Early Prognosis Research Articles

Relationship between hepatic function recovery and postoperative cholangitis in neonates undergoing hepaticojejunostomy for biliary atresia.

To explore the association between hepatic function recovery and the incidence of postoperative cholangitis in neonates with biliary atresia (BA) who underwent hepaticojejunostomy. We conducted a retrospective analysis of medical records from 173 newborns diagnosed with BA and treated with hepaticojejunostomy (Kasai procedure) between February 2020 and October 2022. Participants were categorized into two cohorts: those who developed cholangitis post-surgery (cholangitis group, n=125) and those who did not (non-cholangitis group, n=48). Liver function indices pre- and post-treatment, the extent of postoperative liver function recovery, and jaundice resolution rates were compared. Risk factors for cholangitis development post-surgery were identified using univariate and multifactorial logistic regression analyses. The cholangitis group exhibited higher surgical weight (P=0.030) and elevated preoperative levels of total bilirubin (TB, P<0.001), direct bilirubin (DB, P<0.001), aspartate aminotransferase (AST, P<0.001), and gamma-glutamyl transferase (GGT, P<0.001). This group also showed better postoperative liver function recovery (P=0.002) and jaundice clearance rates (P=0.003). Logistic regression identified postoperative jaundice clearance (P=0.013), TB (P=0.004), DB (P=0.011), AST (P<0.001), and GGT (P<0.001) as independent risk factors for cholangitis. The nomogram model had a C-index of 0.930 with a goodness-of-fit test p-value of 0.873, and an AUC of 0.930. Postoperative jaundice clearance, TB, DB, AST, and GGT are independent risk factors for cholangitis. The nomogram model offers high predictive accuracy for cholangitis development, aiding early intervention and prognosis improvement in high-risk neonates.

The role of biomarkers in diagnosing breast cancer: a review

Breast cancer remains a leading cause of cancer-related mortality worldwide, with late-stage detection significantly impacting prognosis. The emergence of novel biomarkers has revolutionized early diagnosis and prognosis, offering hope for improved outcomes. This review comprehensively explores the diverse types of biomarkers and evaluates their pivotal role in breast cancer diagnosis. Our review employed a systematic search strategy across reputable scientific platforms, utilizing varied keywords to ensure comprehensive coverage. Articles were meticulously selected based on predefined inclusion criteria. New emerging biomarkers are thought to be promising in early diagnosis and, thereby, prognosis. The main uses of tumor markers are to track the effectiveness of treatment and to identify recurrences of the disease before they manifest clinically noticeable symptoms.

Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis.

To develop a clinical-radiomics model using a multimodal machine learning method for distinguishing ductal carcinoma in situ (DCIS) from breast fibromatosis. The clinical factors, ultrasound features, and related ultrasound images of 306 patients (198 DCIS patients) were retrospectively collected. Patients in the development and validation cohort were 184 and 122, respectively. The independent clinical and ultrasound factors identified by the multivariable logistic regression analysis were used for the clinical-ultrasound model construction. Then, the region of interest of breast lesions was delineated and radiomics features were extracted. Six machine learning algorithms were trained to develop a radiomics model. The algorithm with higher and more stable prediction ability was chosen to convert the output of the results into the Radscore. Further, the independent clinical predictors and Radscore were enrolled into the logistic regression analysis to generate a combined clinical-radiomics model. The receiver operating characteristic curve analysis, DeLong test, and decision curve analysis were adopted to compare the prediction ability and clinical efficacy of three different models. Among the six classifiers, logistic regression model was selected as the final radiomics model. Besides, the combined clinical-radiomics model exhibited a superior ability in distinguishing DCIS from breast fibromatosis to the clinical-ultrasound model and the radiomics model. The combined model by integrating clinical-ultrasound factors and radiomics features performed well in predicting DCIS, which might promote prompt interventions to improve the early diagnosis and prognosis of the patients.

Assessment of Lipid Profile in Hepatocellular Carcinoma Patients: A Prospective Study In Bangladesh

Background: Hepatocellular carcinoma (HCC) is a significant cause of cancer-related mortality globally, with a high incidence in Bangladesh. Disruptions in lipid metabolism are a hallmark of liver diseases, including HCC. Understanding the lipid profile in HCC patients can provide insights into the disease's pathophysiology and aid in developing diagnostic and therapeutic strategies. Objective: This study aims to assess the lipid profile in HCC patients in Bangladesh to identify potential diagnostic markers and understand the metabolic alterations associated with the disease. Method: A prospective study was conducted on 356 HCC patients admitted to IBN Sina Specialized Hospital, Dhaka, between June 2022 and December 2023. Blood samples were collected to measure total cholesterol levels, triglycerides, high-density lipoprotein (HDL), and low-density lipoprotein (LDL). The lipid profiles of these patients were compared with a control group of healthy individuals. Results: HCC patients exhibited significant alterations in their lipid profiles compared to the control group. Total cholesterol increased by 18.3% (220 mg/dL vs. 186 mg/dL), triglycerides by 25% (190 mg/dL vs. 152 mg/dL), LDL cholesterol by 29.6% (140 mg/dL vs. 108 mg/dL), and HDL cholesterol decreased by 22.4% (38 mg/dL vs. 49 mg/dL) (p&lt;0.001). Advanced tumor stages showed an additional 10% reduction in HDL and a 15% increase in LDL. Impaired liver function led to a further 12% rise in total cholesterol and 20% in triglycerides. These lipid changes were significantly associated with HCC progression and poorer prognosis. Conclusions: The study demonstrates significant dyslipidemia in HCC patients, suggesting that lipid profile alterations could serve as potential biomarkers for early detection and prognosis of HCC. Further research into lipid metabolism pathways in HCC could lead to novel therapeutic targets.

Molecular beacon-peptide probe based double recycling amplification for multiplexed detection of serum exosomal microRNAs.

Exosomal microRNAs (exomiRs) have been shown to play crucial roles as biomarkers for early detection and prognosis of cancer. However, simultaneous quantification of multiplex exomiRs is hindered by methods that require additional steps, such as labeling with fluorophores or gel visualization, which are susceptible to various factors. Herein, we developed a mass spectrometry-detectable and target-triggered method for multiplexed exomiR detection using three enzyme-based double recycling amplification in combination with well-designed molecular beacon-peptide (MBP) probes, called molecular beacon-peptide probe-based double recycling amplification (MBPDRA). MBP probes mediated the double recycling amplification reaction and were released as mass-detectable reporter peptides. In particular, the hybridization of the target microRNAs (miRNAs) with the stem-loop of the probe triggers two consecutive processes. The first cycle involved polymerase strand displacement amplification, leading to the production of complementary DNA (cycle I), and the second cycle encompassed the recycling exonuclease cleavage of the MBP probe (cycle II). Subsequently, excess probes were removed by interaction with streptavidin beads via biotin-streptavidin binding. The reporter peptides were released using trypsin and subsequently detected by mass spectrometry. Our method enables quantitative detection of multiple exomiRs with a dynamic range from 0.1 fM to 10 pM and a limit of quantification of 0.1 fM. Moreover, the proposed assay was successfully employed for quantification of three exomiRs, exmiR-21, exmiR-191, and exmiR-451a, in the sera of patients with pancreatic cancer. Based on these findings, we believe that the MBPDRA assay holds significant promise as a reliable method for quantifying multiple miRNAs in biomedical research and clinical diagnostics.

Synergistic Assembly of 1DZnO and Anti-CYFRA 21-1: A Physicochemical Approach to Optical Biosensing.

Objective: We conducted a comprehensive physicochemical analysis of one-dimensional ZnO nanowires (1DZnO), incorporating anti-CYFRA 21-1 immobilization to promote fast optical biomarker detection up to 10 ng ml-1. Impact Statement: This study highlights the effectiveness of proof-of-concept 1DZnO nanoplatforms for rapid cancer biomarker detection by examining the nanoscale integration of 1DZnO with these bioreceptors to deliver reliable photoluminescent output signals. Introduction: The urgent need for swift and accurate prognoses in healthcare settings drives the rise of sensitive biosensing nanoplatforms for cancer detection, which has benefited from biomarker identification. CYFRA 21-1 is a reliable target for the early prediction of cancer formation that can be perceptible in blood, saliva, and serum. However, 1DZnO nanostructures have been barely applied for CYFRA 21-1 detection. Methods: We assessed the nanoscale interaction between 1DZnO and anti-CYFRA 21-1 antibodies to develop rapid CYFRA 21-1 detection in two distinct matrices: PhosphateBuffered Saline (PBS) buffer and artificial saliva. The chemical modifications were tracked utilizing Fourier transform infrared spectroscopy, while transmission electron microscopy and energy dispersive spectroscopy confirmed antigen-antibody interplay over nanostructures. Results: Our results show high antibody immobilization efficiencies, affirming the effectiveness of 1DZnO nanoplatforms for rapid CYFRA 21-1 testing within a 5-min detection window in both PBS and artificial saliva. Photoluminescence measurements also revealed distinct optical responses across biomarker concentrations ranging from 10 to 1,000 ng ml-1. Conclusion: Discernible PL signal responses obtained after 5 min affirm the potential of 1DZnO nanoplatforms for further advancement in optical biomarker detection for application in early cancer prognosis.

Ferroptosis: the balance between death and survival in colorectal cancer.

Colorectal cancer (CRC) is a common malignant tumor associated with high morbidity and mortality. Despite an increase in early screening and treatment options, people with CRC still have a poor prognosis and a low 5-year survival rate. Therefore, mining more therapeutic targets and developing means of early diagnosis and determining prognosis are now imperative in the clinical treatment of CRC. Ferroptosis is a recently identified type of regulated cell death (RCD) characterized, which is identified by the accumulation of iron-dependent lipid peroxidation, thereby causing membrane damage and cell death. Recent studies have shown that ferroptosis is associated with tumors, including CRC, and can be involved in CRC progression; however, the underlying mechanisms are complex and heterogeneous and have not been thoroughly summarized. Therefore, this study reviewed the roles of ferroptosis in CRC progression to target ferroptosis-related factors for CRC treatment. The significance of ferroptosis-related biomarkers and genes in the early diagnosis and prognosis of CRC was also investigated. Furthermore, the limitations of ferroptosis studies in the current treatment of CRC, as well as future research perspectives, are discussed.

Role of cell-free DNA levels in the diagnosis and prognosis of sepsis and bacteremia: A systematic review and meta-analysis.

Sepsis remains a major cause of mortality in intensive care units (ICUs). Prompt diagnosis and effective management are imperative for better outcomes. In this systematic review and meta-analysis, we explore the potential of circulating cell-free DNA (cfDNA), as a promising tool for early sepsis detection and prognosis assessment, aiming to address limitations associated with traditional diagnostic methods. Following PRISMA guidelines, we collected relevant literature from thirteen databases. Studies were included if they analyzed quantitative diagnostic or prognostic cfDNA levels in humans in case of sepsis. We collected data on basic study characteristics, baseline patient demographics (e.g. age and sex), and cfDNA levels across different stages of sepsis. Pooled SMD with 95%-CI was calculated, and Comprehensive Meta-Analysis (CMA) software facilitated meta-analysis. Receiver operating characteristic (ROC) curves were generated to assess cfDNA's combined sensitivity and specificity in diagnostics and prognostics. We included a final of 44 studies, of which, only 32 with 2950 participants were included in the meta-analysis. cfDNA levels were higher in septic patients compared to healthy controls (SMD = 3.303; 95%-CI [2.461-4.145], p<0.01). Furthermore, cfDNA levels were higher in non-survivors than survivors (SMD = 1.554; 95%-CI [0.905-2.202], p<0.01). Prognostic studies demonstrated a pooled sensitivity and specificity of 0.78, while diagnostic studies showed a sensitivity of 0.81 and a specificity of 0.87. These findings show that cfDNA levels are significantly higher in sepsis patients compared to control groups and non-survivors in comparison to survivors among both adult and pediatric populations.

A DNA octahedral amplifier for endogenous circRNA detection and bioimaging in living cells and its biomarker study.

The discovery of reliable biomarkers is essential for early diagnosis, treatment, and prognosis assessment of diseases. Many research studies have shown that circRNA is a potential biomarker for diagnosis and prognosis of diseases. However, in situ monitoring circRNA in live cells is still a challenge at present, which brings a major limitation to the development and verification of circRNA as a disease biomarker. In this study, a catalytic hairpin assembly (CHA) reaction-based DNA octahedral amplifier (DOA) was developed for fluorescence resonance energy transfer (FRET) detection and bioimaging of circRNA in living cells. The DOA was first produced by self-assembling a DNA octahedron with six customized single-stranded DNAs, and two hairpins H1 (Cy3) and H2 (Cy5) were then hybridized to four vertices of the DNA octahedron. Idiopathic pulmonary fibrosis (IPF)-related circHIPK3 was used as the target. Once the CHA reaction from H1 and H2 on DOA was activated by a sequence-specific back-splice junction (BSJ) of circHIPK3, a significant FRET signal can be obtained from Cy3 to Cy5. The circHIPK3 was subsequently released to cause the next CHA reaction. Because the DOA has the advantages of the spatial-confinement effect, resistance to nuclease degradation and easy penetration into cells, rapid and excellent signal amplification FRET detection and bioimaging of endogenous circHIPK3 can be achieved in various cells. This study provides a high-precision assay platform to explore the possibility of using circRNA as a biomarker, and it is valuable for circRNA-related early diagnosis and treatment of diseases.

Machine Learning Algorithms for Early Prediction of Multiple Sclerosis Progression: A Comparative Study

Multiple Sclerosis (MS) is a chronic autoimmune disease characterized by central nervous system (CNS) degeneration, leading to diverse neurological symptoms. Managing MS poses a challenge due to its unpredictable progression. This study focuses on early prediction of MS progression using machine learning (ML) algorithms, comparing the effectiveness of Random Forest, XGBoost, Decision Tree, and Logistic Regression. Clinical, genetic, and environmental factors were analyzed in a cohort of Mexican mestizo patients recently diagnosed with Clinically Isolated Syndrome (CIS). Data preprocessing addressed missing values, and feature selection tailored to the population’s characteristics was applied. The dataset was split into training and testing sets, maintaining stratification for CDMS and nonCDMS cases. Machine learning models were trained with optimized hyperparameters. Performance evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, were employed. The Random Forest model exhibited superior performance (AUC: 0.93, accuracy: 87%), outperforming other models. Variable importance analysis identified top predictors, including Periventricular MRI, Age, Infratentorial MRI, and Oligoclonal Bands. The study’s clinical implications highlight ML’s potential in enhancing early MS prognosis, aiding timely interventions. The Random Forest model, with its robust performance, emerges as a valuable tool for identifying patients at risk. While the study contributes to predictive analytics in neurological disorders, limitations include cohort specificity and retrospective data use. Prospective studies and further exploration of data sources are recommended for broader applicability. This research demonstrates the efficacy of ML in early MS progression prediction, providing clinicians with a promising tool for personalized patient care. The findings contribute to advancing predictive healthcare analytics and emphasize the significance of tailored interventions in neurological disorders.

Microfluidics-aided fabrication of 3D micro-nano hierarchical SERS substrate for rapid detection of dual hepatocellular carcinoma biomarkers.

The simultaneous analysis of trace amounts of dual biomarkers is crucial in the early diagnosis, treatment, and prognosis of hepatocellular carcinoma (HCC). In this study, we prepared SERS-active hydrogel microparticles (SAHMs) with 3D hierarchical gold nanoparticles (AuNPs) micro-nanostructures by microdroplet technology and in situ synthesis, which demonstrated high reproducibility and sensitivity. Compared with traditional 2D SERS substrates, this newly prepared 3D SERS substrate provided a high density of nano-wrinkled structures and numerous AuNPs. Furthermore, a newly designed SERS-active substrate was proposed for the simultaneous microfluidic detection of AFP and AFU. The Raman signals of sandwich immunocomplexes on the surface of the SAHMs were measured for the trace analysis of these biomarkers. The proposed microfluidic platform achieved AFP and AFU detection in the range of 0.1-100 ng mL-1 and 0.01-100 ng mL-1, respectively, which represents a good response. Indeed, this platform is easy to fabricate, of low cost and has short detection time and comparable detection limits to other methods. As far as we know, this is the first study to achieve the simultaneous detection of AFP and AFU on a microfluidic platform. Therefore, we proposed a new simultaneous detection platform for dual HCC biomarkers that shows strong potential for the early diagnosis of HCC.

CSER: a gene regulatory network construction method based on causal strength and ensemble regression.

Gene regulatory networks (GRNs) reveal the intricate interactions between and among genes, and understanding these interactions is essential for revealing the molecular mechanisms of cancer. However, existing algorithms for constructing GRNs may confuse regulatory relationships and complicate the determination of network directionality. We propose a new method to construct GRNs based on causal strength and ensemble regression (CSER) to overcome these issues. CSER uses conditional mutual inclusive information to quantify the causal associations between genes, eliminating indirect regulation and marginal genes. It considers linear and nonlinear features and uses ensemble regression to infer the direction and interaction (activation or regression) from regulatory to target genes. Compared to traditional algorithms, CSER can construct directed networks and infer the type of regulation, thus demonstrating higher accuracy on simulated datasets. Here, using real gene expression data, we applied CSER to construct a colorectal cancer GRN and successfully identified several key regulatory genes closely related to colorectal cancer (CRC), including ADAMDEC1, CLDN8, and GNA11. Importantly, by integrating immune cell and microbial data, we revealed the complex interactions between the CRC gene regulatory network and the tumor microenvironment, providing additional new biomarkers and therapeutic targets for the early diagnosis and prognosis of CRC.

Short delay to initiate plasma exchange or immunoadsorption as synergistic therapies for patients in the acute phase of anti-NMDAR encephalitis.

Combined first-line therapies have been frequently adopted for patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis. Plasma exchange (PE) or immunoadsorption (IA) was used as an add-on option following initial immunotherapies, including high-dose steroids and intravenous immunoglobulin (IVIG). However, whether a shorter delay of PE or IA can improve the early recovery prognosis of patients with anti-NMDAR encephalitis remains largely unknown. To compare short-term clinical improvement between patients with early and late initiation of PE or IA in anti-NMDAR encephalitis. A retrospective study was conducted for patients admitted with anti-NMDAR encephalitis between January 2015 and December 2023 (n = 29), including 21 patients who received PE or IA as synergistic therapies. The clinical prognosis was compared between the early PE/IA group and the late PE/IA group in the research. Primary outcome included changes in the Clinical Assessment Scale for Autoimmune Encephalitis (∆CASE) at 90 and 120 days after encephalitis onset. Secondary outcomes included changes in the modified Rankin scale (∆mRS) after 90 and 120 days from encephalitis onset, and the length of intensive care unit (ICU) stay for patients with severe anti-NMDAR encephalitis. The ∆CASE scores after 90 and 120 days from encephalitis onset revealed a significant difference between patients with early and late initiation of PE or IA (p ⩽ 0.05). A significant difference in the ∆mRS was also found between patients with early and late initiation of PE or IA in severe encephalitis (p ⩽ 0.05). No significant difference was found in the length of ICU admission (p = 0.101). Our findings emphasize the importance of considering PE or IA as early as feasible for patients with anti-NMDAR encephalitis, even when steroids and IVIG are in use.

Assessment of long noncoding RNA CCAT1 using real time-polymerase chain reaction in colorectal cancer patients

Colorectal cancer (CRC) is linked to high mortality, mainly when discovered in its advanced stages. Several studies have pointed to the role of epigenetic factors in CRC and other cancers. Long non-coding RNAs (lncRNAs) are involved in the initiation, progression, metastasis, and modulation of the response to chemotherapeutic modalities of CRC as vital contributors to epigenetic mechanisms. Colon cancer-associated transcript-1 (CCAT1) is one of the lncRNAs that have been dysregulated in serum samples, providing a non-invasive route for diagnosing CRC patients. This study aimed to determine the role of CCAT1 expression as diagnostic and prognostic markers. We tested the associations of CCAT1 expression with serum carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA 19-9). The study included three groups: 41 patients with colorectal cancer, 39 patients with precancerous benign colorectal diseases, and 20 normal control individuals. CEA and CA 19-9 were measured by an immunoassay automated system. The expression level of CCAT1 was assessed by a real-time polymerase chain reaction. There was a statistically significant elevation of serum CEA levels in patients with CRC compared to patients with precancerous benign colorectal diseases. Furthermore, there was no statistically significant difference in serum CA 19-9 levels between all groups (p = 0.102). Interestingly, CCAT1 expression was significantly upregulated in the blood of CRC patients compared to the precancerous benign colorectal diseases group (p = 0.009) and the control group (p&lt;0.001). Also, expression of CCAT1 was significantly elevated in patients with precancerous benign colorectal diseases compared to the control group (p=0.004). In conclusion, measuring the expression level of CCAT1 is more advised than assessment of CEA and CA 19-9 for the early diagnosis and prognosis of colorectal cancer.

Advances in circulating tumor cells for early detection, prognosis and metastasis reduction in lung cancer.

Globally, lung cancer stands as the leading type of cancer in terms of incidence and is the major source of mortality attributed to cancer. We have outlined the molecular biomarkers for lung cancer that are available clinically. Circulating tumor cells (CTCs) spread from the original location, circulate in the bloodstream, extravasate, and metastasize, forming secondary tumors by invading and establishing a favorable environment. CTC analysis is considered a common liquid biopsy method for lung cancer. We have enumerated both in vivo and ex vivo techniques for CTC separation and enrichment, examined the advantages and limitations of these methods, and also discussed the detection of CTCs in other bodily fluids. We have evaluated the value of CTCs, as well as CTCs in conjunction with other biomarkers, for their utility in the early detection and prognostic assessment of patients with lung cancer. CTCs engage with diverse cells of the metastatic process, interfering with the interaction between CTCs and various cells in metastasis, potentially halting metastasis and enhancing patient prognosis.

Emerging biomarkers in gestational diabetes: perspectives for improving diagnosis and prognoses

Gestational diabetes mellitus is a pathology suffered by one in seven pregnant women worldwide, in which carbohydrate metabolism is altered, that is, glucose intolerance occurs. It is recognized for the first time in pregnancy since these adaptations occur with the objective of providing an adequate amount of glucose to the fetus for its development. They are related to risk factors such as age, diet, lack of physical activity, among others, and can cause fetal macrosomia, neonatal jaundice, and cardiorespiratory complications. In the mother, it is diagnosed through different blood glucose tests such as the oral glucose tolerance test, O'Sullivan test, however currently new biomarkers have been implemented for an early prognosis such as IL-27, a protein retinol transporter 4, maternal fetuin A, high sensitivity C-reactive protein and molecular biomarkers, but more studies are required so that these biochemical markers can be standardized by an organization

2020 JAPAN Critical Limb Ischemia Database (JCLIMB) Annual Report.

Since 2013, the Japanese Society for Vascular Surgery has started the project of nationwide registration and tracking database for patients with critical limb ischemia (CLI) who are treated by vascular surgeons. The purpose of this project is to clarify the current status of the medical practice for the patients with CLI to contribute to the improvement of the quality of medical care. This database, called JAPAN Critical Limb Ischemia Database (JCLIMB), is created on the National Clinical Database and collects data of patients' background, therapeutic measures, early results, and long-term prognosis as long as 5 years after the initial treatment. The limbs managed conservatively are also registered in JCLIMB, together with those treated by surgery and/or endovascular treatment. In 2020, 1299 CLI limbs (male 890 limbs: 69%) were registered by 85 facilities. Arteriosclerosis obliterans has accounted for 99% of the pathogenesis of these limbs. In this manuscript, the background data and the early prognosis of the registered limbs are reported. (This is a translation of Jpn J Vasc Surg 2023; 32: 363-391.).

Determinants of Early Prognosis after Acute Type A Aortic Dissection Surgery.

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Construction of prognostic nomogram based on the SEER database for esophageal cancer patients

Currently, the incidence of esophageal cancer continues to rise around the world. Because of its good early prognosis, it is of great significance to establish an effective model for predicting the survival of EC patients. The purpose of this study was to predict survival after diagnosis in Esophageal Cancer (EC) patients by constructing a valid clinical nomogram. In this study, 5037 EC patient samples diagnosed from 2010 to 2015 were screened by accessing the SEER database, and 8 independent prognostic factors were screened by various methods, and Cox multivariate regression was included to construct a prognostic model and nomogram for esophageal cancer. to estimate esophageal cancer recurrence and overall survival. Calibration of the nomogram predicted probabilities of 1-year, 3-year and 5-year survival probability, which were closely related to actual survival. In conclusion, this study validated that the column-line graphical model can be considered an individualized quantitative tool for predicting the prognosis of patients with EC in order to assist clinicians in making therapeutic decisions.

Artificial Intelligence in Laboratory Technologies for Early Detection and Prognostication of Sepsis: A Systematic Review

Background: Sepsis: a complex clinical syndrome--represents life-threatening organ dysfunction instigated by an infection's dysregulated host response. Early detection and accurate prognostication of sepsis are crucial; they pave the way for timely intervention, ultimately enhancing patient outcomes. The rise in interest towards Artificial Intelligence (AI) applications within laboratory technologies is directly related to its potential for improving early detection and prognosis forecasting in sepsis cases; this interest comes as AI continues its advancement. Methods: We conducted a systematic review of studies utilizing AI algorithms in laboratory settings for early sepsis detection and prognostication: our methods entailed searching relevant databases for research published until October 2023. Our inclusion criteria spanned original articles; these applied machine learning (ML) and deep learning (DL) techniques to laboratory data--with the aim being sepsis prediction. We assessed the quality of the studies, extracted and synthesized data on AI model performance metrics - including: area under receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: The review encompassed eight studies meeting the inclusion criteria; AI models showcased exceptional predictive capabilities--evidenced by a range of AUROC values from 0.799 to 0.9213, signifying noticeably acceptable performance. However, there was wide variation in sensitivity and specificity among these analyses: an indicator of heterogeneity in model performance. Superior prognostic accuracy and potential for real-time monitoring of patients' early sepsis signs emerged in several models: notably, within the first 12 hours of patient admission - their highest predictive period. The models frequently outperformed traditional scoring systems. Conclusion: Laboratory technology's AI applications significantly promise sepsis' early detection and prognostication. Reviewed studies suggest AI models may surpass traditional methods, offering potential integration into clinical workflows for rapid sepsis identification aid. Nevertheless, we also acknowledged both the variability in model performance and necessity of additional validation across diverse clinical settings. Future research: it must concentrate on two key aspects--the refinement of AI algorithms to enhance sensitivity and precision; furthermore, it should delve into evaluating the clinical impact of tools for sepsis prediction that are assisted by AI.