Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.

The accuracy of predicting hospital admission by emergency medical service and emergency department personnel compared to the prehospital MEWS: a prospective multicenter study.

INTRODUCTION: Overcrowding in the emergency department (ED) is a global problem. Early and accurate recognition of a patient's disposition could limit time spend at the ED and thus improve throughput and quality of care provided. This study aims to compare the accuracy among healthcare providers and the prehospital Modified Early Warning Score (MEWS) in predicting the requirement for hospital admission. METHODS: A prospective, observational, multi-centre study was performed including adult patients brought to the ED by ambulance. Involved Emergency Medical Service (EMS) personnel, ED nurses and physicians were asked to predict the need for hospital admission using a structured questionnaire. Primary endpoint was the comparison between the accuracy of healthcare providers and prehospital MEWS in predicting patients' need for hospital admission. RESULTS: In total 798 patients were included of whom 393 (49.2%) were admitted to the hospital. Sensitivity of predicting hospital admission varied from 80.0 to 91.9%, with physicians predicting hospital admission significantly more accurately than EMS and ED nurses (p < 0.001). Specificity ranged from 56.4 to 67.0%. All healthcare providers outperformed MEWS ≥ 3 score on predicting hospital admission (sensitivity 80.0-91.9% versus 44.0%; all p < 0.001). Predictions for ward admissions specifically were significantly more accurate than MEWS (specificity 94.7-95.9% versus 60.6%, all p < 0.001). CONCLUSIONS: Healthcare providers can accurately predict the need for hospital admission, and all providers outperformed the MEWS score.

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI). METHODS: We selected two representative hospitals in southern and northern China, and collected NIS data from 2014 to 2021. Thirty-nine factors including hospital operation volume, nosocomial infection, antibacterial drug use and outdoor temperature data, etc. Five ML methods were used to fit the INI prediction model respectively, and to evaluate and compare their performance. RESULTS: Compared with other models, Random Forest showed the best performance (5-fold AUC = 0.983) in both hospitals, followed by Support Vector Machine. Among all the factors, 12 indicators were significantly different between high-risk and low-risk groups for INI (P < 0.05). After screening the effective predictors through importance analysis, prediction model of the time trend was successfully constructed (R2 = 0.473 and 0.780, BIC = -1.537 and -0.731). CONCLUSIONS: The number of surgeries, antibiotics use density, critical disease rate and unreasonable prescription rate and other key indicators could be fitted to be the threshold predictions of INI and quantitative early warning.

Assessing spatiotemporal change in coral reef social-ecological systems.

Coral reef resilience is eroding at multiple spatial scales globally, with broad implications for coastal communities, and is thus a critical challenge for managing marine social-ecological systems (SESs). Many researchers believe that external stressors will cause key coral reefs to die by the end of the 21st century, virtually eliminating essential ecological and societal benefits. Here, we propose the use of resilience-based approaches to understand the dynamics of coral reef SESs and subsequent drivers of coral reef decline. Previous research has demonstrated the effectiveness of these methods, not only for tracking environmental change, but also for providing warning in advance of transitions, possibly allowing time for management interventions. The flexibility and utility of these methods make them ideal for assessing complex systems; however, they have not been used to study aquatic ecosystem dynamics at the global scale. Here, we evaluate these methods for examining spatiotemporal change in coral reef SESs across the global seascape and assess the subsequent impacts on coral reef resilience. We found that while univariate indicators failed to provide clear signals, multivariate resilience-based approaches effectively captured coral reef SES dynamics, unveiling distinctive patterns of variation throughout the global coral reef seascape. Additionally, our findings highlight global spatiotemporal variation, indicating patterns of degraded resilience. This degradation was reflected regionally, particularly in the Pacific Ocean and Indian Ocean SESs. These results underscore the utility of resilience-based approaches in assessing environmental change in SESs, detecting spatiotemporal variation at the global and regional scales, and facilitating more effective monitoring and management of coral reef SESs.

Cross-cultural adaptation of National Early Warning Score 2 to Angolan Portuguese.

Objective: To make a cross-cultural adaptation of the National Early Warning Score 2 (NEWS 2) from English to Angolan Portuguese. Methods: A methodological research of cross-cultural adaptation was conducted, involving sequential stages of forward translation, translation synthesis, back-translation, and the application of the Delphi Panel methodology for analyzing semantic, idiomatic, experiential, and conceptual equivalence between the translated and the original versions. This process culminated in the development of a pre-final version, which subsequently underwent testing in a cohort of nurses (n = 37). The Intraclass Correlation Coefficient was calculated to assess inter-rater reliability of ratings. Cronbach's alpha was used for evaluating the internal consistency and reliability within the items of the NEWS 2 score. Results: The cross-cultural adaptation process allowed us to prepare the final version of this tool. The data collected during the testing phase facilitated the examination of inter-rater reliability of ratings and the internal consistency and reliability within the items of the NEWS2 score. The Intraclass Correlation Coefficient observed at this step was 0.992. The Cronbach's alpha was 0.993. Conclusion: The cross-cultural adaptation of the NEWS 2 scoring system to Angolan Portuguese was successful, providing healthcare professionals in Angola with the means to effectively use the tool.

Social determinants of health and sepsis: a case-control study.

PURPOSE: We aimed to identify whether social determinants of health (SDoH) are associated with the development of sepsis and assess the differences between individuals living within systematically disadvantaged neighbourhoods compared with those living outside these neighbourhoods. METHODS: We conducted a single-centre case-control study including 300 randomly selected adult patients (100 patients with sepsis and 200 patients without sepsis) admitted to the emergency department of a large academic tertiary care hospital in Hamilton, ON, Canada. We collected data on demographics and a limited set of SDoH variables, including neighbourhood household income, smoking history, social support, and history of alcohol disorder. We analyzed study data using multivariate logistic regression models. RESULTS: The study included 100 patients with sepsis with a median [interquartile range (IQR)] age of 75 [58-84] yr and 200 patients without sepsis with a median [IQR] age of 72 [60-83] yr. Factors significantly associated with sepsis included arrival by ambulance, absence of a family physician, higher Hamilton Early Warning Score, and a recorded history of dyslipidemia. Important SDoH variables, such as individual or household income and race, were not available in the medical chart. In patients with SDoH available in their medical records, no SDoH was significantly associated with sepsis. Nevertheless, compared with their proportion of the Hamilton population, the rate of sepsis cases and sepsis deaths was approximately two times higher among patients living in systematically disadvantaged neighbourhoods. CONCLUSIONS: This study revealed the lack of available SDoH data in electronic health records. Despite no association between the SDoH variables available and sepsis, we found a higher rate of sepsis cases and sepsis deaths among individuals living in systematically disadvantaged neighbourhoods. Including SDoH in electronic health records is crucial to study their effect on the risk of sepsis and to provide equitable care.

RéSUMé: OBJECTIF: Nous avons cherché à déterminer si les déterminants sociaux de la santé (DSS) étaient associés à l'apparition de sepsis et à évaluer les différences entre les personnes vivant dans des quartiers systématiquement défavorisés et celles vivant à l'extérieur de ces quartiers. MéTHODE: Nous avons mené une étude cas témoins monocentrique portant sur 300 patient·es adultes sélectionné·es au hasard (100 personnes atteintes de sepsis et 200 témoins sans sepsis) admis·es au service des urgences d'un grand hôpital universitaire de soins tertiaires à Hamilton, ON, Canada. Nous avons recueilli des données démographiques et un ensemble limité de variables de DSS, y compris le revenu des ménages du quartier, les antécédents de tabagisme, le soutien social et les antécédents de troubles liés à l'alcool. Nous avons analysé les données de l'étude à l'aide de modèles de régression logistique multivariés. RéSULTATS: L'étude a inclus 100 patient·es atteint·es de sepsis avec un âge médian [écart interquartile (ÉIQ)] de 75 [58-84] ans et 200 patient·es sans sepsis avec un âge médian [ÉIQ] de 72 [60-83] ans. Les facteurs significativement associés au sepsis comprenaient l'arrivée en ambulance, l'absence de médecin de famille, un score Hamilton Early Warning Score plus élevé et des antécédents enregistrés de dyslipidémie. D'importantes variables de DSS, telles que le revenu individuel et du ménage et la race, n'étaient pas disponibles dans le dossier médical. Chez les personnes dont les DSS étaient disponibles dans leur dossier médical, aucun DSS n'était significativement associé au sepsis. Néanmoins, comparativement à leur proportion dans la population de Hamilton, le taux de cas de sepsis et de décès dus au sepsis était environ deux fois plus élevé chez les personnes vivant dans des quartiers systématiquement défavorisés. CONCLUSION: Cette étude a révélé le manque de données disponibles sur les DSS dans les dossiers de santé électroniques. Bien qu'il n'y ait pas d'association entre les variables disponibles et le sepsis, nous avons constaté un taux plus élevé de cas de sepsis et de décès dus à la septicémie chez les personnes vivant dans des quartiers systématiquement défavorisés. L'inclusion des DSS dans les dossiers de santé électroniques est cruciale pour étudier leur effet sur le risque de sepsis et pour dispenser des soins équitables.

Analysis of the effects of early screening combined with blood lactate on the severity of patients with sepsis.

This work aimed to investigate the adoption value of blood lactic acid (BLA) combined with the National Early Warning Score (NEWS) in the early screening of sepsis patients and assessing their severity. The data and materials utilized in this work were obtained from the electronic medical record system of 537 anonymized sepsis patients who received emergency rescue in the emergency rescue area of Liuzhou People's Hospital, Guangxi, from July 1, 2020, to December 26, 2020. Based on the 28-day outcomes of sepsis patients, the medical records were rolled into Group S (407 survival cases) and Group D (130 dead cases). Basic information such as the mode of hospital admission, initial management, use of emergency ventilator within 24 h of admission, NEWS score, arterial oxygen pressure/alveolar oxygen pressure ratio (PaO2/PAO2), alveolar-arterial oxygen difference (A-aDO2), serum creatinine (SCr), blood urea nitrogen (BUN), oxygenation index (OI), Glasgow Coma Scale (GCS), D-dimer, use of vasoactive drugs within 24 h of admission, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), quick Sequential Organ Failure Assessment (qSOFA) score, SOFA score, BLA level, NEWS with lactate (NEWS-L) score, SOFA score including lactate level (SOFA-L) score, Intensive Care Unit (ICU) length of stay, total hospital stay, ICU stay/total hospital stay, and septic shock condition were compared between groups. Logistic regression analysis was performed to assess the impact of various predictive factors on prognosis and to plot the receiver operating characteristic (ROC) curve. The results suggested marked differences between Group S and Group D in terms of mean age (t = -5.620; OR = -9.96, 95 % CI: -13.44∼-6.47; P < 0.001). Group S showed drastic differences in terms of mode of hospital admission (χ2 = 9.618, P < 0.01), method of initial management (χ2 = 51.766, P < 0.001), use of emergency ventilator within 24 h of admission (χ2 = 98.564, P < 0.001), incidence of septic shock (χ2 = 77.545, P < 0.001), use of vasoactive drugs within 24 h of admission (χ2 = 102.453, P < 0.001), heart rate (t = -4.063, P < 0.001), respiratory rate (t = -4.758, P < 0.001), oxygenation status (χ2 = 20.547, P < 0.001), NEWS score (t = -6.120, P < 0.001), PaO2/PAO2 ratio (t = 2.625, P < 0.01), A-aDO2 value (Z = -3.581, P < 0.001), OI value (Z = -3.106, P < 0.01), PLT value (Z = -2.305, P < 0.05), SCr value (Z = -3.510, P < 0.001), BUN value (Z = -3.170, P < 0.01), D-dimer (Z = -4.621, P < 0.001), CRP level (Z = -4.057, P < 0.001), PCT value (Z = -2.783, P < 0.01), IL-6 level (Z = -2.904, P < 0.001), length of hospital stay (Z = -4.138, P < 0.001), total hospital stay (Z = -8.488, P < 0.001), CCU/total hospital stay (Z = -9.118, P < 0.001), NEWS score (t = -6.120, P < 0.001), SOFA score (t = -6.961, P < 0.001), SOFA-L score (Z = -4.609, P < 0.001), NEWS-L score (Z = -5.845, P < 0.001), BLA level (Z = -6.557, P < 0.001), and GCS score (Z = 6.909, P < 0.001) when compared to Group D. The use of ventilators, septic shock, PCT, NEWS score, GCS score, SOFA score, SOFA-L score, NEWS-L score, and BLA level were identified as independent risk factors for predicting the prognosis of sepsis patients (P < 0.001). The areas under ROC curve (AUC) of blood lactic acid, PCT, NEWS, NEWS-L, GCS, SOFA, and SOFA-L were 0.695, 0.665, 0.692, 0.698, 0.477, 0.700, and 0.653, respectively. These findings indicate that the combination of BLA with NEWS (NEWS-L) score and SOFA score has certain advantages in assessing the prognosis of sepsis.

Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023.

Introduction: Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems. Methods: The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency. Results: ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05). Discussion: The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.

The evolution of Kenya's animal health surveillance system and its potential for efficient detection of zoonoses.

Introduction: Animal health surveillance systems in Kenya have undergone significant changes and faced various challenges throughout the years. Methods: In this article, we present a comprehensive overview of the Kenya animal health surveillance system (1944 to 2024), based on a review of archived documents, a scoping literature review, and an examination of past surveillance assessments and evaluation reports. Results: The review of archived documents revealed key historical events that have shaped the surveillance system. These include the establishment of the Directorate of Veterinary Services in 1895, advancements in livestock farming, the implementation of mandatory disease control interventions in 1944, the growth of veterinary services from a section to a ministry in 1954, the disruption caused by the Mau Mau insurrection from 1952 to 1954, which led to the temporary halt of agriculture in certain regions until 1955, the transition of veterinary clinical services from public to private, and the progressive privatization plan for veterinary services starting in 1976. Additionally, we highlight the development of electronic surveillance from 2003 to 2024. The scoping literature review, assessments and evaluation reports uncovered several strengths and weaknesses of the surveillance system. Among the strengths are a robust legislative framework, the adoption of technology in surveillance practices, the existence of a formal intersectoral coordination platform, the implementation of syndromic, sentinel, and community-based surveillance methods, and the presence of a feedback mechanism. On the other hand, the system's weaknesses include the inadequate implementation of strategies and enforcement of laws, the lack of standard case definitions for priority diseases, underutilization of laboratory services, the absence of formal mechanisms for data sharing across sectors, insufficient resources for surveillance and response, limited integration of surveillance and laboratory systems, inadequate involvement of private actors and communities in disease surveillance, and the absence of a direct supervisory role between the national and county veterinary services. Discussion and recommendations: To establish an effective early warning system, we propose the integration of surveillance systems and the establishment of formal data sharing mechanisms. Furthermore, we recommend enhancing technological advancements and adopting artificial intelligence in surveillance practices, as well as implementing risk-based surveillance to optimize the allocation of surveillance resources.

Novel biomarkers to identify complicated course of febrile neutropenia in hematological patients receiving intensive chemotherapy.

Febrile neutropenia (FN) is a common consequence of intensive chemotherapy in hematological patients. More than 90% of the patients with acute myeloid leukemia (AML) develop FN, and 5%-10% of them die from subsequent sepsis. FN is very common also in autologous stem cell transplant recipients, but the risk of death is lower than in AML patients. In this review, we discuss biomarkers that have been evaluated for diagnostic and prognostic purposes in hematological patients with FN. In general, novel biomarkers have provided little benefit over traditional inflammatory biomarkers, such as C-reactive protein and procalcitonin. The utility of most biomarkers in hematological patients with FN has been evaluated in only a few small studies. Although some of them appear promising, much more data is needed before they can be implemented in the clinical evaluation of FN patients. Currently, close patient follow-up is key to detect complicated course of FN and the need for further interventions such as intensive care unit admission. Scoring systems such as q-SOFA (Quick Sequential Organ Failure Assessment) or NEWS (National Early Warning Sign) combined with traditional and/or novel biomarkers may provide added value in the clinical evaluation of FN patients.

Prescription medication use in the 10 years prior to diagnosis of young onset Alzheimer's disease: a nationwide nested case-control study.

BACKGROUND: Patients with young onset Alzheimer's disease (YOAD) face long diagnostic delays. Prescription medication use may provide insights into early signs and symptoms, which may help facilitate timely diagnosis. METHODS: In a register-based nested case-control study, we examined medication use for everyone diagnosed with YOAD in a Danish memory clinic during 2016-2020 compared to cognitively healthy controls. Prescription medication use were grouped into 13 overall categories (alimentary tract and metabolism, blood and blood forming organs, cardiovascular system, dermatologicals, genitourinary system and sex hormones, systemic hormonal preparations, antiinfectives for systemic use, antineoplastic and immunomodulating agents, musculo-skeletal system, nervous system, antiparasitic products, respiratory system, and sensory organs). Further stratifications were done for predetermined subcategories with a use-prevalence of at least 5% in the study population. Conditional logistic regression produced odds ratios, which given the use of incidence-density matching is interpretable as incidence rate ratios (IRRs). The association between prescription medication use and subsequent YOAD diagnosis was examined in the entire 10-year study period and in three time-intervals. RESULTS: The study included 1745 YOAD cases and 5235 controls. In the main analysis, several overall categories showed significant associations with YOAD in one or more time-intervals, namely blood and blood forming organs and nervous system. Prescription medication use in the nervous system category was increased for YOAD cases compared to controls already 10->5 years prior to diagnosis (IRR 1.17, 95% CI 1.05-1.31), increasing to 1.57 (95% CI 1.39-1.78) in the year preceding diagnosis. This was largely driven by antidepressant and antipsychotic use, and especially prominent for first-time users. CONCLUSIONS: In this study, medication use in several categories was associated with YOAD. Onset of treatment-requiring psychiatric symptoms such as depression or psychosis in mid-life may serve as potential early indicators of YOAD.

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection.

Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.

'White lies and safety nets': The perceptions of nurses on the use of early warning systems and the development of higher-order thinking skills.

BACKGROUND: Algorithmic tools such as early warning systems (EWSs) have been embedded into clinical practice globally to facilitate the early recognition of patient deterioration and to guide the escalation of care. Concerns have been raised that the mandated use of these EWS tools may impact the development of nurses' higher-order thinking. However, the relationship between EWS tools and the development of higher-order thinking is poorly understood. OBJECTIVES: This paper provides the qualitative results of a larger study that sought to explore the impact of EWS tools on the development of nurses' higher-order thinking. The objective of this component of the study was to ascertain the thoughts and perceptions of nurses on the use of EWSs and how this related to the development of higher-order thinking skills. METHODS: A mixed-method, concurrent study design was used to explore the concept of the development of nurses' higher-order thinking in the context of EWS tools. The qualitative responses from a Qualtrics survey were thematically analysed and presented. FINDINGS: Two major themes were uncovered: White Lies and Safety Nets. Our analysis of the data suggested that some nurses amend their documentation practice to accommodate the EWS's escalation process, uncovering a view that the tool did not account for clinical reasoning. Parallel to this, some nurses found that these systems supported clinical decision-making and helped to build confidence, thus acting as a safety net for their practice. CONCLUSION: Reliance on EWSs can both hinder and/or support the development of higher-order thinking. Early warning systems are useful tools in ensuring patient safety but should be used in conjunction with nurses' higher-order thinking.

A prediction model for prehospital clinical deterioration: The use of early warning scores.

BACKGROUND: Various prognosticative approaches to assist in recognizing clinical deterioration have been proposed. To date, early warning scores (EWSs) have been evaluated in hospital with limited research investigating their suitability in the prehospital setting. This study evaluated the predictive ability of established EWSs and other clinical factors for prehospital clinical deterioration. METHODS: A retrospective cohort study investigating adult patients of all etiologies attended by Queensland Ambulance Service paramedics between January 1, 2018, and December 31, 2020, was conducted. With logistic regression, several models were developed to predict adverse event outcomes. The National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Queensland Adult Deterioration Detection System (Q-ADDS), and shock index were calculated from vital signs taken by paramedics. RESULTS: A total of 1,422,046 incidents met the inclusion criteria. NEWS, MEWS, and Q-ADDS were found to have comparably high predictive ability with area under the receiver operating characteristic curve (AUC-ROC) between 70% and 90%, whereas shock index had relatively low AUC-ROC. Sensitivity was lower than specificity for all models. Although established EWSs performed well when predicting adverse events, these scores require complex calculations requiring multiple vital signs that may not be suitable for the prehospital setting. CONCLUSIONS: This study found NEWS, MEWS, and Q-ADDS all performed well in the prehospital setting. Although a simple shock index is easier for paramedics to use in the prehospital environment, it did not perform comparably to established EWSs. Further research is required to develop suitably performing parsimonious solutions until established EWSs are integrated into technological solutions to be used by prehospital clinicians in real time.

A highly specific and ultrasensitive approach to detect Prymnesium parvum based on RPA-CRISPR-LbaCas12a-LFD system.

BACKGROUND: Harmful algal blooms (HABs), caused by the rapid proliferation or aggregation of microorganisms, are catastrophic for the environment. The Prymnesium parvum is a haptophyte algal species that is found worldwide and is responsible for extensive blooms and death of larval amphibians and bivalves, causing serious negative impacts on the ecological environment. For the prevention and management of environmental pollution, it is crucial to explore and develop early detection strategies for HABs on-site using simple methods. The major challenge related to early detection is the accurate and sensitive detection of algae present in low abundance. RESULTS: Herein, recombinase polymerase amplification (RPA) was combined with clustered regularly interspaced short palindromic repeats and Cas12a protein (CRISPR-LbaCas12a) systems, and the lateral flow dipstick (LFD) was used for the first time for early detection of P. parvum. The internal transcribed spacer (ITS) of P. parvum was selected as the target sequence, and the concentration of single-strand DNA reporters, buffer liquid system, reaction time, and amount of gold particles were optimized. The RPA-CRISPR-LbaCas12a-LFD approach demonstrated highly specificity during experimental testing, with no cross-reaction against different microalgae used as controls. In addition, the lowest detection limit was 10,000 times better than the lowest detection limit of the standalone RPA approach. The feasibility and robustness of this approach were further verified by using the different environmental samples. It also observed that P. parvum are widely distributed in Chinese Sea, but the cell density of P. parvum is relatively low (<0.1 cells/mL). SIGNIFICANCE: The developed approach has an excellent specificity and offers 10,000 times better sensitivity than the standalone RPA approach. These advantages make this approach suitable for early warning detection and prevention of HAB events in environmental water. Also, the outcomes of this study could promote a shift from traditional laboratory-based detection to on-site monitoring, facilitating early warning against HABs.

European survey on Paediatric Early Warning Systems, and other processes used to aid the recognition and response to children's deterioration on hospital wards.

BACKGROUND: Internationally, there is an increasing trend in using Rapid Response Systems (RRS) to stabilize in-patient deterioration. Despite a growing evidence base, there remains limited understanding of the processes in place to aid the early recognition and response to deteriorating children in hospitals across Europe. AIM/S: To describe the processes in place for early recognition and response to in-patient deterioration in children in European hospitals. STUDY DESIGN: A cross-sectional opportunistic multi-centre European study, of hospitals with paediatric in-patients, using a descriptive self-reported, web-based survey, was conducted between September 2021 and March 2022. The sampling method used chain referral through members of European and national societies, led by country leads. The survey instrument was an adaptation to the survey of Recognition and Response Systems in Australia. The study received ethics approval. Descriptive analysis and Chi-squared tests were performed to compare results in European regions. RESULTS: A total of 185 questionnaires from 21 European countries were received. The majority of respondents (n = 153, 83%) reported having written policies, protocols, or guidelines, regarding the measurement of physiological observations. Over half (n = 120, 65%) reported that their hospital uses a Paediatric Early Warning System (PEWS) and 75 (41%) reported having a Rapid Response Team (RRT). Approximately one-third (38%) reported that their hospital collects specific data about the effectiveness of their RRS, while 100 (54%) reported providing regular training and education to support it. European regional differences existed in PEWS utilization (North = 98%, Centre = 25%, South = 44%, p < .001) and process evaluation (North = 49%, Centre = 6%, South = 36%, p < .001). CONCLUSIONS: RRS practices in European hospitals are heterogeneous. Differences in the uptake of PEWS and RRS process evaluation emerged across Europe. RELEVANCE TO CLINICAL PRACTICE: It is important to scope practices for the safe monitoring and management of deteriorating children in hospital across Europe. To reduce variance in practice, a consensus statement endorsed by paediatric and intensive care societies could provide guidance and resources to support PEWS implementation and for the operational governance required for continuous quality improvement.

Revolutionizing Chronic Heart Disease Management: The Role of IoT-Based Ambulatory Blood Pressure Monitoring System.

Chronic heart disease (CHD) is a widespread and persistent health challenge that demands immediate attention. Early detection and accurate diagnosis are essential for effective treatment and management of this condition. To overcome this difficulty, we created a state-of-the-art IoT-Based Ambulatory Blood Pressure Monitoring System that provides real-time blood pressure readings, systolic, diastolic, and pulse rates at predefined intervals. This unique technology comes with a module that forecasts CHD's early warning score. Various machine learning algorithms employed comprise Naïve Bayes, K-Nearest Neighbors (K-NN), random forest, decision tree, and Support Vector Machine (SVM). Using Naïve Bayes, the proposed model has achieved an impressive 99.44% accuracy in predicting blood pressure, a vital aspect of real-time intensive care for CHD. This IoT-based ambulatory blood pressure monitoring (IABPM) system will provide some advancement in the field of healthcare. The system overcomes the limitations of earlier BP monitoring devices, significantly reduces healthcare costs, and efficiently detects irregularities in chronic heart diseases. By implementing this system, we can take a significant step forward in improving patient outcomes and reducing the global burden of CHD. The system's advanced features provide an accurate and reliable diagnosis that is essential for treating and managing CHD. Overall, this IoT-based ambulatory blood pressure monitoring system is an important tool for the early identification and treatment of CHD in the field of healthcare.

Wastewater-Based Epidemiology for SARS-CoV-2 in Northern Italy: A Spatiotemporal Model.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring the SARS-CoV-2 prevalence in a city in northern Italy from October 2021 to May 2023. Based on a previously used deterministic model, this study proposed a variation to account for the population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated using healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred during the period considered, demonstrating the ability of the model to predict the relevant fluctuations in the number of cases. The population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied to compare the virus load values across cities with different population demographics and sewer network structures, improving the comparability of the WBE data for effective surveillance and intervention strategies.

A Proposed Systematic Approach to Safe Inter Hospital Patient Transfer in Interventional Radiology: From Referral to Repatriation.

WHAT THIS PAPER ADDS: There is no reference in the literature regarding the transfer of patients between hospitals for interventional radiology procedures. This paper outlines an approach to assist with the safe assessment, reassessment and repatriation of patients requiring urgent procedures in a different hospital.

Food prices and the wages of the poor: A cost-effective addition to high-frequency food security monitoring.

The affordability of nutritious food for "all people, at all times" is a critically important dimension of food security. Yet surprisingly, timely high-frequency indicators of food affordability are rarely collected in any systematic fashion despite price volatility emerging as major source of food insecurity in the 21st Century. The 2008 global food crisis prompted international agencies to invest heavily in monitoring domestic food prices in low and middle income countries (LMICs). However, food price monitoring is not sufficient for measuring changes in diet affordability; for that, one must also measure changes either in income or in an income proxy. We propose using the wages of unskilled workers as a cheap and sufficiently accurate income proxy, especially for the urban and rural non-farm poor. We first outline alternative measures of "food wage" indices, defined as wages deflated either by consumer food price indices or novel healthy diet cost indices. We then discuss the conceptual strengths and limitations of food wages. Finally, we examine patterns and trends in different types of real food wage series during well-known food price crises in Ethiopia (2008, 2011 and 2022), Sri Lanka (2022) and Myanmar (2022). In all these instances, food wages declined by 20-30%, often in the space of a few months. In Myanmar, the decline in real wages during 2022 closely matches declines in household disposable income. We strongly advocate tracking the wages of the poor as a timely, accurate and cost-effective means of monitoring food affordability for important segments of the world's poor.