Enhancing the fever workup utilizing a multi-technique modeling approach to diagnose infections more accurately.

BACKGROUND: Differentiation between infectious and non-infectious etiologies of the systemic inflammatory response syndrome (SIRS) in trauma patients remains elusive. We hypothesized that mathematical modeling in combination with computerized clinical decision support would assist with this differentiation. The purpose of this study was to determine the capability of various mathematical modeling techniques to predict infectious complications in critically ill trauma patients and compare the performance of these models with a standard fever workup practice (identifying infections on the basis of fever or leukocytosis). METHODS: An 18-mo retrospective database was created using information collected daily from critically ill trauma patients admitted to an academic surgical and trauma intensive care unit. Two hundred forty-three non-infected patient-days were chosen randomly to combine with the 243 infected-days, which created a modeling sample of 486 patient-days. Utilizing ten variables known to be associated with infectious complications, decision trees, neural networks, and logistic regression analysis models were created to predict the presence of urinary tract infections (UTIs), bacteremia, and respiratory tract infections (RTIs). The data sample was split into a 70% training set and a 30% testing set. Models were compared by calculating sensitivity, specificity, positive predictive value, negative predictive value, overall accuracy, and discrimination. RESULTS: Decision trees had the best modeling performance, with a sensitivity of 83%, an accuracy of 82%, and a discrimination of 0.91 for identifying infections. Both neural networks and decision trees outperformed logistic regression analysis. A second analysis was performed utilizing the same 243 infected days and only those non-infected patient-days associated with negative microbiologic cultures (n = 236). Decision trees again had the best modeling performance for infection identification, with a sensitivity of 79%, an accuracy of 83%, and a discrimination of 0.87. CONCLUSION: The use of mathematical modeling techniques beyond logistic regression can improve the robustness and accuracy of predicting infections in critically ill trauma patients. Decision tree analysis appears to have the best potential to use in assisting physicians in differentiating infectious from non-infectious SIRS.

Follow-up disparities after trauma: a real problem for outcomes research.

INTRODUCTION: The objectives of this study were to (1) determine risk factors associated with failure to follow-up (FTF) after traumatic injury and (2) in those patients who do follow up, to determine if information within the electronic medical record (EMR) is an adequate data-collection tool for outcomes research. METHODS: A 6-year retrospective analysis was conducted on all admitted trauma patients using data from the trauma registry, National Death Index, 2000 Census Data, and the EMR. Bivariate and logistic regression analyses identified risk factors for FTF. A subgroup analysis evaluated the utility of using the EMR to determine basic functional outcomes (Glasgow outcome scale, diet, ambulation, and employment status). RESULTS: A total of 14,784 patients were discharged, and 61% had follow-up appointments. Lower income, higher poverty rates, and lower education were significantly (P<.05) associated with FTF. Logistic regression analysis (excluding census data) identified that older age, lower Injury Severity Score, less severe head injury, nonwhite race, blunt injury, death after discharge, zip code within 25 miles, and patients discharged to home independently predicted FTF after traumatic injury. A subgroup analysis of the EMR showed the inability to reliably determine functional outcomes. CONCLUSIONS: There are several disparities related to follow-up after trauma. Furthermore, charting deficiencies, even with an EMR, highlight the weaknesses of data available for trauma outcomes research. Trauma process improvement programs could target patients at risk for not following up and use a structured electronic outpatient note.

Critical analysis of empiric antibiotic utilization: establishing benchmarks.

AIM: We critically evaluated empiric antibiotic practice in the surgical and trauma intensive care unit (STICU) with three specific objectives: (1) To characterize empiric antibiotics practice prospectively; (2) to determine how frequently STICU patients started on empiric antibiotics subsequently have a confirmed infection; and (3) to elucidate the complications associated with unnecessary empiric antibiotic therapy. METHODS: We collected data prospectively using the Surgical Intensive Care-Infection Registry (SIC-IR) including all 1,185 patients admitted to the STICU for >2 days from March 2007 through May 2008. Empiric antibiotics were defined as those initiated because of suspected infections. RESULTS: The mean patient age was 56 years and 62% were male. The mean STICU length of stay was eight days, and the mortality rate was 4.6%. Empiric antibiotics were started for 26.3% of the patients. The average length of antibiotic use was three days. Of the 312 patients started on empiric antibiotics, only 25.6% were found to have an infection. Factors associated with correctly starting empiric antibiotics were a longer STICU stay (5 vs. 3 days), prior antibiotics (29% vs. 17%), and mechanical ventilation (93% vs. 79%). Patients who were started on antibiotics without a subsequent confirmed infection were compared with patients not given empiric antibiotics. Incorrect use of empiric antibiotics was associated with younger age (p < 0.001), more STICU days (10.6 vs. 5.9 days; p < 0.001), more ventilator days (p < 0.001), more development of acute renal failure (24.1% vs. 12.1%; p < 0.001), and a significant difference in mortality rate (8.6% vs. 3.2%; p < 0.001). CONCLUSIONS: After admission to the STICU, 26% of patients received at least one course of empiric antibiotics. Only 25.6% of these patients were confirmed to have an infection. These results provide key benchmark data for the critical care community to improve antibiotic stewardship.

The "fever workup" and respiratory culture practice in critically ill trauma patients.

PURPOSE: Fever and leukocytosis (FAL) in critically ill patients often triggers a "workup" that includes a respiratory secretion culture (RCx). We evaluated our respiratory culture practice associated with FAL. We hypothesized that FAL would be associated with a RCx, but would not be associated with a positive culture or treating a respiratory infection in critically injured patients during their first 14 intensive care unit (ICU) days. MATERIALS AND METHODS: An 18-month retrospective analysis was performed on consecutive ICU trauma patients admitted for 2 days or more to a level I trauma center. Data collected included demographics, injuries, RCxs (bronchoalveolar lavage or tracheal aspirate), maximum daily temperature, and a daily leukocyte count during the first 14 ICU days. RESULTS: A total of 510 patients with a mean age of 49 and injury severity score of 19 were evaluated for a total of 3839 patient-days. Two hundred eleven patients had 489 RCxs obtained (2.4 RCxs/patient); 94 (19%) were obtained on consecutive days. Obtaining a RCx was associated with fever (relative risk, 4.8; 95% confidence interval, 4.1-5.8) and the combination of FAL (relative risk, 2.6; 95% confidence interval, 2.2-3.1), but not leukocytosis alone. Fever, leukocytosis, or FAL did not predict a positive RCx. One hundred twenty-eight patients were treated for a respiratory infection. Treatment of respiratory infections was contrary to the RCx results 24% of the time. The sensitivity and specificity of a positive RCx being associated with respiratory infection were 97% and 46%, respectively. CONCLUSIONS: Fever and leukocytosis were associated with the decision to obtain RCxs but were not associated with positive RCxs in our ICU practice. Respiratory secretion culture results had a low specificity and did not consistently impact treatment decisions. Factors other than fever and leukocytosis alone should influence the decision to obtain RCxs during the first 14 days in the ICU after trauma.

Fever and leukocytosis in critically ill trauma patients: it is not the blood.

The diagnosis of bacteremia in critically ill patients is classically based on fever and/or leukocytosis. The objectives of this study were to determine 1) if our intensive care unit obtains blood cultures based on fever and/or leukocytosis over the initial 14 days of hospitalization after trauma; and 2) the efficacy of this diagnostic workup. An 18-month retrospective cohort analysis was performed on consecutively admitted trauma patients. Data collected included demographics, injuries, and the first 14 days maximal daily temperature, leukocyte count, and results of blood and catheter tip cultures. Fever was defined as a maximum daily temperature of 38.5 degrees C or greater and leukocytosis as a leukocyte count 12,000/mm3 or greater of blood. Five hundred ten patients were evaluated for a total of 3,839 patient-days. The mean age and injury severity score were 49 +/- 1 years and 19 +/- 1, respectively. Four hundred twenty-five blood culture episodes were obtained and 25 (6%) bacteremias were identified in 23 patients (5%). A significant association was found between obtaining blood cultures in patients with fever (relative risk [RR], 7.7), leukocytosis (RR, 1.3), and fever + leukocytosis (RR, 3.2). However, no significant association was found between these clinical signs and the diagnosis of bacteremia. In fact, fever alone was inversely associated with bacteremia. Our intensive care unit follows the common "fever workup" practice and obtains blood cultures based on the presence of fever and leukocytosis. However, fever and leukocytosis were not associated with bacteremia, suggesting inefficiency and that other factors are more important after trauma.

Who is monitoring your infections: shouldn't you be?

BACKGROUND: In the era of pay for performance and outcome comparisons among institutions, it is imperative to have reliable and accurate surveillance methodology for monitoring infectious complications. The current monitoring standard often involves a combination of prospective and retrospective analysis by trained infection control (IC) teams. We have developed a medical informatics application, the Surgical Intensive Care-Infection Registry (SIC-IR), to assist with infection surveillance. The objectives of this study were to: (1) Evaluate for differences in data gathered between the current IC practices and SIC-IR; and (2) determine which method has the best sensitivity and specificity for identifying ventilator-associated pneumonia (VAP). METHODS: A prospective analysis was conducted in two surgical and trauma intensive care units (STICU) at a level I trauma center (Unit 1: 8 months, Unit 2: 4 months). Data were collected simultaneously by the SIC-IR system at the point of patient care and by IC utilizing multiple administrative and clinical modalities. Data collected by both systems included patient days, ventilator days, central line days, number of VAPs, and number of catheter-related blood steam infections (CR-BSIs). Both VAPs and CR-BSIs were classified using the definitions of the U.S. Centers for Disease Control and Prevention. The VAPs were analyzed individually, and true infections were defined by a physician panel blinded to methodology of surveillance. Using these true infections as a reference standard, sensitivity and specificity for both SIC-IR and IC were determined. RESULTS: A total of 769 patients were evaluated by both surveillance systems. There were statistical differences between the median number of patient days/month and ventilator-days/month when IC was compared with SIC-IR. There was no difference in the rates of CR-BSI/1,000 central line days per month. However, VAP rates were significantly different for the two surveillance methodologies (SIC-IR: 14.8/1,000 ventilator days, IC: 8.4/1,000 ventilator days; p = 0.008). The physician panel identified 40 patients (5%) who had 43 VAPs. The SIC-IR identified 39 and IC documented 22 of the 40 patients with VAP. The SIC-IR had a sensitivity and specificity of 97% and 100%, respectively, for identifying VAP and for IC, a sensitivity of 56% and a specificity of 99%. CONCLUSIONS: Utilizing SIC-IR at the point of patient care by a multidisciplinary STICU team offers more accurate infection surveillance with high sensitivity and specificity. This monitoring can be accomplished without additional resources and engages the physicians treating the patient.

The Surgical Intensive Care-infection Registry: a research registry with daily clinical support capabilities.

Infections in the surgical and trauma intensive care unit (STICU) are responsible for significant patient morbidity and mortality. Research into these infectious complications often uses administrative databases or clinical information systems designed for documenting and billing daily patient care. Neither of these sources is intended for research, and many investigators have questioned their accuracy. The Surgical Intensive Care-Infection Registry (SIC-IR) was developed as a research data repository to use to monitor STICU infections. SIC-IR is a relational database application designed to collect quality data and to integrate with daily patient care. SIC-IR prospectively collects and archives more than 100 clinical variables daily on each STICU patient to ensure completeness and correctness of the registry. Furthermore, SIC-IR aids in clinical activities by providing patient summaries and medical record documentation. SIC-IR provides accurate data for STICU infection research and enables the users to easily undertake quality-of-care improvement initiatives.

Validation of Surgical Intensive Care-Infection Registry: a medical informatics system for intensive care unit research, quality of care improvement, and daily patient care.

BACKGROUND: We developed a prototype electronic clinical information system called the Surgical Intensive Care-Infection Registry (SIC-IR) to prospectively study infectious complications and monitor quality of care improvement programs in the surgical and trauma intensive care unit. The objective of this study was to validate SIC-IR as a successful health information technology with an accurate clinical data repository. STUDY DESIGN: Using the DeLone and McLean Model of Information Systems Success as a framework, we evaluated SIC-IR in a 3-month prospective crossover study of physician use in one of our two surgical and trauma intensive care units (SIC-IR unit versus non SIC-IR unit). Three simultaneous research methodologies were used: a user survey study, a pair of time-motion studies, and an accuracy study of SIC-IR's clinical data repository. RESULTS: The SIC-IR user survey results were positive for system reliability, graphic user interface, efficiency, and overall benefit to patient care. There was a significant decrease in prerounding time of nearly 4 minutes per patient on the SIC-IR unit compared with the non SIC-IR unit. The SIC-IR documentation and data archiving was accurate 74% to 100% of the time depending on the data entry method used. This accuracy was significantly improved compared with normal hand-written documentation on the non SIC-IR unit. CONCLUSIONS: SIC-IR proved to be a useful application both at individual user and organizational levels and will serve as an accurate tool to conduct prospective research and monitor quality of care improvement programs.

Fever and leukocytosis in critically ill trauma patients: it's not the urine.

BACKGROUND: Infectious complications are a major cause of morbidity and mortality in critically ill trauma patients. Therefore, fever and leukocytosis often trigger an extensive laboratory workup that includes a urine culture (UCx). The purposes of this study were to: 1) Define the current practice for obtaining UCxs in trauma patients admitted to the surgical and trauma intensive care unit (STICU); and 2) determine if there is an association between fever or leukocytosis and urinary tract infections (UTIs) during the initial 14 hospital days. METHODS: An 18-month retrospective cohort analysis was performed on consecutive trauma patients admitted for at least two days to the STICU at a level I trauma center. Data collected included demographics, injuries, and daily maximal temperature (T(max)), leukocyte count, and UCx results for the first 14 days. Fever and leukocytosis were defined as T(max) > or =38.5 degrees C and leukocyte count > or =12,000/mm(3), respectively. Urinary tract infections were diagnosed with a positive UCx (> or =10(5) organisms/mL of urine). RESULTS: Five hundred ten patients were evaluated for a total of 3,839 patient-days. Their mean age and Injury Severity Score were 49 +/- 1 years and 19 +/- 1 points, respectively. Seventy-two percent were men, and 91% had sustained blunt injuries. Four hundred seven UCxs were obtained; 42 patients (8%) had 60 UTIs. The cohort had an indwelling urinary catheter for 97% of the patient-days, yielding an infection density of 16 UTIs/1,000 urinary catheter-days. There was a significant association between obtaining a UCx and fever and between fever and leukocytosis (both, p < 0.001), but no association of UTI with fever, leukocytosis, or the combination of fever and leukocytosis. Analysis using temperature and leukocyte count as continuous variables identified no temperature or leukocyte range associated with UTIs. Independent risk factors for UTI calculated by logistic regression were female sex, older age, low Injury Severity Score, and no antibiotics within 24 h before the UCx was obtained. CONCLUSIONS: The practice of obtaining a UCx from the STICU trauma patient was related to fever and fever with leukocytosis. However, neither fever nor leukocytosis nor both were associated with UTIs. These data suggest that there is an unnecessary emphasis on UTI as a source of fever and leukocytosis in injured patients during their first 14 STICU days. Our results suggest that the paradigm for evaluating UTI as a cause of fever needs to be reevaluated in critically ill trauma patients.

A framework for assessing e-health preparedness.

Whilst healthcare is the biggest service industry on the globe, it has yet to realise the full potential of the e-business revolution in the form of e-health. This is due to many reasons including the fact that the healthcare industry is faced with many complex challenges in trying to deliver cost-effective, high-value, accessible healthcare and has traditionally been slow to embrace new business techniques and technologies. Given that e-health, to a great extent, is a macro level concern that has far reaching micro level implications, this paper firstly develops a framework to assess a country's preparedness with respect to embracing e-health (the application of e-commerce to healthcare) and from this an e-health preparedness grid to facilitate the assessment of any e-health initiative. Taken together, the integrative framework and preparedness grid provide useful and necessary tools to enable successful e-health initiatives to ensue by helping country and/or an organisation within a country to identify and thus address areas that require further attention in order for it to undertake a successful e-health initiative.