scholarly journals Development and validation of a scoring system for mortality prediction and application of standardized W statistics to assess the performance of emergency departments

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinwoo Jeong ◽  
Sung Woo Lee ◽  
Won Young Kim ◽  
Kap Su Han ◽  
Su Jin Kim ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Scoring System ◽  
Operating Characteristic ◽  
Mortality Prediction ◽  
Mortality Prediction Model ◽  
National Emergency ◽  
Receiver Operating

Abstract Background In-hospital mortality and short-term mortality are indicators that are commonly used to evaluate the outcome of emergency department (ED) treatment. Although several scoring systems and machine learning-based approaches have been suggested to grade the severity of the condition of ED patients, methods for comparing severity-adjusted mortality in general ED patients between different systems have yet to be developed. The aim of the present study was to develop a scoring system to predict mortality in ED patients using data collected at the initial evaluation and to validate the usefulness of the scoring system for comparing severity-adjusted mortality between institutions with different severity distributions. Methods The study was based on the registry of the National Emergency Department Information System, which is maintained by the National Emergency Medical Center of the Republic of Korea. Data from 2016 were used to construct the prediction model, and data from 2017 were used for validation. Logistic regression was used to build the mortality prediction model. Receiver operating characteristic curves were used to evaluate the performance of the prediction model. We calculated the standardized W statistic and its 95% confidence intervals using the newly developed mortality prediction model. Results The area under the receiver operating characteristic curve of the developed scoring system for the prediction of mortality was 0.883 (95% confidence interval [CI]: 0.882–0.884). The Ws score calculated from the 2016 dataset was 0.000 (95% CI: − 0.021 – 0.021). The Ws score calculated from the 2017 dataset was 0.049 (95% CI: 0.030–0.069). Conclusions The scoring system developed in the present study utilizing the parameters gathered in initial ED evaluations has acceptable performance for the prediction of in-hospital mortality. Standardized W statistics based on this scoring system can be used to compare the performance of an ED with the reference data or with the performance of other institutions.

scholarly journals Prognostic significance of urine paraquat concentrations of patients with acute paraquat poisoning on admission at the emergency department

2018 ◽  
Vol 25 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Xiaowei Liu ◽  
Tao Ma ◽  
Zhi Liu
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Prognostic Significance ◽  
Urine Concentration ◽  
Paraquat Poisoning ◽  
Independent Risk Factors ◽  
Receiver Operating

Objective: To assess the prognostic significance of urine paraquat concentrations of patients with acute paraquat poisoning on admission at the emergency department. Methods: Patients with acute paraquat poisoning admitted to the emergency department were recruited. Survivors and non-survivors were compared with regard to urinary paraquat concentration. The urinary level predictive of mortality was assessed by receiver operating characteristic curve. Risk factors of mortality were evaluated by regression analysis. Results: The overall mortality rate was 70.9% over the 28-day follow-up period. There was a significant difference in the urine paraquat concentrations recorded on admission between non-surviving and surviving patients ( p = 0.022). Receiver operating characteristic curve analysis revealed that the area under the curve when applied to receiver operating characteristic of the admission urine paraquat concentrations for predicting mortality was 0.854 with a cut-off value of 34.5 µg/mL. The dose of paraquat ingested, arterial lactate, and urine concentration were independent risk factors predicting 28-day mortality. The time interval between ingestion and hemoperfusion, arterial lactate, and urine concentration of paraquat were independent risk factors predicting acute kidney injury, while the partial pressure of carbon dioxide (PaCO2) and urine concentration of paraquat were independent risk factors predicting acute lung injury. Conclusion: The urine concentrations of paraquat on admission at emergency department demonstrated predictive ability for the prognosis of patients with acute paraquat poisoning.

scholarly journals A Novel Immune-Related lncRNA Signature predict the Immune Landscape and Prognosis for Head and Neck Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Zhimou Cai ◽  
Lin Chen ◽  
Yu Lin ◽  
Wenbin Lei
Keyword(s):  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Head And Neck ◽  
Squamous Cell ◽  
Operating Characteristic ◽  
Univariate Analysis ◽  
Risk Groups ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Receiver Operating

Abstract Background: The tumor immune microenvironment is known to play an important role in head and neck squamous cell carcinomas (HNSCC). Reliable prognostic signatures that could accurately predict immune landscape and survival in HNSCC patients are vitally needed to promote a better individualized and effective treatment.Methods: HNSCC transcriptome data and clinical data in The Cancer Genome Atlas (TCGA) were embedded in our study. The differentially expressed irlncRNAs were identified by differential co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. Cox and lasso regression analysis was used to identify DEirlncRNA pairs related to overall survival (OS) and build the prediction model. Then, we compared the areas under curve (AUC) with other published lncRNA signatures, counted the akaike information criterion (AIC) values of 3-year receiver operating characteristic curve, and identified the cut-off point to set up an optimal model for distinguishing the high- or low- risk groups among patients with HNSCC. Receiver operating characteristic (ROC)curves and Kaplan–Meier plot curves were used to validate the prediction model. Besides, We then reevaluated them from the viewpoints of clinical factor, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers.Results: We built a risk score model based on 18 DEirlncRNA pairs. The risk model is closely related to the OS of HNSCC patients. The hazard ratio (HR) is 1.376 [95% CI (confidence interval) 1.302-1.453] and log-rank P-value < 0.0001. Compared with two recently published lncRNA signatures, DEirLncRNA pairs signature has higher AUC score which showed the better prognostic performance. Additionally, the signature score showed a positive correlation with aggressive outcomes of HNSCC, such as low immunity score, significantly reduced CD8+ T cell infiltration and lowly expressed immunosuppressed biomarkers. However, high-risk groups of patients may have high chemotherapeutics sensitivity.Conclusions: The signature established by paring irlncRNA regardless of expression levels showed a promising clinical prediction value and revealed tumor immune microenvironment in HNSCC patients which might help in distinguishing those who could benefit from anti-tumor immunotherapy.

Abstract 238: Validation of In-hospital Mortality Prediction Models for Patients with Heart Failure

2015 ◽  
Vol 8 (suppl_2) ◽  
Author(s):  
Tara Lagu ◽  
Mihaela Stefan ◽  
Quinn Pack ◽  
Auras Atreya ◽  
Mohammad A Kashef ◽  
...  
Keyword(s):  
Heart Failure ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Clinical Data ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Mortality Prediction Model ◽  
Mortality Prediction Models ◽  
Clinical Models ◽  
Administrative Model

Background: Mortality prediction models, developed with the goal of improving risk stratification in hospitalized heart failure (HF) patients, show good performance characteristics in the datasets in which they were developed but have not been validated in external populations. Methods: We used a novel multi-hospital dataset [HealthFacts (Cerner Corp)] derived from the electronic health record (years 2010-2012). We examined the performance of four published HF inpatient mortality prediction models developed using data from: the Acute Decompensated Heart Failure National Registry (ADHERE), the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) study, and the Get With the Guidelines-Heart Failure (GWTG-HF) registry. We compared to an administrative HF mortality prediction model (Premier model) that includes selected patient demographics, comorbidities, prior heart failure admissions, and therapies administered (e.g., inotropes, mechanical ventilation) in the first 2 hospital days. We also compared to a model that uses clinical data but is not heart failure-specific: the Laboratory-Based Acute Physiology Score (LAPS2). We included patients aged ≥18 years admitted with HF to one of 62 hospitals in the database. We applied all 6 models to the data and calculated the c-statistics. Results: We identified 13,163 patients ≥18 years old with a diagnosis of heart failure. Median age was 74 years; approximately half were women; 65% of patients were white and 27% were black. In-hospital mortality was 4.3%. Bland-Altman plots revealed that, at higher predicted mortality, the Premier model outperformed the clinical models. Discrimination of the models varied: ADHERE model (0.68); EFFECT (0.70); GWTG-HF, Peterson (0.69); GWTG-HF, Eapen (0.70); LAPS2 (0.74); Premier (0.81) (Figure). Conclusions: Clinically-derived inpatient heart failure mortality models exhibited similar performance with c statistics hovering around 0.70. A generic clinical mortality prediction model (LAPS2) had slightly better performance, as did a detailed administrative model. Any of these models may be useful for severity adjustment in comparative effectiveness studies of heart failure patients. When clinical data are not available, the administrative model performs similarly to clinical models.

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 “Emergency Room Evaluation and Recommendations” (ER2) Tool for the Screening of Older Emergency Department Visitors With Major Neurocognitive Disorders: Results From the ER2 Database

2022 ◽  
Vol 12 ◽  
Author(s):  
Olivier Beauchet ◽  
Liam A. Cooper-Brown ◽  
Joshua Lubov ◽  
Gilles Allali ◽  
Marc Afilalo ◽  
...  
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Emergency Room ◽  
Receiver Operating Characteristic ◽  
Likelihood Ratio ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Neurocognitive Disorders ◽  
Risk Level ◽  
Receiver Operating

Purpose: The Emergency Room Evaluation and Recommendation (ER2) is an application in the electronic medical file of patients visiting the Emergency Department (ED) of the Jewish General Hospital (JGH; Montreal, Quebec, Canada). It screens for older ED visitors at high risk of undesirable events. The aim of this study is to examine the performance criteria (i.e., sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV], positive likelihood ratio [LR+], negative likelihood ratio [LR-] and area under the receiver operating characteristic curve [AUROC]) of the ER2 high-risk level and its “temporal disorientation” item alone to screen for major neurocognitive disorders in older ED visitors at the JGH.Methods: Based on a cross-sectional design, 999 older adults (age 84.9 ± 5.6, 65.1% female) visiting the ED of the JGH were selected from the ER2 database. ER2 was completed upon the patients' arrival at the ED. The outcomes were ER2's high-risk level, the answer to ER2's temporal disorientation item (present vs. absent), and the diagnosis of major neurocognitive disorders (yes vs. no) which was confirmed when it was present in a letter or other files signed by a physician.Results: The sensitivities of both ER2's high-risk level and temporal disorientation item were high (≥0.91). Specificity, the PPV, LR+, and AROC were higher for the temporal disorientation item compared to ER2's high-risk level, whereas a highest sensitivity, LR-, and NPV were obtained with the ER2 high-risk level. Both area under the receiver operating characteristic curves were high (0.71 for ER2's high-risk level and 0.82 for ER2 temporal disorientation item). The odds ratios (OR) of ER2's high-risk level and of temporal disorientation item for the diagnosis of major neurocognitive disorders were positive and significant with all OR above 18, the highest OR being reported for the temporal disorientation item in the unadjusted model [OR = 26.4 with 95% confidence interval (CI) = 17.7–39.3].Conclusion: Our results suggest that ER2 and especially its temporal disorientation item may be used to screen for major neurocognitive disorders in older ED users.

scholarly journals Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Mohammad M. Banoei ◽  
Roshan Dinparastisaleh ◽  
Ali Vaeli Zadeh ◽  
Mehdi Mirsaeidi
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Biochemical Markers ◽  
Mortality Prediction ◽  
Blood Biochemical ◽  
Mortality Prediction Model ◽  
Predict Hospital Mortality ◽  
Risk Patients

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

Building ICU In-hospital Mortality Prediction Model with Federated Learning

2020 ◽  
pp. 255-268
Author(s):  
Trung Kien Dang ◽  
Kwan Chet Tan ◽  
Mark Choo ◽  
Nicholas Lim ◽  
Jianshu Weng ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Model ◽  
Mortality Prediction ◽  
Mortality Prediction Model
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