Seasonal Variation in Heart Failure Hospitalization

Gallerani, Massimo; Boari, Benedetta; Manfredini, Fabio; Manfredini, Roberto

doi:10.1002/clc.20895

Cited by 62 publications

(51 citation statements)

References 41 publications

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“…[1][2][3] A seasonal variation in acute presentation with various cardiovascular disorders has been reported, with data on acute presentations with myocardial infarction, sudden death, rupture/dissection of aortic aneurysms, heart failure hospitalisations and venous thromboembolism [4][5][6][7][8][9] all showing a seasonal peak during winter. Acute stroke and transient ischaemic attack show a similar pattern.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Seasonality, incidence and prognosis in atrial fibrillation and stroke in Denmark and New Zealand

et al. 2012

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ObjectivesThere are relatively few large studies of seasonal variation in the occurrence of stroke in patients with atrial fibrillation (AF). We investigated the seasonal variation in incidence rates of hospitalisation with stroke in patients from Denmark and New Zealand.DesignCohort study.SettingNationwide hospital discharge data from Denmark and New Zealand.Participants243 381 (median age 75) subjects having a first-time hospitalisation with AF in Denmark and 51 480 (median age 76) subjects in New Zealand constituted the study population. Subjects with previous hospitalisation with stroke were excluded.Primary and secondary effect measuresPeak-to-trough ratio of the seasonal variation in incidence rates of stroke in AF patients adjusted for an overall trend was primary effect measure and was assessed using a log-linear Poisson regression model. Secondary effect measures were incidence rate ratios of AF and 30-day case fatality for stroke patients.ResultsIncidence rates of AF per 1000 person-years in Denmark increased by 5.4% (95% CI 5.3% to 5.7%) for patients aged <65 and 5% (95% CI 4.9% to 5.1%) for patients aged ≥65, whereas the increase was 0.2% (95% CI −0.2% to 0.6%) for patients aged <65 and 2.6% (95% CI 2.4% to 2.8%) for patients aged ≥65 in New Zealand. In Denmark 36 088 subjects were hospitalised with stroke, and 7518 subjects in New Zealand, both showing peaks during winter with peak-to-trough ratios of 1.22 and 1.27, respectively and a decreasing trend. The 30-day case fatality risk for stroke patients having AF is now (2000–2008) about 20% in both countries.ConclusionsAlthough incidence rates of hospitalisation with stroke in patients with AF have decreased in recent years, stroke remains a common AF complication with a high case fatality risk. The marked winter peak in incidence rates of hospitalisation with stroke in AF patients suggests that there are opportunities to reduce this complication. Further studies are necessary to identify how to optimise treatment of AF and prevention of stroke.

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Section: Strengths and Limitationsmentioning

confidence: 99%

Seasonality, incidence and prognosis in atrial fibrillation and stroke in Denmark and New Zealand

et al. 2012

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“…These studies have shown significant temporal variation with periodicity ranging from day of the week to season for specific diseases varying in etiological origin and health burden. Significant seasonal temporal variation of hospital utilization rates has been shown for acute diseases such as stroke [1], venous thromboembolism [2, 3], influenza [4], acute myocardial infarction [5], as well as for chronic diseases including chronic obstructive pulmonary disease [6], heart failure [7, 8], atrial fibrillation [9], and psychiatric disorders [10] such as bipolar disorder and mood disorders [11]. Interestingly, each of these previously studied diseases exhibits annual utilization peaks in the winter.…”

Section: Introductionmentioning

confidence: 99%

Discovery of temporal and disease association patterns in condition-specific hospital utilization rates

et al. 2017

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Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008–2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition’s seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.

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“…These breakthrough findings have suggested that the thus far unidentified causative pathogen may be a windborne microbe. Other groups have sought to confirm anecdotal findings of seasonal or climate related changes in disease rates, including heart failure [5], depression and anxiety [6], varicose vein ulcers [7], urinary tract infection [8,9], and even cancer [10]. While some of these works searched for seasonality using purpose-driven surveys, Upshur [11] used coded administrative data derived from a large EHR system to investigate whether seasonal peaks in incidence were a common feature in a limited set of the most frequent diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Data-driven discovery of seasonally linked diseases from an Electronic Health Records system

2014

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BackgroundPatterns of disease incidence can identify new risk factors for the disease or provide insight into the etiology. For example, allergies and infectious diseases have been shown to follow periodic temporal patterns due to seasonal changes in environmental or infectious agents. Previous work searching for seasonal or other temporal patterns in disease diagnosis rates has been limited both in the scope of the diseases examined and in the ability to distinguish unexpected seasonal patterns. Electronic Health Records (EHR) compile extensive longitudinal clinical information, constituting a unique source for discovery of trends in occurrence of disease. However, the data suffer from inherent biases that preclude a identification of temporal trends.MethodsMotivated by observation of the biases in this data source, we developed a method (Lomb-Scargle periodograms in detrended data, LSP-detrend) to find periodic patterns by adjusting the temporal information for broad trends in incidence, as well as seasonal changes in total hospitalizations. LSP-detrend can sensitively uncover periodic temporal patterns in the corrected data and identify the significance of the trend. We apply LSP-detrend to a compilation of records from 1.5 million patients encoded by ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), including 2,805 disorders with more than 500 occurrences across a 12 year period, recorded from 1.5 million patients.Results and conclusionsAlthough EHR data, and ICD-9 coded records in particular, were not created with the intention of aggregated use for research, these data can in fact be mined for periodic patterns in incidence of disease, if confounders are properly removed. Of all diagnoses, around 10% are identified as seasonal by LSP-detrend, including many known phenomena. We robustly reproduce previous findings, even for relatively rare diseases. For instance, Kawasaki disease, a rare childhood disease that has been associated with weather patterns, is detected as strongly linked with winter months. Among the novel results, we find a bi-annual increase in exacerbations of myasthenia gravis, a potentially life threatening complication of an autoimmune disease. We dissect the causes of this seasonal incidence and propose that factors predisposing patients to this event vary through the year.

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Seasonal Variation in Heart Failure Hospitalization

Cited by 62 publications

References 41 publications

Seasonality, incidence and prognosis in atrial fibrillation and stroke in Denmark and New Zealand

Seasonality, incidence and prognosis in atrial fibrillation and stroke in Denmark and New Zealand

Discovery of temporal and disease association patterns in condition-specific hospital utilization rates

Data-driven discovery of seasonally linked diseases from an Electronic Health Records system

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