Beta-fibrinogen gene promoter A-455 allele associated with poor longterm survival among 55-71 years old Caucasian women in Finnish stroke cohort

Peer reviewe

IRlab - Platform for thermal video analysis in evaluation of peripheral thermal behavior and blood perfusion

Background and objectives: Dynamic thermal imaging in medicine has several advantages in comparison to static thermal image analysis and has potential as a novel patient assessment method e.g. in the area of vascular surgery. Since dynamic thermal imaging has become in the scope of research only during the last decade, the computational available analysis methods are often lacking or not existing. Most of the published software is not available to the research community or are behind a paywall. IRlab provides an easy-to-use dynamic thermal video processing and analysis platform, freely accessible to researchers. Methods: IRlab is programmed in Matlab R2020b. Computational tools for dynamic analysis are divided into spatio-temporal and spectral methods, where spatio-temporal methods consist of region of interest delineation tools, thermal modulation analysis, standard thermal measures such as median, maximum, minimum and deviation values, and subtraction and gamma maps. Spectral methods include spectral band power, spectral flow, and wavelet analysis tools. Preliminary data of a single healthy subject was analyzed with the program as a sample run. Results: IRlab provides a platform for lower limb thermal image and video analysis with a clear workflow and variety of processing and analysis tools for time and frequency space analysis. The whole source code for IRlab is freely available for the research community under the General public license. Conclusions: IRlab is a versatile tool for dynamic thermal image and video processing. Freeware and open-source programs for medical thermal imaging are severely lacking, thus as a completely open-source project IRlab offers a unique platform for researchers within the field of medical thermal imaging.publishedVersionPeer reviewe

Early Warning Software for Emergency Department Crowding

Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead to better resource management and has the potential to improve treatment outcomes. This logic has motivated an increasing number of research articles but there has been little to no effort to move these findings from theory to practice. In this article, we present first results of a prospective crowding early warning software, that was integrated to hospital databases to create real-time predictions every hour over the course of 5 months in a Nordic combined ED using Holt-Winters' seasonal methods. We showed that the software could predict next hour crowding with a nominal AUC of 0.98 and 24 hour crowding with an AUC of 0.79 using simple statistical models. Moreover, we suggest that afternoon crowding can be predicted at 1 p.m. with an AUC of 0.84.Comment: 15 pages, 6 figure

Assessment of chronic limb threatening ischemia using thermal imaging

Objectives: Current chronic limb threatening ischemia (CLTI) diagnostics require expensive equipment, using ionizing radiation or contrast agents, or summative surrogate methods lacking in spatial information. Our aim is to develop and improve contactless, non-ionizing and cost-effective diagnostic methods for CLTI assessment with high spatial accuracy by utilizing dynamic thermal imaging and the angiosome concept. Approach: Dynamic thermal imaging test protocol was suggested and implemented with a number of computational parameters. Pilot data was measured from 3 healthy young subjects, 4 peripheral artery disease (PAD) patients and 4 CLTI patients. The protocol consists of clinical reference measurements, including ankle- and toe-brachial indices (ABI, TBI), and a modified patient bed for hydrostatic and thermal modulation tests. The data was analyzed using bivariate correlation. Results: The thermal recovery time constant was on average higher for the PAD (88%) and CLTI (83%) groups with respect to the healthy young subjects. The contralateral symmetry was high for the healthy young group and low for the CLTI group. The recovery time constants showed high negative correlation to TBI (ρ = -0.73) and ABI (ρ = -0.60). The relation of these clinical parameters to the hydrostatic response and absolute temperatures (|ρ|<0.3) remained unclear. Conclusion: The lack of correlation for absolute temperatures or their contralateral differences with the clinical status, ABI and TBI disputes their use in CLTI diagnostics. Thermal modulation tests tend to augment the signs of thermoregulation deficiencies and accordingly high correlations were found with all reference metrics. The method is promising for establishing the connection between impaired perfusion and thermography. The hydrostatic modulation test requires more research with stricter test conditions.publishedVersionPeer reviewe

Evaluation of a wrist-worn photoplethysmography monitor for heart rate variability estimation in patients recovering from laparoscopic colon resection

To evaluate the accuracy of heart rate variability (HRV) parameters obtained with a wrist-worn photoplethysmography (PPG) monitor in patients recovering from minimally invasive colon resection to investigate whether PPG has potential in postoperative patient monitoring. 31 patients were monitored for three days or until discharge or reoperation using a wrist-worn PPG monitor (PulseOn, Finland) with a Holter monitor (Faros 360, Bittium Biosignals, Finland) as a reference measurement device. Beat-to-beat intervals (BBI) and HRV information collected by PPG were compared with RR intervals (RRI) and HRV obtained from the ECG reference after removing artefacts and ectopic beats. The beat-to-beat mean error (ME) and mean absolute error (MAE) of good quality heartbeat intervals obtained by wrist PPG were estimated as - 1.34 ms and 10.4 ms respectively. A significant variation in the accuracy of the HRV parameters was found. In the time domain, SDNN (9.11%), TRI (11.4%) and TINN (11.1%) were estimated with low relative MAE, while RMSSD (34.3%), pNN50 (139%) and NN50 (188%) had higher errors. The logarithmic parameters in the frequency domain (VLF Log, LF Log and HF Log) exhibited the lowest relative error, and for non-linear parameters, SD2 (7.5%), DFA alpha 1 (8.25%) and DFA alpha 2 (4.71%) were calculated much more accurately than SD1 (34.3%). The wrist PPG shows some potential for use in a clinical setting. The accuracy of several HRV parameters analyzed post hoc was found sufficient to be used in further studies concerning postoperative recovery of patients undergoing laparoscopic colon resection, although there were large errors in many common HRV parameters such as RMSSD, pNN50 and NN50, rendering them unusable. ClinicalTrials.gov Identifier: NCT04996511, August 9, 2021, retrospectively registeredPeer reviewe

Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on the subject, several gaps remain: 1) proposed forecasting models have become outdated due to quick influx of advanced machine learning models (ML), 2) amount of multivariable input data has been limited and 3) discrete performance metrics have been rarely reported. In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead. We use electronic health record data from a large, combined ED with an extensive set of explanatory variables, including the availability of beds in catchment area hospitals, traffic data from local observation stations, weather variables, etc. We show that N-BEATS and LightGBM outpeform benchmarks with 11 % and 9 % respective improvements and that DeepAR predicts next day crowding with an AUC of 0.76 (95 % CI 0.69-0.84). To the best of our knowledge, this is the first study to document the superiority of LightGBM and N-BEATS over statistical benchmarks in the context of ED forecasting

Incidence of sudden cardiac arrest and sudden cardiac death after unstable angina pectoris and myocardial infarction

BACKGROUND: Sudden cardiac arrests (SCA) and sudden cardiac deaths (SCD) are believed to account for a large proportion of deaths due to cardiovascular causes. The purpose of this study is to provide comprehensive information on the epidemiology of SCAs and SCDs after acute coronary syndrome. METHODS: The incidence of SCA (including SCDs) was studied retrospectively among 10,316 consecutive patients undergoing invasive evaluation for acute coronary syndrome (ACS) between 2007 and 2018 at Tays Heart Hospital (sole provider of specialized cardiac care for a catchment area of over 0.5 million residents). Baseline and follow-up information was collected by combining information from the hospital's electronic health records, death certificate data, and a full-disclosure review of written patient records and accounts of the circumstances leading to death. RESULTS: During twelve years of follow-up, the cumulative incidence of SCAs (including SCDs) was 9.8% (0.8% annually) and that of SCDs 5.4% (0.5% annually). Cumulative incidence of SCAs in patients with ST-elevation myocardial infarction, non-ST-elevation myocardial infarction and unstable angina pectoris were: 11.9%,10.2% and 5.7% at twelve years. SCAs accounted for 30.5% (n = 528/1,732) of all deaths due to cardiovascular causes. The vast majority of SCAs (95.6%) occurred in patients without implantable cardioverter defibrillator (ICD) devices or among patients with no recurrent hospitalizations for coronary artery disease (89.1%). CONCLUSIONS: SCAs accounted for less than a third of all deaths due to cardiovascular causes among patients with previous ACS. Incidence of SCA is highest among STEMI and NSTEMI patients. After the hospital discharge, most of SCAs happen to NSTEMI patients.publishedVersionPeer reviewe