The future of bedside monitoring

CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring

Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.

Monitoring Artificial Pancreas Trials Through Agent-based Technologies: A Case Report

The increase in the availability and reliability of network connections lets envision systems supporting a continuous remote monitoring of clinical parameters useful either for overseeing chronic diseases or for following clinical trials involving outpatients. We report here the results achieved by a telemedicine infrastructure that has been linked to an artificial pancreas platform and used during a trial of the AP@home project, funded by the European Union. The telemedicine infrastructure is based on a multiagent paradigm and is able to deliver to the clinic any information concerning the patient status and the operation of the artificial pancreas. A web application has also been developed, so that the clinic staff and the researchers involved in the design of the blood glucose control algorithms are able to follow the ongoing experiments. Albeit the duration of the experiments in the trial discussed in the article was limited to only 2 days, the system proved to be successful for monitoring patients, in particular overnight when the patients are sleeping. Based on that outcome we can conclude that the infrastructure is suitable for the purpose of accomplishing an intelligent monitoring of an artificial pancreas either during longer trials or whenever that system will be used as a routine treatment.

Use of echocardiography and modalities of patient monitoring of trauma patients

PURPOSE OF REVIEW

Trauma patients require evaluation of the anatomic structure as well as the hemodynamic profile of the heart to improve effectiveness of resuscitation. They are prone to hemodynamic instability and must be monitored with various modalities to detect deterioration early. Newer, less invasive ultrasound technologies are replacing familiar 'gold standard' modalities of the past. This article reviews the indications, roles, imaging approaches, and limitations of modern echocardiography. A brief review of other ICU monitoring modalities is also presented.

RECENT FINDINGS

Echocardiography has emerged as a first-line diagnostic tool for assessment of trauma patients, especially those with hemodynamic compromise. It yields crucial information about structural damage as well as the hemodynamic profile and can be performed through either the transesophageal or transthoracic route. Quick and systematic use of echocardiography for diagnosis and management of critically injured patients may lead to improved outcomes.

SUMMARY

Echocardiography plays an important role in the trauma bay for diagnosis of thoracic injury and at the bedside in the ICU for evaluation of the hemodynamic profile.

Prognosis in critical care

Prognostic risk prediction models have been employed in the intensive care unit (ICU) setting since the 1980s and provide health care providers with important information to help inform decisions related to treatment and prognosis, as well as to compare outcomes across institutions. Prognostic models for critical care are among the most widely utilized and tested predictive models in healthcare. In this article, we review and compare mortality prediction models, including the APACHE (1981), SAPS (1984), APACHE-II (1985), MPM (1987), APACHE-III (1991), SAPS-II (1993), and MPM-II (1993). We emphasize the importance of model calibration in this domain. In addition, we present a brief review of the statistical methodology, multiple logistic regression, which underlies most of the models currently used in critical care.

Volatility: A New Vital Sign Identified Using a Novel Bedside Monitoring Strategy

BACKGROUND

SIMON (Signal Interpretation and Monitoring) monitors and archives continuous physiologic data in the ICU (HR, BP, CPP, ICP, CI, EDVI, SVO2, SPO2, SVRI, PAP, and CVP). We hypothesized: heart rate (HR) volatility predicts outcome better than measures of central tendency (mean and median).

METHODS

More than 600 million physiologic data points were archived from 923 patients over 2 years in a level one trauma center. Data were collected every 1 to 4 seconds, stored in a MS-SQL 7.0 relational database, linked to TRACS, and de-identified. Age, gender, race, Injury Severity Score (ISS), and HR statistics were analyzed with respect to outcome (death and ventilator days) using logistic and Poisson regression.

RESULTS

We analyzed 85 million HR data points, which represent more than 71,000 hours of continuous data capture. Mean HR varied by age, gender and ISS, but did not correlate with death or ventilator days. Measures of volatility (SD, % HR >120) correlated with death and prolonged ventilation.

CONCLUSIONS

1) Volatility predicts death better than measures of central tendency. 2) Volatility is a new vital sign that we will apply to other physiologic parameters, and that can only be fully explored using techniques of dense data capture like SIMON. 3) Densely sampled aggregated physiologic data may identify sub-groups of patients requiring new treatment strategies.

Reduced heart rate volatility: an early predictor of death in trauma patients

OBJECTIVE

To determine if using dense data capture to measure heart rate volatility (standard deviation) measured in 5-minute intervals predicts death.

BACKGROUND

Fundamental approaches to assessing vital signs in the critically ill have changed little since the early 1900s. Our prior work in this area has demonstrated the utility of densely sampled data and, in particular, heart rate volatility over the entire patient stay, for predicting death and prolonged ventilation.

METHODS

Approximately 120 million heart rate data points were prospectively collected and archived from 1316 trauma ICU patients over 30 months. Data were sampled every 1 to 4 seconds, stored in a relational database, linked to outcome data, and de-identified. HR standard deviation was continuously computed over 5-minute intervals (CVRD, cardiac volatility-related dysfunction). Logistic regression models incorporating age and injury severity score were developed on a test set of patients (N = 923), and prospectively analyzed in a distinct validation set (N = 393) for the first 24 hours of ICU data.

RESULTS

Distribution of CVRD varied by survival in the test set. Prospective evaluation of the model in the validation set gave an area in the receiver operating curve of 0.81 with a sensitivity and specificity of 70.1 and 80.0, respectively. CVRD predict death as early as 24 hours in the validation set.

CONCLUSIONS

CVRD identifies a subgroup of patients with a high probability of dying. Death is predicted within first 24 hours of stay. We hypothesize CVRD is a surrogate for autonomic nervous system dysfunction.

Saliva as a diagnostic fluid

In the last 10 years, the use of saliva as a diagnostic fluid has become somewhat of a translational research success story. Technologies are now available enabling saliva to be used to diagnose disease and predict disease progression. This review describes some important recent advances in salivary diagnostics and barriers to application and advancement. This review will also stimulate future research activity.

5.3 Global challenges in research and strategic planning

Health sciences research is experiencing dramatic progress. How can dental schools throughout the world best make these research advances relevant for dental students, as well as providing them with the means to assess and utilize the research advances that will occur in the future? This complex question presents a critical challenge to the dental educational community. Research is clearly integral to the mission of dental education. By providing dental students with active learning strategies, dental educators can inculcate the ability for independent scientific thinking and thereby develop reflective as well as technically competent practitioners. However, there is a shortage of well-trained individuals to fill faculty and research positions in certain parts of the world. Global networks for mutual information exchange are imperative to overcome resource limitations in individual institutions, as is dedicated funding for research in the dental educational setting.