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Background To describe outcomes of high-risk patients with coronavirus disease 2019 (COVID-19) treated with sotrovimab, other monoclonal antibodies (mAbs), or antivirals, and patients who did not receive early COVID-19 treatment. We also evaluate the comparative effectiveness of sotrovimab versus no treatment in preventing severe clinical outcomes. Methods This observational retrospective cohort study analyzed Mayo Clinic electronic health records. Non-hospitalized adult patients diagnosed with COVID-19 from May 26, 2021 and April 23, 2022 and at high risk of COVID-19 progression were eligible. The primary outcome was 29-day all-cause hospitalization and/or death. Outcomes were described for patients treated with sotrovimab, other mAbs, or antivirals, and eligible but untreated patients, and compared between sotrovimab-treated and propensity score (PS)-matched untreated cohorts. Results We included 35,485 patients (sotrovimab, 1369; other mAbs, 6488; antivirals, 133; high-risk untreated, 27,495). A low proportion of patients treated with sotrovimab (n = 33/1369, 2.4%), other mAbs (n = 147/6488, 2.3%), or antivirals (n = 2/133, 1.5%) experienced all-cause hospitalization or death. Among high-risk untreated patients, the percentage of all-cause hospitalization or death was 3.3% (n = 910/27,495). In the PS-matched analysis, 2.5% (n = 21/854) of sotrovimab-treated patients experienced all-cause hospitalization and/or death versus 2.8% (n = 48/1708) of untreated patients (difference, –0.4%; p = 0.66). Significantly fewer sotrovimab-treated patients required intensive care unit admission (0.5% vs 1.8%; difference, –1.3%; p = 0.002) or respiratory support (3.5% vs 8.7%; difference, –5.2%; p < 0.001). Conclusions There was no significant difference in the proportion of sotrovimab-treated and PS-matched untreated patients experiencing 29-day all-cause hospitalization or mortality, although significantly fewer sotrovimab-treated patients required intensive care unit admission or respiratory support.

Background Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG. Methods The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage–time data, with patients classified as “PH-likely” or “PH-unlikely” (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6–18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. Results Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. Conclusion The PH-EDA can detect PH at diagnosis and 6–18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

Background: Progression-free survival (PFS) is frequently measured in oncology clinical trials. In analyses outside of the trial setting, various strategies have been utilized to assess real-world PFS (rwPFS), including manual abstraction of clinical records, natural language processing (NLP) of oncology notes and radiology reports, and longitudinal analyses of radiologic images. Here we develop and validate a new semi-automated workflow that combines NLP of clinical notes with structured electronic health record data to facilitate the evaluation of rwPFS in patients with metastatic breast cancer (mBC). Methods: This study analyzes de-identified EHR data using nference nSights. The data is extracted following privacy-preserving protocols and is HIPAA compliant. The overall cohort included 316 patients with HR-positive, HER2-negative mBC who initiated Palbociclib and Letrozole combination therapy between January 1, 2015 and December 31, 2021. We developed and implemented an ensemble of deep-learning NLP frameworks to capture progression events from unstructured clinical notes and radiology reports. A change in the line of therapy, as determined by structured drug order/administration records, was also considered a progression event. We used manually curated “ground-truth” datasets to evaluate the performance of the progression-event capture workflow at the levels of both sentences (N = 1000) and patients (N = 100) by calculating sensitivity, specificity, precision, accuracy, and F1 scores. Progression events and censoring events (death, loss to follow-up, end of study period) were considered to compute rwPFS. Results: At the sentence level, progression events were captured from clinical notes and radiology reports with a sensitivity of 99.8%, specificity of 96.7%, and accuracy of 98.2% (Table). At the patient level, initial progression was correctly captured within a window of +/-30 days with a sensitivity of 92.5%, specificity of 83.0%, and accuracy of 88.0% (Table). In a sample of 100 patients, the median rwPFS was determined to be 25 months (95% CI; 15-35 months) by manual curation and 22 months (95% CI; 15-35 months) by the semi-automated workflow. In the overall cohort, median rwPFS was 20 months (95% CI; 18-25 months). Conclusions: An ensemble of NLP algorithms extracted progression events from clinical notes and radiology reports with high accuracy. A semi-automated workflow enabled rapid and accurate determination of rwPFS in mBC patients receiving a combination chemotherapy regimen. Further evaluation of this workflow to estimate rwPFS in other cancers and therapeutic settings is warranted.

Electronic Health Records (EHRs) provide a rich source of medical information across different modalities such as electrocardiograms (ECG), structured EHRs (sEHR), and unstructured EHRs (text). Inspired by the fact that many cardiac and non-cardiac diseases influence the behavior of the ECG, we leverage structured EHRs and unstructured EHRs from multiple sources by pairing with ECGs and propose a set of three new multi-modal contrastive learning models that combine ECG, sEHR, and text modalities. The performance of these models is compared against different baseline models such as supervised learning models trained from scratch with random weights initialization, and self-supervised learning models trained only on ECGs. We pre-train the models on a large proprietary dataset of about 9 million ECGs from around 2.4 million patients and evaluate the pre-trained models on various downstream tasks such as classification, zero-shot retrieval, and out-of-distribution detection involving the prediction of various heart conditions using ECG waveforms as input, and demonstrate that the models presented in this work show significant improvements compared to all baseline modes.