Risk Prediction Model For Death Research Articles

Developing a risk prediction model for death at first suicide attempt-Identifying risk factors from Thailand's national suicide surveillance system data.

More than 60% of suicides globally are estimated to take place in low- and middle-income nations. Prior research on suicide has indicated that over 50% of those who die by suicide do so on their first attempt. Nevertheless, there is a dearth of knowledge on the attributes of individuals who die on their first attempt and the factors that can predict mortality on the first attempt in these regions. The objective of this study was to create an individual-level risk-prediction model for mortality on the first suicide attempt. We analyzed records of individuals' first suicide attempts that occurred between May 1, 2017, and April 30, 2018, from the national suicide surveillance system, which includes all of the provinces of Thailand. Subsequently, a risk-prediction model for mortality on the first suicide attempt was constructed utilizing multivariable logistic regression and presented through a web-based application. The model's performance was assessed by calculating the area under the receiver operating curve (AUC), as well as measuring its sensitivity, specificity, and accuracy. Out of the 3,324 individuals who made their first suicide attempt, 50.5% of them died as a result of that effort. Nine out of the 21 potential predictors demonstrated the greatest predictive capability. These included male sex, age over 50 years old, unemployment, having a depressive disorder, having a psychotic illness, experiencing interpersonal problems such as being aggressively criticized or desiring plentiful attention, having suicidal intent, and displaying suicidal warning signals. The model demonstrated a good predictive capability, with an AUC of 0.902, a sensitivity of 84.65%, a specificity of 82.66%, and an accuracy of 83.63%. The implementation of this predictive model can assist physicians in conducting comprehensive evaluations of suicide risk in clinical settings and devising treatment plans for preventive intervention.

Accuracy of death risk prediction models for acute coronary syndrome patients: a systematic review and meta-analysis.

This study systematically evaluates the accuracy of several death risk prediction models for patients with acute coronary syndrome (ACS) through evidence-based methods. We identify the most accurate and effective ACS death risk prediction model and provide an evidence-based basis for clinical healthcare personnel to evaluate their choice of death risk prediction model for ACS patients. An evidence-based approach was used to study the current death risk prediction model for ACS. First, a literature search was carried out using computer-based and manual searching. The literature databases searched include Cochrane Library, MEDLINE, EMBASE, PubMed, Web of Science, WanFang Data, CNKI, VPCS, and SinoMed. The search period was limited to 2009 to 2022. Screening, quality evaluation and data extraction were carried out for the included articles. The PROBAST was used to conduct a migration risk assessment. RevMan 5.3 and Meta-DiSc 1.4 were used in combination to determine the model effect sizes. A descriptive analysis was conducted for the data that could not be meta-analyzed. A total of 8277 articles were initially included in this study. After screening, 25 articles were finally included, involving 11 different risk prediction models. A total of 306,390 patients with ACS were included of which 158,080 (51.6%) were male and 147,793 (48.4%) were female. The patients stemmed from 11 different countries (e.g., China, the USA, Spain, the UK, etc.). The total number of deaths was 23,601. The sensitivity of the GRACE risk prediction model was 0.78, with a specificity of 0.76 and an AUC value of 0.86. The sensitivity of the CAMI risk prediction model was 0.78, with a specificity of 0.70 and an AUC value of 0.85. The sensitivity of the TIMI risk prediction model was 0.51, with a specificity of 0.81, and an AUC value of 0.64. The sensitivity of the REMS risk prediction model was 0.78, with a specificity of 0.46 and an AUC value of 0.41. Eight different risk prediction models (EPICOR, CRUSADE, SAMI, GWTG, LNS, SYNTAX II, APACHE II) that could not be combined with the effect size were also included, with sensitivities ranging from 0.77-0.95, specificities ranging from 0.22-0.99, and AUC values ranging from 0.71-0.92. The GRACE and CAMI risk prediction models demonstrate good accuracy for evaluating the death risk of ACS patients. The accuracy of the TIMI risk prediction model is similar to that of the REMS risk prediction model. The APACHE II, SYNTAX II, EPICOR, and CAMI risk prediction models also show good accuracy for estimating the risk of death in ACS patients, although further validation is needed due to limited evidence. For improved predictive accuracy and to help advance medical interventions, the author recommends that clinical medical staff use the GRACE model to predict the death risk of ACS patients.

Performance of the PRIMaCY sudden death risk prediction model for childhood hypertrophic cardiomyopathy: implications for implantable cardioverter-defibrillator decision-making.

The validated HCM Risk-Kids model provides accurate individualized estimates of sudden cardiac death risk in children with hypertrophic cardiomyopathy (HCM). A second validated model, PRIMaCY, also provides individualized estimates of risk, but its performance and clinical impact has not been independently investigated. The aim of this study was to investigate the clinical impact of using the PRIMaCY sudden cardiac death (SCD) risk model in childhood HCM. The estimated 5-year SCD risk was calculated for children meeting diagnostic criteria for HCM in a large single-centre cohort using PRIMaCY (clinical and genetic) and HCM Risk-Kids model, and model performance was assessed. Three hundred one patients [median age 10 (interquartile range 4-14)] were followed up for an average of 4.9 (±3.8) years, during which 30 (10.0%) reached the SCD or equivalent event endpoint. Harrell's C-statistic for the clinical and genetic models was 0.66 [95% confidence interval (CI) 0.52-0.8] and 0.66 (95% CI 0.54-0.80) with a calibration slope of 0.19 (95% CI 0.04-0.54) and 0.26 (95% CI -0.03-0.62), respectively. The number needed to treat to potentially treat one life-threatening arrhythmia for the PRIMaCY clinical, PRIMaCY genetic, and HCM Risk-Kids models was 13.7, 14.5, and 9.4, respectively. Although PRIMaCY has a similar discriminatory ability to that reported for HCM Risk-Kids, estimated risk estimates did not correlate well with observed risk. A higher proportion of patients met implantable cardioverter-defibrillator thresholds using PRIMaCY model compared with HCM Risk-Kids. This has important clinical implications as these patients will be exposed to a lifetime risk of complications and inappropriate therapies.

A cohort study of developing a death risk prediction model in patients with septic shock based on MIMIC-III database

To develop and validate a model for predicting death risk in septic shock patients using LASSO-Logistic methods. A retrospective cohort study was conducted. Based on the open-source database Medical Information Mart for Intensive Care-III v1.4 (MIMIC-III v1.4), the septic shock patients meeting the Sepsis-3 criteria were included, and the data on demographic characteristics, major signs, laboratory examinations, hospitalization, and outcomes were extracted. Predictive variables were selected by LASSO regression and predictive models were derived using Logistic regression. The calibration of the model was evaluated using the Hosmer-Lemeshow test and discrimination was evaluated using the receiver operator characteristic curve (ROC curve). A total of 693 patients with septic shock were enrolled, in which 445 patients survived and 248 patients dead within 30 days and the mortality was 35.8%. Logistic regression model was constructed according to nine predictive variables and outcome variables screened by LASSO regression method, which showed that advanced age, Elixhauser index, blood lactic acid (Lac), K+ level and mechanical ventilation were associated with increased 30-day mortality [odds ratio (OR) and 95% confidence interval (95%CI) was 1.023 (1.010-1.037), 1.047 (1.022-1.074), 1.213 (1.133-1.305), 2.241 (1.664-3.057), 2.165 (1.433-3.301), respectively, all P < 0.01], and reduced systolic blood pressure (SBP), diastolic blood pressure (DBP), body temperature, and pulse oxygen saturation (SpO2) were also associated with increased 30-day mortality [OR (95%CI) was 0.974 (0.957-0.990), 0.972 (0.950-0.994), 0.693 (0.556-0.857), 0.971 (0.949-0.992), respectively, all P < 0.05]. The calibration curve showed that the predicted risk of septic shock death risk prediction model had good agreement with the real situation. ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model was 0.839 (95%CI was 0.803-0.876), which could distinguish patients at risk of death from those at risk of survival. The septic shock death risk prediction model has a good ability to identify the 30-day mortality risk of septic shock patients, including nine hospital readily variables (age, Elixhauser index, mechanical ventilation, Lac, K+, SBP, DBP, body temperature and SpO2). The model could be used by clinicians to calculate the risk of death in septic shock individuals.

Development and validation on death risk model of Stanford type A aortic dissection based on Cox regression

To construct the prediction model of death risk of Stanford type A aortic dissection (AAD) based on Cox proportional risk regression model. AAD patients who were diagnosed and received surgical treatment admitted to the department of cardiothoracic surgery of Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology from January 1st, 2019 to April 30th, 2020 were enrolled. The general situation, clinical manifestations, pre-hospital data, laboratory examination and imaging examination results of the patients were collected. The observation period was up to the death of the patients or ended on April 30th, 2021. They were divided into the model group and the verification group according to the ratio of 7:3. Lasso method was used to screen prognostic variables from the data of the modeling group, and multivariate Cox regression analysis was included to construct the AAD death risk prediction model, which was displayed by nomogram. The receiver operator characteristic curve (ROC curve) was used to evaluate the discrimination of the model, the calibration curve to evaluate the accuracy of the model, and the clinical decision curve (DCA) to evaluate the effectiveness of the model. A total of 454 patients with AAD were finally included, and the mortality was 19.4% (88/454). Lasso regression analysis was used to screen out 10 variables from the data of 317 patients in the model group, and the prediction model of death risk was constructed: 0.511×abdominal pain+1.061×syncope+0.428×lower limb pain/numbness-0.365×emergency admission-1.933×direct admission-1.493×diagnosis before referral+0.662×preoperative systolic blood pressure (SBP) < 100 mmHg (1 mmHg = 0.133 kPa)+0.632×hypersensitivity cardiac troponin I (hs-cTnI) > 34.2 ng/L+1.402×De Bakey type+0.641× pulmonary infection+1.472×postoperative delirium. The area under the ROC curve (AUC) and 95% confidence interval (95%CI) of the AAD death risk prediction model were 0.873 (0.817-0.928), and that of the verification group was 0.828 (0.740-0.916). DCA showed that the net benefit value of the model was higher. The calibration curve showed that there was a good correlation between the actual observation results and the model prediction results. The AAD death risk prediction model based on abdominal pain, syncope, lower limb pain/numbness, mode of admission, diagnosis before referral, preoperative SBP < 100 mmHg, hs-cTnI > 34.2 ng/L, De Bakey type , pulmonary infection, and postoperative delirium can effectively help clinicians identify patients at high risk for AAD, evaluate their postoperative survival and timely adjust treatment strategies.

Development and evaluation of an early death risk prediction model after acute type A aortic dissection.

BackgroundThe purpose of the study was to assess the relationship between preoperative laboratory examination, clinical imaging data, and postoperative death of patients with acute type A aortic dissection (ATAAD) and to establish a prediction model of hospital death risk after the operation.MethodsA total of 224 cases of acute Standford A aortic dissection were treated by total arch replacement using a tetrafurcate graft with stented elephant trunk implantation in Tianjin Chest Hospital. Based on preoperative laboratory examination and clinical imaging data of patients with ATAAD, the independent risk factors of postoperative hospital death were obtained using logistic analysis, and a risk prediction model of postoperative hospital death was developed.ResultsIndependent risk factors of postoperative death in patients with ATAAD were: body mass index (BMI), preoperative neutrophil to lymphocyte ratio (NLR), mean platelet volume (MPV), creatinine (Cr), D-dimer, high-sensitive cardiac troponin T (hs-CTnT), apolipoprotein A1, left subclavian artery involvement, and iliac artery involvement. The regression equation of postoperative death risk was: logitP1 = −9.584 + 1.060 × NLR + 1.586 × MPV + 1.009 × Cr + 1.067 × D-dimer + 2.023 × hs-CTnT; the regression equation of postoperative death risk was: logitP2 = −3.296 + 3.242 × left subclavian artery involved + 4.564 × iliac artery involved; the regression equation of postoperative death risk was: logitP3 = −12.864 + 1.149 × BMI + 4.731 × left subclavian artery involved + 4.150 × iliac artery involved + 1.064 × NLR + 1.011 × Cr + 1.084 × D-dimer + 2.242 × hs-CTnT + 3.233 × apolipoprotein A1.ConclusionsBMI, NLR, MPV, Cr, D-dimer, hs-CTnT, apolipoprotein A1, left subclavian artery involvement and iliac artery involvement can affect the hospital mortality rate of aortic dissection undergoing Sun’s operation to varying degrees, which may be helpful to guide the design of the perioperative treatment strategy.

Prediction of neonatal deaths in NICUs: development and validation of machine learning models

BackgroundPrediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians’ ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques.MethodsThis study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes.Results17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models.ConclusionUsing the developed machine learning models can help physicians predict the neonatal deaths in NICUs.

A novel nomogram for early prediction of death in severe neurological disease patients with electroencephalographic periodic discharges

ObjectiveTo investigate death-related factors in patients with electroencephalographic (EEG) periodic discharges (PDs) and to construct a model for death prediction. MethodsThis case-control study enrolled a total of 80 severe neurological disease patients with EEG PDs within 72 h of admission to the neuroscience intensive care unit (NICU). According to modified Rankin scale (mRS) scores half a year after discharge, patients were divided into a survival group (<6 points) and a death group (6 points). Their relevant clinical and biochemical indicators as well as EEG characteristics were retrospectively analyzed. Logistic regression analysis was used to identify the risk factors associated with the death of patients with EEG PDs. A death risk prediction model and an individualized nomogram prediction model were constructed, and the prediction performance and concordance of the models were evaluated. ResultsMultivariate logistic regression analysis showed that the involvement of both gray and white matter in imaging, disappearance of EEG reactivity, occurrence of stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs), and an interval time of 0.5–4 s were independent risk factors for death. A regression model was established according to the multivariate logistic regression analysis, and the area under the curve of this model was 0.9135. The accuracy of the model was 87.01%, the sensitivity was 87.17%, and the specificity was 89.17%. A nomogram model was constructed, and a concordance index of 0.914 was obtained after internal validation. ConclusionThe regression model based on risk factors has high accuracy in predicting the risk of death of patients with EEG PDs. SignificanceThis model can help clinicians in the early assessment of the prognosis of severe neurological disease patients with EEG PDs.

UNOS Match Runs for Pediatric Heart Transplant Organ Allocation—How Well Do They Sequence Patients Based on Medical Urgency?

<h3>Purpose</h3> The match run is the automated mechanism by which UNOS prioritizes donor hearts to waitlist candidates for heart transplant (HT) based on medical urgency. We sought to determine how well match runs perform in sequencing patients according to their medical urgency after implementation of the 2016 pediatric heart allocation revisions. <h3>Methods</h3> OPTN data were used to identify all match runs between March 21, 2016 and June 9, 2020 for each pediatric donor heart offered through UNOS to US waitlist candidates. The rank-order of candidates on the match run was compared to the rank-order based on medical urgency alone defined as the probability of survival on the day of the match run using a risk prediction model developed and validated during the prior 6 years. <h3>Results</h3> Overall, 634 donors generated 2,524 unique match runs during the study period when a total of 2,608 candidates were listed for pediatric HT. The median age of donors was 5 years (Q1, Q3, 1, 13), median weight 21 (11, 54) kg with the most common cause of death natural causes (26%), non-motor vehicle accidents (MVA) (22%) and MVA (16%). The risk-prediction model for death or deterioration included 8 variables (age, weight, cardiac diagnosis and cardiac support). When the survival model was applied to the match run candidates within zone, 26% of candidates were ranked out of survival order (i.e. had a rank-reversal) (<b>Table</b>). Of these, 69% of rank-reversals involved candidates <i>within</i> the same UNOS status levels whereas 41% involved candidates spread across UNOS status levels. <h3>Conclusion</h3> There is evidence to suggest that the 2016 revision to the pediatric donor heart allocation system fails to sequence waitlisted candidates according to medical urgency, which affects a substantial fraction of match runs. Misclassification of patient risk by UNOS status level and within UNOS status levels each contribute rank-reversals. Rank-ordering patients based on a continuous survival probability model could address both problems.

Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study

Objectives To appraise effective predictors for COVID-19 mortality in a retrospective cohort study. Methods A total of 1270 COVID-19 patients, including 984 admitted in Sino French New City Branch (training and internal validation sets randomly split at 7:3 ratio) and 286 admitted in Optical Valley Branch (external validation set) of Wuhan Tongji hospital, were included in this study. Forty-eight clinical and laboratory features were screened with LASSO method. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed death risk prediction model with simple-tree XGBoost model. Performances of models were evaluated by AUC, prediction accuracy, precision, and F1 scores. Results Six features, including disease severity, age, levels of high-sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), ferritin, and interleukin-10 (IL-10), were selected as predictors for COVID-19 mortality. Simple-tree XGBoost model conducted by these features can predict death risk accurately with >90% precision and >85% sensitivity, as well as F1 scores >0.90 in training and validation sets. Conclusion We proposed the disease severity, age, serum levels of hs-CRP, LDH, ferritin, and IL-10 as significant predictors for death risk of COVID-19, which may help to identify the high-risk COVID-19 cases. KEY MESSAGES A machine learning method is used to build death risk model for COVID-19 patients. Disease severity, age, hs-CRP, LDH, ferritin, and IL-10 are death risk factors. These findings may help to identify the high-risk COVID-19 cases.

Death Risk Prediction of Intensive Care Unit Patients Combined with Treatment Process Mining

Precise death risk prediction of ICU patients plays an important role in developing follow-up treatment options and reducing rescue cost. Previous work mainly relied on the vital signs of patients to predict the risk of death. In fact, patients’ treatment process also implies valuable information about patient’s condition. To use the treatment process information in patient risk assessment, this paper proposes a new death risk prediction method combined with treatment process mining. Specifically, historical patients are firstly clustered into several classes according to their final treatment outcome and the drugs they took. Patients in one cluster are assumed to share the same treatment process. Then, these treatment processes are mined by means of the LDA topic model and the probability suffix tree training method. Next, for a patient to do death risk prediction, the similarity between his/her treatment procedure and those probability suffix trees is computed. The similarity value is taken as a new feature complementary to those traditional ones in disease severity scoring systems. Finally, the random forest algorithm is used to train the death risk prediction model based on the extended feature set. Taking the prescription data of sepsis patients in the MIMIC-III database as input, an experiment is conduced to evaluate the proposed method. As expected, the proposed method achieves better prediction accuracy, recall rate, and F1 score compared to methods.

Maternal Risk Modeling in Critical Care-Development of a Multivariable Risk Prediction Model for Death and Prolonged Intensive Care.

We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. We used data from a high-quality prospectively collected national database, supported with literature review and expert opinion. We tested univariable associations between each risk factor and outcome. We then developed two separate multivariable logistic regression models for the outcomes of acute hospital mortality and death or prolonged ICU length of stay. We validated two parsimonious risk prediction models specific for a maternal population. The Intensive Care National Audit and Research Centre Case Mix Programme is the national clinical audit for adult critical care in England, Wales, and Northern Ireland. All female admissions to adult general critical care units, for the period January 1, 2007-December 31, 2016, 16-50 years old, and admitted either while pregnant or within 42 days of delivery-a cohort of 15,480 women. None. We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. For the primary outcome of acute hospital mortality, our parsimonious risk model consisting of eight variables had an area under the receiver operating characteristic of 0.96 (95% CI, 0.91-1.00); these variables are commonly available for all maternal admissions. For the secondary composite outcome of death or ICU length of stay greater than 48 hours, the risk model consisting of 17 variables had an area under the receiver operating characteristic of 0.80 (95% CI, 0.78-0.83). We developed risk prediction models specific to the maternal critical care population. The models compare favorably against general adult ICU risk prediction models in current use within this population.

Bayesian Model Averaging for Selection of a Risk Prediction Model for Death within Thirty Days of Discharge: The SILVER-AMI Study

We describe a selection process for a multivariable risk prediction model of death within 30 days of hospital discharge in the SILVER-AMI study. This large, multi-site observational study included observational data from 2000 persons 75 years and older hospitalized for acute myocardial infarction (AMI) from 94 community and academic hospitals across the United States and featured a large number of candidate variables from demographic, cardiac, and geriatric domains, whose missing values were multiply imputed prior to model selection. Our objective was to demonstrate that Bayesian Model Averaging (BMA) represents a viable model selection approach in this context. BMA was compared to three other backward-selection approaches: Akaike information criterion, Bayesian information criterion, and traditional p-value. Traditional backward-selection was used to choose 20 candidate variables from the initial, larger pool of five imputations. Models were subsequently chosen from those candidates using the four approaches on each of 10 imputations. With average posterior effect probability ≥ 50% as the selection criterion, BMA chose the most parsimonious model with four variables, with average C statistic of 78%, good calibration, optimism of 1.3%, and heuristic shrinkage of 0.93. These findings illustrate the utility and flexibility of using BMA for selecting a multivariable risk prediction model from many candidates over multiply imputed datasets.

Validation of the 2014 European Society of Cardiology Sudden Cardiac Death Risk Prediction Model Among Various Phenotypes in Japanese Patients With Hypertrophic Cardiomyopathy

Risk stratification for sudden cardiac death (SCD) is essential in the management of hypertrophic cardiomyopathy (HC). The 2014 European Society of Cardiology SCD risk prediction model (Risk-SCD) is a novel risk scoring system; however, whether it can be applied to Japanese HC and its usefulness among various HC phenotypes remain unclear. The aim of this study was to validate the Risk-SCD model in Japanese HC, and to evaluate its usefulness among various HC phenotypes. We studied 370 consecutive Japanese HC patients evaluated for primary SCD prevention at our tertiary referral center. The Risk-SCD model was validated in 289 HC patients with ejection fraction (EF) ≥50% (including left ventricular outflow tract obstruction [LVOTO], mid ventricular obstruction [MVO], apical hypertrophy, and nonobstructive phenotypes), and 81 end-stage HC patients (EF <50%). The end point of the study was SCD or an equivalent event (appropriate implantable cardioverter defibrillator therapy or successful resuscitation after cardiac arrest). Thirty-one SCD events were observed during a median follow-up of 5.2 (interquartile range 3.5 to 6.9) years. The Risk-SCD model showed improved risk prediction in HC with EF ≥50% compared with the previous 2011 American College of Cardiology Foundation/American Heart Association and 2003 American College of Cardiology/European Society of Cardiology guideline approaches (number needed to treat = 3.8 at Risk-SCD >6%) regardless of phenotypes; LVOTO, MVO, apical, and nonobstructive, but misclassified SCD risk in end-stage HC. In the current external validation of the Risk-SCD model in Japanese HC, the model improved SCD prediction compared with previous approaches, and was also shown to be useful in LVOTO, MVO, apical, and nonobstructive phenotypes.

403An international validation study of the 2014 european society of cardiology sudden cardiac death risk prediction model in childhood hypertrophic cardiomyopathy

403An international validation study of the 2014 european society of cardiology sudden cardiac death risk prediction model in childhood hypertrophic cardiomyopathy

Hypertrophic Cardiomyopathy in Liver Transplantation Patients

BackgroundHypertrophic cardiomyopathy (HCM) is an autosomal dominant disorder that presents with a hypertrophied nondilated left ventricle. In the absence of other known causes of cardiomyopathy, it is often associated with left ventricular outflow tract obstruction during systole, systolic anterior motion of the mitral valve, mitral regurgitation, and increased risk of sudden cardiac death. When HCM coexists with end-stage liver disease, it can be further complicated by cirrhosis-associated cardiovascular abnormalities, including hyperdynamic circulation, systolic and diastolic dysfunction, and electrophysiologic abnormalities. MethodsWe retrospectively examined patient characteristics, comorbidities, preoperative echocardiogram results, sudden cardiac death risk prediction model score, and 1-year postoperative mortality of patients with HCM who underwent liver transplantation at our institution from January 1, 2000, through January 1, 2015. ResultsOf the 2,812 liver transplantations performed during the study period, we identified 15 patients with a preoperative diagnosis of HCM. When comparing the patients who did vs did not survive the first year after orthotopic liver transplantation, we identified significant differences in maximal left ventricular wall thickness (P = .004) and resting left ventricular outflow tract gradient (P = .004). Preoperative left atrium size (measured by echocardiography; P = .66) and the sudden cardiac death risk prediction model score (P = .32) were not significantly associated with 1-year survival. ConclusionsPreoperative left ventricular outflow tract gradient exceeding 60 mm Hg was strongly associated with death during the first year after transplant. These results suggest that the severity of HCM influences patient outcomes.

A novel cardiovascular death prediction model for Chinese individuals: A prospective cohort study of 381,963 study participants

Background and aimsWe aimed at developing a novel risk prediction model for death from cardiovascular disease (CVD) for Chinese individuals, based upon a large cohort from Taiwan. MethodsThis Chinese cohort came from Taiwan, with 381,963 individuals free from CVD, recruited from a private health surveillance program. With a median follow-up of 8.8 years, 1894 CVD deaths out of a total of 10,829 deaths were identified by linking cohort ID with the National Death File. ResultsA novel CVD death risk prediction model for Chinese individuals was established from this cohort. An increase in the resting heart rate was the statistically independent predictor in this model. The discriminatory accuracy was measured by generating the receiver operating characteristic (ROC) curve, and the area under the ROC curve was 0.913 (95% CI = 0.907 to 0.920). ConclusionsA novel cardiovascular death prediction model with high predictability for Chinese individuals was demonstrated in the present study.

Validation of the 2014 European Society of Cardiology Sudden Cardiac Death Risk Prediction Model in Hypertrophic Cardiomyopathy in a Reference Center in South America

Sudden cardiac death (SCD) is a common cause of death in hypertrophic cardiomyopathy (HC). Our aim was to conduct an external and independent validation in South America of the 2014 European Society of Cardiology (ESC) SCD risk prediction model to identify patients requiring an implantable cardioverter defibrillator. This study included 502 consecutive patients with HC followed from March, 1993 to December, 2014. A combined end point of SCD or appropriate implantable cardioverter defibrillator therapy was assessed. For the quantitative estimation of individual 5-year SCD risk, we used the formula: 1- 0.998(exp(Prognostic index)). Our database also included the abnormal blood pressure response to exercise as a risk marker. We analyzed the 3 categories of 5-year risk proposed by the ESC: low risk (LR) <4%; intermediate risk (IR) ≥4% to <6%, and high risk (HR) ≥6%. The LR group included 387 patients (77%); the IR group 39 (8%); and the HR group 76 (15%). Fourteen patients (3%) had SCD/appropriate implantable cardioverter defibrillator therapy (LR: 0%; IR: 2 of 39 [5%]; and HR: 12 of 76 [16%]). In a receiver-operating characteristic curve, the new model proved to be an excellent predictor because the area under the curve for the estimated risk is 0.925 (statistical C: 0.925; 95% CI 0.8884 to 0.9539, p <0.0001). In conclusion, the SCD risk prediction model in HC proposed by the 2014 ESC guidelines was validated in our population and represents an improvement compared with previous approaches. A larger multicenter, independent and external validation of the model with long-term follow-up would be advisable.

Impact of CGA in therapeutic decisions: ELDERLY trial.

759 Background: Based on results from phase III clinical trials chemotherapy treatment benefit in patients ≥70 years old with stage III and IV colorectal cancer (CRC III-IV) is a subject in question. The ELDERLY trial aims to explore in a pilot program viability and convenience of an evidence based protocol for Comprehensive Geriatric Asessment (CGA) as well as the contribution of further evaluations. Methods: Patients ≥70 years old with CRC III-IV were prospectively and consecutively included. After initial treatment recommendation was made, study protocol for CGA (functional, nutritional, cognitive, social, psychological, and comorbidities) was carried out. Based on CGA, early death risk prediction model and chemotherpy toxicity risk stratification subsequent final treatment recommendation was obtained. Follow up of patients and comparison to historical controls is underway. Results: 20 patients (10 CRC III, 10 CRC IV), median age 79.2 years (71-85), 65% males, were included. ECOG PS was: 0 in 35% of patients, 1 in 35%, 2 in 20%, and 3 in 10% of patients. Initial treatment recommendation was: standard for 60% of patients, adapted standard for 25%, and no treatment for 15% of patients. According to Köhne et al. (Oncologist 2008) fitness categories 25% of patients were in group I, 55% in group II, and 20% of patients in group III. Early death risk, according to Soubeyran et al. (J Clin Oncol 2012), was high for 5% of patients. Chemotherapy toxicity risk, according to Hurria et al. (J Clin Oncol 2011) was low for 15% of patients, intermediate for 60%, and high for 25% of patients. Final treatment recommendation was: standard for 25% of patients, adapted standard for 55%, and no treatment for 20% of patients. Initial and final treatment recommendation diverged in 55% of patients (more conservative final treatment recommendation in 45%, and more intense final treatment recommendation in 10%). Conclusions: Evidence based protocol CGA modified initial treatment recommendations. Follow up of patients and comparison to historical controls could contribute to improve the knwoledge and convenience of this and further evaluations.

Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models

Objective To compare risk prediction models for death in hospital based on an administrative database with published results based on data derived from three national clinical databases: the national cardiac...