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Ego A, Debillon T, Sourd D, Mitton N, Fresson J, Zeitlin J. Identifying Newborns with Hypoxic-Ischemic Encephalopathy in Hospital Discharge Data: A Validation Study. J Pediatr 2024; 268:113950. [PMID: 38336200 DOI: 10.1016/j.jpeds.2024.113950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/18/2024] [Accepted: 02/04/2024] [Indexed: 02/12/2024]
Abstract
Hospital discharge databases (HDDs) are increasingly used for research on health of newborns. Linkage between a French population-based cohort of newborns with hypoxic-ischemic encephalopathy (HIE) and national HDD showed that the HIE ICD-10 code was not accurately reported. Our results suggest that HDD should not be used for research on neonatal HIE without prior validation of HIE ICD-10 codes.
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Affiliation(s)
- Anne Ego
- Public Health Department CHU Grenoble Alpes, Univ. Grenoble Alpes, CNRS, Grenoble INP∗, TIMC-IMAG, Grenoble, France, ∗Institute of Engineering Univ, Grenoble Alpes; INSERM UMR 1153, Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Center for Epidemiology and Statistics Sorbonne Paris Cité (CRESS), FHU PREMA, Paris Descartes University, Paris, France; Univ. Grenoble Alpes, Inserm CIC1406, CHU de Grenoble, Grenoble, France.
| | - T Debillon
- Department of Neonatology CHU Grenoble Alpes, Univ. Grenoble Alpes, CNRS, Grenoble INP∗, TIMC-IMAG, Grenoble, France, ∗Institute of Engineering Univ, Grenoble Alpes
| | - D Sourd
- Public Health Department CHU Grenoble Alpes, Univ. Grenoble Alpes, CNRS, Grenoble INP∗, TIMC-IMAG, Grenoble, France, ∗Institute of Engineering Univ, Grenoble Alpes
| | - N Mitton
- Department of Bioinformatics CHU Grenoble Alpes, Univ. Grenoble Alpes, Grenoble, France
| | - J Fresson
- Population Health Office, DREES, Paris, France
| | - J Zeitlin
- INSERM UMR 1153, Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Center for Epidemiology and Statistics Sorbonne Paris Cité (CRESS), FHU PREMA, Paris Descartes University, Paris, France
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Deimazar G, Sheikhtaheri A. Machine learning models to detect and predict patient safety events using electronic health records: A systematic review. Int J Med Inform 2023; 180:105246. [PMID: 37837710 DOI: 10.1016/j.ijmedinf.2023.105246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 10/02/2023] [Accepted: 10/08/2023] [Indexed: 10/16/2023]
Abstract
INTRODUCTION Identifying patient safety events using electronic health records (EHRs) and automated machine learning-based detection methods can help improve the efficiency and quality of healthcare service provision. OBJECTIVE This study aimed to systematically review machine learning-based methods and techniques, as well as their results for patient safety event management using EHRs. METHODS We reviewed the studies that focused on machine learning techniques, including automatic prediction and detection of patient safety events and medical errors through EHR analysis to manage patient safety events. The data were collected by searching Scopus, PubMed (Medline), Web of Science, EMBASE, and IEEE Xplore databases. RESULTS After screening, 41 papers were reviewed. Support vector machine (SVM), random forest, conditional random field (CRF), and bidirectional long short-term memory with conditional random field (BiLSTM-CRF) algorithms were mostly applied to predict, identify, and classify patient safety events using EHRs; however, they had different performances. BiLSTM-CRF was employed in most of the studies to extract and identify concepts, e.g., adverse drug events (ADEs) and adverse drug reactions (ADRs), as well as relationships between drug and severity, drug and ADEs, drug and ADRs. Recurrent neural networks (RNN) and BiLSTM-CRF had the best results in detecting ADEs compared to other patient safety events. Linear classifiers and Naive Bayes (NB) had the highest performance for ADR detection. Logistic regression had the best results in detecting surgical site infections. According to the findings, the quality of articles has non-significantly improved in recent years, but they had low average scores. CONCLUSIONS Machine learning can be useful in automatic detection and prediction of patient safety events. However, most of these algorithms have not yet been externally validated or prospectively tested. Therefore, further studies are required to improve the performance of these automated systems.
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Affiliation(s)
- Ghasem Deimazar
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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Li LT, Hebballi NB, Nguyen T, Morice C, Lally KP. Complication rates in very low and extremely low birth weight infants following laparotomy: a prospective study. Pediatr Surg Int 2023; 39:237. [PMID: 37477761 DOI: 10.1007/s00383-023-05520-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/12/2023] [Indexed: 07/22/2023]
Abstract
INTRODUCTION Surgical site occurrences (SSO), including surgical site infection, dehiscence, and incisional hernia, are complications following laparotomy. SSO rates in premature neonates are poorly understood. We hypothesize that SSO rates are higher among extremely low birth weight (ELBW) infants compared to very low birth weight (VLBW) infants and strive to determine the optimal abdominal closure method for these infants. METHODS We conducted a prospective observational study of infants < 1.5 kg (kg) undergoing laparotomy at two institutions from 1/1/2020 to 5/1/2022. Patients were grouped by weight and closure; SSO rates were computed and the association tested using Fisher's exact test. RESULTS We identified 59 patients and 104 total operations. At initial surgery, 37 patients weighed < 1 kg (ELBW); 22 patients weighed 1-1.5 kg (VLBW). Complication rate for ELBW was 6(16%) vs. 2(9%) in VLBW, but not significant (p = 0.45). More complications followed a single-layer compared to a two-layer closure (18 vs. 2), but not significant (p = 0.30). CONCLUSIONS SSO rates are higher for ELBW infants undergoing laparotomy, and fewer complications follow two-layer closure. However, these findings did not reach statistical significance. Further studies are needed to identify modifiable factors to reduce postoperative complications in these infants.
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Affiliation(s)
- Linda T Li
- Department of Surgery, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA.
| | - Nutan B Hebballi
- Department of Pediatric Surgery, McGovern Medical School at UTHealth Houston, 6431 Fannin St., Houston, TX, 77030, USA
| | - Thian Nguyen
- Department of Pediatric Surgery, McGovern Medical School at UTHealth Houston, 6431 Fannin St., Houston, TX, 77030, USA
| | - Christina Morice
- Department of Pediatric Surgery, McGovern Medical School at UTHealth Houston, 6431 Fannin St., Houston, TX, 77030, USA
| | - Kevin P Lally
- Department of Pediatric Surgery, McGovern Medical School at UTHealth Houston, 6431 Fannin St., Houston, TX, 77030, USA
- Children's Memorial Hermann Hospital, 6411 Fannin St., Houston, TX, 77030, USA
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Rafaqat W, Fatima HS, Kumar A, Khan S, Khurram M. Machine Learning Model for Assessment of Risk Factors and Postoperative Day for Superficial vs Deep/Organ-Space Surgical Site Infections. Surg Innov 2023:15533506231170933. [PMID: 37082820 DOI: 10.1177/15533506231170933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Background. Deep and organ space surgical site infections (SSI) require more intensive treatment, may result in more severe clinical disease and may have different risk factors when compared to superficial SSIs. Machine learning (ML) algorithms provide the opportunity to analyze multiple factors to predict of the type and time of development of SSI. Therefore, we developed a ML model to predict type and postoperative week of SSI. Methodology. A case-control study was conducted among patients who developed a SSI after undergoing general surgery procedures at a tertiary care hospital between 2019 to 2020. Patients were followed for 30 days. Six ML algorithms were trained as predictors of type of infection (superficial vs deep/organ space) and time of infection, and tested using area under the receiver operating characteristic curve (AUC-ROC). Results. Data for 113 patients with SSIs was available. Of these 62 (54.8%) had superficial and 51 had (45.2%) deep/organ space infections. Compared with other ML algorithms, the XG boost univariate model had highest AUC-ROC (.84) for prediction of type of SSI and Stochastic gradient boosting univariate, logistic regression univariate, XG boost univariate, and random forest classification univariate model had the highest AUC-ROC (.74) for prediction of week of infection. Conclusions. ML models offer reasonable accuracy in prediction of superficial vs deep SSI and time of developing infection. Follow-up duration and allocation of treatment strategies can be informed by ML predictions.
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Affiliation(s)
| | - Hafiza Sundus Fatima
- Smartcity Lab, National Center of Artificial Intelligence, NED University of Engineering and Technology, Karachi, Pakistan
| | - Ayush Kumar
- Medical College, Aga Khan University, Karachi, Pakistan
| | - Sadaf Khan
- Department of Surgery, Aga Khan University, Karachi, Pakistan
| | - Muhammad Khurram
- Smartcity Lab, National Center of Artificial Intelligence, NED University of Engineering and Technology, Karachi, Pakistan
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Irgang L, Barth H, Holmén M. Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:1-41. [PMID: 36910913 PMCID: PMC9995622 DOI: 10.1007/s41666-023-00129-2] [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: 04/05/2022] [Revised: 01/16/2023] [Accepted: 02/03/2023] [Indexed: 03/02/2023]
Abstract
Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00129-2.
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Affiliation(s)
- Luís Irgang
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
| | - Henrik Barth
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
| | - Magnus Holmén
- School of Business, Innovation and Sustainability - Department of Engineering and Innovation, Halmstad University, Halmstad, Sweden
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Use of machine learning in pediatric surgical clinical prediction tools: A systematic review. J Pediatr Surg 2023; 58:908-916. [PMID: 36804103 DOI: 10.1016/j.jpedsurg.2023.01.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 01/03/2023] [Indexed: 01/20/2023]
Abstract
PURPOSE Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artificial intelligence have resulted in a proliferation of CPTs created using machine learning (ML)-yet the clinical applicability of ML-based CPTs and their validation in clinical settings remain unclear. This systematic review aims to compare the validity and clinical efficacy of ML-based to traditional CPTs in pediatric surgery. METHODS Nine databases were searched from 2000 until July 9, 2021 to retrieve articles reporting on CPTs and ML for pediatric surgical conditions. PRISMA standards were followed, and screening was performed by two independent reviewers in Rayyan, with a third reviewer resolving conflicts. Risk of bias was assessed using the PROBAST. RESULTS Out of 8300 studies, 48 met the inclusion criteria. The most represented surgical specialties were pediatric general (14), neurosurgery (13) and cardiac surgery (12). Prognostic (26) CPTs were the most represented type of surgical pediatric CPTs followed by diagnostic (10), interventional (9), and risk stratifying (2). One study included a CPT for diagnostic, interventional and prognostic purposes. 81% of studies compared their CPT to ML-based CPTs, statistical CPTs, or the unaided clinician, but lacked external validation and/or evidence of clinical implementation. CONCLUSIONS While most studies claim significant potential improvements by incorporating ML-based CPTs in pediatric surgical decision-making, both external validation and clinical application remains limited. Further studies must focus on validating existing instruments or developing validated tools, and incorporating them in the clinical workflow. TYPE OF STUDY Systematic Review LEVEL OF EVIDENCE: Level III.
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Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis. Ann Med Surg (Lond) 2022; 84:104956. [PMID: 36582918 PMCID: PMC9793260 DOI: 10.1016/j.amsu.2022.104956] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/08/2022] [Accepted: 11/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background Medical researchers and clinicians have shown much interest in developing machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However, little is known about the overall performance of ML algorithms in predicting SSIs and how to improve the algorithm's robustness. We conducted a systematic review and meta-analysis to summarize the performance of ML algorithms in SSIs case detection and prediction and to describe the impact of using unstructured and textual data in the development of ML algorithms. Methods MEDLINE, EMBASE, CINAHL, CENTRAL and Web of Science were searched from inception to March 25, 2021. Study characteristics and algorithm development information were extracted. Performance statistics (e.g., sensitivity, area under the receiver operating characteristic curve [AUC]) were pooled using a random effect model. Stratified analysis was applied to different study characteristic levels. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) was followed. Results Of 945 articles identified, 108 algorithms from 32 articles were included in this review. The overall pooled estimate of the SSI incidence rate was 3.67%, 95% CI: 3.58-3.76. Mixed-use of structured and textual data-based algorithms (pooled estimates of sensitivity 0.83, 95% CI: 0.78-0.87, specificity 0.92, 95% CI: 0.86-0.95, AUC 0.92, 95% CI: 0.89-0.94) outperformed algorithms solely based on structured data (sensitivity 0.56, 95% CI:0.43-0.69, specificity 0.95, 95% CI:0.91-0.97, AUC = 0.90, 95% CI: 0.87-0.92). Conclusions ML algorithms developed with structured and textual data provided optimal performance. External validation of ML algorithms is needed to translate current knowledge into clinical practice.
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Machine-Learning Models for Predicting Surgical Site Infections using Patient Pre-Operative Risk and Surgical Procedure Factors. Am J Infect Control 2022; 51:544-550. [PMID: 36002080 DOI: 10.1016/j.ajic.2022.08.013] [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: 03/08/2022] [Revised: 08/03/2022] [Accepted: 08/04/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Surgical site infections (SSIs) are a significant healthcare problem as they can cause increased medical costs and increased morbidity and mortality. Assessing a patient's pre-operative risk factors can improve risk stratification and help guide the surgical decision-making process. Previous efforts to use pre-operative risk factors to predict the occurrence of SSIs have relied upon traditional statistical modeling approaches. The aim of this paper is to develop and validate, using state-of-the-art machine learning (ML) approaches, classification models for the occurrence of SSI to improve upon previous models. METHODS In this work, using the American College of Surgeons' National Surgical Quality Improvement Program (ACS NSQIP) database, the performances (e.g., prediction accuracy) of seven different ML approaches (Logistic Regression (LR), Naïve Bayesian (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Network (DNN)) were compared. The performance of these models was evaluated using the area under the curve, accuracy, precision, sensitivity, and F1-score metrics. RESULTS Overall, 2,882,526 surgical procedures were identified in the study for the SSI predictive models' development. The results indicate that the DNN model offers the best predictive performance with 10-fold compared to the other six approaches considered (area under the curve = 0.8518, accuracy = 0.8518, precision = 0.8517, sensitivity = 0.8527, F1-score = 0.8518). Emergency case surgeries, American Society of Anesthesiologists (ASA) Index of 4 (ASA_4), BMI, Vascular surgeries, and general surgeries were most significant influencing features towards developing an SSI. CONCLUSION Equally important is that the commonly used LR approach for SSI prediction displayed mediocre performance. The results are encouraging as they suggest that the prediction performance for SSIs can be improved using modern ML approaches.
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Wang P, Cheng S, Li Y, Liu L, Liu J, Zhao Q, Luo S. Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms. Front Public Health 2022; 10:910479. [PMID: 35836985 PMCID: PMC9273930 DOI: 10.3389/fpubh.2022.910479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background Lumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms. Methods We utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed. Results In the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy. Conclusions The RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.
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Affiliation(s)
- Peng Wang
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Shuwen Cheng
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Yaxin Li
- West China Fourth Hospital/West China School of Public Health, Sichuan University, Chengdu, China
| | - Li Liu
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Jia Liu
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Qiang Zhao
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Shuang Luo
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
- *Correspondence: Shuang Luo
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Meoli A, Ciavola L, Rahman S, Masetti M, Toschetti T, Morini R, Dal Canto G, Auriti C, Caminiti C, Castagnola E, Conti G, Donà D, Galli L, La Grutta S, Lancella L, Lima M, Lo Vecchio A, Pelizzo G, Petrosillo N, Simonini A, Venturini E, Caramelli F, Gargiulo GD, Sesenna E, Sgarzani R, Vicini C, Zucchelli M, Mosca F, Staiano A, Principi N, Esposito S. Prevention of Surgical Site Infections in Neonates and Children: Non-Pharmacological Measures of Prevention. Antibiotics (Basel) 2022; 11:antibiotics11070863. [PMID: 35884117 PMCID: PMC9311619 DOI: 10.3390/antibiotics11070863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/19/2022] [Accepted: 06/23/2022] [Indexed: 12/04/2022] Open
Abstract
A surgical site infection (SSI) is an infection that occurs in the incision created by an invasive surgical procedure. Although most infections are treatable with antibiotics, SSIs remain a significant cause of morbidity and mortality after surgery and have a significant economic impact on health systems. Preventive measures are essential to decrease the incidence of SSIs and antibiotic abuse, but data in the literature regarding risk factors for SSIs in the pediatric age group are scarce, and current guidelines for the prevention of the risk of developing SSIs are mainly focused on the adult population. This document describes the current knowledge on risk factors for SSIs in neonates and children undergoing surgery and has the purpose of providing guidance to health care professionals for the prevention of SSIs in this population. Our aim is to consider the possible non-pharmacological measures that can be adopted to prevent SSIs. To our knowledge, this is the first study to provide recommendations based on a careful review of the available scientific evidence for the non-pharmacological prevention of SSIs in neonates and children. The specific scenarios developed are intended to guide the healthcare professional in practice to ensure standardized management of the neonatal and pediatric patients, decrease the incidence of SSIs and reduce antibiotic abuse.
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Affiliation(s)
- Aniello Meoli
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Lorenzo Ciavola
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Sofia Rahman
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Marco Masetti
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Tommaso Toschetti
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Riccardo Morini
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Giulia Dal Canto
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Cinzia Auriti
- Neonatology and Neonatal Intensive Care Unit, IRCCS Bambino Gesù Children’s Hospital, 00165 Rome, Italy;
| | - Caterina Caminiti
- Research and Innovation Unit, University Hospital of Parma, 43126 Parma, Italy;
| | - Elio Castagnola
- Infectious Diseases Unit, IRCCS Giannina Gaslini, 16147 Genoa, Italy;
| | - Giorgio Conti
- Pediatric ICU and Trauma Center, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00165 Rome, Italy;
| | - Daniele Donà
- Division of Paediatric Infectious Diseases, Department for Woman and Child Health, University of Padua, 35100 Padua, Italy;
| | - Luisa Galli
- Infectious Disease Unit, Meyer Children’s Hospital, 50139 Florence, Italy; (L.G.); (E.V.)
| | - Stefania La Grutta
- Institute of Translational Pharmacology IFT, National Research Council, 90146 Palermo, Italy;
| | - Laura Lancella
- Paediatric Infectious Disease Unit, Academic Department of Pediatrics, IRCCS Bambino Gesù Children’s Hospital, 00165 Rome, Italy;
| | - Mario Lima
- Pediatric Surgery, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Andrea Lo Vecchio
- Department of Translational Medical Science, Section of Pediatrics, University of Naples “Federico II”, 80138 Naples, Italy; (A.L.V.); (A.S.)
| | - Gloria Pelizzo
- Pediatric Surgery Department, “Vittore Buzzi” Children’s Hospital, 20154 Milano, Italy;
| | - Nicola Petrosillo
- Infection Prevention and Control—Infectious Disease Service, Foundation University Hospital Campus Bio-Medico, 00128 Rome, Italy;
| | - Alessandro Simonini
- Pediatric Anesthesia and Intensive Care Unit, Salesi Children’s Hospital, 60123 Ancona, Italy;
| | - Elisabetta Venturini
- Infectious Disease Unit, Meyer Children’s Hospital, 50139 Florence, Italy; (L.G.); (E.V.)
| | - Fabio Caramelli
- Pediatric Intensive Care Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Gaetano Domenico Gargiulo
- Department of Cardio-Thoracic and Vascular Medicine, Adult Cardiac Surgery, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Enrico Sesenna
- Maxillo-Facial Surgery Unit, Head and Neck Department, University Hospital of Parma, 43126 Parma, Italy;
| | - Rossella Sgarzani
- Servizio di Chirurgia Plastica, Centro Grandi Ustionati, Ospedale M. Bufalini, AUSL Romagna, 47521 Cesena, Italy;
| | - Claudio Vicini
- Head-Neck and Oral Surgery Unit, Department of Head-Neck Surgery, Otolaryngology, Morgagni Piertoni Hospital, 47121 Forli, Italy;
| | - Mino Zucchelli
- Pediatric Neurosurgery, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40138 Bologna, Italy;
| | - Fabio Mosca
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Department of Mother, Child and Infant, 20122 Milan, Italy;
| | - Annamaria Staiano
- Department of Translational Medical Science, Section of Pediatrics, University of Naples “Federico II”, 80138 Naples, Italy; (A.L.V.); (A.S.)
| | | | - Susanna Esposito
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
- Correspondence: ; Tel.: +39-0521-903524
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12
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Abdelgawad MA, Parambi DGT, Ghoneim MM, Alotaibi NH, Alzarea AI, Alanazi AS, Hassan A, Tony SM, Abdelrahim ME. A meta-analysis showing the effect of surgical site wound infections and associated risk factors in neonatal surgeries. Int Wound J 2022; 19:2092-2100. [PMID: 35445789 DOI: 10.1111/iwj.13814] [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: 03/18/2022] [Revised: 03/31/2022] [Accepted: 04/06/2022] [Indexed: 11/27/2022] Open
Abstract
A meta-analysis was performed to assess the effect of surgical site wound infections and risk factors in neonates undergoing surgery. A systematic literature search up to January 2022 incorporated 17 trials involving 645 neonates who underwent surgery at the beginning of the trial; 198 of them had surgical site wound infections, and 447 were control for neonates. The statistical tools like the dichotomous or continuous method used within a random or fixed-influence model to establish the odds ratio (OR) and mean difference (MD) with 95% confidence intervals (CIs) to evaluate the risk factors and influence of surgical site wound infections in neonates undergoing surgery. Surgical site wound infections had significantly higher mortality with OR value 2.03 at 95% CI 1.40-2.95 with P-value <0.001, the longer length of hospital stay (MD, 31.88; 95% CI, 18.17-45.59, P < 0.001), and lower birthweight of neonates (MD, -0.30; 95% CI, -0.53 to -0.07, P = 0.01) compared with neonates with no surgical site wound infections undergoing surgery. However, no remarkable change was observed with surgical site wound infections in the gestational age at birth of neonates (MD, -0.70; 95% CI, -1.46 to 0.05, P = 0.07), and the preoperative antibiotic prophylaxis (OR, 1.28; 95% CI, 0.57-2.87, P = 0.55) compared with no surgical site wound infections for neonates undergoing surgery. Surgical site wound infections had significantly higher mortality, a longer length of hospital stay, and lower birthweight of neonates. However, they had no statistically significant difference in the gestational age at birth of neonates and the preoperative antibiotic prophylaxis compared with no surgical site wound infections for neonates undergoing surgery. Furthermore, evidence is needed to confirm the outcomes.
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Affiliation(s)
- Mohamed A Abdelgawad
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka, Saudi Arabia
| | - Della G T Parambi
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka, Saudi Arabia
| | - Mohammed M Ghoneim
- Department of Pharmacy Practice, Faculty of Pharmacy, AlMaarefa University, Ad Diriyah, Saudi Arabia
| | - Nasser Hadal Alotaibi
- Department of clinical pharmacy, College of pharmacy, Jouf university Sakaka, Al Jouf, Saudi Arabia
| | | | - Abdullah S Alanazi
- Department of clinical pharmacy, College of pharmacy, Jouf university Sakaka, Al Jouf, Saudi Arabia.,Health sciences research unit, Jouf university, Sakaka, Saudi Arabia
| | - Ahmed Hassan
- Clinical Pharmacy Department, Faculty of Pharmacy, University of Sadat City (USC), Sadat City, Egypt
| | - Sara M Tony
- Clinical pharmacy department, Beni-suef specialized hospital, Beni-Suef, Egypt
| | - Mohamed Ea Abdelrahim
- Department of Clinical Pharmacy, Faculty of Pharmacy, Beni-Suef, Beni-Suef University, Beni-Suef, Egypt
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13
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Petrosyan Y, Thavorn K, Smith G, Maclure M, Preston R, van Walravan C, Forster AJ. Predicting postoperative surgical site infection with administrative data: a random forests algorithm. BMC Med Res Methodol 2021; 21:179. [PMID: 34454414 PMCID: PMC8403439 DOI: 10.1186/s12874-021-01369-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 07/28/2021] [Indexed: 12/20/2022] Open
Abstract
Background Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms. Methods All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets. Results Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90–0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p = 0.402). Conclusion We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-021-01369-9.
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Affiliation(s)
- Yelena Petrosyan
- Clinical Epidemiology, Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada
| | - Kednapa Thavorn
- Clinical Epidemiology, Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada. .,School of Epidemiology and Public Health, University of Ottawa, 75 Laurier Ave E, Ottawa, Ontario, K1N 6N5, Canada. .,Institute for Clinical and Evaluative Sciences, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada. .,The Ottawa Hospital - General Campus, 501 Smyth Road, PO Box 201B, Ottawa, ON, K1H 8L6, Canada.
| | - Glenys Smith
- Institute for Clinical and Evaluative Sciences, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada
| | - Malcolm Maclure
- Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada
| | - Roanne Preston
- Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada
| | - Carl van Walravan
- Clinical Epidemiology, Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada.,School of Epidemiology and Public Health, University of Ottawa, 75 Laurier Ave E, Ottawa, Ontario, K1N 6N5, Canada.,Institute for Clinical and Evaluative Sciences, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada
| | - Alan J Forster
- Clinical Epidemiology, Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada.,Institute for Clinical and Evaluative Sciences, 1053 Carling Ave, Ottawa, Ontario, K1Y 4E9, Canada.,Department of Medicine, University of Ottawa, 75 Laurier Ave E, Ottawa, Ontario, K1N 6N5, Canada
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14
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Tang BH, Guan Z, Allegaert K, Wu YE, Manolis E, Leroux S, Yao BF, Shi HY, Li X, Huang X, Wang WQ, Shen AD, Wang XL, Wang TY, Kou C, Xu HY, Zhou Y, Zheng Y, Hao GX, Xu BP, Thomson AH, Capparelli EV, Biran V, Simon N, Meibohm B, Lo YL, Marques R, Peris JE, Lutsar I, Saito J, Burggraaf J, Jacqz-Aigrain E, van den Anker J, Zhao W. Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction. Clin Pharmacokinet 2021; 60:1435-1448. [PMID: 34041714 DOI: 10.1007/s40262-021-01033-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. OBJECTIVE The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates. METHODS Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. RESULTS The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. CONCLUSION A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
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Affiliation(s)
- Bo-Hao Tang
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Zheng Guan
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Center, Leiden, The Netherlands
| | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Yue-E Wu
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Efthymios Manolis
- Modelling and Simulation Working Party, European Medicines Agency, Amsterdam, The Netherlands
| | | | - Bu-Fan Yao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Hai-Yan Shi
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Xiao Li
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Xin Huang
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.,Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Wen-Qi Wang
- Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - A-Dong Shen
- Key Laboratory of Major Diseases in Children and National Key Discipline of Pediatrics (Capital Medical University), Ministry of Education, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xiao-Ling Wang
- Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Tian-You Wang
- Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Chen Kou
- Department of Neonatology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Hai-Yan Xu
- Department of Pediatrics, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Yue Zhou
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Yi Zheng
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Bao-Ping Xu
- Department of Respiratory Diseases, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Alison H Thomson
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Edmund V Capparelli
- Pediatric Pharmacology and Drug Discovery, University of California, San Diego, CA, USA
| | - Valerie Biran
- Neonatal Intensive Care Unit, Hospital Robert Debre, Paris, France
| | - Nicolas Simon
- Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM, Hop Sainte Marguerite, Service de Pharmacologie Clinique, CAP-TV, Marseille, France
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yoke-Lin Lo
- Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.,School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia
| | - Remedios Marques
- Department of Pharmacy Services, La Fe Hospital, Valencia, Spain
| | - Jose-Esteban Peris
- Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Valencia, Spain
| | - Irja Lutsar
- Institute of Medical Microbiology, University of Tartu, Tartu, Estonia
| | - Jumpei Saito
- Department of Pharmacy, National Children's Hospital National Center for Child Health and Development, Tokyo, Japan
| | - Jacobus Burggraaf
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Center, Leiden, The Netherlands
| | - Evelyne Jacqz-Aigrain
- Department of Pediatric Pharmacology and Pharmacogenetics, Hospital Robert Debre, APHP, Paris, France.,Clinical Investigation Center CIC1426, Hoŝpital Robert Debre, Paris, France.,University Paris Diderot, Sorbonne Paris Cite, Paris, France
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA.,Departments of Pediatrics, Pharmacology and Physiology, Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Department of Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Wei Zhao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China. .,Modelling and Simulation Working Party, European Medicines Agency, Amsterdam, The Netherlands. .,Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China. .,Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.
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15
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Betts KS, Kisely S, Alati R. Predicting neonatal respiratory distress syndrome and hypoglycaemia prior to discharge: Leveraging health administrative data and machine learning. J Biomed Inform 2020; 114:103651. [PMID: 33285308 DOI: 10.1016/j.jbi.2020.103651] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVES A major challenge for hospitals and clinicians is the early identification of neonates at risk of developing adverse conditions. We develop a model based on routinely collected administrative data, which accurately predicts two common disorders among early term and preterm (<39 weeks) neonates prior to discharge. STUDY DESIGN The data included all inpatient live births born prior to 39 weeks (n = 154,755) occurring in the Australian state of Queensland between January 2009 and December 2015. Predictor variables included all maternal data captured in administrative records from the beginning of gestation up to, and including, the delivery, as well as neonatal data recorded at the delivery. Gradient boosted trees were used to predict neonatal respiratory distress syndrome and hypoglycaemia prior to discharge, with model performance benchmarked against a logistic regression models. RESULTS The gradient boosted trees model achieved very high discrimination for respiratory distress syndrome [AUC = 0.923, 95% CI (0.917, 0.928)] and good discrimination for hypoglycaemia [AUC = 0.832, 95% CI (0.827, 0.837)] in the validation data, as well as outperforming the logistic regression models. CONCLUSION Our study suggests that routinely collected health data have the potential to play an important role in assisting clinicians to identify neonates at risk of developing selected disorders shortly after birth. Despite achieving high levels of discrimination, many issues remain before such models can be implemented in practice, which we discuss in relation to our findings.
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Affiliation(s)
- Kim S Betts
- School of Public Health, Building 400, Kent Street, Bentley, Curtin University, WA 6101, Australia.
| | - Steve Kisely
- School of Medicine, University of Queensland, Brisbane, Australia.
| | - Rosa Alati
- School of Public Health, Building 400, Kent Street, Bentley, Curtin University, WA 6101, Australia.
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16
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Risk of peri-operative complications in children receiving preoperative steroids. Pediatr Surg Int 2020; 36:1345-1352. [PMID: 32918623 DOI: 10.1007/s00383-020-04742-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/01/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Steroid use predisposes adult patients to increased perioperative complications including wound dehiscence and delayed wound healing. A similar large study investigating the perioperative impact of steroid use in pediatric patients has not been performed. METHODS The National Surgical Quality Improvement Project Pediatric Database was queried from 2012-2017 to identify patients who received steroid preoperatively. Patient demographics, comorbidities, surgical variables, and outcomes were compared between cohorts. Patients were propensity score matched and thirty-day adverse events were compared. RESULTS Of 425,251 pediatric surgery patients, 9716 (2.3%) received preoperative steroids. Pediatric patients treated with steroids were older and had more comorbidities. After propensity score matching, the steroid population had a significantly higher rate of adverse events, including prolonged hospital stay (15.3% vs. 9.1%, p < 0.001), seizure (0.9% vs. 0.4%, p < 0.001), readmission (14.4% vs. 9.2%, p < 0.001), and death (2.2% vs. 1.1%, p < 0.001). CONCLUSION Preoperative steroid use is independently associated with increased 30-day postoperative adverse events among pediatric patients. Given the significant impact of steroid use on surgical outcomes, the risks and benefits of steroid treatment in children receiving surgery should be carefully evaluated.
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17
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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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18
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The damage response framework and infection prevention: From concept to bedside. Infect Control Hosp Epidemiol 2020; 41:337-341. [PMID: 31915082 DOI: 10.1017/ice.2019.354] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Hospital-acquired infections remain a common cause of morbidity and mortality despite advances in infection prevention through use of bundles, environmental cleaning, antimicrobial stewardship, and other best practices. Current prevention strategies and further hospital-acquired infection reduction are limited by lack of recognition of the role that host-microbe interactions play in susceptibility and by the inability to analyze multiple risk factors in real time to accurately predict the likelihood of a hospital-acquired infection before it occurs and to inform medical decision making. Herein, we examine the value of incorporating the damage-response framework and host attributes that determine susceptibility to infectious diseases known by the acronym MISTEACHING (ie, microbiome, immunity, sex, temperature, environment, age, chance, history, inoculum, nutrition, genetics) into infection prevention strategies using machine learning to drive decision support and patient-specific interventions.
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19
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da Silva DA, ten Caten CS, dos Santos RP, Fogliatto FS, Hsuan J. Predicting the occurrence of surgical site infections using text mining and machine learning. PLoS One 2019; 14:e0226272. [PMID: 31834905 PMCID: PMC6910696 DOI: 10.1371/journal.pone.0226272] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 11/22/2019] [Indexed: 12/11/2022] Open
Abstract
In this study we propose the use of text mining and machine learning methods to predict and detect Surgical Site Infections (SSIs) using textual descriptions of surgeries and post-operative patients’ records, mined from the database of a high complexity University hospital. SSIs are among the most common adverse events experienced by hospitalized patients; preventing such events is fundamental to ensure patients’ safety. Knowledge on SSI occurrence rates may also be useful in preventing future episodes. We analyzed 15,479 surgery descriptions and post-operative records testing different preprocessing strategies and the following machine learning algorithms: Linear SVC, Logistic Regression, Multinomial Naive Bayes, Nearest Centroid, Random Forest, Stochastic Gradient Descent, and Support Vector Classification (SVC). For prediction purposes, the best result was obtained using the Stochastic Gradient Descent method (79.7% ROC-AUC); for detection, Logistic Regression yielded the best performance (80.6% ROC-AUC).
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Affiliation(s)
- Daniel A. da Silva
- Industrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Carla S. ten Caten
- Industrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Flavio S. Fogliatto
- Industrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- * E-mail:
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20
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Medic G, Kosaner Kließ M, Atallah L, Weichert J, Panda S, Postma M, EL-Kerdi A. Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review. F1000Res 2019; 8:1728. [PMID: 31824670 PMCID: PMC6894361 DOI: 10.12688/f1000research.20498.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/24/2019] [Indexed: 10/21/2023] Open
Abstract
Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.
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Affiliation(s)
- Goran Medic
- Health Economics, Philips, Eindhoven, Noord-Brabant, 5621JG, The Netherlands
- Department of Pharmacy, Unit of PharmacoTherapy, -Epidemiology & -Economics, University of Groningen, Groningen, 9700 AB, The Netherlands
| | | | | | | | - Saswat Panda
- Global Market Access Solutions Sàrl, St-Prex, 1162, Switzerland
| | - Maarten Postma
- Department of Pharmacy, Unit of PharmacoTherapy, -Epidemiology & -Economics, University of Groningen, Groningen, 9700 AB, The Netherlands
- Department of Health Sciences, University Medical Centre Groningen, University of Groningen, Groningen, 9700 AB, The Netherlands
- Department of Economics, Econometrics & Finance, University of Groningen, Groningen, 9700 AB, The Netherlands
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Medic G, Kosaner Kließ M, Atallah L, Weichert J, Panda S, Postma M, EL-Kerdi A. Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review. F1000Res 2019; 8:1728. [PMID: 31824670 PMCID: PMC6894361 DOI: 10.12688/f1000research.20498.2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/22/2019] [Indexed: 02/01/2023] Open
Abstract
Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.
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Affiliation(s)
- Goran Medic
- Health Economics, Philips, Eindhoven, Noord-Brabant, 5621JG, The Netherlands
- Department of Pharmacy, Unit of PharmacoTherapy, -Epidemiology & -Economics, University of Groningen, Groningen, 9700 AB, The Netherlands
| | | | | | | | - Saswat Panda
- Global Market Access Solutions Sàrl, St-Prex, 1162, Switzerland
| | - Maarten Postma
- Department of Pharmacy, Unit of PharmacoTherapy, -Epidemiology & -Economics, University of Groningen, Groningen, 9700 AB, The Netherlands
- Department of Health Sciences, University Medical Centre Groningen, University of Groningen, Groningen, 9700 AB, The Netherlands
- Department of Economics, Econometrics & Finance, University of Groningen, Groningen, 9700 AB, The Netherlands
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Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure FX, Birgand G, Holmes AH. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect 2019; 26:584-595. [PMID: 31539636 DOI: 10.1016/j.cmi.2019.09.009] [Citation(s) in RCA: 178] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 08/29/2019] [Accepted: 09/09/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). OBJECTIVES We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID. SOURCES References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019. CONTENT We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%). IMPLICATIONS Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.
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Affiliation(s)
- N Peiffer-Smadja
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France.
| | - T M Rawson
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - R Ahmad
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | | | - P Georgiou
- Department of Electrical and Electronic Engineering, Imperial College, London, UK
| | - F-X Lescure
- French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France; Infectious Diseases Department, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, Paris, France
| | - G Birgand
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - A H Holmes
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
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Catania VD, Boscarelli A, Lauriti G, Morini F, Zani A. Risk Factors for Surgical Site Infection in Neonates: A Systematic Review of the Literature and Meta-Analysis. Front Pediatr 2019; 7:101. [PMID: 30984722 PMCID: PMC6449628 DOI: 10.3389/fped.2019.00101] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 03/05/2019] [Indexed: 12/17/2022] Open
Abstract
Purpose: Surgical site infections (SSI) contribute to postoperative morbidity and mortality in children. Our aim was to evaluate the prevalence and identify risk factors for SSI in neonates. Methods: Using a defined strategy, three investigators searched articles on neonatal SSI published since 2000. Studies on neonates and/or patients admitted to neonatal intensive care unit following cervical/thoracic/abdominal surgery were included. Risk factors were identified from comparative studies. Meta-analysis was conducted according to PRISMA guidelines using RevMan 5.3. Data are (mean ± SD) prevalence. Results: Systematic review-of 885 abstracts screened, 48 studies (27,760 neonates) were included. The incidence of SSI was 5.6% (1,564 patients). SSI was more frequent in males (61.8%), premature babies (77.4%), and following gastrointestinal surgery (95.4%). Meta-analysis-10 comparative studies (16,442 neonates; 946 SSI 5.7%) showed that predictive factors for SSI development were gestational age, birth weight, age at surgery, length of surgical procedure, number of procedure per patient, length of preoperative hospital stay, and preoperative sepsis. Conversely, preoperative antibiotic use was not significantly associated with development of SSI. Conclusions: Younger neonates and those undergoing abdominal procedures are at higher risk for SSI. Given the lack of evidence-based literature, prospective studies may help determine the risk factors for SSI in neonates.
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Affiliation(s)
- Vincenzo Davide Catania
- Division of General and Thoracic Surgery, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Alessandro Boscarelli
- Neonatal Surgery Unit, Department of Medical and Surgical Neonatology, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Giuseppe Lauriti
- Department of Pediatric Surgery, Spirito Santo Hospital and G. d'Annunzio University of Chieti and Pescara, Chieti, Italy
| | - Francesco Morini
- Neonatal Surgery Unit, Department of Medical and Surgical Neonatology, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Augusto Zani
- Division of General and Thoracic Surgery, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
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