Assessment and comparison of bacterial load levels determined by quantitative amplifications in blood culture-positive and negative neonatal sepsis

Stranieri, Inês; Kanunfre, Kelly Aparecida; Rodrigues, Jonatas Cristian; Yamamoto, Lídia; Nadaf, Maria Isabel Valdomir; Palmeira, Patrícia; Okay, Thelma Suely

doi:10.1590/s1678-9946201860061

Cited by 28 publications

(25 citation statements)

References 33 publications

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“…6 Furthermore, recognition of sepsis and even septic shock is often very difficult, especially in newborns and infants, so that many cases of critically ill patients could be missed when suppressing SIRS. [7][8][9] Patients suffering from SIRS have a high risk for a longer time of stay in intensive care units (ICUs) 10 and increased morbidity and mortality, especially in the pediatric cohort. 11 SIRS carries the risk to progress to sepsis, severe sepsis, or septic shock, which even more aggravates the hazard of mortality.…”

Section: Introduction Rationale and Backgroundmentioning

confidence: 99%

Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review

et al. 2019

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Background The design of computerized systems able to support automated detection of threatening conditions in critically ill patients such as systemic inflammatory response syndrome (SIRS) and sepsis has been fostered recently. The increase of research work in this area is due to both the growing digitalization in health care and the increased appreciation of the importance of early sepsis detection and intervention. To be able to understand the variety of systems and their characteristics as well as performances, a systematic literature review is required. Existing reviews on this topic follow a rather restrictive searching methodology or they are outdated. As much progress has been made during the last 5 years, an updated review is needed to be able to keep track of current developments in this area of research. Objectives To provide an overview about current approaches for the design of clinical decision-support systems (CDSS) in the context of SIRS, sepsis, and septic shock, and to categorize and compare existing approaches. Methods A systematic literature review was performed in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Searches for eligible articles were conducted on five electronic bibliographic databases, including PubMed/MEDLINE, IEEE Xplore, Embase, Scopus, and ScienceDirect. Initial results were screened independently by two reviewers based on clearly defined eligibility criteria. A backward as well as an updated search enriched the initial results. Data were extracted from included articles and presented in a standardized way. Articles were classified into predefined categories according to characteristics extracted previously. The classification was performed according to the following categories: clinical setting including patient population and mono- or multicentric study, support type of the system such as prediction or detection, systems characteristics such as knowledge- or data-driven algorithms used, evaluation of methodology, and results including ground truth definition, sensitivity, and specificity. All results were assessed qualitatively by two reviewers. Results The search resulted in 2,373 articles out of which 55 results were identified as eligible. Over 80% of the articles describe monocentric studies. More than 50% include adult patients, and only four articles explicitly report the inclusion of pediatric patients. Patient recruitment often is very selective, which can be observed from highly varying inclusion and exclusion criteria. The task of disease detection is covered in 62% of the articles; prediction of upcoming conditions in 33%. Sepsis is covered in 67% of the articles, SIRS as sole entity in only 4%, whereas 27% focus on severe sepsis and/or septic shock. The most common combinations of categories “algorithm used” and “support type” are knowledge-based detection of sepsis and data-driven prediction of sepsis. In evaluations, manual chart review (38%) and diagnosis coding (29%) represent the most frequently used ground truth definitions; most studies present a sample size between 10,001 and 100,000 cases (31%) and performances highly differ with only five articles presenting sensitivities and specificities above 90%; four of them using knowledge-based rather than machine learning algorithms. The presentations of holistic CDSS approaches, including technical implementation details, system interfaces, and data and interoperability aspects enabling the use of CDSS in routine settings are missing in nearly all articles. Conclusions The review demonstrated the high variety of research in this context successfully. A clear trend is observable toward the use of data-driven algorithms, and a lack of research could be identified in covering the pediatric population as well as acknowledging SIRS as an independent and threatening condition. The quality as well as the significance of the presented evaluations for assessing the performances of the algorithms in clinical routine settings are often not meeting the current standard of scientific work. Our future interest will be concentrated on these realistic settings by implementing and evaluating SIRS detection approaches as well as considering factors to make the CDSS useable in clinical routine from both technical and medical perspectives.

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Section: Introduction Rationale and Backgroundmentioning

confidence: 99%

Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review

et al. 2019

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“…The incidence of LOS has increased in parallel with the improvement in the survival of premature infants, particularly those with extremely low birthweight, and is an ongoing major global public health issue. Worldwide, infections cause 40% of neonatal deaths each year . An understanding of the epidemiology and management of neonatal LOS may help to reduce the burden of this disease.…”

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confidence: 99%

“…Worldwide, infections cause 40% of neonatal deaths each year. 3,4 An understanding of the epidemiology and management of neonatal LOS may help to reduce the burden of this disease. Epidemiological data on neonates show that the predominant pathogens causing LOS are coagulase-negative staphylococci (CONS), followed by Gram-negative bacteria, and fungi.…”

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confidence: 99%

Red cell distribution width as a predictor of late‐onset Gram‐negative sepsis

Doğan

Varal

2020

Pediatrics International

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Background Late‐onset sepsis (LOS) remains an important cause of morbidity and mortality in preterm infants. In this study, our aim was to investigate the red‐cell distribution width (RDW) levels during a LOS episode, and its association with the type of growing microorganism and mortality. Methods Preterm infants with culture‐proven sepsis during their neonatal intensive care unit stay were enrolled. Red‐cell distribution width levels were obtained in the first 4 h of postnatal life and at the onset of the LOS episode, and compared for these time frames. The study cohort was divided into two groups according to the type of the growing microorganism. The RDW levels were then assessed in intra‐ and inter‐group analyses. Results Eighty‐six infants were included in the final analysis. RDW levels were increased in the study cohort after a LOS attack (P < 0.001). Infants with Gram‐negative sepsis showed a significant increase in their RDW levels, but they remained unchanged in infants with Gram‐positive sepsis (P < 0.001 and P = 0.4, respectively). An RDW cut‐off of >19.50% was related with a sensitivity of 87% and a specificity of 81% for predicting late‐onset Gram‐negative sepsis (P < 0.001). Logistic regression analysis showed a positive association of RDW with mortality when adjusted for covariants (adjusted odds ratio: 1.40; 95% confidence interval: 1.02–1.80; P = 0.03). Conclusions Our findings show that RDW levels increased during a LOS episode in preterm infants, which was especially evident in Gram‐negative infections. We believe that these findings may be of importance in the early diagnosis and prognosis of LOS in preterm infants.

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“…the majority of the population studied (7). However, the increased precision that dPCR offers has not yet been assessed in a clinical setting (8).…”

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confidence: 99%

Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting

et al. 2020

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Applying digital PCR (dPCR) technology to challenging clinical and industrial detection tasks has become more prevalent because of its capability for absolute quantification and rare target detection. However, practices learned from quantitative PCR (qPCR) that promote assay robustness and wide-ranging utility are not readily applied in dPCR. These include internal amplification controls to account for false-negative reactions and amplicon high-resolution melt (HRM) analysis to distinguish true positives from false positives. Incorporation of internal amplification controls in dPCR is challenging because of the limited fluorescence channels available on most machines, and the application of HRM analysis is hindered by the separation of heating and imaging functions on most dPCR systems. We use a custom digital HRM platform to assess the utility of HRM-based approaches for mitigation of false positives and false negatives in dPCR. We show that detection of an exogenous internal control using dHRM analysis reduces the inclusion of false-negative partitions, changing the calculated DNA concentration up to 52%. The integration of dHRM analysis enables classification of partitions that would otherwise be considered ambiguous “rain,” which accounts for up to ∼3% and ∼10% of partitions in intercalating dye and hydrolysis probe dPCR, respectively. We focused on developing an internal control method that would be compatible with broad-based microbial detection in dPCR-dHRM. Our approach can be applied to a number of DNA detection methods including microbial profiling and may advance the utility of dPCR in clinical applications where accurate quantification is imperative.

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Assessment and comparison of bacterial load levels determined by quantitative amplifications in blood culture-positive and negative neonatal sepsis

Cited by 28 publications

References 33 publications

Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review

Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review

Red cell distribution width as a predictor of late‐onset Gram‐negative sepsis

Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting

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