Descriptors of Sepsis Using the Sepsis-3 Criteria: A Cohort Study in Critical Care Units Within the U.K. National Institute for Health Research Critical Care Health Informatics Collaborative.

OBJECTIVES: To describe the epidemiology of sepsis in critical care by applying the Sepsis-3 criteria to electronic health records. DESIGN: Retrospective cohort study using electronic health records. SETTING: Ten ICUs from four U.K. National Health Service hospital trusts contributing to the National Institute for Health Research Critical Care Health Informatics Collaborative. PATIENTS: A total of 28,456 critical care admissions (14,332 emergency medical, 4,585 emergency surgical, and 9,539 elective surgical). MEASUREMENTS AND MAIN RESULTS: Twenty-nine thousand three hundred forty-three episodes of clinical deterioration were identified with a rise in Sequential Organ Failure Assessment score of at least 2 points, of which 14,869 (50.7%) were associated with antibiotic escalation and thereby met the Sepsis-3 criteria for sepsis. A total of 4,100 episodes of sepsis (27.6%) were associated with vasopressor use and lactate greater than 2.0 mmol/L, and therefore met the Sepsis-3 criteria for septic shock. ICU mortality by source of sepsis was highest for ICU-acquired sepsis (23.7%; 95% CI, 21.9-25.6%), followed by hospital-acquired sepsis (18.6%; 95% CI, 17.5-19.9%), and community-acquired sepsis (12.9%; 95% CI, 12.1-13.6%) (p for comparison less than 0.0001). CONCLUSIONS: We successfully operationalized the Sepsis-3 criteria to an electronic health record dataset to describe the characteristics of critical care patients with sepsis. This may facilitate sepsis research using electronic health record data at scale without relying on human coding.

Optimal intensive care outcome prediction over time using machine learning.

BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission. METHODS AND FINDINGS: This study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined. CONCLUSION: This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.

Clinical Predictors of Survival and Functional Outcome of Stroke Patients Admitted to Critical Care.

OBJECTIVES: To determine the predictive value of commonly used clinical variables upon ICU admission for long-term all-cause mortality and functional outcome of adult stroke patients admitted to the ICU. DESIGN: Retrospective observational cohort study. SETTING: General and neurosurgical ICUs of the University College London Hospitals in North Central London. PATIENTS: All adult ICU patients with a clinical diagnosis of acute stroke admitted between February 2010 and May 2012. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Demographic and clinical data concerning the first 24 hours after ICU admission were obtained. Patients were followed until February 2016 to assess long-term survival. Functional outcome was determined using the modified Rankin Scale. We evaluated 131 critically ill stroke patients, with a median (interquartile range) age of 70 years (55-78 yr). One-year mortality rate was 52.7%. Surviving patients were followed up over a median (interquartile range) period of 4.3 years (4.0-4.8 yr). The multivariable model that best predicted long-term all-cause mortality indicated that mortality of critically ill stroke patients was predicted by high Acute Physiology and Chronic Health Evaluation II score, impaired consciousness (Glasgow Coma Scale score ≤ 8) as reason for ICU admission, low Glasgow Coma Scale sum score after 24 hours, and absence of brainstem reflexes. Long-term independent functional status occurred in 30.9% of surviving patients and was predicted by low Acute Physiology and Chronic Health Evaluation II score, high Glasgow Coma Scale sum score at ICU admission, and absence of mass effect on CT scan. CONCLUSIONS: Mortality in critically ill stroke patients is high and occurs most often shortly after the event. Less than one in three surviving patients is able to function independently after 1 year. This study has identified several clinical variables that predict long-term all-cause mortality and functional outcome among critically ill stroke patients and found that mainly acute physiologic disturbance and absolute values of neurologic clinical assessment are predictive.

Managing severe peripartum hyponatraemia: A case report.

We present a case of severe peripartum hyponatraemia that occurred following a major obstetric haemorrhage causing both an ischaemic stroke and Sheehan's syndrome and outline the investigations and management strategy required.

Potential metabolic consequences of statins in sepsis.

OBJECTIVE: Statins may be important for the prevention and management of sepsis; however, through their impact on ubiquinone synthesis, they may impair mitochondrial and organ function in the septic patient. Here we provide a narrative review of the function and roles of ubiquinone in cellular metabolism, the interactions with statins, and the potential consequences in the critically ill. DATA SOURCE: Literature search using the PubMed database. Search terms included statins, mitochondria, ubiquinone, and sepsis. CONCLUSION: Statins are 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors and act by decreasing mevalonate levels, a precursor for cholesterol synthesis. However, mevalonate is also a precursor for ubiquinone, an integral component of the mitochondrial respiratory chain and an important antioxidant. Plasma ubiquinone is inversely related to statin levels, and impaired statin metabolism or excretion can decrease ubiquinone levels markedly. This is potentially important as critical illness markedly impairs statin metabolism. As mitochondrial dysfunction may be a major contributor to sepsis-induced organ failure, it is plausible that low ubiquinone levels may exacerbate mitochondrial and organ dysfunction. Furthermore, although the clinical relevance of low ubiquinone levels is currently unknown in the critically ill, this is often cited as a possible cause of the myopathy and rhabdomyolysis associated with statin use.

Nitrosyl heme production compared in endotoxemic and hemorrhagic shock.

We compared nitric oxide production and nitrosyl hemoglobin steady state concentrations during the early phases of endotoxemic and hemorrhagic shock of equivalent severity. Sprague-Dawley rats were randomly assigned to (1) sham-operated control, (2) hemorrhage, and (3) intravenous endotoxin. Electron paramagnetic resonance spectroscopy was used to measure NO in the vasculature (binding to hemoglobin) and in the liver (binding to cytochrome P450). Despite similar changes in cardiorespiratory variables and identical microvascular pO(2), nitrosyl hemoglobin concentrations were significantly higher in endotoxemic rats than in rats in hemorrhagic shock, suggesting increased rates of NO production. A substantial venous minus arterial concentration gradient was observed for nitrosyl hemoglobin. This increased in line with the plasma total nitrite + nitrate concentration. Nitrosyl hemoglobin formation is likely to occur predominantly in the venous pool, suggesting that removal of NO from hemoglobin in the presence of oxygen may be faster than previously thought. In the liver, an increase in intracellular heme-NO complexes was detected in endotoxemic rats compared with rats in hemorrhagic shock; this was associated with increased reduction of the mitochondrial respiratory chain and is suggestive of NO inhibition of mitochondrial respiration.