Janeway lesions

Beaulieu, Amanda; Rehman, Habib

doi:10.1503/cmaj.091528

Cited by 6 publications

(3 citation statements)

References 1 publication

(2 reference statements)

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“…On the other hand, Janeway lesions are associated with bacterial endocarditis and present notably on the hands and soles of feet 4 . Janeway lesions are thought to be caused by microembolization from the thrombus of endocarditis and typically last days to weeks.…”

Section: Discussionmentioning

confidence: 99%

A late-onset leg rash, unique Janeway lesions or Jarisch-Herxheimer reaction? A case report

Andrew Kim,

Cristian Valdez,

Tony Alarcon

et al. 2023

World J. Adv. Res. Rev.

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Jarisch-Herxheimer reactions typically manifest as a response to antimicrobial therapy for spirochete infections, although these reactions have also been documented following treatment of other microbes. Almost always occurring within 24 hours of treatment initiation, Jarisch-Herxheimer reactions can cause hypotension, fevers, chills, headaches, myalgia, skin rashes, and exacerbations of existing skin lesions, among others; the most feared complication from such reactions is shock. They are usually transient and self-limiting but can cause significant discomfort and concern for patients. Treatment is usually supportive, consisting mostly of nonsteroidal anti-inflammatory drugs (NSAIDs), steroids, and intravenous fluids. Reactions with delayed onset are exceedingly rare, and to our knowledge, have been documented only one other time in literature. In this clinical vignette, we present a case in which a patient developed what we initially suspected to be Janeway lesions; however, the presence of systemic symptoms suggest that what was observed was delayed-onset Jarisch-Herxheimer reaction 16 days after initiation of antibiotics.

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Section: Discussionmentioning

confidence: 99%

A late-onset leg rash, unique Janeway lesions or Jarisch-Herxheimer reaction? A case report

Andrew Kim,

Cristian Valdez,

Tony Alarcon

et al. 2023

World J. Adv. Res. Rev.

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“…9,10 Inter-and intraobserver agreement between experts has been estimated as low as 29% while false positive rates for identifying an at-risk fetus are as high as 60%. [11][12][13][14][15][16] Human misinterpretation of these patterns has been directly implicated in avoidable early pregnancy intervention, increased adverse pregnancy outcomes (including fetal death) and is a major source of medicolegal litigation globally. [17][18][19][20] Efforts to standardize visual evaluation methods in antepartum fetal heart rate monitoring have faced issues with performance, reproducibility and clinician consensus.…”

Section: Introductionmentioning

confidence: 99%

A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term

Davis Jones,

Albert,

Cooke

et al. 2024

Preprint

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Objectives: This study aims to rigorously evaluate the Dawes-Redman computerised cardiotocography algorithm's effectiveness in assessing antepartum fetal wellbeing. It focuses on analysing the algorithm's performance using extensive clinical data, examining accuracy, sensitivity, specificity, and predictive values in various scenarios. The objectives include assessing the algorithm's reliability in identifying fetal wellbeing across different risk prevalences, its efficacy in the context of temporal proximity to delivery, and its performance across ten specific adverse pregnancy outcomes. This comprehensive evaluation seeks to clarify the algorithm's utility and limitations in contemporary obstetric practice, particularly in high-risk pregnancy scenarios. Methods: Antepartum fetal heart rate recordings from term singleton pregnancies between 37 and 42 gestational weeks were extracted from the Oxford University Hospitals database, spanning 1991 to 2021. Traces with significant data gaps or incomplete Dawes-Redman analyses were excluded. For the ten adverse outcomes, only traces performed within 48 hours prior to delivery were considered, aligning with clinical decision-making practices. A healthy cohort was established using rigorous inclusion and exclusion criteria based on clinical indicators. Propensity score matching, controlling for gestational age and fetal sex, ensured balanced comparisons between healthy and adverse outcome cohorts. The Dawes-Redman algorithm's categorisation of FHR traces as either 'criteria met' (an indicator of wellbeing) or 'criteria not met' (indicating a need for further evaluation) informed the evaluation of predictive performance metrics. Performance was assessed using accuracy, sensitivity, specificity, and predictive values (PPV, NPV), adjusted for various risk prevalences. Results: 4,196 term antepartum FHR traces were identified, matched by fetal sex and gestational age. The Dawes-Redman algorithm showed a high sensitivity of 91.7% for detecting fetal wellbeing. However, specificity for adverse outcomes was low at 15.6%. The PPV varied with population prevalence, high in very low-risk settings (99.1%) and declined with increased risk. Temporal proximity to delivery indicated robust sensitivity (>91.0%). Specificity notably decreased over time, impacting the algorithm's discriminative power for identifying adverse outcomes. Across different adverse conditions, the algorithm's performance remained consistent, with high sensitivity but varying NPVs, confirming its utility in detecting fetal wellbeing rather than adverse outcomes. Conclusion: These findings reveal the Dawes-Redman algorithm is effective for detecting fetal wellbeing in term pregnancies, evidenced by its high sensitivity and PPV. However, its low specificity suggests limitations in its ability to identify fetuses at risk of adverse outcomes. The predictive accuracy of the algorithm is significantly affected by the prevalence of healthy pregnancies within the population. Clinical interpretation of FHR traces that do not satisfy the Dawes-Redman criteria should be approached with caution, as they do not necessarily correlate with heightened risk. While the algorithm proves reliable for its primary objective in low-risk contexts, the development of algorithms optimised for high-risk pregnancy scenarios remains an area for future enhancement.

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“…[10,11] Inter-and intra-observer agreement between experts has also been estimated as low as 29% while false positive rates for identifying an at-risk fetus are as high as 60%. [12][13][14][15][16][17] Human misinterpretation of these patterns has therefore been associated with avoidable early pregnancy intervention, increased adverse pregnancy outcomes (including fetal death) and is a major source of medicolegal litigation globally. [18][19][20][21] Efforts to standardize visual evaluation methods in antepartum fetal heart rate monitoring have faced issues with performance, reproducibility and clinician consensus.…”

Section: Introductionmentioning

confidence: 99%

Identifying high-risk pre-term pregnancies using the fetal heart rate and machine learning

Davis Jones,

Cooke,

Vatish

2024

Preprint

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Introduction: Fetal heart rate (FHR) monitoring is one of the commonest and most affordable tests performed during pregnancy worldwide. It is critical for evaluating the health status of the baby, providing real-time insights into the physiology of the fetus. While the relationship between patterns in these signals and adverse pregnancy outcomes is well-established, human identification of these complex patterns remains sub-optimal, with experts often failing to recognise babies at high-risk of outcomes such as asphyxia, growth restriction and stillbirth. These outcomes are especially relevant in low- and middle-income countries where an estimated 98% of perinatal deaths occur. Pre-term birth complications are also the leading cause of death in children <5 years of age, 75% of which can be prevented. While advances have been made in developing low-cost digital solutions for antenatal fetal monitoring, there is still substantial progress to be made in developing tools for the identification of high-risk, adverse outcome pre-term pregnancies using these FHR systems. In this study, we have developed the first machine learning algorithm for the identification of high-risk preterm pregnancies with associated adverse outcomes using fetal heart rate monitoring. Methods: We sourced antepartum fetal heart rate traces from high-risk, pre-term pregnancies that were assigned at least one of ten adverse conditions. These were matched with normal pregnancies delivered at term. Using an automated, clinically-validated algorithm, seven distinct fetal heart rate patterns were extracted from each trace, subsequently filtered for outliers and normalized. The data were split into 80% for model development and 20% for validation. Six machine learning algorithms were trained using k-fold cross-validation to identify each trace as either normal or high-risk preterm. The best-performing algorithm was further evaluated using the validation dataset based on metrics including the AUC, sensitivity, and specificity at three distinct classification thresholds. Additional assessments included decision curve analysis and gestational age-specific and outcome-specific performance evaluations. Results: We analysed antepartum fetal heart rate recordings from 4,867 high-risk, pre-term pregnancies with adverse outcomes and 4,014 normal pregnancies. Feature extraction and preprocessing revealed significant differences between the groups (p<0.001). The random forest classifier was the most effective model, achieving an AUC of 0.88 (95% CI 0.87-0.88). When evaluating specific adverse outcomes, the median AUC was 0.85 (IQR 0.81-0.89) and the model consistently exceeded an AUC of 0.80 across all gestational ages. The model's robustness was confirmed on the validation dataset with an AUC of 0.88 (95% CI 0.86-0.90) and a Brier score of 0.14. Decision curve analysis showed the model surpassed both the treat-none and treat-all strategies over most probability thresholds (0.11-1.0). Performance metrics when using the Youden index were as follows: sensitivity 76.2% (95% CI 72.6-80.5%), specificity 87.5% (95% CI 83.3-91.0), F1 score 81.7 (95% CI 79.6-83.9), and Cohen's kappa 62.8 (95% CI 59.6-66.4), indicating high discriminative ability between pregnancy outcomes. Conclusions: Our study successfully demonstrated machine learning algorithms are capable of identifying high-risk preterm pregnancies with associated adverse outcomes through fetal heart rate monitoring. These findings demonstrate the potential of machine learning in enhancing the accuracy and effectiveness of antenatal fetal monitoring, particularly for high-risk cases where timely intervention is crucial. This algorithm could substantially improve pregnancy outcome prediction and consequently, maternal and neonatal care, especially in low- to middle-income countries where the burden of adverse outcomes is high.

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Janeway lesions

Cited by 6 publications

References 1 publication

A late-onset leg rash, unique Janeway lesions or Jarisch-Herxheimer reaction? A case report

A late-onset leg rash, unique Janeway lesions or Jarisch-Herxheimer reaction? A case report

A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term

Identifying high-risk pre-term pregnancies using the fetal heart rate and machine learning

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