More challenges around sepsis: definitions and diagnosis

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

BACKGROUND

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.

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.

RESULTS

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.

CONCLUSION

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.

Beyond the Horizon: A Comprehensive Review of Contemporary Strategies in Sepsis Management Encompassing Predictors, Diagnostic Tools, and Therapeutic Advances

This comprehensive review offers a detailed exposition of contemporary strategies in sepsis management, encompassing predictors, diagnostic tools, and therapeutic advances. The analysis elucidates the dynamic nature of sepsis, emphasizing the crucial role of early detection and intervention. The multifaceted strategies advocate for a holistic and personalized approach to sepsis care from traditional clinical methodologies to cutting-edge technologies. The implications for clinical practice underscore clinicians' need to adapt to evolving definitions, integrate advanced diagnostic tools, and embrace precision medicine. Integrating artificial intelligence and telemedicine necessitates a commitment to training and optimization. Judicious antibiotic use and recognition of global health disparities emphasize the importance of a collaborative, global effort in sepsis care. Looking ahead, recommendations for future research underscore priorities such as longitudinal studies on biomarkers, precision medicine trials, implementation science in technology, global health interventions, and innovative antibiotic stewardship strategies. These research priorities aim to contribute to transformative advancements in sepsis management, ultimately enhancing patient outcomes and reducing the global impact of this critical syndrome.

Development of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients

Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned admission to the intensive care unit (ICU). Classical severity scores have been developed typically using relatively few patient features. Recently, deep learning-based models demonstrated better individualized risk assessments compared to classic risk scores, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time-stamped data from electronic health records. We developed a deep learning model based on embedded text from multiple data sources and recurrent neural networks to predict the risk of the composite outcome of unplanned ICU transfer and in-hospital death. The risk was assessed at regular intervals during the admission for different prediction windows. Input data included medical history, biochemical measurements, and clinical notes from a total of 852,620 patients admitted to non-intensive care units in 12 hospitals in Denmark's Capital Region and Region Zealand during 2011-2016 (with a total of 2,241,849 admissions). We subsequently explained the model using the Shapley algorithm, which provides the contribution of each feature to the model outcome. The best model used all data modalities with an assessment rate of 6 hours, a prediction window of 14 days and an area under the receiver operating characteristic curve of 0.898. The discrimination and calibration obtained with this model make it a viable clinical support tool to detect patients at higher risk of clinical deterioration, providing clinicians insights into both actionable and non-actionable patient features.

Sepsis scoring systems and use of the Sepsis six care bundle in maternity hospitals

BACKGROUND

This study aimed to assess the predictive power of three different Sepsis Scoring Systems (SSSs), namely maternity Systematic Inflammatory Response Syndrome (mSIRS), quick Sepsis-related Organ Failure Assessment (qSOFA) and Modified Early Warning System (MEWS) in identifying sepsis by comparing them with positive culture. This study also sought to evaluate compliance with using the Sepsis Six Care Bundle (SSCB) operated in an individual health board.

METHODS

A retrospective cohort study was conducted in 3 maternity hospitals of a single Scottish health board that admitted 2690 pregnancies in a 12 weeks period in 2016. Data for study was obtained from medical notes, handheld and electronic health records for women who were prescribed antibiotics with a confirmed or suspected diagnosis of sepsis. Data on clinical parameters was used to classify women according to mSIRS, qSOFA and MEWS as having sepsis or not and this was compared to results of positive culture to obtain sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under Receiver Operating Characteristic curve (AUROC) along with their 95% confidence intervals. Data was also obtained on SSCB compliance.

RESULTS

A total of 89 women were diagnosed with sepsis, of which 14 had missing data, leaving 75 for final analysis. Sensitivity, specificity, PPV, NPV and AUROC of mSIRS and MEWS were almost similar with AUROC of both being around 50%. Only 33 (37.1%) had identifiable sepsis six sticker displayed on medical notes and only 2 (2.2%) had all elements of SSCB delivered within the recommended one-hour post-diagnosis period. Blood culture and full blood count with other lab tests had been performed for most women (97%) followed by intravenous antibiotics and fluids (93.9%).

CONCLUSIONS

mSIRS and MEWS were quite similar in detecting sepsis when compared to positive culture, with their ability to detect sepsis being close to chance. This underlines the need for creating a valid SSS with high sensitivity and specificity for clinical use in obstetric settings. Clinical use of SSCB was limited despite it being a health board policy, although there is considerable possibility of improvement following detailed audits and removal of barriers for implementing SSCB.

Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms

The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serious adverse events due to fluid non-responsiveness. The current method of deciding if patients are responsive to fluid administration is often subjective and requires manual intervention. This study utilizes MIMIC III and associated matched waveform datasets across the entire ICU stay duration of each patient to develop prediction models for assessing fluid responsiveness in sepsis patients. We developed a pipeline to extract high frequency continuous waveform data and included waveform features in the prediction models. Comparing across five machine learning models, random forest performed the best when no waveform information is added (AUC = 0.84), with mean arterial blood pressure and age identified as key factors. After incorporation of features from physiologic waveforms, logistic regression with L1 penalty provided consistent performance and high interpretability, achieving an accuracy of 0.89 and F1 score of 0.90.

Evaluating antimicrobial appropriateness in a tertiary care pediatric ICU in Saudi Arabia: a retrospective cohort study

Background

Inappropriate antibiotic utilization is associated with the emergence of antimicrobial resistance (AMR) and a decline in antibiotic susceptibility in many pathogenic organisms isolated in intensive care units. Antibiotic stewardship programs (ASPs) have been recommended as a strategy to reduce and delay the impact of AMR. A crucial step in ASPs is understanding antibiotic utilization practices and quantifying the problem of inappropriate antibiotic use to support a targeted solution. We aim to characterize antibiotic utilization and determine the appropriateness of antibiotic prescription in a tertiary care pediatric intensive care unit.

Methods

A retrospective cohort study was conducted at King Abdullah Specialized Children’s Hospital, Riyadh, Saudi Arabia, over a 6-month period. Days of therapy (DOT) and DOT per 1000 patient-days were used as measures of antibiotic consumption. The appropriateness of antibiotic use was assessed by two independent pediatric infectious disease physicians based on the Centers for Disease Control and Prevention 12-step Campaign to prevent antimicrobial resistance among hospitalized children.

Results

During the study period, 497 patients were admitted to the PICU, accounting for 3009 patient-days. A total of 274 antibiotic courses were administered over 2553 antibiotic days. Forty-eight percent of antibiotic courses were found to be nonadherent to at least 1 CDC step. The top reasons were inappropriate antibiotic choice (empirical or definitive) and inappropriate prophylaxis durations. Cefazolin and vancomycin contributed to the highest percentage of inappropriate DOTs.

Conclusions

Antibiotic consumption was high with significant inappropriate utilization. These data could inform decision-making in antimicrobial stewardship programs and strategies. The CDC steps provide a more objective tool and limit biases when assessing antibiotic appropriateness

International Survey on Determinants of Antibiotic Duration and Discontinuation in Pediatric Critically Ill Patients

OBJECTIVES

We hypothesized that antibiotic use in PICUs is based on criteria not always supported by evidence. We aimed to describe determinants of empiric antibiotic use in PICUs in eight different countries.

DESIGN

Cross-sectional survey.

SETTING

PICUs in Canada, the United States, France, Italy, Saudi Arabia, Japan, Thailand, and Brazil.

SUBJECTS

Pediatric intensivists.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We used literature review and focus groups to develop the survey and its clinical scenarios (pneumonia, septic shock, meningitis, and intra-abdominal infections) in which cultures were unreliable due to antibiotic pretreatment. Data analyses included descriptive statistics and linear regression with bootstrapped SEs. Overall response rate was 39% (482/1,251), with individual country response rates ranging from 25% to 76%. Respondents in all countries prolonged antibiotic duration based on patient characteristics, disease severity, pathogens, and radiologic findings (from a median increase of 1.8 d [95% CI, 0.5-4.0 d] to 9.5 d [95% CI, 8.5-10.5 d]). Younger age, severe disease, and ventilator-associated pneumonia prolonged antibiotic treatment duration despite a lack of evidence for such practices. No variables were reported to shorten treatment duration for all countries. Importantly, more than 39% of respondents would use greater than or equal to 7 days of antibiotics for patients with a positive viral polymerase chain reaction test in all scenarios, except in France for pneumonia (29%), septic shock (13%), and meningitis (6%). The use of elevated levels of inflammatory markers to prolong antibiotic treatment duration varied among different countries.

CONCLUSIONS

Antibiotic-related decisions are complex and may be influenced by cultural and contextual factors. Evidence-based criteria are necessary to guide antibiotic duration and ensure the rational use of antibiotics in PICUs.

Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.