Search Results (38)

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Carbapenem-resistant Enterobacteriaceae (CRE) pose an emerging threat, with a high mortality rate among children with cancer. This study aimed to evaluate the impact of routine rectal swab surveillance and rapid PCR-based detection of carbapenemase genes to facilitate the early initiation of appropriate treatment and assess its effects on outcomes. The study compared two groups of pediatric cancer patients with CRE bloodstream infections: a retrospective cohort of 254 patients from 2013 to 2017, and a prospective cohort of 186 patients from 2020 to 2022, following the implementation of these tools. A rapid diagnostic test in the prospective cohort resulted in the early initiation of proper antibiotics in 85% (165/186) of patients, compared to only 58% (147/254) in the retrospective group. This led to a decrease in the need for ICU admission related to sepsis from CRE and a significant reduction in the 30-day mortality rate (16% vs. 30%, p ≤ 0.01). Genotypic profiling revealed that class B carbapenemases were the most prevalent (69%), with the NDM type being identified in 67% of patients. OXA-48 and KPC enzymes were detected in 59% and 4% of patients, respectively. Multivariate analysis revealed that patients having Klebsiella pneumoniae, NDM genotype carbapenemases, presence of pneumonia, and septic shock requiring ICU admission were predictors of poor outcomes. Rapid diagnostics and targeted colonization lead to the appropriate use of targeted antibiotics, resulting in improved patient outcomes. Understanding carbapenemase-producing microorganisms and administering newer antibiotics may further reduce mortality and enhance treatment strategies for high-risk patients. Full article

Background/Objectives: Febrile neutropenia is a frequent and potentially life-threatening complication in pediatric oncology patients receiving chemotherapy. Due to profound immunosuppression, early diagnosis of infections remains a major clinical challenge. This review evaluates the diagnostic and prognostic utility of infection biomarkers in children with chemotherapy-induced severe neutropenia. Methods: We reviewed clinical studies that assessed the diagnostic performance of inflammatory biomarkers—including C-reactive protein (CRP), procalcitonin (PCT), interleukins (IL-6, IL-8, IL-10), and others—in pediatric febrile neutropenia. The review includes data on sensitivity, specificity, predictive value, and clinical applications. Results: CRP remains a common but nonspecific marker, often insufficient for early stratification. PCT showed consistently high negative predictive value and early responsiveness to bacterial infections. IL-6 and IL-10 demonstrated strong early diagnostic accuracy in the early phase (AUC > 0.80 in multiple studies) and were particularly useful in predicting septic shock when combined. IL-8, while less specific, may help rule out infection when levels are low. Emerging biomarkers such as presepsin, MR-proADM, and PSP showed promising diagnostic performance. Presepsin achieved near-perfect accuracy in some cohorts (AUC up to 0.996), outperforming CRP and PCT, though its ability to discriminate bacteremia at fever onset varied. MR-proADM demonstrated consistent AUCs above 0.75 and may support early sepsis identification. PSP was associated with significantly elevated levels in sepsis. Additional novel markers—including sTNFR-II, sIL-2R, IP-10, Flt-3L, MCP-1-a, and MBL—showed encouraging diagnostic profiles in individual studies, particularly due to high specificity, but require external validation. G-CSF also emerged as a promising candidate in multimarker models. In contrast, TNF-α and IL-1β displayed limited utility as standalone indicators. Conclusions: Biomarkers such as PCT, IL-6, Il-8, and IL-10 offer valuable tools for early infection detection and risk stratification in pediatric febrile neutropenia. Emerging markers—including presepsin, MR-proADM, and PSP—further enhance diagnostic precision and may support early identification of sepsis. Multimarker strategies, particularly those incorporating presepsin, IL-10, or MR-proADM, show potential to improve diagnostic performance beyond conventional markers. Further prospective validation is needed to optimize clinical implementation and guide personalized treatment decisions. Full article

Background/Objectives: Probiotic supplementation has been actively investigated in preterm populations to reduce the risk of necrotizing enterocolitis (NEC) and late-onset sepsis. Despite this, few studies have focused on clinically relevant feeding tolerance and growth outcomes, and there is an alarming lack of evidence surrounding extremely preterm infants (defined as birth before 28 weeks gestational age), those most at risk of severe comorbidities. We aimed to investigate whether probiotics improve feeding tolerance, neonatal growth and neonatal morbidity among extremely preterm infants. Methods: A literature search was conducted in Medline, Embase, Cochrane CENTRAL, Web of Science, and clinicaltrials.gov for ongoing trials. We included extremely preterm infants from randomized controlled trials and non-randomized trials with a concurrent control group. Two authors independently performed screening, data extraction and risk of bias assessment using the Risk of Bias 2 tool from Cochrane. The certainty of the evidence was assessed using GRADE. Results: Eleven RCTs and three non-randomized studies with a concurrent control group were included, analyzing a total of 14,888 extremely preterm infants. Meta-analyses revealed lower mean days to full enteral feeds (mean difference 1.1 days lower; 95% CI, 7.83 lower to 5.56 higher) and lower duration of parenteral nutrition (mean difference 2.4 days lower; 95% CI, 7.44 lower to 2.58 higher) in infants treated with probiotics; however, this was not statistically significant. There was a significant reduction in NEC (RR; 0.80, 95% CI; 0.68, 0.93) and all-cause mortality (RR; 0.56, 95% CI; 0.33, 0.93) in the probiotic group. All outcomes were graded at low or very low certainty of evidence. Conclusions: The findings indicate a trend towards a potential beneficial effect of probiotic supplementation in reducing feeding intolerance and a notable reduction of risk of NEC and all-cause mortality in infants receiving probiotics. Future RCTs will focus on feeding intolerance, and growth outcomes are warranted. Full article

Introduction: Neonatal sepsis is a severe and life-threatening condition caused by pathogens in the systemic circulation within the first 28 days of life. The classical definition of neonatal sepsis implies positive central cultures, but recent findings discuss culture-negative sepsis (clinical sepsis associated with laboratory findings). Since infected neonates initially express few non-specific clinical signs and there are unreliable biochemical markers to identify sepsis in the early stages, it is essential to improve the accuracy of diagnosis and reduce unnecessary antibiotic exposure. Objective: Our study aims to assess the influence of risk factors and the utility of currently used biomarkers in culture-negative neonatal early-onset sepsis (CN-EOS). Materials and methods: We performed a retrospective study at Bucharest University Hospital, which included 131 preterm and term newborns at risk for EOS admitted in the Neonatal Intensive Care Unit (NICU) over 12 months. The neonates included were classified into two groups: confirmed negative-culture early-onset sepsis (CN-EOS) and suspected early-onset sepsis (S-EOS). Patients from both groups received antibiotic therapy from the first day of life; the type and duration of antibiotic therapy were different in the two groups. For all the patients, we measured C-reactive protein (CRP), procalcitonin (PCT) and white blood count (WBC) at birth and after 72 h, tested blood culture in the first 24 h of life and correlated the results with clinical signs and prenatal risk factors. Categorical variables were presented as frequencies and percentages, while the continuous variables were the mean and the standard deviation. The differences between the continuous variable groups were determined by Student’s t-test or the Mann–Whitney U test, whereas for the categorical variables, the Chi-square test (X²) was employed. The performance of laboratory biomarkers (CRP and PCT) in diagnosing confirmed EOS was calculated. All the tests were statistically significant at a p-value < 0.05. Results: The findings support the significance of low birth weight and gestational age and low Apgar scores as potential indicators for EOS; PROM diagnosed with chorioamnionitis and smoking during the pregnancy were also important predictive risk factors. Respiratory signs, such as apnea and respiratory distress syndrome, were most encountered in the clinical evaluation of infants with CN-EOS. Inflammatory markers were inconsistent in CN-EOS cases, proving that they are not reliable enough for initiating, continuing or stopping antibiotic therapy. Conclusions: Culture-negative neonatal sepsis remains a significant challenge for the neonatologist, since the time elapsed between the moment sepsis is suspected and the initiation of empirical therapy can make the difference between survival and death. Continued efforts are needed to develop more reliable and effective diagnostic tools for timely and appropriate intervention. Full article

Background/Objectives: Early identification of pediatric sepsis complications in intensive care is challenging and requires improved diagnostic tools. This study aimed to compare the Phoenix Sepsis Score (PSS), pSOFA, PELOD-2, and PRISM III in assessing clinical complexity in children with septic and critical conditions in the PICU and to identify the most suitable scale for this patient cohort. Methods: Data were collected prospectively from 53 children between June 2022 and January 2024. Patients were categorized into septic (n = 42) and non-infectious SIRS (n = 11) and further classified by outcome—with/without complications (n = 23/30). The predictive accuracy of the scoring systems was evaluated by discrimination and calibration and by recalibration for the PSS for improved performance. Results: Respiratory (18.8%) and neurological complications (9.4%) were the most common adverse events. Clinical deterioration was observed in 43.4% of cases, including one fatality. Patients with complications stayed longer in the PICU (14 ± 10 days). In the patients with complications, all scoring systems had higher median values. Only PSS showed a significant difference (p = 0.0023). PSS demonstrated the highest overall predictive accuracy (76.2%) outperforming PRISM III (62.3%) and PELOD-2 (58.5%). The pSOFA scale showed high accuracy (88.0%) in identifying patients without complications. The strongest association was between chronic disease (hazard ratio Exp(B) = 1.718) and deteriorations, while mechanical ventilation suggested a reduced risk of complications (Exp(B) = 0.509). Conclusions: PSS showed superior predictive accuracy (76.2%) for deteriorations in pediatric patients with suspected infection and proved adaptable for further validation in larger populations. Full article

Background: Sepsis is a critical condition characterized by severe immune dysregulation, ranking among the leading causes of morbidity and mortality in intensive care and internal medicine units. Nutritional status plays a pivotal role in modulating these responses, as when inadequate it can compromise immune defenses, the body’s ability to handle stress and inflammation, and the clinical course. Malnutrition is frequently observed in septic patients and is strongly associated with worse clinical outcomes, including increased mortality, prolonged hospital stays, and greater complication rates. In this context, nutritional scoring systems have emerged as valuable tools to evaluate patients’ nutritional status and predict clinical trajectories. Objectives: Given the absence of a direct comparison of their performance in an internal medicine setting, this study aimed to assess the effectiveness of various nutritional scores as predictive tools for clinical outcomes in septic patients, emphasizing their application within the field of internal medicine. Methods and Results: A retrospective analysis was conducted on 143 patients diagnosed with sepsis or septic shock who were admitted to an internal medicine unit. Key variables included clinical and laboratory parameters, comorbidities, and nutritional scores at the time of diagnosis. The modified Glasgow Prognostic Score (mGPS), the Prognostic Nutritional Index (PNI), the Controlling Nutritional Status (CONUT) score, the modified Nutrition Risk in Critically Ill (mNUTRIC) score, and the blood urea nitrogen-to-albumin ratio (BAR) were evaluated in forecasting mortality and clinical outcomes in patients with sepsis. Among them, the mNUTRIC score emerged as the strongest independent predictor of in-hospital mortality, with a good performance and a reasonable threshold for risk stratification. Conclusions: The study highlights the mNUTRIC score’s practicality and reliability in assessing nutritional and inflammatory risks in septic patients, particularly in non-ICU settings. These findings suggest its potential utility in guiding nutritional interventions and improving clinical outcomes, emphasizing the importance of integrating nutritional assessment into sepsis management. Full article

Identifying and managing pediatric sepsis is a major research focus, yet early detection and risk assessment remain challenging. In its early stages, sepsis symptoms often mimic those of mild infections or chronic conditions, complicating timely diagnosis. Although various early warning scores exist, their effectiveness is limited, particularly in prehospital settings where accurate, rapid assessment is crucial. This review examines the roles of clinical prediction tools and biomarkers in pediatric sepsis. Traditional biomarkers, like procalcitonin (PCT), have improved diagnostic accuracy but are insufficient alone, often resulting in overprescription of antibiotics or delayed treatment. Combining multiple biomarkers has shown promise for early screening, though this approach can be resource-intensive and less feasible outside hospitals. Predicting sepsis outcomes to tailor therapy remains underexplored. While serial measurements of traditional biomarkers offer some prognostic insight, their reliability is limited, with therapeutic decisions often relying on clinical judgment. Novel biomarkers, particularly those identifying early organ dysfunction, hold potential for improved prognostic accuracy, but significant barriers remain. Many are only available in hospitals, require further validation, or need specialized assays not commonly available, limiting broader clinical use. Further research is needed to establish reliable protocols and enhance the clinical applicability of these tools. Meanwhile, a multifaceted approach that combines clinical judgment with existing tools and biomarkers remains essential to optimize pediatric sepsis management, improving outcomes and minimizing risks. Full article

Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms. Background/Objectives: Over the past few decades, ML and other AI tools have been explored extensively in sepsis, with models developed for the early detection, diagnosis, prognosis, and even real-time management of treatment strategies. Methods: This review was conducted according to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research Type) framework to define the study methodology. A critical overview of each paper was conducted by three different reviewers, selecting those that provided original and comprehensive data relevant to the specific topic of the review and contributed significantly to the conceptual or practical framework discussed, without dwelling on technical aspects of the models used. Results: A total of 194 articles were found; 28 were selected. Articles were categorized and analyzed based on their focus—early prediction, diagnosis, mortality or improvement in the treatment of sepsis. The scientific literature presents mixed outcomes; while some studies demonstrate improvements in mortality rates and clinical management, others highlight challenges, such as a high incidence of false positives and the lack of external validation. This review is designed for clinicians and healthcare professionals, and aims to provide an overview of the application of AI in sepsis management, reviewing the main studies and methodologies used to assess its effectiveness, limitations, and future potential. Full article

Neonatal sepsis remains a significant cause of neonatal morbidity and mortality globally. At present, no clear consensus definition for sepsis in neonates exists, even though a positive blood culture is considered as the gold standard for definitive diagnosis. The accurate and timely diagnosis of sepsis in neonates presents significant difficulties, since “culture negative” or “suspected” sepsis varies widely worldwide. Antibiotic overuse and resistance are emerging problems, but on the other hand, under-recognition of sepsis and delayed antibiotic treatment could have serious or even fatal adverse outcomes in this vulnerable population. In the context of rapid recognition of sepsis and timely initiation of appropriate antibiotic therapy, various sepsis risk assessment tools have been developed, a variety of biomarkers are in clinical use or under research for future use, and new diagnostic techniques are under evaluation. In this review, we summarize the most recent data on neonatal sepsis, the utility of sepsis risk assessment tools for term and preterm infants with sepsis, and current diagnostic and preventive tools. Full article

Background/Objective: Sepsis is a disease with a high mortality rate, which emphasizes the importance of developing tools for the early identification of high-risk patients and to initiate timely treatments to reduce mortality. The SIL score is a scale that uses the shock index and arterial lactate level to identify early on the patients that are at a high risk of in-hospital mortality due to sepsis. The purpose of this study was to validate the SIL score as a tool for estimating the probability of sepsis in-hospital mortality from the triage room in emergency departments. Additionally, the advantages of the SIL score were evaluated in comparison with NEWS and SOFA. Methods: All of the patients with suspected sepsis were prospectively recruited from the triage room in an emergency department. The SIL score, as well as other evaluation scales, were calculated for these patients. The sensitivity, specificity, predictive values, and areas under the curve (AUC) of each scale were assessed to predict mortality. Results: This study included 315 patients. The total mortality of the cohort was 20.4%. Of the total population, 35.5% were in septic shock. The SIL, NEWS, and SOFA scores had similar sensitivities, approximately 60%; however, a higher specificity was documented in the SIL score over the other scales (67%). The SIL score demonstrated superior discriminatory ability compared to the NEWS and SOFA scores (AUC = 0.754, p = 0.01). Conclusions: The SIL score proved to be a useful tool for predicting in-hospital mortality due to sepsis. Its discriminatory ability surpasses that of other evaluated scales. Therefore, the SIL score can be successfully implemented in the triage room of emergency departments to improve the identification and early management of patients with sepsis. Full article

Background. Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. Methods. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database. Eligible studies investigated the development of ML models incorporating commonly available laboratory and clinical data for predicting survival outcomes in adult ICU patients with sepsis. Study selection followed the PRISMA guidelines and relied on predefined inclusion criteria. All records were independently assessed by two reviewers, with conflicts resolved by a third senior reviewer. Data related to study design, methodology, results, and interpretation of the results were extracted in a predefined grid. Results. Overall, 19 studies were identified, encompassing primarily logistic regression, random forests, and neural networks. Most used datasets were US-based (MIMIC-III, MIMIC-IV, and eICU-CRD). The most common variables used in model development were age, albumin levels, lactate levels, and ventilator. ML models demonstrated superior performance metrics compared to conventional methods and traditional scoring systems. The best-performing model was a gradient boosting decision tree, with an area under curve of 0.992, an accuracy of 0.954, and a sensitivity of 0.917. However, several critical limitations should be carefully considered when interpreting the results, such as population selection bias (i.e., single center studies), small sample sizes, limited external validation, and model interpretability. Conclusions. Through real-time integration of routine laboratory and clinical data, ML-based tools can assist clinical decision-making and enhance the consistency and quality of sepsis management across various healthcare contexts, including ICUs with limited resources. Full article

Background: This study aims to verify whether the blood flow velocity and the diameter size, measured through intra-carotid artery Doppler measurements performed on sepsis patients visiting the emergency department, are useful as tools for predicting the risk of early death. Methods: As a prospective study, this research was performed on sepsis patients who visited a local emergency medical center from August 2021 to February 2023. The sepsis patients’ carotid artery was measured using Doppler imaging, and they were divided into patients measured for the size of systolic and diastolic mean blood flow velocity and diameter size: those measured for their qSOFA (quick sequential organ failure assessment) score and those measured using the SIRS (systemic inflammatory response syndrome) criteria. By measuring and comparing their mortality prediction accuracies, this study sought to verify the usefulness of blood flow velocity and the diameter size of the intra-carotid artery as tools to predict early death. Results: This study was conducted on 1026 patients, excluding 45 patients out of the total of 1071 patients. All sepsis patients were measured using systolic and diastolic blood flow velocity and diameter by Doppler imaging of the intra-carotid artery, assessed using qSOFA and evaluated using SIRS criteria. The results of the analysis performed to compare the mortality prediction accuracy were as follows. First, the hazard ratio (95% CI) of the intra-carotid artery was significant (p < 0.05), at 1.020 (1.004–1.036); the hazard ratio (95% CI) of qSOFA was significant (p < 0.05), at 3.871 (2.526–5.931); and the hazard ratio (95% CI) of SIRS showed no significant difference, at 1.002 (0.995–1.009). After 2 h of infusion treatment, the diameter size was 4.72 ± 1.23, showing a significant difference (p < 0.05). After 2 h of fluid treatment, the blood flow velocity was 101 m/s ± 21.12, which showed a significant difference (p < 0.05). Conclusions: Measuring the mean blood flow velocity in the intra-carotid arteries of sepsis patients who visit the emergency department is useful for predicting the risk of death at an early stage. And this study showed that Doppler measurement of the diameter size of the carotid artery significantly increased after performing fluid treatment after early recognition. Full article

Background: Early risk stratification is crucial due to septic patients’ heterogeneity. Serum albumin level may reflect the severity of sepsis and host status. This study aimed to evaluate the prognostic ability of the initial sequential organ failure assessment (SOFA) score alone and combined with serum albumin levels for predicting 28-day mortality in patients with septic shock. Methods: We conducted an observational study using a prospective, multicenter registry of septic shock patients between October 2015 and May 2022 from 12 emergency departments in the Korean Shock Society and the results were validated by examining those from the septic shock cohort in Asan Medical Center. The primary outcome was 28-day mortality. The area under the receiver operating characteristic (ROC) curve was used to compare the predictive values of SOFA score alone and SOFA score combined with serum albumin level. Results: Among 5805 septic shock patients, 1529 (26.3%) died within 28 days. Mortality increased stepwise with decreasing serum albumin levels (13.6% in albumin ≥3.5, 20.7% in 3.5–3.0, 29.7% in 3.0–2.5, 44.0% in 2.5–2.0, 56.4% in <2.0). The albumin SOFA score was calculated by adding the initial SOFA score to the 4 points assigned for albumin levels. ROC analysis for predicting 28-day mortality showed that the area under the curve for the albumin SOFA score was 0.71 (95% CI 0.70–0.73), which was significantly higher than that of the initial SOFA score alone (0.68, 95% CI: 0.67–0.69). Conclusions: The combination of the initial SOFA score with albumin can improve prognostic accuracy for patients with septic shock, suggesting the albumin SOFA score may be used as an early mortality stratification tool. Full article

Background and aim: Candida infection is a significant cause of morbidity and mortality in neonatal intensive care units (NICU) globally. We aimed to conduct a systematic review to investigate the prevalence of candida among causative organisms of neonatal sepsis and identify the distribution of candida species infecting Saudi neonates. Methods: We comprehensively searched Web of Science, Scopus, PubMed, and Cochrane Library from their inception till November 2023. After screening titles, abstracts, and full texts, we ultimately included 21 eligible studies. The designs of the included studies were randomized clinical trials, cohorts, case–control, and case reports; the methodological quality was appraised using the Cochrane risk of bias assessment tool, NIH tool for observational studies, and Murad tool for assessing case reports. Results: Our systematic review and meta-analysis pooled data reported in 21 studies in the Saudi populations, which provided data on different types of candidal infections in 2346 neonates. The pooled data of ten retrospective studies enrolling 1823 neonates revealed that candida species resembled 4.2% of the causative organisms of neonatal sepsis among Saudi neonates (95%CI [2.5%; 5.9%], p = 0.000). Additionally, out of a total of 402 candida species that were identified among the included studies, C. albicans prevailed mostly among Saudi neonates, followed by C. parapsilosis, NS candida, and C. tropicalis (50.25%, 21.40%, 12.44%, and 9.45%, respectively). Conclusions: We found that candida species prevailed in 4.2% of 1823 cases of neonatal sepsis; the most common candida species was C. albicans. We could not pool data regarding risk factors or susceptibility of candida species to different treatment modalities due to insufficient data, requiring future large-scale, high-quality studies to be conducted. Full article