scholarly journals The HOSPITAL score and LACE index as predictors of 30 day readmission in a retrospective study at a university-affiliated community hospital

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3137 ◽  
Author(s):  
Robert Robinson ◽  
Tamer Hudali
Keyword(s):  
Retrospective Study ◽  
High Risk ◽  
Hospital Readmission ◽  
Patient Population ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Brier Score ◽  
Goodness Of Fit Test ◽  
C Statistic ◽  
Overall Performance

IntroductionHospital readmissions are common, expensive, and a key target of the Medicare Value Based Purchasing (VBP) program. Validated risk assessment tools such as the HOSPITAL score and LACE index have been developed to identify patients at high risk of hospital readmission so they can be targeted for interventions aimed at reducing the rate of readmission. This study aims to evaluate the utility of HOSPITAL score and LACE index for predicting hospital readmission within 30 days in a moderate-sized university affiliated hospital in the midwestern United States.Materials and MethodsAll adult medical patients who underwent one or more ICD-10 defined procedures discharged from the SIU-SOM Hospitalist service from Memorial Medical Center (MMC) from October 15, 2015 to March 16, 2016, were studied retrospectively to determine if the HOSPITAL score and LACE index were a significant predictors of hospital readmission within 30 days.ResultsDuring the study period, 463 discharges were recorded for the hospitalist service. The analysis includes data for the 432 discharges. Patients who died during the hospital stay, were transferred to another hospital, or left against medical advice were excluded. Of these patients, 35 (8%) were readmitted to the same hospital within 30 days. A receiver operating characteristic evaluation of the HOSPITAL score for this patient population shows a C statistic of 0.75 (95% CI [0.67–0.83]), indicating good discrimination for hospital readmission. The Brier score for the HOSPITAL score in this setting was 0.069, indicating good overall performance. The Hosmer–Lemeshow goodness of fit test shows a χ2value of 3.71 with apvalue of 0.59. A receiver operating characteristic evaluation of the LACE index for this patient population shows a C statistic of 0.58 (95% CI [0.48–0.68]), indicating poor discrimination for hospital readmission. The Brier score for the LACE index in this setting was 0.082, indicating good overall performance. The Hosmer–Lemeshow goodness of fit test shows a χ2value of 4.97 with apvalue of 0.66.DiscussionThis single center retrospective study indicates that the HOSPITAL score has superior discriminatory ability when compared to the LACE index as a predictor of hospital readmission within 30 days at a medium-sized university-affiliated teaching hospital.ConclusionsThe internationally validated HOSPITAL score may be superior to the LACE index in moderate-sized community hospitals to identify patients at high risk of hospital readmission within 30 days.

scholarly journals The HOSPITAL score as a predictor of 30 day readmission in a retrospective study at a university affiliated community hospital

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2441 ◽  
Author(s):  
Robert Robinson
Keyword(s):  
Retrospective Study ◽  
High Risk ◽  
Hospital Readmission ◽  
Goodness Of Fit ◽  
Medical Center ◽  
Hospital Readmissions ◽  
Assessment Tools ◽  
Brier Score ◽  
Goodness Of Fit Test ◽  
Discriminatory Ability

IntroductionHospital readmissions are common, expensive, and a key target of the Medicare Value Based Purchasing (VBP) program. Risk assessment tools have been developed to identify patients at high risk of hospital readmission so they can be targeted for interventions aimed at reducing the rate of readmission. One such tool is the HOSPITAL score that uses seven readily available clinical variables to predict the risk of readmission within 30 days of discharge. The HOSPITAL score has been internationally validated in large academic medical centers. This study aims to determine if the HOSPITAL score is similarly useful in a moderate sized university affiliated hospital in the midwestern United States.Materials and MethodsAll adult medical patients discharged from the SIU-SOM Hospitalist service from Memorial Medical Center (MMC) from October 15, 2015 to March 16, 2016, were studied retrospectively to determine if the HOSPITAL score was a significant predictor of hospital readmission within 30 days.ResultsDuring the study period, 998 discharges were recorded for the hospitalist service. The analysis includes data for the 931 discharges. Patients who died during the hospital stay, were transferred to another hospital, or left against medical advice were excluded. Of these patients, 109 (12%) were readmitted to the same hospital within 30 days. The patients who were readmitted were more likely to have a length of stay greater than or equal to 5 days (55% vs. 41%,p= 0.005) and were more likely to have been admitted more than once to the hospital within the last year (100% vs. 49%,p< 0.001). A receiver operating characteristic evaluation of the HOSPITAL score for this patient population shows a C statistic of 0.77 (95% CI [0.73–0.81]), indicating good discrimination for hospital readmission. The Brier score for the HOSPITAL score in this setting was 0.10, indicating good overall performance. The Hosmer–Lemeshow goodness of fit test shows aχ2value of 1.63 with apvalue of 0.20.DiscussionThis single center retrospective study indicates that the HOSPITAL score has good discriminatory ability to predict hospital readmissions within 30 days for a medical hospitalist service at a university-affiliated hospital. This data for all causes of hospital readmission is comparable to the discriminatory ability of the HOSPITAL score in the international validation study (C statistics of 0.72 vs. 0.77) conducted at considerably larger hospitals (975 average beds vs. 507 at MMC) for potentially avoidable hospital readmissions.ConclusionsThe internationally validated HOSPITAL score may be a useful tool in moderate sized community hospitals to identify patients at high risk of hospital readmission within 30 days. This easy to use scoring system using readily available data can be used as part of interventional strategies to reduce the rate of hospital readmission.

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.

Is the EuroSCORE II reliable in surgical mitral valve repair? A single-centre validation study

2020 ◽  
Author(s):  
Davide Carino ◽  
Paolo Denti ◽  
Guido Ascione ◽  
Benedetto Del Forno ◽  
Elisabetta Lapenna ◽  
...  
Keyword(s):  
Mitral Valve ◽  
Mitral Valve Repair ◽  
Goodness Of Fit ◽  
Area Under The Curve ◽  
High Volume ◽  
Brier Score ◽  
Calibration Plot ◽  
Valve Repair ◽  
Goodness Of Fit Test ◽  
High Level

Abstract OBJECTIVES The EuroSCORE II is widely used to predict 30-day mortality in patients undergoing open and transcatheter cardiac surgery. The aim of this study is to evaluate the discriminatory ability of the EuroSCORE II in predicting 30-day mortality in a large cohort of patients undergoing surgical mitral valve repair in a high-volume centre. METHODS A retrospective review of our institutional database was carried on to find all patients who underwent mitral valve repair in our department from January 2012 to December 2019. Discrimination of the EuroSCORE II was assessed using receiver operating characteristic curves. The maximum Youden’s Index was employed to define the optimal cut-point. Calibration was assessed by generating calibration plot that visually compares the predicted mortality with the observed mortality. Calibration was also tested with the Hosmer–Lemeshow goodness-of-fit test. Finally, the accuracy of the models was tested calculating the Brier score. RESULTS A total of 2645 patients were identified, and the median EuroSCORE II was 1.3% (0.6–2.0%). In patients with degenerative mitral regurgitation (MR), the EuroSCORE II showed low discrimination (area under the curve 0.68), low accuracy (Brier score 0.27) and low calibration with overestimation of the 30-day mortality. In patients with secondary MR, the EuroSCORE II showed a good overall performance estimating the 30-day mortality with good discrimination (area under the curve 0.88), good accuracy (Brier score 0.003) and good calibration. CONCLUSIONS In patients with degenerative MR operated on in a high-volume centre with a high level of expertise in mitral valve repair, the EuroSCORE II significantly overestimates the 30-day mortality.

Predictive Performance and Variability of the Cardiac Anesthesia Risk Evaluation Score

2004 ◽  
Vol 100 (6) ◽  
pp. 1405-1410 ◽  
Author(s):  
Alexandre Ouattara ◽  
Michaëla Niculescu ◽  
Sarra Ghazouani ◽  
Ario Babolian ◽  
Marc Landi ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Risk Evaluation ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Cardiac Anesthesia ◽  
Goodness Of Fit Test ◽  
Significant Difference ◽  
Receiver Operating

Background The Cardiac Anesthesia Risk Evaluation (CARE) score, a simple Canadian classification for predicting outcome after cardiac surgery, was evaluated in 556 consecutive patients in Paris, France. The authors compared its performance to those of two multifactorial risk indexes (European System for Cardiac Operative Risk Evaluation [EuroSCORE] and Tu score) and tested its variability between groups of physicians (anesthesiologists, surgeons, and cardiologists). Methods Each patient was simultaneously assessed using the three scores by an attending anesthesiologist in the immediate preoperative period. In a blinded study, the CARE score category was also determined by a cardiologist the day before surgery, by a surgeon in the operating room, and by a second anesthesiologist at arrival in intensive care unit. Calibration and discrimination for predicting outcomes were assessed by goodness-of-fit test and area under the receiver operating characteristic curve, respectively. The level of agreement of the CARE scoring between the three physicians was then assessed. Results The calibration analysis revealed no significant difference between expected and observed outcomes for the three classifications. The areas under the receiver operating characteristic curves for mortality were 0.77 with the CARE score, 0.78 with the EuroSCORE, and 0.73 with the Tu score (not significant). The agreement rate of the CARE scoring between two anesthesiologists, between anesthesiologists and surgeons, and between anesthesiologists and cardiologists were 90%, 83%, and 77%, respectively. Conclusions Despite its simplicity, the CARE score predicts mortality and major morbidity as well the EuroSCORE. In addition, it remains devoid of significant variability when used by groups of physicians of different specialties.

scholarly journals Application of concomitant disease scoring in acute cerebral infarction

2019 ◽  
Author(s):  
Xiao-Jing Zhao ◽  
Qun-Xi Li ◽  
Ying Liu ◽  
Li-Sha Chang ◽  
Rui-Ying Chen ◽  
...  
Keyword(s):  
Cerebral Infarction ◽  
Predictive Value ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Acute Cerebral Infarction ◽  
Concomitant Disease ◽  
Disease Score ◽  
Goodness Of Fit Test ◽  
Ideal System ◽  
Disease Scoring

Abstract Background: This study aims to explore the predictive value of concomitant disease scoring for the prognosis of patients with acute cerebral infarction (ACI). Methods: A total of 399 patients with ACI, who met the inclusion criteria, were enrolled into the present study. The concomitant disease score was assessed within 24 hours after admission, and the risk degree of death was analyzed. Then, the goodness of fit test and validity analysis were carried out, the best survival/death cut-off value was determined, and its predictive value for the prognosis of ACI patients was assessed. Results: The area under the receiver operating characteristic (ROC) curve for the concomitant disease score was 0.700, the distinctiveness was relatively good, and the prediction cut-off value was 10 points. Furthermore, the mortality rate of patients with a higher score was significantly higher, when compared to patients with a lower score. Conclusion: This concomitant disease score has good predictive value for the prognosis of ACI patients, and is an ideal system for evaluating the condition of cerebral infarction. The survival/death cut-off value was 10 points.

84 Development of a Risk Prediction Model and Comparison with Clinical Severity Scores in Burn Patients

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S54-S55
Author(s):  
Dohern Kym
Keyword(s):  
Prediction Model ◽  
Goodness Of Fit ◽  
Scoring Systems ◽  
Clinical Severity ◽  
Brier Score ◽  
P Value ◽  
Burn Patients ◽  
Goodness Of Fit Test ◽  
Prediction Of Mortality ◽  
Severity Scores

Abstract Introduction The purpose of this study was to develop a new prediction model to reflect the risk of mortality and severity of disease and to evaluate the ability of the developed model to predict mortality among adult burn patients. Methods This study included 2009 patients aged more than 18 years who were admitted to the intensive care unit (ICU) within 24 hours after a burn. We divided the patients into two groups; those admitted from January 2007 to December 2013 were included in the derivation group and those admitted from January 2014 to September 2017 were included in the validation group. Shrinkage methods with 10-folds cross-validation were performed to identify variables and limit overfitting of the model. The discrimination was analyzed using the area under the curve (AUC) of the receiver operating characteristic curve. The Brier score, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were also calculated. The calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test (HL test). The clinical usefulness was evaluated using a decision-curve analysis. Results The new prediction model showed good calibration with the HL test (χ2=8.785, p=0.361); the highest AUC and the lowest Brier score were 0.943 and 0.068, respectively. The NRI and IDI were 0.124 (p-value = 0.003) and 0.079 (p-value &lt; 0.001) when compared with FLAMES, respectively. Conclusions This model reflects the current risk factors of mortality among adult burn patients. Furthermore, it was a highly discriminatory and well-calibrated model for the prediction of mortality in this cohort. Applicability of Research to Practice There are many severity scoring systems widely used in the ICU to predict outcomes and characterize the severity of the disease. All of these scoring systems have been developed for the mixed population in the ICU. Their accuracy among subgroups, such as burn patients, is questionable and therefore, burn-specific scoring systems are required for accurate prediction.

The Braden Scale for Predicting the Outcome and Prognosis of Pressure Injuries in Older Inpatients: A Multicenter, Retrospective Cohort Study

2021 ◽  
Vol 33 (5) ◽  
pp. 127-135
Author(s):  
Yi-Ping Song ◽  
Man-Li Zha ◽  
Hong-Wu Shen ◽  
Yang Li ◽  
Lin Du ◽  
...  
Keyword(s):  
Respiratory Diseases ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Healing Rate ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Goodness Of Fit Test ◽  
Braden Scale ◽  
Older Inpatients ◽  
Digestive System Diseases

Introduction. The Braden scale is used to assess the risk of patients with pressure injuries (PIs), but there are limitations to the prediction of PI healing. There is a lack of tools for evaluating PI healing and outcome in clinical practice. Objective. The purpose of this study was to examine the ability of the Braden scale to predict the outcome and prognosis of PIs in older patients. Materials and Methods. Outcome indicator was the wound healing rate of patients with PIs at discharge. The receiver operating characteristic (ROC) and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the discrimination and calibration. Results. Completed data were available for 309 patients, 181 of whom (58.6%) were male. The Braden scale had poor discrimination to predict the outcome and prognosis of PIs with an area under the curve (AUC) of 0.63 (95% CI, 0.56–0.70; P = .01). Subgroup analyses showed the Braden scale had low diagnostic value for patients aged over 90 years (AUCROC = 0.56; 95% CI, 0.17–0.96; P = .738), patients with respiratory diseases (AUCROC = 0.51; 95% CI, 0.37–0.65; P = .908), and digestive system diseases (AUCROC = 0.59; 95% CI, 0.42–0.75; P = .342). The level of calibration ability by Hosmer-Lemeshow goodness-of-fit test was acceptable, defined as P >.200 (χ2 = 6.59; P = .473). In patients aged more than 90 years (χ2 = 4.88; P = .431) and female patients (χ2 = 7.03; P = .425), the Braden scale was also fitting. It was not suitable for patients with respiratory diseases (χ2 = 11.35; P = .078). Conclusions. The Braden scale had low discrimination for predicting the outcome and prognosis of PIs in older inpatients. The development of a new tool is needed to predict healing in patients with preexisting PIs.

scholarly journals Validation of a Visual-Based Analytics Tool for Outcome Prediction in Polytrauma Patients (WATSON Trauma Pathway Explorer) and Comparison with the Predictive Values of TRISS

2021 ◽  
Vol 10 (10) ◽  
pp. 2115
Author(s):  
Cédric Niggli ◽  
Hans-Christoph Pape ◽  
Philipp Niggli ◽  
Ladislav Mica
Keyword(s):  
Injury Severity Score ◽  
Severity Score ◽  
Goodness Of Fit ◽  
Injury Severity ◽  
Early Death ◽  
Trauma Centre ◽  
Brier Score ◽  
Individual Risk ◽  
Predictive Values ◽  
Overall Performance

Introduction: Big data-based artificial intelligence (AI) has become increasingly important in medicine and may be helpful in the future to predict diseases and outcomes. For severely injured patients, a new analytics tool has recently been developed (WATSON Trauma Pathway Explorer) to assess individual risk profiles early after trauma. We performed a validation of this tool and a comparison with the Trauma and Injury Severity Score (TRISS), an established trauma survival estimation score. Methods: Prospective data collection, level I trauma centre, 1 January 2018–31 December 2019. Inclusion criteria: Primary admission for trauma, injury severity score (ISS) ≥ 16, age ≥ 16. Parameters: Age, ISS, temperature, presence of head injury by the Glasgow Coma Scale (GCS). Outcomes: SIRS and sepsis within 21 days and early death within 72 h after hospitalisation. Statistics: Area under the receiver operating characteristic (ROC) curve for predictive quality, calibration plots for graphical goodness of fit, Brier score for overall performance of WATSON and TRISS. Results: Between 2018 and 2019, 107 patients were included (33 female, 74 male; mean age 48.3 ± 19.7; mean temperature 35.9 ± 1.3; median ISS 30, IQR 23–36). The area under the curve (AUC) is 0.77 (95% CI 0.68–0.85) for SIRS and 0.71 (95% CI 0.58–0.83) for sepsis. WATSON and TRISS showed similar AUCs to predict early death (AUC 0.90, 95% CI 0.79–0.99 vs. AUC 0.88, 95% CI 0.77–0.97; p = 0.75). The goodness of fit of WATSON (X2 = 8.19, Hosmer–Lemeshow p = 0.42) was superior to that of TRISS (X2 = 31.93, Hosmer–Lemeshow p < 0.05), as was the overall performance based on Brier score (0.06 vs. 0.11 points). Discussion: The validation supports previous reports in terms of feasibility of the WATSON Trauma Pathway Explorer and emphasises its relevance to predict SIRS, sepsis, and early death when compared with the TRISS method.

scholarly journals Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis–Treated Patients Using Stacked Generalization: Model Development and Validation Study

10.2196/17886 ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. e17886
Author(s):  
Guilan Kong ◽  
Jingyi Wu ◽  
Hong Chu ◽  
Chao Yang ◽  
Yu Lin ◽  
...  
Keyword(s):  
Peritoneal Dialysis ◽  
High Risk ◽  
Prediction Model ◽  
Performance Measures ◽  
Prediction Models ◽  
Data Driven ◽  
Brier Score ◽  
Prolonged Length ◽  
Admission Data ◽  
Overall Performance

Background The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients. Objective This study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data. Methods Patient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model. Results The final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided t tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy. Conclusions This study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.

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