Measuring the impact of ambulatory red blood cell transfusion on home functional status: study protocol for a pilot randomized controlled trial

SPIRIT 2013: SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Checklist for clinical trial protocols. (DOCX 65 kb

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic stroke (AIS) clots. We analyzed 50 AIS blood clots retrieved using mechanical thrombectomy procedures. Following HandE staining, quantification of clot components was performed by two different methods: a pathologist using a reference standard method (Adobe Photoshop CC) and an experienced researcher using Orbit Image Analysis. Following quantification, the clots were categorized into 3 types: RBC dominant (\u3e/=60% RBCs), Mixed and Fibrin dominant ( \u3e /=60% Fibrin). Correlations between clot composition and Hounsfield Units density on Computed Tomography (CT) were assessed. There was a significant correlation between the components of clots as quantified by the Orbit Image Analysis algorithm and the reference standard approach (rho = 0.944**, p \u3c 0.001, n = 150). A significant relationship was found between clot composition (RBC-Rich, Mixed, Fibrin-Rich) and the presence of a Hyperdense artery sign using the algorithmic method (X2(2) = 6.712, p = 0.035*) but not using the reference standard method (X2(2) = 3.924, p = 0.141). Orbit Image Analysis machine learning software can be used for the histological quantification of AIS clots, reproducibly generating composition analyses similar to current reference standard methods

Predictive value of individual Sequential Organ Failure Assessment sub-scores for mortality in the cardiac intensive care unit.

PurposeTo determine the impact of Sequential Organ Failure Assessment (SOFA) organ sub-scores for hospital mortality risk stratification in a contemporary cardiac intensive care unit (CICU) population.Materials and methodsAdult CICU admissions between January 1, 2007 and December 31, 2015 were reviewed. The SOFA score and organ sub-scores were calculated on CICU day 1; patients with missing SOFA sub-score data were excluded. Discrimination for hospital mortality was assessed using area under the receiver-operator characteristic curve (AUROC) values, followed by multivariable logistic regression.ResultsWe included 1214 patients with complete SOFA sub-score data. The mean age was 67 ± 16 years (38% female); all-cause hospital mortality was 26%. Day 1 SOFA score predicted hospital mortality with an AUROC of 0.72. Each SOFA organ sub-score predicted hospital mortality (all p ConclusionsIn CICU patients with complete SOFA sub-score data, risk stratification for hospital mortality is determined primarily by the cardiovascular, central nervous system, renal and respiratory SOFA sub-scores

Development and performance of a novel vasopressor-driven mortality prediction model in septic shock

Abstract Background Vasoactive medications are essential in septic shock, but are not fully incorporated into current mortality prediction risk scores. We sought to develop a novel mortality prediction model for septic shock incorporating quantitative vasoactive medication usage. Methods Quantitative vasopressor use was calculated in a cohort of 5352 septic shock patients and compared using norepinephrine equivalents (NEE), cumulative vasopressor index and the vasoactive inotrope score models. Having best discrimination prediction, log10NEE was selected for further development of a novel prediction model for 28-day and 1-year mortality via backward stepwise logistic regression. This model termed ‘MAVIC’ (Mechanical ventilation, Acute Physiology And Chronic Health Evaluation-III, Vasopressors, Inotropes, Charlson comorbidity index) was then compared to Acute Physiology And Chronic Health Evaluation-III (APACHE-III) and Sequential Organ Failure Assessment (SOFA) scores in an independent validation cohort for its accuracy in predicting 28-day and 1-year mortality. Measurements and main results The MAVIC model was superior to the APACHE-III and SOFA scores in its ability to predict 28-day mortality (area under receiver operating characteristic curve [AUROC] 0.73 vs. 0.66 and 0.60) and 1-year mortality (AUROC 0.74 vs. 0.66 and 0.60), respectively. Conclusions The incorporation of quantitative vasopressor usage into a novel ‘MAVIC’ model results in superior 28-day and 1-year mortality risk prediction in a large cohort of patients with septic shock

Predictive Value of the Sequential Organ Failure Assessment Score for Mortality in a Contemporary Cardiac Intensive Care Unit Population.

BACKGROUND: Optimal methods of mortality risk stratification in patients in the cardiac intensive care unit (CICU) remain uncertain. We evaluated the ability of the Sequential Organ Failure Assessment (SOFA) score to predict mortality in a large cohort of unselected patients in the CICU. METHODS AND RESULTS: Adult patients admitted to the CICU from January 1, 2007, to December 31, 2015, at a single tertiary care hospital were retrospectively reviewed. SOFA scores were calculated daily, and Acute Physiology and Chronic Health Evaluation (APACHE)-III and APACHE-IV scores were calculated on CICU day 1. Discrimination of hospital mortality was assessed using area under the receiver-operator characteristic curve values. We included 9961 patients, with a mean age of 67.5±15.2 years; all-cause hospital mortality was 9.0%. Day 1 SOFA score predicted hospital mortality, with an area under the receiver-operator characteristic curve value of 0.83; area under the receiver-operator characteristic curve values were similar for the APACHE-III score, and APACHE-IV predicted mortality ( CONCLUSIONS: The day 1 SOFA score has good discrimination for short-term mortality in unselected patients in the CICU, which is comparable to APACHE-III and APACHE-IV. Advantages of the SOFA score over APACHE include simplicity, improved discrimination using serial scores, and prediction of long-term mortality

Temporal Trends and Clinical Outcomes Associated with Vasopressor and Inotrope Use in The Cardiac Intensive Care Unit.

BACKGROUND: The use of norepinephrine may be associated with better outcomes in some patients with shock. We sought to determine whether norepinephrine was associated with lower mortality in unselected cardiac intensive care unit (CICU) patients compared with other vasopressors, and whether patterns of vasopressor and inotrope usage in the CICU have changed over time. METHODS: We retrospectively evaluated consecutive adult patients admitted to a tertiary care hospital CICU from January 1, 2007 to December 31, 2015. Vasoactive drug doses were quantified using the peak Vasoactive-Inotropic Score (VIS). Temporal trends were assessed using the Cochran-Armitage trends test and multivariable logistic regression was used to determine predictors of hospital mortality. RESULTS: We included 10,004 patients with a mean age of 67 ± 15 years; vasoactive drugs were used in 2,468 (24.7%) patients. Use of norepinephrine increased over time, whereas dopamine utilization decreased (P \u3c 0.001 for trends). After adjustment for illness severity and other variables, the peak VIS was a predictor of hospital mortality across the entire population (unit odds ratio [OR] 1.013, 95% confidence interval [CI], 1.009-1.017, P \u3c 0.001) and among patients receiving vasoactive drugs (OR 1.018, 95% CI, 1.013-1.022, P \u3c 0.001). Among patients receiving vasoactive drugs, norepinephrine was associated with a lower risk of hospital mortality (OR 0.66, 95% CI, 0.49-0.90, P = 0.008) after adjustment for illness severity and peak VIS. CONCLUSIONS: Vasoactive drug use in CICU patients has a dose-dependent association with short-term mortality. Use of norepinephrine in CICU patients is associated with decreased odds of death when compared with other vasoactive drugs

Creating an atlas of normal tissue for pruning WSI patching through anomaly detection

Abstract Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an “atlas of normal tissue” solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches