Abstract 89: Validating The Identification Of Patients At High Risk For Readmission By Examining Hospitalization History

2012 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
Natalia Egorova ◽  
Prashant Vaishnava ◽  
Maria Basso Lipani ◽  
Doran Ricks ◽  
Claudia Colgan ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
High Risk Patient ◽  
Mount Sinai Hospital ◽  
Patient Identification ◽  
Time High Risk ◽  
Post Discharge ◽  
Predictive Tool ◽  
Readmission Risk

OBJECTIVES: To identify patients at high risk of readmission by validating a simple predictive tool based solely on hospitalization history. BACKGROUND: There is a federal mandate to reduce preventable readmissions. Predicting hospital readmission risk is of great interest to identify which patients would benefit most from transition interventions. Current models perform poorly. Mount Sinai Hospital (MSH) has implemented the Preventable Admissions Care Team (PACT), which has achieved significant results for patients not targeted by other transitional programs. PACT, a social worker-led transitional program, decreased 30-day readmission rate from 30% to 12%, ED visits by 63%, and achieved a 90% primary care show rate at 7-10-days post-discharge. Patients are identified for PACT solely by readmission history: one readmission in 30 days or 2 in 6 months, prior to the index hospitalization. Thus, our objective here was to determine the concordance of predictions based on hospitalization history with a more formal risk model based on factors that characterize patients through demographics and comorbidities. METHODS: Using logistic regression, we developed a risk prediction model for readmission within 30-days. The model, which used patient demographics and co-morbidities (alcohol abuse, AMI, afib, breast cancer, CKD, COPD, CVA, depression, hip fracture, or osteoporosis), was developed in a cohort of Medicare FFS beneficiaries with a high proportion of cardiovascular disease, hospitalized at MSH. The higher the risk score, the higher risk of readmission. Scores of 0-2 had a 7% risk of readmission; scores of 3 or 4 and above 5 had 30-day readmission rates of 19%, and 29%, respectively. We then applied this risk scoring model to patients enrolled in PACT to determine how many of them would have been identified as high risk for readmission based on the regression model. RESULTS: A total of 393 patients were enrolled in PACT in a year and completed a 5 week intervention. Eighty seven percent had 1 cardiac comorbid illness (76% CAD, 66% CHF, and 17% Afib). Readmission data was available through 2010 thus, the analysis was completed for 111 patients. Ninety-five percent of PACT enrollees had a risk score greater than 3: 19 patients (17.1%) had a risk score of 3-4, and 87 patients (78.4%) had a risk score of 5 or greater. CONCLUSIONS: Hospitalization history alone is a reasonable proxy to more formal multivariable regression models in predicting 30-day readmission risk. If substantiated through further study, this could have national implications for real time high risk patient identification for transitional services.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

Finding the High-Risk Patient in Primary Prevention Is Not as Easy as a Conventional Risk Score!

2016 ◽  
Vol 129 (12) ◽  
pp. 1329.e1-1329.e7 ◽  
Author(s):  
John A. Ambrose ◽  
Tushar Acharya ◽  
Micah J. Roberts
Keyword(s):  
Primary Prevention ◽  
High Risk ◽  
Risk Score ◽  
High Risk Patient ◽  
Risk Patient

scholarly journals A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fen Liu ◽  
Zongcheng Yang ◽  
Lixin Zheng ◽  
Wei Shao ◽  
Xiujie Cui ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

Abstract 13677: Usefulness of 2-year Iodine-123 Metaiodobenzylguanidine-based Risk Model for the Post-discharge Risk Stratification in Acute Decompensated Heart Failure Patients With Preserved Left Ventricular Ejection Fraction

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Shunsuke Tamaki ◽  
Takahisa Yamada ◽  
Tetsuya Watanabe ◽  
Takashi Morita ◽  
Yoshio Furukawa ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Score ◽  
Mortality Risk ◽  
Risk Model ◽  
Acute Decompensated Heart Failure ◽  
Decompensated Heart Failure ◽  
Cardiac Mortality ◽  
Post Discharge ◽  
Patients With Heart Failure ◽  
Iodine 123

Background: A four-parameter risk model including cardiac iodine-123 metaiodobenzylguanidine (MIBG) imaging and readily available clinical parameters has been recently developed for the prediction of 2-year cardiac mortality risk in patients with chronic heart failure (CHF) using a Japanese CHF database consisting of 1322 patients. However, there is no information available on the usefulness of 2-year MIBG-based cardiac mortality risk score for the prediction of post-discharge prognosis in patients with heart failure with preserved LVEF (HFpEF) who are admitted with acute decompensated heart failure (ADHF). Methods and Results: Patients' data were extracted from The Prospective mUlticenteR obServational stUdy of patIenTs with Heart Failure with Preserved Ejection Fraction (PURSUIT-HFpEF) study, which is a prospective multicenter observational registry for ADHF patients with LVEF ≥50% in Osaka. We studied 239 patients who survived to discharge. Cardiac MIBG imaging was performed just before discharge. The 2-year cardiac mortality risk score was calculated using four parameters, including age, LVEF, NYHA functional class, and the cardiac MIBG heart-to-mediastinum ratio on delayed image. The patients were stratified into three groups based on the 2-year cardiac mortality risk score: low- (<4%), intermediate- (4-12%), and high-risk (>12%) groups. The endpoint was all-cause death. During a follow-up period of 1.6±0.8 years, 33 patients had all-cause death. Multivariate Cox analysis showed that 2-year MIBG-based cardiac mortality risk score was an independent predictor of all-cause death (p=0.0009). There was significant difference in the rate of all-cause death among the three groups stratified by 2-year cardiac mortality risk score (Figure). Conclusions: In this multicenter study, the 2-year MIBG-based cardiac mortality risk score was shown to be useful for the prediction of post-discharge clinical outcome in HFpEF patients admitted for ADHF.

A Predictive Risk Score for Cancer-Associated Thrombosis: Role of Screening In A Prospective Study

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 3173-3173 ◽  
Author(s):  
Alok A. Khorana ◽  
Kimberly Herman ◽  
Deborah Rubens ◽  
Charles W. Francis
Keyword(s):  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Prospective Cohort ◽  
Risk Model ◽  
Conflicts Of Interest ◽  
Clinical Risk Score ◽  
Clinical Risk ◽  
Predictive Risk

Abstract Abstract 3173 Background: We evaluated the utility of screening for VTE using a previously developed clinical risk score (Khorana et al, Blood 2008) in a prospective cohort of cancer patients initiating outpatient chemotherapy but not receiving thromboprophylaxis. Methods: Cancer patients initiating a new chemotherapy regimen and deemed high-risk based on a predictive risk model (score ≥3) were enrolled on an ongoing prospective cohort study with informed consent. Patients were evaluated with baseline and Q4 (± 1) week serial ultrasonography for upto 16 weeks; additionally, computed tomography scans for restaging were also evaluated for VTE. Results: Of 30 patients enrolled on study, 8 (27%) developed a VTE. This included 5 patients with DVT alone (17%), 1 patient with PE alone (3%) and 2 (7%) with both. Twenty-seven patients underwent a baseline ultrasound. Of these, 3 asymptomatic DVTs were identified (11%). Subsequent ultrasounds were performed in 18 patients at week 4 (0 DVT), 17 patients at week 8 (0 DVT) and 15 patients at week 12 (1 DVT, 7%). An additional two patients developed symptomatic DVT between weeks 1 and 4. Restaging CT scans identified an asymptomatic PE in 1 patient at week 6 and asymptomatic PE in 1 patient at week 9 with subsequent symptomatic DVT at week 10. Conclusions: In a prospective observational study, 27% of cancer outpatients deemed high-risk using a clinical risk score developed VTE, a rate much higher than observed even in hospitalized acutely ill patients. Thus, this study confirms the validity of a previously described risk score. The role of thromboprophylaxis in this population is currently being tested. The value of screening ultrasonography should be considered in high-risk patients based on this risk score. Disclosures: No relevant conflicts of interest to declare.

Characterization of sick visits at an enhanced oncology urgent care center.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 285-285
Author(s):  
Anish Parikh ◽  
Donna Berizzi ◽  
Che-Kai Tsao ◽  
Cardinale B. Smith
Keyword(s):  
High Risk ◽  
Average Length ◽  
Care Center ◽  
High Risk Patient ◽  
Mount Sinai Hospital ◽  
Urgent Care ◽  
Entire Group ◽  
Oncology Patients ◽  
Urgent Care Center ◽  
Therapeutic Services

285 Background: The Oncology Care Unit (OCU) is an urgent care center open during after-hours and weekends for patients with cancer and blood disorders at the Mount Sinai Hospital. This 6-bed, nurse practitioner-run unit aims to decrease the need for emergency room (ER) visits and hospitalization in this high risk patient population. Herein we characterize utilization of this unit for urgent clinical management (“sick visits”). Methods: We identified all patients treated in the OCU between 5/12/17 and 4/8/18, and collected information on diagnosis, treatment, and utilization of the ER or hospitalization. We used descriptive statistics to identify characteristics of those patients treated in the OCU. Results: Of the 1,934 visits to the OCU, 100 (5%) were coded as “sick visits”. Of this cohort, 39% had solid tumors, 44% liquid tumors, and 17% benign hematologic conditions. Among the oncology patients, the average number of prior treatment lines was 4.6 and average time since diagnosis was 51.3 months. Of all cancers, 84% were classified as advanced stage or high-risk. Treatments for the entire group included: transfusion (T, 20%), hydration (H, 20%), and infusion (I, 13%). Similarly, 39% of visits were for H+I, 3% for T+H, 4% for T+I, and 1% for T+H+I. 5% of patients had a repeat, unplanned OCU sick visit in the next 7 days. Among the sick visits, 28% resulted in hospitalization, with a 14-day average length of stay. Further results are shown in Table 1. Conclusions: The OCU provides enhanced diagnostic and therapeutic services for high-risk hematology/oncology patients. These services often exceed the capabilities of a busy practice and would otherwise prompt an ER visit and/or hospitalization. We now aim to study the effect of the OCU on ER utilization and admission rates as well as to analyze its cost effectiveness. [Table: see text]

scholarly journals Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Kang-Wen Xiao ◽  
Zhi-Bo Liu ◽  
Zi-Hang Zeng ◽  
Fei-Fei Yan ◽  
Ling-Fei Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Risk Group ◽  
Curve Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Set ◽  
Cox Regression Analysis

Background. Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. Methods. Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan–Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. Results. 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma ( p  < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. Conclusions. A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

Use of bortezomib (BOR) pharmacogenomics (PG) to identify mechanisms of drug resistance and predict survival in multiple myeloma (MM) treated with total therapy 3 (TT3)

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 8538-8538
Author(s):  
J. D. Shaughnessy ◽  
P. Qu ◽  
J. Haessler ◽  
J. Crowley ◽  
B. Barlogie
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Plasma Cells ◽  
Risk Model ◽  
Test Dose ◽  
High Risk Group ◽  
Baseline Risk ◽  
Molecular Subgroup ◽  
Discriminating Power ◽  
Prognostic Power

8538 Background: The prognosis of patients with MM is best captured by gene expression profiling (GEP) analysis of CD138-purified plasma cells (PC), distinguishing a high-risk group of 15% with dismal survival using a 70-gene baseline risk model (BLR). Translational research in TT3 was designed to investigate whether short-term BOR-induced GEP alterations could advance our understanding of BOR's novel mechanism of action. Methods: PG studies were performed as part of two TT3 trials (TT3a, n=303; TT3b, n=177), obtaining PC prior to and 48hr after a BOR test-dose (1.0mg/m2), which was accomplished in 142 patients in TT3a (training set) and 127 in TT3b (test set). Among 1051 genes significantly altered post-BOR in TT3a, 80 were identified as being significantly associated with EFS. A continuous risk score was calculated and an optimal cut-point for EFS separation determined. The independent prognostic power of the binary risk score was tested in TT3b. Multivariate analyses (MV) were employed to determine post-BOR risk (PBR) in relationship to standard prognostic variables and BLR. Results: The discriminatory power in TT3a (3-yr OS: 95% v 45%, p<0.0001; 3-yr EFS: 90% v 35%, p<0.0001) was confirmed in TT3b (18-mo OS: 100% v 65%, p=0.0004; 18-mo EFS: 95% v 45%, p<0.0001). Evaluating PBR in the context of BLR, 12/26 in TT3a and 7/21 in TT3b deemed as having low BLR had high PBR; conversely, 8/126 in TT3a and 14/106 in TT3b deemed as having high BLR had low PBR. In the context of our 8 molecular subgroup model, high PBR was over-represented in the Proliferation (PR) subgroup (7/15 in TT3a, 8/18 in TT3b) and absent in the Low Bone disease (LB) group (0/28). On MV, PBR was an independent adverse variable for both OS and EFS in TT3a (OS: HR=3.17, p=0.006, R2=55%; EFS: HR=4.40, p<0.001, R2=48%) and in TT3b (OS: HR=13.00, p=0.002, R2=48%; EFS: HR=15.57, p<0.001, R2=55%). Proteasome genes ranked first among those differentially up-regulated by BOR. Conclusions: PG identified a powerful 80-gene PBR model with unprecedented prognosis-discriminating power, dispelling BLR from MV analysis by altering BLR designation mainly from low to high risk. High PBR (18%) could be traced to up-regulation of proteasome genes, the target of BOR. [Table: see text]

Development and validation of a novel disease risk model for patients with AML receiving allogeneic hematopoietic cell transplantation.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 7016-7016
Author(s):  
Piyanuch Kongtim ◽  
Omar Hasan ◽  
Jorge Miguel Ramos Perez ◽  
Ankur Varma ◽  
Julianne Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Hematopoietic Cell Transplantation ◽  
Risk Model ◽  
Disease Risk ◽  
Risk Group ◽  
Molecular Data ◽  
Hematopoietic Cell ◽  
Low Risk ◽  
Related Factors

7016 Background: Molecular data and minimal residual disease (MRD) have been shown to influence outcomes in AML patients undergoing allogeneic hematopoietic cell transplantation (AHCT). Here we developed and validated a novel AML-specific Disease Risk Group (AML-DRG) and revise our previously developed Hematopoietic Cell Transplant - Composite Risk (HCT-CR) model by incorporating molecular data and MRD status before transplant to predict post-transplant outcomes of patients with AML. Methods: The study included 1414 consecutively treated adult AML patients, who received first AHCT between 1/2005-8/2018 at our institution. Patients were randomly assigned into a training (N = 944) and validation set (N = 470). To develop the AML-DRG model, the coefficient of all significant AML-related variables in multivariable Cox’s regression analysis (MVA) in a training dataset was converted into scores.The AML-DRG was the sum of scores from all significant covariates, while the AML-HCT-CR was the sum of disease-related factors assessed by the AML-DRG model with the addition of weighted scores from patient-related factors. Results: Based on results of MVA, the following scores were assigned to each AML-related variable; 1 point to secondary AML; 1 point to the 2017 ELN adverse risk; 2 points to CR with MRD positive/unknown; and 4 points to active disease at transplant. These were used to generate the AML-DRG (low-risk score 0-2; intermediate-risk score 3-4; and high-risk score > 4) with significantly different OS with HR of 2.02 (P < 0.001), and 3.85 (P < 0.001) for intermediate and high risk group compared with low risk group. By adding 1 point for patients > / = 60 years or HCT-CI > 3 to the AML-DRG, we created 4 risk groups of AML-HCT-CR (low-risk: score 0-2; intermediate-risk: score 3; high-risk: score 4 and very high-risk: score ≥5) with distinct survival outcomes. The AML-DRG and AML-HCT-CR model had C-index of 0.672 and 0.715, respectively which were better compared with DRI, ELN2017 and cytogenetic risk model. Conclusions: Prognostic models incorporating molecular data and MRD status before transplant allow better stratification and improved survival estimates of AML patients post-transplant.

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