Mauro D. Santos

The association between the neutrophil-to-lymphocyte ratio and mortality in critical illness: an observational cohort study.

IntroductionThe neutrophil-to-lymphocyte ratio (NLR) is a biological marker that has been shown to be associated with outcomes in patients with a number of different malignancies. The objective of this study was to assess the relationship between NLR and mortality in a population of adult critically ill patients.MethodsWe performed an observational cohort study of unselected intensive care unit (ICU) patients based on records in a large clinical database. We computed individual patient NLR and categorized patients by quartile of this ratio. The association of NLR quartiles and 28-day mortality was assessed using multivariable logistic regression. Secondary outcomes included mortality in the ICU, in-hospital mortality and 1-year mortality. An a priori subgroup analysis of patients with versus without sepsis was performed to assess any differences in the relationship between the NLR and outcomes in these cohorts.ResultsA total of 5,056 patients were included. Their 28-day mortality rate was 19%. The median age of the cohort was 65 years, and 47% were female. The median NLR for the entire cohort was 8.9 (interquartile range, 4.99 to 16.21). Following multivariable adjustments, there was a stepwise increase in mortality with increasing quartiles of NLR (first quartile: reference category; second quartile odds ratio (OR) = 1.32; 95% confidence interval (CI), 1.03 to 1.71; third quartile OR = 1.43; 95% CI, 1.12 to 1.83; 4th quartile OR = 1.71; 95% CI, 1.35 to 2.16). A similar stepwise relationship was identified in the subgroup of patients who presented without sepsis. The NLR was not associated with 28-day mortality in patients with sepsis. Increasing quartile of NLR was statistically significantly associated with secondary outcome.ConclusionThe NLR is associated with outcomes in unselected critically ill patients. In patients with sepsis, there was no statistically significant relationship between NLR and mortality. Further investigation is required to increase understanding of the pathophysiology of this relationship and to validate these findings with data collected prospectively.

Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines—including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology—gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

Heart beat detection in multimodal physiological data using a hidden semi-Markov model and signal quality indices.

Accurate heart beat detection in signals acquired from intensive care unit (ICU) patients is necessary for establishing both normality and detecting abnormal events. Detection is normally performed by analysing the electrocardiogram (ECG) signal, and alarms are triggered when parameters derived from this signal exceed preset or variable thresholds. However, due to noisy and missing data, these alarms are frequently deemed to be false positives, and therefore ignored by clinical staff. The fusion of features derived from other signals, such as the arterial blood pressure (ABP) or the photoplethysmogram (PPG), has the potential to reduce such false alarms. In order to leverage the highly correlated temporal nature of the physiological signals, a hidden semi-Markov model (HSMM) approach, which uses the intra- and inter-beat depolarization interval, was designed to detect heart beats in such data. Features based on the wavelet transform, signal gradient and signal quality indices were extracted from the ECG and ABP waveforms for use in the HSMM framework. The presented method achieved an overall score of 89.13% on the hidden/test data set provided by the Physionet/Computing in Cardiology Challenge 2014: Robust Detection of Heart Beats in Multimodal Data.

Low-Cost Blood Pressure Monitor Device for Developing Countries

Taking the Blood Pressure (BP) with a traditional sphygmomanometer requires a trained user. In developed countries, patients who need to monitor their BP at home usually acquire an electronic BP device with an automatic inflate/deflate cycle that determines the BP through the oscillometric method. For patients in resource constrained regions automated BP measurement devices are scarce because supply channels are limited and relative costs are high. Consequently, routine screening for and monitoring of hypertension is not common place. In this project we aim to offer an alternative strategy to measure BP and Heart Rate (HR) in developing countries. Given that mobile phones are becoming increasingly available and affordable in these regions, we designed a system that comprises low-cost peripherals with minimal electronics, offloading the main processing to the phone. A simple pressure sensor passes information to the mobile phone and the oscillometric method is used to determine BP and HR. Data are then transmitted to a central medical record to reduce errors in time stamping and information loss.

A scalable mHealth system for noncommunicable disease management

Barriers to effective screening and management of NCDs in resource-constrained regions include limited availability of trained personnel, access to affordable automatic medical devices, and longitudinal clinical data. We present an end-to-end mHealth system which takes advantage of the almost universal availability of smartphones in order to address these barriers in a scalable and affordable manner. Our system includes simple, low-cost (

Implementing an electronic observation and early warning score chart in the emergency department: a feasibility study

5-

Respiratory rate estimation from the oscillometric waveform obtained from a non-invasive cuff-based blood pressure device.

20) and open-source peripherals that allow a minimally trained person to collect high-quality medical data at the point-of-care through a standard smartphone; allows the reliable transmission of clinical data even in the case of high-latency network connections; stores data in a cloud-based system, making patient records accessible anywhere; and enables both crowdsourced diagnostics and generation of annotated data for the research and development of automatic decision support and risk assessment systems. We show examples of the different elements of the system tailored for the management of cardiovascular disease and chronic obstructive pulmonary disease, which includes prototypes of the low-cost peripherals. In a validation study (of 40 volunteers), our smartphone-based blood pressure (BP) monitor was shown to measure BP, heart rate and respiration rate with a mean-absolute-error of less than 5 units from the reference values for 80% of the measurements.

Self-Management of Postnatal Hypertension: The SNAP-HT Trial.

Background Use of automated systems to aid identification of patient deterioration in routine hospital practice is limited and their impact on patient outcomes remains unclear. This study was designed to evaluate the feasibility of implementing an electronic observation chart with automated early warning score (EWS) calculation in the high-acuity area of an emergency department. Methods This study enrolled 3219 participants before and 3352 after implementation of an automated system, using bedside vital-sign entry on networked mobile devices. The primary outcome measure was the percentage of participants for whom an EWS was accurately recorded at each stage. Results Of the participants, 52.7% before and 92.9% after implementation of the electronic system had an accurate EWS recorded on charts available to the study team. Participant groups were well balanced for baseline characteristics and acuity. Conclusion In this study, the feasibility and limitations of implementing an electronic observation chart in the ED were demonstrated. Accurate EWS documentation was more frequent after implementation of the electronic observation chart. Retrospective analysis suggests that the use of an electronic observation system may lead to a greater percentage of observations being taken from those patients with a higher EWS.

Performance of Early Warning Scoring Systems to Detect Patient Deterioration in the Emergency Department

The presence of respiratory activity in the electrocardiogram (ECG), the pulse oximeters photoplethysmo-graphic and continuous arterial blood pressure signals is a well-documented phenomenon. In this paper, we demonstrate that such information is also present in the oscillometric signal acquired from automatic non-invasive blood pressure monitors, and may be used to estimate the vital sign respiratory rate (RR). We propose a novel method that combines the information from the two respiratory-induced variations (frequency and amplitude) via frequency analysis to both estimate RR and eliminate estimations considered to be unreliable because of poor signal quality. The method was evaluated using data acquired from 40 subjects containing ECG, respiration and blood pressure waveforms, the latter acquired using an in-house built blood pressure device that is able to connect to a mobile phone. Results demonstrated a good RR estimation accuracy of our method when compared to the reference values extracted from the reference respiration waveforms (mean absolute error of 2.69 breaths/min), which is comparable to existing methods in the literature that extract RR from other physiological signals. The proposed method has been implemented in Java on the Android device for use in an mHealth platform.