Derivation and Validation of the Cancer READMIT Score: A Readmission Risk Scoring System for Patients With Solid Tumor Malignancies

2021 ◽  
pp. OP.20.01077
Author(s):  
Joanna-Grace M. Manzano ◽  
Heather Lin ◽  
Hui Zhao ◽  
Josiah Halm ◽  
Maria E. Suarez-Almazor
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Solid Tumor ◽  
Scoring System ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Risk Scoring ◽  
Operating Characteristic Curve ◽  
Risk Scoring System ◽  
Readmission Risk

PURPOSE Readmissions for the medical treatment of cancer have traditionally been excluded from readmission measures under the Hospital Readmissions Reduction Program. Patients with cancer often have higher readmission rates and may need heightened support to ensure effective care transitions after hospitalization. Estimating readmission risk before discharge may assist in discharge planning efforts and help promote care coordination at time of discharge. PATIENTS AND METHODS We developed and validated a readmission risk scoring system among a cohort of adult cancer patients with solid tumor admitted at a comprehensive cancer center. Multivariate logistic regression analysis was used to develop the model. The model's discriminative capacity was evaluated through a receiver operating characteristic curve analysis. We further compared the performance of the developed score with existing risk scores for 30-day readmission. RESULTS The 30-day unplanned readmission rate in the total cohort was 16.0% (n = 1,078 of 6,720). After multivariate analysis, Cancer site, Recent emergency room visit within 30 days, non-English primary language, Anemia defined as hemoglobin < 10 g/dL, > 4 Days length of stay during the index admission, unmarried Marital status, Increased white blood cell count > 11 × 109/L, and distant Tumor spread were significantly associated with risk of unplanned 30-day readmission. The derived score, which we call the Cancer READMIT score, had modest discriminatory performance in predicting readmissions (area under the curve for the model receiver operating characteristic curve = 0.647). CONCLUSION The Cancer READMIT score was able to predict 30-day unplanned readmissions to our institution with fairly modest performance. External validation of our derived risk scoring system is recommended.

scholarly journals A Probabilistic Model for Cushing’s Syndrome Screening in At-Risk Populations: A Prospective Multicenter Study

2016 ◽  
Vol 101 (10) ◽  
pp. 3747-3754 ◽  
Author(s):  
Antonio León-Justel ◽  
Ainara Madrazo-Atutxa ◽  
Ana I. Alvarez-Rios ◽  
Rocio Infantes-Fontán ◽  
Juan A. Garcia-Arnés ◽  
...  
Keyword(s):  
At Risk ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Risk Score ◽  
Scoring System ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Clinical Signs ◽  
At Risk Populations ◽  
Operating Characteristic Curve

Context: Cushing’s syndrome (CS) is challenging to diagnose. Increased prevalence of CS in specific patient populations has been reported, but routine screening for CS remains questionable. To decrease the diagnostic delay and improve disease outcomes, simple new screening methods for CS in at-risk populations are needed. Objective: To develop and validate a simple scoring system to predict CS based on clinical signs and an easy-to-use biochemical test. Design: Observational, prospective, multicenter. Setting: Referral hospital. Patients: A cohort of 353 patients attending endocrinology units for outpatient visits. Interventions: All patients were evaluated with late-night salivary cortisol (LNSC) and a low-dose dexamethasone suppression test for CS. Main Outcome Measures: Diagnosis or exclusion of CS. Results: Twenty-six cases of CS were diagnosed in the cohort. A risk scoring system was developed by logistic regression analysis, and cutoff values were derived from a receiver operating characteristic curve. This risk score included clinical signs and symptoms (muscular atrophy, osteoporosis, and dorsocervical fat pad) and LNSC levels. The estimated area under the receiver operating characteristic curve was 0.93, with a sensitivity of 96.2% and specificity of 82.9%. Conclusions: We developed a risk score to predict CS in an at-risk population. This score may help to identify at-risk patients in non-endocrinological settings such as primary care, but external validation is warranted.

Development of a Predictive Score for Discharge Disposition After Lumbar Fusion Using the Quality Outcomes Database

Neurosurgery ◽  
2017 ◽  
Vol 83 (3) ◽  
pp. 452-458
Author(s):  
Jian Guan ◽  
John J Knightly ◽  
Erica F Bisson
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Scoring System ◽  
Operating Characteristic ◽  
Lumbar Fusion ◽  
Characteristic Curve ◽  
Quality Outcomes ◽  
Alternative Placement ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract BACKGROUND Lumbar fusion remains the treatment of choice for many degenerative pathologies. Healthcare costs related to the procedure are a concern, and postdischarge needs often contribute to greater expenditure. The Quality Outcomes Database (QOD) is a prospective, multicenter clinical registry designed to analyze outcomes after neurosurgical procedures. OBJECTIVE To create a simple scoring system to predict discharge needs after lumbar fusion. METHODS Institutional QOD data from 2 high-volume neurosurgical centers were collected retrospectively. Univariate and multivariable logistic regression analyses were used to identify factors for our model. A receiver operating characteristic curve was used to set cutoff scores for patients likely to discharge home without ongoing services and those likely to require additional services/alternative placement after discharge. RESULTS Two hundred seventeen patients were included. Five variables—osteoporosis, predominant preoperative symptom, need for assistive ambulation device, American Society of Anesthesiologist grade, and age—were included in our final scoring system. Patients with higher scores are less likely to need additional services. In patients with high scores (8-10), our scale correctly predicted discharge needs in 88.7% of cases. In patients with low scores (0-5), our scale predicted discharge needs (additional home services/alternative placement) in 75% of cases. For our final instrument, the area under the receiver operating characteristic curve was 0.809 (95% confidence interval 0.720-0.897). CONCLUSION We present a simple scoring system to assist in predicting postdischarge needs for patients undergoing lumbar fusion for degenerative disease. Further validation studies are needed to assess the generalizability of our scale.

scholarly journals Explainable Tree-Based Predictions for Unplanned 30-Day Readmission of Patients With Cancer Using Clinical Embeddings

2021 ◽  
pp. 155-167
Author(s):  
Chi Wah Wong ◽  
Chen Chen ◽  
Lorenzo A. Rossi ◽  
Monga Abila ◽  
Janet Munu ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Models ◽  
Operating Characteristic ◽  
Dynamic Models ◽  
Characteristic Curve ◽  
Patient Visit ◽  
Operating Characteristic Curve ◽  
Readmission Risk ◽  
Receiver Operating

PURPOSE Thirty-day unplanned readmission is one of the key components in measuring quality in patient care. Risk of readmission in oncology patients may be associated with a wide variety of specific factors including laboratory results and diagnoses, and it is hard to include all such features using traditional approaches such as one-hot encoding in predictive models. METHODS We used clinical embeddings to represent complex medical concepts in lower dimensional spaces. For predictive modeling, we used gradient-boosted trees and adopted the shapley additive explanation framework to offer consistent individualized predictions. We used retrospective inpatient data between 2013 and 2018 with temporal split for training and testing. RESULTS Our best performing model predicting readmission at discharge using clinical embeddings showed a testing area under receiver operating characteristic curve of 0.78 (95% CI, 0.77 to 0.80). Use of clinical embeddings led to up to 23.1% gain in area under precision-recall curve and 6% in area under receiver operating characteristic curve. Hematology models had more performance gain over surgery and medical oncology. Our study was the first to develop (1) explainable predictive models for the hematology population and (2) dynamic models to keep track of readmission risk throughout the duration of patient visit. CONCLUSION To our knowledge, our study was the first to develop (1) explainable predictive models for the hematology population and (2) dynamic models to keep track of readmission risk throughout the duration of patient visit.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring

2016 ◽  
Vol 25 (6) ◽  
pp. 2750-2766 ◽  
Author(s):  
Hélène Jacqmin-Gadda ◽  
Paul Blanche ◽  
Emilie Chary ◽  
Célia Touraine ◽  
Jean-François Dartigues
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Censored Data ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Disease Onset ◽  
Interval Censoring ◽  
Inverse Probability ◽  
Operating Characteristic Curve ◽  
Semicompeting Risks

Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account for these two issues. In recent years, methods for estimating the time-dependent receiver operating characteristic curve and the associated area under the ROC curve have been extended to account for right censored data and competing risks. In this paper, we show how an approximation allows to use the inverse probability of censoring weighting estimator for semicompeting events with interval censored data. Then, using an illness-death model, we propose two model-based estimators allowing to rigorously handle these issues. The first estimator is fully model based whereas the second one only uses the model to impute missing observations due to censoring. A simulation study shows that the bias for inverse probability of censoring weighting remains modest and may be less than the one of the two parametric estimators when the model is misspecified. We finally recommend the nonparametric inverse probability of censoring weighting estimator as main analysis and the imputation estimator based on the illness-death model as sensitivity analysis.

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