Methods applied to neonatal dried blood spot samples for secondary research purposes: a scoping review.

This scoping review aimed to synthesize the analytical techniques used and methodological limitations encountered when undertaking secondary research using residual neonatal dried blood spot (DBS) samples. Studies that used residual neonatal DBS samples for secondary research (i.e. research not related to newborn screening for inherited genetic and metabolic disorders) were identified from six electronic databases: Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Medline, PubMed and Scopus. Inclusion was restricted to studies published from 1973 and written in or translated into English that reported the storage, extraction and testing of neonatal DBS samples. Sixty-seven studies were eligible for inclusion. Included studies were predominantly methodological in nature and measured various analytes, including nucleic acids, proteins, metabolites, environmental pollutants, markers of prenatal substance use and medications. Neonatal DBS samples were stored over a range of temperatures (ambient temperature, cold storage or frozen) and durations (two weeks to 40.5 years), both of which impacted the recovery of some analytes, particularly amino acids, antibodies and environmental pollutants. The size of DBS sample used and potential contamination were also cited as methodological limitations. Residual neonatal DBS samples retained by newborn screening programs are a promising resource for secondary research purposes, with many studies reporting the successful measurement of analytes even from neonatal DBS samples stored for long periods of time in suboptimal temperatures and conditions.

Potential Applications of Artificial Intelligence (AI) in Managing Polypharmacy in Saudi Arabia: A Narrative Review.

Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI's potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.

OPTIMA-BP: empOwering PaTients in MAnaging Blood Pressure - protocol for a randomised parallel group study comparing use of Kvatchii web-based patient education portal as an addition to home blood pressure monitoring.

INTRODUCTION: Hypertension is the leading modifiable risk factor for cardiovascular disease and is implicated in half of all strokes and myocardial infarctions. One-third of the adults in Scotland have hypertension yet only a quarter of them have their blood pressure (BP) controlled to target (<140/90 mm Hg). Empowering patients to have a better understanding of their condition and becoming actively involved in the monitoring and management of hypertension may lead to improved patient satisfaction, improved BP control and health outcomes and reduction in the use of primary/secondary care hypertension clinics. METHODS AND ANALYSIS: OPTIMA-BP is a randomised parallel group pilot study comparing the use of home BP monitoring accompanied by access to the web-based cardiovascular educational portal (Kvatchii) and home BP monitoring (HBPM) alone in 200 patients with hypertension attending the Glasgow Blood Pressure Clinic, Queen Elizabeth University Hospital, Glasgow. Consented participants will be asked to complete surveys on lifestyle factors, medication adherence, quality of life and hypertension knowledge, understanding and home monitoring. The intervention group will be asked to complete a survey to help evaluate the Kvatchii portal. At 6 and 12 months, the surveys will be repeated via the CASTOR EDC. Both groups will input their HBPM results at 2-month intervals into a CASTOR-EDC survey. OPTIMA-BP will follow-up with participants over 12 months with the study running over 24 months. The primary outcome is HBPM systolic BP area under the curve between baseline and 6 months ETHICS AND DISSEMINATION: OPTIMA-BP was approved by the North of Scotland Research Ethics Committee 2 (22/NS/0095). Current protocol version 1.2 date 6 June 2023. Written informed consent will be provided by all study participants. Study findings will be submitted to international peer-reviewed journals and will be presented at national and international scientific meetings. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov: NCT05575453. Registered 12 October 2022. https://clinicaltrials.gov/ct2/show/NCT05575453.

Scrutinizing science to save lives: uncovering flaws in the data linking L-type calcium channels blockers to CRAC channels and heart failure.

Hypertension is estimated to affect almost 1 billion people globally and significantly increases risk of myocardial infarction, heart failure, stroke, retinopathy and kidney disease. One major front line therapy that has been used for over 50 years involves L-type Ca 2+ channel blockers (LCCBs). One class of LCCBs is the dihydropyridine family, with amlodipine being widely prescribed regardless of gender, race, ethnicity or age. In 2020, Johnson et al. 7 reported that all LCCBs significantly increased the risk of heart failure, and attributed this effect to non-canonical activation of store-operated Ca 2+ entry. A major approach on which they based many of their arguments was to measure cytosolic Ca 2+ using the fluorescent Ca 2+ indicator dye fura-2. We recently demonstrated that amlodipine is highly fluorescent within cells and overwhelms the fura-2 signal, precluding the use of the indicator dye with amlodipine 24 . Our meta-analyses and prospective real world study showed that dihydropyridines were not associated with an increase in heart failure, likely explained by the lack of consideration by Johnson et al. 7 of well-known confounding factors such as age, race, obesity, prior anti-hypertensive treatment or diabetes 24 . Trebak and colleagues have responded to our paper with a forthright and unwavering defence of their work 27 . In this paper, we carry out a forensic dissection of Johnson et al., 7 and conduct new experiments that address directly points raised by Trebak et al. 27 . We show that there are major flaws in the design and interpretation of their key experiments, that fura-2 cannot be used with amlodipine, that there are fundamental mathematical misunderstandings and mistakes throughout their study leading to critical calculations on heart failure that are demonstrably wrong, and several of their own results are inconsistent with their interpretation. We therefore believe the study by Johnson et al. 7 is flawed at many levels and we stand by our conclusions.

UMOD Genotype-Blinded Trial of Ambulatory Blood Pressure Response to Torasemide.

BACKGROUND: UMOD (uromodulin) has been linked to hypertension through potential activation of Na+-K+-2Cl- cotransporter (NKCC2), a target of loop diuretics. We posited that hypertensive patients carrying the rs13333226-AA UMOD genotype would demonstrate greater blood pressure responses to loop diuretics, potentially mediated by this UMOD/NKCC2 interaction. METHODS: This prospective, multicenter, genotype-blinded trial evaluated torasemide (torsemide) efficacy on systolic blood pressure (SBP) reduction over 16 weeks in nondiabetic, hypertensive participants uncontrolled on ≥1 nondiuretic antihypertensive for >3 months. The primary end point was the change in 24-hour ambulatory SBP (ABPM SBP) and SBP response trajectories between baseline and 16 weeks by genotype (AA versus AG/GG) due to nonrandomized groups at baseline (ClinicalTrials.gov: NCT03354897). RESULTS: Of 251 enrolled participants, 222 received torasemide and 174 demonstrated satisfactory treatment adherence and had genotype data. The study participants were middle-aged (59±11 years), predominantly male (62%), obese (body mass index, 32±7 kg/m2), with normal eGFR (92±17 mL/min/1.73 m²) and an average baseline ABPM of 138/81 mm Hg. Significant reductions in mean ABPM SBP were observed in both groups after 16 weeks (AA, -6.57 mm Hg [95% CI, -8.44 to -4.69]; P<0.0001; AG/GG, -3.22 [95% CI, -5.93 to -0.51]; P=0.021). The change in mean ABPM SBP (baseline to 16 weeks) showed a difference of -3.35 mm Hg ([95% CI, -6.64 to -0.05]; P=0.048) AA versus AG/GG genotypes. The AG/GG group displayed a rebound in SBP from 8 weeks, differing from the consistent decrease in the AA group (P=0.004 for difference in trajectories). CONCLUSIONS: Our results confirm a plausible interaction between UMOD and NKCC2 and suggest a potential role for genotype-guided use of loop diuretics in hypertension management. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03354897.

Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring.

Background: Inaccurate blood pressure (BP) classification results in inappropriate treatment. We tested whether machine learning (ML), using routine clinical data, can serve as a reliable alternative to ambulatory BP monitoring (ABPM) in classifying BP status. Methods: This study employed a multicentre approach involving 3 derivation cohorts from Glasgow, Gdansk, and Birmingham, and a fourth independent evaluation cohort. ML models were trained using office BP, ABPM, and clinical, laboratory, and demographic data, collected from patients referred for hypertension assessment. Seven ML algorithms were trained to classify patients into 5 groups, named as follows: Normal/Target; Hypertension-Masked; Normal/Target-White-Coat (WC); Hypertension-WC; and Hypertension. The 10-year cardiovascular outcomes and 27-year all-cause mortality risks were calculated for the ML-derived groups using the Cox proportional hazards model. Results: Overall, extreme gradient boosting (using XGBoost open source software) showed the highest area under the receiver operating characteristic curve of 0.85-0.88 across derivation cohorts, Glasgow (n = 923; 43% female; age 50.7 ± 16.3 years), Gdansk (n = 709; 46% female; age 54.4 ± 13 years), and Birmingham (n = 1222; 56% female; age 55.7 ± 14 years). But accuracy (0.57-0.72) and F1 (harmonic mean of precision and recall) scores (0.57-0.69) were low across the 3 patient cohorts. The evaluation cohort (n = 6213; 51% female; age 51.2 ± 10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-WC and the Hypertension-WC groups, with heightened 27-year all-cause mortality observed in all groups, except the Hypertension-Masked group, compared to the Normal/Target group. Conclusions: ML has limited potential in accurate BP classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted.

Contexte: Les erreurs dans la classification des valeurs de la pression artérielle (PA) entraînent une inadéquation du traitement. Nous avons tâché de déterminer si l'apprentissage machine, à l'aide de données cliniques routinières, constituait une solution de rechange fiable à la surveillance ambulatoire de la PA pour définir le statut de la PA. Méthodologie: Cette étude a utilisé une approche multicentrique incluant trois cohortes de dérivation de Glasgow, Gdansk et Birmingham, et une quatrième cohorte d'évaluation indépendante. Les modèles d'apprentissage machine ont été développés en analysant les données démographiques, les valeurs de la PA mesurée au cabinet, les données relatives à la surveillance ambulatoire de la PA et aux épreuves de laboratoire recueillies auprès de patients adressés pour une évaluation de l'hypertension. Sept algorithmes d'apprentissage machine ont été appliqués pour classer les patients en cinq groupes : Normale/Cible; Hypertension-Masquée; Normal/Cible-Blouse blanche; Hypertension-Blouse blanche; Hypertension. Les événements cardiovasculaires sur 10 ans et le risque de mortalité toutes causes confondues sur 27 ans ont été calculés dans les groupes dérivés de l'apprentissage machine à l'aide d'un modèle de risques proportionnels de Cox. Résultats: D'une manière générale, l'amplification de gradient extrême (à l'aide du logiciel ouvert XGBoost) a mis en évidence l'aire sous la courbe de la fonction d'efficacité du récepteur (courbe ROC pour Receiver Operating Characteristic) la plus haute, soit 0,85 à 0,88, pour toutes les cohortes de dérivation : Glasgow (n = 923; 43 % de femmes; âge : 50,7 ± 16,3 ans); Gdansk (n = 709; 46 % de femmes; âge : 54,4 ± 13 ans); Birmingham (n = 1 222; 56 % de femmes; âge : 55,7 ± 14 ans). La précision (0,57 ­ 0,72) et le score F1 (moyenne harmonique de la précision et du rappel) (0,57 ­ 0,69) ont été faibles dans les trois cohortes de patients. La cohorte d'évaluation (n = 6 213; 51 % de femmes; âge : 51,2 ± 10,8 ans) a indiqué un risque d'événements cardiovasculaires composites sur 10 ans élevé dans les groupes Normale/Cible-Blouse blanche et Hypertension-Blouse blanche, tandis qu'une hausse de la mortalité toutes causes confondues sur 27 ans a été observée dans tous les groupes, sauf dans le groupe Hypertension-Masquée, comparativement au groupe Normale/Cible. Conclusions: Le potentiel d'exactitude de la classification de la PA à l'aide de l'apprentissage machine lorsque la surveillance ambulatoire de la PA n'est pas possible est limité. Des études de plus grande envergure portant sur des groupes de patients et des niveaux de ressources diversifiés s'imposent.

Cardiovascular precision medicine - A pharmacogenomic perspective.

Precision medicine envisages the integration of an individual's clinical and biological features obtained from laboratory tests, imaging, high-throughput omics and health records, to drive a personalised approach to diagnosis and treatment with a higher chance of success. As only up to half of patients respond to medication prescribed following the current one-size-fits-all treatment strategy, the need for a more personalised approach is evident. One of the routes to transforming healthcare through precision medicine is pharmacogenomics (PGx). Around 95% of the population is estimated to carry one or more actionable pharmacogenetic variants and over 75% of adults over 50 years old are on a prescription with a known PGx association. Whilst there are compelling examples of pharmacogenomic implementation in clinical practice, the case for cardiovascular PGx is still evolving. In this review, we shall summarise the current status of PGx in cardiovascular diseases and look at the key enablers and barriers to PGx implementation in clinical practice.