Dataset Information

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients.

ABSTRACT:

Background

Major depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.

Methods

The 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2?weeks for a median of 11?weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated "DTW distances"). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level.

Results

The sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories.

Conclusions

Symptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care.

SUBMITTER: Hebbrecht K

PROVIDER: S-EPMC7756914 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients.

Hebbrecht K K Stuivenga M M Birkenhäger T T Morrens M M Fried E I EI Sabbe B B Giltay E J EJ

BMC medicine 20201223 1

<h4>Background</h4>Major depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.<h4>Methods</h4>T ...[more]

PMID: 33353539

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Project description:BackgroundRetrospective studies conducted in psychiatric wards have indicated a shorter duration of stay for depressed inpatients in bright compared to dim daylight-exposed rooms, pointing to a possible antidepressant effect of daylight conditions. Dynamic LED lighting, aiming to mimic daylight conditions, are currently been installed in several hospitals, but their feasibility is poorly investigated.MethodsTo investigate the feasibility of these systems, we developed and installed a LED-lighting system in four rooms in a psychiatric inpatient ward. The system could function statically or dynamically regarding light intensity and colour temperature. The system consisted of (A) a large LED luminaire built into the window jamb mimicking sunlight reflections, (B) two LED light luminaires in the ceiling and (C) a LED reading luminaire. In the static mode, the systems provided constant light from A and B. In the dynamic mode, the system changed light intensity and colour temperature using A, B and C. Patients with unipolar or bipolar depression were randomised to dynamic or static LED lighting for 4 weeks, in addition to standard treatment. Primary outcome was the rate of patients discontinuing the trial due to discomfort from the lighting condition. Secondary outcomes were recruitment and dropout rates, visual comfort, depressive symptoms and suicidal ideation.ResultsNo participants discontinued due to discomfort from the LED lighting. Recruitment rate was 39.8%, dropout from treatment rates were 56.3% in the dynamic group and 33.3% in the static group. 78.1% in the dynamic group were satisfied with the lighting compared with 71.8% in the static group. Discomfort from the light (glare) was reported by 11.5% in the dynamic group compared to 5.1% in the static group. Endpoint suicidal scores were 16.8 (10.4) in the dynamic and 16.3 (14.9) in the static group. The lighting system was 100% functional. The light sensor system proved unstable.ConclusionDropout from treatment was high primarily due to early discharge and with a lack of endpoint assessments. The feasibility study has influenced an upcoming large-scale dynamic lighting efficacy trial where we will use a shorter study period of 3 weeks and with more emphasis on endpoint assessments. The lighting was well tolerated in both groups, but some found intensity too low in the evening. Thus, we will use higher intensity blue-enriched light in the morning and higher intensity amber (blue-depleted) light in the evening in the upcoming study. The light sensor system needs to be improved.Trial registrationClinicalTrials.gov: NCT03363529.

Dataset Information

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients.

Background

Methods

Results

Conclusions

Publications

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients.

Similar Datasets

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