Mohammed Saeed

Predicting ICU hemodynamic instability using continuous multiparameter trends

Background: Identifying hemodynamically unstable patients in a timely fashion in intensive care units (ICUs) is crucial because it can lead to earlier interventions and thus to potentially better patient outcomes. Current alert algorithms are typically limited to detecting dangerous conditions only after they have occurred and suffer from high false alert rates. Our objective was to predict hemodynamic instability at least two hours before a major clinical intervention (e.g., vasopressor administration), while maintaining a low false alert rate. Study population: From the MIMIC II database, containing ICU minute-by-minute heart rate (HR) and invasive arterial blood pressure (BP) monitoring trend data collected between 2001 and 2005, we identified 132 stable and 104 unstable patients that met our stability-instability criteria and had sufficient data points. Method: We first derived additional physiological parameters of shock index, rate pressure product, heart rate variability, and two measures of trending based on HR and BP. Then we developed 220 statistical features and systematically selected a small set to use for classification. We applied multi-variable logistic regression modeling to do classification and implemented validation via bootstrapping. Results: Area under receiver-operating curve (ROC) 0.83±0.03, sensitivity 0.75±0.06, and specificity 0.80±0.07; if the specificity is targeted at 0.90, then the sensitivity is 0.57±0.07. Based on our preliminary results, we conclude that the algorithms we developed using HR and BP trend data may provide a promising perspective toward reliable predictive alerts for hemodynamically unstable patients.

Identifying artifacts in arterial blood pressure using morphogram variability

In this article we present a simple technique that utilizes the cross correlations between ECG signals and haemodynamic signals for the purpose of assessing signal quality and detecting artifacts in the arterial blood pressure (ABP) signal. The technique was tested using cases from a physician-annotated patient monitoring signal database from Beth IsraeUHarvard-MIT University data bank. The results were encouraging: 90% of the manually annotated artifacts were correctly classified as artifacts and 99% of the manually annotated true events were correctly classified (out of a tQtd Of 683 manualiy annotated alarms).

Predicting respiratory instability in the ICU

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) contribute to the morbidity and mortality of intensive care patients worldwide, and have large associated human and financial costs. We identified a reference data set of 624 mechanically-ventilated patients in the MIMIC-II intensive care database with and without low PaO2/FiO2 ratios (termed respiratory instability), and developed prediction algorithms for distinguishing these patients prior to the critical event. In the end, we had four rule sets using mean airway pressure, plateau pressure, total respiratory rate and oxygen saturation (SpO2), where the specificity/sensitivity rates were either 80%/60% or 90%/50%.

Hemodynamic Instability Prediction Through Continuous Multiparameter Monitoring in ICU

Current algorithms identifying hemodynamically unstable intensive care unit patients typically are limited to detecting existing dangerous conditions and suffer from high false alert rates. Our objective was to predict hemodynamic instability at least two hours before patient deterioration while maintaining a low false alert rate, using minute-by-minute heart rate (HR) and blood pressure (BP) data. We identified 66 stable and 104 unstable patients meeting our stability-instability criteria from the MIMIC II database, and developed multi-parameter measures using HR and BP. An instability index combining measures of BP, shock index, rate pressure product, and HR variation was developed from a multivariate regression model to predict hemodynamic instability (ROC of 0.82±0.03, sensitivity of 0.57±0.07 when the specificity was targeted at 0.90; the alert rate ratio of unstable to stable patients was 7.62). We conclude that these algorithms could form the basis for reliable predictive clinical alerts which identify patients likely to become hemodynamically unstable within the next few hours so that the clinicians can proactively manage these patients and provide necessary care.

Heuristics to determine ventilation times of ICU patients from the MIMIC-II database

Mechanical ventilation is an important life support tool for patients in intensive care units (ICU). For various research purposes related to patient hemodynamic and cardiopulmonary monitoring, it is important to know when a patient is on a ventilator. Unfortunately, the widely used MIMIC-II database contains results from user charted data, where the user did not always store ventilation on and off times explicitly and accurately. The resulting ventilation-related data are subject to error. Therefore, there are no simple rules to define ventilation times retrospectively for this dataset. Hence, we designed a simple set of rules to determine the ventilation times using multiple sources of mechanical ventilator-related settings and physiological measurements by expert heuristics. The rules worked well in comparison with nursing notes regarding ventilation events. We conclude that our rule sets for determining ventilation times may be useful in assisting with MIMIC-II database analysis.