Search Results (138)

Prognostic scores that help allocate resources and time to the most critical patients could have potentially improved the response to the SARS-CoV-2 pandemic. We assessed the performance of five risk scores in predicting death or transfer to the intensive care unit (ICU) or sub-intensive care unit (SICU) in hospitalised patients with SARS-CoV-2 infection, with the three aims of retrospectively analysing the effectiveness of these tools, identifying frail patients at risk of death or complications due to infection, and applying these tools in the event of future pandemics. A retrospective observational study was conducted by evaluating data from patients hospitalised with SARS-CoV-2 infection. Among 134 patients considered, 119 were enrolled. All patients were adults, with a mean age of 64 years, and were hospitalised in the Infectious Diseases Division. We compared the five scores using receiver operating characteristic curves and calculation of the areas under the curve (AUCs) to determine their predictive performance. Four of the five scores demonstrated a high accuracy in predicting mortality among COVID-19-positive patients, with AUCs between 0.749 and 0.885. However, only two of the five scores showed good performance in predicting transfer to the ICU or SICU, with AUCs ranging from 0.740 to 0.802. The 4C Mortality Score and COVID-GRAM presented the highest performance for both outcomes. These two scores are easy to apply and low cost. They could still be used in clinical practice as predictive tools for frail and elderly patients with SARS-CoV-2 infection, as well as in the event of future pandemics. Full article

Background: Uterine rupture is a rare but severe obstetric complication with significant maternal and neonatal consequences. While partial uterine ruptures (PURs) are generally associated with less severe outcomes, complete uterine ruptures (CURs) carry a higher risk of serious impact on both mother and child. The present study aimed to evaluate outcomes and identify risk factors for each type of rupture, and also to define high- and low-risk uterine ruptures based on clinical outcomes. Methods: A retrospective analysis of 112 uterine rupture cases, including 29 CURs and 83 PURs, was conducted at the Women’s Hospital of the University of Cologne from October 2010 to January 2021. Results: Maternal outcomes revealed that CUR was associated with higher risks of prolonged hospitalization (p = 0.003), postpartum hemorrhage (p < 0.001), maternal transfusion (p = 0.003), and ICU transfer (p = 0.004) compared to PUR. Neonatal outcomes showed a significantly higher risk of severe acidosis (p < 0.001), low APGAR scores (p < 0.001), NICU transfers (p = 0.004), and resuscitation needs (p = 0.016) in CUR cases. Factors increasing the risk of CUR included pathological CTG (OR = 1.9, 95% CI: 0.99–7.14, p = 0.05), abdominal pain (OR = 2.63, 95% CI: 1.10–6.25, p = 0.03), previous vaginal birth (OR = 7.14, 95% CI: 0.025–20, p < 0.001), and no uterine contractions (OR = 7, 95% CI: 1.21–40.56, p = 0.03). A previous cesarean section significantly increased the risk of CUR (OR = 4.94, 95% CI: 1.38–17.67, p = 0.014), whereas more than two cesarean sections reduced the risk (OR = 0.66, 95% CI: 0.13–3.22, p = 0.61). A comparison of CUR with maternal and neonatal high-risk rupture groups revealed that low gestational age and a history of previous cesarean sections were significant risk factors for neonatal high-risk rupture. Conclusion: Vaginal birth and abdominal pain were identified as key risk factors for CUR, which lead to severe maternal and neonatal outcomes. Recognizing these risk factors can help clinicians optimize risk stratification and decision-making, and enhance monitoring strategies to prevent adverse outcomes. Full article

Introduction: Methadone, a synthetic opioid used for opioid substitution therapy (OST), is typically associated with arrhythmias rather than direct myocardial depression. Neurological complications, especially with concurrent antipsychotic use, have also been reported. Acute left ventricular failure in young adults is uncommon and often linked to genetic or infectious causes. We present a rare case of reversible cardiogenic shock and cerebellar insult due to methadone toxicity. Case Presentation: A 37-year-old man with a history of drug abuse on OST with methadone (130 mg/day) was admitted to the ICU with hemodynamic instability, seizures, and focal neurological deficits. Diagnostic workup revealed low cardiac output syndrome and a right cerebellar insult, attributed to methadone toxicity. The patient received individualized catecholamine support. After 10 days in the ICU, he was transferred to a general ward for ongoing cardiac and neurological rehabilitation and discharged in stable condition seven days later. Conclusions: Methadone-induced reversible left ventricular failure, particularly when accompanied by cerebellar insult, is rare but potentially life-threatening. Early recognition and multidisciplinary management are essential for full recovery in such complex toxicological presentations. Full article

The case-fatality rate of severe leptospirosis can exceed 50%. This retrospective cohort study examined 111 individuals with laboratory-confirmed leptospirosis admitted to Cairns Hospital, a referral hospital in tropical Australia, between January 2015 and June 2024. We examined the patients’ demographic, clinical, laboratory and imaging findings at presentation and then correlated them with the patients’ subsequent clinical course. Severe disease was defined as the presence of pulmonary haemorrhage or a requirement for intensive care unit (ICU) admission. The patients’ median (interquartile range) age was 38 (24–55) years; 85/111 (77%) were transferred from another health facility. Only 13/111 (12%) had any comorbidities. There were 63/111 (57%) with severe disease, including 56/111 (50%) requiring ICU admission. Overall, 56/111 (50%) required vasopressor support, 18/111 (16%) needed renal replacement therapy, 14/111 (13%) required mechanical ventilation and 2/111 (2%) needed extracorporeal membrane oxygenation. Older age—but not comorbidity—was associated with the presence of severe disease. Hypotension, respiratory involvement, renal involvement and myocardial injury—but not liver involvement—frequently heralded a requirement for ICU care. Every patient in the cohort survived to hospital discharge. Leptospirosis can cause multi-organ failure in otherwise well young people in tropical Australia; however, patient outcomes are usually excellent in the country’s well-resourced health system. Full article

Background/Objectives: This study aims to investigate ICU mortality rates and to identify predictors of ICU mortality, focusing on clinical and demographic variables, including age, comorbidities, hemoglobin and creatinine values, intubation in the Emergency department, and Glasgow Coma Scale (GCS) and APACHE II scores at presentation in the Emergency department, and how these factors influence patients’ clinical outcomes. Methods: This retrospective observational cross-sectional study analyzed patients admitted to the Jordan University Hospital (JUH) ICU from 1 January 2022 to 31 December 2023. A total of 1323 patients were included, with a mean age of 65 ± 17 years, of whom 442 (34%) died during their ICU stay. Results: A delay of 6 h or more in ICU admission was reported for 77% of the participants. Mortality rates were significantly lower among patients admitted to the ICU through the Emergency department (32%) compared to those transferred from other wards (41%) (p = 0.003). Higher mortality rates were observed among patients on vasopressors and those intubated in the Emergency department, with lower median hemoglobin (Hb) levels, higher APACHE II scores, and pneumonia as the main diagnosis or urosepsis as the secondary diagnosis (p < 0.001). Conclusions: This study identified predictors of mortality in a medical ICU at a tertiary hospital in Jordan. Full article

Background/Objectives: In hospital settings, the wide variability of acute and complex chronic conditions—among both adult and pediatric patients—requires advanced approaches to detect early signs of clinical deterioration and the risk of transfer to the intensive care unit (ICU). Nursing diagnoses (NDs), standardized representations of patient responses to actual or potential health problems, reflect nursing complexity. However, most studies have focused on the total number of NDs rather than the individual role each diagnosis may play in relation to outcomes such as ICU transfer. This study aimed to identify and rank the specific NDs most strongly associated with ICU transfers in hospitalized adult and pediatric patients. Methods: A retrospective, monocentric observational study was conducted using electronic health records from an Italian tertiary hospital. The dataset included 42,735 patients (40,649 adults and 2086 pediatric), and sociodemographic, clinical, and nursing data were collected. A random forest model was applied to assess the predictive relevance (i.e., variable importance) of individual NDs in relation to ICU transfers. Results: Among adult patients, the NDs most strongly associated with ICU transfer were Physical mobility impairment, Injury risk, Skin integrity impairment risk, Acute pain, and Fall risk. In the pediatric population, Acute pain, Injury risk, Sleep pattern disturbance, Skin integrity impairment risk, and Airway clearance impairment emerged as the NDs most frequently linked to ICU transfer. The models showed good performance and generalizability, with stable out-of-bag and validation errors across iterations. Conclusions: A prioritized ranking of NDs appears to be associated with ICU transfers, suggesting their potential utility as early warning indicators of clinical deterioration. Patients presenting with high-risk diagnostic profiles should be prioritized for enhanced clinical surveillance and proactive intervention, as they may represent vulnerable populations. Full article

Introduction: Managing glycemic fluctuations in critically ill elderly patients with type 2 diabetes mellitus (T2DM) poses significant challenges. This case report presents a unique scenario in which increased intravenous glucose (Glc) infusion, together with insulin therapy, improved glycemic control and reduced insulin requirements during a septic episode. This finding adds to the scientific literature by suggesting that adequate Glc administration may enhance insulin sensitivity in critically ill T2DM patients. Case report: An 84-year-old female patient with T2DM, hypertension, and chronic renal failure was admitted to the intensive care unit with fever, nausea, loss of appetite, and profound weakness. Laboratory findings revealed severe hyperglycemia, electrolyte imbalances, and markedly elevated inflammatory markers, leading to the diagnosis of decompensated T2DM that was complicated by sepsis. The initial treatment consisted of continuous intravenous (IV) insulin, crystalloid infusions, and broad-spectrum antibiotics. Despite insulin therapy and the absence of nutritional intake, the patient experienced extreme fluctuations in their blood glucose levels, ranging from hyperglycemia to hypoglycemia. Due to persistent glycemic instability, IV Glc infusion was initiated alongside continuous insulin therapy. Paradoxically, increasing Glc infusion administration rate led to a reduction in the required insulin doses and stabilization of blood glucose levels below 10 mmol·L⁻¹. The patient’s C-peptide levels were initially elevated but subsequently decreased following Glc administration as well, suggesting a reduction in endogenous insulin secretion and therefore higher insulin sensitivity. The patient’s clinical condition improved, allowing for the transition to a subcutaneous insulin regime and the initiation of oral feeding. She was later transferred to a general medical ward and discharged without further complications. Conclusions: This case highlights the complex interplay between Glc and insulin in critically ill elderly patients with T2DM during sepsis. The main takeaway is that carefully managed Glc infusion, in conjunction with flexible insulin therapy, can enhance insulin sensitivity and stabilize blood glucose levels without causing further hyperglycemia. Frequent glycemia monitoring and adaptable glycemic management strategies are essential in the ICU to address rapid glycemic fluctuations in this patient population. Full article

Background: Manganese (Mn) is an essential trace element for humans. It has been recognized as a potential occupational toxin, but its danger as a toxin in patients under parenteral nutrition is often forgotten. Case Presentation: A 73-year-old man was logged for 210 days in the intensive care unit (ICU), receiving parenteral nutrition (PN) for a month, and was then transferred, first, to the internal medicine ward and, then, to the rehabilitation hospital, and 223 days after discharge from the ICU, he had current disease, chorea-type movements in the head and neck, and left hemibody. Diagnostic tests: The magnetic resonance imaging findings suggested manganese deposits, with a total blood manganese concentration of 34 µg·L⁻¹ (reference range: less than 13 µg·L⁻¹). Discussion: Abnormal movements can be caused by manganese poisoning due to parenteral nutrition and are associated with liver failure in the ICU. Our patient showed toxic Mn concentrations in whole blood after 31 days of receiving 300 μg·d⁻¹ of Mn in PN, a shorter duration than typically reported. Neurotoxicity was observed several months later (223 days). Factors such as liver dysfunction and iron deficiency can modulate neurotoxicity. Age may also be a susceptibility factor due to increased expression of Mn transport proteins. Magnetic resonance imaging (MRI) intensity in the globus pallidus is useful for detecting brain Mn accumulation, but it is not feasible for routine clinical practice. Conclusions: In this case, choreiform movements were attributed to manganese (Mn) accumulation in the basal ganglia. It is essential to monitor patients receiving parenteral nutrition (PN) solutions containing Mn, especially in those who have biomarkers of susceptibility, even if they have not yet shown neurological signs, and routinely measure whole-blood Mn concentrations, iron levels, age, and liver function. If Mn intoxication is suspected, a brain MRI examination should be conducted. Full article

Neonatal endotracheal intubation (ETI) is a complex medical procedure that demands extensive training before practicing on real patients. Clinical studies indicate that the conventional training approach, typically conducted in idealized conditions with task trainers, has a low skill transferability rate compared to performance in the dynamic environments common in intensive care units (ICUs). According to cognitive load theory, novices encounter difficulties in multitasking scenarios, exhibiting performance declines due to competition among tasks for cognitive resources; experts, having achieved automaticity, have more cognitive resources to handle additional tasks present in high-stress environments and therefore exhibit less performance degradation. Current ETI skill assessment methods do not capture these differences in expertise. To bridge this gap, we develop an innovative dual-task mixed-reality (MR) simulator to evaluate the influence of cognitive distractors on ETI and substantiate effective performance measurement metrics. Results affirm that experts demonstrate superior proficiency in handling extraneous cognitive loads compared to novices. This has important implications for understanding how to measure novice performance in ETI settings. Taken together, the dual-task ETI training simulator and the associated automated skill evaluation metric system hold promise for enhancing training in neonatal ETI practice and ultimately leading to improved patient care outcomes. Full article

Introduction: Patients in intensive care units (ICUs) face factors that cause anxiety, fear, pain, depression, and adverse health behaviors. This qualitative study aims to determine patients’ experiences when transferred from the ICU to the ward. Methods: Thirteen individuals who were transferred from the ICU to the ward were included in this study. Interviews were conducted using a face-to-face method in the patient’s room. The interviews were recorded with a voice recorder with the consent of the patients. Codes, categories, and themes were created, and content analysis and descriptive analysis were carried out after the audio recordings were converted into text. Results: Patients reported receiving adequate physical and personal care in the ICU and were satisfied with its continuity. They felt safe due to the close attention of healthcare professionals and continuous treatment. Although they received psychological and social support from nurses, they were negatively affected by constant lights, patient noises, and nursing conversations. Patients experienced anxiety about not knowing the health status and time of day, about their relatives, their homes, and other critically ill patients in intensive care. Some patients reported fear of not being able to leave the intensive care unit, relapse, disability, or death. Patients reported pain due to the cold environment, lighting, probes, drains, and positioning. Patients suggested that healthcare personnel communicate better with them, have a clock they can see, reduce noise, and have caregivers of the same gender. They emphasized the need for moral support. Conclusions: Constant light in the intensive care unit, sounds from other patients, nurses talking among themselves, not being able to see their relatives, not knowing what time of day it is, and wondering caused anxiety in the patients. It was determined that patients experienced pain due to catheter, drain, aspiration procedures, cold environment, and position in bed. Notably, patients reported that they needed moral support and wanted to receive care from caregivers of the same gender. Full article

Background: Recent evidence suggests that alternative postoperative surveillance approaches for patients undergoing elective neurosurgical procedures are less resource-intensive and result in similar or fewer complications compared to high-care settings such as Intensive Care Units (ICUs). A new postoperative care protocol was established at our facility including routine PACU admission and predefined criteria for ICU admission. We aimed to demonstrate that PACU admission is a safe option for patients undergoing elective craniotomy following eventless surgery. Methods: This retrospective analysis included patients undergoing elective supratentorial craniotomy before and after the implementation of the new protocol. Patients with surgery between January 2020 and January 2022 and routine ICU admission were compared to patients undergoing surgery between February 2022 and March 2023 with either PACU or ICU admission based on the new protocol regarding lengths of hospital stay (LOSs), costs, and complications. Results: Data from a total of 405 patients, 198 patients before and 209 patients after the protocol implementation, were included. Both groups were comparable regarding demographics, American Society of Anesthesiologists (ASA) physical status classification, preexisting health conditions, and tumor entity and volume. Postoperative LOSs were significantly shorter in PACU compared to ICU patients of the same cohort (6 d vs. 11 d, p = 0.002). Patients with postoperative PACU transfer suffered fewer intracranial infections, surgical site infections, and pneumonia occurrences. Surgery-related complications, 30- and 90-day readmissions, and mortality rates were comparable in both groups. Conclusions: Postoperative PACU admission is a safe and viable option for patients undergoing elective craniotomy when selection is thorough and is associated with fewer ICU-related complications. Full article

Antibiotic resistance is a global health crisis exacerbated by the misuse of antibiotics in healthcare, agriculture, and the environment. In an intensive care unit (ICU), where high antibiotic usage, invasive procedures, and immunocompromised patients converge, resistance risks are amplified, leading to multidrug-resistant organisms (MDROs) and poor patient outcomes. The human microbiome plays a crucial role in the development and dissemination of antibiotic resistance genes (ARGs) through mechanisms like horizontal gene transfer, biofilm formation, and quorum sensing. Disruptions to the microbiome balance, or dysbiosis, further exacerbate resistance, particularly in high-risk ICU environments. This study explores microbiome interactions and antibiotic resistance in the ICU, highlighting machine learning (ML) as a transformative tool. Machine learning algorithms analyze high-dimensional microbiome data, predict resistance patterns, and identify novel therapeutic targets. By integrating genomic, microbiome, and clinical data, these models support personalized treatment strategies and enhance infection control measures. The results demonstrate the potential of machine learning to improve antibiotic stewardship and predict patient outcomes, emphasizing its utility in ICU-specific interventions. In conclusion, addressing antibiotic resistance in the ICU requires a multidisciplinary approach combining advanced computational methods, microbiome research, and clinical expertise. Enhanced surveillance, targeted interventions, and global collaboration are essential to mitigate antibiotic resistance and improve patient care. Full article

Background and Objectives: Chronic obstructive pulmonary disease (COPD) poses a significant healthcare challenge worldwide, frequently leading to exacerbations necessitating intensive care unit admissions for potentially life-threatening complications. We aimed to investigate correlations between laboratory parameters, bacteriological agents, ventilation mode, and survival rates among COPD patients admitted to the ICU. Materials and Methods: Data were collected from the Pulmonology Department of Mures Clinical County Hospital, Romania, from 1 January 2022 to 30 October 2023. Eighty-four COPD patients required ICU transfer, except for concurrent SARS-CoV-2 infections. Results: Ventilation modes exhibited a significant correlation with specific bacteriological agents, orotracheal intubation being more prevalent in infections with Acinetobacter baumanii, Staphylococcus aureus, and Streptococcus pneumoniae (p < 0.001). Negative cultures were predominantly found in patients managed with non-invasive ventilation. Laboratory parameters revealed an association between elevated white blood cell counts and bacteriological superinfection, particularly with Escherichia coli (p < 0.001). Different bacteriological agents had different survival rates. Patients infected with Acinetobacter baumanii exhibited the highest mortality rate, while those with Staphylococcus aureus had the lowest (p < 0.01). Conclusions: The importance of prompt evaluation of laboratory parameters and bacteriological findings is underscored by these findings, particularly in ICU settings where ventilation and bacteriological profiles influence patient outcomes. The identification of elevated WBC counts is a marker of bacterial superinfection. The association between specific bacterial agents and ventilation modes highlights the importance of tailored treatment based on microbial profiles. These insights can be applied to refine treatment protocols and enhance survival rates in severe COPD exacerbations that require ICU management. Full article

Objectives: Predicting intensive care unit (ICU) admissions during pandemic outbreaks such as COVID-19 can assist clinicians in early intervention and the better allocation of medical resources. Artificial intelligence (AI) tools are promising for this task, but their development can be hindered by the limited availability of training data. This study aims to explore model development strategies in data-limited scenarios, specifically in detecting the need for ICU admission using chest X-rays of COVID-19 patients by leveraging transfer learning and data extension to improve model performance. Methods: We explored convolutional neural networks (CNNs) pre-trained on either natural images or chest X-rays, fine-tuning them on a relatively limited dataset (COVID-19-NY-SBU, n = 899) of lung-segmented X-ray images for ICU admission classification. To further address data scarcity, we introduced a dataset extension strategy that integrates an additional dataset (MIDRC-RICORD-1c, n = 417) with different but clinically relevant labels. Results: The TorchX-SBU-RSNA and ELIXR-SBU-RSNA models, leveraging X-ray-pre-trained models with our training data extension approach, enhanced ICU admission classification performance from a baseline AUC of 0.66 (56% sensitivity and 68% specificity) to AUCs of 0.77–0.78 (58–62% sensitivity and 78–80% specificity). The gradient-weighted class activation mapping (Grad-CAM) analysis demonstrated that the TorchX-SBU-RSNA model focused more precisely on the relevant lung regions and reduced the distractions from non-relevant areas compared to the natural image-pre-trained model without data expansion. Conclusions: This study demonstrates the benefits of medical image-specific pre-training and strategic dataset expansion in enhancing the model performance of imaging AI models. Moreover, this approach demonstrates the potential of using diverse but limited data sources to alleviate the limitations of model development for medical imaging AI. The developed AI models and training strategies may facilitate more effective and efficient patient management and resource allocation in future outbreaks of infectious respiratory diseases. Full article

Background/Objectives: Data availability can affect the performance of AI-based early warning scores (EWSs). This study evaluated how the extent of missing data and imputation strategies influence the predictive performance of the VitalCare–Major Adverse Event Score (VC-MAES), an AI-based EWS that uses last observation carried forward and normal-value imputation for missing values, to forecast clinical deterioration events, including unplanned ICU transfers, cardiac arrests, or death, up to 6 h in advance. Methods: We analyzed real-world data from 6039 patient encounters at Keimyung University Dongsan Hospital, Republic of Korea. Performance was evaluated under three scenarios: (1) using only vital signs and age, treating all other variables as missing; (2) reintroducing a full set of real-world clinical variables; and (3) imputing missing values drawn from a distribution within one standard deviation of the observed mean or using Multiple Imputation by Chained Equations (MICE). Results: VC-MAES achieved the area under the receiver operating characteristic curve (AUROC) of 0.896 using only vital signs and age, outperforming traditional EWSs, including the National Early Warning Score (0.797) and the Modified Early Warning Score (0.722). Reintroducing full clinical variables improved the AUROC to 0.918, whereas mean-based imputation or MICE decreased the performance to 0.885 and 0.827, respectively. Conclusions: VC-MAES demonstrates robust predictive performance with limited inputs, outperforming traditional EWSs. Incorporating actual clinical data significantly improved accuracy. In contrast, mean-based or MICE imputation yielded poorer results than the default normal-value imputation, potentially due to disregarding the “informative presence” embedded in missing data patterns. These findings underscore the importance of understanding missingness patterns and employing imputation strategies that consider the decision-making context behind data availability to enhance model reliability. Full article