ArticlePloS one2022
A predictor model of treatment resistance in schizophrenia using data from electronic health records.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry.Biological psychiatry · 2024Review
- Development and initial evaluation of a clinical prediction model for risk of treatment resistance in first-episode psychosis: Schizophrenia Prediction of Resistance to Treatment (SPIRIT).The British journal of psychiatry : the journal of mental science · 2024Article
- Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.BMC medical research methodology · 2023Article
- Antipsychotic Use: Cross-Sectional Opinion Survey of Psychiatrists in India and United Kingdom.Pharmacy (Basel, Switzerland) · 2023Article
- Clinical correlates of early onset antipsychotic treatment resistance.Journal of psychopharmacology (Oxford, England) · 2022Article
- Ethnic inequalities in clozapine use among people with treatment-resistant schizophrenia: a retrospective cohort study using data from electronic clinical records.Social psychiatry and psychiatric epidemiology · 2022Article
Corrections and comments
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Authors and funding
18 authors at 5 institutions in 4 countries.
Funding
Abstract
objectivesTo develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs.
methodsWe used the Least Absolute Shrinkage and Selection Operator (LASSO) for time-to-event data, to develop a risk prediction model from the first antipsychotic prescription to the development of TRS, using data from electronic health records.
resultsWe reviewed the clinical records of 1,515 patients with a schizophrenia spectrum disorder and observed that 253 (17%) developed TRS. The Cox LASSO survival model produced an internally validated Harrel's C index of 0.60. A Kaplan-Meier curve indicated that the hazard of developing TRS remained constant over the observation period. Predictors of TRS were: having more inpatient days in the three months before and after the first antipsychotic, more community face-to-face clinical contact in the three months before the first antipsychotic, minor cognitive problems, and younger age at the time of the first antipsychotic.
conclusionsRoutinely collected information, readily available at the start of treatment, gives some indication of TRS but is unlikely to be adequate alone. These results provide further evidence that earlier onset is a risk factor for TRS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.