Sex differences in the diagnostic algorithm of screening for heart failure by symptoms and NT-proBNP in patients with type 2 diabetes.

OBJECTIVES: This study aimed to assess the guideline recommended diagnostic tools NT-proBNP and NYHA classification, with a focus on sex-specific differences. BACKGROUND: Patients with Type 2 Diabetes (T2D) face a heart failure (HF) risk up to four times higher than those without T2D, particularly affecting women more than twice as much as men. Despite distinct pathophysiological differences between men and women, there are currently no sex-specific recommendations for the diagnostic algorithm of HF in diabetic patients. METHODS: A total of 2083 patients with T2D were enrolled, and the primary endpoint was heart failure during hospitalization within a 5-year timeframe. The secondary endpoint was all-cause death. RESULTS: In female patients, frequency of HF diagnosis prior to or during hospitalization and mortality did not differ significantly between NYHA II and III, in contrast to male patients. Additionally, there was no notable difference in mean NT-proBNP levels between NYHA stage II and III only in female patients. The multivariable regression analysis highlighted NYHA classification not to be a predictor of NT-proBNP levels in female but solely in male patients. On multivariable Cox regression NYHA score was also no significant risk factor for occurence of HF in female patients. Furthermore, there was no significant disparity in mortality between men with NT-proBNP levels between 125 and 400 pg/ml and those below 125 pg/ml, whereas in women mortality was significantly higher in the group with NT-proBNP levels between 125 and 400 pg/ml than below 125 pg/ml. CONCLUSION: These findings suggest that NYHA classification may not be the most suitable tool for assessing the diagnosis of HF in female patients with T2D. Moreover, the need for consideration of a more symptom-independent screening for HF in female patients with T2D and re-evaluation of current guidelines especially regarding sex-specific aspects is highlighted.

Improving Lameness Detection in Cows: A Machine Learning Algorithm Application.

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.

Genomic surveillance of SARS-CoV-2 evolution by a centralised pipeline and weekly focused sequencing, Austria, January 2021 to March 2023.

BackgroundThe COVID-19 pandemic was largely driven by genetic mutations of SARS-CoV-2, leading in some instances to enhanced infectiousness of the virus or its capacity to evade the host immune system. To closely monitor SARS-CoV-2 evolution and resulting variants at genomic-level, an innovative pipeline termed SARSeq was developed in Austria.AimWe discuss technical aspects of the SARSeq pipeline, describe its performance and present noteworthy results it enabled during the pandemic in Austria.MethodsThe SARSeq pipeline was set up as a collaboration between private and public clinical diagnostic laboratories, a public health agency, and an academic institution. Representative SARS-CoV-2 positive specimens from each of the nine Austrian provinces were obtained from SARS-CoV-2 testing laboratories and processed centrally in an academic setting for S-gene sequencing and analysis.ResultsSARS-CoV-2 sequences from up to 2,880 cases weekly resulted in 222,784 characterised case samples in January 2021-March 2023. Consequently, Austria delivered the fourth densest genomic surveillance worldwide in a very resource-efficient manner. While most SARS-CoV-2 variants during the study showed comparable kinetic behaviour in all of Austria, some, like Beta, had a more focused spread. This highlighted multifaceted aspects of local population-level acquired immunity. The nationwide surveillance system enabled reliable nowcasting. Measured early growth kinetics of variants were predictive of later incidence peaks.ConclusionWith low automation, labour, and cost requirements, SARSeq is adaptable to monitor other pathogens and advantageous even for resource-limited countries. This multiplexed genomic surveillance system has potential as a rapid response tool for future emerging threats.

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria.

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

A network-based explanation of why most COVID-19 infection curves are linear.

Many countries have passed their first COVID-19 epidemic peak. Traditional epidemiological models describe this as a result of nonpharmaceutical interventions pushing the growth rate below the recovery rate. In this phase of the pandemic many countries showed an almost linear growth of confirmed cases for extended time periods. This new containment regime is hard to explain by traditional models where either infection numbers grow explosively until herd immunity is reached or the epidemic is completely suppressed. Here we offer an explanation of this puzzling observation based on the structure of contact networks. We show that for any given transmission rate there exists a critical number of social contacts, [Formula: see text], below which linear growth and low infection prevalence must occur. Above [Formula: see text] traditional epidemiological dynamics take place, e.g., as in susceptible-infected-recovered (SIR) models. When calibrating our model to empirical estimates of the transmission rate and the number of days being contagious, we find [Formula: see text] Assuming realistic contact networks with a degree of about 5, and assuming that lockdown measures would reduce that to household size (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fine-tuning of parameters. In particular, we compare the United States and Austria, as examples for one country that initially did not impose measures and one that responded with a severe lockdown early on. Our findings question the applicability of standard compartmental models to describe the COVID-19 containment phase. The probability to observe linear growth in these is practically zero.

Quantification of the resilience of primary care networks by stress testing the health care system.

There are practically no quantitative tools for understanding how much stress a health care system can absorb before it loses its ability to provide care. We propose to measure the resilience of health care systems with respect to changes in the density of primary care providers. We develop a computational model on a 1-to-1 scale for a countrywide primary care sector based on patient-sharing networks. Nodes represent all primary care providers in a country; links indicate patient flows between them. The removal of providers could cause a cascade of patient displacements, as patients have to find alternative providers. The model is calibrated with nationwide data from Austria that includes almost all primary care contacts over 2 y. We assign 2 properties to every provider: the "CareRank" measures the average number of displacements caused by a provider's removal (systemic risk) as well as the fraction of patients a provider can absorb when others default (systemic benefit). Below a critical number of providers, large-scale cascades of patient displacements occur, and no more providers can be found in a given region. We quantify regional resilience as the maximum fraction of providers that can be removed before cascading events prevent coverage for all patients within a district. We find considerable regional heterogeneity in the critical transition point from resilient to nonresilient behavior. We demonstrate that health care resilience cannot be quantified by physician density alone but must take into account how networked systems respond and restructure in response to shocks. The approach can identify systemically relevant providers.

Relative risk of arterial and venous thromboembolism in persons with cancer vs. persons without cancer-a nationwide analysis.

AIMS: An interrelation between cancer and thrombosis is known, but population-based studies on the risk of both arterial thromboembolism (ATE) and venous thromboembolism (VTE) have not been performed. METHODS AND RESULTS: International Classification of Disease 10th Revision (ICD-10) diagnosis codes of all publicly insured persons in Austria (0-90 years) were extracted from the Austrian Association of Social Security Providers dataset covering the years 2006-07 (n = 8 306 244). Patients with a history of cancer or active cancer were defined as having at least one ICD-10 'C' diagnosis code, and patients with ATE and/or VTE as having at least one of I21/I24 (myocardial infarction), I63/I64 (stroke), I74 (arterial embolism), and I26/I80/I82 (venous thromboembolism) diagnosis code. Among 158 675 people with cancer, 8559 (5.4%) had an ATE diagnosis code and 7244 (4.6%) a VTE diagnosis code. In contrast, among 8 147 569 people without cancer, 69 381 (0.9%) had an ATE diagnosis code and 29 307 (0.4%) a VTE diagnosis code. This corresponds to age-stratified random-effects relative risks (RR) of 6.88 [95% confidence interval (CI) 4.81-9.84] for ATE and 14.91 (95% CI 8.90-24.95) for VTE. ATE proportion was highest in patients with urinary tract malignancies (RR: 7.16 [6.74-7.61]) and lowest in patients with endocrine cancer (RR: 2.49 [2.00-3.10]). The corresponding VTE proportion was highest in cancer of the mesothelium/soft tissue (RR: 19.35 [17.44-21.47]) and lowest in oropharyngeal cancer (RR: 6.62 [5.61-7.81]). CONCLUSION: The RR of both ATE and VTE are significantly higher in persons with cancer. Our population-level meta-data indicate a strong association between cancer, ATE and VTE, and support the concept of shared risk factors and pathobiology between these diseases.Relative risk of ATE and VTE in persons with a cancer diagnosis code versus persons without a cancer diagnosis code.

Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine.

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

High-risk multimorbidity patterns on the road to cardiovascular mortality.

BACKGROUND: Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. METHODS: Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. RESULTS: In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2-0.3%), medium (0.3-1%) and high in-hospital mortality (2-11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50-59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. CONCLUSIONS: Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.

Healthcare utilization, psychiatric medication and risk of rehospitalization in suicide-attempting patients with common mental disorders.

OBJECTIVE: Common mental disorders are strong risk factors for suicide attempt. We compared common mental disorder patients with and without suicide attempt regarding health care utilization and psychiatric medication, assessed gender differences and identified how psychotropic medication of attempters is associated with subsequent rehospitalization. METHODS: We used administrative claims data of 22,276 common mental disorder patients with inpatient care in Lower Austria between 1 January 2006 and 31 December 2011. Suicide attempters (cases, n = 615) and non-attempters (controls, n = 21,661) were compared regarding specific healthcare utilization by calculating mean differences of time-dependent contact probabilities and psychiatric medication (i.e. prescribed defined daily doses) ± 0.5 years around their suicide attempt (cases)/common mental disorder diagnosis (controls). Cluster analysis was used to group suicide attempters according to their psychiatric medication. The risk of rehospitalization 0.5-3 years after the attempt was calculated with regression analysis controlling for sex, age and morbidity-related factors. RESULTS: Contacts with general practitioners were lower for attempters than non-attempters (mean difference of contact probabilities over observation period, males = -0.05, 95% confidence interval = [-0.07, -0.03]; females: mean difference = -0.04, 95% confidence interval = [-0.05, -0.03]). Regarding psychiatrists, female attempters had markedly higher contact probabilities after the attempt compared to female non-attempters (mean difference = 0.02, 95% confidence interval = [0.007, 0.04]); male attempters had lower contact probabilities before the attempt compared to male non-attempters (mean difference = -0.01, 95% confidence interval = [-0.004, -0.02]). Attempters had higher dosages of psychiatric medication across the entire period. Antidepressant and antipsychotic medication peaked at the time of common mental disorder diagnosis/attempt. Benzodiazepine prescriptions were considerably higher for male attempters than their female counterparts and were constantly elevated for male attempters across the observation period. A cluster of attempters with long-term benzodiazepine prescriptions had an increased risk of rehospitalization (adjusted odds ratio = 2.4, 95% confidence interval = [1.1, 5.5]). CONCLUSION: Despite lower contact probabilities, common mental disorder patients with suicide attempt are prescribed more psychiatric medication, particularly benzodiazepines, with an elevated risk of rehospitalization. Strong sex differences were found.

Diagnosis of osteoporosis in statin-treated patients is dose-dependent.

OBJECTIVE: Whether HMG-CoA-reductase inhibition, the main mechanism of statins, plays a role in the pathogenesis of osteoporosis, is not entirely known so far. Consequently, this study was set out to investigate the relationship of different kinds and dosages of statins with osteoporosis, hypothesising that the inhibition of the synthesis of cholesterol could influence sex-hormones and therefore the diagnosis of osteoporosis. METHODS: Medical claims data of all Austrians from 2006 to 2007 was used to identify all patients treated with statins to compute their daily defined dose averages of six different types of statins. We applied multiple logistic regression to analyse the dose-dependent risks of being diagnosed with osteoporosis for each statin individually. RESULTS: In the general study population, statin treatment was associated with an overrepresentation of diagnosed osteoporosis compared with controls (OR: 3.62, 95% CI 3.55 to 3.69, p<0.01). There was a highly non-trivial dependence of statin dosage with the ORs of osteoporosis. Osteoporosis was underrepresented in low-dose statin treatment (0-10 mg per day), including lovastatin (OR: 0.39, CI 0.18 to 0.84, p<0.05), pravastatin (OR: 0.68, 95% CI 0.52 to 0.89, p<0.01), simvastatin (OR: 0.70, 95% CI 0.56 to 0.86, p<0.01) and rosuvastatin (OR: 0.69, 95% CI 0.55 to 0.87, p<0.01). However, the exceeding of the 40 mg threshold for simvastatin (OR: 1.64, 95% CI 1.31 to 2.07, p<0.01), and the exceeding of a 20 mg threshold for atorvastatin (OR: 1.78, 95% CI 1.41 to 2.23, p<0.01) and for rosuvastatin (OR: 2.04, 95% CI 1.31 to 3.18, p<0.01) was related to an overrepresentation of osteoporosis. CONCLUSION: Our results show that the diagnosis of osteoporosis in statin-treated patients is dose-dependent. Thus, osteoporosis is underrepresented in low-dose and overrepresented in high-dose statin treatment, demonstrating the importance of future studies' taking dose-dependency into account when investigating the relationship between statins and osteoporosis.

Quantification of diabetes comorbidity risks across life using nation-wide big claims data.

Despite substantial progress in the study of diabetes, important questions remain about its comorbidities and clinical heterogeneity. To explore these issues, we develop a framework allowing for the first time to quantify nation-wide risks and their age- and sex-dependence for each diabetic comorbidity, and whether the association may be consequential or causal, in a sample of almost two million patients. This study is equivalent to nearly 40,000 single clinical measurements. We confirm the highly controversial relation of increased risk for Parkinson's disease in diabetics, using a 10 times larger cohort than previous studies on this relation. Detection of type 1 diabetes leads detection of depressions, whereas there is a strong comorbidity relation between type 2 diabetes and schizophrenia, suggesting similar pathogenic or medication-related mechanisms. We find significant sex differences in the progression of, for instance, sleep disorders and congestive heart failure in diabetic patients. Hypertension is a highly sex-sensitive comorbidity with females being at lower risk during fertile age, but at higher risk otherwise. These results may be useful to improve screening practices in the general population. Clinical management of diabetes must address age- and sex-dependence of multiple comorbid conditions.

Quantification of excess risk for diabetes for those born in times of hunger, in an entire population of a nation, across a century.

Based on a unique dataset comprising all 325,000 Austrian patients that were under pharmaceutical treatment for diabetes during 2006 and 2007, we measured the excess risk of developing diabetes triggered by undernourishment in early life. We studied the percentage of all diabetes patients in the total population specifically for each year of birth, from 1917 to 2007. We found a massive excess risk of diabetes in people born during the times of the three major famines and immediately after, which occurred in Austria in the 20th century: 1918-1919, 1938, and 1946-1947. Depending on the region, there was an up to 40% higher chance of having diabetes when born in 1919-1921, compared with 1918 or 1922, where age-specific typical diabetes ratios are observed. The excess risk for diabetes was practically absent in those provinces of Austria that were less affected by the famines. We show that diabetes rates exhibit nontrivial, age-specific sex differences, and correlate with the economic wealth of the region. Our results might be of relevance for establishing higher awareness in the health system for those born in high-risk years, and underline the importance of ensuring sufficient nutrition in prenatal and early stages of life.

Statistical detection of systematic election irregularities.

Democratic societies are built around the principle of free and fair elections, and that each citizen's vote should count equally. National elections can be regarded as large-scale social experiments, where people are grouped into usually large numbers of electoral districts and vote according to their preferences. The large number of samples implies statistical consequences for the polling results, which can be used to identify election irregularities. Using a suitable data representation, we find that vote distributions of elections with alleged fraud show a kurtosis substantially exceeding the kurtosis of normal elections, depending on the level of data aggregation. As an example, we show that reported irregularities in recent Russian elections are, indeed, well-explained by systematic ballot stuffing. We develop a parametric model quantifying the extent to which fraudulent mechanisms are present. We formulate a parametric test detecting these statistical properties in election results. Remarkably, this technique produces robust outcomes with respect to the resolution of the data and therefore, allows for cross-country comparisons.