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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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 ◽  
Optimal Number ◽  
Transitions Of Care ◽  
High Risk Population ◽  
Health State ◽  
Class 1 ◽  
Latent Subgroups

Abstract Background: High-risks patients are vulnerable during transitions of care. Segmentation of such heterogenous patients into distinct subgroups help facilitate healthcare resource planning. We aimed to segment a high-risk population using latent class analysis (LCA) and assess its association with 30-day and 90-day hospital readmission and mortality. Methods: We extracted data from all H2H program participants from June to November 2018. 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 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 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 demonstrated the applicability of LCA in identifying 3 unique subgroups with distinct readmission and mortality risks among high-risk patients, providing important information for tailoring future integrated care interventions.

Identifying High-Risk Patients for Hospital Readmission Following Radical Cystectomy and Urinary Diversion

2014 ◽  
Vol 186 (2) ◽  
pp. 496
Author(s):  
K.E. Omernick ◽  
S.E. Tevis ◽  
G.E. Leverson ◽  
E.J. Abel ◽  
D.F. Jarrard ◽  
...  
Keyword(s):  
High Risk ◽  
Radical Cystectomy ◽  
Urinary Diversion ◽  
Hospital Readmission ◽  
High Risk Patients ◽  
Risk Patients

Development and validation of the Tool for Pharmacists to Predict 30-day hospital readmission in patients with Heart Failure (ToPP-HF)

2021 ◽  
Author(s):  
Melissa R Riester ◽  
Laura McAuliffe ◽  
Christine Collins ◽  
Andrew R Zullo
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Hospital Readmission ◽  
Left Ventricular Ejection ◽  
Day Hospital ◽  
High Risk Patients ◽  
C Statistic ◽  
Patients With Heart Failure ◽  
Risk Patients ◽  
Development And Validation

Abstract Purpose Pharmacists are well positioned to provide transitions of care (TOC) services to patients with heart failure (HF); however, hospitalizations for patients with HF likely exceed the capacity of a TOC pharmacist. We developed and validated a tool to help pharmacists efficiently identify high-risk patients with HF and maximize their potential impact by intervening on patients at the highest risk for 30-day all-cause readmission. Methods We conducted a retrospective cohort study including adults with HF admitted to a health system between October 1, 2016, and October 31, 2019. We randomly divided the cohort into development (n = 2,114) and validation (n = 1,089) subcohorts. Nine models were applied to select the most important predictors of 30-day readmission. The final tool, called the Tool for Pharmacists to Predict 30-day hospital readmission in patients with Heart Failure (ToPP-HF) relied upon multivariable logistic regression. We assessed discriminative ability using the C statistic and calibration using the Hosmer-Lemeshow goodness-of-fit test. Results The risk of 30-day all-cause readmission was 15.7% (n = 331) and 18.8% (n = 205) in the development and validation subcohorts, respectively. The ToPP-HF tool included 13 variables: number of hospital admissions in previous 6 months; admission diagnosis of HF; number of scheduled medications; chronic obstructive pulmonary disease diagnosis; number of comorbidities; estimated glomerular filtration rate; hospital length of stay; left ventricular ejection fraction; critical care requirement; renin-angiotensin-aldosterone system inhibitor use; antiarrhythmic use; hypokalemia; and serum sodium. Discriminatory performance (C statistic of 0.69; 95% confidence interval [CI], 0.65-0.73) and calibration (Hosmer-Lemeshow P = 0.28) were good. Conclusions The ToPP-HF performs well and can help pharmacists identify high-risk patients with HF most likely to benefit from TOC services.

P914Balloon aortic valve valvuloplasty as palliative therapy in severe aortic valve stenosis: 10 years experience in a single centre

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
F Mantovani ◽  
M A Clavel ◽  
F Jayme ◽  
L Valli ◽  
R M De Mola ◽  
...  
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Aortic Valve ◽  
Aortic Stenosis ◽  
Palliative Therapy ◽  
Medical Attention ◽  
High Risk Population ◽  
High Risk Patients ◽  
Risk Patients

Abstract Background Improved technology together with greater operator experience has led to refinement of balloon aortic valve valvuloplasty (BAV) in recent years. It may provide a palliative treatment option in high-risk patients, highly symptomatic, for whom no other invasive therapy is available. However, there has not been universal adoption of BAV as a standalone therapy. Methods A retrospective analysis of ten years of practice of BAV as palliative strategy in patient with symptomatic aortic stenosis between March 2008 and June 2018 was performed. Demographic, clinical, procedural, and follow-up data on all patients were collected. Results A total of 152 patients (95 women, 63%) with a mean age of 85±6 years underwent BAV. All patients had severe aortic stenosis, were considered not suitable to aortic valve replacement nor Trans-catheter aortic valve implantation (TAVI) for appreciable comorbidity (STS score 9±5) and had severe symptoms mainly of heart failure which required medical attention. A statistically significant decrease in trans-valvular gradient was observed (peak to peak gradient before BAV 52±22 mmHg, after BAV 29±16 mmHg, delta gradient 24±14 mmHg; p<0.0001). Only one patient, who undergone BAV because of cardiogenic shock, died during the procedure. Considering the high-risk population, intra-hospital mortality was low (7 patients died, 4%). Mortality at 1-year follow-up was 43% and survival free from new hospitalization for heart failure was 63% at 1-year follow-up and 53% at 2 years follow-up. 19 patients (13%) required repeated BAV during follow-up. Conclusion BAV as a palliative procedure in high-risk patients who are highly symptomatic, has a low operative mortality in our experience. BAV is associated with a significant reduction in aortic valve gradient and is valuable since half of the patients were alive without re-hospitalizations for heart failure at 2 years follow-up. Acknowledgement/Funding None

scholarly journals Molecular profiling and risk classification of patients with myeloproliferative neoplasms and splanchnic vein thromboses

2020 ◽  
Vol 4 (15) ◽  
pp. 3708-3715 ◽  
Author(s):  
Pierre-Edouard Debureaux ◽  
Bruno Cassinat ◽  
Juliette Soret-Dulphy ◽  
Barbara Mora ◽  
Emmanuelle Verger ◽  
...  
Keyword(s):  
High Risk ◽  
Primary Outcome ◽  
Myeloproliferative Neoplasms ◽  
Molecular Profiling ◽  
Janus Kinase ◽  
High Risk Population ◽  
Jak2 Mutation ◽  
Allele Burden ◽  
High Risk Patients ◽  
Risk Patients

Abstract Myeloproliferative neoplasms (MPNs) are the most frequent underlying causes of splanchnic vein thromboses (SVTs). MPN patients with SVTs (MPN-SVT) often have a unique presentation including younger age, female predominance, and low Janus kinase 2 (JAK2) mutation allele burden. This study aimed at identifying risk factors for adverse hematologic outcomes in MPN-SVT patients. We performed a retrospective study of a fully characterized cohort of MPN-SVT patients. The primary outcome was the incidence of evolution to myelofibrosis, acute leukemia, or death. Eighty patients were included in the testing cohort. Median follow-up was 11 years. Most of the patients were women with a mean age of 42 years and a diagnosis of polycythemia vera. The primary outcome was met in 13% of the patients and was associated with a JAK2V617F allele burden ≥50% (odds ratio [OR], 14.7) and presence of additional mutations in genes affecting chromatin/spliceosome (OR, 9). We identified high-risk patients (29% of the cohort) as those harboring at least 1 molecular risk factor: JAK2-mutant allele burden ≥50%, presence of chromatin/spliceosome/TP53 mutation. High-risk patients had worse event-free survival (81% vs 100%; P = .001) and overall survival at 10 years (89% vs 100%; P = .01) than low-risk patients. These results were confirmed in an independent validation cohort of 30 MPN-SVT patients. In conclusion, molecular profiling identified MPN-SVT patients with dismal outcome. In this high-risk population, a disease-modifying therapy should be taken into consideration to minimize the probability of transformation.

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