Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder

Fan, Peihao; Guo, Xiaojiang; Qi, Xiguang; Matharu, Mallika; Patel, Ravi; Sakolsky, Dara; Kirisci, Levent; Silverstein, Jonathan C.; Wang, Lirong

doi:10.3390/brainsci10110784

Cited by 7 publications

(16 citation statements)

References 53 publications

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“…The purpose of the feature selection process is to use an adequate but minimal number of features to achieve optimal prediction results of the model ( 41 ). Two methods for feature selection achieved high AUC values.…”

Section: Discussionmentioning

confidence: 99%

A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms

Chen

et al. 2022

Front. Psychiatry

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BackgroundBipolar disorder (BD) is easy to be misdiagnosed as major depressive disorder (MDD), which may contribute to a delay in treatment and affect prognosis. Circadian rhythm dysfunction is significantly associated with conversion from MDD to BD. So far, there has been no study that has revealed a relationship between circadian rhythm gene polymorphism and MDD-to-BD conversion. Furthermore, the prediction of MDD-to-BD conversion has not been made by integrating multidimensional data. The study combined clinical and genetic factors to establish a predictive model through machine learning (ML) for MDD-to-BD conversion.MethodBy following up for 5 years, 70 patients with MDD and 68 patients with BD were included in this study at last. Single nucleotide polymorphisms (SNPs) of the circadian rhythm genes were selected for detection. The R software was used to operate feature screening and establish a predictive model. The predictive model was established by logistic regression, which was performed by four evaluation methods.ResultsIt was found that age of onset was a risk factor for MDD-to-BD conversion. The younger the age of onset, the higher the risk of BD. Furthermore, suicide attempts and the number of hospitalizations were associated with MDD-to-BD conversion. Eleven circadian rhythm gene polymorphisms were associated with MDD-to-BD conversion by feature screening. These factors were used to establish two models, and 4 evaluation methods proved that the model with clinical characteristics and SNPs had the better predictive ability.ConclusionThe risk factors for MDD-to-BD conversion have been found, and a predictive model has been established, with a specific guiding significance for clinical diagnosis.

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Section: Discussionmentioning

confidence: 99%

A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms

Chen

et al. 2022

Front. Psychiatry

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“…Antipsychotic and antidepressant medications, a diagnosis of autistic disorder, schizophrenic disorder, and substance use disorder were reported by Fan et al. ( 37 ) as risk factors for suicidal thoughts, suicide attempts, or death by suicide among patients with post-traumatic stress disorder and bipolar disorder. Hettige et al.…”

Section: Resultsmentioning

confidence: 98%

“…When the aim was to predict death by suicide using administrative data, the most important risk factors were record/indication of mental or behavioral disorders such as schizophrenia, antipsychotic medication use, depression, anxiety, stress disorders, and alcohol use ( 26 , 29 – 33 ), followed by a prior suicide attempt or self-harm ( 26 , 29 , 30 , 34 ), and age ( 30 – 33 , 35 ). Four studies predicted suicidal thoughts and behaviors combined, and based on their findings, schizophrenia, personality disorders (borderline), depressive disorder, substance use disorder, family history of these disorders, related medications (such as antipsychotics and antidepressants), and intentional self-harm were the most important risk factors ( 36 , 37 ) (see Supplementary Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review

Somé,

Noormohammadpour,

Lange

2024

Front. Psychiatry

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BackgroundMachine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature.ObjectiveOur study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified.MethodsA systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed.ResultsThe search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study.ConclusionThe predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.

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“…However, medication usage information was controlled which might mitigate the influence of some biases. In our recent publication, we predicted patients at increased risk for SREs with high accuracy through utilizing the demographic, comorbidities, and medication use information from EMR, without socioeconomic information [ 13 ]. Secondly, we defined SREs as ideation, attempts, and death by suicide.…”

Section: Discussionmentioning

confidence: 99%

“…In our recent publication on the study on SREs in PTSD patients with bipolar disorder, we found that use of antipsychotics is a good feature for SREs prediction [ 13 ]. Antipsychotics are a class of medications indicated for the treatment of psychotic symptoms such as hallucinations, delirium, and mania [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

An Emulation of Randomized Trials of Administrating Antipsychotics in PTSD Patients for Outcomes of Suicide-Related Events

Delapaz

Hor

Gilbert

et al. 2021

JPM

Self Cite

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Post-traumatic stress disorder (PTSD) is a prevalent mental disorder marked by psychological and behavioral changes. Currently, there is no consensus of preferred antipsychotics to be used for the treatment of PTSD. We aim to discover whether certain antipsychotics have decreased suicide risk in the PTSD population, as these patients may be at higher risk. A total of 38,807 patients were identified with a diagnosis of PTSD through the ICD9 or ICD10 codes from January 2004 to October 2019. An emulation of randomized clinical trials was conducted to compare the outcomes of suicide-related events (SREs) among PTSD patients who ever used one of eight individual antipsychotics after the diagnosis of PTSD. Exclusion criteria included patients with a history of SREs and a previous history of antipsychotic use within one year before enrollment. Eligible individuals were assigned to a treatment group according to the antipsychotic initiated and followed until stopping current treatment, switching to another same class of drugs, death, or loss to follow up. The primary outcome was to identify the frequency of SREs associated with each antipsychotic. SREs were defined as ideation, attempts, and death by suicide. Pooled logistic regression methods with the Firth option were conducted to compare two drugs for their outcomes using SAS version 9.4 (SAS Institute, Cary, NC, USA). The results were adjusted for baseline characteristics and post-baseline, time-varying confounders. A total of 5294 patients were eligible for enrollment with an average follow up of 7.86 months. A total of 157 SREs were recorded throughout this study. Lurasidone showed a statistically significant decrease in SREs when compared head to head to almost all the other antipsychotics: aripiprazole, haloperidol, olanzapine, quetiapine, risperidone, and ziprasidone (p < 0.0001 and false discovery rate-adjusted p value < 0.0004). In addition, olanzapine was associated with higher SREs than quetiapine and risperidone, and ziprasidone was associated with higher SREs than risperidone. The results of this study suggest that certain antipsychotics may put individuals within the PTSD population at an increased risk of SREs, and that careful consideration may need to be taken when prescribed.

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Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder

Cited by 7 publications

References 53 publications

A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms

A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms

The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review

An Emulation of Randomized Trials of Administrating Antipsychotics in PTSD Patients for Outcomes of Suicide-Related Events

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