scholarly journals Development and Validation of a Risk Prediction Model of Linezolid-induced Thrombocytopenia in Elderly Patients

2021 ◽  
Author(s):  
Yan Qin ◽  
Zhe Chen ◽  
Shuai Gao ◽  
Ming Kun Pan ◽  
Yu Xiao Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Elderly Patients ◽  
Predictive Model ◽  
Bacterial Infections ◽  
Demographic Data ◽  
Multivariate Logistic Regression Analysis ◽  
Platelet Counts ◽  
Linezolid Therapy ◽  
Validation Set ◽  
Development And Validation

Abstract Background Linezolid is an oxazolidinone antimicrobial agent developed for treating multi-drug-resistant gram-positive bacterial infections. Objective This study aimed at investigating risk factors of linezolid (LI)-induced thrombocytopenia (LI-TP) and establishing a risk predictive model for LI-TP.Setting ZhongShan Hospital, FuDan University, China. Method A retrospective study was performed in patients aged ≥ 65 years receiving linezolid therapy from January 2015 to April 2021. Clinical characteristics and demographic data were collected and compared between patients with LI-TP and those without.Main outcome measures Incidence and risk factors of LI-TP in elderly patients.Results A total of 343 inpatients were included as the train set from January 2015 to August 2020. Among them, 67 (19.5%) developed LI-TP. Multivariate logistic regression analysis revealed that baseline platelet counts < 150×109·L-1 (OR=3.576; P< 0.001), age ≥ 75 years (OR=2.258; P=0.009), eGFR< 60 mL·(min·1.73m2)-1 (OR=2.553; P=0.002), duration of linezolid therapy ≥ 10 d (OR=3.218; P<0.001), ICU admittance (OR=2.682; P=0.004), and concomitant with piperacillin-tazobactam (PTZ) (OR=3.863; P=0.006) were independent risk factors for LI-TP. The risk predictive model was established and exhibited a moderate discriminative power, with an AUC of 0.795 [95%CI 0.740-0.851] and 0.849 [95%CI 0.760-0.939] in train set (n=343) and validation set (n=90), respectively.Conclusion The risk factors of LI-TP in elderly patients were duration of linezolid therapy, age, eGFR, ICU admittance, baseline platelet counts, and concomitant with PTZ. A risk predictive model based on these risk factors may be useful to identify patients with high risk of LI-TP.

scholarly journals Development and validation of a nomogram to predict the mortality risk in elderly patients with ARF

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11016
Author(s):  
Junnan Xu ◽  
Jie Weng ◽  
Jingwen Yang ◽  
Xuan Shi ◽  
Ruonan Hou ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Elderly Patients ◽  
Mortality Risk ◽  
Systolic Pressure ◽  
Multivariate Logistic Regression Analysis ◽  
Prediction Of Mortality ◽  
Short Term Mortality ◽  
Life Threatening ◽  
Validation Set ◽  
Development And Validation

Background Acute respiratory failure (ARF) is a life-threatening complication in elderly patients. We developed a nomogram model to explore the risk factors of prognosis and the short-term mortality in elderly patients with ARF. Methods A total of 759 patients from MIMIC-III database were categorized into the training set and 673 patients from our hospital were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. A 30-day follow-up was performed for all patients. Results Multivariate logistic regression analysis showed that the heart rate, respiratoryrate, systolic pressure, SPO2, albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had a C-index of 0.741 (95% CI [0.7058–0.7766]), and the C-index was 0.687 (95% CI [0.6458–0.7272]) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality. Conclusion Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

scholarly journals A model for predicting carotid instability plaque in high risk group of stroke individuals

2019 ◽  
Author(s):  
Junxiong Yin ◽  
Chuanyong Yu ◽  
Hongxing Liu ◽  
Mingyang Du ◽  
Feng Sun ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Predictive Model ◽  
Doppler Ultrasound ◽  
Scoring System ◽  
Risk Group ◽  
High Risk Group ◽  
High Risk Population ◽  
Auc Value ◽  
Validation Set

Abstract Objective: To establish a predictive model of carotid vulnerable plaque through systematic screening of high-risk population for stroke.Patients and methods: All community residents who participated in the screening of stroke high-risk population by the China National Stroke Screening and Prevention Project (CNSSPP). A total of 19 risk factors were analyzed. Individuals were randomly divided into Derivation Set group and Validation Set group. According to carotid ultrasonography, the derivation set group patients were divided into instability plaque group and non-instability plaque group. Univariate and multivariable logistic regression were taken for risk factors. A predictive model scoring system were established by the coefficient. The AUC value of both derivation and validation set group were used to verify the effectiveness of the model.Results: A total of 2841 high-risk stroke patients were enrolled in this study, 266 (9.4%) patients were found instability plaque. According to the results of Doppler ultrasound, Derivation Set group were divided into instability plaque group (174 cases) and non-instability plaque group (1720 cases). The independent risk factors for carotid instability plaque were: male (OR 1.966, 95%CI 1.406-2.749)，older age (50-59, OR 6.012, 95%CI 1.410-25.629; 60-69, OR 13.915, 95%CI 3.381-57.267;≥70, OR 31.267, 95%CI 7.472-130.83) , married(OR 1.780, 95%CI 1.186-2.672)，LDL-c(OR 2.015, 95%CI 1.443-2.814), and HDL-C(OR 2.130, 95%CI 1.360-3.338). A predictive scoring system was created, range 0-10. The cut-off value of prediction model score is 6.5. The AUC value of derivation and validation set group were 0.738 and 0.737.Conclusion：For a high risk group of stroke individual, We provide a model that could distinguishing those who have a high probability of having carotid instability plaque. When resident’s predictive model score exceeds 6.5, the incidence of carotid instability plaque is high, carotid artery Doppler ultrasound would be checked immediately. This model can be helpful in the primary prevention of stroke.

scholarly journals Predicting the risk of sarcopenia in elderly patients with patellar fracture: Development and assessment of a new predictive nomogram

2019 ◽  
Author(s):  
Chen Yisheng ◽  
Tao Jie
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Elderly Patients ◽  
Prediction Model ◽  
Predictive Model ◽  
Body Mass ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Low Body Mass Index ◽  
Patellar Fractures

AbstractPurposeThis study was aimed at developing a risk prediction model for postoperative dysplasia in elderly patients with patellar fractures in China.Patients and methodsWe conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (Lasso analysis). The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We conducted internal sampling methods for qualitative assessment.ResultWe recruited 61 participants (males: 20, mean age: 68.1 years). Various risk factors were assessed, and low body mass index and diabetes mellitus were identified as the most important risk factors (P<0.05). The model showed a good prediction rate (C-index: 0.909; 95% confidence interval: 0.81–1.00) and good correction effect. The C-index remained high (0.828) even after internal sample verification. Decision curve analysis showed that the risk of sarcopenia was 8.3–80.0%, suggesting good clinical practicability.ConclusionOur prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, longer postoperative hospital stay, no higher education, no postoperative rehabilitation, removal of internal fixation, and less outdoor exercise.

scholarly journals Nomogram Based on Preoperative CT Imaging Predicts The EGFR Mutation Status in Lung Adenocarcinoma

2020 ◽  
Author(s):  
Guojin Zhang ◽  
Jing zhang ◽  
Yuntai Cao ◽  
Zhiyong Zhao ◽  
Shenglin Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Adenocarcinoma ◽  
Egfr Mutation ◽  
Multivariate Logistic Regression Analysis ◽  
Clinical Risk Factors ◽  
Smoking History ◽  
Mutation Status ◽  
Clinical Risk ◽  
Development And Validation ◽  
Ct Features

Abstract Background: Tyrosine kinase inhibitors (TKIs) provide clinical benefits to the lung cancer patients with epidermal growth factor receptor (EGFR) mutations. However, non-invasively determine EGFR mutation status in patients before targeted therapy remains a challenge. This study aimed to develop and validate a nomogram for preoperative prediction of EGFR mutation status in patients with lung adenocarcinoma.Methods: This study retrospectively collected medical records of 403 patients with histologically confirmed lung adenocarcinoma from January 2016 and June 2020. The patients were divided into development and validation cohorts. The preoperative information on all patients was obtained, including clinical characteristics and computed tomography (CT) features. Multivariate logistic regression analysis was used to develop the predictive model. We combined CT features and clinical risk factors and used them to build a prediction nomogram. The performance of the nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness. The nomogram was further validated in an independent external cohort.Results: The predictive factors incorporated in the personalized prediction nomogram included smoking history (OR, 0.2; 95% CI: 0.1, 0.4; P < 0.001), bubble-like lucency (OR, 2.2; 95% CI: 1.3, 3.8; P = 0.003), pleural attachment (OR, 0.4; 95% CI: 0.2, 0.7, P = 0.001) and thickened adjacent bronchovascular bundles (OR, 3.1; 95% CI: 1.8, 5.3; P < 0.001). Based on these parameters, the prediction model has good discrimination and calibration ability. The area under the curve in the development and validation cohorts were 0.784 (95% CI: 0.733, 0.835) and 0.740 (95% CI: 0.643, 0.838), respectively. Decision curve analysis showed that the model was clinically useful.Conclusions: This study presented a nomogram that contained CT features and clinical risk factors, which could conveniently and non-invasively predict EGFR mutation status in patients with lung adenocarcinoma before surgery.

scholarly journals Incidence and Risk Factors of Post-Operative Delirium in Elderly Patients Who Underwent Spinal Anesthesia in Orotta, Halibet and Sembel Hospitals: A Cross Sectional Study in Eritrea

2021 ◽  
pp. 1-8
Author(s):  
Michael Beraki Mengistu ◽  
◽  
Yonatan Mehari Andemeske ◽  
AriamTsegay Emhatsion ◽  
HabtomTareke Wrieta ◽  
...  
Keyword(s):  
Risk Factors ◽  
Elderly Patients ◽  
Spinal Anesthesia ◽  
Significant Risk ◽  
Multivariate Logistic Regression Analysis ◽  
Cross Sectional Study ◽  
P Value ◽  
Sectional Study ◽  
Cross Sectional ◽  
History Of

Background: Delirium is a common but often underdiagnosed set of transient symptoms often seen in elderly patients following surgeries under spinal anesthesia. With early recognition and diagnosis based on the established standard criteria delirium can be improved. Early identification of the possible contributingfactors and the magnitude of the burden will help in the management of the fragile elderly patients. The aim of this study is to determine the incidence of postoperative delirium and associated risk factors in elderly patients who have done surgery under spinal anesthesia. Method: This cross sectional study was conducted in the National Referral Hospitals and Sembel PrivateHospital, in Asmara, Eritrea from February to May, 2019. The study participants were elderly patients (age ≥65 years) having no known history of dementia or delirium or no mental disorder, no history of acute cerebrovascular disease. Basic background and clinical characteristics of the patients was collected. To assess the status of cognitive impairment level, the Mini Mental State Examination and Confusion Assessment Method tools were used. Data was collected through an interview method. After the data was entered into SPSS version 22 software, data was analyzed with frequency, percentage, bivariate and multivariate logistic regression analysis method as appropriate. P value <0.05 was considered as statistically significant. Results: The mean age of the participants was 74 ±6.62 and 102 (79.1%) of the participants were males. POD occurred in 14 (10.9%) of the patients. Adjusting all the potential factors, age was identified as arisk factor for developing POD. Conclusion: Age was determined to be a significant risk factor of delirium. Elderly patients require more attention and care and the findings might help to develop preventive strategies to the occurrence of POD

scholarly journals Incidence and risk factors associated with postoperative stroke in the elderly patients undergoing hip fracture surgery

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Lili Yu ◽  
Yanbin Zhu ◽  
Wei Chen ◽  
Hui Bu ◽  
Yingze Zhang
Keyword(s):  
Risk Factors ◽  
Hip Fracture ◽  
Elderly Patients ◽  
Hip Fractures ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
The Elderly ◽  
Individual Risk ◽  
Factors Associated ◽  
Postoperative Stroke

Abstract Objectives Stroke is one of the rare but devastating complications after hip fracture in the elderly. By far, there is still scarce data on postoperative stroke in elderly patients with hip fractures. Methods This was a retrospective study of prospectively collected data. Between October 2014 to December 2018, patients aged above 65 years who underwent operative treatment for hip fractures were included. Inpatient medical surveillance and scheduled telephone follow-up at 1, 3, 6, and 12 months after operation was conducted to identify who developed an incident stroke. Variables of interests were extracted from patients’ inpatient medical records. Univariate analysis and multivariate logistic regression analysis were used to identify the independent risk factors associated with stroke. Results During the study period, a total of 3743 patients were included, among whom 56 were found to have a stroke after operation, representing an incidence of 1.5% (95% CI, 1.1 to 1.9%). The multivariate analyses showed that advanced age (1-year increment; OR, 1.32; 95% CI, 1.08 to 1.48), history of previous stroke (OR, 4.79; 95% CI, 1.86 to 6.56), ASA III and above (OR, 2.62; 95% CI, 1.27 to 3.68), long-term use of aspirin (OR, 3.63; 95% CI, 1.41 to 4.78), and elevated RDW level (each increment of 1%, OR, 1.21; 95% CI, 1.02 to 1.36) were independently associated with postoperative stroke. Conclusions Although most are not modifiable, these risk factors help in counseling patients regarding the risk of postoperative stroke, individual risk stratification, and targeted optimization of medical conditions and should be firmly kept in treating surgeon’s mind.

scholarly journals Risk Factors Predicting Candida glabrata Bloodstream Infection

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S75-S75
Author(s):  
Jane O’Halloran ◽  
Ryan Kronen ◽  
Charlotte Lin ◽  
Kevin Hsueh ◽  
William Powderly ◽  
...  
Keyword(s):  
Risk Factors ◽  
Predictive Model ◽  
Candida Glabrata ◽  
Demographic Data ◽  
Univariate Analysis ◽  
Blood Stream ◽  
Central Line ◽  
Parenteral Feeding ◽  
Tertiary Referral Hospital ◽  
History Of

Abstract Background Increased incidence of Candida glabrata (CG) infection is a growing concern in recent years due to the higher rates of fluconazole resistance associated with C. glabrata . This study aimed to create a risk predictive model for C. glabrata in patients with culture-positive candidemia. Methods Demographic data, risk factors, laboratory parameters, and outcomes were retrospectively collected on all cases of candidemia occurring at a large tertiary referral hospital between January 2002 and January 2015. Between-group differences were compared using 2 square tests. A risk predictive model was built using multivariate logistic regression. Results Of 1,913 subjects with candidemia, 398 (21%) had C. glabrata isolated. Those with C. glabrata were older (mean [SD] 61 [23] vs. 58 [23] years; P &lt; 0.001) and more often female (231 (58%) vs. 681 (45%); P &lt; 0.001). On univariate analysis, age (OR 1.01 [95% CI 1.01,1.02]), gender (0.6 [0.5, 0.7]), history of rectal cancer (2.00 [1.2, 3.5]), other GI malignancy (3.0 [1.5, 6.2]), breast cancer (1.8 [1.1, 3.0]), enteral and parenteral feeding (1.9 [1.2, 3.2]), bowel resection (3.0 [1.4, 6.2]), temperature (0.9 [0.8, 1.0], recent fluconazole use (2.0 [1.4, 2.9]), and The presence of urinary catheter (2.3 [1.4, 3.6]), central line (1.4 [1.1, 1.7) or ventilator (2.2 [1.3, 3.8]) were all associated with C. glabrata infection (P &lt; 0.05) and included in the multivariate modeling. Age, gender, history of rectal malignancy, other GI malignancies, use of enteral or parenteral feeding and recent fluconazole use remained significant (effect size 1.2 [95% CI 1.1, 1.3]; 1.8 [1.4, 2.3]; 2.0 [1.1, 3.6]; 3.0 [1.3, 6.9]; 1.9 [1.0, 3.3]; 2.0 [1.3, 3.0], respectively). The final model had a c-statistic of 0.66 [95% CI 0.63–0.69]). Ninety-day mortality in the C. glabrata group was not significantly different from the non-C. glabrata group (40% (158/398) vs. 42.5% (644/1515). Conclusion Underlying bowel pathology was more commonly associated with C. glabrata candidemia than with other candida species. Further exploration of the direct association between C. glabrata and GI malignancy and indirect effects of prior surgery or antifungal use on risk of C. glabrata candidemia are required. Interestingly, mortality did not differ between groups with glabrata and non-glabrata candida blood stream infections. This may reflect increasing empiric use of echinocandin therapy. Disclosures A. Spec, Astellas Pharma US, Inc.: Grant Investigator, Research grant

scholarly journals A Novel Prediction Model for Long-Term SARS-CoV-2 RNA Shedding in Non-death Hospitalized Patients with COVID-19: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Xiaoli Lei ◽  
Junli Wang ◽  
Lijie Kou ◽  
Zhigang Yang
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Predictive Model ◽  
Goodness Of Fit ◽  
Multivariate Logistic Regression Analysis ◽  
Hospitalized Patients ◽  
Multivariate Logistic Regression ◽  
Data Set

Abstract Background: Because of the lack of compelling evidence for predicting the duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA shedding, the purpose of this retrospective study was to establish a predictive model for long-term SARS-CoV-2 RNA shedding in non-death hospitalized patients with coronavirus disease-19 (COVID-19).Methods: 97 non-death hospitalized patients with COVID-19 admitted to two hospitals in Henan province of China from February 3, 2020 to March 31, 2020 were retrospectively enrolled. Multivariate logistic regression was performed to identify the high risk factors associated with long-term SARS-CoV-2 RNA shedding and a predictive model was established and represented by a nomogram. Its performance was assessed with discrimination and calibration.Results: 97 patients were divided into the long-term (>21 days) group (n = 27, 27.8%) and the short-term (≤ 21 days) group (n = 70, 72.2%) based on their viral shedding duration. Multivariate logistic regression analysis showed that time from illness onset to diagnosis (OR 1.224, 95% CI 1.070-1.400, P = 0.003) and interstitial opacity in chest computerized tomography(CT) scan (OR 6.516, 95% CI 2.041-20.798, P = 0.002) were independent risk factors for long-term SARS-CoV-2 RNA shedding. A prediction model, which is presented with a nomogram, was established by incorporating the two risk factors. The goodness-of-fit statistics for the nomogram was not statistically significant (χ2 = 8.292; P = 0.406), and its area under the receiver operator characteristic curve was 0.834 (95% CI 0.731- 0.936; P < 0.001).Conclusion: The established model has a good predictive performance on the long-term viral RNA shedding in non-death hospitalized patients with COVID-19, but it still needs further validation by independent data set of large samples in the future.

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