Abstract
Introduction
The clinical impact of the implementation of rapid response teams (RRTs) remains controversial in the literature. Furthermore, data on the financial impact of this intervention remain scarce. Therefore, we aim to assess the impact of the implementation of a dedicated RRT on hospital mortality and hospital expenses of patients experiencing acute clinical deterioration requiring an unplanned intensive care unit (ICU) admission.
Methods
We conducted a retrospective single-centre cohort study of adult patients experiencing acute clinical deterioration requiring an unplanned ICU admission before and after the transition of the RRT leadership to a dedicated group on 1 June 2014. Admissions that occurred 30 days before and 30 days after were excluded because they included the training period of the team members. Therefore, the PRE group encompassed patients who required an unplanned ICU admission between 1 May 2012, and 30 April 2014, and the POST group included those admitted to the ICU between 1 July 2014, and 30 June 2016. Patients were matched by propensity score according to a calibration of 0.2 and at a 1:1 ratio using the nearest neighbour matching method. The primary outcome was in-hospital mortality, with secondary outcomes including ICU mortality, hospital and ICU length of stay, ICU readmission rate within 48 h, and hospital expenses.
Results
The study included 977 consecutive patients: 470 in the PRE group and 507 in the POST group. Following propensity score matching, 343 pairs (totalling 686 patients) were identified. Analyses revealed reductions in in-hospital mortality rate (34.7% PRE vs. 22.7% POST; odds ratio 0.590 [95% CI: 0.254–0.927], P < .001) and ICU mortality rate (19.5% PRE vs. 12.8% POST; odds ratio 0.501 [95% CI: 0.087–0.915]; P = .022). Decreases in hospital and ICU length of stay and use of ICU support measures were also observed, accompanied by a 23.2% reduction in hospital expenditure (P < .001).
Conclusion
Transitioning to a dedicated RRT was associated with reduced in-hospital mortality and hospital resource utilization. Future research in diverse settings and cost-effectiveness analyses are warranted to confirm these findings and explore the economic impacts of RRTs.
Keywords: rapid response team, hospital mortality, ICU mortality, acute illness, medical emergency team, hospitalizations costs
Introduction
In the early 1990s, a study by Daffurn and colleagues [1] reported a post-cardiac arrest mortality rate of 71% in general wards and 64% in the Emergency Department of Liverpool Hospital. To promote the early identification and intervention in critically ill patients, thereby preventing progression to cardiac arrest, a specialized rapid response team (RRT) was established, replacing the pre-existing cardiac arrest team. This appears to be one of the earliest instances of RRT deployment, explicitly aimed at the early identification of acute clinical deterioration to reduce in-hospital mortality, as documented in the medical literature.
Acute clinical deterioration in hospitalized patients refers to a sudden and significant decline in a patient’s physiological status, often characterized by abnormal vital signs, altered mental state, or worsening organ function. This deterioration may result from various underlying conditions, including sepsis, respiratory failure, cardiovascular diseases, or multi-organ dysfunction. Early recognition and timely intervention are crucial, as delayed responses can lead to increased morbidity, prolonged hospitalization, and higher mortality rates, with reported mortality rates up to seven times higher compared to patients without this condition in some studies [2]. In general, this condition precedes a major in-hospital complication, as a cardiac arrest, 6–12 h [3–5]. The early identification of an acute clinical deterioration creates a window of opportunity for health professionals to prevent cardiac arrest or the death of these patients.
Despite the substantial effort and ongoing work required for the implementation and refinement of these teams within healthcare services, there remains uncertainty about their tangible benefits for patients and cost-effectiveness for financial sources. Numerous studies, predominantly observational and single-centre, have indicated that the introduction of RRT is associated with a reduction in the incidence of cardiac arrest events outside the intensive care unit (ICU) and/or in-hospital mortality rates [6–16]. However, the only multicentre, randomized, and controlled clinical trial on this topic, the MERIT study, did not demonstrate this benefit [17]. Certain authors argue that the unexpectedly low mortality rate, coupled with low activation rates in the intervention group, may have rendered the MERIT study statistically underpowered with regard to its primary outcome [18].
In terms of economic implications, there are few published studies analysing these aspects during the implementation of RRTs. In a Dutch study, the introduction of an RRT led to an increased rate of unplanned transfers to the ICU, from 2.5% to 4.2% [19]. This increase in the number of unplanned ICU transfers could potentially lead to a considerable rise in the billed cost of hospitalization for these patients, particularly in private healthcare services that predominantly operate on a fee-for-service payment model, as is the case in our institution. However, to the best of our knowledge, no study has directly quantified this financial impact yet.
Our institution, a philanthropic quaternary hospital with a capacity of approximately 450 beds, has had an RRT since 2006. Initially, it was led by physicians drawn from the ICU, the semi-intensive unit, or the Emergency Department, alongside the nursing team from the unit that had triggered the alert. Depending on the patient’s clinical condition, a respiratory physiotherapist could be summoned to provide additional support.
On 1 June 2014, our institution’s RRT was restructured, shifting its leadership to a dedicated medical team operating continuously 24/7. The dedicated RRT was composed of physicians with a minimum of 3 years of postgraduate experience, at least one completed medical residency, and a background in urgent and emergency medical care.
After this transition, during urgent care interventions, the RRT was led by a dedicated physician who conducted the interventions with the support of the nursing staff from the unit that triggered the response, including at least one nurse and one nursing technician. During emergency care interventions, this team also received support from the emergency department nursing staff, consisting of at least one nurse and two nursing technicians. In both scenarios, the team also had the support of a respiratory physiotherapist, who was available on demand.
To investigate the clinical and financial impacts of an RRT in a hospital setting, we conducted a retrospective single-centre cohort study to assess the impact of implementing a dedicated RRT on hospital mortality and hospital expenses of patients experiencing acute clinical deterioration requiring unplanned transfer to an ICU bed.
Methods
Study design
This was a retrospective single-centre cohort study composed of consecutive adult patients (≥18 years) who presented with some acute clinical deterioration requiring an unplanned transfer from an inpatient unit bed or from diagnostic units to the ICU before and after the implementation of the dedicated RRT.
Preliminary analyses indicated that approximately 30 admissions per month met the inclusion criteria for this study. These initial analyses also suggested that the mortality rate was around 33% before the intervention. If a reduction of approximately 7.5% in this rate occurred, we would need to study about 604 individuals in each group to reject the null hypothesis that the event proportions in the two groups are equal, considering a two-tailed risk α ≤ 5% for type I error and a power of 80% (risk β ≤ 20% for type II error).
Therefore, patients who had an unplanned ICU admission from 1 May 2012 to 30 June 2016 were eligible for this study. Patients transferred internally from one ICU to another were excluded from the analysis, as were external transfers with direct hospitalization in the ICU and patients hospitalized less than 24 h in the ICU, except for deaths (Fig. 1). Only the first admission was considered when the same patient had more than one admission to the ICU. The demographic and clinical data for these patients were extracted from the Epimed Monitor database (Epimed Solutions, Rio de Janeiro, Brazil) and from the electronic patient records. It was decided to exclude ICU admissions that occurred 30 days before and 30 days after implementation of the dedicated RRT, which was the time required for the training of the team members. Patient cohorts were stratified into two distinct groups. The PRE group encompassed patients who required an unplanned ICU admission between 1 May 2012 and 30 April 2014, prior to the inception of the dedicated RRT. The POST group, in contrast, consisted of individuals who required an unplanned ICU admission subsequent to the dedicated RRT’s implementation, specifically between 1 July 2014 and 30 June 2016.
Figure 1.
Flowchart of the database according to the inclusion and exclusion criteria.
This research was approved by the local Ethical Committee (CAAE: 05586919.3.0000.5461). The research was carried out through an anonymized institutional database. The medical records were reviewed by researchers registered and linked to the hospital and thus committed to all confidentiality and compliance institution’s parameters. As a result, the local Ethical Committee waived the use of consent form for this research.
Studied endpoints
The primary endpoint of this study was in-hospital mortality rate. Secondary clinical endpoints included mortality within the ICU, length of hospital and ICU stay, and ICU readmission rates within a 48-h interval. We further conducted exploratory analyses to assess the impacts on the use of supportive interventions inside the ICU, which comprised the administration of vasoactive drugs, application of non-invasive ventilation, incidents of non-invasive ventilation failure, initiation of mechanical ventilation, and implementation of renal replacement therapy. Additionally, the total hospitalization expenses constituted the secondary financial endpoint of the study.
Statistical analysis
Patients in the PRE and POST groups were paired using propensity scores. The propensity scores were estimated using logistic regression analysis, with the POST group as the dependent variable and age, sex, Charlson Comorbidity Index [20], probability of death according to Simplified Acute Physiology Score (SAPS) III [21], and Sequential Organ Failure Assessment (SOFA) score [22] as independent variables. Pairing was performed according to a calibration of 0.2 and at a 1:1 ratio using the nearest neighbour matching method. In parallel, we collected data on the hospital mortality rate of all patients admitted to the ICU during the study period. This analysis aimed to identify any changes in this indicator over time and compare it with the values observed in the study population.
To analyse the total hospital expenditure per patient, it was decided to adjust these values for inflation using Brazil’s official inflation index, the Índice Nacional de Preços ao Consumidor Amplo (Broad National Consumer Price Index – IPCA), published by the Central Bank of Brazil (CBB). For this purpose, these values were adjusted for inflation from the patient’s discharge date to April 2023. These data were processed using the R package deflateBR (version 1.1.2) [23], developed for R software (version 4.0.0). After adjustment for inflation, these values were converted into US dollars using the exchange rate on 14 April 2023, as published by the CBB on its official website [24]. On that day, USD 1.00 was equivalent to BRL 4.9449.
A two-tailed significance level of α ≤ 5% for type I error and a power of 80% (β ≤ 20% for type II error) were considered for this study. All other calculations were performed using JASP statistical software (version 0.16.3), developed by the University of Amsterdam, Amsterdam, NH.
Results
Of the 977 consecutive patients who had some clinical complications and required an unplanned transfer to the ICU, 470 patients were in the PRE group, and 507 patients were in the POST group (Table 1). There was no difference between the groups regarding age, sex, probability of in-hospital death according to the SAPS III, or SOFA score at ICU admission. However, the POST group had a higher score on the Charlson Comorbidity Index (score of 2 [0–3] in the PRE group versus 2 [1–4] in the POST group [P < .001]; median [interquartile range (IQR)]).
Table 1.
Variables used in the propensity score to pair samples. Characteristics of patients before and after propensity score matching.
| Before propensity score matching | After propensity score matching | |||||
|---|---|---|---|---|---|---|
| PRE | POST | PRE | POST | |||
| Variable | n = 470 | n = 507 | P | n = 343 | n = 343 | P |
| Age (years), median (IQR) | 71 (58–83) | 72 (60–82) | .782a | 71 (57–82) | 72 (59–82) | .812a |
| Gender (male) | 263 (56.0%) | 258 (51.0%) | .112b | 176 (51.3%) | 177 (51.6%) | .939b |
| Charlson Comorbidity Index, median (IQR) | 2 (0–3) | 2 (1–4) | <.001a | 2 (0–4) | 2 (0–3) | .913a |
| SAPS III for South America (probability), median (IQR) | 28.6% (15.6–45.4) |
30.8% (15.6–50.4) |
.129a | 30.8% (15.6–47.9) |
30.8% (15.6–50.4) |
.845a |
| SOFA score, median (IQR) | 3 (1–6) | 3 (2–5) | .728a | 3 (1–6) | 3 (2–5) | .565a |
Mann‒Whitney test; bChi-square test.
After matching, 343 pairs were found with the same propensity score, with one member from each pair belonging to the PRE group and another to the POST group, totalling 686 patients (Table 1). After pairing, the groups did not show statistically significant differences regarding age, sex, Charlson Comorbidity Index, probability of in-hospital death according to the SAPS III, and SOFA score at ICU admission.
Table 2 shows the data before and after pairing regarding the type of hospitalization of the patient, unit of origin, and the clinical reason for admission to the ICU.
Table 2.
Clinical characteristics and type of hospitalization of patients before and after propensity score matching.
| Before propensity score matching | After propensity score matching | |||||
|---|---|---|---|---|---|---|
| PRE | POST | PRE | POST | |||
| Variable | n = 470 (%) | n = 507 (%) | P | n = 343 (%) | n = 343 (%) | P |
| Type of hospitalization | 1.000a | 1.000a | ||||
| Elective surgery | 1 (0.2) | 2 (0.4) | 1 (0.3) | 1 (0.3) | ||
| Clinical | 469 (99.8) | 505 (99.6) | 342 (99.7) | 342 (99.7) | ||
| Unit of origin | .420b | .496c | ||||
| Semi-intensive units | 113 (24.0) | 115 (22.7) | 72 (21.0) | 71 (21.0) | ||
| Diagnostic imaging centre | 1 (0.2) | 0 (0) | 1 (0.3) | 0 (0) | ||
| Ward/room | 356 (75.7) | 392 (77.3) | 270 (78.7) | 272 (79.3) | ||
| Reason for admission to the ICU | .524b | .529b | ||||
| Infectious diseases or sepsis | 169 (36.0) | 178 (35.1) | 120 (35.0) | 128 (37.3) | ||
| Respiratory diseases (except sepsis/infection) | 85 (18.1) | 84 (16.6) | 67 (19.5) | 57 (16.6) | ||
| Neurological or psychiatric diseases | 72 (15.3) | 81 (16.0) | 52 (15.2) | 50 (14.6) | ||
| Cardiovascular disease | 55 (11.7) | 74 (14.6) | 37 (10.8) | 46 (13.4) | ||
| Gastrointestinal diseases | 44 (9.4) | 40 (7.9) | 33 (9.6) | 26 (7.6) | ||
| Kidney diseases | 18 (3.8) | 13 (2.6) | 12 (3.5) | 8 (2.3) | ||
| Haematological diseases | 1 (0.2) | 5 (1.0) | 1 (0.3) | 5 (1.5) | ||
| Trauma | 3 (0.6) | 2 (0.4) | 2 (0.6) | 2 (0.6) | ||
| Post-cardiorespiratory arrest | 2 (0.4) | 2 (0.4) | 2 (0.6) | 0 (0) | ||
| Patient monitoring | 2 (0.4) | 1 (0.2) | 2 (0.6) | 1 (0.3) | ||
| Surgical procedure complications | 1 (0.2) | 1 (0.2) | 1 (0.3) | 1 (0.3) | ||
| Autoimmune diseases | 0 (0) | 1 (0.2) | 0 (0) | 0 (0) | ||
| Other | 3 (0.6) | 4 (0.8) | 3 (0.9) | 3 (0.9) | ||
Fisher’s exact test; bLikelihood ratio test; cChi-square test.
Upon analysis after pairing (as shown in Table 3), a significant reduction in the in-hospital mortality rate from 34.7% in the PRE group to 22.7% in the POST group was observed (odds ratio 0.590 [95% CI: 0.254, 0.927], P < .001). The ICU mortality rate also showed a significant decrease from 19.5% in the PRE group to 12.8% in the POST group (odds ratio 0.501 [95% CI: 0.087, 0.915], P = .022). Concomitantly, there was a notable reduction in both hospital and ICU length of stay. Notably, there was no significant difference in the 48-h ICU readmission rate between the two groups.
Table 3.
Hospital and ICU mortality rate, length of hospital stay, and length of stay in the ICU.
| Before propensity score matching | After propensity score matching | |||||
|---|---|---|---|---|---|---|
| PRE | POST | PRE | POST | |||
| Variable | n = 470 | n = 507 | P | n = 343 | n = 343 | P |
| Hospital mortality | 152 (32.3%) | 123 (24.3%) | .005a | 119 (34.7%) | 78 (22.7%) | <.001a |
| ICU mortality | 86 (18.3%) | 63 (12.4%) | .011a | 67 (19.5%) | 44 (12.8%) | .017a |
| Length of hospitalization (days), median (IQR) | 25 (13–42) | 20 (11–34) | <.001b | 25 (13–41) | 19 (10–33) | .003b |
| Length of ICU stay (days), median (IQR) | 4 (2–7) | 3 (2–6) | <.001b | 4 (2–7) | 3 (2–5) | .009b |
| Length of hospitalization before transfer to ICU (days), median (IQR) | 6 (2–14) | 4 (1–10) | <.001b | 6 (2–14) | 4 (1–11) | <.001b |
| Length of hospitalization after transfer to ICU (days), median (IQR) | 14 (7–29) | 13 (6–24) | .133b | 14 (6–28) | 12 (6–24) | .211b |
| Readmission to the ICU in 48 h | 15 (3.2%) | 24 (4.7%) | .219a | 10 (2.9%) | 11 (3.2%) | .825a |
Chi-square test; bMann‒Whitney test.
In our exploratory analyses of ICU support measures (refer to Table 4), we found a reduced requirement for vasoactive drugs, non-invasive ventilation, and fewer instances of non-invasive ventilation failure. Conversely, the use of mechanical ventilation and renal replacement therapy did not show statistically significant differences between the groups.
Table 4.
Intensive care used throughout the ICU stay.
| Before propensity score matching | After propensity score matching | |||||
|---|---|---|---|---|---|---|
| PRE | POST | PRE | POST | |||
| Variable | n = 470 (%) | n = 507 (%) | P | n = 343 (%) | n = 343 (%) | P |
| Use of vasoactive drugs | 194 (41.3) | 148 (29.2) | <.001a | 147 (42.9) | 101 (29.4) | <.001a |
| Use of non-invasive ventilation | 184 (39.1) | 175 (34.5) | .039a | 137 (39.9) | 110 (32.0) | .013a |
| Non-invasive ventilation failure | 47 (10.0) | 25 (4.9) | .001a | 35 (10.2) | 15 (4.4) | .002a |
| Use of mechanical ventilation | 124 (26.4) | 123 (24.3) | .237a | 91 (26.5) | 85 (24.8) | .437a |
| Use of renal replacement therapy | 40 (8.5) | 41 (8.1) | .649a | 30 (8.7) | 25 (7.3) | .407a |
Chi-square test.
With respect to the financial data analysis (Table 5), we observed a 23.20% reduction in the median hospitalization expenses in the POST group compared to the PRE group (P < .001) after propensity score matching.
Table 5.
Final amount charged for hospitalization.
| Before propensity score matching | After propensity score matching | |||||
|---|---|---|---|---|---|---|
| PRE | POST | PRE | POST | |||
| Variable | n = 470 | n = 507 | P | n = 343 | n = 343 | P |
| Hospital expenditure deflated by the IPCA (R $), median (IQR) | 255,893.33 (126,685.38–559,562.31) |
202,725.07 (99,068.05–417,246.05) |
<.001a | 261,996.01 (129,293.13–551,290.95) |
201,203.89 (98,431.00–404,337.70) |
<.001a |
| Hospital expenditure (US $), median (IQR) | 51,748.94 (25,619.40–113,159.48) |
40,996.80 (20,034.39–84,379.07) |
<.001a | 52,983.08 (26,146.76–111,486.77) |
40,689.17 (19,905.56–81,768.63) |
<.001a |
Mann‒Whitney test.
Discussion
Statement of principal findings
The transition of leadership to a dedicated RRT was associated with a statistically significant reduction in both mortality rates and hospitalization expenditures for patients at high risk of in-hospital death. Using a propensity score matching approach, we observed an absolute reduction of 12% in the in-hospital mortality rate from all causes.
Strengths and limitations
Several factors may account for the significant reductions observed in these outcomes. Current guidelines endorse early and structured intervention in scenarios of acute clinical deterioration, such as sepsis, acute myocardial infarction, or stroke, leading to better clinical outcomes and reduced mortality rates [25–29]. A study by Nallamothu BK and colleagues [30] identified a correlation between hospitals with high in-hospital cardiac arrest survival rates and the characteristics of their resuscitation teams, which included dedicated resuscitation staff.
Our findings suggest that care provided by a dedicated RRT was associated with earlier patient transfers to the ICU, as evidenced by a decrease in the median number of hospitalization days prior to ICU transfer in the POST group. Early ICU admission may contribute to a reduced need for ICU support measures, subsequently decreasing both ICU and overall hospital length of stay. We propose that early identification of clinical deterioration, coupled with appropriate support, also led to a decrease in recovery time, contributing to improved patient survival.
It is essential to acknowledge certain limitations in our study. First, the observational and retrospective design of our study may introduce significant limitations in data quality. Despite this potential challenge, our study relied on information meticulously and automatically extracted from clinical and financial databases. As required by our external international accreditation system, these databases are subject to rigorous and regular audits and validations.
Another constraint involves the discrepancy in baseline characteristics between the two groups under study. To mitigate these differences, we performed a comprehensive propensity score matching. Despite the direct adjustments based on the analysis of five variables (age, gender, Charlson Comorbidity Index, SAPS III, and SOFA score), it indirectly considered 60 variables: age, gender, and the other 58 variables used to compute the Charlson Comorbidity Index, SAPS III, and SOFA score, 46 of which were unique. Although we achieved a successful and precise matching, it could only be applied to the patient characteristics available in this database.
It is also important to emphasize that our research was limited to examining the impacts of these interventions on patients who experienced clinical deterioration necessitating an unplanned transfer to an ICU bed. The impacts of these interventions on patients who did not require ICU admission are not addressed in this research.
Moreover, over the 4-year span of this study, notable advances in patient care, drug development, and changes in medical practice have occurred and may have differentially impacted the two groups. Measuring the influence of these evolutions in medical practice over time presents a challenging endeavour. Nonetheless, we analysed the in-hospital mortality rate of all ICU admissions during the study period. Before the implementation of a dedicated RRT, the in-hospital mortality rate for these patients was 14.26%, which decreased to 12.26% in the second period. This absolute reduction in in-hospital mortality of 2.0%, which was statistically significant (P = .008), likely reflects a general improvement in care over the study period. However, it remains substantially lower than the absolute reduction of 12.0% observed in the study population. Therefore, it is possible to infer that the implementation of a dedicated RRT may have had a positive impact on the reduction in in-hospital mortality in the POST group, in addition to the overall advancements in care during this period.
Lastly, financial analyses are seldom included in clinical studies due to their complexity and the challenges associated with their execution. Moreover, their external validation tends to be even more constrained given that the pricing of services and their billing methods can significantly vary even within healthcare services in the same city, let alone between different countries. We examined the final charges imposed by the hospital in relation to the patients’ hospitalization costs. We worked with financial data from a private philanthropic hospital operating on a fee-for-service billing system for all admissions in this study and observed a significant reduction in these amounts following the applied interventions. Given that these data are derived from a fee-for-service billing system, it can be inferred that there was a significant reduction in hospital resource utilization. This is substantiated by the observed reduction in the length of ICU and hospital stays, in addition to the decreased need for support within the ICU, as evidenced by our exploratory analyses.
Interpretation within the context of the wider literature
A study conducted by Factora and colleagues [16] examined the effects of the establishment of a RRT in 2009, the transition of its leadership to an anaesthesiologist-led team in 2012, and other modifications to the RRS and their impact on the in-hospital mortality rate at Cleveland Clinic. Their analysis included patient hospital admission data from 1 March 2005 to 31 December 2018, encompassing a total of 628 533 hospitalizations. The researchers observed a significant reduction in the in-hospital mortality rate following the interventions. This led them to conclude that improvements in hospital outcomes following the introduction of an RRT may take years to manifest.
Similar to the Cleveland Clinic study, our study also observed a significant reduction in the in-hospital mortality rate following the implementation of a dedicated RRT initiated in the early 2010s. Although our cohort included fewer patients and covered a shorter period, our patient population was at a substantially higher risk of in-hospital death compared to the Cleveland Clinic cohort, which increased the statistical power of our study.
Implications for policy, practice, and research
Our findings reinforce the clinical impact of a carefully organized implementation of a dedicated RRT in healthcare institutions. Furthermore, the association between the reduction in in-hospital mortality and a significant decrease in hospital expenditure should draw the attention of health managers to the potential positive economic impact of the implementation of these teams on health systems.
Conclusions
To our knowledge, this is the first study to identify an association between the implementation of a dedicated RRT and reductions in both in-hospital mortality and hospital resource utilization. Based on the data from a population at high risk of in-hospital death over a 4-year period, we infer that early clinical intervention by a well-structured RRT in patients experiencing acute clinical deterioration provided an opportunity for rapid recovery and improved survival. Additionally, this was not associated with an increase but rather with a significant reduction in the use of hospital resources. These findings highlight the importance of a broader implementation of dedicated RRTs to achieve high clinical standards, particularly in resource-limited settings. Further studies are required to replicate these findings in institutions with different profiles. Additionally, cost-effectiveness analyses could further contribute to assessing the economic impact of RRTs.
Acknowledgements
This study was conducted as a component of the first author’s doctoral project. Therefore, we would like to express our gratitude for the support provided by Nurse Teresa Cristina Dias Cunha Nascimento and Professor Márlon Juliano Romero Aliberti in the conceptualization of this project.
Contributor Information
Jacob Sessim-Filho, Inpatient Care Coordination, Sírio-Libanês Hospital, 91, Adma Jafet Street, Bela Vista, São Paulo, SP, 01308-050, Brazil.
Renato Palacio de Azevedo, Medical Team of Hospitalists, Sírio-Libanês Hospital, 91, Adma Jafet Street, Bela Vista, São Paulo, SP 01308-050, Brazil.
Antonildes N Assuncao-Jr, Research and Education Institute, Sírio-Libanês Hospital, 91, Adma Jafet Street, Bela Vista, São Paulo, SP 01308-050, Brazil.
Marcia Martiniano de Sousa e Sá Morgado, Clinical Governance Board, Sírio-Libanês Hospital, 91, Adma Jafet Street, Bela Vista, São Paulo, SP 01308-050, Brazil.
Felipe Duarte Silva, Clinical Governance Board, Sírio-Libanês Hospital, 91, Adma Jafet Street, Bela Vista, São Paulo, SP 01308-050, Brazil.
Laerte Pastore, Jnr, Intensive Care Unit Management, Sírio-Libanês Hospital, 91, Adma Jafet Street, Bela Vista, São Paulo, SP 01308-050, Brazil.
Luiz Francisco Cardoso, Clinical Governance Board, Sírio-Libanês Hospital, 91, Adma Jafet Street, Bela Vista, São Paulo, SP 01308-050, Brazil.
Fernando Ganem, General Management, Sírio-Libanês Hospital, 91, Adma Jafet Street, Bela Vista, São Paulo, SP 01308-050, Brazil.
Author contributions
Following the recommendations of the editors of the International Journal for Quality in Health Care, we have listed the authors along with their respective contributions to this research:
Conception and design of the study: J.S.F., R.P.A., L.F.C., and F.G.
Data acquisition: J.S.F, R.P.A., M.M.S.S, F.D.S, and L.P.Jr.
Analysis of data: J.S.F. and A.N.A.
Drafting the manuscript: J.S.F and R.P.A.
All authors participated in data interpretation, manuscript revision, and gave final approval.”
Conflict of interest
The authors declare no conflicts of interest.
Funding
Financial support for this research was exclusively from institutional resources. None of the authors have personal financial interests related to this study.
Data availability
Owing to ethical considerations, datasets remain restricted for use exclusively within the study team.
Ethics and other permissions
This research received approval from the Institutional Review Board (IRB) of Sírio-Libanês Hospital (CAAE: 05586919.3.0000.5461). The study was conducted utilizing an institutional database, anonymized and extracted by the first author. In cases of suspected database errors or incomplete data, a meticulous review of the patients’ medical records was conducted by both the first and second authors, ensuring data quality and adherence to strict confidentiality and institutional standards. As a result, the IRB waived the requirement for a consent form in this study. This study complies with the Declaration of Helsinki.
Disclosure
The abstract of an early version of this paper was presented at the 2020 Scientific Sessions of the American Heart Association as a poster presentation with interim findings. The poster’s abstract was published in Circulation’s Poster Abstracts Sessions on 9 November 2020: https://doi.org/10.1161/circ.142.suppl_4.288
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Owing to ethical considerations, datasets remain restricted for use exclusively within the study team.