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INTRODUCTION: Unplanned pregnancies are associated with increased risks. Despite this, they are currently not routinely detected during antenatal care. This study evaluates the implementation of the London Measure of Unplanned Pregnancy (LMUP) - a validated measure of pregnancy planning - into antenatal care at University College London Hospital, Homerton Hospital, and St Thomas' Hospital, England, 2019-2023. METHODS: We conducted a mixed methods evaluation of the pilot. Uptake and acceptability were measured using anonymized data with non-completion of the LMUP as a proxy measure of acceptability overall. We conducted focus groups with midwives, and one-to-one interviews with women, to explore their thoughts of asking, or being asked the LMUP, which we analyzed with a Framework Analysis. RESULTS: Asking the LMUP at antenatal appointments is feasible and acceptable to women and midwives, and the LMUP performed as expected. Advantages of asking the LMUP, highlighted by participants, include providing additional support and personalizing care. Midwives' concerns about judgment were unsubstantiated; women with unplanned pregnancies valued such discussions. CONCLUSIONS: These findings support the implementation of the LMUP in routine antenatal care and show how it can provide valuable insights into the circumstances of women's pregnancies. This can be used to help midwives personalize care, and potentially reduce adverse outcomes and subsequent unplanned pregnancy. Integration of the LMUP into the Maternity Services Data Set will establish national data collection of a validated measure of unplanned pregnancy and enable analysis of the prevalence, factors, and implications of unplanned pregnancies across subpopulations and over time to inform implementation.
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Background: Vertebral osteomyelitis and discitis (VOD), an infection of intervertebral discs, often requires spine surgical intervention and timely management to prevent adverse outcomes. Our study aims to develop a machine learning (ML) model to predict the indication for surgical intervention (during the same hospital stay) versus nonsurgical management in patients with VOD. Methods: This retrospective study included adult patients (≥18 years) with VOD (ICD-10 diagnosis codes M46.2,3,4,5) treated at a single institution between 01/01/2015 and 12/31/2019. The primary outcome studied was surgery. Candidate predictors were age, sex, race, Elixhauser comorbidity index, first-recorded lab values, first-recorded vital signs, and admit diagnosis. After splitting the dataset, XGBoost, logistic regression, and K-neighbor classifier algorithms were trained and tested for model development. Results: A total of 1,111 patients were included in this study, among which 30% (n=339) of patients underwent surgical intervention. Age and sex did not significantly differ between the two groups; however, race did significantly differ (P<0.0001), with the surgical group having a higher percentage of white patients. The top ten model features for the best-performing model (XGBoost) were as follows (in descending order of importance): admit diagnosis of fever, negative culture, Staphylococcus aureus culture, partial pressure of arterial oxygen to fractional inspired oxygen ratio (PaO2:FiO2), admit diagnosis of intraspinal abscess and granuloma, admit diagnosis of sepsis, race, troponin I, acid-fast bacillus culture, and alveolar-arterial gradient (A-a gradient). XGBoost model metrics were as follows: accuracy =0.7534, sensitivity =0.7436, specificity =0.7586, and area under the curve (AUC) =0.8210. Conclusions: The XGBoost model reliably predicts the indication for surgical intervention based on several readily available patient demographic information and clinical features. The interpretability of a supervised ML model provides robust insight into patient outcomes. Furthermore, it paves the way for the development of an efficient hospital resource allocation instrument, designed to guide clinical suggestions.