Prediction of 90-day mortality among sepsis patients based on a nomogram integrating diverse clinical indices

Abstract Background Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work aimed to define a nomogram for reliably predicting mortality. Methods We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots and decision curve analysis. Results Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

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Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices

BioMed Research International ◽

10.1155/2021/1023513 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Qingbo Zeng ◽

Longping He ◽

Nianqing Zhang ◽

Qingwei Lin ◽

Lincui Zhong ◽

...

Keyword(s):

Risk Factors ◽

Intensive Care ◽

Validation Cohort ◽

International Normalized Ratio ◽

Curve Analysis ◽

Individual Risk ◽

Decision Curve Analysis ◽

Training Cohort ◽

Receiver Operating Characteristic Curves ◽

Individual Risk Factors

Background. Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work was aimed at defining a nomogram for reliably predicting mortality. Methods. We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis. Results. Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. Conclusion. This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

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A novel nomogram to predict 90-day mortality of patients with sepsis

10.21203/rs.3.rs-396188/v1 ◽

2021 ◽

Author(s):

Qingbo Zeng ◽

Long-Ping He ◽

Nianqing Zhang ◽

Qin-Wei Lin ◽

lin-Cui Zhong ◽

...

Keyword(s):

Risk Factors ◽

Multivariate Logistic Regression Analysis ◽

Area Under The Curve ◽

Clinical Decision ◽

International Normalized Ratio ◽

Mortality Prediction ◽

Curve Analysis ◽

Decision Curve Analysis ◽

Training Cohort ◽

Prevalent Disease

Abstract Background Sepsis is a prevalent disease among intensive care units and continues to be a frequent cause of death. This study aimed to establish a nomogram for mortality prediction in patients with sepsis. Methods We carried out a retrospective, single-center study based on 231 patients with sepsis and data was collected from May 2018 to October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression analysis and a stepwise algorithm were performed to identify risk factors, which were presented with a predictive nomogram. The receiver operating characteristic (ROC), calibration plots and decision curve analysis (DCA) were used to estimate the performance of the nomogram in both the training and validation cohorts. Results A total of 231 patients with sepsis were enrolled in the study, and the 90-day mortality was 31.6%. There were 161 and 70 cases in training and validation cohorts respectively. Statistical analyses showed that Age, international normalized ratio (INR), lactate (Lac), and thrombomodulin (TM) were the risk factors for 90-day mortality. The area under the curve was 0.810 (95% CI, 0.739 to 0.881) in training cohort and 0.813(95% CI, 0.708 to 0.917) in the validation cohort. Calibration curve showed good performance of this nomogram. Decision curve analysis demonstrated that the nomogram was clinical utility. Conclusion This nomogram offering a probability of mortality for a given patient can benefit outcome improvement and clinicians in making clinical decision.

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A novel risk-predicted nomogram for sepsis associated-acute kidney injury among critically ill patients

BMC Nephrology ◽

10.1186/s12882-021-02379-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Shanglin Yang ◽

Tingting Su ◽

Lina Huang ◽

Lu-Huai Feng ◽

Tianbao Liao

Keyword(s):

Acute Kidney Injury ◽

Intensive Care ◽

Critically Ill ◽

Critically Ill Patients ◽

Validation Cohort ◽

Kidney Injury ◽

Curve Analysis ◽

High Morbidity ◽

Baseline Serum ◽

Decision Curve Analysis

Abstract Background Acute kidney injury (AKI) is a prevalent and severe complication of sepsis contributing to high morbidity and mortality among critically ill patients. In this retrospective study, we develop a novel risk-predicted nomogram of sepsis associated-AKI (SA-AKI). Methods A total of 2,871 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database were randomly assigned to primary (2,012 patients) and validation (859 patients) cohorts. A risk-predicted nomogram for SA-AKI was developed through multivariate logistic regression analysis in the primary cohort while the nomogram was evaluated in the validation cohort. Nomogram discrimination and calibration were assessed using C-index and calibration curves in the primary and external validation cohorts. The clinical utility of the final nomogram was evaluated using decision curve analysis. Results Risk predictors included in the prediction nomogram included length of stay in intensive care unit (LOS in ICU), baseline serum creatinine (SCr), glucose, anemia, and vasoactive drugs. Nomogram revealed moderate discrimination and calibration in estimating the risk of SA-AKI, with an unadjusted C-index of 0.752, 95 %Cl (0.730–0.774), and a bootstrap-corrected C index of 0.749. Application of the nomogram in the validation cohort provided moderate discrimination (C-index, 0.757 [95 % CI, 0.724–0.790]) and good calibration. Besides, the decision curve analysis (DCA) confirmed the clinical usefulness of the nomogram. Conclusions This study developed and validated an AKI risk prediction nomogram applied to critically ill patients with sepsis, which may help identify reasonable risk judgments and treatment strategies to a certain extent. Nevertheless, further verification using external data is essential to enhance its applicability in clinical practice.

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Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

Journal of Oncology ◽

10.1155/2021/4533175 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yuyuan Chen ◽

Changxing Chi ◽

Dedian Chen ◽

Sanjun Chen ◽

Binbin Yang ◽

...

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Curve Analysis ◽

Training Set ◽

Characteristic Curves ◽

Decision Curve Analysis ◽

Receiver Operating Characteristic Curves ◽

Independent Risk Factors

Background. The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients. Methods. Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created. The accuracy of the prognostic model in terms of survival was demonstrated by the area under the curve (AUC) of the receiver operating characteristic curves. In addition, the prediction consistency and clinical value of the nomogram were validated by calibration curves and decision curve analysis. Results. All included patients were divided into a training set (n = 308) and a validation set (n = 131). Based on univariate and multivariate analyses, we determined that age, race, grade, and historic stage were independent risk factors for overall survival after surgery in patients with fibrosarcoma. The AUC of the receiver operating characteristic curves demonstrated the high predictive accuracy of the prognostic nomogram, while the decision curve analysis revealed the high clinical application of the model. The calibration curves showed good agreement between predicted and observed survival rates. Conclusion. We developed a new nomogram to estimate 1-year, 3-year, and 5-year OS based on the independent risk factors. The model has good discriminatory performance and calibration ability for predicting the prognosis of patients with fibrosarcoma after surgery.

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Construction and validation of a prognostic nomogram for primary vulvar melanoma: a SEER population-based study

Japanese Journal of Clinical Oncology ◽

10.1093/jjco/hyaa137 ◽

2020 ◽

Vol 50 (12) ◽

pp. 1386-1394

Author(s):

Hongyu Zhou ◽

Xuan Zou ◽

Haoran Li ◽

Lihua Chen ◽

Xi Cheng

Keyword(s):

Overall Survival ◽

Validation Cohort ◽

Risk Group ◽

Curve Analysis ◽

Lymph Node Status ◽

Calibration Plot ◽

Decision Curve Analysis ◽

Training Cohort ◽

Clinical Applicability ◽

Vulvar Melanoma

Abstract Background Primary vulvar melanoma was an aggressive and poorly understood gynecological tumor. Unlike cutaneous melanoma, the incidence of vulvar melanoma was low but the survival was poor. There were no standard staging system and no census on treatment strategies of vulvar melanoma. Therefore, we aimed to conduct and validate a comprehensive prognostic model for predicting overall survival of vulvar melanoma and provide guidance for clinical management. Methods Patients diagnosed with vulvar melanoma between year 2004 and 2015 from Surveillance, Epidemiology, and End Result (SEER) database were randomized to training cohort and validation cohort. Multivariate survival analysis was performed to screen for independent factors of survival. A nomogram was established to predict overall survival of vulvar melanoma. Receiver operating characteristic curve and calibration plot were performed to verify the discrimination and accuracy of the model. The decision curve analysis was performed to verify the clinical applicability of the model. Results Total 737 patients with vulvar melanoma were randomized to the training cohort (n = 517) and the validation cohort (n = 220). Nomogram including age, race, tumor site, depth of tumor invasion, lymph node status, distant metastasis, tumor size, surgery, chemotherapy and radiotherapy was established and validated. The c-indexes for SEER stage, American Joint Committee on Cancer stage and this model were 0.561, 0.635 and 0.826, respectively. The high-risk group scored by this model had worse survival than the low-risk group (P < 0.001). Decision curve analysis revealed this model was superior in predicting survival. Conclusions Our model was deemed to be a useful tool for predicting overall survival of vulvar melanoma with good discrimination and clinical applicability. We hoped this model would assist gynecologists in clinical decision and management of patients diagnosed with vulvar melanoma.

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Development and External Validation of a Prognostic Nomogram for Angioimmunoblastic T-Cell Lymphoma

Blood ◽

10.1182/blood-2021-152054 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1440-1440

Author(s):

Hua Wang ◽

Bibo Fu ◽

Guanjun Chen

Keyword(s):

T Cell ◽

Validation Cohort ◽

Cell Lymphoma ◽

External Validation ◽

Prognostic Index ◽

Curve Analysis ◽

T Cell Lymphoma ◽

Survival Prediction ◽

Decision Curve Analysis ◽

Training Cohort

Abstract Introduction: Heterogeneity exists in prognosis of Angioimmunoblastic T-cell lymphoma (AITL) patients. Thus, a personalized prognostic model is crucial for survival prediction for each AITL patient. Nomogram is a powerful mathematical tool to predict survival. In this study, we aimed to develop a prognostic nomogram for AITL based on data from a large clinical database and validate it in an independent external cohort. In addition, we compared the accuracy of the nomogram with previous prognostic models used in AITL including International Prognostic Index (IPI) and Prognostic Index of T-cell lymphoma (PIT) model. Methods: Totally, 1071 patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database as the training cohort; 156 patients diagnosed from 2009-2021 in Sun Yat-Sen University Cancer Center or The First Affiliated Hospital of Guangzhou Medical University were recruited as the external validation cohort. 105 patients with IPI information in the training cohort were used to compare the nomogram and IPI. 156 patients in the external cohort were used to compare the nomogram and IPI or PIT. The Prognostic risk factors in the nomogram were identified by cox proportional hazards model. Concordance index (C-index) and calibration curves were used for internal and external validation. C-index and decision curve analysis (DCA) curves were used to compare the models. Results: Age, sex, systematic symptoms, Ann Arbor stage and chemotherapy were risk factors finally included to develop the nomogram. C-indexes of the nomogram were 0.676 and 0.652 in the training cohort and the validation cohort. Favorable agreement of nomogram-predicted and actual probability of overall survival (OS) was detected by calibration curves in both training and validation cohorts. In the cohort of 105 patients, C-indexes of the nomogram and IPI were 0.696 vs 0.616 (P<0.05); in the cohort of 156 patients, C-indexes were 0.652 vs 0.597 (P=0.08) of the nomogram and IPI while 0.652 vs 0.616 (P=0.176) of the nomogram and PIT. Decision curve analysis (DCA) showed superiority of the nomogram as compared with the IPI or the PIT model in both 105 and 156 patients' cohorts. A web calculator was published for convenient clinical application. Based on the prognostic scores calculated by the nomogram, three cut points were identified by X-tile program to establish a classification system that could significantly distinguish patients in four risk groups. Conclusion: We establish a nomogram for survival prediction of AITL, which may assist in treatment strategy making and survival consultation. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

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Multidrug-Resistant Pseudomonas aeruginosa: Risk Factors and Clinical Impact

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.50.1.43-48.2006 ◽

2006 ◽

Vol 50 (1) ◽

pp. 43-48 ◽

Cited By ~ 348

Author(s):

Valerie Aloush ◽

Shiri Navon-Venezia ◽

Yardena Seigman-Igra ◽

Shaltiel Cabili ◽

Yehuda Carmeli

Keyword(s):

Risk Factors ◽

Pseudomonas Aeruginosa ◽

Intensive Care ◽

Hospital Stay ◽

Clinical Outcomes ◽

Protective Factor ◽

Multidrug Resistant ◽

Clinical Impact ◽

Individual Risk ◽

Individual Risk Factors

ABSTRACT Pseudomonas aeruginosa, a leading nosocomial pathogen, may become multidrug resistant (MDR). Its rate of occurrence, the individual risk factors among affected patients, and the clinical impact of infection are undetermined. We conducted an epidemiologic evaluation and molecular typing using pulsed-field gel electrophoresis (PFGE) of 36 isolates for 82 patients with MDR P. aeruginosa and 82 controls matched by ward, length of hospital stay, and calendar time. A matched case-control study identified individual risk factors for having MDR P. aeruginosa, and a retrospective matched-cohort study examined clinical outcomes of such infections. The 36 isolates belonged to 12 PFGE clones. Two clones dominated, with one originating in an intensive care unit (ICU). Cases and controls had similar demographic characteristics and numbers of comorbid conditions. A multivariate model identified ICU stay, being bedridden, having high invasive devices scores, and being treated with broad-spectrum cephalosporins and with aminoglycosides as significant risk factors for isolating MDR P. aeruginosa. Having a malignant disease was a protective factor (odds ratio [OR] = 0.2; P = 0.03). MDR P. aeruginosa was associated with severe outcomes compared to controls, including increased mortality (OR = 4.4; P = 0.04), hospital stay (hazard ratio, 2; P = 0.001), and requirement for procedures (OR = 5.4; P = 0.001). The survivors functioned more poorly at discharge than the controls, and more of the survivors were discharged to rehabilitation centers or chronic care facilities. The epidemiology of MDR P. aeruginosa is complex. Critically ill patients that require intensive care and are treated with multiple antibiotic agents are at high risk. MDR P. aeruginosa infections are associated with severe adverse clinical outcomes.

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A Prediction Model Based on Noninvasive Indicators to Predict the 8-Year Incidence of Type 2 Diabetes in Patients with Nonalcoholic Fatty Liver Disease: A Population-Based Retrospective Cohort Study

BioMed Research International ◽

10.1155/2021/5527460 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Xintian Cai ◽

Qing Zhu ◽

Yuanyuan Cao ◽

Shasha Liu ◽

Mengru Wang ◽

...

Keyword(s):

Type 2 Diabetes ◽

Cohort Study ◽

Fatty Liver Disease ◽

Validation Cohort ◽

Japanese Patients ◽

Curve Analysis ◽

Decision Curve Analysis ◽

Training Cohort ◽

Nonalcoholic Fatty Liver

Background. The prevention of type 2 diabetes (T2D) and its associated complications has become a major priority of global public health. In addition, there is growing evidence that nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of diabetes. Therefore, the purpose of this study was to develop and validate a nomogram based on independent predictors to better assess the 8-year risk of T2D in Japanese patients with NAFLD. Methods. This is a historical cohort study from a collection of databases that included 2741 Japanese participants with NAFLD without T2D at baseline. All participants were randomized to a training cohort ( n = 2058 ) and a validation cohort ( n = 683 ). The data of the training cohort were analyzed using the least absolute shrinkage and selection operator method to screen the suitable and effective risk factors for Japanese patients with NAFLD. A cox regression analysis was applied to build a nomogram incorporating the selected features. The C-index, receiver operating characteristic curve (ROC), calibration plot, decision curve analysis, and Kaplan-Meier analysis were used to validate the discrimination, calibration, and clinical usefulness of the model. The results were reevaluated by internal validation in the validation cohort. Results. We developed a simple nomogram that predicts the risk of T2D for Japanese patients with NAFLD by using the parameters of smoking status, waist circumference, hemoglobin A1c, and fasting blood glucose. For the prediction model, the C-index of training cohort and validation cohort was 0.839 (95% confidence interval (CI), 0.804-0.874) and 0.822 (95% CI, 0.777-0.868), respectively. The pooled area under the ROC of 8-year T2D risk in the training cohort and validation cohort was 0.811 and 0.805, respectively. The calibration curve indicated a good agreement between the probability predicted by the nomogram and the actual probability. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusions. We developed and validated a nomogram for the 8-year risk of incident T2D among Japanese patients with NAFLD. Our nomogram can effectively predict the 8-year incidence of T2D in Japanese patients with NAFLD and helps to identify people at high risk of T2D early, thus contributing to effective prevention programs for T2D.

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Establishment and validation of prognosis model for patients with cerebral contusion

BMC Neurology ◽

10.1186/s12883-021-02482-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yufeng Zhu ◽

Xiaoqing Jin ◽

Lulu Xu ◽

Pei Han ◽

Shengwu Lin ◽

...

Keyword(s):

Prediction Model ◽

Validation Cohort ◽

Characteristic Curve ◽

Third Ventricle ◽

Curve Analysis ◽

Imaging Features ◽

Lasso Regression ◽

Cerebral Contusion ◽

Decision Curve Analysis ◽

Training Cohort

Abstract Background And Objective Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients. Methods A total of 426 patients with cerebral contusion (CC) admitted to the People’s Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026–0.952) in the training cohort and (0.93, 95% CI, 0.032–0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients. Conclusion This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.

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Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection

Frontiers in Oncology ◽

10.3389/fonc.2021.638362 ◽

2021 ◽

Vol 11 ◽

Author(s):

Liebin Huang ◽

Bao Feng ◽

Yueyue Li ◽

Yu Liu ◽

Yehang Chen ◽

...

Keyword(s):

Risk Factors ◽

Gastric Cancer ◽

Computed Tomography ◽

Validation Cohort ◽

Radical Resection ◽

Curve Analysis ◽

Internal Validation ◽

Training Cohort ◽

Clinical Model ◽

Radiomics Signature

ObjectiveAccurate prediction of postoperative recurrence risk of gastric cancer (GC) is critical for individualized precision therapy. We aimed to investigate whether a computed tomography (CT)-based radiomics nomogram can be used as a tool for predicting the local recurrence (LR) of GC after radical resection.Materials and Methods342 patients (194 in the training cohort, 78 in the internal validation cohort, and 70 in the external validation cohort) with pathologically proven GC from two centers were included. Radiomics features were extracted from the preoperative CT imaging. The clinical model, radiomics signature, and radiomics nomogram, which incorporated the radiomics signature and independent clinical risk factors, were developed and verified. Furthermore, the performance of these three models was assessed by using the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsThe radiomics signature, which was comprised of two selected radiomics features, namely, contrast_GLCM and dissimilarity_GLCM, showed better performance than the clinical model in predicting the LR of GC, with AUC values of 0.83 in the training cohort, 0.84 in the internal validation cohort, and 0.73 in the external cohort, respectively. By integrating the independent clinical risk factors (N stage, bile acid duodenogastric reflux and nodular or irregular outer layer of the gastric wall) into the radiomics signature, the radiomics nomogram achieved the highest accuracy in predicting LR, with AUC values of 0.89, 0.89 and 0.80 in the three cohorts, respectively. DCA in the validation cohort showed that radiomics nomogram added more net benefit than the clinical model within the range of 0.01-0.98.ConclusionThe CT-based radiomics nomogram has the potential to predict the LR of GC after radical resection.

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