Development And Validation of a Risk Scoring System to Predict Pneumothorax in CT-Guided Percutaneous Transthoracic Needle Biopsy

Abstract Objective: To develop and validate a risk scoring system using variables easily obtained for the prediction of pneumothorax in CT-guided percutaneous transthoracic needle biopsy (PTNB).Methods: The derivation cohort was comprised of 1001 patients who underwent CT-guided PTNB. Multivariate logistic regression was used to identify risk factors for pneumothorax, which were treated as the foundation to develop the risk scoring system. To validate the system, a validation cohort group of 230 patients was enrolled.Results: Age, puncture times, puncture depth, smoking index, number of specimens, bleeding from the needle path, and lobular lesion were identified as risk factors in the derivation cohort. A risk scoring system (Hosmer-Lemeshow goodness-of-fit test p =0.33) was developed. The area under the receiver operating characteristic curve (AUROC) was 0.601 by using the risk score system. This risk score system demonstrated a better diagnostic effect with increasing age. In the group of patients older than 80 years, the AUROC was 0.76, showing good predictive power. This risk scoring system was confirmed in the validation cohort with an AUROC of 0.736.Conclusion: This scoring system has a good predictive effect in both derivation and validation cohort.

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Development and Validation of a New Risk Scoring System for Cancer Patients with Suspected Infection

10.21203/rs.3.rs-720132/v1 ◽

2021 ◽

Author(s):

Bora Chae ◽

Seonok Kim ◽

Yoon-Seon Lee

Keyword(s):

Cancer Patients ◽

Scoring System ◽

Validation Cohort ◽

Performance Status ◽

Eastern Cooperative Oncology Group ◽

Peripheral Oxygen Saturation ◽

Suspected Infection ◽

Risk Scoring ◽

Risk Scoring System ◽

New Scoring

Abstract Purpose: This study aimed to develop a new prognostic model for predicting 30-day mortality in cancer patients with suspected infection.Methods: This study is a retrospective cohort study and was conducted from August 2019 to December 2019 at a single center. Adult active cancer patients with suspected infection were enrolled among visitors to the emergency room (ER). Logistic regression analysis was used to identify potential predictors for a new model. Results: A total of 899 patients were included; 450 in the development cohort and 449 in the validation cohort. Six independent variables predicted 30-day mortality: Eastern Cooperative Oncology Group (ECOG) performance status (PS), peripheral oxygen saturation (SpO2), creatinine, bilirubin, C-reactive protein (CRP), and lactate. The C-statistic of the new scoring system was 0.799 in the development cohort and 0.793 in the validation cohort. The C-statistics in the development cohort was significantly higher than those of SOFA [0.723 (95% CI: 0.663–0.783)], qSOFA [0.596 (95% CI: 0.537–0.655)], and SIRS [0.547 (95% CI: 0.483–0.612)]. Conclusions: The discriminative capability of the new cancer-specific risk scoring system was good in cancer patients with suspected infection. The new scoring system was superior to SOFA, qSOFA, and SIRS in predicting mortality.

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

Neurosurgery ◽

10.1093/neuros/nyx417.188 ◽

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.

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A Risk Scoring System to Predict Progression to Severe Pneumonia in Patients with Covid-19

10.21203/rs.3.rs-40573/v1 ◽

2020 ◽

Author(s):

Ji Yeon Lee ◽

Byung-Ho Nam ◽

Mhinjine Kim ◽

Jongmin Hwang ◽

Jin Young Kim ◽

...

Keyword(s):

High Risk ◽

Sensitivity And Specificity ◽

Scoring System ◽

Resource Constraints ◽

Validation Cohort ◽

Severe Pneumonia ◽

High Risk Group ◽

Risk Scores ◽

Risk Scoring ◽

Risk Scoring System

Abstract Rapid outbreak of coronavirus disease 2019 (Covid-19) raised major concern regarding medical resource constraints. We constructed and validated a scoring system for early prediction of progression to severe pneumonia in patients with Covid-19. A total of 561 patients from a Covid-19 designated hospital in Daegu, South Korea were randomly divided into two cohorts: development cohort (N=421) and validation cohort (N=140). We used multivariate logistic regression to identify four independent risk predictors for progression to severe pneumonia and constructed a risk scoring system by giving each factor a number of scores corresponding to its regression coefficient. We calculated risk scores for each patient and defined two groups: low risk (0 to 8 points) and high risk (9 to 20 points). In the development cohort, the sensitivity and specificity were 83.8% and 78.9%. In the validation cohort, the sensitivity and specificity were 70.8% and 79.3%, respectively. The C-statistics was 0.884 (95% CI, 0.833-0.934) in the development cohort and 0.828 (95% CI, 0.733-0.923) in the validation cohort. This risk scoring system is useful to identify high-risk group for progression to severe pneumonia in Covid-19 patients and can prevent unnecessary overuse of medical care in limited-resource settings.

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Development and Validation of a Risk Score for Predicting 1-Year Mortality Risk of STEMI Based on a Nationwide Cohort in China

10.21203/rs.3.rs-236809/v1 ◽

2021 ◽

Author(s):

Si Chen ◽

Qianzi Che ◽

Yan Zhang ◽

Jia Jia ◽

Yiqun Wu ◽

...

Keyword(s):

Myocardial Infarction ◽

Risk Score ◽

Mortality Risk ◽

Scoring System ◽

Validation Cohort ◽

Predictive Score ◽

Inferior Wall ◽

Risk Scoring ◽

Risk Scoring System

Abstract BackgroundA risk assessment for identifying long-term risk of post-discharge mortality in Chinese STEMI patients remains a concern. The aim of this study is to establish a bedside available risk scoring system for predicting 1-year mortality risk among Chinese STEMI patients. Methods STEMI patients(n=12611) were enrolled from the China STEMI Care Project Phase 2(CSCAP-2) collected between 2015 and 2016. Confounding bias was controlled using propensity score matching. Epidemiological, clinical, laboratory, and imaging variables, treatment strategy and medicine records were screened using extreme gradient boosting and nomogram according to the hazard ratio of Cox regression analysis to construct a predictive score. A validation cohort included 7342 patients collected in 2017 from CSCAP-2 was analyzed using receiver ROC and expectation (E)/observation (O) ratio to validate the risk scoring system. Results From 39 potential predictors, 8 variables were independent predictive factor and were included in the risk score: Killip class, early reperfusion strategy, Non-PCI intraoperative anticoagulants, heart rate, gender, age, anterior-wall myocardial infarction (AWMI) and inferior-wall myocardial infarction (IWMI). The new model demonstrated an excellent discrimination and calibration. The c-statistic and E/O ratio were 0.87(95%CI, 0.80-0.93) and 1.14(95%CI, 0.93-1.39) in the train set, 0.88 (95%CI, 0.78-0.96) and 1.15(95%CI, 0.85-1.56) in the test set, meanwhile, 0.89(95%CI, 0.82-0.95) and 1.00(95%CI, 0.81-1.23) in the validation cohort. The score has better sensitivity than the GRACE score and can recognize risk stratification among STEMI patients (P<0.001).Conclusions We developed a risk scoring system for predicting 1-year mortality risk of STEMI in a large Chinese population. The new score is easy-to-use and demonstrating a good discriminatory accuracy in predicting both short-term and long-term mortality risk in Chinese patients with STEMI.

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976. Development and Validation of a Risk Score for Predicting Cardiovascular Events in HIV-Infected Patients

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz359.078 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S35-S36

Author(s):

Styliani Karanika ◽

Theodoros Karantanos ◽

Herman Carneiro ◽

Sabrina A Assoumou

Keyword(s):

Cardiovascular Disease ◽

Logistic Regression ◽

Scoring System ◽

Validation Cohort ◽

Assessment Tools ◽

Operating Characteristics ◽

Cvd Risk ◽

Risk Scoring ◽

Development And Validation ◽

Risk Scoring System

Abstract Background HIV-infected individuals are at higher risk for developing cardiovascular disease (CVD). We aimed to develop a model to predict 10-year cardiovascular (CV) risk given that commonly used CVD risk assessment tools might not be accurate for HIV-infected patients. Methods We conducted a retrospective cohort study of HIV-infected patients seen at Boston Medical Center between March 2012 and January 2017. Exclusion criteria are shown in Figure 1. Patients were divided into model development and validation cohorts. Logistic regression was used to create a risk model for CV events using data from the development cohort. The relationship between risk factors and CVD risk was summarized using a point-based risk-scoring system. Areas under the receiver-operating-characteristics curve (AUC) were used to evaluate model discrimination. The model was subsequently tested using the validation cohort. Results Of 3,867 eligible HIV-infected patients, 1,914 individuals met inclusion criteria (Figure 1). There were 256 CV events in the development cohort. Ten independent prognostic factors were incorporated into the prediction function (Pmodel < 0.001). The model had excellent discrimination for CVD risk [AUC 0.94; (95% CI:0.93–0.96)] (Figure 2) and included the following variables: male sex (P < 0.001), African-American ethnicity (P = 0.023), current age (P = 0.020), age at HIV diagnosis (P = 0.006), peak HIV viral load (P = 0.012), nadir CD4 lymphocyte count (P < 0.001), hypertension (P < 0.001), hyperlipidemia (P = 0.001), diabetes (P < 0.001), and chronic kidney disease (P < 0.001). Scoring system and score sheets of risk estimates were developed to predict CV events in a 10-year follow-up period (Figures 3 and 4). The 10-parameter multiple logistic regression model also had excellent discrimination [AUC 0.96; (95% CI: 0.89–0.99)] when applied to the validation cohort. Conclusion We developed and validated a risk-scoring system based on 10 clinical factors that accurately predict the 10-year risk for CV events in an HIV-infected population. This assessment tool may provide clinicians with a rapid assessment of cardiovascular disease risk among HIV-infected patients and inform prevention measures during the era of effective antiretroviral therapy. Disclosures All Authors: No reported Disclosures.

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A Risk Scoring System Based on Tumor Microenvironment Cells to Predict Prognosis and Immune Activity in Triple-Negative Breast Cancer

10.21203/rs.3.rs-539886/v1 ◽

2021 ◽

Author(s):

Anli Yang ◽

Minqing Wu ◽

Mengqian Ni ◽

Lijuan Zhang ◽

Mingyue Li ◽

...

Keyword(s):

Breast Cancer ◽

T Cells ◽

Tumor Microenvironment ◽

Scoring System ◽

Triple Negative ◽

Validation Cohort ◽

Risk Group ◽

Training Cohort ◽

Risk Scoring ◽

Risk Scoring System

Abstract The tumor microenvironment (TME) interacting with the malignant cells plays a vital role in cancer development. Herein, we aim to establish and verify a scoring system based on the characteristics of TME cells for prognosis prediction and personalized treatment guidance in patients with triple-negative breast cancer (TNBC). 158 TNBC samples from The Cancer Genome Atlas (TCGA) were included as the training cohort, and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (N = 297), as well as GSE58812 (N = 107), were included as the validation cohort. The enrichment scores of 64 immune and stromal cells were estimated by the xCell algorithm. In the training cohort, cells with prognostic significance were found out using univariate Cox regression analysis and further applied to the random survival forest (RSF) model. Basing on the scores of M2 macrophages, CD8+ T cells, and CD4+ memory T cells, a risk scoring system was constructed, which divided TNBC patients into 4 phenotypes (M2low, M2highCD8+ThighCD4+Thigh, M2highCD8+ThighCD4+Tlow, and M2highCD8+Tlow) and 2 groups. The low-risk group had superior survival outcomes than the high-risk one, which was further confirmed in the validation cohort. Moreover, in the low-risk group, immune-related pathways were significantly enriched, and a higher level of antitumoral immune cells and immune checkpoint molecules, including PD-L1, PD-1, and CTLA-4, could be observed. Additionally, consistent results were achieved in the SYSUCC cohort when the scoring system was applied. In summary, this novel scoring system might predict the survival and immune activity of patients and might serve as a potential index for immunotherapy.

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