scholarly journals A Predictive Model for Early Detection of Hospital-Wide All-Cause 30-Day Hospital Readmission (Preprint)

2019 ◽  
Author(s):  
Peng Zhao ◽  
Illhoi Yoo ◽  
Syed H Naqvi
Keyword(s):  
Multivariate Analysis ◽  
High Risk ◽  
Early Detection ◽  
Predictive Model ◽  
Hospital Readmission ◽  
Inpatient Care ◽  
Index Admission ◽  
Discharge Process ◽  
Abnormal Result ◽  
Readmission Risk

BACKGROUND Unplanned hospital readmission is frequent and costly. Existing readmission reduction solutions focus on complementing inpatient care with enhanced care transition and post-discharge interventions, which are initiated near or after discharge when clinicians’ impact on inpatient care is ending. Preventive intervention during hospitalization is an under-explored area and holds potential for reducing readmission risk. However, it is challenging for clinicians to identify high-risk patients early during hospitalization. OBJECTIVE The objective was to build a predictive model for early detection of hospital-wide all-cause 30-day unplanned hospital readmission. We were also interested at identifying novel readmission predictors. METHODS We extracted index admissions and previous encounters up to one year from the Cerner Health Facts® database. The model was only built with data of previous encounters and index admission data that can be available within 24 hours. Candidate models were evaluated in terms of performance, interpretability, timeliness, and generalizability. Multivariate analysis was used to identify readmission predictors. RESULTS Based on 96,550 patients’ data, we developed a readmission predictive model with AUC of 0.754. By multivariate analysis, we identified 16 novel readmission predictors, including patients with 1 maintenance chemotherapy last year (OR 1.476, 95% CI 1.218-1.790), the number of lymphocyte count test with abnormal result last year was 1 (OR 1.247, 95% CI 1.144-1.359) or ≥ 2 (OR 1.257, 95% CI 1.091-1.447), the number of monocyte count test with abnormal result last year was 1 (OR 1.199, 95% CI 1.056-1.362), the number of monocyte percent test with abnormal result last year was ≥ 2 (OR 1.371, 95% CI 1.178-1.596), the number of serum calcium quantitative test with abnormal result last year was 1 (OR 1.254 95% CI 1.107-1.420) or ≥ 2 (OR 1.345, 95% CI 1.122-1.612), the number of prescriptions of albuterol ipratropium last year was 1 (OR 1.073, 95% CI 1.010-1.141) or ≥ 2 (OR 1.157, 95% CI 1.052-1.272), the number of prescriptions of cefazolin last year was 1 (OR 0.884, 95% CI 0.822-0.950), the index admission hospital was in the Northeast census region (OR 1.441, 95% CI 1.345-1.543), prescribed gabapentin in index admission (OR 1.176, 95% CI 1.113-1.243), prescribed ondansetron in index admission (OR 1.111, 95% CI 1.057-1.168), prescribed polyethylene glycol 3350 in index admission (OR 1.076, 95% CI 1.017-1.139), prescribed cefazolin in index admission (OR 0.863, 95% CI 0.798-0.934), the number of lab tests with abnormal results in index admission was ≥ 16 (OR 1.151, 95% CI 1.043-1.269). CONCLUSIONS The performance of our model is better than the most widely used models in the US health care settings. By multivariate analysis, we identified 16 novel readmission predictors. This model can help clinicians to identify readmission risk early during hospitalization so that clinicians can pay extra attention to high-risk patient’s discharge process.

Factors predictive of response to cisplatin-based chemotherapy in stage IVB persistent or recurrent cervical carcinoma: A multivariate analysis of three Gynecologic Oncology Group trials

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5534-5534
Author(s):  
D. H. Moore ◽  
C. Tian ◽  
B. J. Monk ◽  
H. J. Long ◽  
G. Omura
Keyword(s):  
Risk Factors ◽  
Multivariate Analysis ◽  
African American ◽  
High Risk ◽  
Predictive Model ◽  
Single Agent ◽  
Phase Iii ◽  
Gynecologic Oncology Group ◽  
Recurrent Cervical Cancer ◽  
Risk Of Death

5534 Purpose: A number of patients with advanced/recurrent cervical cancer do not respond to cisplatin-based chemotherapy. A pool analysis of three published phase III GOG studies was undertaken to identify the predictive factors and develop a model predictive of (non-) response to chemotherapy. Methods: The study population consisted of patients who received single-agent cisplatin or a cisplatin-containing combination in GOG protocols 110, 169 and 179. Prognostic variables (age, race, performance status, stage, histology, grade, disease site, prior chemotherapy—with primary radiation, time to recurrence, single-agent versus combination) were analyzed and multivariate analysis was conducted to identify factors independently predictive of response and survival. These analyses were used to establish a predictive model. Results: 816 patients were evaluable for response. In addition to single-agent treatment, multivariate analysis identified six factors (age, African-American, PS > 0, pelvic disease, prior radiosensitizer, recurrence ≤ one year) independently predictive of poor response. Those factors, but not age and African-American, were also independently associated with increased risk of death. 428 patients treated with a cisplatin- containing combination were classified into three risk groups based on the total number of risk factors (low risk: 0–1 factor; mid risk: 2–3 factors; high risk: 4–5 factors). Patients with 4–5 of risk factors were predicted to have a treatment response of only 13% (observed 10%), and median progression-free and overall survival of PFS of 2.8 months and 5.5 months, respectively. This subgroup of patients consist ∼14% of the target population in clinical practice. The predictive model was externally validated using GOG protocol 149 data that were not used for model development and further supported the predictive accuracy. Conclusions: High risk patients should be directed to non-cisplatin chemotherapy or investigational trials. No significant financial relationships to disclose.

scholarly journals Predictors of 30-Day Readmission Following Inpatient Rehabilitation for Patients at High Risk for Hospital Readmission

2016 ◽  
Vol 96 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Steve R. Fisher ◽  
James E. Graham ◽  
Shilpa Krishnan ◽  
Kenneth J. Ottenbacher
Keyword(s):  
High Risk ◽  
Functional Status ◽  
Health Care Providers ◽  
Hospital Readmission ◽  
Relative Effectiveness ◽  
Inpatient Rehabilitation ◽  
Classification And Regression Tree ◽  
Care Hospital ◽  
Care Providers ◽  
Readmission Risk

Background The proposed Centers for Medicare & Medicaid Services (CMS) 30-day readmission risk standardization models for inpatient rehabilitation facilities establish readmission risk for patients at admission based on a limited set of core variables. Considering functional recovery during the rehabilitation stay may help clinicians further stratify patient groups at high risk for hospital readmission. Objective The purpose of this study was to identify variables in the full administrative medical record, particularly in regard to physical function, that could help clinicians further discriminate between patients who are and are not likely to be readmitted to an acute care hospital within 30 days of rehabilitation discharge. Design This study used an observational cohort with a 30-day follow-up of Medicare patients who were deconditioned and had medically complex diagnoses and who were receiving postacute inpatient rehabilitation in 2010 to 2011. Methods Patients in the highest risk quartile for readmission (N=25,908) were selected based on the CMS risk prediction model. Hierarchical generalized linear models were built to compare the relative effectiveness of motor functional status ratings in predicting 30-day readmission. Classification and regression tree analysis was used to create a hierarchical order among predictors based on variable importance in classifying patients based on readmission status. Results Approximately 34% of patients in the high-risk quartile were readmitted within 30 days. Functional outcomes and rehabilitation length of stay were the best predictors of 30-day rehospitalization. A 3-variable algorithm classified 4 clinical subgroups with readmission probabilities ranging from 28% to 75%. Limitations Although planned readmissions were accounted for in the outcome, potentially preventable readmissions were not distinguished from unpreventable readmissions. Conclusion For older patients who are deconditioned and have medically complex diagnoses admitted to postacute inpatient rehabilitation, information on functional status measures that are easily monitored by health care providers may improve plans for care transition and reduce the risk of hospital readmission.

scholarly journals Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

10.2196/16306 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e16306
Author(s):  
Peng Zhao ◽  
Illhoi Yoo ◽  
Syed H Naqvi
Keyword(s):  
Risk Factors ◽  
Protective Factors ◽  
Hospital Readmission ◽  
Inpatient Care ◽  
Early Stage ◽  
Multivariate Logistic Regression Analysis ◽  
Day Hospital ◽  
Multivariate Logistic Regression ◽  
Data Set ◽  
Readmission Risk

Background Existing readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians’ impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. Objective The objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors. Methods We extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors. Results We developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported. Conclusions The performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

Validation of a Predictive Model to Identify Patients at High Risk for Hospital Readmission

2016 ◽  
Vol 38 (1) ◽  
pp. 34-41 ◽  
Author(s):  
LeeAnna Spiva ◽  
Marti Hand ◽  
Lewis VanBrackle ◽  
Frank McVay
Keyword(s):  
High Risk ◽  
Predictive Model ◽  
Hospital Readmission

Early Detection of Hepatocellular Carcinoma Requires a Combined Surveillance Protocol in High-Risk Individuals

Endoscopy ◽  
2006 ◽  
Vol 38 (11) ◽  
Author(s):  
A Qasim ◽  
T Tajjudin ◽  
B Zaman ◽  
D Maguire ◽  
J Geoghegan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Early Detection ◽  
Surveillance Protocol

scholarly journals Independent Validation of Sepsis Index for Sepsis Screening in the Emergency Department

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1292
Author(s):  
Luisa Agnello ◽  
Alessandro Iacona ◽  
Salvatore Maestri ◽  
Bruna Lo Sasso ◽  
Rosaria Vincenza Giglio ◽  
...  
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Early Detection ◽  
Diagnostic Accuracy ◽  
Positive Likelihood Ratio ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Distribution Width ◽  
Independent Validation ◽  
Sepsis Screening

(1) Background: The early detection of sepsis is still challenging, and there is an urgent need for biomarkers that could identify patients at a high risk of developing it. We recently developed an index, namely the Sepsis Index (SI), based on the combination of two CBC parameters: monocyte distribution width (MDW) and mean monocyte volume (MMV). In this study, we sought to independently validate the performance of SI as a tool for the early detection of patients at a high risk of sepsis in the Emergency Department (ED). (2) Methods: We enrolled all consecutive patients attending the ED with a request of the CBC. MDW and MMV were measured on samples collected in K3-EDTA tubes on the UniCel DxH 900 haematology analyser. SI was calculated based on the MDW and MMV. (3) Results: We enrolled a total of 703 patients stratified into four subgroups according to the Sepsis-2 criteria: control (498), infection (105), SIRS (52) and sepsis (48). The sepsis subgroup displayed the highest MDW (median 27.5, IQR 24.6–32.9) and SI (median 1.15, IQR 1.05–1.29) values. The ROC curve analysis for the prediction of sepsis showed a good and comparable diagnostic accuracy of the MDW and SI. However, the SI displayed an increased specificity, positive predictive value and positive likelihood ratio in comparison to MDW alone. (4) Conclusions: SI improves the diagnostic accuracy of MDW for sepsis screening.

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