Three rounds of a national external quality assessment reveal a link between disharmonic anti-SARS-CoV-2 antibody quantifications and the infection stage.

OBJECTIVES: The WHO's standardized measuring unit, "binding antibody units per milliliter (BAU/mL)," should allow the harmonization of quantitative results by different commercial Anti-SARS-CoV-2 immunoassays. However, multiple studies demonstrate inter-assay discrepancies. The antigenic changes of the Omicron variant affect the performance of Spike-specific immunoassays. This study evaluated the variation of quantitative Anti-SARS-CoV-2-Spike antibody measurements among 46, 50, and 44 laboratories in three rounds of a national external quality assessment (EQA) prior to and after the emergence of the Omicron variant in a diagnostic near-to-real-life setting. METHODS: We analyzed results reported by the EQA participant laboratories from single and sequential samples from SARS-CoV-2 convalescent, acutely infected, and vaccinated individuals, including samples obtained after primary and breakthrough infections with the Omicron variant. RESULTS: The three immunoassays most commonly used by the participants displayed a low intra-assay and inter-laboratory variation with excellent reproducibility using identical samples sent to the participants in duplicates. In contrast, the inter-assay variation was very high with all samples. Notably, the ratios of BAU/mL levels quantified by different immunoassays were not equal among all samples but differed between vaccination, past, and acute infection, including primary infection with the Omicron variant. The antibody kinetics measured in vaccinated individuals strongly depended on the applied immunoassay. CONCLUSIONS: Measured BAU/mL levels are only inter-changeable among different laboratories when the same assay was used for their assessment. Highly variable ratios of BAU/mL quantifications among different immunoassays and infection stages argue against the usage of universal inter-assay conversion factors.

Performance of an open-source heart sound segmentation algorithm on eight independent databases.

OBJECTIVE: Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we aim to evaluate the performance of the recently published logistic regression based hidden semi-Markov model (HSMM) heart sound segmentation method, by using a wider variety of independently acquired data of varying quality. APPROACH: Firstly, we constructed a systematic evaluation scheme based on a new collection of heart sound databases, which we assembled for the PhysioNet/CinC Challenge 2016. This collection includes a total of more than 120 000 s of heart sounds recorded from 1297 subjects (including both healthy subjects and cardiovascular patients) and comprises eight independent heart sound databases sourced from multiple independent research groups around the world. Then, the HSMM-based segmentation method was evaluated using the assembled eight databases. The common evaluation metrics of sensitivity, specificity, accuracy, as well as the [Formula: see text] measure were used. In addition, the effect of varying the tolerance window for determining a correct segmentation was evaluated. MAIN RESULTS: The results confirm the high accuracy of the HSMM-based algorithm on a separate test dataset comprised of 102 306 heart sounds. An average [Formula: see text] score of 98.5% for segmenting S1 and systole intervals and 97.2% for segmenting S2 and diastole intervals were observed. The [Formula: see text] score was shown to increases with an increases in the tolerance window size, as expected. SIGNIFICANCE: The high segmentation accuracy of the HSMM-based algorithm on a large database confirmed the algorithm's effectiveness. The described evaluation framework, combined with the largest collection of open access heart sound data, provides essential resources for evaluators who need to test their algorithms with realistic data and share reproducible results.

Automated signal quality assessment of mobile phone-recorded heart sound signals.

Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiate poor quality from diagnostically useful recordings since non-experts are more likely to make poor-quality recordings. This paper investigates the automated signal quality assessment of heart sound recordings performed using both mobile phone-based and commercial medical-grade electronic stethoscopes. The recordings, each 60 s long, were taken from 151 random adult individuals with varying diagnoses referred to a cardiac clinic and were professionally annotated by five experts. A mean voting procedure was used to compute a final quality label for each recording. Nine signal quality indices were defined and calculated for each recording. A logistic regression model for classifying binary quality was then trained and tested. The inter-rater agreement level for the stethoscope and mobile phone recordings was measured using Conger's kappa for multiclass sets and found to be 0.24 and 0.54, respectively. One-third of all the mobile phone-recorded phonocardiogram (PCG) signals were found to be of sufficient quality for analysis. The classifier was able to distinguish good- and poor-quality mobile phone recordings with 82.2% accuracy, and those made with the electronic stethoscope with an accuracy of 86.5%. We conclude that our classification approach provides a mechanism for substantially improving auscultation recordings by non-experts. This work is the first systematic evaluation of a PCG signal quality classification algorithm (using a separate test dataset) and assessment of the quality of PCG recordings captured by non-experts, using both a medical-grade digital stethoscope and a mobile phone.

Logistic Regression-HSMM-Based Heart Sound Segmentation.

The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold-based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when a priori information about the expected duration of the states is incorporated into the model, such as in a hidden semi-Markov model (HSMM). This paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real-world PCG recordings using an HSMM, extended with the use of logistic regression for emission probability estimation. In addition, we implement a modified Viterbi algorithm for decoding the most likely sequence of states, and evaluated this method on a large dataset of 10,172 s of PCG recorded from 112 patients (including 12,181 first and 11,627 second heart sounds). The proposed method achieved an average F1 score of 95.63 ± 0.85%, while the current state of the art achieved 86.28 ± 1.55% when evaluated on unseen test recordings. The greater discrimination between states afforded using logistic regression as opposed to the previous Gaussian distribution-based emission probability estimation as well as the use of an extended Viterbi algorithm allows this method to significantly outperform the current state-of-the-art method based on a two-sided paired t-test.

Heart beat detection in multimodal physiological data using a hidden semi-Markov model and signal quality indices.

Accurate heart beat detection in signals acquired from intensive care unit (ICU) patients is necessary for establishing both normality and detecting abnormal events. Detection is normally performed by analysing the electrocardiogram (ECG) signal, and alarms are triggered when parameters derived from this signal exceed preset or variable thresholds. However, due to noisy and missing data, these alarms are frequently deemed to be false positives, and therefore ignored by clinical staff. The fusion of features derived from other signals, such as the arterial blood pressure (ABP) or the photoplethysmogram (PPG), has the potential to reduce such false alarms. In order to leverage the highly correlated temporal nature of the physiological signals, a hidden semi-Markov model (HSMM) approach, which uses the intra- and inter-beat depolarization interval, was designed to detect heart beats in such data. Features based on the wavelet transform, signal gradient and signal quality indices were extracted from the ECG and ABP waveforms for use in the HSMM framework. The presented method achieved an overall score of 89.13% on the hidden/test data set provided by the Physionet/Computing in Cardiology Challenge 2014: Robust Detection of Heart Beats in Multimodal Data.

Impact of immigration on health and human services: Florida's experience.

Florida has been the destination for large numbers of immigrants fleeing political persecution or economic hardships. Cubans and Haitians have been two of the largest immigrant groups arriving and settling in Florida. Both have received national and local attention. This article describes the immigration experience of Haitians and Cubans in Florida. The descriptions emphasize the differences between these two groups in their adjustment to life in south Florida. The article also addresses Florida's reaction to federal policies regarding immigration and highlights Florida's struggle to meet the service needs of these immigrant populations. Fiscal impacts of immigration are quantified in several service categories, including education, social services, health care, and criminal justice. Florida's action based on the documentation of the immigration fiscal impact is explained. Finally, how the state allocated the $18 million in federal funding provided as a response to Florida's documented impact is covered.