Routine outcome monitoring to improve mental healthcare practice for patients with severe mental illness:Insights from a micro, meso and macro perspective

“Routine outcome monitoring bij ernstige psychiatrische aandoeningen” Patiënten met ernstige psychiatrische aandoeningen (EPA) hebben langdurige psychische klachten. Deze gaan vaak gepaard met problemen op andere gebieden, zoals op sociaal-maatschappelijk vlak. De behandeling vraagt om een intensieve en multidisciplinaire aanpak. In de praktijk blijkt het lastig om de zorg voor deze groep patiënten goed in te richten. Geconfronteerd met een grote zorgvraag en een beperkt pakket aan middelen staat de GGZ voor een grote uitdaging om efficiënter te werken. Routine outcome monitoring (ROM) is een methodiek waarbij herhaaldelijk voortgangsmetingen worden verricht ter ondersteuning van de behandeling van de patiënt. ROM wordt zowel ingezet op individueel niveau, als op organisatieniveau en in het GGZ-systeem als geheel. Het proefschrift van Sascha Kwakernaak laat zien dat ROM-data goede aanknopingspunten geven voor verbetering van de zorg en richt zich op 3 niveaus: micro (individu), meso (organisatie) en macro (GGZ zorgstelsel). Het onderzoek richt zich vooral op de zorg voor patiënten met psychotische aandoeningen, omdat zij een groot deel van de EPA-populatie uitmaken. De ROM-data die gebruikt zijn in het onderzoek zijn afkomstig van de Zorgmonitor (Altrecht) en het GROUP-project1. Op microniveau is ROM-data ondersteunend in het bepalen van de behandelfocus. Consistent gebruik van de data door clinici geeft meer inzicht in de behoeften van patiënten. Daardoor is meer op maat gesneden zorg mogelijk. Bovendien veranderen de ernst van de symptomen en het niveau van functioneren in de loop van de tijd en lijken ze voor een groot deel onafhankelijk van elkaar te zijn. Daarnaast komt naar voren dat de behandeling niet alleen gericht moet zijn psychiatrische symptomen, maar ook op sociaal-maatschappelijk gebied, zoals werk, dagbesteding en sociaal contact. Dit vereist samenwerking en afstemming tussen GGZ-organisaties en maatschappelijke dienstverlening. Op mesoniveau blijkt ROM-data, naast voorgaand zorggebruik, goede voorspellers zijn om inzicht te krijgen in toekomstige zorggebruik. Een kleine groep patiënten zorgt voor een groot deel van de totale GGZ-kosten, maar het zorggebruik van deze groep is moeilijk te voorspellen. Door ROM te combineren met machine learning technieken kunnen we toekomstig zorggebruik steeds nauwkeuriger voorspellen. Voor GGZ-organisaties is een goed beleid ten aanzien van ROM daarom essentieel. Dit draagt onder andere bij aan transparante onderhandelingen tussen zorgaanbieders en financiers. Omdat ROM-data inzicht geven in de zorgvraag kunnen aanbieders hun middelen efficiënter inzetten. Gelet op de lange wachtlijsten en een tekort aan zorgprofessionals is dit erg belangrijk. Op macroniveau is het indelen van patiënten met EPA in groepen een uitdaging. Met een verdeling van patiënten op basis van eerder zorggebruik kan geen duidelijk onderscheid worden gemaakt tussen de groepen met betrekking tot zorgbehoeften en beperkingen in functioneren. Om toe te werken naar een vraaggericht en transparant systeem is het wenselijk om informatie over zorggebruik te combineren met ROM-data. In zo’n situatie is de kans groter dat men tot gemeenschappelijke doelen komt

The impact of machine learning in predicting risk of violence: a systematic review

Background: Inpatient violence in clinical and forensic settings is still an ongoing challenge to organizations and practitioners. Existing risk assessment instruments show only moderate benefits in clinical practice, are time consuming, and seem to scarcely generalize across different populations. In the last years, machine learning (ML) models have been applied in the study of risk factors for aggressive episodes. The objective of this systematic review is to investigate the potential of ML for identifying risk of violence in clinical and forensic populations.Methods: Following Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, a systematic review on the use of ML techniques in predicting risk of violence of psychiatric patients in clinical and forensic settings was performed. A systematic search was conducted on Medline/Pubmed, CINAHL, PsycINFO, Web of Science, and Scopus. Risk of bias and applicability assessment was performed using Prediction model Risk Of Bias ASsessment Tool (PROBAST).Results: We identified 182 potentially eligible studies from 2,259 records, and 8 papers were included in this systematic review. A wide variability in the experimental settings and characteristics of the enrolled samples emerged across studies, which probably represented the major cause for the absence of shared common predictors of violence found by the models learned. Nonetheless, a general trend toward a better performance of ML methods compared to structured violence risk assessment instruments in predicting risk of violent episodes emerged, with three out of eight studies with an AUC above 0.80. However, because of the varied experimental protocols, and heterogeneity in study populations, caution is needed when trying to quantitatively compare (e.g., in terms of AUC) and derive general conclusions from these approaches. Another limitation is represented by the overall quality of the included studies that suffer from objective limitations, difficult to overcome, such as the common use of retrospective data.Conclusion: Despite these limitations, ML models represent a promising approach in shedding light on predictive factors of violent episodes in clinical and forensic settings. Further research and more investments are required, preferably in large and prospective groups, to boost the application of ML models in clinical practice

Toward precision psychiatry in bipolar disorder : staging 2.0

Personalized treatment is defined as choosing the “right treatment for the right person at the right time.” Although psychiatry has not yet reached this level of precision, we are on the way thanks to recent technological developments that may aid to detect plausible molecular and genetic markers. At the moment there are some models that are contributing to precision psychiatry through the concept of staging. While staging was initially presented as a way to categorize patients according to clinical presentation, course, and illness severity, current stagingmodels integratemultiple levels of information that can help to define each patient’s characteristics, severity, and prognosis in a more precise and individualized way. Moreover, staging might serve as the foundation to create a clinical decision-making algorithm on the basis of the patient’s stage. In this review we will summarize the evolution of the bipolar disorder staging model in relation to the new discoveries on the neurobiology of bipolar disorder. Furthermore, we will discuss how the latest and future progress in psychiatry might transform current staging models into precision staging models

Predicting high-cost care in a mental health setting

Background: The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes. Aims: We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation. Method: We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves. Results: In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12–46%). Conclusions: EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance

Machine-Learning for Prescription Patterns: Random Forest in the Prediction of Dose and Number of Antipsychotics Prescribed to People with Schizophrenia

Objective: We aimed to predict antipsychotic prescription patterns for people with schizophrenia using machine learning (ML) algorithms.Methods: In a cross-sectional design, a sample of community mental health service users (SUs; n = 368) with a primary diagnosis of schizophrenia was randomly selected. Socio-demographic and clinical features, including the number, total dose, and route of administration of the antipsychotic treatment were recorded. Information about the number and the length of psychiatric hospitalization was retrieved. Ordinary Least Square (OLS) regression and ML algorithms (i.e., random forest [RF], supported vector machine, K-nearest neighborhood, and Naive Bayes) were used to estimate the predictors of total antipsychotic dosage and prescription of antipsychotic polytherapy (APP).Results: The strongest predictor of the total dose was APP. The number of Community Mental Health Centers (CMHC) contacts was the most important predictor of APP and, with APP omitted, of dosage. Treatment with anticholinergics predicted APP, emphasizing the strong correlation between APP and higher antipsychotic dose. RF performed better than OLS regression and the other ML algorithms in predicting both antipsychotic dose (root square mean error = 0.70, R-2 = 0.31) and APP (area under the receiving operator curve = 0.66, true positive rate = 0.41, and true negative rate = 0.78).Conclusion: APP is associated with the prescription of higher total doses of antipsychotics. Frequent attenders at CMHCs, and SUs recently hospitalized are often treated with APP and higher doses of antipsychotics. Future prospective studies incorporating standardized clinical assessments for both psychopathological severity and treatment efficacy are needed to confirm these findings

Brain functioning, networks and topology during naturalistic stimulus in first-episode psychosis

Psychosis is a condition where the ability to distinguish between external and internal experiences is diminished or distorted. It is a core symptom of several psychiatric disorders which have a crucial impact on the quality of patients’ lives. Although the neuronal basis of psychosis is unknown, studies suggest that psychosis is derived from glutamate-driven and dopamine-related altered signaling within and between different brain regions. Brain imaging studies have typically used resting state or simple task paradigms to study brain mechanisms related to psychosis. In this thesis, naturalistic stimulus during functional magnetic imaging was used to explore more complex stimulus processing. Movies have been shown to synchronize brain activity across subjects more efficiently than resting state and are increasingly used in neuroscience. The main aim of this thesis was to characterize differences in brain functioning between first-episode psychosis patients and control subjects during naturalistic stimulus, i.e., movie viewing. Participants were drawn from the Helsinki Psychosis Study. The patient group consisted of individuals treated for first psychotic episode in the hospitals and outpatient clinics of the Helsinki University hospital and the city of Helsinki. Age-, sex- and area of residence matched control subjects were recruited via the Finnish Population Registration System. Papers I and II include data from 32 patients and 46 control subjects, and paper III includes 71 patients and 57 control subjects. In paper I, machine learning was used to identify a bilateral region in the precuneus, where activation patterns during movie viewing distinguished patients from control subjects. Classification accuracy was associated with positive symptoms in that the higher the symptom score the more reliable the classification. In paper II, both functional and structural connectivity were studied to establish that the precuneus region showed functional connectivity across the movie to the default mode network and that patients had increased function connectivity between the precuneus seed region and the medial prefrontal cortex. No differences were observed in the underlying white matter structural connectivity. In paper III functional connectivity was used on the whole-brain level to identify a sub-network, or component, where connectivity differed between patients and control subjects. Patients had mainly decreased but also, in some connections, increased functional connectivity. Also, differences between patients and control subjects in graph measures were observed, most prominently in the centrality of the insula. The results suggest brain functioning during naturalistic stimulus is altered in first-episode psychosis. Most prominent differences are concentrated in the hub regions of the brain and regions and functional networks related to salience attribution and model updating. Understanding naturalistic stimulus related brain correlates of aberrant cognitive processes already present during early stages of psychosis might provide better targeted and more efficient biological, cognitive and behavioral interventions in the future.Psykoosilla tarkoitetaan todellisuudentajun vakavaa heikentymistä, mikä voi ilmetä aistiharhoina, harhaluuloina, puheen tai käytöksen hajanaisuutena sekä katatonisina oireina. Psykoosi on usean vakavan mielenterveyden häiriön keskeinen oire. Ensipsykoosilla tarkoitetaan ensimmäistä sairausjaksoa, johon liittyy merkittäviä psykoosioireita. Psykoosiin liittyviä aivojen toiminnallisia muutoksia ei vielä täysin tunneta, mutta niiden uskotaan olevan yhteydessä poikkeavaan viestintään eri aivoalueiden välillä. Tämän tutkielman päätavoitteena on tutkia eroja ensipsykoosiin sairastuneiden ja verrokkihenkilöiden aivotoiminnassa elokuvan katsomisen aikana. Aivokuvantamistutkimuksissa on tyypillisesti käytetty ärsykkeenä lepotila-asetelmaa tai yksinkertaisia tehtäviä. Tässä tutkielmassa käytettiin naturalistista elokuvastimulusta, jolloin ärsykkeen käsittely vastaa paremmin arjen vaatimuksia. Tutkielman potilasaineisto koostuu henkilöistä, joilla oli ensimmäinen hoitokontakti psykoosioireiden vuoksi. Verrokkihenkilöt on rekrytoitu Suomen väestörekisterin kautta. Osatöissä I ja II aineistossa on mukana 32 potilasta ja 46 vertailuhenkilöä ja osatyössä III 71 potilasta ja 57 vertailuhenkilöä. Osatyössä I hyödynnettiin koneoppimismenetelmiä ja tunnistettiin molemminpuolinen alue aivojen etukiilassa (precuneus), jossa aivotoimintaa mittaavan fMRI-signaalin vaihtelu elokuvan aikana erotteli potilaita verrokeista sattumaa merkittävästi paremmalla tarkkuudella. Potilaiden luokittelu oli sitä varmempaa, mitä voimakkaampia positiivisia oireita eli harhaluuloja tai aistiharhoja heillä oli. Osatyössä II tarkasteltiin toiminnallisia ja rakenteellisia potilas - verrokki eroja etukiila-alueen ja muiden aivoalueiden yhteyksissä. Potilailla havaittiin vahvempi toiminallinen yhteys eli konnektiviteetti etukiilan ja etuotsalohkon välillä. Alueita yhdistävissä rakenteissa ei ollut ryhmäeroja. Osatyössä III tarkasteltiin elokuvan aikaista aivotoimintaa koko aivoja kattavan verkoston näkökulmasta ja tunnistettiin alaverkosto, jossa konnektiviteetti poikkesi potilaiden ja verrokkien välillä. Lisäksi havaittiin potilas – verrokki eroja verkoston rakennetta kuvaavissa mittareissa, selkeimmin aivosaarekkeen (insulan) keskeisyydessä

Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling

INTRODUCTION: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data. METHODS: A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation. RESULTS: Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values. CONCLUSION: DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment

Steroid Psychosis, History of Corticosteroid Use and Associated Side Effects: Implications for Rehabilitation Counselors

Throughout the medical community, corticosteroid medications are widely used for the treatment of ailments ranging from inflammatory disorders such as arthritic conditions and spinal cord injuries, to skin conditions such eczema and psoriasis. Additional conditions could also include such disorders as inflammatory bowel disease. Also associated with corticosteroid treatments is the anabolic steroid family. These types of steroids are most commonly used for the enhancement of human tissues such as muscle growth associated with athletes referred to as performance enhancing drugs. Additionally, the use of diuretics, predominately in women, have a base of some type of steroid. For the users of corticosteroids and anabolic steroids, there is the danger of an associated condition known as neuropsychiatric psychosis, or steroid psychosis. Although in most cases, the cessation of the steroid use or even tapering the use can reverse the condition. There is approximately 7% to 10% of cases that the psychosis becomes a permanent condition. In many cases the psychosis goes un-diagnosed due to the mimicking of various mental illnesses such as bi-polar disorder, mood disorders, and even schizophrenia. The need for careful monitoring of the administration of these medications as well as tracking of medical. History while treating suspected mental illness are imperative to early identification of steroid psychosis as well as the rapid and effective treatment of said condition. The purpose of this paper is to raise awareness with rehabilitation counselors that due to treatment of some clients with steroid medications could be the cause of symptoms similar to those conditions previously mentioned. The steroid condition can be reduced or eliminated through medication management which very well may reduce the time needed for treatment. The implications for rehabilitation counselors could be the shortened treatment time, adjustments to the treatment plan as well as identifying the condition to the clients so the client will have a clearer understanding of their condition. An additional implication for the counselor will that it is prudent to always review all available medical history information for each client to be aware of all medications or medical procedures that may have been conducted in the client’s history