Predicting New-Onset Psychiatric Disorders Throughout the COVID-19 Pandemic: A Machine Learning Approach.

OBJECTIVE: The investigators estimated new-onset psychiatric disorders (PsyDs) throughout the COVID-19 pandemic in Italian adults without preexisting PsyDs and developed a machine learning (ML) model predictive of at least one new-onset PsyD in subsequent independent samples. METHODS: Data were from the first (May 18-June 20, 2020) and second (September 15-October 20, 2020) waves of an ongoing longitudinal study, based on a self-reported online survey. Provisional diagnoses of PsyDs (PPsyDs) were assessed via DSM-based screening tools to maximize assessment specificity. Gradient-boosted decision trees as an ML modeling technique and the SHapley Additive exPlanations technique were applied to identify each variable's contribution to the model. RESULTS: From the original sample of 3,532 participants, the final sample included 500 participants in the first wave and 236 in the second. Some 16.0% of first-wave participants and 18.6% of second-wave participants met criteria for at least one new-onset PPsyD. The final best ML predictive model, trained on the first wave, displayed a sensitivity of 70% and a specificity of 73% when tested on the second wave. The following variables made the largest contributions: low resilience, being an undergraduate student, and being stressed by pandemic-related conditions. Living alone and having ceased physical activity contributed to a lesser extent. CONCLUSIONS: Substantial rates of new-onset PPsyDs emerged among Italians throughout the pandemic, and the ML model exhibited moderate predictive performance. Results highlight modifiable vulnerability factors that are suitable for targeting by public campaigns or interventions to mitigate the pandemic's detrimental effects on mental health.

Novel Emerging Molecular Targets in Non-Small Cell Lung Cancer.

In the scenario of systemic treatment for advanced non-small cell lung cancer (NSCLC) patients, one of the most relevant breakthroughs is represented by targeted therapies. Throughout the last years, inhibitors of the epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), c-Ros oncogene 1 (ROS1), and V-raf murine sarcoma viral oncogene homolog B (BRAF) have been approved and are currently used in clinical practice. However, other promising molecular drivers are rapidly emerging as therapeutic targets. This review aims to cover the molecular alterations with a potential clinical impact in NSCLC, including amplifications or mutations of the mesenchymal-epithelial transition factor (MET), fusions of rearranged during transfection (RET), rearrangements of the neurotrophic tyrosine kinase (NTRK) genes, mutations of the Kirsten rat sarcoma viral oncogene (KRAS) and phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), as well as amplifications or mutations of human epidermal growth factor receptor 2 (HER2). Additionally, we summarized the current status of targeted agents under investigation for such alterations. This revision of the current literature on emerging molecular targets is needed as the evolving knowledge on novel actionable oncogenic drivers and targeted agents is expected to increase the proportion of patients who will benefit from tailored therapeutic approaches.

Multiple systemic treatment options in a patient with malignant tenosynovial giant cell tumour.

Tenosynovial giant cell tumour (TGCT) is a group of rare soft tissues neoplasia affecting synovial joints, bursae and tendon sheaths and is classified as localized type or diffuse type. The diffuse type (TGCT-D), also known as 'pigmented villonodular (teno)synovitis' is characterized by local aggressivity, with invasion and destruction of adjacent soft-tissue structures, and high local recurrence rate. Radical surgery remains the standard therapy while adjuvant radiotherapy may help to control local spread. Malignant TGCT is characterized by high rate of local recurrences and distant metastasis. Few cases of malignant TGCT and very few evidences on systemic therapies are described in the literature, so, to date, no systemic treatment is approved for this rare disease. We report the case of a malignant TGCT patient treated with many different systemic therapies, including chemotherapy and tyrosine-kinase inhibitors, and performed a review of the literature on the systemic treatment options of this rare tumour.

'Precision' or 'personalized' psychiatry: different terms - same content?

Due to the increased lifetime prevalence and personal, social, and economic burden of mental disorders, psychiatry is in need of a significant change in several aspects of its clinical and research approaches. Over the last few decades, the development of personalized / precision medicine in psychiatry focusing on tailored therapies that fit each patient's unique individual, physiological, and genetic profile has not achieved the same results as those obtained in other branches, such as oncology. The long-awaited revolution has not yet surfaced. There are various explanations for this including imprecise diagnostic criteria, incomplete understanding of the molecular pathology involved, absence of available clinical tools and, finally, the characteristics of the patient. Since then, the co-existence of the two terms has sparked a great deal of discussion around the definition and differentiation between the two types of psychiatry, as they often seem similar or even superimposable. Generally, the two terminologies are used indiscriminately, alternatively, and / or separately, within the same scientific works. In this paper, an overview is provided on the overlap between the application and meaning of the terms 'precision psychiatry' and 'personalized psychiatry'.

Cabozantinib-related cardiotoxicity: a prospective analysis in a real-world cohort of metastatic renal cell carcinoma patients.

AIMS: Data regarding the cardiac toxicity of cabozantinib lacks. The aim of our study was to assess the risk of cabozantinib-related cardiotoxicity in mRCC patients. METHODS: We performed a multicentre prospective study on mRCC patients treated with cabozantinib between October 2016 and November 2017. Transthoracic echocardiogram and plasma biomarkers assay were assessed at baseline, 3 and 6 months after cabozantinib initiation. RESULTS: The study population included 22 mRCC patients. At baseline, 9.1% had a reduced left ventricular ejection fraction (LVEF), but none had a left ventricular systolic dysfunction. Patients with baseline reduced LVEF did not show further significant LVEF modification after 3 months. After 6 months, only 1 had an LVEF decline >10% compared to baseline, resulting in LV systolic dysfunction. At baseline, 64.7% and 27.3% of patients had elevated precursor brain natriuretic peptide (proBNP) and high-sensitivity troponin I (hsTnI), respectively. Among patients with basal normal proBNP and hsTnI, none had elevated values at 3 and 6 months. No correlation was found between basal elevated proBNP and basal reduced LVEF (P = .29), and between elevated proBNP and reduced LVEF after 6 months (P = .37). Similarly, we found no correlations between elevated hsTnI and reduced LVEF or elevated proBNP at baseline (P = .47; P = .38), at 3 (P = .059; P = .45) and after 6 months (P = .72; P = 1.0). CONCLUSIONS: This prospective study revealed a modest risk of developing left ventricular systolic dysfunction related to cabozantinib. A lack of correlation between elevated cardiac biomarkers and reduced LVEF at different time-points was detected. Assessments of the cardiac function should be reserved at the occurrence of clinical symptoms.

A clinically-translatable machine learning algorithm for the prediction of Alzheimer's disease conversion: further evidence of its accuracy via a transfer learning approach.

BACKGROUND: In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer's disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach. METHODS: We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study. RESULTS: Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705-0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706). CONCLUSIONS: These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.

Features of mood associated with high body weight in females with fibromyalgia.

BACKGROUND: Fibromyalgia (FM) is a common syndrome whose main characteristic is chronic widespread musculoskeletal pain, the severity of which is frequently worsened by concomitant obesity. Major depression (MD), particularly as part of a bipolar spectrum disorder (BSD), is associated with both obesity and FM. OBJECTIVE: To evaluate the relationship between lifetime MD, hypomanic symptoms and the body mass index (BMI) in patients with FM. METHOD: Of the 115 patients originally screened, 87 women with FM finally entered the study. Forty-nine patients (57%) had a lifetime diagnosis of MD, assessed by a structured clinical interview based on DSM-IV criteria, and four of them (4.6%) had a current MD episode. Lifetime hypomanic symptoms were measured by means of the self-rated Hypomania Checklist. According to the international criteria for BMI, FM patients were classified as under/normal-weight (61%), overweight (30%) and obese (9%). RESULTS: 62 patients (71.2%) with FM had a bipolar spectrum disorder (BSD). Thirty (48.3%) of them met criteria for bipolar II disorder, 32 (51,6%) for bipolar disorder NOS (18 FM patients with MD associated to sub-syndromal hypomanic syndrome and 14 with hypomanic syndrome without MD). No patient had a bipolar I disorder. Only one patient met the criteria for a major depressive disorder (MDD). There was no significant difference in mean BMI between the patients with and without a lifetime diagnosis of MD, but there was a positive association between the level of hypomanic symptoms and BMI values (p<0.009). When hypomania was considered categorically as hypomanic syndrome there was no significant effect on BMI. CONCLUSIONS: Our finding adds to previous evidence indicating that hypomanic symptoms are a central feature of FM. In the case of the early identification of high-level hypomanic symptoms, body weight should be closely monitored in order to prevent obesity and its detrimental impact on females with FM.

Childhood adversities in patients with fibromyalgia: are they related to comorbid lifetime major depression?

OBJECTIVES: Fibromyalgia (FM) is a syndrome of unknown aetiology that is frequently associated with depressive disorders, and childhood adversities (including maltreatment and parental loss) are frequently described in subjects with FM and depression. The aim of this study was to investigate the extent to which the high percentage of childhood adversities reported by patients with FM is related to FM itself or to a comorbid lifetime depressive disorder. METHODS: Ninety-four consecutive FM patients were assessed for lifetime major depression using the DSM-IVSCID-CV interview. Childhood maltreatment was investigated using the Childhood Trauma Questionnaire, and information relating to parental death or separation before the age of 18 years was collected by means of a semi-structured interview. The Zung Self-Rating Depression Scale, used as a quantitative measure of depressive symptoms, and the childhood adversity assessment were recorded at the same time. RESULTS: Sixty of the 94 FM patients (63.8%) were diagnosed as having a lifetime major depressive disorder. There were no significant associations between childhood parental loss, the presence/level of maltreatment, the occurrence of a lifetime major depression episode, and the Zung Self-Rating Depression Scale scores. CONCLUSIONS: The results of this study suggest that there is no association between childhood adversities and comorbid lifetime major depression in patients with FM. As it would be helpful to prevent the development of FM because of the high cost and limited efficacy of therapeutic interventions, childhood adversities may offer targets for primary prevention.

Is panic disorder associated with clinical severity of fibromyalgia? A preliminary study in a tertiary-care centre.

OBJECTIVES: To investigate the influence of panic disorder (PD) with/without agoraphobia on the clinical severity of fibromyalgia (FM). METHODS: Eighty-one patients with FM, among those consecutively referring to a tertiary-care setting, were included in this cross-sectional study. Psychiatric diagnoses were made by the structured clinical interview in accordance with the 4th-TR version of the diagnostic and statistical manual of mental disorders. The clinical severity of FM was measured by means of the following self-administered scales: Fibromyalgia Impact Questionnaire (FIQ), Fibromyalgia Assessment Status (FAS), Health Assessment Questionnaire (HAQ). RESULTS: A final sample of 66 females with FM with or without past PD was included in the analyses. The two groups did not significantly differ in age, years of education, length of illness or medication distribution. We did not find significant differences between the two groups in the FIQ and FAS scale scores, whereas subjects with FM and past PD showed significantly higher HAQ scale scores than those without past PD (p<.001). CONCLUSIONS: A history of PD in patients with FM increases the severity of functional impairment in performing a wide range of daily-life activities, as measured by the HAQ scale, with no effects on the severity of other clinical dimensions of FM. Potential underlying mechanisms and clinical implications will be discussed.

Are respiratory abnormalities specific for panic disorder? A meta-analysis.

OBJECTIVES: There is evidence of baseline respiratory abnormalities in panic disorder (PD), but whether they are specific to PD remains unclear. To investigate this issue, we meta-analyzed results from studies comparing baseline respiratory and hematic variables between subjects with PD and subjects with other anxiety disorders. METHODS: A literature search in bibliographic databases was performed. Fixed-effects models were applied. Several moderator analyses and publication bias diagnostics were performed. RESULTS: We found: (1) significantly lower mean end-tidal partial pressure of CO(2) (et-pCO(2)) in subjects with PD than in those with social phobia (SP) or generalized anxiety disorder (GAD), and (2) higher mean respiratory rate, lower venous et-pCO(2) and HCO(3)(-) concentration in subjects with PD than in those with SP. No publication bias was found. CONCLUSIONS: Subjects with PD show a condition of baseline hyperventilation when compared to subjects with SP or GAD. Hematic variables suggest that the hyperventilation may be chronic. These results support the idea that baseline respiratory abnormalities are specific to PD pathophysiology. Further studies are needed to clarify whether these abnormalities are related to a malfunction of the respiratory system or to specific cognitive/emotional/behavioral factors in this population.

Self-reported quality of life and clinician-rated functioning in mood and anxiety disorders: relationships and neuropsychological correlates.

This study aimed to investigate 1) the relationship between subjective perception of quality of life (QoL) and clinician-rated levels of psychosocial functioning and 2) the relationship of these indicators with neuropsychological performances, in a sample of 117 subjects with mood and anxiety disorders hospitalized for a 4-week psychiatric rehabilitation program. At the beginning of the hospitalization, QoL and clinician-rated functioning were respectively measured by the World Health Organization Quality of Life Assessment-Brief Form (WHOQOL-BREF) and the Global Assessment of Functioning (GAF) scale, and subjects were administered a neuropsychological battery evaluating verbal and visual memory, working memory, attention, visual-constructive ability, language fluency and comprehension. We did not find any association between WHOQOL-BREF and GAF scores and between cognitive impairment and lower QoL or clinician-rated functioning. Our results suggest that 1) the individuals' condition encompasses different dimensions that are not fully captured by using only clinician-rated or self-administered evaluations; 2) the GAF scale seems unable to indicate the cognitive impairments of our subjects and the WHOQOL-BREF does not appear to be influenced by these deficits. Overall, our findings suggest the need of simultaneously use of multiple assessment tools, including objective evaluations of functioning and different measures of QoL, in order to obtain a more complete clinical picture of the patients. This may allow to identify more specific targets of therapeutic interventions and more reliable measures of outcome.

High prevalence of major depression in US sleep clinics: the need for routine depression screening in sleep services.

Depression screening is not part of routine clinical practice in US sleep clinics. Our study aimed to report the prevalence of depression among individuals referred to US sleep clinics. According to our findings, approximately 21% of patients had depression, with about 4% reporting severe symptoms, 9% had frequent death and/or self-harming thoughts, and 61% were taking antidepressants. Our results highlighted a considerable risk of prevalent depression in sleep clinics and supported the limited existing data on this topic. Our study advocates for the need for routine depression screening in sleep services to reduce the detrimental consequences of a delayed depression diagnosis and the risk of a worse prognosis for both depression and sleep-wake disorders. CITATION: Daccò S, Caldirola D, Grassi M, Alciati A, Perna G, Defillo A. High prevalence of major depression in US sleep clinics: the need for routine depression screening in sleep services. J Clin Sleep Med. 2023;19(4):835-836.

Balance Rehabilitation with Peripheral Visual Stimulation in Patients with Panic Disorder and Agoraphobia: An Open-Pilot Intervention Study.

Given the involvement of balance system abnormalities in the pathophysiology of panic disorder and agoraphobia (PD-AG), we evaluated initial evidence for feasibility, acceptability, and potential clinical usefulness of 10 sessions of balance rehabilitation with peripheral visual stimulation (BR-PVS) in an open-pilot 5-week intervention study including six outpatients with PD-AG who presented residual agoraphobia after selective serotonin reuptake inhibitor (SSRI) treatment and cognitive-behavioral therapy, dizziness in daily life, and peripheral visual hypersensitivity measured by posturography. Before and after BR-PVS, patients underwent posturography, otovestibular examination (no patients presented peripheral vestibular abnormalities), and panic-agoraphobic symptom and dizziness evaluation with psychometric tools. After BR-PVS, four patients achieved postural control normalization measured by posturography, and one patient exhibited a favorable trend of improvement. Overall, panic-agoraphobic symptoms and dizziness decreased, even though to a lesser extent in one patient who had not completed the rehabilitation sessions. The study presented reasonable levels of feasibility and acceptability. These findings suggest that balance evaluation should be considered in patients with PD-AGO presenting residual agoraphobia and that BR-PVS might be an adjunctive therapeutic option worth being tested in larger randomized controlled studies.

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.

Molecular Tailored Therapeutic Options for Advanced Gastrointestinal Stromal Tumors (GISTs): Current Practice and Future Perspectives.

Gastrointestinal stromal tumors (GISTs) are one of the most common mesenchymal tumors characterized by different molecular alterations that lead to specific clinical presentations and behaviors. In the last twenty years, thanks to the discovery of these mutations, several new treatment options have emerged. This review provides an extensive overview of GISTs' molecular pathways and their respective tailored therapeutic strategies. Furthermore, current treatment strategies under investigation and future perspectives are analyzed and discussed.

Cardiorespiratory Assessments in Panic Disorder Facilitated by Wearable Devices: A Systematic Review and Brief Comparison of the Wearable Zephyr BioPatch with the Quark-b2 Stationary Testing System.

Abnormalities in cardiorespiratory measurements have repeatedly been found in patients with panic disorder (PD) during laboratory-based assessments. However, recordings performed outside laboratory settings are required to test the ecological validity of these findings. Wearable devices, such as sensor-imbedded garments, biopatches, and smartwatches, are promising tools for this purpose. We systematically reviewed the evidence for wearables-based cardiorespiratory assessments in PD by searching for publications on the PubMed, PsycINFO, and Embase databases, from inception to 30 July 2022. After the screening of two-hundred and twenty records, eight studies were included. The limited number of available studies and critical aspects related to the uncertain reliability of wearables-based assessments, especially concerning respiration, prevented us from drawing conclusions about the cardiorespiratory function of patients with PD in daily life. We also present preliminary data on a pilot study conducted on volunteers at the Villa San Benedetto Menni Hospital for evaluating the accuracy of heart rate (HR) and breathing rate (BR) measurements by the wearable Zephyr BioPatch compared with the Quark-b2 stationary testing system. Our exploratory results suggested possible BR and HR misestimation by the wearable Zephyr BioPatch compared with the Quark-b2 system. Challenges of wearables-based cardiorespiratory assessment and possible solutions to improve their reliability and optimize their significant potential for the study of PD pathophysiology are presented.

Reliability of patient-reported toxicities during adjuvant chemotherapy.

BACKGROUND: Patient-reported outcomes (PROs) are validated tools to assess the impact of efficacy and toxicities of cancer treatments on patients' health status. Because of the demonstrated little reliability of humans in reporting memories of painful experiences, this work explores the reliability of cancer patients in reporting chemotherapy-related toxicities. AIM: This study aims to evaluate the concordance between toxicities experienced by the patients during chemotherapy and toxicities reported to the doctor at the end of the cycles. METHODS: Questionnaires concerning chemotherapy-related toxicities were administered on days 2, 5, 8, 11, 14, and 17 of each chemo cycle and at the end of the same cycle to patients undergoing adjuvant chemotherapy. The co-primary end-points were Lins's concordance correlation coefficient (CCC) and mean difference between real-time and retrospective toxicity assessments. RESULTS: In total, 7182 toxicity assessments were collected from 1096 questionnaires. Concordance was observed between the retrospective evaluations and the toxicity assessments at early (day 2), peak (maximum toxicity), late (day 14 or 17), and mean real-time evaluations for each chemotherapy cycle (CCC for mean ranging from 0.52 to 0.77). No systematic discrepancy was found between real-time and retrospective evaluations, except for peak, which was systematically underestimated retrospectively. CONCLUSIONS: Toxicities reported by the patients to the doctor at the end of each chemotherapy cycle reflect what they actually experienced without any substantial distortion. This result is very relevant both for the clinical implications in daily patients' management and in the light of the current growing impact on digital monitoring of PROs.

Temperament, character and anxiety sensitivity in panic disorder: a high-risk study.

BACKGROUND: Adult patients with panic disorder (PD) show high levels of harm avoidance and anxiety sensitivity. Peculiar temperament profiles and high anxiety sensitivity have been proposed as developmental risk factors for PD in adult age. Since familial-genetic influences play a role both in PD and in anxiety sensitivity and temperament profiles, this study aims to investigate the possible association between family history of PD and peculiar temperament-character profiles or high anxiety sensitivity in offspring of patients with PD. SAMPLING AND METHODS: Thirty-four children of patients with PD with/without agoraphobia and 30 children of healthy subjects were compared. Temperament and character dimensions and anxiety sensitivity levels of children were obtained by the Junior Temperament and Character Inventory and the Childhood Anxiety Sensitivity Index. RESULTS: Children of patients with PD and children of healthy subjects differed neither in temperament and character dimensions nor in anxiety sensitivity levels. CONCLUSIONS: Our results show that family history of PD is not associated with peculiar temperament and character profiles or high anxiety sensitivity in children, suggesting that these factors may not be early expressions of familial vulnerability to PD. Since the sample is small and the study has a cross-sectional design, longitudinal studies in larger samples are warranted to confirm these findings and to clarify the role of anxiety sensitivity and temperament-character dimensions in the development of PD.

First-onset major depression during the COVID-19 pandemic: A predictive machine learning model.

BACKGROUND: This study longitudinally evaluated first-onset major depression rates during the pandemic in Italian adults without any current clinician-diagnosed psychiatric disorder and created a predictive machine learning model (MLM) to evaluate subsequent independent samples. METHODS: An online, self-reported survey was released during two pandemic periods (May to June and September to October 2020). Provisional diagnoses of major depressive disorder (PMDD) were determined using a diagnostic algorithm based on the DSM criteria of the Patient Health Questionnaire-9 to maximize specificity. Gradient-boosted decision trees and the SHapley Additive exPlanations technique created the MLM and estimated each variable's predictive contribution. RESULTS: There were 3532 participants in the study. The final sample included 633 participants in the first wave (FW) survey and 290 in the second (SW). First-onset PMDD was found in 7.4% of FW participants and 7.2% of the SW. The final MLM, trained on the FW, displayed a sensitivity of 76.5% and a specificity of 77.8% when tested on the SW. The main factors identified in the MLM were low resilience, being an undergraduate student, being stressed by pandemic-related conditions, and low satisfaction with usual sleep before the pandemic and support from relatives. Current smoking and taking medication for medical conditions also contributed, albeit to a lesser extent. LIMITATIONS: Small sample size; self-report assessment; data covering 2020 only. CONCLUSIONS: Rates of first-onset PMDD among Italians during the first phases of the pandemic were considerable. Our MLM displayed a good predictive performance, suggesting potential goals for depression-preventive interventions during public health crises.

Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning.

INTRODUCTION: The course of OCD differs widely among OCD patients, varying from chronic symptoms to full remission. No tools for individual prediction of OCD remission are currently available. This study aimed to develop a machine learning algorithm to predict OCD remission after two years, using solely predictors easily accessible in the daily clinical routine. METHODS: Subjects were recruited in a longitudinal multi-center study (NOCDA). Gradient boosted decision trees were used as supervised machine learning technique. The training of the algorithm was performed with 227 predictors and 213 observations collected in a single clinical center. Hyper-parameter optimization was performed with cross-validation and a Bayesian optimization strategy. The predictive performance of the algorithm was subsequently tested in an independent sample of 215 observations collected in five different centers. Between-center differences were investigated with a bootstrap resampling approach. RESULTS: The average predictive performance of the algorithm in the test centers resulted in an AUROC of 0.7820, a sensitivity of 73.42%, and a specificity of 71.45%. Results also showed a significant between-center variation in the predictive performance. The most important predictors resulted related to OCD severity, OCD chronic course, use of psychotropic medications, and better global functioning. LIMITATIONS: All recruiting centers followed the same assessment protocol and are in The Netherlands. Moreover, the sample of the data recruited in some of the test centers was limited in size. DISCUSSION: The algorithm demonstrated a moderate average predictive performance, and future studies will focus on increasing the stability of the predictive performance across clinical settings.