Nomogram Prediction of Severe Risk in Patients With COVID-19 Pneumonia: A Retrospective Study

Abstract Background COVID-19 elicits a range of different responses in patients and can manifest into mild to very severe cases in different individuals, depending on many factors. We aimed to establish a prediction model of severe risk in COVID-19 patients, to help clinicians achieve early prevention, intervention, and aid them in choosing effective therapeutic strategy. Methods We selected confirmed COVID-19 patients who admitted to First Hospital of Changsha city between January 29 and February 15, 2020 and collected their clinical data. Multivariate logical regression was used to identify the risk factors associated with severe risk. These factors were incorporated into the nomogram to establish the model. The ROC curve, calibration plot and decision curve were used to assess the performance of model. Results 239 patients were enrolled and 45 (18.83%) patients developed severe pneumonia. Univariate and multivariate analysis showed that age, COPD, shortness of breath, fatigue, creatine kinase, D-dimer, lymphocytes and h CRP were independent risk factors for severe risk in COVID-19 patients. Incorporating these factors, the nomogram achieved good concordance indexes of 0.873 (95% CI: 0.819–0.927), and well-fitted calibration plot curves. The model provided superior net benefit when clinical decision thresholds were between 10–70% predicted risk. Conclusions Using the model, clinicians can intervene early, improve therapeutic effects and reduce the severity of COVID-19, thus ensuring more targeted and efficient use of medical resources.

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Development and Validation of a Nomogram for Predicting Bladder Calculi Risk in Patients With Benign Prostatic Hyperplasia

10.21203/rs.3.rs-934816/v1 ◽

2021 ◽

Author(s):

Euxu Xie ◽

Xuelian Gu ◽

Chen Ma ◽

Li Guo ◽

Man Li ◽

...

Keyword(s):

Risk Factors ◽

Benign Prostatic Hyperplasia ◽

Characteristic Curve ◽

Prostatic Hyperplasia ◽

Calibration Plot ◽

Lasso Regression ◽

Net Benefit ◽

Bladder Calculi ◽

Validation Set ◽

Selection Operator

Abstract Objective To develop and validate a nomogram for predicting bladder calculi risk in patients with benign prostatic hyperplasia (BPH).Methods A total of 368 patients who underwent transurethral resection of the prostate (TURP) and had histologically proven BPH from January 2018 to January 2021 were retrospectively collected. Eligible patients were randomly assigned to the training and validation datasets. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal risk factors. A prediction model was established based on the selected characteristics. The performance of the nomogram was assessed by calibration plots and the area under the receiver operating characteristic curve (AUROC). Furthermore, decision curve analysis (DCA) was used to determine the net benefit rate of of the nomogram. Results Among 368 patients who met the inclusion criteria, older age, a history of diabetes and hyperuricemia, longer intravesical prostatic protrusion (IPP)and larger prostatic urethral angulation (PUA) were independent risk factors for bladder calculi in patients with BPH. These factors were used to develop a nomogram, which had a good identification ability in predicting the risk of bladder calculi in patients, with AUROCs of 0.911 (95% CI: 0.876–0.945) in the training set and 0.884 (95% CI: 0.820–0.948) in the validation set. The calibration plot showed that the model had good calibration. Moreover, DCA indicated that the model had a goodclinical benefit. Conclusion We developed and internally validated the first nomogram to date to help physicians assess the risk of bladder calculi in patients with BPH, which may help physicians improve individual interventions and make better clinical decisions.

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Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study

BMC Medical Imaging ◽

10.1186/s12880-020-00513-z ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yixing Yu ◽

Ximing Wang ◽

Min Li ◽

Lan Gu ◽

Zongyu Xie ◽

...

Keyword(s):

Risk Factors ◽

Calibration Curve ◽

Severity Score ◽

Early Stage ◽

Area Under The Curve ◽

Clinical Impact ◽

Imaging Data ◽

Net Benefit ◽

Training Cohort ◽

Independent Risk Factors

Abstract Background To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. Methods The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. Results In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5–9) was significantly higher than that of the mild group (4, IQR,2–5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889–0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867–1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. Conclusion The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

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Serum MMP-9 and SAA in the Diagnosis of Severe Pneumonia Caused by Radiotherapy of Esophageal Cancer

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/6032546 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Yu Zhou ◽

Li Liu ◽

Wenjun Gu

Keyword(s):

Risk Factors ◽

Esophageal Cancer ◽

Lung Function ◽

White Blood Cells ◽

Severe Pneumonia ◽

Diagnostic Value ◽

Control Group ◽

Radiation Group ◽

Severe Group ◽

Independent Risk Factors

Objective. To explore the relationship and diagnostic value of serum MMP-9 and SAA in severe pneumonia (sCAP) caused by radiotherapy of esophageal cancer. Methods. A total of 144 esophageal cancer patients who underwent radiotherapy in our hospital from April 2016 to February 2018 were collected. Among them, 58 patients without radiation pneumonitis (RP) were in the control group, 49 patients with grade 1∼2 RP were in the radiation group, and 37 patients with sCAP were in the severe group. The levels of serum MMP-9 and SAA in every group of patients were detected. The ROC curve was used to determine the diagnostic value of serum MMP-9 and SAA in the diagnosis of RP and sCAP. The correlation between serum MMP-9 and SAA and the patient’s lung function indexes was analyzed, and the logistic single-factor and multivariate analyses were performed to analyze the factors of sCAP in esophageal cancer radiotherapy. Results. PaO2, FVC, and FEV1 decreased in RP and sCAP, and PaCO2, white blood cells, serum MMP-9, and SAA levels increased ( P < 0.05 ); serum MMP-9 and SAA were negatively correlated with lung function ( P < 0.05 ); the AUC of serum MMP-9 and SAA in RP was 0.833 and 0.823, respectively, and the AUC of the two combined diagnosis of RP was 0.919. The AUC of serum MMP-9 and SAA in sCAP was 0.809 and 0.797, respectively, and the AUC of both combined diagnosis of sCAP was 0.873; logistics multivariate analysis found that serum MMP-9, serum SAA, double lung V5, and V20 were independent risk factors for sCAP caused by radiotherapy for esophageal cancer ( P < 0.05 ). Conclusion. Serum MMP-9 and SAA increase in RP and sCAP and are negatively correlated with lung function in patients with pneumonia. They are independent risk factors for severe pneumonia caused by radiotherapy of esophageal cancer and have good diagnostic value.

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Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting the Risk of Surgical Site Infection Following Minimally Invasive Transforaminal Lumbar Interbody Fusion Under Quadrant Channel

10.21203/rs.3.rs-882632/v1 ◽

2021 ◽

Author(s):

Haosheng Wang ◽

Tingting Fan ◽

Bo Yang ◽

Qiang Lin ◽

Wenle Li ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Interbody Fusion ◽

Clinical Decision Making ◽

Transforaminal Lumbar Interbody Fusion ◽

Clinical Decision ◽

Lumbar Interbody Fusion ◽

Regression Analyses ◽

Multivariate Logistic Regression ◽

Independent Risk Factors

Abstract Background: Machine Learning (ML) is rapidly growing in capability and is increasingly applied to model outcomes and complications in medicine. Surgical site infections (SSI) are a common postoperative complication in spinal surgery. This study aimed to develop and validate supervised ML algorithms for predicting the risk of SSI following minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) under the Quadrant channel.Methods: This single-central retrospective study included a total of 705 cases between May 2012 and October 2019. Data of patients who underwent MIS-TLIF under the Quadrant channel was extracted by the electronic medical record system. The patient’s clinical characteristics, surgery-related parameters, and routine laboratory tests were collected. Univariate and multivariate logistic regression analyses were used to screen and identify independent risk factors for SSI. Then, the independent risk factors were imported into six ML algorithms, including k-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), and Naïve Bayes (NB), to develop a prediction model for predicting the risk of SSI following MIS-TLIF under Quadrant channel. During the training process, 10-fold cross-validation was used for validation. Indices like the area under the receiver operating characteristic (AUC), sensitivity, specificity, and accuracy (ACC) were reported to test the performance of ML models.Results: Among the 705 patients, SSI occurred in 33 patients (4.68%). The univariate and multivariate logistic regression analyses showed that preoperative glycated hemoglobin A1c (HbA1c), estimated blood loss (EBL), preoperative albumin, body mass index (BMI), and age were all independent predictors of SSI. In predicting SSI, six ML models posted an average AUC of 0.60-0.80 and an ACC of 0.80-0.95, with the NB model standing out, registering an average AUC and an ACC of 0.78 and 0.90. Then, the feature importance of the NB model was reported.Conclusions: ML algorithms are impressive tools in clinical decision-making, which can achieve satisfactory prediction of SSI with the NB model performing the best. The NB model may help access the risk of SSI following MIS-TLIF under the Quadrant channel and facilitate clinical decision-making. However, future external validation is needed.

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Establishment of prediction score for hematoma expansion after intracerebral hemorrhage

10.21203/rs.2.306/v1 ◽

2019 ◽

Author(s):

Xiangyu Kong ◽

Wei Qian ◽

Jun Dong ◽

Zhiyuan Qian

Keyword(s):

Risk Factors ◽

High Risk ◽

Intracerebral Hemorrhage ◽

High Risk Group ◽

Prediction Score ◽

Hematoma Expansion ◽

Net Benefit ◽

Group Score ◽

Enhanced Ct ◽

Independent Risk Factors

Abstract Background: Identification of intracerebral hemorrhage (ICH) patients at risk of hematoma expansion(HE) could facilitate the selection of candidates likely to benefit from therapies aiming to minimize ICH growth. We, therefore, aimed to develop a prediction score for HE that can be quickly used during the critical phase. Methods: A retrospective analysis of clinical features of 317 ICH patients in the Second Affiliated Hospital of Soochow University from January, 2016 to May, 2018 was conducted. Independent risk factors of HE were obtained according to multiple logistic regression, and a prediction score was established and preliminarily evaluated. Results: History of anticoagulants, ultraearly hematoma growth≥2.7ml/h, GCS≤8, and non-enhanced CT signs (island sign, black hole sign, blend sign, niveau formation) exist one or more, were independent risk factors for HE (P <0.05).The C statistics of score was 0.854 (95%CI, 0.803~0.904); P < 0.001); calibration was outstanding (c2=3.323, P = 0.344); decision curve analysis showed the score was safe and reliable, with high net benefit. After dichotomized, the sensitivity, specificity and accuracy of the high-risk group (score≥4.5) were 0.77, 0.85 and 0.83, respectively. Conclusion The score can accurately identify high-risk individuals with HE, swift guide treatment decisions, and can also be used in clinical trials. Keywords: Intracerebral hemorrhage; Hematoma expansion; Non-enhanced CT; Prediction; Score.

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Nomogram to identify severe Coronavirus Disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study

10.21203/rs.3.rs-41684/v3 ◽

2020 ◽

Author(s):

Yixing Yu ◽

Ximing Wang ◽

Min Li ◽

Lan Gu ◽

Zongyu Xie ◽

...

Keyword(s):

Risk Factors ◽

Calibration Curve ◽

Severity Score ◽

Early Stage ◽

Area Under The Curve ◽

Clinical Impact ◽

Imaging Data ◽

Net Benefit ◽

Training Cohort ◽

Independent Risk Factors

Abstract Background: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics.Methods: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. 217 patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart.Results: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR:2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19.Conclusion: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

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Risk Factors of Very Severe Pneumonia Incidence in Children

Jurnal Respirologi Indonesia ◽

10.36497/jri.v40i4.147 ◽

2020 ◽

Vol 40 (4) ◽

pp. 243-250

Author(s):

Retno Asih Setyoningrum ◽

Hedi Mustiko

Keyword(s):

Risk Factors ◽

Severe Pneumonia ◽

Chi Square ◽

Cross Sectional ◽

Mortality And Morbidity ◽

Childhood Pneumonia ◽

Risk Of Mortality ◽

Chi Square Test ◽

Independent Risk Factors ◽

In Infants And Children

Background: Childhood pneumonia is a significant cause of mortality and morbidity in developing countries. About 7-13% of childhood pneumonia present with very severe pneumonia with a high risk of mortality. Identification of risk factors is necessary for early intervention and better management. Methods: Analytic observational study with a cross-sectional approach was conducted with subjects of pneumonia patients aged 2-59 months admitted in Respirology Ward and PICU Department of Pediatrics Dr. Soetomo Surabaya from January 2017 to December 2018. Results: A total of 253 were roled in this study. Group with very severe pneumonia are 140 patients and 113 patients with severe pneumonia. Independent risk factors were analysed by chi-square test and Continuity Correction. Independent risk factors that intluence the incidence of very severe pneumonia in infants and children are patient's age (PR=1.365;P=0.009;95% confidence interval (CI)=1.089-1.712), low birth weight (PR=1.380;P=0.010;95% CI=1.115-1,708), prematurity (PR=1,412;P=0.007;95% CI=1,141-1,747), exclusive breastfeeding (PR=1,434;P=0.007;95% CI=1,093-1,880), nutritional status (PR=2,412;P

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Therapeutic Effects of Microsurgical Clipping at Different Time Points on Intracranial Aneurysm and Prognostic Factors

Artery Research ◽

10.1007/s44200-021-00001-x ◽

2021 ◽

Author(s):

Ge Huang ◽

Yang Sun ◽

Jinhong Li ◽

Zhengyuan Xie ◽

Xiaoguang Tong

Keyword(s):

Risk Factors ◽

Prognostic Factors ◽

Postoperative Complications ◽

Poor Prognosis ◽

Therapeutic Effects ◽

Time Points ◽

Microsurgical Clipping ◽

History Of ◽

Prognosis Group ◽

Independent Risk Factors

Abstract Background Microsurgical clipping is effective for treating early rupture hemorrhage in intracranial aneurysm (IA) patients. We aimed to evaluate the therapeutic effects of microsurgical clipping at different time points on IA and to explore prognostic factors. Methods A total of 102 eligible patients were divided into good prognosis group (n = 87) and poor prognosis group (n = 15) according to Glasgow Outcome Scale (GOS) scores at discharge. The effects of microsurgical clipping at different time points (within 24 h, 48 h and 72 h) were compared. The incidence rates of postoperative complications in patients with different Hunt–Hess grades were compared. Prognostic factors were determined by multivariate logistic regression analysis. The nomogram prediction model was established based on independent risk factors and validated. Results The good recovery and success rates of complete aneurysm clipping were significantly higher in patients undergoing surgery within 24 h after rupture. The incidence rate of complications was significantly higher in patients with Hunt–Hess grade IV. Good and poor prognosis groups had significantly different age, history of hypertension, preoperative intracranial hematoma volume, aneurysm size, preoperative Hunt–Hess grade, later surgery, postoperative complications and National Institute of Health Stroke Scale (NIHSS) score, as independent risk factors for prognosis. The nomogram model predicted that poor prognosis rate was 14.71%. Conclusion Timing (within 24 h after rupture) microsurgical clipping benefits the prognosis of IA patients. Age, history of hypertension, preoperative intracranial hematoma volume, aneurysm size, preoperative Hunt–Hess grade, later surgery, postoperative complications and NIHSS score are independent risk factors for poor prognosis.

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Development of a nomogram for predicting the presence of combined pulmonary fibrosis and emphysema

BMC Pulmonary Medicine ◽

10.1186/s12890-021-01725-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xueting Yuan ◽

Jin Jin ◽

Xiaomao Xu

Keyword(s):

Risk Factors ◽

Pulmonary Fibrosis ◽

Early Recognition ◽

Pulmonary Function Tests ◽

Characteristic Curve ◽

Calibration Plot ◽

Multivariable Logistic Regression Analysis ◽

Significant Difference ◽

Lemeshow Test ◽

Good Concordance

Abstract Background In the clinical management of patients with combined pulmonary fibrosis and emphysema (CPFE), early recognition and appropriate treatment is essential. This study was designed to develop an accurate prognostic nomogram model to predict the presence of CPFE. Methods We retrospectively enrolled 85 patients with CPFE and 128 patients with idiopathic pulmonary fibrosis (IPF) between January 2015 and January 2020. Clinical characteristics were compared between groups. A multivariable logistic regression analysis was performed to identify risk factors for CPFE. Then, and a nomogram to predict the presence of CPFE was constructed for clinical use. Concordance index (C-index), area under the receiver operating characteristic curve (AUC), and calibration plot was used to evaluate the efficiency of the nomogram. Results Compared to the IPF group, the proportion of patients with male, smoking and allergies were significantly higher in the CPFE group. In terms of pulmonary function tests, patients with CPFE had lower FEV1/FVC%, DLCO/VA% pred, and higher RV, RV%pred, VC, VC%pred, TLC%pred, VA, TLC, TLC%pred, FVC, FVC%pred and FEV1 with significant difference than the other group. Positive correlation was found between DLCO and VA%, RV%, TLC% in patients with IPF but not in patients with CPFE. By multivariate analysis, male, smoking, allergies, FEV1/FVC% and DLCO/VA%pred were identified as independent predictors of the presence of CPFE. The nomogram was then developed using these five variables. After 1000 internal validations of bootstrap resampling, the C-index of the nomogram was 0.863 (95% CI 0.795–0.931) and the AUC was 0.839 (95% CI 0.764–0.913). Moreover, the calibration plot showed good concordance of incidence of CPFE between nomogram prediction and actual observation (Hosmer–Lemeshow test: P = 0.307). Conclusions Patients of CPFE have a characteristic lung function profile including relatively preserved lung volumes and ventilating function, contrasting with a disproportionate reduction of carbon monoxide transfer. By incorporating clinical risk factors, we created a nomogram to predict the presence of CPFE, which may serve as a potential tool to guide personalized treatment.

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Severe pneumonia in Chinese patients with systemic lupus erythematosus

Lupus ◽

10.1177/0961203320922609 ◽

2020 ◽

Vol 29 (7) ◽

pp. 735-742 ◽

Cited By ~ 2

Author(s):

Lingli Peng ◽

Yaling Wang ◽

Lin Zhao ◽

Ting Chen ◽

Anbin Huang

Keyword(s):

Risk Factors ◽

Systemic Lupus Erythematosus ◽

T Lymphocytes ◽

Lupus Erythematosus ◽

Central Nervous System Involvement ◽

Severe Pneumonia ◽

Chinese Patients ◽

Systemic Lupus ◽

Cell Counts ◽

Independent Risk Factors

Objective This study aimed to investigate the clinical characteristics and risk factors associated with severe pneumonia in systemic lupus erythematosus (SLE) patients from China. Method We performed a retrospective study in 112 hospitalized SLE patients who had had pneumonia for 8 years. The primary outcome was severe pneumonia, followed by descriptive analysis, group comparison and bivariate analysis. Results A total of 28 SLE patients were diagnosed with severe pneumonia, with a ratio of 5:23 between men and women. The mean age at diagnosis was 44.36 ± 12.389 years. The median disease duration was 72 months, and the median SLE Disease Activity Index 2000 (SLEDAI 2K) score was 8. The haematological system was the most affected, with an incidence of anaemia in 85.7% of cases and thrombocytopenia in 75% of cases, followed by lupus nephritis in 50% of cases and central nervous system involvement in 10.71% of cases. Cultured sputum specimens were positive in 17 (68%) SLE patients with severe pneumonia, of whom nine (36%) were cases of fungal infection, five (20%) were cases of bacterial infection and three (12%) were cases of mixed infection. Using multivariate logistic regression analysis, we concluded that a daily dosage of prednisone (>10 mg; odds ratio (OR) = 3.193, p = 0.005), a low percentage of CD4+ T lymphocytes (OR = 0.909, p = 0.000), a high SLEDAI 2K score (OR = 1.182, p = 0.001) and anaemia (OR = 1.182, p = 0.001) were all independent risk factors for pneumonia in SLE patients, while a low percentage of CD4+ T lymphocytes (OR = 0.908, p = 0.033), a daily dose of prednisone of >10 mg (OR = 35.67, p = 0.001) were independent risk factors for severe pneumonia in SLE patients. Conclusion Severe pneumonia is not rare in lupus, and is associated with high mortality and poor prognosis. Monitoring CD4+ T-cell counts and giving a small dose of glucocorticoids can reduce the occurrence of severe pneumonia and improve the prognosis of patients with lupus.

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