Assessment of Patient Ambulation Profiles to Predict Hospital Readmission, Discharge Location, and Length of Stay in a Cardiac Surgery Progressive Care Unit

Evaluation of strategies to support implementation of a hospital walking program: protocol for a type III effectiveness-implementation hybrid trial

BACKGROUND

STRIDE is a supervised walking program designed to address the negative consequences of immobility during hospitalization for older adults. In an 8-hospital stepped wedge randomized controlled trial, STRIDE was associated with reduced odds of hospital discharge to skilled nursing facility. STRIDE has the potential to become a system-wide approach to address hospital-associated disability in Veteran's Affairs; however, critical questions remain about how best to scale and sustain the program. The overall study goal is to compare the impact of two strategies on STRIDE program penetration (primary), fidelity, and adoption implementation outcomes.

METHODS

Replicating Effective Programs will be used as a framework underlying all implementation support activities. In a parallel, cluster randomized trial, we will use stratified blocked randomization to assign hospitals (n = 32) to either foundational support, comprised of standard, low-touch activities, or enhanced support, which includes the addition of tailored, high-touch activities if hospitals do not meet STRIDE program benchmarks at 6 and 8 months following start date. All hospitals begin with foundational support for 6 months until randomization occurs. The primary outcome is implementation penetration defined as the proportion of eligible hospitalizations with ≥ 1 STRIDE walks at 10 months. Secondary outcomes are fidelity and adoption with all implementation outcomes additionally examined at 13 and 16 months. Fidelity will be assessed for STRIDE hospitalizations as the percentage of eligible hospital days with "full dose" of the program, defined as two or more documented walks or one walk for more than 5 min. Program adoption is a binary outcome defined as ≥ 5 patients with a STRIDE walk or not. Analyses will also include patient-level effectiveness outcomes (e.g., discharge to nursing home, length of stay) and staffing and labor costs. We will employ a convergent mixed-methods approach to explore and understand pre-implementation contextual factors related to differences in hospital-level adoption.

DISCUSSION

Our study results will dually inform best practices for promoting successful implementation of an evidence-based hospital-based walking program. This information may support other programs by advancing our understanding of how to apply and scale-up national implementation strategies.

TRIAL REGISTRATION

This study was registered on June 1, 2021, at ClinicalTrials.gov (identifier NCT04868656 ).

Effects of Implementation of a Supervised Walking Program in Veterans Affairs Hospitals : A Stepped-Wedge, Cluster Randomized Trial

BACKGROUND

In trials, hospital walking programs have been shown to improve functional ability after discharge, but little evidence exists about their effectiveness under routine practice conditions.

OBJECTIVE

To evaluate the effect of implementation of a supervised walking program known as STRIDE (AssiSTed EaRly MobIlity for HospitalizeD VEterans) on discharge to a skilled-nursing facility (SNF), length of stay (LOS), and inpatient falls.

DESIGN

Stepped-wedge, cluster randomized trial. (ClinicalTrials.gov: NCT03300336).

SETTING

8 Veterans Affairs hospitals from 20 August 2017 to 19 August 2019.

PATIENTS

Analyses included hospitalizations involving patients aged 60 years or older who were community dwelling and admitted for 2 or more days to a participating medicine ward.

INTERVENTION

Hospitals were randomly assigned in 2 stratified blocks to a launch date for STRIDE. All hospitals received implementation support according to the Replicating Effective Programs framework.

MEASUREMENTS

The prespecified primary outcomes were discharge to a SNF and hospital LOS, and having 1 or more inpatient falls was exploratory. Generalized linear mixed models were fit to account for clustering of patients within hospitals and included patient-level covariates.

RESULTS

Patients in pre-STRIDE time periods (n = 6722) were similar to post-STRIDE time periods (n = 6141). The proportion of patients with any documented walk during a potentially eligible hospitalization ranged from 0.6% to 22.7% per hospital. The estimated rates of discharge to a SNF were 13% pre-STRIDE and 8% post-STRIDE. In adjusted models, odds of discharge to a SNF were lower among eligible patients hospitalized in post-STRIDE time periods (odds ratio [OR], 0.6 [95% CI, 0.5 to 0.8]) compared with pre-STRIDE. Findings were robust to sensitivity analyses. There were no differences in LOS (rate ratio, 1.0 [CI, 0.9 to 1.1]) or having an inpatient fall (OR, 0.8 [CI, 0.5 to 1.1]).

LIMITATION

Direct program reach was low.

CONCLUSION

Although the reach was limited and variable, hospitalizations occurring during the STRIDE hospital walking program implementation period had lower odds of discharge to a SNF, with no change in hospital LOS or inpatient falls.

PRIMARY FUNDING SOURCE

U.S. Department of Veterans Affairs Quality Enhancement Research Initiative (Optimizing Function and Independence QUERI).

Early initiation of physical and occupational therapy while on extracorporeal life support improves patients' functional activity

PURPOSE

Managing acute respiratory distress syndrome (ARDS) patients on venovenous extracorporeal membrane oxygenation (V-V ECMO), without sedation/neuromuscular blockade to allow physical and occupational therapy (PT/OT) participation, is untraditional. Here, we investigate the impact of early PT/OT initiation on discharge functional activity for ARDS patients managed on V-V ECMO.

METHODS

This is a retrospective review of 67 ARDS patients managed with V-V ECMO at a single academic center from February 2018 to June 2021. Data collected included patient characteristics, days of V-V ECMO support, day of PT/OT initiation, and ambulation distance and Activity Measure for Post-Acute Care (AMPAC) Six-Clicks score on day of discharge.

RESULTS

Patients with >7 days of V-V ECMO support had decreased ambulation and AMPAC scores compared to those with <7 days (70.5 vs. 162.1, p < 0.01 and 12.3 vs. 16.4, p = 0.01, respectively). PT/OT initiation within 7 days after starting V-V ECMO significantly improved ambulation and AMPAC scores (163.5 vs. 59.5, p < 0.001, and 16.6 vs. 11.8, p < 0.01, respectively). Additionally, in patients with >7 days of V-V ECMO support, those who began PT/OT within 8 days of V-V ECMO cannulation had significantly improved ambulation and AMPAC scores (151.8 vs. 44.2, p < 0.01, and 16.5 vs. 11.0, p < 0.01, respectively).

CONCLUSION

Early PT/OT initiation in severe ARDS patients managed on V-V ECMO is associated with improved patient functional activity on day of discharge. Our study further supports the use of V-V ECMO in treatment of severe ARDS without sedation/neuromuscular blockade and specifically demonstrates PT/OT should be started early following V-V ECMO cannulation.

Comparing three wearable accelerometers to measure early activity after cardiac surgery

Objective

Wearable activity monitors can provide detailed data on activity after cardiac surgery and discriminate a patient's risk for hospital-based outcomes. However, comparative data for different monitoring approaches, as well as predictive ability over clinical characteristics, are lacking. In addition, data on specific thresholds of activity are needed. The objective of this study was to compare 3 wearable activity monitors and 1 observational mobility scale in discriminating risk for 3 hospital-based outcomes, and to establish clinically relevant step thresholds.

Methods

Cardiac surgery patients were enrolled between June 2016 and August 2017 in a cohort study. Postoperative activity was measured by 3 accelerometry monitors (StepWatch Ambulation Monitor, Fitbit Charge HR, and ActiGraph GT9X) and 1 nurse-based observation scale. Monitors represent a spectrum of characteristics, including wear location (ankle/wrist), output (activity counts/steps), consumer accessibility, and cost. Primary outcomes were duration of hospitalization >7 days, discharge to a nonhome location, and 30-day readmission.

Results

Data were available from 193 patients (median age 67 years [interquartile range, 58-72]). All postoperative day 2 activity metrics (ie, from StepWatch, Fitbit, ActiGraph, and the observation scale) were independently associated with prolonged hospitalization and discharge to a nonhome location. Only steps as measured by StepWatch was independently associated with 30-day readmission. Overall, StepWatch provided the greatest discrimination (C-statistics 0.71-0.76 for all outcomes). Step thresholds between 250 and 500 steps/day identified between 74% and 96% of patients with any primary outcome.

Conclusions

Data from wearable accelerometers provide additive value in early postoperative risk-stratification for hospital-based outcomes. These results both support and provide guidance for activity-monitoring programs after cardiac surgery.

Predictors of Functional Improvement, Length of Stay, and Discharge Destination in the Context of an Assess and Restore Program in Hospitalized Older Adults

Assess and restore programs such as Humber’s Elderly Assess and Restore Team (HEART) provide short-term restorative care to prevent functional decline in hospitalized older adults. The aim of this retrospective observational study was to determine which HEART participant characteristics are predictive of functional improvement, decreased length of stay, return to home, and decreased readmission to hospital. Electronic health records were retrospectively examined to gather predictor data. Differences in functional status, excessive length of stay, discharge destination, and hospital readmissions were compared in 547 HEART patients and 547 matched eligible non-participants using ANOVAs, Mann–Whitney, and chi-square tests. The greatest functional improvements (percent Barthel change) were seen in those requiring a one-person assist (M = 39.56) and using a walker (M = 46.07). Difference in excessive length of stay between HEART and non-HEART participants was greatest in those who used a walker (Mdn = 3.80), required a one-person assist (Mdn = 2.00), had a high falls risk (Mdn = 1.80), and had either a lower urinary tract infection (Mdn = 2.25) or pneumonia (Mdn = 1.70). Predictor variables did not affect readmission to the hospital nor return to home. Predictive characteristics should be considered when enrolling patients to assess and restore programs for optimal clinical outcomes.

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.

Patient Ambulations Predict Hospital Readmission

Improved functional ability and physical activity are strongly associated with a broad range of positive health outcomes including reduced risk of hospital readmission. This study presents an algorithm for detecting ambulations from time-resolved step counts gathered from remote monitoring of patients receiving hospital care in their homes. It examines the statistical power of these ambulations in predicting hospital readmission. A diverse demographic cohort of 233 patients of age 70.5±16.8 years are evaluated in a retrospective analysis. Eleven statistical features are derived from raw time series data, and their F-statistics are assessed in discriminating between patients who were and were not readmitted within 30 days of discharge. Using these features, logistic regression models are trained to predict readmission. The results show that the fraction of days with at least one ambulation was the strongest feature, with an F-statistic of 17.2. The models demonstrate AUROC performances of 0.741, 0.766 and 0.769 using stratified 5-fold train-test splits in all included patients (n=233), congestive heart failure (CHF, n=105) and non-CHF (n=128) patient subgroups, respectively. This study suggests that patient ambulation metrics derived from wearable sensors can offer powerful predictors of adverse clinical outcomes such as hospital readmission, even in the absence of other features such as physiological vital signs.Index Terms-readmission, ambulation, step count, heart failure, physical activity, regression, actigraphy, accelerometer.