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Research and practice in critical care medicine have long been defined by syndromes, which, despite being clinically recognizable entities, are, in fact, loose amalgams of heterogeneous states that may respond differently to therapy. Mounting translational evidence—supported by research on respiratory failure due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection—suggests that the current syndrome-based framework of critical illness should be reconsidered. Here we discuss recent findings from basic science and clinical research in critical care and explore how these might inform a new conceptual model of critical illness. De-emphasizing syndromes, we focus on the underlying biological changes that underpin critical illness states and that may be amenable to treatment. We hypothesize that such an approach will accelerate critical care research, leading to a richer understanding of the pathobiology of critical illness and of the key determinants of patient outcomes. This, in turn, will support the design of more effective clinical trials and inform a more precise and more effective practice at the bedside. The authors propose a new conceptual model of critical illness that moves away from the current syndrome-based framework in favor of more precise biological descriptors—spurred by mounting translational evidence and insights from Coronavirus Disease 2019 (COVID-19) research.

In this Consensus Statement, a group of experts propose that high-quality immunoassays, rather than indirect immunofluorescence, should be used as the primary screening method for detecting anti-neutrophil cytoplasmic antibodies when diagnosing patients with suspected granulomatosis with polyangiitis or microscopic polyangiitis. Anti-neutrophil cytoplasmic antibodies (ANCAs) are valuable laboratory markers used for the diagnosis of well-defined types of small-vessel vasculitis, including granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA). According to the 1999 international consensus on ANCA testing, indirect immunofluorescence (IIF) should be used to screen for ANCAs, and samples containing ANCAs should then be tested by immunoassays for proteinase 3 (PR3)-ANCAs and myeloperoxidase (MPO)-ANCAs. The distinction between PR3-ANCAs and MPO-ANCAs has important clinical and pathogenic implications. As dependable immunoassays for PR3-ANCAs and MPO-ANCAs have become broadly available, there is increasing international agreement that high-quality immunoassays are the preferred screening method for the diagnosis of ANCA-associated vasculitis. The present Consensus Statement proposes that high-quality immunoassays can be used as the primary screening method for patients suspected of having the ANCA-associated vaculitides GPA and MPA without the categorical need for IIF, and presents and discusses evidence to support this recommendation.

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms. Smartphone-based mobile health platforms have drawn increasing attention from researchers developing point-of-care testing devices. Here the authors summarize recent progress and future directions of approaches combining microfluidics and artificial intelligence.

Reference intervals are essential for the interpretation of laboratory test results in medicine. We propose a novel indirect approach to estimate reference intervals from real-world data as an alternative to direct methods, which require samples from healthy individuals. The presented refineR algorithm separates the non-pathological distribution from the pathological distribution of observed test results using an inverse approach and identifies the model that best explains the non-pathological distribution. To evaluate its performance, we simulated test results from six common laboratory analytes with a varying location and fraction of pathological test results. Estimated reference intervals were compared to the ground truth, an alternative indirect method ( kosmic ), and the direct method (N = 120 and N = 400 samples). Overall, refineR achieved the lowest mean percentage error of all methods (2.77%). Analyzing the amount of reference intervals within ± 1 total error deviation from the ground truth, refineR (82.5%) was inferior to the direct method with N = 400 samples (90.1%), but outperformed kosmic (70.8%) and the direct method with N = 120 (67.4%). Additionally, reference intervals estimated from pediatric data were comparable to published direct method studies. In conclusion, the refineR algorithm enables precise estimation of reference intervals from real-world data and represents a viable complement to the direct method.

Early use of effective antimicrobial treatments is critical for the outcome of infections and the prevention of treatment resistance. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based techniques can take up to 72 hours to generate results. We have developed a novel machine learning approach to predict antimicrobial resistance directly from matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF) mass spectra profiles of clinical isolates. We trained calibrated classifiers on a newly created publicly available database of mass spectra profiles from the clinically most relevant isolates with linked antimicrobial susceptibility phenotypes. This dataset combines more than 300,000 mass spectra with more than 750,000 antimicrobial resistance phenotypes from four medical institutions. Validation on a panel of clinically important pathogens, including Staphylococcus aureus , Escherichia coli and Klebsiella pneumoniae , resulting in areas under the receiver operating characteristic curve of 0.80, 0.74 and 0.74, respectively, demonstrated the potential of using machine learning to substantially accelerate antimicrobial resistance determination and change of clinical management. Furthermore, a retrospective clinical case study of 63 patients found that implementing this approach would have changed the clinical treatment in nine cases, which would have been beneficial in eight cases (89%). MALDI-TOF mass spectra-based machine learning may thus be an important new tool for treatment optimization and antibiotic stewardship. A machine learning method speeds antimicrobial resistance determination to help tailor treatment decisions.

The world is entering a new era of the COVID-19 pandemic in which there is an increasing call for reliable antibody testing. To support decision making on the deployment of serology for either population screening or diagnostics, we present a detailed comparison of serological COVID-19 assays. We show that among the selected assays there is a wide diversity in assay performance in different scenarios and when correlated to virus neutralizing antibodies. The Wantai ELISA detecting total immunoglobulins against the receptor binding domain of SARS CoV-2, has the best overall characteristics to detect functional antibodies in different stages and severity of disease, including the potential to set a cut-off indicating the presence of protective antibodies. The large variety of available serological assays requires proper assay validation before deciding on deployment of assays for specific applications. SARS-CoV-2 is causing a global pandemic in which the implementation of serology can support decision making in different scenarios. Here, the authors compare the outcome of eight commercially available assays to virus neutralization and discuss their use in diagnostics and exposure assessment of SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is commonly diagnosed by reverse transcription polymerase chain reaction (RT-PCR) to detect viral RNA in patient samples, but RNA extraction constitutes a major bottleneck in current testing. Methodological simplification could increase diagnostic availability and efficiency, benefitting patient care and infection control. Here, we describe methods circumventing RNA extraction in COVID-19 testing by performing RT-PCR directly on heat-inactivated or lysed samples. Our data, including benchmarking using 597 clinical patient samples and a standardised diagnostic system, demonstrate that direct RT-PCR is viable option to extraction-based tests. Using controlled amounts of active SARS-CoV-2, we confirm effectiveness of heat inactivation by plaque assay and evaluate various generic buffers as transport medium for direct RT-PCR. Significant savings in time and cost are achieved through RNA-extraction-free protocols that are directly compatible with established PCR-based testing pipelines. This could aid expansion of COVID-19 testing. SARS-CoV-2 infection is widely diagnosed by RT-PCR, but RNA extraction is a bottleneck for fast and cheap diagnosis. Here, the authors develop protocols to perform RT-PCR directly on heat-inactivated subject samples or samples lysed with readily available detergents and benchmark performance against 597 clinically diagnosed patient samples.

Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests. Data from wearable sensors, including heart rate, body temperature, electrodermal activity and movement, can predict clinical laboratory measurements, with highest accuracy for hematological tests such as hematocrit.

Pancreatic ductal adenocarcinoma (PDAC) is predicted to be the second most common cause of death within the next 10 years. The prognosis for this disease is poor despite diagnostic progress and new chemotherapeutic regimens. The oncogenic KRAS mutation is the major event in pancreatic cancer; it confers permanent activation of the KRAS protein, which acts as a molecular switch to activate various intracellular signalling pathways and transcription factors inducing cell proliferation, migration, transformation and survival. Several laboratory methods have been developed to detect KRAS mutations in biological samples, including digital droplet PCR (which displays high sensitivity). Clinical studies have revealed that a KRAS mutation assay in fine-needle aspiration material combined with cytopathology increases the sensitivity, accuracy and negative predictive value of cytopathology for a positive diagnosis of pancreatic cancer. In addition, the presence of KRAS mutations in serum and plasma (liquid biopsies) correlates with a worse prognosis. The presence of mutated KRAS can also have therapeutic implications, whether at the gene level per se, during its post-translational maturation, interaction with nucleotides and after activation of the various oncogenic signals. Further pharmacokinetic and toxicological studies on new molecules are required, especially small synthetic molecules, before they can be used in the therapeutic arsenal for pancreatic ductal adenocarcinoma.KRAS mutations are identified in >80% of patients with pancreatic ductal adenocarcinoma and tend to be associated with reduced overall survival. This Review discusses the role of oncogenic KRAS in the biology, diagnosis, prognosis and treatment of pancreatic cancer.

Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children’s diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group ( n  = 81) than the control group ( n  = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes. Diabetic retinopathy is a complication of diabetes that can be prevented through screening, yet adherence is low. Here, the authors show that autonomous AI increases diabetic eye exam completion in a diverse cohort of youth with diabetes.