Asthma Self-assessment in a Medicaid Population

Background: Self-assessment of symptoms by patients with chronic conditions is an important element of disease management. A recent study in a commercially-insured population found that patients who received automated telephone calls for asthma self-assessment felt they benefitted from the calls. Few studies have evaluated the effectiveness of disease self-assessment in Medicaid populations. The goals of this study were to: (1) assess the feasibility of asthma self-assessment in a population predominantly insured by Medicaid, (2) study whether adding a gift card incentive increased completion of the self-assessment survey, and (3) evaluate how the self-assessment affected processes and outcomes of care. Methods: We studied adults and children aged 4 years and older who were insured by a Medicaid-focused managed care organization (MCO) in a pre- and post-intervention study. During the pre-incentive period, patients with computerized utilization data that met specific criteria for problematic asthma control were mailed the Asthma Control Test (ACT), a self-assessment survey, and asked to return it to the MCO. During the intervention period, patients were offered a $20 gift card for returning the completed ACT to the MCO. To evaluate clinical outcomes, we used computerized claims data to assess the number of hospitalization visits and emergency department visits experienced in the 3 months after receiving the ACT. To evaluate whether the self-management intervention improved processes of care, we conducted telephone interviews with patients who returned or did not return the ACT by mail. Results: During the pre-incentive period, 1183 patients were identified as having problems with asthma control; 25 (2.0%) of these returned the ACT to the MCO. In contrast, during the incentive period, 1612 patients were identified as having problems with asthma control and 87 (5.4%) of these returned the ACT to the MCO (p < 0.0001). Of all 95 ACTs that were returned, 87% had a score of 19 or less, which suggested poor asthma control. During the 3 months after they received the ACT, patients who completed it had similar numbers of outpatient visits, emergency department visits, and hospitalizations for asthma as patients who did not complete the ACT. We completed interviews with 95 patients, including 28 who had completed the ACT and 67 who had not. Based on an ACT administered at the time of the interview, patients who had previously returned the ACT to the MCO had asthma control similar to those who had not (mean scores of 14.2 vs. 14.6, p = 0.70). Patients had similar rates of contacting their providers within the past 2 months whether they had completed the mailed ACT or not (71% vs. 76%, p = 0.57). Conclusion: Mailing asthma self-assessment surveys to patients with poorly controlled asthma was not associated with better asthma-associated outcomes or processes of care in the Medicaid population studied. Adding a gift card incentive did not meaningfully increase response rates. Asthma disease management programs for Medicaid populations will most likely need to involve alternative strategies for engaging patients and their providers in managing their conditions

_In vivo_ photoacoustic molecular imaging with simultaneous multiple selective targeting using antibody-conjugated gold nanorods

The use of gold nanorods for photoacoustic molecular imaging in vivo with simultaneous multiple selective targeting is reported. The extravasation of multiple molecular probes is demonstrated, and used to probe molecular information of cancer cells. This technique allows molecular profiles representing tumor characteristics to be obtained and a heterogeneous population of cancer cells in a lesion to be determined. The results also show that the image contrast can be enhanced by using a mixture of different molecular probes. In this study, HER2, EGFR, and CXCR4 were chosen as the primary target molecules for examining two types of cancer cells, OECM1 and Cal27. OECM1 cells overexpressed HER2 but exhibited a low expression of EGFR, while Cal27 cells showed the opposite expression profile. Single and double targeting resulted in signal enhancements of up to 3 dB and up to 5 dB, respectively, and hence has potential in improving cancer diagnoses

Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs

Deep learning has demonstrated radiograph screening performances that are comparable or superior to radiologists. However, recent studies show that deep models for thoracic disease classification usually show degraded performance when applied to external data. Such phenomena can be categorized into shortcut learning, where the deep models learn unintended decision rules that can fit the identically distributed training and test set but fail to generalize to other distributions. A natural way to alleviate this defect is explicitly indicating the lesions and focusing the model on learning the intended features. In this paper, we conduct extensive retrospective experiments to compare a popular thoracic disease classification model, CheXNet, and a thoracic lesion detection model, CheXDet. We first showed that the two models achieved similar image-level classification performance on the internal test set with no significant differences under many scenarios. Meanwhile, we found incorporating external training data even led to performance degradation for CheXNet. Then, we compared the models' internal performance on the lesion localization task and showed that CheXDet achieved significantly better performance than CheXNet even when given 80% less training data. By further visualizing the models' decision-making regions, we revealed that CheXNet learned patterns other than the target lesions, demonstrating its shortcut learning defect. Moreover, CheXDet achieved significantly better external performance than CheXNet on both the image-level classification task and the lesion localization task. Our findings suggest improving annotation granularity for training deep learning systems as a promising way to elevate future deep learning-based diagnosis systems for clinical usage.Comment: 22 pages of main text, 18 pages of supplementary table

DPPMask: Masked Image Modeling with Determinantal Point Processes

Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. Despite the empirical success, most previous works have neglected the important fact that it is unreasonable to force the model to reconstruct something beyond recovery, such as those masked objects. In this work, we show that uniformly random masking widely used in previous works unavoidably loses some key objects and changes original semantic information, resulting in a misalignment problem and hurting the representative learning eventually. To address this issue, we augment MIM with a new masking strategy namely the DPPMask by substituting the random process with Determinantal Point Process (DPPs) to reduce the semantic change of the image after masking. Our method is simple yet effective and requires no extra learnable parameters when implemented within various frameworks. In particular, we evaluate our method on two representative MIM frameworks, MAE and iBOT. We show that DPPMask surpassed random sampling under both lower and higher masking ratios, indicating that DPPMask makes the reconstruction task more reasonable. We further test our method on the background challenge and multi-class classification tasks, showing that our method is more robust at various tasks

Mesangial cells of lupus-prone mice are sensitive to chemokine production

Infectious antigens may be triggers for the exacerbation of systemic lupus erythematosus. The underlying mechanism causing acceleration and exacerbation of lupus nephritis (LN) is largely unknown. Bacterial lipopolysaccharide (LPS) is capable of inducing an accelerated model of LN in NZB/W mice, featuring diffuse proliferation of glomerular resident cells. We hypothesized that mesangial cells (MCs) from LN subjects are more responsive to LPS than normal subjects. Cultured primary NZB/W and DBA/W (nonautoimmune disease-prone strain with MHC class II molecules identical to those of NZB/W) MCs were used. Monocyte chemoattractant protein-1 (MCP-1) and osteopontin (OPN) expressions either in the baseline (normal culture) condition or in the presence of LPS were evaluated by real-time PCR, ELISA, or western blot analysis. NF-κB was detected by ELISA, electrophoresis mobility-shift assay, and immunofluorescence. First, either in the baseline condition or in the presence of LPS, NZB/W MCs produced significantly higher levels of MCP-1 and OPN than the DBA/W MC controls. Second, NZB/W MCs expressed significantly higher levels of Toll-like receptor 4, myeloid differentiation factor 88, and NF-κB than the DBA/W MC controls, both receiving exactly the same LPS treatment. In conclusion, NZB/W MCs are significantly more sensitive than their normal control DBA/W MCs in producing both MCP-1 and OPN. With LPS treatment, the significantly elevated levels of both chemokines produced by NZB/W MCs are more likely due to a significantly greater activation of the Toll-like receptor 4-myeloid differentiation factor 88-associated NF-κB pathway. The observed abnormal molecular events provide an intrarenal pathogenic pathway involved in an accelerated type of LN, which is potentially infection triggered

Statistical Evaluations of the Reproducibility and Reliability of 3-Tesla High Resolution Magnetization Transfer Brain Images: A Pilot Study on Healthy Subjects

Magnetization transfer imaging (MT) may have considerable promise for early detection and monitoring of subtle brain changes before they are apparent on conventional magnetic resonance images. At 3 Tesla (T), MT affords higher resolution and increased tissue contrast associated with macromolecules. The reliability and reproducibility of a new high-resolution MT strategy were assessed in brain images acquired from 9 healthy subjects. Repeated measures were taken for 12 brain regions of interest (ROIs): genu, splenium, and the left and right hemispheres of the hippocampus, caudate, putamen, thalamus, and cerebral white matter. Spearman's correlation coefficient, coefficient of variation, and intraclass correlation coefficient (ICC) were computed. Multivariate mixed-effects regression models were used to fit the mean ROI values and to test the significance of the effects due to region, subject, observer, time, and manual repetition. A sensitivity analysis of various model specifications and the corresponding ICCs was conducted. Our statistical methods may be generalized to many similar evaluative studies of the reliability and reproducibility of various imaging modalities

The Privacy Exposure Problem in Mobile Location-Based Services

Mobile location-based services (LBSs) empowered by mobile crowdsourcing provide users with context- aware intelligent services based on user locations. As smartphones are capable of collecting and disseminating massive user location-embedded sensing information, privacy preservation for mobile users has become a crucial issue. This paper proposes a metric called privacy exposure to quantify the notion of privacy, which is subjective and qualitative in nature, in order to support mobile LBSs to evaluate the effectiveness of privacy-preserving solutions. This metric incorporates activity coverage and activity uniformity to address two primary privacy threats, namely activity hotspot disclosure and activity transition disclosure. In addition, we propose an algorithm to minimize privacy exposure for mobile LBSs. We evaluate the proposed metric and the privacy-preserving sensing algorithm via extensive simulations. Moreover, we have also implemented the algorithm in an Android-based mobile system and conducted real-world experiments. Both our simulations and experimental results demonstrate that (1) the proposed metric can properly quantify the privacy exposure level of human activities in the spatial domain and (2) the proposed algorithm can effectively cloak users' activity hotspots and transitions at both high and low user-mobility levels

Ventricular divergence correlates with epicardial wavebreaks and predicts ventricular arrhythmia in isolated rabbit hearts during therapeutic hypothermia

INTRODUCTION: High beat-to-beat morphological variation (divergence) on the ventricular electrogram during programmed ventricular stimulation (PVS) is associated with increased risk of ventricular fibrillation (VF), with unclear mechanisms. We hypothesized that ventricular divergence is associated with epicardial wavebreaks during PVS, and that it predicts VF occurrence. METHOD AND RESULTS: Langendorff-perfused rabbit hearts (n = 10) underwent 30-min therapeutic hypothermia (TH, 30°C), followed by a 20-min treatment with rotigaptide (300 nM), a gap junction modifier. VF inducibility was tested using burst ventricular pacing at the shortest pacing cycle length achieving 1:1 ventricular capture. Pseudo-ECG (p-ECG) and epicardial activation maps were simultaneously recorded for divergence and wavebreaks analysis, respectively. A total of 112 optical and p-ECG recordings (62 at TH, 50 at TH treated with rotigaptide) were analyzed. Adding rotigaptide reduced ventricular divergence, from 0.13±0.10 at TH to 0.09±0.07 (p = 0.018). Similarly, rotigaptide reduced the number of epicardial wavebreaks, from 0.59±0.73 at TH to 0.30±0.49 (p = 0.036). VF inducibility decreased, from 48±31% at TH to 22±32% after rotigaptide infusion (p = 0.032). Linear regression models showed that ventricular divergence correlated with epicardial wavebreaks during TH (p<0.001). CONCLUSION: Ventricular divergence correlated with, and might be predictive of epicardial wavebreaks during PVS at TH. Rotigaptide decreased both the ventricular divergence and epicardial wavebreaks, and reduced the probability of pacing-induced VF during TH