scholarly journals Ethnic comparison in takotsubo syndrome: novel insights from the International Takotsubo Registry

2021 ◽  
Author(s):  
Yoichi Imori ◽  
Ken Kato ◽  
Victoria L. Cammann ◽  
Konrad A. Szawan ◽  
Manfred Wischnewsky ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Clinical Characteristics ◽  
Ethnic Disparities ◽  
Japanese Patients ◽  
Learning Approach ◽  
Takotsubo Syndrome ◽  
Unique Identifier ◽  
Hospital Outcomes ◽  
Machine Learning Approach ◽  
The Impact

Abstract Background Ethnic disparities have been reported in cardiovascular disease. However, ethnic disparities in takotsubo syndrome (TTS) remain elusive. This study assessed differences in clinical characteristics between Japanese and European TTS patients and determined the impact of ethnicity on in-hospital outcomes. Methods TTS patients in Japan were enrolled from 10 hospitals and TTS patients in Europe were enrolled from 32 hospitals participating in the International Takotsubo Registry. Clinical characteristics and in-hospital outcomes were compared between Japanese and European patients. Results A total of 503 Japanese and 1670 European patients were included. Japanese patients were older (72.6 ± 11.4 years vs. 68.0 ± 12.0 years; p < 0.001) and more likely to be male (18.5 vs. 8.4%; p < 0.001) than European TTS patients. Physical triggering factors were more common (45.5 vs. 32.0%; p < 0.001), and emotional triggers less common (17.5 vs. 31.5%; p < 0.001), in Japanese patients than in European patients. Japanese patients were more likely to experience cardiogenic shock during the acute phase (15.5 vs. 9.0%; p < 0.001) and had a higher in-hospital mortality (8.2 vs. 3.2%; p < 0.001). However, ethnicity itself did not appear to have an impact on in-hospital mortality. Machine learning approach revealed that the presence of physical stressors was the most important prognostic factor in both Japanese and European TTS patients. Conclusion Differences in clinical characteristics and in-hospital outcomes between Japanese and European TTS patients exist. Ethnicity does not impact the outcome in TTS patients. The worse in-hospital outcome in Japanese patients, is mainly driven by the higher prevalence of physical triggers. Trial Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT01947621.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

scholarly journals Phenotyping Cardiogenic Shock

2021 ◽  
Author(s):  
Elric Zweck ◽  
Katherine L. Thayer ◽  
Ole K. L. Helgestad ◽  
Manreet Kanwar ◽  
Mohyee Ayouty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Cardiogenic Shock ◽  
Treatment Strategies ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Tailored Treatment ◽  
Machine Learning Approach ◽  
Clinical Profiles ◽  
Patient Enrollment

Background Cardiogenic shock (CS) is a heterogeneous syndrome with varied presentations and outcomes. We used a machine learning approach to test the hypothesis that patients with CS have distinct phenotypes at presentation, which are associated with unique clinical profiles and in‐hospital mortality. Methods and Results We analyzed data from 1959 patients with CS from 2 international cohorts: CSWG (Cardiogenic Shock Working Group Registry) (myocardial infarction [CSWG‐MI; n=410] and acute‐on‐chronic heart failure [CSWG‐HF; n=480]) and the DRR (Danish Retroshock MI Registry) (n=1069). Clusters of patients with CS were identified in CSWG‐MI using the consensus k means algorithm and subsequently validated in CSWG‐HF and DRR. Patients in each phenotype were further categorized by their Society of Cardiovascular Angiography and Interventions staging. The machine learning algorithms revealed 3 distinct clusters in CS: "non‐congested (I)", "cardiorenal (II)," and "cardiometabolic (III)" shock. Among the 3 cohorts (CSWG‐MI versus DDR versus CSWG‐HF), in‐hospital mortality was 21% versus 28% versus 10%, 45% versus 40% versus 32%, and 55% versus 56% versus 52% for clusters I, II, and III, respectively. The "cardiometabolic shock" cluster had the highest risk of developing stage D or E shock as well as in‐hospital mortality among the phenotypes, regardless of cause. Despite baseline differences, each cluster showed reproducible demographic, metabolic, and hemodynamic profiles across the 3 cohorts. Conclusions Using machine learning, we identified and validated 3 distinct CS phenotypes, with specific and reproducible associations with mortality. These phenotypes may allow for targeted patient enrollment in clinical trials and foster development of tailored treatment strategies in subsets of patients with CS.

scholarly journals A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

2022 ◽  
Vol 21 (1) ◽  
Author(s):  
Luca Boniardi ◽  
Federica Nobile ◽  
Massimo Stafoggia ◽  
Paola Michelozzi ◽  
Carla Ancona
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Air Pollution ◽  
Random Forest ◽  
Citizen Science ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Forest Models ◽  
Random Forest Models ◽  
The Impact

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

scholarly journals Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach

2016 ◽  
Vol 23 (3) ◽  
pp. 269-278 ◽  
Author(s):  
R. Andrew Taylor ◽  
Joseph R. Pare ◽  
Arjun K. Venkatesh ◽  
Hani Mowafi ◽  
Edward R. Melnick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Big Data ◽  
Hospital Mortality ◽  
Data Driven ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Emergency Department Patients
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