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Background Hemophagocytic lymphohistiocytosis (HLH) is a rare though often fatal hyperinflammatory syndrome mimicking sepsis in the critically ill. Diagnosis relies on the HLH-2004 criteria and HScore, both of which have been developed in pediatric or adult non-critically ill patients, respectively. Therefore, we aimed to determine the sensitivity and specificity of HLH-2004 criteria and HScore in a cohort of adult critically ill patients. Methods In this further analysis of a retrospective observational study, patients ≥ 18 years admitted to at least one adult ICU at Charité – Universitätsmedizin Berlin between January 2006 and August 2018 with hyperferritinemia of ≥ 500 μg/L were included. Patients’ charts were reviewed for clinically diagnosed or suspected HLH. Receiver operating characteristics (ROC) analysis was performed to determine prediction accuracy. Results In total, 2623 patients with hyperferritinemia were included, of whom 40 patients had HLH. We found the best prediction accuracy of HLH diagnosis for a cutoff of 4 fulfilled HLH-2004 criteria (95.0% sensitivity and 93.6% specificity) and HScore cutoff of 168 (100% sensitivity and 94.1% specificity). By adjusting HLH-2004 criteria cutoffs of both hyperferritinemia to 3000 μg/L and fever to 38.2 °C, sensitivity and specificity increased to 97.5% and 96.1%, respectively. Both a higher number of fulfilled HLH-2004 criteria [OR 1.513 (95% CI 1.372–1.667); p  <  0.001] and a higher HScore [OR 1.011 (95% CI 1.009–1.013); p  <  0.001] were significantly associated with in-hospital mortality. Conclusions An HScore cutoff of 168 revealed a sensitivity of 100% and a specificity of 94.1%, thereby providing slightly superior diagnostic accuracy compared to HLH-2004 criteria. Both HLH-2004 criteria and HScore proved to be of good diagnostic accuracy and consequently might be used for HLH diagnosis in critically ill patients. Clinical trial registration The study was registered with www.ClinicalTrials.gov ( NCT02854943 ) on August 1, 2016.

Ferritin is the major iron storage protein and an acute phase reactant. Hyperferritinemia is frequently seen in the critically ill where it has been hypothesized that not only underlying conditions but also factors such as transfusions, hemodialysis and extracorporeal life support (ECLS) lead to hyperferritinemia. This study aims to investigate the influence of transfusions, hemodialysis, and ECLS on hyperferritinemia in a multidisciplinary ICU cohort. This is a post-hoc analysis of a retrospective observational study including patients aged [greater than or equal to] 18 years who were admitted to at least one adult ICU between January 2006 and August 2018 with hyperferritinemia [greater than or equal to] 500 [mu]g/L and of [greater than or equal to] 14 days between two ICU ferritin measurements. Patients with hemophagocytic lymphohistiocytosis (HLH) were excluded. To identify the influence of transfusions, hemodialysis, and ECLS on ferritin change, multivariable linear regression analysis with ferritin change between two measurements as dependent variable was performed. A total of 268 patients was analyzed. Median duration between measurements was 36 days (22-57). Over all patients, ferritin significantly increased between the first and last measurement (p = 0.006). Multivariable linear regression analysis showed no effect of transfusions, hemodialysis, or ECLS on ferritin change. Changes in aspartate aminotransferase (ASAT) and sequential organ failure assessment (SOFA) score were identified as influencing factors on ferritin change [unstandardized regression coefficient (B) = 2.6; (95% confidence interval (CI) 1.9, 3.3); p < 0.001 and B = 376.5; (95% CI 113.8, 639.1); p = 0.005, respectively]. Using the same model for subgroups of SOFA score, we found SOFA platelet count to be associated with ferritin change [B = 1729.3; (95% CI 466.8, 2991.9); p = 0.007]. No association of ferritin change and in-hospital mortality was seen in multivariable analysis. The present study demonstrates that transfusions, hemodialysis, and ECLS had no influence on ferritin increases in critically ill patients. Hyperferritinemia appears to be less the result of iatrogenic influences in the ICU thereby underscoring its unskewed diagnostic value.

Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited. In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard. We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database. We present the FHIR-DHP workflow in respect of the transformation of \"raw\" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients.BACKGROUNDAs one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients.This study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies.OBJECTIVEThis study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies.This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation.METHODSThis observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation.We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed).RESULTSWe developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed).Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.CONCLUSIONSAnalyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.

Background Alarm fatigue, a multifactorial desensitization of staff to alarms, can harm both patients and health care staff in intensive care units (ICUs), especially due to false and nonactionable alarms. Increasing amounts of routinely collected alarm and ICU patient data are paving the way for training machine learning (ML) models that may help reduce the number of nonactionable alarms, potentially increasing alarm informativeness and reducing alarm fatigue. At present, however, there is no publicly available dataset or process that routinely collects information on alarm actionability (ie, whether an alarm triggers a medical intervention or not), which is a key feature for developing meaningful ML models for alarm management. Furthermore, case-based manual annotation is too slow and resource intensive for large amounts of data. Objective We propose a scalable method to annotate patient monitoring alarms associated with patient-related variables regarding their actionability. While the method is aimed to be used primarily in our institution, other clinicians, scientists, and industry stakeholders could reuse it to build their own datasets. Methods The interdisciplinary research team followed a mixed methods approach to develop the annotation method, using data-driven, qualitative, and empirical strategies. The iterative process consisted of six steps: (1) defining alarm terms; (2) reaching a consensus on an annotation concept and documentation structure; (3) defining physiological alarm conditions, related medical interventions, and time windows to assess; (4) developing mapping tables; (5) creating the annotation rule set; and (6) evaluating the generated content. All decisions were made based on feasibility criteria, clinical relevance, occurrence frequency, data availability and quantity, structure, and storage mode. The annotation guideline development process was preceded by the analysis of the institution’s data and systems, the evaluation of device manuals, and a systematic literature review. Results In a multidisciplinary consensus-based approach, we defined preprocessing steps and a rule-based annotation method to classify alarms as either actionable or nonactionable based on data from the patient data management system. We have presented our experience in developing the annotation method and provided the generated resources. The method focuses on respiratory and medication management interventions and includes 8 general rules in a tabular format that are accompanied by graphical examples. Mapping tables enable handling unstructured information and are referenced in the annotation rule set. Conclusions Our annotation method will enable a large number of alarms to be labeled semiautomatically, retrospectively, and quickly, and will provide information on their actionability based on further patient data. This will make it possible to generate annotated datasets for ML models in alarm management and alarm fatigue research. We believe that our annotation method and the resources provided are universal enough and could be used by others to prepare data for future ML projects, even beyond the topic of alarms.

Background: Elevated serum ferritin is a common condition in critically ill patients. It is well known that hyperferritinemia constitutes a good biomarker for hemophagocytic lymphohistiocytosis (HLH) in critically ill patients. However, further differential diagnoses of hyperferritinemia in adult critically ill patients remain poorly investigated. We sought to systematically investigate hyperferritinemia in adult critically ill patients without HLH. Methods: In this secondary analysis of a retrospective observational study, patients ≥18 years admitted to at least one adult intensive care unit at Charité–Universitätsmedizin Berlin between January 2006 and August 2018, and with hyperferritinemia of ≥500 μg/L were included. Patients with HLH were excluded. All patients were categorized into non-sepsis, sepsis, and septic shock. They were also classified into 17 disease groups, based on their ICD-10 codes, and pre-existing immunosuppression was determined. Uni- and multivariable linear regression analyses were performed in all patients. Results: A total of 2583 patients were analyzed. Multivariable linear regression analysis revealed positive associations of maximum SOFA score, sepsis or septic shock, liver disease (except hepatitis), and hematological malignancy with maximum ferritin. T/NK cell lymphoma, acute myeloblastic leukemia, Kaposi’s sarcoma, acute or subacute liver failure, and hepatic veno-occlusive disease were positively associated with maximum ferritin in post-hoc multivariable linear regression analysis. Conclusions: Sepsis or septic shock, liver disease (except hepatitis) and hematological malignancy are important differential diagnoses in hyperferritinemic adult critically ill patients without HLH. Together with HLH, they complete the quartet of important differential diagnoses of hyperferritinemia in adult critically ill patients. As these conditions are also related to HLH, it is important to apply HLH-2004 criteria for exclusion of HLH in hyperferritinemic patients. Hyperferritinemic critically ill patients without HLH require quick investigation of differential diagnoses.

Brown adipose tissue (BAT) activation via cold exposure is increasingly scrutinized as a potential approach to ameliorate cardio-metabolic risk. Transition to cold temperatures requires changes in the partitioning of energy substrates, re-routing fatty acids to BAT to fuel non-shivering thermogenesis. However, the mechanisms behind the redistribution of energy substrates to BAT remain largely unknown. Angiopoietin-like 4 (ANGPTL4), a protein that inhibits lipoprotein lipase (LPL) activity, is highly expressed in BAT. Here, we demonstrate that ANGPTL4 is part of a shuttling mechanism that directs fatty acids derived from circulating triglyceride-rich lipoproteins to BAT during cold. Specifically, we show that cold markedly down-regulates ANGPTL4 in BAT, likely via activation of AMPK, enhancing LPL activity and uptake of plasma triglyceride-derived fatty acids. In contrast, cold up-regulates ANGPTL4 in WAT, abolishing a cold-induced increase in LPL activity. Together, our data indicate that ANGPTL4 is an important regulator of plasma lipid partitioning during sustained cold.

Brown adipose tissue (BAT) combusts high amounts of fatty acids, thereby lowering plasma triglyceride levels and reducing obesity. However, the precise role of BAT in plasma cholesterol metabolism and atherosclerosis development remains unclear. Here we show that BAT activation by β3-adrenergic receptor stimulation protects from atherosclerosis in hyperlipidemic APOE*3-Leiden.CETP mice, a well-established model for human-like lipoprotein metabolism that unlike hyperlipidemic Apoe −/− and Ldlr −/− mice expresses functional apoE and LDLR. BAT activation increases energy expenditure and decreases plasma triglyceride and cholesterol levels. Mechanistically, we demonstrate that BAT activation enhances the selective uptake of fatty acids from triglyceride-rich lipoproteins into BAT, subsequently accelerating the hepatic clearance of the cholesterol-enriched remnants. These effects depend on a functional hepatic apoE-LDLR clearance pathway as BAT activation in Apoe −/− and Ldlr −/− mice does not attenuate hypercholesterolaemia and atherosclerosis. We conclude that activation of BAT is a powerful therapeutic avenue to ameliorate hyperlipidaemia and protect from atherosclerosis.

The Supplemental Nutrition Assistance Program (SNAP) helps working families meet their nutritional needs. Families whose earned income increases in a given month may have their SNAP benefits abruptly reduced or cut off in the following month. Using sentinel sample data from 2007-15 for families with children younger than age four, we investigated how SNAP benefit reductions or cutoffs resulting from increased income were related to economic hardships (food and energy insecurity, unstable housing, forgone health and/or dental care, and health cost sacrifices) and to caregiver and child health. After we controlled for covariates, we found that the groups whose SNAP benefits were reduced or cut off had significantly increased odds of household and child food insecurity, compared to a group with consistent participation in SNAP. Reduced benefits were associated with 1.43 and 1.22 times greater odds of fair or poor caregiver and child health, respectively. Policy modifications to smooth changes in benefit levels as work incomes improve may protect working families with young children from increased food insecurity, poor health, and forgone care.

Brown and beige adipocytes combust nutrients for thermogenesis and through their metabolic activity decrease pro-atherogenic remnant lipoproteins in hyperlipidemic mice. However, whether the activation of thermogenic adipocytes affects the metabolism and anti-atherogenic properties of high-density lipoproteins (HDL) is unknown. Here, we report a reduction in atherosclerosis in response to pharmacological stimulation of thermogenesis linked to increased HDL levels in APOE*3-Leiden.CETP mice. Both cold-induced and pharmacological thermogenic activation enhances HDL remodelling, which is associated with specific lipidomic changes in mouse and human HDL. Furthermore, thermogenic stimulation promotes HDL-cholesterol clearance and increases macrophage-to-faeces reverse cholesterol transport in mice. Mechanistically, we show that intravascular lipolysis by adipocyte lipoprotein lipase and hepatic uptake of HDL by scavenger receptor B-I are the driving forces of HDL-cholesterol disposal in liver. Our findings corroborate the notion that high metabolic activity of thermogenic adipocytes confers atheroprotective properties via increased systemic cholesterol flux through the HDL compartment. Activation of brown adipose tissue (BAT) reduces the development of atherosclerosis in animal models. Here the authors show that BAT activation also increases reverse cholesterol transport and turnover of high-density lipoprotein, which likely contributes to the anti-atherosclerotic effect of BAT activation.