Self-Healing, Flexible and Smart 3D Hydrogel Electrolytes Based on Alginate/PEDOT:PSS for Supercapacitor Applications.

Hydrogel electrolytes for energy storage devices have made great progress, yet they present a major challenge in the assembly of flexible supercapacitors with high ionic conductivity and self-healing properties. Herein, a smart self-healing hydrogel electrolyte based on alginate/poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) (alginate/PEDOT:PSS)(A/P:P) was prepared, wherein H2SO4 was employed as a polymeric initiator, as well as a source of ions. PEDOT:PSS is a semi-interpenetrating network (IPN) that has been used in recent studies to exhibit quick self-healing properties with the H2SO3 additive, which further improves its mechanical strength and self-healing performance. A moderate amount of PEDOT:PSS in the hydrogel (5 mL) was found to significantly improve the ionic conductivity compared to the pure hydrogel of alginate. Interestingly, the alginate/PEDOT:PSS composite hydrogel exhibited an excellent ability to self-heal and repair its original composition within 10 min of cutting. Furthermore, the graphite conductive substrate-based supercapacitor with the alginate/PEDOT:PSS hydrogel electrolyte provided a high specific capacitance of 356 F g-1 at 100 mV/s g-1. The results demonstrate that the A/P:P ratio with 5 mL PEDOT:PSS had a base sheet resistance of 0.9 Ω/square. This work provides a new strategy for designing flexible self-healing hydrogels for application in smart wearable electronics.

Post-acute sequelae of SARS-CoV-2 with clinical condition definitions and comparison in a matched cohort.

Disease characterization of Post-Acute Sequelae of SARS-CoV-2 (PASC) does not account for pre-existing conditions and time course of incidence. We utilized longitudinal data and matching to a COVID PCR-negative population to discriminate PASC conditions over time within our patient population during 2020. Clinical Classification Software was used to identify PASC condition groupings. Conditions were specified acute and persistent (occurring 0-30 days post COVID PCR and persisted 30-120 days post-test) or late (occurring initially 30-120 days post-test). We matched 3:1 COVID PCR-negative COVIDPCR-positive by age, sex, testing month and service area, controlling for pre-existing conditions up to four years prior; 28,118 PCR-positive to 70,293 PCR-negative patients resulted. We estimated PASC risk from the matched cohort. Risk of any PASC condition was 12% greater for PCR-positive patients in the late period with a significantly higher risk of anosmia, cardiac dysrhythmia, diabetes, genitourinary disorders, malaise, and nonspecific chest pain. Our findings contribute to a more refined PASC definition which can enhance clinical care.

Continuously updated forecasting of SARS-CoV-2 in a regional health system.

OBJECTIVES: To build a model of local hospital utilization resulting from SARS-CoV-2 and to continuously update it with new data. STUDY DESIGN: Retrospective analysis of real performance resulting from a model deployed in a major regional health system. METHODS: Using hospitalization data from the Kaiser Permanente Mid-Atlantic States integrated care system during the period from March 10, 2020, through December 31, 2020, and a custom-developed genetic particle filtering algorithm, we modeled the SARS-CoV-2 outbreak in the mid-Atlantic region. This model produced weekly forecasts of COVID-19-related hospital admissions, which we then compared with actual hospital admissions over the same period. RESULTS: We found that the model was able to accurately capture the data-generating process (weekly mean absolute percentage error, 10.0%-48.8%; Anderson-Darling P value of .97 when comparing percentiles of observed admissions with the uniform distribution) once the effects of social distancing could be accurately measured in mid-April. We also found that our estimates of key parameters, including the reproductive rate, were consistent with consensus literature estimates. CONCLUSIONS: The genetic particle filtering algorithm that we have proposed is effective at modeling hospitalizations due to SARS-CoV-2. The methods used by our model can be reproduced by any major health care system for the purposes of resource planning, staffing, and population care management to create an effective forecasting regimen at scale.

Early Referral, Living Donation, and Preemptive Kidney Transplant.

BACKGROUND: Preemptive kidney transplant (PKT) is recognized as the most beneficial and cost-effective form of renal replacement therapy among patients with end-stage renal disease. Despite optimal outcomes and improved quality of life associated with PKT, its use as a first renal replacement therapy remains low among patients with end-stage renal disease. The goal of this retrospective cohort study was to compare, among adult kidney transplant recipients, characteristics across PKT status. METHODS: We compared the characteristics of patients who did and did not have a PKT over 5 years, from 2010 to 2014, using the electronic health records of Kaiser Permanente Mid-Atlantic States. RESULTS: A total of 233 patients received a kidney-alone transplant, and, of these, 44 patients (19%) were PKT and 189 patients (81%) were non-PKT. Of the patients in the PKT group, 43% received a kidney from a deceased donor. PKT recipients were more often White, had polycystic kidney disease or glomerulonephritis, received a living donor organ, and were transplanted at certain transplant centers. Estimated glomerular filtration rate on listing for those who received a deceased donor transplant was higher in PKT than non-PKT patients listed pre-dialysis. CONCLUSIONS: PKT was associated with having a living kidney donor and with having a higher estimated glomerular filtration rate at listing for deceased donor recipients.

Patient Demographics and the Utilization of a Novel 3-Anatomic-Site Testing Panel Including Rectal Self-Collection as Compared With Usual Care Testing.

BACKGROUND: At Kaiser Permanente Mid-Atlantic States, we designed a 3-anatomic-site panel (urine, oropharynx, and rectum) with a self-collect feature for rectal sites. We compared the proportion tested at each anatomic site, demographic factors, and HIV status between those who received the 3-site panel versus usual care. METHODS: Patients entered our laboratories without a prior appointment and underwent urine (usual care [patient collected]), oropharynx (laboratory technician collected), and rectal site (patient collected) testing. Providers recommended the panel to their patients. Patients then had the choice to accept or to reject the panel. Multivariate and logistic regressions were conducted to explore the relationship of age, sex, race, and HIV status with Neisseria gonorrhoeae (GC) and Chlamydia trachomatis (CT) test results as well as the type of testing (3-site panel vs. usual care testing) received. RESULTS: A total of 145,854 patients received usual care testing as compared with 9227 who received the panel. For those who underwent usual care testing, 4.0% tested positive for CT and 0.85% for GC. For those who received the panel, 9.1% tested positive for CT and 6.4% for GC. Those who received the 3-site panel were more likely to test positive for CT (odds ratio [OR], 2.70; confidence interval [CI], 2.46-2.97) and GC (OR, 4.00; CI, 3.59-4.64). White patients were the most likely to receive the panel compared with Black patients (OR, 3.14; CI, 2.96-3.33). Patients with HIV had greater odds of undergoing the panel (OR, 15.62; CI, 14.67-16.64) and of testing positive for CT (OR, 1.27; CI, 1.07-1.51) and GC (OR, 1.39; CI, 1.14-1.68). CONCLUSIONS: Patients who received the panel had higher odds of testing positive for CT and GC compared with patients with usual testing. Physician training may address the racial and sex differences observed in the panel enrollment and increase utilization. Self-collection for rectal sites should lead to higher detection of CT and GC.

Exploiting hierarchy in medical concept embedding.

OBJECTIVE: To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation. MATERIALS AND METHODS: We trained concept embeddings using several new extensions to the Word2Vec algorithm using a dataset of approximately 600,000 patients from a major integrated healthcare organization in the Mid-Atlantic US. Our concept embeddings included additional entities to account for the medical categories assigned to codes by the Clinical Classification Software Revised (CCSR) dataset. We compare these results to sets of publicly released pretrained embeddings and alternative training methodologies. RESULTS: We found that Word2Vec models which included hierarchical data outperformed ordinary Word2Vec alternatives on tasks which compared naïve clusters to canonical ones provided by CCSR. Our Skip-Gram model with both codes and categories achieved 61.4% normalized mutual information with canonical labels in comparison to 57.5% with traditional Skip-Gram. In models operating on two different outcomes, we found that including hierarchical embedding data improved classification performance 96.2% of the time. When controlling for all other variables, we found that co-training embeddings improved classification performance 66.7% of the time. We found that all models outperformed our competitive benchmarks. DISCUSSION: We found significant evidence that our proposed algorithms can express the hierarchical structure of medical codes more fully than ordinary Word2Vec models, and that this improvement carries forward into classification tasks. As part of this publication, we have released several sets of pretrained medical concept embeddings using the ICD-10 standard which significantly outperform other well-known pretrained vectors on our tested outcomes.

Patient experience after modifying visit delivery during the COVID-19 pandemic.

OBJECTIVES: To describe real-time changes in medical visits (MVs), visit mode, and patient-reported visit experience associated with rapidly deployed care reorganization during the coronavirus disease 2019 (COVID-19) pandemic. STUDY DESIGN: Cross-sectional time series from September 29, 2019, through June 20, 2020. METHODS: Responding to official public health and clinical guidance, team-based systematic structural changes were implemented in a large, integrated health system to reorganize and transition delivery of care from office-based to virtual care platforms. Overall and discipline-specific weekly MVs, visit mode (office-based, telephone, or video), and associated aggregate measures of patient-reported visit experience were reported. A 38-week time-series analysis with March 8, 2020, and May 3, 2020, as the interruption dates was performed. RESULTS: After the first interruption, there was a decreased weekly visit trend for all visits (ß3 = -388.94; P < .05), an immediate decrease in office-based visits (ß2 = -25,175.16; P < .01), increase in telephone-based visits (ß2 = 17,179.60; P < .01), and increased video-based visit trend (ß3 = 282.02; P < .01). After the second interruption, there was an increased visit trend for all visits (ß5 = 565.76; P < .01), immediate increase in video-based visits (ß4 = 3523.79; P < .05), increased office-based visit trend (ß5 = 998.13; P < .01), and decreased trend in video-based visits (ß5 = -360.22; P < .01). After the second interruption, there were increased weekly long-term visit trends for the proportion of patients reporting "excellent" as to how well their visit needs were met for all visits (ß5 = 0.17; P < .01), telephone-based visits (ß5 = 0.34; P < .01), and video-based visits (ß5 = 0.32; P < .01). Video-based visits had the highest proportion of respondents rating "excellent" as to how well their scheduling and visit needs were met. CONCLUSIONS: COVID-19 required prompt organizational transformation to optimize the patient experience.