Developing a Scoring Model to Predict the Risk of Injurious Falls in Elderly Patients: A Retrospective Case-Control Study in Multicenter Acute Hospitals

Validation of the Saga Fall Injury Risk Model

Background: Predicting fall injuries can mitigate the sequelae of falls and potentially utilize medical resources effectively. This study aimed to externally validate the accuracy of the Saga Fall Injury Risk Model (SFIRM), consisting of six factors including age, sex, emergency transport, medical referral letter, Bedriddenness Rank, and history of falls, assessed upon admission. Methods: This was a two-center, prospective, observational study. We included inpatients aged 20 years or older in two hospitals, an acute and a chronic care hospital, from October 2018 to September 2019. The predictive performance of the model was evaluated by calculating the area under the curve (AUC), 95% confidence interval (CI), and shrinkage coefficient of the entire study population. The minimum sample size of this study was 2,235 cases. Results: A total of 3,549 patients, with a median age of 78 years, were included in the analysis, and men accounted for 47.9% of all the patients. Among these, 35 (0.99%) had fall injuries. The performance of the SFIRM, as measured by the AUC, was 0.721 (95% CI: 0.662-0.781). The observed fall incidence closely aligned with the predicted incidence calculated using the SFIRM, with a shrinkage coefficient of 0.867. Conclusions: The external validation of the SFIRM in this two-center, prospective study showed good discrimination and calibration. This model can be easily applied upon admission and is valuable for fall injury prediction.

The association of peripheral neuropathy detected by monofilament testing with risk of falls and fractures in older adults

BACKGROUND

In persons with diabetes, annual screening for peripheral neuropathy (PN) using monofilament testing is the standard of care. However, PN detected by monofilament testing is common in older adults, even in the absence of diabetes. We aimed to assess the association of PN with risk of falls and fractures in older adults.

METHODS

We included participants in the Atherosclerosis Risk in Communities (ARIC) Study who underwent monofilament testing at visit 6 (2016-2017). Incident falls and fractures were identified based on ICD-9 and ICD-10 codes from active surveillance of all hospitalizations and linkage to Medicare claims. We used Cox models to assess the association of PN with falls and fractures (combined and as separate outcomes) after adjusting for demographics and risk factors for falls.

RESULTS

There were 3617 ARIC participants (mean age 79.4 [SD 4.7] years, 40.8% male, and 21.4% Black adults), of whom 1242 (34.3%) had PN based on monofilament testing. During a median follow-up of 2.5 years, 371 participants had a documented fall, and 475 participants had a documented fracture. The incidence rate (per 1000 person-years) for falls or fractures for participants with PN versus those without PN was 111.1 versus 74.3 (p < 0.001). The age-, sex-, and race-adjusted 3-year cumulative incidence of incident fall or fracture was significantly higher for participants with PN versus those without PN (26.5% vs. 18.4%, p < 0.001). After adjusting for demographics, PN remained independently associated with falls and fractures (HR 1.48, 95% CI 1.26, 1.74). Results were similar for models including traditional risk factors for falls, when falls and fractures were analyzed as separate outcomes, and after adjustment for competing risk of death.

CONCLUSIONS

PN, as measured by monofilament testing, is common in older adults and associated with risk of falls and fracture. Screening with monofilament testing may be warranted to identify older adults at high risk for falls.

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

Purpose

Exploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis.

Methods

This retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models.

Results

Five clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical-radiographic model (P=0.049).

Conclusions

The proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Using Root Cause Analysis to Inform a Falls Practice Change in the Home Care Setting

Falls are a significant health problem in community-dwelling older adults, resulting in injuries, deaths, and increased healthcare costs. Falls were a quality concern for a Northeastern home care agency and this project aimed to evaluate the falls prevention process for older adults receiving home care services by determining potential root causes of falls and to identify a practice change. This quality improvement project used a root cause analysis methodology with a retrospective matched case-control design. Records of patients with falls were assessed for falls prevention process fidelity and compared with patients without a fall matched on the Missouri Alliance for Home Care-10 (MAHC-10) assessment, examining plan of care accuracy and patient fall risk factors. Findings indicated fidelity concerns in the fall prevention process, with gaps in care planning aligned with identified risk factors. Interventions to mitigate identified MAHC-10 risk factors on care plans were present less than 50% of the time for four of the six factors. Polypharmacy (7.46%) and pain affecting function (9.21%) were most frequently unaddressed risk factors in the care plan. Recommendations included implementation of a falls prevention pathway, including standardized falls risk assessment, universal falls precautions in the care plan with tailored interventions based on risk factors, and referral initiation when necessary.