IntroductionPrevious studies have suggested an effect of gender on outcome after out-of-hospital cardiac arrest (OHCA), but the results are conflicting. We aimed to investigate the association of gender to outcome, coronary angiography (CAG) and adverse events in OHCA survivors treated with mild induced hypothermia (MIH).MethodsWe performed a retrospective analysis of prospectively collected data from the International Cardiac Arrest Registry. Adult patients with a non-traumatic OHCA and treated with MIH were included. Good neurological outcome was defined as a cerebral performance category (CPC) of 1 or 2.ResultsA total of 1,667 patients, 472 women (28%) and 1,195 men (72%), met the inclusion criteria. Men were more likely to receive bystander cardiopulmonary resuscitation, have an initial shockable rhythm and to have a presumed cardiac cause of arrest. At hospital discharge, men had a higher survival rate (52% vs. 38%, P <0.001) and more often a good neurological outcome (43% vs. 32%, P <0.001) in the univariate analysis. When adjusting for baseline characteristics, male gender was associated with improved survival (OR 1.34, 95% CI 1.01 to 1.78) but no longer with neurological outcome (OR 1.24, 95% CI 0.92 to 1.67). Adverse events were common; women more often had hypokalemia, hypomagnesemia and bleeding requiring transfusion, while men had more pneumonia. In a subgroup analysis of patients with a presumed cardiac cause of arrest (n = 1,361), men more often had CAG performed on admission (58% vs. 50%, P = 0.02) but this discrepancy disappeared in an adjusted analysis.ConclusionsGender differences exist regarding cause of arrest, adverse events and outcome. Male gender was independently associated with survival but not with neurological outcome.

Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristics could be predicted with an accuracy of 88.9% and 72.5% respectively. It could be concluded that machine learning methods show promising potential to enrich building databases with building characteristics relevant for energy retrofitting, which in turn can improve estimations of national energy savings potential.