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Revista Brasileira de Terapia Intensiva

AMIB - Associação de Medicina Intensiva Brasileira

OFFICIAL JOURNAL OF THE ASSOCIAÇÃO BRASILEIRA DE MEDICINA INTENSIVA AND THE SOCIEDADE PORTUGUESA DE CUIDADOS INTENSIVOS

ISSN: 0103-507X
Online ISSN: 1982-4335

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Taniguchi LU, Azevedo LCP, Bozza FA, Cavalcanti AB, Ferreira EM, Carrara FSA, et al. Disponibilidade de recursos para tratamento da sepse no Brasil: uma amostra aleatória de instituições brasileiras. Rev Bras Ter Intensiva. 2019;31(2):193-201

 

 

2019 2019;31(2):193-201
ORIGINAL ARTICLES

10.5935/0103-507X.20190033

Availability of resources to treat sepsis in Brazil: a random sample of Brazilian institutions

Disponibilidade de recursos para tratamento da sepse no Brasil: uma amostra aleatória de instituições brasileiras

Leandro Utino Taniguchi1,2,3, Luciano Cesar Pontes de Azevedo1,2,3,4, Fernando Augusto Bozza3,4,5,6, Alexandre Biasi Cavalcanti3,4,7, Elaine Maria Ferreira4, Fernanda Sousa Angotti Carrara4, Juliana Lubarino Sousa4, Reinaldo Salomão4,8, Flávia Ribeiro Machado3,4,9

1 Emergency Medicine Discipline, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo - São Paulo (SP), Brazil.
2 Hospital Sirio-Libanês - São Paulo (SP), Brazil.
3 Brazilian Research in Intensive Care Network - São Paulo (SP), Brazil.
4 Instituto Latino Americano da Sepse - São Paulo (SP), Brazil.
5 Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz - Rio de Janeiro (RJ), Brazil.
6 Instituto D’Or de Pesquisa e Ensino - Rio de Janeiro (RJ), Brazil.
7 Research Institute, HCor-Hospital do Coração - São Paulo (SP), Brazil.
8 Infectious Disease Department, Universidade Federal de São Paulo - São Paulo (SP), Brazil.
9 Anesthesiology, Pain, and Intensive Care Department, Universidade Federal de São Paulo - São Paulo (SP), Brazil.

Conflicts of interest: None.

Responsible editor: Pedro Póvoa

Author contributions

L.U. Taniguchi, L.C.P. Azevedo, F.A. Bozza, A.B. Cavalcanti, E.M. Ferreira, F.S.A. Carrara, J.L. Souza, R. Salomão and F.R. Machado contributed substantially to the study design, data analysis and interpretation, and the writing and final approval of the manuscript. L.U. Taniguchi and L.C.P. Azevedo contributed equally to this work.

Submitted on November 22, 2018
Accepted on February 04, 2019

Corresponding author: Leandro Utino Taniguchi, Emergency Medicine Discipline, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, Avenida Doutor Enéas de Carvalho Aguiar, 255, Sala 5.023, Zip code: 05403-000 - São Paulo (SP), Brazil, E-mail: leandrout@hotmail.com

 

Abstract

OBJECTIVE: To characterize resource availability from a nationally representative random sample of intensive care units in Brazil.
METHODS: A structured online survey of participating units in the Sepsis PREvalence Assessment Database (SPREAD) study, a nationwide 1-day point prevalence survey to assess the burden of sepsis in Brazil, was sent to the medical director of each unit.
RESULTS: A representative sample of 277 of the 317 invited units responded to the resources survey. Most of the hospitals had fewer than 500 beds (94.6%) with a median of 14 beds in the intensive care unit. Providing care for public-insured patients was the main source of income in two-thirds of the surveyed units. Own microbiology laboratory was not available for 26.8% of the surveyed intensive care units, and 10.5% did not always have access to blood cultures. Broad spectrum antibiotics were not always available in 10.5% of surveyed units, and 21.3% could not always measure lactate within three hours. Those institutions with a high resource availability (158 units, 57%) were usually larger and preferentially served patients from the private health system compared to institutions without high resource availability. Otherwise, those without high resource availability did not always have broad-spectrum antibiotics (24.4%), vasopressors (4.2%) or crystalloids (7.6%).
CONCLUSION: Our study indicates that a relevant number of units cannot perform basic monitoring and therapeutic interventions in septic patients. Our results highlight major opportunities for improvement to adhere to simple but effective interventions in Brazil.

Keywords: Critical care; Health resources; Sepsis/epidemiology; Therapeutics; Epidemiological monitoring; Brazil/epidemiology; Developing countries; Intensive care units.

 

INTRODUCTION

Sepsis is a global health priority, as recently stated by the World Health Organization.(1,2) Current extrapolation based on a recent systematic review estimates 31.5 million cases of sepsis per year worldwide, with a potential of 5.3 million deaths. However, this extrapolation was based on data from high-income countries.(3) Since more than 80% of the world's population lives in low- and middle-income countries (LMICs), where resource limitations are frequent, the lethality rates are likely much higher.(1,4,5) The lack of reliable data on resource availability from LMICs is also noteworthy.(6) Although some information is available,(7-10) these studies are largely single-center descriptions or questionnaire-based surveys without random sampling, which might induce selection bias.

Brazil is a middle-income country according to the World Bank(11) with an estimated population of approximately 209 million people;(12) some data suggest an increase in sepsis-related deaths from 2002 to 2010 in Brazil.(13) The Sepsis PREvalence Assessment Database study (SPREAD), a nationwide 1-day point prevalence survey of Brazilian intensive care units (ICU), observed an ICU sepsis incidence of 36.3 cases per 1000 patient-days and an alarming hospital mortality of 55.7%. Low resource availability was independently associated with mortality (odds ratio 1.67, p = 0.045).(14) Since this survey generated a nationally representative random sample from Brazilian ICUs with a description of institution infrastructure, resource availability, and ICU organizational aspects from participating units, this information is more representative than previous small convenience cohorts.(15,16)

Thus, we performed a post hoc analysis of the SPREAD database to characterize and compare the resource availability of participating units. Patient characterization and outcomes were described in the original publication.(14)

METHODS

The SPREAD study was conducted as a 1-day, prospective, point prevalence study to assess the epidemiology of sepsis in adult ICUs in Brazil.(14) A stratified random sample of all Brazilian adult ICUs was generated from the Associação de Medicina Intensiva Brasileira (AMIB) 2010 Census.(17) It comprised 2,623 ICUs with 28,849 beds. After excluding neonatal and pediatric ICUs, cardiac care units, and burn units, a list of 1,690 ICUs and 19,316 eligible beds remained.

Our sampling method is explained in the original publication.(14) Briefly, we created similarly sized strata, each composed of 100 - 500 ICU beds to enhance the representativeness of our random selection of ICUs. Based on the AMIB list, we produced a sampling frame initially stratified by geographic region and size of the cities (considering the location, whether in capital cities or the countryside). Each stratum was then stratified by the hospitals' main source of income (serving public or privately insured individuals) and ICU size (ten or fewer beds versus more than ten beds), finally generating 40 strata. We applied the "randomize" (RAND) function in Excel 2010, which generates random numbers for ICUs within each stratum and sequentially contacted their medical directors by telephone and email, inviting them to participate in the study. This study was approved by the research ethics committee at the coordinating center (Universidade Federal de São Paulo, Brazil) under the number CAAE: 04719512.0.1001.5505. Informed consent was waived because of the observational nature of the study.

Participants and survey instrument

We assessed the ICU organizational factors and institution resource availability through a self-reported, questionnaire-based web survey (Supplementary material). The medical director of each ICU answered the questionnaire before study entry and patient data collection. No financial incentive to complete the survey or to participate in the SPREAD study was offered.

The questionnaire was designed by the Steering Committee of the SPREAD study and contained 97 items, which were grouped into eight main categories (general information, hospital facilities, use of clinical protocols and availability of drugs, monitoring tools, laboratory exams, equipment and disposables). The "general information" section had two open-ended responses ('number of hospital beds' and 'number of ICU beds' in the institution), which were later categorized by the study investigators. The responses were classified as 'yes', 'no' and 'I don't know' for the "hospital facilities" section; 'yes, a managed protocol', 'yes, but not managed', 'no' and 'I don't know' for the "clinical protocols" section; and 'always', 'most of the time', 'in the minority of times', 'never', and 'I don't know' for the other sections. No missing variables were allowed. To assess the most relevant resources, the Steering Committee selected eight items using an informal Delphi process before performing any analyses, under the premise that they would be required to comply with the Surviving Sepsis Campaign 6-h bundle.(18) These eight items were: blood gas analysis within 3 hours; lactate results within 3 hours; blood, urine and tracheal aspirate (quantitative or qualitative) cultures; antibiotics both for gram-negative (a third-generation cephalosporin plus carbapenems or piperacillin/tazobactam) and gram-positive coverage (vancomycin, teicoplanin or linezolid); crystalloids; noradrenaline; central venous catheter (single or double lumen); and availability for central venous pressure measurement.

Study variables and data analysis

Since previous literature(19) and data from the SPREAD(14) study suggested that compliance with the 6-h bundle was associated with lower hospital mortality, we categorized the units according to the availability of all eight items ('high resource availability' when all 8 items were always available and 'without high resource availability' when one or more of the 8 items were not always available). For the analysis, we considered the units as having the resource available only when the answer was 'always'.(7) We also compared the microbiology analysis resource availability and the possibility to administer broad-spectrum antibiotics (defined as antibiotics for both gram-negative and gram-positive coverage as defined in the 8-item section). The possibility to adhere to the Surviving Sepsis Campaign recommendations labeled as 'strong' and the recent 1-h bundle were evaluated.(20)

Continuous data are presented as the median (25th - 75th percentile) and were compared using the Mann-Whitney U test. Categorical variables are presented as counts and rates or percentages and were compared with the chi-squared test. P-values < 0.05 were considered statistically significant. The software Statistical Package for Social Science (SPSS), version 20 (SPSS Inc., Chicago, IL, USA) was used for the statistical analysis.

RESULTS

Of the 368 contacted ICUs, 317 were eligible and 13 (4%) refused to participate. Of the 317 eligible units, 277 (87%) answered the resources survey and are further described in the present publication. Most of the hospitals were small- to medium-sized (262 hospitals, 94.6%) with a median of 14 (9 - 30) ICU beds. In two-thirds of hospitals, the main source of income was the care for public-insured patients (169 ICUs, 61%). The geographic distribution of participating institutions paralleled the Brazilian population distribution among regions. The nurse/patient ratio was 0.13 (0.10 - 0.19), and the nurse technician/patient ratio was 0.5 (0.5 - 0.5). Although most hospitals had emergency departments (247 hospitals, 89.5%) and operating rooms (274 hospitals, 98.9%), only 73.2% had their own microbiology laboratory, and almost half lacked their own blood bank (Table 1). Twenty-nine units (10.5%) did not always have the possibility to administer broad-spectrum antibiotics, nine (3.2%) did not always have access to crystalloids and five (1.8%) did not always have vasopressors available (neither norepinephrine nor dopamine) (Table 2). In twenty-nine institutions (10.5%), access to blood cultures was not always possible, and 59 (21.3%) could not always measure lactate levels within three hours (Table 3).

Table 1 - General institution characteristics
Variable Global
(n = 277)
High resource availability
(n = 158)
Without high resource availability
(n = 119)
p value*
Hospital size       0.669
    ≤ 100 beds 77 (27.8) 41 (25.9) 36 (30.3)  
    101 to 500 beds 185 (66.8) 109 (69.0) 76 (63.9)  
    > 500 beds 15 (5.4) 8 (5.1) 7 (5.9)  
ICU beds       < 0.001
    ≤ 10 115 (41.5) 48 (30.4) 67 (56.3)  
    11 to 50 130 (46.9) 89 (56.3) 41 (34.5)  
    > 50 32 (11.6) 21 (13.3) 11 (9.2)  
    ICU beds (number) 14 (9 - 30) 19 (10 - 35.25) 10 (8 - 20) < 0.001
Hospital location       < 0.001
    Capitals 140 (50.5) 100 (63.3) 40 (33.6)  
    Countryside 137 (49.5) 58 (36.7) 79 (66.4)  
Hospital characteristics       < 0.001
    Private health system 108 (39.0) 83 (52.5) 25 (21.0)  
    SUS 169 (61.0) 75 (47.5) 94 (79.0)  
Geographic region       0.095
    Southeast 138 (49.8) 86 (54.4) 52 (43.7)  
    South 46 (16.6) 24 (15.2) 22 (18.5)  
    Middle-West 19 (6.9) 14 (8.9) 5 (4.2)  
    Northeast 53 (19.1) 24 (15.2) 29 (24.4)  
    North 21 (7.6) 10 (6.3) 11 (9.2)  
Teaching status       0.051
    University 57 (20.6) 26 (16.5) 31 (26.1)  
    Non-university 220 (79.4) 132 (83.5) 88 (73.9)  
Healthcare staff        
    Nurse/patient ratio 0.13 (0.10 - 0.19) 0.13 (0.10 - 0.20) 0.14 (0.10 - 0.18) 0.510
    Nurse technician/patient ratio 0.5 (0.5 - 0.5) 0.5 (0.5 - 0.5) 0.5 (0.5 - 0.5) 0.004
    Physician/patient ratio (day) 0.13 (0.10 - 0.17) 0.13 (0.10 - 0.17) 0.13 (0.10 - 0.16) 0.852
    Physician/patient ratio (night) 0.11 (0.10 - 0.14) 0.10 (0.10 - 0.13) 0.11 (0.10 - 0.14) 0.043
Hospital facilities        
    Emergency 247 (89.5) 140 (88.6) 107 (89.9) 0.842
    Operating theater 274 (98.9) 157 (99.4) 117 (98.3) 0.404
    Own blood bank 162 (59.1) 89 (56.3) 73 (61.3) 0.342
    Own laboratory 232 (83.8) 136 (86.1) 96 (80.7) 0.227
    Own microbiology 202 (73.2) 125 (79.1) 77 (64.7) 0.010

ICU - intensive care unit; SUS - Brazilian public health system.

* Chi-square or Mann-Whitney U tests between institutions with high resource availability compared to those without. The results are expressed as numbers (%) or the median (25%-75% percentiles).

Table 1 - General institution characteristics
Table 2 - Availability of medicines according to the institution availability of resources
Variable Global
(n = 277)
High resource availability
(n = 158)
Without high resource availability
(n = 119)
p value*
Antibiotics (answer: always)        
    3rd generation cephalosporins 263 (94.9) 158 (100.0) 105 (88.2) < 0.001
    4th generation cephalosporins 249 (89.9) 157 (99.4) 92 (77.3) < 0.001
    Piperacillin/tazobactam 230 (83.0) 155 (98.1) 75 (63.0) < 0.001
    Carbapenems 246 (88.8) 157 (99.4) 89 (74.8) < 0.001
    Vancomycin 257 (92.8) 158 (100) 99 (83.2) < 0.001
    Linezolid 141 (51.5) 105 (66.5) 36 (30.3) < 0.001
    Macrolide 217 (78.9) 140 (88.6) 77 (64.7) < 0.001
    Echinocandins 113 (41.4) 96 (60.8) 17 (14.3) < 0.001
    Tigecycline 112 (41.3) 91 (57.6) 21 (17.6) < 0.001
Other drugs (answer: always)        
    Hydrocortisone 267 (96.4) 157 (99.4) 110 (92.4) 0.002
    Crystalloids 268 (96.8) 158 (100.0) 110 (92.4) < 0.001
    Albumin 212 (76.5) 138 (87.3) 73 (61.3) < 0.001
    Norepinephrine 272 (98.2) 158 (100.0) 114 (95.8) 0.009
    Dopamine 260 (93.9) 152 (96.2) 108 (90.8) 0.062
    Dobutamine 271 (97.8) 158 (100.0) 113 (95.0) 0.004
    Adrenaline 272 (98.2) 158 (100.0) 114 (95.8) 0.009
    Vasopressin 138 (50.5) 103 (65.2) 35 (29.4) < 0.001
    Red blood cell within 6 hours 249 (89.9) 149 (94.3) 100 (84.0) 0.005

* Chi-square test between institutions with high resource availability compared to those without. The results are expressed as numbers (%).

Table 2 - Availability of medicines according to the institution availability of resources
Table 3 - Availability of laboratory exams according to the institution availability of resources
Variable Global
(n = 277)
High resource availability
(n = 158)
Without high resource availability
(n = 119)
p value*
Laboratory (answer: always)        
    Direct microscopy/Gram 0231 (83.4) 150 (94.9) 81 (68.1) < 0.001
    Blood culture 248 (89.5) 158 (100.0) 90 (75.6) < 0.001
    Respiratory secretions (qualitative) 210 (76.6) 151 (95.6) 59 (49.6) < 0.001
    Respiratory secretions (quantitative) 196 (71.8) 143 (90.5) 53 (44.5) < 0.001
        Urine culture 250 (90.3) 158 (100.0) 92 (77.3) < 0.001
        Blood gas analysis within 3 hours 254 (91.7) 158 (100.0) 96 (80.7) < 0.001
    Lactate within 3 hours 218 (78.7) 158 (100.0) 50 (60.4) < 0.001
    C-reactive protein 246 (89.1) 151 (95.6) 95 (79.8) < 0.001
    Procalcitonin 38 (14.5) 28 (17.7) 10 (8.4) 0.026

* Chi-square test between institutions with high resource availability compared to those without. The results are expressed as numbers (%).

Table 3 - Availability of laboratory exams according to the institution availability of resources

The units with high resource availability were usually larger, located in capitals and cared for patients from the private health system compared to those without high resource availability. They also had a higher number of nurse technicians per patient but a similar number of registered nurses and daily physicians per patient (Table 1). Among the units without high resource availability, 24.4% did not have broad-spectrum antibiotics, 4.2% did not have vasopressors and 7.6% did not have crystalloids (Table 2). Microbiology laboratory resources, lactate measures, disposables, equipment and monitoring devices availability were systematically different between these two types of units (Tables 3 and 4). Protocolized care was also different (Table 5). Institutions with lower access to microbiology analyses also had lower access to broad-spectrum antibiotics (Table 6).

Table 4 - Availability of disposables and monitoring/diagnosis devices according to the institution availability of resources
Variable Global
(n = 277)
High resource availability
(n = 158)
Without high resource availability
(n = 119)
p value*
Disposables (answer: always)        
    Oxygen mask/nasal probes 271 (97.8) 155 (98.1) 116 (97.5) 0.75
    Noninvasive ventilation 241 (87.3) 148 (93.7) 93 (78.2) < 0.001
    Mechanical ventilator 264 (95.6) 154 (97.5) 110 (92.4) 0.05
    Tracheal tube 277 (100.0) 158 (100.0) 119 (100.0) 1.00
    Infusion pump 273 (98.5) 157 (99.4) 116 (97.5) 0.152
    Bedside RRT 239 (86.5) 151 (95.6) 88 (73.9) < 0.001
    Urinary catheter 274 (98.9) 158 (100.0) 116 (97.5) 0.045
    Enteral tube feeding 268 (96.7) 157 (99.4) 111 (93.3) 0.005
    Peripheral catheters 273 (98.5) 158 (100.0) 115 (96.6) 0.020
    Central line catheters 267 (97.4) 151 (95.6) 95 (79.8) < 0.001
Monitoring devices (answer: always)        
    Automatic blood pressure 267 (96.4) 157 (99.4) 110 (92.4) 0.002
    Invasive blood pressure 161 (58.1) 129 (81.6) 32 (26.9) < 0.001
    CVP 214 (77.3) 158 (100.0) 56 (47.1) < 0.001
    Noninvasive cardiac output 61 (22.1) 49 (31.0) 12 (10.1) < 0.001
    Pulmonary artery catheter 79 (28.6) 67 (42.4) 12 (10.1) < 0.001
    Continuous SvO2 26 (9.6) 24 (15.2) 2 (1.7) < 0.001
    Bedside X-ray 262 (94.5) 156 (98.7) 106 (89.1) < 0.001
    Bedside ultrasound 142 (51.2) 103 (65.2) 39 (32.8) < 0.001
    Bedside echocardiography 131 (47.2) 98 (62.0) 33 (27.7) < 0.001
    Computed tomography 223 (80.5) 142 (89.9) 81 (68.1) < 0.001

RRT - renal replacement therapy; CVP - central venous pressure; SvO2 - central venous oxygen saturation.

* Chi-square test between institutions with high resource resources compared to those without. The results are expressed as numbers (%).

Table 4 - Availability of disposables and monitoring/diagnosis devices according to the institution availability of resources
Table 5 - Clinical management according to the institution availability of resources
Variable Global
(n = 277)
High resource availability
(n = 158)
Without high resource availability
(n = 119)
p value*
Management (answer: always + almost always)        
    Invasive blood pressure in shock 199 (71.8) 134 (84.8) 65 (54.6) < 0.001
    CVP in shock 237 (85.6) 143 (90.5) 94 (79.0) 0.007
    CVP in hyperlactatemia 217 (78.3) 141 (89.2) 106 (63.9) < 0.001
    Fluid responsiveness 83 (30.3) 65 (41.1) 18 (15.3) < 0.001
    SvcO2 in shock 231 (83.4) 138 (87.3) 93 (78.2) 0.042
    SvcO2 in hyperlactatemia 218 (78.7) 137 (86.7) 81 (68.6) < 0.001
    Lactate in severe sepsis suspicious 247 (89.2) 155 (98.1) 92 (77.3) < 0.001
Protocolized care        
    Sepsis 228 (82.3) 140 (88.6) 88 (73.9) 0.002
    Glycemic control 255 (92.1) 149 (94.3) 106 (89.1) 0.111
    Sedation 227 (81.9) 133 (84.2) 94 (79.0) 0.267
    MV weaning 239 (86.3) 139 (88.0) 100 (84.0) 0.345
    Nutrition 214 (77.8) 134 (84.8) 80 (68.4) 0.001

CVP - central venous pressure; SvO2 - central venous oxygen saturation; MV - mechanical ventilation.

* Chi-square test between institutions with high resource availability compared to those without. The results are expressed as numbers (%).

Table 5 - Clinical management according to the institution availability of resources
Table 6 - Microbiology resources according to antibiotic availability
Variable Has broad-spectrum ATB availability
(n = 248)
Does not have broad-spectrum ATB availability
(n = 29)
p value*
Laboratory (answer: always)      
    Direct microscopy/Gram 215 (86.7) 16 (55.2) < 0.001
    Blood culture 231 (93.1) 17 (58.6) < 0.001
    Respiratory secretions (qualitative) 196 (79.0) 14 (48.3) 0.001
    Respiratory secretions (quantitative) 185 (74.6) 11 (37.9) < 0.001
    Urine culture 233 (94.0) 17 (58.6) < 0.001

ATB - antibiotic. Adequate broad-spectrum ATB availability - antibiotics both for gram-negative (a third-generation cephalosporin plus carbapenens or piperacillin/tazobactam) and gram-positive coverage (vancomycin, teicoplanin or linezolid).

* Chi-square test between institutions with high resource availability compared to those without. The results are expressed as numbers (%).

Table 6 - Microbiology resources according to antibiotic availability

Among all units, 214 (77.3%) were able to adhere to the 1-h bundle, and 219 (79.1%) were able to adhere to the 'strong' recommendations from the Surviving Sepsis Campaign. Notable differences were observed between the units with high resource availability and those without (Table 7).

Table 7 - Possibility to adhere to the 1-hour bundle and to the Surviving Sepsis Campaign ‘strong’ recommendations
Variable Global
(n = 277)
High resource availability
(n = 158)
Without high resource availability
(n = 119)
p value*
1-hour bundle 214 (77.3) 158 (100.0) 56 (47.1) < 0.001
‘Strong’ recommendations 219 (79.1) 139 (88.0) 80 (67.2) < 0.001

* Chi-square test between institutions with high resource availability compared to those without. The results are expressed as numbers (%).

Table 7 - Possibility to adhere to the 1-hour bundle and to the Surviving Sepsis Campaign ‘strong’ recommendations

DISCUSSION

The results of our nationwide, random, self-reported, questionnaire-based survey of a representative sample of Brazilian adult ICUs indicate that a substantial number of units cannot perform some basic monitoring (e.g., lactate measurement) and therapeutic interventions (e.g., broad-spectrum antibiotics) in septic patients. Human resources, medicine, equipment and laboratory availability are systematically different when comparing units with high resource availability (as a surrogate to adhere to the 6-h bundle) and those without. Almost one-quarter of ICUs could not comply with the 1-h bundle because of the lack of resources rather than the short time frame. Our results are relevant both for our country and as a framework to study the availability of resources in LMICs.

Information on resource availability in LMICs is scarce and is mostly limited to single-center data instead of representative national samples.(21) In the ICON study, only 8.5% of participating centers were from low-income countries. Notably, a higher in-hospital risk of death was independently associated with a lower national income.(22) One of the possible explanations is the difference in equipment, training and resource availability among centers. These differences might affect the possibility to adhere to first-line treatments. In fact, in the SPREAD study, lower resource availability was associated with a higher mortality in the multivariate analysis.(14) Conversely, the IMPRESS study suggests that compliance with evidence based-bundles is associated with a lower mortality.(19) Since resource availability in critical care seems to be associated with outcomes, the health care inequalities of LMICs, albeit notorious,(23) should be further characterized.

Previous publications have suggested that the implementation of sepsis bundles in some LMICs is compromised because the availability of equipment, drugs and disposables are inadequate.(7-10) Baelani et al. reported that in some African countries, 16.3% of units could implement the resuscitation bundles, which is much lower than the percentage in high-income countries (93.2%).(7) Although our results for the 1-h bundle were better than those from African units, only 77.3% of our institutions had availability of required resources. When evaluating the individual components of the 1-h bundle in our study, it is particularly striking that some key therapeutic interventions are not always available (e.g., 3.2% lacked crystalloids, 1.8% lacked vasopressors, and 10.5% did not have broad-spectrum antibiotics). The unavailability of antibiotics is particularly worrisome since 60% of observed infections in SPREAD patients were health-care associated infections (which usually occur due to multiresistant microorganisms). We also observed a relationship between microbiology analysis resources and antibiotic availability (i.e., a lack of microbiology tests was associated with a lower availability of antibiotics). Although some institutions cannot perform all microbiology analyses, they should have antibiotics available to avoid treatment delays since the time from infection to antibiotic use is associated with sepsis outcomes.(24)

We also evaluated ICU staffing in our sample, with encountered values lower than those observed in high-income countries(25) and even Mongolian centers.(9) Unfortunately, there is a paucity of current ICU staffing data from LMICs and its relationship with outcomes. Previous information has demonstrated the association between both nurse staffing(26) and the intensivist-patient ratio(27) with hospital mortality and severe burnout,(28) but these data are mainly from high-income countries. In Brazil, Tironi et al. observed a burnout prevalence of 61.7% in intensivists and the lack of resources as a stressor during ICU shifts in 47.4% of staff.(29) Recently, the ORCHESTRA study failed to demonstrate a significant impact of physician or nurse staffing patterns on hospital mortality in Brazil.(30) Although we acknowledge that the ORCHESTRA study was not meant to specifically address septic patients and some differences between participating units in the ORCHESTRA and our study exist (such as the number of participating units from the private health system, geographic distribution alongside Brazilian regions and capitals, the nurse/patient ratio), we also did not identify staffing pattern as a significant factor associated with hospital mortality (Supplementary web appendix and Table 5 published with the SPREAD study - Lancet Infect Dis. 2017;17(11):1180-9).(14)

Our study has some strengths. Our sampling was representative of Brazilian institutions with ICUs. Our study design is original and might help explain the dynamics of resource availability in upper middle-income countries and may help plan future studies at the national level. The low rate of refusal to participate also improves our internal and external validity.

This study also has some limitations. First, the survey was self-reported, and we did not perform audits to evaluate whether the responses were accurate. However, the questionnaire was required to be fully completed before the units could participate in the SPREAD study, and the random stratified sampling method increases the validity and representativeness of our results. Second, although the questionnaire was designed by a committee with previous experience in critical care research and ICU organization aspects and reviewed by board-certified intensivists involved with ICU management, no assessment of test-retest reliability was performed. Third, our data might not be applicable to other countries, even LMICs, although the methods might be replicable in other countries to obtain high-quality data.(4)

CONCLUSION

Our nationwide, randomized survey of a representative sample of Brazilian adult intensive care units indicates that in a substantial number of institutions, there is a lack of required resources to perform basic monitoring and interventions in septic patients. Our results highlight major opportunities for the improvement of effective evidence-based interventions in Brazil. This study may also serve as a framework to evaluate resource availability in low- and middle-income countries.

Declarations: Leandro Utino Taniguchi, Section Editor for Revista Brasileira de Terapia Intensiva, was not involved in the evaluation or decision to publish this article. Luciano Cesar Pontes de Azevedo, Associated Editor for Revista Brasileira de Terapia Intensiva, was not involved in the evaluation or decision to publish this article. Fernando Augusto Bozza, member of the Editorial Board for Revista Brasileira de Terapia Intensiva, was not involved in the evaluation or decision to publish this article. Alexandre Biasi Cavalcanti, Section Editor for Revista Brasileira de Terapia Intensiva, was not involved in the evaluation or decision to publish this article. Flávia Ribeiro Machado, Associated Editor for Revista Brasileira de Terapia Intensiva, was not involved in the evaluation or decision to publish this article.

ACKNOWLEDGMENTS

This study was coordinated by the Instituto Latino Americano da Sepse and was supported by the Brazilian Research in Intensive Care Network (BRICNet). The Research Institute HCor-Hospital do Coração provided data management for the study.

This research was supported by a research grant from Fundação de Apoio a Pesquisa do Estado de São Paulo (FAPESP), under the number 2011/20401-4. The funding source had no influence on the study design; in the collection, analysis, and interpretation of data; in the writing of the original or the present report; and in the decision to submit the paper for publication. We would like to thank the Associação Brasileira de Medicina Intensiva (AMIB) for providing us the list of Brazilian intensive care units in 2010.

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