Prediction Of Sepsis Research Articles

Role of Hematological Parameters in the Early Diagnosis of Neonatal Sepsis.

The major cause of neonatal mortality and morbidity is bacterial sepsis. Blood culture is the most reliable method in neonatal sepsis. This study was conducted to study the usefulness of hematological parameters in the early diagnosis of neonatal sepsis and to assess the most sensitive and specific variables in diagnosing neonatal sepsis. This prospective study was conducted in a tertiary care hospital from January 2017 to January 2018. Peripheral blood smears were prepared from patients with clinical suspicion of sepsis or predisposing perinatal factors for sepsis and stained with Field's stain and examined. The hematological findings were analyzed according to the hematologic scoring system of Rodwell. It was found that immature PMN count, I: M ratio, and I: T ratio had the highest sensitivity (92.06%, 87.30%, and 74.60%, respectively) while I: M ratio, I: T ratio, and degenerative changes in PMN had the highest specificity (97.50%, 96.50%, and 94.0%, respectively) in the prediction of sepsis.

Prevalence, early predictors, and outcomes of sepsis in neurocritical illnesses: A prospective cohort study

Patients with neurocritically illness are an under-recognized population at high risk of sepsis. We aimed to investigate the prevalence, early predictors, and outcomes of sepsis in neuro-ICU. Daily and accumulative incidences of sepsis in neuro-ICU were explored. Demographics, medical history, baseline disease severity scores, and baseline biomarkers regarding inflammation, immunology, organ function, and nutritional status were collected and analyzed as potential predictors of sepsis. Logistic regression analyses were used to determine the independent predictors, and a nomogram was used to estimate the individual probability of sepsis in neuro-ICU. 153 patients were included in this study. Fifty-nine (38.6%) patients developed sepsis, and 21 (14%) patients developed septic shock. More than 86% of the septic cases occurred within the first week. Sequential organ failure assessment score ((relative risk) RR 1.334, P=.026), history of diabetes (RR 2.346, P=.049), and transferrin (RR 0.128, P=.042) on admission are independent predictors of sepsis. Septic patients had significantly higher mortality (P=.011), higher medical cost (P=.028), and a lower rate of functional independence (P=.010), compared to patients without sepsis. Sepsis afflicted more than one-third of neurocritically-ill patients and occurred mostly in the first week of admission. History of diabetes, serum transferrin, and sequential organ failure assessment score on admission were early predictors. Sepsis led to significantly worse outcomes and higher medical costs.

Sepsis Prediction at Emergency Department Triage Using Natural Language Processing: Retrospective Cohort Study.

Despite its high lethality, sepsis can be difficult to detect on initial presentation to the emergency department (ED). Machine learning-based tools may provide avenues for earlier detection and lifesaving intervention. The study aimed to predict sepsis at the time of ED triage using natural language processing of nursing triage notes and available clinical data. We constructed a retrospective cohort of all 1,234,434 consecutive ED encounters in 2015-2021 from 4 separate clinically heterogeneous academically affiliated EDs. After exclusion criteria were applied, the final cohort included 1,059,386 adult ED encounters. The primary outcome criteria for sepsis were presumed severe infection and acute organ dysfunction. After vectorization and dimensional reduction of triage notes and clinical data available at triage, a decision tree-based ensemble (time-of-triage) model was trained to predict sepsis using the training subset (n=950,921). A separate (comprehensive) model was trained using these data and laboratory data, as it became available at 1-hour intervals, after triage. Model performances were evaluated using the test (n=108,465) subset. Sepsis occurred in 35,318 encounters (incidence 3.45%). For sepsis prediction at the time of patient triage, using the primary definition, the area under the receiver operating characteristic curve (AUC) and macro F1-score for sepsis were 0.94 and 0.61, respectively. Sensitivity, specificity, and false positive rate were 0.87, 0.85, and 0.15, respectively. The time-of-triage model accurately predicted sepsis in 76% (1635/2150) of sepsis cases where sepsis screening was not initiated at triage and 97.5% (1630/1671) of cases where sepsis screening was initiated at triage. Positive and negative predictive values were 0.18 and 0.99, respectively. For sepsis prediction using laboratory data available each hour after ED arrival, the AUC peaked to 0.97 at 12 hours. Similar results were obtained when stratifying by hospital and when Centers for Disease Control and Prevention hospital toolkit for adult sepsis surveillance criteria were used to define sepsis. Among septic cases, sepsis was predicted in 36.1% (1375/3814), 49.9% (1902/3814), and 68.3% (2604/3814) of encounters, respectively, at 3, 2, and 1 hours prior to the first intravenous antibiotic order or where antibiotics where not ordered within the first 12 hours. Sepsis can accurately be predicted at ED presentation using nursing triage notes and clinical information available at the time of triage. This indicates that machine learning can facilitate timely and reliable alerting for intervention. Free-text data can improve the performance of predictive modeling at the time of triage and throughout the ED course.

A time series driven model for early sepsis prediction based on transformer module

Sepsis remains a critical concern in intensive care units due to its high mortality rate. Early identification and intervention are paramount to improving patient outcomes. In this study, we have proposed predictive models for early sepsis prediction based on time-series data, utilizing both CNN-Transformer and LSTM-Transformer architectures. By collecting time-series data from patients at 4, 8, and 12 h prior to sepsis diagnosis and subjecting it to various network models for analysis and comparison. In contrast to traditional recurrent neural networks, our model exhibited a substantial improvement of approximately 20%. On average, our model demonstrated an accuracy of 0.964 (± 0.018), a precision of 0.956 (± 0.012), a recall of 0.967 (± 0.012), and an F1 score of 0.959 (± 0.014). Furthermore, by adjusting the time window, it was observed that the Transformer-based model demonstrated exceptional predictive capabilities, particularly within the earlier time window (i.e., 12 h before onset), thus holding significant promise for early clinical diagnosis and intervention. Besides, we employed the SHAP algorithm to visualize the weight distribution of different features, enhancing the interpretability of our model and facilitating early clinical diagnosis and intervention.

Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery

Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery is critical for surgical planning and patient counseling. Due to the relatively small number of cases of ASD surgery, machine learning applications have been limited to traditional models (e.g., logistic regression or standard neural networks) and coarse clinical variables. We present the novel application of advanced models (CNN, LLM, GWAS) using complex data types (radiographs, clinical notes, genomics) for ASD outcome prediction. Methods: We developed a CNN trained on 209 ASD patients (1549 radiographs) from the Stanford Research Repository, a CNN pre-trained on VinDr-SpineXR (10,468 spine radiographs), and an LLM using free-text clinical notes from the same 209 patients, trained via Gatortron. Additionally, we conducted a GWAS using the UK Biobank, contrasting 540 surgical ASD patients with 7355 non-surgical ASD patients. Results: The LLM notably outperformed the CNN in predicting pulmonary complications (F1: 0.545 vs. 0.2881), neurological complications (F1: 0.250 vs. 0.224), and sepsis (F1: 0.382 vs. 0.132). The pre-trained CNN showed improved sepsis prediction (AUC: 0.638 vs. 0.534) but reduced performance for neurological complication prediction (AUC: 0.545 vs. 0.619). The LLM demonstrated high specificity (0.946) and positive predictive value (0.467) for neurological complications. The GWAS identified 21 significant (p < 10−5) SNPs associated with ASD surgery risk (OR: mean: 3.17, SD: 1.92, median: 2.78), with the highest odds ratio (8.06) for the LDB2 gene, which is implicated in ectoderm differentiation. Conclusions: This study exemplifies the innovative application of cutting-edge models to forecast outcomes in ASD, underscoring the utility of complex data in outcome prediction for neurosurgical conditions. It demonstrates the promise of genetic models when identifying surgical risks and supports the integration of complex machine learning tools for informed surgical decision-making in ASD.

A biomarker panel of C-reactive protein, procalcitonin and serum amyloid A is a predictor of sepsis in severe trauma patients

Severe trauma could induce sepsis due to the loss of control of the infection, which may eventually lead to death. Accurate and timely diagnosis of sepsis with severe trauma remains challenging both for clinician and laboratory. Combinations of markers, as opposed to single ones, may improve diagnosis. We compared the diagnostic characteristics of routinely used biomarkers of sepsis alone and in combination, trying to define a biomarker panel to predict sepsis in severe patients. This prospective observational study included patients with severe trauma (Injury severity score, ISS = 16 or more) in the emergency intensive care unit (EICU) at a university hospital. Blood samples were collected and plasma levels of procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6) and serum amyloid A (SAA) were measured using commercial enzyme linked immunosorbent assay (ELISA) kits. A total of 100 patients were eligible for analysis. Of these, 52 were diagnosed with sepsis. CRP yielded the highest discriminative value followed by PCT. In multiple logistic regression, SAA, CRP, and PCT were found to be independent predictors of sepsis. Bioscore which was composed of SAA, CRP, and PCT was shown to be far superior to that of each individual biomarker taken individually. Therefore, compared with single markers, the biomarker panel of PCT, CRP, and SAA was more predictive of sepsis in severe polytrauma patients.

Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning.

Sepsis is one of the main causes of mortality in intensive care units (ICUs). Early prediction is critical for reducing injury. As approximately 36% of sepsis occur within 24 h after emergency department (ED) admission in Medical Information Mart for Intensive Care (MIMIC-IV), a prediction system for the ED triage stage would be helpful. Previous methods such as the quick Sequential Organ Failure Assessment (qSOFA) are more suitable for screening than for prediction in the ED, and we aimed to find a light-weight, convenient prediction method through machine learning. We accessed the MIMIC-IV for sepsis patient data in the EDs. Our dataset comprised demographic information, vital signs, and synthetic features. Extreme Gradient Boosting (XGBoost) was used to predict the risk of developing sepsis within 24 h after ED admission. Additionally, SHapley Additive exPlanations (SHAP) was employed to provide a comprehensive interpretation of the model's results. Ten percent of the patients were randomly selected as the testing set, while the remaining patients were used for training with 10-fold cross-validation. For 10-fold cross-validation on 14,957 samples, we reached an accuracy of 84.1%±0.3% and an area under the receiver operating characteristic (ROC) curve of 0.92±0.02. The model achieved similar performance on the testing set of 1,662 patients. SHAP values showed that the five most important features were acuity, arrival transportation, age, shock index, and respiratory rate. Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage. This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

The Utility of Early Onset Sepsis Calculator and Procalcitonin in Prediction of Early Onset Neonatal Sepsis

The Utility of Early Onset Sepsis Calculator and Procalcitonin in Prediction of Early Onset Neonatal Sepsis

Usefulness of Platelet Count and Other Platelet Indices in Sepsis and Septic Shock Mortality Prediction

Usefulness of Platelet Count and Other Platelet Indices in Sepsis and Septic Shock Mortality Prediction

Early Prediction of Sepsis in the Intensive Care Unit Using the GRU-D-MGP-TCN Model

Early Prediction of Sepsis in the Intensive Care Unit Using the GRU-D-MGP-TCN Model

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department.

This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores (EWSs) and three shock indices in early sepsis prediction in the emergency department (ED). We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong. The primary outcome was sepsis (Sepsis-3 definition) within 48 h of ED presentation. Using c-statistics and the DeLong test, we compared 11 EWSs, including the National Early Warning Score 2 (NEWS2), Modified Early Warning Score, and Worthing Physiological Scoring System (WPS), etc., and three shock indices (the shock index [SI], modified shock index [MSI], and diastolic shock index [DSI]), with Systemic Inflammatory Response Syndrome (SIRS) and quick Sequential Organ Failure Assessment (qSOFA) in predicting the primary outcome, intensive care unit admission, and mortality at different time points. We analyzed 601 patients, of whom 166 (27.6%) developed sepsis. NEWS2 had the highest point estimate (area under the receiver operating characteristic curve [AUROC] 0.75, 95%CI 0.70-0.79) and was significantly better than SIRS, qSOFA, other EWSs and shock indices, except WPS, at predicting the primary outcome. However, the pooled sensitivity and specificity of NEWS2 ≥ 5 for the prediction of sepsis were 0.45 (95%CI 0.37-0.52) and 0.88 (95%CI 0.85-0.91), respectively. The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point. NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

Risk factors for sepsis and mortality in patients with emphysematous pyelonephritis : a series of 68 cases (case series).

Emphysematous pyelonephritis (EPN) is a rare and severe necrotizing infection of the kidney with a high rate of complications and mortality. Our aim was to investigate risk factors of urosepsis and mortality in case of EPN. Between January 2010 and December 2022 the charts of patients diagnosed with EPN were retrospectively reviewed. Patients medical records were collected and data including demographics, BMI, EPN type, the organism causing the infection and biochemical variables were registered. The authors performed an univariate and multivariate logistic regression analysis for sepsis, septic shock, and mortality. Statistical significance was defined as a P-value of <0.05. Our study included 68 patients (63% females, mean age 58.6 years old). Forty-eight patients (70.6%) had diabetes. Half of the patients (50%) presented with sepsis and 11 patients (16.2%) developed a septic shock. The following factors were associated with sepsis by univariate analysis: diabetes (P=0.01), higher blood sugar on admission (P=0.01), higher leukocytic count (P<0.001), higher lymphocytic count (P<0.001), and lower platelet to leukocytes ratio (P<0.001). Multivariate regression analysis revealed that the main risk factors of urosepsis were the leukocytic (OR: 85.7; 95% CI: 9.177-800.486; P<0.001) and lymphocytic count (OR: 6.65; 95% CI: 1.228-36.050; P=0.028). Neither of the variables was significantly associated with a higher risk of mortality. Leukocytic and lymphocytic count on admission are independent simple predictors for sepsis in patients with EPN.

Potential impact of a new sepsis prediction model for the primary care setting: early health economic evaluation using an observational cohort

ObjectivesTo estimate the potential referral rate and cost impact at different cut-off points of a recently developed sepsis prediction model for general practitioners (GPs).DesignProspective observational study with decision tree modelling.SettingFour...

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.

Role of Inflammatory Markers in Early Diagnosis of Neonatal Sepsis; C-Reactive Protein or Procalcitonin or Red Cell Distribution Width; A Hospital-Based Study

Objective: To find out the utilization of C-reactive protein (CRP), Procalcitonin (PCT) and red cell distribution width (RDW) in diagnosing neonatal sepsis considering blood culture findings as gold standard. Study Design: Cross-sectional validation study. Place and Duration of Study: Neonatal Intensive Care Unit (NICU) of Dr Ziauddin Medical University, Karachi Pakistan, from Feb to Jul 2022. Methodology: A total of 42 neonates of either gender with the clinical diagnosis of neonatal sepsis were enrolled. Demographical and clinical characteristics of all neonates were noted at the time of enrollment. A blood sample of 5 ml was withdrawn for the evaluation of CRP, PCT and RDS. The association of different inflammatory markers with the outcome (neonatal sepsis) was assessed. Results: In 42 neonates, the mean age was 7.24±9.24 days, and 26 (61.9%) neonates were boys. Respiratory distress, reluctance to feed and hypoglycemia were the most frequent clinical presentations observed (19,45.2%), (14,33.3%) and (8,19.0%) neonates, respectively. Mean CRP levels among blood culture positive and negative neonates were 40.02±57.84mg/dl and 7.57±13.34 mg/dl, respectively, while the difference was statistically significant (p=0.0068). RDW (p=0.6488) and procalcitonin (0.9021) levels were not statistically significant differences among positive and negative neonates of blood culture. Conclusion: The CRP was a significant predictor of neonatal sepsis, while RDW and procalcitonin did not have a significant relationship with early prediction of neonatal sepsis.

Plasma soluble suppression of tumorigenesis 2 measured in the emergency department for diagnosis and outcome prediction of sepsis: A single-center prospective study

Background and aimsThe diagnostic and prognostic performance of soluble Suppression of Tumorigenicity 2 (sST2) in suspected septic patients presenting to the Emergency Department (ED) is largely unknown. Materials and methodsPatients were included in this prospective study if there was high suspicion of sepsis. The plasma level of sST2 was measured during initial ED evaluation. Outcomes were the evaluation of (1) sST2 diagnostic performance (alone and in combination with procalcitonin [PCT]), and (2) sST2 ability to predict 30-day and 90-day all-cause mortality. ResultsAmong 569 patients included, 481 (84.5 %) had sepsis or septic shock. Plasma sST2 levels were more elevated in septic patients (159 [71–331] vs 50 [31–103] ng/mL, P < 0.001). The AUC of sST2 for sepsis diagnosis was lower than the AUC of PCT (0.76 vs 0.85, P = 0.03). The best cut-off for sST2 was 61.7 ng/mL, with a sensitivity of 79.9 % and a specificity of 70.6 %. sST2 was able to correctly reclassify septic patients with PCT <0.5 (NRI 28.9 % [P = 0.02]). sST2 level was an independent predictor of 30-day mortality in a model including clinical variables (aHR 2.03 [1.24–3.33], C-index 0.69). ConclusionsST2 could be a useful adjunct in diagnosing sepsis and in all-cause mortality prediction.

Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis

To develop an artificial intelligence-based software system for predicting late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in infants admitted to the neonatal intensive care unit (NICU). Single-center, retrospective cohort study, conducted in the NICU of the Antwerp University Hospital. Continuous monitoring data of 865 preterm infants born at <32weeks gestational age, admitted to the NICU in the first week of life, were used to train an XGBoost machine learning (ML) algorithm for LOS and NEC prediction in a cross-validated setup. Afterward, the model's performance was assessed on an independent test set of 148 patients (internal validation). The ML model delivered hourly risk predictions with an overall sensitivity of 69% (142/206) for all LOS/NEC episodes and 81% (67/83) for severe LOS/NEC episodes. The model showed a median time gain of ≤10 hours (IQR, 3.1-21.0 hours), compared with historical clinical diagnosis. On the complete retrospective dataset, the ML model made 721 069 predictions, of which 9805 (1.3%) depicted a LOS/NEC probability of ≥0.15, resulting in a total alarm rate of <1 patient alarm-day per week. The model reached a similar performance on the internal validation set. Artificial intelligence technology can assist clinicians in the early detection of LOS and NEC in the NICU, which potentially can result in clinical and socioeconomic benefits. Additional studies are required to quantify further the effect of combining artificial and human intelligence on patient outcomes in the NICU.

Comparing artificial intelligence strategies for early sepsis detection in the ICU: an experimental study

Sepsis is a life-threatening condition whose early recognition is key to improving outcomes for patients in intensive care units (ICUs). Artificial intelligence can play a crucial role in mining and exploiting health data for sepsis prediction. However, progress in this field has been impeded by a lack of comparability across studies. Some studies do not provide code, and each study independently processes a dataset with large numbers of missing values. Here, we present a comparative analysis of early sepsis prediction in the ICU by using machine learning (ML) algorithms and provide open-source code to the community to support future work. We reviewed the literature and conducted two phases of experiments. In the first phase, we analyzed five imputation strategies for handling missing data in a clinical dataset (which is often sampled irregularly and requires hand-crafted preprocessing steps). We used the MIMIC-III dataset, which includes more than 5,800 ICU hospital admissions from 2001 to 2012. In the second phase, we conducted an extensive experimental study using five ML methods and five popular deep learning models. We evaluated the performance of the methods by using the area under the precision-recall curve, a standard metric for clinical contexts. The deep learning methods (TCN and LSTM) outperformed the other methods, particularly in early detection tasks more than 4 hours before sepsis onset. The motivation for this work was to provide a benchmark framework for future research, thus enabling advancements in this field.Graphical

Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data.

Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning. Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment. DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346). The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation.