Mohammed Saeed

Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*

Objective:We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. Design:Data collection and retrospective analysis. Setting:All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. Patients:Adult patients admitted to intensive care units between 2001 and 2007. Interventions:None. Measurements and Main Results:The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed users guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1–4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). Conclusions:MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.

Risk Factors for ARDS in Patients Receiving Mechanical Ventilation for > 48 h

BACKGROUND Low tidal volume (Vt) ventilation for ARDS is a well-accepted concept. However, controversy persists regarding the optimal ventilator settings for patients without ARDS receiving mechanical ventilation. This study tested the hypothesis that ventilator settings influence the development of new ARDS. METHODS Retrospective analysis of patients from the Multi Parameter Intelligent Monitoring of Intensive Care-II project database who received mechanical ventilation for > or = 48 h between 2001 and 2005. RESULTS A total of 2,583 patients required > 48 h of ventilation. Of 789 patients who did not have ARDS at hospital admission, ARDS developed in 152 patients (19%). Univariate analysis revealed high peak inspiratory pressure (odds ratio [OR], 1.53 per SD; 95% confidence interval [CI], 1.28 to 1.84), increasing positive end-expiratory pressure (OR, 1.35 per SD; 95% CI, 1.15 to 1.58), and Vt (OR, 1.36 per SD; 95% CI, 1.12 to 1.64) to be significant risk factors. Major nonventilator risk factors for ARDS included sepsis, low pH, elevated lactate, low albumin, transfusion of packed RBCs, transfusion of plasma, high net fluid balance, and low respiratory compliance. Multivariable logistic regression showed that peak pressure (OR, 1.31 per SD; 95% CI, 1.08 to 1.59), high net fluid balance (OR, 1.3 per SD; 95% CI, 1.09 to 1.56), transfusion of plasma (OR, 1.26 per SD; 95% CI, 1.07 to 1.49), sepsis (OR, 1.57; 95% CI, 1.00 to 2.45), and Vt (OR, 1.29 per SD; 95% CI, 1.02 to 1.52) were significantly associated with the development of ARDS. CONCLUSIONS The associations between the development of ARDS and clinical interventions, including high airway pressures, high Vt, positive fluid balance, and transfusion of blood products, suggests that ARDS may be a preventable complication in some cases.

Methods of Blood Pressure Measurement in the ICU

Objective:Minimal clinical research has investigated the significance of different blood pressure monitoring techniques in the ICU and whether systolic vs. mean blood pressures should be targeted in therapeutic protocols and in defining clinical study cohorts. The objectives of this study are to compare real-world invasive arterial blood pressure with noninvasive blood pressure, and to determine if differences between the two techniques have clinical implications. Design:We conducted a retrospective study comparing invasive arterial blood pressure and noninvasive blood pressure measurements using a large ICU database. We performed pairwise comparison between concurrent measures of invasive arterial blood pressure and noninvasive blood pressure. We studied the association of systolic and mean invasive arterial blood pressure and noninvasive blood pressure with acute kidney injury, and with ICU mortality. Setting:Adult intensive care units at a tertiary care hospital. Patients:Adult patients admitted to intensive care units between 2001 and 2007. Interventions:None. Measurements and Main Results:Pairwise analysis of 27,022 simultaneously measured invasive arterial blood pressure/noninvasive blood pressure pairs indicated that noninvasive blood pressure overestimated systolic invasive arterial blood pressure during hypotension. Analysis of acute kidney injury and ICU mortality involved 1,633 and 4,957 patients, respectively. Our results indicated that hypotensive systolic noninvasive blood pressure readings were associated with a higher acute kidney injury prevalence (p = 0.008) and ICU mortality (p < 0.001) than systolic invasive arterial blood pressure in the same range (⩽70 mm Hg). Noninvasive blood pressure and invasive arterial blood pressure mean arterial pressures showed better agreement; acute kidney injury prevalence (p = 0.28) and ICU mortality (p = 0.76) associated with hypotensive mean arterial pressure readings (⩽60 mm Hg) were independent of measurement technique. Conclusions:Clinically significant discrepancies exist between invasive and noninvasive systolic blood pressure measurements during hypotension. Mean blood pressure from both techniques may be interpreted in a consistent manner in assessing patients’ prognosis. Our results suggest that mean rather than systolic blood pressure is the preferred metric in the ICU to guide therapy.

Open-access MIMIC-II database for intensive care research

The critical state of intensive care unit (ICU) patients demands close monitoring, and as a result a large volume of multi-parameter data is collected continuously. This represents a unique opportunity for researchers interested in clinical data mining. We sought to foster a more transparent and efficient intensive care research community by building a publicly available ICU database, namely Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II). The data harnessed in MIMIC-II were collected from the ICUs of Beth Israel Deaconess Medical Center from 2001 to 2008 and represent 26,870 adult hospital admissions (version 2.6). MIMIC-II consists of two major components: clinical data and physiological waveforms. The clinical data, which include patient demographics, intravenous medication drip rates, and laboratory test results, were organized into a relational database. The physiological waveforms, including 125 Hz signals recorded at bedside and corresponding vital signs, were stored in an open-source format. MIMIC-II data were also deidentified in order to remove protected health information. Any interested researcher can gain access to MIMIC-II free of charge after signing a data use agreement and completing human subjects training. MIMIC-II can support a wide variety of research studies, ranging from the development of clinical decision support algorithms to retrospective clinical studies. We anticipate that MIMIC-II will be an invaluable resource for intensive care research by stimulating fair comparisons among different studies.

The cardiac output from blood pressure algorithms trial.

OBJECTIVE The value of different algorithms that estimate cardiac output (CO) by analysis of a peripheral arterial blood pressure (ABP) waveform has not been definitively identified. In this investigation, we developed a testing data set containing a large number of radial ABP waveform segments and contemporaneous reference CO by thermodilution measurements, collected in an intensive care unit (ICU) patient population during routine clinical operations. We employed this data set to evaluate a set of investigational algorithms, and to establish a public resource for the meaningful comparison of alternative CO-from-ABP algorithms. DESIGN A retrospective comparative analysis of eight investigational CO-from-ABP algorithms using the Multiparameter Intelligent Monitoring in Intensive Care II database. SETTING Mixed medical/surgical ICU of a university hospital. PATIENTS A total of 120 cases. INTERVENTIONS None. MEASUREMENTS CO estimated by eight investigational CO-from-ABP algorithms, and CO(TD) as a reference. MAIN RESULTS All investigational methods were significantly better than mean arterial pressure (MAP) at estimating direction changes in CO(TD). Only the formula proposed by Liljestrand and Zander in 1928 was a significantly better quantitative estimator of CO(TD) compared with MAP (95% limits-of-agreement with CO(TD): -1.76/+1.41 L/min versus -2.20/+1.82 L/min, respectively; p < 0.001, per the Kolmogorov-Smirnov test). The Liljestrand method was even more accurate when applied to the cleanest ABP waveforms. Other investigational algorithms were not significantly superior to MAP as quantitative estimators of CO. CONCLUSIONS Based on ABP data recorded during routine intensive care unit (ICU) operations, the Liljestrand and Zander method is a better estimator of CO(TD) than MAP alone. Our attempts to fully replicate commercially-available methods were unsuccessful, and these methods could not be evaluated. However, the data set is publicly and freely available, and developers and vendors of CO-from-ABP algorithms are invited to test their methods using these data.

Estimating cardiac output from arterial blood pressurewaveforms: a critical evaluation using the MIMIC II database

Cardiac output (CO) estimation using arterial blood pressure (ABP) waveforms has been an active area of physiology research over the past century. However, the effectiveness of the estimators has not been extensively studied in a clinical setting. In this paper, we evaluate 11 well-known CO estimators using clinical radial ABP waveforms from the multi-parameter intelligent monitoring for intensive care II (MIMIC II) database, using thermodilution CO (TCO) as reference for comparison. We compare estimations to 988 TCO measurements in 84 patients, totaling 165 hours of ABP waveforms sampled at 125 Hz. As a necessary step for producing absolute CO estimates, we also present 3 methods of calibrating the estimators, each tailored towards a different use model. The results show that the standard deviation of error between TCO and the best CO estimators is approximately 1 L/min for absolute CO estimates. For relative estimates without calibration, the best CO estimator has 18% error at 1 standard deviation

A new multiresolution algorithm for image segmentation

We present here a novel multiresolution-based image segmentation algorithm. The proposed method extends and improves the Gaussian mixture model (GMM) paradigm by incorporating a multiscale correlation model of pixel dependence into the standard approach. In particular, the standard GMM is modified by introducing a multiscale neighborhood clique that incorporates the correlation between pixels in space and scale. We modify the log likelihood function of the image field by a penalization term that is derived from a multiscale neighborhood clique. Maximum likelihood (ML) estimation via the expectation-maximization (EM) algorithm is used to estimate the parameters of the new model. Then, utilizing the parameter estimates, the image field is segmented with a MAP classifier. It is demonstrated that the proposed algorithm provides superior segmentations of synthetic images, yet is computationally efficient.

Similarity-based searching in multi-parameter time series databases

We present a similarity-based searching and pattern matching algorithm that identifies time series data with similar temporal dynamics in large-scale, multi-parameter databases. We represent time series segments by feature vectors that reflect the dynamical patterns of single and multi-dimensional physiological time series. Features include regression slopes at varying time scales, maximum transient changes, auto-correlation coefficients of individual signals, and cross correlations among multiple signals. We model the dynamical patterns with a Gaussian mixture model (GMM) learned with the Expectation Maximization algorithm, and compute similarity between segments as Mahalanobis distances. We evaluate the use of our algorithm in three applications: search-by-example based data retrieval, event classification, and forecasting, using synthetic and real physiologic time series from a variety of sources.

Scalable subband image coding with segmented orthogonal matching pursuit

In this paper, a novel algorithm for low bit-rate image compression is presented. In this technique, we use a new image representation algorithm called segmented orthogonal matching pursuit (SOMP) (Rabiee and Kashyap, 1998) to encode the subbands of an image. Our preliminary results show that our algorithm performs better than the segmentation based matching pursuit (QTMP) (Rabiee et al. 1996) and EZW (Shapiro 1993) encoders at lower bit rates, based on subjective image quality and peak signal-to-noise ratio (PSNR).

Multiparameter trend monitoring and intelligent displays using wavelet analysis

An Intelligent Patient Monitoring (IPM) framework is defined for the analysis and display of multiparameter trends from ICU patients. Wavelet analysis was utilized for detection of physiological events and artifacts in long-term trends. A group of 58 patients from the MIMIC database were identified in which the heart rate (HR), arterial blood pressure (ABP), and pulmonary artery pressure (PAP) were monitored. An estimated cardiac output (CO) signal, using HR and ABP, was shown to correlate strongly (r=.67) with actual CO measurements. Using wavelet analysis, automated artifact and physiological event detection algorithms were developed to monitor left ventricular hemodynamic function. Finally, an intelligent display system is presented for presentation of the data in ICU monitors.