Patent foramen ovale screening by ear oximetry in divers

The aim of this study was to test the hypothesis that ear oximetry immediately after the release of a sustained Valsalva maneuver accurately detects patent foramen ovale (PFO). One hundred sixty-five scuba divers underwent transesophageal echocardiography (TEE; reference method) for PFO assessment. Ear oximetry of the right earlobe was performed in a different room within a time frame of 2 hours before or after TEE. The subject and the oximetry operator were unaware of the results of TEE. Oxygen saturation (SO(2)) measurements were obtained at baseline and during the release phase of 4 Valsalva maneuvers within 10 minutes, and the average SO(2) change (SO(2) at baseline minus SO(2) at Valsalva release) was determined as the primary study end point. One hundred seventeen divers had no PFO, and 48 (29%) had PFO by TEE (mean age 39 ± 8 years). The average SO(2) change was 0.79 ± 1.13% (i.e., a slight absolute SO(2) decrease in response to the Valsalva maneuver) in the group without PFO and 1.67 ± 1.19% in the PFO group (p <0.0001). Using receiver-operating characteristic curve analysis, a PFO as defined by TEE could be detected at a threshold of a Valsalva-induced decrease in SO(2) of ≥0.825 percentage points in comparison to baseline (sensitivity 0.756, specificity 0.706, area under the receiver-operating characteristic curve 0.763, p <0.0001, negative predictive value 0.882). In conclusion, the entirely noninvasive method of ear oximetry in response to repetitive Valsalva maneuvers is accurate and useful as a screening method for the detection of a PFO, as shown in this study of divers.

Expanding the cone location and magnitude index to include corneal thickness and posterior surface information for the detection of keratoconus

PURPOSE To extend the capabilities of the Cone Location and Magnitude Index algorithm to include a combination of topographic information from the anterior and posterior corneal surfaces and corneal thickness measurements to further improve our ability to correctly identify keratoconus using this new index: ConeLocationMagnitudeIndex_X. DESIGN Retrospective case-control study. METHODS Three independent data sets were analyzed: 1 development and 2 validation. The AnteriorCornealPower index was calculated to stratify the keratoconus data from mild to severe. The ConeLocationMagnitudeIndex algorithm was applied to all tomography data collected using a dual Scheimpflug-Placido-based tomographer. The ConeLocationMagnitudeIndex_X formula, resulting from analysis of the Development set, was used to determine the logistic regression model that best separates keratoconus from normal and was applied to all data sets to calculate PercentProbabilityKeratoconus_X. The sensitivity/specificity of PercentProbabilityKeratoconus_X was compared with the original PercentProbabilityKeratoconus, which only uses anterior axial data. RESULTS The AnteriorCornealPower severity distribution for the combined data sets are 136 mild, 12 moderate, and 7 severe. The logistic regression model generated for ConeLocationMagnitudeIndex_X produces complete separation for the Development set. Validation Set 1 has 1 false-negative and Validation Set 2 has 1 false-positive. The overall sensitivity/specificity results for the logistic model produced using the ConeLocationMagnitudeIndex_X algorithm are 99.4% and 99.6%, respectively. The overall sensitivity/specificity results for using the original ConeLocationMagnitudeIndex algorithm are 89.2% and 98.8%, respectively. CONCLUSIONS ConeLocationMagnitudeIndex_X provides a robust index that can detect the presence or absence of a keratoconic pattern in corneal tomography maps with improved sensitivity/specificity from the original anterior surface-only ConeLocationMagnitudeIndex algorithm.

Establishing the skill of climate field reconstruction techniques for precipitation with pseudoproxy experiments

This study aims at assessing the skill of several climate field reconstruction techniques (CFR) to reconstruct past precipitation over continental Europe and the Mediterranean at seasonal time scales over the last two millennia from proxy records. A number of pseudoproxy experiments are performed within the virtual reality ofa regional paleoclimate simulation at 45 km resolution to analyse different aspects of reconstruction skill. Canonical Correlation Analysis (CCA), two versions of an Analog Method (AM) and Bayesian hierarchical modeling (BHM) are applied to reconstruct precipitation from a synthetic network of pseudoproxies that are contaminated with various types of noise. The skill of the derived reconstructions is assessed through comparison with precipitation simulated by the regional climate model. Unlike BHM, CCA systematically underestimates the variance. The AM can be adjusted to overcome this shortcoming, presenting an intermediate behaviour between the two aforementioned techniques. However, a trade-off between reconstruction-target correlations and reconstructed variance is the drawback of all CFR techniques. CCA (BHM) presents the largest (lowest) skill in preserving the temporal evolution, whereas the AM can be tuned to reproduce better correlation at the expense of losing variance. While BHM has been shown to perform well for temperatures, it relies heavily on prescribed spatial correlation lengths. While this assumption is valid for temperature, it is hardly warranted for precipitation. In general, none of the methods outperforms the other. All experiments agree that a dense and regularly distributed proxy network is required to reconstruct precipitation accurately, reflecting its high spatial and temporal variability. This is especially true in summer, when a specifically short de-correlation distance from the proxy location is caused by localised summertime convective precipitation events.