Data-Driven Course Trajectories in Primary Care Patients With Major Depressive Disorder

Wardenaar, Klaas J.; Conradi, Henk Jan; Jonge, Peter de

doi:10.1002/da.22228

Cited by 30 publications

(33 citation statements)

References 39 publications

(58 reference statements)

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“…A recent study showed differences in longer term outcomes based on MDD patients’ initial symptom trajectories in primary care (Wardenaar et al, 2014). The first year of assessment clarified patients’ trajectories, and 2 subsequent years’ data allowed outcome comparisons among trajectory groups: Patients with a “chronic” trajectory showed small improvements (year 1) but maintained elevated symptoms (year 2); “early remission” patients showed relatively quick and large symptom reductions (especially during the first 9 months of year 1) and maintained their improvements (year 2); “remission + recurrence” patients showed improvements but then deterioration (both during year 1) and elevated symptoms thereafter (year 2); and “late remission” patients showed slower improvements than early remission patients (year 1) that were largely maintained (year 2).…”

Section: Introductionmentioning

confidence: 99%

Defined symptom-change trajectories during acute-phase cognitive therapy for depression predict better longitudinal outcomes

Vittengl

Clark

Thase

et al. 2016

Behaviour Research and Therapy

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Background Acute-phase cognitive therapy (CT) is an efficacious treatment for major depressive disorder (MDD), but responders experience varying post-acute outcomes (e.g., relapse vs. recovery). Responders’ symptom-change trajectories during response to acute-phase CT may predict longer term outcomes. Method We studied adult outpatients (N=220) with recurrent MDD who responded to CT but had residual symptoms. Responders with linear (steady improvement), log-linear (quicker improvement earlier and slower later), one-step (a single, relatively large, stable improvement between adjacent assessments), or undefined (not linear, log-linear, or one-step) symptom trajectories were assessed every 4 months for 32 additional months. Results Defined (linear, log-linear, one-step) versus undefined acute-phase trajectories predicted lower depressive symptoms (d=0.36), lower weekly probability of being in a major depressive episode (OR=0.46), higher weekly probabilities of remission (OR=1.93) and recovery (OR=2.35), less hopelessness (d=0.41), fewer dysfunctional attitudes (d=0.31), and better social adjustment (d=0.32) for 32 months after acute-phase CT. Differences among defined trajectory groups were nonsignificant. Conclusions Responding to acute-phase CT with a defined trajectory (orderly pattern) of symptom reduction predicts better longer term outcomes, but which defined trajectory (linear, log-linear, or one-step) appears unimportant. Frequent measurement of depressive symptoms to identify un/defined CT response trajectories may clarify need for continued clinical monitoring and treatment.

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Section: Introductionmentioning

confidence: 99%

Defined symptom-change trajectories during acute-phase cognitive therapy for depression predict better longitudinal outcomes

Vittengl

Clark

Thase

et al. 2016

Behaviour Research and Therapy

View full text Add to dashboard Cite

show abstract

“…En la literatura internacional, la historia de depresión se ha asociado con una mayor recurrencia del cuadro 25 , observándose que la patología tiene un desarrollo crónico en un tercio de los pacientes deprimidos del nivel primario [26][27][28] , incrementando el riesgo de padecer un peor estado de salud y discapacidad funcional por períodos prolongados 25,29 .…”

Section: Discussionunclassified

“…También se detectó una frecuencia de riesgo suicida superior a 70%, cifra cercana a la reportada en hospitales comunitarios de la X región 24 , pero más alta que la señalada en el extranjero [31][32] . Estos indicadores de compromiso del cuadro clínico son predictores de mala respuesta y recurrencia/cronicidad 27,28,33,34 , y parecieran influirse recíprocamente en pacientes depresivos de APS 31,32 .…”

Section: Discussionunclassified

Comorbilidad en personas con depresión que consultan en centros de la atención primaria de salud en Santiago, Chile

et al. 2017

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“…For example, one approach commonly used in prognosis studies of recurrent and long-term conditions presenting to primary care is for information on predictors (such as pain intensity) to be ascertained by mailed self-complete questionnaires, or personal interview and examination in research clinics several days after their index consultation (e.g. [8][9][10][11][12][13][14]). This approach offers several advantages, as it facilitates: 1) a wider range of predictor information to be collected than would be possible within the time-constrained primary care consultation, 2) greater standardisation of data collection procedures, 3) a "cooling off period" between being informed about the study at the point of care, and consenting to provide information on potential predictors that would not be considered part of routine care.…”

Section: Introductionmentioning

confidence: 99%

Prognosis research ideally should measure time-varying predictors at their intended moment of use

et al. 2017

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Background: Prognosis research studies (e.g. those deriving prognostic models or examining potential predictors of outcome) often collect information on time-varying predictors after their intended moment of use, sometimes using a measurement method different to that which would be used. We aimed to illustrate how estimates of predictor-outcome associations and prognostic model performance obtained from such studies may differ to those at the earlier, intended moment of use. Methods: We analysed data from two primary care cohorts of patients consulting for non-inflammatory musculoskeletal conditions: the Prognostic Research Study (PROG-RES: n = 296, aged >50 years) and the Primary care Osteoarthritis Screening Trial (POST: n = 756, >45 years). Both cohorts had collected comparable information on a potentially important time-varying predictor (current pain intensity: 0-10 numerical rating scale), other predictors (age, gender, practice) and outcome (patient-perceived non-recovery at 6 months). Using logistic regression models, we compared the direction and magnitude of predictor-outcome associations and model performance measures under two scenarios: (i) current pain intensity ascertained by the treating general practitioner in the consultation (the intended moment of use) and (ii) current pain intensity ascertained by a questionnaire mailed several days after the consultation. Results: In both cohorts, the predictor-outcome association was substantially weaker for pain measured at the consultation (OR (95% CI): PROG-RES 1.06 (0.95, 1.18); POST 1.04 (0.96, 1.12)) than for pain measured in the questionnaire (PROG-RES 1.34 (1.20, 1.48); POST 1.26 (1.18, 1.34)). The c-statistic of the multivariable model was lower when pain was measured at the consultation (c-statistic (95% CI): PROG-RES 0.57 (0.51, 0.64); POST 0.66 (0. 62, 0.70)) than when pain was measured in the questionnaire 0.75); POST 0.72 (0.68, 0.76)), reflecting the lower OR for pain at the consultation. Conclusions: Prognostic research studies ideally should measure time-varying predictors at their intended moment of use and using the intended measurement method. Otherwise, they may produce substantially different estimates of predictor-outcome associations and model performance. Researchers should report when, how and where predictors were measured and identify any significant departures from their intended use that may limit the applicability of findings in practice.

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Data-Driven Course Trajectories in Primary Care Patients With Major Depressive Disorder

Cited by 30 publications

References 39 publications

Defined symptom-change trajectories during acute-phase cognitive therapy for depression predict better longitudinal outcomes

Defined symptom-change trajectories during acute-phase cognitive therapy for depression predict better longitudinal outcomes

Comorbilidad en personas con depresión que consultan en centros de la atención primaria de salud en Santiago, Chile

Prognosis research ideally should measure time-varying predictors at their intended moment of use

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