scholarly journals The ED-PLANN Score: A Simple Risk Stratification Tool for Out-of-Hospital Cardiac Arrests Derived from Emergency Departments in Korea

2021 ◽  
Vol 11 (1) ◽  
pp. 174
Author(s):  
Hyouk Jae Lim ◽  
Young Sun Ro ◽  
Ki Hong Kim ◽  
Jeong Ho Park ◽  
Ki Jeong Hong ◽  
...  

Early risk stratification of out-of-hospital cardiac arrest (OHCA) patients with insufficient information in emergency departments (ED) is difficult but critical in improving intensive care resource allocation. This study aimed to develop a simple risk stratification score using initial information in the ED. Adult patients who had OHCA with medical etiology from 2016 to 2020 were enrolled from the Korean Cardiac Arrest Research Consortium (KoCARC) database. To develop a scoring system, a backward logistic regression analysis was conducted. The developed scoring system was validated in both external dataset and internal bootstrap resampling. A total of 8240 patients were analyzed, including 4712 in the development cohort and 3528 in the external validation cohort. An ED-PLANN score (range 0–5) was developed incorporating 1 point for each: P for serum pH ≤ 7.1, L for serum lactate ≥ 10 mmol/L, A for age ≥ 70 years old, N for non-shockable rhythm, and N for no-prehospital return of spontaneous circulation. The area under the receiver operating characteristics curve (AUROC) for favorable neurological outcome was 0.93 (95% CI, 0.92–0.94) in the development cohort, 0.94 (95% CI, 0.92–0.95) in the validation cohort. Hosmer–Lemeshow goodness-of-fit tests also indicated good agreement. The ED-PLANN score is a practical and easily applicable clinical scoring system for predicting favorable neurological outcomes of OHCA patients.

2020 ◽  
Vol 14 ◽  
pp. 175346662096301
Author(s):  
Jun Duan ◽  
Mei Liang ◽  
Yongpu Li ◽  
Dan Wu ◽  
Ying Chen ◽  
...  

Background: A simple scoring system for triage of suspected patients with COVID-19 is lacking. Methods: A multi-disciplinary team developed a screening score taking into account epidemiology history, clinical feature, radiographic feature, and routine blood test. At fever clinics, the screening score was used to identify the patients with moderate to high probability of COVID-19 among all the suspected patients. The patients with moderate to high probability of COVID-19 were allocated to a single room in an isolation ward with level-3 protection. And those with low probability were allocated to a single room in a general ward with level-2 protection. At the isolation ward, the screening score was used to identify the confirmed and probable cases after two consecutive real-time reverse transcription polymerase chain reaction (RT-PCR) tests. The data in the People’s Hospital of Changshou District were used for internal validation and those in the People’s Hospital of Yubei District for external validation. Results: We enrolled 76 and 40 patients for internal and external validation, respectively. In the internal validation cohort, the area under the curve of receiver operating characteristics (AUC) was 0.96 [95% confidence interval (CI): 0.89–0.99] for the diagnosis of moderate to high probability of cases among all the suspected patients. Using 60 as cut-off value, the sensitivity and specificity were 88% and 93%, respectively. In the isolation ward, the AUC was 0.94 (95% CI: 0.83–0.99) for the diagnosis of confirmed and probable cases. Using 90 as cut-off value, the sensitivity and specificity were 78% and 100%, respectively. These results were confirmed in the validation cohort. Conclusion: The scoring system provides a reference on COVID-19 triage in fever clinics to reduce misdiagnosis and consumption of protective supplies. The reviews of this paper are available via the supplemental material section.


2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
E Zweck ◽  
M Spieker ◽  
P Horn ◽  
C Iliadis ◽  
C Metze ◽  
...  

Abstract Background Transcatheter Mitral Valve Repair (TMVR) with MitraClip is an important treatment option for patients with severe mitral regurgitation. The lack of appropriate, validated and specific means to risk stratify TMVR patients complicates the evaluation of prognostic benefits of TMVR in clinical trials and practice. Purpose We aimed to develop an optimized risk stratification model for TMVR patients using machine learning (ML). Methods We included a total of 1009 TMVR patients from three large university hospitals, of which one (n=317) served as an external validation cohort. The primary endpoint was all-cause 1-year mortality, which was known in 95% of patients. Model performance was assessed using receiver operating characteristics. In the derivation cohort, different ML algorithms, including random forest, logistic regression, support vectors machines, k nearest neighbors, multilayer perceptron, and extreme gradient boosting (XGBoost) were tested using 5-fold cross-validation in the derivation cohort. The final model (Transcatheter MITral Valve Repair MortALIty PredicTion SYstem; MITRALITY) was tested in the validation cohort with respect to existing clinical scores. Results XGBoost was selected as the final algorithm for the MITRALITY Score, using only six baseline clinical features for prediction (in order of predictive importance): blood urea nitrogen, hemoglobin, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), mean arterial pressure, body mass index, and creatinine. In the external validation cohort, the MITRALITY Score's area under the curve (AUC) was 0.783, outperforming existing scores which yielded AUCs of 0.721 and 0.657 at best. 1-year mortality in the MITRALITY Score quartiles across the total cohort was 0.8%, 1.3%, 10.5%, and 54.6%, respectively. Odds of mortality in MITRALITY Score quartile 4 as compared to quartile 1 were 143.02 [34.75; 588.57]. Survival analyses showed that the differences in outcomes between the MITRALITY Score quartiles remained even over a timeframe of 3 years post intervention (log rank: p<0.005). With each increase by 1% in the MITRALITY score, the respective proportional hazard ratio for 3-year survival was 1.06 [1.05, 1.07] (Cox regression, p<0.05). Conclusion The MITRALITY Score is a novel, internally and externally validated ML-based tool for risk stratification of patients prior to TMVR. These findings may potentially allow for more precise design of future clinical trials, may enable novel treatment strategies tailored to populations of specific risk and thereby serve future daily clinical practice. FUNDunding Acknowledgement Type of funding sources: None. Summary Figure


2020 ◽  
Author(s):  
Keita Shibahashi ◽  
Kazuhiro Sugiyama ◽  
Yusuke Kuwahara ◽  
Takuto Ishida ◽  
Atsushi Sakurai ◽  
...  

Abstract Background Out-of-hospital cardiac arrest (OHCA) is a global medical problem. The newly-developed simplified out-of-hospital cardiac arrest (sOHCA) and cardiac arrest hospital prognosis (sCAHP) scores used for prognostication of patients admitted alive have not been validated externally. This study was, thus, conducted to externally validate sOHCA and sCAHP scores in a Japanese population. Methods Adult patients resuscitated and admitted to hospitals after intrinsic OHCA (n=2,428, age ≥18 years) were selected from a prospectively collected Japanese database (January 2012–March 2013). We validated sOHCA and sCAHP scores with reference to the original ones in predicting 1-month unfavourable neurological outcomes based on discrimination and calibration measures. Discrimination and calibration were assessed using area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test with calibration plot, respectively. Results One-month unfavourable neurological outcome was observed in 82% of patients. Score availability was significantly higher in the simplified scores than in the original ones and was highest in the sCAHP score (76%). The AUCs of simplified scores were not significantly different from those of original ones, whereas the AUC of the sCAHP score was significantly higher than that of the sOHCA score (0.88 vs. 0.81, P <0.001). Goodness-of-fit was poor in the sOHCA score (ν= 8, χ 2 =19.1, Hosmer-Lemeshow test: P =0.014) but not in the sCAHP score (ν= 8, χ 2 =13.5, Hosmer-Lemeshow test: P =0.10). Conclusion Performance of original and simplified OHCA and CAHP scores in predicting neurological outcomes in successfully resuscitated OHCA patients were acceptable. Based on the highest availability, similar discrimination, and good calibration, the sCAHP score was the better candidate for clinical implementation. The validated predictive score can help patients’ families, healthcare providers, and researchers by accurately stratifying patients.


2021 ◽  
pp. emermed-2020-210103
Author(s):  
Keita Shibahashi ◽  
Kazuhiro Sugiyama ◽  
Yusuke Kuwahara ◽  
Takuto Ishida ◽  
Atsushi Sakurai ◽  
...  

BackgroundThe novel simplified out-of-hospital cardiac arrest (sOHCA) and simplified cardiac arrest hospital prognosis (sCAHP) scores used for prognostication of hospitalised patients have not been externally validated. Therefore, this study aimed to externally validate the sOHCA and sCAHP scores in a Japanese population.MethodsWe retrospectively analysed data from a prospectively maintained Japanese database (January 2012 to March 2013). We identified adult patients who had been resuscitated and hospitalised after intrinsic out-of-hospital cardiac arrest (OHCA) (n=2428, age ≥18 years). We validated the sOHCA and sCAHP scores with reference to the original scores in predicting 1-month unfavourable neurological outcomes (cerebral performance categories 3–5) based on the discrimination and calibration measures of area under the receiver operating characteristic curves (AUCs) and a Hosmer-Lemeshow goodness-of-fit test with a calibration plot, respectively.ResultsIn total, 1985/2484 (82%) patients had a 1-month unfavourable neurological outcome. The original OHCA, sOHCA, original cardiac arrest hospital prognosis (CAHP) and sCAHP scores were available for 855/2428 (35%), 1359/2428 (56%), 1130/2428 (47%) and 1834/2428 (76%) patients, respectively. The AUCs of simplified scores did not differ significantly from those of the original scores, whereas the AUC of the sCAHP score was significantly higher than that of the sOHCA score (0.88 vs 0.81, p<0.001). The goodness of fit was poor in the sOHCA score (ν=8, χ2=19.1 and Hosmer-Lemeshow test: p=0.014) but not in the sCAHP score (ν=8, χ2=13.5 and Hosmer-Lemeshow test: p=0.10).ConclusionThe performances of the original and simplified OHCA and CAHP scores in predicting neurological outcomes in successfully resuscitated OHCA patients were acceptable. With the highest availability, similar discrimination and good calibration, the sCAHP score has promising potential for clinical implementation, although further validation studies to evaluate its clinical acceptance are necessary.


2020 ◽  
Author(s):  
Liwei Liu ◽  
Jin Liu ◽  
Li Lei ◽  
Bo Wang ◽  
Guoli Sun ◽  
...  

Abstract Background: Risk stratification is recommended as the key step to prevent contrast-associated acute kidney injury (CA-AKI) by allowing for prevention among at-risk patients undergoing coronary angiography (CAG) or percutaneous coronary intervention (PCI). Patients with hypoalbuminemia are prone to CA-AKI and do not have their own risk stratification tool. Therefore, we developed and validated a model for predicting CA-AKI in patients with hypoalbuminemia undergoing CAG/PCI.Methods: A total of 1272 consecutive patients with hypoalbuminemia undergoing CAG/PCI were enrolled and randomly assigned (2:1 ratio) to a development cohort (n = 848) and a validation cohort (n = 424). CA-AKI was defined as a serum creatinine (SCr) increase of ≥ 0.3 mg/dL or 50% from baseline within the first 48 to 72 hours following CAG/PCI. A prediction model was established with independent predictors according to multivariate logistic regression and a stepwise approach, showing as a nomogram. The discrimination of the nomogram was assessed by the area under the receiver operating characteristic (ROC) curve and was compared to the classic Mehran CA-AKI score. Calibration was assessed using the Hosmer–Lemeshow test.Results: Overall, 8.4% (71/848) of patients in the development cohort and 11.2% (48/424) of patients in the validation cohort experienced CA-AKI. The simple nomogram included estimated glomerular filtration rate (eGFR), serum albumin (ALB), age and the use of intra-aortic balloon pump (IABP); showed better predictive ability than the Mehran score (C-index 0.756 vs. 0.693, p = 0.02); and had good calibration (Hosmer–Lemeshow test p = 0.187). Conclusions: Our data suggested that the simple model might be a good tool for predicting CA-AKI in high-risk patients with hypoalbuminemia undergoing CAG/PCI, but our findings require further external validation.Trial registration number NCT01400295


Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 758
Author(s):  
Andoni Elola ◽  
Elisabete Aramendi ◽  
Enrique Rueda ◽  
Unai Irusta ◽  
Henry Wang ◽  
...  

A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision–recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.


CJEM ◽  
2015 ◽  
Vol 17 (3) ◽  
pp. 286-294 ◽  
Author(s):  
Jason E. Buick ◽  
Katherine S. Allan ◽  
Joel G. Ray ◽  
Alexander Kiss ◽  
Paul Dorian ◽  
...  

AbstractBackgroundTraditional variables used to explain survival following out-of-hospital cardiac arrest (OHCA) account for only 72% of survival, suggesting that other unknown factors may influence outcomes. Research on other diseases suggests that neighbourhood factors may partly determine health outcomes. Yet, this approach has rarely been used for OHCA. This work outlines a methodology to investigate multiple neighbourhood factors as determinants of OHCA outcomes.MethodsA retrospective, observational cohort study design will be used. All adult non-emergency medical service witnessed OHCAs of cardiac etiology within the city of Toronto between 2006 and 2010 will be included. Event details will be extracted from the Toronto site of the Resuscitation Outcomes Consortium Epistry—Cardiac Arrest, an existing population-based dataset of consecutive OHCA patients. Geographic information systems technology will be used to assign patients to census tracts. Neighbourhood variables to be explored include the Ontario Marginalization Index (deprivation, dependency, ethnicity, and instability), crime rate, and density of family physicians. Hierarchical logistic regression analysis will be used to explore the association between neighbourhood characteristics and 1) survival-to-hospital discharge, 2) return-of-spontaneous circulation at hospital arrival, and 3) provision of bystander cardiopulmonary resuscitation (CPR). Receiver operating characteristics curves will evaluate each model’s ability to discriminate between those with and without each outcome.DiscussionThis study will determine the role of neighbourhood characteristics in OHCA and their association with clinical outcomes. The results can be used as the basis to focus on specific neighbourhoods for facilitating educational interventions, CPR awareness programs, and higher utilization of automatic defibrillation devices.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 750-750
Author(s):  
Ang Li ◽  
Kylee L Martens ◽  
Daniel Nguyen ◽  
Gabriela Rondon ◽  
Christopher I Amos ◽  
...  

Abstract Introduction: In patients undergoing allogeneic hematopoietic cell transplantation (HCT), venous thromboembolism (VTE) remains a serious complication that lacks validated risk assessment models to guide optimal timing and implementation of thromboprophylaxis. We recently derived the HIGH-2-LOW score that incorporated 7 simple clinical predictors assessed at day 30 post-transplant (Table 1, PMID 33570631). In this present study, we performed validation in two independent datasets. Methods: We selected consecutive patients undergoing first allogeneic HCT from Fred Hutchinson Cancer Research Center (FHCRC, 2015-2019) and MD Anderson Cancer Center (MDACC, 2016-2020). Patients who died, received therapeutic anticoagulation, or did not engraft platelets at day 30 were excluded (Table 2). Day 30 was chosen as the index date because most patients would be transfusion-dependent before that time. We used a combination of ICD9/10 codes and natural language processing (NLP) algorithms to identify possible cases of VTE, followed by confirmation via individual chart review. VTE was defined as radiology-confirmed pulmonary embolism (PE), lower-extremity deep venous thromboembolism (LE-DVT), or catheter-related DVT (CR-DVT). Covariates were captured and weighted according to the original model, except that grade 2-4 was used instead of grade 3-4 GVHD. Discrimination was assessed in each cohort by fitting logistic regression models with VTE and PE/LE-DVT outcomes at 100 days to estimate the c statistic, where a higher c statistic is desirable. Both continuous scores and categorical models were assessed. Kaplan Meier failure curves were plotted to compare the final risk stratification with high- vs. low/intermediate-risk groups. Results: The two cohorts (n=772 in FHCRC, n=1109 in MDACC) had similar characteristics in age, sex, race, weight, disease, and conditioning intensity. Key differences between the two cohorts included a higher number of umbilical cord transplants at FHCRC (vs. haploidentical at MDACC), higher numbers of acute GVHD at FHCRC (due to differences in grading criteria), fewer historical CR-DVT at FHCRC (4.0% vs. 6.8%), and more anticoagulation treatment at day 30 at FHCRC (9.0% vs. 3.5% - excluded). Incident VTE was 2.5% by 100 days and 7.8% by 365 days at FHCRC; incident VTE was 5.4% by 100 days and 9.4% by 365 days at MDACC (Table 2). Incident PE or LE-DVTs were similar in the two cohorts. When treated as a continuous score, every 1-point increase in the HIGH-2-LOW score was associated with odds ratio (OR) of 1.55 for VTE (1.06-2.27, c=0.64) and 2.50 (1.40-4.44, c=0.84) for PE/LE-DVT in the FHCRC cohort. The same increase was associated with OR of 1.93 (1.55-2.39, c=0.64) for VTE and 2.46 (1.61-3.76, c=0.79) for PE/LE-DVT in the MDACC cohort (Table 3). A total of 24% and 19% of patients were classified as high-risk (2+ points), respectively. High vs. low/intermediate-risk was associated with OR of 2.99 (1.20-7.49, c=0.62) for VTE and 19.93 (2.38-166.67, c=0.81) for PE/LE-DVT in the internal validation cohort. High vs. low/intermediate-risk was associated with OR of 4.58 (2.69-7.79, c=0.66) for VTE and 12.05 (3.17-45.81, c=0.77) for PE-LE-DVT in the external validation cohort. The VTE risk stratification separated early and persisted beyond 100 days (Figure 1). Conclusion: Despite differences in HCT and patient characteristics, the HIGH-2-LOW score identified ~20% of allogeneic HCT recipients at high-risk for VTE, particularly that of PE or LE-DVT, in both independent validation cohorts. The lower-than-expected absolute VTE incidence in the FHCRC cohort was likely driven by the increasing use of anticoagulation immediately post-transplant (exclusion criteria); however, the model retained similar OR with modest discrimination in both cohorts. In patients with prior history of PE/LE-DVT off anticoagulation, or those with prolonged admission and at least 1 additional risk factors from the HIGH-2-LOW score (2+ points), VTE prophylaxis should be considered upon platelet engraftment. Figure 1 Figure 1. Disclosures Lee: Kadmon: Research Funding; Syndax: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Novartis: Other: clinical trials, Research Funding; JANSSEN: Other; Incyte: Research Funding; AstraZeneca: Research Funding; Amgen: Research Funding; National Marrow Donor Program: Membership on an entity's Board of Directors or advisory committees. Shpall: Bayer HealthCare Pharmaceuticals: Honoraria; Novartis: Consultancy; Takeda: Patents & Royalties; Affimed: Patents & Royalties; Magenta: Honoraria; Magenta: Consultancy; Navan: Consultancy; Novartis: Honoraria; Axio: Consultancy; Adaptimmune: Consultancy.


Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Mohsin Khan ◽  
Susan Olet ◽  
Mohammad E Mortada ◽  
Firas Zahwe ◽  
Jodi Zilinski ◽  
...  

Introduction: ICD implantation is recommended in patients with LVEF<35%, while those with LVEF between 35 to 40% are not considered at high risk for primary prevention ICD implantation. A subset of these patients develops life threatening ventricular arrhythmias (VA) and improvement in risk stratification may help identify and implement life-saving intervention. Hypothesis: Prolonged repolarization is a marker of electrical instability and JTc interval on ECG could provide prognostic information in patients with LVEF 35-40% incremental to that from LVEF. Methods: Patients ≥18 yr with no history of VA and an ECG and echocardiogram obtained at initial encounter between 11/2011 to 12/2016 with long-term follow-up were identified. The incremental predictive ability of JTc interval on improvement in risk stratification for VA was determined by receiver operating characteristics (ROC) curve, integrated discrimination improvement (IDA) and net reclassification improvement (NRI) analysis. All tests were performed at a 5% level of significance. Results: Out of 29,700 pts that met inclusion criteria, 1,102 (3.7%) had LVEF 35-40% (mean age 70.5±14.6 yrs, 49% males, CAD 67%) and 24,894 (84%) LVEF >40% (65.9±16.3 yrs , 61.8% M). Over the mean follow-up of 4.6±4.2 years, the incidence of VT/VF/cardiac arrest was 16.1% in patients with LVEF 35-40% compared to 4.1% with LVEF >40%. For every 50 ms increase in JTc interval above 300 ms, the risk for arrhythmic event in LVEF 35-40% increased two-fold (Odds Ratio=1.83 (95 % CI 1.72-1.94, P=0.013). Incorporation of JTc to LVEF improved the C statistics (95% Confidence Limit) in the model with only LVEF from 0.56 (0.54-0.57) to 0.72 (0.70-0.73) for the model combining LVEF and JTc. In addition, NRI was estimated at 0.57, which was statistically significant with p values <0.001 while IDI was estimated as 0.015 with p values <0.001 for the model incorporating JTc to LVEF. Conclusions: In patients with LVEF 35-40% considered low risk for life threatening VA by EF, incorporating JTc interval information improved risk stratification and identified those who subsequently developed VT/VF or cardiac arrest and thus identifies a subgroup that can benefit from prophylactic ICD implantation.


BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e034209 ◽  
Author(s):  
Roman Hájek ◽  
Sebastian Gonzalez-McQuire ◽  
Zsolt Szabo ◽  
Michel Delforge ◽  
Lucy DeCosta ◽  
...  

Objectives and designA novel risk stratification algorithm estimating risk of death in patients with relapsed multiple myeloma starting second-line treatment was recently developed using multivariable Cox regression of data from a Czech registry. It uses 16 parameters routinely collected in medical practice to stratify patients into four distinct risk groups in terms of survival expectation. To provide insight into generalisability of the risk stratification algorithm, the study aimed to validate the risk stratification algorithm using real-world data from specifically designed retrospective chart audits from three European countries.Participants and settingPhysicians collected data from 998 patients (France, 386; Germany, 344; UK, 268) and applied the risk stratification algorithm.MethodsThe performance of the Cox regression model for predicting risk of death was assessed by Nagelkerke’s R2, goodness of fit and the C-index. The risk stratification algorithm’s ability to discriminate overall survival across four risk groups was evaluated using Kaplan-Meier curves and HRs.ResultsConsistent with the Czech registry, the stratification performance of the risk stratification algorithm demonstrated clear differentiation in risk of death between the four groups. As risk groups increased, risk of death doubled. The C-index was 0.715 (95% CI 0.690 to 0.734).ConclusionsValidation of the novel risk stratification algorithm in an independent ‘real-world’ dataset demonstrated that it stratifies patients in four subgroups according to survival expectation.


Sign in / Sign up

Export Citation Format

Share Document