Clinical phenotype of adolescent and adult patients with extracranial vascular malformation

OBJECTIVE

In recent years, genotypic characterization of congenital vascular malformations (CVMs) has gained attention; however, the spectrum of clinical phenotype remains difficult to attribute to a genetic cause and is rarely described in the adult population. The aim of this study is to describe a consecutive series of adolescent and adult patients in a tertiary center, where a multimodal phenotypic approach was used for diagnosis.

METHODS

We analyzed clinical findings, imaging, and laboratory results at initial presentation, and set a diagnosis according to the International Society for the Study of Vascular Anomalies (ISSVA) classification for all consecutively registered patients older than 14 years of age who were referred to the Center for Vascular Malformations at the University Hospital of Bern between 2008 and 2021.

RESULTS

A total of 457 patients were included for analysis (mean age, 35 years; females, 56%). Simple CVMs were the most common (n = 361; 79%), followed by CVMs associated with other anomalies (n = 70; 15%), and combined CVMs (n = 26; 6%). Venous malformations (n = 238) were the most common CVMs overall (52%), and the most common simple CVMs (66%). Pain was the most frequently reported symptom in all patients (simple, combined, and vascular malformation with other anomalies). Pain intensity was more pronounced in simple venous and arteriovenous malformations. Clinical problems were related to the type of CVM diagnosed, with bleeding and skin ulceration in arteriovenous malformations, localized intravascular coagulopathy in venous malformations, and infectious complications in lymphatic malformations. Limb length difference occurred more often in patients with CVMs associated with other anomalies as compared with simple or combined CVM (22.9 vs 2.3%; P < .001). Soft tissue overgrowth was seen in one-quarter of all patients independent of the ISSVA group.

CONCLUSIONS

In our adult and adolescent population with peripheral vascular malformations, simple venous malformations predominated, with pain as the most common clinical symptom. In one-quarter of cases, patients with vascular malformations presented with associated anomalies on tissue growth. The differentiation of clinical presentation with or without accompanying growth abnormalities need to be added to the ISSVA classification. Phenotypic characterization considering vascular and non-vascular features remains the cornerstone of diagnosis in adult as well as pediatric patients.

The Accuracy of Wrist Skin Temperature in Detecting Ovulation Compared to Basal Body Temperature: Prospective Comparative Diagnostic Accuracy Study

Background

As a daily point measurement, basal body temperature (BBT) might not be able to capture the temperature shift in the menstrual cycle because a single temperature measurement is present on the sliding scale of the circadian rhythm. Wrist skin temperature measured continuously during sleep has the potential to overcome this limitation.

Objective

This study compares the diagnostic accuracy of these two temperatures for detecting ovulation and to investigate the correlation and agreement between these two temperatures in describing thermal changes in menstrual cycles.

Methods

This prospective study included 193 cycles (170 ovulatory and 23 anovulatory) collected from 57 healthy women. Participants wore a wearable device (Ava Fertility Tracker bracelet 2.0) that continuously measured the wrist skin temperature during sleep. Daily BBT was measured orally and immediately upon waking up using a computerized fertility tracker with a digital thermometer (Lady-Comp). An at-home luteinizing hormone test was used as the reference standard for ovulation. The diagnostic accuracy of using at least one temperature shift detected by the two temperatures in detecting ovulation was evaluated. For ovulatory cycles, repeated measures correlation was used to examine the correlation between the two temperatures, and mixed effect models were used to determine the agreement between the two temperature curves at different menstrual phases.

Results

Wrist skin temperature was more sensitive than BBT (sensitivity 0.62 vs 0.23; P<.001) and had a higher true-positive rate (54.9% vs 20.2%) for detecting ovulation; however, it also had a higher false-positive rate (8.8% vs 3.6%), resulting in lower specificity (0.26 vs 0.70; P=.002). The probability that ovulation occurred when at least one temperature shift was detected was 86.2% for wrist skin temperature and 84.8% for BBT. Both temperatures had low negative predictive values (8.8% for wrist skin temperature and 10.9% for BBT). Significant positive correlation between the two temperatures was only found in the follicular phase (rmcorr correlation coefficient=0.294; P=.001). Both temperatures increased during the postovulatory phase with a greater increase in the wrist skin temperature (range of increase: 0.50 °C vs 0.20 °C). During the menstrual phase, the wrist skin temperature exhibited a greater and more rapid decrease (from 36.13 °C to 35.80 °C) than BBT (from 36.31 °C to 36.27 °C). During the preovulatory phase, there were minimal changes in both temperatures and small variations in the estimated daily difference between the two temperatures, indicating an agreement between the two curves.

Conclusions

For women interested in maximizing the chances of pregnancy, wrist skin temperature continuously measured during sleep is more sensitive than BBT for detecting ovulation. The difference in the diagnostic accuracy of these methods was likely attributed to the greater temperature increase in the postovulatory phase and greater temperature decrease during the menstrual phase for the wrist skin temperatures.

Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study

Background

Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women’s nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women’s resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window’s closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown.

Objective

In this study, we probed what phase-based differences a wearable bracelet could detect in users’ WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time.

Methods

We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window.

Results

We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92).

Conclusions

Our contributions highlight the impact of artificial intelligence and machine learning’s integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.