Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A Systematic Review and Meta-Analysis.

As of May 5, 2023, when the World Health Organization (WHO) declared the end of the coronavirus disease 2019 (COVID-19) pandemic, more than 1.1 million deaths and nearly 7 million emergency department visits had been recorded in the United States (US) []. The COVID-19 pandemic caused significant disruptions to prenatal care, which were associated with increased risks of maternal and neonatal morbidity and mortality [–]. Previous studies reported cancellations of prenatal appointments, difficulties accessing prenatal classes, and changes to birth plans [,]. These disruptions were linked to elevated symptoms of depression and elevated pregnancy-related anxiety []. Population-level analyses revealed overall decreases in prenatal care visits and hospital birth rates, with increases in labor induction rates []. Regarding COVID-19 infection, a living systematic review by Allotey et al. reported higher odds of mortality, admission to an intensive care unit (ICU), invasive ventilation, cesarean delivery, and preterm birth were observed among delivering women with COVID-19 infection, along with higher odds of stillbirth and neonatal ICU admissions among neonates []. While systematic reviews and meta-analyses have provided valuable pooled estimates, these were primarily based on smaller observational studies with heterogeneous designs. There remains an opportunity to build upon prior research by leveraging a large, nationally diverse, multi-payer database with the comprehensive volume and granularity needed to more fully evaluate maternal and neonatal outcomes. Additionally, prior studies have typically evaluated COVID-19 exposure as a single dichotomous indicator, masking potential variant-period-specific risks. Finally, few have assessed incremental healthcare costs related to COVID-19 infection across distinct phases of the pandemic [,–]. While the acute phase of the COVID-19 pandemic has passed, understanding the variant-specific obstetric burden remains critical for informing maternal health surveillance and clinical readiness for future infectious disease outbreaks. This study aimed to assess the association between COVID-19 infection and key delivery-related outcomes during periods corresponding to predominant COVID-19 variants among delivery-related hospitalizations occurring between April 1, 2020, and March 31, 2023, across 688 hospitals across the US. Maternal outcomes included severe maternal morbidity (SMM), a set of 20 unexpected outcomes of delivery indicators defined and maintained by the Centers for Disease Control (CDC) and Alliance for Innovation on Maternal Health (AIM), and mortality; utilization measures included cesarean delivery, ICU admission, hospital costs, and hospital length of stay (LOS); and fetal outcomes included fetal death and preterm birth [].