Ambulatory colectomy: A pilot protocol for same day discharge in minimally invasive colorectal surgery.

BACKGROUND: Since its inception colectomy has routinely been performed in the inpatient setting. The advent of Enhanced Recovery After Surgery (ERAS) protocols has led improved outcomes, including decreased length of stay (LOS). These improvements have introduced the possibility of ambulatory colectomy. However, indications, protocols, and limitations of ambulatory colectomy have not been extensively explored. METHODS: We conducted a retrospective review on ambulatory colectomies performed between February 2019 and August 2021. Patients were candidates for same day discharge (SDD) if they met rigorous preoperative criteria. Following an uncomplicated operation, strict postoperative parameters were required for safe discharge. If the patient underwent SDD following their operation, they were monitored closely via telehealth visits and/or patient communication messages until their one-week postoperative visit. RESULTS: From our review, we identified sixty-nine (n = 69) patients who underwent SDD after colectomy. Of the 69, only one patient was readmitted after discharge (1.4%). All procedures were performed via a robotic-assisted approach (Da Vinci Xi). None of the patients underwent conversion to an open procedure. The most frequently performed procedures included: low anterior resection (LAR) (n = 32, 46.4%) and right hemicolectomy (n = 11, 15.9%). CONCLUSION: Through proper patient education and strictly defined communication between the patient care teams, safe and effective care in the setting of SDD after colectomy can be provided. With recent technological advancements, enhanced mechanisms for patient education throughout all phases, and emerging means of patient-physician communication, via the data included herein the opportunity for same day discharge (SDD) after colectomy is a feasible and safe management plan in the proper patient.

A-current and type I/type II transition determine collective spiking from common input.

The mechanisms and impact of correlated, or synchronous, firing among pairs and groups of neurons are under intense investigation throughout the nervous system. A ubiquitous circuit feature that can give rise to such correlations consists of overlapping, or common, inputs to pairs and populations of cells, leading to common spike train responses. Here, we use computational tools to study how the transfer of common input currents into common spike outputs is modulated by the physiology of the recipient cells. We focus on a key conductance, g(A), for the A-type potassium current, which drives neurons between "type II" excitability (low g(A)), and "type I" excitability (high g(A)). Regardless of g(A), cells transform common input fluctuations into a tendency to spike nearly simultaneously. However, this process is more pronounced at low g(A) values. Thus, for a given level of common input, type II neurons produce spikes that are relatively more correlated over short time scales. Over long time scales, the trend reverses, with type II neurons producing relatively less correlated spike trains. This is because these cells' increased tendency for simultaneous spiking is balanced by an anticorrelation of spikes at larger time lags. These findings extend and interpret prior findings for phase oscillators to conductance-based neuron models that cover both oscillatory (superthreshold) and subthreshold firing regimes. We demonstrate a novel implication for neural signal processing: downstream cells with long time constants are selectively driven by type I cell populations upstream and those with short time constants by type II cell populations. Our results are established via high-throughput numerical simulations and explained via the cells' filtering properties and nonlinear dynamics.

Time scales of spike-train correlation for neural oscillators with common drive.

We examine the effect of the phase-resetting curve on the transfer of correlated input signals into correlated output spikes in a class of neural models receiving noisy superthreshold stimulation. We use linear-response theory to approximate the spike correlation coefficient in terms of moments of the associated exit time problem and contrast the results for type I vs type II models and across the different time scales over which spike correlations can be assessed. We find that, on long time scales, type I oscillators transfer correlations much more efficiently than type II oscillators. On short time scales this trend reverses, with the relative efficiency switching at a time scale that depends on the mean and standard deviation of input currents. This switch occurs over time scales that could be exploited by downstream circuits.