Complex networks approach to study comorbidities in patients with unruptured intracranial aneurysms.

The role of complex network analysis in patients with diagnosis of unruptured intracranial aneurysm is unexplored. The objective of this study is to assess the applicability of this methodology in aneurysm patients. We retrospectively analyze comprehensive unbiased local digital data of a large number of patients treated for any reason between January 2004 and July 2019. We apply an age-cohort approach to a total of 628,831 patients and construct the diagnostic history of each patient-and include the information how old the patient was when diagnosed for the first time with each diagnosis coded according to International Classification of Diseases. For each cohort of age within a 10 year interval and for each gender, we construct a statistically validated comorbidity network and focused on crucial comorbidity links that the aneurysm code has to other disease codes within the whole network. For all cohorts of different age and gender, the analysis shows that 267 diagnose codes have nearest neighbour statistically validated links to unruptured aneurysm ICD code. Among the 267 comorbidities, 204 (76%) were found in patients aged from 40 to 69-years old. Patterns of connectivity with aneurysms were found for smoking, hypertension, chronic obstructive pulmonary disease, dyslipidemia, and mood disorders. A few uncommon connections are also detected in cohorts of female patients. Our study explored the applicability of network analysis and statistical validation in aneurysm observational study.

Characterization of clinicopathological features, treatment practices, and outcomes among Finnish advanced breast cancer patients in real-life clinical practice.

PURPOSE: In recent years, several new targeted therapies have emerged for advanced breast cancer (aBC). However, real-life data specific to aBC and different breast cancer subtypes are scarce. This retrospective cohort study was designed to describe the distribution of aBC subtypes, incidence, treatment patterns, survival, and PIK3CA hotspot mutation frequency. METHODS: The study included all patients in the Hospital District of Southwest Finland diagnosed with aBC between 2004 and 2013 and with a sample available in Auria Biobank. In addition to registry-based data collection, 161 HR+/HER2- aBCs were screened for PIK3CA mutations. RESULTS: Altogether, 54.7% of the 444 patients included in the study had luminal B subtype. The smallest representations were in HR-/HER2+ (4.5%) and triple-negative (5.6%) subgroups. The percentage of aBC among all diagnosed breast cancers increased until 2010, after which it remained stable. The triple-negative cancers were associated with shorter median overall survival (5.5 months) compared to other subgroups (16.5-24.6 months). Most (84%) triple-negative cancers also metastasized during the first two years, whereas this was more evenly distributed over time in other subgroups. Of the HR+/HER2- tumors, 32.3% harbored a PIK3CA hotspot mutation. These patients, however, did not have inferior survival compared to patients with PIK3CA wild-type cancers. CONCLUSION: This study described real-world aBC subgroups and indicated that the clinical outcomes of subgroups vary. Although PIK3CA hotspot mutations did not lead to inferior survival, they are relevant as possible treatment targets. Overall, these data could be utilized to further evaluate the subgroup-specific medical needs in breast cancer.

Burden of idiopathic inflammatory rheumatic diseases in occupational healthcare: increased absenteeism and healthcare resource utilization.

OBJECTIVES: Patients with idiopathic inflammatory rheumatic diseases (IIRD) often have decreased working capacity resulting in indirect costs. However, data on patients' short-term sick leave has been limited. This retrospective cohort study evaluated the number and length of sick leave, including short-term leave, and occupational healthcare resource utilization (HCRU) of the working-aged patients with IIRD compared to controls. METHODS: The data on sick leave and occupational HCRU were gathered from the electronic medical records of the largest occupational healthcare provider in Finland from January 2012 to December 2019. Employed patients with an IIRD (including rheumatoid arthritis, spondyloarthritis, psoriatic and enteropathic arthritis, juvenile arthritis, and reactive arthritis) with at least a 12-months follow-up were identified and compared to age-, sex-, and follow-up matched controls without IIRD. RESULTS: Altogether 5405 patients with IIRD were identified and compared with an equal number of controls. The patients incurred approximately 2.5 times more sick leave than controls: 21.7 versus 8.5 days per patient year, respectively. Short-term sick leave was common: 83% of sickness absence periods of the patients lasted 1-9 days and represented 30% of the total absenteeism. Loss of productivity due to lost workdays was on average €4572 (95% confidence interval €4352-4804) per patient year. Occupational HCRU was approximately 1.8 times higher among IIRD patients than controls. CONCLUSIONS: Workers with an IIRD incur considerably more sick leave and use more occupational healthcare services than controls. Short sick leave not registered in national insurance registers constitute a significant portion of days off work among patients with IIRD.

Smoking is an Independent Marker of Poor Prognosis in Cutaneous Melanoma.

Previous studies have suggested that persistent tobacco smoking impairs survival in cutaneous melanoma, but the effects of smoking and other prognostic factors have not been described in detail. This study examined the association of smoking (persistent, former, or never) with melanoma-specific (MSS) and overall survival (OS) in patients with cutaneous melanoma treated in Southwest Finland during 2005 to 2019. Clinical characteristics were obtained from electronic health records for 1,980 patients. Smoking status was available for 1,359 patients. Patients were restaged according to the 8th edition of the tumour-node-metastasis (TNM) classification. Smoking remained an independent prognostic factor for inferior melanoma-specific survival regardless of age, sex, stage, and comorbidities. The hazard ratio of death from melanoma was 1.81 (1.27-2.58, p = 0.001) in persistent and 1.75 (1.28-2.40, p = 0.001) in former smokers compared with never smokers. In 351 stage IV patients, smoking was associated with increased melanoma-specific and overall mortality: median MSS 10.4 (6.5-14.3), 14.6 (9.1-20.1), and 14.9 (11.4-18.4) months, p = 0.01 and median OS 10.4 (6.5-14.3), 13.9 (8.6-19.2), and 14.9 (11.7-18.1) months, p = 0.01 in persistent, former, and never smokers, respectively. In conclusion, since smoking represents an independent modifiable poor prognostic factor in patients with cutaneous melanoma, smoking habits should be proactively asked about by healthcare professionals, in order to support smoking cessation.

Identification of metastatic primary cutaneous squamous cell carcinoma utilizing artificial intelligence analysis of whole slide images.

Cutaneous squamous cell carcinoma (cSCC) harbors metastatic potential and causes mortality. However, clinical assessment of metastasis risk is challenging. We approached this challenge by harnessing artificial intelligence (AI) algorithm to identify metastatic primary cSCCs. Residual neural network-architectures were trained with cross-validation to identify metastatic tumors on clinician annotated, hematoxylin and eosin-stained whole slide images representing primary non-metastatic and metastatic cSCCs (n = 104). Metastatic primary tumors were divided into two subgroups, which metastasize rapidly (≤ 180 days) (n = 22) or slowly (> 180 days) (n = 23) after primary tumor detection. Final model was able to predict whether primary tumor was non-metastatic or rapidly metastatic with slide-level area under the receiver operating characteristic curve (AUROC) of 0.747. Furthermore, risk factor (RF) model including prediction by AI, Clark's level and tumor diameter provided higher AUROC (0.917) than other RF models and predicted high 5-year disease specific survival (DSS) for patients with cSCC with 0 or 1 RFs (100% and 95.7%) and poor DSS for patients with cSCCs with 2 or 3 RFs (41.7% and 40.0%). These results indicate, that AI recognizes unknown morphological features associated with metastasis and may provide added value to clinical assessment of metastasis risk and prognosis of primary cSCC.

Automated detection of pulmonary embolism from CT-angiograms using deep learning.

BACKGROUND: The aim of this study was to develop and evaluate a deep neural network model in the automated detection of pulmonary embolism (PE) from computed tomography pulmonary angiograms (CTPAs) using only weakly labelled training data. METHODS: We developed a deep neural network model consisting of two parts: a convolutional neural network architecture called InceptionResNet V2 and a long-short term memory network to process whole CTPA stacks as sequences of slices. Two versions of the model were created using either chest X-rays (Model A) or natural images (Model B) as pre-training data. We retrospectively collected 600 CTPAs to use in training and validation and 200 CTPAs to use in testing. CTPAs were annotated only with binary labels on both stack- and slice-based levels. Performance of the models was evaluated with ROC and precision-recall curves, specificity, sensitivity, accuracy, as well as positive and negative predictive values. RESULTS: Both models performed well on both stack- and slice-based levels. On the stack-based level, Model A reached specificity and sensitivity of 93.5% and 86.6%, respectively, outperforming Model B slightly (specificity 90.7% and sensitivity 83.5%). However, the difference between their ROC AUC scores was not statistically significant (0.94 vs 0.91, p = 0.07). CONCLUSIONS: We show that a deep learning model trained with a relatively small, weakly annotated dataset can achieve excellent performance results in detecting PE from CTPAs.

Documentation of the patient's smoking status in common chronic diseases - analysis of medical narrative reports using the ULMFiT based text classification.

INTRODUCTION: Smoking cessation is essential part of a successful treatment in many chronic diseases. Our aim was to analyse how actively clinicians discuss and document patients' smoking status into electronic health records (EHR) and deliver smoking cessation assistance. METHODS: We analysed the results using a combination of rule and deep learning-based algorithms. Narrative reports of all adult patients, whose treatment started between years 2010 and 2016 for one of seven common chronic diseases, were followed for two years. Smoking related sentences were first extracted with a rule-based algorithm. Subsequently, pre-trained ULMFiT-based algorithm classified each patient's smoking status as a current smoker, ex-smoker, or never smoker. A rule-based algorithm was then again used to analyse the physician-patient discussions on smoking cessation among current smokers. RESULTS: A total of 35,650 patients were studied. Of all patients, 60% were found to have a smoking status in EHR and the documentation improved over time. Smoking status was documented more actively among COPD (86%) and sleep apnoea (83%) patients compared to patients with asthma, type 1&2 diabetes, cerebral infarction and ischemic heart disease (range 44-61%). Of the current smokers (N=7,105), 49% had discussed smoking cessation with their physician. The performance of ULMFiT-based classifier was good with F-scores 79-92. CONCLUSION: Ee found that smoking status was documented in 60% of patients with chronic disease and that the clinician had discussed smoking cessation in 49% of patients who were current smokers. ULMFiT-based classifier showed good/excellent performance and allowed us to efficiently study a large number of patients' medical narratives.

Are Breast Cancer Nomograms Still Valid to Predict the Need for Axillary Dissection?

BACKGROUND: Nomograms can help in estimating the nodal status among clinically node-negative patients. Yet their validity in external cohorts over time is unknown. If the nodal stage can be estimated preoperatively, the need for axillary dissection can be decided. OBJECTIVES: The aim of this study was to validate three existing nomograms predicting 4 or more axillary lymph node metastases. METHOD: The risk for ≥4 lymph node metastases was calculated for n = 529 eligible breast cancer patients using the nomograms of Chagpar et al. [Ann Surg Oncol. 2007;14:670-7], Katz et al. [J Clin Oncol. 2008;26(13):2093-8], and Meretoja et al. [Breast Cancer Res Treat. 2013;138(3):817-27]. Discrimination and calibration were calculated for each nomogram to determine their validity. RESULTS: In this cohort, the AUC values for the Chagpar, Katz, and Meretoja models were 0.79 (95% CI 0.74-0.83), 0.87 (95% CI 0.83-0.91), and 0.82 (95% CI 0.76-0.86), respectively, showing good discrimination between patients with and without high nodal burdens. CONCLUSION: This study presents support for the use of older breast cancer nomograms and confirms their current validity in an external population.

The Gaucher earlier diagnosis consensus point-scoring system (GED-C PSS): Evaluation of a prototype in Finnish Gaucher disease patients and feasibility of screening retrospective electronic health record data for the recognition of potential undiagnosed patients in Finland.

BACKGROUND: Gaucher disease (GD) is a rare inherited multiorgan disorder, yet a diagnosis can be significantly delayed due to a broad spectrum of symptoms and lack of disease awareness. Recently, the prototype of a GD point-scoring system (PSS) was established by the Gaucher Earlier Diagnosis Consensus (GED-C) initiative, and more recently, validated in Gaucher patients in UK. In our study, the original GED-C PSS was tested in Finnish GD patients. Furthermore, the feasibility of point scoring large electronic health record (EHR) data set by data mining to identify potential undiagnosed GD cases was evaluated. METHODS: This biobank study was conducted in collaboration with two Finnish biobanks. Five previously diagnosed Finnish GD patients and ~ 170,000 adult biobank subjects were included in the study. The original PSS was locally adjusted due to data availability issues and applied to the Finnish EHR data representing special health care recordings. RESULTS: All GD patients had high levels of the biomarker lyso-Gb1 and deleterious GBA mutations. One patient was a compound heterozygote with a novel variant, potentially pathogenic mutation. Finnish EHR data allowed the retrospective assessment of 27-30 of the 32 original GED-C signs/co-variables. Total point scores of GD patients were high but variable, 6-18.5 points per patient (based on the available data on 28-29 signs/co-variables per patient). All GD patients had been recorded with anaemia while only three patients had a record of splenomegaly. 0.72% of biobank subjects were assigned at least 6 points but none of these potential "GD suspects" had a point score as high as 18.5. Splenomegaly had been recorded for 0.25% of biobank subjects and was associated with variable point score distribution and co-occurring ICD-10 diagnoses. DISCUSSION: This study provides an indicative GED-C PSS score range for confirmed GD patients, also representing potential mild cases, and demonstrates the feasibility of scoring Finnish EHR data by data mining in order to screen for undiagnosed GD patients. Further prioritisation of the "GD suspects" with more developed algorithms and data-mining approaches is needed. FUNDING: This study was funded by Shire (now part of Takeda).

Development of self-assessed work ability among middle-aged asthma patients-a 10 year follow-up study.

OBJECTIVE: The prevalence of asthma has been growing among working age people over the last decades. In this study, we examine the development of Work Ability Score (WAS) among middle-aged asthmatics in a longitudinal setting, in order to find risk factors for poor development. METHODS: We followed the development of WAS trends during 10 years in a cohort of 529 middle-aged asthmatics, who were active in working life. Follow-up questionnaires were mailed in years 1, 2, 4, 6, 8, and 10. To study the development of WAS over time, we computed the discrete Frechet distance, which describes the similarity between the shapes of WAS curves. RESULTS: Sixty-eight percent of the patients' WAS remained good or excellent throughout the follow-up period, while 24% of the patients WAS trend remained moderate. However, in 8%, the WAS was poor already in baseline and decreased further throughout the study. Using logistic regression, the moderate/poor development was associated significantly with high body mass index (BMI), pack years, adult onset asthma, physically strenuous work, number of co-morbidities, especially in psychiatric conditions, hypertension, and gastroesophageal reflux disease(GERD). When the model was adjusted for age and gender, adulthood onset of asthma and pack years lost their significance. Based on medication (high dose of inhaled corticosteroids (ICS) and second controller in use), 8% of the patients had severe asthma. CONCLUSION: In the great majority of middle-aged asthma patients WAS remained stable throughout the follow-up period. However, 8% of the patients, who had more severe asthma and multiple co-morbidities, showed significantly poorer outcomes.

High tumor mutation burden predicts favorable outcome among patients with aggressive histological subtypes of lung adenocarcinoma: A population-based single-institution study.

OBJECTIVES: Tumor mutation burden (TMB) is an emerging predictive cancer biomarker. Few studies have addressed the prognostic role of TMB in non-small cell lung carcinoma, with conflicting results. Moreover, the association of TMB with different histological subtypes of lung adenocarcinoma has hitherto not been systematically evaluated. Here we studied the prognostic value of TMB and its distribution in different histological subtypes of lung adenocarcinomas in a retrospective cohort using the most recent updated classification guidelines. MATERIALS AND METHODS: 176 surgically resected stage I-IV lung adenocarcinomas were histologically reclassified according to WHO 2015 guidelines. A modified classification subdividing the acinar subtype into classic acinar, complex glandular and cribriform subtypes was further applied and potentially prognostic histopathological characteristics such as tumor-infiltrating lymphocytes were evaluated. 148 patients with stage I-III tumors and complete follow-up data were included in the survival analyses. TMB was determined by a commercial next generation sequencing panel from 131 tumors, out of which 105 had survival data available. RESULTS: Predominant micropapillary, solid and complex glandular as well as nonpredominant cribriform histological subtypes were associated with significantly shorter survival. High TMB concentrated in micropapillary, solid and acinar predominant subtypes. Interestingly, TMB ≥ 14 mutations/MB conferred a stage- and histology-independent survival benefit compared to TMB < 14 in multivariable analysis for overall (HR 0.284, 95% CI 0.14-0.59, P=0.001) and disease-specific survival (HR 0.213, 95% CI 0.08-0.56, P=0.002). CONCLUSION: TMB was an independent biomarker of favorable prognosis in our cohort of lung adenocarcinoma despite being associated with predominant histological subtypes considered aggressive.

The Burden Of Chronic Obstructive Pulmonary Disease (COPD) In Finland: Impact Of Disease Severity And Eosinophil Count On Healthcare Resource Utilization.

Purpose: The burden associated with chronic obstructive pulmonary disease (COPD) is substantial. The objectives of this study were to describe healthcare resource utilization (HCRU) and HCRU-associated costs in patients with COPD in Finland, according to disease severity and blood eosinophil count (BEC). Patients and methods: This non-interventional, retrospective registry study (GSK ID: HO-17-17558) utilized data from the specialist care hospital register. Data extraction was from first hospital visit with a COPD diagnosis (index date) from January 1, 2004 until December 31, 2015 or death. Patients (aged >18 years with ≥1 report of post-bronchodilation forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio <0.7) were categorized as having non-severe or severe COPD (FEV1 >50% or ≤50% of reference, respectively). Patients who were initially non-severe but progressed to severe were classified as having progressing COPD. Patients without spirometry registry data were classified as having clinically verified COPD. Patients were grouped according to BEC (≥300 cells/µL, <300 cells/µL or BEC unknown). HCRU, estimated associated costs and mortality were evaluated according to COPD severity and BEC. Results: There were 9042 patients with COPD; 340 non-severe, 326 progressing, 394 severe, and 7982 clinically verified. BEC was available for 31.8% of patients. The mean follow-up time was 3.7-6.5 years in the classified patient-groups. All-cause mortality was 46% during follow-up. Severe COPD was associated with more COPD-related HCRU and higher mortality than non-severe COPD. Patients with BEC ≥300 cells/µL had higher overall HCRU but improved survival compared with those with BEC <300 cells/µL. Overall direct costs were similar across COPD severity categories, 3300-3900€/patient-year, although COPD-related costs were higher in patients with severe versus non-severe COPD. Conclusion: This study demonstrated a substantial burden associated with severe and/or eosinophilic COPD for patients in Finland.

Health care resource utilization and characteristics of patients with eosinophilic asthma in secondary health care in Finland.

Background: Eosinophilic airway inflammation is common in asthma patients and appears to be associated with severe exacerbations and loss of asthma control. Objective: To describe the resource utilization and clinical characteristics of patients with eosinophilic asthma. Design: Asthma patients ≥18 years with ≥1 blood eosinophil count in secondary care (South West Finland) during 2003‒2013 were included. Clinical characteristics (age, lung function, body mass index, and comorbidities) and asthma-related resource utilization (hospital admissions, outpatient visits, and emergency room [ER] visits) were retrieved. Resource utilization rates were compared for patients with blood eosinophil ≤ or >300 cells/µL, using adjusted negative binomial regression models. Results: Overall, 4,357 eligible patients were identified (mean age 60 years, females 68%), of which 1,927 (44%) had >300 eosinophil cells/µL blood. Patients with ≤300 and >300 eosinophil counts, exhibited similar clinical characteristics, including advanced age, poor lung function, and overweight. Comorbidities such as pneumonia, sinusitis, and nasal polyps, were more frequent among those with >300 eosinophil cells/µL blood compared with patients with lower counts. Eosinophil counts >300 cells/µL were associated with greater hospital admissions (rate ratio [RR] [95% confidence interval CI]: 1.13 [1.02;1.24]) and outpatient visits (RR [95% CI]: 1.11 [1.03;1.20]) compared with patients with lower eosinophil counts. Rates of ER visits were similar between the patient groups (RR [95% CI]: 0.99 [0.87;1.12]). Conclusions: Hospital admissions and outpatient visits occurred more often for patients with eosinophil counts >300 cells/µL, than for patients with lower eosinophil counts. Routine blood eosinophil screening might be useful to identify patients with an eosinophilic phenotype eligible for more targeted treatments.

Quantitative Evaluation of 2 Scatter-Correction Techniques for 18F-FDG Brain PET/MRI in Regard to MR-Based Attenuation Correction.

In PET, corrections for photon scatter and attenuation are essential for visual and quantitative consistency. MR attenuation correction (MRAC) is generally conducted by image segmentation and assignment of discrete attenuation coefficients, which offer limited accuracy compared with CT attenuation correction. Potential inaccuracies in MRAC may affect scatter correction, because the attenuation image (µ-map) is used in single scatter simulation (SSS) to calculate the scatter estimate. We assessed the impact of MRAC to scatter correction using 2 scatter-correction techniques and 3 µ-maps for MRAC. Methods: The tail-fitted SSS (TF-SSS) and a Monte Carlo-based single scatter simulation (MC-SSS) algorithm implementations on the Philips Ingenuity TF PET/MR were used with 1 CT-based and 2 MR-based µ-maps. Data from 7 subjects were used in the clinical evaluation, and a phantom study using an anatomic brain phantom was conducted. Scatter-correction sinograms were evaluated for each scatter correction method and µ-map. Absolute image quantification was investigated with the phantom data. Quantitative assessment of PET images was performed by volume-of-interest and ratio image analysis. Results: MRAC did not result in large differences in scatter algorithm performance, especially with TF-SSS. Scatter sinograms and scatter fractions did not reveal large differences regardless of the µ-map used. TF-SSS showed slightly higher absolute quantification. The differences in volume-of-interest analysis between TF-SSS and MC-SSS were 3% at maximum in the phantom and 4% in the patient study. Both algorithms showed excellent correlation with each other with no visual differences between PET images. MC-SSS showed a slight dependency on the µ-map used, with a difference of 2% on average and 4% at maximum when a µ-map without bone was used. Conclusion: The effect of different MR-based µ-maps on the performance of scatter correction was minimal in non-time-of-flight 18F-FDG PET/MR brain imaging. The SSS algorithm was not affected significantly by MRAC. The performance of the MC-SSS algorithm is comparable but not superior to TF-SSS, warranting further investigations of algorithm optimization and performance with different radiotracers and time-of-flight imaging.

Quantum Zeno-type effect and non-Markovianity in a three-level system.

We study the coexistence of the quantum Zeno-type effect and non-Markovianity for a system decaying in a structured bosonic environment and subject to a control field. The interaction with the environment induces decay from the excited to the ground level, which, in turn, is coherently coupled to another meta-stable state. The control of the strength of the coherent coupling between the stable levels allows the engineering of both the dissipation and of the memory effects, without modifying neither the system-reservoir interaction, nor environmental properties. We use this framework in two different parameter regimes corresponding to fast (bad cavity limit) and slow dissipation (good cavity limit) in the original and un-controlled qubit system. Our results show a non-monotonic behavior of memory effects when increasing the effectiveness of the Zeno-like freezing. Moreover, we identify a new source of memory effects which allows the persistence of non-Markovianity for long times while the excited state has already been depleted.

Verifying the Quantumness of Bipartite Correlations.

Entanglement is at the heart of most quantum information tasks, and therefore considerable effort has been made to find methods of deciding the entanglement content of a given bipartite quantum state. Here, we prove a fundamental limitation to deciding if an unknown state is entangled or not: we show that any quantum measurement which can answer this question for an arbitrary state necessarily gives enough information to identify the state completely. We also extend our treatment to other classes of correlated states by considering the problem of deciding if a state has negative partial transpose, is discordant, or is fully classically correlated. Remarkably, only the question related to quantum discord can be answered without resorting to full state tomography.