Prediction Model Risk Of Bias Assessment Tool Research Articles

Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis

Background Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications, often presenting with obvious fever in the early stages. In current clinical practice, distinguishing KD from other febrile illnesses remains a significant challenge. In recent years, some researchers have explored the potential of machine learning (ML) methods for the differential diagnosis of KD versus other febrile illnesses, as well as for predicting coronary artery lesions (CALs) in people with KD. However, there is still a lack of systematic evidence to validate their effectiveness. Therefore, we have conducted the first systematic review and meta-analysis to evaluate the accuracy of ML in differentiating KD from other febrile illnesses and in predicting CALs in people with KD, so as to provide evidence-based support for the application of ML in the diagnosis and treatment of KD. Objective This study aimed to summarize the accuracy of ML in differentiating KD from other febrile illnesses and predicting CALs in people with KD. Methods PubMed, Cochrane Library, Embase, and Web of Science were systematically searched until September 26, 2023. The risk of bias in the included original studies was appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Stata (version 15.0; StataCorp) was used for the statistical analysis. Results A total of 29 studies were incorporated. Of them, 20 used ML to differentiate KD from other febrile illnesses. These studies involved a total of 103,882 participants, including 12,541 people with KD. In the validation set, the pooled concordance index, sensitivity, and specificity were 0.898 (95% CI 0.874-0.922), 0.91 (95% CI 0.83-0.95), and 0.86 (95% CI 0.80-0.90), respectively. Meanwhile, 9 studies used ML for early prediction of the risk of CALs in children with KD. These studies involved a total of 6503 people with KD, of whom 986 had CALs. The pooled concordance index in the validation set was 0.787 (95% CI 0.738-0.835). Conclusions The diagnostic and predictive factors used in the studies we included were primarily derived from common clinical data. The ML models constructed based on these clinical data demonstrated promising effectiveness in differentiating KD from other febrile illnesses and in predicting coronary artery lesions. Therefore, in future research, we can explore the use of ML methods to identify more efficient predictors and develop tools that can be applied on a broader scale for the differentiation of KD and the prediction of CALs.

Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: aSystematic Review.

Subarachnoid haemorrhage is apotentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk. MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023. Studies incorporating any machine learning algorithm to predict the risk of rupture of an intracranial aneurysm were included. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). PROSPERO registration: CRD42023452509. Out of 10,307 records screened, 20studies met the eligibility criteria for this review incorporating atotal of 20,286 aneurysm cases. The machine learning models gave a0.66-0.90 range for performance accuracy. The models were compared to current clinical standards in six studies and gave mixed results. Most studies posed high or unclear risks of bias and concerns for applicability, limiting the inferences that can be drawn from them. There was insufficient homogenous data for ameta-analysis. Machine learning can be applied to predict the risk of rupture for intracranial aneurysms. However, the evidence does not comprehensively demonstrate superiority to existing practice, limiting its role as aclinical adjunct. Further prospective multicentre studies of recent machine learning tools are needed to prove clinical validation before they are implemented in the clinic.

Risk-Prediction Model of Restenosis after Endovascular Treatment for Peripheral Arterial Disease: A Systematic Review and Meta-analysis.

Peripheral artery disease (PAD) patients after endovascular treatment (EVT) have a relatively high restenosis rate. However, this risk can be mitigated through precise risk assessment and individualized self-management intervention plans. Moreover, the number of predictive models for restenosis risk in PAD patients after EVT is gradually increasing, yet these results of study exhibit certain discrepancies, raising uncertainties regarding the quality and applicability in clinical practice and future research. The objective of this study was to systematically evaluate risk-predictive models for restenosis in patients with PAD after EVT. A systematic review and meta-analysis of predictive model construction and validation using observational studies was undertaken. The China National Knowledge Infrastructure, China Science and Technology Journal Database, Wanfang Database, SinoMed, PubMed, Web of Science, Embase, and the Cochrane Library were searched from inception to January 1, 2024. Two researchers independently conducted literature screening and data extraction, encompassing study design, data sources, outcome definition, sample size, predictive factors, model development, and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used for risk of bias and applicability assessment of the models. A total of 4275 studies were retrieved, ultimately resulting in the inclusion of 7 articles comprising 7 predictive models for restenosis in PAD patients after EVT, with a restenosis incidence ranging from 21.8% to 39.7%. The total sample size of the included models ranged from 137 to 1578 cases, with logistic regression analysis being the most commonly used modeling method. All models were built using R software. Only 2 models underwent external validation, and the reported area under the curve ranged from 0.728 to 0.864. The summary area-under-the-curve statistic was 0.80 (95% confidence interval [CI], 0.74-0.86), with an approximate prediction interval of 0.80 (95% CI, 0.62-0.91) . The number of included predictive factors ranged from 3 to 10, with the most common factors being age, Trans-Atlantic Inter-Society Consensus Ⅱ classification, hypertension, diabetes, high-sensitivity C-reactive protein, and surgical approach. All studies exhibited high risk of bias, primarily attributed to inappropriate sources of data and poor reporting of the analysis domain. Predictive models for restenosis after EVT in PAD patients demonstrate overall good predictive performance but are still in the developmental stage with higher risk of bias. Future studies should follow the TRIPOD statement, focusing on the development of new models with larger samples, rigorous study designs, and multicenter external validation. This systematic review adheres to the PRISMA 2020 statement, offering the most recent systematic assessment of risk prediction models for restenosis following endovascular treatment in peripheral arterial disease.The newly developed PROBAST tool was employed to assess the risk of bias and the applicability of the existing evidence.This review emphasizes the practical utility, limitations of the current evidence, and recommendations for future research, with the goal of providing valuable information for clinicians and patients in their decision-making process, while also supporting the advancement of future research endeavors.

Risk prediction models for incident heart failure: a systematic review and meta-analysis

Abstract Background Heart failure (HF) risk prediction models combine multivariable patient data to estimate an individual's risk of developing HF. By detecting at-risk and early-stage patients, models may facilitate earlier intervention to prevent or delay HF development. Previous systematic reviews were unable to recommend any existing prediction models for clinical use due to insufficient evidence and lack of guidelines on appraising study quality at their time of publication. Purpose To summarize the performance of risk prediction models for incident HF and identify models for further validation and potential clinical use. Methods We searched MEDLINE and EMBASE in June 2021 for English-language studies developing or validating HF risk prediction models. Studies were also retrieved from two previous systematic reviews. We narratively summarized model characteristics (e.g. model type, predictors used, prediction horizon) and study methodology (e.g. validation methods). Performance was assessed among all models validated in ≥ 1 cohort. For all models validated in ≥ 2 cohorts, we pooled discrimination measures using random-effects meta-analyses. Calibration was descriptively summarized based on individual study results from statistical tests and graph digitization of calibration plots. Study quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Of 18,937 publications screened, 41 studies consisting of 120 prediction models were included. Twenty models were both derived and validated, 99 only derived, and 1 only validated. Risk of bias was rated as high in nearly all (94.7%) PROBAST assessments, mostly attributable to issues with analysis. Among 21 models validated in ≥ 1 cohort, most had moderate (61.9%, C-statistic 0.7 to <0.8) or high (23.8%, C-statistic 0.8 to <0.9) discrimination. In patients with low predicted risk (<10%), the calibration was adequate. Nine (42.9%) of these 21 models were presented as web-based calculators and five (23.8%) as points-based risk scores. Based on performance, number of validation cohorts, study risk of bias, and user friendliness, the Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study of Atherosclerosis (MESA), Pooled Cohort equations to Prevent Heart Failure (PCP-HF), and Health ABC models emerged as the most promising risk scores for clinical practice. Conclusions Given their acceptable performance but high risk of bias, future studies should focus on the external validation of these models in studies of high methodological rigor. Models should be validated in a greater diversity of patient populations, particularly with respect to race. Impact analyses assessing how the clinical implementation of these models affects patient outcomes are also required prior to their routine use. Once validated, these models may help guide clinical decision making to prevent the onset of HF with early, aggressive risk factor modification.Discrimination in 21 validated modelsCharacteristics of recommended models

Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review.

Background/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.

Meta-analysis of prediction models for predicting lymph node metastasis in thyroid cancer

BackgroundThe purpose of this systematic review and meta-analysis is to assess the efficacy of various machine learning (ML) techniques in predicting preoperative lymph node metastasis (LNM) in patients diagnosed with papillary thyroid carcinoma (PTC). Although prior studies have investigated the potential of ML in this context, the current evidence is not sufficiently strong. Hence, we undertook a thorough analysis to ascertain the predictive accuracy of different ML models and their practical relevance in predicting preoperative LNM in PTC patients.Materials and methodsIn our search, we thoroughly examined PubMed, Cochrane Library, Embase, and Web of Science, encompassing their complete database history until December 3rd, 2022. To evaluate the potential bias in the machine learning models documented in the included studies, we employed the Prediction Model Risk of Bias Assessment Tool (PROBAST).ResultsA total of 107 studies, involving 136,245 patients, were included. Among them, 21,231 patients showed central LNM (CLNM) and 4,637 had lateral LNM (LLNM). The meta-analysis results revealed that the c-index for predicting LNM, CLNM, and LLNM were 0.762 (95% CI: 0.747–0.777), 0.762 (95% CI: 0.747–0.777), and 0.803 (95% CI: 0.773–0.834) in the training set, and 0.773 (95% CI: 0.754–0.791), 0.762 (95% CI: 0.747–0.777), and 0.829 (95% CI: 0.779–0.879) in the validation set, respectively. A total of 134 machine learning-based prediction models were included, covering 10 different types. Logistic Regression (LR) was the most commonly used model, accounting for 81.34% (109/134) of the included models.ConclusionsMachine learning methods have shown a certain level of accuracy in predicting preoperative LNM in PTC patients, indicating their potential as a predictive tool. Their use in the clinical management of PTC holds great promise. Among the various ML models investigated, the performance of logistic regression-based nomograms was deemed satisfactory.

Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review.

Colorectal cancer (CRC) screening is essential to reduce incidence and mortality rates. However, participation in screening remains suboptimal. ColonFlag, a machine learning algorithm using complete blood count (CBC), identifies individuals at high CRC risk using routinely performed tests. This study aims to review the existing literature assessing the efficacy of ColonFlag across diverse populations in multiple countries. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) were followed in reporting this systematic review. Searches were conducted on PubMed, Cochrane, ScienceDirect, and Google Scholar for English articles, using keywords related to CBC, machine learning, ColonFlag, and CRC, covering the first development study from 2016 to August 2023. The Cochrane Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias. A total of 949 articles were identified during the literature search. Ten studies were found to be eligible. ColonFlag yielded Area Under the Curve (AUC) values ranging from 0.736 to 0.82. The sensitivity and specificity ranged from 3.91% to 35.4% and 82.73% to 94%, respectively. The positive predictive values ranged between 2.6% and 9.1%, while the negative predictive values ranged from 97.6% to 99.9%. ColonFlag performed better in shorter time windows, tumors located more proximally, in advanced stages, and in cases of CRC compared to adenoma. While ColonFlag exhibits low sensitivity compared to established screening methods such as the fecal immunochemical test (FIT) or colonoscopy, its potential to detect CRC before clinical diagnosis suggests an opportunity for identifying more cases than regular screening alone.

Risk Prediction Models for Adverse Drug Reactions and Adverse Drug Events in Older Adults – A Systematic Review and Meta-analysis

Abstract Background Adverse drug reactions (ADRs) and adverse drug events (ADEs) are common and result in significant morbidity, mortality and healthcare costs. As the world’s population ages, the consumption of medications and healthcare will increase. Models predicting ADR risks in older adults have previously been found to lack reliability and validity. The aim of this systematic review and meta-analysis is to provide an updated, comprehensive quality assessment and analysis of ADR-risk prediction tools in older adults. Methods Standard computerised-databases and citations were searched (2012 to 2023) to identify relevant peer-reviewed studies. Studies which developed and/or validated an ADR/ADE prediction model for use in older adults were included. Four studies from a previous systematic review were also included. The TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) guidelines were used to evaluate each of the included studies. Random effects models were used to derive a pooled discrimination estimate (area under the receiver operation curve; AUROC) for model development studies and model validation studies. The prediction model risk of bias assessment tool (PROBAST) was applied to evaluate bias. Results Five out of 11,481 titles, plus four previous studies, met all inclusion criteria and underwent TRIPOD evaluation. Meta-analysis resulted in a favourable pooled AUROC 0.75 (95% CI 0.57, 0.87; I-squared= 96.65%) for model development studies and 0.70 (95% CI 0.57, 0.80; I-squared= 90.53%) for model validation studies but there was substantial heterogeneity. Studies had poor adherence (range 32-50%; median 44%; IQR 11.5%) to TRIPOD guidelines. The overall risk of bias was high for all included studies. Conclusion Seven risk prediction models were identified, all exhibiting poor adherence to TRIPOD guidelines which may question the investigational rigor of the studies and their usability. This underscores the need for the development of a validated, robust and reliable tool worthy of implementation and testing in a real-world setting.

Artificial Intelligence in Predicting the Mode of Delivery: A Systematic Review.

The integration of artificial intelligence (AI) into obstetric care offers significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods for mode of delivery often rely on subjective clinical judgment and limited statistical models, which may not fully capture complex patient data. This systematic review aims to evaluate the current state of research on AI applications in predicting the mode of delivery, comparing the performance of AI models with traditional methods, and identifying gaps for future research. A comprehensive literature search was conducted across PubMed, Google Scholar, Web of Science, and Scopus databases, covering publications from January 2010 to July 2024. Inclusion criteria were studies employing AI techniques to predict the mode of delivery, published in peer-reviewed journals, and involving human subjects. Studies were assessed for quality using the Prediction Model Risk of Bias Assessment Tool (PROBAST), and data were synthesized narratively due to heterogeneity. In total, 18 studies met the inclusion criteria, employing various AI models such as logistic regression, random forest, gradient boosting, and neural networks. Sample sizes ranged from 40 to 94,480 participants across diverse geographic settings. AI models demonstrated high accuracy rates, often exceeding 90%, and strong predictive metrics (area under the curve (AUC) values from 0.745 to 0.932). Key predictors included maternal age, gravidity, parity, gestational age, labor induction type, and fetal weight. Notable models like the Adana Systemand Categorical Boosting (CatBoost, Yandex LLC, Moscow, Russia) highlighted the effectiveness of AI in enhancing prediction accuracy and supporting clinical decisions. AI models significantly outperform traditional statistical methods in predicting the mode of delivery, providing a robust tool for obstetric care. Future research should focus on standardizing data collection, improving model interpretability, addressing ethical concerns, and ensuring fairness in AI predictions to enhance clinical trust and application.

Systematic Review of Clinical Prediction Models for the Risk of Emergency Caesarean Births.

Globally, caesarean births (CB), including emergency caesareans births (EmCB), are rising. It is estimated that nearly a third of all births will be CB by 2030. Identify and summarise the results from studies developing and validating prognostic multivariable models predicting the risk of EmCBs. Ultimately understanding the accuracy of their development, and whether they are operationalised for use in routine clinical practice. Studies were identified using databases: MEDLINE, CINAHL, Cochrane Central and Scopus with a search strategy tailored to models predicting EmCBs. Prospective studies developing and validating clinical prediction models, with two or more covariates, to predict risk of EmCB. Data were extracted onto a proforma using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). In total, 8083 studies resulted in 56 unique prediction modelling studies and seven validating studies, with a total of 121 different predictors. Frequently occurring predictors included maternal height, maternal age, parity, BMI and gestational age. PROBAST highlighted 33 studies with low overall bias, and these all internally validated their model. Thirteen studies externally validated; only eight of these were graded an overall low risk of bias. Six models offered applications that could be readily used, but only one provided enough time to offer a planned caesarean birth (pCB). These well-refined models have not been recalibrated since development. Only one model, developed in a relatively low-risk population, with data collected a decade ago, remains useful at 36 weeks for arranging a pCB. To improve personalised clinical conversations, there is a pressing need for a model that accurately predicts the timely risk of an EmCB for women across diverse clinical backgrounds. PROSPERO registration number: CRD42023384439.

Prediction models for postpartum stress urinary incontinence: A systematic review

BackgroundPostpartum stress urinary incontinence significantly impacts the quality of life and the physical and mental health of women. A reliable predictive model for postpartum stress urinary incontinence can serve as a preventive tool. Currently, there have been numerous studies developing predictive models to assess the risk of postpartum stress urinary incontinence, but the quality and clinical applicability of these models remain unknown. ObjectiveTo systematically review and evaluate existing models for predicting stressful postpartum risks. MethodsPubMed, EBSCO, The Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, WanFang Data, SinoMed, and VIP Data databases were systematically searched from the time of database construction to October 2023. Two researchers used Critical appraisal and data extraction for systematic reviews of prediction modeling studies: the CHARMS checklist for data extraction. Three researchers used The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist for bias and applicability assessment. ResultsEight papers including ten postpartum stress urinary incontinence prediction models were finalized. The most common predictors in the prediction models were urinary incontinence (UI) during pregnancy, followed by mode of delivery, Maternal age, parity, and UI before pregnancy. Nine of the prediction models reported discrimination with an area under the ROC curve (AUC) or C-index between 0.680 and 0.850. All included studies were at high risk of bias, mainly due to mishandling of continuous predictors, unreported or mishandled missing data, and inadequate assessment of predictive model performance. ConclusionsPostpartum stress urinary incontinence risk prediction models are in the initial development stage, and existing prediction models have a high risk of bias and poor modeling methodological quality, which may hinder their clinical application. In the future, healthcare practitioners should follow the norms of predictive model development and reporting to develop risk prediction models with superior predictive performance, low risk of bias, and easy clinical application.

Predicting Complete Cytoreduction with Preoperative [18F]FDG PET/CT in Patients with Ovarian Cancer: A Systematic Review and Meta-Analysis.

The cornerstone of ovarian cancer treatment is complete surgical cytoreduction. The gold-standard option in the absence of extra-abdominal metastases and intra-abdominal inoperable circumstances is primary cytoreductive surgery (CRS). However, achieving complete cytoreduction is challenging, and only possible in a selected patient population. Preoperative imaging modalities such as [18F]FDG PET/CT could be useful in patient selection for cytoreductive surgery. In our systematic review and meta-analysis, we aimed to evaluate the role of preoperative [18F]FDG PET/CT in predicting complete cytoreduction in primary and secondary debulking surgeries. Publications were pooled from two databases (PubMed, Mendeley) with predefined keywords "(ovarian cancer) AND (FDG OR PET) AND (cytoreductive surgery)". The quality of the included studies was assessed with the Prediction model Risk Of Bias Assessment Tool (PROBAST). During statistical analysis, MetaDiSc 1.4 software and the DerSimonian-Laird method (random effects models) were used. Primary and secondary cytoreductive surgeries were evaluated. Pooled sensitivities, specificities, positive predictive values (PPVs), and negative predictive values (NPVs) were calculated and statistically analyzed. Results were presented in forest plot diagrams and summary receiver operating characteristic (SROC) curves. Overall, eight publications were included in our meta-analysis. Four publications presented results of primary, three presented results of secondary cytoreductions, and two presented data related to both primary and secondary surgery. Pooled sensitivities, specificities, and positive and negative predictive values were the following: in the case of primary surgeries: 0.65 (95% CI 0.60-0.71), 0.73 (95% CI 0.66-0.80), 0.82 (95% CI 0.77-0.87), 0.52 (95% CI 0.46-0.59); and in the case of secondary surgeries: 0.91 (95% CI 0.84-0.95), 0.48 (95% CI 0.30-0.67), 0.88 (95% CI 0.81-0.93), 0.56 (95% CI 0.35-0.75), respectively. The PPVs of [18F]FDG PET/CT proved to be higher in cases of secondary debulking surgeries; therefore, it can be a valuable predictor of complete successful secondary cytoreduction.

Risk prediction model for postoperative pneumonia in esophageal cancer patients: A systematic review.

Numerous studies have developed or validated prediction models to estimate the likelihood of postoperative pneumonia (POP) in esophageal cancer (EC) patients. The quality of these models and the evaluation of their applicability to clinical practice and future research remains unknown. This study systematically evaluated the risk of bias and applicability of risk prediction models for developing POP in patients undergoing esophageal cancer surgery. PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), WanFang Database and Chinese Biomedical Literature Database were searched from inception to March 12, 2024. Two investigators independently screened the literature and extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability. A total of 14 studies involving 23 models were included. These studies were mainly published between 2014 and 2023. The applicability of all studies was good. However, all studies exhibited a high risk of bias, primarily attributed to inappropriate data sources, insufficient sample size, irrational treatment of variables and missing data, and lack of model validation. The incidence of POP in patients undergoing esophageal cancer surgery ranged from 14.60% to 39.26%. The most frequently used predictors were smoking, age, chronic obstructive pulmonary disease(COPD), diabetes mellitus, and methods of thoracotomy. Inter-model discrimination ranged from 0.627 to 0.850, sensitivity ranged between 60.7% and 84.0%, and specificity ranged from 59.1% to 83.9%. In all included studies, good discrimination was reported for risk prediction models for POP in patients undergoing esophageal cancer surgery, indicating stable model performance. However, according to the PROBAST checklist, all studies had a high risk of bias. Future studies should use the predictive model assessment tool to improve study design and develop new models with larger samples and multicenter external validation. https://www.crd.york.ac.uk/prospero, identifier CRD42024527085.

Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.

Urinary tract infections (UTIs) have been one of the most common bacterial infections in clinical practice worldwide. Artificial intelligence (AI) and machine learning (ML) based algorithms have been increasingly applied in UTI case identification and prediction. However, the overall performance of AI/ML algorithms in identifying and predicting UTI has not been evaluated. The purpose of this paper is to quantitatively evaluate the application value of AI/ML in identifying and predicting UTI cases. MEDLINE, EMBASE, Web of Science, and PubMed databases were systematically searched for articles published up to December 31, 2023. Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) and Prediction Model Risk of Bias Assessment Tool (PROBAST) were used to assess the risk of bias. Study characteristics and detailed algorithm information were extracted. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were synthesized using a bivariate mix-effects model. Meta-regression and subgroup analysis were conducted to test the source of heterogeneity. In total, 11 studies with 14 AI/ML models were included in the final meta-analysis. The overall pooled AUC was 0.89 (95%CI 0.86-0.92). Additionally, the pooled Sen, Spe, PLR, NLR, and DOR were 0.78 (95%CI 0.71-0.84), 0.89 (95%CI 0.83-0.93), 6.99 (95%CI 4.38-11.14), 0.25 (95%CI 0.18-0.34) and 28.07 (95%CI 14.27-55.20), respectively. The results of meta-regression suggested that reference standard definitions might be the source of heterogeneity. AI/ML algorithms appear to be promising to help clinicians detect and identify patients at high risk of UTIs. However, further studies are demanded to evaluate the application value of AI/ML more thoroughly.

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.

This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70-0.79 vs. 0.66-0.76, and for all sub-sites including oral cavity (0.73-0.89 vs. 0.69-0.77) and larynx (0.71-0.85 vs. 0.57-0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75-0.97, with an F1-score of 0.65-0.89 for HNC; AUROC of 0.61-0.91 and F1-score of 0.58-0.86 for the oral cavity; and AUROC of 0.76-0.97 and F1-score of 0.63-0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.

Machine learning in the prediction of treatment response in rheumatoid arthritis: A systematic review

ObjectiveThis study aimed to investigate the current status and performance of machine learning (ML) approaches in providing reproducible treatment response predictions. MethodsThis systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist. We searched PubMed, Cochrane Library, Web of Science, Scopus, and EBSCO databases for cohort studies that derived and/or validated ML models focused on predicting rheumatoid arthritis (RA) treatment response. We extracted data and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. ResultsFrom 210 unduplicated records identified by the literature search, we retained 29 eligible studies. Of these studies, 10 developed a predictive model and reported a mean adherence to the TRIPOD guidelines of 45.6 % (95 % CI: 38.3–52.8 %). The remaining 19 studies not only developed a predictive model but also validated it externally, with a mean adherence of 42.9 % (95 % CI: 39.1–46.6 %). Most of the articles had an unclear risk of bias (41.4 %), followed by a high risk of bias, which was present in 37.9 %. ConclusionsIn recent years, ML methods have been increasingly used to predict treatment response in RA. Our critical appraisal revealed unclear and high risk of bias in most of the identified models, suggesting that researchers can do more to address the risk of bias and increase transparency, including the use of calibration measures and reporting methods for handling missing data. FundingNone.

Performance of risk prediction models for diabetic foot ulcer: a meta-analysis.

The number of prediction models for diabetic foot ulcer (DFU) risk is increasing, but their methodological quality and clinical applicability are uncertain. We conducted a systematic review to assess their performance. We searched PubMed, Cochrane Library, and Embase databases up to 10 February 2024 and extracted relevant information from selected prediction models. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) checklist was used to assess bias risk and applicability. All statistical analyses were conducted in Stata 14.0. Initially, 13,562 studies were retrieved, leading to the inclusion of five development and five validation models from eight studies. DFU incidence ranged from 6% to 16.8%, with age and hemoglobin A1C (HbA1c) commonly used as predictive factors. All included studies had a high risk of bias, mainly due to disparities in population characteristics and methodology. In the meta-analysis, we observed area under the curve (AUC) values of 0.78 (95% CI [0.69-0.89]) for development models and 0.84 (95% CI [0.79-0.90]) for validation models. DFU risk prediction models show good overall accuracy, but there is a risk of bias. Adherence to the PROBAST checklist is crucial for improving their clinical applicability.

A systematic review on artificial intelligence evaluating PSMA PET scan for intraprostatic cancer.

To assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate-specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy). This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: 'artificial intelligence', 'prostate cancer', and 'PSMA PET'. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used. Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high-grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non-PSMA-avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume. Although the current state of AI differentiating high-grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed.

Prediction models for skin tears in the elderly: A systematic review and meta-analysis

BackgroundThe prevalence of risk prediction models for skin tears in the elderly is growing; however, there is still debate regarding the usefulness and suitability of these models for clinical use and additional study. ObjectiveThe purpose of this work is to perform a systematic review and meta-analysis of published research on skin tear risk prediction models in the elderly. MethodsWe conducted a comprehensive search of various databases, including Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, PubMed, Web of Science, MEDLINE, Scopus, The Cochrane Library, Wanfang Database, China Science and Technology Journal Database (VIP), and China National Knowledge Infrastructure (CNKI), from the beginning until November 27, 2023. Data extraction from the chosen studies encompassed various elements, such as study design, sample size, outcome definition, data source, predictors, model development, and performance. The assessment of bias and applicability was conducted using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was utilized to assess the transparency in reporting the prediction models—a meta-analysis of the most common predictors to assess predictor reliability. In addition, a narrative synthesis was carried out to provide an overview of the qualities, bias risk, and effectiveness of the current models. The reporting procedures of this meta-analysis conformed to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement. ResultsOut of the initially retrieved 1499 studies, this review included eight prediction models from eight selected studies. All the studies employed logistic regression to develop prediction models for skin tears. The prevalence of skin tears in the elderly varied from 3.0% to 33.3%. Senile purpura and a history of previous skin tears were the most commonly utilized predictors. The reported values for the area under the curve (AUC) ranged from 0.765 to 0.854. All the studies exhibited a high risk of bias, primarily due to inadequate reporting in the outcome and analysis domains. Furthermore, serious questions concerning their applicability were highlighted by four studies. ConclusionBased on the PROBAST checklist, the current models for predicting skin tears in the elderly showed a high risk of bias. The development of new prediction models with bigger sample sizes, appropriate study designs, and external validation from multiple sources ought to be the primary focus of future research. Patient or public contributionThere was no patient or public contribution to this systematic review. RegistrationPROSPERO registration number: CRD42023494387.

Critical appraisal and assessment of bias among studies evaluating risk prediction models for in-hospital and 30-day mortality after percutaneous coronary intervention: a systematic review

ObjectiveWe systematically assessed prediction models for the risk of in-hospital and 30-day mortality in post-percutaneous coronary intervention (PCI) patients.DesignSystematic review and narrative synthesis.Data sourcesSearched PubMed, Web of Science, Embase, Cochrane...