Data collected through the Better Care platform can be used for research purposes. Crossing data from ECG, ventilators (Invasive and non-invasive), EEG, pulseoxymeters and other biomedical signals provide a rich database in knowledge.
Thanks to this data provided by Better Care, several papers has been published, and some others are in revision.
Citation in scientific papers:
1.- Asynchronies during mechanical ventilation are associated with mortality.
Blanch L, Villagra A, Sales B, Montanya J, Lucangelo U, Luján M, García-Esquirol O, Chacón E, Estruga A, Oliva JC, Hernández-Abadia A, Albaiceta GM, Fernández-Mondejar E, Fernández R, Lopez-Aguilar J, Villar J, Murias G, Kacmarek RM.
Intensive Care Med. 2015 Apr;41(4):633-41. doi: 10.1007/s00134-015-3692-6. Epub 2015 Feb 19.
This study aimed to assess the prevalence and time course of asynchronies during mechanical ventilation (MV).
Prospective, noninterventional observational study of 50 patients admitted to intensive care unit (ICU) beds equipped with Better Care™ software throughout MV. The software distinguished ventilatory modes and detected ineffective inspiratory efforts during expiration (IEE), double-triggering, aborted inspirations, and short and prolonged cycling to compute the asynchrony index (AI) for each hour. We analyzed 7,027 h of MV comprising 8,731,981 breaths.
Asynchronies were detected in all patients and in all ventilator modes. The median AI was 3.41 % [IQR 1.95-5.77]; the most common asynchrony overall and in each mode was IEE [2.38 % (IQR 1.36-3.61)]. Asynchronies were less frequent from 12 pm to 6 am [1.69 % (IQR 0.47-4.78)]. In the hours where more than 90 % of breaths were machine-triggered, the median AI decreased, but asynchronies were still present. When we compared patients with AI > 10 vs AI ≤ 10 %, we found similar reintubation and tracheostomy rates but higher ICU and hospital mortality and a trend toward longer duration of MV in patients with an AI above the cutoff.
Asynchronies are common throughout MV, occurring in all MV modes, and more frequently during the daytime. Further studies should determine whether asynchronies are a marker for or a cause of mortality.
2.- Effect of dynamic random leaks on the monitoring accuracy of home mechanical ventilators: a bench study.
Sogo A, Montanyà J, Monsó E, Blanch L, Pomares X, Lujàn M.
BMC Pulm Med. 2013 Dec 10;13:75. doi: 10.1186/1471-2466-13-75.
So far, the accuracy of tidal volume (VT) and leak measures provided by the built-in software of commercial home ventilators has only been tested using bench linear models with fixed calibrated and continuous leaks. The objective was to assess the reliability of the estimation of tidal volume (VT) and unintentional leaks in a single tubing bench model which introduces random dynamic leaks during inspiratory or expiratory phases.
The built-in software of four commercial home ventilators and a fifth ventilator-independent ad hoc designed external software tool were tested with two levels of leaks and two different models with excess leaks (inspiration or expiration). The external software analyzed separately the inspiratory and expiratory unintentional leaks.
In basal condition, all ventilators but one underestimated tidal volume with values ranging between -1.5 ± 3.3% to -8.7% ± 3.27%. In the model with excess of inspiratory leaks, VT was overestimated by all four commercial software tools, with values ranging from 18.27 ± 7.05% to 35.92 ± 17.7%, whereas the ventilator independent-software gave a smaller difference (3.03 ± 2.6%). Leaks were underestimated by two applications with values of -11.47 ± 6.32 and -5.9 ± 0.52 L/min. With expiratory leaks, VT was overestimated by the software of one ventilator and the ventilator-independent software and significantly underestimated by the other three, with deviations ranging from +10.94 ± 7.1 to -48 ± 23.08%. The four commercial tools tested overestimated unintentional leaks, with values between 2.19 ± 0.85 to 3.08 ± 0.43 L/min.
In a bench model, the presence of unintentional random leaks may be a source of error in the measurement of VT and leaks provided by the software of home ventilators. Analyzing leaks during inspiration and expiration separately may reduce this source of error.
3.- Interpretation of ventilator curves in patients with acute respiratory failure.
Correger E, Murias G, Chacon E, Estruga A, Sales B, López-Aguilar J, Montanya J, Lucangelo U, García-Esquirol O, Villagrá A, Villar J, Kacmarek RM, Burgueño MJ, Blanch L.
Medicina Intensiva 2011;doi:10.1016 /j.medin.2011.08.005.
Mechanical ventilation is a therapeutic intervention involving the temporary replacement of ventilatory function with the purpose of improving symptoms in patients with acute respiratory failure. Technological advances have facilitated the development of sophisticated ventilators for viewing and recording the respiratory waveforms, which are a valuable source of information for the clinician. The correct interpretation of these curves is crucial for the correct diagnosis and early detection of anomalies, and for understanding physiological aspects related to mechanical ventilation and patient-ventilator interaction. The present study offers a guide for the interpretation of the airway pressure and flow and volume curves of the ventilator, through the analysis of different clinical scenarios.
4.- Validation of the Better Care© system to detect ineffective efforts during expiration in mechanically ventilated patients. A pilot study.
Blanch L, Sales B, Montanya J et al .
Intensive Care Med doi: 10.1007/s00134-012-2493-4
Ineffective respiratory efforts during expiration (IEE) are a problem during mechanical ventilation (MV). The goal of this study is to validate mathematical algorithms that automatically detect IEE in a computerized (Better Care(®)) system that obtains and processes data from intensive care unit (ICU) ventilators in real time.
The Better Care(®) system, integrated with ICU health information systems, synchronizes and processes data from bedside technology. Algorithms were developed to analyze airflow waveforms during expiration to determine IEE. Data from 2,608,800 breaths from eight patients were recorded. From these breaths 1,024 were randomly selected. Five experts independently analyzed the selected breaths and classified them as IEE or not IEE. Better Care(®) evaluated the same 1,024 breaths and assigned a score to each one. The IEE score cutoff point was determined based on the experts’ analysis. The IEE algorithm was subsequently validated using the electrical activity of the diaphragm (EAdi) signal to analyze 9,600 breaths in eight additional patients.
Optimal sensitivity and specificity were achieved by setting the cutoff point for IEE by Better Care(®) at 42%. A score >42% was classified as an IEE with 91.5% sensitivity, 91.7% specificity, 80.3% positive predictive value (PPV), 96.7% negative predictive value (NPV), and 79.7% Kappa index [confidence interval (CI) (95%) = (75.6%; 83.8%)]. Compared with the EAdi, the IEE algorithm had 65.2% sensitivity, 99.3% specificity, 90.8% PPV, 96.5% NPV, and 73.9% Kappa index [CI (95%) = (71.3%; 76.3%)].
In this pilot, Better Care(®) classified breaths as IEE in close agreement with experts and the EAdi signal.
5.- Nurses’ detection of ineffective inspiratory efforts during mechanical ventilation.
Chacón E, Estruga A, Murias G, Sales B, Montanya J, Lucangelo U, Garcia-Esquirol O, Villagrá A, Villar J, Kacmarek RM, Burgueño MJ, Blanch L, Jam R.
Am J Crit Care. 2012 Jul;21(4):e89-93. doi: 10.4037/ajcc2012108.
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.
To evaluate the accuracy of specifically trained critical care nurses in detecting ineffective inspiratory efforts during expiration.
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.
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).
Specifically trained nurses can reliably detect ineffective inspiratory efforts during expiration.
6.- Telemedicine: Improving the quality of care for critical patients from the pre-hospital phase to the intensive care unit
Murias G, Sales B, García-Esquirol O, Blanch L.
Med Intensiva. 2010 Jan-Feb;34(1):46-55. doi: 10.1016/j.medin.2009.05.002. Epub 2009 Oct 6. Review. Spanish.
The Health System is in crisis and critical care (from transport systems to the ICU) cannot escape from that. Lack of integration between ambulances and reference Hospitals, a deep shortage of critical care specialists and assigned economical resources that increase less than critical care demand are the cornerstones of the problem. Moreover, the analysis of the situation anticipated that the problem will be worse in the future. “Closed” ICUs in which critical care specialists direct patient care outperform “open” ones in which primary admitting physicians direct patient care in consultation with critical care specialists. However, the current paradigm in which a critical care specialist is close to the patient is in the edge of the trouble so, only a new paradigm could help to increase the number of patients under intensivist care. Current information technology and networking capabilities should be fully exploited to improve both the extent and quality of intensivist coverage. Far to be a replacement of the existing model Telemedicine might be a complimentary tool. In fact, to centralize medical data into servers has many additional advantages that could even improve the way in which critical care physicians take care of their patients under the traditional system.
7.- Telemedicine in critical care
Murias G, Sales B, Garcia-Esquirol O, Blanch L.
Open Respir Med J. 2009 Mar 12;3:10-6. doi: 10.2174/1874306400903010010.
Critical care medicine is the specialty that cares for patients with acute life-threatening illnesses where intensivists look after all aspects of patient care. Nevertheless, shortage of physicians and nurses, the relationship between high costs and economic restrictions, and the fact that critical care knowledge is only available at big hospitals puts the system on the edge. In this scenario, telemedicine might provide solutions to improve availability of critical care knowledge where the patient is located, improve relationship between attendants in different institutions and education material for future specialist training. Current information technologies and networking capabilities should be exploited to improve intensivist coverage, advanced alarm systems and to have large critical care databases of critical care signals.
8.- Mechanical ventilation: looking for new paradigms.
Murias G, Sales B, Blanch L.
Curr Opin Crit Care. 2007 Feb;13(1):1-5