A systematic review of efforts to predict day of surgery cancellation

Risk prediction model for chemotherapy-induced nausea and vomiting in cancer patients: a systematic review.

Day-of-surgery cancellation (DOSC) in elective surgery occurs in roughly 18% of elective surgeries worldwide. This impacts patient physical health, psychological wellbeing and social function. Further impacts include reduced health service efficiency and wider economic productivity. There is a range of contributing variables including patient factors, resource constraints and health service pressures which could be integrated into predictive models. This article describes the protocol for a systematic review to evaluate peer-reviewed original research articles and implementation studies of models to predict DOSC. Such statistical models could, if properly integrated into clinical practice, yield benefits to patients and healthcare providers. The systematic review will provide a comprehensive synthesis of evidence in this area to inform future efforts at gold-standard statistical modelling. Predictor-finding studies, subsequent publications of the same model and publications in which the predictive variables have not been disclosed will be excluded. Searches will be conducted in Medline, Embase, Scopus and Web of science. Risk of bias will be assessed using the prediction model risk of bias assessment tool. Data will be collected on included variables, method of prediction, whether prediction was made at the level of the patient or the system, and training and assessment processes. These data will be subject to qualitative synthesis and used to generate a narrative summary and figures. This systematic review will abide by the 2020 PRISMA guidelines. This review is registered on PROSPERO, registration CRD42023478984.

Identifying high-risk individuals using a risk prediction model could be a crucial first stage of screening pathways to improve the early detection of pancreatic cancer. A systematic review was conducted to critically evaluate the published primary literature on the development or validation of clinical risk prediction models for pancreatic cancer risk. MEDLINE, Embase, and Web of Science were searched for relevant articles from the inception of each database up to November 2021. Study selection and data extraction were conducted by 2 independent reviewers. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to assess risk of bias. In total, 33 studies were included, describing 38 risk prediction models. Excluding studies with an overlapping population, this study consist of 15,848,100 participants, of which 58,313 were diagnosed with pancreatic cancer. Eight studies externally validated their model, and 13 performed internal validation. The studies described risk prediction models for pancreatic cancer in the general population (n = 14), patients with diabetes (n = 8), and individuals with gastrointestinal (and other) symptoms (symptoms included abdominal pain, unexplained weight loss, jaundice, and change in bowel habits and indigestion; n = 11). The commonly used clinical risk factors in the model were cigarette smoking (n = 27), age (n = 25), diabetes history (n = 22), chronic pancreatitis (n = 18), and body mass index (n = 14). In the 25 studies that assessed model performance, C-statistics ranged from 0.61 to 0.98. Of the 33 studies included, 6 were rated as being at a low risk of bias based on PROBAST. Many clinical risk prediction models for pancreatic cancer had been developed for different target populations. Although low risk-of-bias studies were identified, these require external validation and implementation studies to ensure that these will benefit clinical decision making.

Risk prediction models for postoperative delirium in elderly patients with fragility hip fracture: A systematic review and critical appraisal

The Prediction model Risk Of Bias ASsessment Tool (PROBAST) is used to assess the quality, risk of bias, and applicability of prediction models or algorithms and of prediction model/algorithm studies....

BackgroundRisk prediction models for depression in patients with coronary heart disease are increasingly being developed. However, the quality and applicability of these models in clinical practice remain uncertain.ObjectiveTo systematically evaluate depression risk prediction models in patients with coronary heart disease (CHD).MethodsDatabases including PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed were searched for relevant studies from inception to September 29, 2024. Two researchers independently screened the literature, extracted data, and used the Prediction Model Risk of Bias Assessment Tool (PROBAST) to evaluate the models' risk of bias and applicability.ResultsEight studies, encompassing 13 risk prediction models and involving 8,035 CHD patients, were included, with 1,971 patients diagnosed with depression. Common predictors included age, educational level, gender, and cardiac function classification. The area under the curve (AUC) for the models ranged from 0.772 to 0.961, indicating overall good performance; however, risk of bias was high, primarily due to issues in the analysis phase, such as inadequate handling of missing values, univariate analysis for variable selection, and lack of external validation.ConclusionDepression risk prediction models for CHD patients generally perform well, but high risk of bias and limited applicability remain concerns. Future studies should focus on developing and validating more robust models to aid healthcare professionals in early identification of high-risk patients for depression.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024625641, identifier (CRD42024625641).

BackgroundWith the rapid development of artificial intelligence (AI) technology, especially generative AI, large language models (LLMs) have shown great potential in the medical field. Through massive medical data training, it can understand complex medical texts and can quickly analyze medical records and provide health counseling and diagnostic advice directly, especially in rare diseases. However, no study has yet compared and extensively discussed the diagnostic performance of LLMs with that of physicians.ObjectiveThis study systematically reviewed the accuracy of LLMs in clinical diagnosis and provided reference for further clinical application.MethodsWe conducted searches in CNKI (China National Knowledge Infrastructure), VIP Database, SinoMed, PubMed, Web of Science, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) from January 1, 2017, to the present. A total of 2 reviewers independently screened the literature and extracted relevant information. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), which evaluates both the risk of bias and the applicability of included studies.ResultsA total of 30 studies involving 19 LLMs and a total of 4762 cases were included. The quality assessment indicated a high risk of bias in the majority of studies, primary cause is known case diagnosis. For the optimal model, the accuracy of the primary diagnosis ranged from 25% to 97.8%, while the triage accuracy ranged from 66.5% to 98%.ConclusionsLLMs have demonstrated considerable diagnostic capabilities and significant potential for application across various clinical cases. Although their accuracy still falls short of that of clinical professionals, if used cautiously, they have the potential to become one of the best intelligent assistants in the field of human health care.

This systematic review evaluated the performance and risk of bias in Systemic Lupus Erythematosus (SLE) disease manifestations and case fatality prediction models, based on a search of PubMed, Embase, and Cochrane Library up to October 17, 2025. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects meta-analysis pooled the Area Under the Curve (AUC) values with 95% Confidence Intervals (CIs), with sensitivity and subgroup analyses. The study included 35 studies comprising 89 prediction models, primarily from China (92.3%). Designs were mainly cross-sectional (46.2%) or retrospective cohort (42.3%). Common predictor categories included immunologic/autoantibody profile s (n = 148) and biochemical parameters (n = 126). During development, pulmonary (AUC = 0.92, 95% CI: 0.78-0.87), perinatal (0.92, 0.83-1.03), and case fatality models (0.91, 0.89-0.95) performed highly, while cardiovascular models scored lower (0.78, 0.74-0.81). Upon validation, pulmonary models remained superior (0.86, 0.81-0.91); perinatal (0.82, 0.77-0.87) and cardiovascular models (0.80, 0.76-0.83) remained robust, whereas case fatality models declined markedly. Machine learning models showed greater potential for pulmonary outcomes (0.83, 0.78-0.89). Predictor number improved renal model performance but reduced accuracy for case fatality. All 89 models were rated high risk of bias and mostly low applicability per PROBAST.

ObjectiveThe prognosis for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is not optimistic, and severe AECOPD leads to an increased risk of mortality. Prediction models help distinguish between high‐ and low‐risk groups. At present, many prediction models have been established and validated, which need to be systematically reviewed to screen out more suitable models that can be used in the clinic and provide evidence for future research.MethodsWe searched PubMed, EMBASE, Cochrane Library and Web of Science databases for studies on risk models for AECOPD mortality from their inception to 10 April 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Stata software (version 16) was used to synthesize the C‐statistics for each model.ResultsA total of 37 studies were included. The development of risk prediction models for mortality in patients with AECOPD was described in 26 articles, in which the most common predictors were age (n = 17), dyspnea grade (n = 11), altered mental status (n = 8), pneumonia (n = 6) and blood urea nitrogen (BUN, n = 6). The remaining 11 articles only externally validated existing models. All 37 studies were evaluated at a high risk of bias using PROBAST. We performed a meta‐analysis of five models included in 15 studies. DECAF (dyspnoea, eosinopenia, consolidation, acidemia and atrial fibrillation) performed well in predicting in‐hospital death [C‐statistic = 0.91, 95% confidence interval (CI): 0.83, 0.98] and 90‐day death [C‐statistic = 0.76, 95% CI: 0.69, 0.82] and CURB‐65 (confusion, urea, respiratory rate, blood pressure and age) performed well in predicting 30‐day death [C‐statistic = 0.74, 95% CI: 0.70, 0.77].ConclusionsThis study provides information on the characteristics, performance and risk of bias of a risk model for AECOPD mortality. This pooled analysis of the present study suggests that the DECAF performs well in predicting in‐hospital and 90‐day deaths. Yet, external validation in different populations is still needed to prove this performance.

ObjectiveThis study aims to evaluate the risk of bias (ROB) and reporting quality of idiopathic pulmonary fibrosis (IPF) prediction models by assessing characteristics of these models.MethodsThe development and/or validation of IPF prognostic models were identified via an electronic search of PubMed, Embase, and Web of Science (from inception to 12 August, 2021). Two researchers independently assessed the risk of bias (ROB) and reporting quality of IPF prediction models based on the Prediction model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariable prognostic model for Individual Prognosis or Diagnosis (TRIPOD) checklist.ResultsTwenty prognostic model studies for IPF were included, including 7 (35%) model development and external validation studies, 8 (40%) development studies, and 5 (25%) external validation studies. According to PROBAST, all studies were appraised with high ROB, because of deficient reporting in the domains of participants (45.0%) and analysis (67.3%), and at least 55% studies were susceptible to 4 of 20 sources of bias. For the reporting quality, none of them completely adhered to the TRIPOD checklist, with the lowest mean reporting score for the methods and results domains (46.6% and 44.7%). For specific items, eight sub-items had a reporting rate ≥80% and adhered to the TRIPOD checklist, and nine sub-items had a very poor reporting rate, less than 30%.ConclusionStudies adhering to PROBAST and TRIPOD checklists are recommended in the future. The reproducibility and transparency can be improved when studies completely adhere to PROBAST and TRIPOD checklists.

Introduction With increasing caesarean section (c-section) rates, personalised communication of risk has become paramount. A reliable tool to predict complications would support evidence-based discussion around planned mode of birth. Method MEDLINE, Embase, Web of Science, CINAHL, and the Cochrane Library were searched on 27th January with the terms relating to c-section, prognostic models, and complications such as infection. Any study developing and/or validating a prognostic model for maternal complications of c-section in the English language after January 1995 was included. Data extracted encompassed: source of data, participant criteria, outcome to be predicted, candidate predictors, actual predictors, sample size, model development, and model performance. PROBAST (Prediction model Risk Of Bias Assessment Tool) was utilised for risk of bias analysis and applicability concern in the prognostic model studies. Result 7,752 studies were identified, of which 16 were reviewed producing 3 studies where 3 prognostic models were identified which predicted risk of: blood transfusion, spinal hypotension, and postpartum haemorrhage. From the 3 studies, a total of 29 unique candidate predictors were identified and 15 predictors in the final model. Study authors deemed their studies to be exploratory, exploratory, and confirmatory respectively. None were externally validated and all had a high risk of bias due to analysis technique. Conclusion Few models have been developed to predict complications of elective c-section. Existing models predicting blood transfusion, spinal hypotension, and postpartum haemorrhage cannot be recommended for clinical practice. Future research should focus on identifying predictors known before surgery and validating resulting models. Take-home Message Systematic review of prediction models for planned C-section complications found none suitable for practice.

Artificial intelligence (AI) prediction models can accurately identify high-risk populations by integrating multi-dimensional clinical data, providing decision support for doctors in formulating individualized discharge plans and optimizing follow-up intervention strategies, thereby reducing the risk of readmission from the source. Currently, the number of AI prediction models for readmission of critically ill patients is increasing, but the quality and applicability of these models in clinical practice and future research remain uncertain. To systematically evaluate published studies on AI prediction models for critically ill patients. This study conducted a computerized search of the CNKI, Wanfang Data, VIP, SinoMed, PubMed, Web of Science, Cochrane, and Embase databases, with the time range from 2020 to June 25, 2025. Information such as study design, data sources, outcome definitions, sample size, predictors, model development, and performance was extracted from the selected studies. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was used to evaluate the risk of bias and applicability. A total of 387 studies were retrieved, and after screening, 31 studies with their 31 prediction models were included in this review. All studies developed risk prediction models for readmission of critically ill patients using artificial intelligence algorithms. The readmission risk of critically ill patients ranged from 1.3% to 13.7%. The most commonly used predictors were structured data. The reported area under the curve (AUC) ranged from 0.66 to 0.98. All studies had a high risk of bias, mainly due to poor reporting quality in the analysis domain and insufficient applicability. The pooled AUC of the 24 validation models was 0.82, with a 95% confidence interval of 0.77-0.87. These study results constitute a comprehensive set of high-quality evidence, demonstrating that AI prediction models exhibit moderate-to-high predictive performance and that their predictive performance is significantly higher than that of traditional prediction models. No Patient or Public Contribution. This Meta-analysis is based on the systematic review and statistical combination of the published clinical research data. The processes of research design, data extraction, and result interpretation did not involve the participation of patients or the public. The protocol for this study has been registered in PROSPERO (registration number: CRD42025637829).

ObjectiveTo systematically evaluate the methodological quality and predictive performance of risk prediction models for permanent pacemaker implantation (PPMI) following transcatheter aortic valve replacement (TAVR), identify key predictive factors, and assess the risk of bias and clinical applicability of these models.MethodsA comprehensive search was conducted across multiple databases, including PubMed, Web of Science, The Cochrane Library, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and SinoMed. The search included all records from database inception to January 1, 2025. Two independent researchers screened studies and extracted relevant data.ResultsA total of 11 studies were included, covering 11 risk prediction models with sample sizes ranging from 184–35,410. The incidence of PPMI after TAVR varied between 7.3% and 31.0%. Frequently identified predictors (present in at least two studies) included right bundle branch block (RBBB), self-expandable valves, PR interval, QRS interval, and atrioventricular block (AVB). All models reported the area under the receiver operating characteristic curve (AUROC), ranging from 0.660–0.916, with seven studies providing calibration metrics. Internal validation was performed in three studies, while one study included both internal and external validation. Ten studies were assessed as having a high risk of bias, primarily due to deficiencies in data analysis. The pooled AUROC for the nine validated models was 0.76 (95% confidence interval: 0.72–0.80), indicating moderate discriminatory ability.ConclusionExisting risk prediction models for PPMI after TAVR demonstrate moderate predictive performance but are limited by a high risk of bias, as assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Future research should focus on developing more robust models through larger sample sizes, rigorous methodologies, and multi-center external validation.Systematic Review RegistrationThe protocol for this study is registered with https://www.crd.york.ac.uk/PROSPERO/view/CRD42025629869, PROSPERO CRD42025629869.

Background/Objectives: Renal cancer has a heterogeneous characteristic. Prognostic models can enable a better evaluation of the prognosis. This study aimed to analyze the clinical applicability and risk of bias of prognostic models described in the literature for predicting cancer-specific mortality in renal cancer patients who have undergone nephrectomy. Methods: A systematic review (PROSPERO [CRD42021243529]) of all scientific articles that evaluate prognostic models for cancer-specific mortality due to renal cancer was performed. Descriptive analysis and application of the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) and the Prediction Model Risk Of Bias Assessment Tool (PROBAST) were used. Results: Most of the 40 reviewed studies used retrospective cohort designs, mainly based on hospital records or the SEER database, and focused on patients undergoing nephrectomy. While 50% developed models without validation, the rest included internal or external validation methods, with nomograms being the most common format for presenting results. Cox regression was the main modeling technique, although problems such as poor treatment of missing data, inadequate reporting of events per variable, and limited assessment of model assumptions were prevalent. According to the PROBAST assessment, all studies showed a high risk of bias, particularly in the scope of analysis, and only 40% had good applicability. Conclusions: All the studies analyzed were found to have a high risk of bias, and only 40% demonstrated good applicability. Hence, it is necessary to develop cancer-specific prognostic models for renal cancer based on the CHARMS and PROBAST frameworks.

We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance. We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist. Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models. We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.