Risk Score For In-hospital Mortality Research Articles

AB2CO risk score for in-hospital mortality of COVID-19 patients admitted to intensive care units

PurposeTo develop a mortality risk score for COVID-19 patients admitted to intensive care units (ICU), and to compare it with other existing scores. Materials and methodsThis retrospective observational study included consecutive adult patients with laboratory-confirmed COVID-19 admitted to ICUs of 18 hospitals from nine Brazilian cities, from September 2021 to July 2022. Potential predictors were selected based on the literature review. Generalized Additive Models were used to examine outcomes and predictors. LASSO regression was used to derive the mortality score. ResultsFrom 558 patients, median age was 69 years (IQR 58–78), 56.3 % were men, 19.7 % required mechanical ventilation (MV), and 44.8 % died. The final model comprised six variables: age, pO2/FiO2, respiratory function (respiratory rate or if in MV), chronic obstructive pulmonary disease, and obesity. The AB2CO had an AUROC of 0.781 (95 % CI 0.744 to 0.819), good overall performance (Brier score = 0.191) and an excellent calibration (slope = 1.063, intercept = 0.015, p-value = 0.834). The model was compared with other scores and displayed better discrimination ability than the majority of them. ConclusionsThe AB2CO score is a fast and easy tool to be used upon ICU admission.

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.

Delirium and IL-6 added to clinical scores improves their performance: a prospective analysis of CALL, PREDI-CO, MRS score applied to a population of patients admitted to internal medicine ward

This study aimed to evaluate the effectiveness of various scoring systems in predicting in-hospital mortality for COVID-19 patients admitted to the internal medicine ward. We conducted a prospective collection of clinical data from patients admitted to the Internal Medicine Unit at Santa Maria Nuova Hospital in Florence, Italy, with confirmed pneumonia caused by SARS-CoV-2. We calculated three scoring systems: the CALL score, the PREDI-CO score, and the COVID-19 in-hospital Mortality Risk Score (COVID-19 MRS). The primary endpoint was in-hospital mortality. : A total of 681 patients were enrolled in the study, with a mean age of 68.8 ± 16.1 years, and 54.8% of them were male. Non-survivors had significantly higher scores in all prognostic systems compared to survivors (MRS: 13 [12- 15] vs. 10 [8-12]; CALL: 12 [10-12] vs. 9 [7-11]; PREDI-CO: 4 [3-6] vs. 2 [1-4]; all p<0.001). The receiver operating characteristic (ROC) analysis yielded the following area under the curve (AUC) values: MRS 0.85, CALL 0.78, PREDI-CO 0.77. The addition of Delirium and IL6 to the scoring systems improved their discriminative ability, resulting in AUC values of 0.92 for MRS, 0.87 for CALL, and 0.84 for PREDI-CO. The mortality rate increased significantly across increasing quartiles (p<0.001). In conclusion the COVID-19 in-hospital Mortality Risk Score (MRS) demonstrated reasonable prognostic stratification for patients admitted to the internal medicine ward with SARS-CoV-2-induced pneumonia. The inclusion of Delirium and IL6 as additional prognostic indicators in the scoring systems enhanced their predictive performance, specifically in determining in-hospital mortality among COVID-19 patients.

Development and internal validation of a clinical risk score for in-hospital mortality after stroke: a single-centre retrospective cohort study in Northwest Ethiopia

ObjectiveTo develop and validate a clinical risk score for in-hospital stroke mortality.DesignThe study used a retrospective cohort study design.SettingThe study was carried out in a tertiary hospital in the Northwest...

Development and validation of COVID-19 Radiological Risk Score (COVID-RRS): a multivariable radiological score to estimate the in-hospital mortality risk in COVID-19 patients.

To develop and validate in-hospital mortality risk score comprising radiological aberrances in chest computed tomography (CT) performed on admission. Single-center, longitudinal cohort study in adult patients admitted with Coronavirus Disease 2019 (COVID-19) to our ward. Patients were followed-up during hospitalization until discharge or death. Eligibility criteria for the study comprised positive real-time reverse transcription-polymerase chain reaction test (RT-PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and ground-glass opacities in chest CT. In-hospital death was the outcome of interest. Radiological, laboratory, and clinical data were analyzed. Radiological determinants of mortality were used as variables in multivariate logistic regression analysis, and results were used to build a radiological risk score. 371 patients were enrolled in development and validation cohorts (181 and 190 respectively), with a total of 47 non-survivors. Univariate analysis data determined 12 predictive factors (nine risk and three protective). In multivariate analysis, we developed COVID-RRS (COVID-19 Radiological Risk Score) - a radiological score predicting in-hospital COVID-19 mortality risk comprising estimated lung involvement percentage, pleural effusion, and domination of consolidation-type changes in chest CT. Our score was superior in the prediction of COVID-19 mortality to the percentage of lung involvement alone, Chest Computed Tomography Severity Score (CTSS), and Total Severity Score (TSS) in both groups with AUC of 0.910 and 0.902, respectively (p <0.001). Additional imaging features independently contribute to COVID-19 mortality risk. Our model comprising lung involvement estimation, pleural effusion, and domination of consolidations performed significantly better than scores based on the extent of the changes alone. COVID-RRS is a simple, reliable, and ready-to-use tool for clinical practice.

Predictive Model and Risk Score for In-Hospital Mortality in Patients with All-Cause Cardiogenic Shock.

Cardiogenic shock (CS) is a condition associated with high morbidity and mortality. Our study aimed to perform a risk score for in-hospital mortality that allows for stratifying the risk of death in patients with CS.This is a retrospective analysis, which included 135 patients from a Spanish university hospital between 2011 and 2020. The Santiago Shock Score (S3) was created using clinical, analytical, and echocardiographic variables obtained at the time of admission.The in-hospital mortality rate was 41.5%, and acute coronary syndrome (ACS) was the responsible cause of shock in 60.7% of patients. Mitral regurgitation grade III-IV, age, ACS etiology, NT-proBNP, blood hemoglobin, and lactate at admission were included in the score. The S3 had good accuracy for predicting in-hospital mortality area under the receiver operating characteristic curve (AUC) 0.85 (95% confidence interval (CI) 0.78-0.90), higher than the AUC of the CardShock score, which was 0.74 (95% CI 0.66-0.83). Predictive power in a cohort of 131 patients with profound CS was similar to that of CardShock with an AUC of 0.601 (95% CI 0.496-0.706) versus an AUC of 0.558 (95% CI 0.453-0.664). Three risk categories were created according to the S3: low (scores 0-6), intermediate (scores 7-10), and high (scores 11-16) risks, with an observed mortality of 12.9%, 49.1%, and 87.5% respectively (P < 0.001).The S3 score had excellent predictive power for in-hospital mortality in patients with nonprofound CS. It could aid the initial risk stratification of patients and thus, guide treatment and clinical decision making in patients with CS.

Predicting in-hospital mortality in pulmonary embolism patients: development and external validation of the PATHOS score.

According to the 2019 European Society of Cardiology (ESC) guidelines on pulmonary embolism (PE), prognosis is calculated using the Pulmonary Embolism Severity Index (PESI), a complex score with debated validity, or simplified PESI (sPESI). We have developed and validated a new risk score for in-hospital mortality (IHM) of patients with PE in the emergency department. This retrospective, dual-center cohort study was conducted in the emergency departments of two third-level university hospitals. Patients aged >18 years with a contrast-enhanced computed tomography-confirmed PE were included. Clinical variables and laboratory tests were evaluated blindly to IHM. Multivariable logistic regression was performed to identify the new score's predictors, and the new score was compared with the PESI, sPESI, and shock index. A total of 1,358 patients were included in this study: 586 in the derivation cohort and 772 in the validation cohort, with a global 10.6% of IHM. The PATHOS scores were developed using independent variables to predict mortality: platelet count, age, troponin, heart rate, oxygenation, and systolic blood pressure. The PATHOS score showed good calibration and high discrimination, with an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [CI], 0.77-0.89) in the derivation population and 0.74 (95% CI, 0.68-0.80) in the validation cohort, which is significantly higher than the PESI, sPESI, and shock index in both cohorts (P<0.01 for all comparisons). PATHOS is a simple and effective prognostic score for predicting IHM in patients with PE in an emergency setting.

Abstract 11505: Predicting In-Hospital All-Cause Mortality in Chronic Total Occlusion Percutaneous Coronary Intervention: PROGRESS-CTO Mortality Score

Introduction: Chronic total occlusion (CTO) percutaneous coronary intervention (PCI) is associated with increased risk of periprocedural complications. Estimating the risk of complications facilitates risk-benefit assessment and procedural planning. Methods: We analyzed the Prospective Global Registry for the Study of Chronic Total Occlusion Intervention (PROGRESS-CTO, NCT02061436) and created a risk score for in-hospital all-cause mortality. Logistic regression prediction modeling was used to identify independently associated variables and the model was internally validated with bootstrapping. Results: Of the 10,480 CTO PCI cases performed between 2012-2022 at 40 US and non-US centers, in-hospital mortality occurred in 47 (0.45%). The final prediction model identified 4 independent predictors of mortality: Age ≥65 years, Odds Ratio [OR]: 2.83 (95% confidence interval [CI], 1.30-6.16), 1-point; Left ventricular ejection fraction ≤45%, OR: 2.13 (95% CI, 1.14-3.98), 1-point; Moderate-severe calcification, OR: 2.45 (95% CI, 1.18-5.08), 1-point; and Antegrade dissection re-entry, OR: 2.81 (95% CI, 1.06-7.43), 1-point; or Retrograde strategy, OR: 3.51 (95% CI, 1.67-7.37), 1-point; with a bootstrap corrected c-statistics of 0.71 (95% confidence interval: 0.63-0.81). The calculated risk percentages for mortality based on the PROGRESS-CTO mortality score ranged from 0.05-2.42% for mortality, and 63% of patients in the PROGRESS-CTO cohort had PROGRESS-CTO mortality score of 1 or 2, corresponding to a mortality risk of 0.1-0.5% (Figure 1). Conclusions: The PROGRESS-CTO in-hospital mortality risk score can facilitate risk-benefit assessment and procedural planning in patients undergoing CTO PCI.

Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study.

Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach to identifying a prediction model with a good discriminatory capacity to be easily used in clinical practice. The aim of this study was to obtain a risk score for in-hospital mortality in patients with coronavirus disease infection (COVID-19) based on a limited number of features collected at hospital admission. We studied an Italian cohort of consecutive adult Caucasian patients with laboratory-confirmed COVID-19 who were hospitalized in 13 cardiology units during Spring 2020. The Lasso procedure was used to select the most relevant covariates. The dataset was randomly divided into a training set containing 80% of the data, used for estimating the model, and a test set with the remaining 20%. A Random Forest modeled in-hospital mortality with the selected set of covariates: its accuracy was measured by means of the ROC curve, obtaining AUC, sensitivity, specificity and related 95% confidence interval (CI). This model was then compared with the one obtained by the Gradient Boosting Machine (GBM) and with logistic regression. Finally, to understand if each model has the same performance in the training and test set, the two AUCs were compared using the DeLong's test. Among 701 patients enrolled (mean age 67.2 ± 13.2 years, 69.5% male individuals), 165 (23.5%) died during a median hospitalization of 15 (IQR, 9-24) days. Variables selected by the Lasso procedure were: age, oxygen saturation, PaO2/FiO2, creatinine clearance and elevated troponin. Compared with those who survived, deceased patients were older, had a lower blood oxygenation, lower creatinine clearance levels and higher prevalence of elevated troponin (all P < 0.001). The best performance out of the samples was provided by Random Forest with an AUC of 0.78 (95% CI: 0.68-0.88) and a sensitivity of 0.88 (95% CI: 0.58-1.00). Moreover, Random Forest was the unique model that provided similar performance in sample and out of sample (DeLong test P = 0.78). In a large COVID-19 population, we showed that a customizable machine learning-based score derived from clinical variables is feasible and effective for the prediction of in-hospital mortality.

Establishment of a novel risk score for in-hospital mortality in adult sepsis patients.

BackgroundExisting scoring systems have limitations in predicting the in-hospital mortality of adult sepsis patients. We aimed to develop and validate a novel risk score for predicting the in-hospital mortality of adult sepsis patients.MethodsThe clinical data of 1,335 adult sepsis inpatients were retrospectively analyzed. Enrolled patients were randomly divided into a modeling group and a validation group at a 3:2 ratio. The modeling group (n=801) was used to develop the risk score by univariate and multivariate logistic regression analyses. The score’s performance was validated in the validation group (n=534). We classified patients into four risk levels according to the novel risk score.ResultsAge, central vein catheterization, mechanical ventilation, vasopressin, Charlson comorbidity index (CCI), respiratory rate (RR), heart rate (HR), Glasgow coma scale (GCS) score, platelet (PLT), hematocrit (HCT), aspartate aminotransferase (AST), and activated partial thrombin time (APTT) were independent risk factors for in-hospital death in adult sepsis patients. Continuous variables were converted into classified variables to develop the risk score, with a total score of 39 points. Adult sepsis patients with low, lower medium, higher medium, and high risk levels had in-hospital mortality rates of 9.8%, 24.7%, 55.8%, and 83.5%, respectively.ConclusionsCompared with the Acute Physiology and Chronic Health Evaluation II scoring system (APACHE II) and the Modified Early Warning Score (MEWS), the novel risk score showed good predictive performance for in-hospital mortality in adult sepsis patients.

ST yükselmesiz miyokard enfarktüslü yaşlı hastalarda hastane içi mortaliteyi öngörmek için yüksek duyarlı modifiye Glasgow prognostik skoru

Aim: Inflammation and malnutrition are poor prognostic markers in acute coronary syndromes. In this study, we aimed to investigate the association between high sensitive modified Glasgow prognostic score (HS-mGPS), derived from C-reactive protein and serum albumin levels, and in-hospital mortality of elderly patients with non-ST elevation myocardial infarction (NSTEMI).&#x0D; Material and Methods: Included subjects were recruited from three different tertiary health centers. Totally, 282 eligible patients aged &gt;65 years with diagnosis of NSTEMI were retrospectively enrolled. Global Registry of Acute Coronary Events (GRACE) risk score for in-hospital mortality and HS-mGPS was calculated for each patient. Subjects were categorized according to their inflammation-based scores ((high HS-mGPS group (HS-mGPS ≥1, n=124) vs. low HS-mGPS group (HS-mGPS =0, n=158)). &#x0D; Results: Both groups were similar regarding admission blood pressure levels, coronary angiography findings, treatment modalities and GRACE scores. Patients with high HS-mGPS had higher admission heart rate and longer hospitalization duration compared to low HS-mGPS group. In-hospital mortality rates were higher in high HS-mGPS group compared to low HS-mGPS group (21.8% (n=27) vs. 3.2% (n=5), respectively, P

An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study.

IntroductionIdentifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.Material and methodsWe conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.Results1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality.ConclusionsMachine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

Development and validation of the ABC2-SPH risk score for in-hospital mortality in COVID-19 patients

Abstract Introduction Fast and efficient assessment of prognosis of coronavirus disease 19 (COVID-19) is needed to optimize the allocation of health care and human resources, to empower early identification and intervention of patients at higher risk of poor outcome. A proper assessment tool may guide decision making, to develop an appropriate plan of care for each patient. Although different scores have been proposed, the majority of them are limited due to high risk of bias, and there is a lack of reliable prognostic prediction models. Purpose To develop and validate an easy applicable rapid scoring system that employs routinely available clinical and laboratory data at hospital presentation, to predict in-hospital mortality in patients with COVID-19, able to discriminate high vs non-high risk patients. Additionally, we aimed to compare this score with other existing ones. Method Cohort study, conducted in 36 Brazilian hospitals in 17 cities. Consecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Primary outcome was in-hospital mortality. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Results Median (25th-75th percentile) age of the model-derivation cohort was 60 (48–72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (&amp;gt;25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator. Conclusions We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Minas Gerais State Agency for Research and Development (Fundação de Amparo à Pesquisa do Estado de Minas Gerais - FAPEMIG) [grant number APQ-00208-20], National Institute of Science and Technology for Health Technology Assessment (Instituto de Avaliação de Tecnologias em Saúde – IATS)/ National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnolόgico - CNPq) [grant number 465518/2014-1]

The risk score for in-hospital mortality in patients with ST-segment elevation myocardial infarction

Aim To develop a scale (score system) for predicting the individual risk of in-hospital death in patients with ST segment elevation acute myocardial infarction (STEMI) with an account of results of percutaneous coronary intervention (PCI).Material and methods The analysis used data of 1 649 sequential patients with STEMI included into the hospital registry of PCI from 2006 through 2017. To test the model predictability, the original sample was divided into two groups: a training group consisting of 1150 (70 %) patients and a test group consisting of 499 (30 %) patients. The training sample was used for computing an individual score. To this purpose, β-coefficients of each variable obtained at the last stage of the multivariate logistic regression model were subjected to linear transformation. The scale was verified using the test sample.Results Seven independent predictors of in-hospital death were determined: age ≥65 years, acute heart failure (Killip class III-IV), total myocardial ischemia time ≥180 min, anterior localization of myocardial infarction, failure of PCI, SYNTAX scale score ≥16, glycemia on admission ≥7.78 mmol/l for patients without a history of diabetes mellitus and ≥14.35 mmol/l for patients with a history of diabetes mellitus. The contribution of each value to the risk of in-hospital death was ranked from 0 to 7. A threshold total score of 10 was determined; a score ≥10 corresponded to a high probability of in-hospital death (18.2 %). In the training sample, the sensitivity was 81 %, the specificity was 80.6 %, and the area under the curve (AUC) was 0.902. In the test sample, the sensitivity was 96.2 %, the specificity was 83.3 %, and the AUC was 0.924.Conclusion The developed scale has a good predictive accuracy in identifying patients with acute STEMI who have a high risk of fatal outcome at the hospital stage.

From Other Journals: A Review of Recent Articles by Our Editorial Team.

In this review we provide a brief description of recently published articles addressing topics relevant to pediatric cardiologists. Our hope is to provide a summary of the latest articles published recently in other journals in our field. The articles address (1) a summary of a scientific statement of the American Heart Association for diagnosis and treatment of myocarditis, (2) development of a perioperative risk score for in-hospital mortality after cardiac surgery in adults with congenital heart disease, (3) using a machine learning algorithm to predict cardiopulmonary deterioration in patients in the interstage period 1–2 h in advance using hospital monitor generated data, (4) risk factors for reoperation after the arterial switch operation, (5) the effect of mitochondrial transplantation for cardiogenic shock in pediatric patients, (6) comparing outcomes of primary or staged repair in tetralogy of Fallot with pulmonary atresia.

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

ObjectivesThe majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. MethodsConsecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. ResultsMedian (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). ConclusionsAn easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.

Clinical risk score to predict in-hospital mortality in COVID-19 patients: a retrospective cohort study.

ObjectivesSeveral physiological abnormalities that develop during COVID-19 are associated with increased mortality. In the present study, we aimed to develop a clinical risk score to predict the in-hospital mortality in COVID-19 patients, based on a set of variables available soon after the hospitalisation triage.SettingRetrospective cohort study of 516 patients consecutively admitted for COVID-19 to two Italian tertiary hospitals located in Northern and Central Italy were collected from 22 February 2020 (date of first admission) to 10 April 2020.ParticipantsConsecutive patients≥18 years admitted for COVID-19.Main outcome measuresSimple clinical and laboratory findings readily available after triage were compared by patients’ survival status (‘dead’ vs ‘alive’), with the objective of identifying baseline variables associated with mortality. These were used to build a COVID-19 in-hospital mortality risk score (COVID-19MRS).ResultsMean age was 67±13 years (mean±SD), and 66.9% were male. Using Cox regression analysis, tertiles of increasing age (≥75, upper vs <62 years, lower: HR 7.92; p<0.001) and number of chronic diseases (≥4 vs 0–1: HR 2.09; p=0.007), respiratory rate (HR 1.04 per unit increase; p=0.001), PaO2/FiO2 (HR 0.995 per unit increase; p<0.001), serum creatinine (HR 1.34 per unit increase; p<0.001) and platelet count (HR 0.995 per unit increase; p=0.001) were predictors of mortality. All six predictors were used to build the COVID-19MRS (Area Under the Curve 0.90, 95% CI 0.87 to 0.93), which proved to be highly accurate in stratifying patients at low, intermediate and high risk of in-hospital death (p<0.001).ConclusionsThe COVID-19MRS is a rapid, operator-independent and inexpensive clinical tool that objectively predicts mortality in patients with COVID-19. The score could be helpful from triage to guide earlier assignment of COVID-19 patients to the most appropriate level of care.

P845Comparison of the performance of the five validated risk scores in acute myocardial infarction patients undergoing primary PCI

Abstract Background Several risk scores have been developed to predict mortality of patients with acute myocardial infarction (AMI) undergoing primary percutaneous coronary intervention (pPCI), with limited data on the comparative prognostic value of these models. Purpose We aimed to compare the prognostic value of five validated risk scores for in-hospital and one-year mortality of patients with AMI undergoing pPCI. ume catheterization laboratory in a period from January 2009 to December 2017, a total of 3868 consecutive patients who underwent pPCI were available for analysis. For each patient, the Thrombolysis In Myocardial Infarction (TIMI), Controlled Abciximab and Device Investigation to Lower Late Angioplasty complications (CADILLAC), ACTION Registry-GWTG in-hospital mortality risk score (ACTION), Age, Creatinine, and Ejection Fraction (ACEF), and ZWOLLE risk scores were calculated using required clinical and angiographic characteristics. In-hospital and one-year mortality were assessed (follow-up available for 92% of pts). Calibration and discrimination of the three risk models were evaluated by the Hosmer-Lemeshow (H-L) goodness-of-fit test and C-statistic, respectively. Results Mortality rates for in-hospital and one-year mortality were 1.8% and 6.9% respectively. All five scores showed good model calibration as assessed by the H-L test and very good discriminative power for in-hospital and one-year mortality as assessed by C-statistics (Table 1 &amp; Figure 1): Table 1 Risk score H-L H-L p AUC in-hospital 95% CI Significant p AUC one-year 95% CI Significant p ZWOLLE 1.3 0.7 0.90 0.89–0.91 vs. CADILLAC &lt;0.05 0.75 0.74–0.77 vs. TIMI &lt;0.005 ACTION 13.1 0.1 0.87 0.86–0.88 vs. TIMI &lt;0.005 0.79 0.77–0.80 CADILLAC 5.5 0.2 0.85 0.84–0.86 vs. TIMI &lt;0.01 0.81 0.80–0.83 vs. ZWOLLE &lt;0.000 vs. TIMI &lt;0.000 ACEF 9.9 0.3 0.814 0.83–0.85 0.80 0.78–0.81 vs. ZWOLLE &lt;0.000 vs. TIMI &lt;0.05 TIMI 7.1 0.3 0.79 0.78–0.80 0.76 0.75–0.78 Figure 1 Conclusion Risk stratification of patients with AMI undergoing pPCI using the ZWOLLE, ACTION, CADILLAC, ACEF or TIMI risk scores enables accurate identification of high-risk patients for in-hospital and one-year mortality in an all-comers population. Among evaluated scores, ZWOLLE model was better fitted for prediction of in-hospital mortality while CADILLAC and ACEF better predicted late events.

Prognostic value of TIMI and GRACE risk scores for in-hospital mortality in Chinese patients with non-ST-segment elevation myocardial infarction

Objective: To evaluate the prognostic value of the thrombolysis in myocardial infarction (TIMI) and global registry of acute coronary events (GRACE) risk scores for in-hospital mortality in Chinese non-ST-segment elevation myocardial infarction (NSTEMI) patients. Methods: Data of present study derived from the prospective, multi-center registry trial of Chinese AMI (CAMI). Among 31 provinces, municipalities or autonomous districts in China, at least one tertiary and secondary hospital was selected. From January 2013 to September 2014, 5 896 consecutive non-ST-segment elevation myocardial infarction patients who were admitted to 107 hospitals within 7 days of symptom onset were enrolled. For each patient, TIMI and GRACE risk scores were calculated using specific variables collected at admission. Their prognostic value was evaluated by the endpoint of in-hospital mortality. Results: Among 5 896 NSTEMI patients (age was (65.4±12.1) years old), 68.2% (n=4 020) were males. The in-hospital mortality was 6.0% (n=353) and the median length of hospital stay was 10.0 (7.0, 13.0) days. The incidence of pre-hospital cardiac arrest was 3.6% (n=213) among 5 896 NSTEMI patients. Six hundreds and forty five patients (10.9%) received primary percutaneous coronary intervention, and 6 patients underwent emergent coronary artery bypass grafting surgery (0.1%), and the median time of reperfusion was 529.5 (256.0, 1 065.0) minutes. The prescription percentage of statins, β-blocker, angiotensin converting enzyme inhibitors or angiotensin Ⅱ receptor blockers, and aldosterone antagonists were 94.8% (n=5 587), 71.7% (n=4 228), 65.5% (n=3 864) and 26.0% (n=1 533) respectively. The area under the curve of GRACE risk score for in-hospital mortality (0.7930 (95%CI 0.767-0.818)) was better than that of TIMI risk score (0.5588 (95%CI 0.532-0.586), P<0.001). Conclusion: GRACE risk score demonstrates better predictive accuracy than TIMI risk score for in-hospital mortality in NSTEMI patients in this patient cohort.

Outcomes and Mortality Prediction Model of Critically Ill Adults With Acute Respiratory Failure and Interstitial Lung Disease

We aimed to examine short- and long-term mortality in a mixed population of patients with interstitial lung disease (ILD) with acute respiratory failure, and to identify those at lower vshigher risk of in-hospital death. We conducted a single-center retrospective cohort study of 126 consecutive adults with ILD admitted to an ICU for respiratory failure at a tertiary care hospital between 2010 and 2014 and who did not undergo lung transplantation during their hospitalization. We examined associations of ICU-day 1 characteristics with in-hospital and 1-year mortality, using Poisson regression, and examined survival using Kaplan-Meier curves. We created a risk score for in-hospital mortality, using a model developed with penalized regression. In-hospital mortality was 66%, and 1-year mortality was 80%. Those with connective tissue disease-related ILD had better short-term and long-term mortality compared with unclassifiable ILD (adjusted relative risk, 0.6; 95%CI, 0.3-0.9; and relative risk, 0.6; 95%CI, 0.4-0.9, respectively). Our prediction model includes male sex, interstitial pulmonary fibrosis diagnosis, use of invasive mechanical ventilation and/or extracorporeal life support, no ambulation within 24h of ICU admission, BMI, and Simplified Acute Physiology Score-II. The optimism-corrected C-statistic was 0.73, and model calibration was excellent (P= .99). In-hospital mortality rates for the low-, moderate-, and high-risk groups were 33%, 65%, and 96%, respectively. We created a risk score that classifies patients with ILD with acute respiratory failure from low to high risk for in-hospital mortality. The score could aid providers in counseling these patients and their families.