Development Of Predictive Models Research Articles

Development of predictive model for the neurological deterioration among mild traumatic brain injury patients using machine learning algorithms.

Mild traumatic brain injury (mTBI) comprises a majority of traumatic brain injury (TBI) cases. While some mTBI would suffer neurological deterioration (ND) and therefore have poorer prognosis. This study was designed to develop the predictive model for the ND among mTBI using machine learning algorithms. mTBI patients recorded in the Medical Information Mart for Intensive Care-III were selected for the study. The ND was defined as a drop of Glasgow Coma Scale ≥ 2 within the first 7day after admission. Eight machine learning algorithms were trained and validated with 5-fold cross validation including extreme gradient boosting, logistic regression, light gradient boosting machine, random forest, adaptive boosting, decision tree, complement naïve Bayes, and support vector machine. The value of eight machine learning algorithms was compared by the area under the receiver operating characteristic curve (AUC). 361 mTBI patients suffered the ND with the incidence of 30.7%. The ND group had higher 30-day mortality (p = 0.001). In the training cohort of mTBI patients, the random forest performed the best on predicting the ND with the AUC of 1.000. The XGBoost and AdaBoost had an AUC of 0.827 and 0.815, respectively. The logistic regression performed the best on predicting the ND in the validation cohort with the AUC of 0.741. The XGBoost, random forest and AdaBoost had an AUC of 0.729, 0.735, 0.736 in the validation cohort, respectively. After adjusting confounding effects, the multivariate logistic regression found only two independent risk factors for the ND including Sequential Organ Failure Assessment (SOFA) (p < 0.001) and hypertension (p = 0.001). The logistic regression predictive model composed of SOFA and hypertension had an AUC of 0.741. SOFA score and complicated hypertension are two independent risk factors for the neurological deterioration among mTBI patients. The logistic regression predictive model incorporating SOFA and hypertension is helpful to identify mTBI patients with the high risk of ND.

Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features.

Background: Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic-clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification. Methods: A retrospective study was conducted on patients with JME at the Severance Hospital, analyzing clinical variables and magnetic resonance imaging (MRI) data. Machine learning models were developed to predict prognosis using clinical and radiological features. Results: The study utilized six machine learning models, with the XGBoost model demonstrating the highest predictive accuracy (AUROC 0.700). Combining clinical and MRI data outperformed models using either type of data alone. The key features identified through a Shapley additive explanation analysis included the volumes of the left cerebellum white matter, right thalamus, and left globus pallidus. Conclusions: This study demonstrated that integrating clinical and radiological data enhances the predictive accuracy of JME prognosis. Combining these neuroanatomical features with clinical variables provided a robust prediction of JME prognosis, highlighting the importance of integrating multimodal data for accurate prognosis.

Development of a spontaneous preterm birth predictive model using a panel of serum protein biomarkers for early pregnant women: A nested case-control study.

To develop a model based on maternal serum liquid chromatography tandem mass spectrometry (LC-MS/MS) proteins to predict spontaneous preterm birth (sPTB). This nested case-control study used the data from a cohort of 2053 women in China from July 1, 2018, to January 31, 2019. In total, 110 singleton pregnancies at 11-13+6 weeks of pregnancy were used for model development and internal validation. A total of 72 pregnancies at 20-32 weeks from an additional cohort of 2167 women were used to evaluate the scalability of the model. Maternal serum samples were analyzed by LC-MS/MS, and a predictive model was developed using machine learning algorithms. A novel predictive panel with four proteins, including soluble fms-like tyrosine kinase-1, matrix metalloproteinase 8, ceruloplasmin, and sex-hormone-binding globulin, was developed. The optimal model of logistic regression had an AUC of 0.934, with additional prediction of sPTB in second and third trimester (AUC = 0.868). First-trimester modeling based on maternal serum LC-MS/MS identifies pregnant women at risk of sPTB, which may provide utility in identifying women at risk at an early stage of pregnancy before clinical presentation to allow for earlier intervention.

Chemometrics and neural networks for estimating the chilling injury severity of kimchi cabbage (Brassica rapa L. ssp. pekinensis) based on hyperspectral images

The objective of this study was to develop prediction models for the severity of chilling injury in kimchi cabbages (KCs) using short-wave infrared hyperspectral imaging (HSI) combined with supervised classifications (partial least square discriminant analysis, PLS-DA; and artificial neural network, ANN) at early stages. Classifications were implemented for the two-class (i.e., normal and chilled), three-class (i.e., normal, semi, and severe) and four-class (i.e., normal, slight, moderate, and severe) classifications at full and selected optimal wavelengths (associated with the C–H stretch of sugar and cellulose). The PLS-DA classifier with Savitzky-golay (SG) preprocessing showed improved average classification accuracy of 0.912 and 0.896 for three-class and four-class classification developed with optimal wavelength, respectively. The best classification results based on ANN using optimal wavelengths and SG achieved overall accuracies above 0.924 for all-class classifications. ANN models have higher potential and are simplified to predict chilling damage characteristics of KCs compared to PLS-DA models. By visualizing injured areas, we showed the association between the internal changes as detected by HSI. These results demonstrate the capability of HSI and the central role of chemometrics and neural networks in developing efficient models for prediction of chilling damage properties of KCs.

Nomogram predicting the prognosis of primary liver cancer after radiofrequency ablation combined with transcatheter arterial chemoembolization.

The incidence and mortality rates of primary hepatocellular carcinoma (HCC) are high, and the conventional treatment is radiofrequency ablation (RFA) with transcatheter arterial chemoembolization (TACE); however, the 3-year survival rate is still low. Further, there are no visual methods to effectively predict their prognosis. To explore the factors influencing the prognosis of HCC after RFA and TACE and develop a nomogram prediction model. Clinical and follow-up information of 150 patients with HCC treated using RFA and TACE in the Hangzhou Linping Hospital of Traditional Chinese Medicine from May 2020 to December 2022 was retrospectively collected and recorded. We examined their prognostic factors using multivariate logistic regression and created a nomogram prognosis prediction model using the R software (version 4.1.2). Internal verification was performed using the bootstrapping technique. The prognostic efficacy of the nomogram prediction model was evaluated using the concordance index (CI), calibration curve, and receiver operating characteristic curve. Of the 150 patients treated with RFA and TACE, 92 (61.33%) developed recurrence and metastasis. Logistic regression analysis identified six variables, and a predictive model was created. The internal validation results of the model showed a CI of 0.882. The correction curve trend of the prognosis prediction model was always near the diagonal, and the mean absolute error before and after internal validation was 0.021. The area under the curve of the prediction model after internal verification was 0.882 [95% confidence interval (95%CI): 0.820-0.945], with a specificity of 0.828 and sensitivity of 0.656. According to the Hosmer-Lemeshow test, χ 2 = 3.552 and P = 0.895. The predictive model demonstrated a satisfactory calibration, and the decision curve analysis demonstrated its clinical applicability. The prognosis of patients with HCC after RFA and TACE is affected by several factors. The developed prediction model based on the influencing parameters shows a good prognosis predictive efficacy.

Risk factors for the development of refeeding syndrome in adults: A systematic review.

Identifying patients with a particularly high risk of refeeding syndrome (RFS) is essential for taking preventive measures. To guide the development of clinical decision-making and risk prediction models or other screening tools for RFS, increased knowledge of risk factors is needed. Therefore, we conducted a systematic review to identify risk factors for the development of RFS. PubMed, EMBASE, Cochrane Library, and Web of Science were searched from January 1990 until March 2023. Studies investigating demographic, clinical, drug use, laboratory, and/or nutrition factors for RFS were considered. The Newcastle-Ottawa Scale was used to appraise the methodological quality of included studies. Of 1589 identified records, 30 studies were included. Thirty-three factors associated with increased risk of RFS after multivariable adjustments were identified. The following factors were reported by two or more studies, with 0-1 study reporting null findings: a previous history of alcohol misuse, cancer, comorbid hypertension, high Acute Physiology and Chronic Health Evaluation II score, high Sequential Organ Failure Assessment score, low Glasgow coma scale score, the use of diuretics before refeeding, low baseline serum prealbumin level, high baseline level of creatinine, and enteral nutrition. The majority of the studies (20, 66.7%) were of high methodological quality. In conclusion, this systematic review informs on several risk factors for RFS in patients. To improve risk stratification and guide development of risk prediction models or other screening tools, further confirmation is needed because there were a small number of studies and a low number of high-quality studies on each factor.

Common DNA sequence variation influences epigenetic aging in African populations.

Aging is associated with genome-wide changes in DNA methylation in humans, facilitating the development of epigenetic age prediction models. However, most of these models have been trained primarily on European-ancestry individuals, and none account for the impact of methylation quantitative trait loci (meQTL). To address these gaps, we analyzed the relationships between age, genotype, and CpG methylation in 3 understudied populations: central African Baka (n = 35), southern African ‡Khomani San (n = 52), and southern African Himba (n = 51). We find that published prediction methods yield higher mean errors in these cohorts compared to European-ancestry individuals, and find that unaccounted-for DNA sequence variation may be a significant factor underlying this loss of accuracy. We leverage information about the associations between DNA genotype and CpG methylation to develop an age predictor that is minimally influenced by meQTL, and show that this model remains accurate across a broad range of genetic backgrounds. Intriguingly, we also find that the older individuals and those exhibiting relatively lower epigenetic age acceleration in our cohorts tend to carry more epigenetic age-reducing genetic variants, suggesting a novel mechanism by which heritable factors can influence longevity.

Relationship between GPS-based community mobility data and orthopedic trauma admissions during the COVID-19 pandemic in Austria: amulticenter analysis.

The main objective of this study was to examine the relationship between mobility patterns during the coronavirus disease 2019 (COVID-19) pandemic and orthopedic trauma patients in Austria. Utilizing global positioning system (GPS)-based mobility data, the attempt was to assess both the impact of COVID-19 lockdowns on reducing orthopedic trauma patients and the degree of compliance to the imposed movement restrictions. This retrospective analysis included all patients (283,501) treated at 3 major levelI trauma centers in Austria. Analyzed time periods were 1 January 2019 to 8 February 2021. Freely available GPS-based mobility data from Google and Apple Inc. was gathered. Amoderate to strong correlation between the cumulative average outpatients and the assessed mobility index was observed for all cities (Google: r = 0.70 p < 0.001, 95% confidence interval, CI: 0.67-0.73; Apple: r = 0.64 p < 0.001, 95% CI: 0.61-0.67). Asignificant linear regression equation was found for Vienna (adjusted r2 = 0.48; F(1, 350) = 328,05; p < 0.01). During the first lockdown there was adrastic decline in mobility (up to -75.36%) and in numbers of orthopedic trauma outpatients (up to -64%, from 153 patients/day 2019 to 55patients/day 2020) in comparison to the prepandemic era. The decline diminished as time passed. Analyses of GPS-based mobility patterns show acorrelation with trauma patient numbers. These findings can be used to develop prediction models, leading to better resource planning and public health policy, enhancing patient care and cost-effectiveness, especially in the event of future pandemics. Furthermore, the results suggest that compliance to mobility restrictions decreased over time during the COVID-19 pandemic, resulting in increased mobility and trauma patients.

Machine learning models and computational simulation techniques for prediction of anti-corrosion properties of novel benzimidazole derivatives

The present paper delves into the development of predictive models for the optimum prediction of inhibition efficiencies and anti-corrosion properties of newly designed benzimidazole compounds in an HCl medium. Density functional theory (DFT) was used to obtain the molecular descriptors. 17 descriptors considered as input variables were reduced to 9 after redundant variables were eliminated using the variance inflation factor (VIF). Machine learning models such as random forest regression (Rf), K-nearest neighbor (KNN), and gradient boosting (GB) were used to develop a predictive model using data from 50 benzimidazole derivatives whose inhibition efficiencies for steel alloy in HCl medium have been determined experimentally and are available in the literature. The cross-validation (CV) technique was used to evaluate the predictive capability of the models. KNN gave the best result with a coefficient of correlation (R2) of 0.654, compared to GB (0.591) and RF (0.512). Subsequently, the three models were used to predict the inhibition efficiencies of three novel benzimidazole compounds. The corrosion inhibition efficiency of three newly developed benzimidazole compounds was 91 % - 98.5 % when assessed with the best-performing model, K-nearest neighbor (KNN) with MSBAH recording the highest value of 98.5 %. Fukui indices parameters fk+k,fk−k,andf2(r) showed that both the electrophilic and nucleophilic sites are concentrated around specific atoms most of whom are heteroatoms that participate actively in electron-donor and acceptor interaction with the metal atom leading to the formation of coordinate covalent bonds. The results of electron distribution and Fukui dual descriptors support the results of the predictive model. The study also revealed that machine learning algorithms in conjunction with DFT and MD simulation is an innovative technique for the prediction of anti-corrosion properties of newly designed molecules such as corrosion inhibition efficiencies, and mechanism of inhibitor adsorption on metal/alloy surfaces exposed to an aggressive environment. This technique can be harnessed in proffering solutions to acid corrosion especially those that occur during descaling operation.

Development and validation of optical coherence tomography (OCT) and OCT angiography prediction model for short-term vitreous haemorrhage secondary to polypoidal choroidal vasculopathy

AimsTo construct and validate an optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) image model for predicting the occurrence of short-term vitreous haemorrhage (VH) in polypoidal choroidal vasculopathy...

Retrospective analysis of molecular characteristics, risk factors, and outcomes in carbapenem-resistant Klebsiella pneumoniae bloodstream infections

BackgroundKlebsiella pneumoniae (KP) is the second most prevalent Gram-negative bacterium causing bloodstream infections (BSIs). In recent years, the management of BSIs caused by KP has become increasingly complex due to the emergence of carbapenem-resistant Klebsiella pneumoniae (CRKP). Although numerous studies have explored the risk factors for the development of CRKP-BSIs, the mortality of patients with KP-BSIs, and the molecular epidemiological characteristics of CRKP, the variability in data across different populations, countries, and hospitals has led to inconsistent conclusions. In this single-center retrospective observational study, we utilized logistic regression analyses to identify independent risk factors for CRKP-BSIs and factors associated with mortality in KP-BSI patients. Furthermore, a risk factor-based prediction model was developed. CRKP isolates underwent whole-genome sequencing (WGS), followed by an evaluation of microbiological characteristics, including antimicrobial resistance and virulence genes, as well as epidemiological characteristics and phylogenetic analysis.ResultsOur study included a total of 134 patients with KP-BSIs, comprising 50 individuals infected with CRKP and 84 with carbapenem-susceptible Klebsiella pneumoniae (CSKP). The independent risk factors for CRKP-BSIs were identified as gastric catheterization (OR = 9.143; CI = 1.357–61.618; P = 0.023), prior ICU hospitalization (OR = 4.642; CI = 1.312–16.422; P = 0.017), and detection of CRKP in non-blood sites (OR = 8.112; CI = 2.130-30.894; P = 0.002). Multivariate analysis revealed that microbiologic eradication after 6 days (OR = 3.569; CI = 1.119–11.387; P = 0.032), high Pitt bacteremia score (OR = 1.609; CI = 1.226–2.111; P = 0.001), and inappropriate empirical treatment after BSIs (OR = 6.756; CI = 1.922–23.753; P = 0.003) were independent risk factors for the 28-day mortality in KP-BSIs. The prediction model confirmed that microbiologic eradication after 6.5 days and a Pitt bacteremia score of 4.5 or higher were significant predictors of the 28-day mortality. Bioinformatics analysis identified ST11 as the predominant CRKP sequence type, with blaKPC−2 as the most prevalent gene variant. CRKP stains carried multiple plasmid-mediated resistance genes along with some virulence genes. Phylogenetic analysis indicated the presence of nosocomial transmission of ST11 CRKP within the ICU.ConclusionsThe analysis of risk factors for developing CRKP-BSIs and the association between KP-BSIs and 28-day mortality, along with the development of a risk factor-based prediction model and the characterization of CRKP strains, enhances clinicians’ understanding of the pathogens responsible for BSIs. This understanding may help in the timely administration of antibiotic therapy for patients with suspected KP-BSIs, potentially improving outcomes.

Analysis of risk factors and development of a nomogram prediction model for renal tubular acidosis in primary Sjogren syndrome patients

ObjectiveTo investigate the risk factors of renal tubular acidosis (RTA) in patients with primary Sjögren’s syndrome (pSS) and create a personalized nomogram for predicting pSS-RTA patients.MethodData from 99 pSS patients who underwent inpatient treatment at our hospital from January 2012 to January 2024 were retrospectively collected and analyzed. Bootstrap resampling technique, single-factor, and multi-factor logistic regression analyses were used to explore the risk factors for pSS-RTA. A nomogram was developed based on the results of the multivariate logistic model. The model was evaluated through receiver operating characteristic curve, C-index, calibration curve, and decision curve analysis. In addition, we graded the severity of pSS-RTA patients and used univariate analysis to assess the relationship between pSS-RTA severity and risk factors.ResultsA multivariate logistic regression analysis revealed that concurrent thyroid disease, long symptom duration, subjective dry mouth, and positive RF were independent risk factors for pSS-RTA patients. Based on them, a personalized nomogram predictive model was established. With a p-value of 0.657 from the Hosmer-Lemeshow test, the model demonstrated a good fit. The AUC values in the training and validation groups were 0.912 and 0.896, indicating a strong discriminative power of the nomogram. The calibration curves for the training and validation groups closely followed the diagonal line with a slope of 1, confirming the model’s reliable predictive ability. Furthermore, the decision curve analysis showed that the nomogram model had a net benefit in predicting pSS-RTA, emphasizing its clinical value.This study did not find an association between the severity of pSS-RTA and risk factors.DiscussionWe developed a nomogram to predict RTA occurrence in pSS patients, and it is believed to provide a foundation for early identification and intervention for high-risk pSS patients.

Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality. In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45-80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45-80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE. Graphical models, applied on the COPDGene Phase 1 samples (n=8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n=3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n=2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions. Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them. This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.

How to Accurately Detect Renal Replacement Therapy Weaning in Intensive Care: Data Quality and Standardization Considerations for the OMOP Common Data Model.

This study assesses the effectiveness of the Observational Medical Outcomes Partnership common data model (OMOP CDM) in standardising Continuous Renal Replacement Therapy (CRRT) data from intensive care units (ICU) of two French university hospitals. Our objective was to extract and standardise data from various sources, enabling the development of predictive models for CRRT weaning that are agnostic to the data's origin. Data for 1,696 ICU stays from the two data sources were extracted, transformed, and loaded into the OMOP format after semantic alignment of 46 CRRT standard concepts. Although the OMOP CDM demonstrated potential in harmonising CRRT data, we encountered challenges related to data variability and the lack of standard concepts. Despite these challenges, our study supports the promise of the OMOP CDM for ICU data standardization, suggesting that further refinement and adaptation could significantly improve clinical decision making and patient outcomes in critical care settings.

Development, Calibration and Validation of Time Series Analysis and Artificial Neural Network Joint Model for Urban Noise Prediction

Noise in large urban areas, which is mainly generated by road traffic and by the human activities carried out nearby and inside the area under study, is a relevant problem. The continuous exposure to high noise levels, in fact, can lead to several problems, largely documented in the scientific literature. The analysis and forecasting of the noise level in a given area are, then, fundamental for control and prevention, especially when field measurements present peculiar trends and slopes, which can be modeled with a Time Series Analysis approach. In this paper, a hybrid model is presented for the analysis and the forecasting of noise time series in urban areas: this technique is based on the application of a deterministic decomposition model followed in cascade by a predictor of the forecasting errors based on an artificial neural network. Two variants of the hybrid model have been implemented and presented. The time series used to calibrate and validate the model is composed of sound pressure level measurements detected on a busy road near the commercial port of an Italian city. The proposed hybrid model has been calibrated on a part of the entire time series and validated on the remaining part. Residuals and error analysis, together with a detailed statistical description of the simulated noise levels and error metrics describe in detail the method’s performances and its limitations.

The strategic use of Big Data - A study protocol for a multicenter clinical trial testing if the use of the Swespine Dialogue Support alter outcomes in degenerative spine surgery

BackgroundPatients surgically treated for lumbar spinal stenosis or cervical radiculopathy report improvement in approximately two out of three cases. Advancements in Machine Learning and the utility of large datasets have enabled the development of prognostic prediction models within spine surgery. This trial investigates if the use of the postoperative outcome prediction model, the Dialogue Support, can alter patient-reported outcome and satisfaction compared to current practice.MethodsThis is a prospective, multicenter clinical trial. Patients referred to a spine clinic with cervical radiculopathy or lumbar spinal stenosis will be screened for eligibility. Participants will be assessed at baseline upon recruitment and at 12 months follow-up. The Dialogue Support will be used on all participants, and they will thereafter be placed into either a surgical or a non-surgical treatment arm, depending on the decision made between patient and surgeon. The surgical treatment group will be studied separately based on diagnosis of either cervical radiculopathy or lumbar spinal stenosis. Both the surgical and the non-surgical group will be compared to a retrospective matched control group retrieved from the Swespine register, on which the Dialogue Support has not been used. The primary outcome measure is global assessment regarding leg/arm pain in the surgical treatment group. Secondary outcome measures include patient satisfaction, Oswestry Disability Index (ODI), EQ-5D, and Numeric Rating Scales (NRS) for pain. In the non-surgical treatment group primary outcome measures are EQ-5D and mortality, as part of a selection bias analysis.DiscussionThe findings of this study may provide evidence on whether the use of an advanced digital decision tool can alter patient-reported outcomes after surgery.Trial registrationThe trial was retrospectively registered at ClinicalTrials.gov on April 17th, 2023, NCT05817747.Protocol version1.Trial designClinical multicenter trial.

Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review

BackgroundNavigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment. Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes.MethodsAn extensive search was conducted to retrieve relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results are presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study.ResultsOf the 176 articles initially identified, 12 were selected for our study after removing duplicates and applying the inclusion and exclusion criteria. AI’s primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI is categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches. The most commonly employed AI models include Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3). Furthermore, Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, whereas MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images.ConclusionsAI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in chronic myeloid leukemia. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.

External validation of and improvement upon a model for the prediction of placenta accreta spectrum severity using prospectively collected multicenter ultrasound data.

This study aimed to validate the Sargent risk stratification algorithm for the prediction of placenta accreta spectrum (PAS) severity using data collected from multiple centers and using the multicenter data to improve the model. We conducted a multicenter analysis using data collected for the IS-PAS database. The Sargent model's effectiveness in distinguishing between abnormally adherent placenta (FIGO grade 1) and abnormally invasive placenta (FIGO grades 2 and 3) was evaluated. A new model was developed using multicenter data from the IS-PAS database. The database included 315 cases of suspected PAS, of which 226 had fully documented standardized ultrasound signs. The final diagnosis was normal placentation in 5, abnormally adherent placenta/FIGO grade 1 in 43, and abnormally invasive placenta/FIGO grades 2 and 3 in 178. The external validation of the Sargent model revealed moderate predictive accuracy in a multicenter setting (C-index 0.68), compared to its higher accuracy in a single-center context (C-index 0.90). The newly developed model achieved a C-index of 0.74. The study underscores the difficulty in developing universally applicable PAS prediction models. While models like that of Sargent etal. show promise, their reproducibility varies across settings, likely due to the interpretation of the ultrasound signs. The findings support the need for updating the current ultrasound descriptors and for the development of any new predictive models to use data collected by different operators in multiple clinical settings.

Prediction of transforaminal epidural injection success in sciatica (POTEISS): a protocol for the development of a multivariable prediction model for outcome after transforaminal epidural steroid injection in patients with lumbar radicular pain due to disc herniation or stenosis

BackgroundTransforaminal epidural injections (TEI) can alleviate symptoms and help to maintain physical functioning and quality of life in patients with lumbar radicular pain. We aim to develop a prediction model for patient outcome after TEI in patients suffering from unilateral lumbar radicular pain due to lumbar disc herniation (LDH) or single-level spinal stenosis (LSS). The secondary aim is to estimate short-term patient outcome differences between LDH and LSS patients, the association between psychological variables and patient outcome, the rate of additional injections, surgery and complications, and to explore the short-term cost-effectiveness of TEI.MethodsThis study is designed as a multi-centre, observational, prospective cohort study in two large regional hospitals in the Netherlands. Patients diagnosed with unilateral lumbar radicular pain secondary to LDH or LSS and congruent with MRI findings, who are referred for TEI along usual care pathways, are eligible for study participation. A total of 388 patients with LDH or LSS will be included. A pre-defined set of demographic, clinical and radiological variables will be used as the predictors in the model. The primary outcome measure is the Numerical Rating Scale (NRS) for leg pain. Secondary outcome measures include back pain, physical functioning, perceived recovery, pain coping strategies, anxiety and depression and use of analgesics and physical therapy. Patients will be evaluated at baseline, 2 weeks and 6 weeks after treatment. NRS leg pain and Likert perceived recovery data will be used as the dependent variables in a generalized linear mixed model for prediction of TEI outcome, with internal validation of performance (explained variation) by bootstrap resampling. Cost-effectiveness for a period of 6 weeks prior to and after treatment will be performed with decision-analytic modelling.DiscussionPatients with severe lumbar radicular pain often request additional treatment when conservative care is insufficient. TEI can offer relief of symptoms. Currently, it is not possible to predict responsiveness to this treatment for individual patients. This study is designed to explore predictors that can differentiate between patients that will and will not have a positive outcome after TEI. This information may support treatment strategies for this patient group.Trial registrationThis study is registered at ClinicalTrials.gov database under registry number NCT04540068 on September 1, 2020.

A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building

Accurate machine learning (ML) predictions for the early stages of the building design are crucial to construct energy-efficient buildings utilizing limited resources. Several studies have employed ML methods for energy consumption (EC) prediction without considering the utmost crucial PCM-integrated building design parameters. In addition, reducing the dataset by considering only the most significant building design parameters before applying the ML-based method would be beneficial for reducing the computational power and memory usage of the system as well as utilizing less time in the modelling process. To this end, this research presents a novel framework to establish the most robust and reliable ML-based prediction model with less complexity, considering only the most influential PCM-integrated building design parameters. These parameters were identified for future scenarios of hot semi-arid (BSh) climate zones using multi-stage sensitivity analysis. Afterward, a reduced EC database based on the most significant building's early-design-stage parameters (EDSPs) was utilized to formulate several multi-expression programming (MEP) and support vector machines (SVM)-based forecasting models, considering the variations in their hyperparameter values. Formulated prediction models have shown less time utilization through the training and testing phases for the EC evaluations of selected PCM-integrated building compared to the physical-modelling process. Several statistical parameters were used to test and validate the performance of the formulated prediction models. The acquired model evaluation and validation results demonstrated that the MEP-based prediction model (MEP15) exhibited the highest level of reliability and accuracy, showing an R2 value of >95% for both the training and testing phases. The model's interpretability showed that, throughout the parametric analysis, the developed prediction model adhered to the system's physical boundary constraints. Also, the best-performing prediction model showed energy savings of up to 12% for building integrated with PCM having a melting temperature of 28 °C. Conclusively, this research demonstrated that the developed MEP-based prediction model could be employed to precisely forecast the EC for selected PCM-incorporated building in the whole BSh climate, considering important EDSPs while utilizing minimal resources.