Impact of radiological surveillance for major blood vessels on complications of percutaneous dilatational tracheostomy: A retrospective cohort study.

PURPOSE: Percutaneous dilatational tracheostomy (PDT) is the preferred method for managing long-term ventilator-dependent patients in ICUs. This study aimed to evaluate the association between preprocedural screening (ultrasound Doppler [USD] or computed tomography [CT]) for major neck blood vessels and complications in ICU patients undergoing PDT. MATERIALS AND METHODS: This was a retrospective cohort study of patients who underwent PDT between 2012 and 2023 at a tertiary referral center. We performed a multivariable analysis and created a propensity-matched cohort. The primary outcome was bleeding within the first seven days after PDT. Secondary outcomes included early and late PDT complications and PTD-related mortality. RESULTS: A total of 1766 consecutive critically ill patients hospitalized at a tertiary academic hospital were evaluated for PDT. Of these, 881 (49.9 %) underwent only physical examination before PDT, while 885 (50.1 %) underwent additional imaging (CT/USD). A higher proportion of patients in the imaging group were referred to open surgery due to suspected major blood vessels interfering with the procedure (6.2 % vs. 3.0 %, p = 0.001). Among the 1685 patients who underwent PDT, there was no significant difference in the rate of early bleeding between the physical examination group and the imaging group (4.6 % vs. 6.3 %, p = 0.12). Similarly, the overall early complication rates (5.5 % vs. 7.6 %, p = 0.08), late complication rates (1.6 % vs. 2.2 %, p = 0.42), and PDT-related mortality rates (0.7 % vs. 0.6 %, p = 0.73) did not exhibit significant differences between the two groups. In a propensity score-matched cohort, results remained consistent. CONCLUSIONS: Physical examination can effectively identify major neck blood vessels without increasing the risk of bleeding during and after PDT.

Association Between Admission Ionized Calcium Level and Neurological Outcome of Patients with Isolated Severe Traumatic Brain Injury: A Retrospective Cohort Study.

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. Pathophysiological processes following initial insult are complex and not fully understood. Ionized calcium (Ca++) is an essential cofactor in the coagulation cascade and platelet aggregation, and hypocalcemia may contribute to the progression of intracranial bleeding. On the other hand, Ca++ is an important mediator of cell damage after TBI and cellular hypocalcemia may have a neuroprotective effect after brain injury. We hypothesized that early hypocalcemia might have an adverse effect on the neurological outcome of patients suffering from isolated severe TBI. In this study, we aimed to evaluate the relationship between admission Ca++ level and the neurological outcome of these patients. METHODS: This was a retrospective, single-center, cohort study of all patients admitted between January 2014 and December 2020 due to isolated severe TBI, which was defined as head abbreviated injury score ≥ 4 and an absence of severe (abbreviated injury score > 2) extracranial injuries. The primary outcome was a favorable neurological status at discharge, defined by a modified Rankin Scale of 0-2. Multivariable logistic regression was performed to determine whether admission hypocalcemia (Ca++ < 1.16 mmol L-1) is an independent predictor of neurological status at discharge. RESULTS: The final analysis included 201 patients. Hypocalcemia was common among patients with isolated severe TBI (73.1%). Most of the patients had mild hypocalcemia (1 < Ca++ < 1.16 mmol L-1), and only 13 (6.5%) patients had Ca++ ≤ 1.00 mmol L-1. In the entire cohort, hypocalcemia was independently associated with higher rates of good neurological status at discharge (adjusted odds ratio of 3.03, 95% confidence interval 1.11-8.33, p = 0.03). In the subgroup of 81 patients with an admission Glasgow Coma Scale > 8, 52 (64.2%) had hypocalcemia. Good neurological status at discharge was recorded in 28 (53.8%) of hypocalcemic patients compared with 14 (17.2%) of those with normal Ca++ (p = 0.002). In multivariate analyses, hypocalcemia was independently associated with good neurological status at discharge (adjusted odds ratio of 6.67, 95% confidence interval 1.39-33.33, p = 0.02). CONCLUSIONS: Our study demonstrates that among patients with isolated severe TBI, mild admission hypocalcemia is associated with better neurological status at hospital discharge. The prognostic value of Ca++ may be greater among patients with admission Glasgow Coma Scale > 8. Trials are needed to investigate the role of hypocalcemia in brain injury.

Clinical impact of fungal colonization of burn wounds in patients hospitalized in the intensive care unit: a retrospective cohort study.

Background: Invasive fungal infections in burn victims significantly increase mortality and hospitalization. The effect of fungal burn wound colonization has not been established. Methods: All adult patients hospitalized in the intensive care unit (ICU) with burns ≥10% of total body surface area (TBSA) between 2005 and 2021 were included. Superficial swabs were collected whenever clinical suspicion of wound colonization was raised, and deep tissue samples were sent at any wound excision. The primary outcome was the incidence of invasive fungal infections defined as any deep tissue fungal infection or fungemia. Results: Of 242 patients included, 39 (16.1%) had fungal wound colonization, 22 (56.4%) with yeasts and 24 (61.5%) molds. Patients with fungal colonization had a significantly higher rate of invasive fungal infections (82.1% vs 3.9%, p<0.001), candidemia (15.4% vs 3.4%, p=0.002), as well as longer ICU stay (61.5±57.6 vs 19±40.5 days, p<0.001), and higher in-ICU mortality (43.6% vs 15.8%, p<0.001). Survival analysis showed fungal colonization to be associated with significantly increased risk of invasive infection (aHR 25, 95% CI (9.67 to 64.62)), even when adjusted for age, TBSA, sequential organ failure assessment scores, Charlson Comorbidity Index and the presence of bacteremia. Conclusions: Fungal burn wound colonization is associated with increased risk of invasive fungal infections and mortality. Level of Evidence: This a single center, retrospective cohort study.

Early risk factors for prolonged mechanical ventilation in patients with severe blunt thoracic trauma: A retrospective cohort study.

BACKGROUND: A significant proportion of patients with severe chest trauma require mechanical ventilation (MV). Early prediction of the duration of MV may influence clinical decisions. We aimed to determine early risk factors for prolonged MV among adults suffering from severe blunt thoracic trauma. METHODS: This retrospective, single-center, cohort study included all patients admitted between January 2014 and December 2020 due to severe blunt chest trauma. The primary outcome was prolonged MV, defined as invasive MV lasting more than 14 days. Multivariable logistic regression was performed to identify independent risk factors for prolonged MV. RESULTS: The final analysis included 378 patients. The median duration of MV was 9.7 (IQR 3.0-18.0) days. 221 (58.5 %) patients required MV for more than 7 days and 143 (37.8 %) for more than 14 days. Male gender (aOR 3.01, 95 % CI 1.63-5.58, p < 0.001), age (aOR 1.40, 95 % CI 1.21-1.63, p < 0.001, for each category above 30 years), presence of severe head trauma (aOR 3.77, 95 % CI 2.23-6.38, p < 0.001), and transfusion of >5 blood units on admission (aOR 2.85, 95 % CI 1.62-5.02, p < 0.001) were independently associated with prolonged MV. The number of fractured ribs and the extent of lung contusions were associated with MV for more than 7 days, but not for 14 days. In the subgroup of 134 patients without concomitant head trauma, age (aOR 1.63, 95 % CI 1.18-2.27, p = 0.004, for each category above 30 years), respiratory comorbidities (aOR 9.70, 95 % CI 1.49-63.01, p = 0.017), worse p/f ratio during the first 24 h (aOR 1.55, 95 % CI 1.15-2.09, p = 0.004), and transfusion of >5 blood units on admission (aOR 5.71 95 % CI 1.84-17.68, p = 0.003) were independently associated with MV for more than 14 days. CONCLUSIONS: Several predictors have been identified as independently associated with prolonged MV. Patients who meet these criteria are at high risk for prolonged MV and should be considered for interventions that could potentially shorten MV duration and reduce associated complications. Hemodynamically stable, healthy young patients suffering from severe thoracic trauma but no head injury, including those with extensive lung contusions and rib fractures, have a low risk of prolonged MV.

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study.

OBJECTIVE: The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics. MATERIALS AND METHODS: We develop a model of patient clinical course based on an advanced multistate survival model. The model predicts the patient's disease course in terms of clinical states-critical, severe, or moderate. The model also predicts hospital utilization on the level of entire hospitals or healthcare systems. We cross-validated the model using a nationwide registry following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1 to May 2, 2020 (n = 2703). RESULTS: Per-day mean absolute errors for predicted total and critical care hospital bed utilization were 4.72 ± 1.07 and 1.68 ± 0.40, respectively, over cohorts of 330 hospitalized patients; areas under the curve for prediction of critical illness and in-hospital mortality were 0.88 ± 0.04 and 0.96 ± 0.04, respectively. We further present the impact of patient influx scenarios on day-by-day healthcare system utilization. We provide an accompanying R software package. DISCUSSION: The proposed model accurately predicts total and critical care hospital utilization. The model enables evaluating impacts of patient influx scenarios on utilization, accounting for the state of currently hospitalized patients and characteristics of incoming patients. We show that accurate hospital load predictions were possible using only a patient's age, sex, and day-by-day clinical state (critical, severe, or moderate). CONCLUSIONS: The multistate model we develop is a powerful tool for predicting individual-level patient outcomes and hospital-level utilization.

Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms.

PURPOSE: We aimed to develop a machine-learning (ML) algorithm that can predict intensive care unit (ICU)-acquired bloodstream infections (BSI) among patients suspected of infection in the ICU. METHODS: The study was based on patients' electronic health records at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, USA, and at Rambam Health Care Campus (RHCC), Haifa, Israel. We included adults from whom blood cultures were collected for suspected BSI at least 48 h after admission. Clinical data, including time-series variables and their interactions, were analyzed by an ML algorithm at each site. Prediction ability for ICU-acquired BSI was assessed by the area under the receiver operating characteristics (AUROC) of ten-fold cross-validation and validation sets with 95% confidence intervals. RESULTS: The datasets comprised 2351 patients from BIDMC (151 with BSI) and 1021 from RHCC (162 with BSI). The median (inter-quartile range) age was 62 (51-75) and 56 (38-69) years, respectively; the median Acute Physiology and Chronic Health Evaluation II scores were 26 (21-32) and 24 (20-29), respectively. The means of the cross-validation AUROCs were 0.87 ± 0.02 for BIDMC and 0.93 ± 0.03 for RHCC. AUROCs of 0.89 ± 0.01 and 0.92 ± 0.02 were maintained in both centers with internal validation, while external validation deteriorated. Valuable predictors were mainly the trends of time-series variables such as laboratory results and vital signs. CONCLUSION: An ML approach that uses temporal and site-specific data achieved high performance in recognizing BC samples with a high probability for ICU-acquired BSI.