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The algorithm was further validated in a subsequent cohort of 150 women (n= 139 term birth and n= 11 preterm birth), achieving a sensitivity of 91% (95% confidence interval, 62-100%) and a specificity of 78% (95% confidence interval, 70-84%).

We have identified a panel of biomarkers that yield clinically useful diagnostic values when combined in a multiplex algorithm. The early identification of asymptomatic women at risk for preterm birth would allow women to be triaged to specialist clinics for further assessment and appropriate preventive treatment.

We have identified a panel of biomarkers that yield clinically useful diagnostic values when combined in a multiplex algorithm. The early identification of asymptomatic women at risk for preterm birth would allow women to be triaged to specialist clinics for further assessment and appropriate preventive treatment.

Incidence, risk factors, and perinatal morbidity and mortality rates related to amniotic fluid embolism remain a challenge to evaluate, given the presence of differing international diagnostic criteria, the lack of a gold standard diagnostic test, and a significant overlap with other causes of obstetric morbidity and mortality.

The aims of this study were (1) to analyze the clinical features and outcomes of women using the largest United States-based contemporary international amniotic fluid embolism registry, and (2) to investigate differences in demographic and obstetric variables, clinical presentation, and outcomes between women with typical versus atypical amniotic fluid embolism, using previously published and validated criteria for the research reporting of amniotic fluid embolism.

The AFE Registry is an international database established at Baylor College of Medicine (Houston, TX) in partnership with the Amniotic Fluid Embolism Foundation (Vista, CA) and the Perinatology Research Branch of the Diew, using recently published and validated criteria for research reporting of amniotic fluid embolism. Although no definitive risk factors were identified, a high rate of placenta previa, reported allergy, and conceptions achieved through in vitro fertilization was observed.

Maternal hyperoxygenation is widely used during labor as an intrauterine resuscitation technique. However, robust evidence regarding its beneficial effect and potential side effects is scarce, and previous studies show conflicting results.

To assess the effect of maternal hyperoxygenation upon suspected fetal distress during the second stage of term labor on fetal heart rate, neonatal outcome, maternal side effects, and mode of delivery.

In a single-center randomized controlled trial in a tertiary hospital in The Netherlands, participants were randomized in case of an intermediary or abnormal fetal heart rate pattern during the second stage of term labor, to receive either conventional care or 100% oxygen at 10 L/min until delivery. The primary outcome was the change in fetal heart rate pattern. Prespecified secondary outcomes were Apgar score, umbilical cord blood gas analysis, neonatal intensive care unit admission, perinatal death, free oxygen radical activity, maternal side effects, and mode of deli delivery or neonatal outcome; however, significantly fewer episiotomies on fetal indication were performed following maternal hyperoxygenation in the subgroup with abnormal fetal heart rate pattern.

Maternal hyperoxygenation has a positive effect on the fetal heart rate in the presence of suspected fetal distress during the second stage of labor. There was no significant difference in the mode of delivery or neonatal outcome; however, significantly fewer episiotomies on fetal indication were performed following maternal hyperoxygenation in the subgroup with abnormal fetal heart rate pattern.

Asymptomatic short cervical length is an independent risk factor for spontaneous preterm birth. However, most studies have focusedon the associated risk of a short cervical length when encountered between 16and 23 weeks' gestation. The relationship between cervical length and riskof spontaneous preterm birth after 23 weeks is not well known.

To evaluate the risk of spontaneous preterm birth in asymptomatic women with a short cervix (≤25 mm) at 23-28 weeks' gestation.

A retrospective cohort study of women with asymptomatic short cervix (cervical length ≤25 mm) at extreme prematurity, defined as 23-28 weeks' gestation, was performed at a single center from January 2015 to March 2018. Women with symptoms of preterm labor, multiple gestations, fetal or uterine anomalies, cervical cerclage, or those with incomplete data were excluded from the study. Demographic information as well as data on risk factors for spontaneous preterm birth were collected. Patients were divided into 4 groups based on the cervical lcorticosteroids in asymptomatic patients with a cervical length of ≤25 mm at 23-28 weeks' gestation may be delayed until additional indications are present.

The risk of spontaneous preterm birth in asymptomatic women with a sonographic short cervix increases as cervical length decreases. The risk is substantially higher in women with a cervical length of ≤10 mm. Women with a cervical length of ≤10 mm also had the shortest time interval to delivery. Nevertheless, delivery within 1 or 2 weeks is highly unlikely, regardless of the cervical length at the time of enrollment. Therefore, based on our data, we suggest that management decisions such as timing of administration of antenatal corticosteroids in asymptomatic patients with a cervical length of ≤25 mm at 23-28 weeks' gestation may be delayed until additional indications are present.

Chronic cough (CC) of 8weeks or more affects about 10%of adults and may lead to expensive treatments and reduced quality of life. TP-0903 Incomplete diagnostic coding complicates identifying CC in electronic health records (EHRs). Natural language processing (NLP) of EHR text could improve detection.

We assessed NLP in identifying cough in EHRs, and characterized adults and encounters with CC.

A Midwestern EHR system identified patients aged 18 to 85 years during 2005 to 2015. NLP was used to evaluate text notes, except prescriptions and instructions, for mentions of cough. Two physicians and a biostatistician reviewed 12 sets of 50 encounters each, with iterative refinements, until the positive predictive value for cough encounters exceeded 90%. NLP, International Classification of Diseases, 10th revision, or medication was used to identify cough. Three encounters spanning 56 to 120days defined CC. Descriptive statistics summarized patients and encounters, including referrals.

Optimizing NLP required identifts is important for characterizing treatment and unmet needs.

NLP successfully identified a large cohort with CC. Most patients were identified through NLP alone, rather than diagnoses or medications. link2 NLP improved detection of patients nearly sevenfold, addressing the gap in ability to identify and characterize CC disease burden. Nearly all cases appeared to be managed in primary care. Identifying these patients is important for characterizing treatment and unmet needs.To assess airway and lung parenchymal damage noninvasively in cystic fibrosis (CF), chest MRI has been historically out of the scope of routine clinical imaging because of technical difficulties such as low proton density and respiratory and cardiac motion. However, technological breakthroughs have emerged that dramatically improve lung MRI quality (including signal-to-noise ratio, resolution, speed, and contrast). At the same time, novel treatments have changed the landscape of CF clinical care. In this contemporary context, there is now consensus that lung MRI can be used clinically to assess CF in a radiation-free manner and to enable quantification of lung disease severity. MRI can now achieve three-dimensional, high-resolution morphologic imaging, and beyond this morphologic information, MRI may offer the ability to sensitively differentiate active inflammation vs scarring tissue. MRI could also characterize various forms of inflammation for early guidance of treatment. Moreover, functional information from MRI can be used to assess regional, small-airway disease with sensitivity to detect small changes even in patients with mild CF. Finally, automated quantification methods have emerged to support conventional visual analyses for more objective and reproducible assessment of disease severity. This article aims to review the most recent developments of lung MRI, with a focus on practical application and clinical value in CF, and the perspectives on how these modern techniques may converge and impact patient care soon.

Pulmonary arterial hypertension (PAH) is a rare disease, and much of our understanding stems from single-center studies, which are limited by sample size and generalizability. Administrative data offer an appealing opportunity to inform clinical, research, and quality improvement efforts for PAH. Yet, currently no standardized, validated method exists to distinguish PAH from other subgroups of pulmonary hypertension (PH) within this data source.

Can a collection of algorithms be developed and validated to detect PAH in administrative data in two diverse settings all Veterans Health Administration (VA) hospitals and Boston Medical Center (BMC), a PAH referral center.

In each setting, we identified all adult patients with incident PH from 2006 through 2017 using International Classification of Diseases PH diagnosis codes. From this baseline cohort of all PH subgroups, we sequentially applied the following criteria diagnosis codes for PAH-associated conditions, procedure codes for right heart catheterizati of validated algorithms to identify PAH in administrative data can be used by the PAH scientific and clinical community to enhance the reliability and value of research findings, to inform quality improvement initiatives, and ultimately to improve health for PAH patients.

Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment.

Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24h in advance?

We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio

, and pH) were used to assess future need for MV. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value.

We obtained data from more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs0.882, respectively), providing significant improvement over traditional clinical criteria (P< .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943.

A transparent DL algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.

A transparent DL algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. link3 Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.