Utility of skin tone on pulse oximetry in critically ill patients: a prospective cohort study.

Importance: Pulse oximetry, a ubiquitous vital sign in modern medicine, has inequitable accuracy that disproportionately affects Black and Hispanic patients, with associated increases in mortality, organ dysfunction, and oxygen therapy. Although the root cause of these clinical performance discrepancies is believed to be skin tone, previous retrospective studies used self-reported race or ethnicity as a surrogate for skin tone. Objective: To determine the utility of objectively measured skin tone in explaining pulse oximetry discrepancies. Design Setting and Participants: Admitted hospital patients at Duke University Hospital were eligible for this prospective cohort study if they had pulse oximetry recorded up to 5 minutes prior to arterial blood gas (ABG) measurements. Skin tone was measured across sixteen body locations using administered visual scales (Fitzpatrick Skin Type, Monk Skin Tone, and Von Luschan), reflectance colorimetry (Delfin SkinColorCatch [L*, individual typology angle {ITA}, Melanin Index {MI}]), and reflectance spectrophotometry (Konica Minolta CM-700D [L*], Variable Spectro 1 [L*]). Main Outcomes and Measures: Mean directional bias, variability of bias, and accuracy root mean square (ARMS), comparing pulse oximetry and ABG measurements. Linear mixed-effects models were fitted to estimate mean directional bias while accounting for clinical confounders. Results: 128 patients (57 Black, 56 White) with 521 ABG-pulse oximetry pairs were recruited, none with hidden hypoxemia. Skin tone data was prospectively collected using 6 measurement methods, generating 8 measurements. The collected skin tone measurements were shown to yield differences among each other and overlap with self-reported racial groups, suggesting that skin tone could potentially provide information beyond self-reported race. Among the eight skin tone measurements in this study, and compared to self-reported race, the Monk Scale had the best relationship with differences in pulse oximetry bias (point estimate: -2.40%; 95% CI: -4.32%, -0.48%; p=0.01) when comparing patients with lighter and dark skin tones. Conclusions and relevance: We found clinical performance differences in pulse oximetry, especially in darker skin tones. Additional studies are needed to determine the relative contributions of skin tone measures and other potential factors on pulse oximetry discrepancies.

Altered consciousness mediates the effect of hypernatremia, but not hyponatremia, on mortality among patients observed in the ICU.

Purpose: Dysnatremias - hypernatremia and hyponatremia - may be associated with mortality through their impact on altered consciousness. We examined the mediating effect of decreased consciousness on the relationship between dysnatremia and mortality. Methods: Among 195,568 critically ill patients in the United States contained in the eICU database, we categorized serum sodium into bands of 5mEq/L. Using causal mediation analysis, we compared bands in the hypernatremia and hyponatremia ranges to a reference band of 135-139mEq/L to determine the proportion of mortality mediated by decreased consciousness as determined by the Glasgow Coma Score (GCS). Results: Both hyponatremia (OR [95%CI] for bands: <120mEq/L: 1.58 [1.26-1.97]; 120-<125mEq/L: 1.92 [1.64-2.25]; 125-<130mEq/L: 1.76 [1.60-1.93]; 130-<135mEq/L: 1.32 [1.24-1.41]) and hypernatremia (OR [95%CI] for bands: 140-<145mEq/L: 1.12 [1.05-1.19]; 145-<150mEq/L: 1.89 [1.70-2.11]; ≥150mEq/L: 1.86 [1.57-2.19]) were significantly associated with increased mortality. GCS mediated the effect of hypernatremia on mortality risk (Proportion mediated [95%CI]: 140-144mEq/L: 0.38 [0.23 to 0.89]; 145-149mEq/L: 0.27 [0.22 to 0.34]; ≥150mEq/L: 0.53 [0.41 to 0.81]) but not hyponatremia (proportion mediated 95%CI upper bound <0.05 for all bands). Conclusion: Decreased consciousness mediates the association between increased mortality and hypernatremia, but not hyponatremia. Further studies are needed to explore neurologic mechanisms and directionality in this relationship.

Knowledge about Hepatitis E and Influencing Factors among Residents in Qingdao: a Cross-Sectional Study.

Hepatitis E infection poses a serious health problem in developing countries. Hepatitis E vaccination is important for prevention, but it is influenced by residents' knowledge. Qingdao residents' knowledge of hepatitis E remains unknown. This study used an online survey on the WeChat platform. The chi-square test was used to compare the hepatitis E influencing factors between the subgroups. Binary logistic regression was used for multiple factor analysis to explore the hepatitis E influencing factors. The total awareness rate of hepatitis E was 60.51%. Females aged between 51 and 60, aged 61 and above, and working in government-affiliated departments were found to have higher awareness rates than other subgroups. Participants with family members infected with hepatitis E had a lower awareness rate. The government and relevant departments should focus on education regarding the hepatitis E vaccination and disease process.

Emulating Target Trials Comparing Early and Delayed Intubation Strategies.

BACKGROUND: Whether intubation should be initiated early in the clinical course of critically ill patients remains a matter of debate. Results from prior observational studies are difficult to interpret because of avoidable flaws including immortal time bias, inappropriate eligibility criteria, and unrealistic treatment strategies. RESEARCH QUESTION: Do treatment strategies that intubate patients early in the critical care admission improve 30-day survival compared with strategies that delay intubation? STUDY DESIGN AND METHODS: We estimated the effect of strategies that require early intubation of critically ill patients compared with those that delay intubation. With data extracted from the Medical Information Mart for Intensive Care-IV database, we emulated three target trials, varying the flexibility of the treatment strategies and the baseline eligibility criteria. RESULTS: Under unrealistically strict treatment strategies with broad eligibility criteria, the 30-day mortality risk was 7.1 percentage points higher for intubating early compared with delaying intubation (95% CI, 6.2-7.9). Risk differences were 0.4 (95% CI, -0.1 to 0.9) and -0.9 (95% CI, -2.5 to 0.7) percentage points in subsequent target trial emulations that included more realistic treatment strategies and eligibility criteria. INTERPRETATION: When realistic treatment strategies and eligibility criteria are used, strategies that delay intubation result in similar 30-day mortality risks compared with those that intubate early. Delaying intubation ultimately avoids intubation in most patients.

MIMIC-IV, a freely accessible electronic health record dataset.

Digital data collection during routine clinical practice is now ubiquitous within hospitals. The data contains valuable information on the care of patients and their response to treatments, offering exciting opportunities for research. Typically, data are stored within archival systems that are not intended to support research. These systems are often inaccessible to researchers and structured for optimal storage, rather than interpretability and analysis. Here we present MIMIC-IV, a publicly available database sourced from the electronic health record of the Beth Israel Deaconess Medical Center. Information available includes patient measurements, orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. MIMIC-IV is intended to support a wide array of research studies and educational material, helping to reduce barriers to conducting clinical research.

The application of artificial intelligence in the management of sepsis.

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide. Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit (ICU) supervision, where a multitude of apparatus is poised to produce high-granularity data. This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice. However, existing reviews currently lack the inclusion of the latest advancements. This review examines the evolving integration of artificial intelligence (AI) in sepsis management. Applications of artificial intelligence include early detection, subtyping analysis, precise treatment and prognosis assessment. AI-driven early warning systems provide enhanced recognition and intervention capabilities, while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy. Precision medicine harnesses the potential of artificial intelligence for pathogen identification, antibiotic selection, and fluid optimization. In conclusion, the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift, ushering in novel prospects to elevate diagnostic precision, therapeutic efficacy, and prognostic acumen. As AI technologies develop, their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.

Prediction of Alzheimer's Disease-Associated Genes by Integration of GWAS Summary Data and Expression Data.

Alzheimer's disease (AD) is the most common cause of dementia. It is the fifth leading cause of death among elderly people. With high genetic heritability (79%), finding the disease's causal genes is a crucial step in finding a treatment for AD. Following the International Genomics of Alzheimer's Project (IGAP), many disease-associated genes have been identified; however, we do not have enough knowledge about how those disease-associated genes affect gene expression and disease-related pathways. We integrated GWAS summary data from IGAP and five different expression-level data by using the transcriptome-wide association study method and identified 15 disease-causal genes under strict multiple testing (α < 0.05), and four genes are newly identified. We identified an additional 29 potential disease-causal genes under a false discovery rate (α < 0.05), and 21 of them are newly identified. Many genes we identified are also associated with an autoimmune disorder.