Search

El endometrioma ovárico es un quiste con tejido endometrial ectópico que se asociaa disminución de la reserva ovárica, siendo su manejo en infertilidad controversial.Presentamos el caso de una mujer de 32 años con reserva ovárica disminuiday endometrioma mayor de 100 mm. Fue sometida a aspiración transvaginalecoguiada y a escleroterapia con etanol, lográndose reducción del tamaño en 65% alos tres meses. Posteriormente se realizó fecundación in vitro (FIV), consiguiéndoseembarazo. La exéresis del endometrioma es controversial, debido a que reduce lareserva ovárica. La escleroterapia demuestra conservarla, se asocia a una tasa bajade recurrencia y facilita la accesibilidad ovárica. La escleroterapia con etanol delendometrioma es una técnica ambulatoria, segura y eficaz que permite el embarazoen mujeres con infertilidad.

In the increasingly complex health care system, physicians require skills and knowledge to participate with multidisciplinary team members in quality improvement (QI) that adds value to health care organizations. The Educational and Clinical Leaders Improving Performance with Structured E3L training (ECLIPSE) program was developed to address this challenge. Clinically relevant components of lean management were leveraged to create an online, flipped-classroom curriculum, and this was paired with Kaizen adapted specifically for physicians and multidisciplinary clinicians to promote experiential skills utilization. The focus of each adapted Kaizen was a topic of institutional QI priority, such as improving patient throughput or reducing readmission rates. Participants were awarded certification in the E3 Leadership management system-a patient-centered, equity-focused system based on lean principles. After 4 years, 50 E3 Leadership certificates were awarded to multidisciplinary clinicians, including 30 to physicians; participants scored an average 85% on module quizzes. The ECLIPSE program has improved physician participation in multidisciplinary QI projects with institutional alignment.

BACKGROUND Regular medical care is important for people living with HIV. A no-show predictive model among people with HIV could improve clinical care by allowing providers to proactively engage patients at high risk of missing appointments. Epic, a major provider of electronic medical record systems, created a model that predicts a patient’s probability of being a no-show for an outpatient health care appointment; however, this model has not been externally validated in people with HIV. OBJECTIVE We examined the performance of Epic’s no-show model among people with HIV at an academic medical center and assessed whether the performance was impacted by the addition of demographic and HIV clinical information. METHODS We obtained encounter data from all in-person appointments among people with HIV from January 21 to March 30, 2022, at the University of Chicago Medicine. We compared the predicted no-show probability at the time of the encounter to the actual outcome of these appointments. We also examined the performance of the Epic model among people with HIV for only HIV care appointments in the infectious diseases department. We further compared the no-show model among people with HIV for HIV care appointments to an alternate random forest model we created using a subset of seven readily accessible features used in the Epic model and four additional features related to HIV clinical care or demographics. RESULTS We identified 674 people with HIV who contributed 1406 total scheduled in-person appointments during the study period. Of those, we identified 331 people with HIV who contributed 440 HIV care appointments. The performance of the Epic model among people with HIV for all appointments in any outpatient clinic had an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI 0.63-0.66) and for only HIV care appointments had an AUC of 0.63 (95% CI 0.59-0.67). The alternate model we created for people with HIV attending HIV care appointments had an AUC of 0.78 (95% CI 0.75-0.82), a significant improvement over the Epic model restricted to HIV care appointments (P200 copies per mL, lower CD4 T cell counts (both 50 to 3 and 200 to 3), and female sex. CONCLUSIONS For both models among people with HIV, performance was significantly lower than reported by Epic. The improvement in the performance of the alternate model over the proprietary Epic model demonstrates that, among people with HIV, the inclusion of demographic information may enhance the prediction of appointment attendance. The alternate model further reveals that the prediction of appointment attendance in people with HIV can be improved by using HIV clinical information such as CD4 count and HIV viral load test results as features in the model.

BACKGROUND Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in usual care of patients hospitalized with conditions related to unhealthy substance use. Rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. OBJECTIVE To provide a study protocol to evaluate health outcomes and cost-benefit of an AI-driven automated screener compared to manual human screening for unhealthy substance use. METHODS A pre-post design to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards in a single medical center. Effectiveness in terms of patient outcomes will be determined by non-inferior rates of interventions (brief intervention/motivational interviewing, medication assisted treatment, naloxone dispensing, referral to outpatient care) in the post-period by a substance use intervention team compared to pre-period. A separate analysis will be performed to assess the cost-benefit to the health system of using automated screening. RESULTS A natural language processing tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a non-inferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. The study is approved by the Institutional Review Board and registered at clinicaltrials.gov with a plan for implementation beginning in September 2022. CONCLUSIONS The use of augmented intelligence for clinical decision support is growing with an increasing number of artificial intelligence tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a non-inferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. CLINICALTRIAL ClinicalTrials.gov NCT03833804

COPD is the 5th-leading cause of admissions and third-leading cause of readmissions among US adults. Recent policies instituted financial penalties for excessive COPD readmissions.To evaluate changes in the quality of care for patients hospitalized for COPD following implementation of the Hospital Readmissions Reduction Program (HRRP).Retrospective cohort study of patients older than 40 years hospitalized for COPD across 995 US hospitals (Premier Healthcare Database).Quality of care before and after HRRP implementation was measured via adherence to recommended inpatient care treatments for acute exacerbations of COPD (recommended care, non-recommended care, "ideal care" [all recommended and no non-recommended care]). We included 662,842 pre-HRRP January 2010- September 2014) and 285,508 post-HRRP (October 2014- December 2018) admissions. Recommended care increased at a rate of 0.16% per-month pre-HRRP and 0.01% per-month post-HRRP (p0.001). Non-recommended care decreased at a rate of 0.15% per-month pre-HRRP and 0.13% per-month post-HRRP. Ideal care increased at a rate of 0.24% per-month pre-HRRP and 0.11% per-month post-HRRP (p0.001).The pre-HRRP trends toward improving care quality for inpatient COPD care slowed post-HRRP implementation. This suggests that financial penalties for readmissions did not stimulate higher quality of care for patients hospitalized with COPD. It remains unclear what policies or approaches will be effective to ensure high care quality for patients hospitalized with COPD exacerbations.

Matthew Bonomo,1,* Michael G Hermsen,1,* Samuel Kaskovich,1 Maximilian J Hemmrich,1 Juan C Rojas,2 Kyle A Carey,3 Laura Ruth Venable,4 Matthew M Churpek,5 Valerie G Press3,6 1Pritzker School of Medicine, University of Chicago, Chicago, IL, USA; 2Department of Medicine, Section of Pulmonary/Critical Care, University of Chicago, Chicago, IL, USA; 3Department of Medicine, Section of General Internal Medicine, University of Chicago, Chicago, IL, USA; 4Department of Medicine, Section of Hospitalist Medicine, University of Chicago, Chicago, IL, USA; 5Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin-Madison, Madison, WI, USA; 6Department of Pediatrics, Section of Academic Pediatrics, University of Chicago, Chicago, IL, USA*These authors contributed equally to this workCorrespondence: Valerie G Press, University of Chicago, 5841 S Maryland, MC 2007, Chicago, IL, 60637, USA, Tel +773-702-5170, Email vpress@medicine.bsd.uchicago.eduBackground: Chronic obstructive pulmonary disease (COPD) is a leading cause of hospital readmissions. Few existing tools use electronic health record (EHR) data to forecast patients’ readmission risk during index hospitalizations.Objective: We used machine learning and in-hospital data to model 90-day risk for and cause of readmission among inpatients with acute exacerbations of COPD (AE-COPD).Design: Retrospective cohort study.Participants: Adult patients admitted for AE-COPD at the University of Chicago Medicine between November 7, 2008 and December 31, 2018 meeting International Classification of Diseases (ICD)-9 or − 10 criteria consistent with AE-COPD were included.Methods: Random forest models were fit to predict readmission risk and respiratory-related readmission cause. Predictor variables included demographics, comorbidities, and EHR data from patients’ index hospital stays. Models were derived on 70% of observations and validated on a 30% holdout set. Performance of the readmission risk model was compared to that of the HOSPITAL score.Results: Among 3238 patients admitted for AE-COPD, 1103 patients were readmitted within 90 days. Of the readmission causes, 61% (n = 672) were respiratory-related and COPD (n = 452) was the most common. Our readmission risk model had a significantly higher area under the receiver operating characteristic curve (AUROC) (0.69 [0.66, 0.73]) compared to the HOSPITAL score (0.63 [0.59, 0.67]; p = 0.002). The respiratory-related readmission cause model had an AUROC of 0.73 [0.68, 0.79].Conclusion: Our models improve on current tools by predicting 90-day readmission risk and cause at the time of discharge from index admissions for AE-COPD. These models could be used to identify patients at higher risk of readmission and direct tailored post-discharge transition of care interventions that lower readmission risk.Keywords: chronic obstructive lung disease, COPD, readmissions, machine learning

Background Regular medical care is important for people living with HIV. A no-show predictive model among people with HIV could improve clinical care by allowing providers to proactively engage patients at high risk of missing appointments. Epic, a major provider of electronic medical record systems, created a model that predicts a patient’s probability of being a no-show for an outpatient health care appointment; however, this model has not been externally validated in people with HIV. Objective We examined the performance of Epic’s no-show model among people with HIV at an academic medical center and assessed whether the performance was impacted by the addition of demographic and HIV clinical information. Methods We obtained encounter data from all in-person appointments among people with HIV from January 21 to March 30, 2022, at the University of Chicago Medicine. We compared the predicted no-show probability at the time of the encounter to the actual outcome of these appointments. We also examined the performance of the Epic model among people with HIV for only HIV care appointments in the infectious diseases department. We further compared the no-show model among people with HIV for HIV care appointments to an alternate random forest model we created using a subset of seven readily accessible features used in the Epic model and four additional features related to HIV clinical care or demographics. Results We identified 674 people with HIV who contributed 1406 total scheduled in-person appointments during the study period. Of those, we identified 331 people with HIV who contributed 440 HIV care appointments. The performance of the Epic model among people with HIV for all appointments in any outpatient clinic had an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI 0.63-0.66) and for only HIV care appointments had an AUC of 0.63 (95% CI 0.59-0.67). The alternate model we created for people with HIV attending HIV care appointments had an AUC of 0.78 (95% CI 0.75-0.82), a significant improvement over the Epic model restricted to HIV care appointments (P200 copies per mL, lower CD4 T cell counts (both 50 to Conclusions For both models among people with HIV, performance was significantly lower than reported by Epic. The improvement in the performance of the alternate model over the proprietary Epic model demonstrates that, among people with HIV, the inclusion of demographic information may enhance the prediction of appointment attendance. The alternate model further reveals that the prediction of appointment attendance in people with HIV can be improved by using HIV clinical information such as CD4 count and HIV viral load test results as features in the model.

Background The intensive care unit (ICU)–ward transfer poses a particularly high-risk period for patients. The period after transfer has been associated with adverse events and additional work for care teams related to miscommunication or omission of information. Standardized handoff processes have been found to reduce communication errors and adverse patient events in other clinical environments but are understudied at the ICU-ward interface. We previously developed an electronic ICU-ward transfer tool, ICU-PAUSE, which embeds the key elements and diagnostic reasoning to facilitate a safe transfer of care at ICU discharge. Objective The aim of this study is to evaluate the implementation process of the ICU-PAUSE handoff tool across 10 academic medical centers, including the rate of adoption and acceptability, as perceived by clinical care teams. Methods ICU-PAUSE will be implemented in the medical ICU across 10 academic hospitals, with each site customizing the tool to their institution’s needs. Our mixed methods study will include a combination of a chart review, quantitative surveys, and qualitative interviews. After a 90-day implementation period, we will conduct a retrospective chart review to evaluate the rate of uptake of ICU-PAUSE. We will also conduct postimplementation surveys of providers to assess perceptions of the tool and its impact on the frequency of communication errors and adverse events during ICU-ward transfers. Lastly, we will conduct semistructured interviews of faculty stakeholders with subsequent thematic analysis with the goal of identifying benefits and barriers in implementing and using ICU-PAUSE. Results ICU-PAUSE was piloted in the medical ICU at Barnes-Jewish Hospital, the teaching hospital of Washington University School of Medicine in St. Louis, in 2019. As of July 2022, implementation of ICU-PAUSE is ongoing at 6 of 10 participating sites. Our results will be published in 2023. Conclusions Our process of ICU-PAUSE implementation embeds each step of template design, uptake, and customization in the needs of users and key stakeholders. Here, we introduce our approach to evaluate its acceptability, usability, and impact on communication errors according to the tenets of sociotechnical theory. We anticipate that ICU-PAUSE will offer an effective handoff tool for the ICU-ward transition that can be generalized to other institutions. International Registered Report Identifier (IRRID) DERR1-10.2196/40918

Intensive care unit (ICU)-ward patient transfers are inherently high risk, and clinician miscommunication has been linked to adverse events and negative outcomes. Despite these risks, few educational tools exist to improve resident handoff communication at ICU-ward transfer.We used human-centered design (HCD) methods to cocreate a novel electronic health record ICU-ward transfer tool alongside Internal Medicine residents at three academic hospitals.We conducted HCD workshops at each hospital, performing process mapping, brainstorming, and rapid prototyping. We performed thematic analysis on verbatim-transcribed workshop audio recordings to inform development and adaptation of the final resident prototype into the ICU-PAUSE tool.ICU-PAUSE focuses on reasons for ICU admission and problem-based ICU course (I); Code status, goals of care, and family contacts (C); a diagnostic pause acknowledging Uncertainty (U); Pending tests (P); Active consultants (A); high-risk medications, including medications to be Unprescribed (U); Summary of problems and to-dos (S); and a current physical Exam (E).We used HCD to cocreate a novel, more user-friendly electronic ICU-ward transfer tool, ICU-PAUSE, alongside Internal Medicine trainees. Future steps will involve formal usability testing, evidence-driven implementation, and clinical evaluation of ICU-PAUSE across multiple hospitals.

Background Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. Objective This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use. Methods A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models. Results The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025. Conclusions The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. Trial Registration ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804 International Registered Report Identifier (IRRID) DERR1-10.2196/42971

Predictive analytic models leveraging machine learning methods increasingly have become vital to health care organizations hoping to improve clinical outcomes and the efficiency of care delivery for all patients. Unfortunately, predictive models could harm populations that have experienced interpersonal, institutional, and structural biases. Models learn from historically collected data that could be biased. In addition, bias impacts a model’s development, application, and interpretation. We present a strategy to evaluate for and mitigate biases in machine learning models that potentially could create harm. We recommend analyzing for disparities between less and more socially advantaged populations across model performance metrics (eg, accuracy, positive predictive value), patient outcomes, and resource allocation and then identify root causes of the disparities (eg, biased data, interpretation) and brainstorm solutions to address the disparities. This strategy follows the lifecycle of machine learning models in health care, namely, identifying the clinical problem, model design, data collection, model training, model validation, model deployment, and monitoring after deployment. To illustrate this approach, we use a hypothetical case of a health system developing and deploying a machine learning model to predict the risk of mortality in 6 months for patients admitted to the hospital to target a hospital’s delivery of palliative care services to those with the highest mortality risk. The core ethical concepts of equity and transparency guide our proposed framework to help ensure the safe and effective use of predictive algorithms in health care to help everyone achieve their best possible health.

School children are highly susceptible to chemicals in the atmosphere. Some studies have demonstrated that the pollution is related to respiratory problems and impaired cognitive development in children. In this context, the objective of this study was to determine nanoparticles and associated species and their origin in fresh air currents in La Salle Canoas School in Rio Grande do Sul (Brazil). Different analyses by using high-resolution transmission electron microscopy with energy-dispersive, X-ray spectroscopy (HR-TEM/SAED/EDS), and the scanning (STEM) mode were carried out. The EDS analysis of the atmospheric particles demonstrated that materials contain a significant proportion of elements such as Cr, Fe, Mn, Ni, and Si and other trace metals Zn, Pb, Zr, Ca, Cu, K, Cr, and Cd from exhaust and non-exhaust. The main source of the particles was emission from internal combustion in motor vehicles, road dust, and brake and tire wear. This emphasizes the importance of urban planning to reduce exposure of children to traffic gas emissions.

BackgroundThere is limited literature about physician handoffs between the intensive care unit (ICU) and the ward, and best practices have not been described. These patients are uniquely vulnerable given their medical complexity, diagnostic uncertainty and reduced monitoring intensity. We aimed to characterise the structure, perceptions and processes of ICU–ward handoffs across three teaching hospitals using multimodal methods: by identifying the handoff components involved in communication failures and describing common processes of patient transfer.MethodsWe conducted a study at three academic medical centres using two methods to characterise the structure, perceptions and processes of ICU–ward transfers: (1) an anonymous resident survey characterising handoff communication during ICU–ward transfer, and (2) comparison of process maps to identify similarities and differences between ICU–ward transfer processes across the three hospitals.ResultsOf the 295 internal medicine residents approached, 175 (59%) completed the survey. 87% of the respondents recalled at least one adverse event related to communication failure during ICU–ward transfer. 95% agreed that a well-structured handoff template would improve ICU–ward transfer. Rehabilitation needs, intravenous access/hardware and risk assessments for readmission to the ICU were the most frequently omitted or incorrectly communicated components of handoff notes. More than 60% of the respondents reported that notes omitted or miscommunicated pending results, active subspecialty consultants, nutrition and intravenous fluids, antibiotics, and healthcare decision-maker information at least twice per month. Despite variable process across the three sites, all process maps demonstrated flaws and potential for harm in critical steps of the ICU–ward transition.ConclusionIn this multisite study, despite significant process variation across sites, almost all resident physicians recalled an adverse event related to the ICU–ward handoff. Future work is needed to determine best practices for ICU–ward handoffs at academic medical centres.

RATIONALE: Patient transfers from the intensive care unit (ICU) to the hospital ward are a vulnerable time for patients recovering from critical illness. Our prior work showed that ICU-ward transfers vary widely across sites. Previously, we used Human-Centered Design-an iterative, collaborative process for user-focused solutions-to develop a prototype structured ICU-ward transfer communication tool for clinicians at three academic Internal Medicine residency programs. We describe here an iterative approach to rapid prototyping and updating of this novel tool for integration into the electronic health record (EHR), based on structured focus groups using Design Thinking methodology. METHODS: In spring 2020, we conducted voluntary focus groups of PGY-2 and PGY-3 Internal Medicine Residents to co-create new iterations of an ICUward transfer tool created through prior focus groups. Each site conducted 2 focus groups with 4-10 participants per session. All focus groups were conducted via videoconference due to the COVID-19 pandemic. We used participant feedback to iteratively modify the ICU-ward transfer tool towards an ideal state. With participants' consent, we recorded and anonymously transcribed the focus group discussions conducted over the Zoom platform. We performed qualitative inquiry on transcripts for coding and thematic content analysis. Coding was performed independently by two coders (LS, BG) using both a theory-driven (deductive) and data-driven (inductive) approach;disparate coding was reconciled via in-depth discussion between coders. RESULTS: Focus groups identified several main themes around the role, content, and workflows related to an ideal ICU-wards handoff tool: (1) how the tool can best serve the needs of ICU and wards teams;(2) that the ICU-ward handoff process must balance thoroughness and usability;and (3) that design and implementation must consider user experience and training in best practices. Under these themes, participants identified specific attributes of an ideal handoff tool (Table 1), which coalesced around 3 main goals: (1) to synthesize key details and communicate the ICU team's thought process (summarized in Table 1A);(2) to integrate the new tool into the EHR within a customizable note template that minimizes click burden and redundancy (1B);and (3) to serve as a standardized outline to reduce errors of omission in written and verbal handoffs processes (1C). CONCLUSIONS: Participants valued organization of handoff information, EHR integration and user experience, and standardization and adaptability of workflows when modifying a framework based on prior resident user input to create a more user-friendly ICU-ward transfer tool using a Design Thinking Methodology.

Rationale: Determining when an intensive care unit (ICU) patient is ready for discharge to the ward is a complex daily challenge for any ICU care team. Patients who experience unplanned readmissions to the ICU have increased mortality, length of stay, and cost compared with those not readmitted during their hospital stay. The accuracy of clinician prediction for ICU readmission is unknown. Objectives: To determine the accuracy of ICU physicians and nurses for predicting ICU readmissions Methods: We conducted a prospective study in the medical ICU of an academic hospital from October 2015 to September 2017. After daily rounding for patients being transferred to the ward, ICU clinicians (nurses, residents, fellows, and attendings) were asked to report the likelihood of readmission within 48 hours (using a 1–10 scale, with 10 being “extremely likely”). The accuracy of the clinician prediction score (1–10) was assessed for all clinicians and by clinician type using sensitivity, specificity, and area under the curve (AUC) for the receiver operating characteristic curve for predicting the primary outcome, which was ICU readmission within 48 hours of ICU discharge. Results: A total of 2,833 surveys was collected for 938 ICU-to-ward transfers, of which 40 (4%) were readmitted to the ICU within 48 hours of transfer. The median clinician likelihood of readmission score was 3 (interquartile range, 2–4). When physician and nurse likelihood scores were combined, the median clinician likelihood score had an AUC of 0.70 (95% confidence interval [CI], 0.62–0.78) for predicting ICU readmission within 48 hours. Nurses were significantly more accurate than interns at predicting 48-hour ICU readmission (AUC, 0.73 [95% CI, 0.64–0.82] vs. AUC, 0.60 [95% CI, 0.49–0.71]; P = 0.03). All other pairwise comparisons were not significantly different for predicting ICU readmission within 48 hours (P > 0.05 for all comparisons). Conclusions: We found that all clinicians surveyed in our ICU, regardless of the level of experience or clinician type, had only fair accuracy for predicting ICU readmission. Further research is needed to determine if clinical decision support tools would provide prognostic value above and beyond clinical judgment for determining who is ready for ICU discharge.

Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.We aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.This observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).Eleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.A machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

This cross-sectional study evaluates measures of health equity in COVID-19 vaccination plans in the 50 US states and Washington, DC.

Multi-criteria decision-making methods have emerged as a very useful tool to find solutions under different criteria or conflicting points of view, also in energy planning. However, social and environmental criteria have not been fully integrated in rural electrification projects design and play often an opposing role to technical and economic criteria. We propose the combined application of two multi-criteria decision-making methods, namely, the Analytical Hierarchy Process (AHP) and Compromise Ranking method (VIKOR), to facilitate the selection of the best solution for electrical supply of remote rural locations, involving technical, economic, environmental and social criteria. A group of experts were consulted for weighting the preferences of the criteria, sub-criteria and category of deciders. Thirteen discrete alternatives were proposed; the first six were associated with optimal multi-objective solutions pertaining to dispersed decentralized generation and another six multi-objective options to compact decentralized generation (CDG), and last, extension of the public electric grid was considered. The results reveal that the combination of multi-criteria methods facilitates decision-making in a manner that is robust, comprehensive, transparent and coherent to sustainable development requirements. CDG resulted in the best model of electrical supply.

Objetivos: Establecer la prevalencia de positividad, la tasa de conversión de la tuberculina en trabajadores de nuestro hospital y describir las características demográficas y laborales asociadas a esto.Materiales y Métodos Estudio observacional descriptivo en una cohorte de trabajadores del hospital a los que se les realizó la prueba de tuberculina. Se definió la positividad de la prueba de tuberculina como un resultado mayor o igual a 10 mm y la conversión como un aumento de 6 o 10 mm con respecto a la prueba inicial.Resultados Se encontró una prevalencia de 23,7 % y una incidencia de conversión de 13,6 % para el punto de corte mayor de 10 mm y 23 % para el punto de corte de 6 mm. La edad de los sujetos estuvo relacionada a conversión, mientras que no se encontró relación con la ocupación.Conclusión La probabilidad de transmisión de la tuberculosis en trabajadores del hospital es mayor al de la población general. Deben ser implementadas medidas de promoción y prevención para disminuir la transmisión e incrementar el conocimiento de la tuberculosis asociada al cuidado de la salud en los trabajadores.

This work presents and examines the mathematical methods utilised for electrification planning in rural environments with decentralised energy sources. The main objective is to obtain conclusions on the best possible formulation of the multi-criteria decision-making problem related to the alternatives for a robust planning that is coherent to the local environment and needs of resident. From previous works, it is clear that the on-going paradigm shift started in the 1990s. The new paradigm is characterised by the inclusion of more than one objective or evaluation criterion. This objective makes electrification planning a multidimensional process with clear evidence that the multi-criteria decision-making models offer better responses for the present-day requirements of decentralised energy planning in rural environments.

The impact of NPC1L1 and ezetimibe on cholesterol absorption are well documented. However, their potential consequences relative to absorption and metabolism of other nutrients have been only minimally investigated. Thus studies were undertaken to investigate the possible effects of this protein and drug on fat absorption, weight gain, and glucose metabolism by using Npc1l1−/−and ezetimibe-treated mice fed control and high-fat, high-sucrose diets. Results show that lack of NPC1L1 or treatment with ezetimibe reduces weight gain when animals are fed a diabetogenic diet. This resistance to diet-induced obesity results, at least in part, from significantly reduced absorption of dietary saturated fatty acids, particularly stearate and palmitate, since food intake did not differ between groups. Expression analysis showed less fatty acid transport protein 4 (FATP4) in intestinal scrapings of Npc1l1−/−and ezetimibe-treated mice, suggesting an important role for FATP4 in intestinal absorption of long-chain fatty acids. Concomitant with resistance to weight gain, lack of NPC1L1 or treatment with ezetimibe also conferred protection against diet-induced hyperglycemia and insulin resistance. These unexpected beneficial results may be clinically important, given the focus on NPC1L1 as a target for the treatment of hypercholesterolemia.

Objectives To establish the prevalence of positivity and conversion rate of the tuberculin skin test in workers of our hospital, and to describe the related demographic and occupational characteristics. Materials and Methods An observational, descriptive study was conducted in a cohort of hospital workers who underwent the tuberculin skin test. The positivity of the test was defined as a result of greater than or equal to 10 mm, and conversion was defined as an increase of 6 or 10 mm with respect to the initial test. Results Prevalence of 23.7 % and incidence of conversion of 13.6 % for the major cut-off point of 10 mm, and 23 % for the lesser cut-off point of 6 mm, was found. The age of the subjects was related to the conversion, but there was no relation with occupation. Conclusions The probability of tuberculosis transmission in health-care workers is higher than in the general population. Promotion and prevention measures must be implemented to decrease the transmission and to increase awareness of tuberculosis related to occupational activities.

A query written in natural language (NL) may involve several linguistic problems that cause a query not being interpreted or translated correctly into SQL. One of these problems is implicit information or semantic ellipsis, which can be understood as the omission of important words in the wording of a query written in NL. An exhaustive survey on NLIDB works has revealed that most of these works has not systematically dealt with semantic ellipsis. In experiments conducted on commercial NLIDBs, very poor results have been obtained (7% to 16.9%) when dealing with query corpora that involve semantic ellipsis. In this paper we propose a dialogue manager (DM) for a NLIDB for solving semantic ellipsis problems. The operation of this DM is based on a typification of elliptical problems found in queries, which permits to systematically deal with this problem. Additionally, the typification has two important characteristics: domain independence, which permits the typification to be applied to queries of different databases, and generality, which means that it holds for different languages such as English, French, Italian, Spanish, etc. These characteristics are inherited to the dialogue processes implemented in the DM, since they are based on this typification. In experiments conducted with this DM and a NLIDB on a corpus of elliptical queries, an increase of correctly answered queries of 30-35% was attained.

This study evaluated the contributions of carboxyl ester lipase (CEL) and pancreatic triglyceride lipase (PTL) in lipid nutrient absorption. Results showed PTL deficiency has minimal effect on triacylglycerol (TAG) absorption under low fat dietary conditions. Interestingly, PTL(-)(/)(-) mice displayed significantly reduced TAG absorption compared with wild type mice under high fat/high cholesterol dietary conditions (80.1 +/- 3.7 versus 91.5 +/- 0.7%, p < 0.05). Net TAG absorption was reduced further to 61.1 +/- 3.8% in mice lacking both PTL and CEL. Cholesterol absorption was 41% lower in PTL(-/-) mice compared with control mice (p < 0.05), but this difference was not exaggerated in PTL(-/-), CEL(-/-) mice. Retinyl palmitate absorption was reduced by 45 and 60% in PTL(-/-) mice (p < 0.05) and PTL(-/-), CEL(-/-) mice (p < 0.01), respectively. After 15 weeks of feeding, the high fat/high cholesterol diet, wild type, and CEL(-/-) mice gained approximately 24 g of body weight. However, body weight gain was 6.2 and 8.6 g less (p < 0.01) in PTL(-/-) and PTL(-/-), CEL(-/-) mice, respectively, despite their consumption of comparable amounts of the high fat/high cholesterol diet. The decrease body weight gain in PTL(-/-) and PTL(-/-), CEL(-/-) mice was attributed to their absorption of fewer calories from the high fat/high cholesterol diet, thereby resulting in less fat mass accumulation than that observed in wild type and CEL(-/-) mice. Thus, this study documents that PTL and CEL serve complementary functions, working together to mediate the absorption of a major portion of dietary fat and fat-soluble vitamin esters. The reduced lipid absorption efficiency due to PTL and CEL inactivation also resulted in protection against diet-induced obesity.