Nosocomial infections and their relationship to non-traditional risk factors in a medical intensive care unit

Background. Nosocomial infections are a source of concern for many hospitals in the United States and worldwide. These infections are associated with increased morbidity, mortality and hospital costs. Nosocomial infections occur in ICUs at a rate which is five times greater than those in general wards. Understanding the reasons for the higher rates can ultimately help reduce these infections. The literature has been weak in documenting a direct relationship between nosocomial infections and non-traditional risk factors, such as unit staffing or patient acuity.^ Objective. To examine the relationship, if any, between nosocomial infections and non-traditional risk factors. The potential non-traditional risk factors we studied were the patient acuity (which comprised of the mortality and illness rating of the patient), patient days for patients hospitalized in the ICU, and the patient to nurse ratio.^ Method. We conducted a secondary data analysis on patients hospitalized in the Medical Intensive Care Unit (MICU) of the Memorial Hermann- Texas Medical Center in Houston during the months of March 2008- May 2009. The average monthly values for the patient acuity (mortality and illness Diagnostic Related Group (DRG) scores), patient days for patients hospitalized in the ICU and average patient to nurse ratio were calculated during this time period. Active surveillance of Bloodstream Infections (BSIs), Urinary Tract Infections (UTIs) and Ventilator Associated Pneumonias (VAPs) was performed by Infection Control practitioners, who visited the MICU and performed a personal infection record for each patient. Spearman's rank correlation was performed to determine the relationship between these nosocomial infections and the non-traditional risk factors.^ Results. We found weak negative correlations between BSIs and two measures (illness and mortality DRG). We also found a weak negative correlation between UTI and unit staffing (patient to nurse ratio). The strongest positive correlation was found between illness DRG and mortality DRG, validating our methodology.^ Conclusion. From this analysis, we were able to infer that non-traditional risk factors do not appear to play a significant role in transmission of infection in the units we evaluated.^

Hemodynamic changes associated with manual and automated lateral rotation in mechanically ventilated intensive care patients

Objective: To investigate hemodynamic responses to lateral rotation. ^ Design: Time-series within a randomized controlled trial pilot study. ^ Setting: A medical intensive care unit (ICU) and a medical-surgical ICU in two tertiary care hospitals. ^ Patients: Adult patients receiving mechanical ventilation. ^ Interventions: Two-hourly manual or continuous automated lateral rotation. ^ Measurements and Main Results: Heart rate (HR) and arterial pressure were sampled every 6 seconds for > 24 hours, and pulse pressure (PP) was computed. Turn data were obtained from a turning flow sheet (manual turn) or with an angle sensor (automated turn). Within-subject ensemble averages were computed for HR, mean arterial pressure (MAP), and PP across turns. Sixteen patients were randomized to either the manual (n = 8) or automated (n = 8) turn. Three patients did not complete the study due to hemodynamic instability, bed malfunction or extubation, leaving 13 patients (n = 6 manual turn and n = 7 automated turn) for analysis. Seven patients (54%) had an arterial line. Changes in hemodynamic variables were statistically significant increases ( p < .05), but few changes were clinically important, defined as ≥ 10 bpm (HR) or ≥ 10 mmHg (MAP and PP), and were observed only in the manual-turn group. All manual-turn patients had prolonged recovery to baseline in HR, MAP and PP of up to 45 minutes (p ≤ .05). No significant turning-related periodicities were found for HR, MAP, or PP. Cross-correlations between variables showed variable lead-lag relations in both groups. A statistically, but not clinically, significant increase in HR of 3 bpm was found for the manual-turn group in the back compared with the right lateral position ( F = 14.37, df = 1, 11, p = .003). ^ Conclusions: Mechanically ventilated critically ill patients experience modest hemodynamic changes with manual lateral rotation. A clinically inconsequential increase in HR, MAP, and PP may persist for up to 45 minutes. Automated lateral rotation has negligible hemodynamic effects. ^

A COMPARISON OF NEONATAL MORTALITY BETWEEN HOSPITALS WITH LEVEL III NEWBORN INTENSIVE CARE UNITS AND ALL OTHER HOUSTON HOSPITALS

A number of medical and social developments have had an impact on the neonatal mortality over the past ten to 15 years in the United States. The purpose of this study was to examine one of these developments, Newborn Intensive Care Units (NICUs), and evaluate their impact on neonatal mortality in Houston, Texas.^ This study was unique in that it used as its data base matched birth and infant death records from two periods of time: 1958-1960 (before NICUs) and 1974-1976 (after NICUs). The neonatal mortality of single, live infants born to Houston resident mothers was compared for two groups: infants born in hospitals which developed NICUs and infants born in all other Houston hospitals. Neonatal mortality comparisons were made using the following birth-characteristic variables: birthweight, gestation, race, sex, maternal age, legitimacy, birth order and prenatal care.^ The results of the study showed that hospitals which developed NICUs had a higher percentage of their population with high risk characteristics. In spite of this, they had lower neonatal mortality rates in two categories: (1) white 3.5-5.5 pounds birthweight infants, (2) low birthweight infants whose mothers received no prenatal care. Black 3.5-5.5 pounds birthweight infants did equally well in either hospital group. While the differences between the two hospital groups for these categories were not statistically significant at the p < 0.05 level, data from the 1958-1960 period substantiate that a marked change occurred in the 3.5-5.5 pounds birthweight category for those infants born in hospitals which developed NICUs. Early data were not available for prenatal care. These findings support the conclusion that, in Houston, NICUs had some impact on neonatal mortality among moderately underweight infants. ^

An outbreak of Multi-Drug Resistant Acinetobacter in the burn intensive care unit of a large tertiary care center

Risk factors for Multi-Drug Resistant Acinetobacter (MDRA) acquisition were studied in patients in a burn intensive care unit (ICU) where there was an outbreak of MDRA. Forty cases were matched with eighty controls based on length of stay in the Burn ICU and statistical analysis was performed on data for several different variables. Matched analysis showed that mechanical ventilation, transport ventilation, number of intubations, number of bronchoscopy procedures, total body surface area burn, and prior Methicillin Resistant Staphylococcus aureus colonization were all significant risk factors for MDRA acquisition. ^ MDRA remains a significant threat to the burn population. Treatment for burn patients with MDRA is challenging as resistance to antibiotics continues to increase. This study underlined the need to closely monitor the most critically ill ventilated patients during an outbreak of MDRA as they are the most at risk for MDRA acquisition.^

Prediction of sepsis in the burn intensive care unit patient

Sepsis is a significant cause for multiple organ failure and death in the burn patient, yet identification in this population is confounded by chronic hypermetabolism and impaired immune function. The purpose of this study was twofold: 1) determine the ability of the systemic inflammatory response syndrome (SIRS) and American Burn Association (ABA) criteria to predict sepsis in the burn patient; and 2) develop a model representing the best combination of clinical predictors associated with sepsis in the same population. A retrospective, case-controlled, within-patient comparison of burn patients admitted to a single intensive care unit (ICU) was conducted for the period January 2005 to September 2010. Blood culture results were paired with clinical condition: "positive-sick"; "negative-sick", and "screening-not sick". Data were collected for the 72 hours prior to each blood culture. The most significant predictors were evaluated using logistic regression, Generalized Estimating Equations (GEE) and ROC area under the curve (AUC) analyses to assess model predictive ability. Bootstrapping methods were employed to evaluate potential model over-fitting. Fifty-nine subjects were included, representing 177 culture periods. SIRS criteria were not found to be associated with culture type, with an average of 98% of subjects meeting criteria in the 3 days prior. ABA sepsis criteria were significantly different among culture type only on the day prior (p = 0.004). The variables identified for the model included: heart rate>130 beats/min, mean blood pressure<60 mmHg, base deficit<-6 mEq/L, temperature>36°C, use of vasoactive medications, and glucose>150 mg/d1. The model was significant in predicting "positive culture-sick" and sepsis state, with AUC of 0.775 (p < 0.001) and 0.714 (p < .001), respectively; comparatively, the ABA criteria AUC was 0.619 (p = 0.028) and 0.597 (p = .035), respectively. SIRS criteria are not appropriate for identifying sepsis in the burn population. The ABA criteria perform better, but only for the day prior to positive blood culture results. The time period useful to diagnose sepsis using clinical criteria may be limited to 24 hours. A combination of predictors is superior to individual variable trends, yet algorithms or computer support will be necessary for the clinician to find such models useful. ^

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.^

Are Intensive Family Preservation Services Useful?: A Study in the United Kingdom

This evaluation of the first year of an Intensive Family Preservation Service in England is based on the analysis of eighty-six families: fifty-seven families who received the service and a comparison group of twenty-nine families who did not. The study considered whether the program was fulfilling its objectives of reducing the number of children and young people in the public care system; offering a safe, supportive service for children who need protection; integrating the program into family support services as a whole, and improving family functioning. The findings were complex to interpret. Child protection was improved but there was not a reduction in the number of children needing out of home care (indeed there was an increase) meaning that short term savings in costs could not be made. Nor were there lasting improvements in the children’s behavior. There were instead a number of more subtle, arguably more sensitive outcomes: parents’ capacity to tolerate their child’s behavior was greater and overall family functioning was better for most families who received the service. Also families were, on the whole, able to make better use of follow up services.

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.

Implementing Intensive Family Preservation Services: A Case of Infidelity

The importance of treatment fidelity in evaluations of all human service programs, including intensive family preservation services (IFPS), is examined in this article. Special attention is focused on the issue of treatment fidelity in IFPS programs attempting to adhere to a specific program model (Homebuilders©), and on the problems that lack of treatment fidelity has caused for research that has been conducted on this and other program models. Attempts to address the issue of treatment fidelity in other program areas offer models for constructing treatment fidelity assessment tools for IFPS. The authors suggest a schema for assessing treatment fidelity in evaluations of IFPS programs that should help to explore relationships among different approaches to IFPS, the consistency with which they are being implemented, and the outcomes that result.

Domestic Violence Resources for Women Receiving Intensive Family Preservation Services

Intimate partner violence is a common correlate of child abuse and neglect and often is not addressed in family preservation services. In many cases, the ideologies of family preservationists and advocates for women 's safety can be at odds. This article presents a study of a collaborative model of intervention, utilizing family preservation workers and community resource practitioners working with domestic violence as group facilitators. The study utilizes a pretest, post-test design to evaluate a domestic violence resource group for women who were concurrently receiving intensive family preservation services. The study examines the effect of the program on participants' self-perceptions regarding self-esteem, independence, goals, social isolation, and assertiveness. Caseworker perceptions of client characteristics also are evaluated, and qualitative responses of the effects of the program are included.

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.^