Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy.

OBJECTIVE: This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring. DATA SOURCES: A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms. STUDY ELIGIBILITY CRITERIA: Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916). METHODS: Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist. RESULTS: Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias. CONCLUSION: The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.

Autoimmune and Chronic Inflammatory Disease Patients with COVID-19.

OBJECTIVE: There are limited data on the impact of coronavirus disease 2019 (COVID-19) on hospitalized patients with autoimmune and chronic inflammatory disease (AICID) compared with patients who do not have AICID. We sought to evaluate whether patients with AICID who have confirmed COVID-19 presenting to the hospital are at higher risk of adverse outcomes compared with those patients without AICID who are infected with COVID-19 and whether immunosuppressive medications impact this risk. METHODS: We performed a multicenter retrospective cohort study with patients presenting to five hospitals in a large academic health system with polymerase chain reaction-confirmed COVID-19 infection. We evaluated the impact of having an AICID and class of immunosuppressive medication being used to treat patients with AICID (biologics, nonbiologic immunosuppressives, or systemic corticosteroids) on the risk of developing severe COVID-19 defined as requiring mechanical ventilation (MV) and/or death. RESULTS: A total of 6792 patients with confirmed COVID-19 were included in the study, with 159 (2.3%) having at least one AICID. On multivariable analysis, AICIDs were not significantly associated with severe COVID-19 (adjusted odds ratio [aOR] 1.3, 95% confidence interval [CI]: 0.9-1.8). Among patients with AICID, use of biologics or nonbiologic immunosuppressives did not increase the risk of severe COVID-19. In contrast, systemic corticosteroid use was significantly associated with an increased risk of severe COVID-19 (aOR 6.8, 95% CI: 2.5-18.4). CONCLUSION: Patients with AICID are not at increased risk of severe COVID-19 with the exception of those on corticosteroids. These data suggest that patients with AICID should continue on biologic and nonbiologic immunosuppression but limit steroids during the COVID-19 pandemic.

Artificial Intelligence-Aided Colonoscopy Does Not Increase Adenoma Detection Rate in Routine Clinical Practice.

The performance of artificial intelligence-aided colonoscopy (AIAC) in a real-world setting has not been described. We compared adenoma and polyp detection rates (ADR/PDR) in a 6-month period before (pre-AIAC) and after introduction of AIAC (GI Genius, Medtronic) in all endoscopy suites in our large-volume center. The ADR and PDR in the AIAC group was lower compared with those in the pre-AIAC group (30.3% vs 35.2%, P < 0.001; 36.5% vs 40.9%, P = 0.004, respectively); procedure time was significantly shorter in the AIAC group. In summary, introduction of AIAC did not result in performance improvement in our large-center cohort, raising important questions on AI-human interactions in medicine.

Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction.

Clinical EHR data is naturally heterogeneous, where it contains abundant sub-phenotype. Such diversity creates challenges for outcome prediction using a machine learning model since it leads to high intra-class variance. To address this issue, we propose a supervised pre-training model with a unique embedded k-nearest-neighbor positive sampling strategy. We demonstrate the enhanced performance value of this framework theoretically and show that it yields highly competitive experimental results in predicting patient mortality in real-world COVID-19 EHR data with a total of over 7,000 patients admitted to a large, urban health system. Our method achieves a better AUROC prediction score of 0.872, which outperforms the alternative pre-training models and traditional machine learning methods. Additionally, our method performs much better when the training data size is small (345 training instances).

FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study.

OBJECTIVES: To evaluate if radiomics with machine learning can differentiate between F-18-fluorodeoxyglucose (FDG)-avid breast cancer metastatic lymphadenopathy and FDG-avid COVID-19 mRNA vaccine-related axillary lymphadenopathy. MATERIALS AND METHODS: We retrospectively analyzed FDG-positive, pathology-proven, metastatic axillary lymph nodes in 53 breast cancer patients who had PET/CT for follow-up or staging, and FDG-positive axillary lymph nodes in 46 patients who were vaccinated with the COVID-19 mRNA vaccine. Radiomics features (110 features classified into 7 groups) were extracted from all segmented lymph nodes. Analysis was performed on PET, CT, and combined PET/CT inputs. Lymph nodes were randomly assigned to a training (n = 132) and validation cohort (n = 33) by 5-fold cross-validation. K-nearest neighbors (KNN) and random forest (RF) machine learning models were used. Performance was evaluated using an area under the receiver-operator characteristic curve (AUC-ROC) score. RESULTS: Axillary lymph nodes from breast cancer patients (n = 85) and COVID-19-vaccinated individuals (n = 80) were analyzed. Analysis of first-order features showed statistically significant differences (p < 0.05) in all combined PET/CT features, most PET features, and half of the CT features. The KNN model showed the best performance score for combined PET/CT and PET input with 0.98 (± 0.03) and 0.88 (± 0.07) validation AUC, and 96% (± 4%) and 85% (± 9%) validation accuracy, respectively. The RF model showed the best result for CT input with 0.96 (± 0.04) validation AUC and 90% (± 6%) validation accuracy. CONCLUSION: Radiomics features can differentiate between FDG-avid breast cancer metastatic and FDG-avid COVID-19 vaccine-related axillary lymphadenopathy. Such a model may have a role in differentiating benign nodes from malignant ones. KEY POINTS: • Patients who were vaccinated with the COVID-19 mRNA vaccine have shown FDG-avid reactive axillary lymph nodes in PET-CT scans. • We evaluated if radiomics and machine learning can distinguish between FDG-avid metastatic axillary lymphadenopathy in breast cancer patients and FDG-avid reactive axillary lymph nodes. • Combined PET and CT radiomics data showed good test AUC (0.98) for distinguishing between metastatic axillary lymphadenopathy and post-COVID-19 vaccine-associated axillary lymphadenopathy. Therefore, the use of radiomics may have a role in differentiating between benign from malignant FDG-avid nodes.

Machine learning to predict in-hospital mortality among patients with severe obesity: Proof of concept study.

Objectives: Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in-hospital mortality among this population. Methods: Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m2) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient-boosting machine learning model to identify in-hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held-out data from the fifth hospital. Results: A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in-hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden's index, the model had a sensitivity of 0.77 (95% CI: 0.67-0.86) with a false positive rate of 1:9. Conclusion: A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.

Top 25 cited articles on Covid-19 and IBD: A bibliometric analysis.

OBJECTIVES: The use of citation analysis to identify the most cited Covid-19 and inflammatory bowel disease (IBD) manuscripts to provide an insight into the advances and knowledge accumulated regarding the pandemic in this subgroup of patients. METHODS: We've used a public application programming interface (API) U.S. National Center for Biotechnology Information (NCBI) to access the PubMed database. Data lock was performed on April 19, 2022. The API was used to retrieve all available IBD AND Covid-19 -related entries. For each retrieved entry, we've also obtained its citation count. RESULTS: The top 25 manuscripts were published between 2020 and 2021. The total citation count is 2051. The citation count of articles ranged from 41 to 313. The top 25 manuscripts were published in eight journals, while 16 were published in Gastroenterology and Gut. 36% of the most cited manuscripts reported clinical characteristics and patient outcomes, and 32% dealt with patient management. The most impactful manuscripts provided evidence that IBD patients are not at increased risk for severe morbidity or mortality from Covid-19 and that it is not advisable to discontinue the anti-inflammatory treatment for IBD during the pandemic. Two basic science studies demonstrated mechanistic insights for these observations. Studies that examined the immunogenic response of IBD patients treated with biologics were also part of the top-cited list. CONCLUSIONS: Impactful scientific publications on Covid-19 in IBD patients provided reassurance and directed treatment at the time of this newly recognized severe disease.

Trends in inflammatory bowel disease infections and vaccinations in the past four decades: A high-level text mining analysis of PubMed publications.

AIM: We aimed at assessing the published literature on different prophylactic screening and vaccination options in inflammatory bowel disease (IBD) patients between 1980 and 2020. Special attention was attributed to latest data assessing covid-19 vaccinations. METHODS: We have queried PubMed for all available IBD-related entries published during 1980-2020. The following data were extracted for each entry: PubMed unique article ID (PMID), title, publishing journal, abstract text, keywords (if any), and authors' affiliations. Two gastrointestinal specialists decided by consensus on a list of terms to classify entries. The terms belonged to four treatment groups: opportunistic infections, prophylactic screening, prophylactic vaccinations/treatment, and routine vaccines. Annual trends of publications for the years 1980-2020 were plotted for different screening, vaccinations and infection types. Slopes of publication trends were calculated by fitting regression lines to the annual number of publications. RESULTS: Overall, 98,339 IBD entries were published between 1980 and 2020. Of those, 7773 entries belonged to the investigated groups. Entries concerning opportunistic infections showed the sharpest rise, with 19 entries and 1980 to 423 entries in 2020 (slope 11.3, p < .001). Entries concerning prophylactic screening rose from 10 entries in 1980 to 204 entries in 2020 (slope 5.4, p < .001). Both entries concerning prophylactic vaccinations/treatments and routine vaccines did not show a significant rise (slope 0.33 and slope 0.92, respectively). During the COVID 19 pandemic, a total of 44 publications were identified. Of them, 37 were relevant to vaccines and immune reaction. Nineteen publications (51%) were guidelines/recommendations, and 14 (38%) assessed immune reaction to vaccination, most of them (11, 61%) to mRNA vaccines. CONCLUSIONS: During the past two decades, along with a rapid increase in biologic therapy, publications regarding opportunistic infections and prophylactic screening increased in a steep slope compared to the two decades in the pre-biologic area. During the COVID-19 pandemic, most publications included vaccination recommendations and guidelines and only 38% included real-world data assessing reaction to vaccinations. More research is needed.

Innovation in Gastroenterology-Can We Do Better?

The health system can reap significant benefits by adopting and implementing innovative measures, as was recently demonstrated and emphasized during the COVID-19 pandemic. Herein, we present our bird's eye view of gastroenterology's innovative technologies via utilizing a text-mining technique. We analyzed five research fields that comply with innovation: artificial intelligence (AI), virtual reality (VR), telemedicine, the microbiome, and advanced endoscopy. According to gastroenterology literature, the two most innovative fields were the microbiome and advanced endoscopy. Though artificial intelligence (AI), virtual reality (VR), and telemedicine trailed behind, the number of AI publications in gastroenterology has shown an exponential trend in the last couple of years. While VR and telemedicine are neglected compared to other fields, their implementation could improve physician and patient training, patient access to care, cost reduction, and patient outcomes.

Obesity as a mortality risk factor in the medical ward: a case control study.

BACKGROUND: Research regarding the association between severe obesity and in-hospital mortality is inconsistent. We evaluated the impact of body mass index (BMI) levels on mortality in the medical wards. The analysis was performed separately before and during the COVID-19 pandemic. METHODS: We retrospectively retrieved data of adult patients admitted to the medical wards at the Mount Sinai Health System in New York City. The study was conducted between January 1, 2011, to March 23, 2021. Patients were divided into two sub-cohorts: pre-COVID-19 and during-COVID-19. Patients were then clustered into groups based on BMI ranges. A multivariate logistic regression analysis compared the mortality rate among the BMI groups, before and during the pandemic. RESULTS: Overall, 179,288 patients were admitted to the medical wards and had a recorded BMI measurement. 149,098 were admitted before the COVID-19 pandemic and 30,190 during the pandemic. Pre-pandemic, multivariate analysis showed a "J curve" between BMI and mortality. Severe obesity (BMI > 40) had an aOR of 0.8 (95% CI:0.7-1.0, p = 0.018) compared to the normal BMI group. In contrast, during the pandemic, the analysis showed a "U curve" between BMI and mortality. Severe obesity had an aOR of 1.7 (95% CI:1.3-2.4, p < 0.001) compared to the normal BMI group. CONCLUSIONS: Medical ward patients with severe obesity have a lower risk for mortality compared to patients with normal BMI. However, this does not apply during COVID-19, where obesity was a leading risk factor for mortality in the medical wards. It is important for the internal medicine physician to understand the intricacies of the association between obesity and medical ward mortality.

Efficacy and Safety of Third Dose of the COVID-19 Vaccine among Solid Organ Transplant Recipients: A Systemic Review and Meta-Analysis.

Solid organ transplant recipients were demonstrated to have reduced antibody response to the first and second doses of the COVID-19 mRNA vaccine. This review evaluated published data on the efficacy and safety of the third dose among solid organ transplant recipients. We performed a systematic search of PubMed, EMBASE, and Web of Science to retrieve studies evaluating the efficacy of the third dose of anti-SARS-CoV-2 vaccines in adult solid organ transplant recipients. Serologic response after the third vaccine was pooled using inverse variance and generalized linear mixed and random-effects models. Seven studies met our inclusion criteria. A total of 853 patients received the third dose. Except for one randomized controlled trial, all studies were retrospective in design. Following the third COVID-19 vaccine dose, antibody response occurred in 6.4-69.2% of patients. The pooled proportion of antibody response rate after the third vaccine was 50.3% (95% confidence interval (CI): 37.1-63.5, I2 = 90%). Five papers reported the safety profile. No severe adverse events were observed after the third vaccine dose. In conclusion, a third dose of the SARS-CoV-2 mRNA vaccine in solid organ transplant recipients is associated with improved immunogenicity and appears to be safe. Nevertheless, a significant portion of patients remain seronegative.

The association between obesity and peak antibody titer response in COVID-19 infection.

OBJECTIVE: Obesity is associated with severe coronavirus disease 2019 (COVID-19) infection. Disease severity is associated with a higher COVID-19 antibody titer. The COVID-19 antibody titer response of patients with obesity versus patients without obesity was compared. METHODS: The data of individuals tested for COVID-19 serology at the Mount Sinai Health System in New York City between March 1, 2020, and December 14, 2021, were retrospectively retrieved. The primary outcome was peak antibody titer, assessed as a binary variable (1:2,880, which was the highest detected titer, versus lower than 1:2,880). In patients with a positive serology test, peak titer rates were compared between BMI groups (<18.5, 18.5 to 25, 25 to 30, 30 to 40, and ≥40 kg/m2 ). A multivariable logistic regression model was used to analyze the independent association between different BMI groups and peak titer. RESULTS: Overall, 39,342 individuals underwent serology testing and had BMI measurements. A positive serology test was present in 12,314 patients. Peak titer rates were associated with obesity (BMI < 18.5 [34.5%], 18.5 to 25 [29.2%], 25 to 30 [37.7%], 30 to 40 [44.7%], ≥40 [52.0%]; p < 0.001). In a multivariable analysis, severe obesity had the highest adjusted odds ratio for peak titer (95% CI: 2.1-3.0). CONCLUSION: COVID-19 neutralizing antibody titer is associated with obesity. This has implications on the understanding of the role of obesity in COVID-19 severity.

The Effect of the COVID-19 Outbreak on Trauma-Related Visits to a Tertiary Hospital Emergency Department.

BACKGROUND: The novel coronavirus disease (COVID-19) pandemic changed medical environments worldwide. OBJECTIVES: To evaluate the impact of the COVID-19 pandemic on trauma-related visits to the emergency department (ED). METHODS: A single tertiary center retrospective study was conducted that compared ED attendance of patients with injury-related morbidity between March 2020 (COVID-19 outbreak) and pre-COVID-19 periods: February 2020 and the same 2 months in 2018 and 2019. RESULTS: Overall, 6513 patients were included in the study. During the COVID-19 outbreak, the daily number of patients visiting the ED for acute trauma declined by 40% compared to the average in previous months (P < 0.01). A strong negative correlation was found between the number of trauma-related ED visits and the log number of confirmed cases of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in Israel (Pearson's r = -0.63, P < 0.01). In the COVID-19 period there was a significant change in the proportion of elderly patients (7% increase, P = 0.002), admissions ratio (12% increase, P < 0.001), and patients brought by emergency medical services (10% increase, P < 0.001). The number of motor vehicle accident related injury declined by 45% (P < 0.01). CONCLUSIONS: A significant reduction in the number of trauma patients presenting to the ED occurred during the COVID-19 pandemic, yet trauma-related admissions were on the rise.

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.

BACKGROUND: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS: The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. CONCLUSIONS: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Moderate or Severe Impairment in Pulmonary Function is Associated with Mortality in Sarcoidosis Patients Infected with SARS­CoV­2.

PURPOSE: To investigate whether sarcoidosis patients infected with SARS-CoV-2 are at risk for adverse disease outcomes. STUDY DESIGN AND METHODS: This retrospective study was conducted in five hospitals within the Mount Sinai Health System during March 1, 2020 to July 29, 2020. All patients diagnosed with COVID-19 were included in the study. We identified sarcoidosis patients who met diagnostic criteria for sarcoidosis according to accepted guidelines. An adverse disease outcome was defined as the presence of intubation and mechanical ventilation or in-hospital mortality. In sarcoidosis patients, we reported (when available) the results of pulmonary function testing measured within 3 years prior to the time of SARS­CoV­2 infection. A multivariable logistic regression model was used to generate an adjusted odds ratio (aOR) to evaluate sarcoidosis as a risk factor for an adverse outcome. The same model was used to analyze sarcoidosis patients with moderate and/or severe impairment in pulmonary function. RESULTS: The study included 7337 patients, 37 of whom (0.5%) had sarcoidosis. The crude rate of developing an adverse outcome was significantly higher in patients with moderately and/or severely impaired pulmonary function (9/14 vs. 3/23, p = 0.003). While the diagnosis of sarcoidosis was not independently associated with risk of an adverse event, (aOR 1.8, 95% CI 0.9-3.6), the diagnosis of sarcoidosis in patients with moderately and/or severely impaired pulmonary function was associated with an adverse outcome (aOR 7.8, 95% CI 2.4-25.8). CONCLUSION: Moderate or severe impairment in pulmonary function is associated with mortality in sarcoidosis patients infected with SARS­CoV­2.

Association between COVID-19 diagnosis and presenting chief complaint from New York City triage data.

BACKGROUND AND AIM: New York City (NYC) is an epicenter of the COVID-19 pandemic in the United States. Proper triage of patients with possible COVID-19 via chief complaint is critical but not fully optimized. This study aimed to investigate the association between presentation by chief complaints and COVID-19 status. METHODS: We retrospectively analyzed adult emergency department (ED) patient visits from five different NYC hospital campuses from March 1, 2020 to May 13, 2020 of patients who underwent nasopharyngeal COVID-19 RT-PCR testing. The positive and negative COVID-19 cohorts were then assessed for different chief complaints obtained from structured triage data. Sub-analysis was performed for patients older than 65 and within chief complaints with high mortality. RESULTS: Of 11,992 ED patient visits who received COVID-19 testing, 6524/11992 (54.4%) were COVID-19 positive. 73.5% of fever, 67.7% of shortness of breath, and 65% of cough had COVID-19, but others included 57.5% of weakness/fall/altered mental status, 55.5% of glycemic control, and 51.4% of gastrointestinal symptoms. In patients over 65, 76.7% of diarrhea, 73.7% of fatigue, and 69.3% of weakness had COVID-19. 45.5% of dehydration, 40.5% of altered mental status, 27% of fall, and 24.6% of hyperglycemia patients experienced mortality. CONCLUSION: A novel high risk COVID-19 patient population was identified from chief complaint data, which is different from current suggested CDC guidelines, and may help triage systems to better isolate COVID-19 patients. Older patients with COVID-19 infection presented with more atypical complaints warranting special consideration. COVID-19 was associated with higher mortality in a unique group of complaints also warranting special consideration.