The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

Abstract Background: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we hypothesize that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from our transitional care program (TCP) from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p<0.05), 30- and 90-day readmission (p<0.05) and mortality (p<0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR=2.04, 95%CI: 1.21-3.46, p= 0.008), 30- (OR=6.92, 95%CI: 1.76-27.21, p=0.006) and 90-day mortality (OR=11.51, 95%CI: 4.57-29.02, p<0.001). Conclusions: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

10.21203/rs.2.12113/v4 ◽

2019 ◽

Author(s):

Shawn Choon Wee Ng ◽

Yu Heng Kwan ◽

Shi Yan ◽

Chuen Seng Tan ◽

Lian Leng Low

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Latent Class ◽

Resource Planning ◽

Transitions Of Care ◽

High Risk Population ◽

Health State ◽

High Risk Patients ◽

Class 1 ◽

Risk Patients

Abstract Background: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p<0.05), 30- and 90-day readmission (p<0.05) and mortality (p<0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR=2.04, 95%CI: 1.21-3.46, p= 0.008), 30- (OR=6.92, 95%CI: 1.76-27.21, p=0.006) and 90-day mortality (OR=11.51, 95%CI: 4.57-29.02, p<0.001). Conclusions: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

10.21203/rs.2.12113/v3 ◽

2019 ◽

Author(s):

Shawn Choon Wee Ng ◽

Yu Heng Kwan ◽

Shi Yan ◽

Chuen Seng Tan ◽

Lian Leng Low

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Latent Class ◽

Resource Planning ◽

Transitions Of Care ◽

High Risk Population ◽

Health State ◽

High Risk Patients ◽

Class 1 ◽

Risk Patients

Abstract Background: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we hypothesize that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from our transitional care program (TCP) from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p<0.05), 30- and 90-day readmission (p<0.05) and mortality (p<0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR=2.04, 95%CI: 1.21-3.46, p= 0.008), 30- (OR=6.92, 95%CI: 1.76-27.21, p=0.006) and 90-day mortality (OR=11.51, 95%CI: 4.57-29.02, p<0.001). Conclusions: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

BMC Health Services Research ◽

10.1186/s12913-019-4769-7 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Shawn Choon Wee Ng ◽

Yu Heng Kwan ◽

Shi Yan ◽

Chuen Seng Tan ◽

Lian Leng Low

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Latent Class ◽

Resource Planning ◽

Transitions Of Care ◽

High Risk Population ◽

Health State ◽

High Risk Patients ◽

Class 1 ◽

Risk Patients

Abstract Background High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p < 0.05), 30- and 90-day readmission (p < 0.05) and mortality (p < 0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR = 2.04, 95%CI: 1.21–3.46, p = 0.008), 30- (OR = 6.92, 95%CI: 1.76–27.21, p = 0.006) and 90-day mortality (OR = 11.51, 95%CI: 4.57–29.02, p < 0.001). Conclusions We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

10.21203/rs.2.12113/v5 ◽

2019 ◽

Author(s):

Shawn Choon Wee Ng ◽

Yu Heng Kwan ◽

Shi Yan ◽

Chuen Seng Tan ◽

Lian Leng Low

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Latent Class ◽

Resource Planning ◽

Transitions Of Care ◽

High Risk Population ◽

Health State ◽

High Risk Patients ◽

Class 1 ◽

Risk Patients

Abstract Background: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p<0.05), 30- and 90-day readmission (p<0.05) and mortality (p<0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR=2.04, 95%CI: 1.21-3.46, p= 0.008), 30- (OR=6.92, 95%CI: 1.76-27.21, p=0.006) and 90-day mortality (OR=11.51, 95%CI: 4.57-29.02, p<0.001). Conclusions: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns

10.21203/rs.2.12113/v6 ◽

2019 ◽

Author(s):

Shawn Choon Wee Ng ◽

Yu Heng Kwan ◽

Shi Yan ◽

Chuen Seng Tan ◽

Lian Leng Low

Keyword(s):

High Risk ◽

Hospital Readmission ◽

Latent Class ◽

Resource Planning ◽

Transitions Of Care ◽

High Risk Population ◽

Health State ◽

High Risk Patients ◽

Class 1 ◽

Risk Patients

Abstract Background: High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results: Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p<0.05), 30- and 90-day readmission (p<0.05) and mortality (p<0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR=2.04, 95%CI: 1.21-3.46, p= 0.008), 30- (OR=6.92, 95%CI: 1.76-27.21, p=0.006) and 90-day mortality (OR=11.51, 95%CI: 4.57-29.02, p<0.001). Conclusions: We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.

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SIMON: A Digital Protocol to Monitor and Predict Suicidal Ideation

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.554811 ◽

2021 ◽

Vol 12 ◽

Author(s):

Laura Sels ◽

Stephanie Homan ◽

Anja Ries ◽

Prabhakaran Santhanam ◽

Hanne Scheerer ◽

...

Keyword(s):

Suicidal Ideation ◽

High Risk ◽

Psychiatric Hospital ◽

Hospital Readmission ◽

Mobile Sensing ◽

Self Report ◽

Support Vector ◽

High Risk Population ◽

Current Effort ◽

Self Reports

Each year, more than 800,000 persons die by suicide, making it a leading cause of death worldwide. Recent innovations in information and communication technology may offer new opportunities in suicide prevention in individuals, hereby potentially reducing this number. In our project, we design digital indices based on both self-reports and passive mobile sensing and test their ability to predict suicidal ideation, a major predictor for suicide, and psychiatric hospital readmission in high-risk individuals: psychiatric patients after discharge who were admitted in the context of suicidal ideation or a suicidal attempt, or expressed suicidal ideations during their intake. Specifically, two smartphone applications -one for self-reports (SIMON-SELF) and one for passive mobile sensing (SIMON-SENSE)- are installed on participants' smartphones. SIMON-SELF uses a text-based chatbot, called Simon, to guide participants along the study protocol and to ask participants questions about suicidal ideation and relevant other psychological variables five times a day. These self-report data are collected for four consecutive weeks after study participants are discharged from the hospital. SIMON-SENSE collects behavioral variables -such as physical activity, location, and social connectedness- parallel to the first application. We aim to include 100 patients over 12 months to test whether (1) implementation of the digital protocol in such a high-risk population is feasible, and (2) if suicidal ideation and psychiatric hospital readmission can be predicted using a combination of psychological indices and passive sensor information. To this end, a predictive algorithm for suicidal ideation and psychiatric hospital readmission using various learning algorithms (e.g., random forest and support vector machines) and multilevel models will be constructed. Data collected on the basis of psychological theory and digital phenotyping may, in the future and based on our results, help reach vulnerable individuals early and provide links to just-in-time and cost-effective interventions or establish prompt mental health service contact. The current effort may thus lead to saving lives and significantly reduce economic impact by decreasing inpatient treatment and days lost to inability.

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Addictive-Like Behaviors Increase The Risk of Suicide Behaviors in Chinese Middle School Students: A Latent Class Analysis

10.21203/rs.3.rs-735158/v1 ◽

2021 ◽

Author(s):

Chunyu Guo ◽

Xiaoqing Wang ◽

Zhengmei Xia ◽

Yingying Cui ◽

Jie Hu ◽

...

Keyword(s):

Middle School ◽

High Risk ◽

Middle School Students ◽

Latent Class Analysis ◽

Latent Class ◽

Moderate Risk ◽

School Students ◽

Risk Class ◽

Class 1 ◽

Suicide Behaviors

Abstract Background In adolescents, multiple addictive-like behaviors (ALBs) occur frequently together which are likely to be associated with suicide behaviors (SBs), increasing the risk of suicide. This study aimed to clarify the potential subgroups of ALBs in Chinese adolescents, and examine the associations between different patterns of ALBs and SBs. Methods A total of 22,628 middle school students were enrolled in this study. Self-reported ALBs and SBs were investigated by questionnaires. Latent class analysis (LCA) was performed based on five ALBs [smoking, alcohol use (AU), diet pills use (DPU), screen time (ST), problematic mobile phone use (PMPU)]. Multivariate logistic regressions were used to examine the associations between the different patterns of ALBs and SBs. Results Four subgroups of ALBs were identified by LCA, including high-risk class (smoking/AU/DPU/PMPU/ST), moderate-risk class 1 (DPU/PMPU), moderate risk class 2 (smoking/AU/ST), and low-risk class. Compared with the low-risk class, all of the moderate-risk class 1, moderate-risk class 2 and high-risk class had higher risks of suicide ideation, suicide plan, and suicide attempt. Conclusions These findings suggested that the patterns of ALBs were related to SBs in Chinese adolescents. Accordingly, considerations of different classes of ALBs may be essential for developing effective preventive programs.

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Heterogeneity of Learners’ Behavioral Patterns of Watching Videos and Completing Assessments in Massive Open Online Courses (MOOCs): A Latent Class Analysis

The International Review of Research in Open and Distributed Learning ◽

10.19173/irrodl.v21i4.4645 ◽

2020 ◽

Vol 21 (4) ◽

pp. 222-237

Author(s):

In Gu Kang

Keyword(s):

Latent Class Analysis ◽

Low Income ◽

Latent Class ◽

Online Courses ◽

Optimal Number ◽

Massive Open Online Courses ◽

Behavioral Patterns ◽

Class Analysis ◽

Massive Open Online ◽

Latent Subgroups

Massive open online courses (MOOCs) have been touted as an effective way to make higher education accessible for free or for only a small fee, thus addressing the problem of unequal access and providing new opportunities to young people in middle and low income groups. However, many critiques of MOOCs have indicated that low completion rates are a major concern. Using a latent class analysis (LCA), a more advanced methodology to identify latent subgroups, this study examined the heterogeneity of learners’ behavioral patterns in a MOOC, categorized them into distinctive subgroups, and ultimately determined the optimal number of latent subgroups in a MOOC. The five subgroups identified in this study were: completing (6.6%); disengaging (4.8%); auditing (4.6%); sampling (21.1%); and enrolling (62.8%). Results indicated this was the optimal number of subgroups. Given the characteristics of the three at-risk subgroups (disengaging, sampling, and enrolling), tailored instructional strategies and interventions to improve behavioral engagement are discussed.

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The structure of the ultra high risk mental state for psychosis. A latent class cluster analysis study

European Psychiatry ◽

10.1016/s0924-9338(11)73790-7 ◽

2011 ◽

Vol 26 (S2) ◽

pp. 2087-2087 ◽

Cited By ~ 1

Author(s):

L. Valmaggia ◽

D. Stahl ◽

A. Yung ◽

B. Nelson ◽

P. McGorry ◽

...

Keyword(s):

Cluster Analysis ◽

High Risk ◽

Mental State ◽

Negative Symptoms ◽

Latent Class ◽

Community Mental Health Services ◽

Psychotic Symptoms ◽

Latent Class Cluster Analysis ◽

Class 1 ◽

Ultra High Risk

IntroductionIndividuals at Ultra High Risk (UHR) for psychosis typically present with attenuated psychotic symptoms. However it is difficult to predict which individuals will later develop frank psychosis when their mental state is rated in terms of individual symptoms.The objective of the study was to examine the phenomenological structure of the UHR mental state and identify symptom profiles that predict later transition to psychosis.MethodPsychopathological data from a large sample of UHR subjects were analysed using latent class cluster analysis.A total of 318 individuals with a UHR for psychosis. Data were collected from two specialised community mental health services for people at UHR for psychosis: OASIS in London and PACE, in Melbourne.ResultsLatent class cluster analysis produced 4 classes: Class 1 - Mild was characterized by lower scores on all the CAARMS items. Subjects in Class 2 - Moderate scored moderately on all CAARMS items and was more likely to be in employment. Those in Class 3 - Moderate-Severe scored moderately-severe on negative symptoms, social isolation and impaired role functioning. Class 4 - Severe was the smallest group and was associated with the most impairment: subjects in this class scored highest on all items of the CAARMS, had the lowest GAF score and were more likely to be unemployed. This group was also characterized by the highest transition rate (41%).ConclusionsDifferent constellations of symptomatology are associates with varying levels of risk to of transition to psychosis.

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