ABSTRACT

Objective.

To determine the risk factors for admission to the intensive care unit (ICU) or mortality in patients hospitalized for COVID-19 in a hospital in Puno, Peru.

Materials and methods.

Retrospective cohort study in adults hospitalized between April and December 2020. We evaluated Sociodemographic characteristics, vital functions, comorbidities, treatment received and its association with admission to ICU or mortality (adverse outcome). Poisson regression with robust variance was used to calculate crude and adjusted relative risks (RR) with their 95% confidence intervals (95%CI).

Results.

A total of 348 medical records were analyzed. The median age in years was 42.5 (IQR: 30.0; 58.0); 38.2% were male, and 35.3% died or were admitted to the ICU. Those admitted with an oxygen saturation ≤ 75% were 2.79 times more likely to have the adverse outcome (p < 0.001), compared to those admitted with a saturation ≥ 85%; those admitted with a value between 75-79% were 2.92 times more likely to have the adverse outcome (p < 0.001); likewise, those admitted with saturation between 80-84% were 1.70 times more likely to have the adverse outcome; however, the difference was not statistically significant (p=0.066). In addition, male patients, RR= 1.75 (p<0.001); those aged > 40 years, RR 3.5 (p=0.001); those with tachypnea, RR=1.66 (p=0.010); or with diabetes, RR = 1.53 (p=0.011) had higher risk of presenting the adverse outcome.

Conclusions.

The risk factors for ICU admission or mortality due to COVID-19 were male sex, age over 40 years, low saturation, diabetes and tachypnea.

Keywords:
COVID-19; Mortality; Inpatients; Altitude; Oximetry

KEY MESSAGES

Keywords:
COVID-19; Mortality; Inpatients; Altitude; Oximetry

INTRODUCTION

A significant number of severe pneumonia cases were reported in Wuhan, China in December 2019 ¹1. Díaz-Castrillón FJ, Toro-Montoya AI. SARS-CoV-2/COVID-19: el virus, la enfermedad y la pandemia. Med y Lab. 2020;24(3):183-205. doi: 10.36384/01232576.268.
https://doi.org/10.36384/01232576.268... . Subsequently, this new disease would be identified as COVID-19, caused by severe acute respiratory syndrome type 2 virus (SARS-CoV-2) ¹1. Díaz-Castrillón FJ, Toro-Montoya AI. SARS-CoV-2/COVID-19: el virus, la enfermedad y la pandemia. Med y Lab. 2020;24(3):183-205. doi: 10.36384/01232576.268.
https://doi.org/10.36384/01232576.268... . On March 11, 2020, The World Health Organization (WHO) proclaimed COVID-19 as a pandemic due to its extensive worldwide spread, suggesting that countries follow a comprehensive strategy aimed at preventing infections and deaths ²2. World Health Organization [Internet]. Alocución de apertura del director general de la OMS en la rueda de prensa sobre la COVID-19 celebrada el 11 de marzo de 2020. 2020. [citado el 04 de abril del 2020]. Disponible en: https://www.who.int/es/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020.
https://www.who.int/es/dg/speeches/detai... . As of October 26, 2021, more than 4.9 million deaths caused by this disease had been reported worldwide. In response, the Peruvian Ministry of Health (MINSA) decreed a state of national health emergency ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... . Confirmed cases of coronavirus in the country totaled 2,197,052, with 200,118 deaths and a case fatality rate of 9.11% ⁴4. Instituto Nacional de Salud y Centro de Control de Enfermedades [Internet]. Sala situacional de COVID-19 Perú; 2020 [citado el 11 de septiembre de 2020]. Disponible en: https://covid19.minsa.gob.pe/sala_situacional.asp.
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There are studies that suggest that the prevalence and impact of COVID-19 could be diminished by a potential protective factor such as altitude ⁶6. Arias-Reyes C, Zubieta-DeUrioste N, Poma-Machicao L, Aliaga-Raduan F, Carvajal-Rodriguez F, Dutschmann M, et al. Does the pathogenesis of SARS-CoV-2 virus decrease at high-altitude?. Respir Physiol Neurobiol. 2020; 277:103443. doi: 10.1016/j.resp.2020.103443.
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https://doi.org/10.1371/journal.pone.024... ; either due to the drastic changes that usually exist in these environments, high ultraviolet radiation and dryness of the air ⁶6. Arias-Reyes C, Zubieta-DeUrioste N, Poma-Machicao L, Aliaga-Raduan F, Carvajal-Rodriguez F, Dutschmann M, et al. Does the pathogenesis of SARS-CoV-2 virus decrease at high-altitude?. Respir Physiol Neurobiol. 2020; 277:103443. doi: 10.1016/j.resp.2020.103443.
https://doi.org/10.1016/j.resp.2020.1034... . Likewise, the decreased expression of angiotensin-converting enzyme (ACE) in the pulmonary endothelium of high-altitude inhabitants could generate a physiological protective factor as it is the binding site of SARS-CoV-1 and SARS-CoV-2 ⁶6. Arias-Reyes C, Zubieta-DeUrioste N, Poma-Machicao L, Aliaga-Raduan F, Carvajal-Rodriguez F, Dutschmann M, et al. Does the pathogenesis of SARS-CoV-2 virus decrease at high-altitude?. Respir Physiol Neurobiol. 2020; 277:103443. doi: 10.1016/j.resp.2020.103443.
https://doi.org/10.1016/j.resp.2020.1034... ^,77. Stephens KE, Chernyavskiy P, Bruns DR. Impact of altitude on COVID-19 infection and death in the United States: A modeling and observational study. PLOS ONE. 2021;16(1):e0245055. doi: 10.1371/journal.pone.0245055.
https://doi.org/10.1371/journal.pone.024... . However, these are ecological studies, with inconclusive statements, and more research is needed.

There are several factors associated with mortality due to this disease, which can be grouped into sociodemographic characteristics, vital functions, comorbidities, ancillary examinations, time of illness and treatment received ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... . Among the sociodemographic characteristics, male sex and age over 60 years stand out ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
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9. Kang SJ, Jung SI. Age Related Morbidity and Mortality among Patients with COVID-19. Infect Chemother. 2020;52(2):154-64. doi: 10.3947/ic.2020.52.2.154.
https://doi.org/10.3947/ic.2020.52.2.154... ^-1010. Rivera-Izquierdo M, Valero-Ubierna MC, R-DelAmo JL, Fernández-García MÁ, Martínez-Diz S, Tahery-Mahmoud A, et al. Sociodemographic, clinical and laboratory factors on admission associated with COVID-19 mortality in hospitalized patients: A retrospective observational study. PLoS One. 2020;15(6):1-12. doi: 10.1371/journal.pone.0235107
https://doi.org/10.1371/journal.pone.023... . Obesity (BMI > 30) ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... ^,1111. Fernández García L, Puentes Gutiérrez AB, García Bascones M. Relationship between obesity, diabetes and ICU admission in COVID-19 patients. Med Clin (Barc). 2020;(xx):9-10. doi: 10.1016/j.medcli.2020.06.009.
https://doi.org/10.1016/j.medcli.2020.06... ^,1212. Luzi L, Radaelli MG. Influenza and obesity: its odd relationship and the lessons for COVID-19 pandemic. Acta Diabetológica. 2020;57(6):759-64. doi: 10.1007/s00592-020-01522-8.
https://doi.org/10.1007/s00592-020-01522... , diabetes mellitus¹¹11. Fernández García L, Puentes Gutiérrez AB, García Bascones M. Relationship between obesity, diabetes and ICU admission in COVID-19 patients. Med Clin (Barc). 2020;(xx):9-10. doi: 10.1016/j.medcli.2020.06.009.
https://doi.org/10.1016/j.medcli.2020.06...

12. Luzi L, Radaelli MG. Influenza and obesity: its odd relationship and the lessons for COVID-19 pandemic. Acta Diabetológica. 2020;57(6):759-64. doi: 10.1007/s00592-020-01522-8.
https://doi.org/10.1007/s00592-020-01522...

13. Yan Y, Yang Y, Wang F, Ren H, Zhang S, Shi X, et al. Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. BMJ Open Diabetes Res Care. 2020;8(1):1-9. doi: 10.1136/bmjdrc-2020-001343.
https://doi.org/10.1136/bmjdrc-2020-0013... ^-1414. Giralt Herrera A, Rojas-Velázquez JM, Leiva-Enríquez J. Relación entre COVID-19 e Hipertensión Arterial. Rev Habanera Ciencias Médicas. [Internet]. 2020 [citado el 08 noviembre del 2020]. Disponible en: http://www.revhabanera.sld.cu/index.php/rhab/article/view/3246.
http://www.revhabanera.sld.cu/index.php/... and arterial hypertension (AHT) ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... ^,1111. Fernández García L, Puentes Gutiérrez AB, García Bascones M. Relationship between obesity, diabetes and ICU admission in COVID-19 patients. Med Clin (Barc). 2020;(xx):9-10. doi: 10.1016/j.medcli.2020.06.009.
https://doi.org/10.1016/j.medcli.2020.06... ^,1515. Acosta G, Escobar G, Bernaola G, Alfaro J, Taype W, Marcos C, et al. Caracterización de pacientes con COVID-19 grave atendidos en un hospital de referencia nacional del Perú. Rev Peru Med Exp Salud Publica. 2020;37(2):253-8. doi: 10.17843/rpmesp.2020.372.5437.
https://doi.org/10.17843/rpmesp.2020.372... are the most important the comorbidities. These characteristics predispose any individual to a higher risk of SARS-CoV-2 infection or more severe disease.

Oxygen saturation (SatO²) is an important prognostic factor, since having a low percentage at hospital admission is associated with higher mortality ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... ^,1515. Acosta G, Escobar G, Bernaola G, Alfaro J, Taype W, Marcos C, et al. Caracterización de pacientes con COVID-19 grave atendidos en un hospital de referencia nacional del Perú. Rev Peru Med Exp Salud Publica. 2020;37(2):253-8. doi: 10.17843/rpmesp.2020.372.5437.
https://doi.org/10.17843/rpmesp.2020.372... ^,1616. Xie J, Covassin N, Fan Z, Singh P, Gao W, Li G, et al. Association Between Hypoxemia and Mortality in Patients With COVID-19. Mayo Clin Proc. 2020;95(6):1138-47. doi: 10.1016/j.mayocp.2020.04.006.
https://doi.org/10.1016/j.mayocp.2020.04... . A study conducted in Lima, a city at an altitude of 161 m, found that having saturation < 90% on admission was associated with a higher risk of death compared to those who arrived with > 90% ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... . We have not found studies that determine what percentage saturation indicates a poor prognosis in high-altitude residents. However, it is known that high-altitude residents have different SatO₂ values than residents at sea level. A study conducted in healthy residents of Huánuco (1894 m altitude) and Cerro de Pasco (3399 m altitude) found that the mean saturation was 96.2 and 87.0%, respectively, concluding that the higher the altitude of residence, the more noticeable the changes in the normal values of SatO₂¹⁷17. Tinoco Solorzano A, Roman Santamaria A, Charri Victorio J. Gasometría arterial en diferentes niveles de altitud en residentes adultos sanos en el Perú. Horiz Med. 2017;17(3):6-10. doi: 10.24265/horizmed.2017.v17n3.02.
https://doi.org/10.24265/horizmed.2017.v... , it should be noted that this study was conducted outside the context of a pandemic.

Puno is a region located at an altitude of 3827 m, so the results of this study may be useful to understand the dynamics of the disease in high-altitude populations and to propose future studies.

This research aimed to determine the risk factors for intensive care unit (ICU) admission or mortality in a high-altitude population of adults hospitalized for COVID-19 in a hospital in Puno, Peru.

MATERIALS AND METHODS

Design and context

A retrospective cohort study was designed at the Manuel Núñez Butrón Regional Hospital (HRMNB) in Puno, Peru, that included adult patients hospitalized in the COVID area between April and December 2020. The HRMNB is a category II hospital, administered by the Ministry of Health (MINSA) located at an altitude of 3827 m. The first COVID-19 cases were recorded in early April, with the first wave occurring between July and August.

Population

We included adult patients (≥18 years), hospitalized in COVID areas of the HRMNB who previously resided in Puno for at least two months. Patients who voluntarily withdrew and those without confirmed diagnosis by a rapid test were excluded.

Sample

We carried out a census of patients hospitalized in the COVID area of the HRMNB of Puno from April to December 2020, obtaining 542 medical records. Epidat 4.2 was used to define a minimum sample size. Based on a previous study ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... , we constructed a contingency table and calculated the probability of dying between exposed and unexposed; we considered a ratio of 0.33 with a confidence level of 99.9% and power of 99.9%. p1 represents the probability of dying in patients with low saturation (<85%) being 61.6%; while; p2, is the probability of dying in patients without low saturation (≥ 85%) which was 14%. The minimum required sample size was 192 and considering 10% of incorrectly filled histories, it was 214.

Variables

The dependent variable was the adverse outcome, which consisted of admission to the ICU or in-hospital mortality. This variable was categorical dichotomous (died or was admitted to the ICU / did not die or was not admitted to the ICU).

Independent variables were age in years (<40 / 40-59 / ≥60), sex (female / male), time of illness (days), oxygen saturation on admission (≤75% / 76% - 79% / 81% - 84% / ≥85%), respiratory rate (tachypnea > 22 bpm / normal), among other vital functions. Comorbidities had dichotomous responses (yes / no) and include diabetes mellitus, AHT, chronic kidney disease, congestive heart failure and obesity. Regarding laboratory tests, hemoglobin (g/dL) and glucose (mg/dL) are shown as numerical variables, in addition to lymphopenia (≤1400 U/mm³ / >1400 U/mm³) and blood type (O+, A+, B+). Medications received during hospitalization had dichotomous response (yes/no) and included dexamethasone, azithromycin, hydroxychloroquine and ivermectin, among others. It should be noted that the vital functions and laboratory test values we considered were those obtained at hospital admission.

Data Collection

Data collection was carried out after approval from the Research Ethics Committee of the Universidad Peruana de Ciencias Aplicadas and authorization from the Regional Health Directorate (DIRESA) of Puno. We obtained access to the medical records of patients hospitalized by COVID-19 at the HRMNB of Puno between April and December 2020. Those that met the selection criteria were selected. Between June and July 2021, the data collection form was filled out with the variables chosen by literature review, then the data were organized in Excel.

Data analysis

We used the STATA v16 statistical analysis program. For the univariate analysis we calculated percentages for categorical variables, measures of central tendency and dispersion for quantitative variables, the mean and standard deviation (SD) were calculated if there was normality, otherwise, median and interquartile range (IQR). For the bivariate analysis, we used Student’s t test / Mann Whitney U test and chi-square / Fisher’s test, according to compliance with assumptions. Finally, for the multivariate analysis, we used Poisson regression with robust variance to calculate the crude and adjusted relative risk (RR) with 95% confidence intervals (95%CI). An alpha of 0.05 was considered. Variables were entered into the model according to epidemiological/theoretical criteria. Collinearity with VIF (variance inflation factor) was assessed using a cutoff point of 2.

Ethical aspects

The protocol was approved by the Ethics Committee of the Universidad Peruana de Ciencias Aplicadas (SCEI 024-01-22 PI 496-20) and authorized by the DIRESA of Puno. Since the data were already in the medical records, the research team had no direct contact with the research subjects and confidentiality was maintained. PRISA registration code: EI00001585.

RESULTS

A total of 542 medical records were obtained from patients hospitalized due to COVID-19 in the hospital of Puno, from April to December 2020. Of these, 194 were excluded because they did not meet inclusion criteria or because they were not available. Finally, 348 were included as part of the analyzed sample (Figure 1).

Descriptive analysis

With respect to the baseline characteristics (Table 1), 48.2% were under 40 years of age, 29.1% were between 40 and 59 years of age and 22.7% were 60 years of age or older; 61.8% were female. Regarding the outcome, 18.4% were admitted to the ICU, while 5.5% used invasive mechanical ventilation, and in-hospital mortality was 29.9%. In terms of comorbidities, 13.8% had diabetes, 7.5% had AHT, 4.6% had obesity and, finally, 21.8% had one comorbidity and 6.4% had two or more of them.

Regarding oxygen saturation, 69.9% of patients were admitted with a saturation greater than 85%, 5.5% were admitted with a saturation ranging from 80 to 84%, 6.1% with a saturation ranging from 76 to 79% and 18.5% were admitted with less than 75% saturation.

Regarding diagnosis, 48.3% had clinical diagnosis. In addition, 66.9% were positive for the IgM rapid test and 81.9% were positive for the IgG rapid test. As for laboratory tests, the mean hemoglobin was 15.1 g/dL with a standard deviation (SD) of 3.0. In addition, 46.7% had lymphopenia and 33.9% had leukocytosis. The median glucose was 102 mg/dL (IQR: 84.1;124.0). In terms of management, in noninvasive ventilation, i.e. ventilatory support without endotracheal intubation, 29.9% received a reservoir mask; meanwhile, 21.6% of patients received binasal cannula. With respect to the treatment, 37.4% received dexamethasone. On the other hand, 23.3% received azithromycin, 23.9% took hydroxychloroquine and 22.4% took ivermectin.

Bivariate analysis

During the bivariate analysis, we observed a statistically significant association between age and adverse outcome (p < 0.001) as well as with male sex (p < 0.001) and reason for admission (p < 0.001) (Table 2).

Regarding comorbidities, diabetes (p<0.001) and obesity (p<0.001) had a significant association with adverse outcome; no significant association was observed with AHT (p=0.104).

As for vital signs, we found a significant association between oxygen saturation and adverse outcome (p < 0.001), as well as with respiratory frequency (p < 0.001), lower systolic blood pressure (p = 0.017) and lower diastolic blood pressure (p = 0.025).

With respect to laboratory tests, a significant association was found between adverse outcome and higher hemoglobin level (p < 0.001), as well as with higher glucose level (p < 0.001) and lymphopenia (p < 0.001). However, no significant association was found between adverse outcome and blood group (p = 0.200).

Regarding the treatment, a significant association was found between the adverse outcome and dexamethasone (p < 0.001), azithromycin (p < 0.001), hydroxychloroquine (p < 0.001) and ivermectin (p < 0.001).

Analysis of multiple variables

During the multivariate analysis we found a statistically significant association between age and the adverse outcome, with those between 40 and 59 years of age having 3.7 times the probability of developing an adverse outcome (p < 0.001) and those over 60 years of age having 3.5 times the risk compared to those under 40 years of age (p = 0.001) (Table 3). Likewise, men had 1.8 times the risk of adverse outcome (p < 0.001) after adjusting for the rest of the variables in the equation. Regarding comorbidities, patients with diabetes had 1.3 times the risk of developing the adverse outcome (p = 0.011) and obese patients had 1.2 times the risk of developing it; however, the association was not significant (p = 0.260). On the other hand, there was no statistically significant association with AHT (p = 0.851). Patients with oxygen saturation ranging between 80 and 84% had 1.7 times the risk of developing an adverse outcome compared to those admitted with more than 85%, without a significant association (p = 0.066); on the other hand, those with values between 75 and 79% had 2.9 times the probability of developing an adverse outcome (p < 0.001), the same for those admitted with less than 75%, who had a risk of 2.8 (p < 0.001).

Patients admitted with tachypnea (> 22 bpm) had 1.7 times the risk of an adverse outcome, with a statistically significant difference (p = 0.010). There was no association between the use of anticoagulants and the risk of having an adverse outcome (p > 0.05). Similarly, there was no association between adverse outcome and dexamethasone (p = 0.769), azithromycin (p = 0.705) and hydroxychloroquine (p = 0.386). The variables “chronic kidney disease” and “congestive heart failure” were eliminated from the adjusted model due to a significant percentage of missing values and the variable “ivermectin” was eliminated due to collinearity with hydroxychloroquine.

DISCUSSION

The risk factors identified with admission to the ICU or death were age over 40 years, male sex, admission with oxygen saturation less than 80%, admission with respiratory rate > 22, or diabetes mellitus. No significant association was found between adverse outcome and obesity or hypertension. As secondary findings, 18.4% of patients were admitted to the ICU and 29.9% died; 30.1% were admitted with saturation < 85%. The mortality we found was lower (29.9%) than that reported for other hospitals, 49% ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... and 46.4% ¹⁸18. Vences MA, Pareja J, Otero P, Veramendi-Espinoza L, Vega-Villafana M, Mogollón-Lavi J, et al. Factores asociados a mortalidad en pacientes hospitalizados con COVID-19: Cohorte prospectivo en el Hospital Nacional Edgardo Rebagliati Martins. Scielo Prepr. 2020;(1):1-23. doi: 10.1590/SciELOPreprints.1241.
https://doi.org/10.1590/SciELOPreprints.... ; however, these studies would not be comparable as they are from different contexts, characteristics and times of the pandemic in the country.

Nonetheless, our study reports characteristics and risk factors similar to those described in other cohorts ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... ^,1313. Yan Y, Yang Y, Wang F, Ren H, Zhang S, Shi X, et al. Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. BMJ Open Diabetes Res Care. 2020;8(1):1-9. doi: 10.1136/bmjdrc-2020-001343.
https://doi.org/10.1136/bmjdrc-2020-0013... ^,1515. Acosta G, Escobar G, Bernaola G, Alfaro J, Taype W, Marcos C, et al. Caracterización de pacientes con COVID-19 grave atendidos en un hospital de referencia nacional del Perú. Rev Peru Med Exp Salud Publica. 2020;37(2):253-8. doi: 10.17843/rpmesp.2020.372.5437.
https://doi.org/10.17843/rpmesp.2020.372... ^,1919. Anyaypoma-Ocón W, Ñuflo Vásquez S, Bustamante-Chávez HC, Sedano-De la Cruz E, Zavaleta-Gavidia V, Angulo-Bazán Y. Factores asociados a letalidad por COVID-19 en un hospital de la región Cajamarca en Perú. Rev Peru Med Exp Salud Publica. 2021;38(4):501-11. doi: 10.17843/rpmesp.2021.384.8890.
https://doi.org/10.17843/rpmesp.2021.384...

20. Moya-Salazar Jeel, Cañari Betsy, Sánchez-Llanos Alondra, Hernandez Sofia A, Eche-Navarro Marylin, Salazar-Hernandez Richard, et al. Factores de riesgo en población rural andina con COVID-19: un estudio de cohorte retrospectivo. Rev Infectio. 2021;25(4): 256-261. doi: 10.22354/in.v25i4.957.
https://doi.org/10.22354/in.v25i4.957... ^-2121. Hueda-Zavaleta M, Copaja-Corzo C, Bardales-Silva F, Flores-Palacios R, Barreto-Rocchetti L, Benites-Zapata VA. Factores asociados a la muerte por COVID-19 en pacientes admitidos en un hospital público en Tacna, Perú. Rev Peru Med Exp Salud Publica. 2021;38(2):214-23. doi: 10.17843/rpmesp.2021.382.7158.
https://doi.org/10.17843/rpmesp.2021.382... . We observed an association between age over 40 years and the adverse outcome, which is also evident in other studies ⁹9. Kang SJ, Jung SI. Age Related Morbidity and Mortality among Patients with COVID-19. Infect Chemother. 2020;52(2):154-64. doi: 10.3947/ic.2020.52.2.154.
https://doi.org/10.3947/ic.2020.52.2.154... ^,1010. Rivera-Izquierdo M, Valero-Ubierna MC, R-DelAmo JL, Fernández-García MÁ, Martínez-Diz S, Tahery-Mahmoud A, et al. Sociodemographic, clinical and laboratory factors on admission associated with COVID-19 mortality in hospitalized patients: A retrospective observational study. PLoS One. 2020;15(6):1-12. doi: 10.1371/journal.pone.0235107
https://doi.org/10.1371/journal.pone.023... ^,1313. Yan Y, Yang Y, Wang F, Ren H, Zhang S, Shi X, et al. Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. BMJ Open Diabetes Res Care. 2020;8(1):1-9. doi: 10.1136/bmjdrc-2020-001343.
https://doi.org/10.1136/bmjdrc-2020-0013... ^,2323. Takahashi T, Iwasaki A. Sex differences in immune responses. Science. 2021;371(6527):347-8. doi: 10.1126/science.abe7199.
https://doi.org/10.1126/science.abe7199... , possibly due to the increased prevalence of chronic diseases in this age group. It could also be due to vulnerability to infections, explained by immunosenescence ⁹9. Kang SJ, Jung SI. Age Related Morbidity and Mortality among Patients with COVID-19. Infect Chemother. 2020;52(2):154-64. doi: 10.3947/ic.2020.52.2.154.
https://doi.org/10.3947/ic.2020.52.2.154... .

In this study we observed an association between male sex and outcome. This association has been described in studies carried out in cities at sea level ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... ^,88. Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46(5):846-8. doi: 10.1007/s00134-020-05991-x.
https://doi.org/10.1007/s00134-020-05991... ^,1010. Rivera-Izquierdo M, Valero-Ubierna MC, R-DelAmo JL, Fernández-García MÁ, Martínez-Diz S, Tahery-Mahmoud A, et al. Sociodemographic, clinical and laboratory factors on admission associated with COVID-19 mortality in hospitalized patients: A retrospective observational study. PLoS One. 2020;15(6):1-12. doi: 10.1371/journal.pone.0235107
https://doi.org/10.1371/journal.pone.023... . It has been described that biological mechanisms, such as a decreased immune response in men versus women may be involved ²³23. Takahashi T, Iwasaki A. Sex differences in immune responses. Science. 2021;371(6527):347-8. doi: 10.1126/science.abe7199.
https://doi.org/10.1126/science.abe7199... . It has been suggested that sex hormones, such as androgens, could play an important role in regulating proteins such as transmembrane serine protease 2 (TMPRSS2), which is involved in viral entry and spread, resulting in more severe forms of COVID-19 ²⁴24. Jun T, Nirenberg S, Weinberger T, Sharma N, Pujadas E, Cordon-Cardo C, et al. Analysis of sex-specific risk factors and clinical outcomes in COVID-19. Commun Med (Lond). 2021;1:3. doi: 10.1038/s43856-021-00006-2.
https://doi.org/10.1038/s43856-021-00006... .

We identified that having an admission oxygen saturation < 80% was a risk factor, this value differs from the cut-off point proposed in other similar studies performed in populations at sea level ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... ^,1515. Acosta G, Escobar G, Bernaola G, Alfaro J, Taype W, Marcos C, et al. Caracterización de pacientes con COVID-19 grave atendidos en un hospital de referencia nacional del Perú. Rev Peru Med Exp Salud Publica. 2020;37(2):253-8. doi: 10.17843/rpmesp.2020.372.5437.
https://doi.org/10.17843/rpmesp.2020.372... ^,1818. Vences MA, Pareja J, Otero P, Veramendi-Espinoza L, Vega-Villafana M, Mogollón-Lavi J, et al. Factores asociados a mortalidad en pacientes hospitalizados con COVID-19: Cohorte prospectivo en el Hospital Nacional Edgardo Rebagliati Martins. Scielo Prepr. 2020;(1):1-23. doi: 10.1590/SciELOPreprints.1241.
https://doi.org/10.1590/SciELOPreprints.... and in populations with a lower altitude than Puno ¹⁹19. Anyaypoma-Ocón W, Ñuflo Vásquez S, Bustamante-Chávez HC, Sedano-De la Cruz E, Zavaleta-Gavidia V, Angulo-Bazán Y. Factores asociados a letalidad por COVID-19 en un hospital de la región Cajamarca en Perú. Rev Peru Med Exp Salud Publica. 2021;38(4):501-11. doi: 10.17843/rpmesp.2021.384.8890.
https://doi.org/10.17843/rpmesp.2021.384... . This could be explained by the low oxygen pressure, since the saturation described in patients at high altitude without any pathology is lower, compared to that of residents at sea level ¹⁷17. Tinoco Solorzano A, Roman Santamaria A, Charri Victorio J. Gasometría arterial en diferentes niveles de altitud en residentes adultos sanos en el Perú. Horiz Med. 2017;17(3):6-10. doi: 10.24265/horizmed.2017.v17n3.02.
https://doi.org/10.24265/horizmed.2017.v... , so it would be expected that in patients with COVID-19, the saturation would be even lower since it is a pathology that adds hypoxemia. It is difficult to establish a cut-off point in high altitude populations. An inversely proportional relationship between oxygen saturation and altitude level has been previously described ¹⁷17. Tinoco Solorzano A, Roman Santamaria A, Charri Victorio J. Gasometría arterial en diferentes niveles de altitud en residentes adultos sanos en el Perú. Horiz Med. 2017;17(3):6-10. doi: 10.24265/horizmed.2017.v17n3.02.
https://doi.org/10.24265/horizmed.2017.v... . More studies are needed to quantify this relationship and determine cut-off points.

Likewise, we found a significant association between tachypnea (>22 bpm) and the adverse outcome. This relationship is understandable because an increasing respiratory rate is the physiological response to hypoxemia and acidosis ²⁵25. Gutiérrez Muñoz FR. Insuficiencia respiratoria aguda. Acta méd peruana. 2010;27(4):286-97.. Again, it should be noted that more studies evaluating tachypnea in high-altitude conditions are needed to determine the cutoff points for poor prognosis in this population.

Regarding comorbidities, we found an association between the adverse outcome and diabetes mellitus (DM), which is similar in other studies ¹¹11. Fernández García L, Puentes Gutiérrez AB, García Bascones M. Relationship between obesity, diabetes and ICU admission in COVID-19 patients. Med Clin (Barc). 2020;(xx):9-10. doi: 10.1016/j.medcli.2020.06.009.
https://doi.org/10.1016/j.medcli.2020.06...

12. Luzi L, Radaelli MG. Influenza and obesity: its odd relationship and the lessons for COVID-19 pandemic. Acta Diabetológica. 2020;57(6):759-64. doi: 10.1007/s00592-020-01522-8.
https://doi.org/10.1007/s00592-020-01522...

13. Yan Y, Yang Y, Wang F, Ren H, Zhang S, Shi X, et al. Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. BMJ Open Diabetes Res Care. 2020;8(1):1-9. doi: 10.1136/bmjdrc-2020-001343.
https://doi.org/10.1136/bmjdrc-2020-0013... ^-1414. Giralt Herrera A, Rojas-Velázquez JM, Leiva-Enríquez J. Relación entre COVID-19 e Hipertensión Arterial. Rev Habanera Ciencias Médicas. [Internet]. 2020 [citado el 08 noviembre del 2020]. Disponible en: http://www.revhabanera.sld.cu/index.php/rhab/article/view/3246.
http://www.revhabanera.sld.cu/index.php/... , since DM involves immunosuppression due to alterations in the immune response that include increased production of proinflammatory cytokines, which add inflammation to the patient with COVID-19 ²⁶26. Luján D, Guatibonza-García V, Pérez-Londoño A, Mendivil CO. COVID-19 pathophysiology and diabetes mellitus. Revista Colombiana De Endocrinología, Diabetes Metabolismo. 7(2S), 67-71. doi: 10.53853/encr.7.2S.586.
https://doi.org/10.53853/encr.7.2S.586... . However, no association was found between the adverse outcome and obesity, as has been found in other studies ²⁰20. Moya-Salazar Jeel, Cañari Betsy, Sánchez-Llanos Alondra, Hernandez Sofia A, Eche-Navarro Marylin, Salazar-Hernandez Richard, et al. Factores de riesgo en población rural andina con COVID-19: un estudio de cohorte retrospectivo. Rev Infectio. 2021;25(4): 256-261. doi: 10.22354/in.v25i4.957.
https://doi.org/10.22354/in.v25i4.957... ^,2222. Diaz Lazo AV, Montalvo Otivo R, Lazarte Nuñez E, Aquino Lopez E. Caracterización clínica y epidemiológica de los pacientes con COVID-19 en un hospital situado en la altura. Rev Horiz Med. 21(2):e1303. doi: 10.24265/horizmed.2021.v21n2.09.
https://doi.org/10.24265/horizmed.2021.v... where the outcome was mortality and, like DM, obesity involves a proinflammatory state due to cytokines (TNF-alpha, MCP-1 and IL-6) produced by adipose tissue ¹²12. Luzi L, Radaelli MG. Influenza and obesity: its odd relationship and the lessons for COVID-19 pandemic. Acta Diabetológica. 2020;57(6):759-64. doi: 10.1007/s00592-020-01522-8.
https://doi.org/10.1007/s00592-020-01522... . On the other hand, although studies report an association between mortality and AHT ³3. Mejia F, Medina C, Cornejo E, Morello E, Vasquez S, Alave J, et al. Características clínicas y factores pronósticos relacionados con la mortalidad en pacientes adultos hospitalizados por COVID-19 en un hospital público de Lima, Perú. Scielo Prepr. [Internet]. 2020 [citado el 08 julio 2020]. Disponible en: https://preprints.scielo.org/index.php/scielo/preprint/view/858/version/909?fbclid=IwAR0gNkru9G1CGkqCtO8UMJfSzbyU3kRhcAMdNqD-elKsfIpZz2j0EWP7gKo.
https://preprints.scielo.org/index.php/s... ^,1111. Fernández García L, Puentes Gutiérrez AB, García Bascones M. Relationship between obesity, diabetes and ICU admission in COVID-19 patients. Med Clin (Barc). 2020;(xx):9-10. doi: 10.1016/j.medcli.2020.06.009.
https://doi.org/10.1016/j.medcli.2020.06... ^,1515. Acosta G, Escobar G, Bernaola G, Alfaro J, Taype W, Marcos C, et al. Caracterización de pacientes con COVID-19 grave atendidos en un hospital de referencia nacional del Perú. Rev Peru Med Exp Salud Publica. 2020;37(2):253-8. doi: 10.17843/rpmesp.2020.372.5437.
https://doi.org/10.17843/rpmesp.2020.372... , no association was found between AHT and adverse outcome in the adjusted analysis. It has been proposed that there is a higher expression of ACE in hypertensive patients due to treatment with ACE inhibitors, which could increase the risk of severe forms of COVID-19 ¹⁴14. Giralt Herrera A, Rojas-Velázquez JM, Leiva-Enríquez J. Relación entre COVID-19 e Hipertensión Arterial. Rev Habanera Ciencias Médicas. [Internet]. 2020 [citado el 08 noviembre del 2020]. Disponible en: http://www.revhabanera.sld.cu/index.php/rhab/article/view/3246.
http://www.revhabanera.sld.cu/index.php/... .

We did not find statistically significant associations in the laboratory results; however, there is literature describing that severe patients in high altitude areas show a decrease of the platelet count.

Finally, no significant association was found between adverse outcome and receiving anticoagulants (enoxaparin) either in prophylactic dose (40 mg) or full dose (60 mg), nor with dexamethasone, azithromycin or hydroxychloroquine. Regarding hydroxychloroquine, there are studies that show that there is no benefit in reducing mortality compared to those who do not receive it ²⁷27. Bonilla Catherine. Efectividad y seguridad de la hidroxicloroquina/cloroquina en el tratamiento de COVID-19. Unidad de Análisis y Generación de Evidencias en Salud Pública [Internet]. 2020 [citado el 08 de febrero de 2021]. Disponible en: https://web.ins.gob.pe/sites/default/files/Archivos/authenticated%2C%20administrator%2C%20editor/publicaciones/2020-08-04/SE_18%20Hidroxicloroquina.pdf.
https://web.ins.gob.pe/sites/default/fil... ^,2828. Hasdeu S, Guadalupe M, Tortosa F, Sanguine V, Balaciano G, Izcovich A, et al. Tratamiento con hidroxicloroquina en pacientes con COVID-19: informe rápido de evaluación de tecnología sanitaria. Rev argent salud pública. 2020;12(Suppl 1):16-16.. On the contrary, there is literature that supports the association between the use of low-dose dexamethasone and a reduction in mortality in patients with severe COVID-19 ²⁹29. Noreen S, Maqbool I, Madni A. Dexamethasone: Therapeutic potential, risks, and future projection during COVID-19 pandemic. Eur J Pharmacol. 2021;894. doi: 10.1016/j.ejphar.2021.173854.
https://doi.org/10.1016/j.ejphar.2021.17... .

The main limitation of this study is the use of the rapid test, due to its low diagnostic sensitivity. However, it proved to be the most accessible resource to define the cases admitted to the COVID area. This screening method was used to confirm or rule out cases according to the Ministerial Resolution 193-2020; because diagnosis was based on epidemiological history and clinical characteristics of the patient. On the other hand, the laboratory for molecular tests was available since August 2020, before this date the tests were sent to other departments, complicating their use. In addition, some medical records had incomplete information of some of the variables. It is possible that there is insufficient statistical power for some associations. Regarding treatment, only the received drugs were considered, but not the doses. Finally, the results can only be extrapolated to similar populations.

Despite the limitations, this is one of the few studies that explores the relationship between saturation and adverse outcomes in high-altitude populations. In addition, our study identifies different factors that possibly influence the prognosis of the high-altitude resident with COVID-19.

In conclusion, in the Manuel Núñez Butron Regional Hospital, the risk factors for an adverse outcome (admission to the ICU or mortality) due to COVID-19 were male sex, age over 40 years, low saturation (< 80%) on admission, tachypnea (> 22 bpm) on admission, and diabetes.

The definition of admission for patients should be improved for future studies. On the other hand, it would be relevant to define temporal contexts because epidemiological surveillance in Peru determined the existence of two waves of COVID-19 cases; in addition, there are variants of the SARS-CoV-2 virus that appeared over time, as well as vaccines from different laboratories (Pfizer, Astrazeneca, Sinopharm). These variables could be included when replicating this study.

COVID-19 represents a huge public health burden globally and nationally, and identification of risk factors at hospital admission in high-altitude populations could help to take early measures that contribute to clinical improvement of the patients. These findings should be confirmed in future studies.

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