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Objective To study the factors affecting hospital death in elderly patients with novel coronavirus infec-tion/disease 2019(COVID-19),and to build a risk prediction model.Methods According to the diagnostic criteria of Diagnosis and Treatment Protocol for COVID-19 Infection(Trial 10th Edition).Totally 775 elderly patients(≥60 years old)diagnosed as COVID-19 infection in the emergency department and fever clinic of the First Hospital of Changsha were selected as the research objects.General data and serum biomarkers of patients were collected.After treatment,the patients'data were divided into survival group and hospital death group.Binary Logistic regres-sion was used to screen the independent influencing factors of death,and ROC curve was used to analyze the pre-dictive value of related indicators on hospital death.Results After treatment,712 patients(91.9%)survived and 63 patients(8.3%)died in hospital.Binary Logistic regression analysis showed that:≥90 years old[OR=5.065,95%CI(1.427,17.974)],type 2 diabetes mellitus[OR= 3.757,95%CI(1.649,8.559)],COPD[OR= 5.625,95%CI(2.357,13.421)],monocyte ratio[OR=0.908,95%CI(0.857,0.963)],plasma fibringen[OR=1.376,95%CI(1.053,1.800)]and lactate dehydrogenase[OR=1.005,95%CI(1.001,o1.008)]were independent factors of in-hospital death(P＜0.05).The predictive value of diabetes mellitus+COPD+age+monocyte ratio+plasma fibrinogen+lactate dehydrogenase was proved in hospital death from COVID-19 infected patients:the area under the curve(AUC)was 0.883(95%CI:0.827,0.940,P＜0.001),the critical value≥0.710 suggested the risk of death in hospital,the specificity was 0.851,the sensitivity was 0.857.Conclusions The hospital mortality of the elderly after COVID-19 infection is higher and closely related to type 2 diabetes,COPD,monocyte ratio,plasma fibrinogen and lactate dehydrogenase.
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Objective To analyze risk factors for perioperative blood transfusion in elderly patients undergoing uni-lateral primary total hip arthroplasty and develop a prediction model.Methods The study retrospectively collected 467 elderly patients receiving unilateral primary total hip arthroplasty between January 2013 and October 2021 at Peking Union Medical College Hospital.The 70%of the data were used as the training set and the 30%of the data were used as the testing set.Patients were divided into the transfusion and no-transfusion groups based on the presence or absence of perioperative blood transfusion.Univariate analysis and multivariable logistic regression were conducted to analyze patient demographic characteristics,surgical information,and preoperative laboratory tests for identifying risk factors.Clinical experience was combined to establish a prediction model and draw the nomogram.The receiver operating characteristic(ROC)curve and calibration curve were used to evaluate the model in the tes-ting set.Results A total of 91 patients(19.5%)received perioperative blood transfusion.Multivariable logistic re-gression suggested the history of coronary artery disease,prolonged operation time,and lower preoperative hemoglo-bin were risk factors for perioperative blood transfusion(P＜0.05).The prediction model was constructed based on the results of statistical analysis and clinical experience,including the history of coronary artery disease,operation time,preoperative hemoglobin,age,and American Society of Anesthesiologists(ASA)physical status＞Ⅱ.The area under the receiver operating characteristic curve(AUC)of the model was 0.809.Conclusions The prediction model for perioperative blood transfusion in elderly patients undergoing unilateral total hip arthroplasty had a good performance and could assist in clinical practice.
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Objective To explore the influencing factors of spontaneous bacterial peritonitis in patients with primary liver cancer complicated with ascites and establish a prediction model.Methods A total of 292 patients with primary liver cancer complicated with ascites who were hospitalized for the first time in the Third People's Hospital of Kunming from January 2012 to December 2021 were selected as the study objects.General data,etiological indicators,serological indicators and complications of these subjects were collected.Then they were divided into the infection group(n = 114)and the control group(n = 178)according to whether spontaneous bacterial peritonitis(SBP)was complicated.Univariate and multivariate logistic regression were used to analyze the influencing factors of SBP in patients with primary liver cancer complicated with ascites.Finally,ROC curves were constructed to more intuitively represent the individual and combined predictive value of these targets.Results Am-ong 292 hepatocellular carcinoma patients with ascites,there were 235 males(80.48%)and 57 females(19.52%),among which 114 patients with SBP were in the infection group and 178 patients without SBP were in the control group.The results of univariate analysis showed that compared with the control group,the levels of WBC,neutrophils,prothrombin time,total bilirubin,albumin,CD3,CD4,CD8,CD4/CD8 ratio,CD19 procalcitonin,serum amyloid A,hypersensitive C-reactive protein,sodium,chlorine,alcohol consumption,shock,hepatorenal syndrome,hepatic encephalopathy,massive ascites in the infection group had statistically significant difference(P<0.05).Multi-factor analysis revealed that CD8,CD4/CD8 ratio were protective factors for SBP in patients with liver cancer ascites,CD19,procalcitonin,serum amyloid A,and massive ascites were risk factors for SBP in patients with ascites.ROC curve construction showed that serum amyloid A,CD8,CD4/CD8 ratio,CD19,procalcitonin,massive ascites area under curve(AUC)of massive ascites were 0.724,0.637,0.653,0.820,0.705,0.686,respectively.Conclusion CD8,CD4/CD8 ratio,CD19,procalcitonin,serum amyloid A,and a large volume of ascites are significant factors contributing to the development of spontaneous bacterial peritonitis(SBP)in patients with hepatocellular carcinoma ascites.The predictive value of combination is substantial,demonstrating a level of accuracy in forecasting SBP occurrence
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Objective:To construct a risk prediction model for urinary retention in patients undergoing radical cervical cancer surgery based on machine learning, and the prediction effect of the model was internally verified and evaluated, in order to provide reference for the early prevention and treatment of urinary retention in patients undergoing radical cervical cancer surgery.Methods:A total of 981 patients who underwent radical cervical cancer surgery in the First Affiliated Hospital of Anhui Medical University from June 2017 to February 2022 were selected and divided into the training set (687 cases) and the test set (294 cases) according to a ratio of 7∶3. Through literature review and risk factor analysis, the influencing factors of urinary retention after radical treatment of cervical cancer were explored, and the risk prediction model of urinary retention was constructed by using XGBoost, random forest, support vector machine and decision tree in machine learning. The accuracy rate, recall rate, F1 value and AUC of four machine learning algorithms were calculated by using the method of 10-fold cross-validation, and the model with the highest predictive efficiency was selected.Results:Among the 981 patients included, the incidence of urinary retention after radical cervical cancer surgery was 18.86% (185/981). The median age of urinary retention group was 51 years old, and that of non urinary retention group was 50 years old. Statistically significant variables in the univariate analysis and influencing factors summarized by literature review were featured, including patient age, intraoperative blood loss, body mass index (BMI), cancer stage, surgical method, surgical resection scope, whether pelvic lymph node dissection was performed, comorbidities and residual urine. Among the four model building methods of machine learning, the random forest model has the best effect, its training set F1 value was 0.94, the test set F1 value was 0.77, the ROC was plotted and the AUC was calculated to be 0.73. Age, intraoperative blood loss, BMI, cancer stage and surgical method contributed significantly to the classification of random forest model.Conclusions:The prediction model of urinary retention risk after radical cervical cancer surgery based on random forest method has the best efficacy. It is useful to help nursing personnel evaluate the risk of the uroschesis for a patient and then take targeted nursing interventions to actively prevent postoperative urinary retention.
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Hepatic encephalopathy is a clinical syndrome of central nervous system dysfunction caused by liver insufficiency.It severely affects the quality of life of patients and may lead to death.Accurate prediction of the risk of developing hepatic encephalopathy is crucial for early intervention and treatment.In order to identify the risk of hepatic encephalopathy in patients in advance,many studies have been devoted to efforts to develop tools and methods to identify the risk of hepatic encephalopathy as early as possible,so as to develop preventive and early management strategies.Most conventional hepatic encephalopathy risk prediction models currently assess the prob-ability of a patient developing hepatic encephalopathy by analysing factors such as clinical data and biochemical indicators,however,their accuracy,sensitivity and positive predictive value are not high.The application of artificial intelligence to clinical predictive modelling is a very hot and promising area,which can use large amounts of data and complex algorithms to improve the accuracy and efficiency of diagnosis and prognosis.To date,there have been few studies using AI techniques to predict hepatic encephalopathy.Therefore,this paper reviews the research progress of hepatic encephalopathy risk prediction models,and also discusses the prospect of AI application in hepatic encephalopathy risk prediction models.It also points out the challenges and future research directions of AI in HE risk prediction model research in order to promote the development and clinical application of hepatic encephalopathy risk prediction models.
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Objective To construct a risk prediction model of pulmonary involvement based on chest CT and clinical feature in patients with primary Sjogren's syndrome(pSS),and to explore the risk prediction value of the model.Methods A total of 360 pSS patients who had been treated at Handan Hospital of Traditional Chinese Medicine from October 2020 to August 2023 were retrospectively selected as study objects,and were then divided into a modeling group(252 patients)and a verification group(108 patients)according to a ratio of 7∶3.The patients in the modeling group were divided into a control group(201 patients)and an involvement group(51 patients)based on presence or absence of lung involvement.The data on clinical characteristics and features of chest high-resolution CT(HRCT)in the modeling group was collected.Univariate analysis was performed among the groups to determine the relevant factors affecting lung involvement in pSS patients.Binary logistic regression analysis was performed on related factors to screen independent risk factors.A prediction model was established based on the independent risk factors.A verification and value analysis of the column-line prediction model were completed through data collection of the verification group.Results Age,disease course,cough,Raynaud's phenomenon,C-reactive protein(CRP),anti-SSA antibody,and HRCT were the relevant factors affecting lung involvement in pSS patients(all P＜0.05).Further binary logistic regression analysis showed that old age,prolonged disease course,cough and abnormal HRCT imaging were independent risk factors for lung involvement in SS patients(all P＜0.05).A nomogram risk prediction model was constructed based on independent factors.The model verification results indicated that the calibration chart showed better performance in the prediction model.The AUC of the area under the receiver operating characteristic(ROC)curve was 0.993 the modeling group and 0.995 in the validation group.Conclusions The clinical characteristics and the results of chest CT are closely related with lung involvement in patients with pSS.Old age,prolonged disease course,cough,and abnormal HRCT imaging are independent risk factors affecting lung involvement in patients with pSS.The prediction model established on this basis has a higher predictive value for the occurrence of lung involvement in patients receiving after-loading radiotherapy.
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BACKGROUND:Proximal humeral fracture in older adults is one of the three major osteoporotic fractures.Anatomic locking plate fixation is the first choice for most scholars to treat difficult-to-reduce and complex fracture types.However,the probability of reduction failure after the operation is high,which seriously affects patients'quality of life. OBJECTIVE:To investigate the correlation between deltoid tuberosity index and postoperative reduction failure of proximal humeral fractures in the elderly,analyze and filter preoperative independent risk factors for reduction failure of proximal humeral fractures in the elderly,and construct and verify the effectiveness of a clinical prediction model. METHODS:The clinical data of 153 elderly patients with proximal humeral fractures who met the diagnosis and inclusion criteria and received open reduction and locking plate surgery in Foshan Hospital of TCM from June 2012 to June 2021 were collected.The patients were divided into the reduction failure subgroup and the reduction maintenance subgroup.The independent risk factors were selected by multivariate Logistic regression analysis,and the nomogram was constructed by R language.After 1000 times of resampling by Bootstrap method,the Hosmer-Lemeshow goodness of fit correlation test,receiver operating characteristic curve,calibration curve,clinical decision,and influence curve were plotted to evaluate its goodness of fit,discrimination,calibration ability,and clinical application value.Fifty-five elderly patients with proximal humeral fractures from June 2013 to August 2021 were selected as the model's external validation group to evaluate the prediction model's stability and accuracy. RESULTS AND CONCLUSION:(1)Of the 153 patients in the training group,44 patients met reduction failure after internal plate fixation.The prevalence of postoperative reduction failure was 28.8%.Multivariate Logistic regression analysis identified that deltoid tuberosity index[OR=9.782,95%CI(3.798,25.194)],varus displacement[OR=4.209,95%CI(1.472,12.031)],and medial metaphyseal comminution[OR=4.278,95%CI(1.670,10.959)]were independent risk factors for postoperative reduction failure of proximal humeral fractures in older adults(P<0.05).(2)A nomogram based on independent risk factors was then constructed.The Hosmer-Lemeshow test results for the model of the training group showed that χ2=0.812(P=0.976)and area under curve=0.830[95%CI(0.762,0.898)].The calibration plot results showed that the model's predicted risk was in good agreement with the actual risk.The decision and clinical influence curves showed good clinical applicability.(3)In the validation group,the accuracy rate in practical applications was 86%,area under curve=0.902[95%CI(0.819,0.985)].(4)It is concluded that deltoid tuberosity index<1.44,medial metaphyseal comminution,and varus displacement were independent risk factors for reduction failure.(5)The internal and external validation of the risk prediction model demonstrated high discrimination,accuracy,and clinical applicability could be used to individually predict and screen the high-risk population of postoperative reduction failure of proximal humeral fractures in the elderly.The predicted number of patients at high risk is highly matched to the actual number of patients who occur when the model's threshold risk probability is above 65%,and clinicians should use targeted treatment.
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Refeeding syndrome(RFS)has a high incidence among critically ill patients and significantly impacts the re-covery and prognosis of the patients.In this paper,we reviewed the literature on the risk factors and risk prediction models for RFS,finding the risk factors of RFS included patient-related,treatment-related factors and disease-related factors and the risk prediction models encompassed risk stratification model,risk score models and the Logistic regression models.It was concluded from the review that early assessment was crucial to preventing the occurrence of RFS.However,there was still a lack of reliable RFS risk prediction models with good predictive performance.It was found as well that it was crucial for the prevention of RFS to attach importance to nutritional and serological indicators and other factors.It was expected to be a necessity to conduct prospec-tive and multicenter studies to develop a risk prediction model for predicting RFS for ICU patients.Our review provides a refer-ence for early assessment and intervention for critically ill patients with RFS.
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Objective To analyze the influencing factors of hypoglycemia in patients undergoing colonoscopy and to construct a risk prediction model and evaluate the model.Methods A total of 528 patients who underwent colonoscopy were selected by the convenience sampling method from the gastroenterology department of a tertiary A hospital in Qingdao from March 2022 to August 2022.Their general information,laboratory indicators and operation-related data were collected.Multivariate Logistic regression was used to analyze the risk factors of hypoglycemia in patients with colonoscopy for risk prediction model construction,and its prediction effect was evaluated by drawing a nomogram.Results Hypoglycemia occurred in 66 of 528 patients,with an incidence of 12.50％.The risk factors finally in the risk prediction model in Logistic regression were drinking history,long fasting time after operation,polyethylene glycol(PEG)-electrolyte solutions＞3 L,low quality of bowel preparation.The model passed Hosmer-Lemeshow goodness of fit test x2=10.158(P=0.200).The area under the ROC curve was 0.829,while the cut-off was 0.575,with sensitivity of 92.90％and specificity of 64.60％.Conclusion Patients undergoing colonoscopy have a higher risk of hypoglycemia.Patients with a history of drinking,longer fasting after surgery,more than 3 L of PEG-electrolyte solutions,and low quality of bowel preparation were more likely to develop hypoglycemia.The established risk prediction model has a good effect,providing the reference for screening high-risk group of hypoglycemia and taking preventive and protective measures.
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Objective:To construct the risk prediction nomogram model of acute kidney injury (AKI) with R language and traditional statistical methods based on the large sample clinical database, and verify the accuracy of the model.Methods:It was a a retrospective case control study. The patients who met the diagnostic criteria of AKI in Tongji Hospital of Tongji University from January 1 to December 31, 2021 were screened in the clinical database, and the patients with monitored serum creatinine within 48 hours but without AKI were included as the control group. The demographic data, disease history, surgical history, medication history and laboratory test data were collected to screen the risk factors of AKI in clinic.Firstly, based on multivariate logistic regression analysis and forward stepwise logistic regression analysis, the selected risk factors were included to construct the nomogram model. At the same time, cross validation, bootstrap validation and randomly split sample validation were used for internal verification, and clinical data of patients in the sane hospital after one year (January to December, 2022) were collected for external verification. The receiver-operating characteristic curve was used to determine the discrimination of the model, and calibration curve and decision curve analysis were carried out to evaluate the accuracy and clinical net benefit, respectively.Results:A total of 5 671 patients were enrolled in the study, with 1 884 AKI patients (33.2%) and 3 787 non-AKI patients (66.7%). Compared with non-AKI group, age, and proportions of surgical history, renal replacement therapy, hypertension, diabetes, cerebrovascular accident,chronic kidney disease, drug use histories and mortality in AKI group were all higher (all P<0.05). Multivariate logistic regression analysis showed that the independent influencing factors of AKI were surgical history, hypertension, cerebrovascular accident, diabetes, chronic kidney disease, diuretics, nitroglycerin, antidiuretic hormones, body temperature, serum creatinine, C-reactive protein, red blood cells, white blood cells, D-dimer, myoglobin, hemoglobin, blood urea nitrogen, brain natriuretic peptide, aspartate aminotransferase, alanine aminotransferase, triacylglycerol, lactate dehydrogenase, total bilirubin, activated partial thromboplastin time, blood uric acid and potassium ion (all P<0.05). Finally, the predictive factors in the nomogram were determined by forward stepwise logistic regression analysis, including chronic kidney disease, hypertension, myoglobin, serum creatinine and blood urea nitrogen, and the area under the curve of the prediction nomogram model was 0.926 [95% CI 0.918-0.933, P<0.001]. The calibration curve showed that the calibration effect of nomogram was good ( P>0.05). The decision curve showed that when the risk threshold of nomogram model was more than 0.04, the model construction was useful in clinic. In addition, the area under the curve of receiver-operating characteristic curve predicted by nomograph model in external validation set was 0.876 (95% CI 0.865-0.886), which indicated that nomograph model had a high discrimination degree. Conclusion:A nomogram model for predicting the occurrence of AKI is established successfully, which is helpful for clinicians to find high-risk AKI patients early, intervene in time and improve the prognosis.
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@#Objective To construct a risk prediction score model for serious adverse event (SAE) after cardiac catheterization in patients with adult congenital heart disease (ACHD) and pulmonary hypertension (PH) and verify its predictive effect. Methods The patients with PH who underwent cardiac catheterization in Wuhan Asian Heart Hospital Affiliated to Wuhan University of Science and Technology from January 2018 to January 2022 were retrospectively collected. The patients were randomly divided into a model group and a validation group according to the order of admission. The model group was divided into a SAE group and a non-SAE group according to whether SAE occurred after the catheterization. The data of the two groups were compared, and the risk prediction score model was established according to the results of multivariate logistic regression analysis. The discrimination and calibration of the model were evaluated using the area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test, respectively. Results A total of 758 patients were enrolled, including 240 (31.7%) males and 518 (68.3%) females, with a mean age of 43.1 (18.0-81.0) years. There were 530 patients in the model group (47 patients in the SAE group and 483 patients in the non-SAE group) and 228 patients in the validation group. Univariate analysis showed statistical differences in age, smoking history, valvular disease history, heart failure history, N-terminal pro-B-type natriuretic peptide, and other factors between the SAE and non-SAE groups (P<0.05). Multivariate analysis showed that age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, surgical general anesthesia, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients (P<0.05). The risk prediction score model had a total score of 0-139 points and patients who had a score>50 points were high-risk patients. Model validation results showed an area under the ROC curve of 0.937 (95%CI 0.897-0.976). Hosmer-Lemeshow goodness-of-fit test: χ2=3.847, P=0.797. Conclusion Age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, general anesthesia for surgery, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients. The risk prediction model based on these factors has a high predictive value and can be applied to the risk assessment of SAE after interventional therapy in ACHD-PH patients to help clinicians perform early intervention.
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OBJECTIVE To construct a risk prediction model for non-compliance with inhaled medication in patients with chronic obstructive pulmonary disease （COPD）. METHODS A retrospective analysis was conducted on 365 COPD patients admitted to the cough and wheeze pharmaceutical care clinic of the First Hospital of Qinhuangdao from October 2021 to October 2023. The patients admitted from October 2021 to June 2023 were selected as the model group （n＝303）， and the patients admitted from July to October 2023 were selected as the validation group （n＝62）. The model group was divided into compliance subgroup （n＝126） and non-compliance subgroup （n＝177）. Univariate analysis combined with multivariate Logistic regression analysis were used to analyze the risk factors for non-compliance with inhaled formulations in patients； the risk prediction model was established through regression analysis， and the accuracy of the model prediction was evaluated based on the validation group of patients. RESULTS Multivariate Logistic regression analysis showed that simultaneous use of 2 inhaled formulations （OR＝3.730， 95%CI 1.996-6.971， P＜0.001）， the number of acute exacerbations within one year ≥2 （OR＝2.509， 95%CI 1.509-4.173， P＜0.001）， smoking （OR＝2.167， 95%CI 1.309-3.588， P＝0.003）， complicated with anxiety/depression （OR＝2.112， 95%CI 1.257-3.499， P＝0.004） and mMRC grading≥2 levels （OR＝1.701， 95%CI 1.014-2.853， P＝0.044） were risk factors for non-compliance with inhaled preparations. Based on this， a risk prediction model was established and the ROC curve was drawn. The areas under the curve of the model group and validation group were 0.836 and 0.928， and the overall accuracy of the model’s prediction was 88.71%. CONCLUSIONS The predictive model based on the simultaneous use of 2 inhaled formulations， the number of acute exacerbations within one year ≥2， smoking， complicated with anxiety/depression， mMRC grading ≥2 levels has certain predictive value for the risk of non-compliance with inhaled formulations for COPD patients.
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OBJECTIVE To construct a risk prediction model for non-compliance with inhaled medication in patients with chronic obstructive pulmonary disease （COPD）. METHODS A retrospective analysis was conducted on 365 COPD patients admitted to the cough and wheeze pharmaceutical care clinic of the First Hospital of Qinhuangdao from October 2021 to October 2023. The patients admitted from October 2021 to June 2023 were selected as the model group （n＝303）， and the patients admitted from July to October 2023 were selected as the validation group （n＝62）. The model group was divided into compliance subgroup （n＝126） and non-compliance subgroup （n＝177）. Univariate analysis combined with multivariate Logistic regression analysis were used to analyze the risk factors for non-compliance with inhaled formulations in patients； the risk prediction model was established through regression analysis， and the accuracy of the model prediction was evaluated based on the validation group of patients. RESULTS Multivariate Logistic regression analysis showed that simultaneous use of 2 inhaled formulations （OR＝3.730， 95%CI 1.996-6.971， P＜0.001）， the number of acute exacerbations within one year ≥2 （OR＝2.509， 95%CI 1.509-4.173， P＜0.001）， smoking （OR＝2.167， 95%CI 1.309-3.588， P＝0.003）， complicated with anxiety/depression （OR＝2.112， 95%CI 1.257-3.499， P＝0.004） and mMRC grading≥2 levels （OR＝1.701， 95%CI 1.014-2.853， P＝0.044） were risk factors for non-compliance with inhaled preparations. Based on this， a risk prediction model was established and the ROC curve was drawn. The areas under the curve of the model group and validation group were 0.836 and 0.928， and the overall accuracy of the model’s prediction was 88.71%. CONCLUSIONS The predictive model based on the simultaneous use of 2 inhaled formulations， the number of acute exacerbations within one year ≥2， smoking， complicated with anxiety/depression， mMRC grading ≥2 levels has certain predictive value for the risk of non-compliance with inhaled formulations for COPD patients.
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OBJECTIVE To construct a risk prediction model for bloodstream infection （BSI） induced by carbapenem-resistant Klebsiella pneumoniae （CRKP）. METHODS Retrospective analysis was conducted for clinical data from 253 patients with BSI induced by K. pneumoniae in the First Hospital of Qinhuangdao from January 2019 to June 2022. Patients admitted from January 2019 to December 2021 were selected as the model group （n＝223）， and patients admitted from January 2022 to June 2022 were selected as the validation group （n＝30）. The model group was divided into the CRKP subgroup （n＝56） and the carbapenem- sensitive K. pneumoniae （CSKP） subgroup （n＝167） based on whether CRKP was detected or not. The univariate and multivariate Logistic analyses were performed on basic information such as gender， age and comorbid underlying diseases in two subgroups of patients； independent risk factors were screened for CRKP-induced BSI， and a risk prediction model was constructed. The established model was verified with patients in the validation group as the target. RESULTS Admissioning to intensive care unit （ICU）， use of immunosuppressants， empirical use of carbapenems and empirical use of antibiotics against Gram-positive coccus were independent risk factors of CRKP-induced BSI （ORs were 3.749， 3.074， 2.909， 9.419， 95%CIs were 1.639-8.572， 1.292- 7.312， 1.180-7.717， 2.877-30.840， P＜0.05）. Based on this， a risk prediction model was established with a P value of 0.365. The AUC of the receiver operating characteristic （ROC） curve of the model was 0.848 [95%CI （0.779， 0.916）， P＜0.001]， and the critical score was 6.5. In the validation group， the overall accuracy of the prediction under the model was 86.67%， and the AUC of ROC curve was 0.926 [95%CI （0.809， 1.000]， P＜0.001]. CONCLUSIONS Admission to ICU， use of immunosuppressants， empirical use of carbapenems and empirical use of antibiotics against Gram-positive coccus are independent risk factors of CRKP- induced BSI. The CRKP-induced BSI risk prediction model based on the above factors has good prediction accuracy.
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ABSTRACT Introduction: Blood transfusion is a common practice in cardiac surgery, despite its well-known negative effects. To mitigate blood transfusion-associated risks, identifying patients who are at higher risk of needing this procedure is crucial. Widely used risk scores to predict the need for blood transfusions have yielded unsatisfactory results when validated for the Brazilian population. Methods: In this retrospective study, machine learning (ML) algorithms were compared to predict the need for blood transfusions in a cohort of 495 cardiac surgery patients treated at a Brazilian reference service between 2019 and 2021. The performance of the models was evaluated using various metrics, including the area under the curve (AUC), and compared to the commonly used Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST) scoring systems. Results: The study found that the model had the highest performance, achieving an AUC of 0.7350 (confidence interval [CI]: 0.7203 to 0.7497). Importantly, all ML algorithms performed significantly better than the commonly used TRACK and TRUST scoring systems. TRACK had an AUC of 0.6757 (CI: 0.6609 to 0.6906), while TRUST had an AUC of 0.6622 (CI: 0.6473 to 0.6906). Conclusion: The findings of this study suggest that ML algorithms may offer a more accurate prediction of the need for blood transfusions than the traditional scoring systems and could enhance the accuracy of predicting blood transfusion requirements in cardiac surgery patients. Further research could focus on optimizing and refining ML algorithms to improve their accuracy and make them more suitable for clinical use.
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Purpose: To correlate microvascular changes and assess the relationship between microvascular changes and cardiovascular disease (CVD) risk in patients with retinal vein occlusion (RVO). Methods: Patients over 40 years of age with unilateral RVO were included in this prospective study. Those known to have cardiovascular disease were excluded. A detailed medical history was taken and physical exam was done to measure the height, weight, body mass index (BMI), and systolic blood pressure (SBP). A comprehensive eye check?up was followed by optical coherence tomography angiography (OCTA). Microvascular indices such as vessel density (VD) and perfusion density (PD) were noted. A statistical model was developed for prediction of CVD risk and was integrated with the World Health Organization (WHO)’s risk prediction charts. Results: This study included 42 patients with RVO and 22 controls with an age range of 42–82 years. There were 40 males (62.5%) and 24 females (37.5%). Along with age, SBP, and gender, perfusion density was found to have significant impact on CVD risk (P = 0.030). Reduction in PD was associated with increase in CVD risk. PD had a greater influence on CVD in <50 years age than in >70 years group. Using linear regression, a model with accuracy of 72.1% was developed for CVD risk prediction and was converted into color coded charts similar to WHO risk prediction charts. Conclusion: These findings suggest a significant correlation between microvascular parameters and CVD risk in RVO patients. Based on these parameters, an easy?to?use and color?coded risk prediction chart was developed
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Objective To explore the epidemiological characteristics of pulmonary infection in elderly patients with chronic obstructive pulmonary disease (COPD), and to construct a risk prediction model. Methods Among of 125 elderly patients with COPD from May 2020 to June 2022 were selected as the research subjects. The epidemiological characteristics of infected patients were counted, and the risk factors of pulmonary infection in patients were analyzed and a prediction model was constructed. Results A total of the 125 elderly patients with COPD, there were 46 cases of pulmonary infection, with the infection rate of 36.80%. The detection rate of Gram-negative bacteria was higher than that of Gram-positive bacteria or fungi (64.44% vs 33.33% or 2.22%, P2=0.812 and P=0.295. ROC curve analysis revealed that the AUC value of the prediction model on predicting the pulmonary infection in elderly patients with COPD was 0.802. Conclusion The pathogenic bacteria of elderly patients with COPD complicated with pulmonary infection are mainly Gram-negative bacteria. The prediction model constructed according to the risk factors of pulmonary infection in patients has predictive value on pulmonary infection in patients.
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This study aimed to compare the predictive value of six selected anthropometric indicators for benign prostatic hyperplasia (BPH). Males over 50 years of age who underwent health examinations at the Health Management Center of the Second Xiangya Hospital, Central South University (Changsha, China) from June to December 2020 were enrolled in this study. The characteristic data were collected, including basic anthropometric indices, lipid parameters, six anthropometric indicators, prostate-specific antigen, and total prostate volume. The odds ratios (ORs) with 95% confidence intervals (95% CIs) for all anthropometric parameters and BPH were calculated using binary logistic regression. To assess the diagnostic capability of each indicator for BPH and identify the appropriate cutoff values, receiver operating characteristic (ROC) curves and the related areas under the curves (AUCs) were utilized. All six indicators had diagnostic value for BPH (all P ≤ 0.001). The visceral adiposity index (VAI; AUC: 0.797, 95% CI: 0.759-0.834) had the highest AUC and therefore the highest diagnostic value. This was followed by the cardiometabolic index (CMI; AUC: 0.792, 95% CI: 0.753-0.831), lipid accumulation product (LAP; AUC: 0.766, 95% CI: 0.723-0.809), waist-to-hip ratio (WHR; AUC: 0.660, 95% CI: 0.609-0.712), waist-to-height ratio (WHtR; AUC: 0.639, 95% CI: 0.587-0.691), and body mass index (BMI; AUC: 0.592, 95% CI: 0.540-0.643). The sensitivity of CMI was the highest (92.1%), and WHtR had the highest specificity of 94.1%. CMI consistently showed the highest OR in the binary logistic regression analysis. BMI, WHtR, WHR, VAI, CMI, and LAP all influence the occurrence of BPH in middle-aged and older men (all P ≤ 0.001), and CMI is the best predictor of BPH.
Subject(s)
Middle Aged , Male , Humans , Aged , Prostatic Hyperplasia , Obesity/epidemiology , Body Mass Index , China/epidemiology , Waist-Height Ratio , ROC Curve , Waist Circumference , Risk FactorsABSTRACT
The "Lübeck disaster", twins studies, adoptees studies, and other epidemiological observational studies have shown that host genetic factors play a significant role in determining the host susceptibility to Mycobacterium tuberculosis infection and pathogenesis of tuberculosis. From linkage analyses to genome-wide association studies, it has been discovered that human leucocyte antigen (HLA) genes as well as non-HLA genes (such as SLC11A1, VDR, ASAP1 as well as genes encoding cytokines and pattern recognition receptors) are associated with tuberculosis susceptibility. To provide ideas for subsequent studies about risk prediction of MTB infection and the diagnosis and treatment of tuberculosis, we review the research progress on tuberculosis susceptibility related genes in recent years, focusing on the correlation of HLA genes and non-HLA genes with the pathogenesis of tuberculosis. We also report the results of an enrichment analysis of the genes mentioned in the article. Most of these genes appear to be involved in the regulation of immune system and inflammation， and are also closely related to autoimmune diseases.
Subject(s)
Humans , Genome-Wide Association Study , Tuberculosis/genetics , Gene Expression Regulation , Cytokines/genetics , Autoimmune Diseases , Mycobacterium tuberculosis/genetics , Genetic Predisposition to DiseaseABSTRACT
BACKGROUND@#Previous cardiovascular risk prediction models in Japan have utilized prospective cohort studies with concise data. As the health information including health check-up records and administrative claims becomes digitalized and publicly available, application of large datasets based on such real-world data can achieve prediction accuracy and support social implementation of cardiovascular disease risk prediction models in preventive and clinical practice. In this study, classical regression and machine learning methods were explored to develop ischemic heart disease (IHD) and stroke prognostic models using real-world data.@*METHODS@#IQVIA Japan Claims Database was searched to include 691,160 individuals (predominantly corporate employees and their families working in secondary and tertiary industries) with at least one annual health check-up record during the identification period (April 2013-December 2018). The primary outcome of the study was the first recorded IHD or stroke event. Predictors were annual health check-up records at the index year-month, comprising demographic characteristics, laboratory tests, and questionnaire features. Four prediction models (Cox, Elnet-Cox, XGBoost, and Ensemble) were assessed in the present study to develop a cardiovascular disease risk prediction model for Japan.@*RESULTS@#The analysis cohort consisted of 572,971 invididuals. All prediction models showed similarly good performance. The Harrell's C-index was close to 0.9 for all IHD models, and above 0.7 for stroke models. In IHD models, age, sex, high-density lipoprotein, low-density lipoprotein, cholesterol, and systolic blood pressure had higher importance, while in stroke models systolic blood pressure and age had higher importance.@*CONCLUSION@#Our study analyzed classical regression and machine learning algorithms to develop cardiovascular disease risk prediction models for IHD and stroke in Japan that can be applied to practical use in a large population with predictive accuracy.