METHODS: We identified drugs as precipitants of ACLF among prospective cohort of patients with ACLF from the Asian Pacific Association of Study of Liver (APASL) ACLF Research Consortium (AARC) database. Drugs were considered precipitants after exclusion of known causes together with a temporal association between exposure and decompensation. Outcome was defined as death from decompensation.
RESULTS: Of the 3,132 patients with ACLF, drugs were implicated as a cause in 329 (10.5%, mean age 47 years, 65% men) and other nondrug causes in 2,803 (89.5%) (group B). Complementary and alternative medications (71.7%) were the commonest insult, followed by combination antituberculosis therapy drugs (27.3%). Alcoholic liver disease (28.6%), cryptogenic liver disease (25.5%), and non-alcoholic steatohepatitis (NASH) (16.7%) were common causes of underlying liver diseases. Patients with drug-induced ACLF had jaundice (100%), ascites (88%), encephalopathy (46.5%), high Model for End-Stage Liver Disease (MELD) (30.2), and Child-Turcotte-Pugh score (12.1). The overall 90-day mortality was higher in drug-induced (46.5%) than in non-drug-induced ACLF (38.8%) (P = 0.007). The Cox regression model identified arterial lactate (P < 0.001) and total bilirubin (P = 0.008) as predictors of mortality.
DISCUSSION: Drugs are important identifiable causes of ACLF in Asia-Pacific countries, predominantly from complementary and alternative medications, followed by antituberculosis drugs. Encephalopathy, bilirubin, blood urea, lactate, and international normalized ratio (INR) predict mortality in drug-induced ACLF.
METHODS: Data was collected from 13 Asian countries on patients with CLD, known or newly diagnosed, with confirmed COVID-19.
RESULTS: Altogether, 228 patients [185 CLD without cirrhosis and 43 with cirrhosis] were enrolled, with comorbidities in nearly 80%. Metabolism associated fatty liver disease (113, 61%) and viral etiology (26, 60%) were common. In CLD without cirrhosis, diabetes [57.7% vs 39.7%, OR = 2.1 (1.1-3.7), p = 0.01] and in cirrhotics, obesity, [64.3% vs. 17.2%, OR = 8.1 (1.9-38.8), p = 0.002] predisposed more to liver injury than those without these. Forty three percent of CLD without cirrhosis presented as acute liver injury and 20% cirrhotics presented with either acute-on-chronic liver failure [5 (11.6%)] or acute decompensation [4 (9%)]. Liver related complications increased (p
METHODOLOGY: One thousand two hundred and sixteen prospectively enrolled patients with ACLF (males 98%, mean age 42.5 ± 9.4 years, mean CTP, MELD and AARC scores of 12 ± 1.4, 29.7 ± 7 and 9.8 ± 2 respectively) from the Asian Pacific Association for the Study of the Liver (APASL) ACLF Research Consortium (AARC) database were analysed retrospectively. Patients with or without metabolic risk factors were compared for severity (CTP, MELD, AARC scores) and day 30 and 90 mortality. Information on overweight/obesity, type 2 diabetes mellitus (T2DM), hypertension and dyslipidaemia were available in 1028 (85%), 1019 (84%), 1017 (84%) and 965 (79%) patients respectively.
RESULTS: Overall, 392 (32%) patients died at day 30 and 528 (43%) at day 90. Overweight/obesity, T2DM, hypertension and dyslipidaemia were present in 154 (15%), 142 (14%), 66 (7%) and 141 (15%) patients, respectively, with no risk factors in 809 (67%) patients. Patients with overweight/obesity had higher MELD scores (30.6 ± 7.1 vs 29.2 ± 6.9, P = .007) and those with dyslipidaemia had higher AARC scores (10.4 ± 1.2 vs 9.8 ± 2, P = .014). Overweight/obesity was associated with increased day 30 mortality (HR 1.54, 95% CI 1.06-2.24, P = .023). None of other metabolic risk factors, alone or in combination, had any impact on disease severity or mortality. On multivariate analysis, overweight or obesity was significantly associated with 30-day mortality (aHR 1.91, 95% CI 1.41-2.59, P
PATIENTS AND METHODS: Data of 2360 patients from APASL-ACLF Research Consortium (AARC) was analysed. Multivariate logistic regression model (PIRO score) was developed from a derivation cohort (n=1363) which was validated in another prospective multicentric cohort of acute on chronic liver failure patients (n=997).
RESULTS: Factors significant for P component were serum creatinine[(≥2 mg/dL)OR 4.52, 95% CI (3.67-5.30)], bilirubin [(<12 mg/dL,OR 1) vs (12-30 mg/dL,OR 1.45, 95% 1.1-2.63) vs (≥30 mg/dL,OR 2.6, 95% CI 1.3-5.2)], serum potassium [(<3 mmol/LOR-1) vs (3-4.9 mmol/L,OR 2.7, 95% CI 1.05-1.97) vs (≥5 mmol/L,OR 4.34, 95% CI 1.67-11.3)] and blood urea (OR 3.73, 95% CI 2.5-5.5); for I component nephrotoxic medications (OR-9.86, 95% CI 3.2-30.8); for R component,Systemic Inflammatory Response Syndrome,(OR-2.14, 95% CI 1.4-3.3); for O component, Circulatory failure (OR-3.5, 95% CI 2.2-5.5). The PIRO score predicted acute kidney injury with C-index of 0.95 and 0.96 in the derivation and validation cohort. The increasing PIRO score was also associated with mortality (P
METHODS: Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R2, relative prediction errors, and odds ratios.
RESULTS: Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p 12 had the lowest 30-day survival (5.7%).
CONCLUSIONS: APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.
METHODS: Altogether 1021 patients were analyzed for the severity and organ failure at admission to determine transplant eligibility and 28 day survival with or without transplant.
RESULTS: The ACLF cohort [mean age 44 ± 12.2 years, males 81%) was of sick patients; 55% willing for LT at admission, though 63% of them were ineligible due to sepsis or organ failure. On day 4, recovery in sepsis and/or organ failure led to an improvement in transplant eligibility from 37% at baseline to 63.7%. Delay in LT up to 7 days led to a higher incidence of multiorgan failure (p
METHODS: Delayed-onset (>7 days) vaccine-related adverse events (AE), disease flares and AID-related treatment modifications were analysed upon diagnosis of AID vs healthy controls (HC) and the pregnancy/breastfeeding status at the time of at least one dose of vaccine.
RESULTS: Among the 9201 participants to the self-administered online survey, 6787 (73.8%) were women. Forty pregnant and 52 breastfeeding patients with AID were identified, of whom the majority had received at least one dose of COVID-19 vaccine (100% and 96.2%, respectively). AE were reported significantly more frequently in pregnant than in non-pregnant patients (overall AE 45% vs 26%, P = 0.01; minor AE 40% vs 25.9%, P = 0.03; major AE 17.5% vs 4.6%, P
METHODS: The COVAD-1 and -2 global surveys were circulated in early 2021 and 2022, respectively, and we captured demographics, comorbidities, AIRDs details, COVID-19 infection history and vaccination details. Flares of IIMs were defined as (a) patient self-reported, (b) immunosuppression (IS) denoted, (c) clinical sign directed and (d) with >7.9-point minimal clinically significant improvement difference worsening of Patient-Reported Outcomes Measurement Information System (PROMIS) PROMISPF10a score. Risk factors of flares were analysed using regression models.
RESULTS: Of 15 165 total respondents, 1278 IIMs (age 63 years, 70.3% female, 80.8% Caucasians) and 3453 AIRDs were included. Flares of IIM were seen in 9.6%, 12.7%, 8.7% and 19.6% patients by definitions (a) to (d), respectively, with a median time to flare of 71.5 (10.7-235) days, similar to AIRDs. Patients with active IIMs pre-vaccination (OR 1.2; 95% CI 1.03, 1.6, P = 0.025) were prone to flares, while those receiving rituximab (OR 0.3; 95% CI 0.1, 0.7, P = 0.010) and AZA (OR 0.3, 95% CI 0.1, 0.8, P = 0.016) were at lower risk. Female gender and comorbidities predisposed to flares requiring changes in IS. Asthma (OR 1.62; 95% CI 1.05, 2.50, P = 0.028) and higher pain visual analogue score (OR 1.19; 95% CI 1.11, 1.27, P
OBJECTIVES: to create a set of evidence- and expert consensus-based falls prevention and management recommendations applicable to older adults for use by healthcare and other professionals that consider: (i) a person-centred approach that includes the perspectives of older adults with lived experience, caregivers and other stakeholders; (ii) gaps in previous guidelines; (iii) recent developments in e-health and (iv) implementation across locations with limited access to resources such as low- and middle-income countries.
METHODS: a steering committee and a worldwide multidisciplinary group of experts and stakeholders, including older adults, were assembled. Geriatrics and gerontological societies were represented. Using a modified Delphi process, recommendations from 11 topic-specific working groups (WGs), 10 ad-hoc WGs and a WG dealing with the perspectives of older adults were reviewed and refined. The final recommendations were determined by voting.
RECOMMENDATIONS: all older adults should be advised on falls prevention and physical activity. Opportunistic case finding for falls risk is recommended for community-dwelling older adults. Those considered at high risk should be offered a comprehensive multifactorial falls risk assessment with a view to co-design and implement personalised multidomain interventions. Other recommendations cover details of assessment and intervention components and combinations, and recommendations for specific settings and populations.
CONCLUSIONS: the core set of recommendations provided will require flexible implementation strategies that consider both local context and resources.
METHODS: The COVAD surveys were used to extract data on flare demographics, comorbidities, COVID-19 history, and vaccination details for patients with AIRDs. Flares following vaccination were identified as patient-reported (a), increased immunosuppression (b), clinical exacerbations (c) and worsening of PROMIS scores (d). We studied flare characteristics and used regression models to differentiate flares among various AIRDs.
RESULTS: Of 15 165 total responses, the incidence of flares in 3453 patients with AIRDs was 11.3%, 14.8%, 9.5% and 26.7% by definitions a-d, respectively. There was moderate agreement between patient-reported and immunosuppression-defined flares (K = 0.403, P = 0.022). Arthritis (61.6%) and fatigue (58.8%) were the most commonly reported symptoms. Self-reported flares were associated with higher comorbidities (P = 0.013), mental health disorders (MHDs) (P
METHODS: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.
RESULTS: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83.
CONCLUSION: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.
METHODS: The first and second COVAD patient self-reported e-surveys were circulated from March to December 2021, and February to June 2022 (ongoing). We collected data on demographics, comorbidities, COVID-19 infection and vaccination history, reasons for hesitancy, and patient reported outcomes. Predictors of hesitancy were analysed using regression models in different groups.
RESULTS: We analysed data from 18 882 (COVAD-1) and 7666 (COVAD-2) respondents. Reassuringly, hesitancy decreased from 2021 (16.5%) to 2022 (5.1%) (OR: 0.26; 95% CI: 0.24, 0.30, P