Association of Longitudinal Mobility Levels in the Hospital and Injurious Inpatient Falls.

ABSTRACT: Falls are one of the most common adverse events in hospitals, and patient mobility is a key risk factor. In hospitals, risk assessment tools are used to identify patient-centered fall risk factors and guide care plans, but these tools have limitations. To address these issues, we examined daily patient mobility levels before injurious falls using the Johns Hopkins Highest Level of Mobility, which quantifies key patient mobility milestones from low-level to community distances of walking. We aimed to identify longitudinal characteristics of patient mobility before a fall to help identify fallers before the event. Conducting a retrospective matched case-control analysis, we compared mobility levels in the days leading up to an injurious fall between fallers and nonfallers. We observed that patients who experienced an injurious fall, on average, spent 28% of their time prefall at a low mobility level (Johns Hopkins Highest Level of Mobility levels 1-4), compared with nonfallers who spent 19% of their time at a low mobility level (mean absolute difference, 9%; 95% confidence interval, 1%-16%; P = 0.026; relative difference, 44%). This suggests that assessing a patient's mobility levels over time can help identify those at an increased risk for falls and enable hospitals to manage mobility problems more effectively.

An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation.

Infectious disease modelling can serve as a powerful tool for situational awareness and decision support for policy makers. However, COVID-19 modelling efforts faced many challenges, from poor data quality to changing policy and human behaviour. To extract practical insight from the large body of COVID-19 modelling literature available, we provide a narrative review with a systematic approach that quantitatively assessed prospective, data-driven modelling studies of COVID-19 in the USA. We analysed 136 papers, and focused on the aspects of models that are essential for decision makers. We have documented the forecasting window, methodology, prediction target, datasets used, and geographical resolution for each study. We also found that a large fraction of papers did not evaluate performance (25%), express uncertainty (50%), or state limitations (36%). To remedy some of these identified gaps, we recommend the adoption of the EPIFORGE 2020 model reporting guidelines and creating an information-sharing system that is suitable for fast-paced infectious disease outbreak science.

Multi-hazard hospital evacuation planning during disease outbreaks using agent-based modeling.

As different types of hazards, including natural and man-made, can occur simultaneously, to implement an integrated and holistic risk management, a multi-hazard perspective on disaster risk management, including preparedness and planning, must be taken for a safer and more resilient society. Considering the emerging challenges that the COVID-19 pandemic has been introducing to regular hospital operations, there is a need to adapt emergency plans with the changing conditions, as well. Evacuation of patients with different mobility disabilities is a complicated process that needs planning, training, and efficient decision-making. These protocols need to be revisited for multi-hazard scenarios such as an ongoing disease outbreak during which additional infection control protocols might be in place to prevent transmission. Computational models can provide insights on optimal emergency evacuation strategies, such as the location of isolation units or alternative evacuation prioritization strategies. This study introduces a non-ICU patient classification framework developed based on available patient mobility data. An agent-based model was developed to simulate the evacuation of the emergency department at the Johns Hopkins Hospital during the COVID-19 pandemic due to a fire emergency. The results show a larger nursing team can reduce the median and upper bound of the 95% confidence interval of the evacuation time by 36% and 33%, respectively. A dedicated exit door for COVID-19 patients is relatively less effective in reducing the median time, while it can reduce the upper bound by more than 50%.

Responding to a Pandemic: COVID-19 Projects in the Malone Center.

As the scope and scale of the COVID-19 pandemic became clear in early March of 2020, the faculty of the Malone Center engaged in several projects aimed at addressing both immediate and long-term implications of COVID-19. In this article, we briefly outline the processes that we engaged in to identify areas of need, the projects that emerged, and the results of those projects. As we write, some of these projects have reached a natural termination point, whereas others continue. We identify some of the factors that led to projects that moved to implementation, as well as factors that led projects to fail to progress or to be abandoned.

Artificial Intelligence-Based Clinical Decision Support for COVID-19-Where Art Thou?

The COVID-19 crisis has brought about new clinical questions, new workflows, and accelerated distributed healthcare needs. Although artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date. In this perspective piece, the opportunities and requirements for AI-based clinical decision support systems are identified and challenges that impact "AI readiness" for rapidly emergent healthcare challenges are highlighted.

Automated treatment planning for a dedicated multi-source intra-cranial radiosurgery treatment unit accounting for overlapping structures and dose homogeneity.

PURPOSE: The purpose of this work is to advance the two-step approach for Gamma Knife(®) Perfexion™ (PFX) optimization to account for dose homogeneity and overlap between the planning target volume (PTV) and organs-at-risk (OARs). METHODS: In the first step, a geometry-based algorithm is used to quickly select isocentre locations while explicitly accounting for PTV-OARs overlaps. In this approach, the PTV is divided into subvolumes based on the PTV-OARs overlaps and the distance of voxels to the overlaps. Only a few isocentres are selected in the overlap volume, and a higher number of isocentres are carefully selected among voxels that are immediately close to the overlap volume. In the second step, a convex optimization is solved to find the optimal combination of collimator sizes and their radiation duration for each isocentre location. RESULTS: This two-step approach is tested on seven clinical cases (comprising 11 targets) for which the authors assess coverage, OARs dose, and homogeneity index and relate these parameters to the overlap fraction for each case. In terms of coverage, the mean V99 for the gross target volume (GTV) was 99.8% while the V95 for the PTV averaged at 94.6%, thus satisfying the clinical objectives of 99% for GTV and 95% for PTV, respectively. The mean relative dose to the brainstem was 87.7% of the prescription dose (with maximum 108%), while on average, 11.3% of the PTV overlapped with the brainstem. The mean beam-on time per fraction per dose was 8.6 min with calibration dose rate of 3.5 Gy/min, and the computational time averaged at 205 min. Compared with previous work involving single-fraction radiosurgery, the resulting plans were more homogeneous with average homogeneity index of 1.18 compared to 1.47. CONCLUSIONS: PFX treatment plans with homogeneous dose distribution can be achieved by inverse planning using geometric isocentre selection and mathematical modeling and optimization techniques. The quality of the obtained treatment plans are clinically satisfactory while the homogeneity index is improved compared to conventional PFX plans.

Automated treatment planning for a dedicated multi-source intracranial radiosurgery treatment unit using projected gradient and grassfire algorithms.

PURPOSE: The purpose of this work is to develop a framework to the inverse problem for radiosurgery treatment planning on the Gamma Knife(®) Perfexion™ (PFX) for intracranial targets. METHODS: The approach taken in the present study consists of two parts. First, a hybrid grassfire and sphere-packing algorithm is used to obtain shot positions (isocenters) based on the geometry of the target to be treated. For the selected isocenters, a sector duration optimization (SDO) model is used to optimize the duration of radiation delivery from each collimator size from each individual source bank. The SDO model is solved using a projected gradient algorithm. This approach has been retrospectively tested on seven manually planned clinical cases (comprising 11 lesions) including acoustic neuromas and brain metastases. RESULTS: In terms of conformity and organ-at-risk (OAR) sparing, the quality of plans achieved with the inverse planning approach were, on average, improved compared to the manually generated plans. The mean difference in conformity index between inverse and forward plans was -0.12 (range: -0.27 to +0.03) and +0.08 (range: 0.00-0.17) for classic and Paddick definitions, respectively, favoring the inverse plans. The mean difference in volume receiving the prescribed dose (V(100)) between forward and inverse plans was 0.2% (range: -2.4% to +2.0%). After plan renormalization for equivalent coverage (i.e., V(100)), the mean difference in dose to 1 mm(3) of brainstem between forward and inverse plans was -0.24 Gy (range: -2.40 to +2.02 Gy) favoring the inverse plans. Beam-on time varied with the number of isocenters but for the most optimal plans was on average 33 min longer than manual plans (range: -17 to +91 min) when normalized to a calibration dose rate of 3.5 Gy/min. In terms of algorithm performance, the isocenter selection for all the presented plans was performed in less than 3 s, while the SDO was performed in an average of 215 min. CONCLUSIONS: PFX inverse planning can be performed using geometric isocenter selection and mathematical modeling and optimization techniques. The obtained treatment plans all meet or exceed clinical guidelines while displaying high conformity.