroundImproving the quality and safety of care transitions is a priority in many countries. Carrying out performance measurements play a significant role in improving quality of decisions undertaken by different actors involved in reforms. Therefore, the main objective of this paper is to present the development of an evaluation tool for assessing the performance of long-term care systems in relation to care transition, namely the Transitional Care Assessment Tool in Long-Term Care (TCAT-LTC). This study is performed as part of a larger European TRANS-SENIOR project.MethodsThe development of the TCAT-LTC involved three steps. First, we developed a conceptual model based on Donabedian's quality framework and literature review. Second, we carried out a thorough process of item pool generation using deductive (systematic literature review) and deductive-inductive methods (in-depth interviews) with experts in the field of long-term care. Third, we conducted preliminary validation of the tool by asking experts in research and practice to provide an opinion on a tool and to assess content validity. Future fourth step will involve a tool's pilot with country experts from Germany, the Netherlands and Poland.ResultsBy applying methodological triangulation, we developed the TCAT-LTC, which consists of 2 themes, 12 categories and 63 items. Themes include organizational and financial aspects. Organizational aspects include categories such as communication, transfer of information, availability and coordination of resources, training and education of staff, education/support of the patient/informal caregiver, involvement of the patient/informal caregiver, telemedicine and e-Health, and social care. Financial aspects include categories such as primary care, hospital, and long-term care. We also present the instructions on the application of the TCAT-LTC.ConclusionsIn this paper, we presented the development of the TCAT-LTC evaluation tool for assessing the performance of long-term care systems in relation to care transition. The TCAT-LTC is the first tool to assess the performance of long-term care systems in relation to care transition. Assessments can be carried out at the national and international level and enable to monitor, evaluate, and compare performance of the long-term care systems in relation to care transition within and across countries.

Project description:ImportancePerinatal arterial ischemic stroke (PAIS) is a focal brain injury in term neonates that is identified postnatally but is presumed to occur near the time of birth. Many pregnancy, delivery, and fetal factors have been associated with PAIS, but early risk detection is lacking; thus, targeted treatment and prevention efforts are currently limited.ObjectiveTo develop and validate a diagnostic risk prediction model that uses common clinical factors to predict the probability of PAIS in a term neonate.Design, setting, and participantsIn this diagnostic study, a prediction model was developed using multivariable logistic regression with registry-based case data collected between January 2003, and March 2020, from the Alberta Perinatal Stroke Project, Canadian Cerebral Palsy Registry, International Pediatric Stroke Study, and Alberta Pregnancy Outcomes and Nutrition study. Criteria for inclusion were term birth and no underlying medical conditions associated with stroke diagnosis. Records with more than 20% missing data were excluded. Variable selection was based on peer-reviewed literature. Data were analyzed in September 2021.ExposuresClinical pregnancy, delivery, and neonatal factors associated with PAIS as common data elements across the 4 registries.Main outcomes and measuresThe primary outcome was the discriminative accuracy of the model predicting PAIS, measured by the concordance statistic (C statistic).ResultsOf 2571 term neonates in the initial analysis (527 [20%] case and 2044 [80%] control individuals; gestational age range, 37-42 weeks), 1389 (54%) were male, with a greater proportion of males among cases compared with controls (318 [60%] vs 1071 [52%]). The final model was developed using 1924 neonates, including 321 cases (17%) and 1603 controls (83%), and 9 clinical factors associated with risk of PAIS in term neonates: maternal age, tobacco exposure, recreational drug exposure, preeclampsia, chorioamnionitis, intrapartum maternal fever, emergency cesarean delivery, low 5-minute Apgar score, and male sex. The model demonstrated good discrimination between cases and controls (C statistic, 0.73; 95% CI, 0.69-0.76) and good model fit (Hosmer-Lemeshow P = .20). Internal validation techniques yielded similar C statistics (0.73 [95% CI, 0.69-0.77] with bootstrap resampling, 10-fold cross-validated area under the curve, 0.72 [bootstrap bias-corrected 95% CI, 0.69-0.76]), as did a sensitivity analysis using cases and controls from Alberta, Canada, only (C statistic, 0.71; 95% CI, 0.65-0.77).Conclusions and relevanceThe findings suggest that clinical variables can be used to develop and internally validate a model to predict the risk of PAIS in term neonates, with good predictive performance and strong internal validity. Identifying neonates with a high probability of PAIS who could then be screened for early diagnosis and treatment may be associated with reductions in lifelong morbidity for affected individuals and their families.

Dataset Information

Fracture Risk Assessment in Long-term Care (FRAiL): Development and Validation of a Prediction Model.

Publications

Fracture Risk Assessment in Long-term Care (FRAiL): Development and Validation of a Prediction Model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets