Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models.

BACKGROUND: The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain. METHODS: Participants were from the UK Biobank. The primary outcome was a "lifetime" history of depression. The model's performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot). RESULTS: Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a "lifetime" history of depression was 45.7% and varied (25.0-66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a "lifetime" history of depression was 30.2% and varied (21.4-70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI. CONCLUSIONS: There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients' treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them.

A nomogram clinical prediction model for predicting urinary infection stones: development and validation in a retrospective study.

PURPOSE: This study aimed to develop a nomogram prediction model to predict the exact probability of urinary infection stones before surgery in order to better deal with the clinical problems caused by infection stones and take effective treatment measures. METHODS: We retrospectively collected the clinical data of 390 patients who were diagnosed with urinary calculi by imaging examination and underwent postoperative stone analysis between August 2018 and August 2023. The patients were randomly divided into training group (n = 312) and validation group (n = 78) using the "caret" R package. The clinical data of the patients were evaluated. Univariate and multivariate logistic regression analysis were used to screen out the independent influencing factors and construct a nomogram prediction model. The receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA) and clinical impact curves were used to evaluate the discrimination, accuracy, and clinical application efficacy of the prediction model. RESULTS: Gender, recurrence stones, blood uric acid value, urine pH, and urine bacterial culture (P < 0.05) were independent predictors of infection stones, and a nomogram prediction model ( https://zhaoyshenjh.shinyapps.io/DynNomInfectionStone/ ) was constructed using these five parameters. The area under the ROC curve of the training group was 0.901, 95% confidence interval (CI) (0.865-0.936), and the area under the ROC curve of the validation group was 0.960, 95% CI (0.921-0.998). The results of the calibration curve for the training group showed a mean absolute error of 0.015 and the Hosmer-Lemeshow test P > 0.05. DCA and clinical impact curves showed that when the threshold probability value of the model was between 0.01 and 0.85, it had the maximum net clinical benefit. CONCLUSIONS: The nomogram developed in this study has good clinical predictive value and clinical application efficiency can help with risk assessment and decision-making for infection stones in diagnosing and treating urolithiasis.

Addressing the implementation challenge of risk prediction model due to missing risk factors: The submodel approximation approach.

Clinical prediction models have been widely acknowledged as informative tools providing evidence-based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real-time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed "preconditioning") method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing "one-step-sweep" approach as well as the imputation approach. In general, the simulation results show the preconditioning-based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation-based approach, while the "one-step-sweep" approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real-time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home.

Construction of a Prognostic Nomogram for Major Amputation Within 30 Days Postrevascularization in Patients With Acute Lower Limb Ischemia Based on 2D Perfusion Parameters.

PURPOSE: The purpose of the study is to develop a prediction model for major amputation (MA) within 30 days after arterial revascularization in patients with acute lower limb ischemia (ALLI) using 2-dimensional (2D) perfusion imaging parameters. MATERIALS AND METHODS: A retrospective study was performed in ALLI patients undergoing arterial revascularization between October 2015 and May 2022. Patients were randomly assigned into training and validation cohorts in a ratio of 7:3. Variables were selected using univariate and multivariate logistic regression. A nomogram for the MA risk within 30 days after arterial revascularization in ALLI patients was created. Its discrimination, calibration, and clinical effectiveness were reported. RESULTS: A total of 310 ALLI patients (326 limbs) were included. The MA rate within 30 days after arterial revascularization was 11.6%. Skin speckle, myoglobin, and time-to-peak were independent risk factors, while atrial fibrillation was a protective factor (all p<0.05). The nomogram predicted 30-day MA with satisfactory discriminative ability. The integrated discrimination improvement was 0.279 and 0.379 for the training and validation cohorts, respectively (both p<0.001). Calibration curves were close to the standard curve. The decision curve analysis demonstrated net benefits. CONCLUSION: This 2D perfusion imaging parameter-based nomogram could accurately predict the risk of MA within 30 days postrevascularization in ALLI patients. CLINICAL IMPACT: This study introduces a novel nomogram based on 2-dimensional (2D) perfusion imaging that can significantly advance the prognosis prediction in ALLI patients. By calculating the risk of major amputation within 30 days postrevascularization, this nomogram offers an accurate predictive tool and can lead to more informed decision-making on patient management. The innovative aspect of this research lies in its utilization of 2D perfusion parameters, a novel approach that enhances risk assessment accuracy in ALLI patients. This nomogram represents a significant step toward risk stratification and can guide future research for appropriate management on ALLI patients with different risk profiles.

Prediction of postoperative complications following transanal total mesorectal excision in middle and low rectal cancer: development and internal validation of a clinical prediction model.

PURPOSE: The objective of this study is to develop a nomogram for the personalized prediction of postoperative complication risks in patients with middle and low rectal cancer who are undergoing transanal total mesorectal excision (taTME). This tool aims to assist clinicians in early identification of high-risk patients and in addressing preoperative risk factors to enhance surgical safety. METHODS: In this case-control study, 207 patients diagnosed with middle and low rectal cancer and undergoing taTME between February 2018 and November 2023 at The First Affiliated Hospital of Xiamen University were included. Independent risk factors for postoperative complications were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multifactorial logistic regression models. A predictive nomogram was constructed using R Studio. RESULTS: Among the 207 patients, 57 (27.5%) experienced postoperative complications. The LASSO and multifactorial logistic regression analyses identified operation time (OR = 1.010, P = 0.007), smoking history (OR = 9.693, P < 0.001), anastomotic technique (OR = 0.260, P = 0.004), and ASA score (OR = 9.077, P = 0.051) as significant predictors. These factors were integrated into the nomogram. The model's accuracy was validated through receiver operating characteristic curves, calibration curves, consistency indices, and decision curve analysis. CONCLUSION: The developed nomogram, incorporating operation time, smoking history, anastomotic technique, and ASA score, effectively forecasts postoperative complication risks in taTME procedures. It is a valuable tool for clinicians to identify patients at heightened risk and initiate timely interventions, ultimately improving patient outcomes.

Preoperative risk prediction models for acute kidney injury after noncardiac surgery: an independent external validation cohort study.

BACKGROUND: Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them. METHODS: We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases. RESULTS: Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis. CONCLUSIONS: We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery.

Multivariable machine learning models for clinical prediction of subsequent hip fractures in older people using the Chinese population database.

PURPOSE: This study aimed to develop and validate clinical prediction models using machine learning (ML) algorithms for reliable prediction of subsequent hip fractures in older individuals, who had previously sustained a first hip fracture, and facilitate early prevention and diagnosis, therefore effectively managing rapidly rising healthcare costs in China. METHODS: Data were obtained from Grade A Tertiary hospitals for older patients (age ≥ 60 years) diagnosed with hip fractures in southwest China between 1 January 2009 and 1 April 2020. The database was built by collecting clinical and administrative data from outpatients and inpatients nationwide. Data were randomly split into training (80%) and testing datasets (20%), followed by six ML-based prediction models using 19 variables for hip fracture patients within 2 years of the first fracture. RESULTS: A total of 40,237 patients with a median age of 66.0 years, who were admitted to acute-care hospitals for hip fractures, were randomly split into a training dataset (32,189 patients) and a testing dataset (8,048 patients). Our results indicated that three of our ML-based models delivered an excellent prediction of subsequent hip fracture outcomes (the area under the receiver operating characteristics curve: 0.92 (0.91-0.92), 0.92 (0·92-0·93), 0.92 (0·92-0·93)), outperforming previous prediction models based on claims and cohort data. CONCLUSIONS: Our prediction models identify Chinese older people at high risk of subsequent hip fractures with specific baseline clinical and demographic variables such as length of hospital stay. These models might guide future targeted preventative treatments.

Intraoperative quantitative crystalline lens nuclear opacities analysis based on crystalline lenSx platform.

PURPOSE: The main objective is to quantify the lens nuclear opacity using spectral-domain optical coherence tomography (SD-OCT) and to evaluate its association with Lens Opacities Classification System III (LOCS-III) system, lens thickness (LT), and surgical parameters. The secondary objective is to assess the diagnostic model performance for hard nuclear cataract. METHODS: This study included 70 eyes of 57 adults with cataract, with 49 (70%) and 21 (30%) in training and validation cohort, respectively. Correlations of the average nuclear density (AND) /maximum nuclear density (MND) with LOCS-III scores, LT, and surgical parameters were analyzed. Univariate and multivariate logistic regression analysis, receiver operating characteristic curves and calibration curves were performed for the diagnostic of hard nuclear cataract. RESULTS: The pre-operative uncorrected distance visual acuity (UDVA), intraocular pressure (IOP), mean axial length (AL), and LT were 1.20 ± 0.47 log MAR, 15.50 ± 2.87 mmHg, 27.34 ± 3.77 mm and 4.32 ± 0.45 mm, respectively. The average nuclear opalescence (NO) and nuclear colour (NC) scores were 3.61 ± 0.94 and 3.50 ± 0.91 (ranging from 1.00 to 6.90), respectively. The average AND and MND were 137.94 ± 17.01 and 230.01 ± 8.91, respectively. NC and NO scores both significantly correlated with the AND (rNC = 0.733, p = 0.000; rNO = 0.755, p = 0.000) and MND (rNC = 0.643, p = 0.000; rNO = 0.634, p = 0.000). In the training cohort, the area under the curve (AUC) of the model was 0.769 (P < 0.001, 95%CI 0.620-0.919), which had a good degree of differentiation (Fig. 2a). The calibration curve showed good agreement between predicted and actual probability. CONCLUSION: The nuclear density measurement on SD-OCT images can serve as an objective and reliable indicator for quantifying nuclear density.

Comprehensive comorbidity assessment for the ECOPD: a long-term multi-centre retrospective study.

BACKGROUND: Exacerbation of chronic obstructive pulmonary disease (ECOPD) results in severe adverse outcomes and mortality. It is often associated with increased local and systemic inflammation. However, individual susceptibility to exacerbations remains largely unknown. Our study aimed to investigate the association between comorbidities and exacerbation outcomes. METHODS: We included patients with the primary discharge diagnosis of exacerbation for more 10 years in China. Data on all comorbidities were collected and analysed to determine the impact of the comorbidities on 1-year exacerbation readmission, length of hospital stay, and hospital cost. Univariable and multivariable logistic regression analyses were performed, and predictive models were developed. RESULTS: This extensive investigation evaluated a total of 15,708 individuals from five prominent locations in China, revealing notable variations in the prevalence of comorbidities and healthcare expenses among different regions. The study shows that there is a high rate of readmission within one year, namely 15.8%. The most common conditions among readmitted patients are hypertension (38.6%), ischemic heart disease (16.9%), and diabetes mellitus (16.6%). An extensive multivariable study revealed that age, gender, and particular comorbidities such as malnutrition and hyperlipidemia are important factors that can significantly predict greater readmission rates, longer hospital stays or increased healthcare costs. The multivariable models show a moderate to good ability to predict patient outcomes, with concordance index ranging from 0.701 to 0.752. This suggests that targeted interventions in these areas could improve patient outcomes and make better use of healthcare resources. CONCLUSIONS: The results regarding the association between severe exacerbations and systemic disease status support the integration of systematic evaluation of comorbidities into the management of exacerbations and the intensification of treatment of important comorbidities as a appropriate measure for prevention of further exacerbations. Our models also provide a novel tool for clinicians to determine the risk of the 1-year recurrence of severe ECOPD in hospitalised patients.

Predicting Poststroke Cognitive Impairment after Acute Ischemic Stroke Based on Admission Characteristics.

OBJECTIVE: This study aimed to develop a robust clinical prediction model for Poststroke cognitive impairment (PSCI) within 6 months following acute ischemic stroke (AIS) and subsequently validate its effectiveness. METHODS: A total of 386 AIS patients were divided into the PSCI group (174 cases) and the cognitively normal (CN) group (212 cases) based on the occurrence of PSCI. These patients were further categorized into two cohorts: 270 AIS patients in the training set, 116 AIS patients in the validation set. Multifactor logistic regression analysis was performed to identify independent predictors, which were then included in the prediction model for further analysis and validation. The performance of the prediction model was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), calibration plots analyses to assess discrimination, calibration ability, respectively. RESULTS: Based on the selected variables (smoking, alcohol consumption, female gender, low education level, NIHSS score at admission, stroke progression, high systolic blood pressure, diabetes, atrial fibrillation, coronary heart disease, low-density lipoprotein cholesterol, ß2-microglobulin, and Lp-PLA2), a clinical prediction model for the occurrence of PSCI within 6 months in AIS patients was constructed. The AUC-ROC of the model was 0.862, 0.806 in the training, validation sets, respectively. Calibration curve analyses and Hosmer-Lemeshow goodness-of-fit tests, along with other validation metrics, further demonstrated the model's good predictive performance. CONCLUSION: The model exhibits high discriminative ability for PSCI and has substantial guiding value for clinical decision-making. However, further optimization of the model is required with multicenter data to enhance its robustness and applicability.

Advances in applications of artificial intelligence algorithms for cancer-related miRNA research. / äººå·¥æ™ºèƒ½ç®—æ³•åœ¨ç™Œç—‡ç›¸å ³å¾®RNAç ”ç©¶ä¸­çš„åº”ç”¨è¿›å±•.

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

[Study on mechanism of icariin-induced ferroptosis in HepG2 hepatoma carcinoma cells through PPARG/FABP4/GPX4 pathway].

The aim of this study was to explore the mechanism of icaritin-induced ferroptosis in hepatoma HepG2 cells. By bioinformatics screening, the target of icariin's intervention in liver cancer ferroptosis was selected, the protein-protein interaction(PPI) network was constructed, the related pathways were focused, the binding ability of icariin and target protein was evaluated by molecular docking, and the impact on patients' survival prognosis was predicted and the clinical prediction model was built. CCK-8, EdU, and clonal formation assays were used to detect cell viability and cell proliferation; colorimetric method and BODIPY 581/591 C1 fluorescent probe were used to detect the levels of Fe~(2+), MDA and GSH in cells, and the ability of icariin to induce HCC cell ferroptosis was evaluated; RT-qPCR and Western blot detection were used to verify the mRNA and protein levels of GPX4, xCT, PPARG, and FABP4 to determine the expression changes of these ferroptosis-related genes in response to icariin. Six intervention targets(AR, AURKA, PPARG, AKR1C3, ALB, NQO1) identified through bioinformatic analysis were used to establish a risk scoring system that aids in estimating the survival prognosis of HCC patients. In conjunction with patient age and TNM staging, a comprehensive Nomogram clinical prediction model was developed to forecast the 1-, 3-, and 5-year survival of HCC patients. Experimental results revealed that icariin effectively inhibited the activity and proliferation of HCC cells HepG2, significantly modulating levels of Fe~(2+), MDA, and lipid peroxidation ROS while reducing GSH levels, hence revealing its potential to induce ferroptosis in HCC cells. Icariin was found to diminish the expression of GPX4 and xCT(P<0.01), inducing ferroptosis in HCC cells, potentially in relation to inhibition of PPARG and FABP4(P<0.01). In summary, icariin induces ferroptosis in HCC cells via the PPARG/FABP4/GPX4 pathway, providing an experimental foundation for utilizing the traditional Chinese medicine icariin in the prevention or treatment of HCC.

Risk factors in antiphospholipid antibody-associated valvular heart disease: A 383-patient cohort study.

Valvular heart disease (VHD) is a prevalent cardiac manifestation in antiphospholipid syndrome (APS) patients. However, risk factors and predictors for antiphospholipid antibody-associated VHD (aPL-VHD) remain vague. We aimed to assess the risk of developing aPL-VHD in aPL-positive patients, by establishing a clinical prediction model upon a cross-sectional cohort from APS-Shanghai database, including 383 APS patients and durable aPL carriers with transthoracic echocardiography investigation. The prevalence of aPL-VHD was 11.5%. Multivariate logistic regression analysis identified three independent risk factors for aPL-VHD: anti-ß2GPI IgG (OR 5.970, P < 0.001), arterial thrombosis (OR 2.758, P = 0.007), and stratified estimated glomerular filtration rate levels (OR 0.534, P = 0.001). A prediction model for aPL-VHD, incorporating the three factors, was further developed, which demonstrated good discrimination with a C-index of 0.855 and 0.841 (after bootstrapping), and excellent calibration (P = 0.790). We provide a practical tool for assessing the risk of developing VHD among aPL-positive patients.

Clinical prediction models and the multiverse of madness.

BACKGROUND: Each year, thousands of clinical prediction models are developed to make predictions (e.g. estimated risk) to inform individual diagnosis and prognosis in healthcare. However, most are not reliable for use in clinical practice. MAIN BODY: We discuss how the creation of a prediction model (e.g. using regression or machine learning methods) is dependent on the sample and size of data used to develop it-were a different sample of the same size used from the same overarching population, the developed model could be very different even when the same model development methods are used. In other words, for each model created, there exists a multiverse of other potential models for that sample size and, crucially, an individual's predicted value (e.g. estimated risk) may vary greatly across this multiverse. The more an individual's prediction varies across the multiverse, the greater the instability. We show how small development datasets lead to more different models in the multiverse, often with vastly unstable individual predictions, and explain how this can be exposed by using bootstrapping and presenting instability plots. We recommend healthcare researchers seek to use large model development datasets to reduce instability concerns. This is especially important to ensure reliability across subgroups and improve model fairness in practice. CONCLUSIONS: Instability is concerning as an individual's predicted value is used to guide their counselling, resource prioritisation, and clinical decision making. If different samples lead to different models with very different predictions for the same individual, then this should cast doubt into using a particular model for that individual. Therefore, visualising, quantifying and reporting the instability in individual-level predictions is essential when proposing a new model.

External validation of models for predicting cumulative live birth over multiple complete cycles of IVF treatment.

STUDY QUESTION: Can two prediction models developed using data from 1999 to 2009 accurately predict the cumulative probability of live birth per woman over multiple complete cycles of IVF in an updated UK cohort? SUMMARY ANSWER: After being updated, the models were able to estimate individualized chances of cumulative live birth over multiple complete cycles of IVF with greater accuracy. WHAT IS KNOWN ALREADY: The McLernon models were the first to predict cumulative live birth over multiple complete cycles of IVF. They were converted into an online calculator called OPIS (Outcome Prediction In Subfertility) which has 3000 users per month on average. A previous study externally validated the McLernon models using a Dutch prospective cohort containing data from 2011 to 2014. With changes in IVF practice over time, it is important that the McLernon models are externally validated on a more recent cohort of patients to ensure that predictions remain accurate. STUDY DESIGN, SIZE, DURATION: A population-based cohort of 91 035 women undergoing IVF in the UK between January 2010 and December 2016 was used for external validation. Data on frozen embryo transfers associated with these complete IVF cycles conducted from 1 January 2017 to 31 December 2017 were also collected. PARTICIPANTS/MATERIALS, SETTING, METHODS: Data on IVF treatments were obtained from the Human Fertilisation and Embryology Authority (HFEA). The predictive performances of the McLernon models were evaluated in terms of discrimination and calibration. Discrimination was assessed using the c-statistic and calibration was assessed using calibration-in-the-large, calibration slope, and calibration plots. Where any model demonstrated poor calibration in the validation cohort, the models were updated using intercept recalibration, logistic recalibration, or model revision to improve model performance. MAIN RESULTS AND THE ROLE OF CHANCE: Following exclusions, 91 035 women who underwent 144 734 complete cycles were included. The validation cohort had a similar distribution age profile to women in the development cohort. Live birth rates over all complete cycles of IVF per woman were higher in the validation cohort. After calibration assessment, both models required updating. The coefficients of the pre-treatment model were revised, and the updated model showed reasonable discrimination (c-statistic: 0.67, 95% CI: 0.66 to 0.68). After logistic recalibration, the post-treatment model showed good discrimination (c-statistic: 0.75, 95% CI: 0.74 to 0.76). As an example, in the updated pre-treatment model, a 32-year-old woman with 2 years of primary infertility has a 42% chance of having a live birth in the first complete ICSI cycle and a 77% chance over three complete cycles. In a couple with 2 years of primary male factor infertility where a 30-year-old woman has 15 oocytes collected in the first cycle, a single fresh blastocyst embryo transferred in the first cycle and spare embryos cryopreserved, the estimated chance of live birth provided by the post-treatment model is 46% in the first complete ICSI cycle and 81% over three complete cycles. LIMITATIONS, REASONS FOR CAUTION: Two predictors from the original models, duration of infertility and previous pregnancy, which were not available in the recent HFEA dataset, were imputed using data from the older cohort used to develop the models. The HFEA dataset does not contain some other potentially important predictors, e.g. BMI, ethnicity, race, smoking and alcohol intake in women, as well as measures of ovarian reserve such as antral follicle count. WIDER IMPLICATIONS OF THE FINDINGS: Both updated models show improved predictive ability and provide estimates which are more reflective of current practice and patient case mix. The updated OPIS tool can be used by clinicians to help shape couples' expectations by informing them of their individualized chances of live birth over a sequence of multiple complete cycles of IVF. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by an Elphinstone scholarship scheme at the University of Aberdeen and Aberdeen Fertility Centre, University of Aberdeen. S.B. has a commitment of research funding from Merck. D.J.M. and M.B.R. declare support for the present manuscript from Elphinstone scholarship scheme at the University of Aberdeen and Assisted Reproduction Unit at Aberdeen Fertility Centre, University of Aberdeen. D.J.M. declares grants received by University of Aberdeen from NHS Grampian, The Meikle Foundation, and Chief Scientist Office in the past 3 years. D.J.M. declares receiving an honorarium for lectures from Merck. D.J.M. is Associate Editor of Human Reproduction Open and Statistical Advisor for Reproductive BioMed Online. S.B. declares royalties from Cambridge University Press for a book. S.B. declares receiving an honorarium for lectures from Merck, Organon, Ferring, Obstetric and Gynaecological Society of Singapore, and Taiwanese Society for Reproductive Medicine. S.B. has received support from Merck, ESHRE, and Ferring for attending meetings as speaker and is on the METAFOR and CAPRE Trials Data Monitoring Committee. TRIAL REGISTRATION NUMBER: N/A.

Development and validation of a risk prediction model for the recurrence of foot ulcer in type 2 diabetes in China: A longitudinal cohort study based on a systematic review and meta-analysis.

AIMS: To develop and validate a risk prediction model for Chinese patients with type 2 diabetes with the recurrence of diabetic foot ulcers (DFUs) based on a systematic review and meta-analysis. METHODS: A prospective analysis was performed with 1333 participants and followed up for 60 months. Three models were analysed using a derived cohort. The risk factors were screened using meta-analysis and logistic regression, and the missing variables were interpolated by multiple imputation. The internal validation was performed using the bootstrap procedure, and the validation cohort was applied to the external validation. The performance of the model was evaluated in the area under the discrimination Receiver Operating Characteristic Curve (ROC). Calibration and discrimination methods were used for the validation cohort. The variables were selected according to their clinical and statistical importance to construct the nomograms. RESULTS: Three models were developed and validated. Model 1 included seven social and clinical indicators like sex, diabetes mellitus duration, previous DFU, location of ulcer, smoking, history of amputation, and foot deformity. Model 2 included four more indicators besides those in Model 1, which were statin agents used, antiplatelet agents used, systolic blood pressure, and body mass index. Model 3 added further laboratory indicators to Model 2, such as LDL-C, HbA1C, fibrinogen, and blood urea nitrogen. In the derivation cohort, 20.1% (206/1027) participants with DFU recurred as compared to the validation cohort, which was 38.2% (117/306). The areas under the curve in the derivation cohort for Models 1-3 were 0.781 (0.744-0.817), 0.843 (0.813-0.873), and 0.899 (0.876-0.922), respectively. The Youden indexes for Models 1-3 were 0.430, 0.559, and 0.653, respectively. Model 3 showed the highest sensitivity and specificity. All models performed well for both discrimination and calibration. CONCLUSIONS: Models 1-2 were non-invasive, which indicate their role in general screening for patients at a high risk of recurrence of DFU. However, Model 3 offers a more specific screening due to its best performance in predicting the risk of DFU recurrence amongst the three models.

Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques.

INTRODUCTION: This study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis. METHODS: We considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring. RESULTS: Discrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual-outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors. DISCUSSION: We recommend the dual-outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.

Prediction of subsequent vertebral compression fractures after thoracolumbar kyphoplasty: a multicenter retrospective analysis.

OBJECTIVE: Second fractures at the cemented vertebrae (SFCV) are often seen after percutaneous kyphoplasty, especially at the thoracolumbar junction. Our study aimed to develop and validate a preoperative clinical prediction model for predicting SFCV. METHODS: A cohort of 224 patients with single-level thoracolumbar osteoporotic vertebral fractures (T11-L2) from 3 medical centers was analyzed between January 2017 and June 2020 to derive a preoperative clinical prediction model for SFCV. Backward-stepwise selection was used to select preoperative predictors. We assigned a score to each selected variable and developed the SFCV scoring system. Internal validation and calibration were conducted for the SFCV score. RESULTS: Among the 224 patients included, 58 had postoperative SFCV (25.9%). The following preoperative measures on multivariable analysis were summarized in the 5-point SFCV score: bone mineral density (≤-3.05), serum 25-hydroxy vitamin D3 (≤17.55 ng/mL), standardized signal intensity of fractured vertebra on T1-weighted images (≤59.52%), C7-S1 sagittal vertical axis (≥3.25 cm), and intravertebral cleft. Internal validation showed a corrected area under the curve of 0.794. A cutoff of ≤1 point was chosen to classify a low risk of SFCV, for which only 6 of 100 patients (6%) had SFCV. A cutoff of ≥4 points was chosen to classify a high risk of SFCV, for which 28 of 41 (68.3%) had SFCV. CONCLUSION: The SFCV score was found to be a simple preoperative method for identification of patients at low and high risk of postoperative SFCV. This model could be applied to individual patients and aid in the decision-making before percutaneous kyphoplasty.

Evolution of Making Clinical Predictions in Neurosurgery.

Prediction of clinical outcomes is an essential task for every physician. Physicians may base their clinical prediction of an individual patient on their intuition and on scientific material such as studies presenting population risks and studies reporting on risk factors (prognostic factors). A relatively new and more informative approach for making clinical predictions relies on the use of statistical models that simultaneously consider multiple predictors that provide an estimate of the patient's absolute risk of an outcome. There is a growing body of literature in the neurosurgical field reporting on clinical prediction models. These tools have high potential in supporting (not replacing) neurosurgeons with their prediction of a patient's outcome. If used sensibly, these tools pave the way for more informed decision-making with or for individual patients. Patients and their significant others want to know their risk of the anticipated outcome, how it is derived, and the uncertainty associated with it. Learning from these prediction models and communicating the output to others has become an increasingly important skill neurosurgeons have to master. This article describes the evolution of making clinical predictions in neurosurgery, synopsizes key phases for the generation of a useful clinical prediction model, and addresses some considerations when deploying and communicating the results of a prediction model. The paper is illustrated with multiple examples from the neurosurgical literature, including predicting arachnoid cyst rupture, predicting rebleeding in patients suffering from aneurysmal subarachnoid hemorrhage, and predicting survival in glioblastoma patients.

The association between combustible/electronic cigarette use and stroke based on national health and nutrition examination survey.

AIMS: This study aims to analyze the association between combustible/electronic cigarettes and the risk of stroke. METHODS: We obtained data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). The stroke history and combustible/electronic cigarette use were acquired by questionnaires. Considering the sole or dual use of combustible cigarettes and electronic cigarettes (e-cigarettes), we divided all the individuals into four subgroups, including nonsmokers (reference group), sole combustible cigarette, sole e-cigarette, and dual use of both combustible cigarettes and e-cigarettes. We performed multivariable logistic regression to determine the association between cigarette use with the prevalence of stroke. We used odds ratios (ORs) with 95% confidence intervals (CIs) to show the effect size. Finally, we developed a prediction model to evaluate the risk of stroke for individuals with combustible or electronic cigarette use based on a random forest model. RESULTS: We included a total of 4022 participants in the study. The median age was 55, and 48.3% of the participants were males. When we adjusted for age, gender, education attainment, race, total-to-HDL cholesterol (< 5.9 or ≥ 5.9), diabetes, hypertension, and alcohol consumption, the groups of sole e-cigarette use, sole combustible cigarette use, and dual use of combustible and electronic cigarettes were significantly associated with the prevalence of stroke with ORs (with 95%CI) of 2.07 (1.04-3.81), 2.36 (1.52-3.59), 2.34 (1.44-3.68), respectively. In the testing set, the AUC was 0.74 (95%CI = 0.65-0.84), sensitivity was 0.68, and specificity was 0.75. CONCLUSION: Sole e-cigarettes and dual use of e-cigarettes with combustible cigarettes might increase the risk of stroke.