Risk prediction models for emergence delirium in paediatric general anaesthesia: a systematic review.
Ontology highlight
ABSTRACT: Objectives
Emergence delirium (ED) occurs in approximately 25% of paediatric general anaesthetics and has significant adverse effects. The goal of the current systematic review was to identify the existing literature investigating performance of predictive models for the development of paediatric ED following general anaesthesia and to determine their usability.Design
Systematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sources
Medline (Ovid), PubMed, Embase (Ovid), Cochrane Database of Systematic Reviews (Ovid), Cochrane CENTRAL (Ovid), PsycINFO (Ovid), Scopus (Elsevier) and Web of Science (Clarivate Analytics), ClinicalTrials.gov, International Clinical Trials Registry Platform and ProQuest Digital Dissertations and Theses International through 17 November 2020.Eligibility criteria for selecting studies
All randomised controlled trials and cohort studies investigating predictive models for the development of ED in children undergoing general anaesthesia.Data extraction and synthesis
Following title, abstract and full-text screening by two reviewers, data were extracted from all eligible studies, including demographic parameters, details of anaesthetics and performance characteristics of the predictive scores for ED. Evidence quality and predictive score usability were assessed according to the PROBAST framework.Results
The current systematic review yielded 9242 abstracts, of which only one study detailing the development and validation of the Emergence Agitation Risk Scale (EARS) met the inclusion criteria. EARS had good discrimination with c-index of 0.81 (95% CI 0.72 to 0.89). Calibration showed a non-significant Homer-Lemeshow goodness-of-fit test (p=0.97). Although the EARS demonstrated low concern of applicability, the high risk of bias compromised the overall usability of this model.Conclusions
The current systematic review concluded that EARS has good discrimination performance but low usability to predict ED in a paediatric population. Further research is warranted to develop novel models for the prediction of ED in paediatric anaesthesia.Prospero registration number
CRD42019141950.
SUBMITTER: Petre MA
PROVIDER: S-EPMC7789445 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectiveSummarize performance and development of ICU delirium-prediction models published within the past 5 years.Data sourcesSystematic electronic searches were conducted in April 2019 using PubMed, Embase, Cochrane Central, Web of Science, and Cumulative Index to Nursing and Allied Health Literature to identify peer-reviewed studies.Study selectionEligible studies were published in English during the past 5 years that specifically addressed the development, validation, or recalibration of delirium-prediction models in adult ICU populations.Data extractionScreened citations were extracted independently by three investigators with a 42% overlap to verify consistency using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.Data synthesisEighteen studies featuring 23 distinct prediction models were included. Model performance varied greatly, as assessed by area under the receiver operating characteristic curve (0.62-0.94), specificity (0.50-0.97), and sensitivity (0.45-0.96). Most models used data collected from a single time point or window to predict the occurrence of delirium at any point during hospital or ICU admission, and lacked mechanisms for providing pragmatic, actionable predictions to clinicians.ConclusionsAlthough most ICU delirium-prediction models have relatively good performance, they have limited applicability to clinical practice. Most models were static, making predictions based on data collected at a single time-point, failing to account for fluctuating conditions during ICU admission. Further research is needed to create clinically relevant dynamic delirium-prediction models that can adapt to changes in individual patient physiology over time and deliver actionable predictions to clinicians.
| S-EPMC7746201 | biostudies-literature
Project description:OBJECTIVE:To identify existing prognostic delirium prediction models and evaluate their validity and statistical methodology in the older adult (?60 years) acute hospital population. DESIGN:Systematic review. DATA SOURCES AND METHODS:PubMed, CINAHL, PsychINFO, SocINFO, Cochrane, Web of Science and Embase were searched from 1 January 1990 to 31 December 2016. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and CHARMS Statement guided protocol development. INCLUSION CRITERIA:age >60 years, inpatient, developed/validated a prognostic delirium prediction model. EXCLUSION CRITERIA:alcohol-related delirium, sample size ?50. The primary performance measures were calibration and discrimination statistics. Two authors independently conducted search and extracted data. The synthesis of data was done by the first author. Disagreement was resolved by the mentoring author. RESULTS:The initial search resulted in 7,502 studies. Following full-text review of 192 studies, 33 were excluded based on age criteria (<60?years) and 27 met the defined criteria. Twenty-three delirium prediction models were identified, 14 were externally validated and 3 were internally validated. The following populations were represented: 11 medical, 3 medical/surgical and 13 surgical. The assessment of delirium was often non-systematic, resulting in varied incidence. Fourteen models were externally validated with an area under the receiver operating curve range from 0.52 to 0.94. Limitations in design, data collection methods and model metric reporting statistics were identified. CONCLUSIONS:Delirium prediction models for older adults show variable and typically inadequate predictive capabilities. Our review highlights the need for development of robust models to predict delirium in older inpatients. We provide recommendations for the development of such models.
| S-EPMC5931306 | biostudies-other
Project description:Objective To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population.Design Systematic review.Data sources Medline and Embase until June 2013.Eligibility criteria for study selection Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population.Results 9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively.Conclusions There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors.
| S-EPMC4868251 | biostudies-literature
Project description:AimTo provide a comprehensive overview of cardiovascular disease (CVD) risk prediction models for women and models that include female-specific predictors.MethodsWe performed a systematic review of CVD risk prediction models for women in the general population by updating a previous review. We searched Medline and Embase up to July 2017 and included studies in which; (a) a new model was developed, (b) an existing model was validated, or (c) a predictor was added to an existing model.ResultsA total of 285 prediction models for women have been developed, of these 160 (56%) were female-specific models, in which a separate model was developed solely in women and 125 (44%) were sex-predictor models. Out of the 160 female-specific models, 2 (1.3%) included one or more female-specific predictors (mostly reproductive risk factors). A total of 591 validations of sex-predictor or female-specific models were identified in 206 papers. Of these, 333 (56%) validations concerned nine models (five versions of Framingham, SCORE, Pooled Cohort Equations and QRISK). The median and pooled C statistics were comparable for sex-predictor and female-specific models. In 260 articles the added value of new predictors to an existing model was described, however in only 3 of these female-specific predictors (reproductive risk factors) were added.ConclusionsThere is an abundance of models for women in the general population. Female-specific and sex-predictor models have similar predictors and performance. Female-specific predictors are rarely included. Further research is needed to assess the added value of female-specific predictors to CVD models for women and provide physicians with a well-performing prediction model for women.
| S-EPMC6324808 | biostudies-literature
Project description:OBJECTIVE:Older patients with hip fractures who are undergoing surgery are at high risk of significant mortality and morbidity including postoperative delirium. It is unclear whether different types of anaesthesia may reduce the incidence of postoperative delirium. This systematic review will investigate the impact of anaesthetic technique on postoperative delirium. Other outcomes included mortality, length of stay, complications and functional outcomes. DESIGN:Systematic review of randomised controlled trials and non-randomised controlled studies. DATA SOURCES:Bibliographic databases were searched from inception to June 2018. Web of Science and ZETOC databases were searched for conference proceedings. Reference lists of relevant articles were checked, and clinical trial registers were searched to identify ongoing trials. ELIGIBILITY CRITERIA:Studies were eligible if general and regional anaesthesia were compared in patients (aged 60 and over) undergoing hip fracture surgery, reporting primary outcome of postoperative delirium and secondary outcomes of mortality, length of hospital stay, adverse events, functional outcomes, discharge location and quality of life. Exclusion criteria were anaesthetic technique or drug not considered current standard practice; patients undergoing hip fracture surgery alongside other surgery and uncontrolled studies. RESULTS:One hundred and four studies were included. There was no evidence to suggest that anaesthesia type influences postoperative delirium or mortality. Some studies suggested a small reduction in length of hospital stay with regional anaesthesia. There was some evidence to suggest that respiratory complications and intraoperative hypotension were more common with general anaesthesia. Heterogeneity precluded meta-analysis. All findings were described narratively and data were presented where possible in forest plots for illustrative purposes. CONCLUSIONS:While there was no evidence to suggest that anaesthesia types influence postoperative delirium, the evidence base is lacking. There is a need to ascertain the impact of type of anaesthesia on outcomes with an adequately powered, methodologically rigorous study. PROSPERO REGISTRATION NUMBER:CRD42015020166.
| S-EPMC6286489 | biostudies-literature
Project description:BackgroundAlthough postoperative delirium is a frequent complication of surgery, little is known about risk factors for delirium occurring in the post-anaesthesia care unit (PACU). The aim of this study was to determine pre- and intraoperative risk factors for the development of recovery room delirium (RRD) in patients undergoing elective non-cardiovascular surgery.MethodsRRD was diagnosed according to the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). We collected perioperative data in 228 patients undergoing elective non-cardiovascular surgery under general anaesthesia and performed univariate and multivariate logistic regression to identify risk factors related to RRD. PACU and postoperative events were recorded to assess the outcome of RRD.ResultsFifty-seven patients (25%) developed RRD. On multivariate analysis, maintenance of anaesthesia with inhalation anaesthetic agents (OR = 6.294, 95% CI 1.4-28.8, corrected p = 0.03), malignant primary disease (OR = 3.464, 95% CI = 1.396-8.592, corrected p = 0.018), American Society of Anaesthesiologists Physical Status (ASA-PS) III-V (OR = 3.389, 95% CI = 1.401-8.201, corrected p = 0.018), elevated serum total or direct bilirubin (OR = 2.535, 95% CI = 1.006-6.388, corrected p = 0.049), and invasive surgery (OR = 2.431, 95% CI = 1.103-5.357, corrected p = 0.035) were identified as independent risk factors for RRD. RRD was associated with higher healthcare costs (31,428 yuan [17,872-43,674] versus 16,555 yuan [12,618-27,788], corrected p = 0.002), a longer median hospital stay (17 days [12-23.5] versus 11 days [9-17], corrected p = 0.002), and a longer postoperative stay (11 days [7-15] versus 7 days [5-10], corrected p = 0.002]).ConclusionsIdentifying patients at high odds for RRD preoperatively would enable the formation of more timely postoperative delirium management programmes.
| S-EPMC7856719 | biostudies-literature
Project description:ObjectivesThis study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST).Data sourcesBoth PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage.Study selectionArticles published between December 2011 and October 2019 were considered.Data extractionFor each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported.ResultsThe median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively.ConclusionsAmong prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.
| S-EPMC8453657 | biostudies-literature
Project description:Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes.
| S-EPMC7610622 | biostudies-literature
Project description:ContextEarly detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratification of the population into risk categories could allow for the introduction of a screening programme tailored to individuals.ObjectiveThis review will identify and compare published models that predict the risk of developing kidney cancer in the general population.Evidence acquisitionA search identified primary research reporting or validating models predicting the risk of kidney cancer in Medline and EMBASE. After screening identified studies for inclusion, we extracted data onto a standardised form. The risk models were classified using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated using the PROBAST assessment tool.Evidence synthesisThe search identified 15 281 articles. Sixty-two satisfied the inclusion criteria; performance measures were provided for 11 models. Some models predicted the risk of prevalent undiagnosed disease and others future incident disease. Six of the models had been validated, two using external populations. The most commonly included risk factors were age, smoking status, and body mass index. Most of the models had acceptable-to-good discrimination (area under the receiver-operating curve >0.7) in development and validation. Many models also had high specificity; however, several had low sensitivity. The highest performance was seen for the models using only biomarkers to detect kidney cancer; however, these were developed and validated in small case-control studies.ConclusionsWe identified a small number of risk models that could be used to stratify the population according to the risk of kidney cancer. Most exhibit reasonable discrimination, but a few have been validated externally in population-based studies.Patient summaryIn this review, we looked at mathematical models predicting the likelihood of an individual developing kidney cancer. We found several suitable models, using a range of risk factors (such as age and smoking) to predict the risk for individuals. Most of the models identified require further testing in the general population to confirm their usefulness.
| S-EPMC8642244 | biostudies-literature
Project description:ObjectivesTo look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics.DesignSystematic review.Data sourcesMedline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases.Review methodsDatabases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast).Results16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes.ConclusionsMost predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient centred decision making, as well as improve the use of prevention strategies for contrast induced nephropathy.
| S-EPMC4784870 | biostudies-literature