Determination of Clinically Acceptable Analytical Variation of Cardiac Troponin at Decision Thresholds.

BACKGROUND: Clinical decision-making for risk stratification for possible myocardial infarction (MI) uses high-sensitivity cardiac troponin (hs-cTn) thresholds that range from the limit of detection to several-fold higher than the upper reference limit (URL). To establish a minimum analytical variation standard, we can quantify the effect of variation on the population clinical measures of safety (sensitivity) and effectiveness [proportion below threshold, or positive predictive value (PPV)]. METHODS: From large datasets of patients investigated for possible MI with the Abbott hs-cTnI and Roche hs-cTnT assays, we synthesized datasets of 1 000 000 simulated patients. Troponin concentrations were randomly varied several times based on absolute deviations of 0.5 to 3 ng/L and relative changes of 2% to 20% around the low-risk threshold (5 ng/L) and URLs, respectively. RESULTS: For both assays at the low-risk thresholds, there were negligible differences in sensitivity (<0.3%) with increasing analytical variation. The proportion of patients characterized as low risk reduced by 30% to 29% (Roche) and 53% to 44% (Abbott). At the URL, increasing analytical variation also did not change sensitivity; the PPV fell by less than 3%. For risk stratification, increased delta thresholds (change between serial troponin concentrations) increased sensitivity at the cost of a decreased percentage of patients below the delta threshold, with the largest changes at the greatest analytical variation. CONCLUSIONS: At the low-risk threshold, analytical variation up to 3 ng/L minimally impacted the safety metric (sensitivity) but marginally reduced effectiveness. Similarly, at the URL even relative variation up to 25% minimally impacted safety metrics and effectiveness. Analytical variation for delta thresholds did not negatively impact sensitivity but decreased effectiveness.

Prognostic performance of soluble urokinase plasminogen activator receptor for heart failure or mortality in Western and Asian patients with acute breathlessness.

AIMS: The performance of circulating soluble urokinase plasminogen activator receptor (suPAR) for predicting the composite endpoint of subsequent heart failure (HF) hospitalisation and/or death at 1 year was assessed in (i) patients with undifferentiated breathlessness, and generalisability was compared in (ii) disparate Western versus Asian sub-cohorts, and in (iii) the sub-cohort adjudicated with HF. METHODS AND RESULTS: Patients with acute breathlessness were recruited from the emergency departments in New Zealand (NZ, n = 612) and Singapore (n = 483). suPAR measured in the presentation samples was higher in patients incurring the endpoint (n = 281) compared with survivors (5.2 ng/mL vs 3.1 ng/mL, P < 0.0001). The discriminative power of suPAR for endpoint prediction was c-statistic of 0.77 in the combined population, but was superior in Singapore than NZ (c-statistic: 0.83 vs 0.71, P < 0.0001). Although the highest suPAR tertile (>4.37 ng/mL) was associated with risks of >4-fold in NZ, >20-fold in Singapore, and ≥3-fold in HF for incurring the outcome, there was no interaction between country and suPAR levels after adjustment. Multivariable analysis indicated suPAR to be robust in predicting HF/death at 1-year [hazard ratio: 1.9 (95% CI:1.7 to 2.0) per SD increase] and improved risk discrimination for outcome prediction in HF (∆0.06) and for those with NT-proBNP >1000 pg/mL (∆0.02). CONCLUSION: suPAR is a strong independent predictor of HF and/or death at 1 year in acutely breathless patients, in both Asian and Western cohorts, and in HF. suPAR may improve stratification of acutely breathless patients, and in acute HF, for risk of later onset of heart failure or mortality.

Convalescent Growth Differentiation Factor-15 and Long-Term Outcomes after an Acute Coronary Syndrome.

BACKGROUND: Growth differentiation factor-15 (GDF-15) has been shown to be associated with adverse clinical outcomes in patients after an acute coronary syndrome when measured soon after an event. Although dynamic in the acute phase after myocardial injury, GDF-15 has been shown to remain stable during convalescence. In this study, we aimed to assess the value of GDF-15 as a long-term prognostic marker for clinical outcomes when measured in the convalescent phase following an acute coronary syndrome. METHODS: GDF-15 concentrations were measured in 1945 patients who were recruited between 2002 and 2009 to the Coronary Disease Cohort Study. For this analysis, follow-up was curtailed at 10 years and association of GDF-15 with all-cause death, cardiovascular death, recurrent myocardial infarction, and heart failure hospitalizations were assessed with multivariate Cox proportional hazard regression analysis. RESULTS: After 10 years of follow-up, there were 648 deaths (348 from cardiovascular causes), 500 admissions for myocardial infarction, and 436 for heart failure. Four-month convalescent GDF-15 demonstrated a robust independent association with all endpoints, which remained after adjustment for Global Registry of Acute Coronary Events score and other convalescent biomarkers. When compared to the lowest quartile of GDF-15 concentrations, those in the highest quartile had a 3-fold increased risk of all-cause death. CONCLUSIONS: Convalescent plasma GDF-15 is a strong and independent predictor of 10-year all-cause death, cardiovascular death, recurrent myocardial infarction, and heart failure admission following an acute coronary syndrome. AUSTRALIAN NEW ZEALAND CLINICAL TRIALS REGISTRY TRIAL ID: ACTRN12605000431628.

Derivation and Validation of Thresholds Using Synthetic Data Methods for Single-Test Screening of Emergency Department Patients with Possible Acute Myocardial Infarction Using a Point-of-Care Troponin Assay.

BACKGROUND: Single-sample (screening) rule-out of acute myocardial infarction (AMI) with troponin requires derivation of a single-test screening threshold. In data sets with small event numbers, the lowest one or two concentrations of myocardial infarction (MI) patients dictate the threshold. This is not optimal. We aimed to demonstrate a process incorporating both real and synthetic data for deriving such thresholds using a novel pre-production high-precision point-of-care assay. METHODS: cTnI concentrations were measured from thawed plasma using the Troponin I Next (TnI-Nx) assay (i-STAT; Abbott) in adults on arrival to the emergency department with symptoms suggestive of AMI. The primary outcome was an AMI or cardiac death within 30 days. We used internal-external validation with synthetic data production based on clinical and demographic data, plus the measured TnI-Nx concentration, to derive and validate decision thresholds for TnI-Nx. The target low-risk threshold was a sensitivity of 99% and a high-risk threshold specificity of >95%. RESULTS: In total, 1356 patients were included, of whom 191 (14.1%) had the primary outcome. A total of 500 synthetic data sets were constructed. The mean low-risk threshold was determined to be 5 ng/L. This categorized 38% (95% CI, 6%-68%) to low-risk with a sensitivity of 99.0% (95% CI, 98.6%-99.5%) and a negative predictive value of 99.4% (95% CI, 97.6%-99.8%). A similarly derived high-risk threshold of 25 ng/L had a specificity of 95.0% (95% CI, 94.8%-95.1%) and a positive predictive value of 74.8% (95% CI, 71.5%-78.0%). CONCLUSIONS: With the TnI-Nx assay, we successfully demonstrated an approach using synthetic data generation to derive low-risk thresholds for safe and effective screening.

Comparison of three creatinine-based equations to predict adverse outcome in a cardiovascular high-risk cohort: an investigation using the SPRINT research materials.

Background: Novel creatinine-based equations have recently been proposed but their predictive performance for cardiovascular outcomes in participants at high cardiovascular risk in comparison to the established CKD-EPI 2009 equation is unknown. Method: In 9361 participants from the United States included in the randomized controlled SPRINT trial, we calculated baseline estimated glomerular filtration rate (eGFR) using the CKD-EPI 2009, CKD-EPI 2021, and EKFC equations and compared their predictive value of cardiovascular events. The statistical metric used is the net reclassification improvement (NRI) presented separately for those with and those without events. Results: During a mean follow-up of 3.1 ± 0.9 years, the primary endpoint occurred in 559 participants (6.0%). When using the CKD-EPI 2009, the CKD-EPI 2021, and the EKFC equations, the prevalence of CKD (eGFR <60 ml/min/1.73 m2 or >60 ml/min/1.73 m2 with an ACR ≥30 mg/g) was 37% vs. 35.3% (P = 0.02) vs. 46.4% (P < 0.001), respectively. The corresponding mean eGFR was 72.5 ± 20.1 ml/min/1.73 m2 vs. 73.2 ± 19.4 ml/min/1.73 m2 (P < 0.001) vs. 64.6 ± 17.4 ml/min/1.73 m2 (P < 0.001). Neither reclassification according to the CKD-EPI 2021 equation [CKD-EPI 2021 vs. CKD-EPI 2009: NRIevents: -9.5% (95% confidence interval (CI) -13.0% to -5.9%); NRInonevents: 4.8% (95% CI 3.9% to 5.7%)], nor reclassification according to the EKFC equation allowed better prediction of cardiovascular events compared to the CKD-EPI 2009 equation (EKFC vs. CKD-EPI 2009: NRIevents: 31.2% (95% CI 27.5% to 35.0%); NRInonevents: -31.1% (95% CI -32.1% to -30.1%)). Conclusion: Substituting the CKD-EPI 2009 with the CKD-EPI 2021 or the EKFC equation for calculation of eGFR in participants with high cardiovascular risk without diabetes changed the prevalence of CKD but was not associated with improved risk prediction of cardiovascular events for both those with and without the event.

The Drug Burden Index and Level of Frailty as Determinants of Healthcare Costs in a Cohort of Older Frail Adults in New Zealand.

OBJECTIVES: Frailty is common in older people and is associated with increased use of healthcare services and ongoing use of multiple medications. This study provides insights into the healthcare cost structure of a frail group of older adults in Aotearoa, New Zealand. Furthermore, we investigated the relationship between participants' anticholinergic and sedative medication burden and their total healthcare costs to explore the viability of deprescribing interventions within this cohort. METHODS: Healthcare cost analysis was conducted using data collected during a randomized controlled trial within a frail, older cohort. The collected information included participant demographics, medications used, frailty, cost of service use of aged residential care and outpatient hospital services, hospital admissions, and dispensed medications. RESULTS: Data from 338 study participants recruited between 25 September 2018 and 30 October 2020 with a mean age of 80 years were analyzed. The total cost of healthcare per participant ranged from New Zealand $15 (US dollar $10) to New Zealand $270 681 (US dollar $175 943) over 6 months postrecruitment into the study. Four individuals accounted for 26% of this cohort's total healthcare cost. We found frailty to be associated with increased healthcare costs, whereas the drug burden was only associated with increased pharmaceutical costs, not overall healthcare costs. CONCLUSIONS: With no relationship found between a patient's anticholinergic and sedative medication burden and their total healthcare costs, more research is required to understand how and where to unlock healthcare cost savings within frail, older populations.

Aprendizaje automático para la toma de decisiones en cardiología: una revisión narrativa para ayudar a recorrer el nuevo panorama / Machine learning for decision-making in cardiology: a narrative review to aid navigating the new landscape

El aprendizaje automático (machine learning) en cardiología es cada vez más frecuente en la literatura médica, pero los modelos de aprendizaje automático aún no han producido un cambio generalizado de la práctica clínica. En parte esto se debe a que el lenguaje utilizado para describir el aprendizaje automático procede de la informática y resulta menos familiar a los lectores de revistas clínicas. En esta revisión narrativa se proporcionan, en primer lugar, algunas orientaciones sobre cómo leer las revistas de aprendizaje automático y, a continuación, orientaciones adicionales para quienes se plantean iniciar un estudio utilizando el aprendizaje automático. Por último, se ilustra el estado actual de la técnica con breves resúmenes de 5 artículos que van desde un modelo de aprendizaje automático muy sencillo hasta otros muy sofisticados.(AU)

Machine learning in cardiology is becoming more commonplace in the medical literature; however, machine learning models have yet to result in a widespread change in practice. This is partly due to the language used to describe machine, which is derived from computer science and may be unfamiliar to readers of clinical journals. In this narrative review, we provide some guidance on how to read machine learning journals and additional guidance for investigators considering instigating a study using machine learning. Finally, we illustrate the current state of the art with brief summaries of 5 articles describing models that range from the very simple to the highly sophisticated.(AU)

Post Hoc Analyses of a Randomized Controlled Trial for the Effect of Pharmacist Deprescribing Intervention on the Anticholinergic Burden in Frail Community-Dwelling Older Adults.

OBJECTIVES: Anticholinergic burden is detrimental to cognitive health. Multiple studies found that a high anticholinergic burden is associated with an increased risk for dementia, changes to the brain structure, function, and cognitive decline. We performed a post hoc analysis of a randomized controlled deprescribing trial. We compared the effect of the intervention on baseline anticholinergic burden across the treatment and control groups and the time of recruitment before and after a lockdown due to the COVID pandemic with subgroup analyses by baseline frailty index. DESIGN: Randomized controlled trial. SETTINGS AND PARTICIPANTS: We analyzed data from a de-prescribing trial of older adults (>65 years) previously conducted in New Zealand that was focused on reducing the Drug Burden Index (DBI). METHODS: We used the anticholinergic cognitive burden (ACB) to quantify the impact of the intervention on reducing the anticholinergic burden. Participants not taking anticholinergics at the start of the trial were excluded. The primary outcome for this subgroup analysis was a change in ACB, measured with the gHedges statistic describing the difference in standard deviation units of this change between intervention and control. For this analysis, the trial participants were stratified into low, medium, and high frailty and timing into prior- and post-lockdown (public health measures for COVID-19). RESULTS: Among the 295 participants in this analysis, the median (IQR) age was 79 (74, 85), and 67% were women. For the primary outcome gHedges = -0.04 (95% CI -0.26 to 0.19) with a -0.23 mean reduction in ACB in the intervention arm and -0.19 in the control arm. Before lockdown gHedges = -0.38 (95% CI -0.84 to 0.04) and post-lockdown gHedges = 0.07 (95% CI -0.19 to 0.33). The mean change in ACB for each of the frailty strata was as follows: low frailty (-0.02; 95% CI -0.65 to 0.18); medium frailty (0.05; 95% CI -0.28 to 0.38); high frailty (0.08; 95% CI -0.40 to 0.56). CONCLUSIONS AND IMPLICATIONS: The study did not provide evidence for the effect of pharmacist deprescribing intervention on reducing the anticholinergic burden. However, this post hoc analysis examined the impact of COVID on the effectiveness of the intervention, and further research in this area may be warranted.

Implementation of an integrated emergency department acute atrial fibrillation pathway safely reduces cardioversions and hospitalisations: A comparative pre-post study.

OBJECTIVE: Atrial fibrillation/flutter (AF/AFL) accounts for high rates of ED presentations and hospital admissions. There is increasing evidence to suggest that delaying cardioversion for acute uncomplicated AF is safe, and that many patients will spontaneously revert to sinus rhythm (SR). We conducted a before-and-after evaluation of AF/AFL management after a change in ED pathway using a conservative 'rate-and-wait' approach, incorporating next working day outpatient clinic follow-up and delayed cardioversion if required. METHODS: We performed a before-and-after retrospective cohort study examining outcomes for patients who presented to the ED in Christchurch, New Zealand, with acute uncomplicated AF/AFL in the 1-year period before and after the implementation of a new conservative management pathway. RESULTS: A total of 360 patients were included in the study (182 'Pre-pathway' vs 178 'Post-Pathway'). Compared to the pre-pathway cohort, those managed under the new pathway had an 81.2% reduction in ED cardioversions (n = 32 vs n = 6), and 50.7% reduction in all cardioversions (n = 65 vs n = 32). There was a 31.6% reduction in admissions from ED (n = 54 vs n = 79). ED length of stay (3.9 h vs 3.8 h, net difference -0.1 h, 95% confidence interval [CI] -0.6 to 0.3), 1-year ED AF representation (32.4% vs 26.4%, net difference -6.0% [95% CI -16.0% to 3.9%]), 1-year ED ischaemic stroke presentation (2.2% in both groups) and 7-day all-cause mortality rates (hazard ratio 1.05 [95% CI 0.6 to 1.9]) were all similar. CONCLUSIONS: Using a conservative 'rate-and-wait' strategy with early follow-up for patients presenting to ED with AF/AFL can safely reduce unnecessary cardioversions and avoidable hospitalisations.

Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.

Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score (0-100) that corresponds to an individual's probability of myocardial infarction. The models were trained on data from 10,038 patients (48% women), and their performance was externally validated using data from 10,286 patients (35% women) from seven cohorts. CoDE-ACS had excellent discrimination for myocardial infarction (area under curve, 0.953; 95% confidence interval, 0.947-0.958), performed well across subgroups and identified more patients at presentation as low probability of having myocardial infarction than fixed cardiac troponin thresholds (61 versus 27%) with a similar negative predictive value and fewer as high probability of having myocardial infarction (10 versus 16%) with a greater positive predictive value. Patients identified as having a low probability of myocardial infarction had a lower rate of cardiac death than those with intermediate or high probability 30 days (0.1 versus 0.5 and 1.8%) and 1 year (0.3 versus 2.8 and 4.2%; P < 0.001 for both) from patient presentation. CoDE-ACS used as a clinical decision support system has the potential to reduce hospital admissions and have major benefits for patients and health care providers.

Personalized diagnosis in suspected myocardial infarction.

BACKGROUND: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. METHODS: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients. RESULTS: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy. CONCLUSION: We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care. TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www. CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www. CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www. CLINICALTRIALS: gov ; NCT01852123), LUND ( www. CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www. CLINICALTRIALS: gov ; NCT03111862), ROMI ( www. CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www. CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www. CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www. CLINICALTRIALS: gov ; NCT02060760).

An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes.

Background: Patients suffering from acute myocardial infarction (AMI) are at risk of secondary outcomes including major adverse cardiovascular events (MACE) and heart failure (HF). Comprehensive molecular phenotyping and cardiac imaging during the post-discharge time window may provide cues for risk stratification for the outcomes. Materials and methods: In a prospective AMI cohort in New Zealand (N = 464), we measured plasma proteins and lipids 30 days after hospital discharge and inferred a unified partial correlation network with echocardiographic variables and established clinical biomarkers (creatinine, c-reactive protein, cardiac troponin I and natriuretic peptides). Using a network-based data integration approach (iOmicsPASS+), we identified predictive signatures of long-term secondary outcomes based on plasma protein, lipid, imaging markers and clinical biomarkers and assessed the prognostic potential in an independent cohort from Singapore (N = 190). Results: The post-discharge levels of plasma proteins and lipids showed strong correlations within each molecular type, reflecting concerted homeostatic regulation after primary MI events. However, the two molecular types were largely independent with distinct correlation structures with established prognostic imaging parameters and clinical biomarkers. To deal with massively correlated predictive features, we used iOmicsPASS + to identify subnetwork signatures of 211 and 189 data features (nodes) predictive of MACE and HF events, respectively (160 overlapping). The predictive features were primarily imaging parameters, including left ventricular and atrial parameters, tissue Doppler parameters, and proteins involved in extracellular matrix (ECM) organization, cell differentiation, chemotaxis, and inflammation. The network signatures contained plasma protein pairs with area-under-the-curve (AUC) values up to 0.74 for HF prediction in the validation cohort, but the pair of NT-proBNP and fibulin-3 (EFEMP1) was the best predictor (AUC = 0.80). This suggests that there were a handful of plasma proteins with mechanistic and functional roles in predisposing patients to the secondary outcomes, although they may be weaker prognostic markers than natriuretic peptides individually. Among those, the diastolic function parameter (E/e' - an indicator of left ventricular filling pressure) and two ECM proteins, EFEMP1 and follistatin-like 3 (FSTL3) showed comparable performance to NT-proBNP and outperformed left ventricular measures as benchmark prognostic factors for post-MI HF. Conclusion: Post-discharge levels of E/e', EFEMP1 and FSTL3 are promising complementary markers of secondary adverse outcomes in AMI patients.

Chronic health conditions and mortality among older adults with complex care needs in Aotearoa New Zealand.

BACKGROUND: Older people have more comorbidities than younger groups and multimorbidity will increase. Often chronic conditions affect quality of life, functional ability and social participation. Our study aim was to quantify the prevalence of chronic conditions over a three-year period and their association with mortality after accounting for demographics. METHODS: We conducted a retrospective cohort study using routinely collected health data including community-dwelling older adults in New Zealand who had an interRAI Home Care assessment between 1 January 2017 and 31 December 2017. Descriptive statistics and differences between variables of interest among ethnic groups were reported. Cumulative density plots of mortality were developed. Logistic regression models adjusted for age and sex to estimate mortality were created independently for each combination of ethnicity and disease diagnosis. RESULTS: The study cohort consisted of 31,704 people with a mean (SD) age of 82.3 years (8.0), and of whom 18,997 (59.9%) were female. Participants were followed for a median 1.1 (range 0 to 3) years. By the end of the follow-up period 15,678 (49.5%) people had died. Nearly 62% of Maori and Pacific older adults and 57% of other ethnicities had cognitive impairment. Diabetes the next most prevalent amongst Maori and Pacific peoples, and coronary heart disease amongst Non-Maori/Non-Pacific individuals. Of the 5,184 (16.3%) who had congestive heart failure (CHF), 3,450 (66.6%) died. This was the highest mortality rate of any of the diseases. There was a decrease in mortality rate with age for both sexes and all ethnicities for those with cancer. CONCLUSIONS: Cognitive impairment was the most common condition in community dwelling older adults who had an interRAI assessment. Cardiovascular disease (CVD) has the highest mortality risk for all ethnic groups, and in non-Maori/non-Pacific group of advanced age, risk of mortality with cognitive impairment is as high as CVD risk. We observed an inverse for cancer mortality risk with age. Important differences between ethnic groups are reported.

Comparison of SPEED, S-Trap, and In-Solution-Based Sample Preparation Methods for Mass Spectrometry in Kidney Tissue and Plasma.

Mass spectrometry is a powerful technique for investigating renal pathologies and identifying biomarkers, and efficient protein extraction from kidney tissue is essential for bottom-up proteomic analyses. Detergent-based strategies aid cell lysis and protein solubilization but are poorly compatible with downstream protein digestion and liquid chromatography-coupled mass spectrometry, requiring additional purification and buffer-exchange steps. This study compares two well-established detergent-based methods for protein extraction (in-solution sodium deoxycholate (SDC); suspension trapping (S-Trap)) with the recently developed sample preparation by easy extraction and digestion (SPEED) method, which uses strong acid for denaturation. We compared the quantitative performance of each method using label-free mass spectrometry in both sheep kidney cortical tissue and plasma. In kidney tissue, SPEED quantified the most unique proteins (SPEED 1250; S-Trap 1202; SDC 1197). In plasma, S-Trap produced the most unique protein quantifications (S-Trap 150; SDC 148; SPEED 137). Protein quantifications were reproducible across biological replicates in both tissue (R2 = 0.85-0.90) and plasma (SPEED R2 = 0.84; SDC R2 = 0.76, S-Trap R2 = 0.65). Our data suggest SPEED as the optimal method for proteomic preparation in kidney tissue and S-Trap or SPEED as the optimal method for plasma, depending on whether a higher number of protein quantifications or greater reproducibility is desired.

Light curves and colours of the ejecta from Dimorphos after the DART impact.

On 26 September 2022, the Double Asteroid Redirection Test (DART) spacecraft struck Dimorphos, a satellite of the asteroid 65803 Didymos1. Because it is a binary system, it is possible to determine how much the orbit of the satellite changed, as part of a test of what is necessary to deflect an asteroid that might threaten Earth with an impact. In nominal cases, pre-impact predictions of the orbital period reduction ranged from roughly 8.8 to 17 min (refs. 2,3). Here we report optical observations of Dimorphos before, during and after the impact, from a network of citizen scientists' telescopes across the world. We find a maximum brightening of 2.29 ± 0.14 mag on impact. Didymos fades back to its pre-impact brightness over the course of 23.7 ± 0.7 days. We estimate lower limits on the mass contained in the ejecta, which was 0.3-0.5% Dimorphos's mass depending on the dust size. We also observe a reddening of the ejecta on impact.

The cost savings of the rural accelerated chest pain pathway for low-risk chest pain in rural general practice: a cost minimisation analysis.

Introduction The rural accelerated chest pain pathway (RACPP) has been shown to safely reduce the number of transfers to hospital for patients who present with chest pain to rural general practice. Aim This study aimed to estimate the costs associated with assessing patients with low-risk chest pain using the RACPP in rural general practice compared with transporting such patients to a distant emergency department (ED). Methods This was a retrospective cost minimisation analysis. All patients with low-risk chest pain that were assessed in New Zealand (NZ) rural general practice using the RACPP between 1 June 2018 and 31 December 2019 were asked to participate. The costs incurred by patients were determined by an online survey. Patients were also asked to estimate the costs if they would have been transferred to ED. System costs were obtained from the relevant healthcare organisations. The main outcome measure was the total cost for patients who present with low-risk chest pain. Results In total, 15 patients (22.7% response rate) responded to the survey. Using the RACPP in general practice resulted in a median cost saving of NZ$1184 (95% CI: $1111 to $1468) compared with transferring the same patient to ED. Discussion Although limited by low enrolment, this study suggests that there are significant savings if the RACPP is used to assess patients with low-risk chest pain in rural NZ general practice.