Circadian Rhythmicity as a Predictor of Quality of Life in Allogeneic Hematopoietic Cell Transplant Patients

Context

Quality of life (QoL) is increasingly recognized as an important outcome of cancer treatment. Previous studies have examined clinical predictors of QoL, but with the increasing prevalence of wearable sensors that monitor sleep and activity patterns, further investigation into whether these behaviors are predictive of post-treatment QoL is now feasible. Among patients receiving aggressive cancer treatment such as hematopoietic cell transplantation (HCT), analysis of circadian rhythms (24-hour patterns of sleep and activity) via wearable sensors is limited.

Inpatient Versus Outpatient Arthroplasty: A Single-Surgeon,Matched Cohort Analysis of 90-Day Complications

Background

Although some prior work supports the safety of same-day arthroplasty performed in a hospital, concerns remain when these procedures are performed in a free-standing ambulatory surgery center. The purpose of this study is to compare 90-day complication rates between matched cohorts that underwent inpatient vs outpatient arthroplasty at an ambulatory surgery center.

Regulated necrosis and failed repair in cisplatin-induced chronic kidney disease

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Patients with end-stage renal disease have poor outcomes after endovascular abdominal aortic aneurysm repair

Objective

Although endovascular repair of abdominal aortic aneurysms (AAAs) has been demonstrated to have favorable outcomes, not all cohorts of patients with AAA fare equally well. Our goal was to investigate perioperative and 1-year outcomes in patients with end-stage renal disease (ESRD) on dialysis, who have traditionally fared worse after vascular interventions, to assess how ESRD affects outcomes in a large modern cohort of endovascular aneurysm repair (EVAR) patients.

Transmissible vaccines whose dissemination rates vary through time,with applications to wildlife

Transmission is a potential property of live viral vaccines that remains largely unexploited but may lie within the realm of many engineering designs. While likely unacceptable for vaccines of humans, transmission may be highly desirable for vaccines of wildlife, both to protect natural populations and also to limit zoonotic transmissions into humans. Defying intuition, transmission alone does not guarantee that a vaccine will perform well: the benefit of transmission over no transmission depends on and increases with the basic reproductive number of the vaccine, $R_0$. The $R_0$ of an infectious agent in a homogeneous population is typically considered to be a fixed number, but some evidence suggests that dissemination of transmissible vaccines may change through time. One obvious possibility is that transmission will be greater from hosts directly vaccinated than from hosts who acquire the vaccine passively, but other types of change might also accrue. Whenever transmission changes over time, the $R_0$ estimated from directly vaccinated hosts will not reflect the vaccine’s long term impact. As there is no theory on the consequences of changing transmission rates for a vaccine, we derive conditions for a transmissible vaccine with varying transmission rates to protect a population from pathogen invasion. Being the first in the transmission chain, the $R_0$ from directly vaccinated hosts has a larger effect than those from later steps in the chain. This mathematical property reveals that a transmissible vaccine with low long term transmission may nonetheless realize a big impact if early transmission is high. Furthermore, there may be ways to artificially elevate early transmission, thereby achieving high herd immunity from transmission while ensuring that the vaccine will ultimately die out.     

Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports

BACKGROUND AND PURPOSE:The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natural language processing of radiology reports.MATERIALS AND METHODS:We retrospectively analyzed 32,555 radiology reports from brain CTs and MRIs from a comprehensive stroke center, performed from March 1 to April 30 each year from 2017 to 2020, involving 20,414 unique patients. To detect acute or subacute ischemic stroke in free-text reports, we trained a random forest natural language processing classifier using 1987 randomly sampled radiology reports with manual annotation. Natural language processing classifier generalizability was evaluated using 1974 imaging reports from an external dataset.RESULTS:The natural language processing classifier achieved a 5-fold cross-validation classification accuracy of 0.97 and an F1 score of 0.74, with a slight underestimation (−5%) of actual numbers of acute or subacute ischemic strokes in cross-validation. Importantly, cross-validation performance stratified by year was similar. Applying the classifier to the complete study cohort, we found an estimated 24% decrease in patients with acute or subacute ischemic strokes reported on CT or MR imaging from March to April 2020 compared with the average from those months in 2017–2019. Among patients with stroke-related order indications, the estimated proportion who underwent neuroimaging with acute or subacute ischemic stroke detection significantly increased from 16% during 2017–2019 to 21% in 2020 (P = .01). The natural language processing classifier performed worse on external data.CONCLUSIONS:Acute or subacute ischemic stroke cases detected by neuroimaging decreased during the COVID-19 pandemic, though a higher proportion of studies ordered for stroke were positive for acute or subacute ischemic strokes. Natural language processing approaches can help automatically track acute or subacute ischemic stroke numbers for epidemiologic studies, though local classifier training is important due to radiologist reporting style differences.

There is much concern regarding the impact of the coronavirus disease 2019 (COVID-19) pandemic on the quality of stroke care, including issues with hospital capacity, clinical resource re-allocation, and the safety of patients and clinicians.^1,2 Previous reports have shown that there have been substantial decreases in stroke neuroimaging volume during the pandemic.^3,4 In addition, acute ischemic infarcts have been found on neuroimaging studies in many hospitalized patients with COVID-19, though the causal relationship is unclear.^5,6 Studies like these and other epidemiologic analyses usually rely on the creation of manually curated databases, in which identification of cases can be time-consuming and difficult to update in real-time. One way to facilitate such research is to use natural language processing (NLP), which has shown utility for automated analysis of radiology report data.⁷ NLP algorithms have been developed previously for the classification of neuroradiology reports for the presence of ischemic stroke findings and acute ischemic stroke subtypes.^8,9 Thus, NLP has the potential to facilitate COVID-19 research.In this study, we developed an NLP machine learning model that classifies radiology reports for the presence or absence of acute or subacute ischemic stroke (ASIS), as opposed to chronic stroke. We used this model to quantify the change in ASIS detected on all CT or MR imaging studies performed at a large comprehensive stroke center during the COVID-19 pandemic in the United States. We also evaluated NLP model generalizability and different training strategies using a sample of radiology reports from a second stroke center.