Effect of spontaneous breathing on ventilator-free days in critically ill patients-an analysis of patients in a large observational cohort.

BACKGROUND: Mechanical ventilation can injure lung tissue and respiratory muscles. The aim of the present study is to assess the effect of the amount of spontaneous breathing during mechanical ventilation on patient outcomes. METHODS: This is an analysis of the database of the 'Medical Information Mart for Intensive Care (MIMIC)'-III, considering intensive care units (ICUs) of the Beth Israel Deaconess Medical Center (BIDMC), Boston, MA. Adult patients who received invasive ventilation for at least 48 hours were included. Patients were categorized according to the amount of spontaneous breathing, i.e., ≥50% ('high spontaneous breathing') and <50% ('low spontaneous breathing') of time during first 48 hours of ventilation. The primary outcome was the number of ventilator-free days. RESULTS: In total, the analysis included 3,380 patients; 70.2% were classified as 'high spontaneous breathing', and 29.8% as 'low spontaneous breathing'. Patients in the 'high spontaneous breathing' group were older, had more comorbidities, and lower severity scores. In adjusted analysis, the amount of spontaneous breathing was not associated with the number of ventilator-free days [20.0 (0.0-24.2) vs. 19.0 (0.0-23.7) in high vs. low; absolute difference, 0.54 (95% CI, -0.10 to 1.19); P=0.101]. However, 'high spontaneous breathing' was associated with shorter duration of ventilation in survivors [6.5 (3.6 to 12.2) vs. 7.6 (4.1 to 13.9); absolute difference, -0.91 (95% CI, -1.80 to -0.02); P=0.046]. CONCLUSIONS: In patients surviving and receiving ventilation for at least 48 hours, the amount of spontaneous breathing during this period was not associated with an increased number of ventilator-free days.

SEVERITAS: An externally validated mortality prediction for critically ill patients in low and middle-income countries.

OBJECTIVE: Severity of illness scores used in critical care for benchmarking, quality assurance and risk stratification have been mainly created in high-income countries. In low and middle-income countries (LMICs), they cannot be widely utilized due to the demand for large amounts of data that may not be available (e.g. laboratory results). We attempt to create a new severity prognostication model using fewer variables that are easier to collect in an LMIC. SETTING: Two intensive care units, one private and one public, from São Paulo, Brazil PATIENTS: An ICU for the first time. INTERVENTIONS: None. MEASUREMENTS AND MAINS RESULTS: The dataset from the private ICU was used as a training set for model development to predict in-hospital mortality. Three different machine learning models were applied to five different blocks of candidate variables. The resulting 15 models were then validated on a separate dataset from the public ICU, and discrimination and calibration compared to identify the best model. The best performing model used logistic regression on a small set of 10 variables: highest respiratory rate, lowest systolic blood pressure, highest body temperature and Glasgow Coma Scale during the first hour of ICU admission; age; prior functional capacity; type of ICU admission; source of ICU admission; and length of hospital stay prior to ICU admission. On the validation dataset, our new score, named SEVERITAS, had an area under the receiver operating curve of 0.84 (0.82 - 0.86) and standardized mortality ratio of 1.00 (0.91-1.08). Moreover, SEVERITAS had similar discrimination compared to SAPS-3 and better discrimination than the simplified TropICS and R-MPM. CONCLUSIONS: Our study proposes a new ICU mortality prediction model using simple logistic regression on a small set of easily collected variables may be better suited than currently available models for use in low and middle-income countries.

The MIMIC Code Repository: enabling reproducibility in critical care research.

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.

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.