Abstract Body (Do not enter title and authors here): Background: Seasonal variations have been observed in heart failure (HF) hospitalization. Our objective was to identify specific factors that could contribute to seasonal variability using a large longitudinal dataset of HF hospitalizations.

Methods: Hospital discharge data was obtained for all hospitals in New York state from 1994-2007. Records with a primary diagnosis of heart failure. Year and month of admission were used as predictors to evaluate outcomes of in-hospital mortality, population-adjusted daily rate of hospital admissions, and length of stay (LOS) using univariable regression including a sinusoidal model to assess seasonality of HF outcomes. Observations were then adjusted for multiple medical covariables including acute diagnoses such as asthma, COPD, and pneumonia, average local monthly temperature, and humidity at each hospital using data from the Global Historical Climate Network to identify modifiers of seasonal variability.

Results: Among 949,907 records, median age was 76 (IQR 65-84 years old), and 54% female (510,945 records). The population-adjusted rate of HF admissions increased by 1.1 admissions/day/year, whereas in-hospital mortality and LOS decreased by -0.3%/year and -0.3 days/year respectively (p<0.001 for all). Seasonal trends were identified for daily HF admissions (February peak, p<0.0001), mortality (January peak, p<0.001) and LOS (January peak, p<0.01). Cosinor analysis revealed significant periodicity for HF admission rate (amplitude ±0.9/admits/day/100,000 people, p<0.001), in-hospital mortality (amplitude ±0.47%, p<0.001), and LOS (amplitude ±0.23 days, p<0.01). No other patient characteristics were significant modifiers of seasonality. Odds of mortality were highest in July rather than January when adjusted for average local temperature but not humidity.

Conclusion: Adverse outcomes in patients hospitalized with HF were significantly worse in winter months even when adjusted for concurrent diagnoses such as pneumonia. Local ambient temperature was the strongest modifier of the observed seasonality. Given increasing frequency of extreme weather events, additional work to determine mechanisms of this and other environmental risk factors is urgently needed.