Naloxone-Prescribing Practices in a Freestanding Rehabilitation Hospital.

OBJECTIVE: The study aims to determine whether Physical Medicine & Rehabilitation physicians offer naloxone per the Centers for Disease Control and Prevention Guidelines to patients at the highest risk of complications from opioid treatment and whether there is a difference between inpatient and outpatient naloxone prescribing. DESIGN: A retrospective chart review on 389 adults (outpatient n = 166; inpatient n = 223) from May 4 to May 31, 2022, at an academic rehabilitation hospital. Prescribed medications and comorbidities were evaluated to determine whether Centers for Disease Control and Prevention criteria for offering naloxone were met and whether naloxone was offered. RESULTS: One hundred twenty-nine opioid prescriptions were written for 102 outpatients; 61 qualified for naloxone (morphine milliequivalent range = 10-1080, mean = 158.08). On inpatient, 68 patients received 86 opioid prescriptions; 35 qualified for naloxone (morphine milliequivalent range = 3.75-246, mean = 62.36). Overall, there was a significantly lower rate of opioid prescriptions for inpatients (30.49%) than outpatients (61.45%) ( P < 0.0001), a nonsignificant lower rate of inpatient (51.47%) than outpatient (59.80%) "at-risk" prescriptions ( P = 0.351), and a weakly significant lower rate of naloxone prescribing for inpatient (2.86%) than outpatient visits (8.20%) ( P < 0.0519). CONCLUSIONS: At this rehabilitation hospital, there was a low rate of naloxone prescribing by inpatient and outpatient providers, with a higher rate occurring in the outpatient than inpatient setting. More research is needed to understand this prescribing trend to determine potential interventions.

Spasticity Predicts Motor Recovery for Patients with Subacute Motor Complete Spinal Cord Injury.

OBJECTIVE: A motor complete spinal cord injury (SCI) results in the loss of voluntary motor control below the point of injury. Some of these patients can regain partial motor function through inpatient rehabilitation; however, there is currently no biomarker to easily identify which patients have this potential. Evidence indicates that spasticity could be that marker. Patients with motor complete SCI who exhibit spasticity show preservation of descending motor pathways, the pathways necessary for motor signals to be carried from the brain to the target muscle. We hypothesized that the presence of spasticity predicts motor recovery after subacute motor complete SCI. METHODS: Spasticity (Modified Ashworth Scale and pendulum test) and descending connectivity (motor evoked potentials) were tested in the rectus femoris muscle in patients with subacute motor complete (n = 36) and motor incomplete (n = 30) SCI. Motor recovery was assessed by using the International Standards for Neurological Classification of Spinal Cord Injury and the American Spinal Injury Association Impairment Scale (AIS). All measurements were taken at admission and discharge from inpatient rehabilitation. RESULTS: We found that motor complete SCI patients with spasticity improved in motor scores and showed AIS conversion to either motor or sensory incomplete. Conversely, patients without spasticity showed no changes in motor scores and AIS conversion. In incomplete SCI patients, motor scores improved and AIS conversion occurred regardless of spasticity. INTERPRETATION: These findings suggest that spasticity represents an easy-to-use clinical outcome that might help to predict motor recovery after severe SCI. This knowledge can improve inpatient rehabilitation effectiveness for motor complete SCI patients. ANN NEUROL 2023.

Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms.

Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose. Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, "annotations" refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms. Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency. Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.

A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study.

PURPOSE: Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images. We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer. METHODS: We digitized 64 glass slides of hematoxylin- and eosin-stained invasive ductal carcinoma core biopsies prepared at a single clinical site. A collaborating pathologist selected 10 regions of interest (ROIs) per slide for evaluation. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The microscope platform allows the same ROIs to be evaluated in both modes. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI. RESULTS: In total, 19 pathologists made 1645 ROI evaluations during a data collection event and the following 2 weeks. The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm. CONCLUSION: We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will investigate methods to use the dataset as an external validation tool for algorithms. We will also consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the Food and Drug Administration via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned.

Exploring the relationship between Overall Survival (OS), Progression Free Survival (PFS) and Objective Response Rate (ORR) in patients with advanced melanoma.

PURPOSE: From 2011 to 2016, 13 randomized clinical trials with active controls were submitted to U.S. Food and Drug Administration (FDA) for the treatment of advanced melanoma. While regular approval is generally granted due to a substantial improvement in overall survival (OS), or a large, clinically meaningful improvement in progression-free survival (PFS), accelerated approval can be granted based on incremental change in a surrogate end point reasonably likely to predict clinical benefit, such as objective response (ORR) of large magnitude and long duration. However, the relationship between objective response rate and progression free survival or objective response rate and overall survival in advanced melanoma has not been established. PATIENTS AND METHODS: We conducted analyses to assess the correlation of objective response rate with progression free survival as well as overall survival by examining all advanced melanoma trials submitted to the FDA between 2011 and 2016. In order to examine these relationships, associations between trial-level hazard ratio (HR) of progression free survival, hazard ration of survival and odds ratio of objective response rate were analyzed using a weighted linear regression model. Patient-level responder analyses comparing progression free survival and overall survival between patients with and without an objective response were performed using pooled data from all studies. RESULTS: In the trial-level analysis, the linear relationships between progression free survival and objective response rate, and overall survival and objective response rate were weak (R²adj = 0.019 and 0.093, respectively). The linear relationship between overall survival and progression free survival (R²adj = 0.075) was also weak. In the patient-level responder analyses, patients who achieved a response had better progression free survival and overall survival compared with non-responders in both the control drug treatment and the experimental drug treatment. CONCLUSION: Based on this analysis, use of objective response rate as a surrogate endpoint for overall survival or progression free survival in this population appears not appropriate. However, due to the nature of heterogeneity, interpretation needs to be cautious.