Risk Stratification For In-hospital Mortality Research Articles

Geriatric Trauma Outcome Score as a Mortality Predictor in Isolated Moderate to Severe Traumatic Brain Injury: A Single-Center Retrospective Study.

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, with severe cases significantly increasing the risk of complications and long-term mortality. The Geriatric Trauma Outcome Score (GTOS), based on age, injury severity, and transfusion need, has been validated for predicting mortality in older trauma patients, but its utility in predicting mortality for TBI patients remains unexplored. This retrospective study included 5543 adult trauma patients with isolated moderate to severe TBI, defined by head Abbreviated Injury Scale (AIS) scores of ≥ 3, from 1998 to 2021. GTOS was calculated with the following formula: age + (Injury Severity Score × 2.5) + 22 (if transfused within 24 h). The area under the receiver operating characteristic curve (AUROC) assessed GTOS's ability to predict mortality. The optimal GTOS cutoff value was determined using Youden's index. Mortality rates were compared between high- and low-GTOS groups, separated by the optimal GTOS cutoff value, including a propensity score-matched analysis adjusting for baseline characteristics. Among 5543 patients, mortality was 8.3% (462 deaths). Higher mortality is correlated with male sex, older age, higher GTOS, and comorbidities like hypertension, coronary artery disease, and end-stage renal disease. The optimal GTOS cut-off for mortality prediction was 121.5 (AUC = 0.813). Even when the study population was matched by propensity score, patients with GTOS ≥121.5 had much higher odds of death (odds ratio 2.64, 95% confidence interval 1.93-3.61, p < 0.001) and longer hospital stays (mean 16.7 vs. 12.2 days, p < 0.001) than those with GTOS < 121.5. These findings support the idea that GTOS is a useful tool for risk stratification of in-hospital mortality in isolated moderate to severe TBI patients. However, we encourage further research to refine GTOS for better applicability in TBI patients.

Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients.

In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive interpretation of the prediction results. We aimed to develop ML models with high prediction performance, interpretability, and superior risk stratification to predict in-hospital mortality and worsening heart failure (WHF) in patients with acute heart failure (AHF). Based on the Kyoto Congestive Heart Failure registry, which enrolled 4056 patients with AHF, we developed prediction models for in-hospital mortality and WHF using information obtained on the first day of admission (demographics, physical examination, blood test results, etc.). After excluding 16 patients who died on the first or second day of admission, the original dataset (n=4040) was split 4:1 into training (n=3232) and test datasets (n=808). Based on the training dataset, we developed three types of prediction models: (i) the classification and regression trees (CART) model; (ii) the random forest (RF) model; and (iii) the extreme gradient boosting (XGBoost) model. The performance of each model was evaluated using the test dataset, based on metrics including sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and calibration slope. For the complex structure of the XGBoost model, we performed SHapley Additive exPlanations (SHAP) analysis, classifying patients into interpretable clusters. In the original dataset, the proportion of females was 44.8% (1809/4040), and the average age was 77.9±12.0. The in-hospital mortality rate was 6.3% (255/4040) and the WHF rate was 22.3% (900/4040) in the total study population. In the in-hospital mortality prediction, the AUC for the XGBoost model was 0.816 [95% confidence interval (CI): 0.815-0.818], surpassing the AUC values for the CART model (0.683, 95% CI: 0.680-0.685) and the RF model (0.755, 95% CI: 0.753-0.757). Similarly, in the WHF prediction, the AUC for the XGBoost model was 0.766 (95% CI: 0.765-0.768), outperforming the AUC values for the CART model (0.688, 95% CI: 0.686-0.689) and the RF model (0.713, 95% CI: 0.711-0.714). In the XGBoost model, interpretable clusters were formed, and the rates of in-hospital mortality and WHF were similar among each cluster in both the training and test datasets. The XGBoost models with SHAP analysis provide high prediction performance, interpretability, and reproducible risk stratification for in-hospital mortality and WHF for patients with AHF.

Intraoperative Predictors and Proposal for a Novel Prognostic Risk Score for In-Hospital Mortality after Open Repair of Ruptured Abdominal Aortic Aneurysms (SPARTAN Score).

(1) Background: Several mortality risk scores have been developed to predict mortality in ruptured abdominal aortic aneurysms (rAAAs), but none focused on intraoperative factors. The aim of this study is to identify intraoperative variables affecting in-hospital mortality after open repair and develop a novel prognostic risk score. (2) Methods: The analysis of a retrospectively maintained dataset identified patients who underwent open repair for rAAA from January 2007 to October 2023 in three Italian tertiary referral centers. Multinomial logistic regression was used to calculate the association between intraoperative variables and perioperative mortality. Independent intraoperative factors were used to create a prognostic score. (3) Results: In total, 316 patients with a mean age of 77.3 (SD ± 8.5) were included. In-hospital mortality rate was 30.7%. Hemoperitoneum (p < 0.001), suprarenal clamping (p = 0.001), and operation times of >240 min (p = 0.008) were negative predictors of perioperative mortality, while the patency of at least one hypogastric artery had a protective role (p = 0.008). Numerical values were assigned to each variable based on the respective odds ratio to create a risk stratification for in-hospital mortality. (4) Conclusions: rAAA represents a major cause of mortality. Intraoperative variables are essential to estimate patients' risk in surgically treated patients. A prognostic risk score based on these factors alone may be useful to predict in-hospital mortality after open repair.

MitraCox Score Predicts In-Hospital Mortality in Patients Admitted for Transcatheter Edge-to-Edge Mitral Valve Repair

As mitral valve transcatheter edge-to-edge repair (M-TEER) is evolving as an effective treatment for high-risk surgical patients with mitral regurgitation, there is a pressing need for cardiologists to optimize resources through risk stratification of in-hospital mortality for this patient population. Although current risk-prediction models have been shown to predict adverse outcomes with reasonable accuracy, models trained using the US nationwide population are lacking. This study aimed to identify clinical, demographic, and procedural features that predict in-hospital mortality, and to derive and validate an in-hospital mortality risk-prediction tool in patients who have undergone M-TEER. A total of 9,373 admissions from the Nationwide Readmissions Database of patients who underwent M-TEER between 2015 and 2018 were used to develop and validate the model. We first performed least absolute shrinkage and selection operator (LASSO) regularization of Cox regression (Coxnet) that is 10-fold cross-validated. The non-zero coefficients were multiplied with the respective values of each observation of the predictors to build the scoring formula. Out of 9,373 admissions, 196 patients (2%) died in-hospital during index admission. In descending order, the top variables that were most predictive of in-hospital mortality were higher age, presence of fluid and electrolyte disturbance, and large metropolitan location of the hospital. The validation C-statistic of the MitraCox score was 0.82. Using X-tile software (Yale School of Medicine, New Haven, Connecticut), 2 cutoffs of the score were determined on the basis of in-hospital mortality and length of stay, and the survival of the population was classified into 3 risk groups: low, intermediate, and high. The scoring system deployed online as a web-based calculator can be accessed at https://kathavs.shinyapps.io/Mitracox_Kapadia/. In conclusion, MitraCox score is easy to calculate and predicts in-hospital mortality depending on length of stay in a dynamic manner.

Prediction of In-Hospital Mortality in Patients With Traumatic Brain Injury Using the Rotterdam and Marshall CT Scores: A Retrospective Study From Western India.

Objective Head trauma of any severity, including concussions and skull fractures, can cause a traumatic brain injury (TBI). Prognostication plays a vital role in the scenario of urgency put forth by TBI. The application of CT-based scoring systems developed by the Rotterdam CT score and Marshall classification system appears to be appropriate for the early and precise prediction of clinical outcomes in TBI patients. The present study was designed to determine the predictive value of the Rotterdam CT score and Marshall classification system for in-hospital mortality in patients with TBI. Methods All adult patients (≥ 18 years) with acute traumatic brain injury presented over a period from February 2019 to November 2022 were included. Only those patients who had undergone a plain CT scan of the brain during the initial presentation at the emergency department (ED) were considered. Patients who presented with penetrating brain injury as well as those who died on arrival or who died prior to the initial CT scan of the brain were excluded. A total of 127 patients were included in the final data analysis. Based on initial CT-scan findings, the Rotterdam CT scoreand Marshall classification system were calculated in order to predict in-hospital mortality. Results The study was dominated by male patients (85.8%) as compared to female patients (14.2%). The overall mortality rate was 32.3% (n = 41). The mortality rate among males and females was 30.3% (33/109) and 44.4% (8/18), respectively. As per the Glasgow Coma Scale (GCS) classification, the severity of the injury was mild in 12.6% of the study subjects, moderate in 22%, and severe in 65.4%. The mortality rate among the patients with mild severity was 12.5% (2/16), while it was 28.6% in moderate (8/28) and 37.3% (31/83) in the severe category group. The best cut-off point of the Rotterdam score for predicting mortality was >4 (as per the Youden Index), which had a sensitivity and specificity of 60.98% and 90.70%, respectively, while the cut-off point of the Marshall CT classificationfor predicting mortality was >3 (as per the Youden Index), which had a sensitivity of 82.93% and a specificity of 75.58%. There was only a minor difference in the area under the curve (AUC) value of the receiver operating characteristic curve (ROC) curve between the Rotterdam CT score (0.827) and the Marshall classification system (0.833). Conclusion The Rotterdam and Marshall CT scores have demonstrated significant independent prognostic value and may serve as a useful initial evaluation tool for risk stratification of in-hospital mortality among patients with TBI.

Additive prognostic value of serum calcium to the ESC risk stratification in patients with acute pulmonary embolism

BackgroundHypocalcemia has been shown to be involved in the adverse outcomes of acute pulmonary embolism (APE). We aimed to determine the incremental value of adding hypocalcemia, defined as serum calcium level ≤ 2.12 mmol/L, on top of the European Society of Cardiology (ESC) prognostic algorithm, for the prediction of in-hospital mortality in APE patients, which in turn could lead to the optimization of APE management.MethodsThis study was conducted at West China Hospital of Sichuan University from January 2016 to December 2019. Patients with APE were retrospectively analyzed and divided into 2 groups based on serum calcium levels. Associations between hypocalcemia and adverse outcomes were assessed by Cox analysis. The accuracy of risk stratification for in-hospital mortality was assessed with the addition of serum calcium to the current ESC prognostic algorithm.ResultsAmong 803 patients diagnosed with APE, 338 (42.1%) patients had serum calcium levels ≤ 2.12 mmol/L. Hypocalcemia was significantly associated with higher in-hospital and 2-year all-cause mortality compared to the control group. The addition of serum calcium to ESC risk stratification enhanced net reclassification improvement. Low-risk group with serum calcium level > 2.12 mmol/L had a 0% mortality rate, improving the negative predictive value up to 100%, while high-risk group with serum calcium level ≤ 2.12 mmol/L indicated a higher mortality of 25%.ConclusionOur study identified serum calcium as a novel predictor of mortality in patients with APE. In the future, serum calcium may be added to the commonly used ESC prognostic algorithm for better risk stratification of patients suffering from APE.

Risk Stratification for In-Hospital Mortality in Adult Patients with COVID-19

Background: Since the outbreak of coronavirus 2019 (COVID-19), identifying risk factors associated with in-hospital mortality has been a global priority. In this study, the purpose was to evaluate the clinical, laboratory, and radiological characteristics of hospitalized patients with COVID-19 to develop a predictive model and scoring system for in-hospital mortality. Methods: In this retrospective cohort study, 611 adult patients with COVID-19, admitted to Sina hospital were enrolled and followed up. Results: Out of the total number of 611 patients, 104 patients (17%) deceased during hospitalization, including 75 (12.2%) deaths in ICU and 29 (4.7%) deaths in the wards. After multivariate logistic regression analysis, several characteristics including age &gt;55 years, previous history of malignancy, history of cerebrovascular accident, tachypnea on admission, CRP&gt;54 on admission, D-dimer&gt;1300, and bilateral pulmonary consolidation on chest Computed Tomography (CT) were shown to be the main determinants for stratifying the risk for in-hospital death. The factors were finally considered for introducing a new predictive scoring system for COVID-19 related death. Conclusion: In-hospital mortality rate in patients with COVID-19 is estimated to be 17%. A new scoring system for predicting in-hospital mortality in such patients was structured based on determinant factors of advanced age, history of malignancy, cerebrovascular accident, tachypnea, raised CRP, raised D-dimer on admission, and bilateral pulmonary consolidation on chest CT scan.

In-hospital mortality after surgery: a retrospective cohort study in a Japanese university hospital.

BackgroundThe rapidly aging population affects Japan’s health system, which is characterized by equity and full health insurance coverage for the entire population. However, the current outcomes after surgery in tertiary hospitals in Japan are not known. We aimed to gain an overview of postoperative mortality and death in a tertiary university hospital.MethodsUsing the administrative database of Kawasaki Medical School Hospital, we investigated the pattern of in-hospital mortality and death for patients who underwent surgery under general or regional anesthesia between January 2010 and December 2011. We used a logistic regression model to find pre-operative risk factors associated with in-hospital mortality in this derivation cohort and tested its results in the validation cohort obtained from surgical patients between January 2012 and April 2014.ResultsAmong 8414 admissions for surgery patients aged ≥65 years was 41.0 %, reflecting aged population in Japan. There were 170 deaths in the derivation cohort, resulting in in-hospital mortality of 2.0 %, and in 30-day mortality of 1.0 %, because a half of the death occurred later than 30 days. We identified four independent preoperative risk factors for in-hospital mortality: high-risk surgery [odds ratio (OR) 18.64], moderate-risk surgery (OR 5.00), ASA-PS ≥3 (OR 5.55), and emergency (OR 2.35). A good correlation between actual and calculated mortality based on the derivation cohort was confirmed in the validation cohort.ConclusionsThis retrospective study of a single university hospital in Japan shows that aged patients in their 70 s is the largest group undergoing surgery, and that the overall in-hospital mortality is similar to that of other countries, but the 30-day mortality is less than that. Risk stratification for in-hospital mortality using preoperative factors was validated.

Trimethylamine N-oxide and prognosis in acute heart failure

ObjectiveAcute heart failure (AHF) is associated with high mortality and morbidity. Trimethylamine N-oxide (TMAO), a gut-derived metabolite, has reported association with mortality risk in chronic HF but this association in...

Validation of two US risk scores for percutaneous coronary intervention in a single-center Portuguese population of patients with acute coronary syndrome

IntroductionNew scores have been developed and validated in the US for in-hospital mortality risk stratification in patients undergoing coronary angioplasty: the National Cardiovascular Data Registry (NCDR) risk score and the Mayo Clinic Risk Score (MCRS). We sought to validate these scores in a European population with acute coronary syndrome (ACS) and to compare their predictive accuracy with that of the GRACE risk score. MethodsIn a single-center ACS registry of patients undergoing coronary angioplasty, we used the area under the receiver operating characteristic curve (AUC), a graphical representation of observed vs. expected mortality, and net reclassification improvement (NRI)/integrated discrimination improvement (IDI) analysis to compare the scores. ResultsA total of 2148 consecutive patients were included, mean age 63 years (SD 13), 74% male and 71% with ST-segment elevation ACS. In-hospital mortality was 4.5%. The GRACE score showed the best AUC (0.94, 95% CI 0.91–0.96) compared with NCDR (0.87, 95% CI 0.83–0.91, p=0.0003) and MCRS (0.85, 95% CI 0.81–0.90, p=0.0003). In model calibration analysis, GRACE showed the best predictive power. With GRACE, patients were more often correctly classified than with MCRS (NRI 78.7, 95% CI 59.6–97.7; IDI 0.136, 95% CI 0.073–0.199) or NCDR (NRI 79.2, 95% CI 60.2–98.2; IDI 0.148, 95% CI 0.087–0.209). ConclusionThe NCDR and Mayo Clinic risk scores are useful for risk stratification of in-hospital mortality in a European population of patients with ACS undergoing coronary angioplasty. However, the GRACE score is still to be preferred.

Validation of two US risk scores for percutaneous coronary intervention in a single-center Portuguese population of patients with acute coronary syndrome

IntroductionNew scores have been developed and validated in the US for in-hospital mortality risk stratification in patients undergoing coronary angioplasty: the National Cardiovascular Data Registry (NCDR) risk score and the Mayo Clinic Risk Score (MCRS). We sought to validate these scores in a European population with acute coronary syndrome (ACS) and to compare their predictive accuracy with that of the GRACE risk score. MethodsIn a single-center ACS registry of patients undergoing coronary angioplasty, we used the area under the receiver operating characteristic curve (AUC), a graphical representation of observed vs. expected mortality, and net reclassification improvement (NRI)/integrated discrimination improvement (IDI) analysis to compare the scores. ResultsA total of 2148 consecutive patients were included, mean age 63 years (SD 13), 74% male and 71% with ST-segment elevation ACS. In-hospital mortality was 4.5%. The GRACE score showed the best AUC (0.94, 95% CI 0.91–0.96) compared with NCDR (0.87, 95% CI 0.83–0.91, p=0.0003) and MCRS (0.85, 95% CI 0.81–0.90, p=0.0003). In model calibration analysis, GRACE showed the best predictive power. With GRACE, patients were more often correctly classified than with MCRS (NRI 78.7, 95% CI 59.6–97.7; IDI 0.136, 95% CI 0.073–0.199) or NCDR (NRI 79.2, 95% CI 60.2–98.2; IDI 0.148, 95% CI 0.087–0.209). ConclusionThe NCDR and Mayo Clinic risk scores are useful for risk stratification of in-hospital mortality in a European population of patients with ACS undergoing coronary angioplasty. However, the GRACE score is still to be preferred.

The predictive value of non-invasive cardiac output at rest in discharged heart failure patients

Risk stratification in patients suffering from chronic congestive heart failure (CHF) is based on a variety of clinical and laboratory variables. Indeed, several prognostic parameters have been identified, including age, New York Heart Association (NYHA) class, renal function, comorbidity such as atrial fibrillation, diabetes mellitus and ischemic heart disease [ [1] Swedberg K. Cleland J. Dargie H. et al. Guidelines for the diagnosis and treatment of chronic heart failure: executive summary (update 2005): the Task Force for the Diagnosis and Treatment of Chronic Heart Failure of the European Society of Cardiology. Eur. Heart J. 2005; 26: 1115-1140 Crossref PubMed Scopus (246) Google Scholar ]. In acute decompensated heart failure (ADHF) episodes, the degree of renal dysfunction and arterial hypotension easily stratified patients with worst clinical outcome [ [2] Fonarow G.C. Adams K.F. Abraham W.T. Yancy C.W. Boscardin W.J. for the ADHERE Scientific Advisory Committee Risk stratification for in-hospital mortality in acutely decompensated heart failure. JAMA. 2005; 293: 572-580 Crossref PubMed Scopus (1308) Google Scholar ]. A single determination of Brain Natriuretic Peptide (BNP) plasma level represents a reliable risk stratification procedure and its increase is considered a sensitive diagnostic marker of left ventricular dysfunction [ 3 Boland D.G. Abraham W.T. Natriuretic peptides in heart failure. Congest. Heart Fail. 1998; 4: 23-33 Google Scholar , 4 McDonagh T.A. Robb S.D. Murdoch D.R. et al. Biochemical detection of left-ventricular systolic dysfunction. Lancet. 1998; 351: 9-13 Abstract Full Text Full Text PDF PubMed Scopus (909) Google Scholar ] having a clear prognostic relevance in predicting cardiovascular events in congestive heart failure (CHF) patients [ 5 Maisel A.S. Krishnaswamy P. Nowak R.M. et al. Rapid measurement of B-type natriuretic peptide in the emergency diagnosis of heart failure. N. Engl. J. Med. 2002; 347: 161-167 Crossref PubMed Scopus (2785) Google Scholar , 6 Berger R. Huelsman M. Strecker K. et al. B-type natriuretic peptide predicts sudden death in patients with chronic heart failure. Circulation. 2002; 105: 2392-2397 Crossref PubMed Scopus (616) Google Scholar , 7 Feola M. Aspromonte N. Canali C. et al. Prognostic value of plasma brain natriuretic peptide, urea nitrogen and creatinine in outpatients >70 years of age with heart failure. Am. J. Cardiol. 2005; 96: 705-709 Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar ]. In discharged HF patients both right or left ventricular function proved to play a prognostic role [ 8 Kamath S.A. Drazner M.H. Tasissa G. Rogers J.G. Stevenson L.W. Yancy C.W. Correlation of impedance cardiography with invasive hemodynamic measurements in patients with advanced heart failure: the Bioimpedance CardioGraphy (BIG) Substudy of the ESCAPE Trial. Am. Heart J. 2009; 158: 217-223 Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar , 9 Zornoff L.A.M. Skali H. Pfeffer M.A. et al. Right ventricular dysfunction and risk of heart failure and mortality after myocardial infarction. J. Am. Coll. Cardiol. 2002; 39: 1450-1455 Abstract Full Text Full Text PDF PubMed Scopus (366) Google Scholar ]. In particular, in HF patients developed after an MI each reduction of 5% in right ventricular function correlated with a 16% increased in mortality [ [9] Zornoff L.A.M. Skali H. Pfeffer M.A. et al. Right ventricular dysfunction and risk of heart failure and mortality after myocardial infarction. J. Am. Coll. Cardiol. 2002; 39: 1450-1455 Abstract Full Text Full Text PDF PubMed Scopus (366) Google Scholar ]. In the substudy of the ESCAPE trial, the invasive measurement of pulmonary wedge pressure was significantly associated with death at 6-month follow-up [ [8] Kamath S.A. Drazner M.H. Tasissa G. Rogers J.G. Stevenson L.W. Yancy C.W. Correlation of impedance cardiography with invasive hemodynamic measurements in patients with advanced heart failure: the Bioimpedance CardioGraphy (BIG) Substudy of the ESCAPE Trial. Am. Heart J. 2009; 158: 217-223 Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar ].

Comments on the Risk Stratification for the In-Hospital Mortality in Subarachnoid Hemorrhage: The HAIR Score

The HAIR score was published in the August 2014 issue ofNeurocritical Care as a tool to enable risk stratification ofin-hospital mortality after subarachnoid hemorrhage (SAH)[1]. The scale features four variables: Hunt and Hess score,age, intraventricular hemorrhage, and re-bleeding thatcomprise a composite maximum score of 8. Several otherfactors, previously described as being associated with pooroutcome after SAH, were not considered in the HAIRscore, for example larger aneurysm size or the presence ofglobal cerebral edema on follow-up CT scan. In their ret-rospectively reviewed cohort of 400 patients, Lee et al.demonstrated increasing mortality rates with incrementalincreases in HAIR scores across the entire spectrum ofscores between 0 and 7.We sought to externally validate the HAIR score in ourcohort of SAH patients from the Columbia University SAHOutcomes Project (SHOP) registry. Data from 1,629 pa-tients were prospectively collected and adjudicated weeklyby the study team. Our findings show that the HAIR scoreadequately discriminates mortality rates between scores 0and 5, however, fails to discriminate between scores 6 and7 (Fig. 1a). The area under the ROC-curve was 0.90(Fig. 1b).Additionally, Lee et al’s study did not include any pa-tients with a HAIR score of 8, which, in our cohort,corresponded to a 100 % in-hospital mortality rate.In summary, analysis of our larger, prospectively col-lected cohort of patients with SAH confirmed the authors’

Risk stratification for in-hospital mortality after heart transplantation using the modification of diet in renal disease and the chronic kidney disease epidemiology collaboration equations for estimated glomerular filtration rate.

A new equation for estimating glomerular filtration rate (GFR)-the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation-is better at predicting outcomes in the general population than the Modification of Diet in Renal Disease (MDRD) equation. We compared risk stratification of heart transplant (HT) recipients for early post-HT mortality using estimated GFR from the MDRD and the CKD-EPI equations. We identified all patients 18 years or older who underwent their first HT in the United States between January 2007 and October 2010 (n=6,564). We compared risk stratification for posttransplant in-hospital mortality by GFR estimated by the CKD-EPI equation versus that estimated by the MDRD equation. Posttransplant in-hospital mortality was 4.6%. Lower GFR (mL/min/1.73 m2, MDRD) was associated with higher in-hospital mortality in adjusted analysis (GFR, 60-89; odds ratio [OR], 1.5; 95% confidence interval [CI], 1.0-2.3; GFR, 30-59; OR, 2.2; 95% CI, 1.4-3.3; GFR<30; OR, 3.3; 95% CI, 1.8-6.1; vs. GFR ≥ 90). Glomerular filtration rate estimated using the CKD-EPI equation reclassified 10%, 15%, and 18% of patients, respectively, in GFR categories lower than 30, 30-59, and 60-89 to the next higher GFR category. Using the CKD-EPI equation was not associated with a significant net reclassification improvement for mortality risk in the overall cohort or in GFR subgroups. The risk models of in-hospital mortality developed using the two GFR equations performed similarly for discrimination and calibration. Estimated GFR using the CKD-EPI equation is comparable to estimated GFR using the MDRD equation in risk stratification of HT recipients for early posttransplant mortality.

Risk stratification for in-hospital mortality after cardiac surgery: external validation of EuroSCORE II in a prospective regional registry

To evaluate performance of the European System for Cardiac Operation Risk Evaluation (EuroSCORE II), to assess the influence of model updating and to derive a hierarchical tree for modelling the relationship between EuroSCORE II risk factors and hospital mortality after cardiac surgery in a large prospective contemporary cohort of consecutive adult patients. Data on consecutive patients, who underwent on-pump cardiac surgery or off-pump coronary artery bypass graft intervention, were retrieved from Puglia Adult Cardiac Surgery Registry. Discrimination, calibration, re-estimation of EuroSCORE II coefficients and hierarchical tree analysis of risk factors were assessed. Out 6293 procedures, 6191 (98.4%) had complete data for EuroSCORE II assessment with a hospital mortality rate of 4.85% and EuroSCORE II of 4.40 ± 7.04%. The area under the receiver operator characteristic curve (0.830) showed good discriminative ability of EuroSCORE II in distinguishing patients who died and those who survived. Calibration of EuroSCORE II was preserved with lower predicted than observed risk in the highest EuroSCORE II deciles. At logistic regression analysis, the complete revision of the model had most of re-estimated regression coefficients not statistically different from those in the original EuroSCORE II model. When missing values were replaced with the mean EuroSCORE II value according to urgency and weight of intervention, the risk score confirmed discrimination and calibration obtained over the entire sample. A recursive tree-building algorithm of EuroSCORE II variables identified three large groups (55.1, 17.1 and 18.1% of procedures) with low-to-moderate risk (observed mortality of 1.5, 3.2 and 6.4%) and two groups (3.8 and 5.9% of procedures) at high risk (mortality of 14.6 and 32.2%). Patients with low-to-moderate risk had good agreement between observed events and predicted frequencies by EuroSCORE II, whereas those at greater risk showed an underestimation of expected mortality. This study demonstrates that EuroSCORE II is a good predictor of hospital mortality after cardiac surgery in an external validation cohort of contemporary patients from a multicentre prospective regional registry. The EuroSCORE II predicts hospital mortality with a slight underestimation in high-risk patients that should be further and better evaluated. The EuroSCORE II variables as a risk tree provides clinicians and surgeons a practical bedside tool for mortality risk stratification of patients at low, intermediate and high risk for hospital mortality after cardiac surgery.

Relation of Baseline Systolic Blood Pressure and Long-Term Outcomes in Ambulatory Patients With Chronic Mild to Moderate Heart Failure

We studied the impact of baseline systolic blood pressure (SBP) on outcomes in patients with mild to moderate chronic systolic and diastolic heart failure (HF) in the Digitalis Investigation Group trial using a propensity-matched design. Of 7,788 patients, 7,785 had baseline SBP data and 3,538 had SBP ≤ 120 mm Hg. Propensity scores for SBP ≤ 120 mm Hg, calculated for each of the 7,785 patients, were used to assemble a matched cohort of 3,738 patients with SBP ≤ 120 and >120 mm Hg who were well-balanced in 32 baseline characteristics. All-cause mortality occurred in 35% and 32% of matched patients with SBPs ≤ 120 and >120 mm Hg respectively, during 5 years of follow-up (hazard ratio [HR] when SBP ≤ 120 was compared to >120 mm Hg 1.10, 95% confidence interval [CI] 0.99 to 1.23, p = 0.088). HRs for cardiovascular and HF mortalities associated with SBP ≤ 120 mm Hg were 1.15 (95% CI 1.01 to 1.30, p = 0.031) and 1.30 (95% CI 1.08 to 1.57, p = 0.006). Cardiovascular hospitalization occurred in 53% and 49% of matched patients with SBPs ≤ 120 and > 120 mm Hg, respectively (HR 1.13, 95% CI 1.03 to 1.24, p = 0.008). HRs for all-cause and HF hospitalizations associated with SBP ≤ 120 mm Hg were 1.10 (95% CI 1.02 to 1.194, p = 0.017) and 1.21 (95% CI 1.07 to 1.36, p = 0.002). In conclusion, in patients with mild to moderate long-term systolic and diastolic HF, baseline SBP ≤ 120 mm Hg was associated with increased cardiovascular and HF mortalities and all-cause, cardiovascular, and HF hospitalizations that was independent of other baseline characteristics.

Tree-structured risk stratification of in-hospital mortality after percutaneous coronary intervention for acute myocardial infarction: A report from the New York State percutaneous coronary intervention database

Previous risk scores have shown excellent performance. However, the need for real-time risk score computation makes their implementation in an emergent situation challenging. A more simplified approach can provide practitioners with a practical bedside risk stratification tool. We developed an easy-to-use tree-structured risk stratification model for patients undergoing early percutaneous coronary intervention (PCI) for acute myocardial infarction. The model was developed on the New York State PCI database for 1999 to 2000 (consisting of 5385 procedures) and was validated using the subsequent 2001 to 2002 database (consisting of 7414 procedures). Tree-structured modeling identified 3 key presenting features: cardiogenic shock, congestive heart failure, and age. In the validation data set, this risk stratification model identified patient groups with in-hospital mortality ranging from 0.5% to 20.6%, more than a 20-fold increased risk. The performance of this model was similar to the Mayo Clinic Risk Score with a discriminative capacity of 82% (95% confidence interval, 79%-84%) versus 80% (95% confidence interval, 77%-82%), respectively. Patients undergoing PCI for acute myocardial infarction can be readily stratified into risk categories using the tree-structured model. This provides practicing cardiologists with an internally validated and easy-to-use scheme for in-hospital mortality risk stratification.

Risk stratification for in-hospital mortality in spontaneous intracerebral haemorrhage: A Classification and Regression Tree Analysis

Risk stratification for mortality in intracerebral haemorrhage (ICH) helps guide care, but existing clinical prediction rules are too cumbersome for clinical practice because of their complexity. To develop a simple decision tree model of in-hospital mortality risk stratification for ICH patients. We collected information on spontaneous ICH patients hospitalized in a teaching hospital in Japan from August, 1998 to December, 2001 (n = 374). All variables were abstracted from data available at the time of initial evaluation. A prediction rule for in-hospital mortality was developed by the Classification and Regression Tree (CART) methodology. The accuracy of the model was evaluated using the area under receiver-operator characteristic curve. Overall in-hospital mortality rate was 20.2%. The CART methodology identified four groups for mortality risk, varying from low (2.1%) to high (58.9%). Level of consciousness (coma) was the best single predictor for mortality, followed by high ICH volume (cut-off 10.4 ml), and then age (cut-off 75 years). The accuracy of our CART model (0.86) exceeded that of a multivariate logistic regression model (0.81). ICH patients can easily be stratified for mortality risk, based on three predictors available on admission. This simple decision tree model provides clinicians with a reliable and practical tool.

Risk Stratification of In-Hospital Mortality for Coronary Artery Bypass Graft Surgery

The purpose of this research was to develop a risk index for in-hospital mortality for coronary artery bypass graft (CABG) surgery. Risk indexes for CABG surgery are used to assess patients' operative risk as well as to profile hospitals and surgeons. None has been developed using data from a population-based region in the U.S. for many years. Data from New York's Cardiac Surgery Reporting System in 2002 were used to develop a statistical model that predicts mortality and to create a risk index based on a relatively small number of patient risk factors. The fit of the index was tested by applying it to another year (2003) of New York data and testing the correspondence of expected and observed mortality rates for each risk score in the index. The risk index contains a total of 10 risk factors (age, female gender, hemodynamic state, ejection fraction, pre-procedural myocardial infarction, chronic obstructive pulmonary disease, calcified ascending aorta, peripheral arterial disease, renal failure, and previous open heart operations). The score possible for each variable ranges from 0 to 5, and total risk scores possible range from 0 to 34. The highest score observed for any patient was 22, and 93% of the patients had scores of 8 or lower. When the risk index was applied to another year of New York data with a considerably lower mortality rate, the C-statistic was 0.782. The risk index appears to be a valuable tool for predicting patient risk when applied to another year of New York data. It should now be tested against other risk indexes in a variety of geographical regions.

A risk stratification tool predicted in-hospital mortality in acute decompensated heart failure

Source Citation Fonarow GC, Adams KF Jr., Abraham WT, Yancy CW, Boscardin WJ. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tre...