Faculty Bibliography

BACKGROUND: Periprosthetic joint infection (PJI) is a serious complication of total hip arthroplasty (THA). Although the number of revision cases is increasing, the prevalence of PJI as an indication for revision surgery, and the variability of this indication among surgeons and hospitals, is unclear. METHODS: The New York Statewide Planning and Research Cooperative System was used to identify 33,582 patients undergoing revision THA between 2000 and 2013. PJI was identified using International Classification of Diseases, Ninth Revision diagnosis codes. Volume was defined using mean number of revision THAs performed annually by each hospital and surgeon. RESULTS: PJI was the indication for 13.0% of all revision THAs. The percentage of revision THAs for PJI increased between years 2000 and 2007 (odds ratio [OR] = 1.05, P < .001), but decreased between years 2008 and 2013 (OR = 0.96, P = .001). Compared to medium-volume hospitals, the PJI burden at high-volume hospitals decreased during years 2000-2007 (OR = 0.58, P < .001) and 2008-2013 (OR = 0.57, P < .001). Compared to medium-volume surgeons, the PJI burden for high-volume surgeons increased during years 2000-2007 (OR = 1.39, P < .001), but did not differ during years 2008-2013 (P = .618). CONCLUSION: The burden of PJI as an indication for revision THA may be plateauing. High-volume institutions have seen decreases in the percentage of revisions performed for PJI over the complete study duration. Specific surgeon may be associated with the plateauing in PJI rates as high-volume surgeons in 2008-2013 were no longer found to be at increased risk of PJI as an indication for revision THA.

Improvements in the quality of mental health care in the United States depend on the successful implementation of evidence-based treatments (EBT's) in typical settings of care. Unfortunately, there is little evidence that EBT's are used in ways that would approximate their established fidelity standards in such settings. This article describes an approach to more successful implementation of EBT's via a collaborative process between intervention developers and intervention users (e.g. providers, administrators, consumers) called Lead-user Innovation. Lead-user Innovation democratizes the implementation process by integrating the expertise of lead-users in the delivery, adaptation, innovation and evaluation of EBT's.

BACKGROUND: The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. METHODS: ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. RESULTS: Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. CONCLUSIONS: In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy regulation in youth. Recent studies indicate that machine learning techniques can help elucidate the role of neuroimaging measures in classifying individual subjects by specific symptom trajectory. Cortical thickness measures were extracted in sixty-eight anatomical regions covering the entire brain in 115 participants from the Longitudinal Assessment of Manic Symptoms (LAMS) study and 31 healthy comparison youth (12.5 y/o;-Male/Female = 15/16;-IQ = 104;-Right/Left handedness = 24/5). Using a combination of trajectories analyses, surface reconstruction, and machine learning techniques, the present study aims to identify the extent to which measures of cortical thickness can accurately distinguish youth with higher (n = 18) from those with lower (n = 34) trajectories of manic-like behaviors in a large sample of LAMS youth (n = 115; 13.6 y/o; M/F = 68/47, IQ = 100.1, R/L = 108/7). Machine learning analyses revealed that widespread cortical thickening in portions of the left dorsolateral prefrontal cortex, right inferior and middle temporal gyrus, bilateral precuneus, and bilateral paracentral gyri and cortical thinning in portions of the right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, and right parahippocampal gyrus accurately differentiate (Area Under Curve = 0.89;p = 0.03) youth with different (higher vs lower) trajectories of positive mood and energy dysregulation over a period up to 5years, as measured by the Parent General Behavior Inventory-10 Item Mania Scale. Our findings suggest that specific patterns of cortical thickness may reflect transdiagnostic neural mechanisms associated with different temporal trajectories of positive mood and energy dysregulation in youth. This approach has potential to identify patterns of neural markers of future clinical course.

The psychological mechanisms of action involved in psilocybin-assisted psychotherapy are not yet well understood. Despite a resurgence of quantitative research regarding psilocybin, the current study is the first qualitative study of participant experiences in psilocybin-assisted psychotherapy. Semistructured interviews were carried out with 13 adult participants aged 22 to 69 years (M = 50 years) with clinically elevated anxiety associated with a cancer diagnosis. Participants received a moderate dose of psilocybin and adjunctive psychotherapy with an emphasis on the process of meaning-making. Verbatim transcribed interviews were analyzed by a five-member research team using interpretative phenomenological analysis. General themes found in all or nearly all transcripts included relational embeddedness, emotional range, the role of music as conveyor of experience, meaningful visual phenomena, wisdom lessons, revised life priorities, and a desire to repeat the psilocybin experience. Typical themes found in the majority of transcripts included the following: exalted feelings of joy, bliss, and love; embodiment; ineffability; alterations to identity; a movement from feelings of separateness to interconnectedness; experiences of transient psychological distress; the appearance of loved ones as guiding spirits; and sharing the experience with loved ones posttreatment. Variant themes found in a minority of participant transcripts include lasting changes to sense of identity, synesthesia experiences, catharsis of powerful emotion, improved relationships after treatment, surrender or letting go, forgiveness, and a continued struggle to integrate experience. The findings support the conclusion that psilocybin-assisted psychotherapy may provide an effective treatment for psychological distress in cancer patients. Implications for theory and treatment are discussed.

A remarkable example of maladaptive plasticity is the development of epilepsy after a brain insult or injury to a normal animal or human. A structure that is considered central to the development of this type of epilepsy is the dentate gyrus (DG), because it is normally a relatively inhibited structure and its quiescence is thought to reduce hippocampal seizure activity. This characteristic of the DG is also considered to be important for normal hippocampal-dependent cognitive functions. It has been suggested that the brain insults which cause epilepsy do so because they cause the DG to be more easily activated. One type of brain insult that is commonly used is induction of severe seizures (status epilepticus; SE) by systemic injection of a convulsant drug. Here we describe an alteration in the DG after this type of experimental SE that may contribute to chronic seizures that has not been described before: large folds or gyri that develop in the DG by 1 month after SE. Large gyri appeared to increase network excitability because epileptiform discharges recorded in hippocampal slices after SE were longer in duration when recorded inside gyri relative to locations outside gyri. Large gyri may also increase excitability because immature adult-born neurons accumulated at the base of gyri with time after SE, and previous studies have suggested that abnormalities in adult-born DG neurons promote seizures after SE. In summary, large gyri after SE are a common finding in adult rats, show increased excitability, and are associated with the development of an abnormal spatial distribution of adult-born neurons. Together these alterations may contribute to chronic seizures and associated cognitive comorbidities after SE.

Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and stimulus-evoked BOLD-signal changes. These task-based hemodynamic changes are synchronized across subjects and involve large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in BOLD-signal based functional connectivity (FC) across 2 distinct movie conditions and eyes-open rest (n=31 healthy adults, 2 scan sessions each). We found that movies have higher within- and between-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-individual variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching algorithm that identifies individual subjects from within a group based on FC patterns, quantifying the accuracy of the algorithm across the three conditions. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 61 and 100%. Overall, pairings involving movies outperformed rest, and the social, faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration, and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are unique at the individual level.