Alistair E. W. Johnson

An open access database for the evaluation of heart sound algorithms

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Machine Learning and Decision Support in Critical Care

Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply reusing the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability, and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding “secondary use of medical records” and “Big Data” analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of “precision medicine.” This review article covers many of the issues involved in the collection and preprocessing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; online patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data.

The MIMIC Code Repository: enabling reproducibility in critical care research

Abstract Objective Lack of reproducibility in medical studies is a barrier to the generation of a robust knowledge base to support clinical decision-making. In this paper we outline the Medical Information Mart for Intensive Care (MIMIC) Code Repository, a centralized code base for generating reproducible studies on an openly available critical care dataset. Materials and Methods Code is provided to load the data into a relational structure, create extractions of the data, and reproduce entire analysis plans including research studies. Results Concepts extracted include severity of illness scores, comorbid status, administrative definitions of sepsis, physiologic criteria for sepsis, organ failure scores, treatment administration, and more. Executable documents are used for tutorials and reproduce published studies end-to-end, providing a template for future researchers to replicate. The repository’s issue tracker enables community discussion about the data and concepts, allowing users to collaboratively improve the resource. Discussion The centralized repository provides a platform for users of the data to interact directly with the data generators, facilitating greater understanding of the data. It also provides a location for the community to collaborate on necessary concepts for research progress and share them with a larger audience. Consistent application of the same code for underlying concepts is a key step in ensuring that research studies on the MIMIC database are comparable and reproducible. Conclusion By providing open source code alongside the freely accessible MIMIC-III database, we enable end-to-end reproducible analysis of electronic health records.

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.

Bridging the Health Data Divide

Fundamental quality, safety, and cost problems have not been resolved by the increasing digitization of health care. This digitization has progressed alongside the presence of a persistent divide between clinicians, the domain experts, and the technical experts, such as data scientists. The disconnect between clinicians and data scientists translates into a waste of research and health care resources, slow uptake of innovations, and poorer outcomes than are desirable and achievable. The divide can be narrowed by creating a culture of collaboration between these two disciplines, exemplified by events such as datathons. However, in order to more fully and meaningfully bridge the divide, the infrastructure of medical education, publication, and funding processes must evolve to support and enhance a learning health care system.

Prolonged Elevated Heart Rate and 90-Day Survival in Acutely Ill Patients: Data From the MIMIC-III Database

Purpose: We sought to evaluate the association of prolonged elevated heart rate (peHR) with survival in acutely ill patients. Methods: We used a large observational intensive care unit (ICU) database (Multiparameter Intelligent Monitoring in Intensive Care III [MIMIC-III]), where frequent heart rate measurements were available. The peHR was defined as a heart rate >100 beats/min in 11 of 12 consecutive hours. The outcome was survival status at 90 days. We collected heart rates, disease severity (simplified acute physiology scores [SAPS II]), comorbidities (Charlson scores), and International Classification of Diseases (ICD) diagnosis information in 31 513 patients from the MIMIC-III ICU database. Propensity score (PS) methods followed by inverse probability weighting based on the PS was used to balance the 2 groups (the presence/absence of peHR). Multivariable weighted logistic regression was used to assess for association of peHR with the outcome survival at 90 days adjusting for additional covariates. Results: The mean age was 64 years, and the most frequent main disease category was circulatory disease (41%). The mean SAPS II score was 35, and the mean Charlson comorbidity score was 2.3. Overall survival of the cohort at 90 days was 82%. Adjusted logistic regression showed a significantly increased risk of death within 90 days in patients with an episode of peHR (P < .001; odds ratio for death 1.79; confidence interval, 1.69-1.88). This finding was independent of median heart rate. Conclusion: We found a significant association of peHR with decreased survival in a large and heterogenous cohort of ICU patients.

Estimating patient's health state using latent structure inferred from clinical time series and text

Modern intensive care units (ICUs) collect large volumes of data in monitoring critically ill patients. Clinicians in the ICUs face the challenge of interpreting large volumes of high-dimensional data to diagnose and treat patients. In this work, we explore the use of Hierarchical Dirichlet Processes (HDP) as a Bayesian nonparametric framework to infer patients states of health by combining multiple sources of data. In particular, we employ HDP to combine clinical time series and text from the nursing progress notes in a probabilistic topic modeling framework for patient risk stratification. Given a patient cohort, we use HDP to infer latent “topics” shared across multimodal patient data from the entire cohort. Each topic is modeled as a multinomial distribution over a vocabulary of codewords, defined over heterogeneous data sources. We evaluate the clinical utility of the learned topic structure using the first 24-hour ICU data from over 17,000 adult patients in the MIMIC-II database to estimate patients risks of in-hospital mortality. Our results demonstrate that our approach provides a viable framework for combining different data modalities to model patients states of health, and can potentially be used to generate alerts to identify patients at high risk of hospital mortality.

Right Ventricular Function, Peripheral Edema, and Acute Kidney Injury in Critical Illness

Introduction The cardiorenal syndrome generally focuses on left ventricular function, and the importance of the right ventricle as a determinant of renal function is described less frequently. In a cohort of critically ill patients with echocardiographic measurements obtained within 24 hours of admission to the intensive care unit, we examined the association of right ventricular function with acute kidney injury (AKI) and AKI-associated mortality. We also examined whether clinical measurement of volume overload modified the association between ventricular function and AKI in a subpopulation with documented admission physical examinations. Methods Among 1879 critically ill patients with echocardiographic ventricular measurements, 43% (n = 807) had ventricular dysfunction—21% (n = 388), 9% (n = 167), and 13% (n = 252) with isolated left ventricular dysfunction, isolated right ventricular dysfunction, and biventricular dysfunction, respectively. Overall, ventricular dysfunction was associated with a 43% higher adjusted risk of AKI (95% confidence interval [CI] 1.14–1.80; P = 0.002) compared with those with normal biventricular function, whereas isolated left ventricular dysfunction, isolated right ventricular dysfunction, and biventricular dysfunction were associated with a 1.34 (95% CI 1.00-1.77, P = 0.05), 1.35 (95% CI 0.90–2.10, P = 0.14) and 1.67 (95% CI 1.23–2.31, P = 0.002) higher adjusted risk. Although an episode of AKI was associated with an approximately 2-fold greater risk of hospital mortality in those with isolated left ventricular dysfunction and biventricular dysfunction, in those with isolated right ventricular dysfunction, AKI was associated with a 7.85-fold greater risk of death (95% CI 2.89–21.3, P < 0.001). Independent of ventricular function, peripheral edema was an important determinant of AKI. Discussion Like left ventricular function, right ventricular function is an important determinant of AKI and AKI-associated mortality. Volume overload, independently of ventricular function, is a risk factor for AKI. Whether establishment of euvolemia might mitigate AKI risk will require further study.

First Brazilian datathon in critical care

Sistema Único de Saúde (SUS) was established in 1990 by the Brazilian Federal Constitution to ensure comprehensive, universal and free access to healthcare for the entire population.(1) In 1988, half of Brazil’s population had no health coverage. Two decades after establishing SUS, more than 75% of the country’s estimated 190 million people rely exclusively on SUS for their health care coverage.(2) Provision of these services presents a rich opportunity to capture digital health data, which are a resource for developing locally relevant clinical practice guidelines rather than adopting those of other countries. Despite this potential for data acquisition, electronic health records (EHR) in Brazil are currently used primarily to support administrative and billing functions and do not store clinical information in a machine-ready format as required for data analysis.(3) A recent survey showed that 70% of the facilities that have used the Internet in the last 12 months in Brazil have some sort of electronic record for medical information.(4) In 48% of the facilities, records are partially on paper and partially digital. Paperless information systems were present only in 22% of the facilities, and this rate was slightly higher at 33% for private facilities. On the other hand, 30% of the facilities keep all their patient records on paper with a much higher proportion among public facilities at 51%.(4) Two government departments could play leading roles in moving health information technology and data analytics forward in Brazil: (1) DATASUS, which provides information systems support to all divisions of SUS,(5) and (2) Telessaúde Brasil Redes, a national program designed to improve SUS’s quality of care, integrating teaching and service through information technology tools.(6) With the integration of SUS, it is possible to create an EHR for every citizen, a repository of an individual’s records of services carried out and clinical data, and when aggregated, an accurate record of the trajectory of health and disease in Brazil. Databases drawn from EHR in Brazil are limited in scope and accessible only to investigators internally within a hospital or organization. Indeed, Ary Serpa Neto1,2, Guillaume Kugener3, Lucas Bulgarelli3,4, Roberto Rabello Filho1, Miguel Ángel Armengol de la Hoz3,5,6, Alistair EW Johnson3, Kenneth E Paik3, Felipe Torres3, Chen Xie3, Edson Amaro Júnior4,7, Leonardo José Rolim Ferraz1, Leo Anthony Celi3, Rodrigo Octavio Deliberato3,4

Impact of Intensive Care Unit Discharge Delays on Patient Outcomes: A Retrospective Cohort Study

Objective: Patients often overstay in intensive care units (ICU) after they are deemed discharge ready. The objective of this study was to examine the impact of such discharge delays (DD) on subsequent in-hospital morbidity and mortality. Design: Retrospective cohort study. Setting: Single tertiary academic medical center. Patients: Adult patients admitted to the medical ICU between 2005 and 2011. Interventions: For all patients, DD (ie, time between request for a ward bed and time of ICU discharge) was calculated. Discharge delays was dichotomized as long (≥24 hours) or short (<24 hours). Multivariable linear and logistic regressions were used to assess the association between dichotomized DD and post-ICU clinical outcomes. Results: Overall, 9673 discharges were included of which 10.4% patients had long DDs. In the fully adjusted model, a long delay was not associated with increased odds of death (adjusted odds ratio [aOR]: 0.99, 95% confidence interval [CI]: 0.74-1.31, P = .95) but was associated with a shorter log plus one of post-ICU discharge length of stay (LOS; regression coefficient: −0.13, 95% CI: −0.17 to −0.08, P < .001). Longer DD was not associated with more hospital-free days (HFD: a composite of post-ICU LOS and in-hospital mortality). Shorter DDs were associated with shorter LOS when LOS was measured from the time of ward bed request as opposed to the time of ICU discharge. Conclusions: In this study, long DD was associated with a slight decrease in post-ICU LOS but longer LOS when measured from the point of ward bed request, suggesting a potential role for more aggressive discharge planning in the ICU for patients with long DDs. There was no association between long DD and subsequent mortality or HFD.