Risk factors for hospital mortality in intensive care unit survivors: a retrospective cohort study

Luiza Gabriella Antonio e Silva; Claudia Maria Dantas de Maio Carrilho; Thalita Bento Talizin; Lucienne Tibery Queiroz Cardoso; Edson Lopes Lavado; Cintia Magalhães Carvalho Grion

doi:10.4266/acc.2022.01375

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Original Article Epidemiology Risk factors for hospital mortality in intensive care unit survivors: a retrospective cohort study: Luiza Gabriella Antonio e Silva¹, Claudia Maria Dantas de Maio Carrilho², Thalita Bento Talizin³, Lucienne Tibery Queiroz Cardoso², Edson Lopes Lavado⁴, Cintia Magalhães Carvalho Grion²; Acute and Critical Care 2023;38(1):68-75.
DOI: https://doi.org/10.4266/acc.2022.01375
Published online: February 27, 2023

¹Universidade Estadual de Londrina, Londrina, Brazil

²Departamento de Clínica Médica, Universidade Estadual de Londrina, Londrina, Brazil

³Departamento de Cirurgia Geral, Universidade de São Paulo, São Paulo, Brazil

⁴Departamento de Fisioterapia, Universidade Estadual de Londrina, São Paulo, Brazil

Corresponding author: Cintia Magalhães Carvalho Grion Departamento de Clínica Médica, Universidade Estadual de Londrina,. Rua Robert Koch 60, Vila Operária. Londrina, Paraná 86038-440, Brasil Tel: +55-43-33712284 Fax: +55-43-33712466 Email: cintiagrion@hotmail.com

• Received: November 1, 2022 • Revised: January 10, 2023 • Accepted: January 12, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, providewere observed among non-survivors. In the logisticd the original work is properly cited.

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Abstract
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
KEY MESSAGES
NOTES
Acknowledgments
REFERENCES

Abstract

Background
Deaths can occur after a patient has survived treatment for a serious illness in an intensive care unit (ICU). Mortality rates after leaving the ICU can be considered indicators of health care quality. This study aims to describe risk factors and mortality of surviving patients discharged from an ICU in a university hospital.
Methods
Retrospective cohort study carried out from January 2017 to December 2018. Data on age, sex, length of hospital stay, diagnosis on admission to the ICU, hospital discharge outcome, presence of infection, and Simplified Acute Physiology Score (SAPS) III prognostic score were collected. Infected patients were considered as those being treated for an infection on discharge from the ICU. Patients were divided into survivors and non-survivors on leaving the hospital. The association between the studied variables was performed using the logistic regression model.
Results
A total of 1,025 patients who survived hospitalization in the ICU were analyzed, of which 212 (20.7%) died after leaving the ICU. When separating the groups of survivors and non-survivors according to hospital outcome, the median age was higher among non-survivors. Longer hospital stays and higher SAPS III values were observed among non-survivors. In the logistic regression, the variables age, length of hospital stay, SAPS III, presence of infection, and readmission to the ICU were associated with hospital mortality.
Conclusions
Infection on ICU discharge, ICU readmission, age, length of hospital stay, and SAPS III score increased risk of death in ICU survivors.
Keywords: drug resistance, multiple, bacterial; intensive care; mortality; patient discharge; severity of illness index

INTRODUCTION

Despite advances in the quality of health care, deaths can occur after a patient has survived treatment for a serious illness in an intensive care unit (ICU). Post-ICU mortality rates described in the literature can range from 5% to 27% [1-5]. Mortality rates after leaving the ICU can be considered indicators of health care quality. Several factors have been described associated with death in the short-term after ICU discharge. Most of these risk factors are associated with age, disease severity, and length of stay in the ICU, in addition to infections and the need for unplanned readmissions [6-8]. Health care-associated infections (HAIs) are worrisome complications during an intensive care stay and may be associated with increased mortality in ICU survivors [7,9]. Another risk factor that is associated with mortality after an intensive care stay is readmission to the ICU, which can increase intra-hospital mortality by ten times [8]. It is estimated that for every 100 patients discharged from the ICU, 4 to 6 will be readmitted to this sector, noting that between 3 and 7 of these patients will die before hospital discharge, thus demonstrating the high risk in this transition of care [10]. It is estimated that the hospital mortality rate for patients who require readmission to the ICU is 34.3% [9].

Decisions to discharge patients from the ICU should take into consideraticon possible risk factors for hospital mortality present at that moment, to allow safer transition of care. Prognostic scores are widely used, and these tools can contribute as an indicator of the performance of care provided by the units and hospitals through severity-adjusted mortality rates. A prognostic score that is widely used and with good performance is the Simplified Acute Physiology Score (SAPS) III, which corresponds to the clinical analysis of the patient on admission, and is scored according to demographic, clinical, and physiological variables [11,12]. The current study aims to describe the hospital mortality rate and assess risk factors for death in surviving patients discharged from an ICU at a university hospital.

MATERIALS AND METHODS

Research Ethics

This study was performed according to the Helsinki Declaration and approved by the local Research Ethics Committee (CAAE 85685418.7.0000.5231; Opinion 2,568,527) of the institution where the experiment was performed. Written informed consent was waived.

A retrospective cohort study, carried out with adult surviving patients admitted to a mixed clinical-surgical ICU of a University Hospital from January 2017 to December 2018. When a patient was readmitted, only the discharge from the ICU on the first admission was used for data collection and the patient was followed up until the hospital outcome. Patients under 18 years of age or those with incomplete medical records were excluded. This is a 20 bed ICU that provides care for adult patients in a University Hospital with 300 beds. The ICU's occupancy rate was above 95% for entire the study period. All patients who leave the ICU are transferred to the same level of care in the hospital wards. There is no intermediate care facility in the institution. There is a 24-hour rapid response team on duty lead by an intensivist.

Information was collected on age, sex, length of hospital stay, diagnosis in patients with ICU survivor, readmission to the ICU, hospital discharge outcome, presence of infection, microorganisms isolated, and the SAPS III in patients with ICU survivor. The main outcome of the study was death or discharge from the hospital.

An infected patient was defined as those who were being treated with antimicrobials agents for an infection diagnosis on discharge from the ICU. Treatment for infections were revised by an infectologist according to the stewardship program to ensure adequate treatment. Infected patients were divided according to the identification of the etiological agent into unidentified microorganisms, sensitive microorganisms, multidrug-resistant microorganisms (MDR), extensively drug-resistant (XDR), and pandrug-resistant microorganisms (PDR). The definitions of antimicrobial resistance followed the recommendations of the task force of the European Centre for Diseases Prevention and Control and Centers for Disease Control and Prevention, as follows: MDR was defined as acquired non-susceptibility to at least one agent in three or more antimicrobial categories, XDR was defined as non-susceptibility to at least one agent in all but two or fewer antimicrobial categories (i.e. bacterial isolates remain susceptible to only one or two categories) and PDR was defined as non-susceptibility to all agents in all antimicrobial categories [13]. Readmission to the ICU was considered if there was a new unscheduled admission after discharge from the ICU in the same hospital stay.

Statistical Analysis

After applying the Shapiro-Wilk test, continuous variables were summarized as median and interquartile range (IQR) for not assuming normality. Categorical variables are reported as absolute and relative frequencies and their 95% confidence intervals (CIs), expressed in tables and figures. For comparisons of groups, the Mann-Whitney test or chi-square test was used. The association between the studied variables was performed by the logistic regression model and presented as odds ratio (OR) and 95% CI. Additionally, Kaplan-Meier analysis was performed to estimate the probability of survival after ICU discharge at various time intervals. Significant differences were established by P<0.05. IBM ver. 21 software (IBM Corp.) was used for analysis.

RESULTS

During the study period, 1,596 patients were admitted to the ICU and of these, 537 patients died in this unit. Of the 1,059 surviving ICU patients, 33 were excluded for incomplete data and 1 for being under 18 years of age (Figure 1). Data were collected from 1,025 medical records. The median age was 62 years (IQR, 46.0–73.5 years), with a predominance of males, 573 (55.9%). The length of hospital stay of these patients had a median of 17 days (IQR, 8–30 days) and the SAPS III severity score presented a median of 47 (IQR, 35.0–62.5).

Of the total number of patients studied, 314 (30.6%) had some type of infection at ICU discharge, 92 (9%) were readmitted to the unit, and 212 (20.7%) died. Considering the patients who developed some type of infection, in 103 (32.8%) it was not possible to identify the microorganism, 78 (24.8%) were sensitive microorganisms, 81 (25.8%) were XDR, 50 (15, 9%) MDR, and 2 (0.6%) PDR. The most frequently found infectious foci were pulmonary, with 253 cases (77%), followed by the urinary tract with 63 (18.1%) and the surgical site with 24 (6.4%).

There were 588 (57.4%) unplanned ICU admissions and 437 (42.6%) planned admissions in the study period. The most frequent admission diagnoses were 153 lower limb osteosynthesis (14.9%), 128 digestive tract surgeries (12.5%), 84 intracranial hemorrhages (8.20%), 81 sepsis (7.9%), 52 neurosurgeries (5.1%), 44 peripheral vascular surgeries (4.3%) and 44 head traumas (4.3%).

When assessing vital status at hospital discharge, the median age of non-survivors (67.5 years) was higher compared to survivors (60 years, P<0.001). There was no association between sex and hospital mortality (P=0.103). The length of hospital stay and the SAPS III were higher among non-survivors (P<0.001). The presence of infection and readmission to the ICU were associated with hospital death (P<0.001). When analyzing the 212 cases of death, 117 (55.2%) occurred in infected patients; 28 (13.2%) by sensitive microorganisms, 30 (14.2%) by unidentified microorganisms, and 59 (27.8%) by resistant microorganisms (Table 1).

In the logistic regression, the vital status on hospital discharge was considered as the dependent variable, and age, length of hospital stay, SAPS III, presence of infection, readmission to the ICU, and microorganisms were considered as covariates. The variables that remained in the regression model were age (OR, 1.029; 95% CI, 1.01–1.04; P<0.001), length of hospital stay (OR, 1.011; 95% CI, 1.003–1.019; P=0.004), SAPS III (OR, 1.042; 95% CI, 1.031–1.054; P<0.001), presence of infection (OR, 1.251; 95% CI, 1.081–1.447; P=0.003), and readmission to the ICU (OR, 11.659; 95% CI, 6.522–20.842; P<0.001) (Table 2). Kaplan-Meier analysis shows a median 49,000 days (95% CI, 42.987–55.013 days) after ICU discharge and 200 death events in 90 days (Figure 2).

DISCUSSION

The present study describes factors associated with hospital mortality in ICU survivors. The presence of infection on discharge from the ICU and readmissions demonstrated the greatest weight for the risk of death for these patients. These results can support decisions on optimizing HAI prevention and treatment, in addition to the allocation of resources and the prioritization of patients at a higher risk for intermediate care units after leaving the ICU [14,15].

Previous studies have shown that several factors may be associated with worse outcomes after ICU discharge. The intensity of therapeutic interventions and nursing overload at ICU discharge were associated with an increase in hospital mortality [3]. A systematic review described a readmission score that predicts adverse events after ICU discharge. The Minimizing ICU Readmission score takes into account several factors for this prediction, such as the SAPS II prognostic score at ICU admission, the use of a central venous catheter during the ICU stay, the SOFA score at ICU discharge, the presence of SIRS in the last 2 days of ICU stay, and discharge from the ICU at night [16].

Although the prognostic scores present moderate performance, with limitations in their calibration in low and middle-income countries, the SAPS III remains the most applied prognostic assessment tool in several countries [17,18]. The SAPS III calculated at ICU discharge could better reflect the risk of hospital death, but it is not common for units to have this data in routine care furthermore, SAPS III was developed to predict hospital mortality, not ICU mortality. In our study, we chose the SAPS III at ICU admission because it is the score available in the data source and because it is adopted by the institutional policy to evaluate the unit's performance and, therefore, can be easily incorporated to identify patients at a high risk of an unfavorable outcome at ICU discharge.

In a recent study of patients affected by coronavirus disease 2019 (COVID-19), age was the factor identified as associated with increased mortality [19], similar to what was found in our sample, where each year of age increased the chance of post-ICU death by approximately 3%. On the other hand, Madotto et al. [20] describe that the presence of comorbidities and limitations of life support treatments resulted in worse outcomes in ICU survivors.

Our post-ICU discharge mortality can be considered high when compared with results from Europe and New Zealand [21,22]. Although the research institution has implemented follow-up of surviving ICU patients by a rapid response team led by an intensive care physician, there is a high demand for ICU beds that pressures early discharge and there is no availability of intermediate care beds. Reports from low- and middle-income countries show similar results [5].

The prevalence of death in infected patients who survived the ICU was high in our study. The increased risk of post ICU mortality in patients with infections has been pointed out by other authors [23-26]. It is estimated that infection by MDR increases hospital mortality by approximately 1.7 times [23,24], which is similar to the results of the current study.

Pneumonia is the most frequent focus of infection reported by other studies [27-29], and this result is consistent with our findings. Klebsiella species was the most commonly isolated microorganism in our patients. This bacterium is related to almost a third of infections caused by Gram-negative bacteria in general, and presents high resistance to antimicrobials [30,31]. Other Gram-negative microorganisms responsible for HAIs are Enterobacteriaceae, Pseudomonas aeruginosa, Acinetobacter baumannii, and Stenotrophomonas maltophilia. The frequency of pneumonia caused by Klebsiella pneumoniae resistant to carbapenems is above 25%, while the frequency for P. aeruginosa is between 20 and 40% and for A. baumannii, between 40% and 70% [32].

Readmission to the ICU is associated with higher mortality and longer hospital stays [33]. Patients surviving readmission should be monitored and, preferably, remain in intermediate care units. The worse prognosis of patients requiring readmission could be associated with decompensation in the ward, which may result in late intervention. The use of intermediate care units can meet the needs of these higher-risk patients who require surveillance [14]. The length of hospital stay, as a risk factor for death, was also demonstrated in the present study, corroborating studies that show that a period longer than 6 days exposes the patient to more infectious risks [34].

It is possible to reduce readmission rates and increase the safety of surviving ICU patients through the implementation of rapid response teams, in view of the follow-up performed by an intensive care physician in the wards of patients discharged from the ICU [35]. It is estimated that implementation of the rapid response team is associated with a 20.6% decrease in the mortality rate [36].

The study population composed exclusively of ICU survivors, in addition to the sample size, can be considered strengths of the present study. Limitations of the study are the fact that it is a retrospective and single-center analysis, which limits its external validity. Also, a limited number of variables were investigated as risk factors for mortality. Other factors that can affect mortality, such as underlying comorbidities, the use of mechanical ventilation, renal replacement treatment or vasoactive drugs were not included in the regression analysis.

In conclusion, a high mortality rate was observed in patients who survived hospitalization in the ICU and the factors associated with hospital mortality were age, SAPS III, presence of infection, readmission to the ICU, and length of hospital stay.

KEY MESSAGES

▪ Infection diagnosis on intensive care unit discharge increases risk of hospital mortality.

▪ Intensive care readmission is a risk factor for hospital death in critical ill survivors from intensive care unit.

NOTES

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

FUNDING

None.

AUTHOR CONTRIBUTIONS

Conceptualization: LGAS, ELL, CMCG. Data curation: LGAS, CMDMC, TBT, CMCG. Formal analysis: ELL. Methodology: LGAS, CMDMC, TBT, LTQC, CMCG. Project administration: CMCG. Visualization: all authors. Writing–original draft: LGAS, TBT. Writing–review & editing: CMC, LTQC, ELL, CMCG.

Acknowledgments

None.

Figure 1.

Flowchart of patients.

Figure 2.

Analysis of 90-day survival after intensive care unit discharge of 1,025 patients. Kaplan-Meier analysis shows a median 49,000 days (95% confidence interval, 42.987–55.013) intensive care unit discharge and 200 death events in 90 days.

Table 1.

Univariate analysis to describe of intensive care unit survivors according to hospital outcome

Variable	Survivor (n=813)	Non-survivor (n=212)	P-value
Age (yr)	60 (44–72.5)	67.5 (53–77)	<0.001^a)
Male	444 (54.6)	129 (60.8)	0.103^b)
ICU length of stay (day)	2 (1.5–6)	9 (3–15)	<0.001^a)
Hospital length of stay (day)	14 (7–24)	32 (20–45.7)	<0.001^a)
SAPS III	44 (34–58)	61 (50.2–75)	<0.001^a)
Reason for admission			<0.001^b)
Clinical	151 (18.5)	80 (37.7)
Elective PO	394 (48.5)	43 (20.3)
Emergency PO	138 (16.9)	51 (24.1)
Trauma	130 (30.9)	38 (17.9)
BMI (kg/m²)	25.7 (23.4–28.1)	25.1 (23.0–27.9)	0.524^a)
Comorbidity			-
Hypertension	346 (42.6)	95 (44.8)
Cancer	65 (8.0)	20 (9.4)
Cirrhosis	6 (0.7)	4 (1.9)
Congestive heart failure	51 (6.3)	19 (8.9)
Coronary diseases	38 (4.7)	10 (4.7)
COPD	11 (1.3)	8 (3.8)
Diabetes mellitus	139 (17.1)	34 (16.0)
Use of mechanical ventilation	304 (37.4)	141 (66.5)	<0.001^b)
Infection	197 (24.2)	117 (55.2)	<0.001^b)
Readmission to ICU	25 (3.1)	67 (31.6)	<0.001^b)
Microorganism			-
Not identified	73 (8.9)	30 (14.2)	0.026^b)
Sensitive	50 (6.1)	28 (13.2)	0.001^b)
MDR	29 (3.6)	21 (9.9)	<0.001^b)
XDR	43 (5.3)	38 (17.9)	<0.001^b)
PDR	2 (0.2)	0	0.471^b)
MDR/XDR/PDR	74 (9.1)	59 (27.8)	<0.001^b)

Values are presented as median (interquartile range) or number (%).

ICU: intensive care unit; SAPS: Simplified Acute Physiology Score; PO: postoperative; BMI: body mass index; COPD: chronic obstructive pulmonary disease; MDR: multidrug-resistant microorganisms (non-susceptibility to at least one agent in three or more antimicrobial categories); XDR: extensively drug-resistant (non-susceptibility to at least one agent in all but two or fewer antimicrobial categories); PDR: pandrug-resistant microorganisms (non-susceptibility to all agents in all antimicrobial categories).

^a)Mann-Whitney test;

^b)Chi-square test.

Table 2.

Univariate and multivariate analysis for factors associated with mortality in ICU survivors, from January 2017 to December 2018

Variable	Univariate			Multivariate
Variable	Odds ratio	95% CI	P-value	Adjusted odds ratio	95% CI	P-value
Age (yr)	-	-	-	1.029	1.010–1.040	<0.001
Length of hospital stay (day)	-	-	-	1.011	1.003–1.019	0.004
SAPS III	-	-	-	1.042	1.031–1.054	<0.001
Presence of infection	4,305	3,195–5,912	<0.001	1.251	1.081–1.447	0.003
Readmission to ICU	14,564	8,902–27,829	<0.001	11.659	6.522–20.842	<0.001
Microorganisms
Not identified	1,646	1,045–2,592	0.026	1.530	0.900–2.601	0.116
Sensitive	2,322	1,422–3,789	0.001	1.231	0.665–2.277	0.509
MDR	2,972	1,658–5,327	<0.001	1.037	0.076–14.147	0.978
XDR	3,786	2,368–6,052	<0.001	1.096	0.240–13.948	0.899
MDR/XDR/PDR	3,825	2,793–5,239	<0.001	2.326	0.184–29.412	0.514

ICU: intensive care unit; CI: confidence interval; SAPS: Simplified Acute Physiology Score; MDR: multidrug-resistant microorganisms (non-susceptibility to at least one agent in three or more antimicrobial categories); XDR: extensively drug-resistant (non-susceptibility to at least one agent in all but two or fewer antimicrobial categories); PDR: pandrug-resistant microorganisms (non-susceptibility to all agents in all antimicrobial categories).

REFERENCES

1. Goldhill DR, Sumner A. Outcome of intensive care patients in a group of British intensive care units. Crit Care Med 1998;26:1337-45.Article PubMed
2. Rowan KM, Kerr JH, Major E, McPherson K, Short A, Vessey MP. Intensive Care Society’s APACHE II study in Britain and Ireland--II: Outcome comparisons of intensive care units after adjustment for case mix by the American APACHE II method. BMJ 1993;307:977-81.Article PubMed PMC
3. Smith L, Orts CM, O’Neil I, Batchelor AM, Gascoigne AD, Baudouin SV. TISS and mortality after discharge from intensive care. Intensive Care Med 1999;25:1061-5.Article PubMed PDF
4. Azoulay E, Adrie C, De Lassence A, Pochard F, Moreau D, Thiery G, et al. Determinants of postintensive care unit mortality: a prospective multicenter study. Crit Care Med 2003;31:428-32.Article PubMed
5. Ranzani OT, Prada LF, Zampieri FG, Battaini LC, Pinaffi JV, Setogute YC, et al. Failure to reduce C-reactive protein levels more than 25% in the last 24 hours before intensive care unit discharge predicts higher in-hospital mortality: a cohort study. J Crit Care 2012;27:525. Article
6. Campbell AJ, Cook JA, Adey G, Cuthbertson BH. Predicting death and readmission after intensive care discharge. Br J Anaesth 2008;100:656-62.Article PubMed
7. Ponzoni CR, Corrêa TD, Filho RR, Serpa Neto A, Assunção MS, Pardini A, et al. Readmission to the intensive care unit: incidence, risk factors, resource use, and outcomes: a retrospective cohort study. Ann Am Thorac Soc 2017;14:1312-9.Article PubMed
8. Rodrigues CM, Pires EM, Feliciano JP, Vieira JM Jr, Taniguchi LU. Admission factors associated with intensive care unit readmission in critically ill oncohematological patients: a retrospective cohort study. Rev Bras Ter Intensiva 2016;28:33-9.Article PubMed PMC
9. Lin WT, Chen WL, Chao CM, Lai CC. The outcomes and prognostic factors of the patients with unplanned intensive care unit readmissions. Medicine (Baltimore) 2018;97:e11124.Article PubMed PMC
10. Hosein FS, Roberts DJ, Turin TC, Zygun D, Ghali WA, Stelfox HT. A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care. Crit Care 2014;18:715. Article PubMed PMC PDF
11. Keegan MT, Soares M. What every intensivist should know about prognostic scoring systems and risk-adjusted mortality. Rev Bras Ter Intensiva 2016;28:264-9.Article PubMed PMC
12. Serpa Neto A, Assunção MS, Pardini A, Silva E. Feasibility of transitioning from APACHE II to SAPS III as prognostic model in a Brazilian general intensive care unit: a retrospective study. Sao Paulo Med J 2015;133:199-205.Article PubMed
13. Magiorakos AP, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect 2012;18:268-81.Article PubMed
14. Morland M, Haagensen R, Dahl FA, Berdal JE. Epidemiology and prognoses in a medical intermediate care unit. Tidsskr Nor Laegeforen 2018;138.Article PMC
15. Capuzzo M, Volta C, Tassinati T, Moreno RP, Valentin A, Guidet B, et al. Hospital mortality of adults admitted to intensive care units in hospitals with and without intermediate care units: a multicentre European cohort study. Crit Care 2014;18:551. Article PubMed PMC PDF
16. Hosein FS, Bobrovitz N, Berthelot S, Zygun D, Ghali WA, Stelfox HT. A systematic review of tools for predicting severe adverse events following patient discharge from intensive care units. Crit Care 2013;17:R102. Article PubMed PMC
17. Haniffa R, Isaam I, De Silva AP, Dondorp AM, De Keizer NF. Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review. Crit Care 2018;22:18. Article PubMed PMC PDF
18. Fuchs PA, Czech IJ, Krzych ŁJ. The pros and cons of the prediction game: the never-ending debate of mortality in the intensive care unit. Int J Environ Res Public Health 2019;16:3394. Article PubMed PMC
19. Donnelly JP, Wang XQ, Iwashyna TJ, Prescott HC. Readmission and death after initial hospital discharge among patients with COVID-19 in a large multihospital system. JAMA 2021;325:304-6.Article PubMed
20. Madotto F, McNicholas B, Rezoagli E, Pham T, Laffey JG, Bellani G, et al. Death in hospital following ICU discharge: insights from the LUNG SAFE study. Crit Care 2021;25:144. Article PubMed PMC
21. Santamaria JD, Duke GJ, Pilcher DV, Cooper DJ, Moran J, Bellomo R, et al. The timing of discharge from the intensive care unit and subsequent mortality: a prospective, multicenter study. Am J Respir Crit Care Med 2015;191:1033-9.Article PubMed
22. Azoulay E, Alberti C, Legendre I, Buisson CB, Le Gall JR, European Sepsis Group. Post-ICU mortality in critically ill infected patients: an international study. Intensive Care Med 2005;31:56-63.Article PubMed PDF
23. Barrasa-Villar JI, Aibar-Remón C, Prieto-Andrés P, Mareca-Doñate R, Moliner-Lahoz J. Impact on morbidity, mortality, and length of stay of hospital-acquired infections by resistant microorganisms. Clin Infect Dis 2017;65:644-52.Article PubMed
24. Serra-Burriel M, Keys M, Campillo-Artero C, Agodi A, Barchitta M, Gikas A, et al. Impact of multi-drug resistant bacteria on economic and clinical outcomes of healthcare-associated infections in adults: systematic review and meta-analysis. PLoS One 2020;15:e0227139.Article PubMed PMC
25. Rosa RG, Falavigna M, Robinson CC, Sanchez EC, Kochhann R, Schneider D, et al. Early and late mortality following discharge from the ICU: a multicenter prospective cohort study. Crit Care Med 2020;48:64-72.Article PubMed
26. Taniguchi LU, Ramos FJ, Momma AK, Martins Filho AP, Bartocci JJ, Lopes MF, et al. Subjective score and outcomes after discharge from the intensive care unit: a prospective observational study. J Int Med Res 2019;47:4183-93.Article PubMed PMC PDF
27. Kołpa M, Wałaszek M, Gniadek A, Wolak Z, Dobroś W. Incidence, microbiological profile and risk factors of healthcare-associated infections in intensive care units: a 10 year observation in a provincial hospital in Southern Poland. Int J Environ Res Public Health 2018;15:112. Article PubMed PMC
28. Ferreira LL, Azevedo LM, Salvador PT, Morais SH, Paiva RM, Santos VE. Nursing care in healthcare-associated infections: a scoping review. Rev Bras Enferm 2019;72:476-83.Article PubMed
29. Mota EC, Oliveira SP, Silveira BR, Silva PL, Oliveira AC. Incidência da pneumonia associada à ventilação mecânica em unidade de terapia intensiva. Medicina (Ribeirão Preto) 2017;50:39-46.Article PDF
30. Navon-Venezia S, Kondratyeva K, Carattoli A. Klebsiella pneumoniae: a major worldwide source and shuttle for antibiotic resistance. FEMS Microbiol Rev 2017;41:252-75.Article PubMed
31. de Maio Carrilho CM, de Oliveira LM, Gaudereto J, Perozin JS, Urbano MR, Camargo CH, et al. A prospective study of treatment of carbapenem-resistant Enterobacteriaceae infections and risk factors associated with outcome. BMC Infect Dis 2016;16:629. PubMed PMC
32. Breijyeh Z, Jubeh B, Karaman R. Resistance of Gram-negative bacteria to current antibacterial agents and approaches to resolve it. Molecules 2020;25:1340. Article PubMed PMC
33. Tejerina Álvarez EE, Gómez Mediavilla KA, Rodríguez Solís C, Valero González N, Lorente Balanza JÁ. Risk factors for readmission to ICU and analysis of intra-hospital mortality. Med Clin (Barc) 2022;158:58-64.Article PubMed
34. Londoño Restrepo J, Macias Ospina IC, Ochoa Jaramillo FL. Factores de riesgo asociados a infecciones por bacterias multirresistentes derivadas de la atención en salud en una institución hospitalaria de la ciudad de Medellín 2011-2014. Infect 2016;20:77-83.Article
35. Bergamasco E Paula R, Tanita MT, Festti J, Queiroz Cardoso LT, Carvalho Grion CM. Analysis of readmission rates to the intensive care unit after implementation of a rapid response team in a University Hospital. Med Intensiva 2017;41:411-7.Article PubMed
36. Jung B, Daurat A, De Jong A, Chanques G, Mahul M, Monnin M, et al. Rapid response team and hospital mortality in hospitalized patients. Intensive Care Med 2016;42:494-504.Article PubMed PDF

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Risk factors for hospital mortality in intensive care unit survivors: a retrospective cohort study

Acute Crit Care. 2023;38(1):68-75. Published online February 27, 2023

DOI: https://doi.org/10.4266/acc.2022.01375

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Risk factors for hospital mortality in intensive care unit survivors: a retrospective cohort study

Figure 1. Flowchart of patients.

Figure 2. Analysis of 90-day survival after intensive care unit discharge of 1,025 patients. Kaplan-Meier analysis shows a median 49,000 days (95% confidence interval, 42.987–55.013) intensive care unit discharge and 200 death events in 90 days.

Figure 1.

Figure 2.

Risk factors for hospital mortality in intensive care unit survivors: a retrospective cohort study

Variable	Survivor (n=813)	Non-survivor (n=212)	P-value
Age (yr)	60 (44–72.5)	67.5 (53–77)	<0.001^a)
Male	444 (54.6)	129 (60.8)	0.103^b)
ICU length of stay (day)	2 (1.5–6)	9 (3–15)	<0.001^a)
Hospital length of stay (day)	14 (7–24)	32 (20–45.7)	<0.001^a)
SAPS III	44 (34–58)	61 (50.2–75)	<0.001^a)
Reason for admission			<0.001^b)
Clinical	151 (18.5)	80 (37.7)
Elective PO	394 (48.5)	43 (20.3)
Emergency PO	138 (16.9)	51 (24.1)
Trauma	130 (30.9)	38 (17.9)
BMI (kg/m²)	25.7 (23.4–28.1)	25.1 (23.0–27.9)	0.524^a)
Comorbidity			-
Hypertension	346 (42.6)	95 (44.8)
Cancer	65 (8.0)	20 (9.4)
Cirrhosis	6 (0.7)	4 (1.9)
Congestive heart failure	51 (6.3)	19 (8.9)
Coronary diseases	38 (4.7)	10 (4.7)
COPD	11 (1.3)	8 (3.8)
Diabetes mellitus	139 (17.1)	34 (16.0)
Use of mechanical ventilation	304 (37.4)	141 (66.5)	<0.001^b)
Infection	197 (24.2)	117 (55.2)	<0.001^b)
Readmission to ICU	25 (3.1)	67 (31.6)	<0.001^b)
Microorganism			-
Not identified	73 (8.9)	30 (14.2)	0.026^b)
Sensitive	50 (6.1)	28 (13.2)	0.001^b)
MDR	29 (3.6)	21 (9.9)	<0.001^b)
XDR	43 (5.3)	38 (17.9)	<0.001^b)
PDR	2 (0.2)	0	0.471^b)
MDR/XDR/PDR	74 (9.1)	59 (27.8)	<0.001^b)

Variable	Univariate			Multivariate
Variable	Odds ratio	95% CI	P-value	Adjusted odds ratio	95% CI	P-value
Age (yr)	-	-	-	1.029	1.010–1.040	<0.001
Length of hospital stay (day)	-	-	-	1.011	1.003–1.019	0.004
SAPS III	-	-	-	1.042	1.031–1.054	<0.001
Presence of infection	4,305	3,195–5,912	<0.001	1.251	1.081–1.447	0.003
Readmission to ICU	14,564	8,902–27,829	<0.001	11.659	6.522–20.842	<0.001
Microorganisms
Not identified	1,646	1,045–2,592	0.026	1.530	0.900–2.601	0.116
Sensitive	2,322	1,422–3,789	0.001	1.231	0.665–2.277	0.509
MDR	2,972	1,658–5,327	<0.001	1.037	0.076–14.147	0.978
XDR	3,786	2,368–6,052	<0.001	1.096	0.240–13.948	0.899
MDR/XDR/PDR	3,825	2,793–5,239	<0.001	2.326	0.184–29.412	0.514

Table 1. Univariate analysis to describe of intensive care unit survivors according to hospital outcome

Values are presented as median (interquartile range) or number (%).

Mann-Whitney test;

Chi-square test.

Table 2. Univariate and multivariate analysis for factors associated with mortality in ICU survivors, from January 2017 to December 2018