Dynamic data in the ED predict requirement for ICU transfer following acute care admission.

Misidentification of illness severity may lead to patients being admitted to a ward bed then unexpectedly transferring to an ICU as their condition deteriorates. Our objective was to develop a predictive analytic tool to identify emergency department (ED) patients that required upgrade to an intensive or intermediate care unit (ICU or IMU) within 24 h after being admitted to an acute care floor. We conducted a single-center retrospective cohort study to identify ED patients that were admitted to an acute care unit and identified cases where the patient was upgraded to ICU or IMU within 24 h. We used data available at the time of admission to build a logistic regression model that predicts early ICU transfer. We found 42,332 patients admitted between January 2012 and December 2016. There were 496 cases (1.2%) of early ICU transfer. Case patients had 18.0-fold higher mortality (11.1% vs. 0.6%, p < 0.001) and 3.4 days longer hospital stays (5.9 vs. 2.5, p < 0.001) than those without an early transfer. Our predictive analytic model had a cross-validated area under the receiver operating characteristic of 0.70 (95% CI 0.67-0.72) and identified 10% of early ICU transfers with an alert rate of 1.6 per week (162.2 acute care admits per week, 1.9 early ICU transfers). Predictive analytic monitoring based on data available in the emergency department can identify patients that will require upgrade to ICU or IMU if admitted to acute care. Incorporating this tool into ED practice may draw attention to high-risk patients before acute care admit and allow early intervention.

Development of a concise injury severity prediction model for pediatric patients involved in a motor vehicle collision.

OBJECTIVE: Transporting severely injured pediatric patients to a trauma center has been shown to decrease mortality. A decision support tool to assist emergency medical services (EMS) providers with trauma triage would be both as parsimonious as possible and highly accurate. The objective of this study was to determine the minimum set of predictors required to accurately predict severe injury in pediatric patients. METHODS: Crash data and patient injuries were obtained from the NASS and CISS databases. A baseline multivariable logistic model was developed to predict severe injury in pediatric patients using the following predictors: age, sex, seat row, restraint use, ejection, entrapment, posted speed limit, any airbag deployment, principal direction of force (PDOF), change in velocity (delta-V), single vs. multiple collisions, and non-rollover vs. rollover. The outcomes of interest were injury severity score (ISS) ≥16 and the Target Injury List (TIL). Accuracy was measured by the cross-validation mean of the receiver operator curve (ROC) area under the curve (AUC). We used Bayesian Model Averaging (BMA) based on all subsets regression to determine the importance of each variable separately for each outcome. The AUC of the highest performing model for each number of variables was compared to the baseline model to assess for a statistically significant difference (p < 0.05). A reduced variable set model was derived using this information. RESULTS: The baseline models performed well (ISS ≥ 16: AUC 0.91 [95% CI: 0.86-0.95], TIL: AUC 0.90 [95% CI: 0.86-0.94]). Using BMA, the rank of the importance of the predictors was identical for both ISS ≥ 16 and TIL. There was no statistically significant decrease in accuracy until the models were reduced to fewer than five and six variables for predicting ISS ≥ 16 and TIL, respectively. A reduced variable set model developed using the top five variables (delta-V, entrapment, ejection, restraint use, and near-side collision) to predict ISS ≥ 16 had an AUC 0.90 [95% CI: 0.84-0.96]. Among the models that did not include delta-V, the highest AUC was 0.82 [95% CI: 0.77-0.87]. CONCLUSIONS: A succinct logistic regression model can accurately predict severely injured pediatric patients, which could be used for prehospital trauma triage. However, there remains a critical need to obtain delta-V in real-time.

Does obesity affect the position of seat belt loading in occupants involved in real-world motor vehicle collisions?

OBJECTIVE: Previous work has shown that the lap belt moves superior and forward compared to the bony pelvis as body mass index (BMI) increases. The goal of this project was to determine whether the location of lap belt loading is related to BMI for occupants who sustained real-world motor vehicle collisions (MVCs). METHODS: A national MVC database was queried for vehicle occupants over a 10-year period (2003-2012) who were at least 16 years old, restrained by a 3-point seat belt, sitting in the front row, and involved in a front-end collision with a change in velocity of at least 56 km/h. Cases were excluded if there was not an available computed tomography (CT) scan of the abdomen. CT scans were then analyzed using adipose enhancement of 3-dimensional reconstructions. Scans were assessed for the presence a radiographic seat belt sign (rSBS), or subcutaneous fat stranding due to seat belt loading. In scans in which the rSBS was present, anterior and superior displacement of rSBS from the anterior-superior iliac spine (ASIS) was measured bilaterally. This displacement was correlated with BMI and injury severity. RESULTS: The inclusion and exclusion criteria yielded 151 cases for analysis. An rSBS could definitively be identified in 55 cases. Cases in which occupants were older and had higher BMI were more likely to display an rSBS. There was a correlation between increasing BMI and anterior rSBS displacement (P <.01 and P <.01, right and left, respectively). There was no significant correlation between BMI and superior displacement of the rSBS (P =.46 and P =.33, right and left, respectively). When the data were examined in terms of relating increasing superior displacement of the lap belt with Injury Severity Scale (P =.34) and maximum Abbreviated Injury Score (AIS) injury severity (P =.63), there was also no significant correlation. CONCLUSION: The results from this study demonstrated that anterior displacement of the radiographic seat belt sign but not superior displacement increased with higher BMI. These results suggest that obesity may worsen horizontal position but not the vertical position of the lap belt loading during real-world frontal MVCs.

Diagnosis and treatment of neurotransmitter disorders.

The neurotransmitter disorders represent an enigmatic and enlarging group of neurometabolic conditions caused by abnormal neurotransmitter metabolism or transport. A high index of clinical suspicion is important, given the availability of therapeutic strategies. This article covers disorders of monoamine (catecholamine and serotonin) synthesis, glycine catabolism, pyridoxine dependency, and gamma-aminobutyric acid (GABA) metabolism. The technological aspects of appropriate cerebrospinal fluid (CSF) collection, shipment, study, and interpretation merit special consideration. Diagnosis of disorders of monoamines requires analysis of CSF homovanillic acid, 5-hydroxyindoleacetic acid, ortho-methyldopa, BH4, and neopterin. The delineation of new disorders with important therapeutic implications, such as cerebral folate deficiency and PNPO deficiency, serves to highlight the value of measuring CSF neurotransmitter precursors and metabolites. The impressive responsiveness of Segawa fluctuating dystonia to levodopa is a hallmark feature of previously unrecognized neurologic morbidity becoming treatable at any age. Aromatic amino acid decarboxylase and tyrosine hydroxylase deficiency have more severe phenotypes and show variable responsiveness to levodopa. Glycine encephalopathy usually has a poor outcome; benzoate therapy may be helpful in less affected cases. Pyridoxine-dependent seizures are a refractory but treatable group of neonatal and infantile seizures; rare cases require pyridoxal-5-phosphate. Succinic semialdehyde dehydrogenase deficiency is relatively common in comparison to the remainder of this group of disorders. Treatment directed at the metabolic defect with vigabatrin has been disappointing, and multiple therapies are targeted toward specific but protean symptoms. Other disorders of GABA metabolism, as is true of the wide spectrum of neurotransmitter disorders, will require increasing use of CSF analysis for diagnosis, and ultimately, treatment.

Inherited disorders of GABA metabolism.

The inherited disorders of γ-amino butyric acid (GABA) metabolism require an increased index of clinical suspicion. The known genetic disorders are GABA-transaminase deficiency, succinic semialdehyde dehydrogenase (SSADH) deficiency and homocarnosinosis. A recent link has also been made between impaired GABA synthesis and nonsyndromic cleft lip, with or without cleft palate. SSADH deficiency is the most commonly occurring of the inherited disorders of neurotransmitters. The disorder has a nonspecific phenotype with myriad neurological and psychiatric manifestations, and usually has a nonprogressive temporal course. Diagnosis is made by the detection of γ-hydroxybutyrate excretion on urine organic acid testing. The most consistent magnetic resonance imaging abnormality is an increased signal in the globus pallidus. Magnetic resonance spectroscopy has demonstrated the first example of increased endogenous GABA in human brain parenchyma in this disorder. GABA-transaminase deficiency and homocarnosinosis appear to be very rare, but require cerebrospinal fluid for detection, thus allowing for the possibility that these entities, as in the other inherited neurotransmitter disorders, are under-recognized.