Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Longevity-associated BPIFB4 gene counteracts the inflammatory signaling.

BACKGROUND: Increased levels of pro-inflammatory proteins in plasma can be detected in older individuals and associate with the so called chronic low-grade inflammation, which contributes to a faster progression of aged-related cardiovascular (CV) diseases, including frailty, neurodegeneration, gastro-intestinal diseases and disorders reflected by alterations in the composition of gut microbiota. However, successful genetic programme of long-living individuals alters the trajectory of the ageing process, by promoting an efficient immune response that can counterbalance deleterious effects of inflammation and the CV complications. This is the case of BPIFB4 gene in which, homozygosity for a four single-nucleotide polymorphism (SNP) haplotype, the Longevity-Associated Variant (LAV) correlates with prolonged health span and reduced risk of CV complications and inflammation. The relation between LAV-BPIFB4 and inflammation has been proven in different experimental models, here we hypothesized that also human homozygous carriers of LAV-BPIFB4 gene may experience a lower inflammatory burden as detected by plasma proteomics that could explain their favourable CV risk trajectory over time. Moreover, we explored the therapeutic effects of LAV-BPIFB4 in inflammatory disease and monolayer model of intestinal barrier. RESULTS: We used high-throughput proteomic approach to explore the profiles of circulating proteins from 591 baseline participants selected from the PLIC cohort according to the BPIFB4 genotype to identify the signatures and differences of BPIFB4 genotypes useful for health and disease management. The observational analysis identified a panel of differentially expressed circulating proteins between the homozygous LAV-BPIFB4 carriers and the other alternative BPIFB4 genotypes highlighting in the latter ones a higher grade of immune-inflammatory markers. Moreover, in vitro studies performed on intestinal epithelial organs from inflammatory bowel disease (IBD) patients and monolayer model of intestinal barrier demonstrated the benefit of LAV-BPIFB4 treatment. CONCLUSIONS: Homozygosity for LAV-BPIFB4 results in the attenuation of inflammation in PLIC cohort and IBD patients providing preliminary evidences for its therapeutic use in inflammatory disorders that need to be further characterized and confirmed by independent studies.

Predicting Response to In-Hospital Pulmonary Rehabilitation in Individuals Recovering From Exacerbations of Chronic Obstructive Pulmonary Disease / Predicción de la respuesta a la rehabilitación pulmonar hospitalaria en personas que se recuperan de exacerbaciones de la enfermedad pulmonar obstructiva crónica

Background: Predicting the response to pulmonary rehabilitation (PR) could be valuable in defining admission priorities. We aimed to investigate whether the response of individuals recovering from a COPD exacerbation (ECOPD) could be forecasted using machine learning approaches. Method: This multicenter, retrospective study recorded data on anthropometrics, demographics, physiological characteristics, post-PR changes in six-minute walking distance test (6MWT), Medical Research Council scale for dyspnea (MRC), Barthel Index dyspnea (BId), COPD assessment test (CAT) and proportion of participants reaching the minimal clinically important difference (MCID). The ability of multivariate approaches (linear regression, quantile regression, regression trees, and conditional inference trees) in predicting changes in each outcome measure has been assessed. Results: Individuals with lower baseline 6MWT, as well as those with less severe airway obstruction or admitted from acute care hospitals, exhibited greater improvements in 6MWT, whereas older as well as more dyspnoeic individuals had a lower forecasted improvement. Individuals with more severe CAT and dyspnea, and lower 6MWT had a greater potential improvement in CAT. More dyspnoeic individuals were also more likely to show improvement in BId and MRC. The Mean Absolute Error estimates of change prediction were 44.70m, 3.22 points, 5.35 points, and 0.32 points for 6MWT, CAT, BId, and MRC respectively. Sensitivity and specificity in discriminating individuals reaching the MCID of outcomes ranged from 61.78% to 98.99% and from 14.00% to 71.20%, respectively. Conclusion: While the assessed models were not entirely satisfactory, predictive equations derived from clinical practice data might help in forecasting the response to PR in individuals recovering from an ECOPD. Future larger studies will be essential to confirm the methodology, variables, and utility.(AU)

Relationship between maternal obesity and first-trimester TSH in women with negative anti-TPO antibodies.

Objective: Obesity is associated with increased thyroid-stimulating hormone (TSH) in non-pregnant subjects, but this phenomenon has not been fully characterized during pregnancy. Our aim was to evaluate the impact of BMI on first-trimester TSH in a wide cohort of pregnant women with negative anti-thyroperoxidase antibodies (AbTPO) and its implications on uterine artery pulsatility index (UtA-PI), a marker of early placentation. Methods: The study included 2268 AbTPO-negative pregnant women at their first antenatal visit. Anamnestic data, BMI, TSH, anti-nuclear antibody (ANA) and extractable nuclear antigen (ENA) positivity and mean UtA-PI were collected. Results: A total of 1693 women had normal weight, 435 were overweight and 140 were obese. Maternal age, ANA/ENA positivity, history of autoimmune diseases and familiar history of thyroid diseases were similar in the three groups. TSH was significantly higher in obese women (1.8 (IQR: 1.4-2.4) mU/L) when compared to normal weight (1.6 (IQR: 1.2-2.2) mU/L) and overweight (median: 1.6 (IQR: 1.2-2.2) mU/L) ones (P < 0.001). BMI was significantly related with the risk of having a TSH level ≥4 mU/L at logistic regression, independently from non-thyroid autoimmunity, smoking or familiar predisposition for thyroid diseases (OR: 1.125, 95% CI: 1.080-1.172, P < 0.001). A restricted cubic splines regression showed a non-linear relationship between BMI and TSH. Women with a TSH ≥4 mU/L had a higher UtA-PI, independently from BMI. Conclusion: Overweight/obesity is significantly related with TSH serum levels in AbTPO-negative pregnant women, independently from the other risk factors for hypothyroidism during pregnancy. The increase of TSH levels could be clinically relevant, as suggested by its association with abnormal UtA-PI, a surrogate marker of abnormal placentation.

Efficacy and activity of treatments after progression from palbociclib plus endocrine therapy in patients with HR+/HER2- metastatic breast cancer: a prospective, monocentric study.

Background: Breast cancer is the most frequent tumour worldwide, and the HR+/HER2- subtype is the most common. For this tumour type, endocrine therapy (ET) is the mainstay of treatment. The association of ET and CDK4/6 inhibitors (CDK4/6i) represents the gold standard for first-line or second-line therapies. However, the optimal therapeutic strategy after CDK4/6i progression is still a matter of debate, with several randomized clinical trials still ongoing. Patients and methods: This is an observational, prospective, real-world study including women with HR+/HER2- metastatic breast cancer progressing to palbociclib plus ET. Patients received either ET or chemotherapy (CT). The primary objective was the evaluation of efficacy of the different therapeutic strategies after palbociclib in terms of median progression-free survival 2. Secondary objectives were the activity of therapeutic strategies measured with the clinical benefit rate, evaluation of the parameters used for the treatment choice, and progression-free survival 1 related to palbociclib plus ET treatment. Results: Overall, 48 patients (median age 53, range 33-78 years) were included. The median progression-free survival 2 was of 5 months in the overall cohort (95% CI 4-48 months) with a statistically significant difference between the two therapeutic strategies adopted (ET versus CT, 10 months versus 5 months, respectively). Regarding secondary objectives, the clinical benefit rate was 55.2% in the CT cohort and 50% in ET. Moreover, women treated with CT had a greater number of visceral metastases and a shorter median progression-free survival 1 than patients who received ET. Conclusions: ET and CT represent two possible therapeutic alternatives for patients progressing on CDK4/6i plus ET. The choice is based on clinical parameters, with a potential preference for ET.

Predicting Response to In-Hospital Pulmonary Rehabilitation in Individuals Recovering From Exacerbations of Chronic Obstructive Pulmonary Disease.

BACKGROUND: Predicting the response to pulmonary rehabilitation (PR) could be valuable in defining admission priorities. We aimed to investigate whether the response of individuals recovering from a COPD exacerbation (ECOPD) could be forecasted using machine learning approaches. METHOD: This multicenter, retrospective study recorded data on anthropometrics, demographics, physiological characteristics, post-PR changes in six-minute walking distance test (6MWT), Medical Research Council scale for dyspnea (MRC), Barthel Index dyspnea (BId), COPD assessment test (CAT) and proportion of participants reaching the minimal clinically important difference (MCID). The ability of multivariate approaches (linear regression, quantile regression, regression trees, and conditional inference trees) in predicting changes in each outcome measure has been assessed. RESULTS: Individuals with lower baseline 6MWT, as well as those with less severe airway obstruction or admitted from acute care hospitals, exhibited greater improvements in 6MWT, whereas older as well as more dyspnoeic individuals had a lower forecasted improvement. Individuals with more severe CAT and dyspnea, and lower 6MWT had a greater potential improvement in CAT. More dyspnoeic individuals were also more likely to show improvement in BId and MRC. The Mean Absolute Error estimates of change prediction were 44.70m, 3.22 points, 5.35 points, and 0.32 points for 6MWT, CAT, BId, and MRC respectively. Sensitivity and specificity in discriminating individuals reaching the MCID of outcomes ranged from 61.78% to 98.99% and from 14.00% to 71.20%, respectively. CONCLUSION: While the assessed models were not entirely satisfactory, predictive equations derived from clinical practice data might help in forecasting the response to PR in individuals recovering from an ECOPD. Future larger studies will be essential to confirm the methodology, variables, and utility.

Clinical characteristics of acute lacosamide poisoning: Pavia Poison Control Centre experience.

AIMS: Lacosamide is a third-generation antiepileptic drug used as adjunctive therapy for partial seizures. Since its approval in 2008 very few cases of lacosamide overdose have been described in literature. The aim of our study was to evaluate clinical characteristics of acute lacosamide poisoning. METHODS: A retrospective observational study was performed including all cases of acute lacosamide poisoning referred to Pavia Poison Control Centre from January 2012 to December 2021. For each patient age, sex, ingested dose, coingestants, clinical manifestations, treatment and outcome were collected. RESULTS: A total of 31 subjects (median age 39 years, [interquartile range: 26.5-46.5]; females 22/31) were included. The median lacosamide ingested dose was 1500 mg [650-2800]. In 35.5% of cases lacosamide was the single ingested substance, while in 64.5% coingestants were also present. Coingestants varied from a minimum of 1 to a maximum of 3, with the more common being benzodiazepines and valproic acid. Clinical manifestations were present in 87% patients the most common were: vomiting (29%); seizures (29%), coma (25.8%), drowsiness (25.8%), confusion (12.9%), agitation (12.9%), tachycardia (12.9%), tremors (9.7%), bradycardia (9.7%), headache (6.5%) and hypertension (3.2%). The median lacosamide ingested dose was significantly higher in patients that experienced coma compared to patient who did not (2800 vs. 800 mg; P = .0082). Orotracheal intubation was necessary in 32.3% of patients. All patients fully recovered. CONCLUSION: Lacosamide acute overdose may lead to a severe clinical picture. Dentral nervous system symptoms predominated, particularly seizures and coma occurred in a high percentage of cases.

BPIFB4 and its longevity-associated haplotype protect from cardiac ischemia in humans and mice.

Long-living individuals (LLIs) escape age-related cardiovascular complications until the very last stage of life. Previous studies have shown that a Longevity-Associated Variant (LAV) of the BPI Fold Containing Family B Member 4 (BPIFB4) gene correlates with an extraordinarily prolonged life span. Moreover, delivery of the LAV-BPIFB4 gene exerted therapeutic action in murine models of atherosclerosis, limb ischemia, diabetic cardiomyopathy, and aging. We hypothesize that downregulation of BPIFB4 expression marks the severity of coronary artery disease (CAD) in human subjects, and supplementation of the LAV-BPIFB4 protects the heart from ischemia. In an elderly cohort with acute myocardial infarction (MI), patients with three-vessel CAD were characterized by lower levels of the natural logarithm (Ln) of peripheral blood BPIFB4 (p = 0.0077). The inverse association between Ln BPIFB4 and three-vessel CAD was confirmed by logistic regression adjusting for confounders (Odds Ratio = 0.81, p = 0.0054). Moreover, in infarcted mice, a single administration of LAV-BPIFB4 rescued cardiac function and vascularization. In vitro studies showed that LAV-BPIFB4 protein supplementation exerted chronotropic and inotropic actions on induced pluripotent stem cell (iPSC)-derived cardiomyocytes. In addition, LAV-BPIFB4 inhibited the pro-fibrotic phenotype in human cardiac fibroblasts. These findings provide a strong rationale and proof of concept evidence for treating CAD with the longevity BPIFB4 gene/protein.

Health-related quality of life and clinical complexity of a real-life cohort of patients with advanced HR+/HER2- breast cancer treated with CDK4/6 inhibitors and endocrine therapy.

Background: Advanced breast cancer (ABC) is characterized by multidimensional clinical complexity that is usually not considered in randomized clinical trials. In the present real-life study, we investigated the link between clinical complexity and quality of life of patients with HR+/HER2- ABC treated with CDK4/6 inhibitors. Methods: We evaluated multimorbidity burden assessed with the Cumulative Illness Rating Scale (CIRS), polypharmacy and patient-reported outcomes (PROs). PROs were assessed at baseline (T0), after 3 months of therapy (T1), and at disease progression (T2) using EORTC QLC-C30 and QLQ-BR23 questionnaires. Baseline PROs and changes between T0 and T1 were evaluated amongst patients with different multimorbidity burden (CIRS <5 and ≥5) and polypharmacy (<2 or ≥2 drugs). Results: From January 2018 to January 2022, we enrolled 54 patients (median age 66 years, IQR 59-74). The median CIRS score was 5 (IQR 2-7), whilst the median number of drugs taken by patients was 2 (IQR 0-4). No changes in QLQ-C30 final scoring between T0 and T1 were observed in the overall cohort (p=0.8944). At T2, QLQ-C30 global score deteriorated with respect to baseline (p=0.0089). At baseline, patients with CIRS ≥5 had worse constipation than patients without comorbidities (p<0.05) and a lower trend in the median QLQ-C30 global score. Patients on ≥2 drugs had lower QLQ-C30 final scores and worse insomnia and constipation (p<0.05). No change in QLQ-C30 final score from T0 to T1 was observed (p>0.05). Conclusion: Multimorbidity and polypharmacy increase the clinical complexity of patients with ABC and may affect baseline PROs. The safety profile of CDK4/6 inhibitors seems to be maintained in this population. Further studies are needed to assess clinical complexity in patients with ABC.This article is part of the Tackling clinical complexity in breast cancer Special Issue: https://www.drugsincontext.com/special_issues/tackling-clinical-complexity-in-breast-cancer/.

DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms.

BACKGROUND AND OBJECTIVE: Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. METHODS: DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs. RESULTS: The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results. CONCLUSION: To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.

Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study.

Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section.

The prognostic role of variations in tumour markers (CEA, CA15.3) in patients with metastatic breast cancer treated with CDK4/6 inhibitors.

Tumour markers have no established role in the monitoring of the course of metastatic breast cancer during antineoplastic therapy, yet cancer antigen 15.3 (CA15.3) and carcinoembryonic antigen (CEA) are commonly used in clinical practice to aid in the early detection of progression of disease (PD). In our multicentre, prospective, real-life study, we enrolled 142 consecutive patients with advanced breast cancer receiving endocrine therapy in combination with a CDK4/6 inhibitor from January 2017 to October 2020; 75 patients had PD at the time of database closure. We measured serum marker concentrations at regular 4-month intervals together with radiological tumour response assessments and in cases of clinical suspicion of PD. Appropriate descriptive and inferential statistical methods were used to analyse serum marker level trends amongst prespecified subgroups and at specific time points (baseline, best radiologically documented tumour response and first detection of PD) in the subpopulation of patients with PD at the time of database closure. Notably, the median time from treatment initiation to best tumour response was 4.4 months. We evaluated the presence of an association between baseline CA15.3 and CEA levels and prespecified clinical characteristics but found no clinically meaningful correlation. We assessed marker level variations at the time of best radiologically documented disease response and PD: in the subgroup of patients who responded to treatment before progressing, we detected a statistically significant correlation with tumour marker variation between the time of best response and progression; this finding was not confirmed in the subgroup of patients that did not benefit from treatment. In conclusion, serum tumour marker flares can be useful in the early diagnosis of PD but should not be used as the sole factor prompting a change in treatment strategy without radiological confirmation.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality.

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients.

The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09-1.55), heart failure (RR 1.22, 95% CI 1.10-1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07-1.31), and fatigue (RR 1.18, 95% CI 1.07-1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58-2.76), venous embolism (RR 1.34, 95% CI 1.17-1.54), atrial fibrillation (RR 1.30, 95% CI 1.13-1.50), type 2 diabetes (RR 1.26, 95% CI 1.16-1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09-1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90-3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21-2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04-1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2.

OBJECTIVE: To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic. DESIGN, SETTING AND PARTICIPANTS: This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation. RESULTS: Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001). CONCLUSIONS: Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital "Istituti Clinici Salvatore Maugeri" in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

Clinical and Biological Variables Influencing Outcome in Patients with Advanced Non-Small Cell Lung Cancer (NSCLC) Treated with Anti-PD-1/PD-L1 Antibodies: A Prospective Multicentre Study.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) have become the standard of treatment for patients with non-small cell lung cancer (NSCLC). However, there are still many uncertainties regarding the selection of the patient who could benefit more from this treatment. This study aims to evaluate the prognostic and predictive role of clinical and biological variables in unselected patients with advanced NSCLC candidates to receive ICIs. METHODS: This is an observational and prospective study. The primary objective is the evaluation of the relationship between clinical and biological variables and the response to ICIs. Secondary objectives included: safety; assessment of the relationship between clinical and biological parameters/concomitant treatments and progression-free survival at 6 months and overall survival at 6 and 12 months. Nomograms to predict these outcomes have been generated. RESULTS: A total of 166 patients were included. An association with response was found in the presence of the high immunohistochemical PD-L1 expression, squamous cell histotype, and early line of treatment, whereas a higher probability of progression was seen in the presence of anemia, high LDH values and neutrophil/lymphocyte ratio (NLR), pleural involvement, and thrombosis before treatment. The nomogram showed that anemia, PD-L1 expression, NLR, and LDH represented the most informative predictor as regards the three parameters of interest. CONCLUSIONS: In the era of personalized medicine, the results are useful for stratifying the patients and tailoring the treatments, considering both the histological findings and the clinical features of the patients.

Impact of COVID-19 lockdown on PM concentrations in an Italian Northern City: A year-by-year assessment.

In the last century, the increase in traffic, human activities and industrial production have led to a diffuse presence of air pollution, which causes an increase of risk of several health conditions such as respiratory diseases. In Europe, air pollution is a serious concern that affects several areas, one of the worst ones being northern Italy, and in particular the Po Valley, an area characterized by low air quality due to a combination of high population density, industrial activity, geographical factors and weather conditions. Public health authorities and local administrations are aware of this problem, and periodically intervene with temporary traffic limitations and other regulations, often insufficient to solve the problem. In February 2020, this area was the first in Europe to be severely hit by the SARS-CoV-2 virus causing the COVID-19 disease, to which the Italian government reacted with the establishment of a drastic lockdown. This situation created the condition to study how significant is the impact of car traffic and industrial activity on the pollution in the area, as these factors were strongly reduced during the lockdown. Differently from some areas in the world, a drastic decrease in pollution measured in terms of particulate matter (PM) was not observed in the Po Valley during the lockdown, suggesting that several external factors can play a role in determining the severity of pollution. In this study, we report the case study of the city of Pavia, where data coming from 23 air quality sensors were analyzed to compare the levels measured during the lockdown with the ones coming from the same period in 2019. Our results show that, on a global scale, there was a statistically significant reduction in terms of PM levels taking into account meteorological variables that can influence pollution such as wind, temperature, humidity, rain and solar radiation. Differences can be noticed analyzing daily pollution trends too, as-compared to the study period in 2019-during the study period in 2020 pollution was higher in the morning and lower in the remaining hours.

Role of CACNA1C in Brugada syndrome: Prevalence and phenotype of probands referred for genetic testing.

BACKGROUND: Evidence for the role of the CACNA1C gene, which encodes for the α-subunit of the cardiac L-type calcium channel CaV1.2, as a cause of the BrS3 variant of Brugada syndrome (BrS) is contradictory. OBJECTIVE: The purpose of this study was to define in a large BrS cohort the yield of molecular screening and to test whether appropriate patient selection could improve clinical utility. METHODS: A total of 709 patients were included in this study. BrS probands (n = 563, consecutively referred) underwent CACNA1C sequencing. Two matched cohorts where defined: discovery cohort (n = 200) and confirmation cohort (n = 363). In addition, the clinical phenotypes of a matched SCN5A-positive BrS cohort (n = 146) were included for comparative genotype-phenotype correlation. RESULTS: In the discovery cohort, we identified 11 different rare variants in 9 patients; 10 of the variants (5%) were considered potentially causative based on their frequency in the general population. However, American College of Medical Genetics criteria were unable to classify the majority (80%) of them, which eventually were labeled as variants of unknown significance (VUS). Functional studies revealed a loss of function for 9 variants, pointing to a prevalence of CACNA1C causative variants in 4% of the discovery cohort. Genotype-phenotype correlation showed that pathogenic variants are significantly more frequent in patients with shorter QTc (12.9% vs 2.2% in patients with QTc <390 ms). CONCLUSION: CACNA1C is an infrequent but definitive cause of BrS typically associated with short QT. Functional studies are highly relevant to improve variant interpretation.