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Magnetic resonance imagings of 91 children with hemiplegic cerebral palsy were analysed with the aim of clustering their features into fairly homogeneous forms. In addition, the different clinical patterns of each form were described. Four main types of lesion were distinguished: form 1 (13 cases), which comprised brain malformations, form 2 (41 subjects), which grouped abnormalities of the periventricular white matter, form 3 (27 children), which was represented by cortical-subcortical lesions, and form 4 (10 subjects), which grouped non-progressive postnatal brain injuries. None of the children had normal MRI and a high incidence of bilateral lesions was found, especially in form 2. A left motor involvement was prevalent in the sample and was noted in all but the third form. The severity of impairment was mainly moderate in forms 1 and 3, mild in the others. The upper limb was found to be more affected in all forms except the second one, which presented a greater involvement of the lower limb. Mental retardation occurred in about one-third of the children with forms 1 and 4, less often in the other two. Seizures occurred in about half of the children with forms 1 or 3, while the incidence was lower in forms 4 and 2. A strong correlation between the presence of seizures and mental retardation was observed. The results of this study show the importance of MRI in the evaluation of children with hemiplegic cerebral palsy.

In this pilot, Better Care® classified breaths as IEE in close agreement with experts and the EAdi signal.

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In mechanical ventilation, it is paramount to ensure the patient’s ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) – z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction.

Background Patient-ventilator dyssynchrony is common and may influence patients' outcomes. Detection of such dyssynchronies relies on careful observation of patients and airway flow and pressure measurements. Given the shortage of specialists, critical care nurses could be trained to identify dyssynchronies. Objective To evaluate the accuracy of specifically trained critical care nurses in detecting ineffective inspiratory efforts during expiration. Methods We compared 2 nurses' evaluations of measurements from 1007 breaths in 8 patients with the evaluations of experienced critical care physicians. Sensitivity, specificity, positive predictive value, negative predictive value, and the Cohen κ for interobserver agreement were calculated. Results For the first nurse, sensitivity was 92.5%, specificity was 98.3%, positive predictive value was 95.4%, negative predictive value was 97.1%, and κ was 0.92 (95% CI, 0.89-0.94). For the second nurse, sensitivity was 98.5%, specificity was 84.7%, positive predictive value was 70.7%, negative predictive value was 99.3%, and κ was 0.74 (95% CI, 0.70-0.78). Conclusion Specifically trained nurses can reliably detect ineffective inspiratory efforts during expiration.

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BackgroundExpert systems can help alleviate problems related to the shortage of human resources in critical care, offering expert advice in complex situations. Expert systems use contextual information to provide advice to staff. In mechanical ventilation, it is crucial for an expert system to be able to determine the ventilatory mode in use. Different manufacturers have assigned different names to similar or even identical ventilatory modes so an expert system should be able to detect the ventilatory mode. The aim of this study is to evaluate the accuracy of an algorithm to detect the ventilatory mode in use.MethodsWe compared the results of a two-step algorithm designed to identify seven ventilatory modes. The algorithm was built into a software platform (BetterCare® system, Better Care SL; Barcelona, Spain) that acquires ventilatory signals through the data port of mechanical ventilators. The sample analyzed compared data from consecutive adult patients who underwent >24 h of mechanical ventilation in intensive care units (ICUs) at two hospitals. We used Cohen’s kappa statistics to analyze the agreement between the results obtained with the algorithm and those recorded by ICU staff.ResultsWe analyzed 486 records from 73 patients. The algorithm correctly labeled the ventilatory mode in 433 (89 %). We found an unweighted Cohen’s kappa index of 84.5 % [CI (95 %) = (80.5 %: 88.4 %)].ConclusionsThe computerized algorithm can reliably identify ventilatory mode.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-016-1436-9) contains supplementary material, which is available to authorized users.