Antimalarials are not Effective as Pre-Exposure Prophylaxis for COVID-19: A Retrospective Matched Control Study.

The early phase of the COVID-19 pandemic prompted a repurposing of antiviral and immunomodulatory drugs as investigational therapeutics, including hydroxychloroquine and chloroquine. While antimalarials have been well-refuted as a treatment for COVID-19, data on these drugs' role in preventing SARS-CoV-2 infection as pre-exposure prophylaxis is more limited. We investigated the efficacy of antimalarial drugs as pre-exposure SARS-CoV-2 prophylaxis in a US tertiary-care center. We identified all adult patients exposed to antimalarials with active prescriptions from July 1, 2019 to February 29, 2020 and exact-matched antimalarial-treated study patients with controls on age, sex, race, and Charleston Comorbidity Index. We used multivariable logistic regression to calculate the odds ratio (OR) of COVID-19 diagnosis by antimalarial exposure, adjusting for demographics, comorbidities, local infection rates, and specific conditions identified in early studies as risk factors for COVID-19. There were 3,074 patients with antimalarial prescriptions and 58,955 matched controls. Hydroxychloroquine represented 98.8% of antimalarial prescriptions. There were 51 (1.7%) infections among antimalarial-exposed and 973 (1.6%) among controls. No protective effect for SARS-CoV-2 infection was demonstrated among antimalarial-exposed patients in the multivariate model (OR=1.06, 95% CI 0.80-1.40, P=0.70). These findings corroborate prior work demonstrating that hydroxychloroquine and related antimalarials do not have a role in protection against SARS-CoV-2.Klebanov N, Pahalyants V, Said JT, et al. Antimalarials are not effective as pre-exposure prophylaxis for COVID-19: a retrospective matched control study. J Drugs Dermatol. 2023;22(8):840-843. doi:10.36849/JDD.6593.

Prediction of early-stage melanoma recurrence using clinical and histopathologic features.

Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorithms. Models were evaluated internally and externally: (1) five-fold cross-validation of the MGB cohort; (2) the MGB cohort for training and the DFCI cohort for testing independently. In the internal and external validations, respectively, we achieved a recurrence classification performance of AUC: 0.845 and 0.812, and a time-to-event prediction performance of time-dependent AUC: 0.853 and 0.820. Breslow tumor thickness and mitotic rate were identified as the most predictive features. Our results suggest that machine-learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients that may benefit from adjuvant immunotherapy.

Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study.

AIM: Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a late, incurable stage. We sought to determine whether individuals at high risk of developing PDAC could be identified early using routinely collected data. METHODS: Electronic health record (EHR) databases from two independent hospitals in Boston, Massachusetts, providing inpatient, outpatient, and emergency care, from 1979 through 2017, were used with case-control matching. PDAC cases were selected using International Classification of Diseases 9/10 codes and validated with tumour registries. A data-driven feature selection approach was used to develop neural networks and L2-regularised logistic regression (LR) models on training data (594 cases, 100,787 controls) and compared with a published model based on hand-selected diagnoses ('baseline'). Model performance was validated on an external database (408 cases, 160,185 controls). Three prediction lead times (180, 270 and 365 days) were considered. RESULTS: The LR model had the best performance, with an area under the curve (AUC) of 0.71 (confidence interval [CI]: 0.67-0.76) for the training set, and AUC 0.68 (CI: 0.65-0.71) for the validation set, 365 days before diagnosis. Data-driven feature selection improved results over 'baseline' (AUC = 0.55; CI: 0.52-0.58). The LR model flags 2692 (CI 2592-2791) of 156,485 as high risk, 365 days in advance, identifying 25 (CI: 16-36) cancer patients. Risk stratification showed that the high-risk group presented a cancer rate 3 to 5 times the prevalence in our data set. CONCLUSION: A simple EHR model, based on diagnoses, can identify high-risk individuals for PDAC up to one year in advance. This inexpensive, systematic approach may serve as the first sieve for selection of individuals for PDAC screening programs.

Labor therapeutics and BMI as risk factors for postpartum preeclampsia: A case-control study.

OBJECTIVES: This study aims at identifying associations between therapeutics used during labor and the occurrence of postpartum preeclampsia (PPPE), a poorly understood entity. STUDY DESIGN AND MAIN OUTCOME MEASURES: This is a case-control study of women who received an ICD-9 code for PPPE (cases) during the years 2009-2011, compared to women with a normotensive term pregnancy, delivery and postpartum period until discharge (controls), matched on age (±1year) and delivery date (±3months). Cases were defined as women having a normotensive term pregnancy, delivery and initial postpartum period (48h post-delivery) but developing hypertension between 48h and 6weeks postpartum. Single variable and multiple variable models were used to determine significant risk factors. RESULTS: Forty-three women with PPPE were compared to 86 controls. Use of vasopressors and oxytocin did not differ between cases and controls, but rate of fluids administered during labor (OR=1.68 per 100cc/h; 95% CI: 1.09-2.59, p=0.02) and an elevated pre-pregnancy/first trimester BMI (OR=1.18 per kg/m2, 95% CI: 1.07-1.3, p=0.001) were identified as significant risk factors in multivariate analysis. CONCLUSIONS: We identified two potentially modifiable risk factors for PPPE; further studies are needed to better define the role of these two variables in the development of PPPE.

The Biobank Portal for Partners Personalized Medicine: A Query Tool for Working with Consented Biobank Samples, Genotypes, and Phenotypes Using i2b2.

We have designed a Biobank Portal that lets researchers request Biobank samples and genotypic data, query associated electronic health records, and design and download datasets containing de-identified attributes about consented Biobank subjects. This do-it-yourself functionality puts a wide variety and volume of data at the fingertips of investigators, allowing them to create custom datasets for their clinical and genomic research from complex phenotypic data and quickly obtain corresponding samples and genomic data. The Biobank Portal is built upon the i2b2 infrastructure [1] and uses an open-source web client that is available to faculty members and other investigators behind an institutional firewall. Built-in privacy measures [2] ensure that the data in the Portal are utilized only according to the processes to which the patients have given consent.