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Autoimmune skin diseases are characterized by significant local and systemic inflammation that is largely mediated by the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. Advanced understanding of this pathway has led to the development of targeted inhibitors of Janus kinases (JAKinibs). As a class, JAK inhibitors effectively treat a multitude of hematologic and inflammatory diseases. Growing evidence suggests that JAK inhibitors are efficacious in atopic dermatitis, alopecia areata, psoriasis, and vitiligo. Additional evidence suggests that JAK inhibition might be broadly useful in dermatology, with early reports of efficacy in several other conditions. JAK inhibitors can be administered orally or used topically and represent a promising new class of medications. Here we review the evolving data on the role of the JAK-STAT pathway in inflammatory dermatoses and the potential therapeutic benefit of JAK-STAT antagonism.

The 2016 definitions of sepsis included the quick Sepsis-related Organ Failure Assessment (qSOFA) score to identify high-risk patients outside the intensive care unit (ICU). We sought to compare qSOFA with other commonly used early warning scores. All admitted patients who first met the criteria for suspicion of infection in the emergency department (ED) or hospital wards from November 2008 until January 2016 were included. The qSOFA, Systemic Inflammatory Response Syndrome (SIRS), Modified Early Warning Score (MEWS), and the National Early Warning Score (NEWS) were compared for predicting death and ICU transfer. Of the 30,677 included patients, 1,649 (5.4%) died and 7,385 (24%) experienced the composite outcome (death or ICU transfer). Sixty percent (n = 18,523) first met the suspicion criteria in the ED. Discrimination for in-hospital mortality was highest for NEWS (area under the curve [AUC], 0.77; 95% confidence interval [CI], 0.76-0.79), followed by MEWS (AUC, 0.73; 95% CI, 0.71-0.74), qSOFA (AUC, 0.69; 95% CI, 0.67-0.70), and SIRS (AUC, 0.65; 95% CI, 0.63-0.66) (P < 0.01 for all pairwise comparisons). Using the highest non-ICU score of patients, ≥2 SIRS had a sensitivity of 91% and specificity of 13% for the composite outcome compared with 54% and 67% for qSOFA ≥2, 59% and 70% for MEWS ≥5, and 67% and 66% for NEWS ≥8, respectively. Most patients met ≥2 SIRS criteria 17 hours before the combined outcome compared with 5 hours for ≥2 and 17 hours for ≥1 qSOFA criteria. Commonly used early warning scores are more accurate than the qSOFA score for predicting death and ICU transfer in non-ICU patients. These results suggest that the qSOFA score should not replace general early warning scores when risk-stratifying patients with suspected infection.

Abstract Rationale Tools that screen inpatients for sepsis use the systemic inflammatory response syndrome (SIRS) criteria and organ dysfunctions, but most studies of these criteria were performed in intensive care unit or emergency room populations. Objectives To determine the incidence and prognostic value of SIRS and organ dysfunctions in a multicenter dataset of hospitalized ward patients. Methods Hospitalized ward patients at five hospitals from November 2008 to January 2013 were included. SIRS and organ system dysfunctions were defined using 2001 International Consensus criteria. Patient characteristics and in-hospital mortality were compared among patients meeting two or more SIRS criteria and by the presence or absence of organ system dysfunction. Measurements and Main Results A total of 269,951 patients were included in the study, after excluding 48 patients with missing discharge status. Forty-seven percent (n = 125,841) of the included patients met two or more SIRS criteria at least once during their ward stay. On ward admission, 39,105 (14.5%) patients met two or more SIRS criteria, and patients presenting with SIRS had higher in-hospital mortality than those without SIRS (4.3% vs. 1.2%; P < 0.001). Fourteen percent of patients (n = 36,767) had at least one organ dysfunction at ward admission, and those presenting with organ dysfunction had increased mortality compared with those without organ dysfunction (5.3% vs. 1.1%; P < 0.001). Conclusions Almost half of patients hospitalized on the wards developed SIRS at least once during their ward stay. Our findings suggest that screening ward patients using SIRS criteria for identifying those with sepsis would be impractical.

Hidradenitis suppurativa (HS) is a chronic skin disease of the pilo-sebaceous apocrine unit characterized by significant inflammation and an impaired quality of life. The pathogenesis of HS remains unclear. To determine the HS skin and blood transcriptomes and HS blood proteome, patient data from previously published studies were analysed and integrated from a cohort of patients with moderate to severe HS (n = 17) compared to healthy volunteers (n = 10). The analysis utilized empirical Bayes methods to determine differentially expressed genes (DEGs) (fold change (FCH) >2.0 and false discovery rate (FDR) <0.05), and differentially expressed proteins (DEPs) (FCH>1.5, FDR<0.05). In the HS skin transcriptome (lesional skin compared to non-lesional skin), there was an abundance of immunoglobulins, antimicrobial peptides, and an interferon signature. Gene-sets related to Notch signalling and Interferon pathways were differentially activated in lesional compared to non-lesional skin. CIBERSORT analysis of the HS skin transcriptome revealed a significantly increased proportion of plasma cells in lesional skin. In the HS skin and blood transcriptomes and HS blood proteome, gene-sets related to the complement system changed significantly (FDR<0.05), with dysregulation of complement-specific DEGs and DEPs. These data point towards an exaggerated immune response in lesional skin that may be responding to commensal cutaneous bacterial presence and raise the possibility that this may be an important driver of HS disease progression.

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient’s chart.Artificial intelligence: Algorithm predicts clinical outcomes for hospital inpatientsArtificial intelligence outperforms traditional statistical models at predicting a range of clinical outcomes from a patient’s entire raw electronic health record (EHR). A team led by Alvin Rajkomar and Eyal Oren from Google in Mountain View, California, USA, developed a data processing pipeline for transforming EHR files into a standardized format. They then applied deep learning models to data from 216,221 adult patients hospitalized for at least 24 h each at two academic medical centers, and showed that their algorithm could accurately predict risk of mortality, hospital readmission, prolonged hospital stay and discharge diagnosis. In all cases, the method proved more accurate than previously published models. The authors provide a case study to serve as a proof-of-concept of how such an algorithm could be used in routine clinical practice in the future.

Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show—on a diverse set of single and multi-site health studies—that federated models can achieve similar accuracy, precision, and generalizability, and lead to the same interpretation as standard centralized statistical models while achieving considerably stronger privacy protections and without significantly raising computational costs. This work is the first to apply modern and general federated learning methods that explicitly incorporate differential privacy to clinical and epidemiological research—across a spectrum of units of federation, model architectures, complexity of learning tasks and diseases. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science—aspects that used to be at odds with each other.

The capabilities of artificial intelligence (AI) have accelerated over the past year, and they are beginning to impact healthcare in a significant way. Could this new technology help address issues that have been difficult and recalcitrant problems for quality and safety for decades? While we are early in the journey, it is clear that we are in the midst of a fundamental shift in AI capabilities. It is also clear these capabilities have direct applicability to healthcare and to improving quality and patient safety, even as they introduce new complexities and risks. Previously, AI focused on one task at a time: for example, telling whether a picture was of a cat or a dog, or whether a retinal photograph showed diabetic retinopathy or not. Foundation models (and their close relatives, generative AI and large language models) represent an important change: they are able to handle many different kinds of problems without additional datasets or training. This review serves as a primer on foundation models’ underpinnings, upsides, risks and unknowns—and how these new capabilities may help improve healthcare quality and patient safety.

ABSTRACT BACKGROUND Differences among hospitals in the use of inpatient consultation may contribute to variation in outcomes and costs for hospitalized patients, but basic epidemiologic data on consultations nationally are lacking. OBJECTIVE The purpose of the study was to identify physician, hospital, and geographic factors that explain variation in rates of inpatient consultation. DESIGN This was a retrospective observational study. SETTING AND PARTICIPANTS This work included 3,118,080 admissions of Medicare patients to 4,501 U.S. hospitals in 2009 and 2010. MAIN MEASURES The primary outcome measured was number of consultations conducted during the hospitalization, summarized at the hospital level as the number of consultations per 1,000 Medicare admissions, or “consultation density.” KEY RESULTS Consultations occurred 2.6 times per admission on average. Among non-critical access hospitals, use of consultation varied 3.6-fold across quintiles of hospitals (933 versus 3,390 consultations per 1,000 admissions, lowest versus highest quintiles, p  < 0.001). Sicker patients received greater intensity of consultation (rate ratio [RR] 1.18, 95 % CI 1.17–1.18 for patients admitted to ICU; and RR 1.19, 95 % CI 1.18–1.20 for patients who died). However, even after controlling for patient-level factors, hospital characteristics also predicted differences in rates of consultation. For example, hospital size (large versus small, RR 1.31, 95 % CI 1.25–1.37), rural location (rural versus urban, RR 0.78, CI 95 % 0.76–0.80), ownership status (public versus not-for-profit, RR 0.94, 95 % CI 0.91–0.97), and geographic quadrant (Northeast versus West, RR 1.17, 95 % CI 1.12–1.21) all influenced the intensity of consultation use. CONCLUSIONS Hospitals exhibit marked variation in the number of consultations per admission in ways not fully explained by patient characteristics. Hospital “consultation density” may constitute an important focus for monitoring resource use for hospitals or health systems.

Abstract Rationale Opioid abuse is increasing, but its impact on critical care resources in the United States is unknown. Objectives We hypothesized that there would be a rising need for critical care among opioid-associated overdoses in the United States. Methods We analyzed all adult admissions, using a retrospective cohort study from 162 hospitals in 44 states, discharged between January 1, 2009, and September 31, 2015 to describe the incidence of intensive care unit (ICU) admissions for opioid overdose during this time. Admissions were identified using the Clinical Database/Resource Manager of Vizient, the successor to the University Health System Consortium. Results Our primary outcome was opioid-associated overdose admissions to the ICU. The outcome was defined on the basis of previously validated ICD-9 codes. Our secondary outcomes were in-hospital death and markers of ICU resources. The final cohort included 22,783,628 admissions; 4,145,068 required ICU care. There were 52.4 ICU admissions for overdose per 10,000 ICU admissions over the entire study (95% confidence interval [CI], 51.8–53.0 per 10,000 ICU admissions). During this time period, opioid overdose admissions requiring intensive care increased 34%, from 44 per 10,000 (95% CI, 43–46 per 10,000) to 59 per 10,000 ICU admissions (95% CI, 57–61 per 10,000; P < 0.0001). The mortality rate of patients with ICU admissions with overdoses averaged 7% (95% CI, 7.0–7.6%) but increased to 10% in 2015 (95% CI, 8.8–10.8%). Conclusions The number of deaths of ICU patients with opioid overdoses increased substantially in the 7 years of our study, reflecting increases in both the incidence and mortality of this condition. Our findings raise the need for a national approach to developing safe strategies to care for patients with overdose in the ICU, to providing coordinated resources in the hospital for patients and families, and to helping survivors maintain sobriety on discharge.

Although “respect” and “dignity” are intuitive concepts, little formal work has addressed their systematic application in the ICU setting. After convening a multidisciplinary group of relevant experts, we undertook a review of relevant literature and collaborative discussions focused on the practice of respect in the ICU. We report the output of this process, including a summary of current knowledge, a conceptual framework, and a research program for understanding and improving the practice of respect and dignity in the ICU. We separate our report into findings and proposals. Findings include the following: 1) dignity and respect are interrelated; 2) ICU patients and families are vulnerable to disrespect; 3) violations of respect and dignity appear to be common in the ICU and overlap substantially with dehumanization; 4) disrespect may be associated with both primary and secondary harms; and 5) systemic barriers complicate understanding and the reliable practice of respect in the ICU. Proposals include: 1) initiating and/or expanding a field of research on the practice of respect in the ICU; 2) treating “failures of respect” as analogous to patient safety events and using existing quality and safety mechanisms for improvement; and 3) identifying both benefits and potential unintended consequences of efforts to improve the practice of respect. Respect and dignity are important considerations in the ICU, even as substantial additional research remains to be done.