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Development and Validation of a Machine Learning-Based Model of Mortality Risk in First-Episode Psychosis.
Lieslehto, Johannes; Tiihonen, Jari; Lähteenvuo, Markku; Leucht, Stefan; Correll, Christoph U; Mittendorfer-Rutz, Ellenor; Tanskanen, Antti; Taipale, Heidi.
Afiliación
  • Lieslehto J; Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio.
  • Tiihonen J; Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Lähteenvuo M; Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio.
  • Leucht S; Center for Psychiatry Research, Stockholm City Council, Stockholm, Sweden.
  • Correll CU; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Mittendorfer-Rutz E; Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio.
  • Tanskanen A; Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany.
  • Taipale H; The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, New York.
JAMA Netw Open ; 7(3): e240640, 2024 Mar 04.
Article en En | MEDLINE | ID: mdl-38497965
ABSTRACT
Importance There is an absence of mortality risk assessment tools in first-episode psychosis (FEP) that could enable personalized interventions.

Objective:

To examine the feasibility of machine learning (ML) in discerning mortality risk in FEP and to assess whether such risk predictions can inform pharmacotherapy choices. Design, Setting, and

Participants:

In this prognostic study, Swedish nationwide cohort data (from July 1, 2006, to December 31, 2021) were harnessed for model development and validation. Finnish cohort data (from January 1, 1998, to December 31, 2017) were used for external validation. Data analyses were completed between December 2022 and December 2023. Main Outcomes and

Measures:

Fifty-one nationwide register variables, encompassing demographics and clinical and work-related histories, were subjected to ML to predict future mortality risk. The ML model's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The comparative effectiveness of pharmacotherapies in patients was assessed and was stratified by the ML model to those with predicted high mortality risk (vs low risk), using the between-individual hazard ratio (HR). The 5 most important variables were then identified and a model was retrained using these variables in the discovery sample.

Results:

This study included 24 052 Swedish participants (20 000 in the discovery sample and 4052 in the validation sample) and 1490 Finnish participants (in the validation sample). Swedish participants had a mean (SD) age of 29.1 (8.1) years, 62.1% were men, and 418 died with 2 years. Finnish participants had a mean (SD) age of 29.7 (8.0) years, 61.7% were men, and 31 died within 2 years. The discovery sample achieved an AUROC of 0.71 (95% CI, 0.68-0.74) for 2-year mortality prediction. Using the 5 most important variables (ie, the top 10% [substance use comorbidities, first hospitalization duration due to FEP, male sex, prior somatic hospitalizations, and age]), the final model resulted in an AUROC of 0.70 (95% CI, 0.63-0.76) in the Swedish sample and 0.67 (95% CI, 0.56-0.78) in the Finnish sample. Individuals with predicted high mortality risk had an elevated 15-year risk in the Swedish sample (HR, 3.77 [95% CI, 2.92-4.88]) and an elevated 20-year risk in the Finnish sample (HR, 3.72 [95% CI, 2.67-5.18]). For those with predicted high mortality risk, long-acting injectable antipsychotics (HR, 0.45 [95% CI, 0.23-0.88]) and mood stabilizers (HR, 0.64 [95% CI, 0.46-0.90]) were associated with decreased mortality risk. Conversely, for those predicted to survive, only oral aripiprazole (HR, 0.38 [95% CI, 0.20-0.69]) and risperidone (HR, 0.38 [95% CI, 0.18-0.82]) were associated with decreased mortality risk. Conclusions and Relevance In this prognostic study, an ML-based model was developed and validated to predict mortality risk in FEP. These findings may help to develop personalized interventions to mitigate mortality risk in FEP.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 6_ODS3_enfermedades_notrasmisibles Problema de salud: 6_mental_health_behavioral_disorders Asunto principal: Trastornos Psicóticos / Antipsicóticos Límite: Adult / Female / Humans / Male Idioma: En Revista: JAMA Netw Open Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 6_ODS3_enfermedades_notrasmisibles Problema de salud: 6_mental_health_behavioral_disorders Asunto principal: Trastornos Psicóticos / Antipsicóticos Límite: Adult / Female / Humans / Male Idioma: En Revista: JAMA Netw Open Año: 2024 Tipo del documento: Article
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