Digital PCR Applications for the Diagnosis and Management of Infection in Critical Care Medicine

Irene Merino; Amanda de la Fuente; Marta Domínguez-Gil; José María Eiros; Ana P. Tedim; Jesús F. Bermejo-Martín

Crit Care. 2022;26(63)

Abstract and Introduction

Abstract

Infection (either community acquired or nosocomial) is a major cause of morbidity and mortality in critical care medicine. Sepsis is present in up to 30% of all ICU patients. A large fraction of sepsis cases is driven by severe community acquired pneumonia (sCAP), which incidence has dramatically increased during COVID-19 pandemics. A frequent complication of ICU patients is ventilator associated pneumonia (VAP), which affects 10–25% of all ventilated patients, and bloodstream infections (BSIs), affecting about 10% of patients. Management of these severe infections poses several challenges, including early diagnosis, severity stratification, prognosis assessment or treatment guidance. Digital PCR (dPCR) is a next-generation PCR method that offers a number of technical advantages to face these challenges: it is less affected than real time PCR by the presence of PCR inhibitors leading to higher sensitivity. In addition, dPCR offers high reproducibility, and provides absolute quantification without the need for a standard curve. In this article we reviewed the existing evidence on the applications of dPCR to the management of infection in critical care medicine. We included thirty-two articles involving critically ill patients. Twenty-three articles focused on the amplification of microbial genes: (1) four articles approached bacterial identification in blood or plasma; (2) one article used dPCR for fungal identification in blood; (3) another article focused on bacterial and fungal identification in other clinical samples; (4) three articles used dPCR for viral identification; (5) twelve articles quantified microbial burden by dPCR to assess severity, prognosis and treatment guidance; (6) two articles used dPCR to determine microbial ecology in ICU patients. The remaining nine articles used dPCR to profile host responses to infection, two of them for severity stratification in sepsis, four focused to improve diagnosis of this disease, one for detecting sCAP, one for detecting VAP, and finally one aimed to predict progression of COVID-19. This review evidences the potential of dPCR as a useful tool that could contribute to improve the detection and clinical management of infection in critical care medicine.

Introduction

Infection in Critical Care Medicine

Infectious pathology represents a leading cause of admission to the intensive care units (ICU). Sepsis (defined by the presence of a dysregulated host response to infection inducing organ dysfunction) is present in up to 30% of all ICU patients, as recently reported by Sakr et al. in a large study with 10,000 patients from 730 ICUs.^[1] One of the leading causes of sepsis is severe community acquired pneumonia (sCAP) of bacterial or viral origin.^[2] Current Coronavirus disease 2019 (COVID-19) pandemics has largely boosted the cases of sCAP all over the world.

In turn, infection is one of the most frequent complications in patients who are critically ill. Compromise of body's physical barriers by invasive devices, surgical aggression or traumatic injury, disruption of the mucosa, pressure sores, ventilator-induced lung injury, immune suppression, poor nutritional state, the use of broad-spectrum antibiotics which alter the commensal microbiota, combined with the increased exposition to opportunistic (often multi-drug resistant, MDR) pathogens,^[3] all represent predisposing factors favouring ICU acquired infections.^[4] In fact, approximately 19.2% of ICU patients develop infections compared to approximately 5.2% of infections developed by patients staying in all other hospital wards.^[3,5] Ventilator-associated pneumonia (VAP) affects 10–25% of all ventilated patients after at least 48 h on mechanical ventilation.^[6] Other frequent nosocomial infections affecting critically ill patients are catheter-associated urinary tract infection, bloodstream infection (BSIs), skin and wound infections, sinusitis, and gastrointestinal infection (often with Clostridium difficile).^[4] Clinical management of these infectious diseases or complications of the critically ill patient faces several challenges, including early diagnosis with microorganism identification, severity stratification, prognosis assessment and treatment guidance. Digital polymerase chain reaction (dPCR) is a next-generation PCR method that represents an opportunity to address these challenges.

dPCR: Technical Principles and Applications

dPCR has emerged as a promising technology that might fill in the current gaps of other standard or emerging diagnostic technologies employed in microbiology (Table S1 – additional file 1). dPCR is based on the division of the PCR mastermix (all components including DNA or RNA targets) into thousands of partitions. PCR amplification of target genes occurs in each individual partition, acting as an individual microreactor.^[7] These partitions can be created using a number of different mechanisms, such as emulsified microdroplets suspended in oil (droplet digital PCR, ddPCR), manufactured microwells, or microfluidic valving.^[8] The distribution of target sequences in the partitions is detected by fluorescence at endpoint. Quantification of target genes is estimated based on Poisson's distribution, by calculating the ratio of positive partitions (presence of fluorescence) over the total number of partitions.^[9] This technology has several advantages: i) it is less affected by PCR inhibitors than other standard or real-time PCR (qPCR) methods, as target sequences are concentrated in the microreactors; ii) it also offers a high reproducibility of the results; iii) it provides an absolute quantification of the target sequence without the need for standard curves; and iv) it has an improved analytical sensitivity ideal for detecting microbial genes, for species identification or for genes conferring antimicrobial resistance or higher pathogenicity. dPCR also presents some limitations: i) it is unable to distinguish between viable and non-viable microorganisms (an inconvenient which affects all PCR-based methods); ii) it might have different sensitivity for different types of microorganisms; iii) it needs specialized training; and iv) it has a high cost, particularly to acquire the devices. This represents a major drawback for applications in low or middle income countries, for example during the COVID-19 pandemics.^[7,9] In spite its limitations, the previously mentioned dPCR properties make it an ideal tool for clinical applications in the field of microbiology and infectious diseases.^[7] In this article we reviewed the existing evidence on the use of dPCR to improve the clinical management of infection in critical care medicine.

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Abstract and Introduction
Methods
Evidence on the use of dPCR for the Diagnosis and Management of Infection in Critical Care Medicine (Figure 2)
Conclusions
References

Table 1. Articles included in the review depicting the applications of dPCR for infection diagnosis and management in critical care medicine
Study	Year	Usage	Entity	Objective	References
Hu et al.	2021	Bacterial identification in blood or plasma	Sepsis	Identification of DNA from bacterial pathogens and antimicrobial resistance genes in blood from patients BSI	[10]
Yamamoto et al.	2018	Bacterial identification in blood or plasma	Sepsis	Identification of Mycobacterium tuberculosis through the detection of circulating cell-free DNA	[14]
Shin et al.	2021	Bacterial identification in blood or plasma	BSI	Diagnosis of Gram-Negative pathogens and antimicrobial resistance genes in plasma from patients with BSIs	[15]
Zheng et al.	2021	Bacterial identification in blood or plasma	Sepsis	Identification of DNA from Acinetobacter baumannii and Klebsiella pneumoniae in blood from patients BSI	[16]
Chen et al.	2021	Fungal identification in blood	BSI	Diagnosis of Candida spp in blood from patients with BSI	[18]
Zhou et al.	2021	Bacterial and fungal identification in other clinical samples	Pleural or peritoneal infections	Identification of pathogens from pleural or peritoneal infections	[19]
Simms et al.	2021	Viral identification	COVID-19	Confirmation of detection of SARS-CoV-2 in renal allograft and lung tissue (initially detected by immunohistochemistry)	[20]
Alteri et al.	2020	Viral identification	COVID-19	Quantification of SARS-CoV-2 viral load in plasma of patients with negative qPCR results	[21]
Jiang et al.	2020	Viral identification	COVID-19	Quantification of SARS-CoV-2 in plasma and hospital environment	[22]
Ziegler et al.	2019	Quantification of microbial burden to assess severity, prognosis and treatment guidance	Sepsis	Quantification of DNA load overtime in patients with Staphylococcus aureus BSIs	[29]
Ziegler et al.	2019	Quantification of microbial burden to assess severity, prognosis and treatment guidance	Sepsis	Quantification of DNA from bacterial pathogens load (16S rDNA) overtime in patients BSI to access patients' progression	[30]
Bialasiewicz et al.	2019	Quantification of microbial burden to assess severity, prognosis and treatment guidance	Sepsis	Quantification of DNA in blood from Capnocytophaga canimorsus to access patients progression	[31]
Dickson et al.	2020	Quantification of microbial burden to assess severity, prognosis and treatment guidance	VAP	Quantification of bacteria DNA burden in the lung and association with disease progression and outcomes	[32]
Goh et al.	2020	Quantification of microbial burden to assess severity, prognosis and treatment guidance	SepsissCAP	Quantification of EBV (to detect reactivation) in patients with sepsis to monitor disease progression	[33]
Veyer et al.	2021	Quantification of microbial burden to assess severity, prognosis and treatment guidance	COVID-19	Quantification of SARS-CoV-2 viral load in plasma and correlation with disease severity	[34]
Chen et al.	2021	Quantification of microbial burden to assess severity, prognosis and treatment guidance	COVID-19	Quantification of SARS-CoV-2 viral load in plasma and correlation with disease severity	[35]
Bermejo-Martin et al.	2020	Quantification of microbial burden to assess severity, prognosis and treatment guidance	COVID-19	Quantification of SARS-CoV-2 viral load in plasma and correlation with disease severity	[36]
Ram-Mohan et al.	2021	Quantification of microbial burden to assess severity, prognosis and treatment guidance	COVID-19	Quantification of SARS-CoV-2 viral load in plasma and correlation with disease severity	[37]
Tedim et al.	2021	Quantification of microbial burden to assess severity, prognosis and treatment guidance	COVID-19	Quantification of SARS-CoV-2 viral load in plasma, comparison with qPCR	[38]
Martin-Vicente et al.	2022	Quantification of microbial burden to assess severity, prognosis and treatment guidance	COVID-19	Quantification of SARS-CoV-2 viral load in plasma	[39]
Bruneau et al.	2021	Quantification of microbial burden to assess severity, prognosis and treatment guidanceHost Response	COVID-19	Quantification of SARS-CoV-2 viral load and host biomarkers in plasma to predict disease severity	[40]
Chanderraj et al.	2022	Microbial ecology studies	Sepsis	Quantification of bacterial density in rectal swabs and risk of extraintestinal infection	[41]
Brooks et al.	2018	Microbial ecology studies	Microbiological burden and microbiome	Quantification of total microbiological burden in hospital neonates ICU and correction with microbiome establishment	[42]
Tamayo et al.	2014	Host Response	Sepsis	Quantification of the expression of the constant region of the mu heavy chain of IgM in blood to differentiate sepsis from SIRS	[46]
Almansa et al.	2019	Host Response	Sepsis	Gene expression ratio between MMP8 or LCN2 with HLA-DRA to differentiate surgical patients with sepsis from those with no sepsis	[47]
Almansa et al.	2018	Host Response	Sepsis	Ratio between HLA-DRA expression and procalcitonin to differentiate surgical patients with sepsis from those with no sepsis	[48]
Link et al.	2020	Host Response	Sepsis	Quantification of miRNA in blood for the early diagnosis of sepsis	[49]
Martin-Fernandez et al.	2020	Host Response	Sepsis	Quantification of emergency granulopoiesis gene expression to stratify severity in patients with infection, sepsis and septic shock	[50]
Menéndez et al.	2019	Host Response	sCAP	Gene expression levels of the immunological synapse genes to identify patients with sCAP	[52]
Almansa et al.	2018	Host Response	VAP	Gene expression levels of the immunological synapse genes to identify VAP	[53]
Busani et al.	2020	Host Response	Sepsis	Quantification of mtDNA in patients with Septic shock cause by MDR pathogens predicts disease severity	[54]
Sabbatinelli et al.	2021	Host Response	COVID-19	Quantification of miRNA associated with inflammation and aging (Inflammaging) to predict COVID-19 disease progression	[56]

DNA, deoxyribonucleic acid; BSIs, bloodstream infections; spp, Species; COVID-19, Coronavirus disease 2019; SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2; qPCR, real-time polymerase chain reaction; VAP, ventilator-associated pneumoniae; sCAP, severe community acquired pneumoniae; EBV, Epstein Barr Virus; rDNA, ribosomal deoxyribonucleic acid; ICU, intensive care unit; IgM, immunoglobulin M; SIRS, Systemic inflammatory response syndrome; MMP8, matrix metalloproteinase-8; LCN2, Lipocalin 2; HLA-DRA, major histocompatibility complex class II; miRNA, micro ribonucleic acid; mtDNA, mitochondrial deoxyribonucleic acid; MDR: multidrug-resistant