Using procalcitonin (PCT) to guide antibiotics escalation in suspicious infected patients: a new application of PCT in ICU

2020 ◽  
Author(s):  
Xu Wang ◽  
Huaiwu He ◽  
Yun Long ◽  
Guangliang Shan ◽  
Dongkai Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Antibiotic Therapy ◽  
Binary Logistic Regression ◽  
Empirical Antibiotic Therapy ◽  
Binary Logistic Regression Model ◽  
College Hospital ◽  
Suspected Infection ◽  
Significant Difference ◽  
Before And After ◽  
Wbc Count

Abstract Background Empirical antibiotic therapy before pathogen recognition is a tricky problem for suspicious infected patients in ICU. Procalcitonin (PCT) is efficient in degrading antibiotics level without increasing complications for its sensitivity and specificity in bacterial infection. Considering this, we hypothesized that PCT might be significant in indicating timely antibiotics escalation and improve patients’ outcomes. Methods This was a single-center retrospective study including patients with suspected infection who were admitted to Peking Union Medical College Hospital from January 2014 to June 2018. Patients were labelled as “antibiotic escalation” or “non-escalation” according to their antimicrobial use 48 h before and after the "PCT alert". “PCT alert” was defined as PCT ≥1.0 ng/mL that had not decreased by at least 10% from the previous day or from baseline, or a single measurement ≥ 1.0 ng/ml. Indicators that possibly influenced the prognosis were collected. 28-day intensive care unit (ICU)-free days were calculated; ICU stays >20 days and ≤20 days were considered nonprolonged ICU stays (nPISs) and prolonged ICU stays (PISs), respectively. Difference analysis and binary logistic regression were performed to determine the factors that influenced the 28-day ICU-free days . Results A total of 1109 patients were included, 654 in the PIS group, other 455 in nPIS group. The PIS group had higher rates of pathogen identification (33.94% vs 28.13%, P=0.047) and escalated antibiotic therapy (35.47% vs 20.66%, P<0.001) but a lower proportion of surgical patients (39.45% vs 54.95%, P<0.001) than the nPIS group. Regarding PCT, the values on the 1st day (20.36±43.89 vs 14.89±30.37 ug/L, P=0.014) and on the “alert day” (24.24±46.38 vs 18.75±32.69 ug/L; P=0.021) were higher in the PIS group than nPIS group, but no significant difference in the white blood cell (WBC) count was revealed. According to the binary logistic regression model, antibiotic escalation (OR=0.552, 95% CI 0.347-0.877, P=0.012) was a negative factor for PIS, while postsurgical status (OR=1.959, 95% CI 1.269-3.023, P=0.002) and age (OR=1.020, 95% CI 1.007-1.034, P=0.003) were positive factors. Conclusions PCT is significant in evaluating the effect of empirical antibiotic therapy. Escalating microbial ranks when “alert PCT” appeared can increase 28-day ICU-free days.

scholarly journals Increased procalcitonin indicates antibiotic escalation in ICU patients: an observational study

2019 ◽  
Author(s):  
Xu Wang ◽  
Huaiwu He ◽  
YUN LONG ◽  
Guangliang Shan ◽  
Dongkai Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Antibiotic Therapy ◽  
Binary Logistic Regression ◽  
Pathogen Identification ◽  
Binary Logistic Regression Model ◽  
College Hospital ◽  
Suspected Infection ◽  
Significant Difference ◽  
Before And After ◽  
Wbc Count

Abstract Background: Empirical antibiotic therapy often fails to cover all pathogens for patients with critical infection without pathogen identification. In these patients, progressive infection can manifest as a “procalcitonin (PCT) alert”. Delayed proper antibiotic escalation could worsen their prognosis. We hypothesized that for these patients, escalating antibiotics after a “PCT alert” would improve their outcomes.Methods: This was a single-center retrospective study including patients with suspected infection who were admitted to Peking Union Medical College Hospital from January 2014 to June 2018. Patients were labelled as “antibiotic escalation” or “nonescalation” according to their antimicrobial use 48 h before and after the "PCT alert". “PCT alert” was defined as PCT ≥1.0 ng/mL that had not decreased by at least 10% from the previous day or from baseline, or a single measurement ≥ 1.0 ng/ml. Indicators that possibly influenced the prognosis were collected. 28-day intensive care unit (ICU)-free days were calculated; ICU stays >20 days and ≤20 days were considered nonprolonged ICU stays (nPISs) and prolonged ICU stays (PISs), respectively. Difference analysis and binary logistic regression were performed to determine the factors that influenced the 28-day ICU-free days . Results: A total of 1109 patients were included, 654 in the PIS group, other 455 in nPIS group. The PIS group had higher rates of pathogen identification (33.94% vs 28.13%, P=0.047) and escalated antibiotic therapy (35.47% vs 20.66%, P<0.001) but a lower proportion of surgical patients (39.45% vs 54.95%, P<0.001) than the nPIS group. Regarding PCT, the values on the 1st day (20.36±43.89 vs 14.89±30.37 ug/L, P=0.014) and on the “alert day” (24.24±46.38 vs 18.75±32.69 ug/L; P=0.021) were higher in the PIS group than nPIS group, but no significant difference in the white blood cell (WBC) count was revealed. According to the binary logistic regression model, antibiotic escalation (OR=0.552, 95% CI 0.347-0.877, P=0.012) was a negative factor for PIS, while postsurgical status (OR=1.959, 95% CI 1.269-3.023, P=0.002) and age (OR=1.020, 95% CI 1.007-1.034, P=0.003) were positive factors. Conclusions Escalating antibiotics in high-risk infection patients whose PCT does not decrease expectedly after administering broad-spectrum antimicrobials may reduce their ICU stay.

Prevalence of gastrointestinal helminthes in camels of hyper-arid partially irrigated zone of Rajasthan

2018 ◽  
Author(s):  
K. P. Parmar ◽  
Abhishek Gupta ◽  
P. K. Pilania ◽  
N. Kumar ◽  
Monika . ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Faecal Examination ◽  
Helminthic Infections ◽  
Faecal Samples ◽  
Significant Difference ◽  
Examination Techniques ◽  
Age And Sex

A total 509 faecal samples were collected from camel population of different age and sex from hyper-arid partially irrigated zone of Rajasthan from March, 2016 to January, 2017.An overall prevalence rate of helminthic infectionswas recorded in 60.70% camelsby conventional faecal examination techniques. Among helminthes, highest prevalence was of strongyles (49.31%) followed by Trichuris sp. (24.75%), Strongyloides sp. (14.14%) and Nematodirus sp.(13.16%). Egg per gram counts of Strongyloides sp, Trichuris sp, and strongyle group was recorded from 100-900 (261.11±40.08); 100-1600 (564.81±23.17) and 100-1900 (624.07±47.73), respectively. Statistical analysis using multivariate binary logistic regression model revealed a highly significant difference (p less than 0.01) in seasonal dynamics and district wise prevalence of gastrointestinal helminthic infections in camel population whereasage and sex wise prevalence analysis revealed statistically non-significant difference. Coproculture study revealed the presence of Haemonchus sp., Trichostrongylus sp., Strongyloides sp. and Nematodirus sp. in the decreasing order of prevalence

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

Could Be % MMEF and sGaw Determinant Diagnostic Parameters Between Partial Reversibility and Reversibility in Evaluating Short-Acting Bronchodilator Response?

2021 ◽  
Author(s):  
Gökhan Erdoğan ◽  
Elif Altuğ ◽  
Sacide Rana Işık ◽  
Levent Tabak
Keyword(s):  
Independent Predictor ◽  
Binary Logistic Regression ◽  
Threshold Value ◽  
Test Results ◽  
Binary Logistic Regression Model ◽  
Bronchodilator Response ◽  
Significant Difference ◽  
Post Hoc ◽  
The Relationship ◽  
Airway Conductance

INTRODUCTION: By examining the relationship between changes in maximum mild-expiratory flow( MMEF) and specific airway conductance(sGaw), parameters with the change in FEV1 when evaluating the spirometer test and the bronchodilator response, we investigated their diagnostic contribution to the bronchodilator response in those with partial responses to FEV1. METHODS: The retrospective study sample consists of data from 112 patients between Jun 1, 2019, and Feb 1, 2020 who applied to the pulmonary function laboratory with a pre-diagnosis of bronchial hyperreactivity as well as body plethysmography test performed together with the reversibility test. RESULTS: MMEF% and ▲sGaw were linearly correlated with ▲FEV1 (respectively r = 0.752; p <0.001, r = 0.611; p <0.001). While there was a significant difference between ▲MMEF% and ▲sGaw between reversible, partially reversible, and irreversible groups (P <0.001), there was no significant difference in ▲sGaw between partial reversible and reversible groups in post-hoc comparisons (P> 0.05). In the binary logistic regression model created between the partially reversible and reversible groups, demographic characteristics, MMEF% and ▲sGaw variables, ▲MMEF was an independent predictor [OR: 1.132; 95% CI (1.036-1.238), p = 0.006]. The 24% threshold for absolute reversibility or partial reversibility calculated with MMEF% was significant, indicating significance at 86.2% sensitivity and 80.8% specificity (AUC: 0.811, 95% CI: 0.686-0.936; p <0.001). This value we found defined 81% of the partially reversible group as reversible. DISCUSSION AND CONCLUSION: We observed that ▲sGaw alone didn' have a determinant contribution for determining reversibility in bronchodilator response, which showed partial reversibility with respect to FEV1 change. We believe that ▲MMEF% can be an independent predictor between these two groups and the calculated threshold value of 24% can be used as a criterion for determining the reversibility in cases where FEV1 is not determinant.

scholarly journals A predictive model for early diagnosis of keratoconus

2020 ◽  
Author(s):  
GRACIA CASTRO-LUNA ◽  
ANTONIO PÉREZ-RUEDA
Keyword(s):  
Logistic Regression ◽  
Predictive Model ◽  
Early Stage ◽  
Corneal Thickness ◽  
Binary Logistic Regression ◽  
Cross Sectional Study ◽  
Bivariate Analysis ◽  
Diagnostic Model ◽  
Binary Logistic Regression Model ◽  
Cross Sectional

Abstract Background: The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models Methods: The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables Results: Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90º and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90º and posterior coma to 90º. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%Conclusions: The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90º and the posterior coma to 90º.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PEROLEHAN KREDIT OLEH PENGUSAHA MIKRO

2018 ◽  
Vol 4 (3) ◽  
Author(s):  
Abdul Azis Safii ◽  
Tri Suwarno
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Large Scale ◽  
Logistic Regression Model ◽  
Banking Sector ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Access To Credit ◽  
The Poor ◽  
Micro Enterprises

Abstract: The number of micro-entrepreneurs and the dominant number of micro enterprises compared to medium and large-scale enterprises in Indonesia are not balanced by the provision of access to credit and venture capital for micro businesses. This resulted in a micro-sector sector identical to the poor being vulnerable to exploitation by moneylenders who exploit the difficulties of micro entrepreneurs accessing credit from the banking sector. This study examines the factors that determine the accessibility of credit by micro entrepreneur in Bojonegoro regency. A total sum of 270 micro entrepreneurs who have applied for banking loan were sampled from the study area. With an binary logistic regression model the research resulting that education, skill on entrepreneur, and monthly net profits generated by the microenterprise are significant in determining the accessibility of microcredit. Keywords: micro entrepreneur, microcredit, credit accessibility Abstrak: Perkembangan jumlah pengusaha mikro serta dominannya jumlah usaha mikro dibandingkan dengan usaha menengah dan usaha besar di Indonesia, tidak diimbingi dengan penyediaan akses kredit dan modal usaha bagi para pelaku usaha mikro. Hal tersebut mengakibatkan sektor usaha mikro yang identik dengan masyarakat miskin rentan dieksploitasi oleh rentenir yang memanfaatkan sulitnya para pengusaha mikro mengakses kredit dari sektor perbankan. Penelitian ini menggunakan data primer yang di ambil langsung dari pengusaha mikro dengan teknik kuesioner. Analisis data dengan metode binary logistic regression mendapatkan hasil variabel yang berpengaruh signifikan terhadap akses kredit para pengusaha mikro adalah variabel usia pengusaha, laba bersih usaha tiap bulan, dan jumlah karyawan yang di pekerjakan. Kata kunci : usaha mikro, microcredit, akses kredit

scholarly journals Using the Econometric Models for Identification of Risk Factors for Albanian Smes (Case Study: Smes of Gjirokastra Region)

2021 ◽  
Vol 18 ◽  
pp. 163-170
Author(s):  
Lorenc Koçiu ◽  
Kledian Kodra
Keyword(s):  
Logistic Regression ◽  
Statistical Models ◽  
Logistic Regression Model ◽  
Qualitative Data ◽  
Linear Models ◽  
Binary Logistic Regression ◽  
Random Variable ◽  
Econometric Models ◽  
Binary Logistic Regression Model

Using the econometric models, this paper addresses the ability of Albanian Small and Medium-sizedEnterprises (SMEs) to identify the risks they face. To write this paper, we studied SMEs operating in theGjirokastra region. First, qualitative data gathered through a questionnaire was used. Next, the 5-level Likertscale was used to measure it. Finally, the data was processed through statistical software SPSS version 21,using the binary logistic regression model, which reveals the probability of occurrence of an event when allindependent variables are included. Logistic regression is an integral part of a category of statistical models,which are called General Linear Models. Logistic regression is used to analyze problems in which one or moreindependent variables interfere, which influences the dichotomous dependent variable. In such cases, the latteris seen as the random variable and is dependent on them. To evaluate whether Albanian SMEs can identifyrisks, we analyzed the factors that SMEs perceive as directly affecting the risks they face. At the end of thepaper, we conclude that Albanian SMEs can identify risk

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