Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study

Global Epidemiology of Invasive Infections by Uncommon Candida Species: A Systematic Review

Emerging and uncommon Candida species have been reported as an increasing cause of invasive Candida infections (ICI). We aim to systematize the global epidemiology associated with emergent uncommon Candida species responsible for invasive infections in adult patients. A systematic review (from 1 January 2001 to 28 February 2023) regarding epidemiological, clinical, and microbiological data associated to invasive Candida infections by uncommon Candida spp. were collected. In total, 1567 publications were identified, and 36 were selected according to inclusion criteria (45 cases). The chosen studies covered: C. auris (n = 21), C. haemulonii (n = 6), C. fermentati (n = 4), C. kefyr (n = 4), C. norvegensis (n = 3), C. nivariensis (n = 3), C. bracarensis (n = 1), C. duobushaemulonii (n = 1), C. blankii (n = 1), and C. khanbhai (n = 1). Over the recent years, there has been an increase in the number of invasive infections caused by uncommon Candida spp. Asia and Europe are the continents with the most reported cases. The challenges in strain identification and antifungal susceptibility interpretation were significant. The absence of clinical breakpoints for the susceptibility profile determination for uncommon Candida spp. makes interpretation and treatment options a clinical challenge. It is crucial that we focus on new and accessible microbiology techniques to make fast and accurate diagnostics and treatments.

Risk Factors for 30-Day Mortality of Community-Acquired Bloodstream Infection Patients in Changsha City, Hunan Province, China

Purpose

To analyze the factors affecting patients' prognoses based on the community acquired-bloodstream infection patient data from 2017 to 2021.

Patients and Methods

The data of 940 patients were retrieved, having at least one positive bilateral blood culture within 48 hours of hospitalization, and grouped into survivor and non-survivor groups. The clinical characteristics, laboratory results, causative pathogen and other indicators were collected and compared, and risk factors were identified by applying Cox proportional hazard regression model to the data.

Results

Community acquired-bloodstream infection is most commonly caused by Escherichia coli, Klebsiella species and Staphylococcus hominis. Among the total of 940 selected patients, 52 (5.5%) died during hospitalization. The demographic parameters like age and gender, clinical protocols like maintenance hemodialysis, glucocorticoid use during hospitalization, catheter placement, procaicitonin, total protein, albumin, creatinine, uric acid contents and Sequential Organ Failure Assessment scores were significantly different between the survivor and non-survivor groups. The survival analysis results revealed that age (HR=1.02, 95% CI: 1.00-1.05, P=0.002), glucocorticoid use during hospitalization (HR=3.69, 95% CI: 1.62-8.37, P=0.021) and Sequential Organ Failure Assessment score (HR=1.10, 95% CI: 1.03-1.18, P=0.004) might be the risk factors affecting 30-day mortality in patients with community acquired-bloodstream infection.

Conclusion

The identified risk factors may help guide clinical treatment protocol for patients with community acquired-bloodstream infection, providing more effective treatment strategy selection with improved clinical outcomes.

In silico method and bioactivity evaluation to discover novel antimicrobial agents targeting FtsZ protein: Machine learning, virtual screening and antibacterial mechanism study

This research paper utilizes a fused-in-silico approach alongside bioactivity evaluation to identify active FtsZ inhibitors for drug discovery. Initially, ROC-guided machine learning was employed to obtain almost 13182 compounds from three libraries. After conducting virtual screening to assess the affinity of 2621 acquired compounds, cluster analysis and bonding model analysis led to the discovery of five hit compounds. Additionally, antibacterial activity assays and time-killing kinetics revealed that T3995 could eliminate Staphylococcus aureus ATCC6538 and Bacillus subtilis ATCC9732, with MIC values of 32 and 2 μg/mL. Further morphology and FtsZ polymerization assays indicated that T3995 could be an antimicrobial inhibitor by targeting FtsZ protein. Moreover, hemolytic toxicity evaluation demonstrated that T3995 is safe at or below 16 ug/mL concentration. Additionally, bonding model analysis explained how the compound T3995 can display antimicrobial activity by targeting the FtsZ protein. In conclusion, this study presents a promising FtsZ inhibitor that was discovered through a fused computer method and bioactivity evaluation.

Prediction of candidemia with machine learning techniques: state of the art

In this narrative review, we discuss studies assessing the use of machine learning (ML) models for the early diagnosis of candidemia, focusing on employed models and the related implications. There are currently few studies evaluating ML techniques for the early diagnosis of candidemia as a prediction task based on clinical and laboratory features. The use of ML tools holds promise to provide highly accurate and real-time support to clinicians for relevant therapeutic decisions at the bedside of patients with suspected candidemia. However, further research is needed in terms of sample size, data quality, recognition of biases and interpretation of model outputs by clinicians to better understand if and how these techniques could be safely adopted in daily clinical practice.

Hospital Acquired Sepsis, Disease Prevalence, and Recent Advances in Sepsis Mitigation

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, commonly associated with nosocomial transmission. Gram-negative bacterial species are particularly problematic due to the release of the lipopolysaccharide toxins upon cell death. The lipopolysaccharide toxin of E. coli has a greater immunogenic potential than that of other Gram-negative bacteria. The resultant dysregulation of the immune system is associated with organ failure and mortality, with pregnant women, ICU patients, and neonates being particularly vulnerable. Additionally, sepsis recovery patients have an increased risk of re-hospitalisation, chronic illness, co-morbidities, organ damage/failure, and a reduced life expectancy. The emergence and increasing prevalence of antimicrobial resistance in bacterial and fungal species has impacted the treatment of sepsis patients, leading to increasing mortality rates. Multidrug resistant pathogens including vancomycin-resistant Enterococcus, beta lactam-resistant Klebsiella, and carbapenem-resistant Acinetobacter species are associated with an increased risk of mortality. To improve the prognosis of sepsis patients, predominantly high-risk neonates, advances must be made in the early diagnosis, triage, and control of sepsis. The identification of suitable biomarkers and biomarker combinations, coupled with machine learning and artificial intelligence, show promise in early detection protocols. Rapid diagnosis of sepsis in patients is essential to inform on clinical treatment, especially with resistant infectious agents. This timely review aims to discuss sepsis prevalence, aetiology, and recent advances towards disease mitigation and control.

Efficacy of Random Forest Models in Predicting Multidrug-Resistant Gram-Negative Bacterial Nosocomial Infections Compared to Traditional Logistic Regression Models

This study evaluates whether random forest (RF) models are as effective as traditional Logistic Regression (LR) models in predicting multidrug-resistant Gram-negative bacterial nosocomial infections. Data were collected from 541 patients with hospital-acquired Gram-negative bacterial infections at two tertiary-level hospitals in Urumqi, Xinjiang, China, from August 2022 to November 2023. Relevant literature informed the selection of significant predictors based on patients' pre-infection clinical information and medication history. The data were split into a training set of 379 cases and a validation set of 162 cases, adhering to a 7:3 ratio. Both RF and LR models were developed using the training set and subsequently evaluated on the validation set. The LR model achieved an accuracy of 84.57%, sensitivity of 82.89%, specificity of 80.10%, positive predictive value of 84%, negative predictive value of 85.06%, and a Yoden index of 0.69. In contrast, the RF model demonstrated superior performance with an accuracy of 89.51%, sensitivity of 90.79%, specificity of 88.37%, positive predictive value of 87.34%, negative predictive value of 91.57%, and a Yoden index of 0.79. Receiver operating characteristic curve analysis revealed an area under the curve of 0.91 for the LR model and 0.94 for the RF model. These findings indicate that the RF model surpasses the LR model in specificity, sensitivity, and accuracy in predicting hospital-acquired multidrug-resistant Gram-negative infections, showcasing its greater potential for clinical application.

Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis

BACKGROUND

Since AI algorithms can analyze patient data, medical records, and imaging results to suggest treatment plans and predict outcomes, they have the potential to support pathologists and clinicians in the diagnosis and treatment of oral and maxillofacial pathologies, just like every other area of life in which it is being used. The goal of the current study was to examine all of the trends being investigated in the area of oral and maxillofacial pathology where AI has been possibly involved in helping practitioners.

METHODS

We started by defining the important terms in our investigation's subject matter. Following that, relevant databases like PubMed, Scopus, and Web of Science were searched using keywords and synonyms for each concept, such as "machine learning," "diagnosis," "treatment planning," "image analysis," "predictive modelling," and "patient monitoring." For more papers and sources, Google Scholar was also used.

RESULTS

The majority of the 9 studies that were chosen were on how AI can be utilized to diagnose malignant tumors of the oral cavity. AI was especially helpful in creating prediction models that aided pathologists and clinicians in foreseeing the development of oral and maxillofacial pathology in specific patients. Additionally, predictive models accurately identified patients who have a high risk of developing oral cancer as well as the likelihood of the disease returning after treatment.

CONCLUSIONS

In the field of oral and maxillofacial pathology, AI has the potential to enhance diagnostic precision, personalize care, and ultimately improve patient outcomes. The development and application of AI in healthcare, however, necessitates careful consideration of ethical, legal, and regulatory challenges. Additionally, because AI is still a relatively new technology, caution must be taken when applying it to this industry.

Procalcitonin and C-reactive protein as diagnostic biomarkers in COVID-19 and Non-COVID-19 sepsis patients: a comparative study

BACKGROUND

This study aimed to assess and compare procalcitonin (PCT) and C-reactive protein (CRP) levels between COVID-19 and non-COVID-19 sepsis patients. Additionally, we evaluated the diagnostic efficiency of PCT and CRP in distinguishing between Gram-positive (GP) and Gram-negative (GN) bacterial infections. Moreover, we explored the associations of PCT with specific pathogens in this context.

METHODS

The study included 121 consecutive sepsis patients who underwent blood culture testing during the COVID-19 epidemic. PCT and CRP were measured, and reverse transcriptase-polymerase chain reaction (RT-PCR) was employed for the detection of COVID-19 nucleic acid. The Mann-Whitney U-test was used to compare PCT and CRP between the COVID-19 and non-COVID-19 groups. Receiver operating characteristic (ROC) curves were generated to compare PCT and CRP levels in the GN group versus the GP group for assessing the diagnostic efficiency. The kruskal-Wallis H test was applied to assess the impact of specific pathogen groups on PCT concentrations.

RESULTS

A total of 121 sepsis patients were categorized into a COVID-19 group (n = 25) and a non-COVID-19 group (n = 96). No significant differences in age and gender were observed between the COVID-19 and non-COVID-19 groups. The comparison of biomarkers between these groups showed no statistically significant differences. The optimal cut-off values for PCT and CRP in differentiating between GP and GN infections were 1.03 ng/mL and 34.02 mg/L, respectively. The area under the ROC curve was 0.689 (95% confidence interval (CI) 0.591-0.786) for PCT and 0.611 (95% CI 0.505-0.717) for CRP. The diagnostic accuracy was 69.42% for PCT and 58.69% for CRP. The study found a significant difference in PCT levels among specific groups of pathogens (P < 0.001), with the highest levels observed in Escherichia coli infections. The frequency of Staphylococcus spp. positive results was significantly higher (36.0%) in COVID-19 compared to non-COVID-19 sepsis patients (P = 0.047).

CONCLUSION

Sepsis patients with COVID-19 revealed a significantly higher culture positivity for staphylococcus spp. than the non-COVID-19 group. Both PCT and CRP showed moderate diagnostic efficiency in differentiating between GP and GN bacterial infections. PCT showed potential utility in identifying E. coli infections compared to other pathogens.

Invasive Candidiasis in Patients with Solid Tumors: A Single-Center Retrospective Study

Background

Invasive candidiasis (ICs) is one of the common causes of death in patients with solid tumors. However, studies on the clinical characteristics of ICs with solid tumors are limited.

Methods

The purpose of this study was to retrospectively analyse the clinical characteristics, laboratory results and risk factor prediction of inpatients with ICs and solid tumors. We reviewed the clinical data and candida specimen information of hospitalized patients diagnosed with solid tumors combined with ICs at the First Hospital of China Medical University from January 2016 to December 2020. Multivariate logistic regression analysis was used to assess the prognostic factors associated with mortality in these patients.

Results

A total of 243 ICs patients with solid tumors were included in this study. The average ± SD age was 62.8 ± 11.7 (range: 27-93 years old), of which nearly 41% were ≥ 65 years old (99/243, 40.7%), and most were male (162/243, 66.6%). Most patients had malignant tumors of the digestive system. The most common candida was Candida parapsilosis (101/243, 41.5%), followed by Candida guilliermondii (83/243, 34.1%), Candida albicans (32/243, 13.1%), Candida glabrata (17/243, 6.9%), Candida tropicalis (7/243, 2.8%) and Candida krusei (3/243, 1.2%). Multivariate logistic regression analysis showed that the length of stay in the ICU, urinary catheter, total parenteral nutrition, stay in the ICU, renal failure and neutrophil count were prognostic factors related to death.

Conclusion

In this study, based on the clinical data of solid tumor patients with ICs in the past 5 years, the results showed that the length of stay in the ICU, urinary catheter, total parenteral nutrition, stay in the ICU, renal failure and neutrophil count were identified as the main prognostic factors. This study can be used to help clinicians carry out early intervention for high-risk patients.

Periodontitis is associated with the development of fungal sinusitis: A nationwide 12-year follow-up study

AIM

The incidence of fungal sinusitis is increasing; however, its pathophysiology has not been investigated previously. We investigate the effect of periodontitis on the incidence of fungal sinusitis over a 12-year follow-up period using nationwide population-based data.

MATERIALS AND METHODS

The periodontitis group was randomly selected from the National Health Insurance Service database. The non-periodontitis group was obtained by propensity score matching considering several variables. The primary end point was the diagnosis of sinonasal fungal balls (SFBs) and invasive fungal sinusitis (IFS).

RESULTS

The periodontitis and non-periodontitis groups included 12,442 and 12,442 individuals, respectively. The overall adjusted hazard ratio (aHR) for SFBs in the periodontitis group was 1.46 (p = .002). In subgroup analysis, the aHR for SFBs was 1.59 (p = 0.008) for those with underlying chronic kidney disease (CKD), 1.58 (p = .022) for those with underlying atopic dermatitis, 1.48 (p = .019) for those with chronic obstructive pulmonary disease (COPD), and 1.36 (p = .030) for those with diabetes mellitus (DM), but these values are applicable only when considering the relationship between periodontitis and SFB. The aHR for IFS in the periodontitis group was higher than in the non-periodontitis group (2.80; p = .004).

CONCLUSIONS

The risk of SFBs and IFS increased after diagnosis of periodontitis. This trend is often more severe in patients with DM, COPD, or CKD, but this association with underlying diseases is applicable only when considering the association between periodontitis and fungal sinusitis.

Random forest model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression model

OBJECTIVE

To explore if random forest (RF) model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression(LR) model.

METHODS

A total of 254 cases of hospital-acquired Klebsiella pneumoniae infection in a tertiary hospital in Beijing from January 2016 to December 2020 were retrospectively collected. Appropriate influencing factors were selected by referring to relevant articles from the aspects of basic clinical information and contact history before infection, and divided into a training set and a test set. Both the RF and LR models were trained by the training set, and using testing set to compare these two models.

RESULTS

The prediction accuracy of the LR model was 87.0%, the true positive rate of the LR model was 94.7%; the false negative rate of the LR model was 5.3%; the false positive rate of the LR model was 35%; the true negative rate of the LR model was 65%; the sensitivity of the LR model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 94.7%; and the specificity was 65%. The prediction accuracy of the RF model was 89.6%; the true positive rate of the RF model was 92.1%; the false negative rate of the RF model was 7.9%; the false positive rate of the RF model was 21.4%; the true negative rate of the RF model was 78.6%; the sensitivity of the RF model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 92.1%; and the specificity was 78.6%. ROC curve shows that the area under curve(AUC) of the LR model was 0.91, and that of the RF model was 0.95.

CONCLUSION

The RF model has higher specificity, sensitivity, and accuracy for the prognostic prediction of hospital-acquired Klebsiella pneumoniae infection than the LR model and has greater clinical application prospects.

Prediction of Prognostic Risk Factors in Patients with Invasive Candidiasis and Cancer: A Single-Centre Retrospective Study

Background

Invasive candidiasis is a common cancer-related complication with a high fatality rate. If patients with a high risk of dying in the hospital are identified early and accurately, physicians can make better clinical judgments. However, epidemiological analyses and mortality prediction models of cancer patients with invasive candidiasis remain limited.

Method

A set of 40 potential risk factors was acquired in a sample of 258 patients with both invasive candidiasis and cancer. To begin, risk factors for Candida albicans vs. non-Candida albicans infections and persistent vs. nonpersistent Candida infections were analysed using classic statistical methods. Then, we applied three machine learning models (random forest, logistic regression, and support vector machine) to identify prognostic indicators related to mortality. Prediction performance of different models was assessed by precision, recall, F1 score, accuracy, and AUC.

Results

Of the 258 patients both with invasive candidiasis and cancer included in the analysis. The median age of patients was 62 years, and 95 (36.82%) patients were older than 65 years, of which 178 (66.28%) were male. And 186 (72.1%) patients underwent surgery 2 weeks before data collection, 100 (39.1%) patients stayed in ICU during hospitalisation, 99 (38.4%) patients had bacterial blood infection, 85 (32.9%) patients had persistent invasive candidiasis, and 41 (15.9%) patients died within 30 days. The usage of drainage catheter and prolonged length of hospitalisation are the dominant risk factors for non-Candida albicans infections and persistent Candida infections, respectively. Risk factors, such as septic shock, history of surgery within the past 2 weeks, usage of drainage tubes, length of stay in ICU, total parenteral nutrition, serum creatinine level, fungal antigen, stay in ICU during hospitalisation, and total bilirubin level, were significant predictors of death. The RF model outperformed the LR and SVM models. Precision, recall, F1 score, accuracy, and AUC for RF were 64.29%, 75.63%, 69.23%, 89.61%, and 91.28%.

Conclusions

In this study, the machine learning-based models accurately predicted the prognosis of cancer and invasive candidiasis patients. The algorithm could be used to help clinicians in high-risk patients' early intervention.

Diagnoses Based on C-Reactive Protein Point-of-Care Tests

C-reactive protein (CRP) is an important part of the immune system's reaction to various pathological impulses such as bacterial infections, systemic inflammation, and internal organ failures. An increased CRP level serves to diagnose the mentioned pathological states. Both standard laboratory methods and simple point-of-care devices such as lateral flow tests and immunoturbidimetric assays serve for the instrumental diagnoses based on CRP. The current method for CRP has many flaws and limitations in its use. Biosensor and bioassay analytical devices are presently researched by many teams to provide more sensitive and better-suited tools for point-of-care tests of CRP in biological samples when compared to the standard methods. This review article is focused on mapping the diagnostical relevance of CRP, the applicability of the current analytical methods, and the recent innovations in the measurement of CRP level.