scholarly journals Development and Validation of a 90-day Mortality Prediction Nomogram for AMI Patients: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Rui Yang ◽  
Wen Ma ◽  
Tao Huang ◽  
Lu-Ming Zhang ◽  
Di-Di Han ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Random Forest ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Mortality Prediction ◽  
Multiple Logistic Regression ◽  
Goodness Of Fit Test ◽  
Standard Curve ◽  
Mimic Iii

Abstract Background: The purpose of this study was to identify the factors influencing the 90-day mortality of acute myocardial infarction(AMI) patients, and to establish a prognostic model for these patients based on the MIMIC-III database.Methods: Retrospective study methods were used to collect AMI patient data that met the inclusion criteria from the MIMIC-III database. Variable importance selection was determined using the random forest algorithm. Multiple logistic regression was used to determine AMI-related risk factors, with the results represented as a nomogram.Results: The baseline scores for the training and validation groups were very flat, and indicators for developing risk-model nomograms were obtained after random forest and multiple logistic regression. The AUC of the risk model was the highest (0.826 and 0.818 in the training and validation groups, respectively) . The Hosmer-Lemeshow goodness-of-fit test and standard curve both produced very consistent results. Both the NRI and IDI values indicated that the risk model had significant predictive power, and DCA results indicated that the risk model had good net benefits for clinical application.Conclusions: The results of this study indicated that age, troponinT, VT, VFI, MI_his, APS-III, bypass, and PCI were risk factors for 90-day mortality in AMI patients. Interactive nomograms could provide intuitive and concise personalized 90-day mortality predictions for AMI patients.

scholarly journals Risk factors for peripheral venous catheter failure: A prospective cohort study of 5345 patients

2021 ◽  
pp. 112972982110150
Author(s):  
Ya-mei Chen ◽  
Xiao-wen Fan ◽  
Ming-hong Liu ◽  
Jie Wang ◽  
Yi-qun Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Cohort Study ◽  
Goodness Of Fit ◽  
Insertion Site ◽  
Healthcare Providers ◽  
Venous Catheter ◽  
Goodness Of Fit Test ◽  
Peripheral Venous Catheter ◽  
Independent Risk Factors

Purpose: The objective of this study was to determine the independent risk factors associated with peripheral venous catheter (PVC) failure and develop a model that can predict PVC failure. Methods: This prospective, multicenter cohort study was carried out in nine tertiary hospitals in Suzhou, China between December 2017 and February 2018. Adult patients undergoing first-time insertion of a PVC were observed from catheter insertion to removal. Logistic regression was used to identify the independent risk factors predicting PVC failure. Results: This study included 5345 patients. The PVC failure rate was 54.05% ( n = 2889/5345), and the most common causes of PVC failure were phlebitis (16.3%) and infiltration/extravasation (13.8%). On multivariate analysis, age (45–59 years: OR, 1.295; 95% CI, 1.074–1.561; 60–74 years: OR, 1.375; 95% CI, 1.143–1.654; ⩾75 years: OR, 1.676; 95% CI, 1.355–2.073); department (surgery OR, 1.229; 95% CI, 1.062–1.423; emergency internal/surgical ward OR, 1.451; 95% CI, 1.082–1.945); history of venous puncture in the last week (OR, 1.298, 95% CI 1.130–1.491); insertion site, number of puncture attempts, irritant fluid infusion, daily infusion time, daily infusion volume, and type of sealing liquid were independent predictors of PVC failure. Receiver operating characteristic curve analysis indicated that a logistic regression model constructed using these variables had moderate accuracy for the prediction of PVC failure (area under the curve, 0.781). The Hosmer-Lemeshow goodness of fit test demonstrated that the model was correctly specified (χ2 = 2.514, p = 0.961). Conclusion: This study should raise awareness among healthcare providers of the risk factors for PVC failure. We recommend that healthcare providers use vascular access device selection tools to select a clinically appropriate device and for the timely detection of complications, and have a list of drugs classified as irritants or vesicants so they can monitor patients receiving fluid infusions containing these drugs more frequently.

scholarly journals Development and Validation of a Risk Scoring System for Cephamycin-Associated Hemorrhagic Events

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tong-Ling Chien ◽  
Fei-Yuan Hsiao ◽  
Li-Ju Chen ◽  
Yu-Wen Wen ◽  
Shu-Wen Lin
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Scoring System ◽  
Goodness Of Fit ◽  
Bleeding Tendency ◽  
Multivariate Logistic Regression ◽  
Goodness Of Fit Test ◽  
Hemorrhagic Events ◽  
Risk Scoring ◽  
Risk Scoring System

Abstract Cephamycin-associated hemorrhages have been reported since their launch. This research aimed to determine risk factors for cephamycin-associated hemorrhagic events and produce a risk scoring system using National Taiwan University Hospital (NTUH) database. Patients who were older than 20 years old and consecutively used study antibiotics for more than 48 hours (epidode) at NTUH between January 1st, 2009 and December 31st, 2015 were included. The population was divided into two cohorts for evaluation of risk factors and validation of the scoring system. Multivariate logistic regression was used for the assessment of the adjusted association between factors and the outcome of interest. Results of the multivariate logistic regression were treated as the foundation to develop the risk scoring system. There were 46402 and 22681 episodes identified in 2009–2013 and 2014–2015 cohorts with 356 and 204 hemorrhagic events among respective cohorts. Use of cephamycins was associated with a higher risk for hemorrhagic outcomes (aOR 2.03, 95% CI 1.60–2.58). Other risk factors included chronic hepatic disease, at least 65 years old, prominent bleeding tendency, and bleeding history. A nine-score risk scoring system (AUROC = 0.8035, 95% CI 0.7794–0.8275; Hosmer-Lemeshow goodness-of-fit test p = 0.1044) was developed based on the identified risk factors, with higher scores indicating higher risk for bleeding. Use of cephamycins was associated with more hemorrhagic events compared with commonly used penicillins and cephalosporins. The established scoring system, CHABB, may help pharmacists identify high-risk patients and provide recommendations according to the predictive risk, and eventually enhance the overall quality of care.

scholarly journals Binary Logistic Model to Identify the Factors Associated with Households with Bank Accounts in Nepal

2020 ◽  
Vol 2 (2) ◽  
pp. 323-336
Author(s):  
Santosh Kumar Shah
Keyword(s):  
Logistic Regression ◽  
Financial Institutions ◽  
Goodness Of Fit ◽  
Receiver Operating Curve ◽  
P Value ◽  
Ratio Test ◽  
Multiple Logistic Regression ◽  
Goodness Of Fit Test ◽  
Factors Associated ◽  
Bank Accounts

Introduction: Banks play an important role in ensuringthe economicand social stability, and the sustainablegrowth of the economy. The savings and other accounts in financial institutions, including banks, finances, microfinances and cooperatives, enable people to execute important financial functions. Thus, households that have accounts in any of financial institutions can have access to various banking services. Objective: The objective of the study is to identify the factors associated with households having bank accounts in Nepal. Methods: The analysis is based on household data extracted from the dataset of Nepal Demographic and Health Survey, 2016. The dependent variable is dichotomous, as the households with bank accounts and without bank accounts in any formal financial channels. In order to identify the factors associated with households receiving financial services in Nepal, multiple logistic regression models were developed by examining the model adequacy test. Results: The study finds that a total of 66.9% of the households had bank accounts. Several variables were found to be 1% of significance level. The predictive power of the model is found to be 31.2% and multicollinearity among the independent variables was absent. The Hosmer-Lemoshow goodness of fit test revealed that the data were poorly (p-value=0.056) fitted by the model. However, Osius-Rojek goodness of fit test (z=0.11; p-value=0.911), Stukel test (Z=0.683, p-value=0.494), likelihood ratio test (χ2=2770; p-value<0.0001) and area under receiver operating curve (79.8%) revealed that fitted model was good. Conclusion: Multiple logistic regression model revealed that in mountainous and hilly regions, women-headed households have less chances of not having bank accounts compared to the Terai region and men-headed households. The chances of having a bank account in province-2 is even worse than in Karnali and other provinces. The odds of not having bank accounts gradually decreased with the increase in size of agricultural land, wealth index, increase in family size and the number of family members who have completed secondary education.

Risk factors and outcomes of postoperative emergency response team activation: a matched case-control study

2020 ◽  
Vol 22 (1) ◽  
pp. 6-14
Author(s):  
Matthew I Hardman ◽  
◽  
S Chandralekha Kruthiventi ◽  
Michelle R Schmugge ◽  
Alexandre N Cavalcante ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Postoperative Complications ◽  
Emergency Response ◽  
Case Control Study ◽  
Case Control ◽  
Multiple Logistic Regression ◽  
Increased Risk ◽  
Response Team ◽  
Control Study

OBJECTIVE: To determine patient and perioperative characteristics associated with unexpected postoperative clinical deterioration as determined for the need of a postoperative emergency response team (ERT) activation. DESIGN: Retrospective case–control study. SETTING: Tertiary academic hospital. PARTICIPANTS: Patients who underwent general anaesthesia discharged to regular wards between 1 January 2013 and 31 December 2015 and required ERT activation within 48 postoperative hours. Controls were matched based on age, sex and procedure. MAIN OUTCOME MEASURES: Baseline patient and perioperative characteristics were abstracted to develop a multiple logistic regression model to assess for potential associations for increased risk for postoperative ERT. RESULTS: Among 105 345 patients, 797 had ERT calls, with a rate of 7.6 (95% CI, 7.1–8.1) calls per 1000 anaesthetics (0.76%). Multiple logistic regression analysis showed the following risk factors for postoperative ERT: cardiovascular disease (odds ratio [OR], 1.61; 95% CI, 1.18–2.18), neurological disease (OR, 1.57; 95% CI, 1.11–2.22), preoperative gabapentin (OR, 1.60; 95% CI, 1.17–2.20), longer surgical duration (OR, 1.06; 95% CI, 1.02–1.11, per 30 min), emergency procedure (OR, 1.54; 95% CI, 1.09–2.18), and intraoperative use of colloids (OR, 1.50; 95% CI, 1.17–1.92). Compared with control participants, ERT patients had a longer hospital stay, a higher rate of admissions to critical care (55.5%), increased postoperative complications, and a higher 30-day mortality rate (OR, 3.36; 95% CI, 1.73–6.54). CONCLUSION: We identified several patient and procedural characteristics associated with increased likelihood of postoperative ERT activation. ERT intervention is a marker for increased rates of postoperative complications and death.

Case control study of gastrointestinal complications after cardiopulmonary bypass heart surgery

Perfusion ◽  
2009 ◽  
Vol 24 (3) ◽  
pp. 173-178 ◽  
Author(s):  
Guowei Zhang ◽  
Naishi Wu ◽  
Hongyu Liu ◽  
Hang Lv ◽  
Zhifa Yao ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Cardiopulmonary Bypass ◽  
Heart Surgery ◽  
Perfusion Pressure ◽  
Multiple Logistic Regression Analysis ◽  
Univariate Analysis ◽  
Multiple Logistic Regression ◽  
Gastrointestinal Complications ◽  
Preoperative Serum

Background: Gastrointestinal complications (GIC) after cardiopulmonary bypass (CPB) surgery are rare, but, nevertheless, extremely dangerous.The identification of risks for GIC may be helpful in planning appropriate perioperative management strategies. The aim of the present study was to analyze perioperative factors of GIC in patients undergoing CPB surgery. Methods: We retrospectively analysed 206 patients who underwent GIC after cardiopulmonary bypass surgery from 2000 to 2007 and compared them with 206 matched control patients (matched for surgery, temperature, hemodilution and date). Univariate analysis and multiple logistic regression analysis were performed on 12 risk factors. Result: Sex and types of cardioplegia perfusate did not significantly influence the GIC after CPB surgery. Multiple logistic regression revealed that CPB time, preoperative serum creatinine (PSC) ≥ 179 mg/dL, emergency surgery, perfusion pressure ≤40mmHg, low cardiac output syndrome (LCOS), age ≥ 61, mechanical ventilation ≥96 h, New York Heart Association (NYHA) class III and IV were predictors of the occurrence of GIC after CPB surgery. Perfusion pressure and aprotinin administration were protective factors. Conclusion: Gastrointestinal complications after CPB surgery could be predictive in the presence of the above risk factors. This study suggests that GIC can be reduced by maintenance of higher perfusion pressure and shortening the time on CPB and ventilation.

scholarly journals Predicting mortality in hemodialysis patients using machine learning analysis

2020 ◽  
Author(s):  
Victoria Garcia-Montemayor ◽  
Alejandro Martin-Malo ◽  
Carlo Barbieri ◽  
Francesco Bellocchio ◽  
Sagrario Soriano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Prediction Models ◽  
Initial Period ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Prediction Of Mortality ◽  
Mortality Prediction Models

Abstract Background Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. Results There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

scholarly journals Incidence and Risk Factors for Prolonged Opioid Use after Arthroscopic Meniscal Surgery: An Analysis of 107,717 Cases

2020 ◽  
Vol 8 (7_suppl6) ◽  
pp. 2325967120S0046
Author(s):  
Jacqueline Baron ◽  
Alan Shamrock ◽  
Trevor Gulbrandsen ◽  
Brian Wolf ◽  
Kyle Duchman ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Logistic Regression ◽  
Chronic Obstructive ◽  
Multiple Logistic Regression ◽  
Opioid Use ◽  
Obstructive Pulmonary Disease ◽  
Factors Associated ◽  
Meniscal Surgery

Objectives: The current opioid epidemic in the United States is a significant cause of increasing morbidity and mortality. The purpose of this study was to determine rate of opioid use before and after arthroscopic meniscal surgery, and assess patient factors associated with prolonged opioid use following primary arthroscopic meniscal surgery. Methods: Patients undergoing primary arthroscopic meniscal surgery procedures from 2007-2016 were retrospectively accessed from the Humana Inc. administrative claims database. Patients were categorized as patients who filled opioid prescriptions within 3 months (OU), within 1 month (A-OU), between 1 to 3 months (C-OU), and never filled opioid prescriptions (N-OU) before surgery. Rates of opioid use were evaluated preoperatively and longitudinally tracked for OU and N-OU cohorts. Prolonged opioid use was defined as continued opioid prescription filling at ≥3 months after surgery. Multiple logistic regression analysis was used to control for various patient characteristics and identify factors associated with opioid use at 12 months after surgery, with significance defined as P<0.05 Results: There were 107,717 patients (54% female) that underwent arthroscopic meniscal surgery during the study period, of which 46.1% (n=49,630) were N-OU. One year after surgery, opioid fill rate was significantly higher in the OU group compared to the N-OU group with a relative risk of 6.98 (21.1% vs 3.02%; 95% CI: 6.61-7.36; p<0.0001). Multiple logistic regression model identified C-OU (OR:10.23, 95% CI: 9.74-10.76, p<0.0001) as the strongest predictor of opioid use at 12 months postoperatively. Furthermore, patients with acute preoperative opioid use (p<0.0001), preoperative diagnosis of diabetes mellitus (p<0.0001), hypertension (p<0.0001), chronic obstructive pulmonary disease (p<0.0001), anxiety or depression (p<0.0001), alcohol abuse (p= 0.0019), and tobacco use (p=0.0345) had a significantly increased odds of opioid use at 12 months postoperatively. However, males (p<0.0001) and patients <40 years (p<0.0001) had a significantly decreased odds of opioid use 12 months postoperatively. Conclusion: Preoperative opioid use is a significant risk factor for opioid use at 12 months following surgery. Diabetes mellitus, hypertension, chronic obstructive pulmonary disease, smoking status, and psychiatric diagnosis were independent risk factors for opioid use 1-year following surgery.

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