Abstract 127: Machine Learning Techniques to Predict Cardiac Re-Arrest in Out-Of-Hospital Setting

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Andoni Elola ◽  
Elisabete Aramendi ◽  
Unai Irusta ◽  
Naroa Amezaga ◽  
Jon Urteaga ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Prediction Model ◽  
Prehospital Care ◽  
Hospital Setting ◽  
Area Under The Curve ◽  
Interquartile Range ◽  
Machine Learning Techniques ◽  
Survival Prediction ◽  
Poincaré Plot ◽  
Rr Intervals

Background: Re-arrest occurs when a cardiac arrest patient being treated by the emergency medical services experiences another cardiac arrest after return of spontaneous circulation (ROSC).The incidence of re-arrest is high, close to 40% in out-of-hospital cardiac arrest (OHCA), and it is associated with lower survival. Prediction of re-arrest could improve prehospital care. The aim of this study was to develop a re-arrest prediction model based on heart rate variability (HRV) features. Materials and methods: OHCA cases treated by Dallas-FortWorth Center of Resuscitation Research were analyzed. Patients with at least two minutes of ROSC were included. Re-arrest was ascertained by the presence of life-threatening ECG and/or presence of chest compressions within 12 minutes after ROSC. Eighteen HRV characteristics for 1 min and 2 min intervals after ROSC were computed. Features were fed into a Random Forest (RF) classifier with 100 trees to predict re-arrest cases. Ten-fold cross-validation with 30 repetitions was applied to train the model and assess the performance in terms of area under the curve (AUC). Results: Inclusion criteria were met by 98 patients, 41 of which suffered re-arrest. The median time (interquartile range) to re-arrest from ROSC onset was 5 (3-7) min. The re-arrest prediction model showed a median AUC of 0.71 and 0.75 for 1 and 2 min post ROSC intervals, respectively. The most important HRV features in the RF predictor were the SD1/SD2 ratio (where SD1 and SD2 are the dispersions of points both perpendicular and parallel to the line-of-identity in the Poincaré plot), SD2, the interquartile range of the RR intervals, peak frequency in the high frequency band (0.15-0.4 Hz) and coefficient of variation of RR intervals (the ratio between the mean and standard deviation of RR intervals). Conclusions: HRV metrics predict re-arrest in OHCA. Further studies with larger datasets are needed to better understand re-arrest dynamics and confirm conclusions.

Out-of-Hospital Resuscitation: Have We Gone Too Far?

2006 ◽  
Vol 21 (6) ◽  
pp. 445-450 ◽  
Author(s):  
Corita Grudzen
Keyword(s):  
Cardiac Arrest ◽  
Prehospital Care ◽  
Sudden Cardiac Arrest ◽  
Hospital Setting ◽  
The United States ◽  
Spontaneous Circulation ◽  
Emergency Medical ◽  
Clinical Scenarios ◽  
Life Sustaining Treatment ◽  
Future Policy

AbstractAmericans are living longer and are more likely to be chronically or terminally ill at the time of death. Although surveys indicate that most people prefer to die at home, the majority of people in the United States die in acute care hospitals. Each year, approximately 400,000 persons suffer sudden cardiac arrest in the US, the majority occurring in the out-of-hospital setting. Mortality rates are high and reach almost 100% when prehospital care has failed to restore spontaneous circulation. Nonetheless, patients who receive little benefit or may wish to forgo life-sustaining treatment often are resuscitated. Risk versus harm of resuscitation efforts can be differentiated by various factors, including cardiac rhythm. Emergency medical services policy regarding resuscitation should consider its utility in various clinical scenarios. Patients, family members, emergency medical providers, and physicians all are important stakeholders to consider in decisions about out-of-hospital cardiac arrest. Ideally, future policy will place greater emphasis on patient preferences and quality of life by including all of these viewpoints.

scholarly journals A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

scholarly journals Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma

2021 ◽  
Vol 10 (13) ◽  
pp. 2869
Author(s):  
Indah Jamtani ◽  
Kwang-Woong Lee ◽  
Yun-Hee Choi ◽  
Young-Rok Choi ◽  
Jeong-Moo Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Prediction Model ◽  
Risk Group ◽  
Significant Risk ◽  
Risk Scores ◽  
Accurate Information ◽  
Survival Prediction ◽  
Good Prediction ◽  
Recurrence Rates

This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno’s C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65–0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time “t” was calculated using the formula: [1 − (HCC-specific death(t’))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named “SALT calculator” could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC.

scholarly journals Comparing the characteristics and predicting the survival of patients with head and neck melanoma versus body melanoma: a population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuxin Ding ◽  
Runyi Jiang ◽  
Yuhong Chen ◽  
Jing Jing ◽  
Xiaoshuang Yang ◽  
...  
Keyword(s):  
Head And Neck ◽  
Health Management ◽  
External Validation ◽  
Area Under The Curve ◽  
Population Based ◽  
Survival Prediction ◽  
Cancer Specific Survival ◽  
Cox Models ◽  
Lentigo Maligna Melanoma ◽  
Clinical Tools

Abstract Background Previous studies reported cutaneous melanoma in head and neck (HNM) differed from those in other regions (body melanoma, BM). Individualized tools to predict the survival of patients with HNM or BM remain insufficient. We aimed at comparing the characteristics of HNM and BM, developing and validating nomograms for predicting the survival of patients with HNM or BM. Methods The information of patients with HNM or BM from 2004 to 2015 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The HNM group and BM group were randomly divided into training and validation cohorts. We used the Kaplan-Meier method and multivariate Cox models to identify independent prognostic factors. Nomograms were developed via the rms and dynnom packages, and were measured by the concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration plots. Results Of 70,605 patients acquired, 21% had HNM and 79% had BM. The HNM group contained more older patients, male sex and lentigo maligna melanoma, and more frequently had thicker tumors and metastases than the BM group. The 5-year cancer-specific survival (CSS) and overall survival (OS) rates were 88.1 ± 0.3% and 74.4 ± 0.4% in the HNM group and 92.5 ± 0.1% and 85.8 ± 0.2% in the BM group, respectively. Eight variables (age, sex, histology, thickness, ulceration, stage, metastases, and surgery) were identified to construct nomograms of CSS and OS for patients with HNM or BM. Additionally, four dynamic nomograms were available on web. The internal and external validation of each nomogram showed high C-index values (0.785–0.896) and AUC values (0.81–0.925), and the calibration plots showed great consistency. Conclusions The characteristics of HNM and BM are heterogeneous. We constructed and validated four nomograms for predicting the 3-, 5- and 10-year CSS and OS probabilities of patients with HNM or BM. These nomograms can serve as practical clinical tools for survival prediction and individual health management.

scholarly journals An Early Predictive Scoring Model for In-Hospital Cardiac Arrest of Emergent Hemodialysis Patients

2021 ◽  
Vol 10 (15) ◽  
pp. 3241
Author(s):  
Shih-Hao Chen ◽  
Ya-Yun Cheng ◽  
Chih-Hao Lin
Keyword(s):  
Cardiac Arrest ◽  
Risk Group ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Hemodialysis Patients ◽  
High Risk Group ◽  
Predictive Score ◽  
Primary Group ◽  
Sum Scores ◽  
Hospital Cardiac Arrest

Background: Patients undergoing hemodialysis are prone to cardiac arrests. Methods: This study aimed to develop a risk score to predict in-hospital cardiac arrest (IHCA) in emergency department (ED) patients undergoing emergency hemodialysis. Patients were included if they received urgent hemodialysis within 24 h after ED arrival. The primary outcome was IHCA within three days. Predictors included three domains: comorbidity, triage information (vital signs), and initial biochemical results. The final model was generated from data collected between 2015 and 2018 and validated using data from 2019. Results: A total of 257 patients, including 52 with IHCA, were analyzed. Statistical analysis selected significant variables with higher sensitivity cutoff, and scores were assigned based on relative beta coefficient ratio: K > 5.5 mmol/L (score 1), pH < 7.35 (score 1), oxygen saturation < 85% (score 1), and mean arterial pressure < 80 mmHg (score 2). The final scoring system had an area under the curve of 0.78 (p < 0.001) in the primary group and 0.75 (p = 0.023) in the validation group. The high-risk group (defined as sum scores ≥ 3) had an IHCA risk of 47.2% and 41.7%, while the low-risk group (sum scores < 3) had 18.3% and 7%, in the primary and validation databases, respectively. Conclusions: This predictive score model for IHCA in emergent hemodialysis patients could help healthcare providers to take necessary precautions and allocate resources.

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