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.     

Rapid behavioral assessment of barriers and opportunities to improve vaccination coverage among displaced Rohingyas in Bangladesh,January 2018

Background

In November 2017, the World Health Organization received initial reports of suspected diphtheria cases in camps established for displaced Rohingyas in Cox’s Bazar district, Bangladesh. By January 11, 2018, over 4,000 suspected cases of diphtheria and 30 deaths were reported. The Bangladesh government and partners implemented a diphtheria vaccination campaign in December 2017. Outbreak response staff reported anecdotal evidence of vaccine hesitancy. Our assessment aimed to understand vaccination barriers and opportunities to enhance vaccine demand among displaced Rohingyas in Bangladesh.

The First Case Report of Schnitzler Syndrome Presenting with Eye Pain

ABSTRACT

Schnitzler syndrome is a rare, auto inflammatory condition known to manifest with bone pain, urticarial rash, fevers, relapsing arthralgia, and fatigue. In this case report, we describe a patient who was diagnosed with Schnitzler Syndrome that had initially presented with a unilateral pressure-type headache with a sensation of a ‘dagger’ stabbing into the back of the eye. He also had an associated ipsilateral redness of the conjunctiva, eyelid swelling, subtle optic disc elevations bilaterally and facial flushing - but with no visual acuity, pupillary, or lacrimatory changes. Anterior segment, fundoscopy, intraocular pressures and extraocular muscle movements were otherwise normal.     

Pituitary Adenylate Cyclase-Activating Polypeptide Attenuates Brain Edema by Protecting Blood–Brain Barrier and Glymphatic System After Subarachnoid Hemorrhage in Rats

Brain edema is a vital contributor to early brain injury after subarachnoid hemorrhage (SAH), which is responsible for prolonged hospitalization and poor outcomes. Pharmacological therapeutic targets on edema formation have been the focus of research for decades. Pituitary adenylate cyclase-activating polypeptide (PACAP) has been shown to participate in neural development and brain injury. Here, we used PACAP knockout CRISPR to demonstrate that endogenous PACAP plays an endogenous neuroprotective role against brain edema formation after SAH in rats. The exogenous PACAP treatment provided both short- and long-term neurological benefits by preserving the function of the blood–brain barrier and glymphatic system after SAH. Pretreatment of inhibitors of PACAP receptors showed that the PACAP-involved anti-edema effect and neuroprotection after SAH was facilitated by the selective PACAP receptor (PAC1). Further administration of adenylyl cyclase (AC) inhibitor and sulfonylurea receptor 1 (SUR1) CRISPR activator suggested that the AC–cyclic adenosine monophosphate (cAMP)–protein kinase A (PKA) axis participated in PACAP signaling after SAH, which inhibited the expression of edema-related proteins, SUR1 and aquaporin-4 (AQP4), through SUR1 phosphorylation. Thus, PACAP may serve as a potential clinical treatment to alleviate brain edema in patients with SAH.Electronic supplementary materialThe online version of this article (10.1007/s13311-020-00925-3) contains supplementary material, which is available to authorized users.Key Words: Subarachnoid hemorrhage, brain edema, pituitary adenylate cyclase-activating polypeptide, blood–brain barrier, glymphatic system     

Mind the gap: Teachers’ conceptions of student-staff partnership and its potential to enhance educational quality

Abstract

Introduction: Student-staff partnerships as a concept to improve medical education have received a growing amount of attention. Such partnerships are collaborations in which students and teachers seek to improve education by each adding their unique contribution to decision-making and implementation processes. Although previous research has demonstrated that students are favourable to this concept, teachers remain hesitant. The present study investigated teachers’ conceptions of student-staff partnerships and of the prerequisites that are necessary to render such partnerships successful and enhance educational quality.

Method: We conducted semi-structured interviews with 14 course coordinators who lead course design teams and also teach in 4 bachelor health programmes, using Bovill and Bulley’s levels of student participation as sensitising concepts during data analysis.

Results: The results pointed to three different conceptions of student-staff partnerships existing among teachers: Teachers teach and students study; teachers teach and value students’ feedback; and teachers and students co-create. The prerequisites for effective co-creation teachers identified were: Teachers must be open to involve students and create dialogues; students must be motivated and have good communication skills; the organisation must be supportive; and teachers should have the final say.

Conclusion: We conclude that teachers’ conceptions are consistent with Bovill and Bulley’s levels of student participation. Under certain conditions, teachers are willing to co-create and reach the highest levels of student participation.     

Shared decision-making for the management of small renal masses: Development and acceptability testing of a novel patient decision aid

IntroductionShared decision-making incorporates patients’ values and preferences to achieve high-quality decisions. The objective of this study was to develop an acceptable patient decision aid to facilitate shared decision-making for the management of small renal masses (SRMs).MethodsThe International Patient Decision Aids Standards were used to guide an evidence-based development process. Management options included active surveillance, thermal ablation, partial nephrectomy, and radical nephrectomy. A literature review was performed to provide incidence rates for outcomes of each option. Once a prototype was complete, alpha-testing was performed using a 10-question survey to assess acceptability with patients, patient advocates, urologists, and methodological experts. The primary outcome was acceptability of the decision aid.ResultsA novel patient decision aid was created to facilitate shared decision-making for the management of SRMs. Acceptability testing was performed with 20 patients, 10 urologists, two patient advocates, and one methodological expert. Responders indicated the decision aid was appropriate in length (82%, 27/33), well-balanced (82%, 27/33), and had language that was easy to follow (94%, 31/33). All patient responders felt the decision aid would have been helpful during their consultation and would recommend the decision aid for future patients (100%, 20/20). Most urologists reported they intend to use the decision aid (90%, 9/10).ConclusionsA novel patient decision aid was created to facilitate shared decision-making for management of SRMs. This clinical tool was acceptable with patients, patient advocates, and urologists and is freely available at: https://decisionaid.ohri.ca/decaids.html.     

Building on the success of anti-vascular endothelial growth factor therapy: a vision for the next decade

This article aims to identify key opportunities for improvement in the diagnosis and treatment of retinal disease, and describe recent innovations that will potentially facilitate improved outcomes with existing intravitreal vascular endothelial growth factor (VEGF) therapies and lay the groundwork for new treatment approaches. The review begins with a summary of the key discoveries that led to the development of anti-VEGF therapies and briefly reviews their impact on clinical practice. Opportunities for improvements in diagnosis, real-world outcomes with existing therapies, long-acting therapeutics and personalised health care are discussed, as well as the need to identify new targets for therapeutic intervention. Low-cost, remote patient screening and monitoring using artificial intelligence (AI)-based technologies can help improve diagnosis rates and enable remote disease monitoring with minimal patient burden. AI-based tools can be applied to generate patient-level prognostic data and predict individual treatment needs, reducing the time needed to optimise a patient’s treatment regimen. Long-acting therapeutics can help improve visual outcomes by reducing the treatment burden. When paired with AI-generated prognoses, long-acting therapeutics enable the possibility of vision loss prevention. Dual-acting drugs may help improve efficacy and/or durability beyond what is possible with anti-VEGF agents alone. Recent developments and ongoing innovations will help build upon the success of anti-VEGF therapies to further reduce vision loss owing to retinal disease while lowering the overall burden of care.Subject terms: Retinal diseases, Quality of life     

Prolonged mechanical ventilation after cardiac surgery: substudy of the Transfusion Requirements in Cardiac Surgery III trial

Purpose

Prolonged mechanical ventilation (MV) is a major complication following cardiac surgery. We conducted a secondary analysis of the Transfusion Requirements in Cardiac Surgery (TRICTS) III trial to describe MV duration, identify factors associated with prolonged MV, and examine associations of prolonged MV with mortality and complications.

Methods

Four thousand, eight hundred and nine participants undergoing cardiac surgery at 71 hospitals worldwide were included. Prolonged MV was defined based on the Society of Thoracic Surgeons definition as MV lasting 24 hr or longer. Adjusted associations of patient and surgical factors with prolonged MV were examined using multivariable logistic regression. Associations of prolonged MV with complications were assessed using odds ratios, and adjusted associations between prolonged MV and mortality were evaluated using multinomial regression. Associations of shorter durations of MV with survival and complications were explored.

Results

Prolonged MV occurred in 15% (725/4,809) of participants. Prolonged MV was associated with surgical factors indicative of complexity, such as previous cardiac surgery, cardiopulmonary bypass duration, and separation attempts; and patient factors such as critical preoperative state, left ventricular impairment, renal failure, and pulmonary hypertension. Prolonged MV was associated with perioperative but not long-term complications. After risk adjustment, prolonged MV was associated with perioperative mortality; its association with long-term mortality among survivors was weaker. Shorter durations of MV were not associated with increased risk of mortality or complications.

Conclusion

In this substudy of the TRICS III trial, prolonged MV was common after cardiac surgery and was associated with patient and surgical risk factors. Although prolonged MV showed strong associations with perioperative complications and mortality, it was not associated with long-term complications and had weaker association with long-term mortality among survivors.

Study registration

www.ClinicalTrials.gov (NCT02042898); registered 23 January 2014. This is a substudy of the Transfusion Requirements in Cardiac Surgery (TRICS) III trial.

     

A second wave of COVID-19 in Cook County: What lessons can be applied?

During the ongoing public health crisis, many agencies are reporting COVID-19 health outcome information based on the overall population. This practice can lead to misleading results and underestimation of high risk areas. To gain a better understanding of spatial and temporal distribution of COVID-19 deaths; the long term care facility (LTCF) and household population (HP) deaths must be used. This approach allows us to better discern high risk areas and provides policy makers with reliable information for community engagement and mitigation strategies. By focusing on high-risk LTCFs and residential areas, protective measures can be implemented to minimize COVID-19 spread and subsequent mortality.  These areas should be a high priority target when COVID-19 vaccines become available

During the current public health crisis, many agencies and media outlets are reporting COVID-19 health outcome information based on the overall population of Cook County. As we have demonstrated, overall COVID-19 case counts and mortality can be misleading (details in >Story Map 1). Moreover, they offer little guidance for delivering public health interventions to high risk populations, a critical need during this second and potentially more devastating wave of the pandemic. The University of Illinois Chicago School of Public Health’s Public Health Geographic Information System Program (UIC-SPH-PHGIS) and Purdue research team has been examining spatial and temporal patterns of COVID-19 mortality with a focus on the significant loss of life from COVID-19 among Long-Term Care Facility (LTCF) residents in contrast to mortality in the community among residents of private households (non-LTCF; referred to as household population, HP). The goals of the study are:

• Improve the accuracy of commonly quoted COVID-19 mortality indicators;
• Gain a better understanding of spatial and temporal distribution of COVID-19 deaths;
• Examine the role of race, ethnicity, and socioeconomic status in COVID-19 mortality;
• Identify population and organizational parameters that can inform strategies for public health interventions.

Prioritizing the allocation of resources based on reliable information is a prerequisite of a successful mitigation strategy and immunization plan. Findings from our research have significant practical implications. The state and federal government face a series of policy decisions both due to the recent surge in positive cases and, when the time comes, the need to rationalize distribution of vaccines to high priority groups beyond healthcare workers and nursing home residents in critical areas. The research team seeks to modify prevailing practices in order to derive reliable information that guides policy decisions. At this stage of the study, we identified high-risk LTCFs and residential areas (HP) of Cook County from readily available, real-time mortality data.