Evaluating Epidemiology and Improving Surveillance of Infections Associated with Health Care, United States

Distinct Origins and Transmission Pathways of blaKPC Enterobacterales across Three U.S. States

Carbapenem-resistant Enterobacterales (CRE) are among the most concerning antibiotic resistance threats due to high rates of multidrug resistance, transmissibility in health care settings, and high mortality rates. We evaluated the potential for regional genomic surveillance to track the spread of blaKPC-carrying CRE (KPC-CRE) by using isolate collections from health care facilities in three U.S. states. Clinical isolates were collected from Connecticut (2017 to 2018), Minnesota (2012 to 2018), and Tennessee (2016 to 2017) through the U.S. Centers for Disease Control and Prevention's Multi-site Gram-negative Surveillance Initiative (MuGSI) and additional surveillance. KPC-CRE isolates were whole-genome sequenced, yielding 255 isolates from 214 patients across 96 facilities. Case report data on patient comorbidities, facility exposures, and interfacility patient transfer were extracted. We observed that in Connecticut, most KPC-CRE isolates showed evidence of importation from outside the state, with limited local transmission. In Minnesota, cases were mainly from sporadic importation and transmission of blaKPC-carrying Klebsiella pneumoniae ST258, and clonal expansion of blaKPC-carrying Enterobacter hormaechei ST171, primarily at a single focal facility and its satellite facilities. In Tennessee, we observed transmission of diverse strains of blaKPC-carrying Enterobacter and Klesbiella, with evidence that most derived from the local acquisition of blaKPC plasmids circulating in an interconnected regional health care network. Thus, the underlying processes driving KPC-CRE burden can differ substantially across regions and can be discerned through regional genomic surveillance. This study provides proof of concept that integrating genomic data with information on interfacility patient transfers can provide insights into locations and drivers of regional KPC-CRE burden that can enable targeted interventions.

A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster

The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of methods for automatic data-driven variable construction and machine learning techniques (MLT) show promising results. In this framework, varicella-zoster virus (VZV) infection was chosen to perform an automatic case identification with MLT. Pedianet, an Italian pediatric primary care database, was used to train a series of models to identify whether a child was diagnosed with VZV infection between 2004 and 2014 in the Veneto region, starting from free text fields. Given the nature of the task, a recurrent neural network (RNN) with bidirectional gated recurrent units (GRUs) was chosen; the same models were then used to predict the children’s status for the following years. A gold standard produced by manual extraction for the same interval was available for comparison. RNN-GRU improved its performance over time, reaching the maximum value of area under the ROC curve (AUC-ROC) of 95.30% at the end of the period. The absolute bias in estimates of VZV infection was below 1.5% in the last five years analyzed. The findings in this study could assist the large-scale use of EHRs for clinical outcome predictive modeling and help establish high-performance systems in other medical domains.

Epidemiology of extended-spectrum β-lactamase–producing Enterobacterales in five US sites participating in the Emerging Infections Program, 2017

Objective

The incidence of infections from extended-spectrum β-lactamase (ESBL)–producing Enterobacterales (ESBL-E) is increasing in the United States. We describe the epidemiology of ESBL-E at 5 Emerging Infections Program (EIP) sites.

Methods

During October–December 2017, we piloted active laboratory- and population-based (New York, New Mexico, Tennessee) or sentinel (Colorado, Georgia) ESBL-E surveillance. An incident case was the first isolation from normally sterile body sites or urine of Escherichia coli or Klebsiella pneumoniae/oxytoca resistant to ≥1 extended-spectrum cephalosporin and nonresistant to all carbapenems tested at a clinical laboratory from a surveillance area resident in a 30-day period. Demographic and clinical data were obtained from medical records. The Centers for Disease Control and Prevention (CDC) performed reference antimicrobial susceptibility testing and whole-genome sequencing on a convenience sample of case isolates.

Results

We identified 884 incident cases. The estimated annual incidence in sites conducting population-based surveillance was 199.7 per 100,000 population. Overall, 800 isolates (96%) were from urine, and 790 (89%) were E. coli. Also, 393 cases (47%) were community-associated. Among 136 isolates (15%) tested at the CDC, 122 (90%) met the surveillance definition phenotype; 114 (93%) of 122 were shown to be ESBL producers by clavulanate testing. In total, 111 (97%) of confirmed ESBL producers harbored a blaCTX-M gene. Among ESBL-producing E. coli isolates, 52 (54%) were ST131; 44% of these cases were community associated.

Conclusions

The burden of ESBL-E was high across surveillance sites, with nearly half of cases acquired in the community. EIP has implemented ongoing ESBL-E surveillance to inform prevention efforts, particularly in the community and to watch for the emergence of new ESBL-E strains.

Epidemiological Characterization and the Impact of Healthcare-Associated Pneumonia in Patients Admitted in a Northern Portuguese Hospital

Pneumonia is one of the main causes of hospitalization and mortality. It’s the fourth leading cause of death worldwide. Healthcare-associated infections are the most frequent complication of healthcare and affect hundreds of millions of patients around the world, although the actual number of patients affected is unknown due to the difficulty of reliable data. The main goal of this manuscript is to describe the epidemiological characteristics of patients admitted with pneumonia and the impact of healthcare-associated pneumonia (HCAP) in those patients. It is a quantitative descriptive study with retrospective analysis of the clinical processes of 2436 individuals for 1 year (2018) with the diagnosis of pneumonia. The individuals with ≤5 years old represented 10.4% (n = 253) and ≥65 were 72.6% (n = 1769). 369 cases resulted in death, which gives a sample lethality rate of 15.2%. The severity and mortality index were not sensitive to the death event. We found 30.2% (n = 735) individuals with HCAP and 0.41% (n = 59) with ventilator-associated pneumonia (VAP). In only 59 individuals (2.4%) the agent causing pneumonia was isolated. The high fatality rate obtained shows that pneumonia is a major cause of death in vulnerable populations. Moreover, HCAP is one of the main causes of hospital admissions from pneumonia and death and the most pneumonias are treated empirically. Knowledge of the epidemiology characterization of pneumonia, especially associated with healthcare, is essential to increase the skills of health professionals for the prevention and efficient treatment of pneumonia.

Reductions in positive Clostridioides difficile events reportable to National Healthcare Safety Network (NHSN) with adoption of reflex enzyme immunoassay (EIA) testing in 13 Atlanta hospitals

In total, 13 facilities changed C. difficile testing to reflexive testing by enzyme immunoassay (EIA) only after a positive nucleic acid-amplification test (NAAT); the standardized infection ratio (SIR) decreased by 46% (range, -12% to -71% per hospital). Changing testing practice greatly influenced a performance metric without changing C. difficile infection prevention practice.

Detection of CTX-M-27 β-Lactamase Genes on Two Distinct Plasmid Types in ST38 Escherichia coli from Three U.S. States

Infections caused by extended-spectrum-β-lactamase (ESBL)-producing Escherichia coli are a significant cause of morbidity and health care costs. Globally, the prevailing clonal type is ST131 in association with the bla_CTX-M-15 β-lactamase gene. However, other ESBLs, such as bla_CTX-M-14 and bla_CTX-M-27, can also be prevalent in some regions. We identified ST38 ESBL-producing E. coli from different regions in the United States which carry bla_CTX-M-27 embedded on two distinct plasmid types, suggesting the potential emergence of new ESBL lineages.

Framing Bacterial Genomics for Public Health(care)

Advancements in comparative genomics have generated significant interest in defining applications for healthcare-associated pathogens. Clinical microbiology, however, relies on increasingly automated platforms to quickly identify pathogens, resistance mechanisms, and therapy options within CLIA- and FDA-approved frameworks. Additionally, and most notably, healthcare-associated pathogens, especially those that are resistant to antibiotics, represent a diverse spectrum of genera harboring complex genetic targets including antibiotic, biocide, and virulence determinants that can be highly transmissible and, at least for antibiotic resistance, serve as potential targets for containment efforts. U.S. public health investments have focused on rapidly detecting outbreaks and emerging resistance in healthcare-associated pathogens using reference, culture-based, and molecular methods that are distributed, for example, across national laboratory network infrastructures. Herein we describe the public health applications of genomic science that are built from the top-down for broad surveillance, as well as the bottom-up, starting with identification of infections and infectious clusters. For healthcare-associated, including antimicrobial-resistant, pathogens, we propose a combination of top-down and bottom-up genomic approaches leveraged across the public health spectrum, from local infection control, to regional and national containment efforts, to national surveillance for understanding emerging strain ecology and fitness of healthcare pathogens.

Assessment of Health Care Exposures and Outcomes in Adult Patients With Sepsis and Septic Shock

IMPORTANCE

Current information on the characteristics of patients who develop sepsis may help in identifying opportunities to improve outcomes. Most recent studies of sepsis epidemiology have focused on changes in incidence or have used administrative data sets that provided limited patient-level data.

OBJECTIVE

To describe sepsis epidemiology in adults.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study reviewed the medical records, death certificates, and hospital discharge data of adult patients with sepsis or septic shock who were discharged from the hospital between October 1, 2014, and September 30, 2015. The convenience sample was obtained from hospitals in the Centers for Disease Control and Prevention Emerging Infections Program in 10 states (California, Colorado, Connecticut, Georgia, Maryland, Minnesota, New Mexico, New York, Oregon, and Tennessee). Patients 18 years and older with discharge diagnosis codes for severe sepsis or septic shock were randomly selected. Data were analyzed between May 1, 2018, and January 31, 2019.

MAIN OUTCOMES AND MEASURES

The population's demographic characteristics, health care exposures, and sepsis-associated infections and pathogens were described, and risk factors for death within 30 days after sepsis diagnosis were assessed.

RESULTS

Among 1078 adult patients with sepsis (569 men [52.8%]; median age, 64 years [interquartile range, 53-75 years]), 973 patients (90.3%) were classified as having community-onset sepsis (ie, sepsis diagnosed within 3 days of hospital admission). In total, 654 patients (60.7%) had health care exposures before their hospital admission for sepsis; 260 patients (24.1%) had outpatient encounters in the 7 days before admission, and 447 patients (41.5%) received medical treatment, including antimicrobial drugs, chemotherapy, wound care, dialysis, or surgery, in the 30 days before admission. A pathogen associated with sepsis was found in 613 patients (56.9%); the most common pathogens identified were Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Clostridioides difficile. After controlling for other factors, an association was found between underlying comorbidities, such as cirrhosis (odds ratio, 3.59; 95% CI, 2.03-6.32), immunosuppression (odds ratio, 2.52; 95% CI, 1.81-3.52), vascular disease (odds ratio, 1.54; 95% CI, 1.10-2.15), and 30-day mortality.

CONCLUSIONS AND RELEVANCE

Most adults experienced sepsis onset outside of the hospital and had recent encounters with the health care system. A sepsis-associated pathogen was identified in more than half of patients. Future efforts to improve sepsis outcomes may benefit from examination of health maintenance practices and recent health care exposures as potential opportunities among high-risk patients.

Use of Machine Learning Techniques for Case-Detection of Varicella Zoster Using Routinely Collected Textual Ambulatory Records: Pilot Observational Study

Background

The detection of infectious diseases through the analysis of free text on electronic health reports (EHRs) can provide prompt and accurate background information for the implementation of preventative measures, such as advertising and monitoring the effectiveness of vaccination campaigns.

Objective

The purpose of this paper is to compare machine learning techniques in their application to EHR analysis for disease detection.

Methods

The Pedianet database was used as a data source for a real-world scenario on the identification of cases of varicella. The models’ training and test sets were based on two different Italian regions’ (Veneto and Sicilia) data sets of 7631 patients and 1,230,355 records, and 2347 patients and 569,926 records, respectively, for whom a gold standard of varicella diagnosis was available. Elastic-net regularized generalized linear model (GLMNet), maximum entropy (MAXENT), and LogitBoost (boosting) algorithms were implemented in a supervised environment and 5-fold cross-validated. The document-term matrix generated by the training set involves a dictionary of 1,871,532 tokens. The analysis was conducted on a subset of 29,096 tokens, corresponding to a matrix with no more than a 99% sparsity ratio.

Results

The highest predictive values were achieved through boosting (positive predicative value [PPV] 63.1, 95% CI 42.7-83.5 and negative predicative value [NPV] 98.8, 95% CI 98.3-99.3). GLMNet delivered superior predictive capability compared to MAXENT (PPV 24.5% and NPV 98.3% vs PPV 11.0% and NPV 98.0%). MAXENT and GLMNet predictions weakly agree with each other (agreement coefficient 1 [AC1]=0.60, 95% CI 0.58-0.62), as well as with LogitBoost (MAXENT: AC1=0.64, 95% CI 0.63-0.66 and GLMNet: AC1=0.53, 95% CI 0.51-0.55).

Conclusions

Boosting has demonstrated promising performance in large-scale EHR-based infectious disease identification.

Identification of a Carbapenemase-Producing Hypervirulent Klebsiella pneumoniae Isolate in the United States

We report on a carbapenemase-producing hypervirulent Klebsiella pneumoniae (CP-hvKP) isolate collected from a U.S. patient at an outpatient clinic. The isolate was identified as K. pneumoniae serotype K1 sequence type 23 and included both a hypervirulence (with rmpA, rmpA2 iroBCDN, peg-344, and iucABCD-iutA genes) and a carbapenemase-encoding (bla _KPC-2) plasmid. The emergence of CP-hvKP underscores the importance of clinical awareness of this pathotype and the need for continued monitoring of CP-hvKP in the United States.

Clostridioides (Clostridium) difficile infection burden in Japan: A multicenter prospective study

Clostridioides (Clostridium) difficile is the leading cause of healthcare-associated infectious diarrhea in the developed world. Retrospective studies have shown a lower incidence of C. difficile infection (CDI) in Japan than in Europe or North America. Prospective studies are needed to determine if this is due lack of testing for C. difficile or a true difference in CDI epidemiology. A prospective cohort study of CDI was conducted from May 2014 to May 2015 at 12 medical facilities (20 wards) in Japan. Patients with at least three diarrheal bowel movements (Bristol stool grade 6-7) in the preceding 24 h were enrolled. CDI was defined by positive result on enzyme immunoassay for toxins A/B, nucleic acid amplification test for the toxin B gene or toxigenic culture. C. difficile isolates were subjected to PCR-ribotyping (RT), slpA-sequence typing (slpA-ST), and antimicrobial susceptibility testing. The overall incidence of CDI was 7.4/10,000 patient-days (PD). The incidence was highest in the five ICU wards (22.2 CDI/10,000 PD; range: 13.9-75.5/10,000 PD). The testing frequency and CDI incidence rate were highly correlated (R² = 0.91). Of the 146 isolates, RT018/018″ was dominant (29%), followed by types 014 (23%), 002 (12%), and 369 (11%). Among the 15 non-ICU wards, two had high CDI incidence rates (13.0 and 15.9 CDI/10,000 PD), with clusters of RT018/slpA-ST smz-02 and 018"/smz-01, respectively. Three non-RT027 or 078 binary toxin-positive isolates were found. All RT018/018" isolates were resistant to moxifloxacin, gatifloxacin, clindamycin, and erythromycin. This study identified a higher CDI incidence in Japanese hospitals than previously reported by actively identifying and testing patients with clinically significant diarrhea. This suggests numerous patients with CDI are being overlooked due to inadequate diagnostic testing in Japan.

Changes in Prevalence of Health Care-Associated Infections in U.S. Hospitals

BACKGROUND

A point-prevalence survey that was conducted in the United States in 2011 showed that 4% of hospitalized patients had a health care-associated infection. We repeated the survey in 2015 to assess changes in the prevalence of health care-associated infections during a period of national attention to the prevention of such infections.

METHODS

At Emerging Infections Program sites in 10 states, we recruited up to 25 hospitals in each site area, prioritizing hospitals that had participated in the 2011 survey. Each hospital selected 1 day on which a random sample of patients was identified for assessment. Trained staff reviewed medical records using the 2011 definitions of health care-associated infections. We compared the percentages of patients with health care-associated infections and performed multivariable log-binomial regression modeling to evaluate the association of survey year with the risk of health care-associated infections.

RESULTS

In 2015, a total of 12,299 patients in 199 hospitals were surveyed, as compared with 11,282 patients in 183 hospitals in 2011. Fewer patients had health care-associated infections in 2015 (394 patients [3.2%; 95% confidence interval {CI}, 2.9 to 3.5]) than in 2011 (452 [4.0%; 95% CI, 3.7 to 4.4]) (P<0.001), largely owing to reductions in the prevalence of surgical-site and urinary tract infections. Pneumonia, gastrointestinal infections (most of which were due to Clostridium difficile [now Clostridioides difficile]), and surgical-site infections were the most common health care-associated infections. Patients' risk of having a health care-associated infection was 16% lower in 2015 than in 2011 (risk ratio, 0.84; 95% CI, 0.74 to 0.95; P=0.005), after adjustment for age, presence of devices, days from admission to survey, and status of being in a large hospital.

CONCLUSIONS

The prevalence of health care-associated infections was lower in 2015 than in 2011. To continue to make progress in the prevention of such infections, prevention strategies against C. difficile infection and pneumonia should be augmented. (Funded by the Centers for Disease Control and Prevention.).

Smartphone-based pathogen diagnosis in urinary sepsis patients

Background

There is an urgent need for rapid, sensitive, and affordable diagnostics for microbial infections at the point-of-care. Although a number of innovative systems have been reported that transform mobile phones into potential diagnostic tools, the translational challenge to clinical diagnostics remains a significant hurdle to overcome.

Methods

A smartphone-based real-time loop-mediated isothermal amplification (smaRT-LAMP) system was developed for pathogen ID in urinary sepsis patients. The free, custom-built mobile phone app allows the phone to serve as a stand-alone device for quantitative diagnostics, allowing the determination of genome copy-number of bacterial pathogens in real time.

Findings

A head-to-head comparative bacterial analysis of urine from sepsis patients revealed that the performance of smaRT-LAMP matched that of clinical diagnostics at the admitting hospital in a fraction of the time (~1 h vs. 18–28 h). Among patients with bacteremic complications of their urinary sepsis, pathogen ID from the urine matched that from the blood – potentially allowing pathogen diagnosis shortly after hospital admission. Additionally, smaRT-LAMP did not exhibit false positives in sepsis patients with clinically negative urine cultures.

Interpretation

The smaRT-LAMP system is effective against diverse Gram-negative and -positive pathogens and biological specimens, costs less than $100 US to fabricate (in addition to the smartphone), and is configurable for the simultaneous detection of multiple pathogens. SmaRT-LAMP thus offers the potential to deliver rapid diagnosis and treatment of urinary tract infections and urinary sepsis with a simple test that can be performed at low cost at the point-of-care.

Fund

National Institutes of Health, Chan-Zuckerberg Biohub, Bill and Melinda Gates Foundation.

Failure to Communicate: Transmission of Extensively Drug-Resistant bla OXA-237-Containing Acinetobacter baumannii-Multiple Facilities in Oregon, 2012-2014

OBJECTIVE To determine the scope, source, and mode of transmission of a multifacility outbreak of extensively drug-resistant (XDR) Acinetobacter baumannii. DESIGN Outbreak investigation. SETTING AND PARTICIPANTS Residents and patients in skilled nursing facilities, long-term acute-care hospital, and acute-care hospitals. METHODS A case was defined as the incident isolate from clinical or surveillance cultures of XDR Acinetobacter baumannii resistant to imipenem or meropenem and nonsusceptible to all but 1 or 2 antibiotic classes in a patient in an Oregon healthcare facility during January 2012-December 2014. We queried clinical laboratories, reviewed medical records, oversaw patient and environmental surveillance surveys at 2 facilities, and recommended interventions. Pulsed-field gel electrophoresis (PFGE) and molecular analysis were performed. RESULTS We identified 21 cases, highly related by PFGE or healthcare facility exposure. Overall, 17 patients (81%) were admitted to either long-term acute-care hospital A (n=8), or skilled nursing facility A (n=8), or both (n=1) prior to XDR A. baumannii isolation. Interfacility communication of patient or resident XDR status was not performed during transfer between facilities. The rare plasmid-encoded carbapenemase gene bla OXA-237 was present in 16 outbreak isolates. Contact precautions, chlorhexidine baths, enhanced environmental cleaning, and interfacility communication were implemented for cases to halt transmission. CONCLUSIONS Interfacility transmission of XDR A. baumannii carrying the rare blaOXA-237 was facilitated by transfer of affected patients without communication to receiving facilities. Infect Control Hosp Epidemiol 2017;38:1335-1341.

Comparison of Data Collection for Healthcare-Associated Infection Surveillance in Nursing Homes

OBJECTIVE

To facilitate surveillance and describe the burden of healthcare-associated infection (HAI) in nursing homes (NHs), we compared the quality of resident-level data collected by NH personnel and external staff.

DESIGN

A 1-day point-prevalence survey

SETTING AND PARTICIPANTS

Overall, 9 nursing homes among 4 Centers for Disease Control and Prevention (CDC) Emerging Infection Program (EIP) sites were included in this study.

METHODS

NH personnel collected data on resident characteristics, clinical risk factors for HAIs, and the presence of 3 HAI screening criteria on the day of the survey. Trained EIP surveillance officers collected the same data elements via retrospective medical chart review for comparison; surveillance officers also collected available data to identify HAIs (using revised McGeer definitions). Overall agreement was calculated among residents identified by both teams with selected risk factors and HAI screening criteria. The impact of using NH personnel to collect screening criteria on HAI prevalence was assessed.

RESULTS

The overall prevalence of clinical risk factors among the 1,272 residents was similar between NH personnel and surveillance officers, but the level of positive agreement (residents with factors identified by both teams) varied between 39% and 87%. Surveillance officers identified 253 residents (20%) with ≥1 HAI screening criterion, resulting in 67 residents with an HAI (5.3 per 100 residents). The NH personnel identified 152 (12%) residents with ≥1 HAI screening criterion; 42 residents had an HAI (3.5 per 100 residents).

CONCLUSION

We identified discrepancies in resident-level data collection between surveillance officers and NH personnel, resulting in varied estimates of the HAI prevalence. These findings have important implications for the design and implementation of future HAI prevalence surveys.

Infect Control Hosp Epidemiol 2016;1440–1445

Improving Outcomes in Patients Receiving Dialysis: The Peer Kidney Care Initiative

The past decade has witnessed a marked reduction in mortality rates among patients receiving maintenance dialysis. However, the reasons for this welcome development are uncertain, and greater understanding is needed to translate advances in care into additional survival gains. To fill important knowledge gaps and to enable dialysis provider organizations to learn from one another, with the aim of advancing patient care, the Peer Kidney Care Initiative (Peer) was created in 2014 by the chief medical officers of 14 United States dialysis provider organizations and the Chronic Disease Research Group. Areas of particular clinical importance were targeted to help shape the public health agenda in CKD and ESRD. Peer focuses on the effect of geographic variation on outcomes, the implications of seasonality for morbidity and mortality, the clinical significance of understudied disorders affecting dialysis patients, and the debate about how best to monitor and evaluate progress in care. In the realm of geovariation, Peer has provided key observations on regional variation in the rates of ESRD incidence, hospitalization, and pre-ESRD care. Regarding seasonality, Peer has reported on variation in both infection-related and non-infection-related hospitalizations, suggesting that ambient environmental conditions may affect a range of health outcomes in dialysis patients. Specific medical conditions that Peer highlights include Clostridium difficile infection, which has become strikingly more common in patients in the year after dialysis initiation, and chronic obstructive pulmonary disease, the treatments for which have the potential to contribute to sudden cardiac death. Finally, Peer challenges the nephrology community to consider alternatives to standardized mortality ratios in assessing progress in care, positing that close scrutiny of trends over time may be the most effective way to drive improvements in patient care.

Cultivation of an Adaptive Domestic Network for Surveillance and Evaluation of Emerging Infections

Accomplishments of this program have provided numerous dividends and might benefit areas outside infectious diseases.

An Evaluation of Serum Procalcitonin and C-Reactive Protein Levels as Diagnostic and Prognostic Biomarkers of Severe Sepsis

Background

Recommendations have been made, following the multicenter Surviving Sepsis Campaign study, to standardize the definition of severe sepsis with reference to several parameters such as haemodynamic stability, acid-base balance, bilirubin, creatinine, International Normalized Ratio (INR), urine output and pulmonary functional value of the ratio between arterial oxigen partial pressure and inspiratory oxigen concentration. Procalcitonin (PCT) is considered to be a gold standard biomarker for the inflammatory response, and recent studies have shown that it may help to discover whether a seriously ill person is developing sepsis. C-reactive protein (CRP) is also used as a marker of inflammation in the body, as its blood levels increase if there is any inflammation in the body. The aim of this study was to evaluate serum procalcitonin and C-reactive protein levels as diagnostic and prognostic biomarkers of severe sepsis.

Material and method

Sixty patients, diagnosed as being "septic", were admitted to the intensive care unit (ICU). Based on laboratory results and clinical findings a diagnosis of "severe sepsis" was made, and correlated with PCT and CRP values. The APACHE II, SAPS II and SOFA severity scores were calculated, analyzed and correlated with PCT and CRP.

Results

Fifty two patients (86.67%) presented with criteria for severe sepsis. Multivariate correlation analysis indicated a significant positive association between procalcitonin and all severity scores (APACHEII p<0.0001, SOFA p<0.0001, SAPS II p<0.0001). CRP proved to be significantly correlated only with the SAPS II score (p=0.0145). Mortality rate was high, with 48 patients (80%) dying. There was no significant correlation between the levels of the PCT and CRP biomarkers and severe sepsis (p=0.2059 for PCT, p=0.6059 for CRP).

Conclusions

The procalcitonin levels are highly correlated with the severity scores (APACHE II, SAPS II, SOFA) regularly used in ICUs and therefore can be used for determining the severity of the septic process. Quantitive procalcitonin and C-reactive protein analysis was not shown to be useful in diagnosing severe sepsis. However, PCT and CRP can be used to predict the fatal progression of the septic patient.

Emerging Infections Program as Surveillance for Antimicrobial Drug Resistance

Across the United States, antimicrobial drug-resistant infections affect a diverse population, and effective interventions require concerted efforts across various public health and clinical programs. Since its onset in 1994, the Centers for Disease Control and Prevention Emerging Infections Program has provided robust and timely data on antimicrobial drug-resistant infections that have been used to inform public health action across a spectrum of partners with regard to many highly visible antimicrobial drug-resistance threats. These data span several activities within the Program, including respiratory bacterial infections, health care-associated infections, and some aspects of foodborne diseases. These data have contributed to estimates of national burden, identified populations at risk, and determined microbiological causes of infection and their outcomes, all of which have been used to inform national policy and guidelines to prevent antimicrobial drug-resistant infections.