key: cord-0823677-p2d2ajeg
authors: Pepe, Martino; Maroun-Eid, Charbel; Romero, Rodolfo; Arroyo-Espliguero, Ramón; Fernàndez-Rozas, Inmaculada; Aparisi, Alvaro; Becerra-Muñoz, Víctor Manuel; Garcìa Aguado, Marcos; Brindicci, Gaetano; Huang, Jia; Alfonso-Rodríguez, Emilio; Castro-Mejía, Alex Fernando; Favretto, Serena; Cerrato, Enrico; Albiol, Paloma; Raposeiras-Roubin, Sergio; Vedia, Oscar; Feltes Guzmãn, Gisela; Carrero-Fernández, Ana; Perez Cimarra, Clara; Buzón, Luis; Jativa Mendez, Jorge Luis; Abumayyaleh, Mohammad; Corbi-Pascual, Miguel; Macaya, Carlos; Estrada, Vicente; Nestola, Palma Luisa; Biondi-Zoccai, Giuseppe; Núñez-Gil, Iván J.
title: Clinical presentation, therapeutic approach, and outcome of young patients admitted for COVID-19, with respect to the elderly counterpart
date: 2021-02-08
journal: Clin Exp Med
DOI: 10.1007/s10238-021-00684-1
sha: 6237e48cfadf2d5693a93d82ecf7932acb9b6640
doc_id: 823677
cord_uid: p2d2ajeg

There is limited information on the presenting characteristics, prognosis, and therapeutic approaches of young patients hospitalized for coronavirus disease 2019 (COVID-19). We sought to investigate the baseline characteristics, in-hospital treatment, and outcomes of a wide cohort < 65 years admitted for COVID-19. Using the international multicenter HOPE-COVID-19 registry, we evaluated the baseline characteristics, clinical presentation, therapeutic approach, and prognosis of patients < 65 years discharged (deceased or alive) after hospital admission for COVID-19, also compared with the elderly counterpart. Of the included 5746 patients, 2676 were < 65 and 3070 ≥ 65 years. All risk factors and several parameters suggestive of worse clinical presentation augmented through increasing age classes. In-hospital mortality rates were 6.8% and 32.1% in the younger and older cohort, respectively (p < 0.001). Among young patients, mortality, access to ICU and treatment with IMVwere positively correlated with age. Contrariwise, over 65 years of age this trend was broken so that only the association between age and mortality was persistent, while the rates of access to ICU and IMV started to decline. Younger patients also recognized specific predictors of case fatality, such as obesity and gender. Age negatively impacts on mortality, access to ICU and treatment with IMV in patients < 65 years. In elderly patients only case fatality rate keeps augmenting in a stepwise manner through increasing age categories, while therapeutic approaches become more conservative. Besides age, obesity, gender, history of cancer, and severe dyspnea, tachypnea, chest X-ray bilateral abnormalities, abnormal level of creatinine and leucocyte among admission parameters seem to play a central role in the outcome of patients younger than 65 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1007/s10238-021-00684-1).

SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first reported in China in late December 2019. Since then, due to the rapid and global spread of the disease, WHO declared a pandemic indicating

Epidemiological, clinical, and outcome data were extracted from electronic medical records. Patients' data were anonymously collected in a locked, password-protected website.

Demographic information included age, sex, race, weight, and height. Coexisting conditions included any lung disease (chronic obstructive pulmonary disease [COPD], asthma, restrictive or interstitial pulmonary disease), any immunosuppressed condition (immunosuppressant use, a preexisting immunologic condition, or ongoing chemotherapy for cancer disease), current or remote history of smoking, history of hypertension, diabetes mellitus, dyslipidemia, or underlying cardiovascular disease (coronary artery disease, heart failure, valvular disease, and cardiac arrhythmia). Home medications, recorded at the time of hospital admission, included any antiplatelet/anticoagulation therapy, use of betablockers, ARBs or ACE inhibitors, inhaled betaagonist or glucocorticoids, benzodiazepines, and antidepressants.

Data regarding admission signs and symptoms (dyspnea, tachypnea, fever, cough, dysgeusia, hypo/anosmia, sorethroat, vomiting, diarrhea, arthromyalgia), initial laboratory tests and instrumental diagnostic exams (chest X-ray), inpatient medications (glucocorticoids, chloroquine, antiviral drugs, antibiotics, tolicizumab or similar, interferon or similar, ACE or ARBs, and anticoagulants), non-pharmacological treatments (Intensive Care Unit [ICU] care, oxygen therapy, high-flow nasal cannula therapy, non-invasive or invasive mechanical ventilation [IMV] , prone position, extracorporeal mechanical oxygenation [ECMO] or other support), in-hospital adverse events such as mortality or clinically relevant complications (respiratory insufficiency, heart failure, renal failure, pneumonia [uni or bilateral], sepsis, systemic inflammatory response syndrome [SIRS] , clinically relevant bleeding, hemoptysis, and embolic events), and discharge data were extracted for all patients.

For the present analysis, the focus was mainly on the patients aged 18 to 64 years, according to the WHO definition of elderly as individuals aged 65 years or more [3] . Age assessment was made at the time of the hospital admission. The primary endpoint of the study was death from any cause occurring during hospital stay; secondary endpoints were access to ICU and IMV. The study endpoints were also analyzed in the rest of the registry population, which included patients aged 65 or older.

Patients were considered to have confirmed infection by a positive result on high-throughput sequencing or realtime reverse transcriptase polymerase-chain-reaction (PCR) assay of nasal or pharyngeal swab specimens; patients with compatible signs or symptoms together with any other diagnostic finding (e.g., radiological evidence of pulmonary involvement) or with inconclusive PCR assay were deemed as highly suspected of SARS-CoV-2 infection. Leukopenia was defined as white blood cells count < 4000/L, whereas lymphocytopenia as lymphocytes count < 1500/L [2] . For blood tests whose normality thresholds were not predefined (e.g., troponin I, d-dimers, procalcitonin), abnormal levels were according to local laboratory cutoffs. Severe chronic kidney disease (CKD) was defined as an estimated Glomerular Filtration Rate (eGFR) ≤ 30 ml/min calculated by means of the Cockcroft-Gault formula. Body mass index was calculated through the formula weight (in kilograms) divided by the square of the height (in meters). Details of all the remaining variables assessed in the analysis are available online (https ://hopep rojec tmd.com). We referred to the Charlson Comorbidity Index to identify the chronic comorbid conditions which might impact the long-term survival: hypertension, diabetes mellitus, coronary artery disease, heart failure, COPD, cerebrovascular events, severe renal failure, connective disease, liver disease, history of cancer, HIV infection [4] .

Furthermore, four different age groups(< 35; 35-44; 45-54; 55-64) were generated in the younger cohort and three for the elderly patients (65-74; 75-84; ≥ 85 years). Trends through increasing age categories of the following parameters were evaluated: mortality, multiple comorbidities (defined as ≥ 3 comorbid diseases), combined pharmacological therapies (defined as the association of Chloroquine and an antiviral drug), access to ICU, and treatment with IMV. In order to evaluate the differential case fatality rate according to age among patients undergoing IMV or admitted in ICU, in view of the reduced numerosity, a division in four age groups was used (< 55; 55-64; 65-74; ≥ 75).

The study population was primarily divided into two groups: patients younger than 65 years and patients ≥ 65 years; moreover all the assessed variables were also presented according to age categories within the younger cohort. Continuous variables were summarized as means with standard deviations and categorical variables as frequencies or percentages. Baseline characteristics, hospital admission parameters, inpatients medications, ICU admission, in-hospital instrumental treatments, in-hospital complications, and mortality rates were compared between age groups using the Pearson's Chi-squared test or Fisher exact test, when appropriated, for categorical variables and the unpaired Student's t test or analysis of variance for continuous variables. For the primary endpoint of the study, the association with all the baseline characteristics and hospital admission findings was tested in the whole population and in the group < 65 years; a stepwise logistic regression with the forward selection method (P for entry < 0.05) was used to choose the final multivariable model to predict in-hospital death, reporting results as point estimates and 95% confidence intervals (CI) of the odds ratio. Additional sensitivity analyses were based on penalized logistic regression, missing data imputation, and classification and regression tree (CART) analyses. Statistical significance was set at the 2-tailed 0.05 level, without multiplicity adjustment.

A receiver-operating characteristic (ROC) curve analysis with Youden index measure was performed to determine the best cutoff value of age for predicting the in-hospital mortality. Computations were performed with SPSS 22.0 (SPSS; Chicago, IL, USA) and Stata 13.0 (Stata Corp, College Station, TX, USA).

A total of 5868 hospitalized patients with confirmed or highly suspected SARS-CoV-2 infection from 39 centers in 31 cities and seven countries who completed their hospital course were finally included in the HOPE registry by May 05, 2020. Our study population included 5746 patients, owing to the exclusion of 122 patients from the analysis for incompleteness of demographic data or because aged < 18 years (Appendix Fig. 3 ). Enrollment rates by country of citizenship are shown in Appendix Fig. 4 . Table 1 depicts the distribution of demographic characteristics, coexisting conditions, and home medications among young (< 65 years) patients overall and by the four predefined age classes, along with the between-groups differences. In brief, overall patients younger than 65 years were 2676 (mean age 49.63 ± 10.44 years, male 59.4%). All risk factors showed to significantly augment through increasing age classes, as well as several comorbidities such as severe CKD, any lung disease, COPD, previous cardiac, cerebrovascular, liver, and cancer disease. The same trend was found in the analysis of the rates of comorbidities per age classes and was maintained when the investigation was extended over the age of 65 (Appendix Table 4 ). Symptoms, signs, and laboratory results recorded at admission are displayed in Table 2 . Several parameters suggestive of worse clinical presentation showed to be associated with age. Indeed, between the four age groups of the young cohort, a stepwise increasing prevalence of severe dyspnea, fatigue, tachypnea, peripheral oxygen saturation < 92%, instrumental evidence of bilateral pulmonary infiltrates, and more pronounced signs of systemic inflammation and multi-organ involvement (proven by the levels of procalcitonin, C-Reactive protein, D-dimer, troponin I, transaminases, LDH) were detected. In-hospital clinical course and treatments are described in Table 3 . Progressively worse clinical conditions are demonstrated through incremental age classes, as expected, and are paralleled with more aggressive therapies, either pharmacological and/or supportive of the respiratory function.

Appendix Table5 depicts the distribution of demographic characteristics, coexisting conditions, home medications, and clinical information at admission among young (< 65 years) and elderly patients (≥ 65 years), along with the between-groups differences. Older patients had a greater prevalence of risk factors and comorbidities, as predictable. The 83.9% of patients ≥ 65 years had at least 1 comorbidity, while the 24.8% had ≥ 3 comorbid diseases, compared to the 4.0% of the younger counterpart. At admission symptoms, signs, and laboratory results are in line with the "age related" trend already seen among the age groups generated within the younger cohort: Patients ≥ 65 years more frequently presented with severe pulmonary and multi-organ involvement (Appendix Table 6 ). According to pharmacological regimens and intensive treatments, it seems noteworthy to describe some discrepancies between younger and older patients (Appendix Table 7 ). Although the rates of ICU admission were comparable between the two age groups, IMV was applied to the 8.4% and the 6.4% of the younger and older population, respectively (p = 0.005), being the opposite for the non-invasive respiratory support use (15.3% in the elderly vs. 11.7% in the young counterpart, p < 0.001). Additionally, if glucocorticoids and antibiotics were the most common inpatients' medications in the elderly group, chloroquine and antiviral drugs (the drugs probably trusted as the most effective) were more frequently used in patients < 65 years.

The in-hospital case fatality rate in the overall population was 20.3%: death occurred in 182 (6.8%) of patients < 65 years and in 985 (32.1%) of patients in the older cohort (p < 0.001). Between the four age groups of the young population a stepwise increasing mortality rate was depicted through age categories and was paralleled by a concomitant increasing rates of ICU access, IMV, and use of combined pharmacological therapies (Appendix Table 8 and Fig. 1a) .As the optimal threshold value (cutoff point) for mortality was detected by the mean of the Youden index Fig. 1 a Trends of in-hospital death, multiple comorbidities (defined as ≥ 3 comorbid diseases), combined pharmacological therapies (defined as the association of chloroquine and an antiviral drug), access to Intensity Care Unit (ICU), and treatment with invasive mechanical ventilation through increasing age categories among the young population; b Youden index measure performed to determine the best cutoff value of age for predicting in-hospital mortality; c Trends of in-hospital death, multiple comorbidities (defined as ≥ 3 comorbid diseases), combined pharmacological therapies (defined as the association of chloroquine and an antiviral drug), access to Intensity Care Unit (ICU), and treatment with invasive mechanical ventilation through increasing age categories among the whole population around 65-70 years (Fig. 1b) , case fatality rate was also evaluated in the entire study population separated into seven age-groups as described in the methods and displayed in Fig. 1c . The bend of the mortality curve was confirmed after 65 years of age. What is noteworthy is the change of the trend of in-hospital treatments when the entire study sample is considered: The rates of access to ICU, combined pharmacological therapies, and IMV did not follow the trend of mortality any longer, but described a dome-like trend peaking between the age of 55 and 75, and declining afterward (Appendix Table 9 and Fig. 1) . Mortality rates were also evaluated in both the subpopulations of patients admitted to ICU and assisted with IMV: also in this subanalysis, after the division in four age groups (< 55; 55-64; 65-74; ≥ 75),the case fatality rate showed to increase with age (Appendix Table 5 and Fig. 2 ).

A multivariable logistic-regression model was developed. Independent predictors of in-hospital death, their corresponding odds ratios, and 95% confidence intervals are shown in Appendix Table 10 . In the overall population, among baseline characteristics, age, severe CKD, partially dependence status, and oral anticoagulation treatment were associated with a higher risk of in-hospital death together with some clinical vitals and instrumental/laboratory parameters at admission: tachypnea, bilateral abnormalities at chest X-ray, elevated procalcitonin, and WBC count.

Considering the younger population (< 65 years) only, body mass index and cancer were the only independent predictors of in-hospital mortality among demographic and coexisting conditions, while at triage severe dyspnea, tachypnea, bilateral abnormalities at chest X-ray, creatinine > 1.5 mg/dL, and lymphocytopenia were associated with higher rate of case fatality.

The primary and secondary endpoints were investigated in male vs. female patients younger than 65 years. As displayed in Appendix Table 11 , female patients showed better prognosis in terms of mortality, access to ICU, and need for IMV. Baseline characteristics were also analyzed and compared between gender(Appendix Table 12 ), showing higher prevalence of risk factors and cardiac disease among male patients. In the subpopulation of the youngest patients (aged < 45), in female individuals significantly lower rates of in-hospital death and IMV were confirmed, in this case despite the lack of significant differences in terms of cardiovascular risk factors or coexisting conditions between the genders (Appendix Table 13 ).

Since the beginning of the COVID-19 outbreak clinical data from multicentre registries have been collected worldwide [5] [6] [7] [8] . To the best of authors' knowledge, this is the largest investigation on clinical characteristics, therapy, and in-hospital outcome of patients < 65 years admitted with COVID-19, also in comparison with elderly patients. The main findings of the present study are: (1) among patients < 65 years in-hospital mortality was positively correlated with age and the same association was also proven for the access to ICU and the treatment with IMV, secondary endpoints of the study; (2) over 65 years of age only the association between age and mortality was persistent, while the rates of access to ICU and IMV started to decline; (3) younger patients recognized specific predictors of case fatality.

Overall in-hospital mortality rate in our study was 20.3%, being deaths unequally distributed between patients younger than 65 years and older (6.8% vs. 32.1%). Moreover, when multiple age classes were considered, case fatality rate showed to increase in a stepwise fashion among both the younger and older cohort (Appendix Table 11 ). Relevance Fig. 2 Case fatality rate in patients admitted to Intensity Care Unit (ICU) and patients assisted with invasive mechanical ventilation divided according to age categories of age as one of the most powerful mortality predictors is confirmed in our regression analysis (Appendix Table 10 ). The explanation for the increasing mortality through age categories among patients < 65 years can be easily found in the escalating rate of risk factors and comorbidities, which led to worse clinical presentation at admission and less favorable in-hospital clinical course (Table 1) . These differences were enhanced when evaluated between larger age classes, such as in the case of patients younger than 65 years vs older. Patients aged more than 65 years, at the time of hospital access, more frequently presented symptoms and signs of severe pulmonary involvement such as severe dyspnea, tachypnea, low peripheral oxygen saturation (Appendix Table 6 ). This difference could suggest a different stage of the disease at the moment of admission, which might play a role in patients' prognosis.

Moreover, it seems noteworthy to describe the different trends of the primary and secondary endpoints before and after the age cutoff of 65 years. In the younger cohort mortality, ICU access, and IMV consensually increased through age decades; in the elderly group, despite an even sharper mortality curve (in line with the result of the Youden index measure), admission to ICU and treatment with IMV progressively lessened, as well as the treatment with complex pharmacological regimens (Fig. 1a,b,c) [9, 10] . The more "conservative" treatment in the elderly patients, relative to the patients < 65 years, can recognize several reasons. One reason for this age-related differential approach could be the higher rate of comorbidities (e.g., chronic kidney disease or liver disease), which were often simultaneously coexisting in the same patient and made the more aggressive drugs therapies contraindicated or deemed to be poorly tolerated. In the second place, starting compromised general conditions and short life-expectancy might have advised the treating physicians to avoid therapeutic obstinacy. In the third place, it should be taken into consideration that the enrollment period entirely covered the peak of the pandemic, when high pressure was exerted on the healthcare systems. The hypothesis that at the climax of the pandemic, resources, such as mechanical ventilators, could have not coped with all the needs seems possible. In the context of the COVID 19 epidemic, national societies of Anesthesiology have indicated indeed that, in the presence of serious shortage of healthcare resources, intensive treatments must be guaranteed to the patients with greater chances of therapeutic success, evaluated on the basis of the type and severity of the disease, the presence of comorbidities, the impairment of other organs and systems, and their reversibility [11] [12] [13] [14] . Despite all the enrolling nations have been making all the possible efforts to increase health service resources (especially ICU beds) and to optimize their exploitation by patients' transfer toward centers with greater availability, the application of the rationing criterion during the peak of this maxi-emergency cannot be ruled out. Our data, nevertheless, exclude the use of age as the sole criterion for the allocation of possibly limited invasive treatments, as proved by the stepwise increase in the number of coexisting comorbidities through incremental age categories (Appendix Table 4 ).

The influence of a differential therapeutic approach (both pharmacological and instrumental) through different age classes on patients' outcome is impossible to infer in the absence of randomized controlled data, which are not expected. Appendix Table 9 shows indeed the influence of age on mortality rate among patients undergone IMV: case fatality ranged from 44.2% in patients younger than 55 years to the 82.5% in patients aged 75 or older, proving in this category very poor survival expectance. Moreover, further caution in the interpretation of these data is advised as it is licit to hypothesize a selection bias in the choice of the elderly patients to be treated more invasively, so that the latter mortality rate could be underestimated. On the basis of this evidence, what is conversely noteworthy is the potential unreliability of surrogate endpoints such as access to ICU or IMV as prognosis indicators when the cutoff for elderly definition is passed. Indeed, in ours as in several other recent reports these parameters have been used single handedly or within composite endpoints as indicators of negative clinical course [3, 6] .

Besides age, in the younger population (< 65 years) independent predictors of in-hospital mortality among anamnestic factors and coexisting conditions were body mass index and history of cancer. The analysis of the population younger than 65 years, stratified by the presence or absence of obesity, demonstrated that obesity was associated with a significant increase in all the predefined endpoints, both primary and secondary; in detail, as shown in Appendix Table 14 , obese young patients faced a mortality rate almost double as compared to the non-obese counterpart (11.6% vs. 6.4%, respectively). This finding seems noteworthy since confirms some initial analogous evidences [15] . Moreover, despite not included among the most powerful predictors of mortality in our young cohort, recent evidences suggested a potential effect of gender on mortality [16, 17] . The study endpoints were thus investigated relative to the patients' gender, with the evidence of a better outcome in terms of mortality, access to ICU, and need for IMV for the female sex (Appendix Table 11 ). In the whole category of patients younger than 65 years the higher prevalence of risk factors and cardiac disease among male patients could explain this finding. Nevertheless, the same finding in the subpopulation of patients aged less than 45, in which baseline characteristics are very similar between genders, opened to different hypotheses such as possible hormonal protection, in line with other initial reports [18] .

Our study has some limitations. First, the study design is observational, and thus, data would result in selection bias.

As a consequence, even though our dataset was large and the study provides a wide overview of the 'real-world' prognosis and management of patients hospitalized for COVID-19, the study should be considered as hypotheses generating. Second, some clinical characteristics and incident events in the participating centers could have not been diagnosed and/or been reported.

In conclusion, our study confirmed that age negatively impacts on both the primary and the secondary endpoints in patients younger than 65 years. In older patients, only case fatality rate keeps augmenting in a stepwise manner through increasing age categories, while therapeutic approaches become more conservative. Besides age, obesity, and gender seem to both play a role in the outcome of patients younger than 65 years.

See Tables 4,5 , 6, 7, 8, 9, 10, 11, 12, 13, 14 and Figs. 3, 4 Author contributions MP had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis; MP ideated the study, analyzed and interpreted the data, and wrote the manuscript; GBZ contributed substantially to the study design, analyzed and interpreted the data, IJNG contributed to the study design, critically revised the manuscript and approved the final draft of the manuscript; the other authors contributed to the collection of the data, read, reviewed and finally approved the manuscript.

Funding No funding was received.

Availability of data and material Not applicable.

Conflicts of interest The authors declare that they have no conflict of interest.

Ethics approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by Ethics Research Committee from the Hospital Clínico San Carlos (Madrid, Spain) (20/241-E) and the Spanish Agency for Medicines and Health Products classification (EPA-0D).

Consent to participate Written informed consent was waived because of the anonymized nature of the registry and the health alarm situation generated by the pandemia. There were no exclusion criteria, except for patients' explicit refusal to participate.

Former smokers 72/1087 (6.6%) 204/1589 (12.8%)

Any lung disease 124/1087

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Martino Pepe 1 · Charbel Maroun-Eid 2 · Rodolfo Romero 3 · Ramón Arroyo-Espliguero 4 · Inmaculada Fernàndez-Rozas 5 · Alvaro Aparisi 6 · Víctor Manuel Becerra-Muñoz 7 · Marcos Garcìa Aguado 8 · Gaetano Brindicci 1 · Jia Huang 9 · Emilio Alfonso-Rodríguez 10 · Alex Fernando Castro-Mejía 11 · Serena Favretto 12 · Enrico Cerrato 13 · Paloma Albiol 14 · Sergio Raposeiras-Roubin 15 The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China