Frailty in the critically ill: a novel concept

The concept of frailty has been defined as a multidimensional syndrome characterized by the loss of physical and cognitive reserve that predisposes to the accumulation of deficits and increased vulnerability to adverse events. Frailty is strongly correlated with age, and overlaps with and extends aspects of a patient's disability status (that is, functional limitation) and/or burden of comorbid disease. The frail phenotype has more specifically been characterized by adverse changes to a patient's mobility, muscle mass, nutritional status, strength and endurance. We contend that, in selected circumstances, the critically ill patient may be analogous to the frail geriatric patient. The prevalence of frailty amongst critically ill patients is currently unknown; however, it is probably increasing, based on data showing that the utilization of intensive care unit (ICU) resources by older people is rising. Owing to the theoretical similarities in frailty between geriatric and critically ill patients, this concept may have clinical relevance and may be predictive of outcomes, along with showing important interaction with several factors including illness severity, comorbid disease, and the social and structural environment. We believe studies of frailty in critically ill patients are needed to evaluate how it correlates with outcomes such as survival and quality of life, and how it relates to resource utilization, such as length of mechanical ventilation, ICU stay and duration of hospitalization. We hypothesize that the objective measurement of frailty may provide additional support and reinforcement to clinicians confronted with end-of-life decisions on the appropriateness of ICU support and/or withholding of life-sustaining therapies

A review of the compressive stiffness of the human head

Published 12 November 2022Synthetic surrogate head models are used in biomechanical studies to investigate skull, brain, and cervical spine injury. To ensure appropriate biofidelity of these head models, the stiffness is often tuned so that the surrogate’s response approximates the cadaveric response corridor. Impact parameters such as energy, and loading direction and region, can influence injury prediction measures, such as impact force and head acceleration. An improved understanding of how impact parameters affect the head’s structural response is required for designing better surrogate head models. This study comprises a synthesis and review of all existing ex vivo head stiffness data, and the primary factors that influence the force–deformation response are discussed. Eighteen studies from 1972 to 2019 were identified. Head stiffness statistically varied with age (pediatric vs. adult), loading region, and rate. The contact area of the impactor likely affects stiffness, whereas the impactor mass likely does not. The head’s response to frontal impacts was widely reported, but few studies have evaluated the response to other impact locations and directions. The findings from this review indicate that further work is required to assess the effect of head constraints, loading region, and impactor geometry, across a range of relevant scenarios.Darcy W. Thompson-Bagshaw, Ryan D. Quarrington, and Claire F. Jone

Acute kidney injury in the era of big data: The 15^th Consensus Conference of the Acute Dialysis Quality Initiative (ADQI)

The world is immersed in "big data". Big data has brought about radical innovations in the methods used to capture, transfer, store and analyze the vast quantities of data generated every minute of every day. At the same time; however, it has also become far easier and relatively inexpensive to do so. Rapidly transforming, integrating and applying this large volume and variety of data are what underlie the future of big data. The application of big data and predictive analytics in healthcare holds great promise to drive innovation, reduce cost and improve patient outcomes, health services operations and value. Acute kidney injury (AKI) may be an ideal syndrome from which various dimensions and applications built within the context of big data may influence the structure of services delivery, care processes and outcomes for patients. The use of innovative forms of "information technology" was originally identified by the Acute Dialysis Quality Initiative (ADQI) in 2002 as a core concept in need of attention to improve the care and outcomes for patients with AKI. For this 15th ADQI consensus meeting held on September 6-8, 2015 in Banff, Canada, five topics focused on AKI and acute renal replacement therapy were developed where extensive applications for use of big data were recognized and/or foreseen. In this series of articles in the Canadian Journal of Kidney Health and Disease, we describe the output from these discussions

Plasma neutrophil gelatinase-associated lipocalin predicts recovery from acute kidney injury following community-acquired pneumonia

Although plasma neutrophil gelatinase-associated lipocalin (NGAL) is a promising biomarker for early detection of acute kidney injury, its ability to predict recovery is unknown. Using RIFLE criteria to define kidney injury, we tested whether higher plasma NGAL concentrations on the first day of RIFLE-F would predict failure to recover in a post hoc analysis of a multicenter, prospective, cohort study of patients with community-acquired pneumonia. Recovery was defined as alive and not requiring renal replacement therapy during hospitalization or having a persistent RIFLE-F classification at hospital discharge. Median plasma NGAL concentrations were significantly lower among the 93 of 181 patients who recovered. Plasma NGAL alone predicted failure to recover with an area under the receiver operating characteristic curve of 0.74. A clinical model using age, serum creatinine, pneumonia severity, and nonrenal organ failure predicted failure to recover with area under the curve of 0.78. Combining this clinical model with plasma NGAL concentrations did not improve prediction. The reclassification of risk of renal recovery, however, significantly improved by 17% when plasma NGAL was combined with the clinical model. Thus, in this cohort of patients with pneumonia-induced severe acute kidney injury, plasma NGAL appears to be a useful biomarker for predicting renal recovery

Magnesium lactate in the treatment of Gitelman syndrome: patient-reported outcomes.

BACKGROUND: Gitelman syndrome (GS) is a rare recessively inherited renal tubulopathy associated with renal potassium (K) and magnesium (Mg) loss. It requires lifelong K and Mg supplementation at high doses that are at best unpalatable and at worst, intolerable. In particular, gastrointestinal side effects often limit full therapeutic usage. METHODS: We report here the analysis of a cohort of 28 adult patients with genetically proven GS who attend our specialist tubular disorders clinic, in whom we initiated the use of a modified-release Mg preparation (slow-release Mg lactate) and who were surveyed by questionnaire. RESULTS: Twenty-five patients (89%) preferred the new treatment regimen. Of these 25, 17 (68%) regarded their symptom burden as improved and seven reported no worsening. Of the 25 who were not Mg-treatment naïve, 13 (59%) patients reported fewer side effects, 7 (32%) described them as the same and only 2 (9%) considered side effects to be worse. Five were able to increase their dose without ill-effect. Overall, biochemistry improved in 91% of the 23 patients switched from therapy with other preparations who chose to continue the modified-release Mg preparation. Eleven (48%) improved both their Mg and K mean levels, 3 (13%) improved Mg levels only and in 7 cases (30%), K levels alone rose. CONCLUSIONS: Patient-reported and biochemical outcomes using modified-release Mg supplements were very favourable, and patient choice should play a large part in choosing Mg supplements with GS patients.This work was supported by the Wellcome Trust and the NIHR Cambridge Biomedical Research Centre, and contains data that were presented in abstract form at ASN Kidney week 2014.This is the final version of the article. It first appeared from Oxford University Press via https://doi.org/10.1093/ndt/gfw01