# Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms

Julie Bourgin, Edouard Duchesnay, Emilie Magaud, Raphaël Gaillard, Mathilde Kazes, Marie-Odile Krebs

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## ► To cite this version:

Julie Bourgin, Edouard Duchesnay, Emilie Magaud, Raphaël Gaillard, Mathilde Kazes, et al. Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms. European Child and Adolescent Psychiatry, 2019, 29 (11), pp.1525 - 1535. 10.1007/s00787-019-01461-y. hal-04996032

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# Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS):
a multivariate model reveals the influence of nonpsychotic prodromal symptoms

*Running title: Individual clinical prediction in ARMS*

Julie Bourgin¹,³, Edouard Duchesnay⁴, Emilie Magaud⁵, Raphaël Gaillard¹,², Mathilde Kazes¹,², Marie-Odile Krebs¹,²

1. INSERM, Laboratoire Physiopathologie des maladies Psychiatriques, IPNP, UMR 1266, Institut de psychiatrie (CNRS GDR 3557) Paris, France

2. GHU Paris Sainte-Anne, Service Hospitalo-Universitaire, University Paris Descartes, Paris, France.

3. GHNE - Site de Orsay. Domaine du Grand Mesnil - Voie Kastler - 91440 BURES SUR YVETTE

4. NeuroSpin, CEA, Paris-Saclay, Gif-sur-Yvette - France

5. Hotchkiss Brain Institute, Mathison Centre for Mental Health Research & Education, Calgary, Alberta, Canada.

**Corresponding author: Julie Bourgin MD, PhD**

Mail : julie.bourgin@gmail.com

Tel : +33(0)6 81 36 96 45
**Abstract**

**Aim:** To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation and using a multivariate machine learning method known as logistic regression with Elastic-net shrinkage.

**Methods:** 46 young individuals who sought help from the specialized outpatient unit at Sainte-Anne hospital and who met CAARMS criteria for UHR were assessed, among whom 27 were reassessed at follow-up (22.4 ± 6.54 months) and included in the analysis. Elastic net logistic regression was trained, using CAARMS items at baseline to predict individual evolution between converters (UHR-P) and non-converters (UHR-NP). Elastic-net was used to select the few CAARMS items that best predict the clinical evolution. All validations and significances of predictive models were computed with non-parametric re-sampling strategies that provide robust estimators even when the distributional assumption cannot be guaranteed.

**Results:** Among the 25 CAARMS items, the Elastic net selected 'obsessive-compulsive symptoms' and 'aggression / dangerous behavior' as risk factors for conversion while 'anhedonia' and 'mood swings / lability' were associated with non-conversion at follow-up. In the ten-fold stratified cross-validation, the classification achieved 81.8% of sensitivity (P = 0.035) and 93.7% of specificity (P = 0.0016).

**Conclusions:** Non-psychotic prodromal symptoms bring valuable information to improve the prediction of conversion to psychosis. Elastic net logistic regression applied to clinical data is a promising way to switch from group prediction to an individualized prediction.

**Keywords:** Machine learning algorithms, obsessive compulsive symptoms, impulsivity, early detection, schizophrenia
INTRODUCTION

Psychotic disorders are preceded by a prodromal period, prior to the onset of the frank disorder[1, 2]. In the mid-1990s, when first episode studies revealed the prognostic value of the duration of untreated psychosis, improving the detection of young patients with those 'prodromal' symptoms became a major issue, with the aim to intervene earlier in the course of psychotic disorders. Clinical criteria, such as the 'ultra-high-risk' (UHR) criteria, were developed in Melbourne to identify help-seeking people at substantially elevated risk of developing psychosis in the near future[3]. The Comprehensive Assessment of At-Risk Mental States (CAARMS)[4] was the first instrument specifically designed for the identification of subclinical psychopathology that indicates the imminence of a full-blown first-episode psychotic disorder. Besides positive symptoms (disorders of thought content, perceptual abnormalities, and disorganized speech), which are used for the operational definition of UHR, the CAARMS covers extensive psychopathological domains such as: cognitive changes, emotional disturbances, negative symptoms, behavioral changes, motor and physical changes, and general psychopathology[4]. Specifically, the CAARMS maps psychopathological symptoms of subclinical intensity, below the sensitivity threshold of clinical scales such as the Brief Psychiatric Rating Scale (BPRS)[5, 6], the Scale for the Assessment of Negative Symptoms and Positive Symptoms (SANS-SAPS)[7, 8] and the Positive and Negative Syndrome Scale (PANSS)[9].

On a psychopathological level, a large body of literature has brought to light the predictive value of attenuated or transient psychotic symptoms to predict conversion to psychosis assessed with CAARMS[4] or with SIPS (Structured Interview for Prodromal Symptoms)[10]. In fact, more severe positive symptoms[13, 14], severity of unusual thought content, suspiciousness, reduced ideational richness and difficulty with focus/concentration[15], physical anhedonia[16] and perceptual disturbance[17] might further increase the risk of transition, whereas negative symptoms in general[18] or initial anxiety[19] do not further contribute to prediction. Disorganized communication seems to be predictive in children[20] and adolescents[21]. This clinically defined population was initially found to have a rate of 'conversion' to a frank psychotic disorder of around 40% in the first 12 months after presentation[3, 22]. This rate of conversion is decreasing in most early intervention services and the reasons for this are unclear. Several theories have been advanced, including heterogeneity in the sample and instrument used, earlier intervention and therefore perhaps more 'false positives' in help-
seeking subjects, and the possibility that standard treatment for UHR patients has become more
effective over the years in delaying or preventing transition to psychosis[23-26].

Nevertheless, despite the promising initial predictive validity of CAARMS UHR criteria, the high rate of
'false positives' (i.e. persons meeting the UHR criteria who do not develop psychosis)[27, 28] has
prompted the investigation of additional predictors. Among the predictive biomarkers studied, cognitive
functioning[29-31], brain imaging[32-34] and biological biomarkers[35-38] have been proposed, but
these investigations are not always feasible in daily clinical practice. In addition, one pitfall limiting further
advancement in this field is the heterogeneity of high-risk groups: recently, Fusar Poli et al. presented
evidence that BLIPS (Brief Limited Intermittent Psychotic Symptoms) represents a separate risk
subgroup compared to the APS (Attenuated Psychotic Symptoms) and to GRD (Genetic Risk and
Deterioration), which are less frequent in clinical settings and not associated with an increased risk of
psychosis[39]. In our study, all UHR belonged to the APS subgroup to reduce sample heterogeneity
across subgroups.

Beyond the attenuated psychotic symptoms, the CAARMS scale measures other more general
psychopathological dimensions. Indeed, help-seeking subjects at high risk for psychosis usually
present, in addition to attenuated psychotic symptoms, other comorbid non-psychotic disorders,
especially anxiety disorders and depression[40]. On the other hand, "psychotic-like" symptoms can
occur in a variety of non-psychotic disorders such as post-traumatic stress disorder or personality
disorders[41]. Non-psychotic symptoms convey information on other psychopathological dimensions,
complementary to the positive psychotic dimension. Several studies have applied a multidomain
approach to test for independent associations of variables of interest with transition to psychosis[29, 42,
43] but, to the best of our knowledge, very few studies have examined the value of non-psychotic
symptoms in the prediction of psychosis conversion using the CAARMS scale[44, 45].

In addition, the absence of reliable and valid predictive biomarkers may reflect a number of factors
described above, including the reduction in transition risk in recent years, but also the lack of external
validation and methodological pitfalls[46]. To overcome methodological difficulties and because
psychosis is a complex and heterogeneous disorder, assessing the predictive value of markers at the
individual level is necessary to accurately translate to the clinic. This requires multivariate statistical
methods that can combine many potential predictor variables in order to build a more efficient predictive
rule. Cannon et al.[47] (291 treatment-seeking patients with 35% transition rate) and Seidman et al.[31]
(689 clinical high-risk with 89 conversions to psychosis) used multivariate Cox regression models to discover associations between clinical scores and transitions. However, these studies did not assess the ability of the models to generalize to independent samples. To address this challenging goal, all studies used regularized (Support Vector Machine, SVM) models: Koutsouleris et al.[48] (20: UHR-NP vs 15: UHR-P) were able to achieve a sensitivity of 80.0% and specificity of 75.0% of a neurocognitive battery. Mechelli et al.[49] (416 ultra-high risk (UHR) for psychosis with 27% transition rate) obtained 68.6% sensitivity and 60.6% specificity with the same regularized algorithm. This suggests that machine learning methods, such as SVM, outperform classical statistical methods. The efficacy of machine learning methods is obtained by a regularization mechanism (ridge penalty) that prevents the learning algorithm from over-fitting the training data.

Our objective is to predict follow-up transition at an individual level while selecting the relevant clinical predictors of the psychotic transition. We hypothesized that we can improve the prediction of the individual risk of conversion to psychosis in UHR subjects, only by considering all 25 CAARMS' symptoms at baseline presentation (including non-psychotic items).

For that, we used a multivariate machine learning method known as logistic regression with Elastic-net shrinkage that combines ridge regularization with sparsity-inducing regularization in order to promote a solution where only few predictors are selected in the predictive model. Elastic-net logistic regression method was applied to the complete psychopathology assessed with CAARMS psychotic and non-psychotic symptoms in UHR patients at baseline. The algorithm was trained to predict psychosis conversion in UHR subjects, ie, those who did (UHR-P) or did not (UHR-NP) convert into psychosis. Moreover the algorithm was used to identify the few predictive symptoms.

# METHODS

## Settings

The individuals participating in this study were consecutive help-seekers (age 15-25 y.o) referred to the C'JAAD (Centre d'Evaluation du Jeune Adulte et de l'ADolescent – Adolescent and Young Adult Assessment Center) between 2004 and 2008[50]. This specialized outpatient unit dedicated to adolescents and young adults is part of a public mental health service (Service Hospitalo-Universitaire) and is located inside Sainte-Anne Hospital, Paris, a hospital renowned for psychiatry. The assessments included in the analysis are part of standardized procedures of this center of excellence. The participants
were referred by general practitioners, school or university preventive services, private psychiatrists or
psychologists and/or via public mental health, from the catchment area associated with our department
(part of the 15th area of Paris). Some patients contacted us directly after having seen our web site. The
first visit ensured that the exclusion criteria for joining the C'JAAD program were not met : presence of
established schizophrenia spectrum disorders (meeting DSM-IV criteria for schizophrenia, schizo-
affective or schizophreniform disorders[51]), pervasive developmental disorders, bipolar or obsessive-
compulsive disorders; a former psychotic episode; current antidepressant treatment; psychoactive
substance dependence or abuse (DSM-IV criteria) during the previous year or for more than five years;
serious or progressive medical and neurological disorders; head injury and mental retardation (IQ<70),
and non-native French speaking. One hundred and twenty five consecutive patients were eligible for the
C'JAAD program and underwent the standardized assessments by fully-trained psychologists or
psychiatrists. The study was in the frame of standardized expert clinical practice. Since we planned to
analyze the results of assessment, in accordance with French regulations, a complete description of the
study was given to the patients, to inform them that the collected data, after anonymization, would be
used in research analysis and may be published. This form was reviewed by the local Clinical Research
Ethics Committee. After being informed, all referred individuals agreed to participate and gave their
written informed consent. Parental informed consent was also obtained for adolescents younger than
18 years old. All the study's procedures fulfilled the recommendations of the Helsinki Declaration and
the EU recommendations for Good Clinical Research Practice and followed French ethical regulations.

Participants

After applying the CAARMS to the 125 individuals eligible for the C'JAAD program, as we are analyzing
just UHR patients, we excluded 18 individuals who were over (Psychosis group criteria) and 61 under
threshold. Finally, 46 UHR patients were selected. The 46 UHR subjects (mean age = 17.63 ± 3.70 y.o)
were all responding to criteria of attenuated psychosis, with psychotic symptoms (Unusual thought
content, Non bizarre ideas, Perceptual abnormalities) remaining under the threshold of severity or
frequency, associated with a drop of 30% in the GAF during the last year or a chronic low GAF (under
50). All UHR subjects were included in a longitudinal follow-up. No participant had taken cannabis or
alcohol in the last 72 h. All patients were invited for a follow-up assessment and we sought information
from the carer or family member declared as a contact person at the baseline visit. Nineteen participants
dropped-out from this follow-up or did not agree to come back for re-assessment. The final sample used
in the present analysis included twenty-seven patients, who were fully re-assessed with a second CAARMS interview after conversion or at the end of follow-up (mean duration 22.4 ± 6.54 months), helping to detect any change in psychopathology and conversion to psychosis.

## Measures

Psychopathology, at baseline and at follow up, was investigated with the Comprehensive Assessment of At-Risk Mental State (CAARMS)[4], in addition to the Global Assessment of Functioning (GAF) scale[51] for the general level of functioning and reduction in functioning (scoring from 0 to 100). The CAARMS is a semi-structured interview designed to assess prodromal psychopathology in people at high clinical risk of psychosis. It was conducted by a psychiatrist (JB, MK, MOK) or psychologist (EM), all specifically trained for the administration of the instrument. The CAARMS has previously been found to have a good to excellent reliability[4]. The scale has a total of 25 items, which are clustered in seven subscales: Positive Symptoms (Unusual thought content, Non bizarre ideas, Perceptual abnormalities); Cognitive Change (Subjective change, Observed cognitive change); Emotional Disturbance (Subjective emotional disturbance, Observed blunted affect, Observed inappropriate affect); Negative Symptoms (Alogia, Avolition/apathy, Anhedonia); Behavioral Change (Social isolation, Impaired role function, Disorganizing/ odd/ stigmatizing behavior, Aggression/ dangerous behavior); Motor/Physical Changes (Subjective complaints of impaired motor functioning, Subjective complaints of impaired bodily sensation, Subjective complaints of impaired autonomic functioning) and General Psychopathology (Depression, Suicidality and self-harm, Mood swings/lability, Anxiety, Obsessive compulsive symptoms, Dissociative symptoms, Impaired tolerance to normal stress). Along with the decline in functioning or sustained low functioning in the past 12 months, the CAARMS' first subscale (including unusual thought content, perceptual abnormality and disorganized speech scales) provides criteria for 'Ultra High Risk' (UHR) or 'At-Risk Mental State' and for 'Psychosis' threshold. The UHR group encompasses 3 sub-groups: (1) 'Attenuated Psychosis' (AP) group, with subthreshold severity or frequency for positive psychotic symptoms, (2) 'Brief Limited Intermittent Psychosis (BLIPS) group corresponding to a brief psychotic episode that spontaneously remits without antipsychotic medication or hospitalization in less than one week, (3) and 'vulnerability' group (schizotypal personality disorder or a first-degree relative with psychosis) plus a marked decline in psychosocial functioning (Genetic Risk and Deterioration syndrome). In this analysis, improving the homogeneity of the group, all UHR subjects belonged to the 'Attenuated Psychotic' (AP) subgroup.
Psychosis conversion was defined based on CAARMS' psychosis criteria that require the occurrence of at least one frank positive psychotic symptom (Unusual thought content with severity ≥ 6 or Non-bizarre ideas with severity ≥ 6 or Perceptual abnormalities with severity ≥ 5) several times a week for over one week. The conversion to psychosis was confirmed in parallel by any available source of information including the family members and the patients' psychiatrists, who were contacted with the agreement of the patients.

## Multivariate Machine Learning Analysis

**Machine learning algorithm** – Elastic-net[52] logistic regression is a linear machine learning algorithm for classification, which includes a combination of two types of regularization. The regularization is used to alleviate the risk of over-fitting the training data, which would lead to perfect predictions on the training samples but poor prediction on independent test samples. The first regularization (lasso shrinkage) enforces sparsity of the solution by forcing coefficients for uninformative predictors, among the 25 CAARMS items, to shrink towards zero. It provides the algorithm with a variables-selection capability. Indeed, such regularization addresses the key challenge of identifying the few relevant CAARMS items. The second regularization forces the coefficients of correlated predictors to shrink towards each other. The Elastic-net logistic regression was minimized with a proximal gradient descent using the FISTA algorithm[53]. Elastic-Net regularization hyperparameters were automatically determined using an internal loop of cross-validation over a grid of possible values ([0.1, 1, 100] for the global penalty and [0.1, 0.5] for the ratio of lasso over ridge shrinkage). This model selection procedure used exclusively training sets that were partitioned into sub-training and validation sets.

**Validation procedure** – Considering the small size of the dataset, all validations and significances of predictive models were computed with non-parametric re-sampling strategies that provide robust estimators even when the distributional assumption cannot be guaranteed. Concerning the prediction performances, statistical learning theory[54, 55] and experimental results[56] state that a multivariate prediction rule must be evaluated on independent datasets to avoid the downward bias of the apparent error rate on the training data. Therefore, we used ten-fold stratified cross-validation (10CV)[57] to evaluate all prediction scores, i.e. the sensitivity (number of true UHR-P / number of UHR-P), the specificity (number of true UHR-NP / number of UHR-NP), the accuracy and the AUC (Area Under Curve of the receiver operating characteristic curve). The significance of predictions, under the 10CV, were assessed with random permutation tests[58]: Empirical p-values were calculated comparing the scores
obtained from the 10CV (on the true data) against the ones obtained with 1000 repetitions of the 10CV under the null hypothesis, i.e., where the conversion status has been randomized. Finally, we used bootstrapping re-sampling[54] to assess the mean, the standard error, the confidence intervals and the significance of the coefficients associated with the potential predictors (CAARMS items).

Analyses were performed using Python (v3.7) with the "statsmodels" (www.statsmodels.org) library for statistics and scikit-learn (scikit-learn.org) and Parsimony (github.com/neurospin/pylearn-parsimony) for machine learning.

# RESULTS

## The Sample—Baseline and Follow-up

Forty-six UHR subjects were assessed at baseline (T0) and twenty-seven at follow-up (T1). At follow-up, 11 subjects had developed psychosis (UHR-P) while 16 subjects did not convert (UHR-NP) (40% conversion rate) according to CAARMS criteria. Conversion to psychosis occurred within a period of 3-20 months after baseline assessment (mean= 7.6 ± 5.481 months).

Descriptive statistics of the sample at baseline are presented in Table 1. No significant differences with respect to sex, age, years of education, exposition to cannabis and treatment by antipsychotics at baseline were found between UHR-P and UHR-NP (Table 1). Only UHR-P were prone to have made a suicide attempt at the end of the follow-up (P < 10⁻³) (Table 1).

## Baseline Prediction of Conversion using CAARMS items

The elastic net logistic regression achieved highly significant prediction of the conversion given the 25 baseline CAARMS items (see Table 2). Using the ten-fold stratified cross-validation the classification achieved 88.9% (Standard Error, SE=6.7%, P < 10⁻³) accuracy, 81.8% (SE=5%, P = 0.035) sensitivity, 93.7% (SE=13%, P < 10⁻³) specificity and an AUC of 0.83 (SE=7.6%, P < 10⁻³).

## Selection of best CAARMS Predictors of Conversion

CAARMS items selected by elastic net among the 25 - Thanks to the Lasso shrinkage, the whole 10-fold cross-validation round led to the selection of only 6 CAARMS items, and noticeably four of them (see Table 3) were systematically selected across the ten folds: anhedonia, aggression/dangerous behavior, mood swings/lability and obsessive compulsive symptoms. Interestingly, two of them are risk factors (aggression/dangerous behavior, obsessive compulsive symptoms) for the conversion while in contrast, the two others are protective factors (anhedonia, mood swings/lability) of psychosis
conversion. This predictive pattern replicates over the cross-validation resampling as confirmed by the
standard errors obtained by bootstrapped re-sampling (Table 3).

*Reduced model based on 4 items* - In order to demonstrate that a reliable conversion predictor could be built upon those four items, we selected a reduced dataset that contains only the following four CAARMS items: anhedonia, aggression/dangerous behavior, mood swings/lability and obsessive compulsive symptoms. We performed the same 10 CV permutation tests using a simpler model based on ridge (without sparsity) logistic regression. We found the coefficients (Table 3) of each of the four items to be significantly (P<0.01) different from zero. Means and standard errors of the coefficients associated with CAARMS items obtained with 1000 bootstraps of the predictive elastic-net model were represented in figure 1: positive/negative coefficients indicate a positive/negative contribution toward conversion.

DISCUSSION

The goal of this study was to determine whether clinical characteristics readily obtained from only the CAARMS and accompanying clinical interview could predict conversion to psychosis. To construct a predictive model based on these measures, we employed a modern machine learning algorithm known as Support Vector Machine (SVM) machine learning method. For that, we investigated whether non-psychotic CAARMS items scores in UHR individuals at admittance could help to predict subsequent conversion to a full-blown psychotic disorder. The results indicate that only a few non-psychotic symptoms predicted whether an individual would convert to psychosis. In our sample of UHR subjects, obsessive-compulsive symptoms (OCS) and dangerous behavior were associated with a higher risk of conversion while anhedonia and mood lability predicted a lower risk of psychosis.

While numerous studies have addressed the prediction of the outcome of subjects found at ultra-high risk of psychosis, this issue still needs further improvement. Several studies have proposed biological or imaging biomarkers, whose translation into the clinic with broad clinical implementation is more than incomplete, and the majority of studies dealing with psychopathology have focused on attenuated psychotic symptoms as common predictor variables[59–62]. Most of these studies reported effects that were statistically significant at the group level, whereas clinicians have to make treatment decisions for individuals. In addition, a systematic review[63] on the methodology of 91 studies developing a multivariable model predicting the transition to psychosis in clinical high-risk patients found that poor
methods and reporting were widespread, leading to models that are likely considerably overfitted and
reported performance estimates that are likely overoptimistic.

Some previous studies[44, 45, 64, 65] used the CAARMS and a Cox regression model to investigate the relationship between symptoms variables and conversion to a psychotic disorder and one of them[64] omitted OC symptoms from the analysis because they were too rare in their sample. Demjaha et al.[44] found that both the disorganization/cognitive and negative dimensions were associated with subsequent conversion to psychosis with a principal component analysis. None of the negative symptoms, such as avolition, anhedonia, and blunted affect has been found predictive of psychosis when studied as individual items. In Thompson's study[45], the presence of formal thought disorder, especially thought blocking and tangentiality, and elevated mood were the most predictive items of psychosis conversion. Kotlicka-Antczak et al. developed a pilot clinical prediction model internally validated with an accuracy of 78% including speech disorganization and unusual thought content as potential predictors of conversion[65]. Furthermore, a recent paper used SVM and 10 CAARMS subscales (together with the psychotic subscale of the Brief Psychiatric Rating Scale (BPRS) and 4 subscales from Scale for Assessment of Negative Symptoms (SANS)) to predict the conversion and the functional outcome[49]. They found that the prediction of transition was driven by disorder of thought content, attenuated positive symptoms and functioning. Their model was able to discriminate between individuals at UHR who subsequently converted to psychosis versus those who did not with a specificity of 60.6%, a sensitivity of 68.6% and an accuracy of 64.6%. Our study is the second one to examine all psychopathological dimensions from the CAARMS to predict conversion to psychosis in UHR individuals using a SV machine learning method. This method provides a stable and reproducible predictive pattern that replicates over the cross-validation resampling, confirmed by the standard errors obtained by bootstrapped re-sampling (Table 3). Taking into account all CAARMS subscales, we were able to obtain 93.7% (P < 10⁻³) specificity, 81.8% (P = 0.035) sensitivity, 88.9% (P < 10⁻³) accuracy, and an AUC of 0.83 (P < 10⁻³).

The value of obsessive-compulsive symptoms (OCS) to predict psychosis conversion in UHR subjects demonstrated here is consistent with the high prevalence of OCS in patients with schizophrenia compared to the general population[66–68]. Furthermore, a substantial proportion of individuals with schizophrenia report significant obsessive or compulsive symptoms, which can appear early in the developmental course of the illness[69]. In UHR subjects, it has been reported that 11% of patients with
at-risk mental states for psychosis (ARMS) present additional OCS and 5% fulfill the diagnostic criteria for obsessive-compulsive disorder [70]. Hur et al. demonstrated that OCS in UHR individuals were associated with more severe clinical presentation, including lower global functioning and more psychotic symptoms on CAARMS [71]. Altogether, higher impairment of psychosocial functioning[71-73] and more severe depressive symptoms[69, 73-75] have consistently been reported associated with OCS in early stage of psychosis. Thus, our result regarding the importance of the intensity of obsessive symptomatology as a predictor of psychotic transition is consistent with the most commonly occurring predictors which tend to be poor social functioning[76] and certain cognitive domains[29, 31]. Along the same lines as our results, Van Dael et al.[77] concluded from their study that the likelihood of conversion to psychotic disorder was higher if early psychosis was accompanied by co-occurring OCS.

Our results also support the hypothesis that UHR subjects with dangerous and impulsive manifestations are more at risk to develop psychosis than those without dangerous behavior. Impulsivity is one of the most commonly reported behavioral characteristics of patients with schizophrenia[78]. Lee et al.[79] reported greater impulsivity in UHR subjects compared with healthy controls and this high level of impulsivity was associated with behavioral problems. Accordingly, previous studies have found a high prevalence of substance use and suicide risk in UHR subjects[80, 81] as well as impaired ability to control emotional expression and aggression [82]. To the best of our knowledge, until now, no study has highlighted 'impulsivity' as a risk factor for developing schizophrenia in UHR subjects. Interestingly, Niendam et al.[69] found that individuals at UHR for psychosis with comorbid OCS exhibited greater levels of depression and suicidality before conversion to psychosis, probably because aggressive obsessions may be associated with suicidal ideation in UHR youth, as they are intrusive[83] and disturbing[84]. This finding is especially interesting when considering the significant difference with respect to the suicide attempt item between the UHR-P and the UHR-NP (p<0.00067) observed in our study, given that suicidality is a well-known expression of high impulsivity. Nevertheless, the high rate of suicidality in subjects who have made the psychotic transition cannot be due solely to impulsivity and it is clearly demonstrated that the intensification of psychotic symptoms is in itself a major risk factor for suicide attempts [85, 86].

On the other hand, we showed that the UHR subjects who present high mood lability and anhedonia were less prone to psychosis onset. Those two items seem to predict another future, perhaps a more affective profile. To the best of our knowledge, this is the first study which gives this type of balanced
model. In a previous study, the presence of comorbid axis I diagnoses of anxiety, depression, or
concurrent anxiety and depression was reported as having no impact on conversion risk[40].
Additionally, the distinction between patients with depression or anxiety associated with (attenuated)
psychotic symptoms and patients with an emerging psychotic disorder associated with depressive and
anxious symptoms is particularly difficult[40]. In this context, our results are of particular interest because
it seems that with a predictor algorithm based on clinical CAARMS' items we could distinguish UHR
subjects with an affective profile, who appear less prone to psychosis compared to the future converters.
Our results allow us to conclude that symptomatic dimensions additional to the positive psychotic
symptoms could influence the rate of conversion to psychosis. For instance, the presence of OCS in
early psychosis could have a specific pathophysiological value. A so-called "schizo-obsessive" subtype
of psychosis has been proposed, based on cross-sectional evaluations[87]. Preliminary reports have
found that it could be associated with specific neurodevelopmental[88, 89] and neuroimaging
features[90]. OCS may also represent prodromal symptoms in childhood-onset schizophrenia[91], and
the mentioned high prevalence rates of OCS in the ARMS led to the perception that specific OCS could
be a part of the basic symptom cluster in the early course of schizophrenia[92]. Hence, it may be useful
to distinguish subgroups within the UHR samples on the basis of the presence of OCS, since OCS may
reflect neurodevelopmental alterations. Taking into account clinical heterogeneity depending on
developmental trajectory could help to better predict psychosis onset.

Several limitations of the study that could limit its generalization should be discussed. First, the size of
the cohort reduces confidence in the prediction scores: a small sample size increases the error bars of
prediction performances evaluated by cross-validation [88]. This paper reports (theoretical and
experimental) errors of ± 15% on a sample of similar size (30). However, even such large uncertainty
leaves the obtained performance (>80%) far above the chance level of 50%. Moreover, we used state-
of-the-art learning algorithms and validation procedures to address the sample size issue: (i) the
sparsity-promoting penalty of the predictive model limits the risks of overfitting. (ii) All validations and
significances were computed with non-parametric resampling strategies (cross-validation, permutation
tests and bootstrapping) that provide conservative results on skewed distributions. By construction,
permutation tests and bootstrapping take into account the sample size in the calculation of the p-values
and confidence intervals. Finally, the subjects have very well documented files and the criteria for
determining the existence of a psychosis conversion were very carefully considered. While there is no
association between classification errors and the sex (Chi-2=0.01, p-value=0.92), the high proportion of males in the cohort (85.2%) prevents the results from being generalized to females. Secondly, the relatively high rate of conversion in UHR and high rate of males. This might be partly explained by the location of the outpatient clinic and the way of referral, 2nd or 3rd line rather than first line (despite direct referrals by some patients). Higher rates of male in clinical settings are well-known and males usually present a higher propensity for negative symptoms, lower social functioning, earlier age at onset and higher comorbid substance abuse than women[93]. In addition, this study was carried out in the frame of a recently established evaluation center, and specific therapeutic programs (e.g. CBT) were not initially put in place. We used the CAARMS to diagnose ARMS and to define conversion to psychosis. Although this instrument is a valid and widely used tool for the assessment of ARMS and conversion to psychosis, it has the limitation that basic cognitive symptoms are under-represented. Concerning OCS, we know that they could be triggered by medication (e.g. some atypical antipsychotics)[94]. However, in our sample, we can exclude the influence of psychopharmacological treatment, because there were no significant differences between UHR-P and UHR-NP in medication and they were minimally treated, and OCS in UHR individuals were more frequently treated with antidepressants rather than antipsychotics.

## CONCLUSION

To date, the assessment and treatment of UHR subjects has largely focused on attenuated positive psychotic symptoms. Our observations showed that non-psychotic symptoms also convey information that could help to predict the risk of conversion. We found OCS and impulsivity are associated with higher risk of conversion to psychosis while UHR with anhedonia and mood swings are associated with a lower rate of psychosis at outcome. Those symptoms could reflect more the developmental background on one hand and more an affective profile on the other hand, both profiles that have been proposed to lie in a continuum with psychotic disorders.

In this study, we have shown that using the complete psychopathological assessment provided by the CAARMS can improve the prediction of conversion among UHR people by refining the prediction in more homogeneous subgroups of UHR. In addition, new statistical procedures, such as elastic net logistic regression applied to clinical data, open new ways to switch from group prediction to an individualized prediction with better accuracy.
## CONTRIBUTIONS

JB and MOK designed the study. MOK & RG supervised the study. JB, MK, EM collected the data. JB, ED, analyzed the data. JB, ED, MK, MOK interpreted the data. JB, ED and MOK drafted the report. All authors contributed to and have approved the final manuscript.

## ACKNOWLEDGEMENTS

We wish to thank the C'JAAD team: François Chauchot, Olivier Gay, Guy Gozlan, Anne Gut-Fayand, Marion Plaze, Claire Daban and Nicolas Brulez and the clinical research team (CERC), including Emilie Magaud and Yannick Morvan. The study received financial support by the Fondation Pierre Deniker, the Fondation MGEN and the Transition Collaborative Network (INSERM).

## CONFLICT OF INTEREST

The authors declare no competing financial interest

<table><caption>Table 1. Demographic and clinical characteristics measured at baseline (except for suicide attempt) of Ultra High Risk (UHR) subjects who did (UHR-P) or did not (UHR-NP) convert to psychosis.</caption><thead><tr><td>Characteristic</td><td>UHR-NP (N= 16)</td><td>UHR-P (N=11)</td><td>Statistics</td><td>P-value</td></tr></thead><tbody><tr><td>Number of males (%)</td><td>13 (81.3%)</td><td>10 (90.9%)</td><td>0.02<sup>(b)</sup></td><td>0.88</td></tr><tr><td>Age in years, mean (±SD)</td><td>17.2 (±3.9)</td><td>18.3 (±3.4)</td><td>0.74<sup>(a)</sup></td><td>0.465</td></tr><tr><td>Years of education, mean (±SD)</td><td>12.9 (±2.4)</td><td>11.00 (±3.0)</td><td>1.82<sup>(a)</sup></td><td>0.08</td></tr><tr><td>Cannabis exposure, count (%)</td><td>9 (56.3%)</td><td>10 (90.9%)</td><td>2.27<sup>(b)</sup></td><td>0.13</td></tr><tr><td>AP at baseline, count (%)</td><td>6 (37%)</td><td>5 (45%)</td><td>&lt;10<sup>-6(b)</sup></td><td>0.98</td></tr><tr><td>Antipsychotic dosage in mg equivalent CPZ, mean (±SD)</td><td>119 (±158)</td><td>178 (±115)</td><td>0.62<sup>(a)</sup></td><td>0.55</td></tr><tr><td>Suicide Attempt at follow up, count (%)</td><td>0 (0%)</td><td>5 (45%)</td><td>6.1674<sup>(b)</sup></td><td>0.01</td></tr></tbody></table>

Legend: UHR-NP = no conversion at follow-up, UHR-P = conversion; SD = standard deviation;

AP= AntiPsychotic. Statistics: (a) T student, (b) Chi-square.
<table><caption>Table 2. Elastic net Logistic Regression: Prediction of Conversion from Baseline CAARMS items.</caption><thead><tr><th>Model</th><th>Accuracy (%)</th><th>Sensitivity (%)</th><th>Specificity (%)</th><th>AUC</th></tr></thead><tbody><tr><td>Full</td><td>88.9***</td><td>81.8*</td><td>93.75***</td><td>0.83***</td></tr><tr><td>Reduced</td><td>88.9***</td><td>81.8*</td><td>93.75***</td><td>0.93***</td></tr></tbody></table>

Abbreviations: AUC Area Under Curve.

Significance notations: ***: P<10⁻³, **: P<10⁻², *: P<0.05.

<table><caption>Table 3. Elastic net Logistic Regression: Association Between Conversion and Baseline CAARMS selected items</caption><thead><tr><th rowspan="2">Baseline CAARMS item</th><th colspan="4">Full model</th><th colspan="4">Reduced model</th></tr><tr><th>Mean</th><th>SE</th><th>5%</th><th>95%</th><th>Mean</th><th>SE</th><th>5%</th><th>95%</th></tr></thead><tbody><tr><td>Anhedonia</td><td>-0.160</td><td>0.158</td><td>-0.470</td><td>0.000</td><td>-0.301***</td><td>0.101</td><td>-0.458</td><td>-0.132</td></tr><tr><td>Aggression/dangerous behavior</td><td>0.232</td><td>0.183</td><td>0.000</td><td>0.551</td><td>0.360***</td><td>0.102</td><td>0.190</td><td>0.525</td></tr><tr><td>Mood swings/lability</td><td>-0.224</td><td>0.167</td><td>-0.509</td><td>0.000</td><td>-0.350***</td><td>0.110</td><td>-0.524</td><td>-0.164</td></tr><tr><td>Obsessive compulsive symptoms</td><td>0.168</td><td>0.174</td><td>0.000</td><td>0.516</td><td>0.263**</td><td>0.100</td><td>0.104</td><td>0.429</td></tr></tbody></table>

Mean, SE: (Standard Error), 5% and 95% of Confidence Intervals of the predictors' coefficient (CAARMS items) using 1000 bootstrap re-sampling. The full model applied Elastic-net on the 25 predictors and the reduced model used a ridge logistic regression on the reduced set of 4 predictors.

No coefficient was significant on the full model since the sparsity (of Elastic net) forced exact null value in approximately 20% (P=0.2) of the bootstrapped samples (over 1000). However, the coefficients were significant on the reduced model.

Significance notations: ***: P<10⁻³, **: P<10⁻², *: P<0.05

<footnote>-3, **: P<10⁻², *: P<0.05</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1: Means and standard errors of the coefficients associated with CAARMS items obtained with 1000 bootstraps of the predictive elastic-net model</figcaption></figure>

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