Drug Repurposing in Crohn's Disease Using Danish Real-World Data.

Aim: Drug repurposing, utilizing electronic healthcare records (EHRs), offers a promising alternative by repurposing existing drugs for new therapeutic indications, especially for patients lacking effective therapies. Intestinal fibrosis, a severe complication of Crohn's disease (CD), poses significant challenges, increasing morbidity and mortality without available pharmacological treatments. This article focuses on identifying medications associated with an elevated or reduced risk of fibrosis in CD patients through a population-wide real-world data and artificial intelligence (AI) approach. Methods: Patients aged 65 or older with a diagnosis of CD from 1996 to 2019 in the Danish EHRs were followed for up to 24 years. The primary outcome was the need of specific surgical procedures, namely proctocolectomy with ileostomy and ileocecal resection as proxies of intestinal fibrosis. The study explored drugs linked to an increased or reduced risk of the study outcome through machine-learning driven survival analysis. Results: Among the 9179 CD patients, 1029 (11.2%) underwent surgery, primarily men (58.5%), with a mean age of 76 years, 10 drugs were linked to an elevated risk of surgery for proctocolectomy with ileostomy and ileocecal resection. In contrast, 10 drugs were associated with a reduced risk of undergoing surgery for these conditions. Conclusion: This study focuses on repurposing existing drugs to prevent surgery related to intestinal fibrosis in CD patients, using Danish EHRs and advanced statistical methods. The findings offer valuable insights into potential treatments for this condition, addressing a critical unmet medical need. Further research and clinical trials are warranted to validate the effectiveness of these repurposed drugs in preventing surgery related to intestinal fibrosis in CD patients.

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study.

Aims: To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway. Method: We employed linked data from the national health registries of Sweden and Norway to conduct our study. We focused on individuals in Sweden with confirmed SARS-CoV-2 infection through RT-PCR testing up to August 2022 as our study cohort. Within this group, we identified hospitalized cases as those who were admitted to the hospital within 14 days of testing positive for SARS-CoV-2 and matched them with five controls from the same cohort who were not hospitalized due to SARS-CoV-2. Additionally, we identified individuals who died within 30 days after being hospitalized for COVID-19. To develop our disease risk scores, we considered various factors, including demographics, infectious, somatic, and mental health conditions, recorded diagnoses, and pharmacological treatments. We also conducted age-specific analyses and assessed model performance through 5-fold cross-validation. Finally, we performed external validation using data from the Norwegian population with COVID-19 up to December 2021. Results: During the study period, a total of 124,560 individuals in Sweden were hospitalized, and 15,877 individuals died within 30 days following COVID-19 hospitalization. Disease risk scores for both hospitalization and mortality demonstrated predictive capabilities with ROC-AUC values of 0.70 and 0.72, respectively, across the entire study period. Notably, these scores exhibited a positive correlation with the likelihood of hospitalization or death. In the external validation using data from the Norwegian COVID-19 population (consisting of 53,744 individuals), the disease risk score predicted hospitalization with an AUC of 0.47 and death with an AUC of 0.74. Conclusion: The disease risk score showed moderately good performance to predict COVID-19-related mortality but performed poorly in predicting hospitalization when externally validated.

AI-based disease risk score for community-acquired pneumonia hospitalization.

Community-acquired pneumonia (CAP) is an acute infection involving the parenchyma of the lungs, which is acquired outside of the hospital. Population-wide real-world data and artificial intelligence (AI) were used to develop a disease risk score for CAP hospitalization among older individuals. The source population included residents in Denmark aged 65 years or older in the period January 1, 1996, to July 30, 2018. 137344 individuals were hospitalized for pneumonia during the study period for which, 5 controls were matched leading to a study population of 620908 individuals. The disease risk had an average accuracy of 0.79 based on 5-fold cross-validation in predicting CAP hospitalization. The disease risk score can be useful in clinical practice to identify individuals at higher risk of CAP hospitalization and intervene to minimize their risk of being hospitalized for CAP.

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.

Aim: To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Study eligibility criteria: Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data sources: Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction: We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment: A bias assessment of AI models was done using PROBAST. Participants: Patients tested positive for COVID-19. Results: We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions: A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.

Ubrogepant and rimegepant: systematic review, meta-analysis, and meta-regression of clinical studies.

OBJECTIVES: This study aimed at providing pooled estimates of the incidence of adverse drug reactions (ADRs) of ubrogepant and rimegepant and to use meta-regression to identify correlations between the occurrence of selected ADRs, socio-demographic, and clinical characteristics from data published in clinical studies. METHODS: Ovid MEDLINE (up to 03/02/2022) was searched along with the references listed in the reviews identified with the research query. Random intercept and slope logistic regression models were used to estimate the logit transformation of the pooled incidence. To examine how selected clinical and socio-demographic characteristics correlated with the pooled incidence rates, we performed random-effects meta-regression. RESULTS: Significant heterogeneity of incidence estimates was observed in clinical studies along with correlations between ADRs and the sociodemographic and clinical characteristics of patients exposed to ubrogepant. In particular, we observed a correlation between ubrogepant dosage and muscle strain and between Body Mass Index (BMI) and liver function values. For rimegepant, significant correlations were observed between age and infections and having aura symptoms at baseline and nausea/dizziness/diarrhea/muscle strain. CONCLUSION: This study provided pooled incidence estimates of ubrogepant and rimegepant's ADRs and highlighted new safety aspects of the pharmacological treatment with ubrogepants and rimigepants from correlations obtained from the meta-regression.

A machine-learning guided method for predicting add-on and switch in secondary data sources: A case study on anti-seizure medications in Danish registries.

Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users. Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm. Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%). Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.

Impact of device programming on the success of the first anti-tachycardia pacing therapy: An anonymized large-scale study.

BACKGROUND: Antitachycardia pacing (ATP) is an effective treatment for ventricular tachycardia (VT). We evaluated the efficacy of different ATP programs based on a large remote monitoring data set from patients with implantable cardioverter-defibrillators (ICDs). METHODS: A dataset from 18,679 ICD patients was used to evaluate the first delivered ATP treatment. We considered all device programs that were used for at least 50 patients, leaving us with 7 different programs and a total of 32,045 episodes. We used the two-proportions z-test (α = 0.01) to compare the probability of success and the probability for acceleration in each group with the corresponding values of the default setting. RESULTS: Overall, the first ATP treatment terminated in 78.4%-97.5% of episodes with slow VT and 81.5%-91.1% of episodes with fast VT. The default setting of the ATP programs with the number of sequences S = 3 was applied to treat 30.1% of the slow and 36.6% of the fast episodes. Reducing the maximum number of sequences to S = 2 decreased the success rate for slow VT (P < 0.0001, h = 0.38), while the setting S = 4 resulted in the highest success rate of 97.5% (P < 0.0001, h = 0.27). CONCLUSION: While the default programs performed well, we found that increasing the number of sequences from 3 to 4 was a promising option to improve the overall ATP performance.

Effectiveness of CRT-D Versus ICD on Prevention of Electrical Storm: Big Data from the USA.

Patients with implantable cardioverter-defibrillator (ICD) are at the risk of electrical storm (ES) occurrence associated with mortality and poor quality of life. Cardiac resynchronization therapy with defibrillator (CRT-D) minimizes inappropriate ICD shocks. However, limited reports exist on the impact of CRT-D versus traditional ICD on ES occurrences in real-life cohorts. We evaluated the implanted-device characteristics associated with ES events in a large data based on daily stored device-summaries obtained from remote monitoring data in US.Between 2004 and 2016, 19,935 US patients were implanted. Survival analyses with Cox regression for device-shock therapy were performed between patients who experienced at least one ES and those without ES. CRT-D devices (bi-ventricular) were implanted in 5522 (28%) patients during this period, and their ES events over time were compared to ICD recipients implanted with RV lead. Primary endpoint was the first ES event.ES occurred with the rate of 7.26% for all patients during the period. Cox regression analyses revealed significantly an increase risk in ES occurrences (the p-value <; 0.05 and hazard ratio >> 1) with shock therapy. CRT-D implant led to lower ES risk comparing with patients received traditional ICD (RV only).

Predicting Electrical Storm Using Episodes' Parameters from ICD Recorded Data.

Electrical storm (ES) is a life-threatening heart condition for patients with implantable cardioverter defibrillators (ICDs). ICD patients experienced episodes are at higher risk for ES. However, predicting ES using previous episodes' parameters recorded by ICDs have never been developed. This study aims to predict ES using machine learning models based on ICD remote monitoring-summaries during episodes in the anonymized large number of patients. Episode ICD-summaries from 16,022 patients were used to construct and evaluate two models, logistic regression and random forest, for predicting the short-term risk of ES. Episode parameters in this study included the total number of sustained episodes, shocks delivered and the cycle length parameters. The models evaluated on the data sections not used for model development. Random forest performed significantly better than logistic regression (P <; 0.01), achieving a test accuracy of 0.99 and an Area Under an ROC Curve (AUC) of 0.93 (vs. an accuracy of 0.98 and an AUC of 0.90). The total number of previous sustained episodes was the most relevant variables in the both models.

Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

AIMS: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-defibrillators (ICDs). Predicting ES could be essential; however, models for predicting this event have never been developed. The goal of this study was to construct and validate machine learning models to predict ES based on daily ICD remote monitoring summaries. METHODS AND RESULTS: Daily ICD summaries from 19 935 patients were used to construct and evaluate two models [logistic regression (LR) and random forest (RF)] for predicting the short-term risk of ES. The models were evaluated on the parts of the data not used for model development. Random forest performed significantly better than LR (P < 0.01), achieving a test accuracy of 0.96 and an area under the curve (AUC) of 0.80 (vs. an accuracy of 0.96 and an AUC of 0.75). The percentage of ventricular pacing and the daytime activity were the most relevant variables in the RF model. CONCLUSION: The use of large-scale machine learning showed that daily summaries of ICD measurements in the absence of clinical information can predict the short-term risk of ES.

Minimal T-wave representation and its use in the assessment of drug arrhythmogenicity.

BACKGROUND: Recently, numerous models and techniques have been developed for analyzing and extracting features from the T wave which could be used as biomarkers for drug-induced abnormalities. The majority of these techniques and algorithms use features that determine readily apparent characteristics of the T wave, such as duration, area, amplitude, and slopes. METHODS: In the present work the T wave was down-sampled to a minimal rate, such that a good reconstruction was still possible. The entire T wave was then used as a feature vector to assess drug-induced repolarization effects. The ability of the samples or combinations of samples obtained from the minimal T-wave representation to correctly classify a group of subjects before and after receiving d,l-sotalol 160 mg and 320 mg was evaluated using a linear discriminant analysis (LDA). RESULTS: The results showed that a combination of eight samples from the minimal T-wave representation can be used to identify normal from abnormal repolarization significantly better compared to the heart rate-corrected QT interval (QTc). It was further indicated that the interval from the peak of the T wave to the end of the T wave (Tpe) becomes relatively shorter after IKr inhibition by d,l-sotalol and that the most pronounced repolarization changes were present in the ascending segment of the minimal T-wave representation. CONCLUSIONS: The minimal T-wave representation can potentially be used as a new tool to identify normal from abnormal repolarization in drug safety studies.

Assessing common classification methods for the identification of abnormal repolarization using indicators of T-wave morphology and QT interval.

Various parameters based on QTc and T-wave morphology have been shown to be useful discriminators for drug induced I(Kr)-blocking. Using different classification methods this study compares the potential of these two features for identifying abnormal repolarization on the ECG. A group of healthy volunteers and LQT2 carriers were used to train classification algorithms using measures of T-wave morphology and QTc. The ability to correctly classify a third group of test subjects before and after receiving d,l-sotalol was evaluated using classification rules derived from training. As a single electrocardiographic feature, T-wave morphology separates normal from abnormal repolarization better than QTc. It is further indicated that nonlinear boundaries can provide stronger classifiers than a linear boundaries. Whether this is true in general with other ECG markers and other data sets is uncertain because the approach has not been tested in this setting.