How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys

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dc.contributor.affiliationDepartment of Health Care Policy, Harvard Medical School, 180 Longwood Ave., Boston, MA 02115, USAes_ES
dc.creatorKessler, Ronald C.
dc.creatorRose, Sherri
dc.creatorKoenen, Karestan C.
dc.creatorKaram, Elie G.
dc.creatorStang, Paul E.
dc.creatorStein, Dan J.
dc.creatorHeeringa, Steven G.
dc.creatorHill, Eric D.
dc.creatorLiberzon, Israel
dc.creatorMcLaughlin, Katie A.
dc.creatorMcLean, Samuel A.
dc.creatorPennell, Beth E.
dc.creatorPetukhova, María
dc.creatorRosellini, Anthony J.
dc.creatorRuscio, Ayelet M.
dc.creatorShahly, Victoria
dc.creatorShalev, Arieh Y.
dc.creatorSilove, Derrick
dc.creatorZaslavsky, Alan M.
dc.creatorAngermeyer, Matthias C.
dc.creatorBromet, Evelyn J.
dc.creatorCaldas De Almeida, José Miguel
dc.creatorDe Girolamo, Giovanni
dc.creatorDe Jonge, Peter
dc.creatorDemyttenaere, Koen
dc.creatorFlorescu, Silvia E.
dc.creatorGureje, Oye
dc.creatorHaro, Josep María
dc.creatorHinkov, Hristo
dc.creatorKawakami, Norito
dc.creatorKovess-Masfety, Viviane
dc.creatorLee, Sing
dc.creatorMedina-Mora, María Elena
dc.creatorMurphy, Samuel D.
dc.creatorNavarro-Mateu, Fernando
dc.creatorPiazza, Marina
dc.creatorPosada-Villa, Jose
dc.creatorScott, Kate
dc.creatorTorres, Yolanda
dc.creatorViana, María Carmen
dc.creator.identificador"http://orcid.org/0000-0002-3338-2055">Stang, Paul E.es_ES
dc.creator.identificador"http://orcid.org/0000-0002-0464-4845">Viana, Maria Carmenes_ES
dc.creator.identificador"http://orcid.org/0000-0001-9300-0752">Medina Mora Icaza, María Elenaes_ES
dc.creator.identificador"http://orcid.org/0000-0002-1362-2410">McLaughlin, Katiees_ES
dc.date.accessioned2017-06-29T03:52:52Z
dc.date.accessioned2026-03-27T14:33:06Z
dc.date.available2017-06-29T03:52:52Z
dc.date.issued2014es_ES
dc.date.published2014es_ES
dc.description.abstractotrodiomaPost-traumatic stress disorder (PTSD) should be one of the most preventable mental disorders, since many people exposed to traumatic experiences (TEs) could be targeted in first response settings in the immediate aftermath of exposure for preventive intervention. However, these interventions are costly and the proportion of TE-exposed people who develop PTSD is small. To be cost-effective, risk prediction rules are needed to target high-risk people in the immediate aftermath of a TE. Although a number of studies have been carried out to examine prospective predictors of PTSD among people recently exposed to TEs, most were either small or focused on a narrow sample, making it unclear how well PTSD can be predicted in the total population of people exposed to TEs. The current report investigates this issue in a large sample based on the World Health Organization (WHO)'s World Mental Health Surveys. Retrospective reports were obtained on the predictors of PTSD associated with 47,466 TE exposures in representative community surveys carried out in 24 countries. Machine learning methods (random forests, penalized regression, super learner) were used to develop a model predicting PTSD from information about TE type, socio-demographics, and prior histories of cumulative TE exposure and DSM-IV disorders. DSM-IV PTSD prevalence was 4.0% across the 47,466 TE exposures. 95.6% of these PTSD cases were associated with the 10.0% of exposures (i.e., 4,747) classified by machine learning algorithm as having highest predicted PTSD risk. The 47,466 exposures were divided into 20 ventiles (20 groups of equal size) ranked by predicted PTSD risk. PTSD occurred after 56.3% of the TEs in the highest-risk ventile, 20.0% of the TEs in the second highest ventile, and 0.0-1.3% of the TEs in the 18 remaining ventiles. These patterns of differential risk were quite stable across demographic-geographic sub-samples. These results demonstrate that a sensitive risk algorithm can be created using data collected in the immediate aftermath of TE exposure to target people at highest risk of PTSD. However, validation of the algorithm is needed in prospective samples, and additional work is warranted to refine the algorithm both in terms of determining a minimum required predictor set and developing a practical administration and scoring protocol that can be used in routine clinical practice.es_ES
dc.description.monthOctes_ES
dc.identifier2754es_ES
dc.identifier.citationMaría Guadalupe Camal Ibáñezes_ES
dc.identifier.doi10.1002/wps.20150es_ES
dc.identifier.eissn2051-5545es_ES
dc.identifier.issn1723-8617es_ES
dc.identifier.numero3es_ES
dc.identifier.organizacionInstituto Nacional de Psiquiatría Ramón de la Fuente Muñizes_ES
dc.identifier.paginacion265-274es_ES
dc.identifier.placeItaliaes_ES
dc.identifier.urihttps://doi.org/10.1002/wps.20150
dc.identifier.urihttps://repositorio.inprf.gob.mx/handle/123456789/4603
dc.identifier.volumen13es_ES
dc.language.isoenges_ES
dc.publisherMasson Italyes_ES
dc.relation13(3):265-274es_ES
dc.relation.jnabreviadoWORLD PSYCHIATRYes_ES
dc.relation.journalWorld Psychiatryes_ES
dc.rightsAcceso Cerradoes_ES
dc.subject.kwPost-traumatic stress disorder
dc.subject.kwPredictive modeling
dc.subject.kwMachine learning
dc.subject.kwPenalized regression
dc.subject.kwRandom forests
dc.subject.kwRidge regression
dc.titleHow well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveyses_ES
dc.typeArtículoes_ES

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