Prediction modelling in the early detection of neonatal sepsis

Abstract Background Prediction modelling can greatly assist the health-care professionals in the management of diseases, thus sparking interest in neonatal sepsis diagnosis. The main objective of the study was to provide a complete picture of performance of prediction models for early detection of neonatal sepsis. Methods PubMed, Scopus, CINAHL databases were searched and articles which used various prediction modelling measures for the early detection of neonatal sepsis were comprehended. Data extraction was carried out based on Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Extricate data consisted of objective, study design, patient characteristics, type of statistical model, predictors, outcome, sample size and location. Prediction model Risk of Bias Assessment Tool was applied to gauge the risk of bias of the articles. Results An aggregate of ten studies were included in the review among which eight studies had applied logistic regression to build a prediction model, while the remaining two had applied artificial intelligence. Potential predictors like neonatal fever, birth weight, foetal morbidity and gender, cervicovaginitis and maternal age were identified for the early detection of neonatal sepsis. Moreover, birth weight, endotracheal intubation, thyroid hypofunction and umbilical venous catheter were promising factors for predicting late-onset sepsis; while gestational age, intrapartum temperature and antibiotics treatment were utilised as budding prognosticators for early-onset sepsis detection. Conclusion Prediction modelling approaches were able to recognise promising maternal, neonatal and laboratory predictors in the rapid detection of early and late neonatal sepsis and thus, can be considered as a novel way for clinician decision-making towards the disease diagnosis if not used alone, in the years to come.

Download Full-text

Prognostic Models for Knee Osteoarthritis: A Protocol for Systematic Review, Critical Appraisal and Meta-Analysis

10.21203/rs.3.rs-70145/v1 ◽

2020 ◽

Author(s):

Jingyu Zhong ◽

Liping Si ◽

Guangcheng Zhang ◽

Jiayu Huo ◽

Yue Xing ◽

...

Keyword(s):

Systematic Review ◽

Knee Osteoarthritis ◽

Prediction Model ◽

Critical Appraisal ◽

Prediction Models ◽

Data Extraction ◽

Risk Of Bias ◽

Prognostic Models ◽

Rapid Progression ◽

Prognostic Prediction

Abstract Background: Osteoarthritis is the most common degenerative joint disease diagnosed in clinical practice. It is associated with significant socioeconomic burden and poor quality of life, a large proportion of which is due to knee osteoarthritis (KOA), mainly driven by total knee arthroplasty (TKA). As the difficulty of being detected early and deficiency of disease-modifying drug, the focus of KOA is shifting to disease prevention and the treatment to delay its rapid progression. Thus, the prognostic prediction models are called for, to stratify individuals to guide clinical decision making. The aim of our review is to identify and characterize reported multivariable prognostic models for KOA which concern about three clinical concerns: (1) the risk of developing KOA in general population; (2) the risk of receiving TKA in KOA patients; and (3) the outcome of TKA in KOA patients who plan to receive TKA.Methods: Studies will be identified by searching seven electronic databases. Title and abstract screening and full-text review will be accomplished by two independent reviewers. Data extraction instrument and critical appraisal instrument will be developed before formal assessment, and will be modified during a training phase in advance. Study reporting transparency, methodological quality, and risk of bias will be assessed according to Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and Prediction model Risk Of Bias ASsessment Tool (PROBAST). Prognostic prediction models will be summarized qualitatively. Quantitative metrics on predictive performance of these models will be synthesized with meta-analyses if appropriate.Discussion: Our systematic review will collate evidence from prognostic prediction models that can be used through the whole process of KOA. The review may identify models which are capable of allowing personalized preventative and therapeutic interventions to be precisely targeted at those individuals who are at the highest risk. To accomplish the prediction models to cross the translational gaps between an exploratory research method and a valued addition to precision medicine workflows, research recommendations relating to model development, validation or impact assessment will be made.Systematic review registration: PROSPERO (registered, waiting for assessment, ID 203543)

Download Full-text

Clinical profile and outcome of early onset sepsis in high risk very low birth weight neonates

International Journal of Contemporary Pediatrics ◽

10.18203/2349-3291.ijcp20180480 ◽

2018 ◽

Vol 5 (2) ◽

pp. 389 ◽

Cited By ~ 1

Author(s):

Omprakash S. Shukla ◽

Aditi Rawat

Keyword(s):

Risk Factors ◽

Birth Weight ◽

Blood Culture ◽

Low Birth Weight ◽

Neonatal Sepsis ◽

Early Onset ◽

Very Low Birth Weight ◽

Gram Negative ◽

Early Onset Sepsis ◽

Culture Positive

Background: Neonatal sepsis is one of the main causes of mortality and morbidity, especially in very low birth weight neonates (birth weight <1499 grams) despite the progress in hygiene, introduction of new and potent antimicrobial agents for treatment and advanced measures for diagnosis. The aim of the study was to find correlation of clinical features and risk factors of neonatal sepsis in culture positive cases.Methods: A cross- sectional study was carried out in one hundred neonates with risk factors of septicemia after obtaining informed consent. Blood culture was done using Bactec Peds Plus/F Culture as a gold standard to diagnose septicaemia. Correlation of risk factors, clinical features with laboratory findings was obtained by using chi-square test. p-value of less than 0.05 was considered as significant.Results: Out of 100 neonates with suspected sepsis, BACTEC culture proven sepsis was seen in 40% cases. Gram negative sepsis was seen in 62.5% cases. The most common bacteria for early onset sepsis were Klebsiella, Pseudomonas and MRSA contributing 17% each to the bacteriological profile. The most common predisposing factor and clinical feature in culture positive cases were Premature rupture of membrane >24 hours (67%) and bleeding/petechia/pupura (72%) respectively. The major cause of mortality was pulmonary hemorrhage.Conclusions: Gram negative organism were more common and associated with higher mortality. Blood culture positivity increases with increase in number of risk factors in neonatal septicemia. A detailed history and thorough clinical examination is vital for early recognition of sepsis.

Download Full-text

Clinical prediction models to diagnose neonatal sepsis: a scoping review protocol

BMJ Open ◽

10.1136/bmjopen-2020-039712 ◽

2020 ◽

Vol 10 (8) ◽

pp. e039712

Author(s):

Samuel R Neal ◽

David Musorowegomo ◽

Hannah Gannon ◽

Mario Cortina Borja ◽

Michelle Heys ◽

...

Keyword(s):

Scoping Review ◽

Low Income ◽

Neonatal Sepsis ◽

Prediction Models ◽

Data Extraction ◽

Cochrane Library ◽

Clinical Prediction ◽

Clinical Prediction Models ◽

Scoping Reviews ◽

Review Protocol

IntroductionNeonatal sepsis is responsible for significant morbidity and mortality worldwide. Diagnosis is often difficult due to non-specific clinical features and the unavailability of laboratory tests in many low-income and middle-income countries (LMICs). Clinical prediction models have the potential to improve diagnostic accuracy and rationalise antibiotic usage in neonatal units, which may result in reduced antimicrobial resistance and improved neonatal outcomes. In this paper, we outline our scoping review protocol to map the literature concerning clinical prediction models to diagnose neonatal sepsis. We aim to provide an overview of existing models and evidence underlying their use and compare prediction models between high-income countries and LMICs.Methods and analysisThe protocol was developed with reference to recommendations by the Joanna Briggs Institute. Searches will include six electronic databases (Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, Global Index Medicus and the Cochrane Library) supplemented by hand searching of reference lists and citation analysis on included studies. No time period restrictions will be applied but only studies published in English or Spanish will be included. Screening and data extraction will be performed independently by two reviewers, with a third reviewer used to resolve conflicts. The results will be reported by narrative synthesis in line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines.Ethics and disseminationThe nature of the scoping review methodology means that this study does not require ethical approval. Results will be disseminated through peer-reviewed publications and conference presentations, as well as through engagement with peers and relevant stakeholders.

Download Full-text

Predictors of disease severity in children presenting from the community with febrile illnesses: a systematic review of prognostic studies

BMJ Global Health ◽

10.1136/bmjgh-2020-003451 ◽

2021 ◽

Vol 6 (1) ◽

pp. e003451

Author(s):

Arjun Chandna ◽

Rainer Tan ◽

Michael Carter ◽

Ann Van Den Bruel ◽

Jan Verbakel ◽

...

Keyword(s):

At Risk ◽

Prognostic Factors ◽

Prediction Model ◽

Disease Severity ◽

Prediction Models ◽

Severe Disease ◽

Risk Of Bias ◽

Clinical Prediction ◽

Children At Risk ◽

Clinical Prediction Models

IntroductionEarly identification of children at risk of severe febrile illness can optimise referral, admission and treatment decisions, particularly in resource-limited settings. We aimed to identify prognostic clinical and laboratory factors that predict progression to severe disease in febrile children presenting from the community.MethodsWe systematically reviewed publications retrieved from MEDLINE, Web of Science and Embase between 31 May 1999 and 30 April 2020, supplemented by hand search of reference lists and consultation with an expert Technical Advisory Panel. Studies evaluating prognostic factors or clinical prediction models in children presenting from the community with febrile illnesses were eligible. The primary outcome was any objective measure of disease severity ascertained within 30 days of enrolment. We calculated unadjusted likelihood ratios (LRs) for comparison of prognostic factors, and compared clinical prediction models using the area under the receiver operating characteristic curves (AUROCs). Risk of bias and applicability of studies were assessed using the Prediction Model Risk of Bias Assessment Tool and the Quality In Prognosis Studies tool.ResultsOf 5949 articles identified, 18 studies evaluating 200 prognostic factors and 25 clinical prediction models in 24 530 children were included. Heterogeneity between studies precluded formal meta-analysis. Malnutrition (positive LR range 1.56–11.13), hypoxia (2.10–8.11), altered consciousness (1.24–14.02), and markers of acidosis (1.36–7.71) and poor peripheral perfusion (1.78–17.38) were the most common predictors of severe disease. Clinical prediction model performance varied widely (AUROC range 0.49–0.97). Concerns regarding applicability were identified and most studies were at high risk of bias.ConclusionsFew studies address this important public health question. We identified prognostic factors from a wide range of geographic contexts that can help clinicians assess febrile children at risk of progressing to severe disease. Multicentre studies that include outpatients are required to explore generalisability and develop data-driven tools to support patient prioritisation and triage at the community level.PROSPERO registration numberCRD42019140542.

Download Full-text

A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study (Preprint)

10.2196/preprints.15965 ◽

2019 ◽

Author(s):

Wongeun Song ◽

Se Young Jung ◽

Hyunyoung Baek ◽

Chang Won Choi ◽

Young Hwa Jung ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intensive Care ◽

High Resolution ◽

Prediction Model ◽

Neonatal Sepsis ◽

Vital Sign ◽

Prediction Models ◽

Late Onset ◽

Model Based

BACKGROUND Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. OBJECTIVE The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. METHODS We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. RESULTS The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. CONCLUSIONS The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

Download Full-text

Patient characteristics, Bacteriological profile & outcome of Neonatal Sepsis: A Hospital Based Study

Community Based Medical Journal ◽

10.3329/cbmj.v2i1.14184 ◽

2013 ◽

Vol 2 (1) ◽

pp. 49-54

Author(s):

Nasim Jahan ◽

Zabrul SM Haque ◽

Md Abdul Mannan ◽

Morsheda Akhter ◽

Sabina Yasmin ◽

...

Keyword(s):

Birth Weight ◽

Low Birth Weight ◽

Neonatal Sepsis ◽

Early Onset ◽

Late Onset ◽

Female Ratio ◽

Gram Negative ◽

Common Pathogen ◽

Late Onset Sepsis ◽

Early Onset Sepsis

Neonatal sepsis is a major cause of mortality and morbidity in newborn. The spectrum of bacteria which causes neonatal sepsis varies in different parts of the world. The organisms responsible for early onset and late onset sepsis are different. The objective of the study was undertaken to determine the pattern of bacterial isolates responsible for early and late onset neonatal sepsis. A prospective descriptive study over the period of one year was conducted at the Department of Neonatal Intensive care unit of Ad-din Womens Medical College and Hospital, Dhaka, Bangladesh.Organisms were isolated from 8.7% of collected blood samples. The male female ratio of culture proven sepsis was 1.7:1. More than half (52.8%) of the evaluated neonates were preterm. & 56.3% had low birth weight. The gram positive and gram negative bacteria accounted for 24.1% and 75.9% of the isolates respectively. Around three fourth of the neonates (75.8%) presented with early onset sepsis, while 24.2% presented with late onset sepsis. Acinetobacter was the most common pathogen both in early onset (70%) and late onset (30%) sepsis. Pseudomonas (89.4%) was the second most common pathogen in early onset sepsis. Total mortality rate was 5.7%. Pre term, low birth weight and gram negative sepsis contributes majority of mortality.Gram negative organism especially Acinetobacter found to be commonest cause of sepsis. Pseudomonas was second most common but contributed highest in late onset sepsis and neonatal death due to sepsis. DOI: http://dx.doi.org/10.3329/cbmj.v2i1.14184 Community Based Medical Journal Vol.2(1) 2013 49-54

Download Full-text

Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients with Primary or Secondary Brain Tumors: A Systematic Review

10.21203/rs.3.rs-1151996/v1 ◽

2021 ◽

Author(s):

Fariba Tohidinezhad ◽

Dario Di Perri ◽

Catharina M.L. Zegers ◽

Jeanette Dijkstra ◽

Monique Anten ◽

...

Keyword(s):

High Risk ◽

Radiation Dose ◽

Brain Tumors ◽

Prediction Model ◽

Prediction Models ◽

Verbal Learning ◽

Risk Of Bias ◽

Neurocognitive Impairments ◽

Radiation Induced ◽

Neurocognitive Decline

Abstract Purpose: Although an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.Methods: MEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy”, “brain”, “prediction model”, and “neurocognitive impairments”. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.Results: Of 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n=7) and the trail making tests (n=4) were the most frequent outcome assessment tools. All studies used regression (n=14 linear, n=8 logistic, and n=4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.Conclusion: Existing models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: develop a standardized instrument for neurocognitive assessment in patients with brain tumors; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

Download Full-text

Prediction of late-onset fetal growth restriction using a combined first- and second-trimester screening model in South Chinese infants: a retrospective study

10.21203/rs.2.17844/v2 ◽

2020 ◽

Author(s):

Haiqing Zheng ◽

Yan Feng ◽

Jiexin Zhang ◽

Kuanrong Li ◽

Huiying Liang ◽

...

Keyword(s):

Retrospective Study ◽

Birth Weight ◽

Prediction Model ◽

Fetal Growth ◽

Fetal Growth Restriction ◽

Detection Rate ◽

Prediction Models ◽

Late Onset ◽

Validation Dataset ◽

Second Trimester

Abstract Background: Prediction models for early and late fetal growth restriction (FGR) have been established in many high-income countries. However, prediction models for late FGR in China are limited. This study aimed to develop a simple combined first- and second-trimester prediction model for screening late-onset FGR in South Chinese infants. Methods: This retrospective study included 2258 women who had singleton pregnancies and received routine ultrasound scans as training dataset. A validation dataset including 565 pregnant women was used to evaluate the model in order to enable an unbiased estimation. Late-onset FGR was defined as a birth weight < the 10th percentile plus abnormal Doppler indices and/or a birth weight below the 3rd percentile after 32 weeks, regardless of the Doppler status. Multivariate logistic regression was used to develop a prediction model. The model included the a priori risk (maternal characteristics), the second-trimester head circumference (HC/AC) / abdomen circumference (HC) ratio and estimated fetal weight (EFW). Results: Ninety-three fetuses were identified as late-onset FGR. The significant predictors for late-onset FGR were maternal age, height, weight, and medical history; the second-trimester HC/ AC ratio; and the EFW. This model achieved a detection rate (DR) of 52.6% for late-onset FGR at a 10% false positive rate (FPR) (area under the curve (AUC): 0.80, 95%CI 0.76-0.85). The AUC of the validation dataset was 0.65 (95%CI 0.54-0.78). Conclusions: A multivariate model combining first- and second-trimester default tests can detect 52.6% of cases of late-onset FGR at a 10% FPR. Further studies with more screening markers are needed to improve the detection rate.

Download Full-text

Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Rapid Systematic Review

10.1101/2021.01.28.21250718 ◽

2021 ◽

Author(s):

Jamie L. Miller ◽

Masafumi Tada ◽

Michihiko Goto ◽

Nicholas Mohr ◽

Sangil Lee

Keyword(s):

Systematic Review ◽

Prediction Model ◽

Prediction Models ◽

External Validation ◽

Clinical Signs ◽

Risk Of Bias ◽

Low Risk ◽

Prognostic Models ◽

Organ Support ◽

Public Data

ABSTRACTBackgroundThroughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.ObjectiveThis systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.MethodsSearches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and July 2020 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.ResultsA primary review found 292 articles relevant based on title and abstract. After further review, 246 were excluded based on the defined inclusion and exclusion criteria. Forty-six articles were included in the qualitative analysis. Inter observer agreement on inclusion was 0.86 (95% confidence interval: 0.79 - 0.93). When the PROBAST tool was applied, 44 of the 46 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Two studied reported prediction models, 4C Mortality Score from hospital data and QCOVID from general public data from UK, and were rated as low risk of bias and low concerns for applicability.ConclusionSeveral prognostic models are reported in the literature, but many of them had concerning risks of biases and applicability. For most of the studies, caution is needed before use, as many of them will require external validation before dissemination. However, two articles were found to have low risk of bias and low applicability can be useful tools.

Download Full-text

Examining Bias and Reporting in Oral Health Prediction Modeling Studies

Journal of Dental Research ◽

10.1177/0022034520903725 ◽

2020 ◽

Vol 99 (4) ◽

pp. 374-387 ◽

Cited By ~ 3

Author(s):

M. Du ◽

D. Haag ◽

Y. Song ◽

J. Lynch ◽

M. Mittinty

Keyword(s):

Oral Health ◽

Prediction Model ◽

Prediction Models ◽

Periodontal Diseases ◽

Risk Of Bias ◽

Future Research ◽

Prediction Modeling ◽

Bias Assessment ◽

Transparent Reporting ◽

Modeling Studies

Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.

Download Full-text