scholarly journals Development and validation of a new scoring system for prognostic prediction of community-acquired pneumonia in older adults

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Masahiro Shirata ◽  
Isao Ito ◽  
Tadashi Ishida ◽  
Hiromasa Tachibana ◽  
Naoya Tanabe ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Scoring System ◽  
Older Patients ◽  
Characteristic Curve ◽  
External Validation ◽  
Albumin Level ◽  
Community Acquired Pneumonia ◽  
Derivation Cohort ◽  
Prognostic Prediction ◽  
New Scoring

AbstractThe discriminative power of CURB-65 for mortality in community-acquired pneumonia (CAP) is suspected to decrease with age. However, a useful prognostic prediction model for older patients with CAP has not been established. This study aimed to develop and validate a new scoring system for predicting mortality in older patients with CAP. We recruited two prospective cohorts including patients aged ≥ 65 years and hospitalized with CAP. In the derivation (n = 872) and validation cohorts (n = 1,158), the average age was 82.0 and 80.6 years and the 30-day mortality rate was 7.6% (n = 66) and 7.4% (n = 86), respectively. A new scoring system was developed based on factors associated with 30-day mortality, identified by multivariate analysis in the derivation cohort. This scoring system named CHUBA comprised five variables: confusion, hypoxemia (SpO2 ≤ 90% or PaO2 ≤ 60 mmHg), blood urea nitrogen ≥ 30 mg/dL, bedridden state, and serum albumin level ≤ 3.0 g/dL. With regard to 30-day mortality, the area under the receiver operating characteristic curve for CURB-65 and CHUBA was 0.672 (95% confidence interval, 0.607–0.732) and 0.809 (95% confidence interval, 0.751–0.856; P < 0.001), respectively. The effectiveness of CHUBA was statistically confirmed in the external validation cohort. In conclusion, a simpler novel scoring system, CHUBA, was established for predicting mortality in older patients with CAP.

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

scholarly journals Dynamic Predictive Scores for Cardiac Surgery-Associated Agitated Delirium: a single-center retrospective observational study

2021 ◽  
Author(s):  
Yu Tian ◽  
Yuefu Wang ◽  
Wei Zhao ◽  
Bingyang Ji ◽  
Xiaolin Diao ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Model ◽  
Scoring System ◽  
Early Recognition ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Derivation Cohort ◽  
Preoperative Prediction ◽  
Agitated Delirium ◽  
Dynamic Scoring

Abstract Background Prevention, screening, and early treatment are the mainstays of postoperative delirium management. Score system is an objective and effective tool to stratify potential delirium risk for patients undergoing cardiac surgery Methods Patients undergoing cardiac surgery from January 1, 2012, to January 1, 2019, were enrolled in our retrospective study. The patients were divided into a derivation cohort (n = 45,744) and a validation cohort (n = 11,436). The agitated delirium (AD) predictive systems were formulated using multivariate logistic regression analysis at three time points: preoperation, ICU admittance, and 24 hours after ICU admittance. Results The prevalence of AD after cardiac surgery in the whole cohort was 3.6% (2,085/57,180). The dynamic scoring system included preoperative LVEF ≤ 45%, serum creatinine > 100 umol/L, emergency surgery, coronary artery disease, hemorrhage volume > 600 mL, intraoperative platelet or plasma use, and postoperative LVEF ≤ 45%. The area under the receiver operating characteristic curve (AUC) values for AD prediction of 0.68 (preoperative), 0.74 (on the day of ICU admission), and 0.75 (postoperative). The Hosmer-Lemeshow test indicated that the calibration of the preoperative prediction model was poor (P = 0.01), whereas that of the pre- and intraoperative prediction model (P = 0.49) and the pre-, intra- and postoperative prediction model (P = 0.35) was good. Conclusions Using perioperative data, we developed a dynamic scoring system for predicting the risk of AD following cardiac surgery. The dynamic scoring system may improve early recognition of and interventions for AD.

Validation of the failure to maturation equation and proposal for a novel scoring system for arteriovenous fistula maturation in multiethnic Asian haemodialysis patients

2019 ◽  
Vol 21 (2) ◽  
pp. 169-175 ◽  
Author(s):  
Alvin Ren Kwang Tng ◽  
Kian Guan Lee ◽  
Ru Yu Tan ◽  
Suh Chien Pang ◽  
Marjorie Wai Yin Foo ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Arteriovenous Fistula ◽  
Odds Ratio ◽  
Scoring System ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Success Predictors ◽  
Fistula Maturation ◽  
Arteriovenous Fistula Maturation

Introduction: A successful arteriovenous fistula is essential for effective haemodialysis. We aim to validate the existing failure to maturation equation and to propose a new clinical scoring system by evaluating arteriovenous fistula success predictors. Methods: Data of end-stage renal disease patients initiated on haemodialysis from January 2010 to December 2012 were retrospectively obtained from medical records with follow-up until 1 January 2014. Application of the failure to maturation equation was evaluated. A nomogram was developed using arteriovenous fistula success predictors and was calibrated with a bootstrapping technique. Results: A total of 694 patients were included with mean duration of follow-up of 2.3 years. Arteriovenous fistula maturation was achieved by 542 patients (78%). Comparing our cohort with the failure to maturation cohort, there were statistically significant differences in mean age, ethnicity and presence of diabetes mellitus. The failure to maturation equation failed to predict arteriovenous fistula outcomes with area under the curve performance of 0.519 on a receiver operating characteristic curve. Multivariate logistic regression showed that Malay patients (odds ratio = 0.628; 95% confidence interval = 0.403–0.978; p < 0.05) and patients requiring preoperative vein mapping (odds ratio = 0.601; 95% confidence interval = 0.410–0.883; p < 0.01) had a lower chance of arteriovenous fistula success, whereas male gender (odds ratio = 1.526; 95% confidence interval = 1.040–2.241; p < 0.05) and presence of postoperative good thrill (odds ratio = 3.137; 95% confidence interval = 2.127–4.625; p < 0.0001) had a higher chance of arteriovenous fistula success. The derived nomogram predicted arteriovenous fistula success (odds ratio = 1.030; 95% confidence interval = 1.022–1.038; p < 0.0001) with the area under the curve of 0.695 on a receiver operating characteristic curve and an adequacy index of 99.86% ( p < 0.0001). Conclusion: The failure to maturation equation was not validated in our cohort. The clinical utility of our proposed arteriovenous fistula scoring system requires external validation in larger studies.

188 Predicting Posttraumatic Hydrocephalus: Derivation and Validation of a Risk Scoring System Based on Clinical Characteristics

Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 250-250
Author(s):  
Hao Chen
Keyword(s):  
High Risk ◽  
Scoring System ◽  
Clinical Characteristics ◽  
Injury Severity ◽  
Characteristic Curve ◽  
Derivation Cohort ◽  
Radiologic Findings ◽  
Fisher Grade ◽  
Risk Scoring ◽  
Risk Scoring System

Abstract INTRODUCTION Posttraumatic hydrocephalus (PTH) is a common complication of traumatic brain injury (TBI) and often has a high risk of clinical deterioration and worse outcomes. The incidence and risk factors for the development of PTH after decompressive craniectomy (DC) has been assessed in previous studies, but rare studies identify patients with higher risk for PTH among all TBI patients. This study aimed to develop and validate a risk scoring system to predict PTH after TBI. METHODS Demographics, injury severity, duration of coma, radiologic findings, and DC were evaluated to determine the independent predictors of PTH during hospitalization until 6 months following TBI through logistic regression analysis. A risk stratification system was created by assigning a number of points for each predictor and validated both internally and externally. The model accuracy was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS >Of 526 patients in the derivation cohort, 57 (10.84%) developed PTH during 6 months follow up. Age >50 (Odd ratio [OR] = 1.91, 95% confidence interval [CI] 1.09 3.75, 4 points), duration of coma = 1 w (OR = 5.68, 95% CI 2.57 13.47, 9 points), Fisher grade III (OR = 2.19, 95% CI 1.24 4.36, 5 points) or IV (OR = 3.87, 95% CI 1.93 8.43, 7 points), bilateral DC (OR = 6.13, 95% CI 2.82 18.14, 9 points), and extra herniation after DC (OR = 2.36, 95% CI 1.46 4.92, 5 points) were independently associated with PTH. Rates of PTH for the low- (0-12 points), intermediate- (13-22 points) and high-risk (23-34 points) groups were 1.16%, 35.19% and 78.57% (P < 0.0001). The corresponding rates in the validation cohort, where 17/175 (9.71%) developed PTH, were 1.35%, 37.50% and 81.82% (P < 0.0001). The risk score model exhibited good-excellent discrimination in both cohorts, with AUC of 0.839 versus 0.894 (derivation versus validation) and good calibration (Hosmer-Lemshow P = 0.56 versus 0.68). CONCLUSION A risk scoring system based on clinical characteristics accurately predicted PTH. This model will be useful to identify patients at high risk for PTH who may be candidates for preventive interventions, and to improve their outcomes.

scholarly journals Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2020-216001
Author(s):  
Juan Berenguer ◽  
Alberto M Borobia ◽  
Pablo Ryan ◽  
Jesús Rodríguez-Baño ◽  
Jose M Bellón ◽  
...  
Keyword(s):  
Prediction Model ◽  
Characteristic Curve ◽  
External Validation ◽  
Laboratory Data ◽  
Emergency Rooms ◽  
Disease Epidemiology ◽  
Mortality Probability ◽  
Hospitalised Patients ◽  
Prognostic Prediction ◽  
Development And Validation

ObjectiveTo develop and validate a prediction model of mortality in patients with COVID-19 attending hospital emergency rooms.DesignMultivariable prognostic prediction model.Setting127 Spanish hospitals.ParticipantsDerivation (DC) and external validation (VC) cohorts were obtained from multicentre and single-centre databases, including 4035 and 2126 patients with confirmed COVID-19, respectively.InterventionsPrognostic variables were identified using multivariable logistic regression.Main outcome measures30-day mortality.ResultsPatients’ characteristics in the DC and VC were median age 70 and 61 years, male sex 61.0% and 47.9%, median time from onset of symptoms to admission 5 and 8 days, and 30-day mortality 26.6% and 15.5%, respectively. Age, low age-adjusted saturation of oxygen, neutrophil-to-lymphocyte ratio, estimated glomerular filtration rate by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, dyspnoea and sex were the strongest predictors of mortality. Calibration and discrimination were satisfactory with an area under the receiver operating characteristic curve with a 95% CI for prediction of 30-day mortality of 0.822 (0.806–0.837) in the DC and 0.845 (0.819–0.870) in the VC. A simplified score system ranging from 0 to 30 to predict 30-day mortality was also developed. The risk was considered to be low with 0–2 points (0%–2.1%), moderate with 3–5 (4.7%–6.3%), high with 6–8 (10.6%–19.5%) and very high with 9–30 (27.7%–100%).ConclusionsA simple prediction score, based on readily available clinical and laboratory data, provides a useful tool to predict 30-day mortality probability with a high degree of accuracy among hospitalised patients with COVID-19.

scholarly journals Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19

2021 ◽  
Author(s):  
Brandon J. Webb ◽  
Nicholas M. Levin ◽  
Nancy Grisel ◽  
Samuel M. Brown ◽  
Ithan D. Peltan ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
High Risk ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Receiver Operator Characteristic Curve ◽  
Multivariable Logistic Regression Model ◽  
White Race ◽  
Derivation Cohort ◽  
Scoring Tool

AbstractBackgroundAccurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.MethodsAll consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.Results22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n=16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n=6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9).ConclusionA prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

scholarly journals Model to Predict Overall Survival in Patients With Hepatocellular Carcinoma After Curative Hepatectomy

2021 ◽  
Vol 10 ◽  
Author(s):  
Li-xiang Zhang ◽  
Pan-quan Luo ◽  
Lei Chen ◽  
Dong-da Song ◽  
A-man Xu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Scoring System ◽  
External Validation ◽  
Area Under The Curve ◽  
Serum Markers ◽  
Validation Dataset ◽  
Roc Curve Analysis ◽  
Scoring Model ◽  
New Scoring ◽  
Curative Hepatectomy

BackgroundThe prognosis of patients with hepatocellular carcinoma (HCC) remains difficult to accurately predict. The purpose of this study was to establish a prognostic model for HCC based on a novel scoring system.MethodsFive hundred and sixty patients who underwent a curative hepatectomy for treatment of HCC at our hospital between January 2007 and January 2014 were included in this study. Univariate and multivariate analyses were used to screen for prognostic risk factors. The nomogram construction was based on Cox proportional hazard regression models, and the development of the new scoring model was analyzed using receiver operating characteristic (ROC) curve analysis and then compared with other clinical indexes. The novel scoring system was then validated with an external dataset from a different medical institution.ResultsMultivariate analysis showed that tumor size, portal vein tumor thrombus (PVTT), invasion of adjacent tissues, microvascular invasion, and levels of fibrinogen and total bilirubin were independent prognostic factors. The new scoring model had higher area under the curve (AUC) values compared to other systems, and the C-index of the nomogram was highly consistent for evaluating the survival of HCC patients in the validation and training datasets, as well as the external validation dataset.ConclusionsBased on serum markers and other clinical indicators, a precise model to predict the prognosis of patients with HCC was developed. This novel scoring system can be an effective tool for both surgeons and patients.

scholarly journals A Hematological-Related Prognostic Scoring System for Patients With Newly Diagnosed Glioblastoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Chao Zhao ◽  
Long-Qing Li ◽  
Feng-Dong Yang ◽  
Ruo-Lun Wei ◽  
Min-Kai Wang ◽  
...  
Keyword(s):  
Scoring System ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
Newly Diagnosed ◽  
Training Set ◽  
Presenting Symptoms ◽  
Hematological Markers ◽  
Newly Diagnosed Glioblastoma ◽  
Validation Set ◽  
Prognostic Prediction

BackgroundGlioblastoma is the most common primary malignant brain tumor. Recent studies have shown that hematological biomarkers have become a powerful tool for predicting the prognosis of patients with cancer. However, most studies have only investigated the prognostic value of unilateral hematological markers. Therefore, we aimed to establish a comprehensive prognostic scoring system containing hematological markers to improve the prognostic prediction in patients with glioblastoma.Patients and MethodsA total of 326 patients with glioblastoma were randomly divided into a training set and external validation set to develop and validate a hematological-related prognostic scoring system (HRPSS). The least absolute shrinkage and selection operator Cox proportional hazards regression analysis was used to determine the optimal covariates that constructed the scoring system. Furthermore, a quantitative survival-predicting nomogram was constructed based on the hematological risk score (HRS) derived from the HRPSS. The results of the nomogram were validated using bootstrap resampling and the external validation set. Finally, we further explored the relationship between the HRS and clinical prognostic factors.ResultsThe optimal cutoff value for the HRS was 0.839. The patients were successfully classified into different prognostic groups based on their HRSs (P &lt; 0.001). The areas under the curve (AUCs) of the HRS were 0.67, 0.73, and 0.78 at 0.5, 1, and 2 years, respectively. Additionally, the 0.5-, 1-y, and 2-y AUCs of the HRS were 0.51, 0.70, and 0.79, respectively, which validated the robust prognostic performance of the HRS in the external validation set. Based on both univariate and multivariate analyses, the HRS possessed a strong ability to predict overall survival in both the training set and validation set. The nomogram based on the HRS displayed good discrimination with a C-index of 0.81 and good calibration. In the validation cohort, a high C-index value of 0.82 could still be achieved. In all the data, the HRS showed specific correlations with age, first presenting symptoms, isocitrate dehydrogenase mutation status and tumor location, and successfully stratified them into different risk subgroups.ConclusionsThe HRPSS is a powerful tool for accurate prognostic prediction in patients with newly diagnosed glioblastoma.

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