Predicting hospital readmissions in the oncology population.

177 Background: The 30-day readmission rate is established as an important indicator of quality of care. The LACE index is commonly used in the general medical setting to predict readmission but its ability to predict readmission with sensitivity and specificity in the oncology population has not yet been examined. At our cancer center, palliative care (PC) consultation is associated with an increased risk for readmission but it is not an element in the LACE index. Methods: We sought to characterize the operating characteristics of the LACE Index using receiver operating characteristics analyses to predict unplanned readmissions to our cancer center over a 6-week period beginning March 2016. Data was gathered from chart review to calculate a total LACE score for each unplanned admission. Logistic regression was used to examine the individual components of the LACE index and whether a PC consult improved the performance of the index. Results: A total of 329 patients with unplanned admissions were included. Fifty-nine (17.9%) were readmitted within 30 days of discharge. There was no difference between the median LACE scores of those readmitted compared to those who were not (Md = 10.0; p = .93). Receiver operating characteristic (ROC) curve analyses of LACE scores yielded an area under the curve estimate relative to 30-day readmissions of .45 indicative of poor overall accuracy. ROC analyses also showed that the previously established LACE cutoff score of 10 had sensitivity of .54 and specificity of .57 relative to readmissions. The positive predictive value was .81 and the negative predictive value was .18. In logistic regression analysis, only direct referral center/emergency department visits were an independent predictor of readmission, with a c-statistics of .64 for readmission. The inclusion of a PC consult did not improve the performance of the index. Conclusions: The LACE Index performed poorly in predicting 30-day readmission in the oncology setting; the inclusion of whether a PC consult took place did not improve the index’s utility. Further research is required to create a new tool or enhance existing indices to predict readmission in the oncology population.

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Applicability of Anatomic and Physiologic Scoring Systems for the Prediction of Outcome in Polytraumatized Patients with Blunt Aortic Injuries

Diagnostics ◽

10.3390/diagnostics11112156 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2156

Author(s):

Alexander Omar ◽

Marcel Winkelmann ◽

Emmanouil Liodakis ◽

Jan-Dierk Clausen ◽

Tilman Graulich ◽

...

Keyword(s):

Logistic Regression ◽

Injury Severity Score ◽

Sensitivity And Specificity ◽

Severity Score ◽

Injury Severity ◽

Scoring Systems ◽

Apache Ii ◽

Operating Characteristics ◽

Aortic Injuries ◽

Receiver Operating

Background: Most patients with blunt aortic injuries, who arrive alive in a clinic, suffer from traumatic pseudoaneurysms. Due to modern treatments, the perioperative mortality has significantly decreased. Therefore, it is unclear how exact the prediction of commonly used scoring systems of the outcome is. Methods: We analyzed data on 65 polytraumatized patients with blunt aortic injuries. The following scores were calculated: injury severity score (ISS), new injury severity score (NISS), trauma and injury severity score (TRISS), revised trauma score coded (RTSc) and acute physiology and chronic health evaluation II (APACHE II). Subsequently, their predictive value was evaluated using Spearman´s and Kendall´s correlation analysis, logistic regression and receiver operating characteristics (ROC) curves. Results: A proportion of 83% of the patients suffered from a thoracic aortic rupture or rupture with concomitant aortic wall dissection (54/65). The overall mortality was 24.6% (16/65). The sensitivity and specificity were calculated as the area under the receiver operating curves (AUC): NISS 0.812, ISS 0.791, APACHE II 0.884, RTSc 0.679 and TRISS 0.761. Logistic regression showed a slightly higher specificity to anatomical scoring systems (ISS 0.959, NISS 0.980, TRISS 0.957, APACHE II 0.938). The sensitivity was highest in the APACHE II with 0.545. Sensitivity and specificity for the RTSc were not significant. Conclusion: The predictive abilities of all scoring systems were very limited. All scoring systems, except the RTSc, had a high specificity but a low sensitivity. In our study population, the RTSc was not applicable. The APACHE II was the most sensitive score for mortality. Anatomical scoring systems showed a positive correlation with the amount of transfused blood products.

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Prognostic value of AIMS65 score in cirrhotic patients with variceal bleeding

MedPharmRes ◽

10.32895/ump.mpr.6.1.4 ◽

2021 ◽

Vol 6 (1) ◽

pp. 22-26

Author(s):

Trong Nguyen Dang Huynh

Keyword(s):

Variceal Bleeding ◽

Predictive Value ◽

Prognostic Value ◽

Area Under The Curve ◽

Scoring Systems ◽

Meld Score ◽

Operating Characteristics ◽

Increased Risk ◽

Cirrhotic Patients ◽

Ctp Score

Background: In cirrhotic patients, variceal bleeding remains a major cause of death. After a variceal bleeding episode, mortality and rebleeding rates spike for the first 6 weeks before levelling off. We aimed to evaluate the performance of AIMS65 score in comparison with Child-Turcotte-Pugh (CTP) score and model for end-stage liver disease (MELD) score in predicting 6-week mortality and rebleeding in cirrhotic patients with variceal bleeding. Method: Data were collected prospectively from patients with cirrhosis and variceal bleeding at Gastroenterology and Hepatology Department of Cho Ray hospital from September 2016 to April 2017. The primary endpoint was 6-week mortality and rebleeding. The prognostic value of AIMS65, CTP, and MELD scoring systems for 6-week mortality and rebleeding was compared by receiver operating characteristics curves (ROC) and the area under the curve (AUC). Results: Among 80 patients, 15% rebled and 25% died during 6-week follow-up. AUCROC of AIMS65, CTP, and MELD scores in predicting 6-week rebleeding were 0.68, 0.54, and 0.48, respectively. AUCROC of AIMS65, CTP, and MELD scores in predicting 6-week mortality were 0.80, 0.74, and 0.64, respectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of AIMS65 score at the cutoff point of 2 were 95%, 55%, 41.3%, and 97%, respectively. Conclusion: AIMS65 score is a simple yet applicable tool for risk stratification in cirrhotic patients with variceal bleeding. We recommend using AIMS65 score with a cut-off point of 2 to identify patients at increased risk for 6-week mortality after variceal bleeding.

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Predictive Value of Admission D-dimer for Contrast-induced Acute Kidney Injury and Poor Outcomes after Primary Percutaneous Coronary Intervention

10.21203/rs.2.17408/v5 ◽

2020 ◽

Author(s):

Kai-Yang Lin ◽

Han-Chuan Chen ◽

Hui Jiang ◽

Sun-Ying Wang ◽

Hong-mei Chen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Serum Creatinine ◽

Logistic Regression Analysis ◽

Predictive Value ◽

Independent Predictor ◽

Multivariate Logistic Regression Analysis ◽

Increased Risk ◽

Long Term Mortality

Abstract Background DD was found to be associated with acute myocardial infarction (AMI) and renal insufficiency. However, it is uncertain whether DD is an independent risk factor of CI-AKI in patients undergoing pPCI. Methods We prospectively enrolled 550 consecutive patients with STEMI undergoing pPCI between January 2012 and December 2016. The predictive value of admission DD for CI-AKI was assessed by receiver operating characteristic(ROC) and multivariable logistic regression analysis. CI-AKI was defined as an absolute serum creatinine increase ≥0.3 mg/dl or a relative increase in serum creatinine ≥50% within 48 h of contrast medium exposure. Results Overall, the incidence of CI-AKI was 13.1%. The ROC analysis showed that the cutoff point of DD was 0.69 ug/ml for predicting CI-AKI with a sensitivity of 77.8% and a specificity of 57.3%. The predictive value of DD was similar to the Mehran score for CI-AKI (AUC DD =0.729 vs AUC Mehran =0.722; p =0.8298). Multivariate logistic regression analysis indicated that DD >0.69 ug/ml was an independent predictor of CI-AKI (odds ratio[OR]=3.37,95%CI:1.80-6.33, p <0.0001). Furthermore, DD >0.69 ug/ml was associated with an increased risk of long-term mortality during during a mean follow-up period of 16 months(hazard ratio=3.41, 95%CI:1.4-8.03, p =0.005). Conclusion admission DD >0.69 ug/ml is a significant and independent predictor of CI-AKI and long-term mortality in patients undergoing pPCI.

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Predictive Value of Admission D-dimer for Contrast-induced Acute Kidney Injury and Poor Outcomes after Primary Percutaneous Coronary Intervention

10.21203/rs.2.17408/v6 ◽

2020 ◽

Author(s):

Kai-Yang Lin ◽

Han-Chuan Chen ◽

Hui Jiang ◽

Sun-Ying Wang ◽

Hong-mei Chen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Serum Creatinine ◽

Logistic Regression Analysis ◽

Predictive Value ◽

Independent Predictor ◽

Multivariate Logistic Regression Analysis ◽

Increased Risk ◽

Long Term Mortality

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A Rapid Heparin Antibody Detection Assay Performs Better Than 4T's Score in Predicting HIT Diagnosis: A Single Community Medical Center Retrospective Analysis

Blood ◽

10.1182/blood.v124.21.1457.1457 ◽

2014 ◽

Vol 124 (21) ◽

pp. 1457-1457 ◽

Cited By ~ 1

Author(s):

Srikant Nannapaneni ◽

Ishan Malhotra ◽

Michael Simon ◽

Phone Oo ◽

Trishala Meghal ◽

...

Keyword(s):

High Risk ◽

Roc Curve ◽

Predictive Value ◽

Risk Groups ◽

Low Risk ◽

Receiver Operating Characteristics ◽

Operating Characteristics ◽

Test Result ◽

4T Score ◽

Receiver Operating

Abstract Introduction: The diagnosis of heparin induced thrombocytopenia (HIT) and thrombosis (HITT) is challenging due to poor availability of the gold standard serotonin releasing assay (SRA) and suboptimal positive predictive value from clinical scoring models such as 4T score. A common algorithm used for diagnosing HIT is: 4T's pretest probability score estimation in cases suspected of HIT; followed by HIT antibody test in the intermediate to high risk groups; followed by confirmation with SRA test in HIT antibody positive patients. Since 2011, a Particle Immune-Filtration Assay (PIFA) Heparin/Platelet Factor 4 Rapid Assay (HPF4-RA) (Akers Bioscience, Inc, Thorofare, NJ) became available in our medical center and test results were available on the same day. We observed that HPF4-RA test was being routinely ordered along with SRA test at the same time. We performed this retrospective analysis to evaluate and compare the predictive performance for SRA positive HIT diagnosis using 4T score or HPF4-RA. We applied a regression analysis model, to calculate area under receiver operating characteristics (ROC) curve. Methods: A list of all consecutive patients who had HIT antibody test and/or SRA test performed between January 2010 and June 2013 was obtained, which consisted of 402 patients. Patients with duplication of tests were deleted from analysis. 283 patients had results reported for both HPF4-RA (positive in n=42, negative in n=241) and SRA tests (positive in n=16 and negative in n=267); and these results were used for calculation of HPF4-RA prediction model. Two patients had HPF4-RA negative result but SRA positive test result. 4T's scores were calculated for 125 patients, consisting of all HPF4-RA positive patients (n=42), and patients randomly selected from the total HPF4-RA negative pool (n=83). Electronic medical records were reviewed for temporal trend of platelet counts, diagnosis, medication use, Doppler tests and competing causes of thrombocytopenia. Persons calculating the 4T's score were blinded to the laboratory test results. Results: Stratification of the patients with 4T's score analysis (n=125) revealed that the distribution of SRA positive patients (n=16) was 31.3% (n=5) in low risk, 31.3% (n=5) in intermediate risk, and 37.5% (n=6) in high risk groups; while the distribution of SRA negative patients (n=109) was 45.9% (n=50) in low risk, 43.1% (n=47) in intermediate risk and 11.0% (n=12) in high risk groups. The area under receiver operating characteristics (ROC) curve for 4T score as a continuous variable to predict SRA positive HIT was 0.659 (95% CI 0.516 - 0.802; p = 0.041), and the area under ROC curve for HPF4-RA to predict SRA positive HIT was 0.818 ( 95% CI 0.712 - 0.924; p = 0.00) (Figure 1). HPF4-RA test also showed better overall prediction parameters for HIT as shown in Table 1. A combination of HIT HPF4-RA positive result and a 4T score ≥ 4 did not increase the area under ROC curve for prediction of SRA positive HIT. Abstract 1457. Table1: Predictive performance of 4T's score and HPF4-RA for HIT (defined by positive SRA) Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) NPV (95% CI) Number of patients (%) 4T's score ≤ 3 (Low Risk) 0.31 (0.11 – 0.59) 0.72 (0.64 - 0.79) 0.11 (0.03 - 0.23) 0.91 (0.84 - 0.95) 56 (44.8) 4T's score ≥ 4 (Intermediate and High Risk) 0.69 (0.41-0.89) 0.39 (0.29 - 0.48) 0.14 (0.72 - 0.24) 0.89 (0.77 - 0.96) 69 (55.2) 4T's score ≥ 6 (High Risk) 0.37 (0.15-0.65) 0.82 (0.74 - 0.89) 0.24 (0.09 - 0.45) 0.90 (0.82 - 0.95) 17 (13.6) HPF4-RA Test 0.88 (0.62-0.98) 0.86 (0.81- 0.90) 0.26 (0.16 - 0.41) 0.99 (0.96 - 0.99) 283 PPV: Positive Predictive Value. NPV: Negative Predictive Value Figure 1: Receiver Operating Characteristics (ROC) curve of the 4T's score and HPF4-RA test result for determining the presence of HIT (defined by positive SRA). Figure 1:. Receiver Operating Characteristics (ROC) curve of the 4T's score and HPF4-RA test result for determining the presence of HIT (defined by positive SRA). Conclusions: Both 4T's score and HPF4-RA testing predict SRA positive HIT more than chance; however HPF4-RA testing predicts SRA positive HIT better than 4T's scores with higher sensitivity, specificity and NPV. This result challenges the pretesting algorithm for selecting patients for confirmatory SRA testing to diagnose HIT. Instead of using 4T's score as a screening tool for selecting patients for HPF4 antibody testing; rapid HPF4 antibody assays when available, should be considered as upfront screening tool and positive results considered for confirmatory SRA testing for diagnosis of HIT. Further studies are warranted to confirm this data. Disclosures No relevant conflicts of interest to declare.

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Machine learning to predict outcomes in patients with acute pulmonary embolism who prematurely discontinued anticoagulant therapy.

Thrombosis and Haemostasis ◽

10.1055/a-1525-7220 ◽

2021 ◽

Author(s):

Damian Mora ◽

José Antonio Nieto ◽

Jorge Mateo ◽

Behnood Bikdeli ◽

Stefano Barco ◽

...

Keyword(s):

Machine Learning ◽

Pulmonary Embolism ◽

Logistic Regression ◽

Anticoagulant Therapy ◽

Roc Curve ◽

Predictive Value ◽

Support Vector ◽

Calibration Plot ◽

Composite Outcome ◽

Increased Risk

Background: Patients with pulmonary embolism (PE) who prematurely discontinue anticoagulant therapy (<90 days) are at an increased risk for death or recurrences. Methods: We used the data from the RIETE registry to compare the prognostic ability of 5 machine-learning (ML) models and logistic regression to identify patients at increased risk for the composite of fatal PE or recurrent venous thromboembolism (VTE) 30 days after discontinuation. ML models included Decision tree, K-Nearest Neighbors algorithm, Support Vector Machine, Ensemble and Neural Network [NN]. A “full” model with 70 variables and a “reduced” model with 23 were analyzed. Model performance was assessed by confusion matrix metrics on the testing data for each model and a calibration plot. Results: Among 34,447 patients with PE, 1,348 (3.9%) discontinued therapy prematurely. Fifty-one (3.8%) developed fatal PE or sudden death and 24 (1.8%) had non-fatal VTE recurrences within 30 days after discontinuation. ML-NN was the best method for identification of patients experiencing the composite endpoint, predicting the composite outcome with an area under receiver operating characteristics (ROC) curve of 0.96 (95% confidence intervals [CI], 0.95-0.98), using either 70 or 23 variables captured before discontinuation. Similar numbers were obtained for sensitivity, specificity, positive predictive value, negative predictive value and accuracy. The discrimination of logistic regression was inferior (area under ROC curve, 0.76 [95% Cl 0.70-0.81]). Calibration plot showed similar deviations from the perfect line for ML-NN and logistic regression. Conclusions: ML-NN method very well predicted the composite outcome after premature discontinuation of anticoagulation and outperformed traditional logistic regression.

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Predictive Value of Admission D-dimer for Contrast-induced Acute Kidney Injury and Poor Outcomes after Primary Percutaneous Coronary Intervention

10.21203/rs.2.17408/v7 ◽

2020 ◽

Author(s):

Kai-Yang Lin ◽

Han-Chuan Chen ◽

Hui Jiang ◽

Sun-Ying Wang ◽

Hong-mei Chen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Serum Creatinine ◽

Logistic Regression Analysis ◽

Predictive Value ◽

Independent Predictor ◽

Multivariate Logistic Regression Analysis ◽

Increased Risk ◽

Long Term Mortality

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Predictive Value of Admission D-dimer for Contrast-induced Acute Kidney Injury and Poor Outcomes after Primary Percutaneous Coronary Intervention

10.21203/rs.2.17408/v2 ◽

2019 ◽

Author(s):

Kai-Yang Lin ◽

Han-Chuan Chen ◽

Hui Jiang ◽

Sun-Ying Wang ◽

Hong-mei Chen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Serum Creatinine ◽

Logistic Regression Analysis ◽

Predictive Value ◽

Independent Predictor ◽

Multivariate Logistic Regression Analysis ◽

Increased Risk ◽

Long Term Mortality

Abstract Background DD was found to be associated with acute myocardial infarction (AMI) and renal insufficiency. However, it is uncertain whether DD is an independent risk factor of CI-AKI in patients undergoing pPCI.Methods We prospectively enrolled 550 consecutive patients with STEMI undergoing pPCI between January 2012 and December 2016. The predictive value of admission DD for CI-AKI was assessed by receiver operating characteristic(ROC) and multivariable logistic regression analysis. CI-AKI was defined as an absolute serum creatinine increase ≥0.3 mg/dl or a relative increase in serum creatinine ≥50% within 48 h of contrast medium exposure.Results Overall, the incidence of CI-AKI was 13.1%. The ROC analysis showed that the cutoff point of DD was 0.69 ug/ml for predicting CI-AKI with a sensitivity of 77.8% and a specificity of 57.3%. The predictive value of DD was similar to the Mehran score for CI-AKI (AUC DD =0.729 vs AUC Mehran =0.722; p =0.8298). Multivariate logistic regression analysis indicated that DD >0.69 ug/ml was an independent predictor of CI-AKI (odds ratio[OR]=3.37,95%CI:1.80-6.33, p <0.0001). Furthermore, DD >0.69 ug/ml was associated with an increased risk of long-term mortality during during a mean follow-up period of 16 months(hazard ratio=3.41, 95%CI:1.4-8.03, p =0.005).Conclusion admission DD >0.69 ug/ml is a significant and independent predictor of CI-AKI and long-term mortality in patients undergoing pPCI.

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Predictive Value of Admission D-dimer for Contrast-induced Acute Kidney Injury and Poor Outcomes after Primary Percutaneous Coronary Intervention

10.21203/rs.2.17408/v3 ◽

2020 ◽

Author(s):

Kai-Yang Lin ◽

Han-Chuan Chen ◽

Hui Jiang ◽

Sun-Ying Wang ◽

Hong-mei Chen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Serum Creatinine ◽

Logistic Regression Analysis ◽

Predictive Value ◽

Independent Predictor ◽

Multivariate Logistic Regression Analysis ◽

Increased Risk ◽

Long Term Mortality

Abstract Background DD was found to be associated with acute myocardial infarction (AMI) and renal insufficiency. However, it is uncertain whether DD is an independent risk factor of CI-AKI in patients undergoing pPCI.Methods We prospectively enrolled 550 consecutive patients with STEMI undergoing pPCI between January 2012 and December 2016. The predictive value of admission DD for CI-AKI was assessed by receiver operating characteristic(ROC) and multivariable logistic regression analysis. CI-AKI was defined as an absolute serum creatinine increase ≥0.3 mg/dl or a relative increase in serum creatinine ≥50% within 48 h of contrast medium exposure.Results Overall, the incidence of CI-AKI was 13.1%. The ROC analysis showed that the cutoff point of DD was 0.69 ug/ml for predicting CI-AKI with a sensitivity of 77.8% and a specificity of 57.3%. The predictive value of DD was similar to the Mehran score for CI-AKI (AUC DD =0.729 vs AUC Mehran =0.722; p =0.8298). Multivariate logistic regression analysis indicated that DD >0.69 ug/ml was an independent predictor of CI-AKI (odds ratio[OR]=3.37,95%CI:1.80-6.33, p <0.0001). Furthermore, DD >0.69 ug/ml was associated with an increased risk of long-term mortality during during a mean follow-up period of 16 months(hazard ratio=3.41, 95%CI:1.4-8.03, p =0.005).Conclusion admission DD >0.69 ug/ml is a significant and independent predictor of CI-AKI and long-term mortality in patients undergoing pPCI.

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Assessment of severity of illness on admission using SICK score and evaluation of the risk factors associated with mortality in children admitted in a paediatric urban tertiary care centre in south India

International Journal of Contemporary Pediatrics ◽

10.18203/2349-3291.ijcp20211069 ◽

2021 ◽

Vol 8 (4) ◽

pp. 636

Author(s):

N. Rajeshwari ◽

A. Savitha ◽

J. Prahada

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Univariate Analysis ◽

Tertiary Care ◽

Assisted Ventilation ◽

Receiver Operating Curve ◽

Multivariate Logistic Regression Model ◽

Operating Characteristics ◽

Multivariate Logistic Regression ◽

Receiver Operating

Background: “Signs of inflammation that can kill” (SICK) score is one of the severities scoring systems used for predicting outcome of children at admission. The aim of the present study was to study the clinical and demographic profile of children admitted to Paediatric ward, to assess the usefulness of SICK score in predicting the mortality and evaluate the risk factors in predicting mortality. Methods: SICK scoring was done for 369 children on admission. The outcome was recorded as death or discharge. The associated factors were analysed using SPSS software package analysis. Receiver operating curve was used to arrive at the cut-off point of SICK score for predicting mortality. Quantitative data differences between children who died and children who were discharged from the hospital were analysed using student independent t test. Need for assisted ventilation, presence of shock, age less than 3 years, and SICK score>2 were studied to find their association with mortality. Statistical analysis was done using univariate analysis and those factors that were significantly associated with mortality were subjected multivariate logistic regression analysis.Results: The performance of SICK score was “excellent” in discriminating between death and survival with area under the receiver operating characteristics curve 0.94. Age<3-year presence of shock, need for mechanical ventilation and SICK score>2 showed statistically significant association with mortality as evidenced by multivariate logistic regression model. Conclusions: SICK score performed extremely well in predicting mortality on admission. Age<3 years, SICK score>2, Presence of Shock and need for assisted ventilation showed statistically significant association with mortality.

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