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Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves.

ABSTRACT: Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were used to retrieve data. Patients and clinical characteristics of patients with available FIB-4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive mortality models during the four waves of the COVID-19 pandemic. A logistic regression model was applied to the training and test set (75%:25%). The model's performance was assessed by receiver operating characteristic (ROC) curves. A total of 4936 patients were included. Hypertension (38.4%), cancer (12.15%) and diabetes (16.3%) were the most common comorbidities. 23.9% of patients were admitted to ICU, and 12.6% had mechanical ventilation. During the study period, 762 patients (15.4%) died. We developed a multivariable logistic regression model on patient data from all waves, which showed that the FIB-4 score > 2.53 was associated with increased mortality risk (OR = 4.53, 95% CI 2.83-7.25; p ≤ 0.001). These data may be useful in the risk stratification at the admission of hospitalized patients with COVID-19.

SUBMITTER: Miele L

PROVIDER: S-EPMC10412472 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves.

Miele Luca L Dajko Marianxhela M Savino Maria Chiara MC Capocchiano Nicola D ND Calvez Valentino V Calvez Valentino V Liguori Antonio A Masciocchi Carlotta C Vetrone Lorenzo L Mignini Irene I Schepis Tommaso T Marrone Giuseppe G Biolato Marco M Cesario Alfredo A Patarnello Stefano S Damiani Andrea A Grieco Antonio A Valentini Vincenzo V Gasbarrini Antonio A

Internal and emergency medicine 20230725 5

Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were u ...[more]

PMID: 37491564

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Project description:BackgroundTo evaluate the association between ventilator type and hospital mortality in patients with acute respiratory distress syndrome (ARDS) related to COVID-19 (SARS-CoV2 infection), a single-center prospective observational study in France.ResultsWe prospectively included consecutive adults admitted to the intensive care unit (ICU) of a university-affiliated tertiary hospital for ARDS related to proven COVID-19, between March 2020 and July 2021. All patients were intubated. We compared two patient groups defined by whether an ICU ventilator or a less sophisticated ventilator such as a sophisticated turbine-based transport ventilator was used. Kaplan-Meier survival curves were plotted. Cox multivariate regression was performed to identify associations between patient characteristics and hospital mortality. We included 189 patients (140 [74.1%] men) with a median age of 65 years [IQR, 55-73], of whom 61 (32.3%) died before hospital discharge. By multivariate analysis, factors associated with in-hospital mortality were age ≥ 70 years (HR, 2.11; 95% CI, 1.24-3.59; P = 0.006), immunodeficiency (HR, 2.43; 95% CI, 1.16-5.09; P = 0.02) and serum creatinine ≥ 100 µmol/L (HR, 3.01; 95% CI, 1.77-5.10; P < 0.001) but not ventilator type. As compared to conventional ICU (equipped with ICU and anesthesiology ventilators), management in transient ICU (equipped with non-ICU turbine-based ventilators) was associated neither with a longer duration of invasive mechanical ventilation (18 [IQR, 11-32] vs. 21 [13-37] days, respectively; P = 0.39) nor with a longer ICU stay (24 [IQR, 14-40] vs. 27 [15-44] days, respectively; P = 0.44).ConclusionsIn ventilated patients with ARDS due to COVID-19, management in transient ICU equipped with non-ICU sophisticated turbine-based ventilators was not associated with worse outcomes compared to standard ICU, equipped with ICU ventilators. Although our study design is not powered to demonstrate any difference in outcome, our results after adjustment do not suggest any signal of harm when using these transport type ventilators as an alternative to ICU ventilators during COVID-19 surge.

Project description:BackgroundEarly identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19.MethodsCollaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score). The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves.ResultsThe incidence of mortality was 4.3% in both the development and validation set. A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85-0.91) and 0.97 (95% CI, 0.84-0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator (https://www.diseaseriskscore.com).ConclusionThis clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.

Project description:BackgroundIdentification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients.MethodsA retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration.ResultsCompared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)).ConclusionsThe COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.

Project description:BACKGROUND:Radical nephrectomy and thrombectomy is the standard surgical procedure for the treatment of renal cell carcinoma (RCC) with tumor thrombus (TT). But the estimation of intra-operative blood loss is only based on the surgeon's experience. Therefore, our study aimed to develop Peking University Third Hospital score (PKUTH score) for the prediction of intra-operative blood loss volume in radical nephrectomy and thrombectomy. METHODS:The clinical data of 153 cases of renal mass with renal vein (RV) or inferior vena cava tumor thrombus admitted to Department of Urology, Peking University Third Hospital from January 2015 to May 2018 were retrospectively analyzed. The total amount of blood loss during operation is equal to the amount of blood sucked out by the aspirator plus the amount of blood in the blood-soaked gauze. Univariate linear analysis was used to analyze risk factors for intra-operative blood loss, then significant factors were included in subsequent multivariable linear regression analysis. RESULTS:The final multivariable model included the following three factors: open operative approach (P?<?0.001), Neves classification IV (P?<?0.001), inferior vena cava resection (P?=?0.001). The PKUTH score (0-3) was calculated according to the number of aforementioned risk factors. A significant increase of blood loss was noticed along with higher risk score. The estimated median blood loss from PKUTH score 0 to 3 was 280?mL (interquartile range [IQR] 100-600?mL), 1250?mL (IQR 575-2700?mL), 2000?mL (IQR 1250-2900?mL), and 5000?mL (IQR 4250-8000?mL), respectively. Meanwhile, the higher PKUTH score was, the more chance of post-operative complications (P?=?0.004) occurred. A tendency but not significant overall survival difference was found between PKUTH risk score 0 vs. 1 to 3 (P?=?0.098). CONCLUSION:We present a structured and quantitative scoring system, PKUTH score, to predict intra-operative blood loss volume in radical nephrectomy and thrombectomy.

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Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves.

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Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves.

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