Search Results (167)

Background/Objectives: Pressure injuries (PIs), a recognized indicator of care quality, have a higher incidence in intensive care units (ICUs). Our objective was to assess whether critically ill patients identified as “unprotected” (COMHON ≥ 11) developed pressure injuries despite additional preventive measures. Methods: A historical cohort study of an adult ICU was carried out. Of the 811 patients admitted in 2022, 400 were selected. All of them were subjected to the ICU’s PI Prevention Protocol, and those with a moving average of the COMHON Index ≥ 11 were given two additional measures: a multilayer dressing on the sacrum, and anti-equinus and heel-pressure-relieving boots. Results: A total of 36 patients presented with PIs (cumulative incidence of 9%). Significant differences were observed in their mean length of stay and in their disease severity score (APACHE-II). Most of the PIs were located on the sacrum, followed by the heel. Prior to the appearance of the PIs, a sacral dressing was applied to 100% of the patients, while anti-equinus and heel-pressure-relieving boots were only applied to 58.3%. Of the 36 patients with PIs, 52.8% had a PI on the sacrum and 22.2% on the heel. Conclusions: Focusing only on those who presented with PIs, we observed that the considered measures were not effective for preventing PIs in all the patients. Not all PIs are preventable, and individual risk factors should be considered. In the future, we will analyze the individual characteristics of these patients and verify whether the Prevention Protocol was followed, in order to determine how they could have been prevented or whether they were so-called unavoidable PIs. Full article

Non-invasive ventilation (NIV) is a cornerstone in the management of acute and chronic respiratory failure, offering critical support without the risks of intubation. However, successful weaning from NIV remains a complex, high-stakes process. Poorly timed or improperly executed weaning significantly increases morbidity and mortality, yet current clinical practice often relies on subjective judgment rather than evidence-based protocols. This manuscript reviews the current landscape of NIV weaning, emphasizing structured approaches, objective monitoring, and predictors of weaning success or failure. It examines guideline-based indications, monitoring strategies, and various weaning techniques—gradual and abrupt—with evidence of their efficacy across different patient populations. Predictive tools such as the Rapid Shallow Breathing Index, Lung Ultrasound Score, Diaphragm Thickening Fraction, ROX index, and HACOR score are analyzed for their diagnostic value. Additionally, this review underscores the importance of care setting—ICU, step-down unit, or general ward—and how it influences outcomes. Finally, it highlights critical gaps in research, especially around weaning in non-ICU environments. By consolidating current evidence and identifying predictors and pitfalls, this article aims to support clinicians in making safe, timely, and patient-specific NIV weaning decisions. In the current literature, there are gaps regarding patient selection and lack of universal protocolization for initiation and de-escalation of NIV as the data has been scattered. This review aims to consolidate the relevant information to be utilized by clinicians throughout multiple levels of care in all hospital systems. Full article

Background/Objectives: Non-pancreatic hyperlipasemia (NPHL) is associated with high in-hospital mortality, with sepsis being one of the most common etiologies. The prognostic value of hematological parameter-derived inflammatory scores has not been extensively studied in NPHL to date. Methods: The prognostic value of eight inflammatory scores for in-hospital mortality was assessed in a total of 545 NPHL patients from two hospitalized patient cohorts (COVID-19 [n = 144] and non-COVID-19 [n = 401], the latter stratified as bacterial sepsis [n = 111] and absence of systemic infection [n = 290]). We assessed the neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), neutrophil-to-lymphocyte and platelet ratio (N/(LP)), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), aggregate index of systemic inflammation (AISI), systemic inflammation index (SII), and systemic inflammation response index (SIRI), comparing their prognostic value among etiological groups. Results: Patients with bacterial sepsis were older, had more comorbidities, and experienced worse outcomes, including longer hospitalization (median: 15, 7, and 11 days; p < 0.001), higher ICU admission rates (75.7%, 33.8%, and 47.9%, p < 0.001), and increased mortality (45.0%, 13.8%, and 38.2%, p < 0.001), compared to those without systemic infection or with COVID-19-induced NPHL. Overall, NLR, dNLR, and N/(LP) were the most accurate predictors of in-hospital mortality at admission (AUROC: non-infection: 0.747; 0.737; 0.772; COVID-19: 0.810; 0.789; 0.773, respectively). The accuracy of NLR decreased in bacterial sepsis, and only N/(LP) and PLR remained associated with in-hospital mortality (AUROC: 0.653 and 0.616, respectively). Conclusions: The prognostic performance of hematological parameter-derived inflammatory scores in NPHL is etiology-dependent. NLR is the most accurate prognostic tool for mortality in the absence of bacterial sepsis, while N/(LP) is the best score in sepsis-induced NPHL. Full article

Background: High-voltage electrical injuries (HVEIs) represent a complex and life-threatening entity, frequently involving multi-organ damage. While traditionally linked to occupational hazards, train surfing—riding on moving trains—and train climbing—scaling stationary carriages—have emerged as increasingly common causes among adolescents. Popularized via social media, these behaviors expose individuals to the invisible danger of electric arcs from 15,000-volt railway lines, often resulting in extensive burns, cardiac complications, and severe trauma. This study presents a 30-year retrospective analysis comparing cardiac biomarkers and clinical outcomes in train-surfing injuries versus work-related HVEIs. Methods: All patients with confirmed high-voltage injury (≥1000 volts) admitted to a Level 1 burn center between 1994 and 2024 were retrospectively analyzed. Exclusion criteria comprised low-voltage trauma, suicide, incomplete records, and external treatment. Clinical and laboratory parameters—including total body surface area (TBSA), Abbreviated Burn Severity Index (ABSI), electrocardiogram (ECG) findings, intensive care unit (ICU) and hospital stay, mortality, and cardiac biomarkers (creatine kinase [CK], CK-MB, lactate dehydrogenase [LDH], aspartate transaminase [AST], troponin, and myoglobin)—were compared between the two cohorts. Results: Of 81 patients, 24 sustained train-surfing injuries and 57 were injured in occupational settings. Train surfers were significantly younger (mean 16.7 vs. 35.2 years, p = 0.008), presented with greater TBSA (49.9% vs. 17.9%, p = 0.008), higher ABSI scores (7.3 vs. 5.1, p = 0.008), longer ICU stays (53 vs. 17 days, p = 0.008), and higher mortality (20.8% vs. 3.5%). ECG abnormalities were observed in 51% of all cases, without significant group differences. However, all cardiac biomarkers were significantly elevated in train-surfing injuries at both 72 h and 10 days post-injury (p < 0.05), suggesting more pronounced cardiac and muscular damage. Conclusions: Train-surfing-related high-voltage injuries are associated with markedly more severe systemic and cardiac complications than occupational HVEIs. The significant biomarker elevation and critical care demands highlight the urgent need for targeted prevention, public awareness, and early cardiac monitoring in this high-risk adolescent population. Full article

Background: Brain injuries, including stroke and traumatic brain injury (TBI), pose a major healthcare challenge due to their severe consequences and complex recovery. While ischemic strokes are more common, hemorrhagic strokes have a worse prognosis. TBI often affects young adults and leads to long-term disability. A critical concern in these patients is the frequent development of chronic critical illness, compounded by metabolic disturbances and malnutrition that hinder recovery. Objective: This study aimed to compare changes in nutritional status parameters under standard enteral nutrition protocols and clinical outcomes in prolonged/chronic critically ill patients with TBI or stroke versus such a population of patients without TBI or stroke. Methods: This matched prospective–retrospective cohort study included intensive care unit (ICU) patients with TBI or stroke from the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology and patients without these conditions from the eICU-CRD database. Inclusion criteria comprised age 18–74 years, ICU stay >5 days, and enteral nutrition. Patients with re-hospitalization, diabetes, acute organ failure, or incomplete data were excluded. Laboratory values and clinical outcomes were compared between the two groups. Propensity score matching (PSM) was used to balance baseline characteristics (age, sex, and body mass index). Results: After PSM, 29 patients with TBI or stroke and 121 without were included. Univariate analysis showed significant differences in 21 laboratory parameters and three hospitalization outcomes. On day 1, the TBI/stroke group had higher hemoglobin, hematocrit, lymphocytes, total protein, and albumin, but lower blood urea nitrogen (BUN), creatinine, and glucose. By day 20, they had statistically significantly lower calcium, BUN, creatinine, and glucose. This group also showed less change in lymphocytes, calcium, and direct bilirubin. Hospitalization outcomes showed longer mechanical ventilation duration (p = 0.030) and fewer cases of acute kidney injury (p = 0.0220) in the TBI/stroke group. Conclusions: TBI and stroke patients exhibit unique metabolic patterns during prolonged/chronic critical illness, differing significantly from other ICU populations in protein/glucose metabolism and complication rates. These findings underscore the necessity for specialized nutritional strategies in neurocritical care and warrant further investigation into targeted metabolic interventions. Full article

Background: Splenectomy remains necessary in selected oncologic and hematologic indications but is associated with significant postoperative morbidity and mortality. The data on outcomes in this high-risk population remain limited, particularly in mixed cohorts. Methods: We conducted a retrospective cohort study including all patients undergoing splenectomy for oncologic or hematologic causes between 2009 and 2022 at a cancer referral center. The primary outcomes were the occurrence of major complications at day 90 and the 1-year all-cause mortality. Multivariate logistic regression was used to identify independent predictors. Results: Among the 8503 ICU admissions from surgical wards, 204 splenectomies were performed; 179 patients were analyzed. The median age was 64 years, and 100 patients (55.9%) were female. Splenectomy was performed for hematologic malignancies in 76 cases (42.5%) and for oncologic causes in 103 cases (57.5%). Laparotomy was used in 154 cases (86.0%), and metastasectomy was performed in 54 patients (30.2%). At day 90, 86 patients (48.0%) developed a major complication: 12 deaths (6.7%), 44 surgical complications (24.6%), and 71 episodes of sepsis (39.7%). In a multivariate analysis, weight loss (OR 3.39, 95% CI [1.32–8.70], p = 0.011), laparotomy (OR 4.38 [1.09–17.60], p = 0.038), and a higher SAPS II score (OR 1.08 per point [1.03–1.13], p = 0.003) were associated with complications, while metastasectomy was protective (OR 0.23 [0.08–0.67], p = 0.007). At one year, the mortality reached 22.4%. Independent predictors of death were sepsis at one year (OR 5.04, 95% CI [1.30–25.96], p = 0.029), the Charlson Comorbidity Index (OR 1.30 per point, 95% CI [1.04–1.68], p = 0.030), invasive mechanical ventilation (OR 14.94, 95% CI [2.83–118.93], p = 0.003), and a performance status >1 (OR 7.84, 95% CI [2.38–27.75], p < 0.001). Encapsulated bacteria were not isolated; sepsis was mainly due to Gram-negative and enterococcal organisms. Conclusions: Splenectomy in onco-hematologic patients is associated with high rates of sepsis and mortality. In addition to surgical factors, frailty, immune status, and infection independently contribute to the patients’ outcomes. These results support risk-adapted perioperative strategies and long-term infectious surveillance in immunocompromised patients. Full article

Background: Atrial fibrillation (AF) frequently occurs in individuals with hypercapnic type 2 respiratory failure and has the potential to adversely affect patient outcomes. This study sought to investigate the clinical features and prognostic significance of atrial fibrillation in patients admitted to the intensive care unit with hypercapnic type 2 respiratory failure. Methods: This retrospective, single-center study included 200 adult patients diagnosed with hypercapnic type 2 respiratory failure between May 2022 and May 2023. Patients were grouped according to whether atrial fibrillation was present or not. Demographic, laboratory, and echocardiographic findings, comorbidities, and outcomes were compared. Kaplan–Meier survival analysis and Cox regression were used to identify mortality predictors. Results: AF was present in 50.5% of patients. Those with AF were older, had higher Charlson Comorbidity Index scores, and a greater prevalence of heart failure (p < 0.001). No significant differences were found in arterial blood gas values. AF patients had higher urea, creatinine, and BNP levels, and lower hemoglobin, lymphocyte, eosinophil, and monocyte counts (p < 0.05). Echocardiography showed more severe tricuspid and mitral regurgitation, lower ejection fractions, and higher systolic pulmonary pressures in the AF group. About 20% of AF patients were not receiving anticoagulants at ICU admission. AF was associated with shorter survival (49.6 ± 4.07 vs. 61.4 ± 3.8 days, p = 0.031) and 1.6-fold higher mortality risk (HR: 1.60, 95% CI: 1.04–2.47). Advanced age and low hemoglobin were independent predictors of mortality. Conclusions: AF is frequent among patients with type 2 respiratory failure and is linked to increased mortality. Despite known complications, treatment remains underutilized. AF should be actively screened during ICU admissions for respiratory failure. Full article

Background: Pleural empyema (PE) is a major cause of morbidity and mortality worldwide. This study aimed to analyze the epidemiological characteristics of patients hospitalized for PE in Spain between 2016 and 2022. Methods: This retrospective observational study of PE cases was based on the hospital discharge records from the National Health System between 2016 and 2022. The variables analyzed were sex, age, comorbidities, discharge diagnoses and procedures, overall severity, whether empyema was a primary or secondary diagnosis, admission to the intensive care unit (ICU), length of stay (LOS), in-hospital mortality, and healthcare costs. Results: Between 2016 and 2022, 19864 PE cases were diagnosed in Spain, revealing an overall rate of 0.64 per 1000 hospitalizations, with the exception of a slight decline in 2021. The mean age of the patients with PE was 61 years, and 73.85% were men. Most patients had low comorbidities, with a median Charlson comorbidity index (CCI) of 1.7. Most cases (63%) involved secondary diagnoses (pneumonia, pneumococcal pneumonia, sepsis, COVID, or lung cancer). The in-hospital mortality rate was higher in the secondary diagnosis group than in the primary diagnosis group (13.4% vs. 6.2%, respectively, p < 0.001). The factors associated with increased mortality included older age (≥66 years), higher CCI scores, ICU admission, and shorter LOS (<10 days). Conversely, pleural drainage and pneumonia as secondary diagnoses were protective factors. Conclusions: PE is an increasingly common pathology in clinical practice, especially in older and frail patients. It is associated with high morbidity and mortality, and its prognosis worsens with age and comorbidities. Therefore, early and appropriate diagnosis and standardized management strategies are required to mitigate the mortality and healthcare costs. Full article

Background: The COVID-19 pandemic clearly posed a global challenge to healthcare systems, where the allocation of limited resources had important logistical and ethical implications. Detecting and prioritizing the population at risk of intensive care unit (ICU) admission is the first step to being able to care for the most vulnerable people and avoid unnecessary consumption of resources by mildly ill patients. Objective: To create a model, using machine learning techniques, capable of identifying the risk of admission to the ICU throughout the hospital stay of the COVID patient and to evaluate the performance of the model. Methods: A retrospective cohort design was used to develop and validate a classification model of adult COVID-19 patients with or without risk of ICU admission. Data from three hospitals in Spain were used to develop the model (n = 1272) and for subsequent external validation (n = 550). Sensitivity, specificity, positive and negative predictive value, accuracy, F1 score, Youden index and area under the curve of the model were evaluated. Results: The LightGBM model, incorporating 40 variables, was used. The area under the curve obtained by the model when the test dataset was used was 1.00 (0.99–1.0), specificity 0.99 (0.97–1.00) and sensitivity 0.92 (0.86–0.98). Conclusions: A model for predicting ICU admission of hospitalized COVID-19 patients was created with very good results. The identification and prioritization of COVID-19 patients at risk of ICU admission allows the right care to be provided to those who are most in need when the healthcare system is under pressure. Full article

Background: Patients who undergo cardiac surgery and procedures often experience functional impairments. However, few studies have compared changes in physical function, cognitive function, mental health, and sleep quality before and after the interventions. Methods: Intensive care unit (ICU) nurses visited the ward and conducted the assessments. The Japanese version of the Cardiovascular Health Study (J-CHS) and the Barthel index for physical function, mini-mental state examination (MMSE) for cognitive function, hospital anxiety and depression scale for anxiety (HADS-A) and depression (HADS-D) for mental health, and a 5-point Likert scale for sleep quality were used. Results: Of the 210 cases, 156 were included. Cardiac surgeries and procedures included valve replacement or valvuloplasty (43%), coronary artery bypass graft (9%), and transcatheter aortic valve implantation (39%). At a median of 7 (4–9) days after ICU discharge, the J-CHS score worsened from 2 (1–3) to 3 (2–3) (p < 0.01), and the Barthel index worsened from 95 (85–100) to 75 (55–85) (p < 0.01). The HADS-A score improved from 3 (1–6) to 1 (0–4) (p < 0.01), and the HADS-D score improved from 4 (1–7) to 2 (1–6) (p < 0.01). The MMSE score remained unchanged at 26 (24–29; p = 0.91). Sleep quality worsened from 4 (3–5) to 3 (2–4) (p < 0.01). In the multivariate analysis, sleep quality deterioration was associated with open thoracotomy. Conclusions: After cardiac surgeries and procedures, physical function and sleep quality worsened, whereas anxiety and depression improved, and cognitive function remained unchanged. Full article

Endotheliopathy plays an essential role in the pathophysiology of COVID-19. The endothelial activation and stress index (EASIX) indicates endothelial dysfunction. We aimed to investigate the relationship between a high EASIX score and mortality in patients with COVID-19. We retrospectively reviewed COVID-19 patients admitted to the ICU (intensive care unit) of the Ankara Bilkent City Hospital. We recorded hematological and biochemical parameters at the ICU admission and further calculated EASIX with the following equation: EASIX = Lactate dehydrogenase (U/L) × creatinine (mg/dL)/platelet count (10⁹/L). Statistical comparisons were made between the surviving and non-surviving groups in terms of EASIX. The median EASIX score was 1.2 (0.7–2.0) in the survivor group and a median of 2.5 (1.6–4.2) in the non-survivor group (p < 0.001). The mean log2-EASIX was 0.2 ± 0.9 in the survivor group and 1.3 ± 1.2 in the non-survivor group (p < 0.001). Lactate dehydrogenase, creatinine, Troponin I, D-dimer, procalcitonin, ferritin, and IL-6 were statistically significantly higher in the non-survivor group compared to the survivor group. The receiver operating characteristic (ROC) curve analysis showed that the cut-off value of the EASIX score was 2.05 (The area under the curve [AUC] = 0.764, p = 0.001, 95% CI: 0.662–0.847). Our study showed an association between high EASIX scores and poor prognosis in COVID-19 patients. Lactate dehydrogenase, creatinine, Troponin I, D-dimer, procalcitonin, ferritin, IL-6, EASIX, and log2-EASIX were statistically significantly higher in the non-survivor group compared to the survivor group. Being old and having chronic kidney disease increases the risk of death. Eventually, EASIX can be used to predict mortality in COVID-19 patients. Full article

Background: Allostatic load reflects the cumulative physiological effects of chronic and repeated stress on the body and is associated with dysregulation of multiple systems. This study aimed to examine the association between the allostatic load score (ALS) and the development of delirium in intensive care unit (ICU) patients. Method: The adult patients from the Medical Information Mart for Intensive Care (MIMIC-IV) database were screened and included in this study. Allostatic load was scored by hemoglobin A1c, high-density lipoprotein, total cholesterol, systolic blood pressure, diastolic blood pressure, body mass index, C-reactive protein, and serum albumin, and varied from 0 to 8. Restricted cubic spline and multivariate logistic regression were used to assess the relationship between ALS and delirium risk in the ICU. The threshold of the ALS was determined by the decision tree approach. A sensitivity analysis was also conducted. Results: A total of 656 patients were included in the study, and the incidence of delirium was 50.6% (n = 332). In a fully adjusted restricted cubic spline model, an increase in ALS was linearly positively correlated with the occurrence of delirium in the ICU (p-overall = 0.039, p-nonlinear = 0.506). The threshold for ALS was determined to be 3. ALS ≥ 3 was associated with increased delirium rates (p < 0.001), longer hospital stays (p < 0.001), and higher in-hospital mortality (p = 0.002). Subgroup analyses revealed no significant interactions (all p values for interactions > 0.05). Conclusions: Higher ALS was linearly associated with increased risk of ICU delirium. An ALS ≥ 3 identified patients with greater delirium incidence, longer hospital stays, and higher mortality. Full article

Background: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion injury and evolving instability. However, risk prediction in later phases—after revascularization—is less explored, despite its importance in guiding intensive care decisions. This study evaluates machine learning (ML) models for dynamic risk assessment in interventional cardiology and cardiac intensive care unit (CICU) phases, where timely detection of deterioration can guide treatment escalation. Methods: We retrospectively analyzed clinical and procedural data from 158 patients diagnosed with STEMI complicated by cardiogenic shock, treated between 2019 and 2022 at the Cardiology Department of the University Emergency Hospital of Bucharest, Romania. Machine learning models—Random Forest (RF), and Quadratic Discriminant Analysis (QDA)—were developed and tested specifically for the interventional cardiology and CICU phases. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy (ACC), sensitivity, specificity, and F1-score. Results: In the interventional phase, RF and QDA achieved the highest accuracy, both reaching 87.50%. In the CICU, RF and QDA demonstrate the best performance, reaching ACCs of 0.843. QDA maintained consistent performance across phases. Relevant predictors included reperfusion strategy, TIMI flow before percutaneous coronary intervention (PCI), Killip class, creatinine, and Creatine Kinase Index (CKI)—all parameters routinely assessed in STEMI patients. These models effectively identified patients at risk for post-reperfusion complications and hemodynamic decline, supporting decisions regarding extended monitoring and ICU-level care. Conclusions: Predictive models implemented in advanced STEMI phases can contribute to dynamic, phase-specific risk reassessment and optimize CICU resource allocation. These findings support the integration of ML-based tools into post-PCI workflows, enabling earlier detection of clinical decline and more efficient deployment of intensive care resources. When combined with earlier-stage models, the inclusion of interventional and CICU phases forms a dynamic, end-to-end risk assessment framework. With further refinement, this system could be implemented as a mobile application to support clinical decisions throughout the STEMI care continuum. Full article

Background: Heart failure (HF) ranks among the foremost causes of mortality globally, exhibiting particularly high prevalence and significant impact within intensive care units (ICUs). This study sought to develop, validate, and deploy a time-dependent machine learning model aimed at predicting the one-year all-cause mortality risk in ICU patients diagnosed with HF, thereby facilitating precise prognostic evaluation and risk stratification. Methods: This study encompassed a cohort of 8960 ICU patients with HF sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (version 3.1). This latest version of the database added data from 2020 to 2022 on the basis of version 2.2 (covering data from 2008 to 2019); therefore, data spanning 2008 to 2019 (n = 5748) were designated for the training set, while data from 2020 to 2022 (n = 3212) were reserved for the test set. The primary endpoint of interest was one-year all-cause mortality. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to select predictive features from an initial pool of 64 candidate variables (including demographic characteristics, vital signs, comorbidities and complications, therapeutic interventions, routine laboratory data, and disease severity scores). Four predictive models were developed and compared: Cox proportional hazards, random survival forest (RSF), Cox proportional hazards deep neural network (DeepSurv), and eXtreme Gradient Boosting (XGBoost). Model performance was assessed using the concordance index (C-index) and Brier score, with model interpretability addressed through SHapley Additive exPlanations (SHAP) and time-dependent Survival SHapley Additive exPlanations (SurvSHAP(t)). Results: This study revealed a one-year mortality rate of 46.1% within the population under investigation. In the training set, LASSO effectively identified 24 features in the model. In the test set, the XGBoost model exhibited superior predictive performance, as evidenced by a C-index of 0.772 and a Brier score of 0.161, outperforming the Cox model (C-index: 0.740, Brier score: 0.175), the RSF model (C-index: 0.747, Brier score: 0.178), and the DeepSur model (C-index: 0.723, Brier score: 0.183). Decision curve analysis validated the clinical utility of the XGBoost model across a broad spectrum of risk thresholds. Feature importance analysis identified the red cell distribution width-to-albumin ratio (RAR), Charlson Comorbidity Index, Simplified Acute Physiology Score II (SAPS II), Acute Physiology Score III (APS III), and the age–bilirubin–INR–creatinine (ABIC) score as the top five predictive factors. Consequently, an online risk prediction tool based on this model has been developed and is publicly accessible. Conclusions: The time-dependent XGBoost model demonstrated robust predictive capability in evaluating the one-year all-cause mortality risk in critically ill HF patients. This model offered a useful tool for early risk identification and supported timely interventions. Full article

Background and Objectives: Secondary bacterial pneumonia can substantially worsen the clinical trajectory of patients hospitalized for Coronavirus Disease 2019 (COVID-19). This study aimed to characterize bacterial superinfections in COVID-19, including pathogen profiles, resistance patterns, inflammatory responses, severity scores, and ICU admission risk. Methods: In a retrospective cohort design, we reviewed 141 patients admitted to a single tertiary-care hospital between February 2021 and December 2024. A total of 58 patients had laboratory-confirmed bacterial superinfection by sputum, bronchoalveolar lavage, or blood cultures (superinfection group), whereas 83 had COVID-19 without any documented bacterial pathogens (COVID-only group). We collected detailed microbiological data from sputum, bronchoalveolar lavage (BAL), and blood cultures. Antibiotic sensitivity testing was performed using standard breakpoints for multidrug resistance (MDR). Inflammatory markers (C-reactive protein, procalcitonin, neutrophil-to-lymphocyte ratio, and systemic immune-inflammation index) and the severity indices Acute Physiology and Chronic Health Evaluation (APACHE) II, Confusion, Urea, Respiratory rate, Blood pressure (CURB), and National Early Warning Score (NEWS) were measured at admission. Primary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and mortality. Results: Patients in the superinfection group showed significantly elevated inflammatory markers and severity scores compared to the COVID-only group (mean APACHE II of 17.2 vs. 13.8; p < 0.001). Pathogens most frequently isolated from sputum and BAL included Klebsiella pneumoniae (27.6%) and Pseudomonas aeruginosa (20.7%). Multidrug-resistant strains were documented in 32.8% of isolates. The superinfection group had higher ICU admissions (37.9% vs. 19.3%; p = 0.01) and more frequent mechanical ventilation (25.9% vs. 9.6%; p = 0.01). Mortality trended higher among superinfected patients (15.5% vs. 7.2%; p = 0.09). A total of 34% of the cohort had prior antibiotic use, which independently predicted MDR (aOR 2.6, p = 0.01). The presence of MDR pathogens such as Klebsiella pneumoniae (OR 2.8), Pseudomonas aeruginosa (OR 2.5), and Staphylococcus aureus (OR 2.1) significantly increases the risk of ICU admission. Conclusions: Bacterial superinfection exacerbates inflammation and worsens outcomes in COVID-19 patients, such as a higher risk of ICU admission. Full article