Random Forest-Based Prediction of Stroke Outcome

[Abstract] We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.This study was partially supported by grants from the Spanish Ministry of Science and Innovation (SAF2017-84267-R), Xunta de Galicia (Axencia Galega de Innovación (GAIN): IN607A2018/3), Instituto de Salud Carlos III (ISCIII) (PI17/00540, PI17/01103), Spanish Research Network on Cerebrovascular Diseases RETICS-INVICTUS PLUS (RD16/0019) and by the European Union FEDER program. T. Sobrino (CPII17/00027), F. Campos (CPII19/00020) are recipients of research contracts from the Miguel Servet Program (Instituto de Salud Carlos III). General Directorate of Culture, Education and University Management of Xunta de Galicia (ED431G/01,252 ED431D 2017/16), “Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), Competitive Reference Groups (ED431C 2018/49), Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13–3503), European Regional Development Funds (FEDER).Xunta de Galicia; IN607A2018/3Xunta de Galicia; ED431G/01,252Xunta de Galicia; ED431D 2017/1

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank

Objective: Atrial fibrillation (AF) is the most common cardiac arrythmia, and it is associated with increased risk for ischemic stroke, which is underestimated, as AF can be asymptomatic. The aim of this study was to develop optimal ML models for prediction of AF in the population, and secondly for ischemic stroke in AF patients. Methods: To develop ML models for prediction of 1) AF in the general population and 2) ischemic stroke in patients with AF we constructed XGBoost, LightGBM, Random Forest, Deep Neural Network, Support Vector Machine and Lasso penalised logistic regression models using UK-Biobank's extensive real-world clinical data, questionnaires, as well as biochemical and genetic data, and their predictive performances were compared. Ranking and contribution of the different features was assessed by SHapley Additive exPlanations (SHAP) analysis. The clinical tool CHA2DS2-VASc for prediction of ischemic stroke among AF patients, was used for comparison to the best performing ML model. Findings: The best performing model for AF prediction was LightGBM, with an area-under-the-roc-curve (AUROC) of 0.729 (95% confidence intervals (CI): 0.719, 0.738). The best performing model for ischemic stroke prediction in AF patients was XGBoost with AUROC of 0.631 (95% CI: 0.604, 0.657). The improved AUROC in the XGBoost model compared to CHA2DS2-VASc was statistically significant based on DeLong's test (p-value = 2.20E-06). In addition, the SHAP analysis showed that several peripheral blood biomarkers (e.g. creatinine, glycated haemoglobin, monocytes) were associated with ischemic stroke, which are not considered by CHA2DS2-VASc. Implications: The best performing ML models presented have the potential for clinical use, but further validation in independent studies is required. Our results endorse the incorporation of some routinely measured blood biomarkers for ischemic stroke prediction in AF patients

딥러닝 기반 생존분석이 적용된 심혈관질환 위험 평가 모델 성능 향상을 위한 콕스 모형과 결합된 하이브리드 접근법: 헬스케어-환경 연계 데이터 활용 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2020. 8. 박상민 .Background and aims: The contribution of different cardiovascular disease (CVD) risk factors for the risk evaluation and predictive modeling for incident CVD is often debated. Also, to what extent data on CVD risk factors from multiple data categories should be collected for comprehensive risk assessment and predictive modeling for CVD risk using survival analysis is uncertain despite the increasing availability of the relevant data sources. This study aimed to evaluate the contribution of different data categories derived from integrated data on healthcare and environmental exposure to the risk evaluation and prediction models for CVD risk using deep learning based survival analysis in combination with Cox proportional hazards regression and Cox proportional hazards regression. Methods: Information on the comprehensive list of CVD risk factors were collected from systematic reviews of variables included in the conventional CVD risk assessment tools and observational studies from medical literature database (PubMed and Embase). Each risk factor was screened for availability in the National Health Insurance Service-National Sample Cohort (NHIS-NSC) linked to environmental exposure data on cumulative particulate matter and urban green space using residential area code. Individual records of 137,249 patients more than 40 years of age who underwent the biennial national health screening between 2009 and 2010 without previous history of CVD were followed up for incident CVD event from January 1, 2011 to December 31, 2013 in the NHIS-NSC with data linkage to environmental exposure. Statistics-based variable selection methods were implemented as follows: statistical significance, subset with the minimum (best) Akaike Information Criteria (AIC), variables selected from the regularized Cox proportional hazards regression with elastic net penalty, and finally a variable set that commonly meets all the criteria from the abovementioned statistical methods. Prediction models using Cox proportional hazards deep neural network (DeepSurv) and Cox proportional hazards regression were constructed in the training set (80% of the total sample) using input feature sets selected from the abovementioned strategies and progressively adding input features by data categories to examine the relative contribution of each data type to the predictive performance for CVD risk. Performance evaluations of the DeepSurv and Cox proportional hazards regression models for CVD risk were conducted in the test set (20% of the total sample) with Unos concordance statistics (C-index), which is the most up-to-date evaluation metrics for the survival models with right censored data. Results: After the comprehensive review, data synthesis, and availability check, a total of 31 risk factors in the categories of sociodemographic, clinical laboratory test and measurement, lifestyle behavior, family history, underlying medical conditions, dental health, medication, and environmental exposure were identified in the NHIS-NSC linked to environmental exposure data. Among the models constructed with different variable selection methods, using statistically significant variables for DeepSurv (Unos C-index: 0.7069) and all of the variables for Cox proportional hazards regression (Unos C-index: 0.7052) showed improved predictive performance for CVD risk, which was a statistically significant increase (p-value for difference in Unos C-index: <0.0001 for both comparisons) compared to the models with basic clinical factors (age, sex, and body mass index), respectively. When all and statistically significant variables in each data category from sociodemographic to environmental exposure were progressively added as input features into DeepSurv and Cox proportional hazards regression for predictive modeling for CVD risk, the DeepSurv model with statistically significant variables pertaining to the sociodemographic factors, clinical laboratory test and measurement, and lifestyle behavior data showed the notable performance that outperformed Cox proportional hazards regression model with statistically significant variables added up to the medication category. Extensive data linkage to environmental exposure on cumulative particulate matter and urban green space offered only marginal improvement for the predictive performance of DeepSurv and Cox proportional hazards regression models for CVD risk. Conclusion: To obtain the best predictive performance of DeepSurv model for CVD risk with minimum number of input features, information on sociodemographic, clinical laboratory test and measurement, and lifestyle behavior should be primarily collected and used as input features in the NHIS-NSC. Also, the overall performance of DeepSurv for CVD risk assessment was improved with a hybrid approach using statistically significant variables from Cox proportional hazards regression as input features. When all the data categories in the NHIS-NSC linked to environmental exposure data are available, progressively adding variables in each data category could incrementally increase the predictive performance of DeepSurv model for CVD risk with the hybrid approach. Data linkage to the environmental exposure with residential area code in the NHIS-NSC offered marginally improved performance for CVD risk in both DeepSurv model with the hybrid approach and Cox proportional hazards regression model.배경 및 목적: 심혈관질환 위험평가 및 예측모델링에서 다양한 심혈관질환 위험인자들의 모델 성능향상에 대한 기여도는 논란의 요지로 보고되어왔다. 또한, 지속적으로 증가하는 활용 가능한 심혈관질환 관련 데이터의 종류와 양에도 불구하고 포괄적인 심혈관질환 위험평가와 최적의 예측 모형 개발을 위해 데이터를 어느 범위와 수준까지 수집해야 하는지에 대한 근거는 부족한 현황이다. 본 연구에서는 콕스 모형과 결합된 딥러닝 기반 생존분석 접근법 및 콕스 모형을 활용한 심혈관질환 위험평가와 예측모델링에서 헬스케어-환경 연계 데이터 활용방법 및 범주에 따른 모델 성능향상에 대한 기여도를 평가하고자 하였다. 연구 방법: 전통적 심혈관질환 위험 평가 도구 및 관찰 연구들에 포함 된 심혈관질환 위험요인 관련 변수들을 체계적 문헌고찰 방법론을 활용하여 의학연구 문헌데이터베이스 (PubMed and Embase)에서 포괄적으로 정보를 수집하였다. 미세먼지 누적장기노출 및 도시녹지면적에 대한 환경 노출 데이터와 연계 된 국민건강보험공단 표본코호트, (National Health Insurance Service-National Sample Cohort, NHIS-NSC)에서 각 심혈관질환 위험인자들의 데이터 확보 가능성을 검토하였다. NHIS-NSC를 기준으로 2009년에서 2010년 사이에 국가건강검진을 받은 40세 이상 대상자 중 과거 심혈관질환 병력이 없는 대상자 137,249명의 환자에 대한 정보를 수집하여 2011 년 1 월 1 일부터 2013 년 12 월 31 일까지 신규 발생한 심혈관질환에 대해 시간 경과에 따라 추적 조사하였다. 통계 기반 변수선택 방법은 콕스비례위험모형에서 통계적 유의성, 최소 (최상의) Akaike Information Criteria (AIC)의 하위 집합, elastic net penalty로 정규화 된 콕스비례위험모형에서 선택된 변수 및 위에 언급된 모든 기준을 충족하는 변수 세트로 선정하였다. 위에 명시된 통계적 방법 외 모든 데이터 범주에 속한 변수 및 콕스비례위험모형에서 통계적으로 유의미한 변수 (하이브리드 접근법)를 점진적으로 입력 피쳐로 추가하는 전략으로 딥러닝 기반 생존분석 (Cox proportional hazards deep neural network, DeepSurv) 및 콕스비례위험모형에서 예측 모델들을 훈련 세트 (전체 샘플의 80 %)를 기반으로 개발하였다. DeepSurv 및 콕스비례 위험모형을 활용한 심혈관질환 예측 모델의 성능평가는 생존분석을 활용한 예측 모델링에 가장 적합한 평가지표로 알려진 Unos concordance statistics (C-index)를 사용하여 테스트 세트 (총 샘플의 20 %)에서 수행하였다. 결과: 체계적 문헌고찰, 데이터 취합 및 추출 가능성 검토 후, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관, 가족력, 건강상태, 구강건강, 약물 및 환경 노출 데이터 범주에서 총 31 개의 심혈관질환 위험인자가 지역환경 자료와 연계된 NHIS-NSC에서 확인되었다. 통계 기반 변수선택 방법으로 개발한 심혈관질환 예측 모델 중 콕스비례위험모형에서 통계적으로 유의미한 변수를 DeepSurv에 적용한 하이브리드 접근법이 Uno 's C-index 값 0.7069, 모든 변수를 콕스비례위험모형에 적용한 콕스비례위험모형이 Uno 's C-index 값 0.7052로 나타나 기본 임상 요인 (연령, 성별 및 체질량지수)이 포함된 예측 모델과 비교하여 통계적으로 유의미한 모델 예측력 증가를 보였다 (두 모델 모두 Unos C-index 차이에 대한 p-value : <0.0001). 인구사회학적 특성에서 환경 노출에 이르기까지 각 데이터 범주에서 모두 통계적으로 유의미한 변수들이 심혈관질환 예측 모델링을위한 DeepSurv 및 Cox 비례 위험 회귀에 입력 피쳐로 점진적으로 추가 된 경우, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관 요인 중 통계적으로 유의미한 변수들로 구성된 DeepSurv 모델이 의약품 사용까지 고려한 Cox 비례 위험 회귀를 기반으로 한 모델 보다 뛰어난 성능을 나타냈다. 미세먼지 및 도시녹지면적에 대한 환경 노출 데이터를 거주지를 기반으로 NHIS-NSC와 연계 후 점진적으로 입력 피쳐로 추가 시 DeepSurv 및 콕스비례위험모형을 활용한 심혈관질환 예측 모델링 성능을 통계적으로 유의미한 수준으로 개선하지 못했다. 결론: 최소 입력 피쳐를 갖춘 생존 분석 기반 심혈관질환 예측 모델에서 최상의 성능을 얻으려면 인구사회학적, 건강검진 및 측정 결과, 및 생활습관에 대한 정보를 NHIS-NSC에서 수집하여 DeepSurv의 입력 피쳐로 활용해야한다. 지역환경 자료와 연계된 NHIS-NSC에서 모든 데이터 범주를 사용할 수 있을 때 점진적으로 각 데이터 범주 중 콕스비례위험모형에서 통계적으로 유의미한 심혈관질환 위험인자를 점진적으로 입력 피쳐로 DeepSurv 모델에 추가하는 하이브리드 접근법에서 심혈관질환 예측 모델링 성능이 점차 향상 될 것으로 기대할 수 있다. 주거 지역 코드를 사용한 NHIS-NSC와 환경 노출 데이터 연계는 DeepSurv 및 콕스비례위험모형 모두에서 심혈관질환 예측 모델링 성능이 향상되었지만 통계적으로 유의미한 증가 수준은 아닌 것으로 나타나 환경 노출 데이터 연계 및 적용 시 검토가 필요할 것으로 추정된다.I. Introduction 1 1. Background 1 2. Research problem 4 3. Hypothesis and objective 6 3.1. Hypothesis 6 3.2. Objective 6 II. Materials and methods 8 1. Comprehensive review and identification of cardiovascular disease (CVD) risk factors 8 1.1. Systematic review on variables included in conventional CVD risk assessment tools 8 1.2. Systematic review on traditional and emerging CVD risk factors from observational studies 9 1.3. Integration of the comprehensive list of CVD risk factors 11 1.4. Screening for data availability 11 2. Cohort analysis for measuring strength of association between risk factors and incident cardiovascular disease 11 2.1 Study population and linkage to environmental exposure data 11 2.2. Variable selection and data processing 15 2.3. Population-based cohort analysis 17 3. Predictive modeling using survival analysis: DeepSurv and Cox proportional hazards regression 17 3.1. Model development 17 3.2. Evaluation of the predictive performance of the models 20 III. Results 21 1. Identification and categorization of cardiovascular disease risk factors 21 2. Magnitude of association between selected risk factors with cardiovascular disease 43 3. Model performance evaluation 56 VI. Discussion 68 1. Key findings and contributions 68 2. Comparison to other studies 69 3. Strengths and limitations 73 4. Implications 74 5. Future perspectives 75 V. Conclusion 77 Reference 78 국문초록 88Docto

Prediction and monitoring of in-hospital cardiac arrest

Background: In-hospital cardiac arrest (IHCA) is a global health concern of major importance, associated with a poor prognosis. IHCA is frequently heralded by a deterioration of vital signs, and many cases are considered preventable. Hence, prevention has become a key strategy. The overall aim of this thesis was to study the prevention of IHCA, by means of prediction and monitoring, with a view to improve patient safety. Methods: Study I and III are observational cohort studies, based on the Swedish Registry of Cardiopulmonary Resuscitation (SRCR). In study III, we also collected additional data from medical records in a small, hypothesis-generating group of patients. Study II and IV are prospective, observational cohort studies based on patients reviewed by Rapid Response Teams (RRTs) in 26 and 24 Swedish hospitals, respectively. In study IV, additional data on long-term survival was obtained from either medical records or the personal information directory, containing population registration data. Specific study aims and results: In study I, we investigated how 30-day survival after IHCA was influenced by ECG monitoring at the time of collapse, as well as clinical factors that determined whether patients were ECG monitored adjacent to cardiac arrest (CA). In all, 24,790 patients were enrolled in the SRCR between 2008 and 2017. After applying the exclusion criteria, 19,225 patients remained, of which 52% were monitored at the time of collapse. In all, 30-day survival was 30%. ECG monitoring at the time of CA was associated with a Hazard Ratio of 0.62 (95% Confidence Interval 0.60-0.64) for 30-day mortality. The strongest predictor of ECG monitoring adjacent to IHCA was location in hospital. There were tangible variations in the frequency of patients who were ECG monitored at the time of collapse between Swedish regions and across hospitals. In study II, we investigated the predictive power of NEWS 2, as compared to NEWS, in identifying patients at risk of Serious Adverse Events (SAEs) within 24 hours of an RRT-review. In all, 1,065 patients, reviewed by RRTs in general wards during the study period between October 2019 and January 2020, were included. After applying the exclusion criteria, 898 patients were eligible for complete case analyses. In all, 37% of the patients were admitted to the Intensive care unit (ICU) within 24 hours of RRT-review. In-hospital mortality and IHCA were uncommon (6% and 1% respectively). The Area Under the Receiver Operating Characteristic (AUROC) for both NEWS and NEWS 2 was 0.62 for the composite outcome, and 0.69/0.67 for mortality. Regarding the outcome unanticipated ICU admission, the AUROC was 0.59 and 0.60, respectively, while the AUROC for IHCA was 0.51 (NEWS) and 0.47 (NEWS 2), respectively. In study III, we investigated 30-day survival and ROSC in patients suffering from IHCA, who were reviewed by an RRT within 24 hours prior to the CA, as compared to those without such review. Furthermore, we studied patient centred factors prior to RRT activation, the timeliness of the RRT-review as well as the reason for the RRT-review. We also investigated the association between RRT interventions and outcome. During the study period between 2014 and 2021, 19,973 patents were enrolled in the SRCR. After applying the exclusion criteria, 12,915 patients remained. Among these IHCA patients, there was an RRT/ICU contact within 24 hours prior to the CA in 2,058 cases (19%). The adjusted 30-day survival was lower among patients reviewed by an RRT prior to IHCA (25% vs. 33%, p <0.001). Regarding ROSC, we did not observe any difference between the groups. The propensity score based Odds Ratio for 30- day survival was 0.92 for patients who were reviewed by an RRT (95% CI 0.90 to 0.94, p <0.001), as compared to those who were not RRT- reviewed within 24 hours prior to IHCA. A respiratory cause of CA was more common among IHCA patients who were reviewed by an RRT. In the small, explorative subgroup (n=82), 24% of the RRT activations were delayed, and respiratory distress was the most common RRT trigger. We observed a significantly lower 30-day survival among patients triaged to remain at ward compared to those triaged to a higher level of care (2% vs. 20%, p 0.016). In study IV, we explored the impact of age on the ability of NEWS 2 to predict IHCA, unanticipated ICU-admission, or death, and the composite of these three SAEs, within 24 hours of review by an RRT. Furthermore, we aimed to investigate 30-, 90- and 180-day mortality, and the discriminative ability of NEWS 2 in the prediction of long-term mortality among RRT-reviewed patients. In this multi-centre study based on data prospectively collected by RRTs, the NEWS 2 scores of all patients were retrospectively, digitally calculated by the study team. Age was analysed as a continuous variable, in a spline regression model, and categorized into five different models, subsequently explored as additive variables to NEWS 2. The discriminative ability of NEWS 2 in predicting 30-day mortality improved by adding age as a covariate (from AUROC 0.66, 0.62-0.70 to 0.70, 0.65-0.73, p=0.01). There were differences across age groups, with the best predictive performance identified among patients aged 45-54 years. The 30-, 90-and 180-day mortality was 31%, 33%, and 36%, respectively. Conclusion: ECG monitoring at the time of IHCA was associated with a 38% reduction of adjusted mortality. Despite this finding, only one in two IHCA patients were ECG monitored. The most important factor influencing ECG monitoring was which type of hospital ward the patient was admitted to. The tangible variations in the frequency of ECG monitoring adjacent to IHCA observed between Swedish regions and across hospitals need to be investigated in future studies. Guidelines for the monitoring of patients at risk of CA could contribute to an improved outcome. The prognostic accuracy of NEWS 2 in predicting mortality within 24 hours of an RRT-review was acceptable, whereas the discriminative ability in prediction of unanticipated ICU-admission and the composite outcome was rather weak. Regarding the prediction of IHCA, NEWS 2 performed poorly. There was no difference in the prognostic accuracy between NEWS and NEWS 2; however, the discriminative ability was not considered sufficient to serve as a triage tool in RRT-reviewed patients. In-hospital cardiac arrest among patients who were reviewed by an RRT prior to CA was associated with a poorer prognosis, and a more frequent respiratory aetiology of the CA. In the explorative sub-group of patients, RRT activation was frequently delayed, the most common trigger for RRT-review was respiratory distress, and escalation of the level of care was associated with an improved prognosis. Early identification of patients with abnormal respiratory vital signs, followed by a timely response, may have a potential to improve the prognosis for patients reviewed by an RRT and prevent IHCA. Adding age as a covariate improved the discriminative ability of NEWS 2 in the prediction of 30-day mortality among RRT-reviewed patients. The ability differed across age categories. Overall, the long-term prognosis of RRT-reviewed patients was poor. Our results indicate that age merits further validation as a covariate to improve the performance of NEWS 2

Bleeding complications following acute myocardial infarction : time trends, risk assessment and associated prognosis

Background: In patients with acute myocardial infarction (MI), bleeding complications are common and associated with worse prognosis. This thesis aimed to investigate the epidemiology, risk assessment and associated outcomes of bleeding complications in patients with acute MI. Methods and results: Study I: Patients with acute MI enrolled in the SWEDEHEART registry from 1995–2018 were included (n=371 431). The incidence of in-hospital and out-of-hospital bleeding at one-year was investigated parallel to treatment changes and ischemic outcomes. From 1995 to 2018, in-hospital bleeding increased from 0.5% to 1.3% and out-of-hospital bleeding increased from 2.5% to 4.8% along with increased use of invasive revascularisation and more efficient antithrombotic treatment. Meanwhile in-hospital and out-of-hospital ischemic outcomes decreased from 12.1% to 5.6% and 27.5% to 15.1%, respectively. Study II: Patients with acute MI enrolled in the SWEDEHEART registry from 2009– 2014 were included (n=97 597). A prediction model for in-hospital bleeding was created using logistic regression and the performance was compared to that of the CRUSADE and ACTION scores. Due to miscalibration, the CRUSADE and ACTION scores were recalibrated. The SWEDEHEART score, consisting of five baseline variables (haemoglobin, age, sex, creatinine, and C-reactive protein) plus one interaction term (haemoglobin and sex) had a C-index of 0.80 as compared with 0.72 and 0.73 for the recalibrated CRUSADE and ACTION scores, respectively. Study III: Patients with acute MI enrolled in the SWEDEHEART registry from 2007–2016 and discharged alive on any antithrombotic treatment were included (n=149 447). The incidence, associated outcomes and predictors of upper gastrointestinal bleeding (UGIB) was investigated. The incidence of UGIB within one year after discharge was 1.5% and experiencing UGIB was associated with increased risk of mortality and stroke, but not significantly associated with MI. Using both logistic regression and machine-learning models, new potential predictors of UGIB were found, such as smoking status and blood glucose. Study IV: Patients with acute MI enrolled in the SWEDEHEART registry and discharged alive on any antithrombotic treatment from 2012–2017 were included (n=86 736). The incidence and associated mortality risk of ischemic (MI or ischemic stroke) and bleeding events was investigated. Within one year after discharge, the incidence rate of ischemic and bleeding events was 5.7/100 person years and 4.8/100 person years, respectively. Both ischemic and bleeding events were associated with higher risk of mortality as compared with no event, with adjusted hazard ratios (HR)s of 4.16 (95% CI 3.91 to 4.43) and 3.43 (95% CI 3.17 to 3.71), respectively. In a direct comparison of ischemic vs bleeding event, the adjusted HR was 1.27 (95% CI 1.15 to 1.40.) Conclusion: In the past two decades, the incidence of both short- and long-term bleeding events has nearly doubled in patients with acute MI. The five-item SWEDEHEART score predicts inhospital bleeding in patients with acute MI more accurately than the recalibrated CRUSADE and ACTION scores. Among patients with a recent MI, upper gastrointestinal bleeding is common and associated with poorer prognosis. Beyond the known risk factors for bleeding, other predictors for upper gastrointestinal bleeding may be present. In patients discharged after an acute MI, ischemic events were more common and associated with higher risk of mortality than bleeding events

Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation

BACKGROUND: There is no specific treatment for sudden cardiac arrest (SCA) manifesting as pulseless electric activity (PEA) and survival rates are low; unlike ventricular fibrillation (VF), which is treatable by defibrillation. Development of novel treatments requires fundamental clinical studies, but access to the true initial rhythm has been a limiting factor. METHODS: Using demographics and detailed clinical variables, we trained and tested an AI model (extreme gradient boosting) to differentiate PEA-SCA versus VF-SCA in a novel setting that provided the true initial rhythm. A subgroup of SCAs are witnessed by emergency medical services personnel, and because the response time is zero, the true SCA initial rhythm is recorded. The internal cohort consisted of 421 emergency medical services-witnessed out-of-hospital SCAs with PEA or VF as the initial rhythm in the Portland, Oregon metropolitan area. External validation was performed in 220 emergency medical services-witnessed SCAs from Ventura, CA. RESULTS: In the internal cohort, the artificial intelligence model achieved an area under the receiver operating characteristic curve of 0.68 (95% CI, 0.61-0.76). Model performance was similar in the external cohort, achieving an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.59-0.84). Anemia, older age, increased weight, and dyspnea as a warning symptom were the most important features of PEA-SCA; younger age, chest pain as a warning symptom and established coronary artery disease were important features associated with VF. CONCLUSIONS: The artificial intelligence model identified novel features of PEA-SCA, differentiated from VF-SCA and was successfully replicated in an external cohort. These findings enhance the mechanistic understanding of PEA-SCA with potential implications for developing novel management strategies