Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Ventilatory support in the intensive care unit

This article focuses on the functional features of positive-pressure ventilators, the modes of invasive and non-invasive mechanical ventilation, and the main ventilator settings. It also highlights the potential complications of mechanical ventilation, the basic principles of weaning, and the pathophysiological basis of patient-ventilator dyssynchrony.     

右美托咪啶与芬太尼联合对ICU 腹部外科术后机械通气患者镇静镇痛的效果比较

目的:探讨右美托咪啶与芬太尼联合在ICU腹部外科术后机械通气患者中的应用效果及对镇静、镇痛作用的影响。方法:选择2018年5月—2019年6月ICU腹部外科术后机械通气患者62例,随机分为对照组(n=31例)和观察组(n=31例)。两组均采用芬太尼持续静脉泵入,对照组采用咪达唑仑镇静镇痛,观察组采用右美托咪啶镇静镇痛,比较两组镇痛镇静效果、镇静剂使用剂量、苏醒及达到镇静所需时间、血流动力学水平及安全性。结果:两组T2、T3时间点VAS评分分别为(2.40±0.31 vs 2.43±0.32和2.01±0.12 vs 2.05±0.15)、Ramsay量表评分分别(3.21±0.35 vs 3.20±0.33和3.01±0.25vs 3.00±0.24)均低于T1时间点(VAS评分2.94±0.69 vs 2.96±0.71;Ramsay量表评分3.57±0.61 vs 3.58±0.62)(P0.05);观察组右美托咪啶联合芬太尼镇痛镇静达到镇静所需时间(34.29±3.56) min长于对照组(23.63±3.21)(t=5.535,P=0.043);观察组镇静剂使用剂量(220.59±15.25)μg、苏醒时间(3.29±0.69)min均少(短)于对照组镇静剂使用剂量(386.44±18.92)μg、苏醒时间(7.56±1.21)min(t=6.294、6.092,P=0.023、0.025);两组T1、T2时间点心率[观察组T1(88.47±9.76)次/min、T2(86.41±9.43)次/min;对照组T1(89.53±10.41)次/min、T2(87.46±9.58)次/min]均高于T0时间点[观察组(78.78±4.35)次/min、对照组(79.12±4.41)次/min](P0.05);观察组T1、T2时间点MVP(79.58±5.71、87.53±6.76)mmHg高于对照组(74.12±4.69、75.26±5.61)mmHg(t=9.613、7.223,P=0.011、0.016);观察组的不良反应发生率为6.45%,与对照组的12.90%差异无统计学意义(χ~2=1.214, P=0.643)。结论:将右美托咪啶联合芬太尼用于ICU腹部外科术后机械通气患者中能获得良好的镇痛、镇静效果,缩短苏醒及达到镇静所需时间,血流动力学相对稳定,药物安全性较高,值得推广应用。     

Is routine extubation overnight safe in cardiac surgery patients?

Objectives

Expedient extubation after cardiac surgery has been associated with improved outcomes, leading to postoperative extubation frequently during overnight hours. However, recent evidence in a mixed medical-surgical intensive care unit population demonstrated worse outcomes with overnight extubation. This study investigated the impact of overnight extubation in a statewide, multicenter Society of Thoracic Surgeons database.

Modern theatre design and usage: do joint replacements need laminar flow?

Since the introduction of laminar flow to theatre systems by Charnley, it has become a necessity in arthroplasty surgery. His initial work in the 1960s and 70s created improvements in terms of reducing overall rates of periprosthetic joint replacement, but it has been hard to ascertain if this stems from the laminar flow itself or improvements in asepsis and the utilization of perioperative antibiotics. The aim of this paper is therefore to establish if laminar flow is still a necessary addition to modern theatre design, particularly in joint replacement surgery, irrespective of other surgical factors. A wide variety of studies were utilized to form an objective analysis of whether laminar flow is essential to asepsis in theatre design. It explores a number of smaller earlier studies and ends with two large trials which provide evidence that laminar flow may in fact have a deleterious effect on the potential for peri-prosthetic joint infection (PJI). The article discusses potential reasons for these findings and seeks to explain them in context of overall asepsis during joint replacement surgery.     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

Lipase elevation on admission predicts worse clinical outcomes in patients with COVID-19

Background and objectivesHyperlipasemia is highly prevalent among coronavirus disease 2019 (COVID-19) patients. The aim of this study was to assess the effect of lipase activity, measured at the time of admission, on the clinical course and mortality in COVID-19 patients.MethodsThe population of this study comprised 12,139 patients who were hospitalized due to COVID-19 between June 2020 and June 2021 in a pandemic hospital. Of these, 8819 patients were excluded from the study due to missing data, four patients were excluded due to a diagnosis of acute pancreatitis (according to the revised Atlanta criteria), and 72 patients were excluded due to alcohol use or having a history of chronic pancreatitis. The final study sample consisted of the remaining 3244 COVID-19 patients. Laboratory results, intensive care unit (ICU) follow-up periods, the need for mechanical ventilation, and mortality rates were compared between the normal lipase activity and high lipase activity groups.ResultsThere were 968 (29.8%) patients with high lipase activity at the time of admission. The rate of ICU admission was 36.1% vs. 9.9% (p < 0.001), mechanical ventilation requirement rates were 33.7% vs. 8.3% (p < 0.001), and mortality rates were as 24.6% vs. 6.4% (p < 0.001) in the high lipase activity group compared to the normal lipase activity group. Multivariate regression analysis revealed that high lipase activity was an independent factor in predicting mortality in hospitalized COVID-19 patients (odds ratio [OR]: 3.191, p < 0.001).ConclusionElevated lipase activity without acute pancreatitis at the time of admission in COVID-19 patients was determined as an independent predictor of poor prognosis.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.