Outcome Prediction Models Research Articles

Automated Speech Analysis in Bipolar Disorder: The CALIBER Study Protocol and Preliminary Results.

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.

Thoracic sarcopenia was a poor prognostic predictor in patients receiving immunotherapy retain-->for advanced non-small-cell retain-->lung cancer

Sarcopenia, as measured at the level of the third lumbar (L3) has been shown to predict the survival of cancer patients. However, many patients with advanced non-small cell lung cancer (NSCLC) do not undergo routine abdominal imaging. The objective of this study was to investigate the association of thoracic sarcopenia with survival outcomes among patients who underwent immunotherapy for NSCLC. In this retrospective study, patients who initiated immunotherapy for advanced NSCLC from 2019 to 2022 were enrolled. and detailed patient data were collected. Cross sectional skeletal muscle area was calculated at the fifth thoracic vertebra (T5) on pretreatment chest computed tomography (CT) scan. Gender-specific lowest quartile values was used to define sarcopenia. The risk factors were analyzed using Cox analyses. The log-rank test and the random survival forest (RSF) were used to compare progression free survival (PFS). The model's performance was assessed using calibration curve and the receiver operating characteristic curve (ROC). A total of 242 patients was included (discovery cohort n=194, validation cohort n=48). In the discovery cohort, patients with sarcopenia exhibited significantly poorer PFS (p<0.001) than patients without sarcopenia. Univariate cox regression revealed that sarcopenia, lung cancer stage, body mass index, smoking status, and neutrophil-to-lymphocyte ratio were predictors of poor PFS. A RSF model was constructed based on the aforementioned parameters, to evaluate the model's efficacy, the ROC curve was utilized. with an area under the curve for predicting 6-month PFS of 0.68 and for 12-month PFS of 0.69. The prediction models for survival outcomes built by the discovery cohort showed similar performance in the validation cohort. Sarcopenia at T5 is independent prognostic factors in patients who received immunotherapy for advanced NSCLC.

A prediction model for nonresponsive outcomes in critically ill patients with acute respiratory distress syndrome undergoing prone position ventilation: A retrospective cohort study

ObjectiveThis study aimed to develop a reliable and effective nomogram model to identify high-risk populations with non-response to prone position ventilation (PPV) in acute respiratory distress syndrome (ARDS) patients. MethodsThis retrospective cohort study included 175 patients with ARDS undergoing PPV. An improvement of ≥ 20 mmHg in the PaO2/FiO2 after the first PPV was defined as a 'response'. For the construction of the model, all patients were randomly assigned to the train and validation cohort according to 2:1. Multivariate logistic regression was useed to develop the nomogram. The area under the receiver operating characteristic curve (AUC), decision curve and calibration curve were assessed to evaluate the efficiency, clinical utility and calibration of the model. ResultsThe overall rate of non-response to PPV in ARDS patients was approximately 32.6 %. In the training cohort and validation cohort, the rate are 29.9 % and 34.5 % respectively. Murray score ≥ 2.5 (OR: 4.29), procalcitonin (PCT) ≥ 2 ng/mL (OR: 2.52), N-terminal pro-B-type natriuretic peptide (Nt-proBNP) ≥ 2000 pg/ml (OR: 2.44), and hemoglobin ≤ 90 g/L (OR: 2.39) were independently associated with the rate of non-response to PPV and combined in prediction model. The model demonstrated good predictive value with AUC of 0.817 and 0.828 in the train and validation cohort. Calibration curve showed good calibration and decision curve analysis indicated favorable clinical utility. ConclusionsThis study constructed a risk prediction model for non-response to PPV, which demonstrated good predictive value and clinical utility. Implications for clinical practiceEarly identification of prone position response in ARDS is essential for timely alternative treatments, improving patient prognosis and healthcare efficiency. The predictive model included representative indicators of patients with ARDS, encompassing parameters such as the acute lung injury (Murray score), cardiac function (Nt-proBNP), infectious status (PCT), and hemoglobin levels.

Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion.

Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency. Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0-2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key features were identified through backward feature selection. We included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89-0.91). Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.

Visual deterioration outcomes following optic pathway glioma treatment: a 12-year single institution retrospective study.

The surgical treatment of optic pathway gliomas (OPG) remains controversial, with visual outcomes often unpredictable. The present study explored surgical and clinical factors influencing visual acuity (VA) after OPG treatment and developed anatomical subtypes correlated with clinical symptoms. Children with OPG who underwent initial partial tumor resection at Beijing Tiantan Hospital from January 2011 to December 2022 were retrospectively analyzed. Multivariate logistic regression and random forest analyses were performed to identify risk factors for post-treatment VA deterioration and a decision tree model was created based on significant factors. A total of 140 patients were enrolled. Multivariate logistic regression analysis identified surgical approach and initial VA as independent predictors of post-treatment VA deterioration (P < 0.05). Surgical approach, initial VA, and extent of tumor resection were the most significant factors for risk assessment and were included in the decision tree model, with surgical approach as the most important "root" node. The model demonstrated good predictive performance, with area under the curve values of 0.75 and 0.66 for the training and test datasets, respectively. A simple anatomical classification was developed, which revealed clinical characteristic differences among OPG types. Meanwhile, a correlation analysis of post-treatment visual deterioration was performed for each of the three anatomical types. This study offers a predictive model for visual outcomes following initial tumor-reduction surgery in OPG patients, which may help in visual outcomes risk stratification. Additionally, the anatomical classification effectively indicates OPG growth direction, offering potential insights into clinical symptoms.

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.

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.

Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional outcome in survivors with moderate to severe TBI (msTBI) but are mostly associated with mortality, which leads to a mortality bias for models predicting a composite outcome of mortality and severe disability. We analyzed the data from the multicenter randomized controlled Continuous Hyperosmolar Therapy in Traumatic Brain-Injured Patients trial and developed predictive models using machine learning methods and baseline characteristics and predictors collected during ICU stay. We compared our models' predictions of 6-month binary Glasgow Outcome Scale extended (GOS-E) score in all patients with msTBI (unfavorable GOS-E 1-4 vs. favorable GOS-E 5-8) with mortality (GOS-E 1 vs. GOS-E 2-8) and binary functional outcome in survivors with msTBI (severe disability GOS-E 2-4 vs. moderate to no disability GOS-E 5-8). We investigated the link between ICU variables and long-term functional outcomes in survivors with msTBI using predictive modeling and factor analysis of mixed data and validated our hypotheses on theInternational Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. Based on data from 370 patients with msTBI and classically used ICU variables, the prediction of the 6-month outcome in survivors was inefficient (mean area under the receiver operating characteristic 0.52). Using factor analysis of mixed data graph, we demonstrated that high-variance ICU variables were not associated with outcome in survivors with msTBI (p = 0.15 for dimension 1, p = 0.53 for dimension 2) but mostly with mortality (p < 0.001 for dimension 1), leading to a mortality bias for models predicting a composite outcome of mortality and severe disability. We finally identified this mortality bias in the IMPACT model. We demonstrated using machine learning-based predictive models that classically used ICU variables are strongly associated with mortality but not with 6-month outcome in survivors with msTBI, leading to a mortality bias when predicting a composite outcome of mortality and severe disability.

A large-scale reaction dataset of mechanistic pathways of organic reactions

Understanding organic reaction mechanisms is crucial for interpreting the formation of products at the atomic and electronic level, but still remains as a domain of knowledgeable experts. The lack of a large-scale dataset with chemically reasonable mechanistic sequences also hinders the development of reliable machine learning models to predict organic reactions based on mechanisms as human chemists do. Here, we present a high-quality and the first large-scale reaction dataset, denoted as mech-USPTO-31K, with chemically reasonable arrow-pushing diagrams validated by synthetic chemists, encompassing a wide spectrum of polar organic reaction mechanisms. We envision this dataset curated by applying a simple and flexible method that automatically generates reaction mechanisms using autonomously extracted reaction templates and expert-coded mechanistic templates to become an invaluable tool to develop future reaction outcome prediction models and discover new reactions.

Pitfalls of single-study external validation illustrated with a model predicting functional outcome after aneurysmal subarachnoid hemorrhage

BackgroundPrediction models are often externally validated with data from a single study or cohort. However, the interpretation of performance estimates obtained with single-study external validation is not as straightforward as assumed. We aimed to illustrate this by conducting a large number of external validations of a prediction model for functional outcome in subarachnoid hemorrhage (SAH) patients.MethodsWe used data from the Subarachnoid Hemorrhage International Trialists (SAHIT) data repository (n = 11,931, 14 studies) to refit the SAHIT model for predicting a dichotomous functional outcome (favorable versus unfavorable), with the (extended) Glasgow Outcome Scale or modified Rankin Scale score, at a minimum of three months after discharge. We performed leave-one-cluster-out cross-validation to mimic the process of multiple single-study external validations. Each study represented one cluster. In each of these validations, we assessed discrimination with Harrell’s c-statistic and calibration with calibration plots, the intercepts, and the slopes. We used random effects meta-analysis to obtain the (reference) mean performance estimates and between-study heterogeneity (I2-statistic). The influence of case-mix variation on discriminative performance was assessed with the model-based c-statistic and we fitted a “membership model” to obtain a gross estimate of transportability.ResultsAcross 14 single-study external validations, model performance was highly variable. The mean c-statistic was 0.74 (95%CI 0.70–0.78, range 0.52–0.84, I2 = 0.92), the mean intercept was -0.06 (95%CI -0.37–0.24, range -1.40–0.75, I2 = 0.97), and the mean slope was 0.96 (95%CI 0.78–1.13, range 0.53–1.31, I2 = 0.90). The decrease in discriminative performance was attributable to case-mix variation, between-study heterogeneity, or a combination of both. Incidentally, we observed poor generalizability or transportability of the model.ConclusionsWe demonstrate two potential pitfalls in the interpretation of model performance with single-study external validation. With single-study external validation. (1) model performance is highly variable and depends on the choice of validation data and (2) no insight is provided into generalizability or transportability of the model that is needed to guide local implementation. As such, a single single-study external validation can easily be misinterpreted and lead to a false appreciation of the clinical prediction model. Cross-validation is better equipped to address these pitfalls.

Abstract Mo030: Deep learning based multi-omics model for prediction of outcomes in HFpEF and HFmrEF

Introduction: Heart failure with preserved ejection fraction (HFpEF) presents a unique challenge in cardiology due to its diverse etiology and clinical presentation. Our study introduces a cutting-edge integrated deep learning model that combines multi-dimensional datasets, encompassing genetic variants from GWAS, cardiac structural and functional assessments from MRI, and electrophysiological data from ECGs, along with comorbidity profiles including CKD and LFT. Research Questions: Can a deep learning model that integrates multi-dimensional datasets accurately predict HFpEF risk? How does the inclusion of different types of data (genetic, MRI, comorbidity) affect the model's predictive accuracy? Goals: To develop a deep learning model that accurately predicts HFpEF risk To assess the impact of different types of data on the model's predictive accuracy. Methods: The model employs a graph convolutional multi-model deep learning approach to dissect the intricate interplay between genetic variants from GWAS, cardiac structural and functional assessments from MRI, electrophysiological data from ECGs, and comorbidity profiles including CKD and LFT. Results: The baseline genetic model achieved a PRS of 2.04 and an outcome accuracy of 81%. The incorporation of MRI features improved the outcome accuracy to 85.91%. The inclusion of comorbidities such as CKD and T2D resulted in the highest outcome accuracy of 88.57%. Discussion: The results suggest that the inclusion of MRI features and comorbidities improves the model's predictive accuracy. This model holds significant promise for clinical adoption and the advancement of personalized treatment strategies.

Volumetric computed tomography measurements as predictors for outcomes in a cohort of Fournier's gangrene patients.

This study evaluates the prognostic value of CT findings, including volumetric measurements, in predicting outcomes for patients with Fournier gangrene (FG), focusing on mortality, ICU admission, hospital stay length, and healthcare costs. A retrospective study was conducted on 38 FG patients who underwent CT scans before surgical debridement. We analyzed demographic data, CT volumetric measurements, and clinical outcomes using logistic and linear regression models. No single CT measurement significantly predicted mortality or ICU admission. The best model for mortality prediction included age, air volume, NSTI score, and male sex, with an AUC of 0.911. Intubation likelihood was modeled with an AUC of 0.913 using age, NSTI score, and visceral to subcutaneous fat ratio. The ICU admission model achieved an AUC of 0.677. Hospital stay was predicted by air volume (β = 0.0002656, p = 0.0505) with an adjusted R-squared of 0.1287. Air volume significantly predicted hospital costs (β = 2.859, p = 0.00558), resulting in an adjusted R-squared of 0.2165. Volumetric CT findings provide valuable prognostic insights for FG patients, suggesting a basis for informed clinical decisions and resource allocation. Further validation in larger, multi-center studies is recommended to develop robust predictive models for FG outcomes.

A novel prediction score determining individual clinical outcome 3 months after juvenile stroke (PREDICT-score)

BackgroundJuvenile strokes (< 55 years) account for about 15% of all ischemic strokes. Structured data on clinical outcome in those patients are sparse. Here, we aimed to fill this gap by systematically collecting relevant data and modeling a juvenile stroke prediction score for the 3-month functional outcome.MethodsWe retrospectively integrated and analyzed clinical and outcome data of juvenile stroke and TIA patients treated at the LMU University Hospital, LMU Munich, Munich. Good outcome was defined as a modified Rankin Scale of 0–2 or return to baseline of function. We analyzed candidate predictors and developed a predictive model. Predictive abilities were inspected using Area Under the ROC curve (AUROC) and visual representation of the calibration. The model was validated internally.Results346 patients were included in the analysis. We observed a good outcome in n = 293 patients (84.7%). The prediction model for an unfavourable outcome had an AUROC of 89.1% (95% CI 83.3–93.1%). The model includes age NIHSS, ASPECTS, blood glucose and type of vessel occlusion as predictors for the individual patient outcome.ConclusionsHere, we introduce the highly accurate PREDICT-score for the 3-month outcome after juvenile stroke derived from clinical routine data. The PREDICT-score might be helpful in guiding individual patient decisions and designing future studies but needs further prospective validation which is already planned.Trial registration The study has been registered at https://drks.de (DRKS00024407) on March 31, 2022.

Advancements in prognostic markers and predictive models for intracerebral hemorrhage: from serum biomarkers to artificial intelligence models.

Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the prediction and management of ICH outcomes. Serum sestrin2, a stress-responsive protein, has been identified as a significant prognostic marker, correlating with severity indicators such as NIHSS scores and hematoma volume. Its levels predict early neurological deterioration and poor prognosis, offering predictive capabilities comparable to traditional measures. Furthermore, a deep learning-based AI model demonstrated superior performance in predicting early hematoma enlargement, with higher sensitivity and specificity than conventional methods. Additionally, long-term outcome prediction models using CT radiomics and machine learning have achieved high accuracy, particularly with the Random Forest algorithm. These advancements underscore the potential of integrating novel biomarkers and advanced computational techniques to improve prognostication and management of ICH, aiming to enhance patient care and survival rates. The incorporation of serum sestrin2, AI, and machine learning in predictive models represents a significant step forward in the clinical management of ICH, offering new avenues for research and clinical application.

Outcome Prediction Model for Radiofrequency Uvulopalatopharyngoplasty with Tonsillectomy in Adult Obstructive Sleep Apnea: Retrospective Cohort Study.

Knowing an individualized outcome prediction is essential when counseling patients before surgery. We aim to identify predictors and build a model for the outcome of radiofrequency uvulopalatopharyngoplasty with tonsillectomy (rfUPPP + TE). All adult patients undergoing rfUPPP + TE for sleep-disordered breathing from 2015 to 2022 in our institution were included. Preoperative evaluations included detailed upper airway examinations and standardized questionnaires. Postoperative outcomes were measured through home sleep apnea testing and repeated questionnaires 3 months post-surgery. The primary endpoint was the postoperative apnea-hypopnea index (AHI) and the AHI responders using the Sher criteria. We analyzed 247 patients with a mean age of 46 ± 11 years, predominantly male (88.7%), and a mean BMI of 29.0 kg/m2. The mean AHI was reduced from 26.4 ± 18.6/h preoperatively to 16.2 ± 14.6/h postoperatively. Daytime sleepiness improved from 8.9 ± 48 to 4.0 ± 3.1 and snoring from 7.9 ± 2.1 to 3.3 ± 2.2. Multivariate analysis indicated that higher tonsil grades, preoperative AHI, and snoring levels were associated with a greater reduction in AHI. Age and body weight were negative predictors for AHI reduction. For AHI responders, according to Sher, tonsil grade was the only predictor in a multivariate analysis. The ROC curve of this simple model, with a corrected AUC of 0.625, compared favorably against two established models. Our study highlights that tonsil grade, preoperative AHI, snoring, and, to a smaller extent, age and weight are key determinants of AHI reduction, emphasizing the importance of preoperative evaluation. Despite the multifactorial nature of obstructive sleep apnea, preoperative evaluation can predict the outcome of rfUPPP + TE and guide surgical planning.

Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques

BackgroundAdverse birth outcomes, including preterm birth, low birth weight, and stillbirth, remain a major global health challenge, particularly in developing regions. Understanding the possible risk factors is crucial for designing effective interventions for birth outcomes. Accordingly, this study aimed to develop a predictive model for adverse birth outcomes among childbearing women in Sub-Saharan Africa using advanced machine learning techniques. Additionally, this study aimed to employ a novel data science interpretability techniques to identify the key risk factors and quantify the impact of each feature on the model prediction.MethodsThe study population involved women of childbearing age from 26 Sub-Saharan African countries who had given birth within five years before the data collection, totaling 139,659 participants. Our data source was a recent Demographic Health Survey (DHS). We utilized various data balancing techniques. Ten advanced machine learning algorithms were employed, with the dataset split into 80% training and 20% testing sets. Model evaluation was conducted using various performance metrics, along with hyperparameter optimization. Association rule mining and SHAP analysis were employed to enhance model interpretability.ResultsBased on our findings, about 28.59% (95% CI: 28.36, 28.83) of childbearing women in Sub-Saharan Africa experienced adverse birth outcomes. After repeated experimentation and evaluation, the random forest model emerged as the top-performing machine learning algorithm, with an AUC of 0.95 and an accuracy of 88.0%. The key risk factors identified were home deliveries, lack of prenatal iron supplementation, fewer than four antenatal care (ANC) visits, short and long delivery intervals, unwanted pregnancy, primiparous mothers, and geographic location in the West African region.ConclusionThe region continues to face persistent adverse birth outcomes, emphasizing the urgent need for increased attention and action. Encouragingly, advanced machine learning methods, particularly the random forest algorithm, have uncovered crucial insights that can guide targeted actions. Specifically, the analysis identifies risky groups, including first-time mothers, women with short or long birth intervals, and those with unwanted pregnancies. To address the needs of these high-risk women, the researchers recommend immediately providing iron supplements, scheduling comprehensive prenatal care, and strongly encouraging facility-based deliveries or skilled birth attendance.

Cardiovascular magnetic resonance parametric mapping in the risk stratification of patients affected by chronic myocarditis.

Chronic myocardial inflammation is the substrate for arrhythmias and dilated cardiomyopathy onset, causing morbidity and mortality. Cardiovascular magnetic resonance (CMR) is the noninvasive gold standard for myocardial inflammation detection, due to the high sensitivity of the parametric mapping techniques. However, the potential prognostic capabilities of CMR mapping have not been studied in the setting of chronic myocarditis. This is a retrospective study on consecutive patients undergoing CMR with suspicion of chronic myocarditis from September 2017 to November 2021. CMR was acquired according to 2018 Lake Louise Criteria recommendations. The outcome (chronic heart failure, recurrent chronic myocarditic chest pain, ICD/PM implantation, arrhythmias [Lown class ≥ 2]) was collected at follow-up. The extent and degree of native T1, T2, and extracellular volume fraction alterations were used to create multivariate binary logistic regression models for outcome prediction, with or without left ventricle ejection fraction; their AUCs were compared with DeLong test. Differences between other parameters were assessed using Chi-square test, Fisher's exact test, or Mann-Whitney U-test. The population included 88 patients (age 43 [32-52] yo), mostly male (53/88, 60%). After a median follow-up of 21 (17-34) months, 31/88 (35%) patients experienced the outcome. The model based on the extension of mapping alterations and LV dysfunction reached a good predictability (AUC 0.71). The model based on the intensity of mapping alterations and LV dysfunction had a very good performance (AUC 0.80). The quantitative analysis of CMR mapping parameters indicative of myocardial damage severity may improve risk stratification in patients with chronic myocarditis. The intensity of myocardial damage, assessed as the degree of native T1, T2, and ECV alteration, together with left ventricle dysfunction, improved patient risk stratification. Further prospective studies will be necessary for validation before clinical application. Risk stratification of patients affected by chronic myocarditis is an unmet clinical need. Cardiovascular MRI (CMR) can role in risk stratification thanks to its multiparametric capabilities of tissue characterization. A model based on CMR parametric mapping and left ventricle ejection fraction can predict arrhythmia, heart failure, and recurrent symptoms.

Pregnancy and perinatal outcomes in pregnancies following frozen embryo transfer (FET) after transcervical resection of adhesions (TCRA): A retrospective cohort study with propensity score matching analysis.

To compare the pregnancy and perinatal outcomes of frozen-thawed embryo transfer (FET) in patients following transcervical resection of adhesions (TCRA) versus patients with normal uterine morphology, and to investigate the factors influencing pregnancy outcomes in patients undergoing FET after TCRA. We retrospectively analyzed FET cycles from September 2014 to September 2023, comparing patients with normal uterine morphology to those with intrauterine adhesions (IUAs) treated with TCRA. Propensity score matching (PSM) adjusted for confounding factors. LASSO regression and multivariate logistic regression identified predictors of outcomes, which were visually represented in nomograms. Model performance was assessed using calibration curves, ROC curves, and DCA, with bootstrap method for internal validation. Post-PSM analysis showed higher live birth rates in patients with normal uterine morphology after clinical pregnancy (75.1% vs. 61.7%, P < 0.001). No significant differences were noted in clinical pregnancy rates and perinatal outcomes between the groups. Factors influencing clinical pregnancy in FET after TCRA included basal progesterone levels, endometrial thickness, parity, infertility cause, embryo stage at transfer, number and quality of embryos transferred, IUA severity, and TCRA surgical procedures. Body mass index, basal LH levels, and day 14 HCG levels post-embryo transfer were determinants of live birth outcome. FET cycles following TCRA showed a lower rate of successful live births, but TCRA did not increase adverse perinatal outcome risks. Our study introduces an innovative predictive model for clinical pregnancy and live birth outcomes in patients undergoing FET following TCRA, addressing a significant void in existing research.

Validation of a model for outcome prediction after endovascular treatment for ischemic stroke.

The recently developed MR-PREDICTS@24 h model showed excellent performance in the MR-CLEAN Registry cohort in patients presenting within 12 h from onset. However, its applicability to an U.S. population and to patients presenting beyond 12 h from last known normal are still undetermined. We aim to externally validate the MR-PREDICTS@24 h model in a new geographic setting and in the late window. In this retrospective analysis of a prospectively collected database from a comprehensive stroke center in the United States, we included patients with intracranial carotid artery or middle cerebral artery M1 or M2 segment occlusions who underwent endovascular therapy and applied the MR-PREDICTS@24 h formula to estimate the probabilities of functional outcome at day 90. The primary endpoint was the modified Rankin Scale (mRS) at 90 days. We included 1246 patients, 879 in the early (<12 h) and 367 in the late (≥12 h) cohort. For both cohorts, calibration and discrimination of the model were accurate throughout mRS levels, with absolute differences between estimated and predicted proportions ranging from 1% to 5%. Calibration metrics and curve inspections showed good performance for estimating the probabilities of mRS ≤ 1 to mRS ≤ 5 for the early cohort. For the late cohort, predictions were reliable for the probabilities of mRS ≤ 1 to mRS ≤ 4. The MR-PREDICTS@24 h was transferrable to a real-world U.S.-based cohort in the early window and showed consistently accurate predictions for patients presenting in the late window without need for updating.

Machine learning-based prediction models for electoral outcomes in India: a comparative analysis of exit polls from 2014–2021

Machine learning-based prediction models for electoral outcomes in India: a comparative analysis of exit polls from 2014–2021