The Future of Remote Monitoring for Pregnancy.
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ABSTRACT:
SUBMITTER: Walter JR
PROVIDER: S-EPMC10727511 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
The Future of Remote Monitoring for Pregnancy.
Walter Jessica R JR Xu Shuai S Stringer Jeffrey S JS Rogers John A JA
Bridge (Washington, D.C. : 1969) 20220329 1
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Project description:BackgroundAlthough remote monitoring (RM) has proven its added value in various health care domains, little is known about the remote follow-up of pregnant women diagnosed with a gestational hypertensive disorders (GHD).ObjectiveThe aim of this study was to evaluate the added value of a remote follow-up program for pregnant women diagnosed with GHD.MethodsA 1-year retrospective study was performed in the outpatient clinic of a 2nd level prenatal center where pregnant women with GHD received RM or conventional care (CC). Primary study endpoints include number of prenatal visits and admissions to the prenatal observation ward. Secondary outcomes include gestational outcome, mode of delivery, neonatal outcome, and admission to neonatal intensive care (NIC). Differences in continuous and categorical variables in maternal demographics and characteristics were tested using Unpaired Student's two sampled t test or Mann-Whitney U test and the chi-square test. Both a univariate and multivariate analysis were performed for analyzing prenatal follow-up and gestational outcomes. All statistical analyses were done at nominal level, Cronbach alpha=.05.ResultsOf the 166 patients diagnosed with GHD, 53 received RM and 113 CC. After excluding 5 patients in the RM group and 15 in the CC group because of the missing data, 48 patients in RM group and 98 in CC group were taken into final analysis. The RM group had more women diagnosed with gestational hypertension, but less with preeclampsia when compared with CC (81.25% vs 42.86% and 14.58% vs 43.87%). Compared with CC, univariate analysis in RM showed less induction, more spontaneous labors, and less maternal and neonatal hospitalizations (48.98% vs 25.00%; 31.63% vs 60.42%; 74.49% vs 56.25%; and 27.55% vs 10.42%). This was also true in multivariate analysis, except for hospitalizations.ConclusionsAn RM follow-up of women with GHD is a promising tool in the prenatal care. It opens the perspectives to reverse the current evolution of antenatal interventions leading to more interventions and as such to ever increasing medicalized antenatal care.
| S-EPMC5364324 | biostudies-literature
Project description:BackgroundThe Ideal Life Blood Pressure Manager measures blood pressure (BP) and automatically transmits results to the patient's medical record independent of internet access, but has not been validated. Our objective was to conduct a validation study of the Ideal Life BP Manager in pregnant women using a validation protocol.MethodsPregnant participants were enrolled into three subgroups per the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization protocol: normotensive (systolic blood pressure (SBP) < 140 and diastolic blood pressure (DBP) < 90), hypertensive without proteinuria (SBP ≥ 140 or DBP ≥ 90), and preeclampsia (SBP ≥ 140 or DBP ≥ 90 with proteinuria). Two trained research staff used a mercury sphygmomanometer to validate the device, alternating sphygmomanometer, and device readings for a total of 9 measurements.ResultsAmong 51 participants, the mean SBP and DBP differences and standard deviations between the device and the mean staff measurements for all participants were 1.7 ± 7.1 and 1.5 ± 7.0 mm Hg, respectively. The standard deviations of the individual participant's paired device and mean staff SBP and DBP measurements were 6.0 and 6.4 mm Hg, respectively. The device was more likely to overestimate rather than underestimate BP (SBP: mean difference = 1.67, 95% CI [-12.15 to 15.49]; DBP: mean difference = 1.51, 95% CI [-12.26 to 15.28]). Most paired readings had a difference of less than 10 mm Hg across averaged paired readings.ConclusionThe Ideal Life BP Manager met internationally recognized validity criteria in this sample of pregnant women.
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Project description:Mobile technology has become a ubiquitous part of everyday life and is changing the way we offer clinical care and perform clinical research. We have unprecedented access to data for one's self-care as well as for sharing with health care providers. Meeting the challenge posed by the influx of wearable device data requires a multidisciplinary team of researchers, clinicians, software developers, information technologists, and statisticians. Although the possibility of what can be achieved with the ever-evolving wearable technologies seems to be unlimited, regulatory agencies have provided a framework to establish standards for clinical applications, which will also affect research applications. Clinical programs and electronic medical records vendors should prepare to establish a framework to implement these technologies into clinicians' workflow and to allow feedback to measure the impact on clinical outcome. In this article, we discuss how a new brand of multidisciplinary care is evolving around mobile health devices and present a vision of up-and-coming technology in this space.
| S-EPMC8325475 | biostudies-literature
Project description:BackgroundObservational data from the retrospective, non-randomized Pregnancy REmote MOnitoring I (PREMOM I) study showed that remote monitoring (RM) may be beneficial for prenatal observation of women at risk for gestational hypertensive disorders (GHD) in terms of clinical outcomes, health economics, and stakeholder perceptions. PREMOM II is a prospective, randomized, multicenter follow-up study that was performed to explore these promising results.MethodsAfter providing written consent, 3922 pregnant women aged ≥18 years who are at increased risk of developing GHD will be randomized (1:1:1 ratio) to (a) conventional care (control group), (b) a patient self-monitoring group, and (c) a midwife-assisted RM group. The women in each group will be further divided (1:1 ratio) to evaluate the outcomes of targeted or non-targeted (conventional) antihypertensive medication. Women will be recruited in five hospitals in Flanders, Belgium: Ziekenhuis Oost-Limburg, Universitaire Ziekenhuis Antwerpen, Universitaire Ziekenhuis Leuven, AZ Sint Jan Brugge-Oostende, and AZ Sint Lucas Brugge. The primary outcomes are: (1) numbers and types of prenatal visits; (2) maternal outcomes; (3) neonatal outcomes; (4) the applicability and performance of RM; and (5) compliance with RM and self-monitoring. The secondary outcomes are: (1) cost-effectiveness and willingness to pay; (2) patient-reported outcome measures (PROMS) questionnaires on the experiences of the participants; and (3) the maternal and perinatal outcomes according to the type of antihypertensive medication. Demographic, and maternal and neonatal outcomes are collected from the patients' electronic records. Blood pressure and compliance rate will be obtained from an online digital coordination platform for remote data handling. Information about the healthcare-related costs will be obtained from the National Coordination Committee of Belgian Health Insurance Companies (Intermutualistisch Agentschap). PROMS will be assessed using validated questionnaires.DiscussionTo our knowledge, this is the first randomized trial comparing midwife-assisted RM and self-monitoring of prenatal blood pressure versus conventional management among women at increased risk of GHD. Positive results of this study may lead to a practical framework for caregivers, hospital management, and payers to introduce RM into the prenatal care programs of high-risk pregnancies.Trial registrationThis study was registered on clinicaltrials.gov , identification number NCT04031430. Registered 24 July 2019, https://clinicaltrials.gov/ct2/show/NCT04031430?cond=premom+ii&draw=2&rank=1 .
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Project description:Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.
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Project description:ObjectiveDaily remote foot temperature monitoring is an evidence-based preventive practice for patients at risk for diabetic foot complications. Unfortunately, the conventional approach requires comparison of temperatures between contralaterally matched anatomy, limiting practice in high-risk cohorts such as patients with a wound to one foot and those with proximal lower extremity amputation (LEA). We developed and assessed a novel approach for monitoring of a single foot for the prevention and early detection of diabetic foot complications. The purpose of this study was to assess the sensitivity, specificity, and lead time associated with unilateral diabetic foot temperature monitoring.Research design and methodsWe used comparisons among ipsilateral foot temperatures and between foot temperatures and ambient temperature as a marker of inflammation. We analyzed data collected from a 129-participant longitudinal study to evaluate the predictive accuracy of our approach. To evaluate classification accuracy, we constructed a receiver operator characteristic curve and reported sensitivity, specificity, and lead time for four different monitoring settings.ResultsUsing this approach, monitoring a single foot was found to predict 91% of impending non-acute plantar foot ulcers on average 41 days before clinical presentation with a resultant mean 4.2 alerts per participant-year. By adjusting the threshold temperature setting, the specificity could be increased to 78% with corresponding reduced sensitivity of 53%, lead time of 33 days, and 2.2 alerts per participant-year.ConclusionsGiven the high incidence of subsequent diabetic foot complications to the sound foot in patients with a history of proximal LEA and patients being treated for a wound, practice of daily temperature monitoring of a single foot has the potential to significantly improve outcomes and reduce resource utilization in this challenging high-risk population.
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