This article isprotected rights by All copyright. reserved. 10.1002/acr.23748doi: lead todifferences version between this andthe ofPleasec Version Record. been and throughtypesetting, proofreadingwhich may thecopyediting, process, pagination This article undergone has for and buthas accepted been not review publication fullpeer Email: Phone: (615) 875 Nashville, TN 37203 3401 West End Avenue, Suite 380 Osher Center for Vanderbilt University Medical Center Rehabilitation Assistant Professor, Departments of Psychiatry & Behavioral Sciences, Physical Medicine & Lindsey C. McKernan, Ph.D. Correspondence concerning manuscript this should be directed to: RUNNING HEAD: FIBROMYALGIA AND OF RISK SUICIDE 4 3 2 1 M.A M.D., Lindsey C. McKernan, Ph.D., Outpatient Engagement Lowers Predicted Risk of Suicide Attempts in Fibromyalgia typeArticle : Original Article DR. LINDSEY COLMAN MCKERNAN (Orcid :ID 0000 Accepted Department ofMedicine, Vanderbilt University Medical Center ArticleDepartment of Department ofPhysical Medicine & Rehabilitation, Department ofPsychiatry & Behavioral Sciences, Vanderbilt University Medical Center [email protected] . 1,3,4
Biomedical Informatics, Vanderbilt University Medical Center Integrative Medicine - 9990
Fax: (615) 936
1 - 2
Matthew
- 6144 C. Lenert,C. B
.A., Vanderbilt University Medical Center - 0001 3
Leslie J. Crofford, M.D.,
- 9419
- 8502)
4 ite this articleite this as
& G. Colin Walsh,
This article isprotected rights by All copyright. reserved. Keywords: fibromy engagement as a protective against factor suicide. suicidality in FM, identifying novel ri C 40x time more face timemore in follow 95%CI 1.12 suicide attempt included obesity (OR=1.18, 95%CI 1.10 (OR=1.25, 95%CI 1.22 ideati (AUC=0.80) and attempts (AUC=0.82) and excellent calibration. Risk factors specific to suicidal documented cases of suicidal ideation. Results: contact. converted penalized regression with bootstrapping for both outcomes. Secondary utilization analyses specificity, and through calibration including calibration plot discrimination including area under the receiver operating characteristic (AUC), sensitivity, identifying FM with cases PheKBvalidated criteria Methods suicidality suicidal ideation and attempts in FM and identify interpretable risk and protective factors population. Objective Abstract Conflict of Interest Acceptedonclusion Article on included polysomatic complaints such as (OR=1.29,fatigue 95%CI 1.25
We We identified 8,879 : This is a case
: Fibromyalgia (FM) arepatients 10x more likely to die by suicide than the general time unique to FM. : - The purpose of this study t was This is the first study to successfully apply machine learning to 1.18). Per utilization analyses, those with FM - based : - algia, suicide, machine learning, factorrisk - There are no financial co to up - - - billing face withproviders 1.28) - annually control study oflarge
, and weakness (OR=1. individuals with FM, with 34 known suicide attempts and 96 codes to equivalent minutes
, and those without documented suicide attempts spent over sk sk factors for suicidality External validity wasgood for both suicidal ideation annually. o externally publishedvalidate models predicting nflicts of interest. - scale EHR data collected from 199
. 17
Model performanceModel was measured through
, 95%CI 1. - 1.27) and drug depe
to and no
s. s. Risk factors were selected by L1 estimate face and highlighting
15
suicidal ideation spent 3.5x - 1. 19 ) . Risk factors specific to - to reliably detect ndence (OR=1.15, -
face provider outpatient - 8 1.32) - 2017 , dizziness , for - This article isprotected rights by All copyright. reserved. reliance on small, prospective cohorts the as mainstay of study in th – other mod status review suggested that general demographic risk factors for suicide (e.g. gender, age, marital regardless of demographics, pain severity, or mis worldwide factorsrisk for SITBs ideation with active intent (6 ofrates suicidal and 3.3times more at risk than other chronic pain patients. patients are up to 10.5 times more at risk of death from suicide than the general population, widespread pain fibromyalgia o
f chronic pain doubles suicide risk, Acceptedinformation exists detailing risk factors for suicidality in FM patients in part because of Article classified accidental as deaths
, education level A recent comprehensive review indicated that the presence of chronic pain alone, Every day, protective factor against suicide. providers annually Fibromyalgia patients without documented suicidality spent up to 40x timemore factorsRisk for suicide attempt include drug dependence and obesity dizziness, R attempts. fibromyalgia, identifying novel factorsrisk for both suicidal ideation and suicide This theis first study to successfully apply machine learning to suicidality in isk factorsisk for suicidal ideation included polysomatic complaints ifiable factors specific to pain may increase suicide risk. , 10
(FM) stigma, ideation,
with cognitive dysfunction, fatigue, and difficultysleep
and weakness. 120 further elevate 11
in ) may not translate to chronic pain populations, and that possible itis that
or of healthcarelack access. people die from suicide FM thoughts and behaviors (SITBs) including , highlighting the importance of outpatient engagement as a
are difficult to prospectivelystudy because of under - 8%),
Significance Innovation and 7
suicide risk.
8,9 (e.g., car accidents) theyif are reported at all. 31
and n
and evidence suggests that specific
on - mental health, doubles suicide risk. Further, this fatal fatal suicide attempts by poisoning (17%). 3,4
in the Unite
F M 11
is characterizedis by
Moreover, S d States
7
Similarly,
ITBs in 31 . 2
suicidal ideation (33 is domain.
At minimum, the presence Very little FM patients the presence of . 5 FM
pain disorders, such as Coll such fatigue as
may may be .
ectively, FM
In FM – - 10
reporting and conflicting
have
- high 8 ,
48%),
with with The
6
This article isprotected rights by All copyright. reserved. quantified, may suggest targets ofclinical intervention. the if FMwith factorsrisk for SITBs identify andrisk protective factors specific to th personalized to adolesc predictive models of suicide attempt risk on a broad, heterogeneous population of adults recordhealth presence of multiple pain conditions common in FM and known to elevate suicide risk, such as post healthcare are delivered such as primary care. Lastly, population characteristics that are occur in tertiary specialty clinic settings and may not reflect settings in wh fac low response rates, or inability to assess individuals over time. Similar to other conditions, risk research include generalizability, small sample sizes, self needrisk to be considered whe apply to some patients, and that both pain psychiatric co morbidity to suicidality in FM, and initial (differing) findings disorder pain populations specifically, r
Accepted Articletors for suicidal ideation and suicidal attempts may differ. , 14
resultant risk patterns
disease severity ents One path tostudy SITBs remains large . .
16 It is unknown
A point of debate the is relative contribution of pain severity versus psychiatric co 20
- at (EHR) morbidity not does fully explain the increased risk of suicide in FM, may only
high a large academic - data including predictive modeling
risk population concurrently , 6 whether
younger age isk factorsisk identified thus far include pain severity differ in FM compared to other n examining suicide risk in FM t medical center hese
or longitudinally in routinely collected EHR data in 16 s ,
7 (e.g., FM) to
have yet to be investigated. general depression/anxiety - specific and general factorsrisk for elevated suicide -
scale retrospective analyses of clinical electronic algorithms ese groups . G 1) riskpredict eneralized models like these
.
F - reported symptoms and diagnoses, or example, we will accurately identify risk in FM or , .
8,9,15
Research to groups
17 may may suggest . - 21
The preponderanceThe of studies traumatic stress
sleep dysfunctionsleep
Limitations of existing before
.
Such risk patterns, once - harm occurs and 2) date yethas to assess have that the presence of ich large quantities of , 4,7,12,13 validated validated 18 may may be
, and the 3,8
widespread
and mood
patients 19
and - This article isprotected rights by All copyright. reserved. Acceptedindividuals with rich data available on over 1 Million patient lives. text, laboratory values, and more collected over twenty years at Vanderbilt on over 2.8Million Derivative Center (VUMC) using the de dataClinical were collected the from electronic health record at Vanderbilt University Medical Data Collection Clinical Predictive Modeling/Clinical Phenotyping (adapted from Walsh Materials and inform prevention strategies directly. novel analyses to identify external validity ofpublished learning algorithms of Articleseparately to enhance prevention efforts. that notes population, novel predictors will need to be considered theylook as do. hypothesizeWe that in intervention, it i these same data. To bridge both they also present opportunities to develop clinical tools to identify and prevent SITBs using pain sub
general risk factors for suicide do not always translate to chronic pain populations, Coupling literature Studies of clinical EHR data arewell . 22
- This repository includes clinical data populations may have different risk factors for suicidality and need to be studied
Methods s paramounts to both identify who is at risk and to consider
suicide attempt
interpretable -
- and domain identified clinical data repository known the as Synthetic
models accurate risk identification with interpretable,
in predicting
risk,
translating existing models risk - knowledge of SITB 21
19
suited to address the profiles
the purpose of this investigation wasto assess the such as suicidal ideation
unique to FM specific
diagnoses, demographics, clinical
s
in FM to this cohort. se .
with with These latterThese findings of suicide risk to a FM knowledge and attempts et al. 2017) validated why Exist gaps. Moreover, actionable
risk profilesrisk
in machine ing evidence FM
and will will 31 ,
and use use This article isprotected rights by All copyright. reserved. Acceptedassurance thatthere were algorithm training set. modeling experiment and removed any patients in the FM cohort from the general model criteria for FM and appliedWe this phenotype to phenotype uses a combination of diagnostic codes definedWe the External Validation Data Collection for this Study Articlecohort wit extend reach its topredict both suicidal ideation without attempt and suicide attempt in a original algorithm was designed to predict suicide attempt. In this investigation, we toseek anwith internal validation c center and aged over eighteen years. This cohort defined the published algorithm reported prior population of VUMC. suicide attempt were compared to a control cohort of12,695 and labeled by multiple experts toestablish areliable gold standard. Clas described sification of Diseases, version 9 (ICD9) codes (E95x.xx) for adults in the Synthetic Derivative ensureTo true external validity te Prior developmentmodel of thegeneral suicide attempt . 19 h no additional training.
In c brief, FM cohort through a validated phenotype publicly available in PheKB with with These charts were identified from a minimum of three visits in the medical andidate charts were identified using self The general algorithm wasthen re 2) thanmore three visits to VUMC over at least 6 months.
no patients in common in the general cohort. - statistic a the VUMC population and selected only those 1) meeting PheKB
s s ashigh 0.92 to predict suicide attempt in 7 days sting on the FM cohort, we returned to the initial
and text phrases to identify casesoftrue FM. fitted and internally validated
adults -
injury International risk algorithm has been drawn from the general
These 3,250 ofcases
. 23
after The The . The The FM
This article isprotected rights by All copyright. reserved. then attempts at included two outcomes and multiple time point Data on the FM cohort was preprocessed identically to theinternal validation sample and External Validity Testing of General Algorithm on FM Cohort imputation usedwas to Zip codes were missing in 7.5%of charts and race missingwas in 0.05% ofcharts. Multiple measured as counts external validity testing and replication here. dataClinical were preprocessed a Data Preprocessing and Missing Data Handling novel features added to the mo marijuana use exposure, violence exposure, abuse exposure (sexual and non diagnostic codes existing algorithm. derived from patient direct experience working with the existing mode factorsrisk (features) and with specific model of SITBs We F
Accepted Article M
Specific Feature Selection combined domain knowledge and existing resea
applied to these data
30 days from the last clinical encounter. , abdominal pain, and polysomatic complaints.
relevant to FM l as new risk factors. Second In – - provider interactions diagn brief .
impute missing values in those instances First
to obtain a posterior probability of suicide attempt risk. This , we added oses, medications and visits
, we conducted a review of existing literature to extrapolate del, their basis, and specific code . These fe informatics co FM s reporteds prior and described as in the Appendix
and SITBs (LCM and LJC) t
model features atures included not accounted for in previous resear
Missing data were rare because thevariables -
, we authors (CW and MCL) s s of prediction used rch to rch inform feature selection in a FM
The The using regular
– clinical expertise from authors with
post general were imputed to zeroes notif present.
The A The - - o include additional features traumatic stress, trauma sexual), sleep dysfunction, –
s useds to derive them 1 . suicide attempt algorithm wa 24 ) suicidal ideation, ppendix
expressions from notes , incorporated those not in i ncludes a ch orthe
to support and . table of
2
known ) -
and s
This article isprotected rights by All copyright. reserved. technique with resampling to yield aset of influential small number ofimportant predictors across complex data FM patients. bootstrapping usedWe De definition of good calibration 10 similar individuals in the new setting actually having that outcome. subsequently calibrated prevalence in the new setting, in this case the FM cohorts. The resultant predictions are domains predictions was performed logisticusing calibration than that in the order to optimize model performance. generalThe model development cohort was enriched to Recalibration in External predict suicide attempts specifically would also generalize topredict suicidal ideation. some shared risk factors between not equate suicidal ideation and behaviors like attemp beenhas used in a panoply ofnew predictive tasks such as hospital readmission risk Charlson Comorbidity Index, acommon risk score originally validated to assess mortality risk, input data but also testing generalizability its to predict different outcomes. For e in this cohort. predicted probability
Acceptedvelopment and Validation theof Novel Explanatory FM Suicide Risk Algorithm Article . 25 the
This This method passes the predictions through a logit function trained on the In brief, bootstrapped L
External validity testing includes not just testing an algorithm on a new of set to gain insight into which factors may have themost influence on su enriched, L
was to validate used both suicidal ideation and suicide attempt outcomes - 1 penalized regression (LASSO)
properly to indicate thata 40% risk of an outcome correlates with 4of Validation
internal development set (~25%), - 1 penalized regression (BoLASSO –
whether predictions reflect real outcome rates. ideation and attempts
Because outcome prevalence in FM (~1%) wasdifferent as weas have used in other predictive predictors and an ability to obtain ts ts clinically, but we hypothesized that is well
a ratio ofa ratio four controls for every case in suggest suggest that an algorithm designed to
.
The BoLASSO enhances this
recalibration of external - 26 accepted for its ability to select a )
with twowith levels of
This latter example theis
xample, the icide riskicide for . Wewould
This article isprotected rights by All copyright. reserved. Accepted baselineThe characterist outcome prevalence, and visits over a six period,month there were 8 November 2017 Using the validated PheKB definition of Results minutes in time 90832 Behavior (H&B) Codes, 96150 cohor SITBs (see results), we conducted a secondary analysis ofhealthcare encounters in study p With Utilization Analyses Article calibration metrics including calibration plots, calibr Operating Performance was measured through discrimination including Area Under the Receiver Performance Evaluation the Appendix. select only those features that were chosen in 80% of interpretable test statistics for those same predictors. ts. - reliminary results identifying differential healthcare utilization as protective factors of 90840, 90846
We We counted Evaluation and Management (E&M) CPT Codes, 99211 Characteristic
-
based billing to estimate time spent in follow with thewith phenotype. After cens
- 90849, 90853
(AUC), sensitivity/recall, specificity, and precision and through also ics ofics 96 documented cases of suicidal ideation, 1.1% outcome prevalence. - 96154, and Outpatient Psychiatry CPT Codes, these cohorts are shown in 1 Table , foreach study cohort. We linked E&M codes to equivalent FM , ,879 patients with 23
we oring only patients those with at threeleast identified 14, ation ation slope/intercept, and scoring rules. bootstraps We tunedWe conservatively the BoLASSO to
-
34 known at up. 430 .
Full .
patients from details tempts, 0.4% - 99215, Health99215, and 90791 can can be
January - 90792, reviewed in
1998
–
This article isprotected rights by All copyright. reserved. Accepted 3Figure Placeholder anticipated. indicate the majority of cases of ideation intofall the highest predicted bins of as risk, proportions of cases of suicidal ideation concentration the is proportion of cases ofideation or attempts by binned quantile ofrisk. The performance afterrecalibration to the outcome prevalence in the novel FM cohort. Risk prevalence in a population. externally The valid predictions demo Calibration is an important tometric illustrate whether predicted probabilities reflect true 2Figure Placeholder 1Figure Placeholder Article attempts and 0.14 forsuicidal id outcomes given the extreme ca for ideation. Precision and recall/sensitivity were also assessed and precision waslow for both 0.01 to1 for specificity for both outcomes and 0 to 1 forsens ideation. Sensitivity and specificity varied based on threshold of case positivity and ranged from Operating Characteristic (AU attempts in a novel generalThe suicide attempt prediction model predicted both suicidal ideation and suicide External Validation Publishedof Model
FM
cohort with good discrimination. The Areas Under the Receive Cs, Figure se imbalance in this context. eation (Precision
1
by predicted bin ofrisk are shown (Figure ) were 0.82 for suicide attempts and 0.8 for suicidal
- Recall Curves shown, Figure
Maximum precision 0.08was for itivity for attempts and 0 to 0.99 nstrated excellent calibration 2 ).
3 )
r and This article isprotected rights by All copyright. reserved. intervention codes (CPTs Accepted documentedwith attempts. Wethen assessed documented suicide attempts spent over 40x more time with outpatient providers than those even pronounced more for suicide attempters. Individuals with timemore in follow that for suicidal ideation, those patients with talliedWe minutes sp Utilization Analysis summarized here. predictors selected over 80% ofthe time, they were not finally selected in the models predictors ofSITBs. However, because of the conservatism ofour approach to only report those abusesexual and trauma, noteWe that commonly held risk factors such as post Article1.18)] 1.1 [e.g., Cocaine Dependence (OR=1.18, 95%CI 1.1 (OR=1.5, 95%CI 1.46 disorderBipolar OtherwiseNot Specified (OR=1.18 1.28), and weakness (OR=1.17, 95%CI 1.15 polysomatic complaints summarized BoLASSOThe Risk Factors 2 - 1. ,
1 and inpatient utilization 8 ) ,
mental illness
of Suiciof
by category selected both and risk protective factors for both outcomes
dal Ideation and - up per year than those with documented suicidal ideation. ratio This - ent in 1.5
[fatigue (OR=1.29, 95%CI 1.25 3 in
90791, 90846,90791, and [e.g., Recurrent Depression with Psychosis ( ) . Concomitant categories for suicide attempt Table Table outpatient outpatient m
(OR=1.32 edications likebenzodiazepines 2 .
Suicide The risk categories for suicidal ideation included: follow 96150 , 95%CI 1.27 - Attempt in FM 1.19)] - FM up in the cohorts in our study and determined psychiatry and - - 96154) and determined that, thesewhile were 1.27)]
who did who did , serious and persistent mental illness ,
95%CI 1.1795%CI - - traumatic stress disorder, histories of ,
1.36) - obesity 1.32), dizziness (OR=1.25, 95%CI 1.22
not
.
have suicidal ideation spent 3.5x mental health behavio
( FM
BMI 50 - were allincluded 1. 20
who did )], OR=1.12, 95%CI 1.07 - were and inpatient utilizat 59, . Risk factors OR=1.1 not : drug dependence
have as potentialas 5 r and , 95%CI are ,
[e.g.,
was - ion -
This article isprotected rights by All copyright. reserved. for future work. capability to capture pain severity and comorbid obesity (e.g., fatigue illness, comorbid medical andillness, frequent inpatient admission. Both ideation and attempt wasrisk conferred by B predict both suicidal ideation and suicide attempts. further model refitting. That is, amodel predicting suicide attempts alone performed well to algorithm validated externally a across novel cohort and for two differen maximum precision 0.08, ideation predictive models of SITB risk perform well inpredicting SITBs in domain expertise to obtain interpretable patterns of risk. This Discussion established. enabled by predictive models that suggest patients attempts and intervention codes were billed for those patients with ideation but none subsequentwith encounters had a documented suicide attempt. Notably, themajority of small proportions ofthe overall study cohort ( AcceptedoLASSO Article study study Adding . ,
These data highlighted different risk patterns for suicidal ideation versus is the first to
, dizziness, disease
may may suggest a straightforward albeit non
increased the risk of suicide attempt
- and weakness) specific risk factors apply machine learning
– or duration
AUC 0.8
typified risk of suicidal ideation, in a rigorous statistical experimental design 0 , maximum precision 0.14)
0.1 in this context though it remains a consideration to suicidality in younger age, ~2%), none of the patients thesewith
on whom on whom outpatient engagement should be
We We .
O f note, we did not have the demonstrated serious and persistent mental - trivial prevention strategy F M (attempts
in the context of clinical
Polysomatic
while drug dependence mental health behavior . suicide attempts t outcomes with no Notably, theinitial
that generalizabl -
AUC ~0.82,
complaints , the
in FM e . This article isprotected rights by All copyright. reserved. engagement to lower predicted risk. (e.g., polysomatic complaints, pharmacologic therapies) Building on existing research, we also highlight known risk factors of SITBs in FM based on b in a risk population with clinical data science forthe time first respect to su risk cohort. subsequent attempts, Outpatient ofuse outpatient resources like health and behavioral interventions in the low cohort.risk primary care, medical specialty, and mental health clinics. There was a concomitant increase in up forthe low risk group compared to those with SITBs dramatic difference in follow o both groups outpatient prescriptions for both mental and medical illnesses investigation. patterns of suicide risk differ for suicidal ideation and suicide attempt in FM, prompting further complaints including fatigue, dizziness, weakness). Further, our investigation shows that 3) in fibromyalgia (mood disorder) with novel risk factors iden persistent mental illness general population (i.e. obesity, younger age, frequent inpatient admission profiles of suicide risk in FM combine those indicate Acceptedutpatient engagement, Article This workThis extends Notably, analysisThis suggests that unique profiles of suicide exist risk in FM. In our sample,
H&B codes were
These findings suggest further research in patterns of outpatient engagement with . Additionally, preventive medications and vaccinations, typical of longitudinal icidality may be indicated.
freque potentially indicating nt outpatient utilization (clinic follow lowered ), 2) in chronic pain (illicit drug use, co existing research by more likely i - up time
risks of SITBs in FM.
–
up to 40x increased time n those with suicidal ideation without evidence of
a preventive effect of H&B intervention in this high oth literature review and clinical expertise.
quantifying, characterizing, and predicting SITB actionable foci d in previous investigations
Subsequent – and the buffering effect of outpatient
across out
- up) and increased rates of .
served as of risk It confirms and builds upon tified in this study (polysomatic - morbid health conditions),
spent with providers utilization analyses showed a patient settings including management protective , severe and 1)
in factors in strategies
the
in follow
and -
- This article isprotected rights by All copyright. reserved. Acceptedoutcomes. While this permits replicability/reproduc structured data within the health record to assess for patient characteristics tha or countries. documentation, differences in self rates Other studies in fibromyalgia billing workflows failing to document diagnostic codes even if thelatter two have occurred. potential sources: 1) patient hesitancy to report symptoms; 2) lack of provider inquiry; 3) documented suicidal ideation in this cohort. Suicidal ideation remains at risk of under ideation working with reproducibility and generali ~0.4 however, this is reasonable given the lowbase Articlemajor academic medical center for study data additional patient characteristics we combined expertise in machine learning, rheumatology, and psychology to identify and disease medical center data FM
in a large EHR cohort , %
. facilitating
Future analyses should address whether these differences in prevale ) These findingsThese should be interpreted Strengths of our includestudy . External codes is typical of this literature, but codes are an imperfect surrogate fortrue SITBs. - specific risk factors concurrently. In addition
An EHR allowed us to sample patients at all pointsof care
or innate differences in our cohort compared to those in other health systems existing limitation of replicable machine learning methods is the reliance on external validation - validity results report compared to retrospective EHR analyses, data, there is always a risk of misclassification. .
The The models were designed to scale to any clinical setting with EHR zability in new settings are reliant on patient sel of this investigation to in this study. clinically
using - reporting. Wereport a 1.1% prevalence of validated models Under - in light of study limitations. reliedWe on a single . Our overall sample size was relatively small; inform risk predict - rate phenomenon of SITB Applying these f - - documentation oc report have been associated with i
bility and the are encouraging, but studies of important to reviewing investigations to
applied to a
, methods i
on and interpret results. assessing steps Our reliance on the suic under potential curs from multiple
of future work - in
reporting, incomplete to a large academic valid phenotype of nce result from
both known general FM
for
(in ourcohort,
t inform larger
higher - . In scale - date,
idal idal
This article isprotected rights by All copyright. reserved. Derivative is through UL1 RR02 AcceptedTranslational Sciences or the National Institutes of Health. the authors and do not necessarily represent official of views theNational Center for Advancing Research and Quality (AHRQ 6K12HS022990 Centered Outcomes Research Education and Training Initiative and the Agency for Healthcare Acknowledgements: decrease thelikelihood of suicidality complex Providers have expressed helplessness and frustration being unable to “intervene” with connection. therapy for ideation, we suggest connection to mental health resources such as cognitive including and pain severity at follow particular has shown to improve outcomes in FM by improving mood, pain including and in continuity Articlewho areboth atrisk of SITBs and who havebeen reduce risk of SITBs. Predictive models like ours may play a rolein identifying patients those thisfrom investigation is the importance of simply maintaining outpatient contacts over time to findings to actionable methods in clinical settings to enhance which remains a future direction for this work. Experts areaddressing this limitation by “unstructured” data such textas of patient notes investigations across networks, nuance can be lost in additional risk factors that may exist in diverse diseases. While our current efforts onfocus identifying risk, theultimate goal is to translate these
FM mental health engagement may attenuate risk of suicide attempt in those with suicidal psychological approaches to pain management.
with FM
patients.
patients with suicidal ideation to enhance outpati at - risk This This manuscript was prepared with support from the Vanderbilt Patient 30
patients an activeis area of This workThis shows that the contact it 20,28,29 - up.
The goldThe standard for pain treatment is multimodal therapy, 33 4975/RR/NCRR, PI: Gordon Bernard.
Given ourfindings that
in this population processing clinicalte - 04). Its conten
(versu lost to follow prevention in .
s a s outpatient engagement ofan 32
diagnostic code self self may Funding for the BioVU Synthetic
Cognitive behavioral therapy in ts are solely the responsibility of suicide x - up. t through natural language
military and civilian ent engagement and provider
Enhancing have intrinsic benefits prevention.
, for example) - related disability, -
behavioral outpatient A clear signal
y type settings settings .
, that 27
This article isprotected rights by All copyright. reserved. 13. 12. 11. 10. 9. 8. 7. 6. 5. 4. 3. 2. 1.
Accepted Article
2):201 in chronic pain patients: factors a MT,Smith Edwards RR, Robinson RC, Dworkin RH. Suicidal ideation, plans, and attempts Symptom Manage. Japanese men: a population Kikuchi N,Ohmori and Life enhancing mental health for care suicidal individuals and other people in crisis. Gould MS, Munfakh JL, Kleinman M, Lake AM. 2015;5(11):e009120. Nationwide re Tøllefsen HelwegIM, journal ofWorld Institute Pain.of chronic pain: predictors of suicidal ideation in fibromyalgia. Trinanes Y, Gonzalez 2011;50(10):1889 withpatients fibromyalgia: a survey in Spanish patients. Calandre EP, Vilchez JS, Molina patients over thirty Wolfe F, H Rheumatology. of Danish patients with fibromyalgia: increased frequency of suicide. Dreyer L, Kendall D S, Arthritis Care Res (Hoboken). preliminary diagnostic criteria for fibromyalgia and measurement ofsymptom severity. Wolfe F, DJ, Clauw Fitzcharles MA, et al. The American College of Rheumatology psychiatry.JAMA Ilgen MA, Kleinberg F, Ignacio RV, et al. psychological links. Tang NK, Crane C. March 5,2018. https://www.nimh.nih.gov/health/statistics/suicide.shtml National Institute ofMental Health. Suicide. 2017; American Journal ofPublic Health. Singh GK.Area Deprivation and Widening Inequalities in US Mortality, 1969 - 208. - Threatening Behavior. assett AL, Walitt B, Michaud K. Mortality in fibromyalgia:
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85. rheumatology (Hoboken, NJ). The The Journal of nervous and mental disease. - L, Sareen J. Chronic pain conditions and suicidal ideation . The risk. The of suicide mortality in chronic pain patients.
Posttraumatic stress di 2014;18(8):436. - al Science. pain controls and patients suffering from low Differences and similarities ofrisk factors for 2014;10:625. - Regression Modeling Strategies: With Rodriguez BM, Condés Clinical Pharma 2017;5(3):457 Journal ofChild Psychology and
The ClinicalThe journal ofpain. through the bootstrap. Paper 2001;323(7314):662 - Wide Association Study of sorder and suicide among risk 2017;69(2):291
cology &Therapeutics. - ‐ 469. scale de
- Moreno E,Rico . New . New York, NY: ‐ J J Biomed - - 300. identified 662.
- back back J - This article isprotected rights by All copyright. reserved. Accepted analysis ofrandomized controlled trials. cognitive behavioural therapies in fibromyalgia syndrome 33. Bernardy, K., Klose, P., Welsch, P., & Häuser, W. (201 https://iprcc.nih.gov/docs/HHSNational_Pain_Strategy.pdf Population Health Strategy for Pain. 32. Department of Health and Human Services. (2015). Article31. 30. 29. 28. 27.
doi:https://doi.org/10.1016/j.pnpbp.2017.08.020 Neuro Racine, M.(2017). Chronic pain and suicide risk: A comprehensive revie where uncertainty meets attitude. Hayes SM, Myhal GC, Thornton JF, et Fibromyalgiaal. and the therapeutic relationship: 2017;174(2):154 LongitudinalFrom Electronic Health Records. Yuval Barak servicemembers (army starrs). hospitalization in us army soldiers: The army study to assess risk and resilience i Kessler RC, Warner CH, Ivany C,et Predictingal. suicides after psychiatric language processing. suicide and accidental death afterdischarge from McCoy Jr,TH, Castro VM, Roberson AM, Snapper LA, Perlis RH. Improving prediction of - Psychopharmacology and BiologicalPsychiatry - Corren, Victor M. Castro, Solomon Javitt, et al. - 16 2. JAMA Psychiatry.
Retrieved from JAMA Psychiatry.JAMA European Journal ofPain, 22 Pain research & management. 2016;73(10):1064 American Journal ofPsychiatry. National Pain Strategy: A 8). 2015;72(1):49 general hospitals with natural
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A systematic review and meta Efficacy, acceptability and safety of . - 1071. Predicting Suicidal Behavior (2), 242 -
57. 2010;15(6):385
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Comprehensive
Progress in
- n 391. ‐
This article isprotected rights by All copyright. reserved. Hyperactivity Comorbidity Mix Mix inPrecedingUtilization Year yearsMedian in (StandardDeviation), Age Unknown/Not Recorded Declined toRespond Alaskan/Native American Asian Black White Race Female Male Gender Baseline Patient Characteristics Table 1 Outpatient, Mean ( Visits Inpatient,( Mean Visits
AcceptedPost Attention Deficit Disorders with Article
-
traumatic Stress Disorder
Percentile
Percentile
)
)
Suicide AttemptsSuicide 6 0 (0 5 7.1 45 (9.0 0 (0 0 0 0 4 30 31 3 N = Cases, No. .8 ( ( ( ( ( ( (
18 0 0 0 12 9 ( ( ( ) ) ) ) ) 88 91 ) 34 62 73
) )
) )
) ) )
(%);
525 149 1. 14.6 57 (14.2 140 141 796 7 8 805 N = Controls 0 0 , , 3 768 040 ( (
0 0 ( ( ( ( ( ( ( 8,845 84 ) ) ( 6 1.7 1.5 1.5 9 9.5
66 ( ( ) ) ) 88 90.5
) ) ) ) ) )
, No.(%); )
)
Suicidal IdeationSuicidal 31 10 1 23.3 50 0 0 0 0 1 86 (90 81 (84 15 N = Cases, No. 0.7 0 ( ( ( (
0 0 0 0 ( (1 (1 ( ( 32 ) ) ) ) 10 16
96
( ( 0) 3.7 72 60 ) ) ) ) )
) ) )
(%);
498 466 1.2 (84 14.5 57 79 (0.8 140 20 42 (0.5 788 7,719 (88 7,992 796 (9 N = Controls, No.
( (0.
14.1 ( ( (1 ( 8,788 ( 6 5 9
65 2 ( ) ) .5 ) ) ) ) ) ) 91
) ) )
) )
(%); This article isprotected rights by All copyright. reserved. Malignancy COPD DM CHF Schizophrenia Bipolar Episodic MoodDisorders Asthma Generalized AnxietyDisorder DefiantDisorderOppositional Accepted ArticleLiver Dz
3 (9 8 (24 3 0 (0 0 (0) 0 (0) 1 (3) 0 (0) 1 (3) 1 (3) 6 (18)
( 9 ) ) )
)
446 1 679 3 (0.03 29 (0.3) 89 (1) 467 (5) 84 (1) 643 (7) 91 (1) 522 (6) , 693
( ( 5 8
( ) ) 1 )
9)
96 25 (26 13 0 (0 0 (0) 0 (0) 11 (11) 2 (2) 12 (13) 8 (8) 35 (36)
( ( 10 14 )
0) ) )
29 (0.3) 89 (1) 464 (5) 80 (1) 639 (7) 82 (1) 496 431 1,676 633 2 (0.02
(6) ( ( 5 7
( ) ) 19 )
)
Non Thiazolidinediones Anti Comorbid Illness Medical Hypersensitivity AngiitisHypersensitivity Cerebral with Infarction Embolism Extremities Blood Clots PurpuraImmune Thrombocytopenic Type I withOther SpecifiedDiabetes Manifestations, Shock History of Septic Ulcer of Ankle Ulcer of Lower Limb Chemotherapy Retinopathy Diabetic Exacerbation Obstructive without Chronic Bronchitis Atrial flutter Antiepileptics Receptor Estrogen Selective Modulator This article isprotected rights by All copyright. reserved. FactorsRisk for Attempts and SITBs 2 Table -
Accepted Article- Nucleoside Transcriptase Reverse Infective Drugs
-
AC inLower DVT/Embolism
-
- Hydantion Derivatives
Pyrimidine AnaloguesPyrimidine
Factor Source Factor ICD ICD ICD ICD ICD ICD ICD ICD List Medication ICD ICD ICD List Medication List Medication List Medication List Medication List Medication ------10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10
Suicidal ------[1.01, 1.06] [1.05, 1.11] [1.11, 1.14] [1.12, 1.15] [1.16, 1.20] 95% CI Ratio Odds Attempts
[1.01, 1.09] [1.01, 1.09] [1.03, 1.07] [1.03, 1.14] [1.04, 1.11] [1.04, 1.12] [1.05, 1.11] [1.05, [1.08, 1.11] [1.08, 1.14] [1.09, 1.13] [1.11, 1.15] - - - - - 95% CI Ratio Odds Ideation Suicidal
1.14]
ICD ICD ICD ICD ICD ICD ICD ICD cytarabine fluorouracil, E.g., gemcitabine, ICD 491.22 ICD ICD fosphenytoin E.g., phenytoin, E.g., raloxifene E.g., etravirine E.g., pioglitazone gentamycin E.g., ciprofloxacin, Examples ------9, 446.20 9, 9, 453.41 9, 287.31 9, 250.81 9, 785.52 9, 707.13 9, 707.10 9, E.g., 362.01 9, 9, 427.32 434.91 E.g., 491.21
- Recurrent withPsychotic Depression Features Indole Derivatives (Anti BorderlineDisorder Personality Illness Mental Visits the Within Inpatient Year Past Utilization Inpatient UnspecifiedCocaine Dependence, Drug Dependence Weakness Dizziness Fatigue Polysomatic Complaints Morbid Obesity BMI 50.0 Obesity Oxidase Inhibitors Monoamine Features Bipolar I Disorder Bipolar Disorder NOS AcceptedThis article isprotected rights by All copyright. reserved. Article
-
59.9
-
Manic, with Psychotic Manic,
-
Psychotics)
ICD ICD ICD ICD ICD ICD ICD ICD ICD ICD ICD ICD Visit Count
------10 Diagnosis 10 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10 Diagnosis 10
[1.10, 1.27] - - - [1.01, 1.12] [1.12, 1.18] - - - [1.07, 1.18] [1.10, 1.15] [1.16, 1.20] [1.27, 1.36]
[1.15, 1.19] [1.22, 1.28] [1.25, 1.32] - - [1.04, 1.06] [1.13, 1.17] [1.17, 1.20] - - - [1.46, 1.53] -
10 M62.81 ICD R42% 438.85; ICD ICD 10, R53% ICD phenelzine tranylcypromine, E.g., 296.44 ICD ICD 296.16 ICD imipramine E.g., clomipramine, ICD ICD ------9, 728.87; ICD 9, 780.4, 9, 780.7; ICD 9, E.g., 296.43 9, E.g., 296.80 9, E.g., 296.31, 9, 301.83 9, 304.20
- 10
-
- - This article isprotected rights by All copyright. reserved. Figure AcceptedIdeation Suicidal Attempt Suicide Article 1 : Discrimination Performance:: Discrimination Receiver OperatingCurves Characteristic
This article isprotected rights by All copyright. reserved. Accepted Figure Article 2 : Precision: - Recall CurvesRecall
This article isprotected rights by All copyright. reserved. Accepted Article 3 : Proportions of Cases of Ideation by Predicted Bin of Risk Figure