Prospective evaluation of specialist inpatient treatment for refractory affective disorders

Multivariate predictive models have revealed promising results for the individual prediction of treatment response, relapse risk as well as for the differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modelling from the research context to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed, based on which machine learning algorithms can be trained. Digital collection of patient-reported outcomes (PROs) is a time- and cost-efficient approach to gain such data throughout the treatment course. However, it remains unclear whether patients with severe affective disorders are willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics and if digitally acquired patient-reported outcomes are of sufficient diagnostic validity. To address these questions, we implemented a system for continuous digital collection of patient-reported outcomes via tablet computers throughout inpatient treatment for affective disorders at the Department of Psychiatry at the University of Muenster. 364 affective disorder patients were approached, 66.5% of which could be recruited to participate in the study. An average of four assessments were completed during the treatment course, none of the participants dropped out of the study prematurely. 89.3% of participants did not require additional support during data entry. Need of support with tablet handling and slower data entry pace was predicted by older age, whereas depression severity at baseline did not influence these measures. Patient-reported outcomes of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Our results indicate that continuous digital collection of patient-reported outcomes is a feasible, accessible and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way towards personalized psychiatric care.

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Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study

JMIR Mental Health ◽

10.2196/24066 ◽

2020 ◽

Vol 7 (12) ◽

pp. e24066

Author(s):

Maike Frederike Richter ◽

Michael Storck ◽

Rogério Blitz ◽

Janik Goltermann ◽

Juliana Seipp ◽

...

Keyword(s):

Affective Disorder ◽

Affective Disorders ◽

Inpatient Treatment ◽

Data Entry ◽

Self Report ◽

Individual Risk ◽

Depression Severity ◽

Symptom Profiles ◽

Longitudinal Data Collection ◽

Digital Collection

Background Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. Objective The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. Methods We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. Results Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Conclusions Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.

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Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study (Preprint)

10.2196/preprints.24066 ◽

2020 ◽

Author(s):

Maike Frederike Richter ◽

Michael Storck ◽

Rogério Blitz ◽

Janik Goltermann ◽

Juliana Seipp ◽

...

Keyword(s):

Affective Disorder ◽

Affective Disorders ◽

Inpatient Treatment ◽

Data Entry ◽

Self Report ◽

Individual Risk ◽

Depression Severity ◽

Symptom Profiles ◽

Longitudinal Data Collection ◽

Digital Collection

BACKGROUND Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. OBJECTIVE The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. METHODS We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. RESULTS Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. CONCLUSIONS Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.

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198: Prospective Evaluation of Short-Term Impact and Recovery of Health-Related Quality of Life (HRQOL) in men Undergoing Radical Prostatectomy (RP)

The Journal of Urology ◽

10.1016/s0022-5347(18)34463-x ◽

2005 ◽

Vol 173 (4S) ◽

pp. 54-54

Author(s):

J. Slade Hubbard ◽

Aubrey Evans ◽

Culley C. Carson ◽

Eric Wallen ◽

Raj S. Pruthi

Keyword(s):

Quality Of Life ◽

Radical Prostatectomy ◽

Prospective Evaluation ◽

Health Related Quality ◽

Short Term ◽

Related Quality ◽

Health Related

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234 Prospective evaluation of a new high voltage lead integrity check algorithm

EP Europace ◽

10.1016/s1099-5129(05)80144-8 ◽

2005 ◽

Vol 7 ◽

pp. 48-49

Keyword(s):

High Voltage ◽

Prospective Evaluation ◽

Check Algorithm ◽

Integrity Check

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Presence and Persistence of Sleep-Related Symptoms and Suicidal Ideation in Psychiatric Inpatients

Crisis ◽

10.1027/0227-5910/a000279 ◽

2014 ◽

Vol 35 (6) ◽

pp. 398-405 ◽

Cited By ~ 17

Author(s):

Michael R. Nadorff ◽

Thomas E. Ellis ◽

Jon G. Allen ◽

E. Samuel Winer ◽

Steve Herrera

Keyword(s):

Risk Factor ◽

Suicidal Ideation ◽

Inpatient Treatment ◽

Improve Method ◽

Sleep Problems ◽

Psychiatric Inpatients ◽

Poor Sleep ◽

Treatment Termination ◽

Suicidal Patients ◽

The Relationship

Background: Although sleep is an important risk factor for suicidal behavior, research has yet to examine the association between sleep problems and suicidality across the course of inpatient treatment. This study examined the relationship among sleep-related symptoms and suicidal ideation across inpatient treatment. Aims: To examine whether poor sleep at admission longitudinally predicts less improvement in suicidal ideation over the course of treatment. Further, to examine whether suicidal ideation is reduced in patients whose sleep does not improve. Method: The study utilized the Beck Depression Inventory (BDI)-II, which contains items measuring depressive symptoms, sleep-related symptoms, and suicidal ideation. The study sample consisted of 1,529 adult psychiatric inpatients. Patients were assessed at admission, biweekly, and at treatment termination. Results: Admission fatigue, loss of energy, and change in sleep pattern were associated with higher levels of suicidal ideation at admission and discharge. Fatigue at admission predicted suicidal ideation at termination independent of admission depression and suicidal ideation. Individuals whose sleep did not improve over the course of treatment had significantly higher suicidal ideation scores at termination relative to those whose sleep symptoms improved, after controlling for sleep, depression, and suicidal ideation scores at admission. Conclusion: These findings suggest that persistence of sleep-related symptoms warrants clinical attention in the treatment of suicidal patients.

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