Estimating the effectiveness of shielding during pregnancy against SARS-CoV-2 in New York City during the first year of the COVID-19 pandemic

Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population.

Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: A New Utility for Deep Reinforcement Learning

As machine learning-based models continue to be developed for healthcare applications, greater effort is needed in ensuring that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. In this study, we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments, and aimed to mitigate any site-specific (hospital) and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically-effective screening performances, while significantly improving outcome fairness compared to current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient ICU discharge status task, demonstrating model generalizability.

Machine Learning Generalizability Across Healthcare Settings: Insights from multi-site COVID-19 screening

As patient health information is highly regulated due to privacy concerns, the majority of machine learning (ML)-based healthcare studies are unable to test on external patient cohorts, resulting in a gap between locally reported model performance and cross-site generalizability. Different approaches have been introduced for developing models across multiple clinical sites, however no studies have compared methods for translating ready-made models for adoption in new settings. We introduce three methods to do this - (1) applying a ready-made model "as-is"; (2) readjusting the decision threshold on the output of a ready-made model using site-specific data; and (3) finetuning a ready-made model using site-specific data via transfer learning. Using a case study of COVID-19 diagnosis across four NHS Hospital Trusts, we show that all methods achieve clinically-effective performances (NPV >0.959), with transfer learning achieving the best results (mean AUROCs between 0.870-0.925). Our models demonstrate that site-specific customization improves predictive performance when compared to other ready-made approaches.

Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: Insights from Rapid COVID-19 Diagnosis by Adversarial Learning

Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how machine learning may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection or magnified during model development. For example, if one class is over-presented or errors/inconsistencies in practice are reflected in the training data, then a model can be biased by these. To evaluate our adversarial training framework, we used the statistical definition of equalized odds. We evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments, and aimed to mitigate regional (hospital) and ethnic biases present. We trained our framework on a large, real-world COVID-19 dataset and demonstrated that adversarial training demonstrably improves outcome fairness (with respect to equalized odds), while still achieving clinically-effective screening performances (NPV>0.98). We compared our method to the benchmark set by related previous work, and performed prospective and external validation on four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.

Real-world evaluation of AI driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions

BackgroundUncertainty in patients COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, typical turnaround times for batch-processed laboratory PCR tests remain 12-24h. Although rapid antigen lateral flow testing (LFD) has been widely adopted in UK emergency care settings, sensitivity is limited. We recently demonstrated that AI-driven triage (CURIAL-1.0) allows high-throughput COVID-19 screening using clinical data routinely available within 1h of arrival to hospital. Here we aimed to determine operational and safety improvements over standard-care, performing external/prospective evaluation across four NHS trusts with updated algorithms optimised for generalisability and speed, and deploying a novel lab-free screening pathway in a UK emergency department. MethodsWe rationalised predictors in CURIAL-1.0 to optimise separately for generalisability and speed, developing CURIAL-Lab with vital signs and routine laboratory blood predictors (FBC, U&E, LFT, CRP) and CURIAL-Rapide with vital signs and FBC alone. Models were calibrated during training to 90% sensitivity and validated externally for unscheduled admissions to Portsmouth University Hospitals, University Hospitals Birmingham and Bedfordshire Hospitals NHS trusts, and prospectively during the second-wave of the UK COVID-19 epidemic at Oxford University Hospitals (OUH). Predictions were generated using first-performed blood tests and vital signs and compared against confirmatory viral nucleic acid testing. Next, we retrospectively evaluated a novel clinical pathway triaging patients to COVID-19-suspected clinical areas where either model prediction or LFD results were positive, comparing sensitivity and NPV with LFD results alone. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser (OLO; SightDiagnostics, Israel) to provide lab-free COVID-19 screening in the John Radcliffe Hospitals Emergency Department (Oxford, UK), as trust-approved service improvement. Our primary improvement outcome was time-to-result availability; secondary outcomes were sensitivity, specificity, PPV, and NPV assessed against a PCR reference standard. We compared CURIAL-Rapides performance with clinician triage and LFD results within standard-care. Results72,223 patients met eligibility criteria across external and prospective validation sites. Model performance was consistent across trusts (CURIAL-Lab: AUROCs range 0.858-0.881; CURIAL-Rapide 0.836-0.854), with highest sensitivity achieved at Portsmouth University Hospitals (CURIAL-Lab:84.1% [95% Wilsons score CIs 82.5-85.7]; CURIAL-Rapide:83.5% [81.8 - 85.1]) at specificities of 71.3% (95% Wilsons score CIs: 70.9 - 71.8) and 63.6% (63.1 - 64.1). For 3,207 patients receiving LFD-triage within routine care for OUH admissions between December 23, 2021 and March 6, 2021, a combined clinical pathway increased sensitivity from 56.9% for LFDs alone (95% CI 51.7-62.0) to 88.2% with CURIAL-Rapide (84.4-91.1; AUROC 0.919) and 85.6% with CURIAL-Lab (81.6-88.9; AUROC 0.925). 520 patients were prospectively enrolled for point-of-care FBC analysis between February 18, 2021 and May 10, 2021, of whom 436 received confirmatory PCR testing within routine care and 10 (2.3%) tested positive. Median time from patient arrival to availability of CURIAL-Rapide result was 45:00 min (32-64), 16 minutes (26.3%) sooner than LFD results (61:00 min, 37-99; log-rank p<0.0001), and 6:52 h (90.2%) sooner than PCR results (7:37 h, 6:05-15:39; p<0.0001). Sensitivity and specificity of CURIAL-Rapide were 87.5% (52.9-97.8) and 85.4% (81.3-88.7), therefore achieving high NPV (99.7%, 98.2-99.9). CURIAL-Rapide correctly excluded COVID-19 for 58.5% of negative patients who were triaged by a clinician to COVID-19-suspected (amber) areas. ImpactCURIAL-Lab & CURIAL-Rapide are generalisable, high-throughput screening tests for COVID-19, rapidly excluding the illness with higher NPV than LFDs. CURIAL-Rapide can be used in combination with near-patient FBC analysis for rapid, lab-free screening, and may reduce the number of COVID-19-negative patients triaged to enhanced precautions ( amber) clinical areas.

Antibody response to SARS-CoV-2 mRNA vaccines in pregnant women and their neonates

Pregnant women were excluded from initial clinical trials for COVID-19 vaccines1-2, thus the immunologic response to vaccination in pregnancy and the transplacental transfer of maternal antibodies are just beginning to be studied4-5.

Seroprevalence of Anti-SARS-CoV-2 Antibodies in a Cohort of New York City Metro Blood Donors using Multiple SARS-CoV-2 Serological Assays: Implications for Controlling the Epidemic and Reopening.

Projections of the stage of the Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) pandemic and local, regional and national public health policies designed to limit the spread of the epidemic as well as "reopen" cities and states, are best informed by serum neutralizing antibody titers measured by reproducible, high throughput, and statically credible antibody (Ab) assays. To date, a myriad of Ab tests, both available and authorized for emergency use by the FDA, has led to confusion rather than insight per se. The present study reports the results of a rapid, point-in-time 1,000-person cohort study using serial blood donors in the New York City metropolitan area (NYC) using multiple serological tests, including enzyme-linked immunosorbent assays (ELISAs) and high throughput serological assays (HTSAs). These were then tested and associated with assays for neutralizing Ab (NAb). Of the 1,000 NYC blood donor samples in late June and early July 2020, 12.1% and 10.9% were seropositive using the Ortho Total Ig and the Abbott IgG HTSA assays, respectively. These serological assays correlated with neutralization activity specific to SARS-CoV-2. The data reported herein suggest that seroconversion in this population occurred in approximately 1 in 8 blood donors from the beginning of the pandemic in NYC (considered March 1, 2020). These findings deviate with an earlier seroprevalence study in NYC showing 13.7% positivity. Collectively however, these data demonstrate that a low number of individuals have serologic evidence of infection during this "first wave" and suggest that the notion of "herd immunity" at rates of [~]60% or higher are not near. Furthermore, the data presented herein show that the nature of the Ab-based immunity is not invariably associated with the development of NAb. While the blood donor population may not mimic precisely the NYC population as a whole, rapid assessment of seroprevalence in this cohort and serial reassessment could aid public health decision making.

Long-Term Endothelial Cell Changes after Angle-Supported Anterior Chamber Lens Implantation in Phakic Eyes

PURPOSE: To evaluate long-term endothelial cell changes in phakic eyes that underwent implantation of an angle-supported anterior chamber lens to correct myopia. METHODS: A retrospective analysis was performed in 110 eyes of 55 patients who underwent implantation of angle-supported anterior chamber lenses with a follow-up period longer than 5 years. Comparisons were made between preoperative and postoperative endothelial cell density, coefficient of variation, and percentage of hexagonal cells. RESULTS: Mean preoperative corneal endothelial cell density was 2951 +/- 336 cells/mm2 and the percentage of cell loss was 3.8% at year 1, 12.6% at year 3, 13.4% at year 5, 22.5% at year 7, and 22.2% at year 9. Explantation was required in 13 eyes (11.8%) due to the decrease of endothelial cell count to 936 +/- 458 cells/mm2 over 9 years of follow-up. CONCLUSIONS: Continuous endothelial cell loss was observed after implantation of angle-supported anterior chamber lens in the long-term follow-up. A constant decline in the endothelial cell density necessitates periodic ophthalmologic evaluation including specular microscopy.

Efficacy of the Sutureless Amniotic Membrane Patch for the Treatment of Ocular Surface Disorders

PURPOSE: To evaluate the efficacy of the sutureless amniotic membrane (AM) patch for the treatment of ocular surface disorders. METHODS: A sutureless AM patch using a silicone ring was utilized to treat neurotrophic ulcer, persistent epithelial defect (PED), Shield ulcer, chemical injury and Stevens-Johnson syndrome. Primary outcome was the time to complete corneal and conjunctival epithelialization. Secondary outcome was the number of repeated insertions and complications of the inserted ring. RESULTS: Neurotrophic ulcer was observed in 4 eyes, PED in 2 eyes, Shield ulcer in 1 eye, chemical injury in 4 eyes and Stevens-Johnson syndrome in 4 eyes. The mean (SD) time to complete epithelialization was 13 (7.2) days (6-20 days) in neurotrophic ulcer, 17.5 (7.7) days (12-23) in PED, 5 days in Shield ulcer, 10.6 (6.6) days (3-15) in chemical injury and 13.5 (0.7) days (13-14) in Stevens-Johnson syndrome. There were no protrusion or mechanical trauma of the inserted ring. In 1 case of neurotrophic ulcer and 1 refractory case of chemical injury, repeated insertion was performed due to incomplete healing after dissolution of the AM. In 2 eyes with Stevens-Johnson syndrome, repeated insertion was necessary with heavy accumulation of inflammatory debris on the AM. No symblepharon or fornix contracture was found in chemical injury or Stevens-Johnson syndrome patients. CONCLUSIONS: The sutureless AM patch using a silicone ring was shown to be effective and safe for the treatment of ocular surface disorders. The patch can help surgeons avoid suture-related trauma to the ocular surface during the acute inflammatory period.

The packaging of human immunodeficiency virus type 1 RNA is restricted by overexpression of an RNA helicase DHX30.

RNA helicases are a large family of proteins that are able to unwind RNA duplex and remodel the structure of RNA-protein (RNP) complexes using energy derived from hydrolysis of nucleotide triphosphates (NTPs). Every step of cellular RNA metabolism involves the activity of RNA helicases. Not surprisingly, more and more RNA helicases are reported to participate in the replication of viruses including the human immunodeficiency virus type 1 (HIV-1). Here, we provide evidence that overexpression of an RNA helicase named DHX30 enhances HIV-1 gene expression, but leads to the generation of viruses that package significantly low levels of viral RNA and exhibit severely decreased infectivity. These data reveal the complex roles of DHX30 in HIV-1 replication and implicate an inhibitory activity of DHX30 in HIV-1 RNA packaging.