Lack of seasonality in occurrence of pericarditis, myocarditis, and endocarditis

Seasonally adjusted laboratory reference intervals to improve the performance of machine learning models for classification of cardiovascular diseases

BACKGROUND

Variation in laboratory healthcare data due to seasonal changes is a widely accepted phenomenon. Seasonal variation is generally not systematically accounted for in healthcare settings. This study applies a newly developed adjustment method for seasonal variation to analyze the effect seasonality has on machine learning model classification of diagnoses.

METHODS

Machine learning methods were trained and tested on ~ 22 million unique records from ~ 575,000 unique patients admitted to Danish hospitals. Four machine learning models (adaBoost, decision tree, neural net, and random forest) classifying 35 diseases of the circulatory system (ICD-10 diagnosis codes, chapter IX) were run before and after seasonal adjustment of 23 laboratory reference intervals (RIs). The effect of the adjustment was benchmarked via its contribution to machine learning models trained using hyperparameter optimization and assessed quantitatively using performance metrics (AUROC and AUPRC).

RESULTS

Seasonally adjusted RIs significantly improved cardiovascular disease classification in 24 of the 35 tested cases when using neural net models. Features with the highest average feature importance (via SHAP explainability) across all disease models were sex, C- reactive protein, and estimated glomerular filtration. Classification of diseases of the vessels, such as thrombotic diseases and other atherosclerotic diseases consistently improved after seasonal adjustment.

CONCLUSIONS

As data volumes increase and data-driven methods are becoming more advanced, it is essential to improve data quality at the pre-processing level. This study presents a method that makes it feasible to introduce seasonally adjusted RIs into the clinical research space in any disease domain. Seasonally adjusted RIs generally improve diagnoses classification and thus, ought to be considered and adjusted for in clinical decision support methods.

Incidence, clinical presentation, management, and outcome of acute pericarditis and myopericarditis

AIMS

Little is known about the epidemiology, clinical presentation, management, and outcome of acute pericarditis and myopericarditis.

METHODS AND RESULTS

The final diagnoses of acute pericarditis, myopericarditis, and non-ST-segment elevation myocardial infarction (NSTEMI) of patients presenting to seven emergency departments in Switzerland with acute chest pain were centrally adjudicated by two independent cardiologists using all information including serial measurements of high-sensitivity cardiac troponin T. The overall incidence of pericarditis and myopericarditis was estimated relative to the established incidence of NSTEMI. Current management and long-term outcome of both conditions were also assessed. Among 2533 chest pain patients, the incidence of pericarditis, myopericarditis, and NSTEMI were 1.9% (n = 48), 1.1% (n = 29), and 21.6% (n = 548), respectively. Accordingly, the estimated incidence of pericarditis and myopericarditis in Switzerland was 10.1 [95% confidence interval (95% CI) 9.3-10.9] and 6.1 (95% CI 5.6-6.7) cases per 100 000 population per year, respectively, vs. 115.0 (95% CI 112.3-117.6) cases per 100 000 population per year for NSTEMI. Pericarditis (85% male, median age 46 years) and myopericarditis (62% male, median age 56 years) had male predominance, and commonly (50% and 97%, respectively) resulted in hospitalization. No patient with pericarditis or myopericarditis died or had life-threatening arrhythmias within 30 days [incidence 0% (95% CI 0.0-4.8%)]. Compared with NSTEMI, the 2-year all-cause mortality adjusted hazard ratio of pericarditis and myopericarditis was 0.40 (95% CI 0.05-2.96), being 0.59 (95% CI 0.40-0.88) for non-cardiac causes of chest pain.

CONCLUSION

Pericarditis and myopericarditis are substantially less common than NSTEMI and have an excellent short- and long-term outcome.

CLINICAL TRIAL REGISTRATION

ClinicalTrial.gov, number NCT00470587, https://clinicaltrials.gov/ct2/show/NCT00470587.

Temporal Trends of Infective Endocarditis in Olmsted County, Minnesota, Between 1970 and 2018: A Population-Based Analysis

Background

A population-based study of infective endocarditis (IE) in Olmsted County, Minnesota, provides a unique opportunity to define temporal and seasonal variations in IE incidence over an extended time period.

Methods

This was a population-based review of all adults (≥18 years) residing in Olmsted County, Minnesota, with definite or possible IE using the Rochester Epidemiology Project from January 1, 1970, through December 31, 2018. Poisson regression was used to characterize the trends in IE incidence; models were fitted with age, sex, calendar time, and season, allowing for nonlinearity and nonadditivity of their effects.

Results

Overall, 269 cases of IE were identified over a 49-year study period. The median age of IE cases was 67.2 years, and 33.8% were female. The overall age- and sex-adjusted incidence of IE was 7.9 cases per 100 000 person-years (95% CI, 7.0-8.9), with corresponding rates of 2.4, 2.4, 0.9, and 0.7 per 100 000 person-years for Staphylococcus aureus, viridans group streptococci (VGS), Enterococcus species, and coagulase-negative staphylococci IE, respectively. Temporal trends varied by age, sex, and season, but on average IE incidence increased over time (P = .021). Enterococcal IE increased the most (P = .018), while S. aureus IE appeared to increase but mostly in the winter months (P = .018). Between 1996 and 2018, the incidence of VGS IE was relatively stable, with no statistically significant difference in the trends before and after the 2007 AHA IE prevention guidelines.

Conclusions

Overall, IE incidence, and specifically enterococcal IE, increased over time, while S. aureus IE was seasonally dependent. There was no statistically significant difference in VGS IE incidence in the periods before and after publication of the 2007 AHA IE prevention guidelines.

Do seasonal microbiome changes affect infection susceptibility, contributing to seasonal disease outbreaks?

The aim of the present paper is to explore whether seasonal outbreaks of infectious diseases may be linked to changes in host microbiomes. This is a very important issue, because one way to have more control over seasonal outbreaks is to understand the factors that underlie them. In this paper, I will evaluate the relevance of the microbiome as one of such factors. The paper is based on two pillars of reasoning. Firstly, on the idea that microbiomes play an important role in their hosts' defence against infectious diseases. Secondly, on the idea that microbiomes are not stable, but change seasonally. These two ideas are combined in order to argue that seasonal changes in a given microbiome may influence the functionality of the host's immune system and consequently make it easier for infectious agents to infect the host at certain times of year. I will argue that, while this is only a theoretical possibility, certain studies may back up such claims. Furthermore, I will show that this does not necessarily contradict other hypotheses aimed at explaining seasonal outbreaks; in fact, it may even enhance them.