Use of chemotherapy in patients with oesophageal, stomach, colon, rectal, liver, pancreatic, lung, and ovarian cancer: an International Cancer Benchmarking Partnership (ICBP) population-based study.

BACKGROUND: There are few data on international variation in chemotherapy use, despite it being a key treatment type for some patients with cancer. Here, we aimed to examine the presence and size of such variation. METHODS: This population-based study used data from Norway, the four UK nations (England, Northern Ireland, Scotland, and Wales), eight Canadian provinces (Alberta, British Columbia, Manitoba, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, and Saskatchewan), and two Australian states (New South Wales and Victoria). Patients aged 15-99 years diagnosed with cancer in eight different sites (oesophageal, stomach, colon, rectal, liver, pancreatic, lung, or ovarian cancer), with no other primary cancer diagnosis occurring from within the 5 years before to 1 year after the index cancer diagnosis or during the study period were included in the study. We examined variation in chemotherapy use from 31 days before to 365 days after diagnosis and time to its initiation, alongside related variation in patient group differences. Information was obtained from cancer registry records linked to clinical or patient management system data or hospital administration data. Random-effects meta-analyses quantified interjurisdictional variation using 95% prediction intervals (95% PIs). FINDINGS: Between Jan 1, 2012, and Dec 31, 2017, of 893 461 patients with a new diagnosis of one of the studied cancers, 111 569 (12·5%) did not meet the inclusion criteria, and 781 892 were included in the analysis. There was large interjurisdictional variation in chemotherapy use for all studied cancers, with wide 95% PIs: 47·5 to 81·2 (pooled estimate 66·4%) for ovarian cancer, 34·9 to 59·8 (47·2%) for oesophageal cancer, 22·3 to 62·3 (40·8%) for rectal cancer, 25·7 to 55·5 (39·6%) for stomach cancer, 17·2 to 56·3 (34·1%) for pancreatic cancer, 17·9 to 49·0 (31·4%) for lung cancer, 18·6 to 43·8 (29·7%) for colon cancer, and 3·5 to 50·7 (16·1%) for liver cancer. For patients with stage 3 colon cancer, the interjurisdictional variation was greater than that for all patients with colon cancer (95% PI 38·5 to 78·4; 60·1%). Patients aged 85-99 years had 20-times lower odds of chemotherapy use than those aged 65-74 years, with very large interjurisdictional variation in this age difference (odds ratio 0·05; 95% PI 0·01 to 0·19). There was large variation in median time to first chemotherapy (from diagnosis date) by cancer site, with substantial interjurisdictional variation, particularly for rectal cancer (95% PI -15·5 to 193·9 days; pooled estimate 89·2 days). Patients aged 85-99 years had slightly shorter median time to first chemotherapy compared with those aged 65-74 years, consistently between jurisdictions (-3·7 days, 95% PI -7·6 to 0·1). INTERPRETATION: Large variation in use and time to chemotherapy initiation were observed between the participating jurisdictions, alongside large and variable age group differences in chemotherapy use. To guide efforts to improve patient outcomes, the underlying reasons for these patterns need to be established. FUNDING: International Cancer Benchmarking Partnership (funded by the Canadian Partnership Against Cancer, Cancer Council Victoria, Cancer Institute New South Wales, Cancer Research UK, Danish Cancer Society, National Cancer Registry Ireland, The Cancer Society of New Zealand, National Health Service England, Norwegian Cancer Society, Public Health Agency Northern Ireland on behalf of the Northern Ireland Cancer Registry, DG Health and Social Care Scottish Government, Western Australia Department of Health, and Public Health Wales NHS Trust).

Modeling the selective growth advantage of genetically variant human pluripotent stem cells to identify opportunities for manufacturing process control.

BACKGROUND AIMS: The appearance of genetically variant populations in human pluripotent stem cell (hPSC) cultures represents a concern for research and clinical applications. Genetic variations may alter hPSC differentiation potential or cause phenotype variation in differentiated cells. Further, variants may have properties such as proliferative rate, or response to the culture environment, that differ from wild-type cells. As such, understanding the behavior of these variants in culture, and any potential operational impact on manufacturing processes, will be necessary to control quality of putative hPSC-based products that include a proportion of variant threshold in their quality specification. METHODS: Here we show a computational model that mathematically describes the growth dynamics between commonly occurring genetically variant hPSCs and their counterpart wild-type cells in culture. RESULTS: We show that our model is capable of representing the growth behaviors of both wild-type and variant hPSCs in individual and co-culture systems. CONCLUSIONS: This representation allows us to identify three critical process parameters that drive critical quality attributes when genetically variant cells are present within the system: total culture density, proportion of variant cells within the culture system and variant cell overgrowth. Lastly, we used our model to predict how the variability of these parameters affects the prevalence of both populations in culture.

From sleep patterns to heart rhythm: Predicting atrial fibrillation from overnight polysomnograms.

BACKGROUND: Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Given the prevalence of obstructive sleep apnea among AF patients, electrocardiogram (ECG) analysis from polysomnography (PSG) offers a unique opportunity for early AF prediction. Our aim is to identify individuals at high risk of AF development from single­lead ECGs during standard PSG. METHODS: We analyzed 18,782 single­lead ECG recordings from 13,609 subjects undergoing PSG at the Massachusetts General Hospital sleep laboratory. AF presence was identified using ICD-9/10 codes. The dataset included 15,913 recordings without AF history and 2054 recordings from patients diagnosed with AF between one month to fifteen years post-PSG. Data were partitioned into training, validation, and test cohorts ensuring that individual patients remained exclusive to each cohort. The test set was held out during the training process. We employed two different methods for feature extraction to build a final model for AF prediction: Extraction of hand-crafted ECG features and a deep learning method. For extraction of ECG-hand-crafted features, recordings were split into 30-s windows, and those with a signal quality index (SQI) below 0.95 were discarded. From each remaining window, 150 features were extracted from the time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1800 features (12 × 150). A pre-trained deep neural network from the PhysioNet Challenge 2021 was updated using transfer learning to discriminate recordings with and without AF. The model processed PSG ECGs in 16-s windows to generate AF probabilities, from which 13 statistical features were extracted. Combining 1800 features from feature extraction with 13 from the deep learning model, we performed a feature selection and subsequently trained a shallow neural network to predict future AF and evaluated its performance on the test cohort. RESULTS: On the test set, our model exhibited sensitivity, specificity, and precision of 0.67, 0.81, and 0.3, respectively, for AF prediction. Survival analysis revealed a hazard ratio of 8.36 (p-value: 1.93 × 10-52) for AF outcomes using the log-rank test. CONCLUSIONS: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy.

The association between social jetlag and depression is independent of sleep debt.

OBJECTIVES: To investigate whether the association between SJLsc (sleep-corrected social jetlag) and depressive mood is significant and independent of sleep debt. METHODS: Participants from the general adult population were interviewed using structured questionnaires on sleep duration, weekday/weekend sleep schedules, and depressive mood (Patient Health Questionnaire-9). Social jetlag (SJL) was measured by SJLsc and standard SJL (SJLs). SJLs was the absolute difference between mid-sleep time on free days (MSF) and workdays (MSW). For SJLsc, both MSF and MSW were adjusted for average sleep duration across the week according to the direction of sleep debt. Sleep debt was defined by sleep extension on free days. The association of SJL with depression was investigated, and covariates included age, sex, sociodemographic factors, insomnia symptoms, sleep duration, and sleep debt. RESULTS: A total of 1982 individuals (1089 men; age 43.1 ± 14.4 years) were analyzed. SJL was present in 24.6% measured by SJLsc and 51.0% by SJLs. SJLsc and SJLs were significantly associated with depressive mood (r = 0.06, P = 0.02; r = 0.06, P = 0.01, respectively), independent of sleep debt. Sleep debt was also associated with depression (r = 0.07, P < 0.01). CONCLUSIONS: By adopting sleep-corrected formula for SJL, this study found that misaligned and insufficient sleep, at levels occurring in routine social life, can negatively affect mood. Minimizing social jetlag and sleep deprivation may promote individual psychological well-being.

High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study.

PURPOSE: Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals. METHODS: Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments. RESULTS: Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor. CONCLUSIONS: Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.

Evaluation of Viral Inactivation on Dry Surface by High Peak Power Microwave (HPPM) Exposure.

Previous research has shown that virus infectivity can be dramatically reduced by radio frequency exposure in the gigahertz (GHz) frequency range. Given the worldwide SARS-CoV-2 pandemic, which has caused over 1 million deaths and has had a profound global economic impact, there is a need for a noninvasive technology that can reduce the transmission of virus among humans. RF is a potential wide area-of-effect viral decontamination technology that could be used in hospital rooms where patients are expelling virus, in grocery and convenience stores where local populations mix, and in first responder settings where rapid medical response spans many potentially infected locations within hours. In this study, we used bovine coronavirus (BCoV) as a surrogate of SARS-CoV-2 and exposed it to high peak power microwave (HPPM) pulses at four narrowband frequencies: 2.8, 5.6, 8.5, and 9.3 GHz. Exposures consisted of 2 µs pulses delivered at 500 Hz, with pulse counts varied by decades between 1 and 10,000. The peak field intensities (i.e. the instantaneous power density of each pulse) ranged between 0.6 and 6.5 MW/m2 , depending on the microwave frequency. The HPPM exposures were delivered to plastic coverslips containing BCoV dried on the surface. Hemagglutination (HA) and cytopathic effect analyses were performed 6 days after inoculation of host cells to assess viral infectivity. No change in viral infectivity was seen with increasing dose (pulse number) across the tested frequencies. Under all conditions tested, exposure did not reduce infectivity more than 1.0 log10. For the conditions studied, high peak power pulsed RF exposures in the 2-10 GHz range appear ineffective as a virucidal approach for hard surface decontamination. © 2023 Bioelectromagnetics Society.

Association between older subjective age and poor sleep quality: a population-based study.

OBJECTIVE: To examine the association of subjective age (SA) with sleep quality in an adult population. METHODS: In the Korean Sleep and Headache Study, 2,349 participants (49.2% men; 48.1 ± 16.4 years old) were interviewed face-to-face using structured questionnaires between September and December 2018. SA was assessed by asking participants their perceived age in years and then compared with their chronological age (CA). Participants were assigned to three groups: feeling younger, feeling their age, and feeling older. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Association between SA and sleep quality was analyzed with multiple linear regression controlling for demographics, psychosocial, and sleep characteristics. RESULTS: The group feeling older (n = 404, 17.2%; men, 58.2%; age, 46.5 ± 16.2 years) had worse sleep quality than the groups feeling younger and feeling their age (PSQI score, 4.3 ± 2.7, 3.8 ± 2.4, 3.4 ± 2.1, respectively, p <.001; prevalence of poor sleep quality, 29.0%, 18.4%, 13.5% respectively, p <.001). The association between SA and the PSQI score remained significant after adjusting for confounders (ß = 1.05, 95% confidence interval 0.26, 1.83; p <.001). Stratified analyses by sex and CA showed that the association between SA and the PSQI score was significant only in women and in middle-aged and older group (aged 50-79), suggesting that sex and CA modified the association. CONCLUSION: Age perception was associated with self-reported sleep quality, independent of CA. SA may be a useful marker that complements the conventional assessment of subjective sleep quality.

Long-term blood pressure patterns in midlife and dementia in later life: Findings from the Framingham Heart Study.

INTRODUCTION: Long-term blood pressure (BP) measures, such as visit-to-visit BP variability (BPV) and cumulative BP, are strong indicators of cardiovascular risks. This study modeled up to 20 years of BP patterns representative of midlife by using BPV and cumulative BP, then examined their associations with development of dementia in later life. METHODS: For 3201 individuals from the Framingham Heart Study, multivariate logistic regression analyses were performed to examine the association between long-term BP patterns during midlife and the development of dementia (ages ≥ 65). RESULTS: After adjusting for covariates, every quartile increase in midlife cumulative BP was associated with a sequential increase in the risk of developing dementia (e.g., highest quartile of cumulative systolic blood pressure had approximately 2.5-fold increased risk of all-cause dementia). BPV was not significantly associated with dementia. DISCUSSION: Findings suggest that cumulative BP over the course of midlife predicts risk of dementia in later life. HIGHLIGHTS Long-term blood pressure (BP) patterns are strong indicators of vascular risks. Cumulative BP and BP variability (BPV) were used to reflect BP patterns across midlife. High cumulative BP in midlife is associated with increased dementia risk. Visit-to-visit BPV was not associated with the onset of dementia.

Risk factors and prognostic implications of diagnosis of cancer within 30 days after an emergency hospital admission (emergency presentation): an International Cancer Benchmarking Partnership (ICBP) population-based study.

BACKGROUND: Greater understanding of international cancer survival differences is needed. We aimed to identify predictors and consequences of cancer diagnosis through emergency presentation in different international jurisdictions in six high-income countries. METHODS: Using a federated analysis model, in this cross-sectional population-based study, we analysed cancer registration and linked hospital admissions data from 14 jurisdictions in six countries (Australia, Canada, Denmark, New Zealand, Norway, and the UK), including patients with primary diagnosis of invasive oesophageal, stomach, colon, rectal, liver, pancreatic, lung, or ovarian cancer during study periods from Jan 1, 2012, to Dec 31, 2017. Data were collected on cancer site, age group, sex, year of diagnosis, and stage at diagnosis. Emergency presentation was defined as diagnosis of cancer within 30 days after an emergency hospital admission. Using logistic regression, we examined variables associated with emergency presentation and associations between emergency presentation and short-term mortality. We meta-analysed estimates across jurisdictions and explored jurisdiction-level associations between cancer survival and the percentage of patients diagnosed as emergencies. FINDINGS: In 857 068 patients across 14 jurisdictions, considering all of the eight cancer sites together, the percentage of diagnoses through emergency presentation ranged from 24·0% (9165 of 38 212 patients) to 42·5% (12 238 of 28 794 patients). There was consistently large variation in the percentage of emergency presentations by cancer site across jurisdictions. Pancreatic cancer diagnoses had the highest percentage of emergency presentations on average overall (46·1% [30 972 of 67 173 patients]), with the jurisdictional range being 34·1% (1083 of 3172 patients) to 60·4% (1317 of 2182 patients). Rectal cancer had the lowest percentage of emergency presentations on average overall (12·1% [10 051 of 83 325 patients]), with a jurisdictional range of 9·1% (403 of 4438 patients) to 19·8% (643 of 3247 patients). Across the jurisdictions, older age (ie, 75-84 years and 85 years or older, compared with younger patients) and advanced stage at diagnosis compared with non-advanced stage were consistently associated with increased emergency presentation risk, with the percentage of emergency presentations being highest in the oldest age group (85 years or older) for 110 (98%) of 112 jurisdiction-cancer site strata, and in the most advanced (distant spread) stage category for 98 (97%) of 101 jurisdiction-cancer site strata with available information. Across the jurisdictions, and despite heterogeneity in association size (I2=93%), emergency presenters consistently had substantially greater risk of 12-month mortality than non-emergency presenters (odds ratio >1·9 for 112 [100%] of 112 jurisdiction-cancer site strata, with the minimum lower bound of the related 95% CIs being 1·26). There were negative associations between jurisdiction-level percentage of emergency presentations and jurisdiction-level 1-year survival for colon, stomach, lung, liver, pancreatic, and ovarian cancer, with a 10% increase in percentage of emergency presentations in a jurisdiction being associated with a decrease in 1-year net survival of between 2·5% (95% CI 0·28-4·7) and 7·0% (1·2-13·0). INTERPRETATION: Internationally, notable proportions of patients with cancer are diagnosed through emergency presentation. Specific types of cancer, older age, and advanced stage at diagnosis are consistently associated with an increased risk of emergency presentation, which strongly predicts worse prognosis and probably contributes to international differences in cancer survival. Monitoring emergency presentations, and identifying and acting on contributing behavioural and health-care factors, is a global priority for cancer control. FUNDING: Canadian Partnership Against Cancer; Cancer Council Victoria; Cancer Institute New South Wales; Cancer Research UK; Danish Cancer Society; National Cancer Registry Ireland; The Cancer Society of New Zealand; National Health Service England; Norwegian Cancer Society; Public Health Agency Northern Ireland, on behalf of the Northern Ireland Cancer Registry; the Scottish Government; Western Australia Department of Health; and Wales Cancer Network.

Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation.

OBJECTIVE: Sleep-related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to detect sleep apnea automatically from a simple, easy-to-wear device. The objective was to detect abnormal respiration and estimate the Apnea-Hypopnea Index (AHI) automatically with a wearable respiratory device with and without SpO2 signals using a large (n = 412) dataset serving as ground truth. DESIGN: Simultaneously recorded polysomnography (PSG) and wearable respiratory effort data were used to train and evaluate models in a cross-validation fashion. Time domain and complexity features were extracted, important features were identified, and a random forest model was employed to detect events and predict AHI. Four models were trained: one each using the respiratory features only, a feature from the SpO2 (%)-signal only, and two additional models that use the respiratory features and the SpO2 (%) feature, one allowing a time lag of 30 s between the two signals. RESULTS: Event-based classification resulted in areas under the receiver operating characteristic curves of 0.94, 0.86, and 0.82, and areas under the precision-recall curves of 0.48, 0.32, and 0.51 for the models using respiration and SpO2, respiration-only, and SpO2-only, respectively. Correlation between expert-labelled and predicted AHI was 0.96, 0.78, and 0.93, respectively. CONCLUSIONS: A wearable respiratory effort signal with or without SpO2 signal predicted AHI accurately, and best performance was achieved with using both signals.