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Kaal A, Meziyerh S, van Burgel N, Dane M, Kolfschoten N, Mahajan P, Julián-Jiménez A, Steyerberg E, van Nieuwkoop C. Procalcitonin for Safe Reduction of Unnecessary Blood Cultures in the Emergency Department: Development and Validation of a Prediction Model. J Infect 2024:106251. [PMID: 39182652 DOI: 10.1016/j.jinf.2024.106251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 08/13/2024] [Accepted: 08/14/2024] [Indexed: 08/27/2024]
Abstract
OBJECTIVES Blood cultures (BCs) are commonly ordered in emergency departments (EDs), while a minority yields a relevant pathogen. Diagnostic stewardship is needed to safely reduce unnecessary BCs. We aimed to develop and validate a bacteremia prediction model for ED patients, with specific focus on the benefit of incorporating procalcitonin. METHODS We included adult patients with suspected bacteremia from a Dutch ED for a one-year period. We defined 23 candidate predictors for a "full model", of which nine were used for an automatable "basic model". Variations of both models with C-reactive protein and procalcitonin were constructed using LASSO regression, with bootstrapping for internal validation. External validation was done in an independent cohort of patients with confirmed infection from 71 Spanish EDs. We assessed discriminative performance using the C-statistic and calibration with calibration curves. Clinical usefulness was evaluated by sensitivity, specificity, saved BCs, and Net Benefit. RESULTS Among 2111 patients in the derivation cohort (mean age 63 years, 46% male), 273 (13%) had bacteremia, versus 896 (20%) in the external cohort (n = 4436). Adding procalcitonin substantially improved performance for all models. The basic model with procalcitonin showed most promise, with a C-statistic of 0.87 (0.86-0.88) upon external validation. At a 5% risk threshold, it showed a sensitivity of 99% and could have saved 29% of BCs while only missing 10 out of 896 (1.1%) bacteremia patients. CONCLUSIONS Procalcitonin-based bacteremia prediction models can safely reduce unnecessary BCs at the ED. Further validation is needed across a broader range of healthcare settings.
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Affiliation(s)
- Anna Kaal
- Department of Internal Medicine, Haga Teaching Hospital, The Hague, the Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
| | - Soufian Meziyerh
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Nathalie van Burgel
- Department of Medical Microbiology, Haga Teaching Hospital, The Hague, the Netherlands
| | - Martijn Dane
- Department of Clinical Chemistry, Haga Teaching Hospital, The Hague, the Netherlands
| | - Nikki Kolfschoten
- Department of Emergency Medicine, Haga Teaching Hospital, The Hague, the Netherlands
| | - Prashant Mahajan
- Department of Emergency Medicine, University of Michigan Hospital, United States
| | - Agustin Julián-Jiménez
- Department of Emergency Medicine, Complejo Hospitalario Universitario de Toledo, Spain; IDISCAM (Instituto de Investigación Sanitaria de Castilla La Mancha), Universidad de Castilla La Mancha, Toledo, Spain
| | - Ewout Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Cees van Nieuwkoop
- Department of Internal Medicine, Haga Teaching Hospital, The Hague, the Netherlands; Health Campus The Hague, Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
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Heinze G, Baillie M, Lusa L, Sauerbrei W, Schmidt CO, Harrell FE, Huebner M. Regression without regrets -initial data analysis is a prerequisite for multivariable regression. BMC Med Res Methodol 2024; 24:178. [PMID: 39117997 PMCID: PMC11308558 DOI: 10.1186/s12874-024-02294-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 07/01/2024] [Accepted: 07/24/2024] [Indexed: 08/10/2024] Open
Abstract
Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regression model can be easily fit with standard software packages. This bears the risk that data analysts may rush to perform sophisticated analyses without sufficient knowledge of basic properties, associations in and errors of their data, leading to wrong interpretation and presentation of the modeling results that lacks clarity. Ignorance about special features of the data such as redundancies or particular distributions may even invalidate the chosen analysis strategy. Initial data analysis (IDA) is prerequisite to regression analyses as it provides knowledge about the data needed to confirm the appropriateness of or to refine a chosen model building strategy, to interpret the modeling results correctly, and to guide the presentation of modeling results. In order to facilitate reproducibility, IDA needs to be preplanned, an IDA plan should be included in the general statistical analysis plan of a research project, and results should be well documented. Biased statistical inference of the final regression model can be minimized if IDA abstains from evaluating associations of outcome and predictors, a key principle of IDA. We give advice on which aspects to consider in an IDA plan for data screening in the context of regression modeling to supplement the statistical analysis plan. We illustrate this IDA plan for data screening in an example of a typical diagnostic modeling project and give recommendations for data visualizations.
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Affiliation(s)
- Georg Heinze
- Center for Medical Data Science, Institute of Clinical Biometrics, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
| | | | - Lara Lusa
- Faculty of Mathematics, Department of Mathematics, University of Primorska, Natural Sciences and Information Technology, Koper, Slovenia
- Faculty of Medicine, Institute of Biostatistics and Medical Informatics, University of Ljubljana, Ljubljana, Slovenia
| | - Willi Sauerbrei
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
| | - Carsten Oliver Schmidt
- Institute of Community Medicine, University Medicine of Greifswald, SHIP-KEF, Greifswald, Germany
| | - Frank E Harrell
- School of Medicine, Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Marianne Huebner
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
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Waterlow NR, Cooper BS, Robotham JV, Knight GM. Antimicrobial resistance prevalence in bloodstream infection in 29 European countries by age and sex: An observational study. PLoS Med 2024; 21:e1004301. [PMID: 38484006 PMCID: PMC10939247 DOI: 10.1371/journal.pmed.1004301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/22/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Antibiotic usage, contact with high transmission healthcare settings as well as changes in immune system function all vary by a patient's age and sex. Yet, most analyses of antimicrobial resistance (AMR) ignore demographic indicators and provide only country-level resistance prevalence values. This study aimed to address this knowledge gap by quantifying how resistance prevalence and incidence of bloodstream infection (BSI) varied by age and sex across bacteria and antibiotics in Europe. METHODS AND FINDINGS We used patient-level data collected as part of routine surveillance between 2015 and 2019 on BSIs in 29 European countries from the European Antimicrobial Resistance Surveillance Network (EARS-Net). A total of 6,862,577 susceptibility results from isolates with age, sex, and spatial information from 944,520 individuals were used to characterise resistance prevalence patterns for 38 different bacterial species and antibiotic combinations, and 47% of these susceptibility results were from females, with a similar age distribution in both sexes (mean of 66 years old). A total of 349,448 isolates from 2019 with age and sex metadata were used to calculate incidence. We fit Bayesian multilevel regression models by country, laboratory code, sex, age, and year of sample to quantify resistant prevalence and provide estimates of country-, bacteria-, and drug-family effect variation. We explore our results in greater depths for 2 of the most clinically important bacteria-antibiotic combinations (aminopenicillin resistance in Escherichia coli and methicillin resistance in Staphylococcus aureus) and present a simplifying indicative index of the difference in predicted resistance between old (aged 100) and young (aged 1). At the European level, we find distinct patterns in resistance prevalence by age. Trends often vary more within an antibiotic family, such as fluroquinolones, than within a bacterial species, such as Pseudomonas aeruginosa. Clear resistance increases by age for methicillin-resistant Staphylococcus aureus (MRSA) contrast with a peak in resistance to several antibiotics at approximately 30 years of age for P. aeruginosa. For most bacterial species, there was a u-shaped pattern of infection incidence with age, which was higher in males. An important exception was E. coli, for which there was an elevated incidence in females between the ages of 15 and 40. At the country-level, subnational differences account for a large amount of resistance variation (approximately 38%), and there are a range of functional forms for the associations between age and resistance prevalence. For MRSA, age trends were mostly positive, with 72% (n = 21) of countries seeing an increased resistance between males aged 1 and 100 years and a greater change in resistance in males. This compares to age trends for aminopenicillin resistance in E. coli which were mostly negative (males: 93% (n = 27) of countries see decreased resistance between those aged 1 and 100 years) with a smaller change in resistance in females. A change in resistance prevalence between those aged 1 and 100 years ranged up to 0.51 (median, 95% quantile of model simulated prevalence using posterior parameter ranges 0.48, 0.55 in males) for MRSA in one country but varied between 0.16 (95% quantile 0.12, 0.21 in females) to -0.27 (95% quantile -0.4, -0.15 in males) across individual countries for aminopenicillin resistance in E. coli. Limitations include potential bias due to the nature of routine surveillance and dependency of results on model structure. CONCLUSIONS In this study, we found that the prevalence of resistance in BSIs in Europe varies substantially by bacteria and antibiotic over the age and sex of the patient shedding new light on gaps in our understanding of AMR epidemiology. Future work is needed to determine the drivers of these associations in order to more effectively target transmission and antibiotic stewardship interventions.
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Affiliation(s)
- Naomi R. Waterlow
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, EPH, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ben S. Cooper
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Julie V. Robotham
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with the UK Health Security Agency, Oxford, United Kingdom
| | - Gwenan Mary Knight
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, EPH, London School of Hygiene and Tropical Medicine, London, United Kingdom
- AMR Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Han H, Kim DS, Kim M, Heo S, Chang H, Lee GT, Lee SU, Kim T, Yoon H, Hwang SY, Cha WC, Sim MS, Jo IJ, Park JE, Shin TG. A Simple Bacteremia Score for Predicting Bacteremia in Patients with Suspected Infection in the Emergency Department: A Cohort Study. J Pers Med 2023; 14:57. [PMID: 38248758 PMCID: PMC10817606 DOI: 10.3390/jpm14010057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/23/2023] [Accepted: 12/24/2023] [Indexed: 01/23/2024] Open
Abstract
Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia. The study population was randomly divided into derivation and development cohorts. Predictors of bacteremia based on the literature and logistic regression were assessed. A weighted value was assigned to predictors to develop a prediction model for bacteremia using the derivation cohort; discrimination was then assessed using the area under the receiver operating characteristic curve (AUC). Among the 22,519 patients enrolled, 18,015 were assigned to the derivation group and 4504 to the validation group. Sixteen candidate variables were selected, and all sixteen were used as significant predictors of bacteremia (model 1). Among the sixteen variables, the top five with higher odds ratio, including procalcitonin, neutrophil-lymphocyte ratio (NLR), lactate level, platelet count, and body temperature, were used for the simple bacteremia score (model 2). The proportion of bacteremia increased according to the simple bacteremia score in both cohorts. The AUC for model 1 was 0.805 (95% confidence interval [CI] 0.785-0.824) and model 2 was 0.791 (95% CI 0.772-0.810). The simple bacteremia prediction score using only five variables demonstrated a comparable performance with the model including sixteen variables using all laboratory results and vital signs. This simple score is useful for predicting bacteremia-assisted clinical decisions.
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Affiliation(s)
- Hyelin Han
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Da Seul Kim
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sunkyunkwan University, Seoul 06351, Republic of Korea
| | - Minha Kim
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Sejin Heo
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Hansol Chang
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Gun Tak Lee
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Se Uk Lee
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Taerim Kim
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Hee Yoon
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Sung Yeon Hwang
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Won Chul Cha
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sunkyunkwan University, Seoul 06351, Republic of Korea
- Digital Innovation, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Min Sub Sim
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Ik Joon Jo
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
| | - Jong Eun Park
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
- Department of Emergency Medicine, College of Medicine, Kangwon National University, Kangwon 20341, Republic of Korea
| | - Tae Gun Shin
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea (W.C.C.); (M.S.S.); (I.J.J.)
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sunkyunkwan University, Seoul 06351, Republic of Korea
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Rodic S, Hryciw BN, Selim S, Wang CQ, Lepage MF, Goyal V, Nguyen LH, Fergusson DA, van Walraven C. Concurrent external validation of bloodstream infection probability models. Clin Microbiol Infect 2023; 29:61-69. [PMID: 35872173 DOI: 10.1016/j.cmi.2022.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/15/2022] [Accepted: 07/12/2022] [Indexed: 01/26/2023]
Abstract
OBJECTIVE Accurately estimating the likelihood of bloodstream infection (BSI) can help clinicians make diagnostic and therapeutic decisions. Many multivariate models predicting BSI probability have been published. This study measured the performance of BSI probability models within the same patient sample. METHODS We retrieved validated BSI probability models included in a recently published systematic review that returned a patient-level BSI probability for adults. Model applicability, discrimination, and accuracy was measured in a simple random sample of 4485 admitted adults having blood cultures ordered in the emergency department or the initial 48 hours of hospitalization. RESULTS Ten models were included (publication years 1991-2015). Common methodological threats to model performance included overfitting and continuous variable categorization. Restrictive inclusion criteria caused seven models to apply to <15% of validation patients. Model discrimination was less than originally reported in derivation groups (median c-statistic 60%, range 48-69). The observed BSI risk frequently deviated from expected (median integrated calibration index 4.0%, range 0.8-12.4). Notable disagreement in expected BSI probabilities was seen between models (median (25th-75th percentile) relative difference between expected risks 68.0% (28.6-113.6%)). DISCUSSION In a large randomly selected external validation population, many published BSI probability models had restricted applicability, limited discrimination and calibration, and extensive inter-model disagreement. Direct comparison of model performance is hampered by dissimilarities between model-specific validation groups.
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Affiliation(s)
- Stefan Rodic
- Department of Medicine, University of Ottawa, Canada
| | | | - Shehab Selim
- Department of Medicine, University of Ottawa, Canada
| | - Chu Qi Wang
- Department of Medicine, University of Ottawa, Canada
| | | | - Vineet Goyal
- Department of Medicine, University of Ottawa, Canada
| | | | - Dean A Fergusson
- Department of Medicine, University of Ottawa, Canada; Department of Epidemiology & Community Medicine, University of Ottawa, Ottawa Hospital Research Institute, ICES (formerly Institute for Clinical Evaluative Sciences), Canada
| | - Carl van Walraven
- Department of Medicine, University of Ottawa, Canada; Department of Epidemiology & Community Medicine, University of Ottawa, Ottawa Hospital Research Institute, ICES (formerly Institute for Clinical Evaluative Sciences), Canada.
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Goh V, Chou YJ, Lee CC, Ma MC, Wang WYC, Lin CH, Hsieh CC. Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study. Diagnostics (Basel) 2022; 12:diagnostics12102498. [PMID: 36292187 PMCID: PMC9600599 DOI: 10.3390/diagnostics12102498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 12/04/2022] Open
Abstract
Introduction: Bacteremia is a common but life-threatening infectious disease. However, a well-defined rule to assess patient risk of bacteremia and the urgency of blood culture is lacking. The aim of this study is to establish a predictive model for bacteremia in septic patients using available big data in the emergency department (ED) through logistic regression and other machine learning (ML) methods. Material and Methods: We conducted a retrospective cohort study at the ED of National Cheng Kung University Hospital in Taiwan from January 2015 to December 2019. ED adults (≥18 years old) with systemic inflammatory response syndrome and receiving blood cultures during the ED stay were included. Models I and II were established based on logistic regression, both of which were derived from support vector machine (SVM) and random forest (RF). Net reclassification index was used to determine which model was superior. Results: During the study period, 437,969 patients visited the study ED, and 40,395 patients were enrolled. Patients diagnosed with bacteremia accounted for 7.7% of the cohort. The area under the receiver operating curve (AUROC) in models I and II was 0.729 (95% CI, 0.718–0.740) and 0.731 (95% CI, 0.721–0.742), with Akaike information criterion (AIC) of 16,840 and 16,803, respectively. The performance of model II was superior to that of model I. The AUROC values of models III and IV in the validation dataset were 0.730 (95% CI, 0.713–0.747) and 0.705 (0.688–0.722), respectively. There is no statistical evidence to support that the performance of the model created with logistic regression is superior to those created by SVM and RF. Discussion: The advantage of the SVM or RF model is that the prediction model is more elastic and not limited to a linear relationship. The advantage of the LR model is that it is easy to explain the influence of the independent variable on the response variable. These models could help medical staff identify high-risk patients and prevent unnecessary antibiotic use. The performance of SVM and RF was not inferior to that of logistic regression. Conclusions: We established models that provide discrimination in predicting bacteremia among patients with sepsis. The reported results could inspire researchers to adopt ML in their development of prediction algorithms.
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Affiliation(s)
- Vivian Goh
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
| | - Yu-Jung Chou
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
| | - Ching-Chi Lee
- Clinical Medicine Research Center, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
| | - Mi-Chia Ma
- Department of Statistics and Institute of Data Science, College of Management, National Cheng Kung University, Tainan 70101, Taiwan
| | | | - Chih-Hao Lin
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (C.-H.L.); (C.-C.H.)
| | - Chih-Chia Hsieh
- Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (C.-H.L.); (C.-C.H.)
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Garnica O, Gómez D, Ramos V, Hidalgo JI, Ruiz-Giardín JM. Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers. EPMA J 2021; 12:365-381. [PMID: 34484472 PMCID: PMC8405861 DOI: 10.1007/s13167-021-00252-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/30/2021] [Indexed: 12/12/2022]
Abstract
Background The bacteraemia prediction is relevant because sepsis is one of the most important causes of morbidity and mortality. Bacteraemia prognosis primarily depends on a rapid diagnosis. The bacteraemia prediction would shorten up to 6 days the diagnosis, and, in conjunction with individual patient variables, should be considered to start the early administration of personalised antibiotic treatment and medical services, the election of specific diagnostic techniques and the determination of additional treatments, such as surgery, that would prevent subsequent complications. Machine learning techniques could help physicians make these informed decisions by predicting bacteraemia using the data already available in electronic hospital records. Objective This study presents the application of machine learning techniques to these records to predict the blood culture's outcome, which would reduce the lag in starting a personalised antibiotic treatment and the medical costs associated with erroneous treatments due to conservative assumptions about blood culture outcomes. Methods Six supervised classifiers were created using three machine learning techniques, Support Vector Machine, Random Forest and K-Nearest Neighbours, on the electronic health records of hospital patients. The best approach to handle missing data was chosen and, for each machine learning technique, two classification models were created: the first uses the features known at the time of blood extraction, whereas the second uses four extra features revealed during the blood culture. Results The six classifiers were trained and tested using a dataset of 4357 patients with 117 features per patient. The models obtain predictions that, for the best case, are up to a state-of-the-art accuracy of 85.9%, a sensitivity of 87.4% and an AUC of 0.93. Conclusions Our results provide cutting-edge metrics of interest in predictive medical models with values that exceed the medical practice threshold and previous results in the literature using classical modelling techniques in specific types of bacteraemia. Additionally, the consistency of results is reasserted because the three classifiers' importance ranking shows similar features that coincide with those that physicians use in their manual heuristics. Therefore, the efficacy of these machine learning techniques confirms their viability to assist in the aims of predictive and personalised medicine once the disease presents bacteraemia-compatible symptoms and to assist in improving the healthcare economy.
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Affiliation(s)
- Oscar Garnica
- Departamento de Arquitectura de Computadores, Universidad Complutense de Madrid, Madrid, Spain
| | - Diego Gómez
- Universidad Complutense de Madrid, Madrid, Spain
| | - Víctor Ramos
- Universidad Complutense de Madrid, Madrid, Spain
| | - J. Ignacio Hidalgo
- Departamento de Arquitectura de Computadores, Universidad Complutense de Madrid, Madrid, Spain
| | - José M. Ruiz-Giardín
- Departamento de Medicina Interna, Hospital Universitario de Fuenlabrada, Madrid, Spain
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Gregorich M, Strohmaier S, Dunkler D, Heinze G. Regression with Highly Correlated Predictors: Variable Omission Is Not the Solution. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18084259. [PMID: 33920501 PMCID: PMC8073086 DOI: 10.3390/ijerph18084259] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 11/16/2022]
Abstract
Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear. Improper statistical handling of this situation will most certainly generate models of little or no practical use and misleading interpretations. By means of two example studies, we demonstrate how diagnostic tools for collinearity or near-collinearity may fail in guiding the analyst. Instead, the most appropriate way of handling collinearity should be driven by the research question at hand and, in particular, by the distinction between predictive or explanatory aims.
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Affiliation(s)
- Mariella Gregorich
- Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria; (M.G.); (S.S.); (D.D.)
| | - Susanne Strohmaier
- Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria; (M.G.); (S.S.); (D.D.)
- Center for Public Health, Department of Epidemiology, Medical University of Vienna, 1090 Vienna, Austria
| | - Daniela Dunkler
- Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria; (M.G.); (S.S.); (D.D.)
| | - Georg Heinze
- Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria; (M.G.); (S.S.); (D.D.)
- Correspondence:
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Sasaki S, Raita Y, Murakami M, Yamamoto S, Tochitani K, Hasegawa T, Fujisaki K, Fukuhara S. Added value of clinical prediction rules for bacteremia in hemodialysis patients: An external validation study. PLoS One 2021; 16:e0247624. [PMID: 33617601 PMCID: PMC7899347 DOI: 10.1371/journal.pone.0247624] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 02/09/2021] [Indexed: 12/29/2022] Open
Abstract
Introduction Having developed a clinical prediction rule (CPR) for bacteremia among hemodialysis (HD) outpatients (BAC-HD score), we performed external validation. Materials & methods Data were collected on maintenance HD patients at two Japanese tertiary-care hospitals from January 2013 to December 2015. We enrolled 429 consecutive patients (aged ≥ 18 y) on maintenance HD who had had two sets of blood cultures drawn on admission to assess for bacteremia. We validated the predictive ability of the CPR using two validation cohorts. Index tests were the BAC-HD score and a CPR developed by Shapiro et al. The outcome was bacteremia, based on the results of the admission blood cultures. For added value, we also measured changes in the area under the receiver operating characteristic curve (AUC) using logistic regression and Net Reclassification Improvement (NRI), in which each CPR was added to the basic model. Results In Validation cohort 1 (360 subjects), compared to a Model 1 (Basic Model) AUC of 0.69 (95% confidence interval [95% CI]: 0.59–0.80), the AUC of Model 2 (Basic model + BAC-HD score) and Model 3 (Basic model + Shapiro’s score) increased to 0.8 (95% CI: 0.71–0.88) and 0.73 (95% CI: 0.63–0.83), respectively. In validation cohort 2 (96 subjects), compared to a Model 1 AUC of 0.81 (95% CI: 0.68–0.94), the AUCs of Model 2 and Model 3 increased to 0.83 (95% CI: 0.72–0.95) and 0.85 (95% CI: 0.76–0.94), respectively. NRIs on addition of the BAC-HD score and Shapiro’s score were 0.3 and 0.06 in Validation cohort 1, and 0.27 and 0.13, respectively, in Validation cohort 2. Conclusion Either the BAC-HD score or Shapiro’s score may improve the ability to diagnose bacteremia in HD patients. Reclassification was better with the BAC-HD score.
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Affiliation(s)
- Sho Sasaki
- Department of Nephrology, Iizuka Hospital, Fukuoka, Japan
- Clinical Research Support Office, Iizuka Hospital, Fukuoka, Japan
- Department of Healthcare Epidemiology, Kyoto University Graduate School of Public Health, Kyoto, Japan
- * E-mail:
| | - Yoshihiko Raita
- Department of Nephrology, Okinawa Prefectural Chubu Hospital, Naha, Japan
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Minoru Murakami
- Department of Nephrology, Saku Central Hospital, Nagano, Japan
| | - Shungo Yamamoto
- Department of Healthcare Epidemiology, Kyoto University Graduate School of Public Health, Kyoto, Japan
- Department of Infectious Disease, Kyoto City Hospital, Kyoto, Japan
| | - Kentaro Tochitani
- Department of Healthcare Epidemiology, Kyoto University Graduate School of Public Health, Kyoto, Japan
- Department of Infectious Disease, Kyoto City Hospital, Kyoto, Japan
| | - Takeshi Hasegawa
- Office for Promoting Medical Research, Showa University, Tokyo, Japan
- Division of Nephrology, Department of Medicine, Showa University Fujigaoka Hospital, Yokohama, Japan
- Fukushima Medical University, Fukushima, Japan
| | | | - Shunichi Fukuhara
- Fukushima Medical University, Fukushima, Japan
- Section of Clinical Epidemiology, Department of Community Medicine, Kyoto University, Kyoto, Japan
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
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Mooney C, Eogan M, Ní Áinle F, Cleary B, Gallagher JJ, O'Loughlin J, Drew RJ. Predicting bacteraemia in maternity patients using full blood count parameters: A supervised machine learning algorithm approach. Int J Lab Hematol 2020; 43:609-615. [PMID: 33347714 DOI: 10.1111/ijlh.13434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 11/27/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
INTRODUCTION Bacteraemia in pregnancy and the post-partum period can lead to maternal and newborn morbidly. The purpose of this study was to use machine learning tools to identify if bacteraemia in pregnant or post-partum women could be predicted by full blood count (FBC) parameters other than the white cell count. METHODS The study was performed on 129 women with a positive blood culture (BC) for a clinically significant organism, who had a FBC taken at the same time. They were matched with controls who had a negative BC taken at the same time as a FBC. The data were split in to a training (70%) and test (30%) data set. Machine learning techniques such as recursive partitioning and classification and regression trees were used. RESULTS A neutrophil/lymphocyte ratio (NLR) of >20 was found to be the most clinically relevant and interpretable construct of the FBC result to predict bacteraemia. The diagnostic accuracy of NLR >20 to predict bacteraemia was then examined. Thirty-six of the 129 bacteraemia patients had a NLR >20, while only 223 of the 3830 controls had a NLR >20. This gave a sensitivity of 27.9% (95% CI 20.3-36.4), specificity of 94.1% (93.3-94.8), positive predictive value of 13.9% (10.6-17.9) and a negative predictive value (NPV) of 97.4% (97.2-97.7) when the prevalence of bacteraemia was 3%. CONCLUSION The NLR should be considered for use in routine clinical practice when assessing the FBC result in patients with suspected bacteraemia during pregnancy or in the post-partum period.
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Affiliation(s)
- Ciarán Mooney
- Department of Haematolgy, Rotunda Hospital, Dublin, Ireland
| | - Maeve Eogan
- Department of Obstetrics and Gynaecology, Rotunda Hospital, Dublin, Ireland
| | - Fionnuala Ní Áinle
- Department of Haematolgy, Rotunda Hospital, Dublin, Ireland.,Department of Haematology, Mater Misericordiae Hospital, Dublin, Ireland
| | - Brian Cleary
- Department of Pharmacy, Rotunda Hospital, Dublin, Ireland.,Department of Pharmacy, Royal College of Surgeons in Ireland, Dublin, Ireland
| | | | | | - Richard J Drew
- Clinical Innovation Unit, Rotunda Hospital, Dublin, Ireland.,Irish Meningitis and Sepsis Reference Laboratory, Childrens' Health Ireland at Temple Street, Dublin, Ireland.,Department of Clinical Microbiology, Royal College of Surgeons in Ireland, Dublin, Ireland
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11
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The Shapiro-Procalcitonin algorithm (SPA) as a decision tool for blood culture sampling: validation in a prospective cohort study. Infection 2020; 48:523-533. [PMID: 32291611 DOI: 10.1007/s15010-020-01423-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 04/03/2020] [Indexed: 01/15/2023]
Abstract
PURPOSE Blood cultures (BC) are the gold standard for bacteremia detection despite a relatively low diagnostic yield and high costs. A retrospective study reported high predictive values for BC positivity when combining the clinical Shapiro score with procalcitonin (PCT). METHODS Single-center, prospective cohort study between 01/2016 and 02/2017 to validate SPA algorithm, including a modified Shapiro score ≥ 3 points (S) PLUS admission PCT > 0.25 µg/l (P), or presence of overruling safety criteria (A) in patients with systemic inflammatory response syndrome. The diagnostic yield of SPA compared to non-standardized clinical judgment in predicting BC positivity was calculated and results presented as odds ratios (OR) with 95% confidence intervals. RESULTS Of 1438 patients with BC sampling, 215 (15%) had positive BC which increased to 31% (173/555) in patients fulfilling SP criteria (OR for BC positivity 9.07 [6.34-12.97]). When adding 194 patients with overruling safety criteria (i.e., SPA), OR increased to 11.12 (6.99-17.69), although BC positivity slightly decreased to 26%. With an area under the receiver operating curve of 0.742, SPA indicated better diagnostic performance than its individual components. Positive BC in 689 patients not fulfilling SPA (sampling according to non-standardized clinical judgment) were rare (3%; OR for BC positivity 0.09 [0.06-0.14]). Eight out of 21 missed pathogens were still identified by sampling the primary infection focus. CONCLUSIONS This study validates the high predictive value of SPA for bacteremia, increasing true BC positivity from 15 to 26%. Restricting BC sampling to SPA would have reduced BC sampling by 48%, while still detecting 194/215 organisms (90%), which makes SPA a valuable diagnostic stewardship tool.
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12
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Risk of bacteremia in patients presenting with shaking chills and vomiting - a prospective cohort study. Epidemiol Infect 2020; 148:e86. [PMID: 32228723 PMCID: PMC7189349 DOI: 10.1017/s0950268820000746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Chills and vomiting have traditionally been associated with severe bacterial infections and bacteremia. However, few modern studies have in a prospective way evaluated the association of these signs with bacteremia, which is the aim of this prospective, multicenter study. Patients presenting to the emergency department with at least one affected vital sign (increased respiratory rate, increased heart rate, altered mental status, decreased blood pressure or decreased oxygen saturation) were included. A total of 479 patients were prospectively enrolled. Blood cultures were obtained from 197 patients. Of the 32 patients with a positive blood culture 11 patients (34%) had experienced shaking chills compared with 23 (14%) of the 165 patients with a negative blood culture, P = 0.009. A logistic regression was fitted to show the estimated odds ratio (OR) for a positive blood culture according to shaking chills. In a univariate model shaking chills had an OR of 3.23 (95% CI 1.35–7.52) and in a multivariate model the OR was 5.9 (95% CI 2.05–17.17) for those without prior antibiotics adjusted for age, sex, and prior antibiotics. The presence of vomiting was also addressed, but neither a univariate nor a multivariate logistic regression showed any association between vomiting and bacteremia. In conclusion, among patients at the emergency department with at least one affected vital sign, shaking chills but not vomiting were associated with bacteremia.
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13
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Luz CF, Vollmer M, Decruyenaere J, Nijsten MW, Glasner C, Sinha B. Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies. Clin Microbiol Infect 2020; 26:1291-1299. [PMID: 32061798 DOI: 10.1016/j.cmi.2020.02.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/01/2020] [Accepted: 02/03/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. OBJECTIVES To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. SOURCES A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. CONTENT Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. IMPLICATIONS Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.
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Affiliation(s)
- C F Luz
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands.
| | - M Vollmer
- Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany
| | - J Decruyenaere
- Ghent University, Ghent University Hospital, Department of Intensive Care, Ghent, Belgium
| | - M W Nijsten
- University of Groningen, University Medical Center Groningen, Department of Critical Care, Groningen, the Netherlands
| | - C Glasner
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands
| | - B Sinha
- University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands
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14
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Pashenkov MV, Murugina NE, Budikhina AS, Pinegin BV. Synergistic interactions between NOD receptors and TLRs: Mechanisms and clinical implications. J Leukoc Biol 2018; 105:669-680. [PMID: 30517768 DOI: 10.1002/jlb.2ru0718-290r] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 10/23/2018] [Accepted: 11/20/2018] [Indexed: 12/12/2022] Open
Abstract
Interactions between pattern recognition receptors (PRRs) shape innate immune responses to particular classes of pathogens. Here, we review interactions between TLRs and nucleotide-binding oligomerization domain 1 and 2 (NOD1 and NOD2) receptors, two major groups of PRRs involved in innate recognition of bacteria. Most of experimental data both in vitro and in vivo suggest that NODs and TLRs synergize with each other at inducing the production of cytokines and antimicrobial peptides. Molecular mechanisms of this synergy remain poorly understood, although several scenarios can be proposed: (i) direct interactions of signaling pathways downstream of NODs and TLRs; (ii) mutual transcriptional regulation of unique components of NOD-dependent and TLR-dependent signaling pathways; and (iii) interactions at the post-transcriptional level. Potential practical implications of NOD-TLR synergy are dual. In sepsis, where synergistic effects probably contribute to excessive proinflammatory cytokine production, blockade of NOD1, and/or NOD2 in addition to TLR4 blockade may be required to achieve therapeutic benefit. On the other hand, synergistic combinations of relatively small doses of NOD and TLR agonists administered before infection could be used to boost innate resistance against bacterial pathogens.
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Affiliation(s)
- Mikhail V Pashenkov
- Laboratory of Clinical Immunology, National Research Center Institute of Immunology, Federal Medical-Biological Agency of Russia, Moscow, Russia
| | - Nina E Murugina
- Laboratory of Clinical Immunology, National Research Center Institute of Immunology, Federal Medical-Biological Agency of Russia, Moscow, Russia
| | - Anna S Budikhina
- Laboratory of Clinical Immunology, National Research Center Institute of Immunology, Federal Medical-Biological Agency of Russia, Moscow, Russia
| | - Boris V Pinegin
- Laboratory of Clinical Immunology, National Research Center Institute of Immunology, Federal Medical-Biological Agency of Russia, Moscow, Russia
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15
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Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study. Sci Rep 2018; 8:12233. [PMID: 30111827 PMCID: PMC6093921 DOI: 10.1038/s41598-018-30236-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 07/16/2018] [Indexed: 01/09/2023] Open
Abstract
Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate: 28.8%) were finally used. A 29 parameter panel of clinical data, cytokine expression levels and standard laboratory markers was used for model training. Model tuning was performed in a ten-fold cross validation and tuned models were validated in a test set (80:20 random split). The random forest strategy presented the best result in the test set validation (ROC-AUC: 0.729, 95%CI: 0.679–0.779). However, procalcitonin (PCT), as the best individual variable, yielded a similar ROC-AUC (0.729, 95%CI: 0.679–0.779). Thus, machine learning methods failed to improve the moderate diagnostic accuracy of PCT.
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16
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Sasaki S, Hasegawa T, Kawarazaki H, Nomura A, Uchida D, Imaizumi T, Furusho M, Nishiwaki H, Fukuma S, Shibagaki Y, Fukuhara S. Development and Validation of a Clinical Prediction Rule for Bacteremia among Maintenance Hemodialysis Patients in Outpatient Settings. PLoS One 2017; 12:e0169975. [PMID: 28081211 PMCID: PMC5231279 DOI: 10.1371/journal.pone.0169975] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 12/25/2016] [Indexed: 12/23/2022] Open
Abstract
Background To our knowledge, no reliable clinical prediction rule (CPR) for identifying bacteremia in hemodialysis (HD) patients has been established. The aim of this study was to develop a CPR for bacteremia in maintenance HD patients visiting the outpatient department. Methods This multicenter cohort study involved consecutive maintenance HD patients who visited the outpatient clinic or emergency room of seven Japanese institutions between August 2011 and July 2013. The outcome measure was bacteremia diagnosed based on the results of blood cultures. The candidate predictors for bacteremia were extracted through a literature review. A CPR for bacteremia was developed using a coefficient-based multivariable logistic regression scoring method, and calibration was performed. The test performance was then assessed for the CPR. Results Of 507 patients eligible for the study, we analyzed the 293 with a complete dataset for candidate predictors. Of these 293 patients, 48 (16.4%) were diagnosed with bacteremia. At the conclusion of the deviation process, body temperature ≥ 38.3°C, heart rate ≥ 125 /min, C-reactive protein ≥ 10 mg/dL, alkaline phosphatase >360 IU/L, and no prior antibiotics use within the past week were retained and scored. The CPR had a good fit for the model on calibration. The AUC of the CPR was 0.76, and for score CPR ≥ 2, the sensitivity and specificity were 89.6% and 51.4%, respectively. Conclusions We established a simple CPR for bacteremia in maintenance HD patients using routinely obtained clinical information in an outpatient setting. This model may facilitate more appropriate clinical decision making.
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Affiliation(s)
- Sho Sasaki
- Department of Healthcare Epidemiology, Kyoto University Graduate School of Public Health, Kyoto, JAPAN
- Center for Innovative Research for Communities and Clinical Excellence, Fukushima Medical University, Fukushima, JAPAN
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki, JAPAN
| | - Takeshi Hasegawa
- Center for Innovative Research for Communities and Clinical Excellence, Fukushima Medical University, Fukushima, JAPAN
- Office for Promoting Medical Research, Showa University, Tokyo, JAPAN
- Division of Nephrology, Department of Internal Medicine, Showa University Fujigaoka Hospital, Yokohama, JAPAN
- * E-mail:
| | - Hiroo Kawarazaki
- Division of Nephrology, Department of Internal Medicine, Inagi Municipal Hospital, Inagi, JAPAN
| | - Atsushi Nomura
- Department of Immunology, Juntendo University School of Medicine, Tokyo, JAPAN
- Department of Nephrology, Chubu Rosai Hospital, Nagoya, JAPAN
| | - Daisuke Uchida
- Division of Nephrology, Department of Internal Medicine, Inagi Municipal Hospital, Inagi, JAPAN
- Department of Nephrology and Hypertension, Kawasaki Municipal Tama Hospital, Kawasaki, JAPAN
| | - Takahiro Imaizumi
- Department of Nephrology, Toyohashi Municipal Hospital, Toyohashi, JAPAN
- Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya, JAPAN
| | | | - Hiroki Nishiwaki
- Center for Innovative Research for Communities and Clinical Excellence, Fukushima Medical University, Fukushima, JAPAN
- Division of Nephrology, Department of Internal Medicine, Showa University Fujigaoka Hospital, Yokohama, JAPAN
| | - Shingo Fukuma
- Department of Healthcare Epidemiology, Kyoto University Graduate School of Public Health, Kyoto, JAPAN
- Center for Innovative Research for Communities and Clinical Excellence, Fukushima Medical University, Fukushima, JAPAN
| | - Yugo Shibagaki
- Division of Nephrology and Hypertension, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki, JAPAN
| | - Shunichi Fukuhara
- Department of Healthcare Epidemiology, Kyoto University Graduate School of Public Health, Kyoto, JAPAN
- Center for Innovative Research for Communities and Clinical Excellence, Fukushima Medical University, Fukushima, JAPAN
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Vender RJ, Vender RL. Clinical Impact of Blood Culture Results in Acutely Ill Hospitalized Adult Patients With Cystic Fibrosis. J Clin Med Res 2016; 8:859-862. [PMID: 27829951 PMCID: PMC5087625 DOI: 10.14740/jocmr2764w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/27/2016] [Indexed: 01/02/2023] Open
Abstract
Background Blood cultures are obtained clinically to confirm site and source of acute infection as well as to guide effective antibiotic therapies. Patients with cystic fibrosis (CF) are at risk for blood stream infection (BSI) as identified from positive blood culture results. Methods A retrospective chart review was performed of 190 adult CF patients from January 1, 2001 through December 1, 2015. All positive blood culture results were identified as to clinical relevance and source of BSI. Results There were a total of 3,053 blood cultures. One hundred fifty-one positive blood cultures were considered pathogenic and clinically significant. Venous access device-related BSI was identified in 31 evaluable patients and 106 blood cultures. Nineteen patients and 45 positive blood cultures were attributable to organ-specific sources. Conclusion Two patterns of BSI were identified: 1) venous access device infections without causal mortality and 2) organ-specific site infections with associated 26% mortality.
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Affiliation(s)
- Robert J Vender
- Penn State College of Medicine, 500 University Drive, Hershey, PA 17033, USA
| | - Robert L Vender
- Department of Medicine, Penn State Milton S. Hershey Medical Center, 500 University Drive, Hershey, PA 17033, USA
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Neither Single nor a Combination of Routine Laboratory Parameters can Discriminate between Gram-positive and Gram-negative Bacteremia. Sci Rep 2015; 5:16008. [PMID: 26522966 PMCID: PMC4629184 DOI: 10.1038/srep16008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 10/05/2015] [Indexed: 12/18/2022] Open
Abstract
Adequate early empiric antibiotic therapy is pivotal for the outcome of patients with bloodstream infections. In clinical practice the use of surrogate laboratory parameters is frequently proposed to predict underlying bacterial pathogens; however there is no clear evidence for this assumption. In this study, we investigated the discriminatory capacity of predictive models consisting of routinely available laboratory parameters to predict the presence of Gram-positive or Gram-negative bacteremia. Major machine learning algorithms were screened for their capacity to maximize the area under the receiver operating characteristic curve (ROC-AUC) for discriminating between Gram-positive and Gram-negative cases. Data from 23,765 patients with clinically suspected bacteremia were screened and 1,180 bacteremic patients were included in the study. A relative predominance of Gram-negative bacteremia (54.0%), which was more pronounced in females (59.1%), was observed. The final model achieved 0.675 ROC-AUC resulting in 44.57% sensitivity and 79.75% specificity. Various parameters presented a significant difference between both genders. In gender-specific models, the discriminatory potency was slightly improved. The results of this study do not support the use of surrogate laboratory parameters for predicting classes of causative pathogens. In this patient cohort, gender-specific differences in various laboratory parameters were observed, indicating differences in the host response between genders.
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Ratzinger F, Haslacher H, Perkmann T, Schmetterer KG, Poeppl W, Mitteregger D, Dorffner G, Burgmann H. Sepsis biomarkers in neutropaenic systemic inflammatory response syndrome patients on standard care wards. Eur J Clin Invest 2015; 45:815-23. [PMID: 26046926 DOI: 10.1111/eci.12476] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Accepted: 06/01/2015] [Indexed: 01/08/2023]
Abstract
BACKGROUND Neutropaenic patients are at a high risk of contracting severe infections. In particular, in these patients, parameters with a high negative predictive value are desirable for excluding infection or bacteraemia. This study evaluated sepsis biomarkers in neutropaenic patients suffering from systemic inflammatory response syndrome (SIRS). Further, the predictive capacities of evaluated biomarkers in neutropaenic SIRS patients were compared to non-neutropaenic SIRS patients. MATERIAL AND METHODS In this prospective observational cohort study, patients with clinically suspected sepsis were screened. The predictive capacities of procalcitonin (PCT), C-reactive protein and lipopolysaccharide-binding protein (LBP) in neutropaenic SIRS patients were evaluated in terms of their potential to identify infection or bacteraemia and were compared to results for non-neutropaenic SIRS patients. To select an appropriate control cohort, propensity score matching was applied, balancing confounding factors between neutropaenic and non-neutropaenic SIRS patients. RESULTS Of 3370 prospectively screened patients with suspected infection, 51 patients suffered from neutropaenic SIRS. For the identification of infection, none of the assessed biomarkers presented a clinically relevant discriminatory potency. Lipopolysaccharide-binding protein and PCT demonstrated discriminatory capacity to discriminate between nonbacteraemic and bacteraemic SIRS in patients with neutropaenia [receiver-operating characteristics-area under the curves (ROC-AUCs): 0.860, 0.818]. In neutropaenic SIRS patients, LBP had a significantly better ROC-AUC than in a comparable non-neutropaenic patient cohort for identifying bacteraemia (P = 0.01). CONCLUSION In neutropaenic SIRS patients, none of the evaluated biomarkers was able to adequately identify infection. LBP and PCT presented a good performance in identifying bacteraemia. Therefore, these markers could be used for screening purposes to increase the pretest probability of blood culture analysis.
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Affiliation(s)
- Franz Ratzinger
- Division of Medical and Chemical Laboratory Diagnostics, Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Helmuth Haslacher
- Division of Medical and Chemical Laboratory Diagnostics, Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Thomas Perkmann
- Division of Medical and Chemical Laboratory Diagnostics, Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Klaus G Schmetterer
- Division of Medical and Chemical Laboratory Diagnostics, Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Poeppl
- Division of Infectious Diseases and Tropical Medicine, Department of Medicine I, Medical University of Vienna, Vienna, Austria
| | - Dieter Mitteregger
- Division of Clinical Microbiology, Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
| | - Georg Dorffner
- Section for Artificial Intelligence, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Heinz Burgmann
- Division of Infectious Diseases and Tropical Medicine, Department of Medicine I, Medical University of Vienna, Vienna, Austria
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