scholarly journals Risk factors for the treatment outcome of retreated pulmonary tuberculosis patients in China: an optimized prediction model

2017 ◽  
Vol 145 (9) ◽  
pp. 1805-1814 ◽  
Author(s):  
X.-M. WANG ◽  
S.-H. YIN ◽  
J. DU ◽  
M.-L. DU ◽  
P.-Y. WANG ◽  
...  
Keyword(s):  
Risk Factors ◽  
Treatment Outcome ◽  
Pulmonary Tuberculosis ◽  
Treatment Failure ◽  
Risk Score ◽  
Risk Scores ◽  
Predictive Values ◽  
Factors Associated ◽  
Positive Culture Result ◽  
Score Model

SUMMARYRetreatment of tuberculosis (TB) often fails in China, yet the risk factors associated with the failure remain unclear. To identify risk factors for the treatment failure of retreated pulmonary tuberculosis (PTB) patients, we analyzed the data of 395 retreated PTB patients who received retreatment between July 2009 and July 2011 in China. PTB patients were categorized into ‘success’ and ‘failure’ groups by their treatment outcome. Univariable and multivariable logistic regression were used to evaluate the association between treatment outcome and socio-demographic as well as clinical factors. We also created an optimized risk score model to evaluate the predictive values of these risk factors on treatment failure. Of 395 patients, 99 (25·1%) were diagnosed as retreatment failure. Our results showed that risk factors associated with treatment failure included drug resistance, low education level, low body mass index (<18·5), long duration of previous treatment (>6 months), standard treatment regimen, retreatment type, positive culture result after 2 months of treatment, and the place where the first medicine was taken. An Optimized Framingham risk model was then used to calculate the risk scores of these factors. Place where first medicine was taken (temporary living places) received a score of 6, which was highest among all the factors. The predicted probability of treatment failure increases as risk score increases. Ten out of 359 patients had a risk score >9, which corresponded to an estimated probability of treatment failure >70%. In conclusion, we have identified multiple clinical and socio-demographic factors that are associated with treatment failure of retreated PTB patients. We also created an optimized risk score model that was effective in predicting the retreatment failure. These results provide novel insights for the prognosis and improvement of treatment for retreated PTB patients.

scholarly journals A Modified CAIDE Risk Score as a Screening Tool for Cognitive Impairment in Older Adults

2021 ◽  
pp. 1-14
Author(s):  
Magdalena I. Tolea ◽  
Jaeyeong Heo ◽  
Stephanie Chrisphonte ◽  
James E. Galvin
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Cognitive Impairment ◽  
Risk Score ◽  
Clinical Sample ◽  
Community Sample ◽  
Community Dwelling ◽  
Cognitive Testing ◽  
Risk Scores ◽  
Predictive Values

Background: Although an efficacious dementia-risk score system, Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) was derived using midlife risk factors in a population with low educational attainment that does not reflect today’s US population, and requires laboratory biomarkers, which are not always available. Objective: Develop and validate a modified CAIDE (mCAIDE) system and test its ability to predict presence, severity, and etiology of cognitive impairment in older adults. Methods: Population consisted of 449 participants in dementia research (N = 230; community sample; 67.9±10.0 years old, 29.6%male, 13.7±4.1 years education) or receiving dementia clinical services (N = 219; clinical sample; 74.3±9.8 years old, 50.2%male, 15.5±2.6 years education). The mCAIDE, which includes self-reported and performance-based rather than blood-derived measures, was developed in the community sample and tested in the independent clinical sample. Validity against Framingham, Hachinski, and CAIDE risk scores was assessed. Results: Higher mCAIDE quartiles were associated with lower performance on global and domain-specific cognitive tests. Each one-point increase in mCAIDE increased the odds of mild cognitive impairment (MCI) by up to 65%, those of AD by 69%, and those for non-AD dementia by >  85%, with highest scores in cases with vascular etiologies. Being in the highest mCAIDE risk group improved ability to discriminate dementia from MCI and controls and MCI from controls, with a cut-off of ≥7 points offering the highest sensitivity, specificity, and positive and negative predictive values. Conclusion: mCAIDE is a robust indicator of cognitive impairment in community-dwelling seniors, which can discriminate well between dementia severity including MCI versus controls. The mCAIDE may be a valuable tool for case ascertainment in research studies, helping flag primary care patients for cognitive testing, and identify those in need of lifestyle interventions for symptomatic control.

scholarly journals Predictive risk score model for severe fever with thrombocytopenia syndrome mortality based on qSOFA and SIRS scoring system

2020 ◽  
Author(s):  
Li Wang ◽  
Zhiqiang Zou ◽  
Kun Ding ◽  
Chunguo Hou ◽  
Song Qin
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Risk Score ◽  
Predictive Value ◽  
Multivariable Logistic Regression Analysis ◽  
Predictive Values ◽  
Independent Risk Factors ◽  
Score Model ◽  
Qsofa Score ◽  
Severe Fever

Abstract Background: Severe fever with thrombocytopenia syndrome (SFTS) is a severe systemic virus infectious disease usually having multi-organ dysfunction which resembles sepsis. Methods: Data of 321 patients with laboratory-confirmed SFTS from May 2013 to July 2017 were retrospectively analyzed. Demographic and clinical characteristics, calculated quick sequential organ failure assessment (qSOFA) score and systemic inflammatory response syndrome (SIRS) criteria for survivors and nonsurvivors were compared. Independent risk factors associated with in-hospital mortality were obtained using multivariable logistic regression analysis. Risk score models containing different risk factors for mortality in stratified patients were established whose predictive values were evaluated using the area under ROC curve (AUC). Results: Of 321 patients, 87 died (27.1%). Age ( p <0.001) and percentage numbers of patients with qSOFA≥2 and SIRS≥2 ( p <0.0001) were profoundly greater in nonsurvivors than in survivors. Age, qSOFA, SIRS score and aspartate aminotransferase (AST) were independent risk factors for mortality for all patients. And qSOFA score was the only common risk factor in all patients, those age≥60 years and those enrolled in the intensive care unit (ICU). A risk score model containing all these risk factors (Model1) has high predictive value for in-hospital mortality in these three groups with AUCs (95% CI): 0.919 (0.883-0.946), 0.929 (0.862-0.944) and 0.815 (0.710-0.894), respectively. A model only including age and qSOFA also has high predictive value for mortality in these groups with AUCs (95% CI): 0.872 (0.830-0.906), 0.885(0.801-0.900) and 0.865 (0.767-0.932), respectively. Conclusions: Risk models containing qSOFA have high predictive validity for SFTS mortality.

scholarly journals Predictive risk score model for severe fever with thrombocytopenia syndrome mortality based on qSOFA and SIRS scoring system

2020 ◽  
Author(s):  
Li Wang ◽  
Zhiqiang Zou ◽  
Kun Ding ◽  
Chunguo Hou
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Risk Score ◽  
Predictive Value ◽  
Multivariable Logistic Regression Analysis ◽  
Predictive Values ◽  
Independent Risk Factors ◽  
Score Model ◽  
Qsofa Score ◽  
Severe Fever

Abstract Background: Severe fever with thrombocytopenia syndrome (SFTS) is a severe systemic virus infectious disease usually having multi-organ dysfunction which resembles sepsis.Methods: Data of 321 patients with laboratory-confirmed SFTS from May 2013 to July 2017 were retrospectively analyzed. Demographic and clinical characteristics, calculated quick sequential organ failure assessment (qSOFA) score and systemic inflammatory response syndrome (SIRS) criteria for survivors and nonsurvivors were compared. Independent risk factors associated with in-hospital mortality were obtained using multivariable logistic regression analysis. Risk score models containing different risk factors for mortality in stratified patients were established whose predictive values were evaluated using the area under ROC curve (AUC).Results: Of 321 patients, 87 died (27.1%). Age (p<0.001) and percentage numbers of patients with qSOFA≥2 and SIRS≥2 (p<0.0001) were profoundly greater in nonsurvivors than in survivors. Age, qSOFA, SIRS score and aspartate aminotransferase (AST) were independent risk factors for mortality for all patients. And qSOFA score was the only common risk factor in all patients, those age≥60 years and those enrolled in the intensive care unit (ICU). A risk score model containing all these risk factors (Model1) has high predictive value for in-hospital mortality in these three groups with AUCs (95% CI): 0.919 (0.883-0.946), 0.929 (0.862-0.944) and 0.815 (0.710-0.894), respectively. A model only including age and qSOFA also has high predictive value for mortality in these groups with AUCs (95% CI): 0.872 (0.830-0.906), 0.885(0.801-0.900) and 0.865 (0.767-0.932), respectively.Conclusions: Risk models containing qSOFA have high predictive validity for SFTS mortality.

scholarly journals Use of Electronic Data to Identify Risk Factors Associated with Clostridium difficile Infection (CDI) and to Develop CDI Risk Scores

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S403-S403
Author(s):  
Laurie Aukes ◽  
Bruce Fireman ◽  
Edwin Lewis ◽  
Julius Timbol ◽  
John Hansen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clostridium Difficile ◽  
Risk Score ◽  
Skilled Nursing Facility ◽  
Risk Scores ◽  
Office Visits ◽  
Skilled Nursing ◽  
Factors Associated ◽  
C Statistic ◽  
Grant Recipient

Abstract Background Clostridium difficile is a major cause of severe diarrhea in the U.S. We described characteristics of Kaiser Permanente Northern California (KPNC) members with C. difficile infection (CDI), identified risk factors associated with CDI, and developed risk scores to predict who may develop CDI. Methods Retrospective cohort study with all KPNC members ≥18 years old from May 2011 to July 2014 comparing demographic and clinical characteristics for those with and without lab-confirmed incident CDI. We included CDI risk factors in logistic regression models to estimate the risk of developing future CDI after an Identification Recruitment Date (IRD), a time when an individual might be a good candidate for a C. difficile vaccine clinical trial. Two risk score models were created and cross validated (70% of the data used for development and 30% for testing). Results During the study period, there were 9,986 CDI cases and 2,230,354 members without CDI. CDI cases tended to be ≥65 years old (59% vs.. 21%), female (61% vs. 53%), and white race (70% vs. 53%), with more hospitalizations (42% vs. 3%), emergency room visits (51% vs. 14%), and skilled nursing facility stays (25% vs. 0.6%) in the year prior to CDI compared with members without CDI. At least 10 office visits within the prior year (53% vs. 16%), use of antibiotics in last 12 weeks (81% vs. 11%), proton pump inhibitors in the last year (36% vs. 7%), and multiple medical conditions within the prior year (e.g., chronic kidney disease, congestive heart failure, and pneumonia) were important risk factors for CDI. Using a hospital discharge event as the IRD, our risk score model yielded excellent performance in predicting the likelihood of developing CDI in the subsequent 31 – 365 days (C-statistic of 0.851). Using a random date as the IRD, our model also predicted CDI risk in the subsequent 1–30 days (C-statistic 0.658) and 31–365 days (C-statistic 0.722) reasonably well. Conclusion CDI can be predicted by increasing age, medications, comorbidities and healthcare exposure, particularly ≥10 office visits, hospitalizations, and skilled nursing stays in the prior year and recent antibiotics. Such risk factors can be used to identify high-risk populations for C. difficile vaccine clinical studies. Disclosures H. Yu, Pfizer, Inc.: Employee, Salary; B. Cai, Pfizer, Inc.: Employee, Salary; E. Gonzalez, Pfizer, Inc.: Employee, Salary; J. Lawrence, Pfizer, Inc.: Employee, Salary; N. P. Klein, GSK: Investigator, Grant recipient; sanofi pasteur: Investigator, Grant recipient; Merck & Co: Investigator, Grant recipient; MedImmune: Investigator, Grant recipient; Protein Sciences: Investigator, Grant recipient; Pfizer: Investigator, Grant recipient

scholarly journals Predictive risk score model for severe fever with thrombocytopenia syndrome mortality based on qSOFA and SIRS scoring system

2019 ◽  
Author(s):  
Li Wang ◽  
Kun Ding ◽  
Chunguo Hou ◽  
Zhiqiang Zou ◽  
Song Qin
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Risk Score ◽  
Risk Groups ◽  
Multivariable Logistic Regression Analysis ◽  
Predictive Values ◽  
Independent Risk Factors ◽  
Score Model ◽  
Qsofa Score ◽  
Severe Fever

Abstract Background: Severe fever with thrombocytopenia syndrome (SFTS) is a severe systemic virus infectious disease usually having multi-organ dysfunction which resembles sepsis.Methods: A total of 321 patients with laboratory-confirmed SFTS from May 2013 to July 2017 were retrospectively analysed. Demographic and clinical characteristics, calculated quick sequential organ failure assessment (qSOFA) score and systemic inflammatory response syndrome (SIRS) criteria for survivors and nonsurvivors were compared. Independent risk factors associated with in-hospital mortality were obtained using multivariable logistic regression analysis. Risk score models containing different risk factors for mortality in stratified patients were established whose predictive values were evaluated using area under ROC curve (AUC).Results: Of 321 patients, 87 died (27.1%). Age (p<0.001) and percentage numbers of patients with qSOFA≥2 and SIRS≥2 (p<0.0001) were profoundly greater in nonsurvivors than in survivors. Age, qSOFA, SIRS score and aspartate aminotransferase (AST) were independent risk factors for mortality for all patients. And qSOFA score was the only common risk factor in all patients, those of age≥60 years and those enrolled in intensive care unit (ICU). A risk score model containing all these risk factors (Model1) has high predictive value for in-hospital mortality in these three groups with AUCs (95% CI): 0.919 (0.883-0.946), 0.929 (0.862-0.944) and 0.815 (0.710-0.894), respectively. Kaplan-Meier survival analysis showed a strong difference between high-risk and low-risk groups at a cutoff value > 9.22 (log-rank c2 = 126.3, p <0.0001) Conclusions: qSOFA and risk models containing qSOFA have high predictive validity for SFTS in-hospital mortality.

scholarly journals Body Mass Index, Multi-Morbidity, and COVID-19 Risk Factors as Predictors of Severe COVID-19 Outcomes

2021 ◽  
Vol 12 ◽  
pp. 215013272110185
Author(s):  
Sanjeev Nanda ◽  
Audry S. Chacin Suarez ◽  
Loren Toussaint ◽  
Ann Vincent ◽  
Karen M. Fischer ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Hospital Admission ◽  
Body Mass ◽  
Risk Score ◽  
The Body ◽  
Risk Scores ◽  
Nasopharyngeal Swab ◽  
Morbidity Score ◽  
The Impact

Purpose The purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes. Patients Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between January 1, 2020 and May 23, 2020; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Measures Demographic and clinical data were extracted from the electronic medical record. The data included: date of birth, gender, ethnicity, race, marital status, medications (active COVID-19 agents), weight and height (from which the Body Mass Index (BMI) was calculated, history of smoking, and comorbid conditions to calculate the Charlson Comorbidity Index (CCI) and the U.S Department of Health and Human Services (DHHS) multi-morbidity score. An additional COVID-19 Risk Score was also included. Outcomes included hospital admission, ICU admission, and death. Results Cox proportional hazards models were used to determine the impact on mortality or hospital admission. Age, sex, and race (white/Latino, white/non-Latino, other, did not disclose) were adjusted for in the model. Patients with higher COVID-19 Risk Scores had a significantly higher likelihood of being at least admitted to the hospital (HR = 1.80; 95% CI = 1.30, 2.50; P < .001), or experiencing death or inpatient admission (includes ICU admissions) (HR = 1.20; 95% CI = 1.02, 1.42; P = .028). Age was the only statistically significant demographic predictor, but obesity was not a significant predictor of any of the outcomes. Conclusion Age and COVID-19 Risk Scores were significant predictors of severe COVID-19 outcomes. Further work should examine the properties of the COVID-19 Risk Factors Scale.

scholarly journals Risk score to stratify miscarriage risk levels in preconception women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Hui Choo ◽  
Chee Wai Ku ◽  
Yin Bun Cheung ◽  
Keith M. Godfrey ◽  
Yap-Seng Chong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Smoking Status ◽  
Characteristic Curve ◽  
Lifestyle Changes ◽  
Risk Scores ◽  
Risk Level ◽  
Multivariable Logistic Regression Model ◽  
Spontaneous Miscarriage ◽  
Risk Levels

AbstractSpontaneous miscarriage is one of the most common complications of pregnancy. Even though some risk factors are well documented, there is a paucity of risk scoring tools during preconception. In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. Multivariable logistic regression model coefficients were used to determine risk estimates for age, ethnicity, history of pregnancy loss, body mass index, smoking status, alcohol intake and dietary supplement intake; from these we derived a risk score ranging from 0 to 17. Miscarriage before 16 weeks of gestation, determined clinically or via ultrasound. Among 465 included women, 59 had miscarriages and 406 had pregnancy ≥ 16 weeks of gestation. Higher rates of miscarriage were observed at higher risk scores (5.3% at score ≤ 3, 17.0% at score 4–6, 40.0% at score 7–8 and 46.2% at score ≥ 9). Women with scores ≤ 3 were defined as low-risk level (< 10% miscarriage); scores 4–6 as intermediate-risk level (10% to < 40% miscarriage); scores ≥ 7 as high-risk level (≥ 40% miscarriage). The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.

Factors associated to first line antiretroviral therapy (ART) failure among HIV/AIDS patients at Sanglah Hospital, Bali

2017 ◽  
pp. 4
Author(s):  
Cok Istri Sri Dharma Astiti ◽  
A.A Sagung Sawitri ◽  
Tuti Parwati
Keyword(s):  
Risk Factors ◽  
Treatment Failure ◽  
Clinical Stage ◽  
Hiv And Aids ◽  
First Line ◽  
Aids Patients ◽  
Factors Associated ◽  
Art Initiation ◽  
Risk Of Treatment ◽  
Hiv Aids

Background and purpose: The incidence of first line ART failure is increasing in the South East Asia region. The main referral hospital in Bali has recorded an increased use of second line ART due to the first line ART failure. This study aims to explore risk factors associated to first line ART failure.Methods: A case control study was conducted among people living with HIV and AIDS at Sanglah Hospital Denpasar who started first line ART between 2004 and 2013. Cases were those who diagnosed as having clinical treatment failure and still on treatment in 2015. Controls were those with no treatment failure. Sex and year of ART initiation were matched between case and control. Data were obtained from medical records that include initial regiments, HIV mode of transmission, the WHO HIV clinical stage, CD4 count, opportunistic infections, body mass index, hemoglobin level, and drug substitution at the beginning and during treatment. Risk factors were analysed using logistic regression.Results: Out of 68 HIV/AIDS patients with clinical ART failure, 72.1% were confirmed with immunological and 36.8% were confirmed with virological failure. Median time before treatment failure was 3.5 years. Factors associated to ART failure were HIV clinical stage IV with (AOR=3.43; 95%CI=1.65-7.13) and being widow/widower (AOR=4.85; 95%CI=1.52-15.53). Patients with TB co-infection have a lower risk for treatment failure due to early diagnosis and treatment through TB-HIV program with (AOR=0.32; 95%CI=0.14-0.70).Conclusions: Higher HIV clinical stage at ART initiation increases the risk of treatment failure. HIV-TB co-infection indirectly reduces the risk of treatment failure.

Abstract 31: Prediction of coronary artery and abdominal aortic calcium in middle-aged adults using the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) risk score: the CARDIA study

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Christopher Prendergast ◽  
Jamal S Rana ◽  
Alex McMahan ◽  
Henry McGill ◽  
Jeff Carr ◽  
...  
Keyword(s):  
Risk Factors ◽  
Young Adults ◽  
Coronary Artery ◽  
Risk Score ◽  
Model Performance ◽  
Baseline Risk ◽  
Risk Scores ◽  
Abdominal Aortic ◽  
Point Increase ◽  
Better Than

Background: Risk scores to predict coronary and abdominal atherosclerosis have been developed from autopsy specimens from the right coronary artery and descending aorta and risk factors measured post-mortem in the Pathobiological Determinants of Atherosclerosis in Youth Study (PDAY), cohort aged 15-34 years. While the PDAY risk score predicts coronary artery calcium (CAC) up to 15 years before its assessment in Coronary Artery Risk Development in Young Adults (CARDIA), the clinical validity at 25 years follow up and of the abdominal score to predict abdominal aortic calcium (AAC) has not been tested. Objectives: To test the hypotheses: 1) PDAY risk scores at CARDIA baseline (cohort aged 18-30 years) predict CAC and abdominal aortic calcium (AAC) at year 25 better than PDAY risk scores measured at subsequent time points. 2) Change in risk over time will improve associations in Aim 1. Methods: The CARDIA study assessed CAC and AAC by computed tomography in young adults participating at year 25. The PDAY risk score was calculated from risk factors measured at the CARDIA examinations at years 0 (cohort aged 18-30 years), 5, 10, 15 and 25 (age 43-55 years). Results: Age and gender independent PDAY risk scores increased from CARDIA year 0 to year 25 (coronary from 1.80 to 4.95, abdomen from 1.88 to 3.18). Prevalence of any CAC was 28% and AAC was 53%. For a one point increase in risk score, baseline risk predicted CAC and AAC better than year 25 risk; change in risk improved the prediction further (Table). C-statistics for CAC prediction were higher at year 0 but similar at years 0 and 25 for AAC. Further analyses using PDAY scores calculated at 5 year intervals prior to the CT scan confirmed that CAC at baseline and AAC at year 5 had the highest Odds Ratios for a one point increase in score (AAC year 5 Odds Ratio 1.35 (1.30-1.41)). Conclusions: PDAY risk scores, applied to living adults in the CARDIA study, predicted CAC and AAC in 43-55 year old adults; including change in risk slightly improves model performance.

Sign in / Sign up

Export Citation Format

Share Document