Predictive Factors for Hospitalization of Patients with Heat Illness in Yamaguchi, Japan.

Abstract

The objective of the study was to investigate the predictive factors for the hospitalization of patients who presented with mild to moderate heat illness at an emergency department. We conducted a retrospective survey of hospitals with an emergency department in Yamaguchi Prefecture, Japan. The survey questionnaire entries included patient age, sex, use of an ambulance, vital signs, blood examination conducted at the emergency department, the length of hospitalization, and outcome. We analyzed the predictive factors for hospitalization in patients with heat illness. A total of 127 patients were analyzed. Of these, 49 (37%) were admitted, with 59% discharged on the day following admission. In univariate analysis, the following inpatient characteristics were predictive for hospitalization: old age, low Glasgow Coma Scale score, elevated body temperature, increased serum C-reactive protein, and increased blood urea nitrogen. In logistic regression multivariate analysis, the following were predictive factors for hospitalization: age of ≥ 65 years (odds ratio (OR) 4.91; 95% confidence interval (CI) 1.42-17.00), body temperature (OR 1.97; 95% CI 1.14-3.41), Glasgow Coma Scale (OR 0.40; 95% CI 0.16-0.98), and creatinine (OR 2.92; 95% CI 1.23-6.94). The results suggest that the elderly with hyperthermia, disturbance of consciousness, and elevated serum creatinine have an increased risk for hospitalization with heat illness.

• Authors: Takahiro Yamamoto, Masaki Todani, Yasutaka Oda, Tadashi Kaneko, Kotaro Kaneda, Motoki Fujita, Takashi Miyauchi, Ryosuke Tsuruta
• Journal: International journal of environmental research and public health (Int J Environ Res Public Health) Vol. 12 Issue 9 Pg. 11770-80 (Sep 18 2015) ISSN: 1660-4601 [Electronic] Switzerland
• PMID: 26393633 (Publication Type: Journal Article)

Topics

• Adult
• Aged
• Emergency Service, Hospital (statistics & numerical data)
• Female
• Heat Stress Disorders (epidemiology, etiology)
• Hospitalization (statistics & numerical data)
• Hot Temperature (adverse effects)
• Humans
• Japan (epidemiology)
• Logistic Models
• Male
• Middle Aged
• Multivariate Analysis
• Retrospective Studies
• Risk Factors