Transdiagnostic phenotypes of compulsive behavior and associations with psychological, cognitive, and neurobiological affective processing

Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18–45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled “Compulsive Non-Avoidant”, “Compulsive Reactive” and “Compulsive Stressed”. They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.

Pandemic-related declines in hospitalization for non-COVID-19-related illness in the United States from January through July 2020

Background The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially impacted healthcare utilization worldwide. The objective of this retrospective analysis of a large hospital discharge database was to compare all-cause and cause-specific hospitalizations during the first six months of the pandemic in the United States with the same months in the previous four years. Methods Data were collected from all hospitals in the Premier Healthcare Database (PHD) and PHD Special Release reporting hospitalizations from January through July for each year from 2016 through 2020. Hospitalization trends were analyzed stratified by age group, major diagnostic categories (MDCs), and geographic region. Results The analysis included 286 hospitals from all 9 US Census divisions. The number of all-cause hospitalizations per month was relatively stable from 2016 through 2019 and then fell by 21% (57,281 fewer hospitalizations) between March and April 2020, particularly in hospitalizations for non-respiratory illnesses. From April onward there was a rise in the number of monthly hospitalizations per month. Hospitalizations per month, nationally and in each Census division, decreased for 20 of 25 MDCs between March and April 2020. There was also a decrease in hospitalizations per month for all age groups between March and April 2020 with the greatest decreases in hospitalizations observed for patients 50–64 and ≥65 years of age. Conclusions Rates of hospitalization declined substantially during the first months of the COVID-19 pandemic, suggesting delayed routine, elective, and emergency care in the United States. These lapses in care for illnesses not related to COVID-19 may lead to increases in morbidity and mortality for other conditions. Thus, in the current stage of the pandemic, clinicians and public-health officials should work, not only to prevent SARS-CoV-2 transmission, but also to ensure that care for non-COVID-19 conditions is not delayed.

Post-concussive Dizziness: A Review and Clinical Approach to the Patient

Dizziness is a frequent complaint after head trauma. Among patients who suffer a concussion (mild traumatic brain injury or mTBI), dizziness is second only to headache in symptom frequency. The differential diagnosis of post-concussive dizziness (PCD) can be divided into non-vestibular, central vestibular and peripheral vestibular causes with growing recognition that patients frequently exhibit both central and peripheral findings on vestibular testing. Symptoms that traditionally have been ascribed to central vestibular dysfunction may be due to peripheral dysfunction. Further, our ability to test peripheral vestibular function has improved and has allowed us to identify peripheral disorders that in the past would have remained unnoticed. The importance of the identification of the peripheral component in PCD lies in our ability to remedy the peripheral vestibular component to a much greater extent than the central component. Unfortunately, many patients are not adequately evaluated for vestibular disorders until long after the onset of their symptoms. Among the diagnoses seen as causes for PCD are (1) Central vestibular disorders, (2) Benign Paroxysmal Positional Vertigo (BPPV), (3) Labyrinthine dehiscence/perilymph fistula syndrome, (4) labyrinthine concussion, (5) secondary endolymphatic hydrops, (6) Temporal bone fracture, and (7) Malingering (particularly when litigation is pending). These diagnoses are not mutually exclusive and PCD patients frequently exhibit a combination of these disorders. A review of the literature and a general approach to the patient with post-concussive dizziness will be detailed as well as a review of the above-mentioned diagnostic categories.

Familial clustering of psychiatric disorders and low IQ

Background Although the ICD and DSM differentiate between different psychiatric disorders, these often share symptoms, risk factors, and treatments. This was a population-based, case–control, sibling study examining familial clustering of all psychiatric disorders and low IQ, using data from the Israel Draft-Board Registry on all Jewish adolescents assessed between 1998 and 2014. Methods We identified all cases with autism spectrum disorder (ASD, N = 2128), severe intellectual disability (ID, N = 9572), attention-deficit hyperactive disorder (ADHD) (N = 3272), psychotic (N = 7902), mood (N = 9704), anxiety (N = 10 606), personality (N = 24 816), or substance/alcohol abuse (N = 791) disorders, and low IQ (⩾2 SDs below the population mean, N = 31 186). Non-CNS control disorders were adolescents with Type-1 diabetes (N = 2427), hernia (N = 29 558) or hematological malignancies (N = 931). Each case was matched with 10 age-matched controls selected at random from the Draft-Board Registry, with replacement, and for each case and matched controls, we ascertained all full siblings. The main outcome measure was the relative recurrence risk (RRR) of the sibling of a case having the same (within-disorder RRR) or a different (across-disorder RRR) disorder. Results Within-disorder RRRs were increased for all diagnostic categories, ranging from 11.53 [95% confidence interval (CI): 9.23–14.40] for ASD to 2.93 (95% CI: 2.80–3.07) for personality disorders. The median across-disorder RRR between any pair of psychiatric disorders was 2.16 (95% CI: 1.45–2.43); the median RRR between low IQ and any psychiatric disorder was 1.37 (95% CI: 0.93–1.98). There was no consistent increase in across-disorder RRRs between the non-CNS disorders and psychiatric disorders and/or low IQ. Conclusion These large population-based study findings suggest shared etiologies among most psychiatric disorders, and low IQ.

From syndromes to symptoms: Advancing our understanding of mental disorders

<p>Traditionally, psychiatric syndromes have formed the primary target of explanation in psychopathology research. However, these syndromes have been significantly criticised for their conceptual weakness and lack of validity. Ultimately, this limits our ability to create valid explanations of these categories; if the target is invalid then our explanations will suffer as a consequence. Using depression as extended example, this doctoral thesis explores the theoretical and methodological challenges associated with classifying and explaining mental disorders, and develops an alternative explanatory approach and associated methodology for advancing our understanding of mental disorders – the Phenomena Detection Method (PDM; Clack & Ward, 2020; Ward & Clack, 2019). This theoretical thesis begins by evaluating the current approaches to defining, classifying, and explaining mental disorders like depression, and explores the methodological and theoretical challenges with building theories of them. Next, in moving forward, I argue that the explanatory target in psychopathology research should shift from arbitrary syndromes to the central symptoms and signs of mental disorders. By conceptualising the symptoms of a disorder as clinical phenomena, and by adopting epistemic model pluralism as an explanatory strategy, we can build multi-faceted explanations of the processes and factors that constitute a disorder’s core symptoms. This core theoretical and methodological work is then followed by the development of the PDM. Unique in the field of psychopathology, the PDM links different phases of the inquiry process to provide a methodology for conceptualising the symptoms of psychopathology and for constructing multi-level models of the pathological processes that comprise them. Next, I apply the PDM to the two core symptoms of depression – ¬anhedonia and depressed mood – as an illustrative example of the advantages of this approach. This includes providing a more secure relationship between the pathology of depression and its phenotypic presentation, as well as greater insight into the relationship between underlying biological and psychological processes, and behavioural dysfunction. Next, I evaluate the PDM in comparison to existing metatheoretical approaches in the field and make some suggestions for future development. Finally, I conclude with a summary of the main contributions of this thesis. Considering the issues with current diagnostic categories, simply continuing to build explanations of syndromes is not a fruitful way forward. Rather, the complexity of mental disorders suggests we need to represent their key psychopathological phenomena or symptoms at different levels or aspects using multiple models. This thesis provides the metatheoretical and methodological foundations for this to successfully occur.</p>

From syndromes to symptoms: Advancing our understanding of mental disorders

<p>Traditionally, psychiatric syndromes have formed the primary target of explanation in psychopathology research. However, these syndromes have been significantly criticised for their conceptual weakness and lack of validity. Ultimately, this limits our ability to create valid explanations of these categories; if the target is invalid then our explanations will suffer as a consequence. Using depression as extended example, this doctoral thesis explores the theoretical and methodological challenges associated with classifying and explaining mental disorders, and develops an alternative explanatory approach and associated methodology for advancing our understanding of mental disorders – the Phenomena Detection Method (PDM; Clack & Ward, 2020; Ward & Clack, 2019). This theoretical thesis begins by evaluating the current approaches to defining, classifying, and explaining mental disorders like depression, and explores the methodological and theoretical challenges with building theories of them. Next, in moving forward, I argue that the explanatory target in psychopathology research should shift from arbitrary syndromes to the central symptoms and signs of mental disorders. By conceptualising the symptoms of a disorder as clinical phenomena, and by adopting epistemic model pluralism as an explanatory strategy, we can build multi-faceted explanations of the processes and factors that constitute a disorder’s core symptoms. This core theoretical and methodological work is then followed by the development of the PDM. Unique in the field of psychopathology, the PDM links different phases of the inquiry process to provide a methodology for conceptualising the symptoms of psychopathology and for constructing multi-level models of the pathological processes that comprise them. Next, I apply the PDM to the two core symptoms of depression – ¬anhedonia and depressed mood – as an illustrative example of the advantages of this approach. This includes providing a more secure relationship between the pathology of depression and its phenotypic presentation, as well as greater insight into the relationship between underlying biological and psychological processes, and behavioural dysfunction. Next, I evaluate the PDM in comparison to existing metatheoretical approaches in the field and make some suggestions for future development. Finally, I conclude with a summary of the main contributions of this thesis. Considering the issues with current diagnostic categories, simply continuing to build explanations of syndromes is not a fruitful way forward. Rather, the complexity of mental disorders suggests we need to represent their key psychopathological phenomena or symptoms at different levels or aspects using multiple models. This thesis provides the metatheoretical and methodological foundations for this to successfully occur.</p>

A Single-Center Retrospective Evaluation of Unplanned Pediatric Critical Care Upgrades

Background Inappropriate triage of critically ill pediatric patients can lead to poor outcomes and suboptimal resource utilization. This study aimed to determine and describe the demographic characteristics, diagnostic categories, and timing of unplanned upgrades to the pediatric intensive care unit (PICU) that required short (< 24 hours of care) and extended (≥ 24 hours of care) stays. In this article, we hypothesized that we will identify demographic characteristics, diagnostic categories, and frequent upgrade timing periods in both of these groups that may justify more optimal triage strategies. Methods This was a single-institution retrospective study of unplanned PICU upgrades between 2012 and 2018. The cohort was divided into two groups (short and extended PICU stay). We reviewed the electronic health record and evaluated for: demographics, mortality scores, upgrade timing (7a-3p, 3p-11p, 11p-7a), lead-in time (time spent on clinical service before upgrade), patient origin, and diagnostic category. Results Four hundred and ninety-eight patients' unplanned PICU upgrades were included. One hundred and nine patients (21.9%) required a short and 389 (78.1%) required an extended PICU stay. Lead-in time (mean, standard deviation) was significantly lower in the short group (0.65 ± 0.66 vs. 0.91 ± 0.82) (p = 0.0006). A higher proportion of short group patients (59, 46.1%) were upgraded during the 3p-11p shift (p = 0.0077). Conclusion We found that approximately one-fifth of PICU upgrades required less than 24 hours of critical care services, were more likely to be transferred between 3p-11p, and had lower lead-in times. In institutions where ill pediatric patients can be admitted to either a PICU or a monitored step-down unit, this study highlights quality improvement opportunities, particularly in recognizing which pediatric patients truly need critical care.

Pain Assessment and Documentation for Older Adults Presenting with Non-surgical Conditions in Emergency Room

Pain is one of the most common reasons for Emergency Department (ED) visits among older adults. However, timely pain assessment and management in this population in ED is a challenging task due to many factors ranging from; sensory, cognitive impairments, chronic pain, reliability of assessment tools, multimorbidity and system factors such as triage-based dynamic ED workflow. Where the implementation of the EMR was anticipated to improve patientcare, literature has indicated the barriers in effective utilization of the EMR for this purpose. We posit that pain assessment and documentation could be variable among older adults presenting with non-surgical conditions. Objectives:1. To examine the proportion of documented initial pain assessment of nonsurgical older adults visiting emergency department 2. To examine the number of initial pain assessments documented in the chart by the five major categories of ICD-10 diagnoses upon discharge. Methods A retrospective exploratory chart review of 4613 emergency room visits for first pain assessment in the EMR conducted for all adults 65 years or older, presenting with non-surgical conditions, who were discharged same day at an urban teaching hospital. Results In our study 75.72% of encounters reviewed had a documented pain assessment. Completed pain assessments for the corresponding five most common non-surgical diagnostic categories presenting to our ED: Abdominal pain (92.59%), MSK (92.11 %), chest pain (83.92%), dyspnea ( 80%) and falls (79.46%). Conclusion Frequency of pain assessment and the management process of older adults presenting with non-surgical conditions in the institution studied was variable and differed based on presenting conditions.