Inpatient and outpatient nephrology management of critically ill patients with acute kidney injury.

INTRODUCTION: Acute kidney injury (AKI) during critical illness increases the risk of subsequent chronic kidney disease. Guidelines recommend inpatient nephrology assessment and review at 3 months. OBJECTIVES: To quantify the prevalence and predictors of inpatient and outpatient nephrology follow-up of AKI patients admitted to critical care areas within a tertiary hospital. METHODS: Retrospective study of all critically ill adults with AKI between January 1, 2012 and December 31, 2016 with a baseline estimated glomerular filtration rate (eGFR) >30 mL/min/1.73 m2 and alive and independent of renal replacement therapy for 30 days after hospital discharge. We used logistic regression models to examine the primary outcome of nephrology review at 3 months. Secondary outcomes included inpatient nephrology review, renal recovery at discharge and the development of a major adverse kidney event (MAKE) at 1 year. RESULTS: Of 702 critically ill patients with AKI (mean age 66 years, 64% male, baseline eGFR 78 mL/min/1.73 m2 ), 43 patients (6%) received nephrology follow-up at 3 months and 63 patients (9%) at 1 year. Nephrology follow-up occurred more frequently in patients with a higher baseline creatinine, a higher discharge creatinine and greater severity of AKI. Seventy patients (10%) underwent inpatient nephrology review. Overall, 414 (59%) had recovery of renal function by the time of discharge and 239 (34%) developed a MAKE at 12 months. CONCLUSION: Inpatient and outpatient nephrology follow-up of AKI patients after admission to a critical care area was uncommon although one-third developed a MAKE. These findings provide the rationale for controlled studies of nephrology follow-up.

Energy-efficient stochastic computing with superparamagnetic tunnel junctions.

Superparamagnetic tunnel junctions (SMTJs) have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. One of their applications is to generate random bitstreams suitable for use in stochastic computing implementations. We describe a method for digitally programmable bitstream generation based on pre-charge sense amplifiers. This generator is significantly more energy efficient than SMTJ-based bitstream generators that tune probabilities with spin currents and a factor of two more efficient than related CMOS-based implementations. The true randomness of this bitstream generator allows us to use them as the fundamental units of a novel neural network architecture. To take advantage of the potential savings, we codesign the algorithm with the circuit, rather than directly transcribing a classical neural network into hardware. The flexibility of the neural network mathematics allows us to adapt the network to the explicitly energy efficient choices we make at the device level. The result is a convolutional neural network design operating at ≈ 150 nJ per inference with 97 % performance on MNIST-a factor of 1.4 to 7.7 improvement in energy efficiency over comparable proposals in the recent literature.

Neural-like computing with populations of superparamagnetic basis functions.

In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for computing. Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements. Here, we show that nanoscale magnetic tunnel junctions can instead be assembled to meet these requirements. We demonstrate experimentally that a population of nine junctions can implement a basis set of functions, providing the data to achieve, for example, the generation of cursive letters. We design hybrid magnetic-CMOS systems based on interlinked populations of junctions and show that they can learn to realize non-linear variability-resilient transformations with a low imprint area and low power.

Controlling the phase locking of stochastic magnetic bits for ultra-low power computation.

When fabricating magnetic memories, one of the main challenges is to maintain the bit stability while downscaling. Indeed, for magnetic volumes of a few thousand nm(3), the energy barrier between magnetic configurations becomes comparable to the thermal energy at room temperature. Then, switches of the magnetization spontaneously occur. These volatile, superparamagnetic nanomagnets are generally considered useless. But what if we could use them as low power computational building blocks? Remarkably, they can oscillate without the need of any external dc drive, and despite their stochastic nature, they can beat in unison with an external periodic signal. Here we show that the phase locking of superparamagnetic tunnel junctions can be induced and suppressed by electrical noise injection. We develop a comprehensive model giving the conditions for synchronization, and predict that it can be achieved with a total energy cost lower than 10(-13) J. Our results open the path to ultra-low power computation based on the controlled synchronization of oscillators.