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Abstract
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OBJECTIVE: Fibromyalgia (FM) patients are 10x more likely to die by suicide than the general population. The purpose of this study was to externally validate published models predicting suicidal ideation and attempts in FM and identify interpretable risk and protective factors for suicidality unique to FM.
METHODS: This is a case-control study of large-scale EHR data collected from 1998-2017, identifying FM cases with validated PheKB criteria. Model performance was measured through discrimination including area under the receiver operating characteristic (AUC), sensitivity, specificity, and through calibration including calibration plots. Risk factors were selected by L1-penalized regression with bootstrapping for both outcomes. Secondary utilization analyses converted time-based-billing codes to equivalent minutes to estimate face-to-face provider contact.
RESULTS: We identified 8,879 individuals with FM, with 34 known suicide attempts and 96 documented cases of suicidal ideation. External validity was good for both suicidal ideation (AUC=0.80) and attempts (AUC=0.82) and excellent calibration. Risk factors specific to suicidal ideation included polysomatic complaints such as fatigue (OR=1.29, 95%CI 1.25-1.32), dizziness (OR=1.25, 95%CI 1.22-1.28), and weakness (OR=1.17, 95%CI 1.15-1.19). Risk factors specific to suicide attempt included obesity (OR=1.18, 95%CI 1.10-1.27) and drug dependence (OR=1.15, 95%CI 1.12-1.18). Per utilization analyses, those with FM and no suicidal ideation spent 3.5x more time in follow-up annually, and those without documented suicide attempts spent over 40x more time face-to-face with providers annually.
CONCLUSION: This is the first study to successfully apply machine learning to reliably detect suicidality in FM, identifying novel risk factors for suicidality and highlighting outpatient engagement as a protective factor against suicide. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.
Language: en |