Development of a standard sign-out procedure for clinicians in a pediatric intensive care unit

Background: Poor communication among health care providers is cited as the most common cause of sentinel events involving patients. Sign-out of patient data at the change of clinician shifts is a component of communication that is especially vulnerable to errors. Sign-outs are particularly extensive and complex in intensive care units (ICUs). There is a paucity of validated tools to assess ICU sign-outs. ^ Objective: To design a valid and reliable survey tool to assess the perceptions of Pediatric ICU (PICU) clinicians about sign-out. ^ Design: Cross-sectional, web-based survey ^ Setting: Academic hospital, 31-bed PICU ^ Subjects: Attending faculty, fellows, nurse practitioners and physician assistants. ^ Interventions: A survey was designed with input from a focus group and administered to PICU clinicians. Test-retest reliability, internal consistency and validity of the survey tool were assessed. ^ Measurements and Main Results: Forty-eight PICU clinicians agreed to participate. We had 42(88%) and 40(83%) responses in the test and retest phases. The mean scores for the ten survey items ranged from 2.79 to 3.67 on a five point Likert scale with no significant test-retest difference and a Pearson correlation between pre and post answers of 0.65. The survey item scores showed internal consistency with a Cronbach's Alpha of 0.85. Exploratory factor analysis revealed three constructs: efficacy of sign-out process, recipient satisfaction and content applicability. Seventy eight % clinicians affirmed the need for improvement of the sign-out process and 83% confirmed the need for face- to-face verbal sign-out. A system-based sign-out format was favored by fellows and advanced level practitioners while attendings preferred a problem-based format (p=0.003). ^ Conclusions: We developed a valid and reliable survey to assess clinician perceptions about the ICU sign-out process. These results can be used to design a verbal template to improve and standardize the sign-out process.^

The cost effective management of Central Line-Associated Bloodstream Infections (CLABSIs) comparing the central line bundle to antimicrobialcoated central venous catheters: A systematic review

Central Line-Associated Bloodstream Infections (CLABSIs) are one of the most costly and preventable cases of morbidity and mortality among intensive care units (ICUs) in health care today. In 2008, the Centers for Medicare and Medicaid Services Medicare Program, under the Deficit Reduction Act, announced it will no longer reimburse hospitals for such adverse events among those related to CLABSIs. This reveals the financial burden shift onto the hospital rather than the health care payer who can now withhold reimbursements. With this weighing more heavily on hospital management, decision makers will need to find a way to completely prevent cases of CLABSI or simply pay for the financial consequences. ^ To reduce the risk of CLABSIs, several clinical, preventive interventions have been studied and even instituted including the Central Line (CL) Bundle and Antimicrobial Coated Central Venous Catheters (AM-CVCs). I carried out a formal systematic review on the topic to compare the cost-effectiveness of the Central Line (CL) Bundle to the commercially available antimicrobial coated central venous catheters (AM-CVCs) in preventing CLABSIs among critically and chronically ill patients in the U.S. Evidence was assessed for inclusion against predefined criteria. I, myself, conducted the data extraction. Ten studies were included in the review. Efficacy in reducing the mean incidence rate of CLABSI by the CL Bundle and AM-CVC interventions were compared with one another including costs. ^ The AM-CVC impregnated with antibiotics, rifampin-minocycline (AI-RM) is more clinically effective than the CL Bundle in reducing the mean rate of CLABSI per 1,000 catheter days. The lowest mean incidence rate of CLABSI per 1,000 catheter days among the AM-CVC studies was as low as zero in favor of the AI-RM. Moreover, the review revealed that the AI-RM appears to be more cost-effective than the CL Bundle. Results showed the adjusted incremental cost of the CL Bundle per ICU patient requiring a CVC to be approximately $196 while the AI-RM at only an additional cost of $48 per ICU patient requiring a CVC. ^ Limited data regarding the cost of the CL Bundle made it difficult to make a true comparison to the direct cost of the AM-CVCs. However, using the result I did have from this review, I concluded that the AM-CVCs do appear to be more cost-effective in decreasing the mean rate of CLABSI while also minimizing incremental costs per CVC than the CL Bundle. This review calls for further research addressing the cost of the CL Bundle and compliance and more effective study designs such as randomized control trials comparing the efficacy and cost of the CL Bundle to the AM-CVCs. Barriers that may face health care managers when implementing the CL Bundle or AM-CVCs include additional costs associated with the intervention, educational training and ongoing reinforcement as well as creating a new culture of understanding.^

Clostridium difficile infection (CDI): Clinical outcomes associated with concomitant non-CDI antibiotic use

This research examined the relationship between concomitant non-CDI antibiotic use and complications arising due to Clostridium difficile infection. To observe the hypothesized association, 160 total CDI patients between the ages of 50-90 were selected, 80 exposed to concomitant antibiotics and 80 unexposed. Samples were matched based upon their age and Horn's index, a severity score for underlying illness. Patients were de-identified by a third party, and analyzed retrospectively for differences between the two groups. In addition, patients exposed to broad spectrum antibiotics at the time of CDI treatment were further studied to demonstrate whether antibiotics had any effect on CDI complications. Between the two groups, the outcomes of interest (recurrent CDI, refractory CDI, mortality, ICU stay, and length of hospitalization) were not associated with concomitant antibiotic use at the time of CDI therapy. However, within the exposed population, certain classes of antibiotics such as cephalosporin, antifungals, and tetracyclines were more common in patients compared to other types of therapy. In addition, days of therapy provided evidence that sustained use of antibiotics affected CDI (p = 0.08), although a more robust sample size and additional study would be needed. Finally, refractory CDI was found to be potentially overestimated within the exposed population due to the possibility of antibiotic-associated diarrhea.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Intensive care unit utilization among patients with cancer at the end of life

Over the last 2 decades, survival rates in critically ill cancer patients have improved. Despite the increase in survival, the intensive care unit (ICU) continues to be a location where end-of-life care takes place. More than 20% of deaths in the United States occur after admission to an ICU, and as baby boomers reach the seventh and eighth decades of their lives, the volume of patients in the ICU is predicted to rise. The aim of this study was to evaluate intensive care unit utilization among patients with cancer who were at the end of life. End of life was defined using decedent and high-risk cohort study designs. The decedent study evaluated characteristics and ICU utilization during the terminal hospital stay among patients who died at The University of Texas MD Anderson Cancer Center during 2003-2007. The high-risk cohort study evaluated characteristics and ICU utilization during the index hospital stay among patients admitted to MD Anderson during 2003-2007 with a high risk of in-hospital mortality. Factors associated with higher ICU utilization in the decedent study included non-local residence, hematologic and non-metastatic solid tumor malignancies, malignancy diagnosed within 2 months, and elective admission to surgical or pediatric services. Having a palliative care consultation on admission was associated with dying in the hospital without ICU services. In the cohort of patients with high risk of in-hospital mortality, patients who went to the ICU were more likely to be younger, male, with newly diagnosed non-metastatic solid tumor or hematologic malignancy, and admitted from the emergency center to one of the surgical services. A palliative care consultation on admission was associated with a decreased likelihood of having an ICU stay. There were no differences in ethnicity, marital status, comorbidities, or insurance status between patients who did and did not utilize ICU services. Inpatient mortality probability models developed for the general population are inadequate in predicting in-hospital mortality for patients with cancer. The following characteristics that differed between the decedent study and high-risk cohort study can be considered in future research to predict risk of in-hospital mortality for patients with cancer: ethnicity, type and stage of malignancy, time since diagnosis, and having advance directives. Identifying those at risk can precipitate discussions in advance to ensure care remains appropriate and in accordance with the wishes of the patient and family.^