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. 2021 Nov 19;16(11):e0260169. doi: 10.1371/journal.pone.0260169

Factors associated with admission to the intensive care unit and mortality in patients with COVID-19, Colombia

Jorge Enrique Machado-Alba 1,*, Luis Fernando Valladales-Restrepo 1,2, Manuel Enrique Machado-Duque 1,2, Andrés Gaviria-Mendoza 1,2, Nicolás Sánchez-Ramírez 1, Andrés Felipe Usma-Valencia 1, Esteban Rodríguez-Martínez 3, Eliana Rengifo-Franco 3, Víctor Hugo Forero-Supelano 4, Diego Mauricio Gómez-Ramirez 5, Alejandra Sabogal-Ortiz 5
Editor: Wenbin Tan6
PMCID: PMC8604321  PMID: 34797857

Abstract

Introduction

Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide, and several sociodemographic variables, comorbidities and care variables have been associated with complications and mortality.

Objective

To identify the factors associated with admission to intensive care units (ICUs) and mortality in patients with COVID-19 from 4 clinics in Colombia.

Methods

This was a follow-up study of a cohort of patients diagnosed with COVID-19 between March and August 2020. Sociodemographic, clinical (Charlson comorbidity index and NEWS 2 score) and pharmacological variables were identified. Multivariate analyses were performed to identify variables associated with the risk of admission to the ICU and death (p<0.05).

Results

A total of 780 patients were analyzed, with a median age of 57.0 years; 61.2% were male. On admission, 54.9% were classified as severely ill, 65.3% were diagnosed with acute respiratory distress syndrome, 32.4% were admitted to the ICU, and 26.0% died. The factors associated with a greater likelihood of ICU admission were severe pneumonia (OR: 9.86; 95%CI:5.99–16.23), each 1-point increase in the NEWS 2 score (OR:1.09; 95%CI:1.002–1.19), history of ischemic heart disease (OR:3.24; 95%CI:1.16–9.00), and chronic obstructive pulmonary disease (OR:2.07; 95%CI:1.09–3.90). The risk of dying increased in those older than 65 years (OR:3.08; 95%CI:1.66–5.71), in patients with acute renal failure (OR:6.96; 95%CI:4.41–11.78), admitted to the ICU (OR:6.31; 95%CI:3.63–10.95), and for each 1-point increase in the Charlson comorbidity index (OR:1.16; 95%CI:1.002–1.35).

Conclusions

Factors related to increasing the probability of requiring ICU care or dying in patients with COVID-19 were identified, facilitating the development of anticipatory intervention measures that favor comprehensive care and improve patient prognosis.

Introduction

In Wuhan (China), at the end of 2019, a series of cases of pneumonia caused by a new coronavirus were reported [1]. The pathogen was named SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) by the International Committee on Taxonomy of Viruses (ICTV), and the pneumonia it produced was called coronavirus disease-2019 (COVID-19) by the World Health Organization (WHO) [2]. On January 30, 2020, COVID-19 was declared an epidemic of international concern [3]. This infection has affected tens of millions of people worldwide, leading to millions of deaths [4]. In Colombia, according to the National Institute of Health and the Ministry of Health, confirmed cases exceed 2 million, with more than 56000 deaths (2.6%), mostly males (63.7%) and those over 60 years (78.4%) [5].

This has led to an unprecedented burden on health systems worldwide, including increased hospital admissions, high demand for intensive care unit (ICU) beds, advanced respiratory support, renal replacement therapy, and other life support interventions [6]. The impact of the COVID-19 pandemic on health systems varies by country, depending on the balance between the supply and demand of services, which has been associated with the ability to expand the number of hospital beds, particularly in the ICU, and public health policies to contain the pandemic [6, 7].

Although the majority of people with SARS-CoV-2 have mild or uncomplicated disease, 14% develop serious disease requiring oxygen therapy, and approximately 5% require treatment in an ICU; of these, most require mechanical ventilation [8]. Among the prognostic factors described for the development of critical illness and mortality, the most important are advanced age; the presence of certain comorbidities, such as arterial hypertension, diabetes, chronic obstructive pulmonary disease, cardiovascular disease, and obesity; abnormalities in some paraclinical tests; and the availability of medications [2, 9]. These factors should be recognized by attending physicians to identify critical patients early, to allocate resources effectively and to adapt management plans to improve patient prognosis [2].

Considering the information from international studies conducted to date, it is important to have epidemiological data at local and national scales because those data may differ from findings in North America, Europe and Asia. Although COVID-19 is a global pandemic, the burden of disease has not been the same in different countries [7], and in this sense, the aim herein was to identify the factors associated with ICU admission and with mortality in patients with COVID-19 in a Colombian population.

Materials and methods

This was an observational study of the factors associated with ICU admission and with mortality in patients with COVID-19, who were identified from a report of positive cases confirmed by RT-PCR (reverse transcription polymerase chain reaction) testing in 4 tertiary care clinics affiliated with the Grupo Ospedale Network, located in the cities of Bogotá, Cali, Pereira and Popayán. All subjects of any age, sex and city of residence treated for COVID-19 between March 6 and August 31, 2020 were selected. Each patient was followed until death or hospital discharge. Those with incomplete medical records or incomplete follow-up by teleconsultation and those diagnosed by screening were excluded.

Based on the information obtained, a database was designed to collect the following groups of variables:

  1. Sociodemographic: sex, age, city of origin, occupation and place of care (city/department).

  2. Clinical:
    1. Physiological variables: body mass index (BMI), mean blood pressure, heart rate, respiratory rate, oxygen saturation, state of consciousness at the time of the emergency room care, physical examination (i.e: crackles, rhonchi, etc);
    2. Comorbidities: hypertension, diabetes mellitus, dyslipidemia, hypothyroidism, ischemic heart disease, heart failure, chronic obstructive pulmonary disease, asthma, solid tumors or hematological malignancies, human immunodeficiency virus infection, rheumatologic diseases (rheumatoid arthritis, systemic lupus erythematosus, vasculitis, and others), chronic kidney disease, stroke, obesity, and smoking, among others. The age-adjusted Charlson comorbidity index was calculated;
    3. Symptoms and signs: documented in the clinical record at hospital admission (i.e: Cough, fever, dyspnea, etc);
    4. Diagnostic intervention: laboratory tests (blood count, creatinine, urea nitrogen, total bilirubin, direct bilirubin, transaminases, lactate dehydrogenase, C-reactive protein, ferritin, D-dimer, troponin I and prothrombin time) and diagnostic imaging (initial chest X-ray and computerized axial tomography (CT-scan) of the chest) at the time of care;
    5. Service: emergency room, hospitalization in general wards and ICU; and
    6. Hospital stay and ICU stay (in days): date of admission and discharge (from the hospital and from the ICU) or date of death.
  3. Therapeutic intervention (pharmacological):
    1. Therapy prescribed for COVID-19: antimalarials (chloroquine and hydroxychloroquine), azithromycin, lopinavir-ritonavir, tocilizumab, colchicine, ivermectin and convalescent plasma;
    2. Other medications: systemic corticosteroids (oral and parenteral), systemic antibiotics, vasopressors and inotropes (norepinephrine, vasopressin, and dopamine, among others), parenteral anticoagulants, sedatives (benzodiazepines, dexmedetomidine, and others), muscle relaxants, analgesics (non-opioids), antihypertensives and diuretics, normoglycemic agents, antiulcer drugs, benzodiazepines, bronchodilators and inhaled corticosteroids, and antipsychotics, among others;
    3. Use of supplemental oxygen: low-flow devices (nasal cannula/simple face mask and non-rebreathing face mask) and high-flow devices (venturi system and noninvasive and invasive mechanical ventilation). For patients who required mechanical ventilation, the total duration in days of orotracheal intubation (date of intubation and date of definitive extubation) was determined; and
    4. Position of the patient: prone.

  4. Severity of COVID-19: classified according to the Colombian Consensus of Care, Diagnosis and Management of SARS-CoV-2/COVID 19 infection in health care facilities [8]. The CURB-65 and NEWS 2 scores were calculated for all patients, and the APACHE-II score was calculated for critically ill patients.

  5. Complications: acidosis, acute heart injury, acute kidney injury, acute respiratory distress syndrome, arrhythmia, coagulopathy, complications of mechanical ventilation, cytokine-related syndrome, delirium, heart failure, kidney replacement therapy (dialysis), respiratory failure, secondary infection, sepsis, shock, spontaneous pneumothorax, thromboembolism.

  6. Primary outcomes: admission to the ICU and death.

The protocol was approved by the Bioethics Committee of the Universidad Tecnológica de Pereira in the category of "risk-free research" (approval code: 03–080620). The principles of confidentiality of information established by the Declaration of Helsinki were respected. All data were fully anonymized before accessed them and the Bioethics Committee waived the requirement for informed consent.

The data were analyzed with the statistical package SPSS Statistics, version 26.0 for Windows (IBM, USA). A descriptive analysis was performed; frequencies and proportions are reported for the qualitative variables, and measures of central tendency and dispersion are reported for the quantitative variables, depending on their parametric behavior established by the Kolmogorov-Smirnov test. Quantitative variables were compared using Student’s t-test or the Mann-Whitney U test and X2 or Fisher’s exact test for categorical variables. Exploratory binary logistic regression models were developed using ICU admission or death as the dependent variable. Covariates included age, sex and those variables that were significantly associated the dependent variables in the bivariate analysis. The level of statistical significance was established as p<0.05.

Results

A total of 780 patients with a confirmed diagnosis of SARS-CoV-2 were identified; the patients were from 48 different cities and were treated at 4 clinics in the country. A total of 477 (61.2%) were male, and the median age was 57.0 years (interquartile range [IQR]: 45.0–68.0 years; range: 0–100 years). The distribution by age group can be seen in Table 1. A total of 4.9% (n = 38) of the patients had a health-related job. The 4.6% (n = 14/303) of women were pregnant.

Table 1. Sociodemographic, pharmacological, clinical and comorbidity variables among patients who survived and died, infected by SARS-CoV-2.

Characteristics Total Survivors Deceased p
n = 780 % n = 577 % n = 203 %
Sociodemographic
 Male 477 61.2 350 60.7 127 62.6 0.632
 Female 303 38.8 227 39.3 76 37.4
 Age, median (IQR) 57.0 (45.0–68.0) 53.0 (42.0–64.0) 67.0 (58.0–76.0) <0.001^
  <40 years 126 16.2 122 21.1 4 2.0 <0.001
  40–64 years 392 50.3 316 54.8 76 37.4 <0.001
  65–79 years 196 25.1 108 18.7 88 43.3 <0.001
  ≥80 years 66 8.5 31 5.4 35 17.2 <0.001
City of Attention
 Bogotá 306 39.2 247 42.8 59 29.1 0.001
 Cali 302 38.7 190 32.9 112 55.2 <0.001
 Pereira 100 12.8 76 13.2 24 11.8 0.621
 Popayan 72 9.2 64 11.1 8 3.9 0.002
Comorbidities
 Charlson index, median (IQR) 2 (0–3) 1 (0–2.5) 3 (2–5) <0.001^
  0 points 217 27.8 202 35.0 15 7.4 <0.001
  1–2 points 299 38.3 231 40.0 68 33.5 0.099
  3–4 points 166 21.3 97 16.8 69 34.0 <0.001
  ≥5 points 98 12.6 47 8.1 51 25.1 <0.001
 Arterial hypertension 299 38.3 184 31.9 115 56.7 <0.001
 Diabetes mellitus 160 20.5 106 18.4 54 26.6 0.013
 Obesity 132 16.9 92 15.9 40 19.7 0.219
 Chronic obstructive pulmonary disease 75 9.6 36 6.2 39 19.2 <0.001
 Hypothyroidism 61 7.8 45 7.8 16 7.9 0.97
 Chronic kidney disease 57 7.3 26 4.5 31 15.3 <0.001
 Tobacco use 53 6.8 37 6.4 16 7.9 0.474
 Dyslipidemia 35 4.5 26 4.5 9 4.4 0.966
 Heart failure 34 4.4 19 3.3 15 7.4 0.014
 Ischemic heart disease 26 3.3 8 1.4 18 8.9 <0.001
 Other comorbidities 160 20.5 98 17.0 62 30.5 <0.001
Medication history
 Antihypertensives and diuretics 249 31.9 163 28.2 86 42.4 <0.001
 Antidiabetic agents 117 15.0 80 13.9 37 18.2 0.134
 Analgesics 63 8.1 56 9.7 7 3.4 0.005
 Lipid-lowering drugs 57 7.3 39 6.8 18 8.9 0.321
 Thyroid hormone 49 6.3 36 6.2 13 6.4 0.934
Symptoms
 Cough 570 73.1 435 75.4 135 66.5 0.014
 Fever / chills 555 71.2 415 71.9 140 69.0 0.424
 Dyspnea 525 67.3 363 62.9 162 79.8 <0.001
 Fatigue 324 41.5 243 42.1 81 39.9 0.582
 Myalgias / arthralgias 226 29.0 185 32.1 41 20.2 0.001
 Headache 158 20.3 133 23.1 25 12.3 0.001
 Odynophagia 157 20.1 139 24.1 18 8.9 <0.001
 Constitutional symptoms 102 13.1 69 12.0 33 16.3 0.118
 Chest pain 100 12.8 74 12.8 26 12.8 0.995
 Diarrhea 89 11.4 71 12.3 18 8.9 0.185
Vital signs (on admission)
 Mean arterial pressure (mmHg), median (IQR) 93.3 (83.8–101.3) 93.3 (84.7–100.8) 93.7 (82.0–103.3) 0.805^
  <65 mmHg 13 1.7 4 0.7 9 4.4 0.002*
 Heart rate (beats/minute), median (IQR) 91.5 (80.0–108) 90.0 (80.0–106.0) 95.0 (80.0–110.0) 0.012^
  ≥ 100 beats/minute 289 38.1 197 35.5 92 45.3 0.014
 Temperature (°C), median (IQR) 36.5 (36.0–37.0) 36.5 (36.0–37.0) 36.5 (36.0–37.0) 0.430^
  > 38 ° C 47 6.3 35 6.4 12 6.0 0.843
 Respiratory rate (breaths/minute), median (IQR) 20.0 (18.0–24.0) 20 (18–22) 20 (19–25) <0.001^
  ≥ 24 breaths/minute 194 25.7 121 21.9 73 36.0 <0.001
 Oxygen saturation (%), median (IQR) 90.0 (84.0–94.0) 91.0 (85.0–94.0) 87.0 (77.0–92.0) <0.001^
  <90% 348 46.0 220 39.6 128 63.4 <0.001
Physical examination (upon admission)
 Body mass index (kg/m2), median (IQR) 27.1 (24.4–29.7) 27.3 (25.3–30.6) 26.7 (23.5–28.4) 0.014^
  ≥30.0 kg/m2 71 24.3 47 28.5 24 18.9 0.058
 Decreased breath sounds 169 21.7 113 19.6 56 27.6 0.017
 Crackles 157 20.1 104 18.0 53 26.1 0.013
 Rhonchi 89 11.4 58 10.1 31 15.3 0.044
 Intercostal retractions 57 7.3 32 5.5 25 12.3 0.001
 Wheezing 23 2.9 14 2.4 9 4.4 0.146
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IQR: Interquartile range;

* Fisher’s exact test;

^ Mann-Whitney U Test

The most common comorbidities were hypertension, diabetes mellitus, obesity and chronic obstructive pulmonary disease. The median age-adjusted Charlson comorbidity index was 2 points (IQR: 0–3 points), and 33.9% (n = 264) had a score of 3 or more (see Table 1). The symptoms most reported by patients were cough, fever and dyspnea. At the time of admission, 46.0% had an oxygen saturation <90%, 38.1% had a heart rate of 100 or higher, and 25.7% (n = 194) had a respiratory rate of 24 or higher. Among the findings on physical examination, the presence of decreased breath sounds and wheezing was highlighted (see Table 1).

Chest X-rays taken at admission showed abnormalities in 50.3% (n = 392) of patients, with infiltrates being the most frequent finding (n = 287; 36.8%); the predominant feature in CT scans was ground-glass opacity (n = 364; 46.7%). Table 2 describes the radiological and laboratory results.

Table 2. Laboratory and imaging studies at the time of initial care among patients who survived and died, infected by SARS-CoV-2.

Characteristics Total Survivors Deceased p
n = 780 % n = 577 % n = 203 %
Laboratory studies, median (IQR)
 Blood count
  Hemoglobin (g / dL) 14.4 (13.1–15.5) 14.6 (13.4–15.7) 13.8 (12.2–14.8) <0.001^
  Hematocrit (%) 42.6 (38.7–46.3) 43.2 (39.9–46.5) 40.6 (37.0–45.3) <0.001^
  Leukocytes (/ mm3) 8.840 (6.280–11.710) 8.155 (6.000–10.885) 9.980 (8.000–14.125) <0.001^
  Neutrophils (/ mm3) 6.870 (4.450–9.630) 6.235 (4.100–8.915) 8.650 (6.087–11.925) <0.001^
  Lymphocytes (/ mm3) 1.000 (730–1.400) 1.050 (792–1.500) 844 (600–1.240) <0.001^
  Platelets (mil / mm3) 248.0 (190.0–309.0) 254.0 (208.2–311.7) 225.0 (171.0–277.0) 0.001^
 Renal function
  Creatinine (mg / dL) 0.9 (0.7–1.1) 0.9 (0.7–1.1) 1.1 (0.8–1.6) <0.001^
  Urea nitrogen (mg / dL) 16.2 (12.5–23.7) 15.3 (12.1–21.7) 24.4 (17.0–36.9) <0.001^
 Liver function
  Total bilirubin (mg / dL) 0.57 (0.39–0.92) 0.52 (0.37–0.81) 0.65 (0.44–0.90) 0.008^
  Direct bilirubin (mg / dL) 0.31 (0.20–0.49) 0.26 (0.18–0.40) 0.40 (0.25–0.56) <0.001^
  Alanine aminotransferase (U / L) 43.4 (26.1–61.0) 38.8 (26.0–59.4) 42.9 (27.0–68.0) 0.377^
  Aspartate aminotransferase (U / L) 43.0 (29.6–61.0) 42.8 (28.2–61.0) 54.9 (33.0–85.0) 0.011^
  Lactic dehydrogenase (U / L) 367.0 (284.0–489.0) 359.0 (282.7–454.7) 458.0 (347.0–611.0) <0.001^
 Others
  C-reactive protein (mg / L) 130.5 (63.6–206.4) 116.9 (49.4–186.1) 166.5 (105.7–257.5) <0.001^
  Ferritin (ng / mL) 1010.5 (451.0–1906.7) 899.0 (408.0–1657.4) 1340.0 (555.3–2000.0) 0.004^
  D-dimer (μg / mL) 340.0 (20.0–648.0) 299.5 (1.763–558.5) 497.0 (283.0–1141.0) <0.001^
  Troponin I (ng / mL) 0.008 (0.004–0.019) 0.007 (0.004–0.011) 0.024 (0.008–0.055) <0.001^
  Prothrombin time (sec) 13.9 (10.6–15.3) 12.9 (10.2–15.2) 14.1 (10.9–15.4) 0.023^
Imaging studies
 Chest x-ray
  Abnormal findings 392 50.3 290 50.3 102 50.2 0.997
   Infiltrate 287 36.8 211 36.6 76 37.4 0.825
   Consolidation 94 12.1 67 11.6 27 13.3 0.525
   Groud-glass opacity 89 11.4 68 11.8 21 10.3 0.579
   Pleural effusion 26 3.3 18 3.1 8 3.9 0.575
   Atelectasis 23 2.9 18 3.1 5 2.5 0.634
   Air bronchogram 10 1.3 7 1.2 3 1.5 0.726*
 Chest Computed Tomography
  Abnormal findings 395 50.6 276 47.8 119 58.6 0.008
   Groud-glass opacity 364 46.7 250 43.3 114 56.2 0.002
   Consolidation 118 15.1 91 15.8 27 13.3 0.398
   Air bronchogram 81 10.4 38 6.6 43 21.2 <0.001
   Atelectasis 45 5.8 31 5.4 14 6.9 0.423
   Lymphadenopathy 38 4.9 29 5.0 9 4.4 0.736
   Bronchiectasis 35 4.5 21 3.6 14 6.9 0.054
   Interlobular septal thickening 28 3.6 12 2.1 16 7.9 <0.001
   Pleural thickening 27 3.5 19 3.3 8 3.9 0.664
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IQR: Interquartile range;

* Fisher’s exact test;

^ Mann-Whitney U Test

For the CURB-65 criteria, the median score was 1 (IQR:0–1), with the majority of patients scoring between 0 and 1 point (n = 608; 77.9%), followed by 2 points (n = 125; 16.0%) and 3 or more points (n = 47; 6.0%). A total of 54.9% (n = 428) of patients had severe pneumonia on admission, 46.5% (n = 363) had a high-risk NEWS 2 score, and the median APACHE II score for 175 patients was 10 (IQR: 8–17). The median overall hospital stay was 7 days (IQR:4–12). The 10.6% (n = 83) of the patients only required care in the emergency room; 83.2% (n = 649) required care in a general ward, with a median stay of 6 days (IQR:3–9), and 32.4% (n = 253) were admitted to the ICU, with a median stay of 8 days (IQR:4–14). A total of 68.2% (n = 532) of all patients presented some type of complication, in particular acute respiratory distress syndrome (n = 509; 65.3%), and 26.0% (n = 203) died. Table 3 shows the complications suffered by patients who survived and those who died.

Table 3. Complications among patients who survived and died, infected with SARS-CoV-2.

Characteristics Total Survivors Deceased p
n = 780 % n = 577 % n = 203 %
Complications 532 68.2 329 57.0 203 100.0 <0.001
 Acute respiratory distress syndrome 509 65.3 314 54.4 195 96.1 <0.001
 Admission to ICU 253 32.4 94 16.3 159 78.3 <0.001
 Respiratory failure 184 23.6 29 5.0 155 76.4 <0.001
 Acute kidney injury 171 21.9 46 8.0 125 61.6 <0.001
 Acidosis 103 13.2 26 4.5 77 37.9 <0.001
 Shock 89 11.4 10 1.7 79 38.9 <0.001
 Secondary infection 81 10.4 27 4.7 54 26.6 <0.001
 Sepsis 75 9.6 12 2.1 63 31.0 <0.001
 Kidney replacement therapy (dialysis) 64 8.2 10 1.7 54 26.6 <0.001
 Complications of mechanical ventilation 42 5.4 7 1.2 35 17.2 <0.001
 Arrhythmia 37 4.7 4 0.7 33 16.3 <0.001
 Acute heart injury 21 2.7 6 1.0 15 7.4 <0.001
 Coagulopathy 21 2.7 9 1.6 12 5.9 0.001
 Heart failure 19 2.4 3 0.5 16 7.9 <0.001*
 Delirium 15 1.9 5 0.9 10 4.9 0.001*
 Thromboembolism 5 0.6 2 0.3 3 1.5 0.114*
 Spontaneous pneumothorax 4 0.5 3 0.5 1 0.5 1.000*
 Cytokine-related syndrome 2 0.3 1 0.2 1 0.5 0.453*
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ICU: Intensive care unit.

* Fisher’s exact test.

A total of 674 (86.4%) patients required supplemental oxygen, especially through low-flow devices (n = 662; 84.9%), in particular, nasal cannulas or simple face masks (n = 630; 80.8%) and non-rebreathing face masks (n = 313; 40.1%), while high-flow devices were used for 30.9% (n = 241) of all patients, in particular, invasive mechanical ventilation (n = 203; 26.0%), venturi devices (n = 76; 9.7%) and noninvasive mechanical ventilation (n = 21; 2.7%). A total of 26.4% (n = 206) of all patients were placed in the prone position. Of the patients who required invasive mechanical ventilation, the median duration of intubation was 8 days (IQR:4–15; range: 0–66 days). The most commonly used drugs in this group of patients were antimicrobials (n = 633; 81.2%), anticoagulants (n = 614; 78.7%), and systemic corticosteroids (n = 462; 59.2%). Table 4 outlines the pharmacological treatment received by patients.

Table 4. Pharmacological management received during medical care among patients who survived and died, infected by SARS-CoV-2.

Characteristics Total Survivors Deceased p
n = 780 % n = 577 % n = 203 %
Antibiotics (without azithromycin) 633 81.2 436 75.6 197 97.0 <0.001
 Ampicillin sulbactam 502 64.4 360 62.4 142 70.0 0.053
 Clarithromycin 278 35.6 205 35.5 73 36.0 0.912
 Cefepime 124 15.9 39 6.8 85 41.9 <0.001
 Vancomycin 96 12.3 27 4.7 69 34.0 <0.001
 Ceftriaxone 90 11.5 61 10.6 29 14.3 0.154
 Meropenem 82 10.5 19 3.3 63 31.0 <0.001
Anticoagulants 614 78.7 424 73.5 190 93.6 <0.001
 Enoxaparin 577 74.0 411 71.2 166 81.8 0.003
 Unfractionated heparin 45 5.8 17 2.9 28 13.8 <0.001
 Dalteparin 7 0.9 1 0.2 6 3.0 0.002*
Antiulcer drugs 566 72.6 392 67.9 174 85.7 <0.001
Analgesics 501 64.2 378 65.5 123 60.6 0.208
 Non-opioid analgesics 473 60.6 363 62.9 110 54.2 0.029
 Opioid analgesics 96 12.3 53 9.2 43 21.2 <0.001
Systemic corticosteroids 462 59.2 321 55.6 141 69.5 0.001
 Dexamethasone 429 55.0 308 53.4 121 59.6 0.125
 Hydrocortisone 46 5.9 10 1.7 36 17.7 <0.001
 Methylprednisolone 22 2.8 11 1.9 11 5.4 0.009
 Prednisolone or prednisone 14 1.8 6 1.0 8 3.9 0.013*
Proposed COVID-19 therapy
 Azithromycin 257 32.9 169 29.3 88 43.3 <0.001
 Ivermectin 118 15.1 95 16.5 23 11.3 0.079
 Colchicine 81 10.4 57 9.9 24 11.8 0.435
 Antimalarials 33 4.2 19 3.3 14 6.9 0.028
  Hydroxychloroquine 19 2.4 12 2.1 7 3.4 0.293*
  Chloroquine 15 1.9 8 1.4 7 3.4 0.077*
 Lopinavir / ritonavir 14 1.8 5 0.9 9 4.4 0.003*
 Plasma 5 0.6 0 0.0 5 2.5 0.001*
 Tocilizumab 1 0.1 0 0.0 1 0.5 0.260
Inhaled bronchodilators and corticosteroids 319 40.9 249 43.2 70 34.5 0.031
Antihypertensives and diuretics 280 35.9 175 30.3 105 51.7 <0.001
Benzodiazepines (without midazolam) 216 27.7 51 8.8 165 81.3 <0.001
Sedatives 210 26.9 41 7.1 169 83.3 <0.001
 Midazolam 208 26.7 41 7.1 167 82.3 <0.001
 Fentanyl 197 25.3 38 6.6 159 78.3 <0.001
 Dexmedetomidine 104 13.3 31 5.4 73 36.0 <0.001
 Ketamine 104 13.3 24 4.2 80 39.4 <0.001
Antidiabetic agents 194 24.9 105 18.2 89 43.8 <0.001
Muscle relaxants 186 23.8 38 6.6 148 72.9 <0.001
Vasopressors and inotropics 180 23.1 29 5.0 151 74.4 <0.001
Antipsychotics 102 13.1 37 6.4 65 32.0 <0.001
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* Fisher’s exact test.

Patients in the city of Cali were significantly older and had higher NEWS 2 scores at admission, higher rates of severe pneumonia and a higher requirement for invasive mechanical ventilation than patients in other cities (see S1 Table).

Multivariate analysis

The binary logistic regression found that in the city of Cali, ischemic heart disease, chronic obstructive pulmonary disease, severe pneumonia, and each 1-point increase in NEWS 2 score increased the probability of being admitted to an ICU. No variables were found that reduced this risk (Table 5). Being 65 or older, each 1-point increase in the Charlson comorbidity index, presenting severe pneumonia, requiring ICU care and presenting complications such as acute respiratory distress syndrome and acute kidney failure were associated with a greater probability of death. There were also no variables that reduced this risk (Table 6).

Table 5. Binary logistic regression of variables associated with the probability of admission to the intensive care unit in patients with a diagnosis of SARS-CoV-2.

Characteristics p OR 95% CI
Lower Upper
Male sex 0.256 1.252 0.85 1.844
Age ≥65 years 0.39 1.21 0.784 1.867
Cali (city of residence) <0.001 3.153 2.149 4.625
Health related profession 0.536 1.47 0.435 4.971
Obesity 0.083 1.521 0.947 2.445
Ischemic heart disease 0.024 3.243 1.167 9.009
Diabetes mellitus 0.053 1.564 0.995 2.457
Chronic kidney disease 0.805 0.918 0.468 1.803
Chronic obstructive pulmonary disease 0.026 2.066 1.093 3.904
Arterial hypertension 0.338 1.229 0.806 1.875
Non-opioid analgesics 0.149 0.509 0.204 1.273
Severe pneumonia <0.001 9.865 5.995 16.232
NEWS2 score 0.044 1.087 1.002 1.179
Open in a new tab

NEWS2: National Early Warning Score 2. OR: Odds ratio. 95% CI: 95% confidence interval.

Table 6. Binary logistic regression of variables associated with the probability of dying in patients diagnosed with SARS-CoV-2.

Characteristics p OR 95% CI
Lower Upper
Male sex 0.187 0.718 0.439 1.175
Age ≥65 years <0.001 3.087 1.667 5.719
Cali (city of residence) 0.394 1.312 0.702 2.452
Charlson Comorbidity Index 0.046 1.164 1.002 1.351
Severe pneumonia 0.008 2.463 1.263 4.801
NEWS2 score 0.559 1.031 0.931 1.142
Admission to ICU <0.001 6.309 3.634 10.954
Antimalarials 0.075 0.407 0.151 1.096
Azithromycin 0.794 1.084 0.592 1.985
Corticosteroids 0.960 0.986 0.570 1.705
Systemic antibiotics 0.416 0.625 0.201 1.941
Acute kidney injury <0.001 6.966 4.116 11.788
Acute respiratory distress syndrome 0.007 3.448 1.408 8.445
Open in a new tab

NEWS2: National Early Warning Score 2. ICU: intensive care units. OR: Odds ratio. 95% CI: 95% confidence interval.

Discussion

The present study identified factors related to increasing the probability of ICU admission or death in a group of patients with confirmed SARS-CoV-2 treated in 4 cities in Colombia. The identification of these risk factors will allow intervention measures to be proposed and thus contribute to improving the prognosis of these patients [10].

The median age of patients with COVID-19 was similar to that found in other studies (56.0–72.0 years) [11–17], with a predominance of males, as also identified in most studies (51.9–62.0%) [11–13, 15–18], except in a cohort of patients in the USA where a higher proportion of females (55.9%) was described [14]. Regarding these sociodemographic variables, some studies have found that males have a higher risk of complications [14, 19, 20] and death [14, 18, 20]; those findings were not identified in this report, but our results are consistent with those described in other studies [15, 21, 22]. It was observed that as age increased, patients had a higher probability of dying, consistent with a large number of international publications [14, 17, 21–24] and local studies [11, 25], probably due to a higher disease burden [10].

The most frequent comorbidities in this cohort of patients were hypertension and diabetes mellitus, a finding that is consistent with those in other reports [11, 12, 14–17, 20, 22, 23]. These pathologies have been associated with a greater probability of presenting complications and severe forms of the disease [10, 26]; however, this was not the case in this study and in some previously published works [14, 23, 27]. However, ischemic heart disease and chronic obstructive pulmonary disease did increase the risk of complications requiring ICU admission, consistent with what was found by other authors [27–29]. Likewise, the probability of dying increased 16% for each 1-point increase in the Charlson comorbidity index, a finding similar to that documented in a group of patients in Spain (OR:1.23; 95%CI:1.15–1.32) [28] and in a cohort of patients in the USA; in those cohorts, mortality increased from 40 to 93% depending the scoring method [19], making it a useful tool to identify patients with a higher risk of mortality and therefore those who require closer clinical monitoring [30].

The clinical manifestations most described in the literature have been cough, fever and dyspnea [11, 14, 15, 17, 22, 23], as found in this analysis. With respect to the NEWS 2 score, which involves different clinical parameters, this report showed that a higher score was associated with a greater probability of requiring ICU care, a result that is consistent with a study conducted in Colombia (Bogotá), where the score was associated with a greater risk of disease severity (for each 1-point increase, (HR:1.15; 95%CI:1.03–1.28) [11]. Different studies have identified that this scale applied at hospital admission is a good predictor of severe disease, ICU admission and mortality in patients with COVID-19 [31, 32].

In addition, some laboratory test results have been associated with an increased risk of mortality [2, 9, 11, 15, 17, 19, 23, 33], such as elevated levels of creatinine [19, 33], C-reactive protein [15, 33], lactate dehydrogenase [11], transaminases [19], and D-dimer [17] or low levels of monocytes [15], lymphocytes [19], and albumin [19], among others [2, 9]. In this report, significant differences were found in paraclinical testing between patients who survived and those who died, similar to that reported in the literature [11, 17, 23] However, paraclinical testing was not included in the multivariate models because the data were not available for all patients. The radiological finding most frequently found was ground-glass opacification, consistent with what has been reported in the literature [11, 17, 27]; this finding is one of the typical characteristics on CT chest scans in the early phases of COVID-19 [34].

In this cohort of patients, more than half were initially classified as having serious disease, similar to that found in China (63.0%) [17], and higher than that previously published in Colombia (31.7%) [11]; serious disease is related to complications and mortality [11]. Among the complications, almost two-thirds of the patients presented with acute respiratory distress syndrome, which has also been found frequently in other studies but at various proportions (24.1%-90.0%) [7, 11, 14, 17, 23, 24]. In addition, 23.6% of the patients in this study progressed to respiratory failure, consistent with what was reported in a systematic review and meta-analysis (16.2%; 95%CI:0.4–43.3%) [9]. This clinical condition was also associated with an increased risk of death, consistent with what was found in Italy [20] but not in Spain [23]. Another complication that also presented a significant association with mortality was acute renal failure, a risk that was previously documented by Ferrando et al. in Spain (OR:2.46; 95%CI:1.62–3.74) [23].

While patients treated in the city of Cali were more likely to be admitted to the ICU, they were less likely to die. The latter is in line with what was found in a study conducted with data from the National Public Health Surveillance System (SIVIGILA) in several cities of Colombia; patients in Valle del Cauca had a lower risk of mortality than did patients in the rest of the country (relative risk:0.81; 95%CI:0.73–0.90; p<0.001) [18]. With respect to the higher risk of admission to the ICU in Cali, this is probably because in Cali, the patients were older and had a higher NEWS 2 score and there were more severe cases on admission, factors that led to many of these patients requiring ICU care [11, 14, 19, 20, 31, 32].

Regarding the management received by these patients, just over a quarter required invasive mechanical ventilation, a finding that is consistent with other reports (12.2%-23.0%) [12, 13, 16, 17], and almost one-third required ICU care, a proportion that was similar to that found in other countries (26.0%-39.7%) [13, 14, 17]. In cohorts of hospitalized patients in Spain, Iran and Italy, most were managed with antimalarials [23, 33, 35] or azithromycin [23], but in this analysis, these drugs were used in less than one-third of patients; systemic corticosteroids were used similarly to what was published in other studies (60.9%-76.3%) [23, 35]. In this report, none of the therapies were associated with improving the prognosis of patients, a result that is consistent with those reported in several studies [21, 28]. The current evidence (at the time of writing this manuscript) indicates that antimalarials, azithromycin and ivermectin do not reduce complications or mortality in patients with COVID-19 [8, 36, 37]; however, a randomized clinical trial (RECOVERY) showed that the use of dexamethasone reduced the risk of death by 36% in patients who received invasive mechanical ventilation and by 18% among patients who required supplemental oxygen [38]. Notably, this drug may be associated with a higher frequency of bacterial infections and electrolyte disorders [39].

Observational studies have certain limitations that should be taken into account when interpreting the results. In this study, because the information was only obtained from the data recorded from a group of patients from 4 tertiary care clinics located in different cities, the findings may not extrapolate to all types of health care institutions or to all regions of the country. In addition, for some variables, especially those related to clinical laboratory tests, information was not available for all patients; therefore, the inclusion of these types of variables in the multivariate analyses was limited.

Conclusions

Based on these findings, it can be concluded that having some comorbidities, such as ischemic heart disease or chronic obstructive pulmonary disease, prior to the diagnosis of COVID-19, suffering from severe pneumonia, each 1-point increase in NEWS 2 score and being treated in the city of Cali increased the probability of being admitted to an ICU. Advanced age, especially >65 years, each 1-point increase in the Charlson comorbidity index, severe pneumonia, complications, such as acute respiratory distress syndrome or acute renal failure, and requiring ICU care increased the probability of death. No variables were identified that would reduce the risk of requiring ICU care or of dying. These results can be useful for clinicians who care for patients with COVID-19 because the recognition of these variables can be used to improve the quality of care.

Supporting information

S1 Table. Comparison of some sociodemographic and clinical variables among the cities of care of a group of patients infected by SARS-CoV-2, Colombia.

(DOCX)

Click here for additional data file. (15.7KB, docx)

Acknowledgments

Declarations

Author responsibility. The corresponding author confirm full access to all data in the study and final responsibility.

Data Availability

Data and material available at protocolos.io (dx.doi.org/10.17504/protocols.io.bud5ns86).

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. China Novel Coronavirus Investigating and Research Team. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382(8):727–733. doi: 10.1056/NEJMoa2001017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, et al. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect. 2020;81(2):e16–e25. doi: 10.1016/j.jinf.2020.04.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Guo YR, Cao QD, Hong ZS, Tan YY, Chen SD, Jin HJ, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak—an update on the status. Mil Med Res. 2020;7(1):11. doi: 10.1186/s40779-020-00240-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Johns Hopkins University Medicine. Coronavirus Resource Center. (Access 09 Jan 2021). https://coronavirus.jhu.edu/map.html.
  • 5.Instituto Nacional de Salud. Corononavirus (Covid-19) en Colombia. Reporte 09 de Febrero de 2021 (Internet). (Access 09 Jan 2021) https://www.ins.gov.co/Noticias/Paginas/Coronavirus.aspx.
  • 6.Serafim RB, Póvoa P, Souza-Dantas V, Kalil AC, Salluh JIF. Clinical course and outcomes of critically ill patients with COVID-19 infection: a systematic review. Clin Microbiol Infect. 2021;27(1):47–54. doi: 10.1016/j.cmi.2020.10.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Tan E, Song J, Deane AM, Plummer MP. Global Impact of Coronavirus Disease 2019 Infection Requiring Admission to the ICU: A Systematic Review and Meta-analysis. Chest. 2021;159(2):524–536. doi: 10.1016/j.chest.2020.10.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Saavedra Trujillo CH. Consenso colombiano de atención, diagnóstico y manejo de la infección por SARS-COV-2/COVID-19 en establecimientos de atención de la salud. Recomendaciones basadas en consenso de expertos e informadas en la evidencia. Infectio. 2020;24:186–261. [Google Scholar]
  • 9.Zhang JJY, Lee KS, Ang LW, Leo YS, Young BE. Risk Factors for Severe Disease and Efficacy of Treatment in Patients Infected With COVID-19: A Systematic Review, Meta-Analysis, and Meta-Regression Analysis. Clin Infect Dis. 2020;71(16):2199–2206. doi: 10.1093/cid/ciaa576 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Zaki N, Alashwal H, Ibrahim S. Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: A systematic review. Diabetes Metab Syndr. 2020;14(5):1133–1142. doi: 10.1016/j.dsx.2020.07.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Motta JC, Novoa DJ, Gómez CC, Moreno JM, Vargas L, Pérez J, et al. Factores pronósticos en pacientes hospitalizados con diagnóstico de infección por SARS-CoV-2 en Bogotá, Colombia. Biomédica. 2020;40(Supl. 2):116–30. doi: 10.7705/biomedica.5764 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Karagiannidis C, Mostert C, Hentschker C, Voshaar T, Malzahn J, Schillinger G, et al. Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study. Lancet Respir Med. 2020;8(9):853–862. doi: 10.1016/S2213-2600(20)30316-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ranzani OT, Bastos LSL, Gelli JGM, Marchesi JF, Baião F, Hamacher S, et al. Characterisation of the first 250,000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. Lancet Respir Med. 2021;9(4):407–418. doi: 10.1016/S2213-2600(20)30560-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Suleyman G, Fadel RA, Malette KM, Hammond C, Abdulla H, Entz A, et al. Clinical Characteristics and Morbidity Associated With Coronavirus Disease 2019 in a Series of Patients in Metropolitan Detroit. JAMA Netw Open. 2020;3(6):e2012270. doi: 10.1001/jamanetworkopen.2020.12270 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Vanhems P, Gustin MP, Elias C, Henaff L, Dananché C, Grisi B, et al. COVID-Outcomes-HCL Consortium. Factors associated with admission to intensive care units in COVID-19 patients in Lyon-France. PLoS One. 2021;16(1):e0243709. doi: 10.1371/journal.pone.0243709 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW; et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020;323(20):2052–2059. doi: 10.1001/jama.2020.6775 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–1062. doi: 10.1016/S0140-6736(20)30566-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Malagon-Rojas J, Ibañez Pinilla E, Parra Barrera EL, Toloza Y, Álvarez S, Mercado M. Analysis of COVID-19 Mortality and Survival in Colombia: A prospective Cohort Study. Infectio. 2021;25:176. [Google Scholar]
  • 19.Ioannou GN, Locke E, Green P, Berry K, O’Hare AM, Shah JA, et al. Risk Factors for Hospitalization, Mechanical Ventilation, or Death Among 10 131 US Veterans With SARS-CoV-2 Infection. JAMA Netw Open. 2020;3(9):e2022310. doi: 10.1001/jamanetworkopen.2020.22310 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, et al. COVID-19 Lombardy ICU Network. Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA. 2020;323(16):1574–1581. doi: 10.1001/jama.2020.5394 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Larsson E, Brattström O, Agvald-Öhman C, Grip J, Campoccia Jalde F, Strålin K, et al. Karolinska Intensive Care COVID-19 Study Group. Characteristics and outcomes of patients with COVID-19 admitted to ICU in a tertiary hospital in Stockholm, Sweden. Acta Anaesthesiol Scand. 2021;65(1):76–81. doi: 10.1111/aas.13694 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Thomson RJ, Hunter J, Dutton J, Schneider J, Khosravi M, Casement A, et al. Clinical characteristics and outcomes of critically ill patients with COVID-19 admitted to an intensive care unit in London: A prospective observational cohort study. PLoS One. 2020;15(12):e0243710. doi: 10.1371/journal.pone.0243710 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Ferrando C, Mellado-Artigas R, Gea A, Arruti E, Aldecoa C, Bordell A, et al. de la Red de UCI Española para COVID-19. Patient characteristics, clinical course and factors associated to ICU mortality in critically ill patients infected with SARS-CoV-2 in Spain: A prospective, cohort, multicentre study. Rev Esp Anestesiol Reanim. 2020;67(8):425–437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Cervantes J, Sureen A, Galura G, Dodoo C, Dwivedi AK, Bashashati M, et al. Factors associated with COVID-19 severity and mortality among Hispanic patients living on the USA-Mexico border. J Investig Med. 2021:jim-2020-001667. doi: 10.1136/jim-2020-001667 [DOI] [PubMed] [Google Scholar]
  • 25.Malagón-Rojas J., Ibañez E, Parra E, Toloza-Perez Y, Alvarez S, Mercado M. Analysis of COVID-19 Mortality and Survival in Colombia: A prospective Cohort Study. Infectio 2021; 25(3): 176–181. [Google Scholar]
  • 26.Nandy K, Salunke A, Pathak SK, Pandey A, Doctor C, Puj K, et al. Coronavirus disease (COVID-19): A systematic review and meta-analysis to evaluate the impact of various comorbidities on serious events. Diabetes Metab Syndr. 2020;14(5):1017–1025. doi: 10.1016/j.dsx.2020.06.064 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Sim BLH, Chidambaram SK, Wong XC, Pathmanathan MD, Peariasamy KM, Hor CP, et al. Clinical characteristics and risk factors for severe COVID-19 infections in Malaysia: A nationwide observational study. Lancet Reg Health West Pac. 2020;4:100055. doi: 10.1016/j.lanwpc.2020.100055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Gómez Antúnez M, Muiño Míguez A, Bendala Estrada AD, Maestro de la Calle G, Monge Monge D, Boixeda R, et al. SEMI-COVID-19 Network. Clinical Characteristics and Prognosis of COPD Patients Hospitalized with SARS-CoV-2. Int J Chron Obstruct Pulmon Dis. 2021;15:3433–3445. doi: 10.2147/COPD.S276692 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Cordero A, Santos García-Gallego C, Bertomeu-González V, Fácila L, Rodríguez-Mañero M, Escribano D, et al. Mortality associated with cardiovascular disease in patients with COVID-19. REC: CardioClinics. 2021;56(1):30–8. [Google Scholar]
  • 30.Mancusi C, Grassi G, Borghi C, Ferri C, Muiesan ML, Volpe M, et al. SARS-RAS Investigator Group. Clinical Characteristics and Outcomes of Patients with COVID-19 Infection: The Results of the SARS-RAS Study of the Italian Society of Hypertension. High Blood Press Cardiovasc Prev. 2021;28(1):5–11. doi: 10.1007/s40292-020-00429-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Gidari A, De Socio GV, Sabbatini S, Francisci D. Predictive value of National Early Warning Score 2 (NEWS2) for intensive care unit admission in patients with SARS-CoV-2 infection. Infect Dis (Lond). 2020;52(10):698–704. doi: 10.1080/23744235.2020.1784457 [DOI] [PubMed] [Google Scholar]
  • 32.Myrstad M, Ihle-Hansen H, Tveita AA, Andersen EL, Nygård S, Tveit A, et al. National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19—a prospective cohort study. Scand J Trauma Resusc Emerg Med. 2020;28(1):66. doi: 10.1186/s13049-020-00764-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Alamdari NM, Afaghi S, Rahimi FS, Tarki FE, Tavana S, Zali A, et al. Mortality Risk Factors among Hospitalized COVID-19 Patients in a Major Referral Center in Iran. Tohoku J Exp Med. 2020;252(1):73–84. doi: 10.1620/tjem.252.73 [DOI] [PubMed] [Google Scholar]
  • 34.Hu Q, Guan H, Sun Z, Huang L, Chen C, Ai T, et al. Early CT features and temporal lung changes in COVID-19 pneumonia in Wuhan, China. Eur J Radiol. 2020;128:109017. doi: 10.1016/j.ejrad.2020.109017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Loffi M, Piccolo R, Regazzoni V, Di Tano G, Moschini L, Robba D, et al. Coronary artery disease in patients hospitalised with Coronavirus disease 2019 (COVID-19) infection. Open Heart. 2020;7(2):e001428. doi: 10.1136/openhrt-2020-001428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Castaneda-Sabogal A, Chambergo Michilot D, Toro-Huamanchumo C, Silva-Rengifo C, Gonzales-Zamora J, Barboza J. Outcomes of Ivermectin in the treatment of COVID-19: a systematic review and meta-analysis 2021.
  • 37.Khani E, Khiali S, Entezari-Maleki T. Potential COVID-19 Therapeutic Agents and Vaccines: An Evidence-Based Review. J Clin Pharmacol. 2021;61(4):429–460. doi: 10.1002/jcph.1822 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.RECOVERY Collaborative Group, Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L, et al. Dexamethasone in Hospitalized Patients with Covid-19. N Engl J Med. 2021;384(8):693–704. doi: 10.1056/NEJMoa2021436 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Yang Z, Liu J, Zhou Y, Zhao X, Zhao Q, Liu J. The effect of corticosteroid treatment on patients with coronavirus infection: a systematic review and meta-analysis. J Infect. 2020;81(1):e13–e20. doi: 10.1016/j.jinf.2020.03.062 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Tai-Heng Chen

13 Jul 2021

PONE-D-21-18267

Factors associated with admission to the intensive care unit and mortality in patients with COVID-19, Colombia

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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: Yes

Reviewer #2: Yes

**********

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: Authors have submitted a manuscript of significant importance. It is essential to present COVID-19 related data that originate outside developed countries since there is obvious and dramatic disparity in availability and quality if patient care between developed and developing countries. I recommend acceptance after minor but careful revision. Specific comments are listed below.

1. Reconsider keywords.

2. In Introduction: The first paragraph is redundant. Overall , Introduction should be shorter and more to the point.

3. A phrase „behavior of the infection“ should be reconsidered.

4. In Methods: A sentence „Subjects of any age, sex and city of residence were selected between March 6 and August 31, 2020.” should be rephrased to clearly state if all patients admitted to hospital during this time frame were screened for inclusion.

5. In Methods: Which CXR or CT scans were used? The ones on admission to hospital or the ones with the worst scores. COVID-19 is fast evolving and repeated CXRs and CT scans are a necessity.

6. Line 112: symptoms and signs should be listed in methods. In fact, list of all variables shown in Table 1 and 2 should be listed in methods. Those variables that are not shown in Results and used for statistical analysis should be omitted.

7. Line 118: „in general“ should be substituted for „from the hospital“.

8. Line 130/131: Were there any patients with tracheotomy?

9. Line 160/161: Sentence „among the females.....were pregnant“ should definitely undergo respectful rephrasing.

10. Line 199: „ days of intubation“ referes to duration of intubation or days between hospital admission and intubation?

11. Tables 1 to 4 show differences between survivors and non survivors. Somehow, we have „jumped“ from there to prediction of ICU admission. Steps before prediction modeling should be shown as well, process of selection of variables included in the prediction model should be clear. On the same note, it is not clear how selection of variables included in the mortality prediction was performed.

Reviewer #2: I would like to thank the editors of Plos One for giving me the opportunity to review the manuscript “factors associated with admission to the intensive care unit and mortality in patients with COVID-19 in Colombia”.

In this observational cohort study, the authors described the characteristics of 780 patients admitted to 4 clinics for COVID-19 in Colombia between march 6 and august 31, 2020 and tried to identify factor associated with death or ICU admission. The authors confirmed previous risk factors for poor outcomes described in much larger cohorts, including from south America. They also identified that, at the time of the study, patients from Cali had poorer outcomes.

This report appears now as completely anachronic since both viruses and treatments have profoundly changed. The study provides no new information that can be relevant for other caregivers.

Another major limitation of this study is that authors provided comparison between survivors and deceased patients (they report in the methods that they followed-up patient until death…) whereas the main result (as indicated in the title) was the factors associated with either ICU admission or death. In multivariate analysis, the found that coming from Cali, COPD and severe pneumoniae were associated with ICU admission or death. I really don’t understand how this finding may “greatly contribute to improving the prognosis of these patients” as concluded by the authors.

**********

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Reviewer #1: Yes: Suzana Bojic

Reviewer #2: No

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PLoS One. 2021 Nov 19;16(11):e0260169. doi: 10.1371/journal.pone.0260169.r002

Author response to Decision Letter 0

28 Jul 2021

Pereira, July 23 of 2021

Response to reviewers

Journal: PLOS ONE

Manuscript ID: PONE-D-21-18267

Title: Factors associated with admission to the intensive care unit and mortality in patients with COVID-19, Colombia

Thank you for the review. Here, we answer the reviewers’ comments point by point.

Comments to the Author

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

Reviewer #1: Yes

Reviewer #2: No

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

Reviewer #1: I Don't Know

Reviewer #2: No

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

Reviewer #1: Yes

Reviewer #2: No

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

Reviewer #1: Yes

Reviewer #2: Yes

5. Review Comments to the Author

Reviewer #1: Authors have submitted a manuscript of significant importance. It is essential to present COVID-19 related data that originate outside developed countries since there is obvious and dramatic disparity in availability and quality if patient care between developed and developing countries. I recommend acceptance after minor but careful revision. Specific comments are listed below.

Response/ Thank you

1. Reconsider keywords.

Response/ we have adjusted the keywords

2. In Introduction: The first paragraph is redundant. Overall, Introduction should be shorter and more to the point.

Response/ the introduction has been shortened and some phrases have been simplified.

3. A phrase „behavior of the infection“ should be reconsidered.

Response/ The phrase was simplified.

4. In Methods: A sentence „Subjects of any age, sex and city of residence were selected between March 6 and August 31, 2020.” should be rephrased to clearly state if all patients admitted to hospital during this time frame were screened for inclusion.

Response/ The phrase has been adjusted.

5. In Methods: Which CXR or CT scans were used? The ones on admission to hospital or the ones with the worst scores. COVID-19 is fast evolving and repeated CXRs and CT scans are a necessity.

Response/ We report these images on admission. We have now clarified this in the methods section.

6. Line 112: symptoms and signs should be listed in methods. In fact, list of all variables shown in Table 1 and 2 should be listed in methods. Those variables that are not shown in Results and used for statistical analysis should be omitted.

Response/ The variables shown in tables / results are now also consistent with those in the methods section.

7. Line 118: „in general“ should be substituted for „from the hospital“.

Response / Corrected

8. Line 130/131: Were there any patients with tracheotomy?

Response/ This information is not available, we did not searched this variable.

9. Line 160/161: Sentence „among the females.....were pregnant“ should definitely undergo respectful rephrasing.

Response/ we rephrased the sentence.

10. Line 199: „ days of intubation“ referes to duration of intubation or days between hospital admission and intubation?

Response/ This refers to “duration”. The phrase has been adjusted accordingly.

11. Tables 1 to 4 show differences between survivors and non survivors. Somehow, we have „jumped“ from there to prediction of ICU admission. Steps before prediction modeling should be shown as well, process of selection of variables included in the prediction model should be clear. On the same note, it is not clear how selection of variables included in the mortality prediction was performed.

Response/ We included survival status in the tables in order to show the distribution of the variables among survivors and deceased. The step-by-step process for variable selection is not shown explicitly, but we have now changed the sentence in methods in this regard. We now explain that “Covariates included age, sex and those variables that were significantly associated the dependent variables in the bivariate analysis”. We have also indicated that this is an exploratory analysis, not a predictive one.

Reviewer #2: I would like to thank the editors of Plos One for giving me the opportunity to review the manuscript “factors associated with admission to the intensive care unit and mortality in patients with COVID-19 in Colombia”.

In this observational cohort study, the authors described the characteristics of 780 patients admitted to 4 clinics for COVID-19 in Colombia between march 6 and august 31, 2020 and tried to identify factor associated with death or ICU admission. The authors confirmed previous risk factors for poor outcomes described in much larger cohorts, including from south America. They also identified that, at the time of the study, patients from Cali had poorer outcomes.

This report appears now as completely anachronic since both viruses and treatments have profoundly changed. The study provides no new information that can be relevant for other caregivers.

Response/ Thank you, but we believe this data are still of importance, especially in the colombian context.

Another major limitation of this study is that authors provided comparison between survivors and deceased patients (they report in the methods that they followed-up patient until death…) whereas the main result (as indicated in the title) was the factors associated with either ICU admission or death.

Response/ We indeed reported both outcomes (i.e: table 5 and 6 show multivariate analysis considering each of these outcomes). We did not use a composite outcome of ICU admission + death.

In multivariate analysis, the found that coming from Cali, COPD and severe pneumoniae were associated with ICU admission or death. I really don’t understand how this finding may “greatly contribute to improving the prognosis of these patients” as concluded by the authors.

Response/ The phrase has been adjusted.

Thank you, we will be aware of new observations.

The authors

Attachment

Submitted filename: Reviewer Answers Viral infections Antibiotics (2).docx

Click here for additional data file. (18.6KB, docx)

Decision Letter 1

Wenbin Tan

4 Nov 2021

Factors associated with admission to the intensive care unit and mortality in patients with COVID-19, Colombia

PONE-D-21-18267R1

Dear Dr. Machado-Alba,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Wenbin Tan

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #3: All comments have been addressed

**********

Acceptance letter

Wenbin Tan

9 Nov 2021

PONE-D-21-18267R1

Factors associated with admission to the intensive care unit and mortality in patients with COVID-19, Colombia

Dear Dr. Machado-Alba:

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.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Wenbin Tan

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Comparison of some sociodemographic and clinical variables among the cities of care of a group of patients infected by SARS-CoV-2, Colombia.

    (DOCX)

    Click here for additional data file. (15.7KB, docx)
    Attachment

    Submitted filename: Reviewer Answers Viral infections Antibiotics (2).docx

    Click here for additional data file. (18.6KB, docx)

    Data Availability Statement

    Data and material available at protocolos.io (dx.doi.org/10.17504/protocols.io.bud5ns86).

    Articles from PLoS ONE are provided here courtesy of PLOS

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