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Blood culture is a simple and basic diagnostic procedure routinely used in clinical practice that yields essential information for the evaluation of various infectious diseases. A positive blood culture can demonstrate not only an infectious cause of disease but also a microbiological response to antibiotic therapy. However, studies have reported that 35% to 50% of positive blood cultures are falsely positive owing to contamination. False-positive cultures often cause serious interpretation problems, leading to the use of inappropriate or unnecessary antibiotics, additional testing and consultation, and increased length of stay, all of which increase health care costs. In a closed culture system in which blood is drawn directly into vacuum culture bottles, blood culture contamination occurs mainly during specimen collection. Various methods have been widely studied to reduce contamination rates, including skin disinfectants, source of culture, specialized phlebotomists, and changing of needles before inoculating culture bottles. To our knowledge, no data are available on the influence of sterile gloving on blood culture contamination rates. Consequently, some controversy exists about whether sterile gloving should be routinely used during collection of blood for culture. The current guidelines do not recommend the routine use of sterile gloving, whereas some experts prefer sterile gloving for collection of blood for culture. We sought to evaluate whether the routine use of sterile gloving before venipuncture reduces blood culture contamination rates compared with the optional use of sterile gloving in actual clinical practice.

Study Design
We conducted a prospective, cluster randomized, assessor-blinded, crossover, controlled trial. Our study was conducted for 6 months in 2009 in 17 medical wards, including 14 general wards, 2 hematology wards, and 1 intensive care unit at Seoul National University Hospital, a 1600-bed, university-affiliated tertiary-care teaching hospital in Seoul, Republic of Korea. At this hospital, medical interns rather than dedicated phlebotomists are in charge of drawing blood for cultures. We did not include the emergency department because the emergency medical technicians, as well as interns, draw blood for culture in the emergency department. The interns in the hospital were rotated from one department to another each month. The interns in the medical wards consented to participate in the study and took part in the study for 1 month. In each month, 6 to 7 interns were in charge of the 14 general wards, 2 interns in the 2 hematology wards and 2 interns in the intensive care unit. We included all cultures using blood drawn from a peripheral vein in adult patients who needed 2 or more sets of blood cultures, and we excluded blood cultures from intravenous lines and similar access devices. Consent was obtained from all participating interns.

All buffers and reagents were prepared to minimize contamination with environmental lipopolysaccharide by using baked (180°C for 4 hours) and autoclaved glassware, disposable plasticware, and pyrogen-free H2O.
Cultivation of B. burgdorferi
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A low-passage, clonal isolate (AH130) of B. burgdorferi strain 29726 was maintained at 23°C in Barbour-Stoenner-Kelley medium containing 6% normal rabbit serum from Pel-Freez Biologicals (Rogers, AR) and then temperature shifted to 37°C. A temperature-dependent increase in OspC expression was confirmed by silver staining of whole borrelial lysates separated by SDS polyacrylamide gel electrophoresis.
Mice and Infection Protocol

All mice of both sexes used in experiments were 4 to 8 weeks old. C3H/HeN (CD14+/+) (Taconic, Germantown, NY), and FVB/N (MMP-9+/+) mice (National Cancer Institute, Bethesda, MD) were housed in the Animal Resources Facility at Albany Medical College. Food and water were provided ad libitum, and all animal procedures conformed to Institutional Animal Care and Use Committee guidelines. CD14−/− mice were generated as previously described27 and subsequently backcrossed 10 generations onto a C3H/HeN background. FVB/N mice deficient in MMP-9 (MMP-9−/−) were obtained from the Jackson Laboratory (Bar Harbor, ME).

Mice were infected via intradermal inoculation of 1 × 105 spirochetes over the sternum. At 1-week intervals, tibiotarsal joint thickness was measured using digital calipers and bacterial burden in infected tissues was determined using isolated genomic DNA and quantitative real-time PCR (qPCR) as previously described. Total RNA also was isolated from infected tissues for qPCR as described below and elsewhere.
Myeloperoxidase Staining

Joints were excised at 3 and 6 weeks post infection (p.i.) and fixed in 10% neutral-buffered formalin. Tissues were processed using standard histologic methods to obtain 5-μm-thick paraffin sections. Tissue sections were deparaffinized by incubation in 100% Xylene (3 minutes, 3×), 100% ethanol (3 minutes, 3×), 95% ethanol (3 minutes), 70% ethanol (3 minutes), 50% ethanol (3 minutes), and a final rinse in H2O. Deparaffinized tissue sections were boiled in sodium citrate buffer (10 mmol/L sodium citrate, 0.05% Tween 20, pH 6.0) for 10 minutes in a microwave oven for antigen retrieval. Tissue sections were incubated in 3% H2O2 for 10 minutes to inactivate endogenous peroxides. Tissue sections then were washed with 0.01% Triton X-100 and blocked with 2.5% normal horse serum for 20 minutes. Myeloperoxidase (MPO) was detected using a rabbit polyclonal antibody (catalog no. ab15484, Abcam Inc., Cambridge, MA) and the ImmPRESS anti-rabbit immunoglobulin detection kit coupled with the NovaRED substrate kit (Vector Laboratories, Burlingame, CA).

Bone marrow–derived macrophages were isolated and expanded in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 20% L929 cell–conditioned medium as previously described. Macrophages were seeded into 6-well tissue culture–treated plates at a concentration of 1 × 106 cells/2 ml per well and allowed to adhere overnight. The following day, B. burgdorferi were enumerated and resuspended as described above. Macrophages were washed twice with serum-free DMEM to remove any traces of fetal bovine serum, and spirochetes (resuspended in DMEM + 4% autologous serum) were added at a multiplicity of infection of 10 and co-incubated for different intervals at 37°C with 5% CO2. Cells incubated with DMEM + 4% autologous serum alone served as mock-infected controls.

Tissue homogenates were generated by suspending excised joints in 1 ml of PBS containing EDTA-free protease inhibitor (Roche Diagnostics GmbH, Mannheim, Germany) and 1 mmol/L Bestatin (Sigma-Aldrich Co., St. Louis, MO) and subjecting them to physical disruption as previously described. Supernatants were clarified by microcentrifugation at 2000 × g for 5 minutes. Cytokine levels were measured in joint tissue homogenates or culture supernatants using a Mouse Cytometric Bead Array Flex Set for the detection of KC. Analyses were performed on a FACSArray flow cytometer (BD Immunocytometry Systems, San Jose, CA), and data were acquired and analyzed using BD FACSArray software and FCAP Array software, version 1.0 (BD Immunocytometry Systems), respectively. Measurement of MIP-2 was accomplished using a commercial enzyme-linked immunosorbent assay (ELISA) kit (Biosource International Inc., Camarillo, CA) according to the manufacturer’s instructions. Cytokine levels shown represent values normalized on the basis of milligrams of protein from which they were isolated.

Total RNA was isolated from macrophages using the RNeasy Mini Kit (Qiagen GmbH, Hilden, Germany) as per the manufacturer’s instructions. RNA (0.5 μg) was used for reverse transcription of cDNA using Superscript II (Invitrogen Corporation, Carlsbad, CA). cDNA was used to amplify the target genes listed in Table 1, and qPCR was performed as described elsewhere.

MMPs belong to a multigenic family of proteinases that perform diverse functions, such as tissue remodeling during development, wound healing, establishment and maintenance of chemokine gradients, and cell migration. There are 26 known genes in humans encoding this class of metalloproteinases, which are grouped on the basis of substrate specificity (eg, gelatinases such as MMP-2 and MMP-9). Like many of the MMPs, MMP-9 is released from activated neutrophils and macrophages as a proenzyme whose N-terminus is cleaved by other proteinases (eg, plasmin) to generate an active gelatinase. The action of MMPs is regulated by a family of molecules known as tissue inhibitors of metalloproteinases (TIMPs), which act by binding either the latent or active form of the enzyme.18 Particularly relevant to the present study, elevated levels of MMP-9 have been observed in the erythema migrans lesions and cerebrospinal and synovial fluid of patients with Lyme disease. Once secreted, MMP-9 cleaves the ECM, thus facilitating cell migration through a normally dense fibrillar environment. In addition to fostering general leukocyte movement, degradation of collagen also liberates small tripeptide fragments such as proline-glycine-proline (PGP), which exhibit an affinity for the chemokine receptors CXCR1/2 as do the neutrophil chemoattractants CXCL8/KC and MIP-2. The PGP gradient thus formed serves to prolong recruitment of neutrophils even after the CXCL chemokine gradient wanes.

CD14 is a glycosylphosphatidylinositol-anchored protein expressed primarily on cells of myeloid origin, such as neutrophils, macrophages, and DCs. The current paradigm suggests CD14 acts as a coreceptor for Toll-like receptor 2 (TLR2) to facilitate elaboration of innate inflammatory responses to bacterial infection; however, infection of CD14−/− mice with B. burgdorferi, which display an abundance of TLR2 agonists (ie, lipoproteins), leads to greater production of proinflammatory cytokines, more severe disease, and increased bacterial burden in a variety of tissues. Recently, we defined changes in B. burgdorferi–induced macrophage activation that occur in the absence of CD14 and their impact on negative regulation of NF-κB signaling. Diminished negative regulation and thus an inability of macrophages to become tolerant to the stimulatory effects of B. burgdorferi underlie the “hyperactive” release of cytokines. However, one perplexing observation was impaired bacterial clearance in CD14−/− mice despite increased levels of cytokines, such as tumor necrosis factor-α and interferon-γ, known for their antimicrobial activities. Herein, we reveal a unique and unanticipated role for CD14 in the activation of MMP-9 in Borrelia-infected joints that regulates the recruitment of neutrophils, which are required for killing, and clearance of invading spirochetes.

CD14 is a glycosylphosphatidylinositol-anchored protein expressed primarily on myeloid cells (eg, neutrophils, macrophages, and dendritic cells). CD14−/− mice infected with Borrelia burgdorferi, the causative agent of Lyme disease, produce more proinflammatory cytokines and present with greater disease and bacterial burden in infected tissues. Recently, we uncovered a novel mechanism whereby CD14−/− macrophages mount a hyperinflammatory response, resulting from their inability to be tolerized by B. burgdorferi. Paradoxically, CD14 deficiency is associated with greater bacterial burden despite the presence of highly activated neutrophils and macrophages and elevated levels of cytokines with potent antimicrobial activities. Killing and clearance of Borrelia, especially in the joints, depend on the recruitment of neutrophils. Neutrophils can migrate in response to chemotactic gradients established through the action of gelatinases (eg, matrix metalloproteinase 9), which degrade collagen components of the extracellular matrix to generate tripeptide fragments of proline-glycine-proline. Using a mouse model of Lyme arthritis, we demonstrate that CD14 deficiency leads to decreased activation of matrix metalloproteinase 9, reduced degradation of collagen, and diminished recruitment of neutrophils. This reduction in neutrophil numbers is associated with greater numbers of Borrelia in infected tissues. Variation in the efficiency of neutrophil-mediated clearance of B. burgdorferi may underlie differences in the severity of Lyme arthritis observed in the patient population and suggests avenues for development of adjunctive therapy designed to augment host immunity.

Lyme disease is caused by three related borrelial species found in North America (ie, Borrelia burgdorferi), Europe, and Asia (Borrelia afzelii and Borrelia garinii). In the United States the etiological agent is exclusively B. burgdorferi, which is transmitted by the deer tick Ixodes scapularis. B. burgdorferi is the most arthritogenic of the three species, with approximately 60% of untreated patients developing Lyme arthritis in response to the bacterium.1 Recruitment of leukocytes [eg, neutrophils, macrophages, dendritic cells (DCs), and lymphocytes] to the site of infection is one of the more important mechanisms whereby spirochetes are cleared from tissues. Neutrophils are the principal early infiltrating cell type observed in the infected joints of humans, a finding mirrored in a mouse model of Lyme arthritis.Depletion of neutrophils results in early onset of arthritis with a higher bacterial burden in murine Lyme borreliosis. Conversely, B. burgdorferi genetically engineered to express KC (the murine equivalent of human CXCL8), a neutrophil-recruiting chemokine, is rapidly cleared from mouse tissues because of a faster and continuous influx of neutrophils to the site of infection.

The host response to invading pathogens often is typified by a local inflammatory reaction, which generates a gradient of CXCR1/2 ligands that promotes the transendothelial migration of neutrophils into sites of infection. Once there, neutrophils degranulate, thus releasing antimicrobial effectors, such as cathelicidin antimicrobial peptide,11 CXC chemokines [eg, CXCL8/KC and macrophage inflammatory protein 2 (MIP-2)], and extracellular matrix (ECM)–degrading enzymes [eg, elastase and matrix metalloproteinase 9 (MMP-9)]. Of these molecules, elastase activity has been implicated in host defense against gram-negative bacteria13; however, a similar role for MMP-9 has not been firmly established. In fact, we recently reported a positive correlation between MMP-9 activity and susceptibility to infection with the gram-negative bacterium Francisella tularensis. Regardless of whether its role is protective or destructive, the ability of MMP-9 to orchestrate the continuous recruitment of neutrophils has been demonstrated using several infectious and noninfectious disease models.

The disease-specific modeling approach differs from generic modeling approaches used by other researchers. These approaches include APACHE IV (Zimmerman et al. 2006) and the Kaiser Permanent risk adjustment systems (Escobar et al. 2008), for which a generic physiological score using numerical variables was devised first and then an aggregated physiology score was reentered into the multivariable model with other variables, including disease groups. The generic method has merits. It requires only a reasonably sized database for model development and validation and it might be easier to dissimilate and implement. In contrast, development and validation of a disease-specific risk adjustment system requires a very large database and the application of such a system may necessitate the incorporation of more complex electronic systems. Although a direct comparison of these two modeling approaches from statistical perspectives might be interesting, it is beyond the scope of the current study. Perhaps more pertinent to health services research is the fact that both modeling approaches yielded convergent results on the importance of numerical laboratory and vital sign data in risk adjustment, which provide compelling evidence for policy makers in setting priority of health care information technology in capturing and utilizing these numerical data.

Our comorbidity variables using secondary diagnoses did not reflect the recent coding change of identification of acute clinical conditions POA. Future studies may further examine directly consistency, reliability, and validity of POA coding in the administrative data as well as the relative contribution of these new data in relation to electronically captured numerical laboratory and vital sign data when they all become widely available. When evaluating the value of these data, it is important to balance objectivity, parsimony, and cost, in addition to statistical performance.

Conclusions

A small number of laboratory findings provide objective, quantitative, and parsimonious measures of the risk of inpatient mortality in a large array of clinical conditions. Clinical models using electronic numerical laboratory and administrative data can be used for population-based comparative outcome studies and hospital performance reporting. Vital signs and mental status should be included in the automated risk adjustment systems when the electronic collection, storage, and transmission of these data become widely available. Based on automated data, these models are cost-efficient to implement as a risk adjustment system.

Our study has limitations. It may be debatable on how to best group a heterogeneous patient population into clinically homogeneous subgroups. Currently, there were multiple clinical grouping systems (Pine et al. 2007; Tabak, Johannes, and Silber 2007; Escobar et al. 2008; Elixhauser, Steiner, and Palmer 2010). The Clinical Classifications Software (CCS) recently updated by the Agency for Healthcare Research and Quality consists of 285 diagnosis groups (Elixhauser et al. 2010). Although the CCS system offers granularity of grouping patients into homogeneous diagnosis-related groups, it would require even larger database than we currently have to insure adequate number of cases and outcome events for model development and validation, especially for those low-volume disease groups. Implementing a more granulated disease grouping system, such as the CCS, for clinical risk adjustment modeling may be achievable in the future if a nation-wide automated clinical database is established for health services research. Our study provided further evidence in support of establishing such a national database to advance health services research.
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The methodology surrounding the use of numerical laboratory data in risk adjustment modeling also varies. Our disease-specific modeling approach encompassed three phases. It was based on review of disease-specific risk adjustment tools for a general inpatient population published by the clinical community, which showed differences in variable selection and weight of the same variable in different risk adjustment models for patients hospitalized for different clinical conditions (Goldman et al. 1996; Fine et al. 1997; Fonarow et al. 2005; Aujesky et al. 2006; Tabak, Johannes, and Silber 2007; Tabak et al. 2009). Our empirical review of the distribution of each variable in relation to the outcome by disease group showed significant differences across disease groups. For example, for patients with WBC counts ([10.sup.9]/L) of [less than or equal to] 4.3, 4.4-10.9, 11.0-14.1, 14.2-19.8, or [greater than or equal to] 19.9, the corresponding observed inpatient mortality was 7.8, 3.6, 4.5, 5.8, or 7.6 percent if pneumonia was the principal diagnosis whereas, for the same laboratory findings, the corresponding mortality for patients of chronic obstructive pulmonary disease (COPD) was 1.7, 1.7, 2.7, 4.0, and 5.5 percent. These data revealed that neutropenia (WBC [less than or equal to] 4.3 [[10.sup.9]/L]) was associated with the highest mortality risk for pneumonia patients, but not for COPD patients, for whom, the mortality was about the same, the lowest (1.7 percent), whether their WBC was below or in the normal range. The feedback from our clinical advisory panels preferred disease-specific models for easy understanding of risk factors and their relative weights pertinent to caregivers’ specialties. Our finding that risk factors and their relative weights vary depending upon the clinical conditions being considered supports this viewpoint.

Although the absolute value of the relative contribution of the laboratory data was slightly smaller in our study compared with previous studies that did not include additional manually collected clinical data as covariates (Escobar et al. 2008; Render et al. 2008), our finding is consistent with previous publication that included additional variables in the models and used a different statistical method to calculate the relative contribution of laboratory data (Tabak, Johannes, and Silber 2007). These findings further validated the stability and consistency of objective and numerical laboratory data when applied to different patient populations across a wide array of disease groups and over time.

We found that vital signs and altered mental status added an average 0.02 in c-statistic above and beyond automated models. The small cumulative c-statistic increase was in line with a previous study on eight clinical conditions (Pine et al. 2009). However, adding vital signs and mental status improved model calibration in joint distribution analysis, which was not investigated previously. The incremental improvement in calibration might be particularly meaningful if risk stratification is of the interest (Cook 2007). Furthermore, these physiologic variables have clinical face validity as mortality risk factors. With the increasing use of full electronic medical records, automated collection, storage, and transmission of voluminous vital signs will likely become more practical. Hence, our findings may have policy implications for setting the next priority of inclusion of electronic clinical data for health services research.

The finding that altered mental status contributed more than laboratory data in predicting mortality among patients suffering from neurologic disorders such as ischemic and hemorrhagic stoke patients is clinically plausible. From a clinical perspective, laboratory results do not necessarily capture neurologic function. Further study on using “present on admission” (POA) for ICD-9 diagnosis codes indicating “coma” may shed light on a practical way to electronically capture and utilize the information of altered mental status on admission for risk adjustment, especially for diseases of the neurological system. It should be noted that current coding conventions may preclude coding of signs and symptoms that are “integral part of” or “associated routinely with a disease process” (CMS 2009). Our finding on the importance of altered mental status in risk adjustment may aid responsible parties to discuss and consider clarifying and modifying rules so that clinically important signs and symptoms, such as “coma,” can be consistently coded across hospitals.

Adoption of a full electronic medical record system that enables interhospital collection, storage, and transmission of vital signs and mental status throughout the United States will likely take time. Hence, a system using data that is already captured electronically across a vast majority of hospitals would have practical value. Our analysis showed that hospital performance ranks generated by automated clinical models (numerical laboratory data and administrative data) were highly correlated with those generated by models with additional vital signs and mental status. As a bridge, hybrid models incorporating the most widely automated numerical laboratory results and information from administrative data may serve as a reasonable intermediate step for aggregated performance reporting.

Overall, the median (interquartile range) age was 72 (57, 81) versus 71 (56, 81) years for the derivation versus validation cohorts, respectively. Approximately 45.4 percent of both cohorts were men. A total of 50.3 versus 58.3 percent of cases were from teaching hospitals and 15.6 versus 14.7 percent were from rural hospitals, respectively, for the two cohorts. The overall mortality was 4.6 percent (n = 58,300) for the derivation cohort and 4.0 percent (n = 47,279) for the validation cohort. Table 1 displays the distribution of patients and mortality by disease group for the derivation versus validation cohorts.

Mortality Predictors

The most common mortality predictors across disease groups included age, albumin, BUN or creatinine, arterial pH, white blood cell (WBC) counts, blood glucose, sodium, hemoglobin, and other abnormal metabolic, or hematologic parameters (Table 2). The most common chronic conditions predicting mortality included metastatic cancer or cancer of major organ systems. The overall results were similar in the recalibrated validation cohort.

Model Discrimination, Calibration, and Relative Contribution of Predictors

The average c-statistic for the automated models was 0.83 for the derivation cohort (Table 3). The addition of vital signs and mental status increased the average c-statistic to 0.85. It also improved model calibration when predicted mortality risk strata were evaluated in the joint distributions (Table 4). Models with vital signs and mental status reclassified 17.3 percent of cases into risk strata that were more accurate representations of observed mortality risks. For example, 57,483 cases in the 1-5 percent mortality risk stratum were reclassified into < 1 percent mortality risk stratum, which was a more accurate patients alive, making the use of laboratory data for risk adjustment parsimonious and efficient from both scientific and economic perspectives. From a clinical perspective, some deranged laboratory findings might not have one-on-one corresponding code to capture the complete spectrum of clinical complexity seen in laboratory results. For example, an abnormally low albumin could indicate chronic malnutrition, liver failure, renal dysfunction, secondary manifestation of cardiac dysfunction, or even acute severe sepsis possibly due to capillary leakage of albumin. Identification and classification of diagnosis codes to cover the broad spectrum of clinical conditions might be more arduous than directly using the laboratory test results themselves.

Our study built on previous studies on automated laboratory data (Tabak, Johannes, and Silber 2007; Escobar et al. 2008; Render et al. 2008) by extending previous research to both male and female patients admitted for a broad range of diseases in a diverse group of acute care hospitals in terms of teaching status, bed size, and rural location. We used a large database consisting of administrative, numerical laboratory, and manually collected clinical data. We found that laboratory data contributed most in predicting mortality even when we included manually collected key clinical findings of vital signs and mental status.

We added manually abstracted clinical data to the automated clinical models for the 39 manual clinical models. We only considered vital signs (systolic blood pressure, diastolic blood pressure, respiration, heart beat, and temperature) and altered mental stares, which was assessed by the Glasgow Coma Scale or a designation of disoriented, stupor, or coma as charted by the attending physicians. We did not include other manually collected clinical variables beyond vital signs and mental status, because previous studies have found that the contribution of these variables to model discrimination is negligible (Tabak, Johannes, and Silber 2007; Hollenbeak et al. 2008).

We compared changes in c-statistics when vital sign and mental status variables were added to the models. Because the c-statistic may be insensitive in distinguishing between models on calibration and the traditional Hosmer-Lemeshow [chi square] test is not suitable when the sample size is very large, we evaluated the change of model calibration using joint distributions of predicted mortality risk by the two sets of models (Cook 2007). This method allowed us to evaluate whether models with manually extracted data would more accurately stratify individuals into higher or lower mortality risk strata compared with models without these data.

Model Validation. We validated each model internally using bootstrapping in the derivation cohort by sampling with replacement for 200 iterations (Efron and Tibshirani 1993). Variables that never changed coefficient signs and were significant in more than 70 percent of reiterations were retained in the model. For external validation, we recalibrated all models using 1,178,561 cases discharged in 2004-2005 because of the significant decrease in in-hospital mortality observed across years.

Relative Contributions of Variables. We examined changes in the model-fit loglikelihood value when each group of variables was retained and removed from the full model (Escobar et al. 2008; Render et al. 2008). We calculated the relative contributions of age, laboratory results, ICD-9 code-based variables, and additional manually abstracted variables for each model.

Comparison of Hospital Performance Using Automated versus Manual Clinical Models

Large-scale implementation of a clinical risk adjustment system requires cost efficiency. Because electronic capture of vital signs and mental status may require more comprehensive implementation of electronic medical records, which is currently less available compared with electronically captured numerical laboratory data (Jha et al. 2009), we evaluated whether models without vital signs and mental status (automated clinical models) can serve as surrogates for models with these additional data that currently requires manual extraction for the majority of hospitals. Specifically, we fit two sets of hierarchical models to compare hospital performance (Normand et al. 1997; Tabak, Johannes, and Silber 2007). First, we obtained hospital ranking for each disease group using risk-standardized mortality rates generated from automated clinical models. Second, we obtained another set of ranking using manual clinical models. We used the Spearman rank correlation coefficient to assess the agreement. A high-level agreement between the two sets of results would suggest that the automated clinical models can be used as surrogates for the manual clinical models.

We used one of the Clinical Research Databases from CareFusion (Formerly Cardinal Health Clinical Research Database [Clinical Research Services, Marlborough, MA]). This database has been used for research since the late 1980s and the data collection system has been fully described elsewhere (Iezzoni and Moskowitz 1988; Silber et al. 1995; Fine et al. 1997; Kollef et al. 2005; Aujesky et al. 2006; Shorr et al. 2006, 2009; Pine et al. 2007; Tabak, Johannes, and Silber 2007; HoHenbeak et al. 2008; Tabak et al. 2009; Weigelt, Lipsky, and Tabak 2010). The current study population consisted of 1,271,663 discharges in 2000-2001 from 217 hospitals for the derivation cohort and 1,178,561 discharges in 2004-2005 from 191 hospitals for the validation cohort.
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The study database included imported hospital administrative data that was comprised of demographics, principal diagnosis, and up to 25 secondary diagnosis codes. The database also contained electronically imported or manually abstracted laboratory data, vital signs, and other clinical findings. The derivation and validation cohorts had similar laboratory data completion rates. A total of 96 percent of patients had laboratory data on the day of admission. For the 2 percent of patients who did not have laboratory data recorded on admission day, data collection extended to 30 hours after admission. For surgical patients, laboratory data were eligible before surgery starting time if surgery was within the admission window. If surgery was later than the admission data collection window, data collected within the admission window was used. About 2 percent of cases were recorded as missing laboratory data for the specified data collection window. For patients with multiple laboratory assessments on admission day, the worst value was collected.

For this study, we selected 39 major disease groups based on volume of admissions and associated inpatient mortality rate. These disease groups covered clinical conditions of all major organ systems, including the nervous, circulatory, digestive, hepatobiliary/pancreatic, musculoskeletal, metabolic, and kidney/urinary systems, as well as infectious diseases. Patients were classified into one of these mutually exclusive disease groups based on their principal diagnosis. Each patient had only one principal diagnosis for a given admission.

Model Development and Validation
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Model Development. We first developed 39 automated clinical models–one for each disease group–using demographics, numerical laboratory findings on admission, principal diagnosis subgroups based on ICD-9 codes, and chronic conditions based on secondary diagnoses. For each disease group, we examined the distribution of each continuous variable in relation to in-hospital death. We partitioned each continuous variable into multiple discrete levels. A category for patients with missing laboratory data was created and the mortality of this group was compared and pooled into a reference group (Pine et al. 2007; Tabak, Johannes, and Silber 2007). This approach allowed us to use data on all the patients and is more practical for large-scale implementation than imputation or dropping patients with missing data. All candidate variables that were statistically associated with mortality (p< .05) were included as potential covariates. Variable selection in multivariable regression models was based on clinical plausibility and statistical significance.

Comparison of health care outcomes is of interest to both the clinical community and public (Halm and Chassin 2001; Fonarow and Peterson 2009; VanLare, Conway, and Sox 2010). New funding for comparative research from the American Recovery and Reinvestment Act of 2009 (U.S. Congress), coupled with the health care reform, has generated renewed interest as well as concern about methods of comparative effectiveness research and performance reporting (Fonarow and Peterson 2009; Gibbons et al. 2009). When comparing health care outcomes in large populations, clinically credible risk adjustment methodology that can be implemented on a large scale at low cost is important. Although clinical trials are the standard method of assessing health care effectiveness, they have high data collection costs, tend to be conducted on relatively small and homogeneous patient populations, and are not practical for all types of research. As a complement, observational studies enable large-scale investigations of outcomes, which may be more applicable to real world settings (VanLare, Conway, and Sox 2010). The observational studies have been further advanced by the development and proliferating use of technology that enables electronic capture of clinical data. A 2008 survey on representative U.S. hospitals found that 77 percent had fully implemented and an additional 14 percent had been partially or were in the process of implementing electronic laboratory reports (Jha et al. 2009).
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Recent publications demonstrated that automated laboratory data offer clinical credibility, objectivity, parsimony, and cost-effectiveness for risk adjustment (Jordan et al. 2007; Tabak, Johannes, and Silber 2007; Escobar et at. 2008; Render et al. 2008). Laboratory data were found to contribute most in predicting mortality among demographics, comorbidities, and other groups of variables (Tabak, Johannes, and Silber 2007; Escobar et al. 2008; Render et at. 2008). However, existing studies either did not assess contribution of additional clinical data, such as vital signs, in predicting mortality (Escobar et al. 2008; Render et al. 2008) or limited patient population to primarily male and ICU patients (Render et at. 2008). Tabak, Johannes, and Silber (2007) developed and validated disease-specific mortality predictive models, evaluating both cumulative and relative contributions of laboratory data in relation to demographics, administrative, and other manually collected clinical data. Their analysis, nevertheless, was limited to only six common clinical conditions. In a large patient population using disease-specific modeling, we sought to extend the previous work to a broad array of clinical conditions by addressing whether promising laboratory results observed in a few common clinical conditions are reproducible for other less frequently studied conditions. We further evaluated the value of manually extracted vital signs and mental status data in model predictive ability in relation to electronically captured laboratory results, demographics, and diagnosis-based administrative data. Because health care data are complex, prioritizing the electronic capture and utilization of the most standardized data elements for population-based research seems prudent. In addition to numerical laboratory results, vital signs are also objective and quantitative. Hence, determining the value of vital signs in risk adjustment may inform policy makers regarding the relative importance and priority of electronic data capture, storage, and transmission, given the federal government’s commitment to invest billions of dollars in the coming years to encourage the widespread adoption of health information technology in the United States (Blumenthal 2010).