| 1. |
- Dave, Nishi, et al.
(författare)
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Nosocomial SARS-CoV-2 infections and mortality during unique COVID-19 epidemic waves
- 2023
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Ingår i: JAMA Network Open. - : American Medical Association (AMA). - 2574-3805. ; 6:11
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Tidskriftsartikel (refereegranskat)abstract
- Importance: Quantifying the burden of nosocomial SARS-CoV-2 infections and associated mortality is necessary to assess the need for infection prevention and control measures.Objective: To investigate the occurrence of nosocomial SARS-CoV-2 infections and associated 30-day mortality among patients admitted to hospitals in Region Stockholm, Sweden.Design, Setting, and Participants: A retrospective, matched cohort study divided the period from March 1, 2020, until September 15, 2022, into a prevaccination period, early vaccination and pre-Omicron (period 1), and late vaccination and Omicron (period 2). From among 303 898 patients 18 years or older living in Region Stockholm, 538 951 hospital admissions across all hospitals were included. Hospitalized admissions with nosocomial SARS-CoV-2 infections were matched to as many as 5 hospitalized admissions without nosocomial SARS-CoV-2 by age, sex, length of stay, admission time, and hospital unit.Exposure: Nosocomial SARS-CoV-2 infection defined as the first positive polymerase chain reaction test result at least 8 days after hospital admission or within 2 days after discharge.Main Outcomes and Measures: Primary outcome of 30-day mortality was analyzed using time-to-event analyses with a Cox proportional hazards regression model adjusted for age, sex, educational level, and comorbidities.Results: Among 2193 patients with SARS-CoV-2 infections or reinfections (1107 women [50.5%]; median age, 80 [IQR, 71-87] years), 2203 nosocomial SARS-CoV-2 infections were identified. The incidence rate of nosocomial SARS-CoV-2 infections was 1.57 (95% CI, 1.51-1.64) per 1000 patient-days. In the matched cohort, 1487 hospital admissions with nosocomial SARS-CoV-2 infections were matched to 5044 hospital admissions without nosocomial SARS-CoV-2 infections. Thirty-day mortality was higher in the prevaccination period (adjusted hazard ratio [AHR], 2.97 [95% CI, 2.50-3.53]) compared with period 1 (AHR, 2.08 [95% CI, 1.50-2.88]) or period 2 (AHR, 1.22 [95% CI, 0.92-1.60]). Among patients with nosocomial SARS-CoV-2 infections, 30-day AHR comparing those with 2 or more doses of SARS-CoV-2 vaccination and those with less than 2 doses was 0.64 (95% CI, 0.46-0.88).Conclusions and Relevance: In this matched cohort study, nosocomial SARS-CoV-2 infections were associated with higher 30-day mortality during the early phases of the pandemic and lower mortality during the Omicron variant wave and after the introduction of vaccinations. Mitigation of excess mortality risk from nosocomial transmission should be a strong focus when population immunity is low through implementation of adequate infection prevention and control measures.
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| 2. |
- Henriksson, Aron, 1985-, et al.
(författare)
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Multimodal fine-tuning of clinical language models for predicting COVID-19 outcomes
- 2023
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Ingår i: Artificial Intelligence in Medicine. - 0933-3657 .- 1873-2860. ; 146
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Tidskriftsartikel (refereegranskat)abstract
- Clinical prediction models tend only to incorporate structured healthcare data, ignoring information recorded in other data modalities, including free-text clinical notes. Here, we demonstrate how multimodal models that effectively leverage both structured and unstructured data can be developed for predicting COVID-19 outcomes. The models are trained end-to-end using a technique we refer to as multimodal fine-tuning, whereby a pre -trained language model is updated based on both structured and unstructured data. The multimodal models are trained and evaluated using a multicenter cohort of COVID-19 patients encompassing all encounters at the emergency department of six hospitals. Experimental results show that multimodal models, leveraging the notion of multimodal fine-tuning and trained to predict (i) 30-day mortality, (ii) safe discharge and (iii) readmission, outperform unimodal models trained using only structured or unstructured healthcare data on all three outcomes. Sensitivity analyses are performed to better understand how well the multimodal models perform on different patient groups, while an ablation study is conducted to investigate the impact of different types of clinical notes on model performance. We argue that multimodal models that make effective use of routinely collected healthcare data to predict COVID-19 outcomes may facilitate patient management and contribute to the effective use of limited healthcare resources.
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| 3. |
- Pawar, Yash, et al.
(författare)
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Leveraging Clinical BERT in Multimodal Mortality Prediction Models for COVID-19
- 2022
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Ingår i: In Proceedings of IEEE International Symposium on Computer-Based Medical Systems (CMBS 2022). - : IEEE. - 9781665467704 ; , s. 199-204
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Konferensbidrag (refereegranskat)abstract
- Clinical prediction models are often based solely on the use of structured data in electronic health records, e.g. vital parameters and laboratory results, effectively ignoring potentially valuable information recorded in other modalities, such as free-text clinical notes. Here, we report on the development of a multimodal model that combines structured and unstructured data. In particular, we study how best to make use of a clinical language model in a multimodal setup for predicting 30-day all-cause mortality upon hospital admission in patients with COVID-19. We evaluate three strategies for incorporating a domain-specific clinical BERT model in multimodal prediction systems: (i) without fine-tuning, (ii) with unimodal fine-tuning, and (iii) with multimodal fine-tuning. The best-performing model leverages multimodal fine-tuning, in which the clinical BERT model is updated based also on the structured data. This multimodal mortality prediction model is shown to outperform unimodal models that are based on using either only structured data or only unstructured data. The experimental results indicate that clinical prediction models can be improved by including data in other modalities and that multimodal fine-tuning of a clinical language model is an effective strategy for incorporating information from clinical notes in multimodal prediction systems.
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| 4. |
- 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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| 5. |
- Alam, Mahbub Ul, et al.
(författare)
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Terminology Expansion with Prototype Embeddings : Extracting Symptoms of Urinary Tract Infection from Clinical Text
- 2021
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Ingår i: Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF. - Setúbal : SciTePress. - 9789897584909 ; , s. 47-57
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Konferensbidrag (refereegranskat)abstract
- Many natural language processing applications rely on the availability of domain-specific terminologies containing synonyms. To that end, semi-automatic methods for extracting additional synonyms of a given concept from corpora are useful, especially in low-resource domains and noisy genres such as clinical text, where nonstandard language use and misspellings are prevalent. In this study, prototype embeddings based on seed words were used to create representations for (i) specific urinary tract infection (UTI) symptoms and (ii) UTI symptoms in general. Four word embedding methods and two phrase detection methods were evaluated using clinical data from Karolinska University Hospital. It is shown that prototype embeddings can effectively capture semantic information related to UTI symptoms. Using prototype embeddings for specific UTI symptoms led to the extraction of more symptom terms compared to using prototype embeddings for UTI symptoms in general. Overall, 142 additional UTI symp tom terms were identified, yielding a more than 100% increment compared to the initial seed set. The mean average precision across all UTI symptoms was 0.51, and as high as 0.86 for one specific UTI symptom. This study provides an effective and cost-effective solution to terminology expansion with small amounts of labeled data.
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| 6. |
- Athlin, Simon, 1971-, et al.
(författare)
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Pneumococcal urinary antigen testing for antimicrobial guidance in community-acquired pneumonia : A register-based cohort study
- 2022
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Ingår i: Journal of Infection. - : Elsevier. - 0163-4453 .- 1532-2742. ; 85:2, s. 167-173
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: To evaluate the effect of pneumococcal urinary antigen test (UAT) usage on broad-spectrum antibiotic treatment in community-acquired pneumonia (CAP).METHODS: Patients admitted to 32 Swedish hospitals between 2011 and 2014 were retrospectively included from the Swedish National Quality Register of CAP. Using propensity score matched data, stratified by CRB-65 score, we studied the effect of performing UAT and of positive test results on treatment with broad-spectrum β-lactam monotherapy (BSBM) and antibiotics with coverage for atypical bacteria compared to narrow-spectrum β-lactam monotherapy (NSBM).RESULTS: UAT was performed for 4,995/14,590 (34.2%) patients, 603/4,995 (12.1%) of whom had positive test results. At day three, performing UAT was not associated with decreased use of BSBM (OR 1.07, 95% CI 0.94-1.23) but was associated with increased atypical coverage among patients with CRB-65 score 2 (OR 1.47, 95% CI 1.06-2.02). A positive UAT was associated with decreased BSBM use (OR 0.39, 95% CI 0.25-0.60) and decreased atypical coverage (OR 0.25, 95% CI 0.16-0.37), predominantly in non-severe CAP. At day one, performing UAT was associated with atypical coverage among patients with CRB-65 scores 2 (OR 2.60, 95% CI 1.69-3.98) and 3-4 (OR 3.69, 95% CI 1.55-8.79), and a positive test reduced the odds of BSBM treatment among CRB-65 score 3-4 patients (OR 3.49, 95% CI 1.02-12.0).CONCLUSIONS: Performing UAT had no overall effect on decreasing the use of BSBM treatment by day three of hospitalization, yet non-severely ill patients with positive UAT results were less likely to be treated with BSBM and antibiotics with atypical coverage.
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| 7. |
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| 8. |
- Block, Nils, et al.
(författare)
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Bacterial meningitis : Aetiology, risk factors, disease trends and severe sequelae during 50 years in Sweden
- 2022
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Ingår i: Journal of Internal Medicine. - : John Wiley & Sons. - 0954-6820 .- 1365-2796. ; 292:2, s. 350-364
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Tidskriftsartikel (refereegranskat)abstract
- Background Bacterial meningitis (BM) is a rare but severe infection. Few population-based studies have characterised BM episodes and sequelae over long periods.Methods This was a population-based observational cohort study with national coverage, using data on aetiological pathogens, sex, premorbid conditions, steroid pretreatment, severe sequelae and birth, death and diagnosis dates collected from 10,339 patients with BM reported to the National Board of Health and Welfare in Sweden between 1964 and 2014.Results During the 50-year study period, the incidence of BM decreased in young children, but not in the elderly. The most common cause of BM was pneumococci (34%), followed by Haemophilus influenzae (26%), and meningococci (18%), mainly community acquired. Premorbid conditions were found in 20%. After the H. influenzae type b vaccine was introduced in 1993, the BM incidence decreased by 36%. Following pneumococcal conjugated vaccine introduction in 2009, the incidence and 30-day mortality from pneumococcal meningitis decreased by 64% and 100%, respectively, in previously healthy children, and the 30-day mortality decreased by 64% among comorbid adults. The BM incidence in immunosuppressed patients increased by 3% annually post vaccine introduction. The 30-day mortality was 3% in children and 14% in adults, and the rate of severe sequelae was 44%. On average, patients lost 11 years of healthy life due to BM.Conclusion The introduction of conjugated vaccines into the childhood vaccination program has reduced the incidence of BM in young children, but not in adults. Post vaccine introduction, patients present with more premorbid conditions and other bacterial causes of BM, emphasising the need for a correct diagnosis when treating these infections.
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| 9. |
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| 10. |
- Froding, Inga, et al.
(författare)
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Extended-Spectrum-beta-Lactamase- and Plasmid AmpC-Producing Escherichia coli Causing Community-Onset Bloodstream Infection : Association of Bacterial Clones and Virulence Genes with Septic Shock, Source of Infection, and Recurrence
- 2020
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Ingår i: Antimicrobial Agents and Chemotherapy. - : AMER SOC MICROBIOLOGY. - 0066-4804 .- 1098-6596. ; 64:8
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Tidskriftsartikel (refereegranskat)abstract
- Invasive infections due to extended-spectrum-beta-lactamase- and pAmpC-producing Escherichia coli (ESBL/pAmpC-EC) are an important cause of morbidity, often caused by the high-risk clone sequence type (ST131) and isolates classified as extraintestinal pathogenic E. coli (ExPEC). The relative influence of host immunocompetence versus microbiological virulence factors in the acquisition and outcome of bloodstream infections (BSI) is poorly understood. Herein, we used whole-genome sequencing on 278 blood culture isolates of ESBL/pAmpC-EC from 260 patients with community-onset BSI collected from 2012 to 2015 in Stockholm to study the association of virulence genes, sequence types, and antimicrobial resistance with severity of disease, infection source, ESBL/pAmpC-EC BSI low-risk patients, and patients with repeated episodes. ST131 subclade C2 comprised 29% of all patients. Factors associated with septic shock in multivariable analysis were patient host factors (hematologic cancer or transplantation and reduced daily living activity), presence of the E. coli virulence factor iss (increased serum survival), absence of phenotypic multidrug resistance, and absence of the genes pap and hsp. Adhesins, particularly pap, were associated with urinary tract infection (UTI) source, while isolates from post-prostate biopsy sepsis had a low overall number of virulence operons, including adhesins, and commonly belonged to ST131 clades A, B, and subclade C1, ST1193, and ST648. ST131 was associated with recurrent episodes. In conclusion, the most interesting finding is the association of iss with septic shock. Adhesins are important for UTI pathogenesis, while otherwise low-pathogenic isolates from the microbiota can cause post-prostate biopsy sepsis.
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| 11. |
- Glimaker, Martin, et al.
(författare)
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Etiology, clinical presentation, outcome and the effect of initial management in immunocompromised patients with community acquired bacterial meningitis
- 2020
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Ingår i: Journal of Infection. - : W B SAUNDERS CO LTD. - 0163-4453 .- 1532-2742. ; 80:3, s. 291-297
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: The aim was to analyze differences in clinical presentation, etiology, management, and outcome between immunocompromised and immunocompetent patients with acute bacterial meningitis (ABM). Methods: Data were extracted from 1056 adult ABM patients prospectively registered in the national Swedish quality register for ABM during 2008-2017. Primary endpoint was 30-day mortality and secondary endpoints 90-day mortality and unfavorable outcome. Results: An immunocompromised state was observed in 352 (33%) of the 1056 patients. Streptococcus pneumoniae dominated in both immunocompromised and immunocompetent patients (53% in both groups), whereas L monocytogenes occurred in 11% and 2%, respectively. The unadjusted odds ratio (OR) for 30-day mortality in immunocompromised compared to immunocompetent patients was 1.68 (95% confidence interval (CI): 1.07-2.63). Adjusted for age, sex, and mental status on admission the OR was 1.34 (CI: 0.82-2.21). Adjusted also for time to antibiotic treatment and corticosteroids the OR was 1.10 (CI: 0.59-2.05), and in patients without Listeria meningitis 0.98 (CI: 0.50-1.90). Although, the ORs were higher for 90-day mortality and unfavorable outcome the effects of adjustments were similar. Conclusion: Mortality in immunocompromised patients with ABM is only moderately increased unless caused by Listeria. This difference is further reduced in patients given early antibiotic treatment and adjunctive corticosteroids. Funding: This work was supported by Stockholm County Council.
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| 12. |
- 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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| 13. |
- 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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| 14. |
- 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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| 15. |
- Naucler, Pontus, et al.
(författare)
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HAI-Proactive : Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden
- 2020
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Ingår i: Infection control and hospital epidemiology. - : Cambridge University Press. - 0899-823X .- 1559-6834. ; 41, s. S39-S39
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None
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| 16. |
- Rhedin, Samuel, et al.
(författare)
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Myxovirus resistance protein A for discriminating between viral and bacterial lower respiratory tract infections in children- The TREND study
- 2022
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Ingår i: Clinical Microbiology and Infection. - : Elsevier BV. - 1198-743X .- 1469-0691. ; 28:9, s. 1251-1257
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Discriminating between viral and bacterial lower respiratory tract infection (LRTI) in children is challenging, leading to an excessive use of antibiotics. Myxovirus resistance protein A (MxA) is a promising biomarker for viral infections. The primary aim of the study was to assess differences in blood MxA levels between children with viral and bacterial LRTI. Secondary aims were to assess differences in blood MxA levels between children with viral LRTI and asymptomatic controls and to assess MxA levels in relation to different respiratory viruses. Methods: Children with LRTI were enrolled as cases at Sachs' Children and Youth Hospital, Stockholm, Sweden. Nasopharyngeal aspirates and blood samples for analysis of viral PCR, MxA, and C-reactive protein were systematically collected from all study subjects in addition to standard laboratory/radiology assessment. Aetiology was defined according to an algorithm based on laboratory and radiological findings. Asymptomatic children with minor surgical disease were enrolled as controls. Results: MxA levels were higher in children with viral LRTI (n = 242) as compared to both bacterial (n = 5) LRTI (p < 0.01, area under the curve (AUC) 0.90, 95% CI: 0.81 to 0.99), and controls (AUC 0.92, 95% CI: 0.88 to 0.95). In the subgroup of children with pneumonia diagnosis, a cutoff of MxA 430 mg/l discriminated between viral (n = 29) and bacterial (n = 4) aetiology with 93% (95% CI: 78-99%) sensi-tivity and 100% (95% CI: 51-100%) specificity (AUC 0.98, 95% CI: 0.94 to 1.00). The highest MxA levels were seen in cases PCR positive for influenza (median MxA 1699 mg/l, interquartile range: 732 to 2996) and respiratory syncytial virus (median MxA 1115 mg/l, interquartile range: 679 to 2489). Discussion: MxA accurately discriminated between viral and bacterial aetiology in children with LRTI, particularly in the group of children with pneumonia diagnosis, but the number of children with bacterial LRTI was low.
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| 17. |
- Strålin, Kristoffer, et al.
(författare)
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Design of a national patient-centred clinical pathway for sepsis in Sweden
- 2023
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Ingår i: Infectious Diseases. - : Taylor & Francis. - 2374-4235 .- 2374-4243. ; 55:10, s. 716-724
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The World Health Organization has adopted a resolution on sepsis and urged member states to develop national processes to improve sepsis care. In Sweden, sepsis was selected as one of the ten first diagnoses to be addressed, when the Swedish government in 2019 allocated funds for patient-centred clinical pathways in healthcare. A national multidisciplinary working group, including a patient representative, was appointed to develop the patient-centred clinical pathway for sepsis.METHODS: The working group mapped challenges and needs surrounding sepsis care and included a survey sent to all emergency departments (ED) in Sweden, and then designed a patient-centred clinical pathway for sepsis.RESULTS: The working group decided to focus on the following four areas: (1) sepsis alert for early detection and management optimisation for the most severely ill sepsis patients in the ED; (2) accurate sepsis diagnosis coding; (3) structured information to patients at discharge after sepsis care and (4) structured telephone follow-up after sepsis care. A health-economic analysis indicated that the implementation of the clinical pathway for sepsis will most likely not drive costs. An important aspect of the clinical pathway is implementing continuous monitoring of performance and process indicators. A national working group is currently building up such a system for monitoring, focusing on extraction of this information from the electronic health records systems.CONCLUSION: A national patient-centred clinical pathway for sepsis has been developed and is currently being implemented in Swedish healthcare. We believe that the clinical pathway and the accompanying monitoring will provide a more efficient and equal sepsis care and improved possibilities to monitor and further develop sepsis care in Sweden.
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| 18. |
- 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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| 19. |
- 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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| 20. |
- 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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| 21. |
- Verberk, Janneke D. M., et al.
(författare)
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The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery
- 2023
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Ingår i: Antimicrobial Resistance and Infection Control. - 2047-2994. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundIn patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR).MethodsRetrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated.ResultsFrom the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm.ConclusionsThe addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.
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| 22. |
- Wolff, Ellen, 1987, et al.
(författare)
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Cost-effectiveness of pneumococcal vaccination for elderly in Sweden
- 2020
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Ingår i: Vaccine. - : Elsevier BV. - 0264-410X .- 1873-2518. ; 38:32, s. 4988-4995
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The aim was to assess cost-effectiveness of including pneumococcal vaccination for elderly in a national vaccination programme in Sweden, comparing health-effects and costs of pneumococcal related diseases with a vaccination programme versus no vaccination. Method: We used a single-cohort deterministic decision-tree model to simulate the current burden of pneumococcal disease in Sweden. The model accounted for invasive pneumococcal disease (IPD) and pneumonia caused by pneumococci. Costs included in the analysis were those incurred when treating pneumococcal disease, and acquisition and administration of the vaccine. Health effects were measured as quality-adjusted life years (QALY). The time-horizon was set to five years, both effects and costs were discounted by 3% annually. Health-effects and costs were accumulated over the time-horizon and used to create an incremental cost-effectiveness ratio. The 23-valent polysaccharide vaccine (PPV23) was used in the base-case analysis. The 13-valent pneumococcal conjugate vaccine PCV13 was included in sensitivity analyses. Results: A vaccination programme using PPV23 would reduce the burden of pneumococcal related disease significantly, both when vaccinating a 65-year-old cohort and a 75-year-old cohort. IPD would decrease by 30% in the 65-year-old cohort, and by 29% in the 75-year-old cohort. The corresponding figures for CAP (communicable acquired pneumonia) are 19% and 15%. The cost per gained QALY was estimated to EUR 94,000 for vaccinating 65-year-olds and EUR 29,500 for 75-year-olds. With one dose PCV13 given instead of PPV23, the cost per gained QALY would increase by around 400% for both cohorts. The results were robust in sensitivity analyses. Conclusion: Introducing a vaccination programme against pneumococcal disease for 65-year-olds in Sweden is unlikely to be cost-effective, whereas it for 75 year-olds and using PPV23 can be considered good value for money. Our model indicates that vaccine price needs to be reduced by 55% for vaccination of 65-year-olds to be cost-effective, given a threshold of EUR 50,000. (C) 2020 Elsevier Ltd. All rights reserved.
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