BMC Medical Research Methodology最新文献

{"title":"Geographically weighted accelerated failure time model for spatial survival data: application to ovarian cancer survival data in New Jersey.","authors":"Jiaxin Cai, Yemian Li, Weiwei Hu, Hui Jing, Baibing Mi, Leilei Pei, Yaling Zhao, Hong Yan, Fangyao Chen","doi":"10.1186/s12874-024-02346-8","DOIUrl":"https://doi.org/10.1186/s12874-024-02346-8","url":null,"abstract":"<p><strong>Background: </strong>In large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power.</p><p><strong>Method: </strong>We propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey.</p><p><strong>Results: </strong>Our simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates.</p><p><strong>Conclusion: </strong>Our proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"239"},"PeriodicalIF":3.9,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11476607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An example of the adaptation of the Nominal Group Technique (NGT) to a virtual format (vNGT) within healthcare research.","authors":"Frances Riley-Bennett, Lal Russell, Rebecca Fisher","doi":"10.1186/s12874-024-02362-8","DOIUrl":"https://doi.org/10.1186/s12874-024-02362-8","url":null,"abstract":"<p><p>The Nominal Group Technique (NGT) has been used to establish clinical priorities and generate guidelines within healthcare since its creation over fifty years ago. It is characterised by its five distinct stages; introduction, silent idea generation, 'round robin', clarifications and rating or ranking. A key element traditionally has been the inclusion of face-to-face discussion, however in the context of COVID-19 innovations were required. This article provides a case study illustrating an adaptation of the NGT to a virtual format (vNGT) and outlines the processes involved in a virtual NGT (vNGT), using an illustrative study exploring the rehabilitation of stroke survivors. The vNGT offers opportunities for global collaborations without the constraints of geography or incurred costs. Future studies should evaluate it's acceptability for stroke survivors to enable their participation within research.Summary statement1. This study provides a guide for the use of virtual nominal group technique (vNGT), using a freely available video-conferencing platform2. vNGT increases opportunities for global collaborations whilst incurring minimal costs.3. It remains unclear how feasible this procedure is with patient populations who have potentially less digital confidence and access.This work was supported by NIHR ARC-East Midlands, Grant number NIHR200171.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"240"},"PeriodicalIF":3.9,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11476593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of sample size estimation accuracy on power in malaria cluster randomised trials measuring epidemiological outcomes.","authors":"Joseph Biggs, Joseph D Challenger, Joel Hellewell, Thomas S Churcher, Jackie Cook","doi":"10.1186/s12874-024-02361-9","DOIUrl":"https://doi.org/10.1186/s12874-024-02361-9","url":null,"abstract":"<p><strong>Introduction: </strong>Cluster randomised trials (CRTs) are the gold standard for measuring the community-wide impacts of malaria control tools. CRTs rely on well-defined sample size estimations to detect statistically significant effects of trialled interventions, however these are often predicted poorly by triallists. Here, we review the accuracy of predicted parameters used in sample size calculations for malaria CRTs with epidemiological outcomes.</p><p><strong>Methods: </strong>We searched for published malaria CRTs using four online databases in March 2022. Eligible trials included those with malaria-specific epidemiological outcomes which randomised at least six geographical clusters to study arms. Predicted and observed sample size parameters were extracted by reviewers for each trial. Pair-wise Spearman's correlation coefficients (r_s) were calculated to assess the correlation between predicted and observed control-arm outcome measures and effect sizes (relative percentage reductions) between arms. Among trials which retrospectively calculated an estimate of heterogeneity in cluster outcomes, we recalculated study power according to observed trial estimates.</p><p><strong>Results: </strong>Of the 1889 records identified and screened, 108 articles were eligible and comprised of 71 malaria CRTs. Among 91.5% (65/71) of trials that included sample size calculations, most estimated cluster heterogeneity using the coefficient of variation (k) (80%, 52/65) which were often predicted without using prior data (67.7%, 44/65). Predicted control-arm prevalence moderately correlated with observed control-arm prevalence (r_s: 0.44, [95%CI: 0.12,0.68], p-value < 0.05], with 61.2% (19/31) of prevalence estimates overestimated. Among the minority of trials that retrospectively calculated cluster heterogeneity (20%, 13/65), empirical values contrasted with those used in sample size estimations and often compromised study power. Observed effect sizes were often smaller than had been predicted at the sample size stage (72.9%, 51/70) and were typically higher in the first, compared to the second, year of trials. Overall, effect sizes achieved by malaria interventions tested in trials decreased between 1995 and 2021.</p><p><strong>Conclusions: </strong>Study findings reveal sample size parameters in malaria CRTs were often inaccurate and resulted in underpowered studies. Future trials must strive to obtain more representative epidemiological sample size inputs to ensure interventions against malaria are adequately evaluated.</p><p><strong>Registration: </strong>This review is registered with PROSPERO (CRD42022315741).</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"238"},"PeriodicalIF":3.9,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11476958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Evaluating quasi-experimental approaches for estimating epidemiological efficacy of non-randomised field trials: applications in Wolbachia interventions for dengue.","authors":"Jo Yi Chow, Lin Geng, Somya Bansal, Borame Sue Lee Dickens, Lee Ching Ng, Ary Anthony Hoffmann, Jue Tao Lim","doi":"10.1186/s12874-024-02345-9","DOIUrl":"https://doi.org/10.1186/s12874-024-02345-9","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"237"},"PeriodicalIF":3.9,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11476659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of two modeling approaches for the identification of predictors of complications in children with cerebral palsy following spine surgery.","authors":"Rachel L Difazio, Tania D Strout, Judith A Vessey, Jay G Berry, Daniel G Whitney","doi":"10.1186/s12874-024-02360-w","DOIUrl":"10.1186/s12874-024-02360-w","url":null,"abstract":"<p><strong>Background: </strong>Children with non-ambulatory cerebral palsy (CP) frequently develop progressive neuromuscular scoliosis and require surgical intervention. Due to their comorbidities, they are at high risk for developing peri- and post-operative complications. The objectives of this study were to compare stepwise and LASSO variable selection techniques for consistency in identifying predictors when modelling these post-operative complications and to identify potential predictors of respiratory complications and infections following spine surgery among children with CP.</p><p><strong>Methods: </strong>In this retrospective cohort study, a large administrative claims database was queried to identify children who met the following criteria: 1) ≤ 25 years old, 2) diagnosis of CP, 3) underwent surgery during the study period, 4) had ≥ 12-months pre-operative, and 5) ≥ 3-months post-operative continuous health plan enrollment. Outcome measures included the development of a post-operative respiratory complication (e.g., pneumonia, aspiration pneumonia, atelectasis, pleural effusion, pneumothorax, pulmonary edema) or an infection (e.g., surgical site infection, urinary tract infection, meningitis, peritonitis, sepsis, or septicemia) within 3 months of surgery. Codes were used to identify CP, surgical procedures, medical comorbidities and the development of post-operative respiratory complications and infections. Two approaches to variable selection, stepwise and LASSO, were compared to determine which potential predictors of respiratory complications and infection development would be identified using each approach.</p><p><strong>Results: </strong>The sample included 220 children. During the 3-month follow-up, 21.8% (n = 48) developed a respiratory complication and 12.7% (n = 28) developed an infection. The prevalence of 11 variables including age, sex and 9 comorbidities were initially considered to be potential predictors based on the intended outcome of interest. Model discrimination utilizing LASSO for variable selection was slightly improved over the stepwise regression approach. LASSO resulted in retention of additional comorbidities that may have meaningful associations to consider for future studies, including gastrointestinal issues, bladder dysfunction, epilepsy, anemia and coagulation deficiency.</p><p><strong>Conclusions: </strong>Potential predictors of the development of post-operative complications were identified in this study and while identified predictors were similar using stepwise and LASSO regression approaches, model discrimination was slightly improved with LASSO. Findings will be used to inform future research processes determining which variables to consider for developing risk prediction models.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"236"},"PeriodicalIF":3.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11468503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beading plot: a novel graphics for ranking interventions in network evidence.","authors":"Chiehfeng Chen, Yu-Chieh Chuang, Edwin Shih-Yen Chan, Jin-Hua Chen, Wen-Hsuan Hou, Enoch Kang","doi":"10.1186/s12874-024-02355-7","DOIUrl":"10.1186/s12874-024-02355-7","url":null,"abstract":"<p><strong>Background: </strong>Network meta-analysis is developed to compare all available treatments; therefore it enriches evidence for clinical decision-making, offering insights into treatment effectiveness and safety when faced with multiple options. However, the complexity and numerous treatment comparisons in network meta-analysis can challenge healthcare providers and patients. The purpose of this study aimed to introduce a graphic design to present complex rankings of multiple interventions comprehensively.</p><p><strong>Methods: </strong>Our team members developed a \"beading plot\" to summary probability of achieving the best treatment (P-best) and global metrics including surface under the cumulative ranking curve (SUCRA) and P-score. Implemented via the \"rankinma\" R package, this tool summarizes rankings across diverse outcomes in network meta-analyses, and the package received an official release on the Comprehensive R Archive Network (CRAN). It includes the `PlotBead()` function for generating beading plots, which represent treatment rankings among various outcomes.</p><p><strong>Results: </strong>Beading plot has been designed based on number line plot, which effectively displays collective metrics for each treatment across various outcomes. Order on the -axis is derived from ranking metrics like P-best, SUCRA, and P-score. Continuous lines represent outcomes, and color-coded beads signify treatments.</p><p><strong>Conclusion: </strong>The beading plot is a valuable graphic that intuitively displays treatment rankings across diverse outcomes, enhancing reader-friendliness and aiding decision-making in complex network evidence scenarios. While empowering clinicians and patients to identify optimal treatments, it should be used cautiously, alongside an assessment of the overall evidence certainty.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"235"},"PeriodicalIF":3.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do authors of systematic reviews of epidemiological observational studies assess the methodologies of the included primary studies? An empirical examination of methodological tool use in the literature.","authors":"Fabian Kemper, Clovis Mariano Faggion","doi":"10.1186/s12874-024-02349-5","DOIUrl":"10.1186/s12874-024-02349-5","url":null,"abstract":"<p><strong>Background: </strong>The procedures used to assess the methodological quality and risk of bias (RoB) of systematic reviews of observational dental studies have not been investigated. The purpose of this research was to examine the way that authors of systematic reviews of epidemiological observational studies published in dentistry conducted the methodological assessment of those primary studies. In the present article, we aimed to assess the characteristics and the level of reporting of tools used to assess the methodologies of these reviews.</p><p><strong>Methods: </strong>We searched Scopus and the Web of Science from their inceptions to June 2023 for systematic reviews with meta-analyses of observational studies published in dentistry. Document selection and data extraction were performed in duplicate and independently by two authors. In a random sample of 10% of the systematic reviews, there was an agreement of more than 80% between the reviewers; data selection and extraction were conducted in the remaining 90% of the sample by one author. Data on the article and systematic review characteristics were extracted and recorded for descriptive reporting.</p><p><strong>Results: </strong>The search in the two databases resulted in the inclusion of 3,214 potential documents. After the elimination of duplicates and the application of the eligibility criteria, a total of 399 systematic reviews were identified and included. A total of 368 systematic reviews reported a methodological tool, of which 102 used the Newcastle-Ottawa scale. Additionally, 76 systematic reviews stated the use of a modified methodological tool. Information about the approach of assessing the methodological quality or RoB of primary studies but reporting no tool or tool name occurred in 25 reviews.</p><p><strong>Conclusions: </strong>The majority of authors of systematic reviews of epidemiological observational studies published in dentistry reported the tools used to assess the methodological quality or RoB of the included primary studies. Modifying existing tools to meet the individual characteristics of various studies should be considered.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"233"},"PeriodicalIF":3.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forced randomization: the what, why, and how.","authors":"Kerstine Carter, Olga Kuznetsova, Volodymyr Anisimov, Johannes Krisam, Colin Scherer, Yevgen Ryeznik, Oleksandr Sverdlov","doi":"10.1186/s12874-024-02340-0","DOIUrl":"10.1186/s12874-024-02340-0","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;When running a randomized controlled trial (RCT), a clinical site may face a situation when an eligible trial participant is to be randomized to the treatment that is not available at the site. In this case, there are two options: not to enroll the participant, or, without disclosing to the site, allocate the participant to a treatment arm with drug available at the site using a built-in feature of the interactive response technology (IRT). In the latter case, one has employed a \"forced randomization\" (FR). There seems to be an industry-wide consensus that using FR can be acceptable in confirmatory trials provided there are \"not too many\" instances of forcing. A better understanding of statistical properties of FR is warranted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We described four different IRT configurations with or without FR and illustrated them using a simple example. We discussed potential merits of FR and outlined some relevant theoretical risks and risk mitigation strategies. We performed a search using Cortellis Regulatory Intelligence database (IDRAC) ( www.cortellis.com ) to understand the prevalence of FR in clinical trial practice. We also proposed a structured template for development and evaluation of randomization designs featuring FR and showcased an application of this template for a hypothetical multi-center 1:1 RCT under three experimental settings (\"base case\", \"slower recruitment\", and \"faster recruitment\") to explore the effect of four different IRT configurations in combination with three different drug supply/re-supply strategies on some important operating characteristics of the trial. We also supplied the Julia code that can be used to reproduce our simulation results and generate additional results under user-specified experimental scenarios.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;FR can eliminate refusals to randomize patients, which can cause frustration for patients and study site personnel, improve the study logistics, drug supply management, cost-efficiency, and recruitment time. Nevertheless, FR carries some potential risks that should be reviewed at the study planning stage and, ideally, prospectively addressed through risk mitigation planning. The Cortellis search identified only 9 submissions that have reported the use of FR; typically, the FR option was documented in IRT specifications. Our simulation evidence showed that under the considered realistic experimental settings, the percentage of FR is expected to be low. When FR with backfilling was used in combination with high re-supply strategy, the final treatment imbalance was negligibly small, the proportion of patients not randomized due to the lack of drug supply was close to zero, and the time to complete recruitment was shortened compared to the case when FR was not allowed. The drug overage was primarily determined by the intensity of the re-supply strategy and to a smaller extent by the presence or absence of the FR feature in IRT","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"234"},"PeriodicalIF":3.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random.","authors":"Elinor Curnow, Rosie P Cornish, Jon E Heron, James R Carpenter, Kate Tilling","doi":"10.1186/s12874-024-02353-9","DOIUrl":"https://doi.org/10.1186/s12874-024-02353-9","url":null,"abstract":"<p><strong>Background: </strong>Epidemiological and clinical studies often have missing data, frequently analysed using multiple imputation (MI). In general, MI estimates will be biased if data are missing not at random (MNAR). Bias due to data MNAR can be reduced by including other variables (\"auxiliary variables\") in imputation models, in addition to those required for the substantive analysis. Common advice is to take an inclusive approach to auxiliary variable selection (i.e. include all variables thought to be predictive of missingness and/or the missing values). There are no clear guidelines about the impact of this strategy when data may be MNAR.</p><p><strong>Methods: </strong>We explore the impact of including an auxiliary variable predictive of missingness but, in truth, unrelated to the partially observed variable, when data are MNAR. We quantify, algebraically and by simulation, the magnitude of the additional bias of the MI estimator for the exposure coefficient (fitting either a linear or logistic regression model), when the (continuous or binary) partially observed variable is either the analysis outcome or the exposure. Here, \"additional bias\" refers to the difference in magnitude of the MI estimator when the imputation model includes (i) the auxiliary variable and the other analysis model variables; (ii) just the other analysis model variables, noting that both will be biased due to data MNAR. We illustrate the extent of this additional bias by re-analysing data from a birth cohort study.</p><p><strong>Results: </strong>The additional bias can be relatively large when the outcome is partially observed and missingness is caused by the outcome itself, and even larger if missingness is caused by both the outcome and the exposure (when either the outcome or exposure is partially observed).</p><p><strong>Conclusions: </strong>When using MI, the naïve and commonly used strategy of including all available auxiliary variables should be avoided. We recommend including the variables most predictive of the partially observed variable as auxiliary variables, where these can be identified through consideration of the plausible casual diagrams and missingness mechanisms, as well as data exploration (noting that associations with the partially observed variable in the complete records may be distorted due to selection bias).</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"231"},"PeriodicalIF":3.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11457445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning models for the prediction of acute postoperative pain in PACU for video-assisted thoracoscopic surgery.","authors":"Cao Zhang, Jiangqin He, Xingyuan Liang, Qinye Shi, Lijia Peng, Shuai Wang, Jiannan He, Jianhong Xu","doi":"10.1186/s12874-024-02357-5","DOIUrl":"10.1186/s12874-024-02357-5","url":null,"abstract":"<p><strong>Background: </strong>Postoperative pain is a prevalent symptom experienced by patients undergoing surgical procedures. This study aims to develop deep learning algorithms for predicting acute postoperative pain using both essential patient details and real-time vital sign data during surgery.</p><p><strong>Methods: </strong>Through a retrospective observational approach, we utilized Graph Attention Networks (GAT) and graph Transformer Networks (GTN) deep learning algorithms to construct the DoseFormer model while incorporating an attention mechanism. This model employed patient information and intraoperative vital signs obtained during Video-assisted thoracoscopic surgery (VATS) surgery to anticipate postoperative pain. By categorizing the static and dynamic data, the DoseFormer model performed binary classification to predict the likelihood of postoperative acute pain.</p><p><strong>Results: </strong>A total of 1758 patients were initially included, with 1552 patients after data cleaning. These patients were then divided into training set (n = 931) and testing set (n = 621). In the testing set, the DoseFormer model exhibited significantly higher AUROC (0.98) compared to classical machine learning algorithms. Furthermore, the DoseFormer model displayed a significantly higher F1 value (0.85) in comparison to other classical machine learning algorithms. Notably, the attending anesthesiologists' F1 values (attending: 0.49, fellow: 0.43, Resident: 0.16) were significantly lower than those of the DoseFormer model in predicting acute postoperative pain.</p><p><strong>Conclusions: </strong>Deep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"232"},"PeriodicalIF":3.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11457357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142387831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}