Prediction of emergency department presentations for acute coronary syndrome using a machine learning approach.

The relationship between weather and acute coronary syndrome (ACS) incidence has been the subject of considerable research, with varying conclusions. Harnessing machine learning techniques, our study explores the relationship between meteorological factors and ACS presentations in the emergency department (ED), offering insights into seasonal variations and inter-day fluctuations to optimize patient care and resource allocation. A retrospective cohort analysis was conducted, encompassing ACS presentations to Dutch EDs from 2010 to 2017. Temporal patterns were analyzed using heat-maps and time series plots. Multivariable linear regression (MLR) and Random Forest (RF) regression models were employed to forecast daily ACS presentations with prediction horizons of one, three, seven, and thirty days. Model performance was assessed using the coefficient of determination (R²), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The study included 214,953 ACS presentations, predominantly unstable angina (UA) (94,272; 44%), non-ST-elevated myocardial infarction (NSTEMI) (78,963; 37%), and ST-elevated myocardial infarction (STEMI) (41,718; 19%). A decline in daily ACS admissions over time was observed, with notable inter-day (estimated median difference: 41 (95%CI = 37-43, p = < 0.001) and seasonal variations (estimated median difference: 9 (95%CI 6-12, p = < 0.001). Both MLR and RF models demonstrated similar predictive capabilities, with MLR slightly outperforming RF. The models showed moderate explanatory power for ACS incidence (adjusted R² = 0.66; MAE (MAPE): 7.8 (11%)), with varying performance across subdiagnoses. Prediction of UA incidence resulted in the best-explained variability (adjusted R² = 0.80; MAE (MAPE): 5.3 (19.1%)), followed by NSTEMI and STEMI diagnoses. All models maintained consistent performance over extended prediction horizons. Our findings indicate that ACS presentation exhibits distinctive seasonal changes and inter-day differences, with marked reductions in incidence during the summer months and a distinct peak prevalence on Mondays. The predictive performance of our model was moderate. Nonetheless, we obtained good explanatory power for UA presentations. Our model emerges as a potentially valuable supplementary tool to enhance ED resource allocation or future predictive models predicting ACS incidence in the ED.

Hypotension during intensive care stay and mortality and morbidity: a systematic review and meta-analysis.

PURPOSE: The aim of this study is to provide a summary of the existing literature on the association between hypotension during intensive care unit (ICU) stay and mortality and morbidity, and to assess whether there is an exposure-severity relationship between hypotension exposure and patient outcomes. METHODS: CENTRAL, Embase, and PubMed were searched up to October 2022 for articles that reported an association between hypotension during ICU stay and at least one of the 11 predefined outcomes. Two independent reviewers extracted the data and assessed the risk of bias. Results were gathered in a summary table and studies designed to investigate the hypotension-outcome relationship were included in the meta-analyses. RESULTS: A total of 122 studies (176,329 patients) were included, with the number of studies varying per outcome between 0 and 82. The majority of articles reported associations in favor of 'no hypotension' for the outcomes mortality and acute kidney injury (AKI), and the strength of the association was related to the severity of hypotension in the majority of studies. Using meta-analysis, a significant association was found between hypotension and mortality (odds ratio: 1.45; 95% confidence interval (CI) 1.12-1.88; based on 13 studies and 34,829 patients), but not for AKI. CONCLUSION: Exposure to hypotension during ICU stay was associated with increased mortality and AKI in the majority of included studies, and associations for both outcomes increased with increasing hypotension severity. The meta-analysis reinforced the descriptive findings regarding mortality but did not yield similar support for AKI.