Seasonal influence on the renal function in hospitalized elderly patients

Effects of cold and hot temperatures on the renal function of people with chronic disease

OBJECTIVES

This study investigated the effects of hot and cold temperature on the renal function of people with chronic diseases, such as diabetes, hypertension, and chronic kidney disease, using large-scale clinical data.

METHODS

We used retrospective cohort data from the Clinical Data Warehouse of the Seoul St Mary's Hospital, which contains clinical, diagnostic, laboratory, and other information about all patients who have visited the hospital since 1997. We obtained climate data from the Automated Synoptic Observing System of the Korea Meteorological Administration. The heat index was used as a measuring tool to evaluate heat exposure by indexing the actual heat that individuals feel according to temperature and humidity. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. To investigate changes in renal function trends with heat index, this study used generalized additive mixed models.

RESULTS

Renal function decreased linearly with increasing heat index after approximately 25°C, which was considered the flexion point of temperature. A linear decrease in the eGFR was observed with the effects of 0 to 5 lag days. Although there was a correlation observed between the decrease in eGFR and temperatures below -10°C, the results did not indicate statistical significance.

CONCLUSIONS

The results of our study provide scientific evidence that high temperatures affect the renal function of people with chronic diseases. These results can help prevent heat-related morbidity by identifying those who are more likely to develop renal disease and experience worsening renal function.

Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records

BACKGROUND

Acute Kidney Injury (AKI) is a multifactorial condition which presents a substantial burden to healthcare systems. There is limited evidence on whether it is seasonal. We sought to investigate the seasonality of AKI hospitalisations in England and use unsupervised machine learning to explore clustering of underlying comorbidities, to gain insights for future intervention.

METHODS

We used Hospital Episodes Statistics linked to the Clinical Practice Research Datalink to describe the overall incidence of AKI admissions between 2015 and 2019 weekly by demographic and admission characteristics. We carried out dimension reduction on 850 diagnosis codes using multiple correspondence analysis and applied k-means clustering to classify patients. We phenotype each group based on the dominant characteristics and describe the seasonality of AKI admissions by these different phenotypes.

RESULTS

Between 2015 and 2019, weekly AKI admissions peaked in winter, with additional summer peaks related to periods of extreme heat. Winter seasonality was more evident in those diagnosed with AKI on admission. From the cluster classification we describe six phenotypes of people admitted to hospital with AKI. Among these, seasonality of AKI admissions was observed among people who we described as having a multimorbid phenotype, established risk factor phenotype, and general AKI phenotype.

CONCLUSION

We demonstrate winter seasonality of AKI admissions in England, particularly among those with AKI diagnosed on admission, suggestive of community triggers. Differences in seasonality between phenotypes suggests some groups may be more likely to develop AKI as a result of these factors. This may be driven by underlying comorbidity profiles or reflect differences in uptake of seasonal interventions such as vaccines.

Estimating policy-relevant health effects of ambient heat exposures using spatially contiguous reanalysis data

BACKGROUND

Regional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes.

METHODS

We assessed the effect of multi-day lag exposure for maximum near-surface air temperature (Tmax) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008-2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression.

RESULTS

We observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in Tmax on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria.

CONCLUSION

Reanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public.