scholarly journals 939. Predicting Attributable Mortality in Pediatric Patients with Cancer Admitted to the Intensive Care Unit for Suspected Infection

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S562-S562
Author(s):  
Zach Rubnitz ◽  
Asya Agulnik ◽  
Pamela Merritt ◽  
Jose Amadeo A Ferrolino ◽  
Ronald Dallas ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Predictive Value ◽  
Pediatric Patients ◽  
Attributable Mortality ◽  
Suspected Infection ◽  
Prism Score ◽  
Patients With Cancer ◽  
Failure Assessment

Abstract Background Infection and sepsis are important contributors to mortality in children with cancer. Although pediatric risk prediction scores have improved identification of children at high risk of death in the PICU, the value of these tests in immunocompromised children is unknown. Methods In this IRB-approved retrospective study performed at St. Jude Children’s Research Hospital, we evaluated the performance of 4 pediatric risk scores, the Pediatric Risk of Mortality (PRISM), Pediatric Sequential Organ Failure Assessment (pSOFA), Quick Sequential Organ Failure Assessment (qSOFA) scores (using data available at 1, 6, 12 and 24 hours) and the Paediatric Index of Mortality 3 (PIM-3) score (at 1 hour), to predict attributable mortality (death ≤ 60 days without organ dysfunction recovery). Inclusion criteria: Age < 24 years, active cancer therapy (other than bone marrow transplantation), and admission to PICU between 2013 and 2019 with suspected infection (collection of a blood culture and initiation of antibiotic therapy). Scores were calculated using the worst value obtained for each variable. Score distributions were compared by the Mann-Whitney U test, and optimal cutoffs selected by maximizing Youden’s index. An unadjusted p-value < 0.05 was considered statistically significant. Results Of 202 episodes of PICU admission for suspected infection in 168 participants, there were 12 attributable (6%) and 4 unrelated (2%) deaths. Demographic and cancer-related characteristics were not associated with mortality (Table 1). Of the 4 prediction scores, only the PRISM score at 24 hours was associated with mortality (P = 0.012; Table 2). For PRISM score ≥ 18, sensitivity was 58.3%, specificity was 81.6%, positive predictive value was 16.7%, and negative predictive value was 96.9% for attributable mortality. Table 1. Risk factors for attributable mortality in pediatric patients with cancer admitted to the intensive care unit with suspected infection. Table 2. Association between risk prediction scores and attributable mortality in pediatric patients with cancer admitted to the intensive care unit with suspected infection. Conclusion In children with cancer admitted to PICU with suspected infection, early pediatric risk prediction scores did not predict mortality. The PRISM score calculated at 24 hours did predict mortality but was relatively insensitive. Further research is needed to develop a risk score for immunocompromised children and to validate the 24 hour PRISM score in this population. Disclosures Joshua Wolf, MBBS, PhD, FRACP, Karius Inc. (Research Grant or Support) Joshua Wolf, MBBS, PhD, FRACP, Nothing to disclose

scholarly journals Mortality of Adult Patients With Cancer Admitted to an Intensive Care Unit in Chile: A Prospective Cohort Study

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sergio Panay ◽  
Carolina Ruiz ◽  
Marcelo Abarca ◽  
Bruno Nervi ◽  
Ignacio Salazar ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prospective Cohort ◽  
Admission Policy ◽  
Short Term ◽  
Assessment Scores ◽  
Patients With Cancer ◽  
Failure Assessment ◽  
Short Term Mortality ◽  
The Mean

Purpose Increasing numbers of reports have shown acceptable short-term mortality of patients with cancer admitted into the intensive care unit (ICU). The aim of this study was to determine the mortality of critically ill patients with cancer admitted to the ICU in a general hospital in Chile. Materials and Methods This was a prospective cohort trial in which we included all patients with cancer admitted to the ICU between July 2015 and September 2016. Demographic, physiologic, and treatment data were registered, and survival at 30 days and 6 months was evaluated. A prespecified subgroup analysis considering the admission policy was performed. These subgroups were (1) ICU admission for full code management and (2) ICU trial (IT). Results During the study period, 109 patients with cancer were included. Seventy-nine patients were considered in the full code management group and 30 in the IT. The mean age of patients was 60 years (standard deviation [SD], 15), and 56% were male. Lymphoma was the most frequent malignancy (17%), and 59% had not received cancer treatment because of a recent diagnosis. The mean Acute Physiology and Chronic Health Evaluation and Sequential-Related Organ Failure Assessment scores were 22.2 (SD, 7.3) and 7 (SD, 3), respectively. There were no differences in vasopressor, fluid, or transfusion requirements between subgroups. Lactate levels, Sequential-Related Organ Failure Assessment scores (day 1, 3, and 5), complications, and ICU length of stay were similar. In the entire cohort, 30-day and 6-month mortality was 47% and 66%, respectively. There was no difference in mortality between subgroups according to the admission policy. Conclusion Patients admitted to the ICU in a developing country are at high risk for short-term mortality. However, there is a relevant subgroup that achieves 6-month survival, even among patients who undergo an IT.

scholarly journals Predictive value of individual Sequential Organ Failure Assessment sub-scores for mortality in the cardiac intensive care unit

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216177 ◽  
Author(s):  
Jacob C. Jentzer ◽  
Courtney Bennett ◽  
Brandon M. Wiley ◽  
Dennis H. Murphree ◽  
Mark T. Keegan ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Sequential Organ Failure Assessment ◽  
Organ Failure ◽  
Predictive Value ◽  
Cardiac Intensive Care Unit ◽  
Failure Assessment ◽  
Cardiac Intensive Care

scholarly journals The Comparison between Sepsis 1.0 and Sepsis 3.0 in Database of Severe Infection in Critical Illness: A Retrospective Study

2019 ◽  
Author(s):  
Wei Zhang ◽  
Yan Zheng ◽  
Juan Gu ◽  
Yan Kang
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Sequential Organ Failure Assessment ◽  
Critically Ill ◽  
Predictive Validity ◽  
Critically Ill Patients ◽  
Severe Infection ◽  
Suspected Infection ◽  
Failure Assessment

Abstract Objective To compared the Sepsis 1.0 criterial with the Sepsis 3.0 criteria predict the efficacy of all-caused mortality of in-hospital in critically ill patients with severe infection. Design This is a retrospective and cohort study based on the database of severe infection. Setting A 48-bed general intensive care unit in affiliated hospital of University. Patients Critically ill patients with suspected infection based on the electronic health records from 1 January to 31 December, 2015. Interventions None. Measurements The variables of exposures included: quick sequential organ failure assessment (qSOFA), systemic inflammatory response syndrome (SIRS) score and sequential organ failure assessment (SOFA). Main outcomes and measures: for predictive validity, we found that the discrimination for hospital mortality was more common with sepsis than with uncomplicated infections. Results are reported as the area under the receiver operating characteristic curve (AUROC).Main Results In the primary cohort, 873 patients had suspected infection cohort (n=634), of whom 188 (29.7%) died; and with the non-infection cohort (n=239), 26 patients died (10.9%). Among intensive care unit (ICU) cases in the infection cohort, the predictive validity for hospital mortality was higher for Sepsis 3.0 (SOFA) criteria (AUROC=0.702; 95%CI, 0.665 −0.737; p≤0.01 for both) than for Sepsis 1.0 (SIRS) criteria (AUROC=0.533; 95% confidence interval [95%CI], 0.493−0.572). Conclusions In our study, we found the Sepsis 3.0 criteria is able to accurately predict the prognosis in critically ill patients with severe infection, and its predictive efficacy is superior to Sepsis 1.0 criteria.

scholarly journals A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit

2019 ◽  
Vol 11 ◽  
pp. 117822261988514 ◽  
Author(s):  
Christopher R Yee ◽  
Niven R Narain ◽  
Viatcheslav R Akmaev ◽  
Vijetha Vemulapalli
Keyword(s):  
Intensive Care Unit ◽  
Septic Shock ◽  
Intensive Care ◽  
High Risk ◽  
Sequential Organ Failure Assessment ◽  
Predictive Value ◽  
Data Driven ◽  
Failure Assessment ◽  
Risk Patients ◽  
Novel Algorithm

Early diagnosis of sepsis and septic shock has been unambiguously linked to lower mortality and better patient outcomes. Despite this, there is a strong unmet need for a reliable clinical tool that can be used for large-scale automated screening to identify high-risk patients. We addressed the following questions: Can a novel algorithm to identify patients at high risk of septic shock 24 hours before diagnosis be discovered using available clinical data? What are performance characteristics of this predictive algorithm? Can current metrics for evaluation of sepsis be improved using novel algorithm? Publicly available data from the intensive care unit setting was used to build septic shock and control patient cohorts. Using Bayesian networks, causal relationships between diagnosis groups, procedure groups, laboratory results, and demographic data were inferred. Predictive model for septic shock 24 hours prior to digital diagnosis was built based on inferred causal networks. Sepsis risk scores were augmented by de novo inferred model and performance was evaluated. A novel predictive model to identify high-risk patients 24 hours ahead of time, with area under curve of 0.81, negative predictive value of 0.87, and a positive predictive value as high as 0.65 was built. The specificity of quick sequential organ failure assessment, systemic inflammatory response syndrome, and modified early warning score was improved when augmented with the novel model, whereas no improvements were made to the sequential organ failure assessment score. We used a data-driven, expert knowledge agnostic method to build a screening algorithm for early detection of septic shock. The model demonstrates strong performance in the data set used and provides a basis for expanding this work toward building an algorithm that is used to screen patients based on electronic medical record data in real time.

Presepsin - New Marker of Sepsis Romanian Neonatal Intensive Care Unit Experience

2019 ◽  
Vol 70 (8) ◽  
pp. 3008-3013
Author(s):  
Silvia Maria Stoicescu ◽  
Ramona Mohora ◽  
Monica Luminos ◽  
Madalina Maria Merisescu ◽  
Gheorghita Jugulete ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Sepsis ◽  
Predictive Value ◽  
Neonatal Intensive Care ◽  
Complete Blood Count ◽  
Clinical Manifestations ◽  
Receiver Operating Curve ◽  
Predictive Values

Difficulties in establishing the onset of neonatal sepsis has directed the medical research in recent years to the possibility of identifying early biological markers of diagnosis. Overdiagnosing neonatal sepsis leads to a higher rate and duration in the usage of antibiotics in the Neonatal Intensive Care Unit (NICU), which in term leads to a rise in bacterial resistance, antibiotherapy complications, duration of hospitalization and costs.Concomitant analysis of CRP (C Reactive Protein), procalcitonin, complete blood count, presepsin in newborn babies with suspicion of early or late neonatal sepsis. Presepsin sensibility and specificity in diagnosing neonatal sepsis. The study group consists of newborns admitted to Polizu Neonatology Clinic between 15th February- 15th July 2017, with suspected neonatal sepsis. We analyzed: clinical manifestations and biochemical markers values used for diagnosis of sepsis, namely the value of CRP, presepsin and procalcitonin on the onset day of the disease and later, according to evolution. CRP values may be influenced by clinical pathology. Procalcitonin values were mainly influenced by the presence of jaundice. Presepsin is the biochemical marker with the fastest predictive values of positive infection. Presepsin can be a useful tool for early diagnosis of neonatal sepsis and can guide the antibiotic treatment. Presepsin value is significantly higher in neonatal sepsis compared to healthy newborns (939 vs 368 ng/mL, p [ 0.0001); area under receiver operating curve (AUC) for presepsine was 0.931 (95% confidence interval 0.86-1.0). PSP has a greater sensibility and specificity compared to classical sepsis markers, CRP and PCT respectively (AUC 0.931 vs 0.857 vs 0.819, p [ 0.001). The cut off value for presepsin was established at 538 ng/mLwith a sensibility of 79.5% and a specificity of 87.2 %. The positive predictive value (PPV) is 83.8 % and negative predictive value (NPV) is 83.3%.

Sign in / Sign up

Export Citation Format

Share Document