Patients In Critical Condition Research Articles

Enhancing Prehospital Decision-Making: Exploring User Needs and Design Considerations for Clinical Decision Support Systems.

In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have found to cause workflow disruptions and usability issues. Therefore, it is critical to investigate how to design CDSS that meet the needs of prehospital providers while accounting for the unique characteristics of prehospital workflows. We conducted semi-structured interviews with 20 prehospital providers recruited from four emergency medical services (EMS) agencies in an urban area in the northeastern U.S. The interviews focused on the decision-making challenges faced by prehospital providers, their technological needs for decision support, and key considerations for the design and implementation of a CDSS that can seamlessly integrate into prehospital care workflows. The data were analyzed using content analysis to identify common themes. Our qualitative study identified several challenges in prehospital decision-making, including limited access to diagnostic tools, insufficient experience with certain critical patient conditions, and a lack of cognitive support. Participants highlighted several desired features to make CDSS more effective in the dynamic, hands-busy, and cognitively demanding prehospital context, such as automatic prompts for possible patient conditions and treatment options, alerts for critical patient safety events, AI-powered medication identification, and easy retrieval of protocols and guidelines using voice commands. Key considerations for successful CDSS adoption included prioritizing alerts to reduce alert fatigue and workflow disruptions, facilitating real-time data collection and documentation to enable decision generation, and ensuring trust and accountability while preventing over-reliance when using CDSS. This study provides empirical insights into the challenges prehospital providers face and offers design recommendations for developing CDSS solutions that align with prehospital workflows.

Baseline heart rate as a key prognostic indicator in AMI patients with cardiogenic shock undergoing successful primary PCI

Abstract Background Cardiogenic shock presents as a critical condition in patients experiencing acute myocardial infarction (AMI). Even that primary percutaneous coronary intervention (PCI) has become widespread, the mortality rate of AMI complicated with cardiogenic shock remains high. However, comprehensive prognostic stratification remains elusive. Objective Our study sought to elucidate the relationship between baseline parameters and prognostic outcomes in AMI patients with cardiogenic shock. Methods From the GETBACK-AMI multicenter retrospective registry encompassing 935 AMI patients who underwent primary PCI and attained TIMI 3 flow upon final angiography. There were 73 patients diagnosed with cardiogenic shock were ultimately analyzed. The primary endpoint of our study was all-cause mortality. Results Over a median follow-up duration of 425 days, 17 patients (23%) succumbed, of which 14 were in-hospital fatalities. Employing the Cox regression model, we identified that heart rate (HR) at the time of admission bore a potent and independent correlation with the primary endpoint (hazard ratio: 1.04 [95% CI: 1.01-1.06], p=0.002), even post-adjustment for various covariates. ROC analysis pinpointed an admission HR of 82 bpm as the threshold for the primary endpoint. Kaplan-Meier curves further highlighted a pronounced elevation in the primary endpoint occurrence rate for patients with HR > 82 bpm compared to those with HR < 82 bpm (53% vs. 13%, p=0.0002). Conclusions Within our multicenter study cohort, a heightened admission HR was demonstrably linked to increased mortality in AMI patients with cardiogenic shock who reached a final TIMI 3 flow. This readily accessible and impactful prognostic marker may guide clinicians in identifying high-risk patients, subsequently allowing for enhanced support and interventions to optimize patient outcomes.

Machine learning to improve the quality of nursing care in open heart intensive care unit: Challenges & opportunities

Open heart intensive care unit (ICU) encounter distinctive challenges due to the critical condition of patients and the intricate nature of required care [1]. Meanwhile, machine learning has emerged as a potent tool to augment decision-making and patient management in this high-stakes setting [2]. This examination delves into how machine learning applications can transform nursing practices, bolster real-time clinical decision-making, and elevate patient outcomes. In this context, research has showcased the effectiveness of machine learning in various facets of ICU management, including real-time monitoring and predictive analytics. For example, machine learning algorithms have effectively anticipated complications like cardiac arrest and acute kidney injury, enabling proactive interventions [3]. Within the realm of open heart ICUs, machine learning models analyze data from continuous monitoring devices to forecast hemodynamic instability [4]. Additionally, machine learning has facilitated personalized care plans by adapting treatments based on individual patient data trends, thereby refining the accuracy of nursing care [5].

Deep Learning Model for Predicting Critical Patient Conditions

Deep Learning Model for Predicting Critical Patient Conditions

GIS-Based Assessment of Best Route in Complex Traffic Environment: A Case of Kolkata Municipal Corporation

In an emergency like an urban fire, road accident, or critical condition of patients, the shortest possible route to reach the destination is one of the most sought-after issues in transportation studies. However, the physical distance of the shortest route becomes longer distance than the travel time distance due to traffic congestion in metropolitan cities like that of Kolkata. So, the selection of an alternative route is essential to organize the journey logically. A well-structured road network provides multiple options to road users for selection of the best route from the shortest and alternative route according to their demand. So, the present study aims to identify the structural characteristics of road networks by graph theory and the selection of the best route using the GIS technique. It has been found that the road network of Kolkata is characterized by complex structure and grid configuration of the network. Selection of the best route also ranges from the shortest route to alternative route due to spatial variation of traffic speed on different roads in peak hours. The current study identifies the optimal route in terms of travel time to reach the required destinations during peak hour duration in the city of Kolkata.

Epidemiological and Prognostic Assessment of Nosocomial Infections in the Surgical Emergency Resuscitation Department IBN Tofail Hospital

Nosocomial infection is an infection acquired in a healthcare establishment. It is acquired more than 48 hours after admission [1]. Intensive care units are considered a reservoir of multi-resistant bacteria due to the critical condition of patients and the practice of certain invasive care [2]. NI constitute a public health problem marked by high morbidity and mortality. The objectives of our study are:  Detail the epidemiological profile of IN  List ATB-resistant germs  Determine the main prognostic factors We carried out a prospective study from January to June 2022 including patients hospitalized during the period in the HIT RUCH department. Patients whose stay was less than 48 hours were excluded. The diagnosis of IN was suggested based on clinical and paraclinical signs and then confirmed by bacteriological analyses. The resistance profile of the germs to ATB was reported by the results of the antibiogram. Among the 92 patients who stayed more than 48 hours in intensive care, 19 presented with IN, an incidence of 20.65%, of which 89% were male and the median age was 51.7 years. The time to IN acquisition was 5.1 days. IN at the pulmonary site were predominant with a percentage of 36.84%. The germs responsible for IN were dominated by BGN including acinetobacter baumanii with 42.1% and its resistance to ATB was 100% to C3G, 65.6% to amikacin; CGP germs were represented by staphylococcus aureus with 15%. IN-related mortality was 42%. It appears in the light of our results that IN and resistance of germs to ATB are worrying. Judicious use of ATB, hand washing and use of sterile equipment are essential to reduce the incidence of nosocomial infection.

Good outcomes after repeated pediatric liver retransplantations: A justified procedure even in times of organ shortage.

Pediatric liver transplantations generally represent advanced surgery for selected patients. In case of acute or chronic graft failure, biliary or vessel complications, a retransplantation (reLT) can be necessary. In these situations massive adhesions, critical patient condition or lack of good vessels for anastomosis often are problematic. Between 2008 and 2021, 208 pediatric patients received a liver transplantation at our center. Retrospectively, all cases with at least one retransplantation were identified and stored in a database. Indication, intra- and postoperative course and overall survival (OS) were analyzed. Altogether 31 patients (14.9%) received a reLT. In 22 cases only one reLT was done, 8 patients received 2 reLTs and 1 patient needed a fourth graft. Median age for primary transplantation, first, second and third reLT was 14 (range: 1-192 months), 60.5 (range: 1-215 months), 58.5 (range: 14-131 months) and 67 months, respectively. Although biliary atresia (42%) and acute liver failure (23%) represented the main indications for the primary liver transplantation, acute and chronic graft failure (1st reLT: 36%, 2nd reLT: 38%), hepatic artery thrombosis (1st reLT: 29%, 2nd reLT: 25%, 3rd reLT: 100%) and biliary complications (1st reLT: 26%, 2nd reLT: 37%) were the most frequent indications for reLT. OS was 81.8% for patients with 1 reLT, 87.5% with 2 reLTs and 100% with 3 reLTs. Pediatric liver retransplantation is possible with a good outcome even after multiple retransplantations in specialized centers. Nevertheless, careful patient and graft selection, as well as good preoperative conditioning, are essential.

Assessment of first-phase COVID-19 pandemic in Europe using hierarchical clustering based on principal components analysis

<p>It is of great interest for researchers to assess the COVID-19 pandemic in Europe. Grouping of COVID-19-affected regions is an effective way to monitor and optimize planning to combat the disease. This paper applied hierarchical clustering based on principal components analysis (HCPCA) to COVID-19 data from affected European countries. Considering several attribute indices, we obtained a new set of indicators using principal components analysis to aggregate and reduce the dimension of attribute indices of affected countries. Further, we obtained groups of affected countries subject to their similarity using hierarchical clustering to the reduced observations of new attributes indices of these countries. This study aims to group European countries with similar epidemic severity using some presumed attribute indices. The study is limited up to 24 May 2020, to assess if the outputs of the study could help governments, administrators, World Health Organization (WHO), healthcare service professionals, and other decision-makers to optimize their policies and plan their regulations in the country level requirements so that transmission of infections, deaths, critical conditions of patients could be minimized. For this purpose, we used hierarchical clustering using principal components analysis to obtain better clusters of countries with similar epidemic severity.</p>

Analysis of risk factors and construction of nomograph model for critical condition of patients with hypertension during pregnancy

ObjectiveThis study aims to construct the risk prediction nomogram model of critical condition in patients with hypertension during pregnancy and to verify its evaluation effect.MethodsA total of 531 patients with hypertension during pregnancy were randomly grouped into 427 model group and 104 validation group. The model group patients included 59 cases of critical group and 368 cases of non-critical group according to the occurrence of critical situation. Multivariate Logistic regression analysis was conducted to determine the risk factors of critical condition in patients with hypertension during pregnancy, and R software was used to construct the nomogram model. Moreover, the prediction efficiency of the model was evaluated.ResultsThe proportions of patients aged over 30 years, with an educational background of junior high school or below, a family history of hypertension, anemia during pregnancy, and a lower erythrocyte count were significantly higher in the critical group compared to the non-critical group (P < 0.05). Age > 30 years old, educational background of junior high school and below, family history of hypertension, anemia during pregnancy, and red blood cell count were independent risk factors for the occurrence of critical condition in patients with hypertension during pregnancy (P < 0.05). The prediction model formula Z = 1.857×Age + 1.167×Education + 1.601×Family history of hypertension + 1.815×Pregnancy anemia + 3.524×Red blood cell count+(-19.769). The area under the curve (AUC) of the nomogram in the modeling group for predicting the risk of critical situations was 0.926 (95% CI = 0.887 ~ 0.964), indicating excellent discrimination. The calibration curve closely resembled the ideal curve, demonstrating good agreement between the predicted and actual values. The AUC of the validation group’s nomogram to predict the risk of critical situation was 0.942 (95% CI = 0.872 ~ 0.998), with good discrimination. The calibration curve was close to the ideal curve, and the actual value was in good agreement with the predicted value.ConclusionThe nomograph model can predict the risk of critical condition in patients with hypertension during pregnancy and screen high-risk population.

Mortality Rate Prediction in the Critically Ill Patients by Using Apache-II Scoring Tool

OBJECTIVES The study’s objective was to implement a methodological approach, "Acute Physiological and Chronic Health Evaluation II (APACHE-II),” to classify critically ill patients based on severity. METHODOLOGY A retrospective study design was conducted at Shaukat Khanum Memorial Cancer Hospital Lahore, Pakistan, from May 2019 to May 2020. A pilot study of 6 months was conducted by reviewing the medical record of 30 adult patients following convenient sampling after the approval of the proposal by ASRB/IRB of Shaukat Khanum hospital. The record of both male and female patients was studied, while no record of paediatric or adult patients outside the ICU was studied. Each patient’s severity score was obtained using the patient’s parameters with the help of the APACHE-II table. RESULTS Among the patients, three out of 30 scored 25, 29 and 30 on APACHE-II. These patients later passed away in ICU. This indicates that the mortality rate increases with an increase in the APACHE-II score. Thus, the scoring system is very beneficial in predicting adult ICU patients' mortality rate. CONCLUSION It is concluded that APACHE II is one of the best severity scoring systems in predicting the critical condition of patients.

A Visualization and Benchmarking Simulator for Clinical Data.

The availability of Big Data has increased significantly in many areas in recent years. Insights from these data sets lead to optimized processes in many industries, which is why understanding as well as gaining knowledge through analyses of these data sets is becoming increasingly relevant. In the medical field, especially in intensive care units, fast and appropriate treatment is crucial due to the usually critical condition of patients. The patient data recorded here is often very heterogeneous and the resulting database models are very complex, so that accessing and thus using this data requires technical background knowledge. We have focused on the development of a web application that is primarily aimed at clinical staff and researchers. It is an easily accessible visualization and benchmarking tool that provides a graphical interface for the MIMIC-III database. The anonymized datasets contained in MIMIC-III include general information about patients as well as characteristics such as vital signs and laboratory measurements. These datasets are of great interest because they can be used to improve digital decision support systems and clinical processes. Therefore, in addition to visualization, the application can be used by researchers to validate anomaly detection algorithms and by clinical staff to assess disease progression. For this purpose, patient data can be individualized through modifications such as increasing and decreasing vital signs and laboratory parameters so that disease progression can be simulated and subsequently analyzed according to the user's specific needs.

Admission Hyperglycemia as a Predictor of COVID-19 Pneumonia, Cytokine Release Syndrome Progression, and Clinical Outcomes in a Tertiary Care Hospital.

IntroductionDiabetes and coronavirus disease 2019 (COVID-19) are interrelated. The presence of hyperglycemia per se during COVID-19 infection regardless of diabetes status has been associated with poor prognosis and increased risk of mortality.ObjectivesThe main aim of the current study was to assess the association between admission hyperglycemia and COVID-19 outcomes.MethodsThis is a retrospective cohort study including 315 patients, mainly employed in the facility, who presented to the emergency department or were admitted with confirmed COVID-19 infection from April 2020 to August 2021.ResultsThe mean age of the studied cohort was 40.2±12.5 years, where 59.68% were males and 37.7% were symptomatic. Older age, male gender, history of diabetes and hypertension, and elevated C-reactive protein (CRP) and lactate dehydrogenase (LDH) levels were associated with a significantly increased risk of developing cytokine release syndrome (CRS). Admission hyperglycemia was significantly associated with poor outcomes. The time to negativity was 9.30±0.1 days for asymptomatic patients; however, it increased significantly according to clinical presentation, presence of comorbidities, and severe outcomes, in patients with cytokine release syndrome.ConclusionsAdmission hyperglycemia was associated with an increased risk of progression to critical condition in patients hospitalized with COVID-19 independent of the history of diabetes. Therefore, it should not be overlooked but instead should be detected and appropriately treated to improve outcomes. In addition, post-COVID-19 care should be individualized, where severe cases require almost double the time needed by mild cases to convert to negative.

Haemoglobin value and red blood cell transfusions in prolonged weaning from mechanical ventilation: a retrospective observational study

IntroductionThe role of haemoglobin (Hb) value and red blood cell (RBC) transfusions in prolonged weaning from mechanical ventilation (MV) is still controversial. Pathophysiological considerations recommend a not too restrictive transfusion...

BARRIERS TO THE IMPLEMENTATION OF THE EARLY WARNING SCORE IN THE HOSPITAL TREATMENT ROOM

Background: Early warning score is one of the tools used by nurses in the treatment room to determine the critical condition of patients undergoing treatment. The early warning score can be a reference for changes in the patient's condition. Nurses have responsibilities, one of which is filling out the early warning score. Seeing the purpose of using the early warning score is very important in its implementation, but in implementing the filling of the early warning score, are there any obstacles felt by nurses. Objective: This research aims to find the barriers to implementing the early warning score in the treatment room. Methods: The method used is descriptive qualitative. The sample in this study was seven nurses in the treatment room. Result: The results of this study are three obstacles in applying the early warning score in the hospital treatment room: It takes a long time, allows for errors in adding up scores, and difficulties in remembering follow-up interventions. Conclusion: This study concludes that there are barriers to implementing the early warning score by nurses in the treatment room. Namely, it takes a long time to apply the early warning score using paper-based. It is possible to add up the scores incorrectly and have difficulty remembering further interventions that must be done.

Novel Concepts of q -Rung Orthopair Fuzzy Topology and WPM Approach for Multicriteria Decision-Making

A q -rung orthopair fuzzy set (q-ROFS) is a robust approach for fuzzy modeling, computational intelligence, and multicriteria decision-making (MCDM) problems. The aim of this paper is to study the topological structure on q-ROFSs and define the idea of q -rung orthopair fuzzy topology (q-ROF topology). The characterization of q-ROF α -continuous mappings between q-ROF topological spaces and q-ROF connectedness is investigated. Some relationships of different types of q -rung orthopair fuzzy connectedness are also investigated. Additionally, the “ q -rung orthopair fuzzy weighted product model” (q-ROF WPM) is developed for MCDM of a hierarchical healthcare system. Due to limited and insufficient resources, a hierarchical healthcare system (HHS) is very effective to deal with the increasing problems of healthcare. Recognizing the stage of a disease with the symptoms, ranking the critical condition of patients, and referring patients to feasible hospitals are key features of HHS. A HHS will provide healthcare services in three levels, a primary health centers for initial stage of disease, secondary hospitals for secondary stage of disease, and tertiary hospital for the third-order stage. A numerical example is illustrated to demonstrate the efficiency of q-ROF WPM and advantages of HHS.

Iranian Nurses’ Perception of Factors Contributing to Medical Errors in Intensive Care Unit: A Qualitative Study

Patient safety is a major health concern throughout the world, and medical errors are the most important factor threatening patient safety, especially in the Intensive Care Unit (ICU). To prevent errors and improve patient safety, it is necessary to identify the underlying causes of error from the perspective of nurses working in ICUs. This study aimed to explore nurses’ experiences of factors contributing to medical errors in the Intensive Care Unit. The present qualitative study was conducted using conventional content analysis. The participants included 17 nurses working in ICUs in two educational hospitals affiliated with the Iran University of Medical Sciences in Tehran. Data were collected using in-depth semi-structured interviews and were analyzed through the conventional content analysis approach based on Graneheim and Lundman's model. Four main categories (a) extra-organizational challenges; (b) Organizational position; (c) The specificity of the care environment, (d) Individual reason and nine subcategories: (accreditation problems, non-native care standards, organizational management, organizational Features, critical condition of patients, physical structure of unit, neglect in the process of care, lack of clinical knowledge and experience, and physical and psychological problems) were identified. From the nurses' perspective, a series of factors associated with the health system, the organization, the ICUs, and the individual play a role in the occurrence of errors; therefore, it is necessary for planners, policy-makers, nursing managers to think about these underlying factors and with in-service training, and a positive, supportive atmosphere; lead nurses to support the patient and improve patient safety.

Occupational challenges of healthcare workers during the COVID-19 pandemic: a qualitative study

ObjectivesTo address structural determinants and healthcare workers’ (HCWs) physical, mental, emotional and professional challenges of working during the COVID-19 pandemic.DesignExploratory qualitative study with semistructured interviews. Collected data were analysed using...

Гормоны и цитокины как биомаркеры тяжелых инфекций у детей

This review summarizes the available information on the condition of two systems that regulate immune responses, namely the hypothalamic–pituitary–adrenal axis and hypothalamic–pituitary–thyroid axis, in children with neuroinfections or sepsis. We used the Pubmed and eLibrary databases to prepare this review. Serum cortisol is a well-studied biomarker of neuroinfections. Its elevated level is known to be associated with disease severity and outcome in both adults and children. A critical condition in patients with sepsis and septic shock is usually accompanied by critical illness-related corticosteroid insufficiency (CIRCI) and non-thyroidal illness syndrome (NTIS), whose pathogenetic role is unclear and is being actively studied. There is a close correlation between cortisol and cytokine levels in adult patients with neuroinfections or sepsis; however few studies demonstrated this correlation in children. Further investigation of the interaction between neuroendocrine and immune mechanisms will clarify the real need for corticosteroid therapy which is widely used in children with severe infections now. The causal therapy for severe neuroinfections with sepsis and septic shock should include drugs with metabolic and endothelium-protective action (such as cytoflavin), since it can alleviate the disease. Key words: infection, neuroendocrine system, sepsis, hormones, glucocorticoid receptor, cytokines, children, cytoflavin

The association between therapeutic plasma exchange and the risk of mortality among patients critically ill with COVID-19: a meta-analysis.

Background: Cytokine storm has been widely known to contribute to the development of the critical condition in patients with coronavirus disease 2019 (COVID-19), and studies had been conducted to assess the potential aspect of cytokine storm elimination by performing therapeutic plasma exchange (TPE). However, contradictory findings were observed. The objective of this study was to assess the association between TPE and the reduction of mortality of critically ill COVID-19 patients. Methods: A meta-analysis was conducted by collecting data from PubMed, Scopus, and Web of Science. Data on the mortality rate of critically ill COVID-19 patients treated with TPE plus standard of care and that of patients treated with standard of care alone were analyzed using a Z test. Results: We included a total of four papers assessing the association between TPE and the risk of mortality among critically ill COVID-19 patients. Our findings suggested that critically ill COVID-19 patients treated with TPE had lower risk of mortality compared to those without TPE treatment. Conclusion: Our study has identified the potential benefits of TPE in reducing the risk of mortality among critically ill COVID-19 patients.

Role of proteins MRP8 (S100A8) and MRP14 (S100A9) in the development of critical condition in patients with pneumonia with A/H1N1 influenza

Background . Today, the critical care medicine is actively developing, and rapid progress is closely related to the achievements of molecular biology, immunology, and pathological physiology. The study of the role of individual molecular structures in the realization of the reactions of innate and adaptive immunity, which underlie the pathogenesis of critical conditions, is an urgent scientific direction and is of interest. Aims. To assess the contribution of the protein complex MRP-8/14 to the development of systemic inflammation by determining its plasma concentration in patients with pneumonia with influenza A/H1N1. Materials and methods . 85 patients with pneumonia associated with influenza A/H1N1 were examined. Of these, 30 patients with severe pneumonia, 55 with non-severe pneumonia. The plasma concentration of the S100A8/A9 protein complex (MRP-8/14) was determined by flow cytometry on an analyzer (Beckman Coulter, USA). Results. It was found that in patients with severe pneumonia with influenza A/H1N1, the concentration of MRP-8/14 increased in 1.9 times compared with healthy. At the same time, in patients with severe pneumonia with influenza A/H1N1 with a fatal outcome, the concentration of MRP-8/14 increased in 2.1 times. Conclusion. An increase in the level of MRP-8/14 in patients with severe pneumonia associated with influenza A/H1N1, on the one hand, reflects the severity of the course of systemic inflammation, on the other hand, the molecules MRP-8 and MRP-14, acting as damage-associated molecular patterns (DAMPs) stimulate a pro-inflammatory response and contribute to the development of critical state. Thereby, the protein complex MRP-8/14 can be considered as a potential point of application of pharmacological action in the intensive care of critical conditions.