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AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD-code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. METHODS AND RESULTS: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VAs External Peer Review Program for HF (EPRP-HF) and non-HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF-related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP-HF as cases (PPV, 0.86; sensitivity, 0.86). From the HF ICD-code universe, we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). CONCLUSIONS: These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation.

Aims: According to the Kidney Disease: Improving Global Outcomes (KDIGO) guideline, the definition of chronic kidney disease (CKD) requires the presence of abnormal kidney structure or function for >3 months with implications for health. CKD in patients with heart failure (HF) has not been defined using this definition, and less is known about the true health implications of CKD in these patients. The objective of the current study was to identify patients with HF who met KDIGO criteria for CKD and examine their outcomes. Methods and results: Of the 1 419 729 Veterans with HF not receiving kidney replacement therapy, 828 744 had data on ≥2 ambulatory serum creatinine >90 days apart. CKD was defined as estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 (n = 185 821) or urinary albumin‐to‐creatinine ratio (uACR) >30 mg/g (n = 32 730) present twice >3 months apart. Normal kidney function (NKF) was defined as eGFR ≥60 ml/min/1.73 m2, present for >3 months, without any uACR >30 mg/g (n = 365 963). Patients with eGFR <60 ml/min/1.73 m2 were categorized into four stages: 45–59 (n = 72 606), 30–44 (n = 74 812), 15–29 (n = 32 077), and <15 (n = 6326) ml/min/1.73 m2. Five‐year all‐cause mortality occurred in 40.4%, 57.8%, 65.6%, 73.3%, 69.7%, and 47.5% of patients with NKF, four eGFR stages, and uACR >30mg/g (albuminuria), respectively. Compared with NKF, hazard ratios (HR) (95% confidence intervals [CI]) for all‐cause mortality associated with the four eGFR stages and albuminuria were 1.63 (1.62–1.65), 2.00 (1.98–2.02), 2.49 (2.45–2.52), 2.28 (2.21–2.35), and 1.22 (1.20–1.24), respectively. Respective age‐adjusted HRs (95% CIs) were 1.13 (1.12–1.14), 1.36 (1.34–1.37), 1.87 (1.84–1.89), 2.24 (2.18–2.31) and 1.19 (1.17–1.21), and multivariable‐adjusted HRs (95% CIs) were 1.11 (1.10–1.12), 1.24 (1.22–1.25), 1.46 (1.43–1.48), 1.42 (1.38–1.47), and 1.13 (1.11–1.16). Similar patterns were observed for associations with hospitalizations. Conclusion: Data needed to define CKD using KDIGO criteria were available in six out of ten patients, and CKD could be defined in seven out of ten patients with data. HF patients with KDIGO‐defined CKD had higher risks for poor outcomes, most of which was not explained by abnormal kidney structure or function. Future studies need to examine whether CKD defined using a single eGFR is characteristically and prognostically different from CKD defined using KDIGO criteria. [ABSTRACT FROM AUTHOR]

Introduction: According to the US Renal Data System (USRDS), patients with end-stage kidney disease (ESKD) on maintenance dialysis had higher mortality during early COVID-19 pandemic. Less is known about the effect of the pandemic on the delivery of outpatient maintenance hemodialysis and its impact on death. We examined the effect of pandemic-related disruption on the delivery of dialysis treatment and mortality in patients with ESKD receiving maintenance hemodialysis in the Veterans Health Administration (VHA) facilities, the largest integrated national healthcare system in the USA. Methods: Using national VHA electronic health records data, we identified 7,302 Veterans with ESKD who received outpatient maintenance hemodialysis in VHA healthcare facilities during the COVID-19 pandemic (February 1, 2020, to December 31, 2021). We estimated the average change in the number of hemodialysis treatments received and deaths per 1,000 patients per month during the pandemic by conducting interrupted time-series analyses. We used seasonal autoregressive moving average (SARMA) models, in which February 2020 was used as the conditional intercept and months thereafter as conditional slope. The models were adjusted for seasonal variations and trends in rates during the pre-pandemic period (January 1, 2007, to January 31, 2020). Results: The number (95% CI) of hemodialysis treatments received per 1,000 patients per month during the pre-pandemic and pandemic periods were 12,670 (12,525–12,796) and 12,865 (12,729–13,002), respectively. Respective all-cause mortality rates (95% CI) were 17.1 (16.7–17.5) and 19.6 (18.5–20.7) per 1,000 patients per month. Findings from SARMA models demonstrate that there was no reduction in the dialysis treatments delivered during the pandemic (rate ratio: 0.999; 95% CI: 0.998–1.001), but there was a 2.3% (95% CI: 1.5–3.1%) increase in mortality. During the pandemic, the non-COVID hospitalization rate was 146 (95% CI: 143–149) per 1,000 patients per month, which was lower than the pre-pandemic rate of 175 (95% CI: 173–176). In contrast, there was evidence of higher use of telephone encounters during the pandemic (3,023; 95% CI: 2,957–3,089), compared with the pre-pandemic rate (1,282; 95% CI: 1,241–1,324). Conclusions: We found no evidence that there was a disruption in the delivery of outpatient maintenance hemodialysis treatment in VHA facilities during the COVID-19 pandemic and that the modest rise in deaths during the pandemic is unlikely to be due to missed dialysis. [ABSTRACT FROM AUTHOR]

The maximum fire smoke temperature beneath tunnel ceilings using longitudinal ventilation was studied by both small-scale experiments and numerical simulations for a small heat release rate (HRR) fire. And then, the accuracy of the numerical simulation is verified. A numerical simulation is subsequently employed to modify the Kurioka model for cases in large HRR. Then, the modified Kurioka model is verified by various on-site high HRR fire experimental results conducted by other authors.

The red-emitting perovskite material has received widespread attention as a long-wavelength optical gain media. But the easy phase change in the air limits its practical application. Herein, red CsPbBr x I3âˆ' x /SiO2 quantum dots (QDs) are prepared by a ligand-mediated hot injection method in which 3-aminopropyl-triethoxysilane (APTES) is used instead of the usual oleylamine (OAm) ligand. Through the hydrolysis of amino groups, a thin silicon layer is formed on the QD surface, improving the stability and without causing the aggregation of QDs. We find that the ratio of I/Br and the size of QDs can be tuned by adjusting the APTES amount. Moreover, this ligand-mediated synthesis effectively passivates the surface defects, so the photoluminescence quantum yield is remarkably improved, and the carrier lifetime is prolonged. The amplified spontaneous emission is achieved under 532 nm nanosecond laser excitation. Compared with the original CsPbBrI2-OAm QD films, the threshold of CsPbBr x I3âˆ' x /SiO2 QD films is reduced from 403.5 to 98.7 ÎĽ J cmâˆ'2, and the radiation stability is significantly enhanced. Therefore, this material shows great potential in the random laser field. [ABSTRACT FROM AUTHOR]

All inorganic cesium lead halide perovskite (CsPbX3; X = Cl, Br, or I) quantum dots (QDs) have received widespread attention as a class of favorable optical gain materials. The key issue of CsPbX3 perovskites is their instability under light or exposure to air. Notably, the inherent phase instability of CsPbI3 QDs severely restricts the development of red-light emission. Herein, we propose a simple method to post-process CsPbBr3 QDs with a ZnI2 solution. The prepared CsPbBryI3−y (named CPBI-z, where z corresponds to the photoluminescence peaks' position) QDs exhibit adjustable photoluminescence in the range of 550–640 nm. The photoluminescence quantum yield is increased from 73.3% to 97.2% as the surface defects of QDs are passivated by I− and Zn2+. Compared with the pristine CsPbBr3 QDs, the post-processed QDs are more stable under ultraviolet irradiation and in the air. Aside from that, under 532 nm nanosecond laser excitation, CPBI-z QD films exhibit excellent amplified spontaneous emission performance. The CPBI-640 QD film shows a low threshold, 102 μJ cm−2 for red stable amplified spontaneous emission. [ABSTRACT FROM AUTHOR]

Abstract Background The purpose of this study was to evaluate the effectiveness of a patient education program developed to facilitate statin adherence. Methods A controlled trial was designed to test the effectiveness of a multifaceted patient education program to facilitate statin adherence. The program included a brief, in-office physician counseling kit followed by patient mailings. The primary end point was adherence to filling statin prescriptions during a 120-day period. Patients new to statins enrolled and completed a survey. Data from a national pharmacy claims database were used to track adherence. Results Patients new to statin therapy exposed to a patient counseling and education program achieved a 12.4 higher average number of statin prescription fill days and were 10% more likely to fill prescriptions for at least 120 days (p = .01). Conclusion Brief in-office counseling on cardiovascular risk followed by patient education mailings can be effective in increasing adherence. Physicians found a one-minute counseling tool and pocket guidelines useful in counseling patients.

Abstract Background The internet has had a strong impact on how physicians access information and on the development of continuing medical education activities. Evaluation of the effectiveness of these activities has lagged behind their development. Methods To determine the effectiveness of a group of 48 internet continuing medical education (CME) activities, case vignette surveys were administered to US physicians immediately following participation, and to a representative control group of non-participant physicians. Responses to case vignettes were analyzed based on evidence presented in the content of CME activities. An effect size for each activity was calculated using Cohen's d to determine the amount of difference between the two groups in the likelihood of making evidence-based clinical decisions, expressed as the percentage of non-overlap, between the two groups. Two formats were compared. Results In a sample of 5621 US physicians, of the more than 100,000 physicians who participated in 48 internet CME activities, the average effect size was 0.75, an increased likelihood of 45% that participants were making choices in response to clinical case vignettes based on clinical evidence. This likelihood was higher in interactive case-based activities, 51% (effect size 0.89), than for text-based clinical updates, 40% (effect size 0.63). Effectiveness was also higher among primary care physicians than specialists. Conclusion Physicians who participated in selected internet CME activities were more likely to make evidence-based clinical choices than non-participants in response to clinical case vignettes. Internet CME activities show promise in offering a searchable, credible, available on-demand, high-impact source of CME for physicians.

Abstract Background Investigators are actively testing interventions intended to increase lifespan and wish to test whether the interventions increase maximum lifespan. Based on the fact that one cannot be assured of observing population maximum lifespans in finite samples, in previous work, we constructed and validated several tests of difference in the upper parts of lifespan distributions between a treatment group and a control group by testing whether the probabilities that observations are above some threshold defining 'old' or being in the tail of the survival distribution are equal in the two groups. However, a limitation of these tests is that they do not consider how much above the threshold any particular observation is. Methods In this article we propose new methods which improve upon our previous tests by considering not only whether an observation is above some threshold, but also the magnitudes by which observations exceed the threshold. Results Simulations show that the new methods control type I error rates quite well and that the power of the new methods is usually higher than that of the tests we previously proposed. In illustrative analyses of two real datasets involving rodents, when setting the threshold equal to 110 (100) weeks for the first (second) datasets, the new methods detected differences in 'maximum lifespan' between groups at nominal alpha levels of 0.01 (0.05) for the first (second) datasets and provided more significant results than competitor tests. Conclusion The new methods not only have good performance in controlling the type I error rates but also improve the power compared with the tests we previously proposed.