Moderate-High Blood Eosinophilia Is Associated with Increased Hospitalization and Other Asthma Comorbidities.

(1) Background: Eosinophilia has traditionally been linked to eosinophilic asthma, for which it is the gold-standard prognostic biomarker. However, the association between eosinophilia and the presence of other diseases and comorbidities is yet unclear. (2) Methods: For this retrospective study, we reviewed the electronic medical records of 49,909 subjects with blood eosinophilia to gather data on the presence of asthma, COPD, sleep apnea, tuberculosis, dyslipidemia, hypertension, and other cardiovascular diseases and severe CRSwNP among these subjects. Demographic features including age, sex, and smoking habits were collected, as well as the number of hospitalizations and emergency department visits. T-tests, ANOVA, Fisher test, and logistic regression models were used. (3) Results: For all age groups studied, eosinophilia was significantly more prevalent among asthmatic subjects than nonasthmatics, especially in patients also presenting CRSwNP, hypertension, and dyslipidemia. The likelihood of developing asthma, COPD, and CRSwNP, and hospitalization, was increased when BEC was above 600 eosinophils/µL. The association between asthma, CRSwNP, and BEC was corroborated by multiple logistic regressions models. (4) Conclusions: We demonstrated the association of having over 600 blood eosinophils/µL with a higher number of hospitalizations and comorbidities (CRSwNP and COPD), which proves that BEC is a highly useful parameter to consider in subjects who present blood eosinophilia.

Using artificial intelligence to reduce orthopedic surgical site infection surveillance workload: Algorithm design, validation, and implementation in 4 Spanish hospitals.

BACKGROUND: Surgical site infection (SSI) surveillance is a labor-intensive endeavor. We present the design and validation of an algorithm for SSI detection after hip replacement surgery, and a report of its successful implementation in 4 public hospitals in Madrid, Spain. METHODS: We designed a multivariable algorithm, AI-HPRO, using natural language processing (NLP) and extreme gradient boosting to screen for SSI in patients undergoing hip replacement surgery. The development and validation cohorts included data from 19,661 health care episodes from 4 hospitals in Madrid, Spain. RESULTS: Positive microbiological cultures, the text variable "infection", and prescription of clindamycin were strong markers of SSI. Statistical analysis of the final model indicated high sensitivity (99.18%) and specificity (91.01%) with an F1-score of 0.32, AUC of 0.989, accuracy of 91.27%, and negative predictive value of 99.98%. DISCUSSION: Implementation of the AI-HPRO algorithm reduced the surveillance time from 975 person/hours to 63.5 person/hours and permitted an 88.95% reduction in the total volume of clinical records to be reviewed manually. The model presents a higher negative predictive value (99.98%) than algorithms relying on NLP alone (94%) or NLP and logistic regression (97%). CONCLUSIONS: This is the first report of an algorithm combining NLP and extreme gradient-boosting to permit accurate, real-time orthopedic SSI surveillance.

Feasibility of Telemedicine in the Management Strategy of Patients With Lymphoma Amid the COVID-19 Pandemic in Spain: Prospective Observational Study.

BACKGROUND: On March 14, 2020, a state of alarm was declared in Spain due to the spread of SARS-CoV-2. Beyond this date, COVID-19 in the country changed the practice of oncologic care. OBJECTIVE: Since recurrent hospital visits were a potential risk factor for contagion, the aim of this prospective observational study was to analyze the consequences of the COVID-19 pandemic in the health care of patients with lymphoma. METHODS: All data were obtained from the electronic medical record. Variables such as age, sex, reason of the visit, use of the patient portal, changes in management, enrollment in clinical trials, and COVID-19 infection were recorded. RESULTS: In all, 290 patients visited the lymphoma clinic, totaling 437 appointments. The median age was 66 (range 18-94) years, and 157 (54.1%) patients were male. Of them, 214 (73.8%) patients had only 1 visit to the clinic. Only 23 (7.9%) patients did not have access to the patient portal. Amid the COVID-19 pandemic, 78 (26.9%) patients remained in active treatment, 35 (12.1%) experienced delays in their treatments, and 6 (2.1%) experienced treatment discontinuation. During the follow-up, only 7 (2.4%) patients had a COVID-19 infection (6 cases with confirmed polymerase chain reaction test and 1 case with clinical suspicion). Despite the implementation of telemedicine strategies to avoid visits to the hospital, 66 (22.8%) patients had in-person visits at the lymphoma clinic. Patients who attended in-person consultations were younger than those who preferred telemedicine consultations (62 vs 66 years; P=.10) and had less use of the patient portal (17/224, 7.6% vs 6/66, 9%; P=.10), although these differences did not reach statistical significance. Patients who attended in-person visits were more likely to have had only 1 visit to the hospital (29/66, 43.9% vs 185/224, 82.6%; P<.001). Regarding the reason of in-person consultations, more patients were on active treatment in comparison to those using telemedicine resources (37/66, 56.1% vs 42/224, 18.3%; P<.001). Patients with a preference for telemedicine strategies had more surveillance visits (147/224, 65.6% vs 24/66, 36.4%; P<.001). Regarding treatment modifications, more treatment delays (29/224, 12.9% vs 6/66, 9.1%; P=.10) and more definite treatment discontinuations (6/224, 2.7% vs 0/66, 0%; P=.10) were seen in patients using telemedicine resources when compared to patients attending in-person visits, although these differences did not reach statistical significance. Regarding the type of therapy, patients attending in-person visits were more likely to receive an intravenous treatment rather than those using telemedicine (23/66, 62.2% vs 17/224, 40.5%; P<.001). CONCLUSIONS: Telemedicine such as patient portals are feasible strategies in the management of patients with lymphoma during the COVID-19 pandemic, with a reduction of in-person visits to the hospital and a very low contagion rate.

Androgen receptor polyQ alleles and COVID-19 severity in men: A replication study.

BACKGROUND: Ample evidence indicates a sex-related difference in severity of COVID-19, with less favorable outcomes observed in men. Genetic factors have been proposed as candidates to explain this difference. The polyglutamine (polyQ) polymorphism in the androgen receptor gene has been recently described as a genetic biomarker of COVID-19 severity. OBJECTIVE: To test the association between the androgen receptor polyQ polymorphism and COVID-19 severity in a large cohort of COVID-19 male patients. MATERIALS AND METHODS: This study included 1136 male patients infected with SARS-CoV-2 as confirmed by positive PCR. Patients were retrospectively and prospectively enrolled from March to November 2020. Patients were classified according to their severity into three categories: oligosymptomatic, hospitalized and severe patients requiring ventilatory support. The number of CAG repeats (polyQ polymorphism) at the androgen receptor was obtained by PCR and patients were classified as either short (<23 repeats) or long (≥23 repeats) allele carriers. The association between polyQ alleles (short or long) and COVID-19 severity was assessed by Chi-squared (Chi2 ) and logistic regression analysis. RESULTS: The mean number of polyQ CAG repeats was 22 (±3). Patients were classified as oligosymptomatic (15.5%), hospitalized (63.2%), and severe patients (21.3%) requiring substantial respiratory support. PolyQ alleles distribution did not show significant differences between severity classes in our cohort (Chi2 test p > 0.05). Similar results were observed after adjusting by known risk factors such as age, comorbidities, and ethnicity (multivariate logistic regression analysis). DISCUSSION: Androgen sensitivity may be a critical factor in COVID-19 disease severity. However, we did not find an association between the polyQ polymorphism and the COVID-19 severity. Additional studies are needed to clarify the mechanism underlying the association between androgens and COVID-19 outcome. CONCLUSIONS: The results obtained in our study do not support the role of this polymorphism as biomarker of COVID-19 severity.

El valor de los datos y su aplicabilidad en el Sector Sanitario / The value of data and its applicability in the Health Sector

Actualmente las noticias y/o artículos sobre la utilización de la Inteligencia Artificial (IA) y el Big Data nos están inundando y esta situación se ha agudizado con la pandemia, donde se ha dado una gran importancia a su utilización y las diversas aplicaciones en todos los sectores. Unos ámbitos tecnológicos y de oportunidades que cada vez se encuentran más presentes en nuestro día a día. El sector que más crecimiento ha experimento durante este tiempo de pandemia es, sin lugar a dudas, el sector sanitario. La imperiosa necesidad ha fomentado y agilizado el uso de estas tecnologías. La utilización de datos para poder acometer tratamientos en un breve tiempo, ver las evoluciones de las diferentes enfermedades y predecir su estado es lo que ha impulsado su utilización y donde debido a la situación cualquier ayuda era y es poca. Desde este artículo pretendemos dar una explicación de los beneficios del uso de la IA y las diferentes técnicas del Big Data, tanto en el estudio y evolución de enfermedades como en su prevención, detección, seguimiento y tratamiento (AU)

Currently news and/or articles on the use of Artificial Intelligence and the Big Data are flooding us and this situation has worsened with the pandemic, where great importance has been given to its use and the various applications in all sectors. Some areas of technology and opportunities that are increasingly are more present in our day to day. The sector that has experienced the most growth during this time of pandemic is, without a doubt, the Health sector. The imperative need has fostered and expedited the use of these technologies. The use of data to be able to undertake treatments in a short time, see the evolutions of the different diseases and predict their state is what has driven its use and where due to the situation any help was and is little. From this article we intend to give an explanation of the benefits of using the Artificial Intelligence and the different Big Data techniques, both in the study and evolution of diseases as in their prevention, detection, monitoring and treatment (AU)

The value of data and its applicability in the Health Sector.

Currently news and/or articles on the use of Artificial Intelligence and the Big Data are flooding us and this situation has worsened with the pandemic, where great importance has been given to its use and the various applications in all sectors. Some areas of technology and opportunities that are increasingly are more present in our day to day. The sector that has experienced the most growth during this time of pandemic is, without a doubt, the Health sector. The imperative need has fostered and expedited the use of these technologies. The use of data to be able to undertake treatments in a short time, see the evolutions of the different diseases and predict their state is what has driven its use and where due to the situation any help was and is little. From this article we intend to give an explanation of the benefits of using the Artificial Intelligence and the different Big Data techniques, both in the study and evolution of diseases as in their prevention, detection, monitoring and treatment.

The relationship between 25(OH) vitamin D levels and COVID-19 onset and disease course in Spanish patients.

OBJECTIVE: Currently, there are no definitive data on the relationship between low levels of vitamin D in the blood and a more severe disease course, in terms of the need for hospital admission, intensive care unit (ICU) stay, and mortality, in patients with coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to study the association between levels of circulating 25-hydroxyvitamin D (25(OH)D) and adverse clinical outcomes linked to SARS-CoV-2 infection. We further aimed to observe the incidence of low, below-average, and normal levels of 25(OH)D in patients hospitalized for COVID-19 between March 12, 2020, and May 20, 2020, and assess whether these values differed between these patients and a normal population. Finally, we determined whether the need for transfer to the intensive care unit (ICU) and the mortality rate were related to low levels of 25(OH)D. STUDY DESIGN: Retrospective observational study. SETTING: Quironsalud Hospitals in Madrid, Spain. PARTICIPANTS: We analyzed 1549 patients (mean age, 70 years; range, 21-104 years); 835 were male (53.9 %; mean age, 73.02 years), and 714 were female (46.1 %; mean age, 68.05 years). Subsequently, infected patients admitted to the ICU (n = 112) and those with a fatal outcome (n = 324) were analyzed. PROCEDURES: Serum concentrations of 25(OH)D were measured by electrochemiluminescence. RESULTS: More hospitalized patients (66 %, n = 1017) had low baseline levels of 25(OH)D (<20 ng/mL) than normal individuals (45 %) (p < 0.001). An analysis by age group revealed that COVID-19 patients between the ages of 20 and 80 years old had significantly lower vitamin D levels than those of the normal population (p < 0.001). Patients admitted to the ICU tended to have lower levels of 25(OH)D than other inpatients (p < 0.001); if we stratified patients by 25(OH)D levels, we observed that the rate of ICU admission was higher among patients with vitamin D deficiency (p < 0.001), indicating that higher vitamin D levels are associated with a lower risk of ICU admission due to COVID-19. ICU admission was related to sex (higher rates in men, p < 0.001) and age (p < 0.001). When using a logistic regression model, we found that vitamin D levels continued to show a statistically significant relationship with ICU admission rates, even when adjusting for sex and age. Therefore, the relationship found between vitamin D levels and the risk of ICU admission was independent of patient age and sex in both groups. Deceased patients (n = 324 tended to have lower levels of 25 (OH)D that normal population of the same age (p < 0.001). CONCLUSION: Vitamin D deficiency in patients with COVID-19 is correlated with an increased risk of hospital admission and the need for critical care. We found no clear relationship between vitamin D levels and mortality.

IL-6-based mortality prediction model for COVID-19: Validation and update in multicenter and second wave cohorts.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a highly variable condition. Validated tools to assist in the early detection of patients at high risk of mortality can help guide medical decisions. OBJECTIVE: We sought to validate externally, as well as in patients from the second pandemic wave in Europe, our previously developed mortality prediction model for hospitalized COVID-19 patients. METHODS: Three validation cohorts were generated: 2 external with 185 and 730 patients from the first wave and 1 internal with 119 patients from the second wave. The probability of death was calculated for all subjects using our prediction model, which includes peripheral blood oxygen saturation/fraction of inspired oxygen ratio, neutrophil-to-lymphocyte ratio, lactate dehydrogenase, IL-6, and age. Discrimination and calibration were evaluated in the validation cohorts. The prediction model was updated by reestimating individual risk factor effects in the overall cohort (N = 1477). RESULTS: The mortality prediction model showed good performance in the external validation cohorts 1 and 2, and in the second wave validation cohort 3 (area under the receiver-operating characteristic curve, 0.94, 0.86, and 0.86, respectively), with excellent calibration (calibration slope, 0.86, 0.94, and 0.79; intercept, 0.05, 0.03, and 0.10, respectively). The updated model accurately predicted mortality in the overall cohort (area under the receiver-operating characteristic curve, 0.91), which included patients from both the first and second COVID-19 waves. The updated model was also useful to predict fatal outcome in patients without respiratory distress at the time of evaluation. CONCLUSIONS: This is the first COVID-19 mortality prediction model validated in patients from the first and second pandemic waves. The COR+12 online calculator is freely available to facilitate its implementation (https://utrero-rico.shinyapps.io/COR12_Score/).