Left ventricular systolic function evaluated by strain echocardiography and relationship with mortality in patients with severe sepsis or septic shock: a systematic review and meta-analysis.

BACKGROUND: Sepsis-induced myocardial dysfunction is associated with poor outcomes, but traditional measurements of systolic function such as left ventricular ejection fraction (LVEF) do not directly correlate with prognosis. Global longitudinal strain (GLS) utilizing speckle-tracking echocardiography (STE) could be a better marker of intrinsic left ventricular (LV) function, reflecting myocardial deformation rather than displacement and volume changes. We sought to investigate the prognostic value of GLS in patients with sepsis and/or septic shock. METHODS: We conducted a systematic review (PubMed and Embase up to 26 October 2017) and meta-analysis to investigate the association between GLS and mortality at longest follow up in patients with severe sepsis and/or septic shock. In the primary analysis, we included studies reporting transthoracic echocardiography data on GLS according to mortality. A secondary analysis evaluated the association between LVEF and mortality including data from studies reporting GLS. RESULTS: We included eight studies in the primary analysis with a total of 794 patients (survival 68%, n = 540). We found a significant association between worse LV function and GLS values and mortality: standard mean difference (SMD) - 0.26; 95% confidence interval (CI) - 0.47, - 0.04; p = 0.02 (low heterogeneity, I2 = 43%). No significant association was found between LVEF and mortality in the same population of patients (eight studies; SMD, 0.02; 95% CI - 0.14, 0.17; p = 0.83; no heterogeneity, I2 = 3%). CONCLUSIONS: Worse GLS (less negative) values are associated with higher mortality in patients with severe sepsis or septic shock, while such association is not valid for LVEF. More critical care research is warranted to confirm the better ability of STE in demonstrating underlying intrinsic myocardial disease compared to LVEF.

A standardized protocol for the perioperative management of myasthenia gravis patients. Experience with 110 patients.

BACKGROUND: Video-assisted thoracoscopic extended thymectomy (VATET) is well established in the treatment of myasthenia gravis; however, patient selection remains controversial. Perioperative management protocol is lacking, and concerns regarding post-operative myasthenic crisis still remain. We performed a retrospective observational study evaluating the impact of the introduction of a protocol in the perioperative management of patients with myasthenia gravis who underwent VATET. METHODS: The perioperative management protocol was developed by a team of neurologists and anesthesiologists who reviewed the literature and their previous experience on myasthenia gravis patients. Respiratory, clinical, and neurological patient features were included in the protocol evaluation. A retrospective review of patients who underwent VATET before and after introduction to the protocol was finally performed. RESULTS: The medical records of 66 patients (pre-protocol group) and 44 patients (protocol group) were available for the study. In the pre-protocol group, 17 patients (26%) were admitted to intensive care unit (ICU) during the post-operative period, while three patients (6.8%) of the protocol group met the criteria for ICU post-operative admission. This resulted in a reduction of 73.5% of patients admitted to ICU (P = 0.023) and in an 80% (P = 0.002) reduction of the use neuromuscular blocking agents. Two post-operative myasthenic crises preceded by bulbar symptoms (1.8%) were identified in the pre-protocol group patients. CONCLUSIONS: Although the application of our protocol results in a substantial reduction in the recovery of patients in the ICU and in hospital costs, there was no substantial difference in mortality and morbidity between patients admitted to the surgical ward or to ICU.

Ventilation mode recognition using artificial neural networks.

This study investigated the capabilities of artificial neural networks to identify spontaneous and pressure support ventilation modes from gas flow and airway pressure signals. After receiving written informed consent, flow and pressure waveforms were recorded from 13 patients undergoing general anesthesia. During analysis, the inspiratory phase of each breath was extracted and normalized in amplitude and wavelength. Neural networks were configured to input flow, pressure, or both waveforms and to output the ventilatory mode. Neural network training was accomplished with data from 500 breaths obtained from 7 patients. Neural network performance was tested with 433 breaths from the remaining 6 patients. Networks using flow, pressure, and both waveforms recognized correctly 78% (337), 97% (423), and 100% (433) of the test waveforms, respectively. Results indicate that neural networks can be used effectively for breathing pattern recognition and encourage the application of neural networks in other types of respiratory pattern recognition problems.