Joseph J. Frassica

Cardiovascular abnormalities in infants prenatally exposed to cocaine

This study utilized a historical cohort to examine the relationship between maternal cocaine use during pregnancy and the occurrence of congenital cardiovascular abnormalities. All neonatal drug screens performed at Boston City Hospital during an 18-month period were reviewed (n = 554); for 214 (39%) screened high-risk neonates, results of toxicologic screens were positive for cocaine, and 340 (61%) neonates had no detectable cocaine. We compared the occurrence of cardiovascular malformations and electrocardiographic abnormalities in these two groups. Matches were sought between these 554 infants and our pediatric cardiology data base, which consisted of inpatient consultation, outpatient consultation, and electrocardiography. Forty-nine patients had drug screens and were also entered into our cardiology data base: 25 had both consultations and electrocardiograms, and 24 had electrocardiograms only. The rate of cardiac anomalies among the cocaine-positive infants was significantly higher (relative risk = 3.7; 95% confidence interval: (1.4, 9.4)) than the rate of these anomalies among the cocaine-negative comparison group (65/100 vs 18/1000); the rate for cocaine-positive infants was also significantly higher than published rates for general populations of infants. Several electrocardiographic abnormalities, high-grade ventricular ectopy, and cardiorespiratory arrests were also noted in our study population. We conclude that cocaine exposure during prenatal life appears to predispose infants to structural cardiovascular malformations, electrocardiographic abnormalities, and, possibly, cardiopulmonary autonomic dysfunction.

Prioritizing the organization and management of intensive care services in the United States: The PrOMIS Conference

Objective:Adult critical care services are a large, expensive part of U.S. health care. The current agenda for response to workforce shortages and rising costs has largely been determined by members of the critical care profession without input from other stakeholders. We sought to elicit the perceived problems and solutions to the delivery of critical care services from a broad set of U.S. stakeholders. Design:A consensus process involving purposive sampling of identified stakeholders, preconference Web-based survey, and 2-day conference. Setting:Participants represented healthcare providers, accreditation and quality-oversight groups, federal sponsoring institutions, healthcare vendors, and institutional and individual payers. Subjects:We identified 39 stakeholders for the field of critical care medicine. Thirty-six (92%) completed the preconference survey and 37 (95%) attended the conference. Interventions:None. Measurements and Main Results:Participants expressed moderate to strong agreement with the concerns identified by the critical care professionals and additionally expressed consternation that the critical care delivery system was fragmented, variable, and not patient-centered. Recommended solutions included regionalizing the adult critical care system into “tiers” defined by explicit triage criteria and professional competencies, achieved through voluntary hospital accreditation, supported through an expanded process of competency certification, and monitored through process and outcome surveillance; implementing mechanisms for improved communication across providers and settings and between providers and patients/families; and conducting market research and a public education campaign regarding critical care’s promises and limitations. Conclusions:This consensus conference confirms that agreement on solutions to complex healthcare delivery problems can be achieved and that problem and solution frames expand with broader stakeholder participation. This process can be used as a model by other specialties to address priority setting in an era of shifting demographics and increasing resource constraints.

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%.

Recombinant human-activated protein C (rhAPC) in childhood sepsis.

In this issue of the Journal of Intensive Care Medicine, Dr Sajan and colleagues present a Case Report detailing the treatment of an infant with gram negative septic shock with the new drug Drotrecogin Alpha (Activated) (rhAPC, Xigris®). The report deserves careful attention because published pediatric experience with this drug is scarce [1], and it represents an early description of the use of this drug outside of its current FDA-approved indication [2]. Recombinant human-activated protein C (rhAPC) has been demonstrated to reduce mortality in adult patients with sepsis (PROWESS study [3]) and is currently FDA approved for the treatment of sepsis in adults. Although there may be similarities between adult and pediatric sepsis, important differences exist with respect to mortality rates, types of organisms, site of infection, end-organ dysfunction, and the potentially greater risk for intracranial bleeding in the very young. We outline some of the important currently available data with regard to the use of rhAPC in children below. Based on the Food and Drug Administration (FDA) response to the Biologics License Application (BLA) submission for rhAPC, complete or partial data are available for only 182 pediatric patients receiving treatment. Eighty-three patients have been treated in an open-label pharmacology/ safety study in pediatric sepsis (EVAO). The 14-day mortality rate in this trial was 9.6%, and the incidence of serious adverse bleeding events reported during the 14-day study period was 4.8%. Another 14 pediatric patients were treated during an openlabel compassionate-use trial in purpura fulminans (EVAS). In this trial, one pediatric patient died (7%) and 1 serious adverse bleeding event was reported at 24 days posttreatment (7%). Eighty-five patients have been treated with rhAPC in open-label studies. There have been no data submitted to the FDA on these patients. Based on these results, the incidence of serious adverse bleeding events in the pediatric population appears comparable to that in the adult population (3.5% in the PROWESS study [3], 4.8% in the EVAO study). However, the mortality benefit of treatment with rhAPC in children was not comparable to that in adults [4,5]. The FDA BLA submission for this drug contains pharmacokinetic and pharmacodynamic information that may be useful in determining the optimal dose of rhAPC for use in children. Based on the preliminary sequential dose escalation study using 6-hour infusions of 6, 12, 24, and 36 mcg/kg/h conducted in 21 pediatric patients during phase I of the EVAO trial, a dose of 24 mcg/kg/h was well tolerated and found to achieve steady-state activated protein C levels approximating those achieved in the adult phase III studies. Additional pharmacokinetic studies conducted during this trial using 24 mcg/kg/h in patients ranging in age from 38 weeks to 18 years of age determined that mean steadystate plasma concentrations and weight-normalized plasma clearance of activated protein C were similar to that of adults. In addition, the time needed to reach undetectable plasma concentrations (< 10 ng/ml) increased with age but ranged up to 1.5 hours versus 2 hours in adults [4,6]. It has been well documented that sepsis-related protein C deficiency is associated with an increased risk of mortality in both adults and children [5,7,8]. The Pediatric EVAO data demonstrate that biomarkers of sepsis such as protein C are positively affected in pediatric patients treated with rhAPC. The effects of rhAPC noted in children in this trial Recombinant Human-Activated Protein C (rhAPC) in Childhood Sepsis

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.

A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit

BackgroundEarly recognition and timely intervention are critical steps for the successful management of shock. The objective of this study was to develop a model to predict requirement for hemodynamic intervention in the pediatric intensive care unit (PICU); thus, clinicians can direct their care to patients likely to benefit from interventions to prevent further deterioration.MethodsThe model proposed in this study was trained on a retrospective cohort of all patients admitted to a tertiary PICU at a single center in the United States, and validated on another retrospective cohort of all patients admitted to the PICU at a single center in the United Kingdom. The PICU clinical information system database (Intellivue Clinical Information Portfolio, Philips, UK) was interrogated to collect physiological and laboratory data. The model was trained using a variant of AdaBoost, which learned a set of low-dimensional classifiers, each of which was age adjusted.ResultsA total of 7052 patients admitted to the US PICU was used for training the model, and a total of 970 patients admitted to the UK PICU was used for validation. On the training/validation datasets, the model showed better prediction of hemodynamic intervention (area under the receiver operating characteristic (AUROC) = 0.81/0.81) than systolic blood pressure-based (AUCROC = 0.58/0.67) or shock index-based (AUCROC = 0.63/0.65) models. Both of these models were age adjusted using the same classifier.ConclusionsThe proposed model reliably predicted the need for hemodynamic intervention in PICU patients and provides better classification performance when compared to systolic blood pressure-based or shock index-based models alone. This model could readily be built into a clinical information system to identify patients at risk of hemodynamic instability.

Estimation of the patient monitor alarm rate for a quantitative analysis of new alarm settings.

In many critical care units, default patient monitor alarm settings are not fine-tuned to the vital signs of the patient population. As a consequence there are many alarms. A large fraction of the alarms are not clinically actionable, thus contributing to alarm fatigue. Recent attention to this phenomenon has resulted in attempts in many institutions to decrease the overall alarm load of clinicians by altering the trigger thresholds for monitored parameters. Typically, new alarm settings are defined based on clinical knowledge and patient population norms and tried empirically on new patients without quantitative knowledge about the potential impact of these new settings. We introduce alarm regeneration as a method to estimate the alarm rate of new alarm settings using recorded patient monitor data. This method enables evaluation of several alarm setting scenarios prior to using these settings in the clinical setting. An expression for the alarm rate variance is derived for the calculation of statistical confidence intervals on the results.

A Methodology for Evaluating the Performance of Alerting and Detection Algorithms Running on Continuous Patient Data

Objectives Clinicians in the intensive care unit (ICU) are presented with a large number of physiological data consisting of periodic and frequently sampled measurements, such as heart rate and blood pressure, as well as aperiodic measurements, such as noninvasive blood pressure and laboratory studies. Because this data can be overwhelming, there is considerable interest in designing algorithms that help integrate and interpret this data and assist ICU clinicians in detecting or predicting in advance patients who may be deteriorating. In order to decide whether to deploy such algorithms in a clinical trial, it is important to evaluate these algorithms using retrospective data. However, the fact that these algorithms will be running continuously, i.e., repeatedly sampling incoming patient data, presents some novel challenges for algorithm evaluation. Commonly used measures of performance such as sensitivity and positive predictive value (PPV) are easily applied to static “snapshots” of patient data, but can be very misleading when applied to indicators or alerting algorithms that are running on continuous data. Our objective is to create a method for evaluating algorithm performance on retrospective data with the algorithm running continuously throughout the patient’s stay as it would in a real ICU. Methods We introduce our evaluation methodology in the context of evaluating an algorithm, a Hemodynamic Instability Indicator (HII), for assisting bedside ICU clinicians with the early detection of hemodynamic instability before the onset of acute hypotension. Each patient’s ICU stay is divided into segments that are labelled as hemodynamically stable or unstable based on clinician interventions typically aimed at treating hemodynamic instability. These segments can be of varying length with varying degrees of exposure to potential alerts, whether true positive or false positive. Furthermore, to simulate how clinicians might interact with the alerting algorithm, we use a dynamic alert supervision mechanism which suppresses subsequent alerts unless the indicator has significantly deteriorated since the prior alert. Under these conditions determining what counts as a positive or negative instance, and calculations of sensitivity, specificity, and positive predictive value can be problematic. We introduce a methodology for consistently counting positive and negative instances. The methodology distinguishes between counts based on alerting events and counts based on sub-segments, and show how they can be applied in calculating measures of performance such as sensitivity, specificity, positive predictive value. Results The introduced methodology is applied to retrospective evaluation of two algorithms, HII and an alerting algorithm based on systolic blood pressure. We use a database, consisting of data from 41,707 patients from 25 US hospitals, to evaluate the algorithms. Both algorithms are evaluated running continuously throughout each patient’s stay as they would in a real ICU setting. We show how the introduced performance measures differ for different algorithms and for different assumptions. Discussion The standard measures of diagnostic tests in terms of true positives, false positives, etc. are based on certain assumptions which may not apply when used in the context of measuring the performance on an algorithm running continuously, and thus repeatedly sampling from the same patient. When such measures are being reported it is important that the underlying assumptions be made explicit; otherwise, the results can be very misleading. Conclusion We introduce a methodology for evaluating how an alerting algorithm or indicator will perform running continuously throughout every patient’s ICU stay, not just for a subset of patients for selected episodes.