Risultati per: Passo 12. Ripulire in modo mirato le cartelle cliniche degli assisiti

Circulation, Volume 148, Issue Suppl_1, Page A15117-A15117, November 6, 2023. Introduction:The 2022 AHA Guidelines for the Management of Heart Failure (HF) emphasized initiating guideline-directed medical therapy (GDMT) as early as possible during acute hospitalization. GDMT significantly differs between HF patients with reduced and preserved ejection fraction (EF). The electrocardiogram (ECG) is one of the first tests performed to assess cardiac function among HF patients.Purpose:To develop a linear model to estimate EF using the ECG and clinical covariates.Methods:Medical record data from 2020-21 of HF patients >18 years and positive Framingham Heart Failure Diagnostic Criteria excluding those with ventricular assist devices were extracted. Demographics, NYHA class, EF, and comorbidities, 12-lead ECG closest to the time of the echocardiogram were analyzed. We extracted 312 different features from the ECG using the Philips DXL Algorithm. Recursive LASSO regression was used to identify a subset of features in domain blocks. All analyses were completed in RStudio (4.3.0).Results:Among 126 patients (age 69.4+15.5 years; BMI 31.2+8.2 kg/m2; EF 40+19%; the time between ECG-echocardiogram 13.30+40.67 hours: minutes), the predominant cardiac rhythm (70%, n=88) was sinus. Among the 312 ECG features, 39 (13%) were significant; the final linear model included 9 (3%) features: S Duration V1, QRS Duration III, Horizontal T Angle, Mean Heart Rate, Q Duration aVL, Maximum QRS Angle, R Duration V3, S Amplitude V3, and P amplitude V3. The final linear model achieved an R2=0.498 and adjusted R2=0.459. We adjusted the model for age, body mass index, and sex, and model performance improved by R2=0.107 adjusted R2=0.104. The addition of time between ECG-echocardiogram to the model did not significantly change performance. From the predicted values, 50% (n=63) were within the margin of error, and the correlation between predicted and true values was moderate (r=0.629 [95%CI, 0.510-0.724]). The mean difference between predicted and true EF values varied between+12.06%.Conclusions:Our initial results demonstrate the ability to estimate EF using a 12-lead ECG. While the results are limited due to the small sample size and significant time between ECG and echocardiogram, these preliminary results support the feasibility of future research.

Circulation, Volume 148, Issue Suppl_1, Page A18141-A18141, November 6, 2023. Background:Unstable ventricular tachycardia is difficult to map and ablate, with high recurrence rates. We developed a 12-lead ECG mapping algorithm based on computational models to localize VT to help guide ablation of unstable VT.Hypothesis:We hypothesized that prospective ECG-mapping can facilitate invasive activation mapping and improve the success of unstable VT ablation.Aims:To compare the time to ICD shock + death in patients undergoing ECG-mapping guided ablation of unstable VT compared to standard ablation controls.Methods:Consecutive patients from 2 centers with unstable VT undergoing ECG-mapping guided VT ablation were prospectively enrolled. Using the ECG, computational mapping localized the VT onto a 3D model. A multielectrode catheter was placed at the predicted site and activation mapping was performed during VT reinduction. Ablation was performed per standard protocol. Time to ICD shock or death was compared using Cox regression (adjusting for age, EF and ICM) between ECG-mapping guided ablation vs standard VT ablation controls with a minimum 3 month f/u. Accuracy of ECG mapping was compared with activation mapping.Results:Out of 32 consecutive patients who underwent ECG-mapping guided VT ablation, 26 had unstable VT (age 66±10 yr, EF 34±17%). All 26 (100%) patients with unstable VT (median VT CL: 326±81ms) underwent successful activation mapping, Fig 1A. Using Cox regression adjusting for covariates, ECG-mapping guided VT ablation had a significant reduction in ICD shock or death compared to standard ablation controls (p=0.01, HR=0.23 [CI 0.07-0.70], Fig 1B). There was a 99% reduction in total ICD shocks during mean 7.5 month f/u. For all 32 patients, the mean accuracy of ECG mapping was 1.3±0.7cm when compared to invasive activation±entrainment mapping.Conclusions:Use of computational 12-lead ECG mapping to guide ablation of unstable VT significantly improved freedom from ICD shocks and death resulting in excellent accuracy.

Circulation, Volume 148, Issue Suppl_1, Page A16473-A16473, November 6, 2023. Introduction:Artificial Intelligence-based 5-lead 3D-vectorcardiography (5L3DVCG-AI) offers additional information over 12-lead electrocardiography (ECG) in the detection of significant coronary stenoses. 5L3DVCG-AI is under investigation as a new screening tool for coronary vascular disease (CVD). Hypothesis: We tested the hypothesis of variables from the reconstructed “12-lead ECG” (5L12L-ECG, modified Dower transformation) corresponding with the standard 12-lead ECG (ECG).Methods:In this monocentric exploratory retrospective study, raw data of 331 patients with 5L3DVCG-AI and ECG were included. Cardiac pathology (CP) was categorised as exclusion of any CP (control), mild CP or overt CP by 2 independent cardiologists. The following variables were compared: RR-interval, P, PQ, QRS, QT, QTcB, QTcF, QRS-morphology, and ST-morphology. Cardiovascular risk factors (CVRF) were quantified with the modified PROCAM score.Results:From 331 patients (m:w 60:40%, 50.0 ± 19.8 years) of mixed ethnicity and moderate CVRF (2.1 ± 1.2), 70% were controls, 21% had mild CP and 9% overt CP. All variables from reconstructed 5L12L-ECG correlated to the corresponding individual variables from ECG (r= 0.49 to 0.7, p

Circulation, Volume 148, Issue Suppl_1, Page A13921-A13921, November 6, 2023. Introduction:Increased left ventricular myocardial mass (LVM) is associated with adverse cardiovascular outcomes. Various rule-based criteria using limited electrocardiogram (ECG) features lacks sensitivity for evaluating LVM. Recent studies using deep learning methods have made progress in LVM evaluation taking advantage of ECG amplitude data.Hypothesis:We hypothesized that LVM prediction with deep learning models are improved by adding inter-lead information and building sex-specific models.Methods:This study proposed a novel deep learning-based method, the eLVMass-Net, by using ECG-LVM (n=1,459) paired data. ECG signals, QRS duration and axis, demographic features were used as input data. ECG signals were encoded by a temporal convolutional network (TCN) encoder. We adopted a total of four models (non-lead-grouping v. lead-grouping, non-sex-specific v. sex-specific) for LVM estimation. For lead-grouping models, the encoders were constructed based on segregated limb and precordial leads. For sex-specific models, we constructed models on both genders separately. Encoded ECG features and demographic features were concatenated for LVM prediction. To evaluate the performance, we utilized a 5-fold cross-validation approach with the evaluation metrics, mean absolute error (MAE) and mean absolute percentage error (MAPE).Results:For non-sex-specific models, eLVMass-Net has achieved an MAE of 14.33±0.71 and an MAPE of 12.90%±1.12%, outperforming the best state-of-the-art method (MAE 19.51±0.82; MAPE 17.62%±0.78%; P < 0.01). Sex-specific models achieved even lower MAPE for both males and females respectively (male MAPE 12.55%±0.88%; female MAPE 12.52%±0.34%), which also surpassed state-of-the-art methods. Adding the information of QRS axis and duration did not significantly improve the model performance (P = 0.28). The saliency map showed that T wave in precordial leads and QRS complex in limb leads are important features with increasing LVM.Conclusions:This study proposed a novel LVM estimation method, outperforming previous methods by emphasizing relevant heartbeat waveforms, inter-lead information, and non-ECG demographic features. The sex-specific analysis is crucial in improving LVM prediction.

Circulation, Volume 148, Issue Suppl_1, Page A17094-A17094, November 6, 2023. Introduction:As echocardiographic screening is limited by access to technology, structural heart disease (SHD) is often diagnosed after the development of clinical symptoms. To enable automated and accessible screening of SHD, we developed and validated a deep-learning model for 12-lead electrocardiograms (ECGs) for various screening populations.Methods:We used 12-lead ECGs with paired TTEs at the Yale New Haven Hospital (2015-2021) to develop convolutional neural networks for detecting SHD. SHD was defined as the presence of any one of the following on a transthoracic echocardiogram (TTE) performed within 30 days of the ECG: hypertrophic cardiomyopathy (IVSd > 1.5cm and diastolic dysfunction), LV ejection fraction < 40%, or severe left-sided valvular disease (aortic/mitral stenosis or regurgitation). We developed an ensemble XGBoost model based on predictions for individual SHDs and patient age and sex as a single screen across all SHDs. We also simulated the model performance across cohorts with varying disease prevalence.Results:The model was developed in 456,927 ECGs/118,623 individuals (66.5±16.0 years, 45.6% women, 16.5% Black) and validated in 13,181 ECGs/13,181 individuals (65.1±17.3 years, 49.5% women, 14.5% Black). In the held-out validation set, the ensemble XGBoost model achieved an AUROC of 0.92 (95% CI: 0.91-0.94) and an AUPRC of 0.64 (95% CI: 0.57-0.70), with a sensitivity of 85% and specificity of 88%. The AUROCs for the individual disease models ranged from 0.72-0.95. (Panel A) In the test set with 10% disease prevalence, the ensemble model had a PPV 44% and an NPV of 98%. (Panel B) In simulated cohorts with 5% and 20% disease prevalence, the model had reached NPV & PPV of 99% & 27%, and 98% & 64%, respectively. (Panel C)Conclusion:We developed a novel ensemble deep-learning model for detecting several SHDs directly from ECGs with high PPV and NPV. This approach represents a robust, scalable, and accessible modality for automated SHD screening.

Circulation, Volume 148, Issue Suppl_1, Page A16503-A16503, November 6, 2023. Introduction:Left coronary dominance is associated with worse clinical outcomes in the setting of coronary artery disease (CAD). Little is known about the determinants of dominance, including whether it is solidified in utero or sometime after birth.Hypothesis:Investigating the genetic determinants of dominance will identify new arteriogenesis pathways targetable for enhancing coronary artery growth and collateralization in humans.Methods:We conducted a stratified multi-population genome-wide association study of dominance using REGENIE and TopMed-imputed genotypes available in 43813 White, 9532 Black, and 3550 Hispanic participants of the Million Veteran Program (MVP) who had undergone a cardiac catheterization. We modelled left and co-dominant combined vs. right, as well as left vs. right dominance, adjusting for sex and 10 genetic principal components. We also explored whether dominance and CAD were genetically correlated using LD score regression. Lastly, we sought to validate compelling population genetic signals in mice using whole organ imaging of the heart, which allows for the quantification of coronary anatomy and variation.Results:Thirteen loci reached genome wide significance (GWS). The most intriguing was a top hit located downstream of C-X-C Motif Chemokine Ligand 12 (CXCL12) which, remarkably, reached GWS in both White and Black Veterans. While the CXCL12 signal robustly colocalized with the signal in this region for CAD (COLOC PPH4: 87.1%), the genome wide genetic correlation of dominance with CAD was otherwise modest in both MVP (rg=-0.18, p=0.007) and CardiogramplusC4D (rg=-0.16, p=0.018). To gain evidence for causality, we modeled dominance in Cxcl12-deficient mice (n=64 wildtype; n=62 heterozygous). Murine coronary arteries develop in a field of Cxcl12 expression and choose right or left dominance during embryogenesis, a time when we found human dominance to also be evident. Reminiscent of our human GWAS, dominance was skewed away from the common phenotype in Cxcl12 heterozygous mice (p=0.038).Conclusions:CXCL12 is a primary determinant of coronary artery dominance in humans and mice. The modest negative genetic correlation between CAD and dominance supports a developmental component to CAD susceptibility.

Circulation, Volume 148, Issue Suppl_1, Page A13690-A13690, November 6, 2023. Introduction:Diagnostic criterion of left ventricular hypertrophy (LVH) on 12-leads electrocardiogram (ECG) were established. We verified them comparing with artificial intelligence (AI) method.Hypothesis:Machine learning on 12-leads ECG show higher diagnostic performance comparing with historical criterion.Methods:First, consecutive 60 patients with LVH were recruited, and one to one matching with age and sex to patients with normal cardiac function was performed. Finally, 120 patients (69.6 ± 12.6years, 38men per group) were enrolled. LVH was defined as at least one LV wall (septum, posterior wall, apex) showed thickness over 15mm on ultrasound echocardiography. No sinus rhythm, and wide QRS cases were excluded.Results:By logistic regression analysis, 77 significant predictors were extracted. Among historical criterion, Cornell voltage showed high accuracy (0.783) and area under receiver operating characteristics curve analysis (AUROC; 0.808). Conversely, among AI methods, light gradient boosting machine demonstrated higher accuracy (0.843) and random forest method higher AUROC (0.882). V2/V2 S-wave amplitude and I/V5 T-wave amplitude played essential roles to build the AI models.Conclusions:AI diagnosis on ECG for LVH showed powerful diagnostic performance comparing historical criterion.

Circulation, Volume 148, Issue Suppl_1, Page A13765-A13765, November 6, 2023. Introduction:There is an increasing clinical interest using wearable single-lead ECG sensors for continuous cardiac monitoring.Hypothesis:However, there is limited to qualify the reliability of those. The purpose of this study is to assess ECG signal quality assessments of real-time wearable SmartPatch (HiCardi Plus) compared to 12-lead ECG.Methods:To analyze the similarity between the standard 12-Lead ECG and HiCardi Plus signals, 31 subjects were simultaneously acquired with SmartPatch (HiCardi Plus) and 12-Lead ECG. The 12-lead ECG equipment used in this experiment is IntelliVue Patient Monitor MX700 (Philips)Results:It was found that even if the distance between electrodes was short, the ECG signal measured from the electrode pair located diagonal (direction of the electrical axis of the heart) and vertical direction with chest had a very high correlation with the standard 12-lead ECG signal and was sufficient for diagnosisConclusions:Our device (HiCardi Plus) has substantial equivalence with standard 12-Lead ECG, in terms of QRS-complex detection and arrhythmia classification in the intended use condition.

Circulation, Volume 148, Issue Suppl_1, Page A18317-A18317, November 6, 2023. Introduction:ECG construction with deep learning can help standardize ECGs and remove noise and artifacts, transforming potentially unusable ECGs into clinically useful ones. However, noise levels, often unpredictable in clinical settings, may impact model performance.Hypothesis:This study explores the relationship between ECG noise levels and deep learning model performance in constructing noise-free ECGs. We hypothesize that beyond a certain noise threshold, the model’s output ceases to be clinically usable.Aims:Our study aims to determine noise effects on the performance of deep learning models in constructing 12-Lead ECGs, providing insights into the model’s robustness and the optimal conditions for its application.Methods:Using 250 digital 12-Lead ECGs from the PTB database, we injected Gaussian noise (mean 0.0, standard deviations between 0.0 and 1.0) into these ECGs, all processed and scaled to unitless values (-1 to +1) and resampled at 100Hz. A model was asked to construct a noise-free ECG from a full 12-lead ECG with noise injection, which was compared to the original noise-free ECG.Results:The findings reveal two distinct linear phases in the relationship between noise and model performance (measured by mean absolute error, MAE). Between standard deviations of 0.0 and 0.03, the MAE increased marginally, while a sharp increase occurred between 0.03 and 1.0. Importantly, critical features of the original ECG were retained for noise levels up to 0.08, implying a noise threshold for clinical usability.Conclusions:Deep learning models, though not entirely resistant to noise, demonstrate efficacy in constructing ECGs up to certain noise levels (standard deviation 0.03). Beyond this, performance declines markedly. These findings outline the limits and potential of deep learning models like ECGio in clinical practice. Future research should focus on resilience to higher noise levels or noise-reduction preprocessing strategies.

Circulation, Volume 148, Issue Suppl_1, Page A12401-A12401, November 6, 2023. Background:Anticoagulant RCTs are thought to have enrolled younger and less comorbid patients with atrial fibrillation (AF) compared to the general population. We developed a representation score summarizing patient characteristics to describe how well RCT participants with AF reflect a nationally representative cohort.Methods:We studied adults >=65 years old with AF by harmonizing two data sources: (1) patient-level data from 12 landmark RCTs testing anticoagulants vs. placebo or antiplatelets from the Atrial Fibrillation Investigators (AFI) consortium and (2) the Health and Retirement Study AF cohort (HRS-AF), a representative cohort of older adults with AF in the U.S. We fit a logistic regression model to estimate the probability of inclusion in the HRS-AF cohort in the pooled sample using age, height, weight, gender, heart failure, hypertension, diabetes, prior stroke, and prior myocardial infarction. This estimate, the Trial Benchmark Score, reflected the probability of belonging to the HRS-AF cohort and ranged from 0 to 1, with higher scores reflecting a greater likelihood of belonging to the HRS-AF cohort. We plotted the distribution of scores for HRS-AF and AFI participants and compared the mean scores using a t-test.Results:Compared to the HRS-AF cohort (n=3542), AFI participants (n=7933) were younger (72 vs. 76yrs, standardized mean difference [SMD] -0.7), more frequently male (64% vs. 46%, SMD 0.3), and had a lower likelihood of prior stroke (19% vs. 23%, SMD -0.4). The mean Trial Benchmark Score differed significantly between the two cohorts (HRS-AF mean 0.47 vs. AFI mean 0.23, p0.47 (the HRS-AF mean score).Conclusion:Differences in the Trial Benchmark Scores distributions indicate a substantial difference in the distribution of observable characteristics and that RCT participants were not fully representative of the benchmark population.

Circulation, Volume 148, Issue Suppl_1, Page A17037-A17037, November 6, 2023. Introduction:The electrocardiogram (ECG) is a widely available diagnostic tool for evaluating cardiac patients. Although automated ECG interpretation has made significant progress, it has yet to match the accuracy demonstrated by physicians.Hypothesis:In this study, we hypothesized that an artificial intelligence based ECG system can achieve comparable performance to physicians in accurately identifying 20 essential ECG patterns.Methods:An AI-powered system comprising six deep neural networks (DNNs) was trained to identify 20 diagnostic patterns from 12-lead ECGs categorized into six groups: rhythm, infarction, conduction abnormalities, ectopy, chamber enlargement, and axis. An independent test set with the consensus of two expert cardiologists was used as a reference standard. We compared the system’s performance to that of three General Practitioners (GPs) and six individual cardiologists, using F1 scores as the evaluation metric.Results:The AI system was trained on 932,711 standard 12-lead ECGs from 173,949 patients. The independent test set comprised 11,932 annotated ECG labels. Figure 1 shows the respective F1 scores of the DNNs, average GP and average cardiologist as follows: Rhythm: 0.957 vs. 0.771 vs. 0.905; Infarction: 0.925 vs. 0.780 vs. 0.852; Conduction abnormalities: 0.893 vs. 0.714 vs. 0.851; Ectopy: 0.966 vs. 0.896 vs. 0.951; Chamber enlargement: 0.972 vs. 0.562 vs. 0.773; Axis: 0.897 vs. 0.601 vs. 0.685. The AI system’s diagnostic performance exceeded that of GPs and was on par with cardiologists for all individual diagnostic patterns.Conclusions:The AI-powered ECG system is able to accurately identify electrocardiographic abnormalities from the 12-lead ECG, highlighting its potential as a clinical tool for healthcare professionals.

Circulation, Volume 148, Issue Suppl_1, Page A16682-A16682, November 6, 2023. Introduction:Electrocardiography (ECG) can be easily obtained at a low cost and includes voltage and time interval representing heart conditions. We hypothesized that artificial intelligence (AI) detects a subtle abnormality in 12-lead ECG and may predict individual mortality.Methods:Among 502,411 population in UK Biobank, 42,096 individuals had 12-lead ECG from 2013 to 2022. Among 41,572 survival group, after adjusting the following inclusion criteria; normal sinus rhythm, age under 60 years, PR interval 120~200ms, QTc interval 350~460ms, and QRS duration 70~100ms, 4,512 individuals were enrolled in this study. We developed and tested convolutional neural network (CNN) model to predict all cause death, cardiovascular (CV) death, or sudden cardiac arrest (SCA). The study population were divided into train (80%), validation (10%), and test (20%) set.Results:Among 4,512 patients with median 3.7 years [IQR; 2.7-5.1] of follow-up, the rate of all-cause mortality was 11.6% (524). In overall study population, median age was 55.5 years and proportion of male sex was 42.2%. The patients with all-cause death were older (p

Circulation, Volume 148, Issue Suppl_1, Page A14025-A14025, November 6, 2023. Introduction:Risk stratification for healthcare utilization in PAD is critical given rising costs. The association between health status measures and hospitalization is unknown. We examined the association between a disease-specific patient-reported outcome measure and risk of hospitalization at 12 months.Methods:Patients with new or worsened lower extremity claudication enrolled at US sites in the PORTRAIT registry from 2011 to 2015 were included. The Peripheral Artery Questionnaire, a PAD-specific patient-reported outcome measure, was used to measure health status. PAQ summary scores range from 0 to 100 (better health status). Kaplan-Meier failure curves and adjusted Cox proportional hazards models assessed the association between baseline PAQ summary score and (1) the combined endpoint of all-cause hospital admission and ED visit (AD-ED) and (2) all-cause hospital admission (AD) at 12 months.Results:Of the 796 patients (mean age 69 ± 10 years, 42% female, 72% white, mean baseline PAQ summary score 46.8 ± 22.0) included, 349 (44%) had a hospital admission or ED visit at 12 months, with a total of 661 visits. Patients in the lowest PAQ quartile had higher rates of AD-ED at 30 days (16.1% vs. 4.3%), 90 days (29.8% vs. 12.8%), and 12 months (53.3% vs. 22.4%). In the fully adjusted model, lower PAQ score was associated with higher risk for AD-ED (HR per 10-point decrease, 1.12, 95% CI, 1.06-1.19, P

Circulation, Volume 148, Issue Suppl_1, Page A18382-A18382, November 6, 2023. Introduction:Oxygen consumption at peak exercise (VO2peak) is the gold standard for cardiorespiratory fitness, but prior genome-wide association studies (GWAS) have been limited by the availability of cardiopulmonary exercise testing (CPET). Recent deep learning methods to estimate VO2peakfrom the resting 12-lead electrocardiogram (ECG) may enhance genetic studies of cardiorespiratory fitness.Methods:We applied a validated deep learning model (Deep ECG-VO2) to estimate VO2peakamong UK Biobank participants with a 12-lead ECG. We assessed for associations between estimated VO2peakand incident hypertension, diabetes, and atrial fibrillation (AF) using Cox proportional hazards models adjusted for age and sex, and plotted cumulative risk of each outcome stratified by tertile of estimated VO2peak. We then performed a multi-ancestry GWAS of estimated VO2peakusing BOLT-LMM, adjusted for age, sex, array, and the first five principal components of ancestry. Candidate genes were prioritized based on proximity to the lead variant.Results:We applied Deep ECG-VO2 to estimate VO2peakusing the resting 12-lead ECG of 40,801 UK Biobank participants (age 65±8, 52% women). Greater estimated VO2peakwas associated with lower risks of hypertension (hazard ratio per 1-standard deviation 0.76, 95% CI 0.70-0.83), diabetes (0.60, 95% CI 0.53-0.67) and AF (0.82, 95% CI 0.74-0.90). Cumulative risk of each outcome was higher with decreasing estimated VO2peak(Figure). In a GWAS of estimated VO2peakwithin 39,716 participants with genetic data (age 65±8, 52% women, 90% European), we identified 10 novel genome-wide significant loci, including variants near genes involved in cardiac structure (CCDC141/TTN, BAG3), cardiac conduction (SCN5A), and adiposity (FTO).Conclusions:Leveraging artificial intelligence-enabled estimation of VO2peakfrom the resting 12-lead ECG, we identify 10 novel common genetic variants associated with cardiorespiratory fitness.

Objective
To examine the use of CT, emergency department (ED)-presentation and hospitalisation and in 12 months before and after a diagnosis of cancer.

Design
Population-based retrospective cohort study.

Setting
West Australian linked administrative records at individual level.

Participants
104 009 adults newly diagnosed with cancer in 2004–2014.

Main outcome measures
CT use, ED presentations, hospitalisations.

Results
As compared with the rates in the 12th month before diagnosis, the rate of CT scans started to increase from 2 months before diagnosis with an increase in both ED presentations and hospitalisation from 1 month before the diagnosis. These rates peaked in the month of diagnosis for CT scans (477 (95% CI 471 to 482) per 1000 patients), and for hospitalisations (910 (95% CI 902 to 919) per 1000 patients), and the month prior to diagnosis for ED (181 (95% CI 178 to 184) per 1000 patients) then rapidly reduced after diagnosis but remained high for the next 12 months. While the patterns of the health services used were similar between 2004 and 2014, the rate of the health services used during after diagnosis was higher in 2014 versus 2004 except for CT use in patients with lymphohaematopoietic cancer with a significant reduction.

Conclusion
Our results showed an increase in demand for health services from 2 months before diagnosis of cancer. Increasing use of health services during and post cancer diagnosis may warrant further investigation to identify factors driving this change.

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