BACKGROUND: In the United States (US), racial and socioeconomic disparities have been implicated in pediatric intensive care unit (PICU) admissions and outcomes, with higher rates of critical illness in more deprived areas. The degree to which this persists despite insurance coverage is unknown. We investigated whether disparities exist in PICU admission and mortality according to socioeconomic position and race in children receiving Medicaid.
METHODS: Using Medicaid data from 2007-2014 from 23 US states, we tested the association between area level deprivation and race on PICU admission (among hospitalized children) and mortality (among PICU admissions). Race was categorized as Black, White, other and missing. Patient-level ZIP Code was used to generate a multicomponent variable describing area-level social vulnerability index (SVI). Race and SVI were simultaneously tested for associations with PICU admission and mortality.
RESULTS: The cohort contained 8,914,347 children (23·0% Black). There was no clear trend in odds of PICU admission by SVI; however, children residing in the most vulnerable quartile had increased PICU mortality (aOR 1·12 (95%CI 1·04-1·20; p = 0·0021). Black children had higher odds of PICU admission (aOR 1·04; 95% CI 1·03-1·05; p < 0·0001) and higher mortality (aOR 1·09; 95% CI 1·02-1·16; p = 0·0109) relative to White children. Substantial state-level variation was apparent, with the odds of mortality in Black children varying from 0·62 to 1·8.
CONCLUSIONS: In a Medicaid cohort from 2007-2014, children with greater socioeconomic vulnerability had increased odds of PICU mortality. Black children were at increased risk of PICU admission and mortality, with substantial state-level variation. Our work highlights the persistence of sociodemographic disparities in outcomes even among insured children.

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OBJECTIVE: Emergency department observation units (EDOUs) are used to manage common pediatric illnesses and reduce the admission rate to the hospital. Most of these patients require a short duration of observation before a determination can be made whether they need to be admitted to the hospital or safely discharged home. The purpose of this study was to determine the characteristics of admissions from a pediatric EDOU for the top 10 diagnoses admitted to the unit. This will help standardize the disposition of such types of patients from the ED, hence improving the efficiency of the unit.
METHODS: We did a retrospective surveillance study of admitted patients from 0 to 18 years of age from the EDOU for the top 10 diagnoses. Descriptive data were reported using percentages and medians with interquartile ranges. Pearson χ2 tests were used to determine significant differences (P < 0.05) between the reason for admission and medical history.
RESULTS: In total, 520 patients were admitted from the EDOU during the study period. The median patient age was 3.39 years, with most being Hispanic and female. The top three primary diagnoses of all admitted patients were cellulitis and abscess, gastroenteritis, and bronchiolitis. Sixty-three percent of all admitted patients had secondary diagnoses. Most of these patients were admitted to the inpatient unit due to progression of the primary condition.
CONCLUSIONS: The characteristics of admissions from the EDOU may help us to understand historical experience regarding diagnoses, timing, and indications of deterioration, resource utilization, and other metrics that resulted in transfers of EDOU patients to the intensive care unit/operating room/inpatient units.

OBJECTIVE: Data on care home admission and survival rates of patients with syndromes associated with frontotemporal lobar degeneration (FTLD) are limited. However, their estimation is essential to plan trials and assess the efficacy of intervention. Population-based registers provide unique samples for this estimate. The aim of this study was to assess care home admission rate, survival rate, and their predictors in incident patients with FTLD-associated syndromes from the European FRONTIERS register-based study.
METHODS: We conducted a prospective longitudinal multinational observational registry study, considering incident patients with FTLD-associated syndromes diagnosed between June 1, 2018, and May 31, 2019, and followed for up to 5 years till May 31, 2023. We enrolled patients fulfilling diagnosis of the behavioral variant frontotemporal dementia (bvFTD), primary progressive aphasia (PPA), progressive supranuclear palsy (PSP) or corticobasal syndrome (CBS), and FTD with motor neuron disease (FTD-MND). Kaplan-Meier analysis and Cox multivariable regression models were used to assess care home admission and survival rates. The survival probability score (SPS) was computed based on independent predictors of survivorship.
RESULTS: A total of 266 incident patients with FTLD were included (mean age ± SD = 66.7 ± 9.0; female = 41.4%). The median care home admission rate was 97 months (95% CIs 86-98) from disease onset and 57 months (95% CIs 56-58) from diagnosis. The median survival was 90 months (95% CIs 77-97) from disease onset and 49 months (95% CIs 44-58) from diagnosis. Survival from diagnosis was shorter in FTD-MND (hazard ratio [HR] 4.59, 95% CIs 2.49-8.76, p < 0.001) and PSP/CBS (HR 1.56, 95% CIs 1.01-2.42, p = 0.044) compared with bvFTD; no differences between PPA and bvFTD were found. The SPS proved high accuracy in predicting 1-year survival probability (area under the receiver operating characteristic curve = 0.789, 95% CIs 0.69-0.87), when defined by age, European area of residency, extrapyramidal symptoms, and MND at diagnosis.
CONCLUSIONS: In FTLD-associated syndromes, survival rates differ according to clinical features and geography. The SPS was able to predict prognosis at individual patient level with an accuracy of ∼80% and may help to improve patient stratification in clinical trials. Future confirmatory studies considering different populations are needed.

BACKGROUND: The utilization of healthcare services in a growing population has raised concerns about its impact on clinical outcomes. Studies have shown that increased hospital census is associated with higher admission rates and unnecessary consults, tests, and procedures in various areas of healthcare. Traumatic brain injuries (TBIs), a significant concern due to their potential for long-term disabilities, are commonly encountered in intensive care units (ICUs) and are a leading cause of patient mortality. Despite extensive research on various aspects of TBI, the effect of the patient census on TBI outcomes remains unexplored. This study aims to investigate the relationship between healthcare provider patient census and clinical outcomes in TBI patients at a level I trauma center.
METHODS: A retrospective review was conducted from 2017 to 2022. The mean number of patients per day in the trauma service was determined, with patients below this average considered to be present on low-census days and those above it on high-census days. Patient demographics, mechanisms of injury, vital signs, TBI severity, and associated injuries were analyzed. Adjusted regression analyses were conducted.
RESULTS: Over the study period, 1,527 TBI patients were identified. Demographics were similar between patients admitted on high- and low-census days. Patients with moderate TBI were 30% less likely to be admitted to the ICU on high-census days, whereas there was no difference in ICU admission for patients with mild or severe TBI. Delirium was significantly higher in patients admitted on high-census days compared to those on low-census days. This was further identified to be predominantly driven by patients with mild TBI admitted on high-census days.
CONCLUSIONS: While most outcomes remained consistent, significant rates of delirium were found in our mild TBI patients admitted on high-census days suggesting the need for additional factors in the evaluation of these patients on admission. This study also reveals potential under-triage in moderate TBI patients on high-census days as they had significantly lower rates of ICU admission. These findings emphasize the need for further investigations to optimize patient care strategies within the context of fluctuating healthcare system demands.

BACKGROUND: Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful transfer into clinical practice has been rare. Especially the hospital domain offers great potential while facing several challenges including many documents per patient, multiple departments and complex interrelated processes.
METHODS: In this work, we survey relevant literature to identify and classify approaches which exploit NLP in the clinical context. Our contribution involves a systematic mapping of related research onto a prototypical patient journey in the hospital, along which medical documents are created, processed and consumed by hospital staff and patients themselves. Specifically, we reviewed which dataset types, dataset languages, model architectures and tasks are researched in current clinical NLP research. Additionally, we extract and analyze major obstacles during development and implementation. We discuss options to address them and argue for a focus on bias mitigation and model explainability.
RESULTS: While a patient\'s hospital journey produces a significant amount of structured and unstructured documents, certain steps and documents receive more research attention than others. Diagnosis, Admission and Discharge are clinical patient steps that are researched often across the surveyed paper. In contrast, our findings reveal significant under-researched areas such as Treatment, Billing, After Care, and Smart Home. Leveraging NLP in these stages can greatly enhance clinical decision-making and patient outcomes. Additionally, clinical NLP models are mostly based on radiology reports, discharge letters and admission notes, even though we have shown that many other documents are produced throughout the patient journey. There is a significant opportunity in analyzing a wider range of medical documents produced throughout the patient journey to improve the applicability and impact of NLP in healthcare.
CONCLUSIONS: Our findings suggest that there is a significant opportunity to leverage NLP approaches to advance clinical decision-making systems, as there remains a considerable understudied potential for the analysis of patient journey data.

The Directorate General of Health Services (DGHS), India, has released guidelines for intensive care unit (ICU) admission and discharge [1] to guide intensivists and registered medical practitioners (RMPs) in an Expert Consensus Statement (ECS). This is based on the recommendations of 24 experts working in different ICU settings. This team deserves applause for their efforts in creating guidelines for clinicians working in ICU settings. The Delphi method [2], considered one of the most scientific methods for such statements, has been used for this ECS.

BACKGROUND: We used the Spanish national hospital discharge data from 2016 to 2022 to analyze procedures and hospital outcomes among patients aged ≥ 18 years admitted for ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) according to diabetes mellitus (DM) status (non-diabetic, type 1-DM or type 2-DM).
METHODS: We built logistic regression models for STEMI/NSTEMI stratified by DM status to identify variables associated with in-hospital mortality (IHM). We analyzed the effect of DM on IHM.
RESULTS: Spanish hospitals reported 201,950 STEMIs (72.7% non-diabetic, 0.5% type 1-DM, and 26.8% type 2-DM; 26.3% female) and 167,285 NSTEMIs (61.6% non-diabetic, 0.6% type 1-DM, and 37.8% type 2-DM; 30.9% female). In STEMI, the frequency of percutaneous coronary intervention (PCI) increased among non-diabetic people (60.4% vs. 68.6%; p < 0.001) and people with type 2-DM (53.6% vs. 66.1%; p < 0.001). In NSTEMI, the frequency of PCI increased among non-diabetic people (43.7% vs. 45.7%; p < 0.001) and people with type 2-DM (39.1% vs. 42.8%; p < 0.001). In NSTEMI, the frequency of coronary artery by-pass grafting (CABG) increased among non-diabetic people (2.8% vs. 3.5%; p < 0.001) and people with type 2-DM (3.7% vs. 5.0%; p < 0.001). In the entire population, lower IHM was associated with undergoing PCI (odds ratio [OR] [95% confidence interval] = 0.34 [0.32-0.35] in STEMI; 0.24 [0.23-0.26] in NSTEMI) or CABG (0.33 [0.27-0.40] in STEMI; 0.45 [0.38-0.53] in NSTEMI). IHM decreased over time in STEMI (OR = 0.86 [0.80-0.93]). Type 2-DM was associated with higher IHM in STEMI (OR = 1.06 [1.01-1.11]).
CONCLUSIONS: PCI and CABG were associated with lower IHM in people admitted for STEMI/NSTEMI. Type 2-DM was associated with IHM in STEMI.

Adding continuous monitoring to usual care at an acute admission ward did not have an effect on the proportion of patients safely discharged. Implementation challenges of continuous monitoring may have contributed to the lack of effect observed.

BACKGROUND: Palliative care evolution focuses on education and medication accessibility. As little as 12% of palliative care needs are met. Assessment of the domains of Palliative care and patients\' and families\' experience are essential in life-limiting conditions. The Lagos University Teaching Hospital (LUTH), have the National Cancer Centre without offering palliative care services.
OBJECTIVE: The aim was to examine pattern of admissions and needs assessment for palliative services among patients admitted into LUTH wards.
METHODS: Responses were entered into a data sheet inputted into Epi info version 7.2. Descriptive characteristics of the participants were presented as frequencies and percentages for age, sex, pattern of disease, domains of Palliative care, Advance care Plan, Preparation for home care, death and Education about the illness and category of medical conditions (palliative and non-palliative conditions). Together for Short Lives (TfSL) tool was used to categorize respondents\' conditions into Palliative and Non-palliative conditions. Chi-square test was used to determine association between independent variables (pattern of diagnoses, stage of disease, advanced care plan, preparation for home care/ death and education on illness) and dependent variables (category of medical condition). Chi-square test was also used to explore the association between specialty of the managing doctor (independent variable) and Advance care plan (dependent variable). The level of statistical significance was P-value < 0.05.
RESULTS: 80.6% of the respondents had palliative care conditions, 83.7% had family members as their caregiver while 13.2% of the participants had no caregiver and 65.9% had no advance care plan. There was no preparation for home care or death in 72.1%, 70.5% had education about their illness, and 68.2% were in the advanced stage of their disease. Participants attending the surgery non-trauma unit (51.6%) were more likely to have advance care plans. Adults were more likely to have palliative care conditions (79.8%) compared to children (20.2%), and was statistically significant.
CONCLUSIONS: Majority of the participants need palliative care services but are unavailable and unmet and the most predominant condition was cancer. Majority had no advance care plan or preparation for home care or death despite having advanced stage of the disease. This survey emphasized the need for symptom management, communication and provision of support.