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Febrile Neutropenia in the Critical Care Unit
Published in Cheston B. Cunha, Burke A. Cunha, Infectious Diseases and Antimicrobial Stewardship in Critical Care Medicine, 2020
Perrine Parize, Anne Pouvaret, Paul-Louis Woerther, Frédéric Pène, Olivier Lortholary
New sepsis definitions have been recently been set, with a new risk stratification to facilitate earlier recognition and more timely management of patients with sepsis: Absence of organ dysfunction, sepsis, and septic shock [17]. Those definitions are based on the evaluation by the Sequential Organ Failure Assessment (SOFA) score and quick-SOFA (qSOFA), while the previous sepsis definitions were set with the Systemic Inflammatory Response Syndrome (SIRS) score [18]. Only two studies with discordant results have evaluated and compared the accuracy of these scores for mortality in cancer patients admitted to CCU with infections or febrile neutropenia. One of them reported a better discriminative power than SIRS for predicting hospital and CCU mortality [19]. The second one concludes that even if qSOFA score was an independent factor predicting sepsis and CCU admission in patients with febrile neutropenia, its performance remained inferior to that of the MASCC score, with a poor sensitivity of 14% for predicting sepsis but a high specificity of 98% [20]. Thus, predictive models may be helpful for some groups of febrile neutropenia but cannot be applied to the overall population. Severity of illness scores, such as Simplified Acute Physiology Score (SAPS II) in febrile neutropenic patients in hematology wards, appear to be inaccurate for predicting mortality [21].
The management of major injuries
Published in Ashley W. Blom, David Warwick, Michael R. Whitehouse, Apley and Solomon’s System of Orthopaedics and Trauma, 2017
The simplified acute physiology score (SAPS) initially used 14 variables and did not provide any probability of survival. In 1993 it was revised to SAPS II with the data originating from European and North American ICUs. The score includes 12 physiological variables (the worst value within the first 24 hours), age, type of admission and three underlying disease variables (acquired immune deficiency syndrome (AIDS), metastatic cancer and haematological malignancy). Using logistic regression, SAPS II can also be used to estimate the probability of survival. It is a simpler scoring system than APACHE and is also in the public domain, resulting in its widespread use, particularly in Europe. It suffers similar disadvantages when compared to APACHE with regards to the timing of data collection, but it is based on more recent and international data. SAPS III was introduced in 2005 and calculates scores based on data collected within the first hour after admission. The rationale is that this allows a prediction of outcome before ICU intervention, and is a better evaluation of an individual patient rather than an ICU. There are limitations including the timing of data collection which may lead to data omissions.
Acute Lung Injury/Acute Respiratory Distress Syndrome
Published in Stephen M. Cohn, Matthew O. Dolich, Kenji Inaba, Acute Care Surgery and Trauma, 2016
Kristin P. Colling, Juan J. Blondet, Greg J. Beilman
In 2004, the ALIVE study (Acute Lung Injury Verification of Epidemiology) published the results of their prospective, multinational, cohort study, conducted in 78 ICUs across 10 European countries [18]. This study evaluated the occurrence, etiologies, outcomes, and risk factors associated with survival in patients with ARDS. In patients with ARDS, mortality rates varied according to the cause of lung injury, and to whether the lung injury was from a direct cause (e.g., pneumonia), an indirect cause (e.g., extrapulmonary sepsis), or a combined insult (pneumonia and septic shock), where the latter had the worst outcome. In univariate analysis, mortality at hospital discharge was the highest in patients with sepsis (43%), lower in patients with pneumonia (36%) or aspiration (37%), and the lowest in patients with trauma (11%). Using multivariable logistic regression analysis, the following variables remained significantly associated with mortality: age, immunocompromise, air leak in the first 2 days, the Simplified Acute Physiology Score (SAPS) II on admission, and a pH of 7.30 or less (OR = 1.88 [1.11–3.18, 95% CI], p = 0.019).
Comparison of intermittent versus continuous infusion of 3% hypertonic saline on intracranial pressure in traumatic brain injury using ultrasound assessment of optic nerve sheath
Published in Egyptian Journal of Anaesthesia, 2022
Amr Samir Wahdan, Ahmed Abdallah Al-Madawi, Khaled Abdelrahman El-Shafey, Safinaz Hassan Othman
On admission to N-ICU, the baseline characteristic data of patients were collected from the medical records of the local trauma database (e.g., age, sex, weight, body mass index [BMI], comorbidity, Injury Severity Score, AIS, and injury diagnosis). Hemodynamics (mean heart rate [HR], MAP, temperature, and oxygen saturation [SpO2]) were then evaluated, and routine laboratory tests (complete blood count, Na, potassium [K], serum urea, serum creatinine, alanine transaminase, aspartate transaminase, international normalized ratio, prothrombin concentration, lactate, bilirubin, plasma osmolarity, and blood gases) were performed. A central venous catheter was inserted. Additionally, at this time, the GCS scores, Simplified Acute Physiology Score (SAPS II), and Acute Physiology and Chronic Health Evaluation (APACHE II) scores were measured.
A novel prognostic model for predicting the mortality risk of patients with sepsis-related acute respiratory failure: a cohort study using the MIMIC-IV database
Published in Current Medical Research and Opinion, 2022
Lina Zhao, Jing Yang, Cong Zhou, Yunying Wang, Tao Liu
SAPS II is a commonly used system score for the severity of critical illness and is a clinically useful tool for predicting short-term prognosis in sepsis27. However, whether it applies to sepsis-associated acute respiratory failure remains unclear. Therefore, we developed a novel prediction model and compared its predictive performance with SAPS II. Our finding suggests that SAPS II is not as good as previously reported in discriminating sepsis-related acute respiratory failure patients under the risk of hospital mortality. This finding was consistent with a study that reported that SAPS II and SOFA scores did not have statistically significant predictive value in sepsis mortality28. In addition, this study showed that our prediction model was superior to SAPS II, as well as to a previously developed model for predicting the mortality of patients with skin and soft tissue infections (ROC AUC of 0.84)29. Thus, targeting a predictive model based on a combination of independent risk factors may be preferable for evaluating the short-term prognosis of sepsis patients with acute respiratory failure in the ICU.
Mortality of critically ill patients with severe influenza starting four years after the 2009 pandemic
Published in Infectious Diseases, 2019
David Vandroux, Jérôme Allyn, Cyril Ferdynus, Bernard-Alex Gaüzere, Hugo Kerambrun, Thomas Galas, Nicolas Allou, Romain Persichini, Olivier Martinet, Julien Jabot
The main objective of this study was to compare the mortality in influenza with the mortality in patients with other community-acquired pneumonia (bacterial, viral or indeterminate). We determined the expected mortality using a standardized mortality ratio (SMR) approach based on the quartiles of the SAPSII score (instead of age). The SAPS II is a severity score used in ICU that is correlated with the probability of hospital mortality. This score includes 17 variables: 12 physiology variables, age, type of admission, and three underlying disease variables (acquired immunodeficiency syndrome, metastatic cancer, and hematologic malignancy). We chose the SAPSII as the standardization variable because this score integrates age of patients and comorbidities. The SAPSII of 954 cases of pneumonia hospitalized during the same period and in the same ICU was calculated to define a reference population. This reference population was constructed using ICD-10 codes for patients hospitalized in our ICU (J189, J159, J152, J13, J151, J129, J110, and J09). Additionally, we evaluated risk factors of mortality in patients with influenza by bivariate analysis.