Trajectories of remitted psychotic depression : identification of predictors of worsening by machine learning
BACKGROUND: Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.
METHOD: One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics.
RESULTS: Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model.
CONCLUSIONS: Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:54 |
|---|---|
| Enthalten in: |
Psychological medicine - 54(2024), 6 vom: 10. Apr., Seite 1142-1151 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Banerjee, Samprit [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Journal Article |
|---|
| Anmerkungen: |
Date Completed 15.03.2024 Date Revised 03.04.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1017/S0033291723002945 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM363117555 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM363117555 | ||
| 003 | DE-627 | ||
| 005 | 20240403234953.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1017/S0033291723002945 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1362.xml |
| 035 | |a (DE-627)NLM363117555 | ||
| 035 | |a (NLM)37818656 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Banerjee, Samprit |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Trajectories of remitted psychotic depression |b identification of predictors of worsening by machine learning |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 15.03.2024 | ||
| 500 | |a Date Revised 03.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory | ||
| 520 | |a METHOD: One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics | ||
| 520 | |a RESULTS: Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model | ||
| 520 | |a CONCLUSIONS: Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms | ||
| 650 | 4 | |a Randomized Controlled Trial | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a outcome | |
| 650 | 4 | |a predictors | |
| 650 | 4 | |a psychotic depression | |
| 650 | 4 | |a relapse | |
| 650 | 4 | |a remission | |
| 650 | 4 | |a residual depressive symptoms | |
| 650 | 4 | |a trajectories | |
| 650 | 7 | |a Olanzapine |2 NLM | |
| 650 | 7 | |a N7U69T4SZR |2 NLM | |
| 650 | 7 | |a Sertraline |2 NLM | |
| 650 | 7 | |a QUC7NX6WMB |2 NLM | |
| 700 | 1 | |a Wu, Yiyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Bingham, Kathleen S |e verfasserin |4 aut | |
| 700 | 1 | |a Marino, Patricia |e verfasserin |4 aut | |
| 700 | 1 | |a Meyers, Barnett S |e verfasserin |4 aut | |
| 700 | 1 | |a Mulsant, Benoit H |e verfasserin |4 aut | |
| 700 | 1 | |a Neufeld, Nicholas H |e verfasserin |4 aut | |
| 700 | 1 | |a Oliver, Lindsay D |e verfasserin |4 aut | |
| 700 | 1 | |a Power, Jonathan D |e verfasserin |4 aut | |
| 700 | 1 | |a Rothschild, Anthony J |e verfasserin |4 aut | |
| 700 | 1 | |a Sirey, Jo Anne |e verfasserin |4 aut | |
| 700 | 1 | |a Voineskos, Aristotle N |e verfasserin |4 aut | |
| 700 | 1 | |a Whyte, Ellen M |e verfasserin |4 aut | |
| 700 | 1 | |a Alexopoulos, George S |e verfasserin |4 aut | |
| 700 | 1 | |a Flint, Alastair J |e verfasserin |4 aut | |
| 700 | 0 | |a STOP-PD II Study Group |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Psychological medicine |d 1970 |g 54(2024), 6 vom: 10. Apr., Seite 1142-1151 |w (DE-627)NLM000076112 |x 1469-8978 |7 nnns |
| 773 | 1 | 8 | |g volume:54 |g year:2024 |g number:6 |g day:10 |g month:04 |g pages:1142-1151 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1017/S0033291723002945 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 54 |j 2024 |e 6 |b 10 |c 04 |h 1142-1151 | ||