Dataset Information

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.

ABSTRACT:

Background

Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis.

Method

Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts.

Results

Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naïve. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P < .0001). It also predicted 6-week remission status in the Danish cohort (ROC-AUC = 67.74, P < .0001), demonstrating reliability.

Conclusions

Using items from common and validated clinical scales, our model significantly predicted early remission in patients with first-episode psychosis. Although replicated in an independent cohort, forward testing between machine learning models and clinicians' assessment should be undertaken to evaluate the possible utility as a routine clinical tool.

SUBMITTER: Soldatos RF

PROVIDER: S-EPMC8781312 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.

Soldatos Rigas F RF Cearns Micah M Nielsen Mette Ø MØ Kollias Costas C Xenaki Lida-Alkisti LA Stefanatou Pentagiotissa P Ralli Irene I Dimitrakopoulos Stefanos S Hatzimanolis Alex A Kosteletos Ioannis I Vlachos Ilias I II Vlachos Ilias I II Selakovic Mirjana M Foteli Stefania S Nianiakas Nikolaos N Mantonakis Leonidas L Triantafyllou Theoni F TF Ntigridaki Aggeliki A Ermiliou Vanessa V Voulgaraki Marina M Psarra Evaggelia E Sørensen Mikkel E ME Bojesen Kirsten B KB Tangmose Karen K Sigvard Anne M AM Ambrosen Karen S KS Meritt Toni T Syeda Warda W Glenthøj Birte Y BY Koutsouleris Nikolaos N Pantelis Christos C Ebdrup Bjørn H BH Stefanis Nikos N

Schizophrenia bulletin 20220101 1

<h4>Background</h4>Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis.<h4>Method</h4>Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was f ...[more]

PMID: 34535800

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Project description:BackgroundEarly illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes. Our study objective is to use routinely collected baseline demographic and clinical characteristics to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at one-year.Methods and findings83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009. Elastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at one-year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation. After excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with receiver operating curve (ROC) area under the curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS. A key limitation of this study is the small sample size (n) relative to the number of predictors (p), whereby p approaches n. The use of elastic net regularised regression rather than ordinary least squares regression helped circumvent this difficulty. Further, we did not have information for biological and additional social variables, such as nicotine dependence, which observational studies have linked to outcomes in psychosis.Conclusions and relevanceUsing advanced statistical machine learning techniques, we provide the first externally validated evidence, in a temporally and geographically independent cohort, for the ability to predict one-year EET status and symptom remission in individual FEP patients.

Project description:BackgroundThe neurodevelopmental model of psychosis was established over 30 years ago; however, the developmental influence on psychotic symptom expression - how age affects clinical presentation in first-episode psychosis - has not been thoroughly investigated.MethodsUsing generalized additive modeling, which allows for linear and non-linear functional forms of age-related change, we leveraged symptom data from a large sample of antipsychotic-naïve individuals with first-episode psychosis (N = 340, 12-40 years, 1-12 visits), collected at the University of Pittsburgh from 1990 to 2017. We examined relationships between age and severity of perceptual and non-perceptual positive symptoms and negative symptoms. We tested for age-associated effects on change in positive or negative symptom severity following baseline assessment and explored the time-varying relationship between perceptual and non-perceptual positive symptoms across adolescent development.ResultsPerceptual positive symptom severity significantly decreased with increasing age (F = 7.0, p = 0.0007; q = 0.003) while non-perceptual positive symptom severity increased with age (F = 4.1, p = 0.01, q = 0.02). Anhedonia severity increased with increasing age (F = 6.7, p = 0.00035; q = 0.0003), while flat affect decreased in severity with increased age (F = 9.8, p = 0.002; q = 0.006). Findings remained significant when parental SES, IQ, and illness duration were included as covariates. There were no developmental effects on change in positive or negative symptom severity (all p > 0.25). Beginning at age 18, there was a statistically significant association between severity of non-perceptual and perceptual symptoms. This relationship increased in strength throughout adulthood.ConclusionsThese findings suggest that as maturation proceeds, perceptual symptoms attenuate while non-perceptual symptoms are enhanced. Findings underscore how pathological brain-behavior relationships vary as a function of development.

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Dataset Information

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.

Background

Method

Results

Conclusions

Publications

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.

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