215 Artificial Intelligence Predicts Sepsis After Burn Injury

Abstract Introduction Artificial intelligence has proven to be useful in a wide range of medical applications. The purpose of this study was to use artificial intelligence, through supervised machine learning, to predict sepsis in patients with burn injuries. Methods Burn-injured patients were identified from the 2010–2014 Nationwide Readmissions Database. Three machine learning classifiers --logistic regression, gradient boosted trees, and neural network-- were trained with different algorithms to predict the primary outcome of sepsis. The classifiers used categorical variables corresponding to: age, gender, TBSA percentage, burn degree, burn site, and burn mechanism. Classifier cross-validation was performed with ten groups including equal proportions of septic patients. Nine groups were used for training and one for validation. This process was repeated using each group for validation once. The receiver operating characteristic curves (ROC) were plotted for each validation and the mean areas under the curve (AUC) were calculated. Results There were 65,029 patients admitted for burns and the rate of sepsis was 2.8%. Logistic regression performed with an AUC of 0.876 ± 0.012 and an accuracy of 97.15%±0.04%. Neural network had an AUC of 0.860 ± 0.011 and an accuracy of 97.14%±0.10%. Gradient boosted trees performed with an AUC of 0.881 ± 0.010 and an accuracy of 97.19%±0.08%. The most important variables were TBSA ≥20% (57.32%), second degree (20.08%), third degree (4.99%), flame mechanism (2.89%), and age ≥65 (2.89%). Conclusions This study demonstrates the utility of artificial intelligence for the development of highly-accurate prediction models for sepsis in burn patients. Applicability of Research to Practice These models could be easily incorporated into future systems designed to identify and prevent septicemia in burn patients

Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement

Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostomy placement. The Multiparameter Intelligent Monitoring in Intensive Care III database was queried for all intensive care unit (ICU) stays with mechanical ventilation. PMV was defined as ventilation >7 d. Classifiers with a gradient-boosted decision trees algorithm were created for the outcomes of PMV and tracheostomy placement. The variables used were six different severity-of-illness scores calculated on the first day of ICU admission including their components and 30 comorbidities. Mean receiver operating characteristic curves were calculated for the outcomes, and variable importance was quantified. There were 20,262 ICU stays identified. PMV was required in 13.6%, and tracheostomy was performed in 6.6% of patients. The classifier for predicting PMV was able to achieve a mean area under the curve (AUC) of 0.820 ± 0.016, and tracheostomy was predicted with an AUC of 0.830 ± 0.011. There were 60.7% patients admitted to a surgical ICU, and the classifiers for these patients predicted PMV with an AUC of 0.852 ± 0.017 and tracheostomy with an AUC of 0.869 ± 0.015. The variable with the highest importance for predicting PMV was the logistic organ dysfunction score pulmonary component (13%), and the most important comorbidity in predicting tracheostomy was cardiac arrhythmia (12%). This study demonstrates the use of artificial intelligence through machine-learning classifiers for the early identification of patients at risk for PMV and tracheostomy. Application of these identification techniques could lead to improved outcomes by allowing for early intervention

Nationwide Outcomes and Risk Factors for Reinjury After Penetrating Trauma

Previous studies have shown that a notable portion of patients who are readmitted for reinjury after penetrating trauma present to a different hospital. The purpose of this study was to identify the risk factors for reinjury after penetrating trauma including reinjury admissions to different hospitals. The 2010-2014 Nationwide Readmissions Database was queried for patients surviving penetrating trauma. E-codes identified patients subsequently admitted with a new diagnosis of blunt or penetrating trauma. Univariable analysis was performed using 44 injury, patient, and hospital characteristics. Multivariable logistic regression using significant variables identified risk factors for the outcomes of reinjury, different hospital readmission, and in-hospital mortality after reinjury. There were 443,113 patients identified. The reinjury rate was 3.5%. Patients presented to a different hospital in 30.0% of reinjuries. Self-inflicted injuries had a higher risk of reinjury (odds ratio [OR]: 2.66, P < 0.05). Readmission to a different hospital increased risk of mortality (OR: 1.62, P < 0.05). Firearm injury on index admission increased risk of mortality after reinjury (OR: 1.94, P < 0.05). This study represents the first national finding that one in three patients present to a different hospital for reinjury after penetrating trauma and have a higher risk of mortality due to this fragmentation of care. These findings have implications for quality and cost improvements by identifying areas to improve continuity of care and the implementation of penetrating injury prevention programs

Uncaptured rates of postpartum venous thromboembolism: a US national analysis

To quantify the proportion of postpartum venous thromboembolism (VTE) readmissions, including those that occur at different hospitals from index admission, and describe risk factors for this outcome. Retrospective observational study. US hospitals included in the Nationwide Readmissions Database. A total of 3 719 238 patients >14 years of age with a delivery-associated hospitalisation in 2014. Univariate analysis was performed to identify patient and hospital factors associated with readmissions. Significant factors were included in multivariate logistic regression to identify independent risk factors. Results were weighted for national estimates. Readmission with VTE to both index and different hospitals at 30, 60 and 90 days. The VTE cumulative readmission rate was 0.053% (n = 1477), 0.063% (n = 1765) and 0.069% (n = 1938) at 30, 60 and 90 days, respectively. Patients were readmitted to different hospitals 31% of the time within 90 days. Risk factors for different hospital VTE readmission were unique and included younger age and initial admission to a small/medium-sized hospital. Initial admission to a for-profit hospital increased the likelihood of readmission to a different hospital. Nearly one in three postpartum VTEs are missed by the current quality metrics, with significant implications for outcomes and quality. For-profit hospitals have a significant portion of their VTE readmissions hidden, falsely lowering their readmission rates relative to public hospitals. US analysis shows 1 in 3 readmissions for postpartum venous thromboembolism currently missed

Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury

Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learning algorithms for predicting AKI in ICU patients. The eICU Collaborative Research Database was queried for five consecutive days of laboratory measurements per patient. Patients with AKI were identified and trends in vital signs and laboratory values were determined by calculating the slope of the least-squares-fit linear equation using three days for each value. Different machine learning classifiers (gradient boosted trees [GBT], logistic regression, and deep learning) were trained to predict AKI using the laboratory values, vital signs, and slopes. There were 151,098 ICU stays identified and the rate of AKI was 5.6 per cent. The best performing algorithm was GBT with an AUC of 0.834 ± 0.006 and an F-measure of 42.96 per cent ± 1.26 per cent. Logistic regression performed with an AUC of 0.827 ± 0.004 and an F-measure of 28.29 per cent ± 1.01 per cent. Deep learning performed with an AUC of 0.817 ± 0.005 and an F-measure of 42.89 per cent ± 0.91 per cent. The most important variable for GBT was the slope of the minimum creatinine (30.32%). This study identifies the best performing machine learning algorithms for predicting AKI using trends in laboratory values in ICU patients. Early identification of these patients using readily available data indicates that incorporating machine learning predictive models into electronic medical record systems is an inevitable requisite for improving patient outcomes

T4 Variation in National Readmission Patterns After Burn Injury

Abstract Introduction The purpose of this study was to identify risk factors and costs associated with readmission after burn injury across the United States. Methods The 2010–2014 Nationwide Readmissions Database was queried for patients admitted for burn. Multivariate logistic regression identified risk factors for 30-day readmission at index and different hospitals. Readmission cost was calculated using cost-to-charge ratios. Results Of the 94,759 patients admitted with a burn, 7.4% (n=7000) were readmitted within 30 days and of those, 29.2% (n=2047) occurred at a different hospital. Infection (29.4% [n=1990]), graft loss, wound healing, late effects, or aftercare issues (15.3% [n=1074]), and pain control and/or hydration (19.2% [n=1346]) were the most common reasons for readmission. Risk factors unique to readmission to a different hospital include: second degree burns (OR 1.23, p 7 days (OR 2.07, p<0.01), and admission to a metropolitan teaching hospital (OR 1.50, p<0.01) compared to a metropolitan non-teaching hospital. Other risk factors for readmission to a different hospital included: burn of face, head and neck (OR 1.53, p<0.01) and admission to non-metropolitan hospital (OR 1.93, p<0.01). Compared to private insurance, Medicare and Medicaid patients were more likely to get readmitted to a different hospital (OR 1.29, p<0.01 and OR 1.21, p<0.049, respectively). Overall risk factors for readmission at 30 days included: depression (OR 1.30, p<0.01), psychoses (OR 1.53, p<0.01), burn of lower limbs (OR 1.29, p<0.01), third degree burns (OR 1.31, p<0.01), leaving against medical advice (OR 3.39, p<0.01), admission to for-profit hospital (OR 1.30, p<0.01), and Charlson Comorbidity Index ≥2 (OR 1.48, p<0.01). Further risk factors are presented in Table 1. The median readmission cost was higher for patients readmitted to a different hospital $9,005 [$4,792–18,615] vs $8,697 [$5,118-$18,030], p<0.041). The median cost of readmission within 1 year was$10,959 [$5,369-$23,940]. Conclusions Previously unreported, 1 in 3 burn readmissions nationally occur at a different hospital, have unique risk factors, are costlier, and are missed by current quality metrics. For-profit hospitals have higher rates of readmissions overall. Applicability of Research to Practice A significant proportion of burn readmissions are missed by benchmarking and have unique risk factors, suggesting prevention programs, quality monitoring, and policy need to be changed

Hidden Readmissions after Carotid Endarterectomy and Stenting

Historically, carotid procedures incur a readmission rate of approximately 6%; however, these studies are not nationally representative and are limited to tracking only the index hospitals. We sought to evaluate a nationally representative database for readmission rates (including different hospitals) after both carotid endarterectomy (CEA) and carotid artery stenting (CAS) and determine risk factors for poor outcomes including postoperative mortality and myocardial infarction. This study was a retrospective analysis utilizing the 2010–2014 Nationwide Readmissions Database to query patients aged >18 years undergoing CEA or CAS. Outcomes included initial admission mortality, and 30-day readmission, including mortality and myocardial infarction (MI). Univariable analysis of 39 demographic, clinical, and hospital variables was conducted with significance set at P < 0.05. Significant variables were included in a multivariable logistic regression to identify independent risk factors for readmission. Results were weighted for national estimates. There were 527,622 patients undergoing carotid procedures and 13% (n = 69,187) underwent CAS. The 30-day readmission rate was 7% (n = 35,782), and of those, 25% (n = 8,862) were readmitted to a different hospital. When controlling for other factors, CAS was a risk factor for mortality at both index admission (odds ratio [OR] 2.29 [2.11–2.49]) and 30-day readmission (OR 1.48 [1.3–1.69]) and 30-day readmissions at both index hospital (OR 1.11 [1.07–1.14]) and different hospital (OR 1.38 [1.29–1.48]). Readmission to a different hospital increased mortality risk (OR 1.45 [1.29–1.63]) but did not have an effect on MI. Postoperative infections comprised 15% of readmissions while 6% of all readmissions were for stroke. Previously unreported, one in 4 readmissions after carotid procedures occur at a different hospital and this fragmentation of care could increase mortality risk after carotid procedures particularly for CAS which was also an independent risk factor for postoperative mortality and readmissions. Further validation is required to decrease unnecessary hospital after carotid procedures

Assessment of neuromonitoring use and postoperative readmission rates in pediatric Chiari I malformation with syrinx

Introduction While operative intervention for Chiari malformation type I (CMI) with syringomyelia is well established, there is limited data on outcomes of intraoperative neuromonitoring (IONM). This study sought to explore differences in procedural characteristics and their effects on postoperative readmission rates. Methods The Nationwide Readmission Database was queried from 2010 to 2014 for patients = 1 (16% vs. 15% without, p = 0.774). IONM was more often used in those with private insurance (63% vs. 58% without, p = 0.0004) and less likely in those with Medicaid (29% vs. 37% without, p = 0.004). Patients receiving IONM were more likely to have a postoperative complication (23% vs 17%, p = 0.004) and were more likely to have hospital lengths of stay > 7 days (9% vs. 5% without, p = 0.005). Readmission rates for CMI were 9% within 30 days and 15% within the year. The majority (89%) of readmissions were unplanned. 25% of readmissions were for infection and 27% of readmissions underwent a CMI reoperation. The 30-day readmission rate was higher for those with IONM (12% vs. 8% without, p = 0.010). Median cost for hospitalization was significantly higher for patients with IONM ($26,663 ($16,933-34,397)) vs. those without ($14,577 ($11,538-18,392)), p < 0.001. Conclusion The use of intraoperative neuromonitoring for operative repair of CMI is associated with higher postoperative complications and readmissions. In addition, there are disparities in its use and increased cost to the healthcare system. Further studies are needed to elucidate the factors underlying this association

Identifying Abuse and Neglect in Hospitalized Children With Burn Injuries

The purpose of this study was to identify the pattern of injuries that relates to abuse and neglect in children with burn injuries. The Nationwide Readmissions Database for 2010-2014 was queried for all patients aged less than 18 y admitted with burn injuries. The primary outcome was child maltreatment identified at the index admission. The secondary outcome was readmission for maltreatment. A subgroup analysis was performed on patients without a diagnosis of maltreatment during the index admission. Multivariable logistic regression was performed for each outcome. There were 57,939 admissions identified and 1960 (3.4%) involved maltreatment at the index admission. Maltreatment was associated with total body surface area burned >20% (odds ratio (OR) 2.79, P < 0.001) and burn of the lower limbs (OR 1.37, P < 0.001). Readmission for maltreatment was found in 120 (0.2%), and the strongest risk factor was maltreatment identified at the index admission (OR 5.11, P < 0.001). After excluding the patients with maltreatment identified at the index admission, 96 (0.17%) children were found to have a readmission for maltreatment that may have been present on the index admission and subsequently missed. The strongest risk factor was burn of the eye or ocular adnexa (OR 3.79, P = 0.001). This study demonstrates that a portion of admissions for burn injuries in children could involve maltreatment that was undiagnosed. Identifying these at-risk individuals is critical to prevention efforts