Preoperative Fall Risk Assessment Score as a Prognostic Factor in Esophageal Cancer Patients after Esophagectomy

The current study investigated the impact of preoperative fall risk assessment score (FRAS) on long-term prognoses in patients with esophageal cancer (EC). A total of 161 patients with EC who underwent curative surgery were classified into a high-risk (95, 41.0%) and low-risk (66, 41.0%) groups according to their FRAS. This study investigated the relationships between the FRAS and clinicopathological findings and prognoses. Accordingly, patients in the high-risk group were significantly older and had a significantly higher Charlson comorbidity index than those in the low-risk group. No significant difference was found in pathological findings between both groups. The high-risk group had significantly lower overall survival (OS) and relapse-free survival (RFS) rates than the low-risk group (p = 0.004 and 0.001, respectively). Multivariate analysis identified high FRAS as an independent prognostic factor for poor OS, with a hazard ratio of 1.75 (p = 0.033). Moreover, re-analysis of the data after excluding age as a category showed that the high-risk group had significantly worse OS (p = 0.004) and RFS (p = 0.003) than the low-risk group. The FRAS can, therefore, be considered a useful method for assessing frailty and a potential prognostic factor for EC.

Using Electronic Health Records to Predict Short-Term Falls: Machine learning Applications in Senior Care Facilities (Preprint)

BACKGROUND Evidence for the best choice of fall risk assessment in long-term care facilities is limited. Short-term fall predictions may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. This can be achieved through the use of electronic health records (EHRs), which contain routinely collected information regarding the majority of known fall risk factors. OBJECTIVE We implemented machine learning algorithms that use EHR data to predict a three-month fall risk in residents from a variety of senior care facilities providing different levels of care. METHODS This retrospective study obtained EHR data (2007-2021) from Juniper communities’ proprietary database of 2,785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performances of three machine learning (ML)-based fall predictions models and the Juniper Communities fall risk assessment across these different facilities. The ML input features included vital signs and several known risk factors, such as history of fall, comorbidities, and medications. These features were identified within the EHR system based on relevant International Classification of Diseases codes, string searches, or keyword queries. Additional analyses were conducted to examine how the changes in the input features, training datasets, and prediction window affected the performance of these models. RESULTS The extreme gradient boosting (XGB) model exhibited the highest performance with an area under the receiver operating characteristic curve (AUROC) of 0.846, specificity of 0.848, and sensitivity of 0.706 while achieving the best tradeoff in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident's number of active diseases, and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features reached a higher prediction accuracy than using either group of features alone. When reducing the prediction window to two months, the XGB model continued to exhibit the highest performance (AUROC = 0.753) in comparison to logistic regression (AUROC = 0.690), multi-layered perceptron (AUROC = 0.678) and Juniper's fall risk assessment (AUROC = 0.582). CONCLUSIONS This study provides novel insights into EHR-based features for predicting short-term fall risk in different types of care facilities. The integration of EHR data into fall prediction models, and particularly vital signs, yields a cost-effective and automated fall risk surveillance. Our XGB model uncovered the impact of a wide range of clinical and pathophysiological fall predictors across heterogenous cohorts while outperforming traditional fall risk assessments and standard ML techniques that are less compatible with EHR data. CLINICALTRIAL N/A

Change Project Using a Fall Prevention Action Program to Decrease Falls on a Behavioral Health Unit

Practice Problem: Fall rates are increasing in the behavioral health units of the East Texas hospital. Due to the worldwide COVID-19 pandemic, the units are short-staffed, which further supports the urgent need for a targeted intervention to reduce fall risk. PICOT: The PICOT question that guided this project was: “In adult behavioral health patients (P), what is the effect of the Edmonson Psychiatric Fall Risk Assessment Tool (I), compared with previous use of the Morse Fall Risk tool (C), on the fall rate (O), in 8 weeks (T)?” Evidence: Falls are the most reported incidents in acute care hospitals and falls of behavioral health patients are more challenging to mitigate than those of other patients. Evidence suggested that the Edmonson Psychiatric Fall Risk Assessment Tool aided in identifying and mitigating fall risks by tailoring care plans to individual patients. Intervention: The Edmonson Psychiatric Fall Risk Assessment Tool was completed on every patient over the age of 18 years, which was admitted, discharged, falling, or had a change in condition, while on the psychiatric unit. The risk assessment was used to determine the risks and other factors that contribute to the patient falling. Once the contributors were identified, the data was used to put interventions into place and revise each individualized care plan to decrease falls. Outcome: The intended outcome was to identify patients that were at substantial risk for falls using the EPFRAT tool, mitigate some of their risks, and therefore decrease falls. Conclusion: Continued emphasis on the fall risks of behavioral health patients will be paramount in the management and success of the continued improvement in patient outcomes.

Development of a Screening Tool for Feet/Footwear- Related Influences on Fall Risk

The effectiveness of multifactorial fall risk assessment and intervention strategies is well documented. Although identifying feet/footwear-related influences on fall risk is a vital fall risk assessment component, few evidence-based resources or screening tools are available. To address this need, we developed the Screening Tool for Feet/Footwear-Related Influences on Fall Risk. Our tool is designed for older adults who are identified as at risk for falling, based on the CDC’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) Algorithm for Fall Risk Screening, Assessment, and Intervention. Tool development was informed by results of our systematic review of lower-limb factors associated with balance and falls. Our initial tool was evaluated by an external group of 9 interprofessional content experts. Those experts recommended modification of 8 items and rated the tool’s clarity as 81.2/100, appeal as 79.1/100, and clinical feasibility as 76.1/100. After incorporating recommended changes, we completed a modified Delphi study using 8 new interprofessional experts (average years of experience: 19.3). During Phase 1, Delphi participants recommended we combine items with similar treatment recommendations, add a question about orthoses, and increase the specificity of 9 items. This refinement resulted in a 20-item screening tool, which met approval after two rounds of consensus voting. Approval was defined based on the Item Content Validation Index, percentage of agreement > 80% on each item. The high level of agreement illustrates the tool’s content validity. Using our tool, an older adult’s feet/footwear-related risk factors can be identified and incorporated into an effective multifactorial fall prevention intervention.

Development and validation of a continuous fall risk score in community-dwelling older people: an ecological approach

Background Fall risk assessment in older people is of major importance for providing adequate preventive measures. Current predictive models are mainly focused on intrinsic risk factors and do not adjust for contextual exposure. The validity and utility of continuous risk scores have already been demonstrated in clinical practice in several diseases. In this study, we aimed to develop and validate an intrinsic-exposure continuous fall risk score (cFRs) for community-dwelling older people through standardized residuals. Methods Self-reported falls in the last year were recorded from 504 older persons (391 women: age 73.1 ± 6.5 years; 113 men: age 74.0 ± 6.1 years). Participants were categorized as occasional fallers (falls ≤1) or recurrent fallers (≥ 2 falls). The cFRs was derived for each participant by summing the standardized residuals (Z-scores) of the intrinsic fall risk factors and exposure factors. Receiver operating characteristic (ROC) analysis was used to determine the accuracy of the cFRs for identifying recurrent fallers. Results The cFRs varied according to the number of reported falls; it was lowest in the group with no falls (− 1.66 ± 2.59), higher in the group with one fall (0.05 ± 3.13, p < 0.001), and highest in the group with recurrent fallers (2.82 ± 3.94, p < 0.001). The cFRs cutoff level yielding the maximal sensitivity and specificity for identifying recurrent fallers was 1.14, with an area under the ROC curve of 0.790 (95% confidence interval: 0.746–0.833; p < 0.001). Conclusions The cFRs was shown to be a valid dynamic multifactorial fall risk assessment tool for epidemiological analyses and clinical practice. Moreover, the potential for the cFRs to become a widely used approach regarding fall prevention in community-dwelling older people was demonstrated, since it involves a holistic intrinsic-exposure approach to the phenomena. Further investigation is required to validate the cFRs with other samples since it is a sample-specific tool.