Multiparameter Intelligent Monitoring In Intensive Care Research Articles

A Visualization of Evolving Clinical Sentiment Using Vector Representations of Clinical Notes.

Our objective in this paper was to visualize the evolution of clinical language and sentiment with respect to several common population-level categories including: time in the hospital, age, mortality, gender and race. Our analysis utilized seven years of unstructured free text notes from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database. The text data was partitioned by category and used to generate several high dimensional vector space representations. We generated visualizations of the vector spaces using Distributed Stochastic Neighbor Embedding (tSNE) and Principal Component Analysis (PCA). We also investigated representative words from clusters in the vector space. Lastly, we inferred the general sentiment of the clinical notes toward each parameter by gauging the average distance between positive and negative keywords and all other terms in the space. We found intriguing differences in the sentiment of clinical notes over time, outcome, and demographic features. We noted a decrease in the homogeneity and complexity of clusters over time for patients with poor outcomes. We also found greater positive sentiment for females, unmarried patients, and patients of African ethnicity.

Continuous cuffless arterial blood pressure measurement based on PPG quality assessment

The continuous cuffless arterial blood pressure (ABP) can be calculated by the pulse transit velocity. Its accuracy is significantly influenced by the selection of the photoplethysmography (PPG) feature points. In this paper, we designed an assessment function to calculate the quality of PPG. The quality index indicates the reliability of PPG and helps to determine proper feature points. Kalman filter is used to fuse different PPG feature points for blood pressure calculation. This method is evaluated by the multiparameter intelligent monitoring in intensive care (MIMIC) database. The evaluation shows that it can get more accurate blood pressure results than those methods using fixed PPG feature points, especially when serious motion artefacts exist.

From Pharmacovigilance to Clinical Care Optimization.

In order to ensure the continued, safe administration of pharmaceuticals, particularly those agents that have been recently introduced into the market, there is a need for improved surveillance after product release. This is particularly so because drugs are used by a variety of patients whose particular characteristics may not have been fully captured in the original market approval studies. Even well-conducted, randomized controlled trials are likely to have excluded a large proportion of individuals because of any number of issues. The digitization of medical care, which yields rich and accessible drug data amenable to analytic techniques, provides an opportunity to capture the required information via observational studies. We propose the development of an open, accessible database containing properly de-identified data, to provide the substrate for the required improvement in pharmacovigilance. A range of stakeholders could use this to identify delayed and low-frequency adverse events. Moreover, its power as a research tool could extend to the detection of complex interactions, potential novel uses, and subtle subpopulation effects. This far-reaching potential is demonstrated by our experience with the open Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) intensive care unit database. The new database could also inform the development of objective, robust clinical practice guidelines. Careful systematization and deliberate standardization of a fully digitized pharmacovigilance process is likely to save both time and resources for healthcare in general.

Leveraging a Critical Care Database: Selective Serotonin Reuptake Inhibitor Use Prior to ICU Admission Is Associated With Increased Hospital Mortality

Observational studies have found an increased risk of adverse effects such as hemorrhage, stroke, and increased mortality in patients taking selective serotonin reuptake inhibitors (SSRIs). The impact of prior use of these medications on outcomes in critically ill patients has not been previously examined. We performed a retrospective study to determine if preadmission use of SSRIs or serotonin norepinephrine reuptake inhibitors (SNRIs) is associated with mortality differences in patients admitted to the ICU.The retrospective study used a modifiable data mining technique applied to the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) 2.6 database. A total of 14,709 patient records, consisting of 2,471 in the SSRI/SNRI group and 12,238 control subjects, were analyzed. The study outcome was in-hospital mortality.After adjustment for age, Simplified Acute Physiology Score, vasopressor use, ventilator use, and combined Elixhauser score, SSRI/SNRI use was associated with significantly increased in-hospital mortality (OR, 1.19; 95% CI, 1.02-1.40; P=.026). Among patient subgroups, risk was highest in patients with acute coronary syndrome (OR, 1.95; 95% CI, 1.21-3.13; P=.006) and patients admitted to the cardiac surgery recovery unit (OR, 1.51; 95% CI, 1.11-2.04; P=.008). Mortality appeared to vary by specific SSRI, with higher mortalities associated with higher levels of serotonin inhibition.We found significant increases in hospital stay mortality among those patients in the ICU taking SSRI/SNRIs prior to admission as compared with control subjects. Mortality was higher in patients receiving SSRI/SNRI agents that produce greater degrees of serotonin reuptake inhibition. The study serves to demonstrate the potential for the future application of advanced data examination techniques upon detailed (and growing) clinical databases being made available by the digitization of medicine.

A Database-driven Decision Support System: Customized Mortality Prediction

We hypothesize that local customized modeling will provide more accurate mortality prediction than the current standard approach using existing scoring systems. Mortality prediction models were developed for two subsets of patients in Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC), a public de-identified ICU database, and for the subset of patients >80 years old in a cardiac surgical patient registry. Logistic regression (LR), Bayesian network (BN) and artificial neural network (ANN) were employed. The best-fitted models were tested on the remaining unseen data and compared to either the Simplified Acute Physiology Score (SAPS) for the ICU patients, or the EuroSCORE for the cardiac surgery patients. Local customized mortality prediction models performed better as compared to the corresponding current standard severity scoring system for all three subsets of patients: patients with acute kidney injury (AUC = 0.875 for ANN, vs. SAPS, AUC = 0.642), patients with subarachnoid hemorrhage (AUC = 0.958 for BN, vs. SAPS, AUC = 0.84), and elderly patients undergoing open heart surgery (AUC = 0.94 for ANN, vs. EuroSCORE, AUC = 0.648). Rather than developing models with good external validity by including a heterogeneous patient population, an alternative approach would be to build models for specific patient subsets using one’s local database.