Novel intervention based on an individualized bundle of care to decrease infection in kidney transplant recipients.

BACKGROUND: Infection remains a relevant complication after kidney transplantation (KT). A well-established strategy in modern medicine is the application of bundles of evidence-based practice in clinical settings. The objective of this study is to explore the application of a personalized bundle of measures aimed to reduce the incidence of infection in the first 12 months after KT. METHODS: A single-center prospective cohort of 148 patients undergoing KT between February 2018 and September 2019 that received an individualized infection prevention strategy was compared to a preintervention cohort (n = 159). The bundle comprised a review of the patient's immunization history, infection risk by country of origin, screening for latent tuberculosis infection (LTBI), antimicrobial prophylaxis, and immunological assessment. Individualized recommendations were accordingly provided at a scheduled visit at day +30 after transplantation. RESULTS: The intervention cohort showed a higher compliance rate with the recommended vaccine schedule, screening for geographically restricted infections and LTBI, and intravenous immunoglobulin and vitamin D supplementation (p values <.001). The 1-year incidence rate of infection was lower in the intervention cohort (42.6% vs. 57.9%; p value = .037), as was the rate of infection-related hospitalization (17.6% vs. 32.1%; p value = .003) and the incidence of severe bacterial infection. There were no differences in graft rejection or mortality rates between groups. CONCLUSIONS: A multifaceted intervention, including a bundle of evidence-based practices, enhanced compliance with recommended preventive measures and was correlated with a reduction in the 12-month incidence of infection after KT.

Guiding safer risperidone prescribing in Alzheimer's disease with therapeutic drug monitoring.

Previous analysis of pharmacokinetic data on risperidone-treated patients with dementia predicted that 20% had concentration-to-dose (C/D) ratios of the active moiety (risperidone and 9-hydroxy(OH)-risperidone) above 14 ng/mL per mg/day, which were in turn associated with a greater risk of extrapyramidal side effects. This study aimed to further explore risperidone pharmacokinetics in a second dataset. Nonlinear mixed effects modelling, using a Bayesian approach, was applied to data from a randomized controlled trial of risperidone in people with dementia. Covariates included age and glomerular filtration rate (GFR). Age had a significant effect on risperidone clearance (ß = -1.5) and GFR on 9-OH-risperidone clearance (ß = 0.2). The model predicted that 26.2% (95% confidence interval 18.6-32.6%) had C/D ratios above 14 ng/mL per mg/day. These findings confirm the importance of age-related risperidone dose adjustments and argue strongly for therapeutic drug monitoring in the initial stages of treatment to identify those at greatest risk of toxicity.

International trade resilience and the Covid-19 pandemic.

The Covid-19 pandemic represents a low-probability, high-impact systemic risk that has severely disrupted international trade, reshaping the patterns of globalization. Drawing from the concept of supply chain resilience, which involves both the ability of a system to withstand an impact (robustness) and recover from it (responsiveness), we investigate country-level trade resilience during the 1st wave of the pandemic. By employing Fuzzy-set Qualitative Comparative Analysis (fsQCA), we identify configurations of country-level factors, i.e., country profiles, based on their effectiveness in engendering trade resilience. These factors include social and economic globalization, logistics performance, healthcare preparedness, national government response, and income level. The results show how these factors coalesced to strengthen (or weaken) international trade resilience, contributing to a holistic understanding of the impact of the pandemic on international trade. The findings inform the post-Covid-19 debate on international trade, with implications for managers and policymakers.

Overview of School-Based Telehealth Network Grant Program Services Delivered to Students in Rural Schools.

Telehealth can expand and enhance access to school-based health care, but its use has been relatively limited. Recognizing that school-based health care is still not reaching many students, the Health Resources and Services Administration (HRSA) funded the School Based Telehealth Network Grant Program to expand telehealth in rural school-based settings to help to increase the availability and use of these services. The 19 grantees delivered telehealth to over 200 schools across 17 states, choosing which services they would deliver and how. Looking across the services, these fell into three categories - primary/urgent care, behavioral health, and other more specialized services. The majority of grantees offered multiple telehealth services with the combination of behavioral health and primary/urgent care the most common. The current study adds to the literature by elucidating that telehealth in schools can address multiple clinical conditions through separate services even though doing so involves using various combinations of clinicians providing different services.

Multidisciplinary Care for Critical Limb Ischemia: Current Gaps and Opportunities for Improvement.

Critical limb ischemia (CLI), defined as ischemic rest pain or nonhealing ulceration due to arterial insufficiency, represents the most severe and limb-threatening manifestation of peripheral artery disease. A major challenge in the optimal treatment of CLI is that multiple specialties participate in the care of this complex patient population. As a result, the care of patients with CLI is often fragmented, and multidisciplinary societal guidelines have not focused specifically on the care of patients with CLI. Furthermore, multidisciplinary care has the potential to improve patient outcomes, as no single medical specialty addresses all the facets of care necessary to reduce cardiovascular and limb-related morbidity in this complex patient population. This review identifies current gaps in the multidisciplinary care of patients with CLI, with a goal toward increasing disease recognition and timely referral, defining important components of CLI treatment teams, establishing options for revascularization strategies, and identifying best practices for wound care post-revascularization.

Interactions of social, terrestrial, and marine sub-systems in the Galapagos Islands, Ecuador.

Galapagos is often cited as an example of the conflicts that are emerging between resource conservation and economic development in island ecosystems, as the pressures associated with tourism threaten nature, including the iconic and emblematic species, unique terrestrial landscapes, and special marine environments. In this paper, two projects are described that rely upon dynamic systems models and agent-based models to examine human-environment interactions. We use a theoretical context rooted in complexity theory to guide the development of our models that are linked to social-ecological dynamics. The goal of this paper is to describe key elements, relationships, and processes to inform and enhance our understanding of human-environment interactions in the Galapagos Islands of Ecuador. By formalizing our knowledge of how systems operate and the manner in which key elements are linked in coupled human-natural systems, we specify rules, relationships, and rates of exchange between social and ecological features derived through statistical functions and/or functions specified in theory or practice. The processes described in our models also have practical applications in that they emphasize how political policies generate different human responses and model outcomes, many detrimental to the social-ecological sustainability of the Galapagos Islands.

Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study.

BACKGROUND: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI. METHODS AND FINDINGS: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors. CONCLUSIONS: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies.

[Trichomycosis (trichobacteriosis) capitis in an infant: Microbiological, dermoscopic and ultrastructural features]. / Tricomicosis (tricobacteriosis) en un infante: Aspectos microbiológicos, dermatoscópicos y ultraestructurales.

Trichomycosis is a superficial infection caused by Corynebacterium flavescens, which regularly affects axillary, and to a a lesser extent, pubic, scrotal and intergluteal, and exceptionally, head hairs or trichomycosis capitis (TC). This condition is characterised by the formation of bacterial nodules. Clinically, it can be confused with white piedra or pediculosis. The diagnosis is made by microscopic and dermoscopic observation and confirmed by culture. OBJECTIVE: To present a case of TC in an infant and illustrate the microscopic, dermoscopic, and ultrastructural characteristics. CLINICAL CASE: A 6 month-old boy, otherwise healthy, with multiple yellowish concretions on the hairs of the head. TC was confirmed by yellow fluorescence with Wood’s light; white-yellowish beads, like “rosaries of crystalline stones’’ were observed on dermoscopy, direct examination showed bacterial masses, and Corynebacterium flavescens was identified by culture. A superficial infection, without perforation of the hairs, was confirmed by electron microscopy. Treatment with fusidic acid for 3 weeks achieved a clinical and microbiological cure. CONCLUSION: TC is a rare condition that affects children, and tends to be mistaken for other diseases of the hair, such as pediculosis and mycotic infections.

Impact of Glycoprotein IIb/IIIa Inhibitors Use on Outcomes After Lower Extremity Endovascular Interventions From Nationwide Inpatient Sample (2006-2011).

OBJECTIVE: The aim of our study was to study the impact of glycoprotein IIb/IIIa inhibitors (GPI) on in-hospital outcomes. BACKGROUND: There is paucity of data regarding the impact of GPI on the outcomes following peripheral endovascular interventions. METHODS: The study cohort was derived from Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database between the years 2006 and 2011. Peripheral endovascular interventions and GPI utilization were identified using appropriate ICD-9 Diagnostic and procedural codes. Two-level hierarchical multivariate mixed models were created. The study outcomes were: primary (in-hospital mortality and amputation studied separately) and secondary (composite of in-hospital mortality and postprocedural complications). Hospitalization costs were also assessed. RESULTS: GPI utilization (OR, 95% CI, P-value) was independently predictive of lower amputation rates (0.36, 0.27-0.49, <0.001). There was no significant difference in terms of in-hospital mortality (0.59, 0.31-1.14, P 0.117), although GPI use predicted worse secondary outcomes (1.23, 1.03-1.47, 0.023). Following propensity matching, the amputation rate was lower (3.2% vs. 8%, P < 0.001), while hospitalization costs were higher in the cohort that received GPI ($21,091 ± 404 vs. 19,407 ± 133, P < 0.001). CONCLUSIONS: Multivariate analysis revealed GPI use in peripheral endovascular interventions to be suggestive of an increase in composite end-point of in-hospital mortality and postprocedural complications, no impact on in-hospital mortality alone, significantly lower rate of amputation, and increase in hospitalization costs. © 2016 Wiley Periodicals, Inc.

Intravascular Ultrasound in Lower Extremity Peripheral Vascular Interventions: Variation in Utilization and Impact on In-Hospital Outcomes From the Nationwide Inpatient Sample (2006-2011).

PURPOSE: To examine the impact of intravascular ultrasound (IVUS) utilization during lower limb endovascular interventions as regards postprocedural complications and amputation. METHODS: The study cohort was derived from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database between the years 2006 and 2011. Peripheral endovascular interventions were identified using appropriate ICD-9 procedure codes. Two-level hierarchical multivariate mixed models were created. The co-primary outcomes were in-hospital mortality and amputation; the secondary outcome was postprocedural complications. Model results are given as the odds ratio (OR) and 95% confidence interval (CI). Hospitalization costs were also assessed. RESULTS: Overall, among the 92,714 patients extracted from the database during the observation period, IVUS was used in 1299 (1.4%) patients. IVUS utilization during lower extremity peripheral vascular procedures was independently predictive of a lower rate of postprocedural complications (OR 0.80, 95% CI 0.66 to 0.99, p=0.037) as well as lower amputation rates (OR 0.59, 95% CI 0.45 to 0.77, p<0.001) without any significant impact on in-hospital mortality. Multivariate analysis also revealed IVUS utilization to be predictive of a nonsignificant increase in hospitalization costs ($1333, 95% CI -$167 to +$2833, p=0.082). CONCLUSION: IVUS use during lower limb endovascular interventions is predictive of lower postprocedural complication and amputation rates with a nonsignificant increase in hospitalization costs.