scholarly journals Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

2020 ◽  
Author(s):  
Bangbo Zhao ◽  
Yingxin Wei ◽  
Wenwu Sun ◽  
Cheng Qin ◽  
Xingtong Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Acute Abdomen ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Multivariable Logistic Regression

ABATRACTIMPORTANCEIn the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively.OBJECTIVETo develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease 2019 (COVID-19).DESIGNDiagnostic model based on retrospective case series.SETTINGTwo hospitals in Wuhan and Beijing, China.PTRTICIPANTS584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020.METHODSLASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram.RESULTSSix potential COVID-19 prediction variables were selected and the variable abdominal pain was excluded for overmuch weight. The five potential predictors, including fever, chest computed tomography (CT), leukocytes (white blood cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p ≤0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale.CONCLUSIONSWe established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.

scholarly journals Distinguishing Coronavirus Disease 2019 Patients From General Surgery Emergency Patients With the CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

2021 ◽  
Vol 8 ◽  
Author(s):  
Bangbo Zhao ◽  
Yingxin Wei ◽  
Wenwu Sun ◽  
Cheng Qin ◽  
Xingtong Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Acute Abdomen ◽  
Roc Curves ◽  
Curve Analysis ◽  
Nucleic Acid Detection ◽  
Multivariable Logistic Regression Analysis ◽  
Lasso Regression ◽  
Decision Curve Analysis ◽  
Multivariable Logistic Regression

Background: During the epidemic, surgeons cannot identify infectious acute abdomen patients with suspected coronavirus disease 2019 (COVID-19) immediately using the current widely applied methods, such as double nucleic acid detection. We aimed to develop and validate a prediction model, presented as a nomogram and scale, to identify infectious acute abdomen patients with suspected COVID-19 more effectively and efficiently.Methods: A total of 584 COVID-19 patients and 238 infectious acute abdomen patients were enrolled. The least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses were conducted to develop the prediction model. The performance of the nomogram was evaluated through calibration curves, Receiver Operating Characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves in the training and validation cohorts. A simplified screening scale and a management algorithm were generated based on the nomogram.Results: Five potential COVID-19 prediction variables, fever, chest CT, WBC, CRP, and PCT, were selected, all independent predictors of multivariable logistic regression analysis, and the nomogram, named the COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration, and it was validated in the validation cohort. Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified as the CIAAD scale.Conclusion: We established an easy and effective screening model and scale for surgeons in the emergency department to use to distinguish COVID-19 patients. The algorithm based on the CIAAD scale will help surgeons more efficiently manage infectious acute abdomen patients suspected of having COVID-19.

scholarly journals The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

2022 ◽  
Vol 9 ◽  
Author(s):  
Wenle Li ◽  
Shengtao Dong ◽  
Bing Wang ◽  
Haosheng Wang ◽  
Chan Xu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Predictive Ability ◽  
Multivariable Logistic Regression Analysis ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Multivariable Logistic Regression

Background: This study aimed to construct a clinical prediction model for osteosarcoma patients to evaluate the influence factors for the occurrence of lymph node metastasis (LNM).Methods: In our retrospective study, a total of 1,256 patients diagnosed with chondrosarcoma were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database (training cohort, n = 1,144) and multicenter dataset (validation cohort, n = 112). Both the univariate and multivariable logistic regression analysis were performed to identify the potential risk factors of LNM in osteosarcoma patients. According to the results of multivariable logistic regression analysis, A nomogram were established and the predictive ability was assessed by calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier plot of overall survival (OS) was plot and a web calculator visualized the nomogram.Results: Five independent risk factors [chemotherapy, surgery, lung metastases, lymphatic metastases (M-stage) and tumor size (T-stage)] were identified by multivariable logistic regression analysis. What's more, calibration plots displayed great power both in training and validation group. DCA presented great clinical utility. ROCs curve provided the predictive ability in the training cohort (AUC = 0.805) and the validation cohort (AUC = 0.808). Moreover, patients in LNN group had significantly better survival than that in LNP group both in training and validation group.Conclusion: In this study, we constructed and developed a nomogram with risk factors, which performed well in predicting risk factors of LNM in osteosarcoma patients. It may give a guide for surgeons and oncologists to optimize individual treatment and make a better clinical decision.

scholarly journals Impact of histologic chorioamnionitis on pulmonary hypertension and respiratory outcomes in preterm infants

2018 ◽  
Vol 8 (2) ◽  
pp. 204589401876016 ◽  
Author(s):  
Sook Kyung Yum ◽  
Min-Sung Kim ◽  
Yoojin Kwun ◽  
Cheong-Jun Moon ◽  
Young-Ah Youn ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Preterm Infants ◽  
Logistic Regression Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Multivariable Logistic Regression ◽  
Histologic Chorioamnionitis ◽  
Respiratory Outcomes ◽  
Lower Birthweight

We aimed to evaluate the association between the presence of histologic chorioamnionitis (HC) and development of pulmonary hypertension (PH) during neonatal intensive care unit (NICU) stay. Data of preterm infants born at 32 weeks of gestation or less were reviewed. The development of PH and other respiratory outcomes were compared according to the presence of HC. Potential risk factors associated with the development of PH during NICU stay were used for multivariable logistic regression analysis. A total of 188 infants were enrolled: 72 in the HC group and 116 in the no HC group. The HC group infants were born at a significantly shorter gestational age and lower birthweight, with a greater proportion presenting preterm premature rupture of membrane (pPROM) > 18 h before delivery. More infants in the HC group developed pneumothorax ( P = 0.008), and moderate and severe bronchopulmonary dysplasia (BPD; P = 0.001 and P = 0.006, respectively). PH in the HC group was significantly more frequent compared to the no HC group (25.0% versus 8.6%, P = 0.002). Based on a multivariable logistic regression analysis, birthweight ( P = 0.009, odds ratio [OR] = 0.997, 95% confidence interval [CI] = 0.995–0.999), the presence of HC ( P = 0.047, OR = 2.799, 95% CI = 1.014–7.731), and duration of invasive mechanical ventilation (MV) > 14 days ( P = 0.015, OR = 8.036, 95% CI = 1.051–43.030) were significant factors. The presence of HC and prolonged invasive MV in infants with lower birthweight possibly synergistically act against preterm pulmonary outcomes and leads to the development of PH. Verification of this result and further investigation to establish effective strategies to prevent or ameliorate these adverse outcomes are needed.

scholarly journals Hounsfield unit attenuation value can differentiate pyonephrosis from hydronephrosis and predict septic complications in patients with obstructive uropathy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Luca Boeri ◽  
Irene Fulgheri ◽  
Franco Palmisano ◽  
Elena Lievore ◽  
Vito Lorusso ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Obstructive Uropathy ◽  
Hounsfield Unit ◽  
Infectious Complications ◽  
Multivariable Logistic Regression Analysis ◽  
Ureteral Catheter ◽  
Septic Complications ◽  
Multivariable Logistic Regression

Abstract We aimed to assess the role of computerized tomography attenuation values (Hounsfield unit—HU) for differentiating pyonephrosis from hydronephrosis and for predicting postoperative infectious complications in patients with obstructive uropathy. We analysed data from 122 patients who underwent nephrostomy tube or ureteral catheter placement for obstructive uropathy. A radiologist drew the region of interest for quantitative measurement of the HU values in the hydronephrotic region of the affected kidney. Descriptive statistics and logistic regression models tested the predictive value of HU determination in differentiating pyonephrosis from hydronephrosis and in predicting postoperative sepsis. A HU cut-off value of 6.3 could diagnose the presence of pyonephrosis with 71.6% sensitivity and 71.5% specificity (AUC 0.76; 95%CI: 0.66–0.85). At multivariable logistic regression analysis HU ≥ 6.3 (p ≤ 0.001) was independently associated with pyonephrosis. Patients who developed sepsis had higher HU values (p ≤ 0.001) than those without sepsis. A HU cut-off value of 7.3 could diagnose the presence of sepsis with 76.5% sensitivity and 74.3% specificity (AUC 0.79; 95%CI: 0.71–0.90). At multivariable logistic regression analysis, HU ≥ 7.3 (p ≤ 0.001) was independently associated with sepsis, after accounting for clinical and laboratory parameters. Measuring HU values of the fluid of the dilated collecting system may be useful to differentiate pyonephrosis from hydronephrosis and to predict septic complications in patients with obstructive uropathy.

scholarly journals Weight Loss in Parkinson’s Disease: The Relationship with Motor Symptoms and Disease Progression

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Maria G. Cersosimo ◽  
Gabriela B. Raina ◽  
Luis A. Pellene ◽  
Federico E. Micheli ◽  
Cristian R. Calandra ◽  
...  
Keyword(s):  
Weight Loss ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Disease Progression ◽  
Logistic Regression Analysis ◽  
Disease Duration ◽  
Multivariable Logistic Regression Analysis ◽  
Single Session ◽  
Multivariable Logistic Regression ◽  
The Relationship

Objectives.To determine the prevalence of weight loss (WL) in PD patients, its relationship to the severity of motor manifestations and appetite changes.Methods.144 PD patients and 120 controls were evaluated in a single session. All subjects were asked about changes in body weight and appetite. PD patients were examined with the UPDRS-III and the Hoehn and Yahr (HY) scales. Subscores of tremor, bradykinesia /rigidity, and non-dopaminergic symptoms (NDS) were analyzed individually. Multivariable logistic regression analysis was used to determine an association between WL and PD motor manifestations.Results.48.6 % of PD patients presented WL compared to 20.8 % of controls (p < 0.001). Weight losers were significantly older and had longer disease duration, higher scores in HY stages, UPDRS-III, and NDS-subscore. Multivariable logistic regression analysis demonstrated that WL was associated with NDS-subscore (p= 0.002; OR: 1.33) and older age (p= 0.037; OR: 1.05). Appetite in PD cases losing weight was unchanged (35.7 %), decreased (31.4 %), or even increased (32.9).Conclusions.Our results showed that WL occurs in almost half of PD patients and it is largely the consequence of disease progression rather than involuntary movements or a decrease in food intake.

scholarly journals Treatment Strategy of Oxaliplatin-induced Peripheral Neuropathy: A Retrospective, Nationwide Study

2021 ◽  
Author(s):  
Satoshi Yokoyama ◽  
Chihiro Nakagawa ◽  
Kouichi Hosomi
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Peripheral Neuropathy ◽  
Logistic Regression Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Prescription Patterns ◽  
Nationwide Study ◽  
Multivariable Logistic Regression ◽  
Oxaliplatin Treatment ◽  
Prolonged Duration

Abstract PurposeChemotherapy-induced peripheral neuropathy (CIPN) is a common adverse events of cancer treatment; however, no drug is recommended for the prevention of CIPN. In Japan, several drugs such as Gosha-Jinki-Gan and duloxetine have been frequently administered for the treatment of CIPN. The aim of this study was to elucidate prescription patterns of drugs administered for the treatment of CIPN caused by oxaliplatin and the association between these drugs and the duration of oxaliplatin treatment.MethodsWe conducted a retrospective nationwide study using the JMDC administrative claims database (January 2005–June 2020). Patients newly treated with oxaliplatin were identified, and prescription patterns of CIPN medication including Gosha-Jinki-Gan, pregabalin, duloxetine, mecobalamin, and mirogabalin were investigated. The primary outcome was the duration of oxaliplatin treatment. Multivariable logistic regression analysis was performed to examine the association between CIPN medication and duration of oxaliplatin treatment.ResultsA total of 4,739 patients who newly received oxaliplatin were identified. Of these, 759 (16.0%) had received CIPN medication. Duloxetine was administered in 99 (2.1%) patients. Multivariable logistic regression analysis revealed that CIPN medication was significantly associated with the prolonged duration of oxaliplatin treatment (odds ratio: 2.35, [95% confidence interval: 1.99-2.77]).ConclusionReal-world data demonstrated that the administration rate of CIPN medication was higher in patients who underwent oxaliplatin treatment for over 6 months. Increasing administration preference of duloxetine and conducting prospective studies to verify the causal relationship between CIPN medication and prolonged duration of oxaliplatin treatment are needed.

scholarly journals A nomogram to predict the upgrading rate of ISUP grades of RP in patients undergoing transrectal prostate biopsy and transperineal prostate biopsy

2021 ◽  
Author(s):  
Zhilei Zhang ◽  
Fei Qin ◽  
Guofeng Ma ◽  
Hang Yuan ◽  
Yongbo Yu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Radical Prostatectomy ◽  
Logistic Regression Analysis ◽  
Prostate Biopsy ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Biopsy Tissue ◽  
Systematic Biopsy

Abstract Backgroud: This study was aimed to develop and internally validate a nomogram for risk of upgrade of ISUP (International Society of Urology Pathology) grade group from biopsy tissue to RP (radical prostatectomy) final histology.Methods: 166 patients with prostate cancer were retrospectively analyzed and divided into two groups based on ISUP upgrade status from needle biopsy to radical prostatectomy specimen, these being the 'ISUP upgrade' group and the 'no ISUP upgrade' group. Logistic regression analysis was used to predict the significant independent factors for ISUP upgrade. A nonogram was then developed based on these independent factors, which would predict risk of ISUP upgrade. The C-index, calibration plot, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the predicting model. Internal validation was evaluated by using the bootstrapping validation. Results: There were 47 patients in the ISUP upgrade group and 119 patients in the no ISUP upgrade group respectively. Patients in the ISUP upgrade group tended to be of younger age, smaller PV (prostate volume), lower GS (Gleason score) of PB (prostate biopsy) tissue than the no ISUP upgrade group (p=0.043, p=0.041, p < 0.001, p =0.04, respectively). Multivariate logistic regression analysis showed that GS ≤6 (OR=14.236, P=0.001), prostate biopsy approach (TB-SB (transperineal prostate systematic biopsy) VS TR-SB (transrectal prostate systematic biopsy), OR=0.361, P=0.03) and number of positive cores < 10 (OR=0.396, P=0.04) were the independent risk factors for ISUP upgrade. A prediction nomogram model of ISUP upgrade was built based on these significant factors above, the area under the receiver operating characteristic (AUC) curve of which was 0.802. The C-index for the prediction nomogram was 0.798 (95%CI: 0.655–0.941) and the nomogram showed good calibration. High C-index value of 0.772 could still be reached in the interval validation. Decision curve analysis also demonstrated that the threshold value of RP-ISUP upgrade risk was 3% to 67%. Conclusion: A novel nomogram incorporating PSA, GS of PCa, ways of prostate biopsy and number of positive cores was built with a relatively good accuracy to assist clinicians to evaluate the risk of ISUP upgrade in the RP specimen, especially for the low-risk prostate cancer diagnosed by TR-SB.

scholarly journals The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichuang Lian ◽  
Yafang Li ◽  
Wenyi Wang ◽  
Wei Ding ◽  
Zongxin Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Underlying Diseases

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

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