High-altitude is associated with better short-term survival in critically ill COVID-19 patients admitted to the ICU.

BACKGROUND: Multiple studies have attempted to elucidate the relationship between chronic hypoxia and SARS-CoV-2 infection. It seems that high-altitude is associated with lower COVID-19 related mortality and incidence rates; nevertheless, all the data came from observational studies, being this the first one looking into prospectively collected clinical data from severely ill patients residing at two significantly different altitudes.
METHODS: A prospective cohort, a two-center study among COVID-19 confirmed adult patients admitted to a low (sea level) and high-altitude (2,850 m) ICU unit in Ecuador was conducted. Two hundred and thirty confirmed patients were enrolled from March 15th to July 15th, 2020.
RESULTS: From 230 patients, 149 were men (64.8%) and 81 women (35.2%). The median age of all the patients was 60 years, and at least 105 (45.7%) of patients had at least one underlying comorbidity, including hypertension (33.5%), diabetes (16.5%), and chronic kidney failure (5.7%). The APACHE II scale (Score that estimates ICU mortality) at 72 hours was especially higher in the low altitude group with a median of 18 points (IQR: 9.5-24.0), compared to 9 points (IQR: 5.0-22.0) obtained in the high-altitude group. There is evidence of a difference in survival in favor of the high-altitude group (p = 0.006), the median survival being 39 days, compared to 21 days in the low altitude group.
CONCLUSION: There has been a substantial improvement in survival amongst people admitted to the high-altitude ICU. Residing at high-altitudes was associated with improved survival, especially among patients with no comorbidities. COVID-19 patients admitted to the high-altitude ICU unit have improved severity-of-disease classification system scores at 72 hours.

Introduction

In December 2019, the first cases of pneumonia due to the SARS-CoV-2 virus were reported in Wuhan, China [1, 2]. On March 11th, 2020, the novel Coronavirus disease (COVID-19), a condition with multiple clinical features, which can rapidly evolve into acute respiratory distress syndrome (ARDS) and other serious complications, was declared a pandemic [2, 3]. The disease spread rapidly, affecting regions and areas located in urban settings but also in rural and geographically distant areas all over the world [4, 5].

The epidemiological behavior of the pandemic showed exponential growth throughout many countries, while others seem to have managed the outbreak better [6]. Several studies have identified differences in morbidity and mortality depending on many factors, including socioeconomic status, the burden of chronic diseases, adequate access to health care, the strength of the epidemiological surveillance systems, and the implementation of control measures as well as strict mobility restrictions [7]. During the first months of the pandemic, very few ecological studies showed a possible epidemiological and survival implication exerted by high-altitude [7, 8]. It has been proposed that these results are in part answered by the well-known physiological acclimatization and the long-term adaptation to high-altitude exposure, generating a greater tolerance to chronic hypoxia [7-10]. Several investigations have tried to determine the potential relationship between high-altitude and COVID-19 related mortality [8, 11–13]. Most studies have established that living at high-altitudes could be related to reduced COVID-19 related mortality and morbidity [8, 11, 14, 15]. These results were discussed from different points of view. The biological one was based on a hypothesized lower viral affinity for the type-2 angiotensin-converting enzyme (ACE2) receptors, but there is no definitive evidence to support this claim [16]. Another hypothesis surrounding the high-altitude-COVID-19 link refers to the involvement of better perfused and better-oxygenated tissues due to the involvement of the hypoxia-triggered protein that regulates angiogenesis, cell proliferation, metabolism, and down regulates ACE-2 levels; the well-known Hypoxia-inducible factor 1-alpha (HIF-1 α) [10, 17–22]. Having an improved ability to use oxygen within the tissues might reduce the effects of systemic hypoxia caused by acute respiratory distress syndrome (ARDS) [23].

Contrariwise, socio-demographic and environmental factors such as population density, UV radiation, ozone, or cold have been proposed to affect SARS-CoV-2 transmission and viral load; nevertheless, no clinical data is available yet [24, 25].

The link between high-altitude hypoxia and COVID-19 mortality is still under investigation [26, 27]. The very few studies on clinical, ventilatory, and respiratory support parameters’ differences have been performed at an elevation below 1,500 m, and no evidence about the role of high-altitude exposure (> 2,500 m) on severally ill COVID-19 patients have been published yet [23].

We believe our study is the first one that has been able to demonstrate the effect of altitude living on COVID-19 mortality and prognosis after controlling for several clinical factors.

Methods

Study design

A prospective cohort study including patients with severe SARS-CoV-2 infection confirmed with real-time polymerase chain reaction (RT-PCR) was performed from March 15th, 2020, to July 15th, 2020.

Setting

The study was carried out in two intensive care units (ICU) located at two different elevations from the same Social Security Health System (IESS) in Ecuador. The ICU from the IESS-Quito Sur’ Hospital located in the city of Quito (located at 2,850 m above sea level) and the IESS-Los Ceibos, located in the city of Guayaquil (Located at sea level).

Quito and Guayaquil are the most important cities in Ecuador, with 2.5 and 2.9 million inhabitants respectively; both have their IESS hospital built in 2017. They also share the distinction of becoming the first COVID-19 sentinel hospitals enabled in Ecuador. Since both hospitals are part of the same IESS social Security Health System, both were the first in receiving COVID-19 patients during the pandemic, and both are ruled by the same therapeutic guidelines; they are a good case-control opportunity to explore differences related to altitude.

Population and study size

All the patients included in this study were admitted to the ICU unit in one of the two hospitals. The present study included a total of 230 patients diagnosed with COVID-19 using the RT-PCR technique, of which 114 patients were treated in the high-altitude group (IESS-Quito Sur), while 116 patients belonged to the low altitude group (IESS-Los Ceibos).

Inclusion criteria

Adult men and women patients admitted to the ICU diagnosed with COVID-19 by means of RT-PCR who lived at least one year in the unit’s coverage region and signed the informed consent for the use of the information was included in this study.

Exclusion criteria

Patients diagnosed with COVID-19 by RT-PCR who did not meet the criteria for admission to the ICU or who lived less than one year in the unit’s coverage region, or who did not sign the informed consent for the use of the information were excluded.

Data sources and variables

Demographic information, clinical characteristics (including medical history, history of symptoms, comorbidities), chest computed tomography (CT) results, laboratory findings, ventilatory values, and medications used were collected from each patient. The dates of disease onset, hospital admission, admission to the ICU, and death or discharge date from the ICU were also recorded, as well as the APACHE II scores (Score that measures disease severity based on physiologic parameters, age, and previous health conditions) and the Charlson index (predicts 10-year survival in patients with multiple comorbidities). The onset date was defined as the day the patient noticed any symptoms. The severity of COVID-19 was defined according to the diagnosis and treatment guide for SARS-CoV-2 issued by the World Health Organization (WHO) [28]. It was designated as a critical illness due to COVID-19 when patients had one of the following criteria: (a) acute respiratory distress syndrome (any grade); (b) Septicemia; and (c) septic shock. The data were obtained from the electronic medical record of a common registry system for both units and analyzed by three independent researchers.

Statistical analysis

Categorical variables were summarized as frequencies and percentages, and continuous variables were described using median values and interquartile ranges (IQR) or mean and standard deviation, as appropriate. The analysis included a two-tailed Student’s t-test, and the Mann-Whitney U test was used. The frequencies of the categorical variables were compared using the chi-square test and expressed in count and percentage. Also, survival curves (Kaplan Meier), the log-rank statistic, and the hazard ratio between groups were obtained.

Bivariate and multivariate analyzes were performed to identify factors associated with death from COVID-19 in all patients using the Cox risk regression model. To obtain a reduced set of variables from the broad set of predictors, we carried out a progressive in bloc procedure assigning the predictor variables into six groups: sociodemographic characteristics and comorbidities, complications, scales, ventilatory values, medications, and laboratory and imaging parameters. A multivariate regression analysis was applied within each block using two criteria to achieve the best set of predictors: relevance to the clinical situation and bivariate as well as multivariate statistical significance (p <0.05) correcting for age. Variables with more than 25% missing values were not considered for the analysis.

All statistical analyzes were performed in SPSS version 25, and graphs were generated using GraphPad Prism version 7.00 software (GraphPad Software Inc).

Bias

To minimize observation bias for systematic differences between the low and high-altitude group, observers who recollected data were blinded for the investigated hypothesis. To reduce investigation bias, coding and analysis were performed by three members of the research team independently, while discrepancies were resolved after achieving consensus.

Ethical approval

This work was approved by the Hospital IESS-Quito-Sur Internal Review Board (IRB). The request for authorization was submitted on March 1st, 2020, and received ethical approval with the following identification number: ID: IESS-HG-SQ-CIE-2020-2656-M.

According to good clinical practices and local regulations, identifiable data from clinical records was only accessed by the medical team that was providing treatment and care to the patients.

Results

The present study included a total of n = 230 patients diagnosed with COVID-19, of which n = 114 patients were treated in the high-altitude group, while 116 patients belonged to the low altitude group.

Socio-demographic characteristic

The median age of all the patients was 60 years, with a range of 49 to 69 years, and the majority (80.9%) of them were over 45 years of age. More than half (64.8%) of the patients were men. The BMI median of all the patients was 27.8 kg/m2, while about half (47.8%) were overweight, and (32.9%) some degree of obesity. A total of n = 105 (45.7%) patients had at least one underlying comorbidity, the most frequent being chronic diseases, such as hypertension (33.5%), diabetes (16.5%), and chronic kidney failure (5.7%). Five patients with COPD were identified (Table 1). When comparing the samples by altitude, no differences were evidenced between age, sex, and BMI. On the other hand, the low altitude group presented a greater number of patients with comorbidities measured by the Charlson index, highlighting the cases of hypertensive and diabetic patients. The mean interval from the onset of symptoms to admission to the ICU for all patients was eight days (IQR: 6–11). However, in the high-altitude group, there was a shorter median of 7 days (IQR: 5–10) (Table 1).

Table 1

Analysis of the chi-square, mean and median differences for demographic and independent risk factors in COVID-19 critically ill patients living at low and high-altitudes which were hospitalized in intensive care units.

Category	All	High-altitude	Low altitude	P-value
	n (%)	n (%)	n (%)
Age—median (IQR)	60.0 (49.0–69.0)	55.5 (49.0–66.0)	62.5 (48.5–69.0)	0.181
Sex
Male	149 (64.8)	77 (67.5)	72 (62.1)	0.385
Female	81 (35.2)	37 (32.5)	44 (37.9)	0.385
BMI—median (IQR)	27.8 (25.7–30.9)	27.4 (24.9–31.0)	28.2 (26.0–30.7)	0.285
Underweight	1 (0.4)	1 (0.9)	0 (0.0)	0.096
Normal weight	43 (18.9)	27 (24.1)	16 (13.8)	0.096
Overweight	109 (47.8)	48 (42.9)	61 (52.6)	0.096
Obesity class I	59 (25.9)	25 (22.3)	34 (29.3)	0.096
Obesity class II	15 (6.6)	10 (8.9)	5 (4.3)	0.096
Obesity class III	1 (0.4)	1 (0.9)	0 (0.0)	0.096
Charlson index
Presence of Comorbidities	135 (58.7)	85 (74.6)	50 (43.1)	0.000
Low comorbidity	28 (12.2)	10 (8.8)	18 (15.5)	0.000
Hight comorbidity	67 (29.1)	19 (16.7)	48 (41.4)	0.000
Comorbidity	105 (45.7)	33 (28.9)	72 (62.1)	0.000
Arterial Hypertension	77 (33.5)	21 (18.4)	56 (48.3)	0.000
CKD	13 (5.7)	3 (2.6)	10 (8.6)	0.049
Asthma	2 (0.9)	1 (0.9)	1 (0.9)	0.99
Diabetes	38 (16.5)	12 (10.5)	26 (22.4)	0.015
Psoriasis	2 (0.9)	0 (0.0)	2 (1.7)	0.159
CHF	2 (0.9)	1 (0.9)	1 (0.9)	0.99
Cancer	4 (1.7)	2 (1.8)	2 (1.7)	0.986
CVD	2 (0.9)	0 (0.0)	2 (1.7)	0.159
COPD	5 (2.2)	4 (3.5)	1 (0.9)	0.169
Rheumatoid arthritis	2 (0.9)	0 (0.0)	2 (1.7)	0.159
TB	2 (0.9)	2 (1.8)	0 (0.0)	0.152
Hepatic cirrhosis	2 (0.9)	1 (0.9)	1 (0.9)	0.99
Pulmonary fibrosis	3 (1.3)	1 (0.9)	2 (1.7)	0.571
Hypothyroidism	5 (2.2)	3 (2.6)	2 (1.7)	0.637

Abbreviations: IQR: Interquartile range; BMI: body mass index; CKD: Chronic kidney disease; CHF: Chronic hepatic failure; CVD: Cerebro-vascular Disease; COPD: Chronic obstructive pulmonary disease; TB: Tuberculosis.

Clinical characteristics

Regarding the scales evaluated, it was evidenced that upon admission to the ICU, the APACHE II scale in the first 24 hours presented a median of 15 points (IQR: 10.0–20.0) in the high-altitude group, whilst the low altitude scored 16 points (12.0–20.5) and did not show a statistical difference for both groups (p = 0.206). However, the same scale at 72 hours was especially higher in the low altitude group with a median of 18 points (IQR: 9.5–24.0), compared to 9 points (IQR: 5.0–22.0) obtained in the group of high-altitude. Concerning the most common complications presented during the ICU stay, acute respiratory distress syndrome in adults was evidenced in n = 219 (95.2%) patients, any type of shock in n = 166 (72.2%) patients, acute / exacerbated renal failure in n = 101 (43.9%) and delirium in n = 88 (38.3%). There were no statistical relationships between complications and altitude (Table 2). Finally, n = 129 deaths (56.1%) were recorded in the entire sample, of which most (n = 77 (66.4%) were recorded in the low altitude group compared to n = 52 (45.6%) at high-altitude, p = 0.002 (Table 2).

Table 2

Analysis of the chi-square, mean and median differences for clinical predictors for COVID-19 mortality among critically ill patients living at low and high-altitudes which were hospitalized in intensive care units.

Category	Measure	All	High-altitude	Low altitude	P-value
Symptom’s onset	median (IQR)	8.0 (6.0–11.0)	7.0 (5.0–10.0)	8.0 (7.0–13.0)	0.003
Waiting time before admission in the UCI (H)	median (IQR)	2.3 (0.0–8.2)	3.0 (0.0–10.0)	0.7 (0.0–8.0)	0.01
Condition of discharge from ICU
Dead	n (%)	129 (56.1)	52 (45.6)	77 (66.4)	0.002
Alive	n (%)	101 (43.9)	62 (54.4)	39 (33.6)	0.002
ApacheII ICU (24H)	median (IQR)	16.0 (11.0–20.0)	15.0 (10.0–20.0)	16.0 (12.0–20.5)	0.206
ApacheII ICU (72H)	median (IQR)	14.0 (6.0–23.0)	9.0 (5.0–22.0)	18.0 (9.5–24.0)	0.001
Shock
No shock	n (%)	64 (27.8)	34 (29.8)	30 (25.9)	0.503
Septic shock	n (%)	114 (49.6)	41 (36.0)	73 (62.9)	0.503
Distributive shock	n (%)	46 (20.0)	37 (32.5)	9 (7.8)	0.503
Obstructive shock	n (%)	3 (1.3)	2 (1.8)	1 (0.9)	0.503
Cardiogenic shock	n (%)	3 (1.3)	0 (0.0)	3 (2.6)	0.503
Respiratory (ARDS)	n (%)	219 (95.2)	106 (93.0)	113 (97.4)	0.115
Renal
Did not present fault	n (%)	129 (56.1)	61 (53.5)	68 (58.6)	0.094
Acute renal failure	n (%)	89 (38.7)	50 (43.9)	39 (33.6)	0.094
Exacerbated chronic kidney failure	n (%)	12 (5.2)	3 (2.6)	9 (7.8)	0.094
Dialysis	n (%)	26 (11.3)	11 (9.6)	15 (12.9)	0.431
Coagulation	n (%)	17 (7.4)	6 (5.3)	11 (9.5)	0.221
Polyneuropathy	n (%)	85 (37.0)	40 (35.1)	45 (38.8)	0.561
Delirium	n (%)	88 (38.3)	45 (39.5)	43 (37.1)	0.708
Hypoxic encephalopathy	n (%)	3 (1.3)	3 (2.6)	0 (0.0)	0.079
Hepatic	n (%)	30 (13.0)	17 (14.9)	13 (11.2)	0.528

Abbreviations: H: Hours; ICU: Intensive care unit; ARDS: Acute respiratory distress syndrome.

The laboratory results are shown in (Table 3), where lower values of platelets, liver enzymes (AST and ALT), and lactate were evidenced in the low altitude group compared to the high-altitude group. Against the leukocyte count was higher in the low altitude group (Fig 1).

Table 3

Analysis of the mean and median differences of the principal hematological and serological parameters in severely ill patients with covid19.

Category	All	High-altitude	Low altitude	P-value
	median (IQR)	median (IQR)	median (IQR)
Hematic Biometry
Hemoglobin mg/dL	13.7 (12.2–14.7)	13.7 (12.4–14.8)	13.5 (11.8–14.6)	0.095
Leukocytes 103/μL	11.8 (8.9–16.1)	10.5 (8.2–14.4)	13.0 (10.2–17.4)	0.000
Leukocytes 103/μL (7D)	11.8 (9.3–16.9)	10.8 (8.7–12.7)	13.5 (10.2–20.7)	0.000
Neutrophils %	86.7 (81.0–90.0)	87.0 (81.0–90.0)	86.2 (81.3–89.5)	0.215
Lymphocytes 103/μL	6.7 (4.2–10.2)	6.7 (4.4–10.0)	6.1 (4.0–10.9)	0.994
Platelets 103/μL	290.0 (215.0–378.0)	277.0 (218.0–367.0)	300.5 (213.0–387.0)	0.605
Platelets 103/μL (72H)	283.0 (202.0–369.0)	300.5 (228.0–383.0)	255.5 (185.0–347.0)	0.015
Platelets 103/μL (7D)	262.0 (185.0–401.0)	306.0 (207.0–435.0)	241.0 (152.0–338.0)	0.002
Blood chemistry
D-Dimer ng/ml	2,193.0 (796.0–6,293.0)	1,895.5 (743.0–4,745.0)	2,600.0 (800.0–7,700.0)	0.475
D-Dimer ng/ml (72H)	2,003.0 (738.0–5,400.0)	2,347.0 (947.0–3,800.0)	1,600.0 (450.0–6,700.0)	0.745
Urea mg/dL	42.0 (27.0–64.0)	36.4 (23.5–57.7)	45.5 (30.0–66.5)	0.011
Creatinine mg/dL	0.8 (0.7–1.2)	0.8 (0.7–1.1)	0.8 (0.7–1.3)	0.150
Ferritin μg/ml	1,469.0 (912.2–2,000.0)	1,418.8 (824.0–1,979.0)	1,600.0 (1,047.0–2,000.0)	0.300
LDH U/L	402.5 (315.0–584.0)	382.0 (303.0–541.0)	427.0 (320.0–608.0)	0.134
CRP mg/L	27.0 (18.5–45.0)	22.8 (18.1–33.3)	36.0 (19.0–48.0)	0.004
PCT ng/ml	0.5 (0.2–1.8)	0.5 (0.2–1.9)	0.6 (0.2–1.6)	0.953
Gasometry
ph	7.4 (7.3–7.4)	7.4 (7.3–7.5)	7.3 (7.2–7.4)	0.000
ph (7D)	7.4 (7.3–7.5)	7.4 (7.3–7.5)	7.4 (7.3–7.4)	0.001
SaO2%	94.0 (89.0–97.0)	91.0 (87.0–94.0)	96.9 (93.0–98.0)	0.000
SaO2% (7D)	96.0 (92.0–98.0)	94.0 (91.0–96.0)	97.7 (96.0–98.2)	0.000
PaO2 mmHg	76.0 (59.0–104.0)	65.0 (52.0–76.0)	97.9 (72.3–138.4)	0.000
PaO2 mmHg (7D)	82.5 (67.0–119.8)	69.5 (61.3–80.7)	110.5 (88.0–145.0)	0.000
PaCO2 mmHg	38.0 (31.7–48.3)	35.8 (31.0–43.0)	43.0 (32.5–56.3)	0.007
PaCO2 mmHg (7D)	42.0 (38.0–51.0)	42.7 (38.0–52.0)	41.9 (37.2–50.2)	0.774
HCO3 mEq/L	20.9 (18.7–23.6)	21.9 (19.1–24.9)	20.2 (17.7–22.6)	0.000
HCO3 mEq/L (7D)	25.3 (21.5–29.1)	28.0 (22.1–30.6)	24.2 (20.0–26.9)	0.000
Lactate mmol/L	1.8 (1.3–2.3)	1.8 (1.4–2.3)	1.7 (1.0–2.1)	0.800

Abbreviations: H: Hours; D: Days; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; LDH: lactate dehydrogenase; PCR: C-reactive protein; PCT: procalcitonin; ph: Potential of hydrogen; SaO2: Oxygen saturation of arterial blood; PaO2: Partial pressure of oxygen in arterial blood; PaCO2: Partial pressure of carbon dioxide in arterial blood; HCO3: Serum bicarbonate.

Fig 1

Statistical hematological differences between the low and high-altitude group.

All comparisons were calculated using a U Mann-Whitney test * = <0.05; ** = <0.01;*** = <0.001; ns = non-significant. Abbreviations: ALT: Alanine aminotransferase; AST: Aspartate aminotransferase.

The acid-base profile for both groups show normal ranges; however, there is a greater alkalotic component (higher median pH and bicarbonate) in the low altitude group (p = 0.000). At the same time, the level of CO2 appears in normal ranges and without difference between the two groups (Table 3).

Ventilatory findings

Ventilatory management in both groups was based on the critical care COVID-19 Treatment Guidelines approved by the National Institute of Health (NIH) and the current recommendations of the emergency committee for the management of COVID-19 in Ecuador (COE). In that sense, there were very few differences in the parameters used to ventilate patients in the low or high-altitude group. The tidal volume differed in less than 7cmH2O, and either the peak or the pulmonary compliance varied significantly based on several guidelines [29-32]. Although there was a difference in terms of the PEEP pressure, this is often seen when using high pressures during ventilation [33, 34].

Mechanical ventilation was maintained for a median of 12 days (IQR: 7.0–20.0) for the two groups. During admission, the FiO2 for both groups did not show differences; however, the measurements at 72 hours and seven days show significant differences with higher values in the low altitude group (Fig 2).

Fig 2

Statistical differences among respiratory and blood serum parameters among COVID-19 patients living at two different elevations.

All comparisons were calculated using a U Mann-Whitney. Except for bicarbonate on seventh day * = <0.05; ** = <0.01;*** = <0.001; ns = non-significant.

Similarly, a higher value was evidenced during the measurements of the partial pressure of oxygen (PO2) in the patients in the low altitude group, compared to the high-altitude group and consequently in the Pa-Fi relationship (Fig 2). The need for a tracheostomy reached 18.7% of patients (Table 4).

Table 4

Ventilatory and pulmonary parameters among COVID-19 patients in the low and high-altitude group.

Category	Measure	All	High-altitude	Low altitude	P-value
Received mechanical ventilation	n (%)	204 (88.7)	105 (92.1)	99 (85.3)	0.105
High Flow	n (%)	12 (5.2)	0 (0.0)	12 (10.3)	0.000
Recruitment	n (%)	72 (31.3)	33 (28.9)	39 (33.6)	0.445
Tracheostomy	n (%)	43 (18.7)	23 (20.2)	20 (17.2)	0.568
Pronation
Intermittent	n (%)	56 (24.3)	18 (15.8)	38 (32.8)	0.014
Extended	n (%)	126 (54.8)	68 (59.6)	58 (50.0)	0.014
Pronation (H)	median (IQR)	50.0 (16.0–96.0)	48.0 (2.0–72.0)	72.0 (24.0–120.0)	0.000
PAFI mmHg	median (IQR)	104.3 (72.9–154.5)	87.0 (61.0–121.0)	133.1 (94.4–176.6)	0.000
PAFI mmHg (72H)	median (IQR)	165.0 (128.3–216.7)	150.0 (125.5–192.4)	187.5 (147.2–255.0)	0.000
PEEP (cmH2O)	median (IQR)	12.0 (10.0–14.0)	12.0 (10.0–14.0)	10.0 (9.0–12.0)	0.000
PEEP (cmH2O) (72H)	median (IQR)	10.0 (9.0–12.0)	12.0 (9.0–14.0)	10.0 (8.0–12.0)	0.002
Peak Pressure	median (IQR)	30.0 (28.0–32.0)	28.0 (25.0–31.0)	31.0 (29.0–35.0)	0.000
Peak Pressure (72H)	median (IQR)	30.0 (26.0–32.0)	28.0 (24.0–30.0)	31.0 (28.0–33.0)	0.000
Plateau Pressure (cmH2O)	median (IQR)	26.0 (23.0–28.0)	25.0 (22.0–28.0)	26.0 (24.0–29.0)	0.047
Plateau Pressure (cmH2O) (72H)	median (IQR)	25.0 (21.0–27.0)	24.0 (20.0–27.0)	25.5 (22.0–28.0)	0.029
Static Compliance (ml/cmH20)	median (IQR)	28.0 (22.0–37.0)	30.5 (26.0–37.0)	27.0 (18.0–34.0)	0.006
Static Compliance (ml/cmH20) (72H)	mean (SD)	31.65 (10.92)	33.63 (9.48)	29.53 (11.97)	0.008
Driving pressure (cmH2O) (24H)	median (IQR)	13.0 (9.0–16.0)	12.0 (9.0–15.0)	14.0 (8.0–18.0)	0.022
Driving pressure (cmH2O) (72H)	median (IQR)	12.5 (9.0–16.0)	12.0 (8.0–14.0)	14.0 (9.0–17.0)	0.021

Abbreviations: H: Hours; D: Days; FiO2: Fraction of inspired oxygen; RR: Respiratory rate; PAFI: Relationship between the alveolar-arterial oxygen gradient and PaO2/FiO2; PEEP: Positive End-Expiratory Pressure.

Medicines

During the hospital stay, 136 (63.6%) patients received corticosteroids, of which up to 60 (39%) in mg/kg doses, for a median time of 4 (IQR: 3–6) days. The most prescribed corticosteroid was methylprednisolone (n = 97; 42.2%). A total of n = 224 (97.4%) of patients received heparins, of which 82 (79.1%) patients received isocoagulation doses. Regarding medications that to date were used for the treatment of COVID, it was evidenced that 118 (51.3%) and 149 (64.8%) of patients received Hydroxychloroquine and Lopinavir / Ritonavir, respectively. In the comparison between groups, the low altitude group received greater numbers of corticosteroid prescriptions and tocilizumab than the high-altitude group. However, the patients did not show differences in the administration of heparins, antimalarials, or lopinavir/ritonavir (Table 5).

Table 5

Pharmaceutical treatment in the low and high-altitude group.

Category	All	High-altitude	Low altitude	P-value
Heparins	224 (97.4)	71 (98.3)	112 (96.6)	0.420
Heparin at isocoagulation doses	182 (79.1)	71 (62.3)	111 (95.7)	0.000
Heparin at anticoagulation doses	42 (18.3)	41 (36.0)	1 (0.9)	0.000
Corticosteroids	146 (63.6)	59 (51.8)	87 (75.0)	0.000
Methylprednisolone	97 (42.2)	40 (35.1)	57 (49.1)	0.000
Dexamethasone	34 (14.8)	5 (4.4)	29 (25.0)	0.000
Hydrocortisone	2 (0.9)	2 (1.8)	0 (0.0)	0.000
Prednisone	13 (5.7)	12 (10.5)	1 (0.9)	0.000
Corticosteroids Days—median (IQR)	4.0 (3.0–6.0)	3.0 (3.0–3.0)	5.0 (3.0–7.0)	0.000
Corticosteroids Doses
mg/kg	60 (39.0)	26 (43.3)	34 (36.2)	0.649
Lower Doses	48 (31.2)	18 (30.0)	30 (31.9)	0.649
Pulses	46 (29.9)	16 (26.7)	30 (31.9)	0.649
Antimalarials	168 (73.0)	88 (77.2)	80 (69.0)	0.312
Hydroxychloroquine	50 (21.7)	13 (11.4)	37 (31.9)	0.000
Chloroquine	118 (51.3)	75 (65.8)	43 (37.1)	0.000
Others
Lopinavir/Ritonavir	149 (64.8)	69 (60.5)	80 (69.0)	0.180
Tocilizumab	23 (10.0)	3 (2.6)	20 (17.2)	0.000

Altitude and mortality from COVID

There is evidence of a difference in survival in favor of the high-altitude group (p = 0.006), with the median survival of 39 days, compared to 21 days of the low altitude group (Fig 3).

Fig 3

The Kaplan-Meier curves for mortality according to altitude.

All comparisons were calculated using a COX regression multivariate analysis.

The hazard ratio obtained was 0.55 (95%CI = 0.39–0.78). Due to the differences found regarding comorbidities and age, the subgroups analysis was performed based on the Charlson classification (Fig 4).

Fig 4

The Kaplan-Meier curves for mortality according to altitude and group analysis using the Charlson index.

4A Curve with absence of comorbidities p = 0.005; 4B Curve with low comorbidities p = 0.900; 4C Curve with high comorbidities p = 0.920. All comparisons were calculated with COX regression multivariate analysis.

It was evident that the patients classified with a low and high Charlson index did not present differences between groups by altitude p = 0.929 and p = 0.920, respectively. However, in the group with no comorbidities, there was evidence of a difference between altitudes (p = 0.005), with the median survival of 17 days in the low altitude group and 49 in the high-altitude group. The hazard ratio found was 0.41 (95% CI = 0.23–0.75).

In the age subgroup analysis, it was demonstrated that the patients with an age between 51–65 years and over 65 years did not present differences by altitude p = 0.250 and p = 0.097, respectively. In contrast, within the younger group (those under 50 years), we found statistically significant differences between altitudes (p = 0.001), with the median survival of 19 days in the low altitude group and 66 in the high-altitude group. The hazard ratio found was 0.27 (95% CI = 0.11–0.64) (Fig 5).

Fig 5

The Kaplan-Meier curves for mortality according to altitude and age categories.

5A Curve with age less than 50 years p = 0.001; 5B Curve with age from 51 to 65 years p = 0.250; 5C Curve with age over 65 years p = 0.097. All comparisons were calculated with COX regression multivariate analysis.

Predictors of death

In the final adjusted analysis, five factors associated with the risk of death were found. As a protection factor, they were high-altitude and the presence of a tracheostomy. On the other hand, as risk factors were the APACHE II score greater than 17 at 72 hours, the relationship between arterial oxygen pressure and inspiratory oxygen fraction (PaO2 / FiO2) on the seventh day less than 300, and the presence of coagulopathy during the hospitalization (Fig 6).

Fig 6

High altitude mortality predictors among COVID-19 patients living at two different altitudes.

Discussion

The COVID-19 pandemic has severely affected the normal functioning of all countries on the planet, with a greater impact on developing countries. The impact of the pandemic caused by the COVID-19 pandemic has been more striking in countries with weakened health systems, causing thousands of deaths attributed to SARS-CoV-2 infections [35-38]. Although several factors have been linked to lower or higher COVID-19 related attack or mortality rates, other factors such as hypobaric hypoxia found at high-altitudes have been proposed as possible covariates. Evidence has been found around a reduced attack rate due to SARS-CoV-2 infection and lower mortality rates in those places located at higher altitudes [7, 15, 25, 39]. The pathophysiological reason for this relationship has not been established yet. Nevertheless, several hypotheses have been proposed. For instance, it seems like the current evidence suggests that lower COVID-19 related deaths are attributed either to a biological adaptation to hypoxia among high-altitude dwellers, due to environmental factors such as high UV or ozone exposure, or more logical; due to demographic denistity [25]. High-altitude patients are exposed to hypoxia, increasing molecular levels of HIF-1α and HIF-2α, which might favors a greater tolerance to hypoxemia and decreases the acute tissue damage triggered by patients with severe acute respiratory conditions [40]. Nevertheless, a recent study denies this [41]. Tian et al., 2021 concluded that SARS-CoV-2 ORF3a and host hypoxia-inducible factor-1α (HIF-1α) play key roles in the virus infection and pro-inflammatory responses, dysregulating oxygen metabolism among COVID-19 patients [41]. On the other hand, it has been shown that some high-altitude resident population groups have developed polymorphisms of the ACE-2 receptor that favor a better tolerance to hypoxia [42, 43]. The ACE-2 receptors, when inactivated, promote a pro-inflammatory state that would increase the repercussions in the lungs and other organs [43-45]. Other physiological mechanisms could justify, at least in part, this apparent protection conferred by geographical altitude. It is believed that at high-altitudes there is a lower expression of ACE-2 receptors, which are precisely the gateway to our cells for the SARS-CoV-2 virus [8]. A more plausible explanation goes along with the fact that high-altitude inhabitants express genes responsible for producing more erythrocytes (increasing oxygen transport) and creating new blood vessels (greater oxygen supply) [10, 46, 47]. On top of this, we must add certain anatomical and morphological characteristics among high-altitude dwellers, such as larger and bigger thoraxes as well as greater ventilatory capacities, that might play a role in reducing hypoxia found during severe ARDS due to COVID-19 [23, 48–50].

Despite the absence of clear pathophysiology, the present study provides relevant epidemiological and clinical data for understanding the influence of altitude on the evolution of seriously ill patients with COVID-19. Our results show a male to female predominance in terms of hospital admission, having a median age of 60 years, results comparable to other already published [3, 51–53]. The biochemical analysis from our cohorts showed the typical spectrum of hematological alterations, including leukocytosis with neutrophilia and lymphopenia, increased values of plasma ferritin, LDH, CRP, and abnormal platelet count and procalcitonin levels as evidenced in most patients elsewhere [54-58].

The high-altitude group had a higher number of leukocytes, a lower number of platelets, and higher levels of CRP; however, the rest of the values were similar in both groups. For the oxygenation parameters expressed in levels of PaO2 / FiO2, PO2, and SaO2 we found that in both groups, a hypoxic profile was evident at the time of admission. These findings are often present in patients with dyspnea, increased heart rate, and decreased PaO2 / FiO2 value, a common scenario for COVID-19 patients admitted to the ICU [59]. In terms of ventilatory parameters, the median static pulmonary compliance ranged from 27 to 35 ml/cm H2O as reported elsewhere [60-62]. However, the high-altitude group showed lower oxygenation values with higher static compliance. This lack of correlation between PaO2 / FiO2 and static compliance in patients with COVID-19 was also reported by Grasseli et al., 2020 [60]. The explanation behind this finding could be linked to the act that COVID-19 lungs have vascular alterations secondary to endothelial damage [63].

Our results demonstrate that the presence of overweight and obesity were consistent characteristics of both groups, similar findings as reported previously [64]. Current evidence is clear linking obesity as an independent predictor of mortality among COVID-19 patients [65]. Our study has similar results to a large UK study, which confirmed that 44% of hospitalized patients were overweight and 34% obese [65]. The information suggests that after adjusting for possible confounding factors, including age, sex, ethnicity, and social deprivation, the relative risk of critical illness from COVID-19 increases by 44% for overweight people and almost doubles for obese people.

As observed, comorbidities are well-associated with an increasing risk of COVID-19 related mortality [66, 67]. We found that mortality is positively associated with having one or more comorbidities; however, in our study, we found that although the presence of comorbidities is higher in populations located at lower altitudes, once we excluded the presence of comorbidities from the model, the hypothesized protective effect of high-altitude is evident. In other words, patients with comorbidities are at higher risk of dying at both altitudes when compared to patients with no comorbidities, and nevertheless, when compared only patients without comorbidities from the low and high-altitude group, we found that highlanders have a greater chance of survival. In the same way, age is an important death risk factor for COVID-19 infection. In our study, we found that oldest people have higher death cases. But when survival analysis was stratified by age groups. It was evidence similar patterns in the oldest groups and surprisingly only under 50-year-old group had a statistical difference.

The treatment basis for moderate-severe ARDS secondary to COVID-19 is based on ventilatory support with low tidal volumes, a prone position, and active management of intravenous fluids [23, 28, 68, 69]. In this sense, it was evident that the strategy the ventilator used for both groups in the present study was protective and up to 79.1% of patients complied with the prone position. Despite these measures, the complications were not different, and the high-altitude group was characterized by higher survival with a median of 39 days, compared to 21 days in the low altitude group. A difference that remained constant in the subgroup without comorbidities provides one of the first measurements of the contribution of altitude as a factor to be considered in this pathology. It is also important to note that currently, the evidence for the use of dexamethasone has been established as a protective factor for mortality [70]; however, the low altitude group that received a greater amount of corticosteroids presented higher mortality than the high-altitude group, supporting, even more, our hypothesis that high-altitude residents tolerated systemic hypoxia better than their low land peers.

In the Cox regression model, five factors associated with the risk of death were evidenced. These findings of increased risk did not differ from what is reported in the literature [71]. In spite of this, the APACHE II score has been an effective clinical tool to predict hospital mortality in patients with COVID-19 [72]. Thromboembolic events have also been shown to pose a significant risk of mortality in critically ill patients [73]. Regarding the SO2 / FiO2 ratio, it has been widely studied as a prognostic factor [74]. Besides, as protection, tracheostomy is a procedure that favors the release of ventilatory support in patients with prolonged mechanical ventilation, for which it has been classified as a protective factor against severe complications [75].

Finally, although altitude has been reported as a possible intervening factor in the clinical outcome of several patients [7, 8], this is the first study to elucidate more causal information on this relationship.

Limitations

This study was carried out in two COVID-19 designed hospitals, part of the social security health system (IESS). The private for-profit health system also receives COVID-19 patients; nevertheless, this population was not included in our analysis. An important limitation is a fact that we cannot adjust respiratory or oxygenation parameters for both populations, as the presence of validated data at these altitudes is scarce. Although our findings are the first to try to identify the role of altitude in relation to COVID-19 related mortality, further studies, including those with a broader altitude range, could provide important information to resolve some of the uncertainties surrounding our research question.

Conclusion

In this series of cases of critically ill patients with COVID-19 who were admitted to the ICU, there has been a substantial improvement in survival amongst people admitted to the high-altitude critical care unit. High-altitude living was associated with improved survival, especially among patients with no comorbidities. COVID-19 patients admitted to the high-altitude ICU unit have improved severity-of-disease classification system scores at 72 hours and reported better respiratory and ventilatory profiles than the low altitude group. Our analysis suggests this improvement is not due to temporal changes in the age, sex, or major comorbidity burden of admitted patients.

25 Aug 2021

PONE-D-21-07862

High-altitude is associated with better short-term survival in critically ill COVID-19 patients admitted to the ICU

PLOS ONE

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Reviewer #2: Partly

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Reviewer #1: No

Reviewer #2: I Don't Know

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Reviewer #1: The hypothesis that high altitude (HA) protects against SARS-CoV-2 infection and COVID-19 has gained traction over the past several months, though admittedly not without contention. The hypothesis is supported both by epidemiological findings as well as biological plausibility for enhanced hypoxia tolerance in HA residents. Here, the authors tested whether short-term survival and ICU outcomes were different in HA versus low altitude regions. The question is undoubtedly significant and timely, though I have suggestions for improving data presentation and discussion.

Abstract: The background in the abstract is so vague “better or worse”, “lower or higher” that it’s confusing to follow what the authors hypothesize. Can APACHE II be defined? “Especially” higher is not quantitative. High altitude vs. high-altitude are used interchangeably. Please be consistent.

Introduction: the reference to HIF-1 as a protein that regulates angiogenesis is odd given all the other cytoprotective properties regulated by HIF that relate to COVID-19 pathogenesis.

Methods:

How do the two settings for HA and sea level differ? Are the cities of comparable size/rurality? Do they differ in quantitative metrics other than elevation?

The APACHE II and Charlson index are not described

What does “strict protocol implemented in both sites” (in the Bias section)?

Results and Discussion:

Given some of the demographic differences between HA and sea level, i wonder if the authors could statistically control for these factors in their analysis? If statistical regression were performed instead of Mann Whitney analyses, we could have more certainty that the differences in survival and lab biochemistry was really due to altitude rather than age and comorbidities, both of which are very different between elevations.

The hematological and serological parameters in Table 3 is simply too bulky to read. Can the authors present a portion of these outcomes which help directly test their hypothesis (and leave the others as supplemental data?). Same comment for the ventilation parameters

How standard were ventilation protocols between hospitals? For a non-expert, this data is really hard to understand.

Can repeated measures analysis be performed to see how some of these hematological and biochemical variables change over time, within the same patient at HA versus sea level? i.e. do HA patients return to normal faster than sea level?

The gasometry is interesting given that residence at HA should make these values different even at baseline. Is there a way to adjust for these, given that HA residence already impacts them? i.e. perhaps a relative change? The idea that SpO2/FiO2 serves as a prognostic factor is really underdeveloped

The txt states that tracheostomy was reached in 17.5% of patients, but the table says 18.7%.

The results for the hazard ratios is difficult to understand. Figure 1 should have five predictors of survival, yet it lists 6 (including PaO2/FiO2 twice, with two different numbers).

Figure 1 is used several times, and in general the figures are not organized correctly.

What is the difference between the survival curves and how were they generated?

The limitations section is simply too underdeveloped.

The references do not appear to be appropriately selected. For example, 23 and 24 should reference ACE2 polymorphisms and hypoxia tolerance, yet these references are reviews of ACE2 and ARB

Reviewer #2: Morocho Jaramillo and colleagues study potential differences of survival after severe Covid-19 infection related to the altitude of treatment. They find an increased survival time at higher altitude.

Generally, the manuscript is well written and the findings are interesting. However, in its current state it is very difficult to read for several reasons (many typos, figure numbers are incorrect, p-values in table seem to be incorrect) and appears not finalized.

Major:

1. Even though the median age of Covid19 patients was similar, the age distribution was clearly not. Much more individuals were in the highest age-categories (>56 years) in the low altitude group. This has to be considered when discussing the elevated death rate in the low altitude group. Without a way of showing, that this difference of age distribution was not the cause for the observed death rate differences, no influence of altitude can be assumed.

2. The figures are currently difficult to interpret:

a. The figure numbers are incorrect. All figure legends say figure 1. Figure 5(?) is the only figure located in the text and also in the end.

b. Please indicate for all figures (e.g. in the legends), which statistical measures were used (mean –median, SD-SEM-IQR …)

c. Fig 4(?): please check if legend is correct: panel C is indicated to be significantly different, but A and B not – seems not to be right

1. Please check all tables for accuracy; even though difficult to assess from IQRs some p-values are for example surprising in table 4 (esp. the many 0.000) and table 5 (e.g. heparins, p=0.42). Table 1: the p-values for the age categories are mostly 0.024, although sometimes (35-45) the differences do not appear significant. Also all the p=0.096 don’t seem right.

Minor:

1. page and line numbers would facilitate the review.

2. P10, Population and study size “We used a non-probabilistic sampling technique…” �  this is formulated very vaguely; please be more specific on this sampling technique: was everybody included if the inclusion/exclusion criteria were fulfilled?

3. P11, Data sources and variables: “The data were obtained from the electronic medical record of a common registry system for both units and analyzed by three independent researchers.” �  Please clarify what was analyzed independently and how the final results were obtained from these independent analyses.

4. P12, Bias: “… and a strict protocol was implemented in both sites.” �  please specify

5. Results, Sociodemographic characteristic: “The BMI of all the patients was 27.8 kg/m2…” – please clarify that this is the median.

6. Discussion: “high-altitude patients present a chronic conversion of the hypoxia-inducing factor type 1 to type 2” – I am not sure, what this statement means, please clarify and add reference

7. What does it mean that the comorbidity rate was higher in lowlanders? May this reflect a higher incidence of those diseases at high altitude or could it also mean that a lower co-morbidity status may be associated with ICU admittance at higher altitude? Possibly indicating a higher risk to develop severe Covid19 at high altitude (even in absence of comorbidities? Or is it merely due to the unequal age-distribution of the 2 groups (see major point)? It would be important to look at this.

8. for the ACE-2 part: please consider the discussion of this topic in the cited work of Pun et al. Lower Incidence of COVID-19 at High Altitude: Facts and Confounders. High Altitude Medicine & Biology 2020

Figures and tables:

1. sometimes the variation seems to be indicated only on one side, while usually both sides are shown (e.g. Fig 2 (?), SaO2)

2. Table 1: CVA should be CVD?

3. Table 4: what is the difference btw. The 2 lines “resistors”?

Typos

Currently there are many typos in the manuscript, a non-exhaustive list is given below:

1. P10, The link between chronic exposure due to high altitude living and the clinical features of COVID-19 patients has been poorly studied. �  remove “due”

2. P10 “…exposure on severally ill COVID-19 patients”… �  should be “severely”?

3. in Fig 4(?) and Fig 5(?) legends :“comorbidtiesties”, “predictorsCaracterística”, “cuagolopathy” – also in Fig

4. incomplete sentences e.g. in the discussion: “In this way, it is evident that the strategy The ventilator used …”

5. …

Suggestions for further discussion points:

1. Could you discuss the result that the median BMI in both altitude categories is clearly above normal weight?

2. Is it possible to provide numbers of how many individuals per population were admitted to ICUs due to Covid19 in the investigated regions? This would not only be informative to put the present study in context but maybe also to understand some characteristics of the groups better – for example, why there is now difference in BMI between the groups, although lower BMIs are sometimes reported at higher altitudes.

3. Related to the previous point; are the catchment areas of the hospitals at low and high altitude comparable in terms of urban – rural, socioeconomic status, etc.?

4. Fig 2(?): Respiratory and physiological parameters among COVID-19 patients living at two different elevations: The great difference in SaO2 after 1 day should be discussed.

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Reviewer #1: No

Reviewer #2: No

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22 Oct 2021

Point by Point Letter

To:

Jamie Males

Staff Editor

PLOS ONE

RE: PONE-D-21-07862 High-altitude is associated with better short-term survival in critically ill COVID-19 patients admitted to the ICU

Dear Editor and reviewers, thank you very much for your observations and comments regarding our manuscript. Your observations and suggestions have improved our manuscript importantly.

Please find enclosed our point-by-point response letter to each of your remarks.

2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

We have updated the data availability statement and all the relevant data can be found here:

All non-identifiable and previously anonymized data can be retrieved from the following link to our open data digital repository: https://github.com/covid19ec/HospitalData. Any additional query or information about our research work can be requested to our email address at e.ortizprado@gmail.com

3. Please include a caption for figures 2,3,4 and 5.

We have added the missing captions within each new figure

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Partly

We have improved our manuscript significantly; we hope this second revision will fulfil your expectations

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: I Don't Know

We have improved our statistical analyses, incorporating all your suggestions

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

We have updated the data availability statement and all the relevant data can be found here:

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

We have incorporated your comments, which has significantly improved the quality of our work

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The hypothesis that high altitude (HA) protects against SARS-CoV-2 infection and COVID-19 has gained traction over the past several months, though admittedly not without contention. The hypothesis is supported both by epidemiological findings as well as biological plausibility for enhanced hypoxia tolerance in HA residents. Here, the authors tested whether short-term survival and ICU outcomes were different in HA versus low altitude regions. The question is undoubtedly significant and timely, though I have suggestions for improving data presentation and discussion.

Thanks for your comments, we have reviewed the manuscript entirely and added all your observations

Abstract: The background in the abstract is so vague “better or worse”, “lower or higher” that it’s confusing to follow what the authors hypothesize. Can APACHE II be defined? “Especially” higher is not quantitative. High altitude vs. high-altitude are used interchangeably. Please be consistent.

Thanks for your observations, we have improved the manuscript as follow:

Background: Multiple studies have attempted to elucidate the relationship between chronic hypoxia and SARS-CoV-2 infection. It seems that high altitude is associated with lower COVID-19 related mortality and incidence rates, nevertheless, all the data came from observational studies, being this the first one looking into prospectively-collected clinical data from severely ill patients residing at two significantly different altitudes.

Methods: A prospective cohort, two-center study among COVID-19 confirmed adult patients admitted to a low (sea level) and high altitude (2,850 m) ICU unit in Ecuador was conducted. Two hundred and thirty confirmed patients were enrolled from March 15th to July 15th, 2020.

Results: From 230 patients, 149 were men (64.8%) and 81 women (35.2%). The median age of all the patients was 60 years and at least 105 (45.7%) of patients had at least one underlying comorbidity including hypertension (33.5%), diabetes (16.5%), and chronic kidney failure (5.7%). The APACHE II scale (Score that estimates ICU mortality) at 72 hours was especially higher in the low altitude group with a median of 18 points (IQR: 9.5-24.0), compared to 9 points (IQR: 5.0-22.0) obtained in the group of high altitude. There is evidence of a difference in survival in favor of the high altitude group (p = 0.006), the median survival being 39 days, compared to 21 days in the low altitude group.

Conclusion: There has been a substantial improvement in survival amongst people admitted to the high altitude ICU. Residing at High altitude was associated with improved survival, especially among patients with no comorbidities. COVID-19 patients admitted to the high altitude ICU unit have improved severity-of-disease classification system scores at 72 hours.

Introduction: the reference to HIF-1 as a protein that regulates angiogenesis is odd given all the other cytoprotective properties regulated by HIF that relate to COVID-19 pathogenesis.

Thanks so much for your observation, we have updated our literature review on this very topic as follow:

During the first months of the pandemic, very few ecological studies showed a possible epidemiological and survival implication exerted by high altitude7,8. It has been proposed that these results are in part answered by the well-known physiological acclimatization and the long term adaptation to high altitude exposure, generating a greater tolerance to chronic hypoxia 7–10. Several investigations have tried to determine the potential relationship between high altitude and COVID-19 related mortality 1–4. Most studies have established that living at high altitudes could be related to reduced COVID-19 related mortality and morbidity 1–3 . These results were discussed from different points of view. The biological one was based on a hypothesized lower viral affinity for the type-2 angiotensin-converting enzyme (ACE2) receptors, but there is no definitive evidence to supporting this claim 1. Another hypothesis surrounding the high altitude-COVID-19 link refers to the involvement of better perfused and better oxygenated tissues due to the involvement of the hypoxia-triggered protein that regulates angiogenesis, cell proliferation, metabolism and downregulates ACE-2 levels; the well-known Hypoxia-inducible factor 1-alpha (HIF-1 α) 1–7. Having improved ability to use oxygen within the tissues might reduce the effects of systemic hypoxia caused by acute respiratory distress syndrome (ARDS)1.

On the other hand, sociodemographic and environmental factors such as population density, UV radiation, ozone or cold have been proposed to affect SARS-CoV-2 transmission and viral load, nevertheless, no clinical data is available yet 1,2.

The link between high altitude hypoxia and COVID-19 mortality is still under investigation17,18. The very few studies on clinical, ventilatory and respiratory support parameters’ differences have been performed at elevation below 1,500 m and no evidence about the role of high altitude exposure (> 2,500 m) on severally ill COVID-19 patients have been published yet 19.

We believe our study is the first one that has been able to demonstrate the effect of altitude living on COVID-19 mortality and prognosis after controlling for several clinical factors.

Methods:

How do the two settings for HA and sea level differ? Are the cities of comparable size/rurality? Do they differ in quantitative metrics other than elevation?

We have added the following paragraph within the settings section of the manuscript

Quito and Guayaquil are the most important cities in Ecuador with 2.5 and 2.9 million inhabitants respectively. The two ICU were built on 2017, both are part of the same Social Security Health System model (IESS) and both were the first COVID-19 sentinel hospitals in Ecuador. Since both hospitals were the first in receiving patients during the pandemic, both used the same therapeutical protocols and receive the same type of patients.

The APACHE II and Charlson index are not described

Thanks for your comments, we have added the meaning of both scales.

What does “strict protocol implemented in both sites” (in the Bias section)?

We have updated our bias section and included other measures used to avoid bias.

Results and Discussion:

Given some of the demographic differences between HA and sea level, i wonder if the authors could statistically control for these factors in their analysis? If statistical regression were performed instead of Mann Whitney analyses, we could have more certainty that the differences in survival and lab biochemistry was really due to altitude rather than age and comorbidities, both of which are very different between elevations.

Thanks so much for your suggestion, we have performed a subgroup analysis and is included within the results section.

The hematological and serological parameters in Table 3 is simply too bulky to read. Can the authors present a portion of these outcomes which help directly test their hypothesis (and leave the others as supplemental data?). Same comment for the ventilation parameters

Thanks so much for your suggestion. We have reduced the number of variables within the tables

How standard were ventilation protocols between hospitals? For a non-expert, this data is really hard to understand.

A paragraph has been added at the beginning of the results clearly explaining the differences between the ventilatory parameters used.

Can repeated measures analysis be performed to see how some of these hematological and biochemical variables change over time, within the same patient at HA versus sea level? i.e. do HA patients return to normal faster than sea level?

Unfortunately, we only have 3 temporary measurements that do not allow us to perform the type of analysis requested.

The gasometry is interesting given that residence at HA should make these values different even at baseline. Is there a way to adjust for these, given that HA residence already impacts them? i.e. perhaps a relative change? The idea that SpO2/FiO2 serves as a prognostic factor is really underdeveloped.

Thank you for your comments, we were not able to recreate a model that could fit the gasometric values reported at high altitude with other variables, however we have added your observation to the limitations section

The txt states that tracheostomy was reached in 17.5% of patients, but the table says 18.7%.

Thanks for pointing this out, we have corrected the mistake.

The results for the hazard ratios is difficult to understand. Figure 5 should have five predictors of survival, yet it lists 6 (including PaO2/FiO2 twice, with two different numbers).

Figure 5 has been modified to ensure a better understanding of the results.

Figure 1 is used several times, and in general the figures are not organized correctly.

We have updated all the figures and tables

What is the difference between the survival curves and how were they generated?

“Bivariate and multivariate analyzes were performed to identify factors associated with death from COVID-19 in all patients using the Cox risk regression model. To obtain a reduced set of variables from the broad set of predictors, we carried out a progressive in bloc procedure assigning the predictor variables into six groups: sociodemographic characteristics and comorbidities, complications, scales, ventilatory values, medications, and laboratory and imaging parameters. A multivariate regression analysis was applied within each block using two criteria to achieve the best set of predictors: relevance to the clinical situation and bivariate and multivariate statistical significance (p <0.05). Variables with more than 25% missing values were not considered for the analysis.”

The limitations section is simply too underdeveloped.

We have updated this section according to all the comments generated by the reviewers

The references do not appear to be appropriately selected. For example, 23 and 24 should reference ACE2 polymorphisms and hypoxia tolerance, yet these references are reviews of ACE2 and ARB

We have updated the entire references list

Reviewer #2: Morocho Jaramillo and colleagues study potential differences of survival after severe Covid-19 infection related to the altitude of treatment. They find an increased survival time at higher altitude.

Generally, the manuscript is well written and the findings are interesting. However, in its current state it is very difficult to read for several reasons (many typos, figure numbers are incorrect, p-values in table seem to be incorrect) and appears not finalized.

Thanks for your observations, we have updated the entire manuscript

Major:

1- Even though the median age of Covid19 patients was similar, the age distribution was clearly not. Much more individuals were in the highest age-categories (>56 years) in the low altitude group. This has to be considered when discussing the elevated death rate in the low altitude group. Without a way of showing, that this difference of age distribution was not the cause for the observed death rate differences, no influence of altitude can be assumed.

The results of the age subgroups were reviewed. Since they were not standardized with population distributions as recommended by the WHO and since the statistical test that established the association was a 6x2 chi test table, the authors decided to eliminate this confounding factor from the study and maintain the quantitative measure expressed in the nonparametric statistics. We have added this to the limitation section

2. The figures are currently difficult to interpret:

a. The figure numbers are incorrect. All figure legends say figure 1. Figure 5(?) is the only figure located in the text and also in the end.

Thanks for your observation, we have updated all the figures and tables

b. Please indicate for all figures (e.g. in the legends), which statistical measures were used (mean –median, SD-SEM-IQR …)

Thanks for your observation, we have updated all the figures and tables

c. Fig 4(?): please check if legend is correct: panel C is indicated to be significantly different, but A and B not – seems not to be right

Thanks for your observation, we have updated all the figures and tables

1. Please check all tables for accuracy; even though difficult to assess from IQRs some p-values are for example surprising in table 4 (esp. the many 0.000) and table 5 (e.g. heparins, p=0.42). Table 1: the p-values for the age categories are mostly 0.024, although sometimes (35-45) the differences do not appear significant. Also all the p=0.096 don’t seem right.

The error in the comparative calculation of age has been corrected, the content of the tables has been reduced and in Table 4, a detailed explanation of the data has been included, which, although they show statistical differences, these differences are not clinically relevant.

Minor:

1. page and line numbers would facilitate the review.

Many thanks for your observations, we have added continuous lines

2. P10, Population and study size “We used a non-probabilistic sampling technique…” �  this is formulated very vaguely; please be more specific on this sampling technique: was everybody included if the inclusion/exclusion criteria were fulfilled?

The wording has been improved and specified that all patients who met the inclusion and exclusion criteria

3. P11, Data sources and variables: “The data were obtained from the electronic medical record of a common registry system for both units and analyzed by three independent researchers.” �  Please clarify what was analyzed independently and how the final results were obtained from these independent analyses.

The clinical data were obtained by the intensivists of both hospitals; however, the analysis was performed by 3 of the physicians who did not collect the data, limiting the role of research bias while collecting all the information

4. P12, Bias: “… and a strict protocol was implemented in both sites.” �  please specify

The bias section was improved thanks to your observations

5. Results, Sociodemographic characteristic: “The BMI of all the patients was 27.8 kg/m2…” – please clarify that this is the median.

We have updated this information

6. Discussion: “high-altitude patients present a chronic conversion of the hypoxia-inducing factor type 1 to type 2” – I am not sure, what this statement means, please clarify and add reference

We have improved our discussion section, making it clearer and correctly referencing when needed.

7. What does it mean that the comorbidity rate was higher in lowlanders? May this reflect a higher incidence of those diseases at high altitude or could it also mean that a lower co-morbidity status may be associated with ICU admittance at higher altitude? Possibly indicating a higher risk to develop severe Covid19 at high altitude (even in absence of comorbidities? Or is it merely due to the unequal age-distribution of the 2 groups (see major point)? It would be important to look at this.

Thanks so much for pointing this out, we have clarified and highlighted our response as follow:

Comorbidities are a well-associated with an increasing risk of COVID-19 related mortality1,2. We found that mortality is positively associated with having one or more comorbidities, however in our study we found that although the presence of comorbidities is higher in populations located at lower altitudes, once we excluded the presence of comorbidities from the model, the hypothesized protective effect of high altitude is evident. In other words, patients with comorbidities are at higher risk of dying at both altitudes when compared to patients with no comorbidities, nevertheless, when compared only patients without comorbidities from the low and high altitude group, we found that highlanders have greater chance of survival.

8. for the ACE-2 part: please consider the discussion of this topic in the cited work of Pun et al. Lower Incidence of COVID-19 at High Altitude: Facts and Confounders. High Altitude Medicine & Biology 2020

The reference was added and discussed as follow:

Other physiological mechanisms could justify, at least in part, this apparent protection conferred by geographical altitude. It is believed that at high altitude there is a lower expression of ACE-2 receptors, which are precisely the gateway to our cells for the SARS-CoV-2 virus1. A more plausible explanation goes along with the fact that high altitude inhabitants express genes responsible for producing more erythrocytes (increasing oxygen transport) and creating new blood vessels (greater oxygen supply)1–3. On top of this, we must add certain anatomical and morphological characteristics, among high altitude dwellers such as larger and bigger thoraxes as well as greater ventilatory capacities, that might play a role reducing hypoxia found during severe ARDS due to COVID-191–4.

Figures and tables:

1. sometimes the variation seems to be indicated only on one side, while usually both sides are shown (e.g. Fig 2 (?), SaO2)

All the figures and tables were updated

2. Table 1: CVA should be CVD?

All the figures and tables were updated, CVD is the correct abbreviation

3. Table 4: what is the difference btw. The 2 lines “resistors”?

We have eliminated this mistake and rephrase the paragraph

Typos

Currently there are many typos in the manuscript, a non-exhaustive list is given below:

Many thanks for your time and observations, we have reviewed the entire manuscript for mistakes and typos

1. P10, The link between chronic exposure due to high altitude living and the clinical features of COVID-19 patients has been poorly studied. �  remove “due”

Thanks, this was done

2. P10 “…exposure on severally ill COVID-19 patients”… �  should be “severely”?

Thanks, this was corrected

3. in Fig 4(?) and Fig 5(?) legends :“comorbidtiesties”, “predictorsCaracterística”, “cuagolopathy” – also in Fig

Thanks, this was reviewed and corrected

4. incomplete sentences e.g. in the discussion: “In this way, it is evident that the strategy The ventilator used …”

We have completed the sentences, thanks

Suggestions for further discussion points:

1. Could you discuss the result that the median BMI in both altitude categories is clearly above normal weight?

Yes, an entire paragraph was added as follow:

Our results demonstrate that the presence of overweight and obesity were consistent characteristics of both groups. Current evidence is clear linking obesity as an independent predictor of mortality among COVID-19 patients1. Our study has similar results to a large UK study, which confirmed that 44% of hospitalized patients were overweight and 34% obese1. The information suggests that after adjusting for possible confounding factors, including age, sex, ethnicity and social deprivation, the relative risk of critical illness from COVID-19 increases by 44% for overweight people and almost doubles for obese people.

2. Is it possible to provide numbers of how many individuals per population were admitted to ICUs due to Covid19 in the investigated regions? This would not only be informative to put the present study in context but maybe also to understand some characteristics of the groups better – for example, why there is now difference in BMI between the groups, although lower BMIs are sometimes reported at higher altitudes.

The present study included a total of 230 patients diagnosed with COVID-19 using the RT-PCR technique, of which 114 patients were treated in the high altitude group (IESS-Quito Sur), while 116 patients belonged to the low altitude group (IESS-Los Ceibos).

3. Related to the previous point; are the catchment areas of the hospitals at low and high altitude comparable in terms of urban – rural, socioeconomic status, etc.?

We have updated this information was also requested by the other reviewer

4. Fig 2(?): Respiratory and physiological parameters among COVID-19 patients living at two different elevations: The great difference in SaO2 after 1 day should be discussed.

We have added an entire paragraph on this

Submitted filename: Point by Point Letter UCI Revision 1.docx

Click here for additional data file.

5 Nov 2021

19 Dec 2021

Point by Point Letter

To:

Danielle R. Bruns, PhD

Academic Editor

PLOS ONE

RE: PONE-D-21-07862R1 “High-altitude is associated with better short-term survival in critically ill COVID-19 patients admitted to the ICU”

Dear Editor and reviewers, thank you very much for your effort, observing our manuscript for the second time. We have completed the revision and we have fulfilled your comments. All the changes are highlighted in red. We have also included a clean version and the rebuttal letter.

Additional Editor Comments:

Thank you for your revised manuscript. We appreciate the attention you took in addressing each reviewer's comments. I disclose that I participated as a reviewer for the initial evaluation of this manuscript. Upon second review, one reviewer still has a few concerns. While the manuscript is substantially improved, the major contribution of age in high- versus low-altitude communities is still inadequately addressed. Given the significant impact of age on COVID pathogenesis, we believe this is still a major concern in the revised version. The authors need to address this concern statistically (correct for the difference in age) or to at least discuss the importance of age in COVID-19.

Dear Editor, we have responded to your observations, and we believe that the further analysis improved our manuscript.

Secondly, some concerns remain with respect to the discussion of HIF-1, especially with the reference provided.

We have addressed this section and included a deeper analysis within the discussion section

Minor comments also remain regarding figure numbering.

We have reviewed the entire manuscript and corrected all the typos and mistakes while numbering tables and figures.

6. Review Comments to the Author

Reviewer #2: The authors have substantially improved the manuscript and most of my comments have been adequately addressed. However, 2 important concerns (+1 formal point) remain for me:

Thanks for your comments, we have improved our second revision

1. the difference in age distribution (much more individuals were in the highest age-categories (>56 years) in the low altitude group): although the problem is now mentioned in the limitations, this difference in distribution could explain the apparent altitude effect, if it were mostly the older people (>56) that died. I think it would be very important to correct for the age effect – at least the importance of age on mortality from COVID-19 has to be discussed. Currently the text reads, like age is a negligible factor in COVID-19 risk, which it clearly is not.

Thanks for your keen observation, we agreed with you, and we have corrected our findings after adjusting our results for age differences. The new analysis are highlighted in red and new surviving curves were elaborated

2. the argumentation about HIF-1 in high altitude dwellers. Unfortunately, the provided reference is behind a paywall (and most likely is not the ideal reference for this purpose), so it might be a confusion on my side: but the following statement needs clarification:

“High altitude patients present a chronic molecular conversion of the hypoxia-inducing factor type 1 to type 2 (HIF-1), which favors a greater tolerance to hypoxemia and decreases the acute tissue damage triggered by patients with severe acute respiratory conditions41”

I am not sure, what is meant with “conversion of HIF-1 type 1 to type 2”. Does this simply refer to increased stabilization of HIF-1alpha in high altitude residents? Does it refer to the interaction of HIF1- and HIF-2 pathways? Or to genetic adaptations in HIF-2alpha associated with its enhanced activity in some high altitude populations (such as in Tibetans)? If any of these effects are eluded to, the sentence needs to be more precise (a review that would provide adequate references is for example this one: Bigham & Lee. Human high-altitude adaptation: forward genetics meets the HIF pathway. Genes Dev. 2014). Otherwise, I think better explanation has to be provided for what is meant with “type 1 and type 2”.

Many thanks for your comments, we have deepened our discussion on this very subject and clarified the doubt raised by you.

3. In addition, please note, that all figures are still labeled Fig. 1.

Thanks for this observation, we have numbered them accordingly.

Submitted filename: Point by Point Letter UCI Revision 2.docx

Click here for additional data file.

27 Dec 2021

High altitude is associated with better short-term survival in critically ill COVID-19 patients admitted to the ICU

PONE-D-21-07862R2

Dear Dr. Ortiz-Prado,

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PLOS ONE

Additional Editor Comments (optional):

We thank the authors for responding to all reviewer and editorial comments.

Reviewers' comments:

22 Mar 2022

PONE-D-21-07862R2

High-altitude is associated with better short-term survival in critically ill COVID-19 patients admitted to the ICU

Dear Dr. Ortiz-Prado:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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