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

A phase I trial of the selective oral cyclin-dependent kinase inhibitor seliciclib (CYC202; R-Roscovitine), administered twice daily for 7 days every 21 days

Seliciclib (CYC202; R-roscovitine) is the first selective, orally bioavailable inhibitor of cyclin-dependent kinases 1, 2, 7 and 9 to enter clinical trial. Preclinical studies showed antitumour activity in a broad range of human tumour xenografts. A phase I trial was performed with a 7-day b.i.d. p.o. schedule. Twenty-one patients (median age 62 years, range: 39–73 years) were treated with doses of 100, 200 and 800 b.i.d. Dose-limiting toxicities were seen at 800 mg b.i.d.; grade 3 fatigue, grade 3 skin rash, grade 3 hyponatraemia and grade 4 hypokalaemia. Other toxicities included reversible raised creatinine (grade 2), reversible grade 3 abnormal liver function and grade 2 emesis. An 800 mg portion was investigated further in 12 patients, three of whom had MAG3 renograms. One patient with a rapid increase in creatinine on day 3 had a reversible fall in renal perfusion, with full recovery by day 14, and no changes suggestive of renal tubular damage. Further dose escalation was precluded by hypokalaemia. Seliciclib reached peak plasma concentrations between 1 and 4 h and elimination half-life was 2–5 h. Inhibition of retinoblastoma protein phosphorylation was not demonstrated in peripheral blood mononuclear cells. No objective tumour responses were noted, but disease stabilisation was recorded in eight patients; this lasted for a total of six courses (18 weeks) in a patient with ovarian cancer

Development and validation of the motivations for selection of medical study (MSMS) questionnaire in India

Background and Objective Understanding medical students' motivation to select medical studies is particularly salient to inform practice and policymaking in countries-such as India-where shortage of medical personnel poses crucial and chronical challenges to healthcare systems. This study aims to develop and validate a questionnaire to assess the motivation of medical students to select medical studies. Methods A Motivation for Selection of Medical Study (MSMS) questionnaire was developed using extensive literature review followed by Delphi technique. The scale consisted of 12 items, 5 measuring intrinsic dimensions of motivations and 7 measuring extrinsic dimensions. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), validity, reliability and data quality checks were conducted on a sample of 636 medical students from six medical colleges of three North Indian states. Results The MSMS questionnaire consisted of 3 factors (subscales) and 8 items. The three principal factors that emerged after EFA were the scientific factor (e.g. research opportunities and the ability to use new cutting edge technologies), the societal factor (e.g. job security) and the humanitarian factor (e.g. desire to help others). The CFA conducted showed goodnessof-fit indices supporting the 3-factor model. Conclusion The three extracted factors cut across the traditional dichotomy between intrinsic and extrinsic motivation and uncover a novel three-faceted motivation construct based on scientific factors, societal expectations and humanitarian needs. This validated instrument can be used to evaluate the motivational factors of medical students to choose medical study in India and similar settings and constitutes a powerful tool for policymakers to design measures able to increase selection of medical curricula

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

Descriptors of sepsis using the Sepsis-3 criteria: A cohort study in critical care units within the UK 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