Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study.

BACKGROUND: Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. METHODS: An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. RESULTS: The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. CONCLUSIONS: We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19.

Underrecognition of Dysmenorrhea Is an Iatrogenic Harm.

Many patients face years of recurrent and debilitating menstrual pain that affects their ability to work and study. Patients often normalize their severe pain as an expected part of menses. Both underrecognition and lack of awareness of available therapies for this remediable condition serve as a quintessential example of hermeneutic injustice. Hermeneutic injustice describes a structural lack of access to epistemic resources, such as shared concepts and knowledge. Pervasive menstrual stigma further discourages people with dysmenorrhea from discussing their symptoms and seeking health care. A lack of respect for women's experiences of pain in clinical encounters acts to worsen these issues and should be considered a source of iatrogenic harm. Health care workers can promote hermeneutic justice by preemptively destigmatizing discussions about menstruation and validating patients' concerns. On a systemic level, there should be greater awareness of dysmenorrhea and the various treatments availabe for it.