Category: Nevin Manimala

BMJ Open. 2023 May 22;13(5):e066560. doi: 10.1136/bmjopen-2022-066560.

ABSTRACT

OBJECTIVE: InterVA-5 is a new version of an analytical tool for cause of death (COD) analysis at the population level. This study validates the InterVA-5 against the medical review method, using mortality data in Papua New Guinea (PNG).

DESIGN AND SETTING: This study used mortality data collected from January 2018 to December 2020 in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System (CHESS), established by the PNG Institute of Medical Research in six major provinces.

METHODS: The CHESS demographic team conducted verbal autopsy (VA) interviews with close relatives of the deceased, who died in communities within the catchment areas of CHESS, using the WHO 2016 VA instrument. COD of the deceased was assigned by InterVA-5 tool, and independently certified by the medical team. Consistency, difference and agreement between the InterVA-5 model and medical review were assessed. Sensitivity and positive predictive value (PPV) of the InterVA-5 tool were calculated with reference to the medical review method.

RESULTS: Specific COD of 926 deceased people was included in the validation. Agreement between the InterVA-5 tool and medical review was high (kappa test: 0.72; p<0.01). Sensitivity and PPV of the InterVA-5 were 93% and 72% for cardiovascular diseases, 84% and 86% for neoplasms, 65% and 100% for other chronic non-communicable diseases (NCDs), and 78% and 64% for maternal deaths, respectively. For infectious diseases and external CODs, sensitivity and PPV of the InterVA-5 were 94% and 90%, respectively, while the sensitivity and PPV of the medical review method were both 54% for classifying neonatal CODs.

CONCLUSION: The InterVA-5 tool works well in the PNG context to assign specific CODs of infectious diseases, cardiovascular diseases, neoplasms and injuries. Further improvements with respect to chronic NCDs, maternal deaths and neonatal deaths are needed.

PMID:37217264 | DOI:10.1136/bmjopen-2022-066560

BMJ Open. 2023 May 22;13(5):e067386. doi: 10.1136/bmjopen-2022-067386.

ABSTRACT

OBJECTIVES: REVEAL-CKD aims to estimate the prevalence of, and factors associated with, undiagnosed stage 3 chronic kidney disease (CKD).

DESIGN: Multinational, observational study.

SETTING: Data from six country-specific electronic medical records and/or insurance claims databases from five countries (France, Germany, Italy, Japan and the USA [two databases]).

PARTICIPANTS: Eligible participants (≥18 years old) had ≥2 consecutive estimated glomerular filtration rate (eGFR) measurements (calculated from serum creatinine values, sex and age) taken from 2015 onwards that were indicative of stage 3 CKD (≥30 and <60 mL/min/1.73 m²). Undiagnosed cases lacked an International Classification of Diseases 9/10 diagnosis code for CKD (any stage) any time before, and up to 6 months after, the second qualifying eGFR measurement (study index).

MAIN OUTCOME MEASURES: The primary outcome was point prevalence of undiagnosed stage 3 CKD. Time to diagnosis was assessed using the Kaplan-Meier approach. Factors associated with lacking a CKD diagnosis and risk of diagnostic delay were assessed using logistic regression adjusted for baseline covariates.

RESULTS: The prevalence of undiagnosed stage 3 CKD was 95.5% (19 120/20 012 patients) in France, 84.3% (22 557/26 767) in Germany, 77.0% (50 547/65 676) in Italy, 92.1% (83 693/90 902) in Japan, 61.6% (13 845/22 470) in the US Explorys Linked Claims and Electronic Medical Records Data database and 64.3% (161 254/250 879) in the US TriNetX database. The prevalence of undiagnosed CKD increased with age. Factors associated with undiagnosed CKD were female sex (vs male, range of odds ratios across countries: 1.29-1.77), stage 3a CKD (vs 3b, 1.81-3.66), no medical history (vs a history) of diabetes (1.26-2.77) or hypertension (1.35-1.78).

CONCLUSIONS: There are substantial opportunities to improve stage 3 CKD diagnosis, particularly in female patients and older patients. The low diagnosis rates in patients with comorbidities that put them at risk of disease progression and complications require attention.

TRIAL REGISTRATION: NCT04847531.

PMID:37217263 | DOI:10.1136/bmjopen-2022-067386

Tob Control. 2023 May 22:tc-2022-057624. doi: 10.1136/tc-2022-057624. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare 50-year forecasts of Australian tobacco smoking rates in relation to trends in smoking initiation and cessation and in relation to a national target of ≤5% adult daily prevalence by 2030.

METHODS: A compartmental model of Australian population daily smoking, calibrated to the observed smoking status of 229 523 participants aged 20-99 years in 26 surveys (1962-2016) by age, sex and birth year (1910-1996), estimated smoking prevalence to 2066 using Australian Bureau of Statistics 50-year population predictions. Prevalence forecasts were compared across scenarios in which smoking initiation and cessation trends from 2017 were continued, kept constant or reversed.

RESULTS: At the end of the observation period in 2016, model-estimated daily smoking prevalence was 13.7% (90% equal-tailed interval (EI) 13.4%-14.0%). When smoking initiation and cessation rates were held constant, daily smoking prevalence reached 5.2% (90% EI 4.9%-5.5%) after 50 years, in 2066. When initiation and cessation rates continued their trajectory downwards and upwards, respectively, daily smoking prevalence reached 5% by 2039 (90% EI 2037-2041). The greatest progress towards the 5% goal came from eliminating initiation among younger cohorts, with the target met by 2037 (90% EI 2036-2038) in the most optimistic scenario. Conversely, if initiation and cessation rates reversed to 2007 levels, estimated prevalence was 9.1% (90% EI 8.8%-9.4%) in 2066.

CONCLUSION: A 5% adult daily smoking prevalence target cannot be achieved by the year 2030 based on current trends. Urgent investment in concerted strategies that prevent smoking initiation and facilitate cessation is necessary to achieve 5% prevalence by 2030.

PMID:37217260 | DOI:10.1136/tc-2022-057624

Genome Res. 2023 May 22:gr.277674.123. doi: 10.1101/gr.277674.123. Online ahead of print.

ABSTRACT

The collection and sharing of genomic data are becoming increasingly commonplace in research, clinical, and direct-to-consumer settings. The computational protocols typically adopted to protect individual privacy include sharing summary statistics, such as allele frequencies, or limiting query responses to the presence/absence of alleles of interest using web-services called Beacons. However, even such limited releases are susceptible to likelihood-ratio-based membership-inference attacks. Several approaches have been proposed to preserve privacy, which either suppress a subset of genomic variants or modify query responses for specific variants (e.g., adding noise, as in differential privacy). However, many of these approaches result in a significant utility loss, either suppressing many variants or adding a substantial amount of noise. In this paper, we introduce optimization-based approaches to explicitly trade off the utility of summary data or Beacon responses and privacy with respect to membership-inference attacks based on likelihood-ratios, combining variant suppression and modification. We consider two attack models. In the first, an attacker applies a likelihood-ratio test to make membership-inference claims. In the second model, an attacker uses a threshold that accounts for the effect of the data release on the separation in scores between individuals in the dataset and those who are not. We further introduce highly scalable approaches for approximately solving the privacy-utility tradeoff problem when information is either in the form of summary statistics or presence/absence queries. Finally, we show that the proposed approaches outperform the state of the art in both utility and privacy through an extensive evaluation with public datasets.

PMID:37217251 | DOI:10.1101/gr.277674.123

Genome Res. 2023 May 22:gr.277677.123. doi: 10.1101/gr.277677.123. Online ahead of print.

ABSTRACT

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) is a common assay to identify chromatin accessible regions by using a Tn5 transposase that can access, cut, and ligate adapters to DNA fragments for subsequent amplification and sequencing. These sequenced regions are quantified and tested for enrichment in a process referred to as “peak calling”. Most unsupervised peak calling methods are based on simple statistical models and suffer from elevated false positive rates. Newly developed supervised deep learning methods can be successful, but they rely on high quality labeled data for training, which can be difficult to obtain. Moreover, though biological replicates are recognized to be important, there are no established approaches for using replicates in the deep learning tools, and the approaches available for traditional methods either cannot be applied to ATAC-seq, where control samples may be unavailable, or are post-hoc and do not capitalize on potentially complex, but reproducible signal in the read enrichment data. Here, we propose a novel peak caller that uses unsupervised contrastive learning to extract shared signals from multiple replicates. Raw coverage data are encoded to obtain low-dimensional embeddings and optimized to minimize a contrastive loss over biological replicates. These embeddings are passed to another contrastive loss for learning and predicting peaks and decoded to denoised data under an autoencoder loss. We compared our Replicative Contrastive Learner (RCL) method with other existing methods on ATAC-seq data, using annotations from ChromHMM genome and transcription factor ChIP-seq as noisy truth. RCL consistently achieved the best performance.

PMID:37217250 | DOI:10.1101/gr.277677.123

BMJ Health Care Inform. 2023 May;30(1):e100712. doi: 10.1136/bmjhci-2022-100712.

ABSTRACT

OBJECTIVES: Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession.

METHODS: We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI’s impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach.

RESULTS: Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making.

CONCLUSIONS: Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare.

PMID:37217249 | DOI:10.1136/bmjhci-2022-100712

J Immunother Cancer. 2023 May;11(5):e006514. doi: 10.1136/jitc-2022-006514.

ABSTRACT

BACKGROUND: Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC).

METHODS: Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis.

RESULTS: Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p<0.001) and validation set (HR=2.625, 95% CI: 1.506 to 4.574, p=0.001), respectively. Subgroup analyses showed that the radiomics model was not influenced by programmed death-ligand 1 status, tumor metastatic burden or molecular subtype.

CONCLUSIONS: This radiomics model provided an innovative and accurate way that could stratify patients with ABC who may benefit more from ICIs-based therapies.

PMID:37217246 | DOI:10.1136/jitc-2022-006514

Int Wound J. 2023 May 22. doi: 10.1111/iwj.14240. Online ahead of print.

ABSTRACT

Head of bed elevation is used to manage some medical and surgical conditions however this may increase a patient’s risk of sacral pressure injuries. Novel point-of-care technologies that measure subepidermal moisture can identify changes in localised subepidermal oedema and potential pressure injury risk. This prospective exploratory study investigated variations in sacral subepidermal oedema in healthy adults during 120-min of 60° head of bed elevation. Sacral subepidermal oedema was measured at 20-min intervals using the Provisio® subepidermal moisture scanner. Descriptive analysis, one-way repeated measures analysis of variance and an independent t-test were conducted. Slightly more male volunteers (n = 11; 55%) were recruited and the sample mean age was 39.3 years (SD 14.7) with an average body mass index of 25.8 (SD 4.3). Little variation in the mean sacral subepidermal moisture of healthy adults was observed. There was a statistically significant difference in the mean sacral subepidermal moisture measurements between males and females (Mean difference 0.18; 95% confidence intervals: 0.02 to 0.35; P = .03). Healthy adults can tolerate prolonged 60° head of bed elevation without developing increased subepidermal sacral oedema. This warrants further investigation in other populations, in various positions and over different time periods.

PMID:37217227 | DOI:10.1111/iwj.14240

Psychol Med. 2023 May 23:1-8. doi: 10.1017/S0033291723001290. Online ahead of print.

ABSTRACT

BACKGROUND: Serum levels of C-reactive protein (CRP) and interleukin-6 (IL-6) have been associated with anxiety and depression in cross-sectional and Mendelian randomisation studies, but results regarding the effect size and direction have been mixed. A recent Mendelian Randomisation (MR) study suggested that CRP may decrease and IL-6 may increase anxiety and depression symptoms.

METHODS: Among 68 769 participants of the population-based Trøndelag Health Study (HUNT), we performed cross-sectional observational and one-sample MR analyses of serum CRP and two-sample MR analysis of serum IL-6. The main outcomes were symptoms of anxiety and depression assessed using the Hospital Anxiety and Depression Scale (HADS) and life satisfaction assessed using a seven-level ordinal questionnaire where higher scores indicate lower life satisfaction.

RESULTS: In cross-sectional observational analyses, a doubling in serum CRP level was associated with 0.27% (95% CI -0.20 to 0.75) difference in HADS depression score (HADS-D), -0.77% (95% CI -1.24 to -0.29) difference in HADS anxiety score (HADS-A) and -0.10% (95% CI -0.41 to 0.21) difference in life satisfaction score. In one-sample MR analyses, a doubling in serum CRP was associated with 2.43% (95% CI -0.11 to 5.03) higher HADS-D, 1.94% (95% CI -0.58 to 4.52) higher HADS-A, and 2.00% (95% CI 0.45 to 3.59) higher life satisfaction score. For IL-6, causal point estimates were in the opposite direction, but imprecise and far from conventional criteria for statistical significance.

CONCLUSIONS: Our results do not support a major causal role of serum CRP on anxiety and depression symptoms and life satisfaction, but provides weak evidence that serum CRP may modestly increase anxiety and depression symptoms and reduce life satisfaction. Our findings do not support the recent suggestion that serum CRP may lower anxiety and depression symptoms.

PMID:37217205 | DOI:10.1017/S0033291723001290

J Am Vet Med Assoc. 2023 May 18:1-7. doi: 10.2460/javma.23.02.0076. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the CT features of cavitary pulmonary lesions and determine their utility to differentiate malignant from benign lesions.

ANIMALS: This retrospective study included cases from 5 veterinary medical centers between January 1 2010, and December 31, 2020. Inclusion criteria included having a gas-filled cavitary pulmonary lesion on thoracic CT and definitive diagnosis by either cytology or histopathology. Forty-two animals (27 dogs and 15 cats) were included in this study.

PROCEDURES: Medical records systems/imaging databases were searched, and cases meeting inclusion criteria were selected. The CT studies were interpreted by a third-year radiology resident, and findings were reviewed by a board-certified veterinary radiologist.

RESULTS: 7 of the 13 lesion characteristics investigated were not statistically associated with the final diagnosis of the lesion, whereas 6 were statistically associated. Those that were associated included the presence of intralesional contrast enhancement, type of intralesional contrast enhancement (heterogenous and homogenous analyzed separately), presence of additional nodules, wall thickness of the lesion at its thickest point, and wall thickness at the thinnest point.

CLINICAL RELEVANCE: Results from the present study showed that thoracic CT imaging of cavitary pulmonary lesions can be used to further refine the list of differential diagnoses. Based on this data set, in lesions that have heterogenous contrast enhancement, additional pulmonary nodules, and wall thickness > 40 mm at their thickest point, it would be reasonable to consider malignant neoplastic disease higher on the list of differentials than other causes.

PMID:37217176 | DOI:10.2460/javma.23.02.0076