Publications

Axon guidance in vertebrates is controlled by genetic cascades as well as by intrinsic activity-dependent refinement of connections. Midline axon crossing is one of the best studied pathfinding models and is fundamental to the establishment of bilaterally symmetric nervous systems. However, it is not known whether crossing requires intrinsic activity in axons, and what controls that activity. Further, a mechanism linking neuronal activity and gene expression has not been identified for axon pathfinding. Using embryonic zebrafish, we found that the NMDA receptor (NMDAR) NR1.1 subunit () is expressed in commissural axons. Pharmacological inhibition of , hypoxia exposure reduction of expression, or CRISPR knock-down of leads to defects in midline crossing. Inhibition of neuronal activity phenocopies the effects of loss on midline crossing. By combining pharmacological inhibition of the NMDAR with optogenetic stimulation to precisely restore neuronal activity, we observed rescue of midline crossing. This suggests that the NMDAR controls pathfinding by an activity-dependent mechanism. We further show that the NMDAR may act, via modulating activity, on the transcription factor (mammalian ), a known regulator of midline pathfinding. These findings uncover a novel role for the NMDAR in controlling activity to regulate commissural pathfinding and identify as a key link between the genetic and activity-dependent regulation of midline axon guidance.

BACKGROUND

Readmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor-to-modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high-risk patients as early as possible during hospitalization.

METHODS AND RESULTS

We used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all-cause nonelective 30-day readmissions, using stepwise backward selection and 5-fold cross-validation. Of 826 patients hospitalized with AMI, 13% had a 30-day readmission. The first-day AMI model (the AMI "READMITS" score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism-corrected C-statistic of 0.73 (95% confidence interval, 0.71-0.74) and was well calibrated. The full-stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism-corrected C-statistic of 0.75 (95% confidence interval, 0.74-0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models.

CONCLUSIONS

The parsimonious AMI READMITS score enables early prospective identification of high-risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full-stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did meaningfully improve reclassification.