scholarly journals Inappropriate antibiotic prescribing for acute bronchiolitis in Colombia: a predictive model

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jefferson Antonio Buendía ◽  
John Edwin Feliciano-Alfonso
Keyword(s):  
Predictive Model ◽  
Bacterial Resistance ◽  
Pediatric Population ◽  
Area Under The Curve ◽  
Internal Validity ◽  
Sampling Bias ◽  
Antibiotic Prescribing ◽  
Inappropriate Prescription ◽  
Acute Bronchiolitis ◽  
Inappropriate Use

Abstract Introduction Acute bronchiolitis is the leading cause of hospitalization in the pediatric population. The inappropriate prescription of antibiotics in acute bronchiolitis is associated with bacterial resistance, higher costs, and risk of adverse effects in this population. The objective of this work is to develop a predictive model of inappropriate use of antibiotics in children with acute bronchiolitis in Colombia. Methods A retrospective cohort study was conducted in patients under 2 years of age with a diagnosis of acute bronchiolitis from two hospitals in Rionegro, Colombia. To identify factors independently associated with inappropriate use of antibiotics, we used logistic regression and estimated odds ratios (ORs). To assess discrimination, area under the curve (AUC) was estimated with a 95% confidence interval and plotted using AUC–ROC plots. To correct sampling bias of variance parameters and to evaluate the internal validity of the model, repeated curved validation “tenfold cross-validation” was used, comparing the area under the ROC curve obtained in the repetitions with that observed in the model Results A total of 415 patients were included. 142 patients (34.13%) had a prescription of some antibiotic during their hospital stay. In 92 patients (64.78%, 95% CI 56.3 to 72.6%) the prescription of antibiotics was classified as inappropriate. Age older than 1 year, chest retractions, temperature between 37.5 °C and 38.5 °C and leukocyte count between 10,000 and 15,000 million/mm3 were the predictive variables of inappropriate use of medications in this population. Conclusion The presence of fever between 37.5 °C and 38.5 °C, leukocytosis between 10,000 and 15,000 million/mm3, and age older than 1 year and presence of chest retractions, should alert the physician regarding the high risk of inappropriate prescription of antibiotics. Patients with acute bronchiolitis with a score on our scale greater than 2 should be carefully evaluated regarding the need for the use of antibiotics, if prescribed.

scholarly journals Study of antimicrobial use in paediatric inpatients in a tertiary care hospital in Ahmedabad, India

2020 ◽  
Vol 8 (1) ◽  
pp. 35
Author(s):  
Hetal N. Jeeyani ◽  
Rutvik H. Parikh ◽  
Sheena Sivanandan ◽  
Harsh J. Muliya ◽  
Shivam N. Badiyani ◽  
...  
Keyword(s):  
Bacterial Resistance ◽  
Tertiary Care ◽  
Antibiotic Use ◽  
Antibiotic Prescribing ◽  
Care Hospital ◽  
Antimicrobial Drugs ◽  
Prescribing Indicators ◽  
Inappropriate Use ◽  
Increased Risk ◽  
The Mean

Background: Inappropriate use and overuse of antibiotics are important factors leading to increased bacterial resistance apart from increased risk of adverse reactions. The aim of this study was to derive antibiotic use percentage, study its pattern and compare antibiotic prescribing indicators with standard indicators.Methods: This prospective observational study was conducted from 1st August 2018 to 31st July 2019 on paediatric inpatients from 1 month to 14 years. All the relevant data was taken from the case records of patients at the time of discharge. The data included: age, sex, hospital stay, clinical diagnosis and details of antimicrobial treatment.Results: From 989 patients, 85.9% were diagnosed with infectious illness, of which 60.1% had viral and 36.7% had bacterial infection. The use of antimicrobial drugs was 42.7% and antibiotics was 40.4%. The mean number of antibiotics received was 1.13±0.31. 90% patients received single antibiotic. 88.8% drugs were prescribed by generic name and 99% drugs were prescribed from essential drug formulary. 17 different antibiotics were used out of which ceftriaxone (62.5%) was the most commonly used. Groupwise, antibiotic use was cephalosporins (68.4%), penicillin (20.2%), aminoglycosides (4.31%), fluoroquinolones (0.9%) and macrolides (0.22%). The use of higher antibiotics like vancomycin (3.86%) and carbapenems (0.68%) was quite less.Conclusions: The antibiotic use in our hospital was higher than the WHO standard but less as compared to majority of other studies. Use of cephalosporins was more and penicillin was less as compared to other studies. This suggests that there is a need of implementing antibiotic stewardship programs to enhance rational antibiotic prescribing.

scholarly journals Prediction of the requirement of open reduction for developmental dysplasia of the hip

2017 ◽  
Vol 46 (1) ◽  
pp. 54-61 ◽  
Author(s):  
Nabil Alassaf
Keyword(s):  
Open Reduction ◽  
Area Under The Curve ◽  
Internal Validity ◽  
Developmental Dysplasia ◽  
Estimation Model ◽  
Cross Sectional ◽  
Noninvasive Treatment ◽  
Dysplasia Of The Hip ◽  
Logistic Regression Equation ◽  
Objective Tool

Objective Closed reduction (CR) is a noninvasive treatment for developmental dysplasia of the hip (DDH), and this treatment is confirmed intraoperatively. This study aimed to develop a preoperative estimation model of the probability of requiring open reduction (OR) for DDH. Methods The study design was cross-sectional by screening all patients younger than 2 years who had attempted CR between October 2012 and July 2016 by a single surgeon. Potential diagnostic determinants were sex, age, side, bilaterality, International Hip Dysplasia Institute (IHDI) grade, and acetabular index (AI). An intraoperative arthrogram was the reference standard. A logistic regression equation was built from a reduced model. Bootstrapping was performed for internal validity. Results A total of 164 hips in 104 patients who met the inclusion criteria were analysed. The prevalence of CR was 72.2%. Independent factors for OR were older age, higher IHDI grade, and lower AI. The probability of OR = 1/[1 + exp − (−2.753 + 0.112 × age (months) + 1.965 × IHDI grade III (0 or 1) + 3.515 × IHDI grade IV (0 or 1) − 0.058 × AI (degrees)]. The area under the curve was 0.79. Conclusion This equation is an objective tool that can be used to estimate the requirement for OR.

scholarly journals Predicting bacteriophage hosts based on sequences of annotated receptor-binding proteins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dimitri Boeckaerts ◽  
Michiel Stock ◽  
Bjorn Criel ◽  
Hans Gerstmans ◽  
Bernard De Baets ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Receptor Binding ◽  
Bacterial Infections ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Area Under The Curve ◽  
Local Alignment ◽  
Search Tool ◽  
Different Levels

AbstractNowadays, bacteriophages are increasingly considered as an alternative treatment for a variety of bacterial infections in cases where classical antibiotics have become ineffective. However, characterizing the host specificity of phages remains a labor- and time-intensive process. In order to alleviate this burden, we have developed a new machine-learning-based pipeline to predict bacteriophage hosts based on annotated receptor-binding protein (RBP) sequence data. We focus on predicting bacterial hosts from the ESKAPE group, Escherichia coli, Salmonella enterica and Clostridium difficile. We compare the performance of our predictive model with that of the widely used Basic Local Alignment Search Tool (BLAST). Our best-performing predictive model reaches Precision-Recall Area Under the Curve (PR-AUC) scores between 73.6 and 93.8% for different levels of sequence similarity in the collected data. Our model reaches a performance comparable to that of BLASTp when sequence similarity in the data is high and starts outperforming BLASTp when sequence similarity drops below 75%. Therefore, our machine learning methods can be especially useful in settings in which sequence similarity to other known sequences is low. Predicting the hosts of novel metagenomic RBP sequences could extend our toolbox to tune the host spectrum of phages or phage tail-like bacteriocins by swapping RBPs.

Inappropriate outpatient antibiotic use in children insured by Kentucky Medicaid

2021 ◽  
pp. 1-7
Author(s):  
Bethany A. Wattles ◽  
Kahir S. Jawad ◽  
Yana Feygin ◽  
Maiying Kong ◽  
Navjyot K. Vidwan ◽  
...  
Keyword(s):  
Claims Data ◽  
Inappropriate Prescribing ◽  
Patient Characteristics ◽  
Antibiotic Use ◽  
Population Based ◽  
Antibiotic Prescribing ◽  
Cross Sectional ◽  
Inappropriate Use ◽  
Pharmacy Claims ◽  
Medicaid Children

Abstract Objective: To describe risk factors associated with inappropriate antibiotic prescribing to children. Design: Cross-sectional, retrospective analysis of antibiotic prescribing to children, using Kentucky Medicaid medical and pharmacy claims data, 2017. Participants: Population-based sample of pediatric Medicaid patients and providers. Methods: Antibiotic prescriptions were identified from pharmacy claims and used to describe patient and provider characteristics. Associated medical claims were identified and linked to assign diagnoses. An existing classification scheme was applied to determine appropriateness of antibiotic prescriptions. Results: Overall, 10,787 providers wrote 779,813 antibiotic prescriptions for 328,515 children insured by Kentucky Medicaid in 2017. Moreover, 154,546 (19.8%) of these antibiotic prescriptions were appropriate, 358,026 (45.9%) were potentially appropriate, 163,654 (21.0%) were inappropriate, and 103,587 (13.3%) were not associated with a diagnosis. Half of all providers wrote 12 prescriptions or less to Medicaid children. The following child characteristics were associated with inappropriate antibiotic prescribing: residence in a rural area (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.07–1.1), having a visit with an inappropriate prescriber (OR, 4.15; 95% CI, 4.1–4.2), age 0–2 years (OR, 1.39; 95% CI, 1.37–1.41), and presence of a chronic condition (OR, 1.31; 95% CI, 1.28–1.33). Conclusions: Inappropriate antibiotic prescribing to Kentucky Medicaid children is common. Provider and patient characteristics associated with inappropriate prescribing differ from those associated with higher volume. Claims data are useful to describe inappropriate use and could be a valuable metric for provider feedback reports. Policies are needed to support analysis and dissemination of antibiotic prescribing reports and should include all provider types and geographic areas.

scholarly journals A predictive model to identify Parkinson disease from administrative claims data

Neurology ◽  
2017 ◽  
Vol 89 (14) ◽  
pp. 1448-1456 ◽  
Author(s):  
Susan Searles Nielsen ◽  
Mark N. Warden ◽  
Alejandra Camacho-Soto ◽  
Allison W. Willis ◽  
Brenton A. Wright ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Predictive Model ◽  
Claims Data ◽  
Demographic Data ◽  
Area Under The Curve ◽  
Rem Sleep Behavior Disorder ◽  
Administrative Claims ◽  
Sleep Behavior ◽  
Administrative Claims Data ◽  
Procedure Codes

Objective:To use administrative medical claims data to identify patients with incident Parkinson disease (PD) prior to diagnosis.Methods:Using a population-based case-control study of incident PD in 2009 among Medicare beneficiaries aged 66–90 years (89,790 cases, 118,095 controls) and the elastic net algorithm, we developed a cross-validated model for predicting PD using only demographic data and 2004–2009 Medicare claims data. We then compared this model to more basic models containing only demographic data and diagnosis codes for constipation, taste/smell disturbance, and REM sleep behavior disorder, using each model's receiver operator characteristic area under the curve (AUC).Results:We observed all established associations between PD and age, sex, race/ethnicity, tobacco smoking, and the above medical conditions. A model with those predictors had an AUC of only 0.670 (95% confidence interval [CI] 0.668–0.673). In contrast, the AUC for a predictive model with 536 diagnosis and procedure codes was 0.857 (95% CI 0.855–0.859). At the optimal cut point, sensitivity was 73.5% and specificity was 83.2%.Conclusions:Using only demographic data and selected diagnosis and procedure codes readily available in administrative claims data, it is possible to identify individuals with a high probability of eventually being diagnosed with PD.

scholarly journals Did admissions caused by respiratory infections and asthma decrease during the COVID-19 pandemic?

2021 ◽  
Author(s):  
Zeynep Onay ◽  
Deniz Mavi ◽  
Yetkin Ayhan ◽  
Sinem Can Oksay ◽  
Gulay Bas ◽  
...  
Keyword(s):  
Respiratory Tract ◽  
Outpatient Clinic ◽  
Lower Respiratory Tract ◽  
Respiratory Tract Infections ◽  
Respiratory Infections ◽  
Pediatric Population ◽  
Pediatric Intensive Care ◽  
Acute Bronchiolitis ◽  
Lower Respiratory Tract Infections ◽  
Tract Infections

Background: COVID-19 outbreak lead to nationwide lockdown on the March 16th, 2020 in Turkey. We aimed to quantitively determine the change in frequency of upper and lower respiratory tract infections and asthma in pediatric population associated with COVID-19. Methods: The electronic medical record data of pediatric population admitted to the emergency department (ED), outpatient and inpatient clinics and pediatric intensive care unit (PICU) were analyzed with the diagnosis of Influenza, upper and lower respiratory tract infections (URTI, LRTI) acute bronchiolitis and asthma. The data of the first year of the pandemic was compared with the previous year. Results: In total 112496 admissions were made between April 1, 2019 and March 31, 2021 in our hospital. A decline was observed in ED admissions (-73%) and outpatient clinic (-70%) visits, hospitalizations (-41.5%) and PICU admissions (-42%). The admissions with the diagnosis of Influenza and URTI had a decline from 4.26% to 0.37% (p=0.0001), and from 81.54% to 75.62% (p=0.0001), respectively. An increase was observed in the LRTI, acute bronchiolitis and asthma (from 8.22% to 10.01% (p=0.0001), from 2.76% to 3.07% (p=0.027) and from 5.96% to 14% (p=0.0001), respectively). Conclusions: A dramatic decrease was observed in the number of admissions to ED and inpatient clinics and outpatient clinic visits and PICU admissions, and, when the rates of admissions were compared, the general rate of admissions to ED showed a decrease while inpatient, outpatient clinics and PICU admissions demonstrated an increase during the pandemic.

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

scholarly journals Analysis of patterns of antibiotic prescribing in public health facilities in Nepal

2020 ◽  
Vol 14 (01) ◽  
pp. 18-27 ◽  
Author(s):  
Anant Nepal ◽  
Delia Hendrie ◽  
Suzanne Robinson ◽  
Linda A Selvey
Keyword(s):  
Public Health ◽  
Cross Sectional Study ◽  
Health Facilities ◽  
Antibiotic Prescribing ◽  
Who Guidelines ◽  
Prescribing Practices ◽  
Cross Sectional ◽  
Middle Income ◽  
Public Health Facilities ◽  
Inappropriate Use

Introduction: Inappropriate use of antibiotics is recognised as a leading cause of antibiotic resistance. Little is known about antibiotic prescribing practices at public health facilities in low- and middle-income countries. We examined patterns of antibiotic prescribing in public health facilities in Nepal and explored factors influencing these practices. Methodology: A cross-sectional study of antibiotic prescribing in public health facilities was conducted in the Rupandehi district of Nepal. Six public health facilities were selected based on WHO guidelines, and data were extracted from administrative records for 6,860 patient encounters. Patterns of antibiotic prescribing were investigated using descriptive statistics. Chi-squared tests and logistic regressions were applied to explore factors associated with antibiotic prescribing. Results: Of patients attending public health facilities, the proportion prescribed at least one antibiotic (44.7%) was approximately twice the WHO recommended value (20.0 to 26.8%). The antibiotic prescribing rate for hospital inpatients (64.6%) was higher than for other facilities, with the prescribing rate also high in primary health care centres (50.4%) and health posts (52.2%). The most frequently (29.9%) prescribed antibiotic classes were third-generation cephalosporins. Females (p = 0.005) and younger (p < 0.001) patients were more likely to be prescribed antibiotics. High prescribing rates of antibiotics for selected diseases appeared contrary to international recommendations. Conclusion: Antibiotic prescribing in public health facilities was high compared with WHO guidelines, suggesting the need for strategies to reduce misuse of antibiotics. This study provides useful information to assist in formulating policies and guidelines to promote more appropriate use of antibiotics in Nepal.

Editorial

2008 ◽  
Vol 7 (1) ◽  
Author(s):  
Chris Roseveare ◽  
Keyword(s):  
Emergency Medicine ◽  
Antibiotic Resistance ◽  
Respiratory Illness ◽  
Community Acquired Pneumonia ◽  
Antibiotic Prescribing ◽  
Acute Medicine ◽  
St Segment Elevation ◽  
Combined Training ◽  
Inappropriate Use ◽  
Exacerbation Of Copd

Infection control and antibiotic prescribing have been high on the list of priorities for clinicians working in acute medicine over recent years. Concerns about antibiotic resistance have encouraged many hospital-based and speciality society guidelines to take a broad spectrum approach to the septic patient. However, this approach risks exacerbating the problems of resistance and increasing the incidence of Clostridium Diffi cile diarrhoea, particularly amongst our elderly patients. Finding the appropriate balance is a key priority for physicians working at the hospital’s ‘front door’. Three of our review articles highlight some of the issues involved in this area. In an extensively researched review, Lille and Barlow provide a microbiological perspective on the topic of antibiotic resistance. Their approach suggests a mechanism for risk assessment in relation to the likelihood of antibiotic resistance, while the results of cultures are awaited. Although most hospitals have their own specifi c prescribing guidelines, the algorithms in this article should provide a useful reference guide. Two other reviews deal specifi cally with issues around management of acute respiratory illness. Distinction between community acquired pneumonia (CAP) and exacerbation of COPD remains a signifi cant area of confusion in relation to antibiotic prescribing. Inappropriate use of CAP severity scoring for patients with COPD frequently leads to over-use of intravenous antibiotics, for a condition which is often non-infective in origin. The medical complications of cocaine use are not as common a cause of hospital admission as pneumonia or COPD. However, the increasing recreational use of this drug, highlighted in the review by Irvine and Penston, requires that acute physicians are familiar with its consequences and their treatment. Cardiac-type chest pain and arrhythmias are the most likely complications to present on the acute medical take, but pneumothorax and pneumomediastinum are also well recognised. Consideration of cocaine as a cause for ST segment elevation is important, given that thrombolysis is generally contraindicated; pressure to administer thrombolytic drugs within 30 minutes for patients with STEMI requires that the appropriate questions are asked at the time of admission. Issues around acute medicine training and the interface with emergency medicine continue to cause controversy. In an article submitted in response to a Viewpoint article published last year, Gallitelli and colleagues imply that the approach in Italy is progressing towards the development of combined training in acute and emergency medicine. Although the development of Acute Care Common Stem rotations in the UK may suggest a step in a similar direction, there remains a need to fi nd ways in which specialist trainees in both areas can work more closely together to attain necessary competencies in management of acutely unwell adults.

scholarly journals Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

2019 ◽  
Vol 14 (3) ◽  
pp. 40-57
Author(s):  
Yazan Alnsour ◽  
Rassule Hadidi ◽  
Neetu Singh
Keyword(s):  
Hospital Mortality ◽  
Predictive Model ◽  
Health Care Providers ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Area Under The Curve ◽  
The United States ◽  
Hospital Death ◽  
Care Providers ◽  
Chi Square

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.

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