Impact of a Hospitalist Co-Management Program on Medical Complications and Length of Stay in Neurosurgical Patients.

BACKGROUND: The impact of co-management on clinical outcomes in neurosurgical patients is uncertain. This study aims to describe the implementation of a hospitalist co-management program in a neurosurgery department and its impact on the incidence of complications, mortality, and length of stay. METHODS: The authors used a quasi-experimental study design that compared a historical control period (July-December 2017) to a prospective intervention arm. During the intervention period, patients admitted to a neurosurgery inpatient unit who were older than 65 years, suffered certain conditions, or were admitted from ICUs were included in the co-management program. Two hospitalists joined the surgical staff and intervened in the diagnostic and therapeutical plan of patients, participating in clinical decisions and coordinating patient navigation with neurosurgeons. The incidence of moderate or severe complications measured by the Accordion Severity Grading System, in-hospital mortality, and length of stay of the two cohorts were compared. Multivariate regression was used to adjust for confounders, and the average treatment effect was estimated using inverse probability of treatment weighting. RESULTS: The adjusted incidence of moderate or severe complications was lower among co-managed patients (odds ratio [OR] 0.60, 95% confidence interval [CI] 0.39-0.91). Mortality was unchanged (OR 0.83, 95% CI 0.15-4.17). Length of stay was lower in co-managed patients, with a 1.3-day reduction observed after inverse probability of treatment weighting analysis. CONCLUSION: Hospitalist co-management was associated with a reduced incidence of complications and length of stay in neurosurgical patients, but there was no difference in in-hospital mortality.

Bacteremia in Patients with Solid Organ Cancer: Insights into Epidemiology and Antibiotic Consumption.

Epidemiology and risk factors associated to bacterial resistance in solid organ cancer (SOC) patients has been barely described. This retrospective monocentric study analyzed clinical variables in SOC patients who developed bacteremia between 1 January 2019 and 31 December 2022. We described rates of bacterial resistance in Gram negative bacteria (80.6%): E. coli-ESBL, K. pneumoniae-ESBL, Carbapenem-Resistant K. pneumoniae and Meropenem-Resistant P. aeruginosa, as well as antibiotic consumption, and compared these rates between the medical and oncology wards. In total, we included 314 bacteremias from 253 patients. SOC patients are frequently prescribed antibiotics (40.8%), mainly fluoroquinolones. Nosocomial bacteremia accounted for 18.2% of the cases and only 14.3% of patients were neutropenic. Hepatobiliary tract was the most frequent tumor (31.5%) and source of bacteremia (38.5%). Resistant bacteria showed a decreased rate of resistance during the years studied in the oncology ward. Both K-ESBL and K-CBP resistance rates decreased (from 45.8% to 20.0%, and from 29.2% to 20.0%, respectively), as well as MRPA, which varied from a resistance rate of 28% to 16.7%. The presence of a urinary catheter (p < 0.001) and previous antibiotic prescription (p = 0.002) were risk factors for bacterial resistance. Identifying either of these risk factors could help in guiding antibiotic prescription for SOC patients.

Enhancing screening adherence for strongyloides infection in latinx inpatients with COVID-19: a local protocol implementation study.

Strongyloides stercoralis hyperinfection syndrome has been observed in immunosuppressed coronavirus disease 2019 (COVID-19) patients. Detecting and treating asymptomatic Strongyloides infection in individuals from endemic areas can effectively prevent hyperinfection. Unfortunately, many clinicians are unaware of this neglected infection. Therefore, we aimed to evaluate whether including Strongyloides screening in COVID-19 management protocols would encourage this practice. To accomplish this, we conducted a retrospective single-center study at 'Hospital Universitario 12 de Octubre' in Madrid, Spain, comparing two consecutive cohorts. The first cohort comprised all Latinx patients over 18 years old who were admitted for COVID-19 between March 1st and April 30th, 2020. The second cohort consisted of Latinx patients admitted between July 1st and December 31st, 2020, following an amendment to the COVID-19 management protocol that recommended screening for strongyloidiasis in at-risk patients. We identified 559 and 795 patients in the first and second periods, respectively. The percentage of individuals screened increased significantly from 8.8% to 51.6% after the screening recommendation was included in the protocol (odds ratio [OR] 11.08, 95% confidence interval [CI] 8.01-15.33). In both periods, the screening rate was significantly higher among those receiving immunosuppression than those who did not receive steroids and/or tocilizumab. No other factors influenced the screening rate. In conclusion, including strongyloidiasis screening recommendations in COVID-19 management protocols led to its increased implementation. However, the overall screening rate remained low, emphasizing the need for further efforts to enhance screening practices.

Clinical outcomes by supplemental oxygen use in remdesivir-treated, hospitalised adults with COVID-19.

BACKGROUND: Clinical trials show different effects of remdesivir on clinical outcomes relative to COVID-19 severity at hospital admission; in Europe, there are few real-world data. METHODS: A multicentre, multinational retrospective cohort study in adult patients hospitalised with PCR-confirmed COVID-19 was conducted to understand remdesivir clinical use in different countries and to describe outcomes for patients receiving remdesivir stratified by oxygen use. Primary endpoints were all-cause mortality at day 28 and hospitalisation duration. Patients were categorised by baseline disease severity: no supplemental oxygen (NSO); low flow oxygen ≤ 6 litres (l)/minute (LFO); high flow oxygen > 6 l/minute (HFO). RESULTS: Four hundred and forty-eight (448) patients (72 [16.1%] HFO; 295 [65.8%] LFO; 81 (18.1%] NSO) were included; median age was 65 years and 64% were male. Mortality was higher in patients on HFO (rate 23.6%) compared to LFO (10.2%; p = 0.001) or NSO (6.2%; p = 0.002). Duration of hospitalisation was longer in patients on HFO (13 days) compared to LFO (9 days; p = 0.003) and NSO (9 days; p = 0.021). Patients who initiated remdesivir ≥ 2 days compared to within a day of hospitalisation had a 4.2 times higher risk of death, irrespective of age, sex, comorbidities, and oxygen support at baseline. Requirement for mechanical ventilation/ECMO and readmission within 28 days of discharge was similar across groups. Remdesivir use and outcomes differed by country. CONCLUSIONS: A higher mortality rate and duration of hospitalisation was seen in remdesivir-treated COVID-19 patients on HFO compared to LFO and NSO. Initiation of remdesivir upon admission as opposed to delayed initiation has a mortality benefit. CLINICAL TRIALS REGISTRATION: NCT04847622.

COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study.

The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).

COVID-19 Severity and Survival over Time in Patients with Hematologic Malignancies: A Population-Based Registry Study.

Mortality rates for COVID-19 have declined over time in the general population, but data in patients with hematologic malignancies are contradictory. We identified independent prognostic factors for COVID-19 severity and survival in unvaccinated patients with hematologic malignancies, compared mortality rates over time and versus non-cancer inpatients, and investigated post COVID-19 condition. Data were analyzed from 1166 consecutive, eligible patients with hematologic malignancies from the population-based HEMATO-MADRID registry, Spain, with COVID-19 prior to vaccination roll-out, stratified into early (February-June 2020; n = 769 (66%)) and later (July 2020-February 2021; n = 397 (34%)) cohorts. Propensity-score matched non-cancer patients were identified from the SEMI-COVID registry. A lower proportion of patients were hospitalized in the later waves (54.2%) compared to the earlier (88.6%), OR 0.15, 95%CI 0.11-0.20. The proportion of hospitalized patients admitted to the ICU was higher in the later cohort (103/215, 47.9%) compared with the early cohort (170/681, 25.0%, 2.77; 2.01-3.82). The reduced 30-day mortality between early and later cohorts of non-cancer inpatients (29.6% vs. 12.6%, OR 0.34; 0.22-0.53) was not paralleled in inpatients with hematologic malignancies (32.3% vs. 34.8%, OR 1.12; 0.81-1.5). Among evaluable patients, 27.3% had post COVID-19 condition. These findings will help inform evidence-based preventive and therapeutic strategies for patients with hematologic malignancies and COVID-19 diagnosis.

Disparities between Latinx migrants and Spanish natives in COVID-19 outcome in Madrid.

Previous studies have suggested an increased susceptibility of COVID-19 among certain populations. We analyzed whether COVID-19 presentation and mortality differ between Latinx migrants and Spanish natives. METHODS AND MATERIALS: COVID-19 patients between 35-64 years old admitted between January 26th-May-5th 2020 were reviewed. Demographics, major comorbidities, symptoms, signs and analytical parameters on admission were recorded. Respiratory failure was defined as PaO2/FiO2 ≤ 200 mmHg, noninvasive or invasive mechanical ventilation requirement at any time during hospitalization. A propensity score (PS) adjustment was created between Latinx and Spanish. A multivariable logistic regression model adjusted by the PS was performed to evaluate the effects of different variables on mortality. RESULTS: 894 patients: 425 (47.5%) Latinx and 469 (52.5%) Spanish natives were included. Latinx were younger (50 vs 55 years p < 0.001) and had less comorbidities (29.4% vs 55.0% p < 0.001) than Spanish natives. More often they exhibited fever (22.1% vs 9.8% p = 0.018) and had higher inflammatory markers (PCR) (11.3 mg/dl vs 7.7 mg/dl p < 0.001). Mortality seemed lower among Latinx (4.7% vs 8.7%, p = 0.017). No association was found between ethnicity and mortality. Respiratory failure [OR = 23.978 (CI 95% 9.4-60.1) p < 0.001], LDH [OR (per unitary increment) = 1.002; CI95% (1.000-1.004;p = 0.036] and PCR [OR (per unitary increment) = 1.044 (CI95% 1.06-1.08); p = 0.02] were independently associated to mortality. CONCLUSIONS: We were unable to identify significant ethnic disparities between Latinx and Spanish natives in terms of COVID-19 mortality. Universal access to the health care system in Spain may have contributed to a better outcome of Latinx patients. Differences previously described might be a consequence of socioeconomic disparities.

Derivation and external validation of a simple prediction rule for the development of respiratory failure in hospitalized patients with influenza.

BACKGROUND: Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients. METHODS: A prospective cohort study was conducted during two consecutive Influenza seasons (December 2016-March 2017 and December 2017-April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 and May 2019. RESULTS: Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p < 0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher's test p > 0.43). CONCLUSION: we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient's stratification during seasonal Influenza epidemics.

Clinical Differences and Outcomes of COVID-19 Associated Pulmonary Thromboembolism in Comparison with Non-COVID-19 Pulmonary Thromboembolism.

(1) Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been reported to increase the risk of pulmonary thromboembolism (PTE). The aim of this study is to elucidate whether Coronavirus disease COVID-19-associated PTE has a different clinical expression than non-COVID-19 PTE due to a different pathophysiology. (2) Methods: retrospective study of PTE episodes conducted at our hospital between January 2019 and December 2020, comparing the group of COVID-19-associated PTE patients with a control group of non-COVID-19 PTE patients. (3) Results: A total of 229 patients with PTE were registered, 79 of whom had COVID-19. Cancer (15.2% vs. 39.3%; p < 0.001), previous surgery (0% vs. 8%; p = 0.01), previous VTE (2.5% vs. 15.3%; p = 0.003), signs and/or symptoms of deep venous thrombosis (DVT) (7.6% vs. 22.7%; p = 0.004) and syncope (1.3% vs. 8.1%; p = 0.035) were less frequent in the COVID-19 group. Central thrombosis was more frequent in the control group (35.3% vs. 13.9%; p = 0.001). No VTE recurrent episodes were observed in the COVID-19 group, whereas four (2.7%) episodes were recorded for the control group. One-month bleeding rate was higher in the COVID-19 group (10.1% vs. 1.3%; p = 0.004). (4) Conclusion: COVID-19-associated PTE has clinical characteristics that differ from those of PTE without COVID-19, including inferior severity and a lower rate of VTE recurrence. Physicians should be aware of the high risk of bleeding in the first month of COVID-19-associated PTE.

In-hospital incidence of and risk factors for influenza-associated respiratory failure.

BACKGROUND: Respiratory failure (RF) is the most important complication of influenza virus infection. Its definition and incidence are heterogeneous in the literature. METHODS: This systematic review and meta-analysis aim to determine the incidence of and risk factors for RF in patients hospitalized with influenza. Electronic databases were searched for articles on RF in patients hospitalized for influenza infection up to December 2021 regardless of their geographical location. Observational and experimental studies were considered for inclusion, excluding case series. The Newcastle-Ottawa and Johanna Briggs scales were used for quality assessment. A random-effects meta-analysis was performed, followed by subgroup analyses according to, among others, presence/absence of pneumonia, RF definition, serotype and time period. PRISMA guidelines were followed for this review. RESULTS: Thirty-six studies were finally included in the meta-analysis. Overall, RF incidence was 24% (range 5%-85%, 95% confidence interval [95CI] 19%-31%). Significantly higher incidences of RF were found in patients with pneumonia (42%, 95CI 28%-57%, p = .006), when RF was defined as hypoxemia (58%, 95CI 31%-81%, p < .001), and during the 2009 pandemic (25%, 95CI 16%-36%) and postpandemic period (23%, 95CI 15%-34%, p = .01). No differences were found between human influenza serotypes. Assessment of risk factors associated with the development of RF was not possible due to their inconsistent and heterogeneous reporting. CONCLUSION: Respiratory failure is frequent in hospitalized influenza patients, especially in patients with pneumonia and since the 2009 pandemic, although its definition and reporting widely vary in the literature. This complicates its characterization and comparison between cohorts and with other respiratory viruses.

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.

While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.

Making EHRs Trustable: A Quality Analysis of EHR-Derived Datasets for COVID-19 Research.

One approach to verifying the quality of research data obtained from EHRs is auditing how complete and correct the data are in comparison with those collected by manual and controlled methods. This study analyzed data quality of an EHR-derived dataset for COVID-19 research, obtained during the pandemic at Hospital Universitario 12 de Octubre. Data were extracted from EHRs and a manually collected research database, and then transformed into the ISARIC-WHO COVID-19 CRF model. Subsequently, a data analysis was performed, comparing both sources through this convergence model. More concepts and records were obtained from EHRs, and PPV (95% CI) was above 85% in most sections. In future studies, a more detailed analysis of data quality will be carried out.

Fluconazole versus micafungin for initial antifungal prophylaxis against Candida in pancreas transplant recipients: A comparative study of two consecutive periods.

BACKGROUND: Invasive fungal infection, particularly intraabdominal candidiasis, exerts a negative impact on the outcome of pancreas transplant recipients (PTRs). Optimal antifungal prophylaxis in this context remains unclear. METHODS: We performed a single-centre retrospective study to compare the incidence of invasive candidiasis during the first 6 post-transplant months in a cohort of 218 PTRs over two periods in which different agents for antifungal prophylaxis were used: fluconazole (Fluco-Px) from March 1995 to June 2012, and micafungin followed by fluconazole (Mica-Px) from July 2012 to December 2018. RESULTS: A total of 152 and 66 PTRs received Fluco-Px and Mica-Px. Mean age was 39.7 ± 7.8 years, 56.4% (123/218) were males, and 85.3% (186/218) underwent simultaneous pancreas-kidney transplantation. Invasive candidiasis occurred in 21.7% (33/152) of PTRs under Fluco-Px compared to 24.2% (16/66) of those under Mica-Px (p-value = .681). Median time from transplantation to infection was 8 days (interquartile range [IQR]: 6-16) under Fluco-Px versus 6.5 (IQR: 3.3-15.8) under Mica-Px (p-value = .623). Non-albicans Candida species comprised 27.5% (11/40) and 25.0% (4/16) of episodes under Fluco-Px and Mica-Px respectively (p-value = .849). Surgical site infection was the most common form in both groups (82.5% [33/40] and 87.5% [14/16]; p-value = .954). Multivariable analysis identified cold ischaemia time of the pancreas and kidney grafts, surgical reintervention and insulin requirement after transplantation as risks factor for invasive candidiasis. CONCLUSION: This retrospective study did not reveal a significant benefit from the initial use of micafungin-based antifungal prophylaxis over fluconazole among PTRs in terms of invasive candidiasis.

Higher mortality of hospitalized haematologic patients with COVID-19 compared to non-haematologic is driven by thrombotic complications and development of ARDS: An age-matched cohorts study.

BACKGROUND AND OBJECTIVES: The characteristics of COVID-19 in haematologic patients compared to non-haematologic patients have seldom been analyzed. Our aim was to analyze whether there are differences in clinical characteristics and outcome of haematologic patients with COVID-19 as compared to non-haematologic. PATIENTS AND METHODS: Retrospective cohort study in 2 University hospitals of patients admitted with laboratory-confirmed COVID-19 included in the SEMICOVID19 database. The cohort with underlying haematologic disease was compared to a cohort of age and date-of-COVID-19-matched controls without haematologic disease (1:2). RESULTS: 71 cases and 142 controls were included from March-May 2020.Twenty (28.1%) had received recent chemotherapy. Twelve (16.9%) were stem cell transplant recipients (SCT). Eleven (15.5%) were neutropenic concurrently with COVID-19 diagnosis.Haematologic patients presented ARDS (58.5 vs 20.7%, p = 0.0001), thrombotic complications (15.7 vs 2.1%, p = 0.002), DIC (5.7 vs 0.0%, p = 0.011), heart failure (14.3 vs 4.9%, p = 0.029) and required ICU admission (15.5 vs 2.8%, p = 0.001), MV (14.1% vs 2.1%, p 0.001), steroid (64.8 vs 33.1%, p = 0.0001), tocilizumab (33.8 vs 8.5%, p = 0.0001) or anakinra treatment (9.9% vs 0%, p = 0.0001) more often. In-hospital mortality was significantly higher (38.0% vs 18.3%, p = 0.002). CONCLUSIONS: Our results suggest COVID-19 has worse outcomes in haematologic patients than in non-haematologic, independently of age, and that the development of ARDS and thrombotic complications drive the higher in-hospital mortality.

An Early Th1 Response Is a Key Factor for a Favorable COVID-19 Evolution.

The Th1/Th2 balance plays a crucial role in the progression of different pathologies and is a determining factor in the evolution of infectious diseases. This work has aimed to evaluate the early, or on diagnosis, T-cell compartment response, T-helper subsets and anti-SARS-CoV-2 antibody specificity in COVID-19 patients and to classify them according to evolution based on infection severity. A unicenter, randomized group of 146 COVID-19 patients was divided into four groups in accordance with the most critical events during the course of disease. The immunophenotype and T-helper subsets were analyzed by flow cytometry. Asymptomatic SARS-CoV-2 infected individuals showed a potent and robust Th1 immunity, with a lower Th17 and less activated T-cells at the time of sample acquisition compared not only with symptomatic patients, but also with healthy controls. Conversely, severe COVID-19 patients presented with Th17-skewed immunity, fewer Th1 responses and more activated T-cells. The multivariate analysis of the immunological and inflammatory parameters, together with the comorbidities, showed that the Th1 response was an independent protective factor for the prevention of hospitalization (OR 0.17, 95% CI 0.03-0.81), with an AUC of 0.844. Likewise, the Th1 response was found to be an independent protective factor for severe forms of the disease (OR 0.09, 95% CI: 0.01-0.63, p = 0.015, AUC: 0.873). In conclusion, a predominant Th1 immune response in the acute phase of the SARS-CoV-2 infection could be used as a tool to identify patients who might have a good disease evolution.

Analysis of the factors predicting clinical response to tocilizumab therapy in patients with severe COVID-19.

BACKGROUND: Controversy remains about the efficacy of tocilizumab (TCZ) for the treatment of severe COVID-19. We aimed to analyze the profile of TCZ-respondent patients. METHODS: We retrospectively analyzed a cohort of patients with severe COVID-19 who received off-label TCZ after recommendation by a local committee and were admitted to the University Hospital "12 de Octubre" until May 2020. The primary end point was a significant clinical improvement (SCI) on day 14 after administration of TCZ. Factors independently related to SCI were analyzed by multivariate logistic regression models. RESULTS: Of 428 (63.3%) patients treated with TCZ, 271 (63.3%) experienced SCI. After adjustment for factors related to unfavorable outcomes, TCZ administration within the first 48 hours from admission (odds ratio [OR]: 1.98, 95% confidence Interval [95% CI]: 1.1-3.55; P = 0.02) and ALT levels >100 UI/L at day 0 (OR: 3.28; 95% CI: 1.3-8.1; P = 0.01) were independently related to SCI. The rate of SCI significantly decreased according to the time of TCZ administration: 70.2% in the first 48 hours from admission, 58.5% on days 3-7, and 45.1% after day 7 (P = 0.03 and P = 0.001, respectively). CONCLUSION: TCZ improves the prognosis of patients with COVID-19 the most if treatment starts within the first 48 hours after admission.

Influenza A-Associated In-Hospital Mortality in Very Older People: Does Inflammation Also Play a Role?

BACKGROUND: The aim of the study was to analyze the clinical manifestations and outcome of the oldest old (people aged ≥85 years) who were admitted to the hospital with a confirmed influenza A virus infection in comparison with younger patients and to assess the role of inflammation in the outcome of influenza infection in this population. METHODS: This is an observational prospective study including all adult patients with influenza A virus infection hospitalized in a tertiary teaching hospital in Madrid, in 2 consecutive influenza seasons (2016-17 and 2017-18). RESULTS: Five hundred nine hospitalized patients with influenza A infection were included, of whom 117 (23%) were older than 85 years (median age: 89.3 ± 3.2). We compared the clinical characteristics and outcome with those of the rest of the population (median age: 72.8 ± 15.7). Overall, mortality was higher in older patients (10% vs. 4%; p = 0.03) with no differences in clinical presentation. Patients older than 85 years who ultimately died (12 out of 117) showed increased systemic inflammation expressed by higher levels of C-reactive protein (CRP) and ferritin compared to survivors who were discharged (odds ratio [OR] of CRP >20 mg/dL: 5.16, 95% confidence interval [CI]: 1.29-20.57, and OR of ferritin >500 mg: 4.3, 95% CI: 1.04-17.35). CONCLUSIONS: Patients aged 85 and older with influenza A virus infection presented a higher in-hospital mortality than younger subjects. CRP and ferritin levels were higher in the oldest old who died, suggesting that inflammation could play a key role in the outcome of this subset of patients.

A predictive score at admission for respiratory failure among hospitalized patients with confirmed 2019 Coronavirus Disease: a simple tool for a complex problem.

Coronavirus Disease 2019 (COVID-19) pandemic has implacably stricken on the wellness of many countries and their health-care systems. The aim of the present study is to analyze the clinical characteristics of the initial wave of patients with COVID-19 attended in our center, and to identify the key variables predicting the development of respiratory failure. Prospective design study with concurrent data retrieval from automated medical records of all hospitalized adult patients who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rRT-PCR assay performed on respiratory samples from March 2nd to 18th, 2020. Patients were followed up to May 1st, 2020 or death. Respiratory failure was defined as a PaO2/FiO2 ratio ≤ 200 mm Hg or the need for mechanical ventilation (either non-invasive positive pressure ventilation or invasive mechanical ventilation). We included 521 patients of whom 416 (81%) had abnormal Chest X-ray on admission. Median age was 64.6 ± 18.2 years. One hundred eighty-one (34.7%) developed respiratory failure after a median time from onset of symptoms of 9 days (IQR 6-11). In-hospital mortality was 23.8% (124/521). The modeling process concluded into a logistic regression multivariable analysis and a predictive score at admission. Age, peripheral pulse oximetry, lymphocyte count, lactate dehydrogenase and C-reactive protein were the selected variables. The model has a good discriminative capacity with an area under the ROC curve of 0.85 (0.82-0.88). The application of a simple and reliable score at admission seems to be a useful tool to predict respiratory failure in hospitalized COVID-19 patients.

Longitudinal dynamics of SARS-CoV-2-specific cellular and humoral immunity after natural infection or BNT162b2 vaccination.

The timing of the development of specific adaptive immunity after natural SARS-CoV-2 infection, and its relevance in clinical outcome, has not been characterized in depth. Description of the long-term maintenance of both cellular and humoral responses elicited by real-world anti-SARS-CoV-2 vaccination is still scarce. Here we aimed to understand the development of optimal protective responses after SARS-CoV-2 infection and vaccination. We performed an early, longitudinal study of S1-, M- and N-specific IFN-γ and IL-2 T cell immunity and anti-S total and neutralizing antibodies in 88 mild, moderate or severe acute COVID-19 patients. Moreover, SARS-CoV-2-specific adaptive immunity was also analysed in 234 COVID-19 recovered subjects, 28 uninfected BNT162b2-vaccinees and 30 uninfected healthy controls. Upon natural infection, cellular and humoral responses were early and coordinated in mild patients, while weak and inconsistent in severe patients. The S1-specific cellular response measured at hospital arrival was an independent predictive factor against severity. In COVID-19 recovered patients, four to seven months post-infection, cellular immunity was maintained but antibodies and neutralization capacity declined. Finally, a robust Th1-driven immune response was developed in uninfected BNT162b2-vaccinees. Three months post-vaccination, the cellular response was comparable, while the humoral response was consistently stronger, to that measured in COVID-19 recovered patients. Thus, measurement of both humoral and cellular responses provides information on prognosis and protection from infection, which may add value for individual and public health recommendations.

Which outcomes are most important to measure in patients with COVID-19 and how and when should these be measured? Development of an international standard set of outcomes measures for clinical use in patients with COVID-19: a report of the International Consortium for Health Outcomes Measurement (ICHOM) COVID-19 Working Group.

OBJECTIVES: The COVID-19 pandemic has resulted in widespread morbidity and mortality with the consequences expected to be felt for many years. Significant variation exists in the care even of similar patients with COVID-19, including treatment practices within and between institutions. Outcome measures vary among clinical trials on the same therapies. Understanding which therapies are of most value is not possible unless consensus can be reached on which outcomes are most important to measure. Furthermore, consensus on the most important outcomes may enable patients to monitor and track their care, and may help providers to improve the care they offer through quality improvement. To develop a standardised minimum set of outcomes for clinical care, the International Consortium for Health Outcomes Measurement (ICHOM) assembled a working group (WG) of 28 volunteers, including health professionals, patients and patient representatives. DESIGN: A list of outcomes important to patients and professionals was generated from a systematic review of the published literature using the MEDLINE database, from review of outcomes being measured in ongoing clinical trials, from a survey distributed to patients and patient networks, and from previously published ICHOM standard sets in other disease areas. Using an online-modified Delphi process, the WG selected outcomes of greatest importance. RESULTS: The outcomes considered by the WG to be most important were selected and categorised into five domains: (1) functional status and quality of life, (2) mental functioning, (3) social functioning, (4) clinical outcomes and (5) symptoms. The WG identified demographic and clinical variables for use as case-mix risk adjusters. These included baseline demographics, clinical factors and treatment-related factors. CONCLUSION: Implementation of these consensus recommendations could help institutions to monitor, compare and improve the quality and delivery of care to patients with COVID-19. Their consistent definition and collection could also broaden the implementation of more patient-centric clinical outcomes research.