Busto COVID-19 Score Identify Low Risk Patients. External Validation.

Background: Coronavirus disease of 2019 (COVID-19) is associated with severe acute respiratory failure. Early identifcation of low-risk COVID-19 patients is crucial, discharging safely patients to home and optimizing the use of available resources. Methods: We aimed to external validate a simple score for the prediction of low-risk outcomes. A retrospective cohort study of patients hospitalized for COVID-19 was carried out by the Busto Hospital and Niguarda hospital. Epidemiological, clinical, laboratory, and treatment variables were collected at hospital admission. Variables included in this retrospective cohort were analized to validate the Busto COVID-19 score as a Clinical Risk Score able to individuate low risk COVID-19 patients. Among COVID-19 patients admitted to the hospital, severe outcome was defned as the composite of the admission to the Intensive Care Unit or death. Results: The development cohort included 427 consecutive patients. The mean (SD) age of patients among the cohort was 60.5 years; 273 (63%) were men. As potential predictors, Busto COVID-19 score variables include: lung ultrasound abnormality, age, total white blood cells count , C-reactive protein value, pO2/FiO2 ratio, lactates value, arterial hypertension and fever from 5 days or more and resulted in the best performing score with an area under the curve in the derivation sample of 0.88 and 0.71 in the external sample. Conclusions: The proposed score can identify patients at low risk for severe outcome who can be safely managed in a low-intensity setting after hospital admission for COVID-19.

Plasma interleukin-6 levels correlate with survival in patients with bacterial sepsis and septic shock

In this study, IL-6 levels were assessed as inflammatory biomarker of bacterial sepsis in patients hospitalized at the ICU of the hospital of Colombia.Materials and methodsProspective study on 62 patients diagnosed with sepsis and septic shock. An ELISA assay was used to test serum levels of IL-6 at admission and 48 h after admission. Variables were analyzed by χ2 test (alfa <0.05). Multivariable Cox regression was used to determine the survival with the statistical program SPSS v23.00.ResultsPatient's median age was 53 years old and 59.7% were male. Lung was the most common primary site of infection (43.5%), and hypertension comorbidity with higher prevalence (40%). Infection by Gram negative bacteria were significantly more frequent among patients than Gram positive (P = 0.037). Overall, survival analysis showed that 10 (16.1%) patients died with a survival median of 7.00 +4.874 (2–3) days. In patients with sepsis we detected a significant decline in the average of IL-6 serum levels after 48 h of admission [7.50 (SD: 7.00–68.00) pg/mL vs. 68.00 [SD: 7.00–300.00] pg/mL (P = 0.000). Only 25% of patients with septic shock who presented high levels of IL-6 at the time of admission and at 48 h had a survival up to 15 days (P = 0.005).ConclusionWe found significant differences between the plasma levels of IL-6 during the first 48 h after admission to the ICU among patients with sepsis and septic shock. Patients with sepsis had a significant decline in IL-6 levels, whereas in patients who developed septic shock, levels of this cytokine remained high and have a lower survival compared to those who maintained low levels of IL-6.

Predictors of mortality in the intensive care unit for adult patients admitted on mechanical ventilation: admission profile

Evaluating risk factors for mortality in local populations such as adult patients admitted on mechanical ventilation in intensive care units (ICU) may provide support for the management and improvement of outcomes in these units. The inclusion of the workload of professionals in these models has offered a different view of predictors. The aim of this study was to evaluate whether Nursing workload assessed by the Nursing Activities Score (NAS), predictors of mortality (APACHEII and SAPS3) and some additional admission variables for patients admitted on mechanical ventilation in an ICU are predictors of death. We evaluated 194 patients who remained on mechanical ventilation for 48 hours before or after admission in one ICU, in a university hospital of high complexity. The clinical and socio-demographic profile, the NAS of admission and some admission variables were evaluated. The outcome discharge or death in the ICU was evaluated for all patients, and from simple or multiple logistic regression models, risk or protective factors for death in the ICU were obtained. Individually, only SAPS3 was significant for prediction of death (OR = 1.03; CI95%: 1.01; 1.05), while the APACHEII and the NAS of admission was not able to predict ICU mortality. In the multiple model, the only risk factors for ICU mortality were the presence of chronic obstructive pulmonary disease (OR = 8.82; CI95%: 1.82; 42.70), having thyroid diseases (OR = 5.98; CI95%: 1.15; 31.22) and the increase in the level of urea in the blood (OR = 1.01; CI95%: 1,002; 1.02). The admission variables of this population were more effective in predicting ICU mortality than the predictors of mortality evaluated here.

ADMISSION VARIABLES ASSOCIATED WITH INDEPENDENT AMBULATION AT TIME OF DISCHARGE FROM A COMPREHENSIVE STROKE UNIT

The aim: To identify admission variables associated with Functional Ambulation Classification (FAC) 1 to 4 (unable to walk without assistance) at time of discharge (dFAC<5) from a comprehensive stroke unit (CSU). Materials and methods: Patients admitted to CSU at Oberig Clinic, Kyiv, Ukraine, August 01, 2012 to July 31, 2018, were screened for study selection criteria. Association of qualifying patients’ data with FAC score at CSU discharge was retrospectively assessed by univariate and multivariate logistic regression, odds ratios (OR) and 95% confidence intervals (95% CI) using MedCalc v. 19.1. Results: The study cohort (442 of 492 admitted patients) had median age: 65.8 years, gender: 43% female, stroke-type: 84% ischemic strokes, median baseline NIHSS total score: 10. Estimated time from stroke onset to CSU admission was from less-than-24-hours to over-180-days. The univariate logistic regression analysis, revealed 28 variables significantly (p<0.05) related to dFAC<5; while in multivariate analysis only 4 admission variables were significantly (p<0.05) associated with dFAC<5: age (OR= 1.07; 95% CI 1.03-1.10, on average, for each additional year, p<0.001), baseline NIHSS score (OR= 1.15; 95% CI 1.08-1.22, on average, with a 1-point increase in the total score, p<0.001), initial FAC score (OR= 0.40; 95% CI 0.31–0.52, on average, with a 1-point decrease in the score, p<0.001), and very late CSU admission (over 180 days; OR= 5.7; 95% CI 1.9–17.1, p=0.002). Conclusions: Four admission variables may be independently associated with dFAC<5 and provide opportunity for improving CSU outcomes and mitigating risk for inability to ambulate without assistance after CSU discharge.

Predicting success in teacher education: Revisiting the influence of high school GPA, admission, and standardized test scores on academic and licensure performance

This predictive study explored the influence of three admission variables on the college grade point average (CGPA), and licensure examination ratings of the 2015 teacher education graduates in a state-run university in Northern Philippines. The admission variables were high school grade point average (HSGPA), admission test (IQ) scores, and standardized test (General Scholastic Aptitude - GSA) scores. The participants were from two degree programs – Bachelor in Elementary Education (BEE) and Bachelor in Secondary education (BSE). The results showed that the graduates’ overall HSGPA were in the proficient level, while their admission and standardized test scores were average. Meanwhile, their mean licensure examination ratings were satisfactory, with high (BEE – 80.29%) and very high (BSE – 93.33%) passing rates. In both degree programs, all entry variables were significantly correlated and linearly associated with the CGPAs and licensure examination ratings of the participants. These entry variables were also linearly associated with the specific area GPAs and licensure ratings, except in the specialization area (for BSE). Finally, in both degrees, CGPA and licensure examination ratings were best predicted by HSGPA and standardized test scores, respectively. The implications of these findings on admission policies are herein discussed.

Readmissions to General ICUs in a Geographic Area of Poland Are Seemingly Associated with Better Outcomes

Background: Various factors can contribute to high mortality rates in intensive care units (ICUs). Here, we intended to define a population of patients readmitted to general ICUs in Poland and to identify independent predictors of ICU readmission. Methods: Data derived from adult ICU admissions from the Silesian region of Poland were analyzed. First-time ICU readmissions (≤30 days from ICU discharge after index admissions) were compared with first-time ICU admissions. Pre-admission and admission variables that independently influenced the need for ICU readmission were identified. Results: Among the 21,495 ICU admissions, 839 were first-time readmissions (3.9%). Patients readmitted to the ICU had lower mean APACHE II (21.2 ± 8.0 vs. 23.2 ± 8.8, p < 0.001) and TISS-28 scores (33.7 ± 7.4 vs. 35.2 ± 7.8, p < 0.001) in the initial 24 h following ICU admission, compared to first-time admissions. ICU readmissions were associated with lower mortality vs. first-time admissions (39.2% vs. 44.3%, p = 0.004). Independent predictors for ICU readmission included the admission from a surgical ward (among admission sources), chronic respiratory failure, cachexia, previous stroke, chronic neurological diseases (among co-morbidities), and multiple trauma or infection (among primary reasons for ICU admission). Conclusions: High mortality associated with first-time ICU admissions is associated with a lower mortality rate during ICU readmissions.

P958Outcomes in nonagenarians with myocardial infarction who activate the primary PCI pathway

Introduction Current guidelines recommend immediate revascularisation in patients with ST elevation myocardial infarction (STEMI). However it remains unclear whether PPCI reduces mortality in nonagenarians. We aimed to compare mortality in nonagenarians, presenting via the PPCI pathway, who were managed medically (MM) versus those who underwent PCI. Methods Electronic records of every nonagenarian who presented as a PPCI activation between 2013–2018 were reviewed. Patients were divided into those who had PCI and those MM. Standard univariate and Kaplan Meier survival analyses were performed. We compared outcomes to an age and sex matched cohort using life tables from the Office for National Statistics (ONS). Results There were 157 nonagenarians presenting via the PPCI pathway, of which 111 were “true” myocardial infarction. Table 1 summarises baseline variables and comorbidities. The cohorts were generally well matched. Both groups had similar BCIS PCI 30-day mortality risk scores. The commonest reason to treat medically was presentation 12 hours after symptom onset. There was a trend towards increased 30-day mortality in the MM group. Kaplan Meier analysis (Figure 1) show the survival curves diverge immediately and reach statistical significance at 3 years. Compared to a matched population from ONS life tables, outcomes are worse in MM. Table 1.S Admission variables & results PCI Group (n=42) Medically Managed Group (n=69) P-value Age 92 (91, 94) 93 (91, 95) 0.22 Female 21 (50.0%) 45 (65.2%) 0.11 Left ventricular failure (EF <45%) 27 (64.3%) 46 (66.6%) >0.99 Cardiogenic shock (Systolic BP <90mmHg) 4 (9.5%) 6 (8.7%) >0.99 Hx of hypertension 24 (57.1%) 45 (65.2%) 0.39 Hx of diabetes 5 (11.9%) 18 (26.1%) 0.07 Hx of chronic kidney disease 12 (28.6%) 25 (36.2%) 0.41 Hx of previous stroke 8 (19.1%) 15 (21.7%) 0.73 Hx of atrial fibrillation 1 (2.4%) 16 (23.2%) 0.003 Presented as non-STEMI 1 (2.4%) 12 (17.4%) 0.017 Presented as completed STEMI 2 (4.8%) 30 (43.5%) <0.001 BCIS PCI 30-day mortality risk 15.7 (14.3, 23.6) 17.5 (15.3, 22.3) 0.17 30-day mortality 10 (23.8%) 28 (40.6%) 0.07 Figure 1. Kaplan Meier Chart Conclusions Long term survival even in nonagenarians is significantly improved by timely PPCI when compared with medical management.

P837Neural networks algorithms improve GRACE Score performance

Background Global Registry of Acute Coronary Events (GRACE) score is a well-known model used to predict the probability of events in acute coronary syndrome (ACS). GRACE model was developed using a logistic regression approach that can only model linear functions, a limitation that could be prevented using artificial neural networks (NN) a recognized tool for nonlinear statistical modeling. The aim of this study was to develop, train and test different NN algorithm-based models to improve the GRACE score performance. Methods We analyzed a prospective database including 1,255 patients admitted with diagnosis of ACS in a community hospital, between June 2008 and June 2017. The database included 40 demographic and laboratory admission variables. In the guided approach, only the individual predictors included in the GRACE score were used to train and test three NN algorithm-based models, one- and two-hidden layer multilayer perceptron (MLP), and a radial basis function network. In addition, three extra unguided models were built using the 40 admission variables. Finally, expected mortality according to the GRACE score was calculated using the logistic regression equation. The database was split into 2 datasets: 70% for model training and 30% for validation. In order to choose the best model, the training process was repeated 50 times. Every time the models were tested on the validation cohort, accuracy, receiver operating characteristic (ROC) area, negative predictive value (NPV), and positive predictive value (PPV) were recorded. Only models showing the best discrimination power were selected for comparison with logistic regression outcomes. The end point was in-hospital all-cause mortality. Results In terms of accuracy, ROC area and NPV, almost all NN algorithms outperformed the logistic regression approach (accuracy 97.1, 96.7, 96.2, 97.3 and 94.1%, p<0.001; ROC area 0.89, 0.86, 0.84, 0.84 and 0.75, Hanley-McNeil p≤0.05; for guided and unguided one- and two-hidden layers MLP and GRACE score, respectively). Only radial basis function models obtained a better accuracy level based on NPV improvement (100 vs. 98.8%, p=0.0001), at the expense of PPV reduction (0.0% vs. 13.2%, p<0.0001) (ROC are 0.84 vs. 0.75, p=0.043). Compared with the logistic regression approach, one- and two-hidden layers in guided and unguided MLP models improved PPV from 13.2 to 18.2% (38% increase), 15.4% (17% increase), 27.3% (107% increase), and 25.0% (89% increase), respectively, although these differences were not statistically significant. Conclusions NN algorithms improve GRACE score performance in terms of discriminatory power for the prediction of in-hospital mortality. Its application should become a useful tool for the decision making in ACS patients