Incidence and characteristic analysis of in-hospital falls after anesthesia

A Case-Control Study of Accidental Falls During Surgical Hospitalizations

BACKGROUND

Postoperative falls are preventable complications. The study aims were to describe the rate and circumstances surrounding postoperative falls and explore potential associations with patient and procedural characteristics with emphasis on the use of sedative medications.

METHODS

Medical records of hospitalized patients undergoing non-lower extremity surgery under general anesthesia from January 1, 2010, through April 30, 2018 were reviewed for falls within 72 postoperative hours. Perioperative use of sedatives, sleep aids, gabapentinoids, and opioids were abstracted. Each fall case was matched with two controls on age, sex, and procedure type. Descriptive statistics and multivariable analysis accounting for the matched design were performed.

RESULTS

There were 343 falls among 200 186 hospitalized surgical patients (incidence of 17.1 [95% CI: 15.4, 19.0] falls per 10 000 procedures) with largest proportion of falls occurring on postoperative day 2 (n = 134, 39.1%). Most falls occurred in the general hospital wards (n = 304, 88.6%) and were unwitnessed (n = 186, 55.9%). The incidence of major injuries was 1.0 (95% CI: .1 - 3.6) per 100 000 procedures. Home use of non-benzodiazepine hypnotics (odds ratio 2.68, 95% CI: 1.47, 4.88, P=.001) and blood transfusions were associated with increased fall risk. Hospital stay was longer in patients who fall (7 [4, 15] vs. 5 [3, 9] days, P < .001).

CONCLUSIONS

The rate of postoperative falls in our institution was low and frequently unwitnessed. The use of non-benzodiazepine hypnotics is a modifiable risk factor associated with postoperative falls. Serious complications after falls were rare.

Usability of Electronic Health records in Predicting Short-term falls: Machine learning Applications in Senior Care Facilities (Preprint)

Background

Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities.

Objective

The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-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 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models.

Results

The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off 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 achieved higher prediction accuracy than using either group of features alone.

Conclusions

This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.

Effects of multimedia-based fall prevention education on the knowledge, attitudes, or behaviors of patients

OBJECTIVES

Patient safety is regarded as a critical quality monitoring indicator for medical institutions. The effects of a multimedia-based patient education intervention on knowledge, attitudes, and behaviors regarding fall prevention were observed.

METHODS

The study had a quasi-experimental research design and enrolled 140 participants. Seventy participants in the experimental group received multimedia-based patient education and a health education leaflet, while those in the control group received only the health education leaflet. A structured questionnaire was used for data collection at baseline, and a posttest was applied after the intervention.

RESULTS

The participants were predominantly treated in the gastroenterology department (45.7%), followed by the pulmonology department (33.6%). A total of 86.4% of patients had not experienced a fall within 3 months. After the intervention, the average scores for all variables in the experimental group were higher than those in the control group. The results indicate that attitudes, knowledge, and behaviors regarding fall prevention among patients in the pulmonology department were higher than those among patients in the gastroenterology department; the differences were statistically significant.

CONCLUSION

The individualized health education content was of substantial significance for patients with different disease backgrounds and facilitated changes in their knowledge, attitudes, and behaviors regarding falls.

RELEVANCE TO CLINICAL PRACTICE

Multimedia-based patient education influenced inpatients' knowledge, attitudes, and behaviors for preventing falls.

Wearable devices for patient monitoring in the early postoperative period: a literature review

The early postoperative period is a crucial stage in a patient's recovery as they are susceptible to a range of complications, with detection and management the key to avoiding long term consequences. Wearable devices are an innovative way of monitoring patient's post-intervention and may translate into improved patient outcomes, and reduced strain on healthcare resources, as they may facilitate safer and earlier discharge from the hospital setting. Several recent studies have investigated the use of wearable devices in postoperative monitoring. This review outlines the current literature including the range of wearable devices used for postoperative monitoring, the variety of surgeries investigated, and the outcomes assessed. A search of five electronic databases was performed. Data on the range of wearable devices, outcomes and surgeries investigated were extracted and synoptically analysed. Twenty-four articles were retrieved. Data on several different types of surgery were available and discussed. Most studies used wrist-mounted wearable devices and accelerometers or pedometers to assess physical activity metrics, including step counts and physical activity intensity (PAI), as markers of recovery. Wearable devices can provide objective data capture in the early postoperative phase to remotely monitor patients using various metrics including temperature, cardiac monitoring and physical activity. The majority of current research is focussed on wrist-mounted accelerometers and pedometers used to assess physical activity as a marker of postoperative function. Further research is required to demonstrate improved safety and cost-effectiveness of this technology.

A Literature Review of Factors Influencing Injurious Falls

Falls pose substantial challenges to health care institutions. This review aims to provide a synthesis and critique of studies that investigated fall injury risk factors and to identify significant risk factors that predispose patients to injurious falls. A comprehensive literature search was conducted in PubMed, COCHRANE, Embase, Cumulative Index to Nursing and Allied Heath Literature, and Scopus. Additional records were searched through Google Scholar and bibliographies of the retrieved articles. Twenty-three primary research studies were included. Demographic, intrinsic, and extrinsic factors have been identified. Demographics include age, gender, and marital status. Intrinsic factors include body mass index, medication, and preexisting conditions, and extrinsic factors include environmental factors. Several factors were found to be inconclusive. These factors should be considered and examined further. Future research may evaluate interventions focusing on targeted risk factors of injurious falls. Clinical guidelines addressing the factors in this review may be considered after further testing and research.