Prediction Of Outcome In Patients Research Articles

The role of test results in the emergency department in the evaluation of major torso injury patients

We studied the role of laboratory tests in the emergency department (ED) for the prediction of outcomes in major torso injury patients. We hypothesized that the laboratory test results in the ED could predict worse outcomes and be complement of the TRISS (trauma and injury severity score) which serves a popular prediction model for trauma patients. Patients with major torso trauma who were sent to a level-I trauma center between January 2016 and December 2020 were retrospectively studied. Demographic information, TRISS, and laboratory data (glucose, lactate, base deficit (BD)) were collected and analyzed. The early death (death within 24 hours from the ED arrival) and the long intensive care unit (ICU) length of stay (LOS) (over seven days) were defined as the primary and secondary outcomes. A prediction model that integrated TRISS and the laboratory test results was created and evaluated by using the receiver operating characteristic (ROC) curve with the area under the curve (AUC). In total, 827 patients were studied. The glucose (255.2 ± 109.9 vs. 192.1 ± 77.0, p < 0.001) and lactate (77.2 ± 43.4 vs. 39.2 ± 26.4, p < 0.001) levels were significantly higher in patients who died early. The performance of glucose, lactate and BD in identifying those major torso trauma patients who would die early demonstrated acceptable discrimination (glucose: AUC = 0.687; lactate: AUC = 0.778; BD: AUC = 0.734). Furthermore, a model that integrated the TRISS and laboratory tests showed calibration of AUC = 0.863. Moreover, the levels of BD and glucose were also independent factors of long ICU LOS. In conclusion, checking lactate, BD and blood glucose is recommended in the evaluation of major torso trauma patients. Biochemical markers are beneficial in predicting worse outcomes for patients with major torso trauma and can complement the TRISS for these patients.

CDKL3 is a promising biomarker for diagnosis and prognosis prediction in patients with hepatocellular carcinoma.

Cyclin-dependent kinase-like 3 (CDKL3) has been identified as an oncogene in certain types of tumors. Nonetheless, its function in hepatocellular carcinoma (HCC) is poorly understood. In this study, we conducted a comprehensive analysis of CDKL3 based on data from the HCC cohort of The Cancer Genome Atlas (TCGA). Our analysis included gene expression, diagnosis, prognosis, functional enrichment, tumor microenvironment and metabolic characteristics, tumor burden, mRNA expression-based stemness, alternative splicing, and prediction of therapy response. Additionally, we performed a cell counting kit-8 assay, TdT-mediated dUTP nick-end Labeling staining, migration assay, wound healing assay, colony formation assay, and nude mouse experiments to confirm the functional relevance of CDKL3 in HCC. Our findings showed that CDKL3 was significantly upregulated in HCC patients compared to controls. Various bioinformatic analyses suggested that CDKL3 could serve as a potential marker for HCC diagnosis and prognosis. Furthermore, CDKL3 was found to be involved in various mechanisms linked to the development of HCC, including copy number variation, tumor burden, genomic heterogeneity, cancer stemness, and alternative splicing of CDKL3. Notably, CDKL3 was also closely correlated with tumor immune cell infiltration and the expression of immune checkpoint markers. Additionally, CDKL3 was shown to independently function as a risk predictor for overall survival in HCC patients by multivariate Cox regression analysis. Furthermore, the knockdown of CDKL3 significantly inhibited cell proliferation in vitro and in vivo, indicating its role as an oncogene in HCC. Taken together, our findings suggest that CDKL3 shows promise as a biomarker for the detection and treatment outcome prediction of HCC patients.

Outcome and response prediction for primary and oligometastatic spinal sarcoma patients who received stereotactic body radiation therapy (SBRT)

Outcome and response prediction for primary and oligometastatic spinal sarcoma patients who received stereotactic body radiation therapy (SBRT)

Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis.

The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases. We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set. For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone. Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.

Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review.

Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus. The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

Analytical Study of Breast Cancer and Treatment Techniques

Breast cells have the potential to evolve into a malignant growth known as breast cancer. Intensive research into breast cancer detection aims to gain a deeper understanding of this intricate ailment, which disproportionately impacts women and constitutes a significant global health challenge. Key objectives in breast cancer analysis encompass improving early detection methods, comprehending diverse disease subtypes, developing efficient treatment approaches, and implementing preventive measures. Advances in mammography, ultrasonography, MRI, and innovative imaging techniques are enhancing early detection rates. Molecular and genetic profiling now allows for more precise categorization of breast cancer subtypes, enabling personalized treatment plans based on DNA mutations, protein markers, and gene expression patterns. The identification of prognostic and predictive biomarkers enhances patient outcome predictions, facilitating customized treatments and higher survival rates. Additionally, molecular analysis aids in understanding drug resistance, tumor diversity, and pinpointing specific therapeutic targets. To effectively analyse breast cancer, collaborative research and data exchange are essential, and massive genomic projects are facilitating interdisciplinary collaborations and expediting achievements. We strive for a future where breast cancer is successfully identified, treated, and prevented, thereby improving patient outcomes and lessening the burden of this disease. To that end, we are constantly increasing our knowledge and sharing resources. Results from various machine learning models demonstrate promising accuracy, including CNN (93%), SVM (91%), AlexNet (93.8%), TRF (89.2%), LR (89.0%), and Naive Bayes (90%).

Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study

ObjectivesThis study combined two novel approaches in oncology patient outcome predictions—body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer. MethodsThe study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms. ResultsThe volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve–receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05). ConclusionAutomatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.

Stone Free Success Rate by Using Modified Guy’s Scoring System in Patients Undergoing Percutaneous Nephrolithotomy

Background: Renal stone disease (RSD) is a prevalent condition globally, with significant impacts on patient health and healthcare systems. The complexity of RSD management, particularly in achieving high stone-free rates (SFR) with minimal complications, necessitates ongoing research into effective treatment modalities. Percutaneous nephrolithotomy (PCNL) has emerged as a standard treatment, but predicting its success pre-operatively remains a challenge. Objective: The study aimed to evaluate the efficacy of the Modified Guy’s Stone Score (M-GSS) in predicting the SFR in patients undergoing PCNL, and to analyze the relationship between various patient and stone characteristics and the success of the procedure. Methods: This descriptive case series study involved 161 patients undergoing PCNL at a tertiary care center. Data on patient demographics, stone characteristics, and operative details were collected. Stones were classified using the M-GSS, and PCNL outcomes were assessed in terms of SFR. Statistical analysis was performed using SPSS 23.0, with a focus on the correlation between M-GSS grades and SFR. Results: The overall SFR was 59.6%. The majority of patients (38.5%) were classified as M-GSS grade 1. The mean stone size was 2.74 ± 1.6 cm, and the mean operative time was 73.4 ± 14.6 minutes. There was no significant difference in SFR across different M-GSS grades (p=0.236). Stone size was the only factor significantly affecting the SFR (p=0.0001). Conclusion: The study indicates that while M-GSS is a useful tool for preoperative assessment in PCNL, stone size remains a crucial determinant of SFR. The findings suggest the need for further research, particularly larger, multi-center randomized studies, to validate these results and refine preoperative patient counseling and outcome prediction strategies in PCNL.

The Role of Immunoglobulin G4 in Outcomes of Primary Sclerosing Cholangitis.

Primary sclerosing cholangitis (PSC) is a chronic, cholestatic liver disease that is characterized by an inflammatory and fibrotic process affecting bile ducts which eventually develops into liver cirrhosis and liver failure. The aim of this study was to investigate serum IgG subclass distribution in patients with PSC and its possible association with PSC outcomes. We performed a retrospective analysis of 181 patients who had been diagnosed with PSC between January 1970 and December 2015 and followed at our outpatient clinic. Their demographic, immunological, and clinical characteristics were recorded and analyzed. This study included 181 patients with PSC (120 males, 61 females). There was no association between IgGs and the development of autoimmune hepatitis, cirrhosis, cholangiocarcinoma, liver transplantation, inflammatory bowel disease, and colectomy. Patients with elevated IgG4 had statistically significant higher rates of cholangitis (p = 0.02) and endoscopic retrograde cholangiopancreatography (ERCP) (p = 0.009). High IgG4 values were observed in nine patients who underwent ERCP. In these nine patients, on average, IgG4 was evaluated 5 years after ERCP (min 3 days, max 11 years). Subanalysis considering only IgG4 values evaluated before ERCP showed no significant difference but remains significant if we consider IgG4 values after ERCP. Elevated IgG4 in our study showed a possible association with higher rates of cholangitis and ERCP among patients with primary sclerosing cholangitis. It seems that IgGs may be a useful tool for the prediction of outcomes in patients with PSC. A prospective study is necessary, especially to study the trends of IgGs values during disease as well as the role of possible seroconversion.

Machine learning score using only echocardiographic data for prediction of in-hospital outcomes in ICCU patients

Machine learning score using only echocardiographic data for prediction of in-hospital outcomes in ICCU patients

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.

To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images. A retrospective, bi-center study of ICH patients was conducted. Firstly, a custom 3D convolutional model was built for predicting the functional outcome of ICH patients based on CT scans from randomly selected ICH patients in H training dataset collected from H hospital. Secondly, clinical data and radiological features were collected at admission and the Extreme Gradient Boosting (XGBoost) algorithm was used to establish a second model, named the XGBoost model. Finally, the Convolution model and XGBoost model were fused to build the third "Fusion model." Favorable outcome was defined as modified Rankin Scale score of 0-3 at discharge. The prognostic predictive accuracy of the three models was evaluated using an H test dataset and an external Y dataset, and compared with the performance of ICH score and ICH grading scale (ICH-GS). A total of 604 patients with ICH were included in this study, of which 450 patients were in the H training dataset, 50 patients in the H test dataset, and 104 patients in the Y dataset. In the Y dataset, the areas under the curve (AUCs) of the Convolution model, XGBoost model, and Fusion model were 0.829, 0.871, and 0.905, respectively. The Fusion model prognostic performance exceeded that of ICH score and ICH-GS (p = 0.043 and p = 0.045, respectively). Deep learning models have good accuracy for predicting functional outcome of patients with spontaneous intracerebral hemorrhage. The proposed deep learning Fusion model may assist clinicians in predicting functional outcome and developing treatment strategies, thereby improving the survival and quality of life of patients with spontaneous intracerebral hemorrhage. • Integrating clinical presentations, CT images, and radiological features to establish deep learning model for functional outcome prediction of patients with intracerebral hemorrhage. • Deep learning applied to CT images provides great help in prognosing functional outcome of intracerebral hemorrhage patients. • The developed deep learning model performs better than clinical prognostic scores in predicting functional outcome of patients with intracerebral hemorrhage.

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases.

Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive computational markers from primary tumor and BM data at [Formula: see text] and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are [Formula: see text], the proliferation rate of a single tumor cell; and [Formula: see text], the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at [Formula: see text] for 20 patients. Parameters [Formula: see text] and [Formula: see text] were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p = 0.0029 and HR 1.95 (1.31-2.91) p = 0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p < 0.0001). We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.

Machine learning-based gait analysis to predict clinical frailty scale in elderly patients with heart failure.

Although frailty assessment is recommended for guiding treatment strategies and outcome prediction in elderly patients with heart failure (HF), most frailty scales are subjective, and the scores vary among raters. We sought to develop a machine learning-based automatic rating method/system/model of the clinical frailty scale (CFS) for patients with HF. We prospectively examined 417 elderly (≥75 years) with symptomatic chronic HF patients from 7 centres between January 2019 and October 2023. The patients were divided into derivation (n = 194) and validation (n = 223) cohorts. We obtained body-tracking motion data using a deep learning-based pose estimation library, on a smartphone camera. Predicted CFS was calculated from 128 key features, including gait parameters, using the light gradient boosting machine (LightGBM) model. To evaluate the performance of this model, we calculated Cohen's weighted kappa (CWK) and intraclass correlation coefficient (ICC) between the predicted and actual CFSs. In the derivation and validation datasets, the LightGBM models showed excellent agreements between the actual and predicted CFSs [CWK 0.866, 95% confidence interval (CI) 0.807-0.911; ICC 0.866, 95% CI 0.827-0.898; CWK 0.812, 95% CI 0.752-0.868; ICC 0.813, 95% CI 0.761-0.854, respectively]. During a median follow-up period of 391 (inter-quartile range 273-617) days, the higher predicted CFS was independently associated with a higher risk of all-cause death (hazard ratio 1.60, 95% CI 1.02-2.50) after adjusting for significant prognostic covariates. Machine learning-based algorithms of automatically CFS rating are feasible, and the predicted CFS is associated with the risk of all-cause death in elderly patients with HF.

Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning

Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.

Outcome Prediction of Patients with Intracerebral Hemorrhage by Measurement of Lesion Volume in the Corticospinal Tract on Computed Tomography

Outcome Prediction of Patients with Intracerebral Hemorrhage by Measurement of Lesion Volume in the Corticospinal Tract on Computed Tomography

Machine Learning Models for Predicting Stroke Mortality in Malaysia: An Application and Comparative Analysis.

Background Stroke is a significant public health concern characterized by increasing mortality and morbidity. Accurate long-term outcome prediction for acute stroke patients, particularly stroke mortality, is vital for clinical decision-making and prognostic management. This study aimed to develop and compare various prognostic models for stroke mortality prediction. Methods In a retrospective cohort study from January 2016 to December 2021, we collected data from patients diagnosed with acute stroke from five selected hospitals. Data contained variables ondemographics, comorbidities, and interventionsretrieved from medical records. The cohort comprised 950 patients with 20 features. Outcomes (censored vs. death) were determined by linking data with the Malaysian National Mortality Registry.We employed three common survival modeling approaches, the Cox proportional hazard regression (Cox), support vector machine (SVM), and random survival forest (RSF), while enhancing the Cox model with Elastic Net (Cox-EN) for feature selection. Models were compared using the concordance index (C-index), time-dependent area under the curve (AUC), and discrimination index (D-index), with calibration assessed by the Brier score. Results The support vector machine (SVM) model excelled among the four, with three-month, one-year, and three-year time-dependent AUC values of 0.842, 0.846, and 0.791; a D-index of 5.31 (95% CI: 3.86, 7.30); and a C-index of 0.803 (95% CI: 0.758, 0.847). All models exhibited robust calibration, with three-month, one-year, and three-year Brier scores ranging from 0.103 to 0.220, all below 0.25. Conclusion The support vector machine (SVM) model demonstrated superior discriminative performance, suggesting its efficacy in developing prognostic models for stroke mortality. This study enhances stroke mortality prediction and supports clinical decision-making, emphasizing the utility of the support vector machine method.

Additional predictive value of optic nerve sheath diameter for neurological prognosis after cardiac arrest: a prospective cohort study

BackgroundThe goal is to estimate the additional value of ultrasonographic optic nerve sheath diameter (ONSD) measurement on days 1–3, on top of electroencephalography (EEG), pupillary light reflexes (PLR), and somatosensory evoked potentials (SSEP), for neurological outcome prediction of comatose cardiac arrest patients. We performed a prospective longitudinal cohort study in adult comatose patients after cardiac arrest. ONSD was measured on days 1–3 using ultrasound. Continuous EEG, PLR, and SSEP were acquired as standard care. Poor outcome was defined as cerebral performance categories 3–5 at 3–6 months. Logistic regression models were created for outcome prediction based on the established predictors with and without ONSD. Additional predictive value was assessed by increase in sensitivity for poor (at 100% specificity) and good outcome (at 90% specificity).ResultsWe included 100 patients, 54 with poor outcome. Mean ONSD did not differ significantly between patients with good and poor outcome. Sensitivity for predicting poor outcome increased by adding ONSD to EEG and SSEP from 25% to 41% in all patients and from 27% to 50% after exclusion of patients with non-neurological death.ConclusionsONSD on days 1–3 after cardiac arrest holds potential to add to neurological outcome prediction.Trialregistration: clinicaltrials.gov, NCT04084054. Registered 10 September 2019, https://www.clinicaltrials.gov/study/NCT04084054.

Predicting adverse outcomes in adults with a community-acquired lower respiratory tract infection: a protocol for the development and validation of two prediction models for (i) all-cause hospitalisation and mortality and (ii) cardiovascular outcomes

BackgroundCommunity-acquired lower respiratory tract infections (LRTI) are common in primary care and patients at particular risk of adverse outcomes, e.g., hospitalisation and mortality, are challenging to identify. LRTIs are also linked to an increased incidence of cardiovascular diseases (CVD) following the initial infection, whereas concurrent CVD might negatively impact overall prognosis in LRTI patients. Accurate risk prediction of adverse outcomes in LRTI patients, while considering the interplay with CVD, can aid general practitioners (GP) in the clinical decision-making process, and may allow for early detection of deterioration. This paper therefore presents the design of the development and external validation of two models for predicting individual risk of all-cause hospitalisation or mortality (model 1) and short-term incidence of CVD (model 2) in adults presenting to primary care with LRTI.MethodsBoth models will be developed using linked routine electronic health records (EHR) data from Dutch primary and secondary care, and the mortality registry. Adults aged ≥ 40 years with a GP-diagnosis of LRTI between 2016 and 2019 are eligible for inclusion. Relevant patient demographics, medical history, medication use, presenting signs and symptoms, and vital and laboratory measurements will be considered as candidate predictors. Outcomes of interest include 30-day all-cause hospitalisation or mortality (model 1) and 90-day CVD (model 2). Multivariable elastic net regression techniques will be used for model development. During the modelling process, the incremental predictive value of CVD for hospitalisation or all-cause mortality (model 1) will also be assessed. The models will be validated through internal-external cross-validation and external validation in an equivalent cohort of primary care LRTI patients.DiscussionImplementation of currently available prediction models for primary care LRTI patients is hampered by limited assessment of model performance. While considering the role of CVD in LRTI prognosis, we aim to develop and externally validate two models that predict clinically relevant outcomes to aid GPs in clinical decision-making. Challenges that we anticipate include the possibility of low event rates and common problems related to the use of EHR data, such as candidate predictor measurement and missingness, how best to retrieve information from free text fields, and potential misclassification of outcome events.

Neurodesarrollo a los 2 años en cardiopatía congénita: utilidad pronóstica de los marcadores de daño cerebral

IntroductionAt present, neurodevelopmental abnormalities are the most frequent type of complication in school-aged children with congenital heart disease (CHD). We analysed the incidence of acute neurologic events (ANEs) in patients with operated CHD and the usefulness of neuromarkers for the prediction of neurodevelopment outcomes. MethodsProspective observational study in infants with a prenatal diagnosis of CHD who underwent cardiac surgery in the first year of life. We assessed the following variables: (1) serum biomarkers of brain injury (S100B, neuron-specific enolase) in cord blood and preoperative blood samples; (2) clinical and laboratory data from the immediate postnatal and perioperative periods; (3) treatments and complications; (4) neurodevelopment (Bayley-III scale) at age 2 years. ResultsThe study included 84 infants with a prenatal diagnosis of CHD who underwent cardiac surgery in the first year of life. Seventeen had univentricular heart, 20 left ventricular outflow obstruction and 10 genetic syndromes. The postoperative mortality was 5.9% (5/84) and 10.7% (9/84) patients experienced ANEs. The mean overall Bayley-III scores were within the normal range, but 31% of patients had abnormal scores in the cognitive, motor or language domains. Patients with genetic syndromes, ANEs and univentricular heart had poorer neurodevelopmental outcomes. Elevation of S100B in the immediate postoperative period was associated with poorer scores. ConclusionsChildren with a history of cardiac surgery for CHD in the first year of life are at risk of adverse neurodevelopmental outcomes. Patients with genetic syndromes, ANEs or univentricular heart had poorer outcomes. Postoperative ANEs may contribute to poorer outcomes. Elevation of S100B levels in the postoperative period was associated with poorer neurodevelopmental outcomes at 2 years. Studies with larger samples and longer followups are needed to define the role of these biomarkers of brain injury in the prediction of neurodevelopmental outcomes in patients who undergo surgery for management of CHD.

Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients.

Accurately predicting functional outcomes in stroke patients remains challenging yet clinically relevant. While brain CTs provide prognostic information, their practical value for outcome prediction is unclear. We analyzed a multi-center cohort of 743 ischemic stroke patients (<72 h onset), including their admission brain NCCT and CTA scans as well as their clinical data. Our goal was to predict the patients' future functional outcome, measured by the 3-month post-stroke modified Rankin Scale (mRS), dichotomized into good (mRS ≤ 2) and poor (mRS > 2). To this end, we developed deep learning models to predict the outcome from CT data only, and models that incorporate other patient variables. Three deep learning architectures were tested in the image-only prediction, achieving 0.779 ± 0.005 AUC. In addition, we created a model fusing imaging and tabular data by feeding the output of a deep learning model trained to detect occlusions on CT angiograms into our prediction framework, which achieved an AUC of 0.806 ± 0.082. These findings highlight how further refinement of prognostic models incorporating both image biomarkers and clinical data could enable more accurate outcome prediction for ischemic stroke patients.