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Pulse oximeters measure peripheral arterial oxygen saturation (SpO2) noninvasively, while the gold standard (SaO2) involves arterial blood gas measurement. There are known racial and ethnic disparities in their performance. BOLD is a dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients. Paired SpO2 and SaO2 measurements were time-aligned and combined with various other sociodemographic and parameters to provide a detailed representation of each patient. BOLD includes 49,099 paired measurements, within a 5-minute window and with oxygen saturation levels between 70–100%. Minority racial and ethnic groups account for ~25% of the data – a proportion seldom achieved in previous studies. The codebase is publicly available. Given the prevalent use of pulse oximeters in the hospital and at home, we hope that BOLD will be leveraged to develop debiasing algorithms that can result in more equitable healthcare solutions. [ABSTRACT FROM AUTHOR]

Purpose: To externally validate four previously developed severity scores (i.e., CALL, CHOSEN, HA2T2 and ANDC) in patients with COVID-19 hospitalised in a tertiary care centre in Switzerland. Methods: This observational analysis included adult patients with a real-time reverse-transcription polymerase chain reaction or rapid-antigen test confirmed severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection hospitalised consecutively at the Cantonal Hospital Aarau from February to December 2020. The primary endpoint was all-cause in-hospital mortality. The secondary endpoint was disease progression, defined as needing invasive ventilation, ICU admission or death. Results: From 399 patients (mean age 66.6 years ± 13.4 SD, 68% males), we had complete data for calculating the CALL, CHOSEN, HA2T2 and ANDC scores in 297, 380, 151 and 124 cases, respectively. Odds ratios for all four scores showed significant associations with mortality. The discriminative power of the HA2T2 score was higher compared to CALL, CHOSEN and ANDC scores [area under the curve (AUC) 0.78 vs. 0.65, 0.69 and 0.66, respectively]. Negative predictive values (NPV) for mortality were high, particularly for the CALL score (≥ 6 points: 100%, ≥ 9 points: 95%). For disease progression, discriminative power was lower, with the CHOSEN score showing the best performance (AUC 0.66). Conclusion: In this external validation study, the four analysed scores had a lower performance compared to the original cohorts regarding prediction of mortality and disease progression. However, all scores were significantly associated with mortality and the NPV of the CALL and CHOSEN scores in particular allowed reliable identification of patients at low risk, making them suitable for outpatient management. [ABSTRACT FROM AUTHOR]

Purpose: Estimating the probability of readmission following hospitalization using prediction scores can be complex. Quality of life (QoL) may provide an easy and effective alternative. Methods: Secondary analysis of the prospective "TRIAGE" cohort. All medical in-patients admitted to a Swiss tertiary care institution (2016–2019) ≥18 years with a length of stay of ≥2 days (23,309 patients) were included. EQ-5D VAS, EQ-5D index, and Barthel index were assessed at a single telephone interview 30-day after admission. Patients lost to follow-up were excluded. Readmission was defined as a non-elective hospital stay at our institution >24 h within 1 year after discharge and assessed using area under the curve (AUC) analysis with adjustment for confounders. Results: 12,842 patients (43% females, median age 68, IQR 55–78) were included. Unadjusted discrimination was modest at 0.59 (95% CI 0.56–0.62) for EQ-5D VAS. Partially adjusted discrimination (for gender) was identical. Additional adjustment for insurance, Charlson comorbidity index, length of stay, and native language increased the AUC to 0.66 (95% CI 0.63–0.69). Results were robust irrespective of time to event (12, 6 or 3 months). A cut-off in the unadjusted model of EQ-5D VAS of 55 could separate cases with a specificity of 80% and a sensitivity of 30%. Conclusion: QoL at day 30 after admission can predict one-year readmission risk with similar precision as more intricate tools. It might help for identification of high-risk patients and the design of tailored prevention strategies. [ABSTRACT FROM AUTHOR]

Background: Several clinical risk scores for unplanned 30-day readmission have been published, but there is a lack of external validation and head-to-head comparison. Objective: Retrospective replication of six clinical risk scores (LACE, HOSPITAL, SEMI, RRS, PARA, Tsui et al.)f Design: Models were fitted with the original intercept and beta coefficients as reported. Otherwise, a logistic model was refitted (SEMI and Tsui et al). We performed subgroup analyses on main admission specialty. This report adheres to the TRIPOD statement for reporting of prediction models. Participants: We used our prospective cohort of 15,639 medical patients from a Swiss tertiary care institution from 2016 through 2018. Main Measures: Thirty-day readmission rate and area under the curve (AUC < 0.50 worse than chance, > 0.70 acceptable, > 0.80 excellent) Conclusions: Among several readmission risk scores, HOSPITAL, PARA, and the score from Tsui et al. showed the best predictive abilities and have high potential to improve patient care. Interventional research is now needed to understand the effects of these scores when used in clinical routine. Key Results: Among the six risk scores externally validated, calibration of the models was overall poor with overprediction of events, except for the HOSPITAL and the PARA scores. Discriminative abilities (AUC) were as follows: LACE 0.53 (95% CI 0.50–0.56), HOSPITAL 0.73 (95% CI 0.72–0.74), SEMI 0.47 (95% CI 0.46–0.49), RRS 0.64 (95% CI 0.62–0.66), PARA 0.72 (95% CI 0.72–0.74), and the score from Tsui et al. 0.73 (95% CI 0.72–0.75). Performance in subgroups did not differ from the overall performance, except for oncology patients in the PARA score (0.57, 95% CI 0.54–0.60), and nephrology patients in the SEMI index (0.25, 95% CI 0.18–0.31), respectively. [ABSTRACT FROM AUTHOR]

Background: Metabolic syndrome is a major risk factor for cardiovascular diseases, which are still the major cause of death in developed countries. Methods: We cross-sectionally studied the association between urinary phytoestrogen excretion and metabolic cardiovascular risk factors. Hence, we used data from the National Health and Nutrition Examination Survey from 1999 to 2004 with 1,748 participants, who had urine levels of isoflavones and lignans measured. Geometric means of waist circumference, blood pressure, fasting glucose, HDL cholesterol, and triglyceride levels were computed by quartiles of isoflavone or lignan urinary excretion. Outcome was assessed as the presence of metabolic syndrome according to NCEP-ATP III criteria. The association between phytoestrogen concentration and the metabolic syndrome was calculated using logistic regression analyses. Results: Plasma triglyceride and HDL cholesterol levels were lower in participants in the highest quartile of lignan excretion compared with the lowest (both P < 0.01). However, blood pressure, waist circumference, and plasma glucose levels did not differ significantly between extreme quartiles. The presence of metabolic syndrome was lower with increasing levels of urinary lignans (OR 0.48, 95 % CI 0.28; 0.80 top vs. bottom quartile), especially when separately computed for the excretion of enterolactone (OR 0.47, 95 % CI 0.28; 0.78). There was no significant association between isoflavone excretion and any component of the metabolic syndrome. Conclusions: Our study shows that an increasing excretion of lignans, especially enterolactone, might be associated with a decreased presence of the metabolic syndrome. [ABSTRACT FROM AUTHOR]

When counseling patients regarding the best first-line treatment for Graves' disease, physicians should use a shared decision making approach outlining the risks and benefits of each treatment option and providing estimates regarding recurrence risks with antithyroid drug therapy based on recently published clinical scores (i.e., GREAT score) [[3]]. 3 Struja T, Kaeslin M, Boesiger F, Jutzi R, Imahorn N, Kutz A. External validation of the GREAT score to predict relapse risk in Graves' disease: results from a multicenter, retrospective study with 741 patients. Predicting the risk of recurrence before the start of antithyroid drug therapy in patients with Graves' hyperthyroidism. [Extracted from the article]