| 1. |
- Alam, Mahbub Ul, et al.
(författare)
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Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis
- 2020
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Ingår i: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 5: HEALTHINF. - Setúbal : SciTePress. - 9789897583988 ; , s. 45-55
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Konferensbidrag (refereegranskat)abstract
- Sepsis is a life-threatening complication to infections, and early treatment is key for survival. Symptoms of sepsis are difficult to recognize, but prediction models using data from electronic health records (EHRs) can facilitate early detection and intervention. Recently, deep learning architectures have been proposed for the early prediction of sepsis. However, most efforts rely on high-resolution data from intensive care units (ICUs). Prediction of sepsis in the non-ICU setting, where hospitalization periods vary greatly in length and data is more sparse, is not as well studied. It is also not clear how to learn effectively from longitudinal EHR data, which can be represented as a sequence of time windows. In this article, we evaluate the use of an LSTM network for early prediction of sepsis according to Sepsis-3 criteria in a general hospital population. An empirical investigation using six different time window sizes is conducted. The best model uses a two-hour window and assumes data is missing not at random, clearly outperforming scoring systems commonly used in healthcare today. It is concluded that the size of the time window has a considerable impact on predictive performance when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis.
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| 2. |
- Behnke, Michael, et al.
(författare)
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Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections
- 2021
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Ingår i: Clinical Microbiology and Infection. - : Elsevier. - 1198-743X .- 1469-0691. ; 27:Suppl 1, s. S29-S39
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Healthcare-associated infections (HAI) are a major public health concern. Monitoring of HAI rates, with feedback, is a core component of infection prevention and control programmes. Digitalization of healthcare data has created novel opportunities for automating the HAI surveillance process to varying degrees. However, methods are not standardized and vary widely between different healthcare facilities. Most current automated surveillance (AS) systems have been confined to local settings, and practical guidance on how to implement large-scale AS is needed. Methods: This document was written by a task force formed in March 2019 within the PRAISE network (Providing a Roadmap for Automated Infection Surveillance in Europe), gathering experts in HAI surveillance from ten European countries. Results: The document provides an overview of the key e-health aspects of implementing an AS system of HAI in a clinical environment to support both the infection prevention and control team and information technology (IT) departments. The focus is on understanding the basic principles of storage and structure of healthcare data, as well as the general organization of IT infrastructure in surveillance networks and participating healthcare facilities. The fundamentals of data standardization, interoperability and algorithms in relation to HAI surveillance are covered. Finally, technical aspects and practical examples of accessing, storing and sharing healthcare data within a HAI surveillance network, as well as maintenance and quality control of such a system, are discussed. Conclusions: With the guidance given in this document, along with the PRAISE roadmap and governance documents, readers will find comprehensive support to implement large-scale AS in a surveillance network.
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| 3. |
- Berkestedt, Ingrid, et al.
(författare)
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Early depletion of contact system in patients with sepsis : a prospective matched control observational study
- 2018
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Ingår i: APMIS. - : Wiley. - 0903-4641 .- 1600-0463. ; , s. 892-898
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Tidskriftsartikel (refereegranskat)abstract
- Activation of the contact system generates bradykinin from high-molecular-weight kininogen and has been suggested to participate in the pathophysiology of sepsis. To test this, we prospectively measured bradykinin and high-molecular-weight kininogen levels in a cohort of sepsis patients requiring intensive care. From 29 patients meeting criteria for sepsis or septic shock according to Sepsis-3, blood was sampled within 24 h and on the fourth day following admittance to intensive care. Patients planned for neurosurgery served as matched controls. Sequential organ failure assessment score and 90-day mortality was registered. Bradykinin levels (median [interquartile range]) were lower in sepsis patients (79 [62–172] pg/ml) compared to controls (130 [86–255] pg/ml, p < 0.025) and did not correlate with mortality or severity of circulatory derangement. High-molecular-weight kininogen levels were lower in sepsis patients (1.6 [0.8–4.8] densitometry units) compared to controls (4.4 [2.9–7.7] densitometry units, p < 0.001), suggesting previous contact system activation. High-molecular-weight kininogen levels were lower in non-survivors than survivors (p = 0.003) and negatively correlated to severity of circulatory derangement. We conclude that a role for bradykinin in later stages of severe sepsis must be challenged. Low high-molecular-weight kininogen concentrations suggest that the decrease in bradykinin is due to substrate depletion.
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| 4. |
- Karlsson Valik, John
(författare)
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Improved diagnosis and management of sepsis and bloodstream infection
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Sepsis is a severe organ dysfunction triggered by infections, and a leading cause of hospitalization and death. Concurrent bloodstream infection (BSI) is common and around one third of sepsis patients have positive blood cultures. Prompt diagnosis and treatment is crucial, but there is a trade-off between the negative effects of over diagnosis and failure to recognize sepsis in time. The emerging crisis of antimicrobial resistance has made bacterial infections more difficult to treat, especially gram-negative pathogens such as Pseudomonas aeruginosa. The overall aim with this thesis was to improve diagnosis, assess the influence of time to antimicrobial treatment and explore prognostic bacterial virulence markers in sepsis and BSI. The papers are based on observational data from 7 cohorts of more than 100 000 hospital episodes. In addition, whole genome sequencing has been performed on approximately 800 invasive P. aeruginosa isolates collected from centers in Europe and Australia. Paper I showed that automated surveillance of sepsis incidence using the Sepsis-3 criteria is feasible in the non-ICU setting, with examples of how implementing this model generates continuous epidemiological data down to the ward level. This information can be used for directing resources and evaluating quality-of-care interventions. In Paper II, evidence is provided for using peripheral oxygen saturation (SpO2) to diagnose respiratory dysfunction in sepsis, proposing the novel thresholds 94% and 90% to get 1 and 2 SOFA points, respectively. This has important implications for improving sepsis diagnosis, especially when conventional arterial blood gas measurements are unavailable. Paper III verified that sepsis surveillance data can be utilized to develop machine learning screening tools to improve early identification of sepsis. A Bayesian network algorithm trained on routine electronic health record data predicted sepsis onset within 48 hours with better discrimination and earlier than conventional NEWS2 outside the ICU. The results suggested that screening may primarily be suited for the early admission period, which have broader implications also for other sepsis screening tools. Paper IV demonstrated that delays in antimicrobial treatment with in vitro pathogen coverage in BSI were associated with increased mortality after 12 hours from blood culture collection, but not at 1, 3, and 6 hours. This indicates a time window where clinicians should focus on the diagnostic workup, and proposes a target for rapid diagnostics of blood cultures. Finally, Paper V showed that the virulence genotype had some influence on mortality and septic shock in P. aeruginosa BSI, however, it was not a major prognostic determinant. Together these studies contribute to better understanding of the sepsis and BSI populations, and provide several suggestions to improve diagnosis and timing of treatment, with implications for clinical practice. Future works should focus on the implementation of sepsis surveillance, clinical trials of time to antimicrobial treatment and evaluating the prognostic importance of bacterial genotype data in larger populations from diverse infection sources and pathogens.
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| 5. |
- Karlsson Valik, John, et al.
(författare)
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Peripheral Oxygen Saturation Facilitates Assessment of Respiratory Dysfunction in the Sequential Organ Failure Assessment Score With Implications for the Sepsis-3 Criteria
- 2022
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Ingår i: Critical Care Medicine. - 0090-3493 .- 1530-0293. ; 50:3, s. e272-e283
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: Sequential Organ Failure Assessment score is the basis of the Sepsis-3 criteria and requires arterial blood gas analysis to assess respiratory function. Peripheral oxygen saturation is a noninvasive alternative but is not included in neither Sequential Organ Failure Assessment score nor Sepsis-3. We aimed to assess the association between worst peripheral oxygen saturation during onset of suspected infection and mortality.DESIGN: Cohort study of hospital admissions from a main cohort and emergency department visits from four external validation cohorts between year 2011 and 2018. Data were collected from electronic health records and prospectively by study investigators.SETTING: Eight academic and community hospitals in Sweden and Canada.PATIENTS: Adult patients with suspected infection episodes.INTERVENTIONS: None.MEASUREMENTS AND MAIN RESULTS: The main cohort included 19,396 episodes (median age, 67.0 [53.0–77.0]; 9,007 [46.4%] women; 1,044 [5.4%] died). The validation cohorts included 10,586 episodes (range of median age, 61.0–76.0; women 42.1–50.2%; mortality 2.3–13.3%). Peripheral oxygen saturation levels 96–95% were not significantly associated with increased mortality in the main or pooled validation cohorts. At peripheral oxygen saturation 94%, the adjusted odds ratio of death was 1.56 (95% CI, 1.10–2.23) in the main cohort and 1.36 (95% CI, 1.00–1.85) in the pooled validation cohorts and increased gradually below this level. Respiratory assessment using peripheral oxygen saturation 94–91% and less than 91% to generate 1 and 2 Sequential Organ Failure Assessment points, respectively, improved the discrimination of the Sequential Organ Failure Assessment score from area under the receiver operating characteristics 0.75 (95% CI, 0.74–0.77) to 0.78 (95% CI, 0.77–0.80; p < 0.001). Peripheral oxygen saturation/Fio2 ratio had slightly better predictive performance compared with peripheral oxygen saturation alone, but the clinical impact was minor.CONCLUSIONS: These findings provide evidence for assessing respiratory function with peripheral oxygen saturation in the Sequential Organ Failure Assessment score and the Sepsis-3 criteria. Our data support using peripheral oxygen saturation thresholds 94% and 90% to get 1 and 2 Sequential Organ Failure Assessment respiratory points, respectively. This has important implications primarily for emergency practice, rapid response teams, surveillance, research, and resource-limited settings.
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| 6. |
- Karlsson Valik, John, et al.
(författare)
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Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population : observational study using electronic health records data
- 2020
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Ingår i: BMJ Quality and Safety. - : BMJ Publishing Group Ltd. - 2044-5415 .- 2044-5423. ; 29:9, s. 735-745
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Forskningsöversikt (refereegranskat)abstract
- Background: Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards.Methods: A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review.Results: In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards.Conclusions: A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.
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| 7. |
- Lamproudis, Anastasios, et al.
(författare)
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Improving the Timeliness of Early Prediction Models for Sepsis through Utility Optimization
- 2022
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Ingår i: 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI). ; , s. 1062-1069
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Konferensbidrag (refereegranskat)abstract
- Early prediction of sepsis can facilitate early intervention and lead to improved clinical outcomes. However, for early prediction models to be clinically useful, and also to reduce alarm fatigue, detection of sepsis needs to be timely with respect to onset, being neither too late nor too early. In this paper, we propose a utility-based loss function for training early prediction models, where utility is defined by a function according to when the predictions are made and in relation to onset as well as to specified early, optimal and late time points. Two versions of the utility-based loss function are evaluated and compared to a cross-entropy loss baseline. Experimental results, using real clinical data from electronic health records, show that incorporating the utility-based loss function leads to superior multimodal early prediction models, detecting sepsis both more accurately and more timely. We argue that improving the timeliness of early prediction models is important for increasing their utility and acceptance in a clinical setting.
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| 8. |
- Valik, John Karlsson, et al.
(författare)
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Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model-SepsisFinder-which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care.
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| 9. |
- van der Werff, S. D., et al.
(författare)
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The accuracy of fully automated algorithms for surveillance of healthcare-associated urinary tract infections in hospitalized patients
- 2021
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Ingår i: Journal of Hospital Infection. - : Elsevier BV. - 0195-6701 .- 1532-2939. ; 110, s. 139-147
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Tidskriftsartikel (refereegranskat)abstract
- Background: Surveillance for healthcare-associated infections such as healthcareassociated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resourceintensive and subject to bias.Aim: To develop and validate a fully automated surveillance algorithm for HA-UTI using electronic health record (EHR) data.Methods: Five algorithms were developed using EHR data from 2979 admissions at Karolinska University Hospital from 2010 to 2011: (1) positive urine culture (UCx); (2) positive UCx + UTI codes (International Statistical Classification of Diseases and Related Health Problems, 10th revision); (3) positive UCx + UTI-specific antibiotics; (4) positive UCx + fever and/or UTI symptoms; (5) algorithm 4 with negation for fever without UTI symptoms. Natural language processing (NLP) was used for processing free-text medical notes. The algorithms were validated in 1258 potential UTI episodes from January to March 2012 and results extrapolated to all UTI episodes within this period (N 1/4 16,712). The reference standard for HA-UTIs was manual record review according to the European Centre for Disease Prevention and Control (and US Centers for Disease Control and Prevention) definitions by trained healthcare personnel.Findings: Of the 1258 UTI episodes, 163 fulfilled the ECDC HA-UTI definition and the algorithms classified 391, 150, 189, 194, and 153 UTI episodes, respectively, as HA-UTI. Algorithms 1, 2, and 3 had insufficient performances. Algorithm 4 achieved better performance and algorithm 5 performed best for surveillance purposes with sensitivity 0.667 (95% confidence interval: 0.594-0.733), specificity 0.997 (0.996-0.998), positive predictive value 0.719 (0.624-0.807) and negative predictive value 0.997 (0.996-0.997).Conclusion: A fully automated surveillance algorithm based on NLP to find UTI symptoms in free-text had acceptable performance to detect HA-UTI compared to manual record review. Algorithms based on administrative and microbiology data only were not sufficient.
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| 10. |
- van Mourik, Maaike S. M., et al.
(författare)
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PRAISE : providing a roadmap for automated infection surveillance in Europe
- 2021
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Ingår i: Clinical Microbiology and Infection. - : Elsevier. - 1198-743X .- 1469-0691. ; 27, s. S3-S19
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Healthcare-associated infections (HAI) are among the most common adverse events of medical care. Surveillance of HAI is a key component of successful infection prevention programmes. Conventional surveillance - manual chart review - is resource intensive and limited by concerns regarding interrater reliability. This has led to the development and use of automated surveillance (AS). Many AS systems are the product of in-house development efforts and heterogeneous in their design and methods. With this roadmap, the PRAISE network aims to provide guidance on how to move AS from the research setting to large-scale implementation, and how to ensure the delivery of surveillance data that are uniform and useful for improvement of quality of care. Methods: The PRAISE network brings together 30 experts from ten European countries. This roadmap is based on the outcome of two workshops, teleconference meetings and review by an independent panel of international experts. Results: This roadmap focuses on the surveillance of HAI within networks of healthcare facilities for the purpose of comparison, prevention and quality improvement initiatives. The roadmap does the following: discusses the selection of surveillance targets, different organizational and methodologic approaches and their advantages, disadvantages and risks; defines key performance requirements of AS systems and suggestions for their design; provides guidance on successful implementation and maintenance; and discusses areas of future research and training requirements for the infection prevention and related disciplines. The roadmap is supported by accompanying documents regarding the governance and information technology aspects of implementing AS. Conclusions: Large-scale implementation of AS requires guidance and coordination within and across surveillance networks. Transitions to large-scale AS entail redevelopment of surveillance methods and their interpretation, intensive dialogue with stakeholders and the investment of considerable resources. This roadmap can be used to guide future steps towards implementation, including designing solutions for AS and practical guidance checklists. (C) 2021 Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.
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