Eribulin demonstrated activity with manageable tolerability (including infrequent grade 3 and no grade 4 neuropathy) in heavily pretreated patients with MBC when dosed as a short IV infusion on days 1 and 8 of a 21-day cycle.

No abstract

Eribulin demonstrated antitumor activity in extensively pretreated patients who had previously received an anthracycline, taxane, and capecitabine, with a manageable tolerability profile.

Purpose: To evaluate the maximum tolerated dose (MTD), dose-limiting toxicities (DLT), and pharmacokinetics of eribulin mesylate (E7389), a halichondrin B analogue, administered every 21days in patients with advanced solid tumors. Experimental Design: Eribulin mesylate was given as a 1-hour infusion every 21days at doses of 0.25, 0.5, 1, 2, 2.8, and 4 mg/m 2 .The MTD was identified using an accelerated titration design. The pharmacokinetics of eribulin were evaluated in the plasma and urine with the first dose. Results: Twenty-one patients were enrolled. At 4 mg/m 2 , three patients experienced a DLT of febrile neutropenia on day 7. The dose level was reduced to 2.8 mg/m 2 where two of three patients experienced dose-limiting febrile neutropenia. Six additional patients were enrolled at 2 mg/m 2 (seven patients in total received this dose) and one of these patients experienced a neutropenic DLT. The MTD of eribulin mesylate was therefore 2 mg/m 2 . Nonhematologic toxicities included alopecia, fatigue, anorexia, and nausea. Pharmacokinetic analysis showed linear kinetics for eribulin over the dose range studied and a terminal half-life of 2 days. The plasmaconcentration-time profile exhibited a rapid distribution phase followed by a slow elimination phase. Drug clearance was nonrenal. One patient with non^small cell lung cancer achieved an unconfirmed partial response and 12 patients had stable disease. Conclusions: Eribulin mesylate administered as a 1-hour infusion every 21 days has a manageable toxicity profile at 2 mg/m 2 , with further dose escalation limited by neutropenia.

This outbreak might reflect improved diagnostic testing, indicating that E. meningoseptica is a pseudo-emerging pathogen.

BackgroundPrognostication 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 findingsThis 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.ConclusionThis 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.

Rationale: There is conflicting evidence on harm related to exposure to supraphysiologic PaO2 (hyperoxemia) in critically ill patients.Objectives: To examine the association between longitudinal exposure to hyperoxemia and mortality in patients admitted to ICUs in five United Kingdom university hospitals.Methods: A retrospective cohort of ICU admissions between January 31, 2014, and December 31, 2018, from the National Institute of Health Research Critical Care Health Informatics Collaborative was studied. Multivariable logistic regression modeled death in ICU by exposure to hyperoxemia.Measurements and Main Results: Subsets with oxygen exposure windows of 0 to 1, 0 to 3, 0 to 5, and 0 to 7 days were evaluated, capturing 19,515, 10,525, 6,360, and 4,296 patients, respectively. Hyperoxemia dose was defined as the area between the PaO2 time curve and a boundary of 13.3 kPa (100 mm Hg) divided by the hours of potential exposure (24, 72, 120, or 168 h). An association was found between exposure to hyperoxemia and ICU mortality for exposure windows of 0 to 1 days (odds ratio [OR], 1.15; 95% compatibility interval [CI], 0.95–1.38; P = 0.15), 0 to 3 days (OR 1.35; 95% CI, 1.04–1.74; P = 0.02), 0 to 5 days (OR, 1.5; 95% CI, 1.07–2.13; P = 0.02), and 0 to 7 days (OR, 1.74; 95% CI, 1.11–2.72; P = 0.02). However, a dose–response relationship was not observed. There was no evidence to support a differential effect between hyperoxemia and either a respiratory diagnosis or mechanical ventilation.Conclusions: An association between hyperoxemia and mortality was observed in our large, unselected multicenter cohort. The absence of a dose–response relationship weakens causal interpretation. Further experimental research is warranted to elucidate this important question.

The CCHIC database is now in use by health care researchers from academia and industry. The 'research ready' suite of data preparation tools have facilitated access, and linkage to national databases of secondary care is underway.