Candidate Predictors Research Articles

Impact of thermal seed treatment on spermosphere microbiome, metabolome and viability of winter wheat

Thermal seed treatment can be used as an alternative method to prevent infection by seed-borne diseases, but exposure duration and temperature during thermal treatment are important to maintain high seed viability and emergence whilst decreasing infection rate. A method for predicting suitable treatment parameters to maintain viability and eliminate seed-borne pathogens is therefore needed. Seeds of winter wheat were subjected to thermal treatment at four levels of intensity and pre-treatments with or without imbibition. Treatment impact was measured by metabolome analysis using LC-MS and GC-MS, analysis of spermosphere bacterial and fungal metagenomes using Illumina MiSeq, and detection of presence of Fusarium spp. and Microdochium spp. using ddPCR. The results showed that moderate treatment intensity reduced signs of infection and increased seedling emergence. In imbibed samples, myo-inositol concentration and myo-inositol: glucose ratio were positively correlated with treatment intensity, whereas concentrations of glucose and citric acid were negatively correlated. No correlations were found for non-imbibed samples. Imbibition had a large significant impact on microbial community composition of the wheat spermosphere. Imbibition of wheat seeds prior to thermal treatment altered wheat spermosphere microbiota. The concentration of myo-inositol, potentially in combination with glucose, could be a candidate predictor for suitable thermal treatment intensity of wheat seeds.

Should We Use Clinician Self-Report to Tailor Implementation Strategies? Predicting Use of Youth CBT with Clinician Self-Report Versus Direct Observation.

Developing tailored implementation strategies to increase the use of evidence-based practice (EBP) requires accurate identification of predictors of their use. However, known difficulties with measuring EBP use complicates interpretation of the extant literature. In this proof-of-concept study, we examined whether the same predictors of use of cognitive behavioral therapy (CBT) are identified when CBT use is measured with clinician self-report compared to direct observation. We examined four candidate predictors of CBT use - clinician participation in an EBP training initiative, years of experience, caseload, and employment status - in a sample of 36 clinicians (64% female; 72% White and 28% Black) from 19 community mental health agencies treating youth in greater Philadelphia. CBT use was captured for 100 unique client sessions (M = 2.8 recorded sessions per clinician) through both clinician self-report and direct observation, using parallel measures. We used three-level (client, clinician, and agency) regression models with random intercepts to estimate the relationship between each predictor variable and CBT use in both measures and compared the magnitude and direction of each model across self-report and direct observation using z-tests. There was no alignment for any of the four candidate predictors between predictive relationships identified by self-report compared to those identified by direct observation. The findings in this study extend literature documenting limitations of using clinician self-report to capture clinician behavior and suggest that even the characteristics that predict higher self-reported CBT use do not align with (and often are discordant with) those that predict directly observed CBT use. This raises questions about the utility of relying on self-reported use to inform implementation strategy design.

Normal tissue complication probability modeling for late rectal bleeding after conventional or hypofractionated radiotherapy for prostate cancer

PurposeTo develop a single NTCP model for grade ≥ 2 late rectal bleeding (G2 LRB) after conventional or hypofractionated radiotherapy for prostate cancer. Methods and MaterialsThe development dataset consisted of prostate cancer patients (n = 656) previously randomized to conventional (39 x 2 Gy) or hypofractionated (19 x 3.4 Gy) external beam radiotherapy with N = 89 G2 LRB cases. Candidate predictors were obtained from literature. We fitted five separate logistic regression models to the data, each with one of the following dose parameters as candidate predictors in biological effective dose (BED), assuming α/β = 3 Gy: Equivalent uniform dose (EUD) with n = 0.1, EUD with n = 0.2, the relative volume receiving ≥ 111.9 Gy in BED (V111.9, the equivalent of physical V70 for a conventional schedule), minimum BED to the hottest 0.1 cm3 (D0.1cm3) or 2 cm3 (D2cm3). Previous abdominal surgery was included in every model and fractionation schedule was tested as predictor in each model. A sensitivity analysis was performed by varying the α/β-ratio, n and dose-volume cutoff. ResultsThe pre-selected candidate dosimetric predictor and previous abdominal surgery were significantly associated with the outcome in all five models. Fractionation schedule was eliminated by the backward scheme in only the EUD (n = 0.1), D0.1cm3 and D2cm3-based models. In internal validation these models showed AUC’s of 0.64, 0.60 & 0.62, respectively. The sensitivity analyses showed that EUD models with n ≥ 0.15 and / or α/β ≥ 4 Gy failed, and EUD models based on α/β = 2 Gy with n = 0.05–0.2 showed good fits as well. ConclusionsOur trial data set with different fractionation schedules offered the unique possibility to generate unbiased BED-based models. EUD (n = 0.1), D0.1cm3 and D2cm3 performed overall best in predicting G2 LRB; with α/β = 2 Gy equally good models were obtained. External validation is required to confirm our results.

Predicting Pancreatic Cancer in New‐Onset Diabetes Cohort Using a Novel Model With Integrated Clinical and Genetic Indicators: A Large‐Scale Prospective Cohort Study

ABSTRACTIntroductionIndividuals who develop new‐onset diabetes have been identified as a high‐risk cohort for pancreatic cancer (PC), exhibiting an incidence rate nearly 8 times higher than the general population. Hence, the targeted screening of this specific cohort presents a promising opportunity for early pancreatic cancer detection. We aimed to develop and validate a novel model capable of identifying high‐risk individuals among those with new‐onset diabetes.MethodsEmploying the UK Biobank cohort, we focused on those developing new‐onset diabetes during follow‐up. Genetic and clinical characteristics available at registration were considered as candidate predictors. We conducted univariate regression analysis to identify potential indicators and used a 5‐fold cross‐validation method to select optimal predictors for model development. Five machine learning algorithms were used for model development.ResultsAmong 12,735 patients with new‐onset diabetes, 100 (0.8%) were diagnosed with PC within 2 years. The final model (area under the curve, 0.897; 95% confidence interval, 0.865–0.929) included 5 clinical predictors and 24 single nucleotide polymorphisms. Two threshold cut‐offs were established: 1.28% and 5.26%. The recommended 1.28% cut‐off, based on model performance, reduces definitive testing to 13% of the total population while capturing 76% of PC cases. The high‐risk threshold is 5.26%. Utilizing this threshold, only 2% of the population needs definitive testing, capturing nearly half of PC cases.ConclusionsWe, for the first time, combined clinical and genetic data to develop and validate a model to determine the risk of pancreatic cancer in patients with new‐onset diabetes using machine learning algorithms. By reducing the number of unnecessary tests while ensuring that a substantial proportion of high‐risk patients are identified, this tool has the potential to improve patient outcomes and optimize healthcare sources.

Whole blood transcriptional profiles and the pathogenesis of tuberculous meningitis.

Mortality and morbidity from tuberculous meningitis (TBM) are common, primarily due to inflammatory response to Mycobacterium tuberculosis infection, yet the underlying mechanisms remain poorly understood. We aimed to uncover genes and pathways associated with TBM pathogenesis and mortality, and determine the best predictors of death, utilizing whole-blood RNA sequencing from 281 Vietnamese adults with TBM, 295 pulmonary tuberculosis (PTB), and 30 healthy controls. Through weighted gene co-expression network analysis, we identified hub genes and pathways linked to TBM severity and mortality, with a consensus analysis revealing distinct patterns between HIV-positive and HIV-negative individuals. We employed multivariate elastic-net Cox regression to select candidate predictors of death, then logistic regression and internal bootstrap validation to choose best predictors. Increased neutrophil activation and decreased T and B cell activation pathways were associated with TBM mortality. Among HIV-positive individuals, mortality associated with increased angiogenesis, while HIV-negative individuals exhibited elevated TNF signaling and impaired extracellular matrix organization. Four hub genes-MCEMP1, NELL2, ZNF354C, and CD4-were strong TBM mortality predictors. These findings indicate that TBM induces a systemic inflammatory response similar to PTB, highlighting critical genes and pathways related to death, offering insights for potential therapeutic targets alongside a novel four-gene biomarker for predicting outcomes.

Whole blood transcriptional profiles and the pathogenesis of tuberculous meningitis

Mortality and morbidity from tuberculous meningitis (TBM) are common, primarily due to inflammatory response to Mycobacterium tuberculosis infection, yet the underlying mechanisms remain poorly understood. We aimed to uncover genes and pathways associated with TBM pathogenesis and mortality, and determine the best predictors of death, utilizing whole-blood RNA sequencing from 281 Vietnamese adults with TBM, 295 pulmonary tuberculosis (PTB), and 30 healthy controls. Through weighted gene co-expression network analysis, we identified hub genes and pathways linked to TBM severity and mortality, with a consensus analysis revealing distinct patterns between HIV-positive and HIV-negative individuals. We employed multivariate elastic-net Cox regression to select candidate predictors of death, then logistic regression and internal bootstrap validation to choose best predictors. Increased neutrophil activation and decreased T and B cell activation pathways were associated with TBM mortality. Among HIV-positive individuals, mortality associated with increased angiogenesis, while HIV-negative individuals exhibited elevated TNF signaling and impaired extracellular matrix organization. Four hub genes—MCEMP1, NELL2, ZNF354C, and CD4—were strong TBM mortality predictors. These findings indicate that TBM induces a systemic inflammatory response similar to PTB, highlighting critical genes and pathways related to death, offering insights for potential therapeutic targets alongside a novel four-gene biomarker for predicting outcomes.

Integrated multi-omics predictive analysis of atherosclerosis: a sub-study from the Mineralocorticoid Receptor Antagonism in Diabetic Atherosclerosis (MAGMA) trial

Abstract Background/Introduction Mineralocorticoid receptor (MR) antagonists (MRA) are beneficial in cardiorenal outcomes in randomized controlled trials but the mechanisms are unclear. MAGMA was an NHLBI sponsored randomized, double-blind, placebo controlled, 12-month trial comparing Spironolactone (n = 37) vs. placebo (n = 42) in Type 2 diabetics with CKD stages 3-4 on maximal renin-angiotensin system (RAS) blockade and a prior atherosclerotic event and/or left ventricular (LV) hypertrophy. The primary outcome of percent (%) change in total aortic wall volume (TWV) at 12 months, measured by magnetic resonance imaging (MRI), was significantly reduced by Spironolactone. Purpose/Originality The purpose of this analysis was to understand mechanistic pathways of MRAs using a multi-omics approach that could help tease out molecular mechanisms of benefit. Revealing for the first time which of these integrated pathways are predictive of the primary outcome will help design treatments for targeted interventions. Methods Plasma and peripheral blood mononuclear cells from patients randomized to Spironolactone or placebo at baseline and 3-months were measured for aptamer-based proteomic biomarkers (7,596 proteins) and 10-X platform-based single-cell RNA-sequencing (scRNAseq), respectively. Pre-selected candidate predictors were used as inputs of a predictive model of changes of TWV. We fit a Mixed-Multivariate Random Forest (RF) model, a variation of the RF supervised tree-based machine learning method to take the experimental design into account in the regression formulation. The two sources of "omics predictors" were integrated into a supervised multi-omics model to jointly explain the outcome using an extension of Sparse Generalized Canonical Correlation Analysis (SGCCA). Integrated multiome functional analyses with graphical visualizations were carried out by statistical enrichment analysis using Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) against databases of gene ontologies, biological pathways, putative regulatory motifs, proteins, or disease annotations. Results The plasma proteome in response to Spironolactone revealed downregulation of MR targets including fibrosis, immune activation/inflammation, leukocyte activation, proliferation and pathways involved in cytokine stimulation. scRNAseq pathways revealed negative regulation of cytokines production such as IL-2 and redistribution of multiple cell types. Predictors of plaque progression involved cytokine-receptor, complement-coagulation, cell adhesion and axonal guidance targets. Multiome integrated functional analyses results of predictive pathways will be presented. Conclusions The changes in plasma proteomic profile with Spironolactone were consistent with the phenotype of reduced atherosclerosis and downregulation of multiple inflammatory, immune response and profibrotic pathways.

Factor XI activity in ST-segment elevation myocardial infarction

Abstract Background ST-segment elevation myocardial infarction (STEMI) is characterized by a significant thrombotic burden, where the coagulation cascade, including Factor XI (FXI) activity, plays a crucial role. In STEMI and other thrombotic conditions, FXI levels vary according to patient characteristics and timing from the event. Understanding the changes in FXI activity may offer valuable insights into the optimal patient selection and timing for use of investigational FXI inhibitors. Purpose Our aim was to characterize variations in FXI activity in STEMI patients. Methods In this prospective, observational study, patients admitted with STEMI underwent two measurements of plasma FXI activity, prior to coronary angiography and at discharge, using the HemosIL assay. The variation in FXI activity at these timepoints was compared between patients with and without coronary thrombus, as detected by an angiographic core-lab. Linear regression analyses were conducted to test the association of candidate predictors with FXI variation between admission and discharge (i.e., presence of coronary thrombus, PCI of the culprit artery, Thrombolysis in Myocardial Infarction flow post-PCI, bailout use of glycoprotein IIb/IIIa inhibitors, time to second measurement of FXI activity) and with FXI value at admission (i.e., age, sex, atrial fibrillation, late [>24 hours] STEMI presentation, presence of coronary thrombus). Results From November 2023 to February 2024, 49 STEMI patients were included. The median variation in FXI activity among admission and discharge was +24.8% (interquartile range [IQR] 11.8; 69.0; p<0.001). This variation was observed in both patients with (+41.6% [IQR 14.3; 74.6]; p<0.001) and without (+21.8% [IQR -2.3;40.7]; p=0.008) thrombus at coronary angiography, with significant interaction effects compared to the global STEMI population (p for interaction <0.001). An inverse linear regression relationship was observed between presence of coronary thrombus and changes in FXI activity (coefficient -33.0, 95% CI -59.5 to -7.2; p=0.012). Furthermore, in the model exploring predictors of FXI activity at admission, thrombus presence (coefficient -40.9, 95% CI -75.3 to -6.4; p=0.024) and subacute presentation (coefficient 90.8, 95% CI 21.9 to 159.8; p=0.015) emerged as significant predictors, albeit with contrasting effects on FXI activity. Figure 1 Conclusions Our findings reveal significant increases in FXI activity from admission to discharge in STEMI patients. The inverse relationship between the presence of coronary thrombus and FXI activity variations, along with the contrasting associations among initial FXI levels and subacute patients’ presentation or thrombus presence, support the importance of FXI in the coagulation process during STEMI. These findings suggest targeted FXI inhibition, especially early in STEMI management, could be beneficial, meriting detailed investigation in future studies.Multivariable linear regression analysis

Utilization and determinants of maternity waiting homes among pastoralist mothers in Dire district, southern Ethiopia: a mixed-methods study

BackgroundMaternity waiting homes are cost-effective, World Health Organization-approved components of comprehensive prenatal, delivery, and postpartum care strategies. However, few community-based studies within Ethiopia's pastoralist communities, and none in the study area, have been conducted to determine actual usage or to gain a thorough understanding of the factors influencing utilization.MethodsA cross-sectional study, supplemented by qualitative methods, was conducted from June 25 to July 25, 2023. A simple random sampling technique was used to select 305 study participants. Data were gathered through an interviewer-administered questionnaire, entered into Epi-data version 3.1, and analyzed using SPSS version 25. Descriptive data were presented in tables, graphs, text, and percentages. Bivariate logistic regression identified candidate predictors at a P-value of <0.25, and predictors of maternity waiting home utilization were identified through multivariate logistic regression at a 95% confidence interval and P-value of <0.05. Qualitative interviews were transcribed, translated, and thematically analyzed.ResultsThe prevalence of maternity waiting home use in the study area was 35.2% (95% CI: 30.9%, 39.5%). Factors associated with maternity waiting home utilization included travel time greater than 60 min to nearby health facilities (AOR: 5.47 CI: 1.77, 16.91), good knowledge of danger signs of pregnancy (AOR: 5.41, CI: 1.86, 15.79), lack of a caretaker to household tasks (AOR: 0.1, CI: 0.03, 0.31), and a refusal to accept a waiting time of 2–4 weeks (AOR: 0.24 CI: 0.08, 0.74). The qualitative findings underscored hurdles such as resource constraints, challenges in providing maternity services, and the importance of community awareness and access to network connectivity in ensuring safe childbirth.ConclusionThis study aims to determine the utilization of maternity waiting homes and the factors associated with their use among women who gave birth within the last year in the Dire district, Borana zone, southern Ethiopia. The prevalence of maternity waiting home use was low compared to national efforts to promote this service. Longer travel time, lack of a caretaker, good knowledge of danger signs of pregnancy, and a refusal to accept a waiting time of 2–4 weeks were associated with maternity waiting home use in this study.

Modeling Upper Limb Rehabilitation-Induced Recovery after Stroke: The Role of Attention as a Clinical Confounder.

People who have survived stroke may have motor and cognitive impairments. High dose of motor rehabilitation was found to provide clinically relevant improvement to upper limb (UL) motor function. Besides, mounting evidence suggests that clinical, neural, and neurophysiological features are associated with spontaneous recovery. However, the association between these features and rehabilitation-induced, rather than spontaneous, recovery has never been fully investigated.The objective was to explore the association between rehabilitation dose and UL motor outcome after stroke, as well as to identify which variables can be considered potential candidate predictors of motor recovery. People who survived stroke were assessed before and after a period of rehabilitation using motor, cognitive, neuroanatomical, and neurophysiological measures. We investigated the association between dose of rehabilitation and UL response (ie, Fugl-Meyer Assessment for upper extremity [FMA-UE]), using ordinary least squares regression as the primary analysis. To obtain unbiased estimates, adjusting covariates were selected using a directed acyclic graph. Baseline FMA-UE was the only factor associated with motor recovery (b = 0.99; 95% CI = 0.83 to 1.15 points). Attention emerged as a confounder of the association between rehabilitation and final FMA-UE (b = 5.5; 95% CI = -0.8 to 11.9 points), influencing both rehabilitation and UL response. Preserved attention in people who have survived stroke might lead to greater UL motor recovery, albeit estimates have high levels of variability. Moreover, the increase in the dose of rehabilitation can lead to 5.5 points improvement on the FMA-UE, a nonsignificant but potentially meaningful finding. The approach described here discloses a new framework for investigating the effect of rehabilitation treatment as a potential driver of recovery. Attentional resources could play a key role in UL motor recovery. There is a potential association between amount of UL recovery and dose of rehabilitation delivered, needing further exploration. Preserved attention and rehabilitation dose are candidate predictors of UL motor recovery.

Landscape Homogeneity May Drive the Distribution of Koala Vehicle Collisions on a Major Highway in the Clarke-Connors Range in Central Queensland, Australia

After the loss and fragmentation of habitat, vehicle collisions are one of the main threats to the long-term survival of wild koalas. Koala road strike data were analysed for a section of the Peak Downs Highway between Nebo and Spencer’s Gap, west of Mackay, Queensland, Australia. The analysis was carried out on 345 records (October 2014 to November 2023), and results suggested the spatial distribution of koala road strike followed a random pattern along this section of the highway, assuming a Poisson point pattern on a linear network. An analysis of the candidate predictors of koala vehicle collisions, including habitat and road variables, found that the amount of high-quality koala habitat (as defined by the local koalas’ tree species preference) present and the driver visibility were the only significant predictors. The relative homogeneity of landuse and vegetation across this landscape may mean that koalas do not concentrate at specific crossing points. More research, including detailed habitat mapping, is needed into this population, which currently lacks government and conservation attention, to inform mitigation efforts and reduce mortality rates for this potentially nationally significant population.

Development, validation, and visualization of a web-based nomogram for predicting chronic kidney disease incidence at health examination centers

Purpose To develop and validate a web-based nomogram for predicting new incident chronic kidney disease (CKD) within 4 years in a cohort undergoing routine physical examination from two health examination centers. Methods One center was utilized for training and internal validation of a nomogram model involving 6515 patients, while a separate center was employed for external validation with 3152 patients. Sixteen candidate predictors, including patient demographics, medical histories, physical examination, and laboratory test data, were included in this study to ascertain factors linked to new incident CKD. A nomogram was created to predict CKD risks using a logistic model. The nomogram’s performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis. Results Out of the 9667 healthy individuals included in the study with mean age of 46 years, sex ratio (male/female) of 1.69 (6075/3592), 118 (2.59%), 51 (2.61%), and 60 (1.90%) individuals developed CKD in the training (n = 4563), internal validation (n = 1952), and external validation (n = 3152) datasets, respectively. Age, history of diabetes mellitus, systolic blood pressure, serum creatinine, albumin, and triglyceride levels were used to build the nomogram, which yielded excellent discrimination ability (training cohort, AUC = 0.8806, 95% confidence interval [CI] 0.8472–0.9141; internal validation cohort, AUC = 0.8506, 95% CI 0.7856–0.9156; external validation cohort, AUC = 0.9183, 95% CI 0.8698–0.9669). We further developed a web-based calculator for convenient application (https://luochuxuan.shinyapps.io/dynnomapp/). Conclusion Our web-based nomogram accurately predicted CKD risks in Chinese health individuals and can be easily used in clinical settings.

Using machine learning to understand social isolation and loneliness in schizophrenia, bipolar disorder, and the community

Social disconnection, including objective social isolation and subjective loneliness, is linked to substantial health risks. Yet, little is known about the predictors of social disconnection in individuals with mental illness. Here, we used machine learning to identify predictors of social isolation and loneliness in schizophrenia (N = 72), a psychiatric condition associated with social disconnection. For comparison, we also included two other groups: a psychiatric comparison sample of bipolar disorder (N = 48) and a community sample enriched for social isolation (N = 151). We fitted statistical models of social isolation and loneliness within and across groups. Each model included five candidate predictors: social avoidance motivation, depression, nonsocial cognition, social anhedonia, and social cognition. The results showed that social anhedonia explained unique variance in social isolation and loneliness in all samples, suggesting that it contributes to social isolation and loneliness broadly. However, nonsocial cognition explained unique variance in social isolation only within schizophrenia. Thus, social anhedonia could be a potential intervention target across populations, whereas nonsocial cognition may play a unique role in determining social disconnection in schizophrenia.

Systematic review of clinical prediction models for psychosis in individuals meeting At Risk Mental State criteria.

This study aims to review studies developing or validating a prediction model for transition to psychosis in individuals meeting At Risk Mental State (ARMS) criteria focussing on predictors that can be obtained as part of standard clinical practice. Prediction of transition is crucial to facilitating identification of patients who would benefit from cognitive behavioural therapy and, conversely, those that would benefit from less costly and less-intensive regular mental state monitoring. The review aims to determine whether prediction models rated as low risk of bias exist and, if not, what further research is needed within the field. Bibliographic databases (PsycINFO, Medline, EMBASE, CINAHL) were searched using index terms relating to the clinical field and prognosis from 1994, the initial year of the first prospective study using ARMS criteria, to July 2024. Screening of titles, abstracts, and subsequently full texts was conducted by two reviewers independently using predefined criteria. Study quality was assessed using the Prediction model Risk Of Bias ASessment Tool (PROBAST). Studies in any setting were included. The primary outcome for the review was the identification of prediction models considering transition risk and a summary of their risk of bias. Forty-eight unique prediction models considering risk of transition to psychosis were identified. Variables found to be consistently important when predicting transition were age, gender, global functioning score, trait vulnerability, and unusual thought content. PROBAST criteria categorised four unique prediction models as having an overall low-risk bias. Other studies were insufficiently powered for the number of candidate predictors or lacking enough information to draw a conclusion regarding risk of bias. Two of the 48 identified prediction models were developed using current best practice statistical methodology, validated their model in independent data, and presented low risk of bias overall in line with the PROBAST guidelines. Any new prediction model built to evaluate the risk of transition to psychosis in people meeting ARMS criteria should be informed by the latest statistical methodology and adhere to the TRIPOD reporting guidelines to ensure that clinical practice is informed by the best possible evidence. External validation of such models should be carefully planned particularly considering generalisation across different countries. https://www.crd.york.ac.uk/PROSPEROFILES/108488_PROTOCOL_20191127.pdf, identifier CRD42018108488.

Predictive Factors of Apparent Treatment Resistant Hypertension Among Patients With Hypertension Identified Using Electronic Health Records.

Early identification of a patient with resistant hypertension (RH) enables quickly intensified treatment, short-interval follow-up, or perhaps case management to bring his or her blood pressure under control and reduce the risk of complications. To identify predictors of RH among individuals with newly diagnosed hypertension (HTN), while comparing different prediction models and techniques for managing missing covariates using electronic health records data. Risk prediction study in a retrospective cohort. Adult patients with incident HTN treated in any of the primary care clinics of one health system between April 2013 and December 2016. Predicted risk of RH at the time of HTN identification and candidate predictors for variable selection in future model development. Among 26,953 individuals with incident HTN, 613 (2.3%) met criteria for RH after 4.7 months (interquartile range, 1.2-11.3). Variables selected by the least absolute shrinkage and selection operator (LASSO), included baseline systolic blood pressure (SBP) and its missing indicator (a dummy variable created if baseline SBP is absent), use of antihypertensive medication at the time of cohort entry, body mass index, and atherosclerosis risk. The random forest technique achieved the highest area under the curve (AUC) of 0.893 (95% CI, 0.881-0.904) and the best calibration with a calibration slope of 1.01. Complete case analysis is not a valuable option (AUC = 0.625). Machine learning techniques and traditional logistic regression exhibited comparable levels of predictive performance after handling the missingness. We suggest that the variables identified by this study may be good candidates for clinical prediction models to alert clinicians to the need for short-interval follow up and more intensive early therapy for HTN.

Development and internal validation of prehospital prediction models for identifying intracerebral haemorrhage in suspected stroke patients

IntroductionDistinguishing patients with intracerebral haemorrhage (ICH) from other suspected stroke cases in the prehospital setting is crucial for determining the appropriate level of care and minimising the onset-to-treatment time, thereby...

Prediction model for recommending coronary artery calcium score screening (CAC-prob) in cardiology outpatient units: A development study.

Despite the well-established significance of the CAC score as a cardiovascular risk marker, the timing of using CAC score in routine clinical practice remains unclear. We aim to develop a prediction model for patients visiting outpatient cardiology units, which can recommend whether CAC score screening is necessary. A prediction model using retrospective cross-sectional design was conducted. Patients who underwent CAC score screening were included. Eight candidate predictors were preselected, including age, gender, DM or primary hypertension, angina chest pain, LDL-C (≥130 mg/dl), presence of low HDL-C, triglyceride (≥150 mg/dl), and eGFR. The outcome of interest was the level of CAC score (CAC score 0, CAC score 1-99, CAC score ≥100). The model was developed using ordinal logistic regression, and model performance was evaluated in terms of discriminative ability and calibration. A total of 360 patients were recruited for analysis, comprising 136 with CAC score 0, 133 with CAC score 1-99, and 111 with CAC score ≥100. The final predictors identified were age, male gender, presence of hypertension or DM, and low HDL-C. The model demonstrated excellent discriminative ability (Ordinal C-statistics of 0.81) with visually good agreement on calibration plots. The implementation of this model (CAC-prob) has the potential to enhance precision in recommending CAC screening. However, external validation is necessary to assess its robustness in new patient cohorts.

Shifts in motivation to quit cigarette smoking associated with IQOS use

IntroductionIQOS, a heated tobacco product (HTP), is among a growing number of noncombustible nicotine delivery alternatives marketed to people who smoke combustible cigarettes and are interested in less harmful alternatives. Little is known regarding whether IQOS use and IQOS-associated effects impact motivation to quit cigarette smoking. MethodsNon-treatment-seeking adult daily smokers (n = 87) completed a within-subjects study consisting of a baseline ad-lib smoking period (days 1–5), two laboratory visits (days 6–7) involving IQOS exposure, and a two-week period where participants were instructed to switch from smoking cigarettes to using IQOS (days 8–21). Motivation to quit smoking was measured on days 1 and 22. ResultsA generalized linear model revealed a significant increase in motivation to quit smoking across the 14-day switch phase with an increase of 1.47 ladder steps (Δ=1.47, sd = 1.98, t(86) = 6.92, p < 0.0001), increasing from 5.30 to 6.80. Candidate predictors were identified through a stepwise selection procedure, which resulted in a final model with two predictors, the standardized HeatStick substitution rate (b = 0.54, (CI95% 0.13–0.95), p = 0.01) and standardized relative risk perception score (b = 0.45, (CI95% 0.04–0.85) p = 0.03), ConclusionsAmong those uninterested in quitting cigarette smoking within the next month, IQOS use was associated with increased motivation to quit. Motivation to quit smoking may not be a necessary prerequisite for promoting smoking behavior change but rather bolstered by smoking behavior change in the context of HTP use. Greater perceptions of risk reduction may ultimately aid the transition from combustible to noncombustible tobacco products.

Predicting the risk of nursing home placement of elderly persons using a population-based stratification score

ObjectiveTo develop and validate a novel score predictive of nursing home placement in elderly. Study designPopulation-based case-control study based on healthcare utilization databases of Lombardy, a region of Northern Italy. MethodsThe 2.4 million citizens aged ≥65 years who on January 1, 2018 lived outside nursing home formed the target population. Cases were citizens who experienced nursing home admission (the outcome of interest) until December 31, 2019. Cases were matched 1:1 by gender, age, and municipality of residence to one control. Conditional logistic regression was fitted to select candidate predictors (the exposure to 69 clinical conditions and 11 social and healthcare services) independently associated with the outcome. The model was built from the 26,156 cases, and as many controls (training set), and applied to a validation set (15,807 case-control couples). Predictive performance was assessed by discrimination and calibration. ResultsTwenty-one factors were identified as predictive of nursing home admission and were included in the “Elderly Nursing Home Placement” (ENHP) score. Mental health disorders and chronic neurological illnesses contributed most to prediction of nursing home admission. ENHP performance showed an area under the receiver operating characteristic curve of 0.77 and a remarkable calibration of observed and predicted outcome risk. ConclusionsA simple score derived from data used for public health management may reliably predict the risk of nursing home placement in elderly. Its use by healthcare decision makers allows to accurately identify high-risk individuals who need home services, thereby avoiding admission to nursing homes.

Predictores del éxito académico en estudiantes de pregrado de la carrera de Nutrición y Dietética de la Pontificia Universidad Católica de Chile

IntroductionIdentifying the factors that determine academic success can help design strategies focused on increasing academic success. We aimed to identify predictors associated with academic success in undergraduate students of the Nutrition and Dietetics Program of the Pontificia Universidad Católica de Chile (PUC). MethodsRetrospective study using the institutional database. Academic success was the main outcome, including two indexes: i) final grade point average (GPA-f) and; ii) timely graduation, defined as graduating in ≤10 semesters. Candidate predictors were: age, sex, region of origin, school attended, score at the university selection test (PSU-score), admission route, and preference for the program. Stepwise linear regression was used to identify predictors of GPA-f. Binary logistic regression was used to identify variables associated with timely graduation. ResultsPSU-score was the only predictor of GPA, explaining 24% of its variance. The odds (OR [95%CI]) for a timely graduation were influenced by PSU-score (1.017 [1.003–1.031]) and admission route (PSU 1.00 [Reference], Inclusive 0.30 [0.10–0.96], Special 0.46 [0.13–1.57]). Conclusions and implicationsPSU-score partially predicts the academic success of Nutrition and Dietetics students. In addition, students who accessed the program by the equity admission had lower odds of a timely graduation. As educational inequalities may affect academic success, the teaching and learning process needs to be addressed with specific strategies for these students to ensure academic success.