Clinical course and outcomes of cancer patients admitted in medical ICU with sepsis.

Background And Aims: Sepsis is not only a leading cause of intensive care unit (ICU) admission but also one of the variables which affect outcomes of cancer patients. We aimed to assess the clinical characteristics, clinical course, mortality and risk factors associated with 30-day mortality in medical oncology patients admitted in a multi-disciplinary medical ICU.

Methods: We conducted a retrospective analysis of 435 consecutive cancer patients admitted in medical ICU over a 28 months period.

Role of cerebrospinal fluid lactate in diagnosing meningitis in critically ill patients.

Background: Meningitis is a life-threatening clinical condition associated with high mortality and morbidity. Early diagnosis and specific treatment may improve outcomes. Lack of specific clinical signs or tests make the diagnosis challenging.

Prognostic Value of Swirl Sign in Acute Epidural Hemorrhage.

 The purpose of this study is to analyze and evaluate the incidence, prognostic value, and impact of swirl sign on the outcome of patients who underwent surgical treatment for epidural hematoma.  A retrospective analytical study on 307 acute epidural hematoma (AEDH) patients with and without swirl sign was conducted at our hospital between 2015 and 2019. All the patients in this study were treated initially as per the protocols of advanced trauma life support and surgically treated by craniotomy and evacuation of epidural hematoma.

Scoring systems in critically ill: Which one to use in cancer patients?

Background: Scoring systems have not been evaluated in oncology patients. We aimed to assess the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, APACHE IV, Simplified Acute Physiology Score (SAPS) II, SAPS III, Mortality Probability Model (MPM) II and Sequential Organ Failure Assessment (SOFA) score in critically ill oncology patients.

Aim: To compare the efficacy of seven commonly employed scoring systems to predict outcomes of critically ill cancer patients.