Assessment of proadrenomedullin as diagnostic or prognostic biomarker of acute appendicitis in children with acute abdominal pain

BackgroundAcute appendicitis (AA) is one of the most frequent surgical pathologies in pediatrics.ObjectivesTo investigate the utility of proadrenomedullin (pro-ADM) for the diagnosis of AA.MethodsProspective, analytical, observational, and multicenter study conducted in 6 pediatric emergency departments. Children up to 18 years of age with suspected AA were included. Clinical, epidemiological, and analytical data were collected.ResultsWe studied 285 children with an average age of 9.5 years (95% confidence interval [CI], 9.1–9.9). AA was diagnosed in 103 children (36.1%), with complications in 10 of them (9.7%). The mean concentration of pro-ADM (nmol/L) was higher in children with AA (0.51 nmol/L, SD 0.16) than in children with acute abdominal pain (AAP) of another etiology (0.44 nmol/L, SD 0.14; p < 0.001). This difference was greater in complicated cases compared with uncomplicated AA (0.64 nmol/L, SD 0.17 and 0.50 nmol/L, SD 0.15, respectively; p = 0.005). The areas under the receiver-operating characteristic curves were 0.66 (95% CI, 0.59–0.72) for pro-ADM, 0.70 (95% CI, 0.63–0.76) for C-reactive protein (CRP), 0.84 (95% CI, 0.79–0.89) for neutrophils, and 0.84 (95% CI, 0.79–0.89) for total leukocytes. The most reliable combination to rule out AA was CRP ≤1.25 mg/dL and pro-ADM ≤0.35 nmol/L with a sensitivity of 96% and a negative predictive value of 93%.ConclusionChildren with AA presented higher pro-ADM values than children with AAP of other etiologies, especially in cases of complicated AA. The combination of low values of pro-ADM and CRP can help to select children with low risk of AA.     

Selective Indication of T-Tube in Liver Transplantation: Prospective Validation of the Results of a Randomized Controlled Trial

Background and Aims

T-tube placement during choledochocholedochostomy (CCS) associated with liver transplantation (LT) remains controversial. This study was designed to validate the results of an earlier prospective randomized controlled trial (RCT) on use versus nonuse of the T-tube during CCS associated with LT.

Structures at Risk From an Intermetatarsal Screw for Lapidus Bunionectomy: A Cadaveric Study

The Lapidus bunionectomy is performed to treat hallux valgus. Recurrence of the deformity remains a concern. A transverse intermetatarsal screw spanning the base of the first metatarsal to the base of the second can increase stability. The neurovascular bundle is located within the proximity of this screw. In this study, we assessed the structures at risks with the use of this technique. In 10 specimens, a guide wire was placed, and a 4.0-mm cannulated screw was inserted. The neurovascular bundle was dissected and inspected for direct trauma to the neurovascular bundle, and the proximity of the screw was measured using a digital caliper. Ten cadaveric specimens were used. The dorsalis pedis artery and deep peroneal nerve were free from injury in 9 of 10 specimens. In those 9 specimens, the neurovascular bundle was located dorsal in relation to the screw. The mean distance of the screw to the neurovascular bundle was 7.1 ± 3.3 mm. The mean distance from the screw to the first tarsometatarsal joint (TMTJ) was 14.7 ± 4.3 mm. The mean distance from the screw as it entered the second metatarsal to the second TMTJ was 18.0 ± 7.2 mm. In 1 specimen, the screw was found to be traversing through the neurovascular bundle. The distance from the screw to the first TMTJ was 15.0 mm. The distance of the screw from where it entered the second metatarsal to the second TMTJ was 24.0 mm. Although the intermetatarsal screw avoided the neurovascular cases in most instances, there is some anatomic risk to the neurovascular bundle. Further study is warranted to evaluate clinical results using the intermetatarsal screw for the modified Lapidus procedure.     

From the Cover: Communication consumes 35 times more energy than computation in the human cortex,but both costs are needed to predict synapse number

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)-consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843–1864 (1960)] and [J. Sawada, D. S. Modha, “Synapse: Scalable energy-efficient neurosynaptic computing” in Application of Concurrency to System Design (ACSD) (2013), pp. 14–15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a 108-fold discrepancy between biological and lowest possible values of a neuron’s computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).

The purpose of the brain is to process information, but that leaves us with the problem of finding appropriate definitions of information processing. We assume that given enough time and given a sufficiently stable environment (e.g., the common internals of the mammalian brain), then Nature’s constructions approach an optimum. The problem is to find which function or combined set of functions is optimal when incorporating empirical values into these function(s). The initial example in neuroscience is ref. 1, which shows that information capacity is far from optimized, especially in comparison to the optimal information per joule which is in much closer agreement with empirical values. Whenever we find such an agreement between theory and experiment, we conclude that this optimization, or near optimization, is Nature’s perspective. Using this strategy, we and others seek quantified relationships with particular forms of information processing and require that these relationships are approximately optimal (1–7). At the level of a single neuron, a recent theoretical development identifies a potentially optimal computation (8). To apply this conjecture requires understanding certain neuronal energy expenditures. Here the focus is on the energy budget of the human cerebral cortex and its primary neurons. The energy audit here differs from the premier earlier work (9) in two ways: The brain considered here is human not rodent, and the audit here uses a partitioning motivated by the information-efficiency calculations rather than the classical partitions of cell biology and neuroscience (9). Importantly, our audit reveals greater energy use by communication than by computation. This observation in turn generates additional insights into the optimal synapse number. Specifically, the bits per joule optimized computation must provide sufficient bits per second to the axon and presynaptic mechanism to justify the great expense of timely communication. Simply put from the optimization perspective, we assume evolution would not build a costly communication system and then not supply it with appropriate bits per second to justify its costs. The bits per joule are optimized with respect to N, the number of synaptic activations per interpulse interval (IPI) for one neuron, where N happens to equal the number of synapses per neuron times the success rate of synaptic transmission (below).To measure computation, and to partition out its cost, requires a suitable definition at the single-neuron level. Rather than the generic definition “any signal transformation” (3) or the neural-like “converting a multivariate signal to a scalar signal,” we conjecture a more detailed definition (8). To move toward this definition, note two important brain functions: estimating what is present in the sensed world and predicting what will be present, including what will occur as the brain commands manipulations. Then, assume that such macroscopic inferences arise by combining single-neuron inferences. That is, conjecture a neuron performing microscopic estimation or prediction. Instead of sensing the world, a neuron’s sensing is merely its capacitive charging due to recently active synapses. Using this sampling of total accumulated charge over a particular elapsed time, a neuron implicitly estimates the value of its local latent variable, a variable defined by evolution and developmental construction (8). Applying an optimization perspective, which includes implicit Bayesian inference, a sufficient statistic, and maximum-likelihood unbiasedness, as well as energy costs (8), produces a quantified theory of single-neuron computation. This theory implies the optimal IPI probability distribution. Motivating IPI coding is this fact: The use of constant amplitude signaling, e.g., action potentials, implies that all information can only be in IPIs. Therefore, no code can outperform an IPI code, and it can equal an IPI code in bit rate only if it is one to one with an IPI code. In neuroscience, an equivalent to IPI codes is the instantaneous rate code where each message is IPI−1. In communication theory, a discrete form of IPI coding is called differential pulse position modulation (10); ref. 11 explicitly introduced a continuous form of this coding as a neuron communication hypothesis, and it receives further development in ref. 12.Results recall and further develop earlier work concerning a certain optimization that defines IPI probabilities (8). An energy audit is required to use these developments. Combining the theory with the audit leads to two outcomes: 1) The optimizing N serves as a consistency check on the audit and 2) future energy audits for individual cell types will predict N for that cell type, a test of the theory. Specialized approximations here that are not present in earlier work (9) include the assumptions that 1) all neurons of cortex are pyramidal neurons, 2) pyramidal neurons are the inputs to pyramidal neurons, 3) a neuron is under constant synaptic bombardment, and 4) a neuron’s capacitance must be charged 16 mV from reset potential to threshold to fire.Following the audit, the reader is given a perspective that may be obvious to some, but it is rarely discussed and seemingly contradicts the engineering literature (but see ref. 6). In particular, a neuron is an incredibly inefficient computational device in comparison to an idealized physical analog. It is not just a few bits per joule away from optimal predicted by the Landauer limit, but off by a huge amount, a factor of 108. The theory here resolves the efficiency issue using a modified optimization perspective. Activity-dependent communication and synaptic modification costs force upward optimal computational costs. In turn, the bit value of the computational energy expenditure is constrained to a central limit like the result: Every doubling of N can produce no more than 0.5 bits. In addition to 1) explaining the 108 excessive energy use, other results here include 2) identifying the largest “noise” source limiting computation, which is the signal itself, and 3) partitioning the relevant costs, which may help engineers redirect focus toward computation and communication costs rather than the 20-W total brain consumption as their design goal.     

Riesgo de recurrencia tras retirada de la anticoagulación en pacientes con enfermedad tromboembólica venosa no provocada: validación externa del nomograma de Viena y del modelo predictivo DASH

IntroductionScales for predicting venous thromboembolism (VTE) recurrence are useful for deciding the duration of the anticoagulant treatment. Although there are several scales, the most appropriate for our setting has not been identified. For this reason, we aimed to validate the DASH prediction score and the Vienna nomogram at 12 months.MethodsThis was a retrospective study of unselected consecutive VTE patients seen between 2006 and 2014. We compared the ability of the DASH score and the Vienna nomogram to predict recurrences of VTE. The validation was performed by stratifying patients as low-risk or high-risk, according to each scale (discrimination) and comparing the observed recurrence with the expected rate (calibration).ResultsOf 353 patients evaluated, 195 were analyzed, with an average age of 53.5 ± 19 years. There were 21 recurrences in 1 year (10.8%, 95% CI: 6.8%-16%). According to the DASH score, 42% were classified as low risk, and the rate of VTE recurrence in this group was 4.9% (95% CI: 1.3%-12%) vs. the high-risk group that was 15% (95% CI: 9%-23%) (p <.05). According to the Vienna nomogram, 30% were classified as low risk, and the rate of VTE recurrence in the low risk group vs. the high risk group was 4.2% (95% CI:0.5%-14%) vs. 16.2% (95% CI: 9.9%-24.4%) (p <.05).ConclusionsOur study validates the DASH score and the Vienna nomogram in our population. The DASH prediction score may be the most advisable, both because of its simplicity and its ability to identify more low-risk patients than the Vienna nomogram (42% vs. 30%).