Carotid Artery Plaque Progression: Proposal of a New Predictive Score and Role of Carotid Intima-Media Thickness

Background: We aimed to investigate if the carotid intima-media thickness (IMT) at baseline and the HAD2S score, composed of the sum of single risk factors (hypertension, age ≥ 75 years, diabetes, dyslipidemia, smoking), were predictive of plaque progression. Methods: We performed a retrospective analysis on real-life prospectively collected data from patients with any detectable carotid plaque at follow up. The plaque score, calculated at baseline (T0) and at a median follow up of 36.6 months (IQR 39.6–34.3) (T3), was defined as 0: no plaque or stenosis < 30%; 1: stenosis in the range 30–49%; 2: in the range 50–69%; 3: in the range 70–99% and 4: occlusion. Carotid IMT was measured at T0 and T3; HAD2S score was calculated at baseline. Results: We included 340 patients with a mean age of 69.9 (9.1) years and 25.3% subjects had plaque progression. Individuals with progression had a median HAD2S score of 3 (1) while those without progression had 2 (1). Patients with progression had a mean baseline IMT of 0.86 (0.17) while those without progression had 0.77 (0.18) (p < 0.0001). A correlation between progression and baseline IMT was found (p = 0.002). Conclusion: Baseline IMT could be considered a predictor of progression. Patients with progression had an HAD2S score higher than those without evolution.

A Simple Predictive Score to Distinguish between Disseminated Histoplasmosis and Tuberculosis in Patients with HIV

Disseminated histoplasmosis is a common differential diagnosis of tuberculosis in disease-endemic areas. We aimed to find a predictive score to orient clinicians towards disseminated histoplasmosis or tuberculosis when facing a non-specific infectious syndrome in patients with advanced HIV disease. We reanalyzed data from a retrospective study in Cayenne Hospital between January 1997–December 2008 comparing disseminated histoplasmosis and tuberculosis: 100 confirmed disseminated histoplasmosis cases and 88 confirmed tuberculosis cases were included. A simple logit regression model was constructed to predict whether a case was tuberculosis or disseminated histoplasmosis. From this model, a score may be obtained, where the natural logarithm of the probability of disseminated histoplasmosis/tuberculosis = +3.917962 × WHO performance score (1 if >2, 0 if ≤2) −1.624642 × Pulmonary presentation (1 yes, 0 no) +2.245819 × Adenopathies > 2 cm (1 yes, 0 no) −0.015898 × CD4 count − 0.001851 × ASAT − 0.000871 × Neutrophil count − 0.000018 × Platelet count + 6.053793. The area under the curve was 98.55%. The sensitivity of the model to distinguish between disseminated histoplasmosis and tuberculosis was 95% (95% CI = 88.7–98.3%), and the specificity was 93% (95% CI = 85.7.3–97.4%). In conclusion, we here present a clinical-biological predictive score, using simple variables available on admission, that seemed to perform very well to discriminate disseminated histoplasmosis from tuberculosis in French Guiana in well characterized patients.

Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score

Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer (HGSOC) patients warrants an availability of Critical Care Unit (CCU) beds. Machine Learning (ML) could be helpful in monitoring CCU admissions to improve standards of care. We aimed to improve the accuracy of predicting CCU admission in HGSOC patients by ML algorithms and developed an ML-based predictive score. A cohort of 291 advanced stage HGSOC patients with fully curated data was selected. Several linear and non-linear distances, and quadratic discriminant ML methods, were employed to derive prediction information for CCU admission. When all the variables were included in the model, the prediction accuracies were higher for linear discriminant (0.90) and quadratic discriminant (0.93) methods compared with conventional logistic regression (0.84). Feature selection identified pre-treatment albumin, surgical complexity score, estimated blood loss, operative time, and bowel resection with stoma as the most significant prediction features. The real-time prediction accuracy of the Graphical User Interface CCU calculator reached 95%. Limited, potentially modifiable, mostly intra-operative factors contributing to CCU admission were identified and suggest areas for targeted interventions. The accurate quantification of CCU admission patterns is critical information when counseling patients about peri-operative risks related to their cytoreductive surgery.

Temporal Validation of a Predictive Score for Death in Children with Visceral Leishmaniasis

Objectives. To perform a temporal validation of a predictive model for death in children with visceral leishmaniasis (VL). Methods. A temporal validation of a children-exclusive predictive model of death due to VL (Sampaio et al. 2010 model), using a retrospective cohort, hereby called validation cohort. The validation cohort convenience sample was made of 156 patients less than 15 years old hospitalized between 2008 and 2018 with VL. Patients included in the Sampaio et al. 2010 study are here denominated derivation cohort, which was composed of 546 patients hospitalized in the same hospital setting in the period from 1996 to 2006. The calibration and discriminative capacity of the model to predict death by VL in the validation cohort were then assessed through the procedure of logistic recalibration that readjusted its coefficients. The calibration of the updated model was tested using Hosmer–Lemeshow test and Spiegelhalter test. A ROC curve was built and the value of the area under this curve represented the model’s discrimination. Results. The validation cohort found a lethality of 6.4%. The Sampaio et al. 2010 model demonstrated inadequate calibration in the validation cohort (Spiegelhalter test: p = 0.007 ). It also presented unsatisfactory discriminative capacity, evaluated by the area under the ROC curve = 0.618. After the coefficient readjustment, the model showed adequate calibration (Spiegelhalter test, p = 0.988 ) and better discrimination, becoming satisfactory (AUROC = 0.762). The score developed by Sampaio et al. 2010 attributed 1 point to the variables dyspnea, associated infections, and neutrophil count <500/mm3; 2 points to jaundice and mucosal bleeding; and 3 points to platelet count <50,000/mm3. In the recalibrated model, each one of the variables had a scoring of 1 point for each. Conclusion. The temporally validated model, after coefficient readjustment, presented adequate calibration and discrimination to predict death in children hospitalized with VL.

Day100 Score predicts moderate-severe cGVHD, transplant mortality, and survival after hematopoietic cell transplantation

The aim of this study was to develop a predictive score for moderate-severe chronic graft-versus-host disease (cGVHD) on day +100 after allogeneic stem cell transplantation (HSCT). We studied 1292 patients allografted between 1990 and 2016, alive on day +100 after transplant, without cGvHD, and with full biochemistry laboratory values available. Patients were randomly assigned to a training and a validation cohort (ratio 1:1). In the training cohort, a multivariate analysis identified four independent predictors of moderate-severe cGvHD: gammaglutamyltransferase ≥75 UI/l, creatinine ≥1 mg/dl, cholinesterase ≤4576 UI/l and albumin ≤4 g/dl. A score of 1 was assigned to each variable, producing a low (0-1), intermediate (2-3) and high (4) score. The cumulative incidence (CI) of moderate-severe cGvHD was 12%, 20% and 52% (p&lt;0.0001) in the training cohort, and 13%, 24% and 33% (p=0.002) in the validation cohort. The 5 year CI of transplant related mortality (TRM) was 5%, 14%, 27%(p&lt;0.0001) and 5%, 16%, 31%(p&lt;0.0001), respectively. The 5 year survival was 64%, 57%, 54%(p=0.009) and 70%, 59%, 42%(p=0.0008) in the two cohorts respectively . In conclusion, Day100 score predicts cGvHD, TRM and survival, and, if validated in a separate group of patients, could be considered for trials of pre-emptive therapy.

Is Elevated Red Blood Cell Distribution Width Value a Prognostic Marker in Sepsis Patients?

Sepsis and its complications are a common cause of infectious disease and death in worldwide. But the infection can be challenges to confirm and there is gold standard methods to confirm it. Red blood cell distribution width (RDW) value frequently measured at every complete blood count. In sepsis the RDW morphology changes are believed to be mainly related to prognosis. RDW has also been studied as an independent variable in different predictive score. We systematically review the articles can RDW be used as prognostic marker in patient with sepsis.

FAT score: an Indian insight to a novel diagnostic score to differentiate non-alcoholic steatohepatitis (NASH) from simple steatosis

Background The aim of this study was to propose a simple predictive score to differentiate NASH from simple steatosis. Results This study included 64 patients who had biopsy-proven NAFLD, of which 34 patients had steatohepatitis and 30 had simple steatosis. Clinical, anthropometric, and biochemical variables of the study population were analyzed. Univariate analysis showed platelet count, ferritin, and transaminases (ALT&AST) were predictors of NASH. This led to the proposal of a new diagnostic tool, FAT score (F signifies Ferritin, A indicates AST&ALT, T denotes t in Platelet) with AUROC of 0.95. The ROC curves for the significant variables were plotted and cutoff values were identified. Each component is awarded a score of 0 or 1, based on this cutoff value. The component is awarded a score of 1 if the component score is above the cutoff value and 0, if the score is below cutoff. The maximum score which can be obtained is 4. A score of ≥ 3 was able to predict NASH from simple steatosis with a sensitivity of 76.5% and a specificity of 100%. The score was validated with a cohort of 84 liver biopsy patients wherein a cutoff ≥ 3 was found to give a specificity of 100% in the validation cohort. Conclusions FAT score is a simple predictive model to differentiate NASH from simple steatosis (cutoff of more than or equal to 3) without performing a liver biopsy. A FAT score less than 3 rules out the need for biopsy.

500 The impact on healthcare of reimplantation and long-term mortality after transvenous lead extraction in patients with device related infection

Aims Transvenous lead extraction (TLE) has become a pivotal part of a comprehensive lead management strategy, dealing with a continuously increasing demand. Nonetheless, literature about long-term outcomes and the impact of a new device implantation on survival is still lacking. Given these knowledge gaps, the aim of our study was to analyse reimplantation and both early and long-term mortality in patients undergoing TLE, even in a public health perspective, specifically clarifying concerns about reimplantation. Methods This prospective, single-centre, observational, real-world registry consecutively enrolled patients (pts) with cardiac implantable electronic device who underwent TLE at our Hospital, from January 2005 to September 2020. The primary endpoint was to analyse major adverse cardiovascular events (MACEs) in both re-implanted (R Group) and non reimplanted (NR Group); secondary end-point was long-term (after discharge) mortality of the whole cohort, in order to investigate long-term mortality predictors. Results We enrolled high-risk cohort of 451 pts (mean population age 70 ± 12, with lead dwelling time 81.7 ± 201.2 months) at baseline findings: 92% of pts had an evidence of device infection, a generally impaired heart function with mean left ventricular ejection fraction (LVEF) 44 ± 13% and high rates of comorbidities (15% of pts with hypertension+ diabetes mellitus + renal failure). Three-hundred thirteen (72%) pts were reimplanted, using endocardiac leads in 86% and epicardial leads in 14%. Total MACEs rate was higher in R Group versus NR Group (64% versus 28%, P ≤ 0.001, CI 95%, respectively). In particular, rehospitalizations occurred more frequently in reimplanted population (R group 43% versus NR group 13%, P = 0.001, CI 95%). Long-term mortality rate was 34% (150 pts) at a mean follow up of 5.2 years. The leading contributor to long-term mortality was represented by multiple non-communicable chronic diseases (62%), being sepsis responsible for only 4% of long-term mortality, with a clear evidence of reduced infective burden after TLE and complete antibiotic therapy. At multivariate analysis, we found three independent predictors of long-term mortality: advanced age (&gt; 77 years, OR 1.04, CI 1.02–1.06, P &lt; 0.001), renal failure (eGFR&lt;30 mL/min, OR 1.66, CI 1.15–2.39, P = 0.007) and left ventricular dysfunction before TLE (LVEF&lt;45%, OR 1.58, CI 1.08–2.13, P = 0.017). Conclusions In patients undergoing TLE for infective indications, our study identified the reimplantation group as high risk group for adverse events before discharge. On the other hand, advanced age, renal failure and systolic dysfunction, as independent predictors of long-term mortality, could be evaluated as a predictive score to assess the mortality risk before the procedure of TLE.