PLoS medicine
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Pressure injuries (PIs) pose a substantial healthcare burden and incur significant costs worldwide. Several risk prediction tools to allow timely implementation of preventive measures and a subsequent reduction in healthcare system burden are available and in use. The ability of risk prediction tools to correctly identify those at high risk of PI (prognostic accuracy) and to have a clinically significant impact on patient management and outcomes (effectiveness) is not clear. We aimed to evaluate the prognostic accuracy and clinical effectiveness of risk prediction tools for PI and to identify gaps in the literature. ⋯ Available systematic reviews suggest a lack of high-quality evidence for the accuracy of risk prediction tools for PI and limited reliable evidence for their use leading to a reduction in incidence of PI. Further research is needed to establish the clinical effectiveness of appropriately developed and validated risk prediction tools for PI.
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Although X-rays are not recommended for routine diagnosis of osteoarthritis (OA), clinicians and patients often use or expect X-rays. We evaluated whether: (i) a radiographic diagnosis and explanation of knee OA influences patient beliefs about management, compared to a clinical diagnosis and explanation that does not involve X-rays; and (ii) showing the patient their X-ray images when explaining radiographic report findings influences beliefs, compared to not showing X-ray images. ⋯ An X-ray-based diagnosis and explanation of knee OA may have potentially undesirable effects on people's beliefs about management.
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Accelerating improvements in maternal and newborn health (MNH) care is a major public health priority in Kenya. While use of formal health care has increased, many pregnant and postpartum women do not receive the recommended number of maternal care visits. Even when they do, visits are often short with many providers not offering important elements of evaluation and counseling, leaving gaps in women's knowledge and preparedness. Digital health tools have been proposed as a complement to care that is provided by maternity care facilities, but there is limited evidence of the impact of digital health tools at scale on women's knowledge, preparedness, and the content of care they receive. We evaluated a digital health platform (PROMPTS (Promoting Mothers in Pregnancy and Postpartum Through SMS)) composed of informational messages, appointment reminders, and a two-way clinical helpdesk, which had enrolled over 750,000 women across Kenya at the time of our study, on 6 domains across the pregnancy-postpartum care continuum. ⋯ Digital health tools indicate promise in addressing shortcomings in pregnant and postpartum women's health care, amidst systems that do not reliably deliver a minimally adequate standard of care. Through providing women with critical information and empowering them to seek recommended care, such tools can improve individuals' preparation for safe childbirth and receipt of more comprehensive postpartum care. Future work is needed to ascertain the impact of at-scale digital platforms like PROMPTS on health outcomes.
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A higher risk of placenta previa after assisted reproductive technology (ART) is well established. The underlying mechanisms are poorly understood, but may relate to embryo culture duration, cryopreservation, and cause of infertility. Within-mother analyses, where each woman is her own control (i.e., sibling design), help disentangle treatment contributions from maternal confounders that are stable between pregnancies. We aimed to investigate the risk of placenta previa in pregnancies achieved after ART according to embryo culture duration, cryopreservation, and infertility factors while accounting for stable maternal factors using within-mother analyses. ⋯ The risk of placenta previa in pregnancies conceived by ART differed by embryo culture duration, cryopreservation, and underlying infertility. The highest risk was seen after fresh embryo transfer and especially fresh blastocyst transfer. Women with endometriosis had a higher risk than women with other infertility factors, and within mothers, their risk was higher after ART than after natural conception. Identifying the responsible mechanisms might provide opportunities for prevention.
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Multicenter Study Observational Study
Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.
Preeclampsia is a potentially life-threatening pregnancy complication. Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML] model, and the logistic regression-based fullPIERS model) accurately identify individuals at greatest or least risk of adverse maternal outcomes within 48 h following admission. Both models were developed and validated to be used as part of initial assessment. In the United Kingdom, the National Institute for Health and Care Excellence (NICE) recommends repeated use of such static models for ongoing assessment beyond the first 48 h. This study evaluated the models' performance during such consecutive prediction. ⋯ In this study, we have evaluated the performance of the fullPIERS and PIERS-ML models for consecutive prediction. We observed deteriorating performance of both models over time. We recommend using the models for consecutive prediction with greater caution and interpreting predictions with increasing uncertainty as the pregnancy progresses. For clinical practice, models should be adapted to retain accuracy when deployed serially. The performance of future models can be compared with the results of this study to quantify their added value.