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Original Research Articles: Original Clinical Research Report

Invasiveness of Treatment Is Gender Dependent in Intensive Care: Results From a Retrospective Analysis of 26,711 Cases

Blecha, Sebastian MD*; Zeman, Florian MSc; Specht, Simon MD*; Lydia Pfefferle, Anna MD*; Placek, Sabine MD*; Karagiannidis, Christian PhD; Bein, Thomas PhD*

Author Information
doi: 10.1213/ANE.0000000000005082
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Abstract

KEY POINTS

  • Question: Did tracheostomy, extracorporeal life support, and other intensive care unit (ICU) therapies or mortality differ by gender?
  • Findings: Male patients more often received invasive treatment and had higher rates of tracheostomy, dialysis, and extracorporeal membrane oxygenation.
  • Meaning: In critically ill patients, ICU treatment is marked by gender-related differences.

Potential gender-related bias in the provision of medical services has been of growing interest in recent years. Gender-related differences were previously reported in a 2003 study of critically ill patients.1 In more recent analyses, critically ill men were more likely to receive invasive monitoring and treatment, including mechanical ventilation, dialysis, pulmonary arterial catheterization (PAC), and central venous catheterization (CVC).2,3

Several studies have also reported gender-related differences in the treatment and outcome of specific disease states. In patients with cardiovascular disease, more men than women undergo intensive evaluation and treatment.4–7 Yet, survival after out-of-hospital cardiac arrest or interventional cardiac procedures does not differ between men and women.8–10 In a recent study of patients with acute respiratory distress syndrome (ARDS), women were less likely to receive lung-protective ventilation, and female sex was associated with higher mortality in patients with severe ARDS.11

We hypothesized that gender would be associated with different implementation rates of tracheostomy and extracorporeal membrane oxygenation (ECMO) in critically ill patients. To test our hypothesis, we reviewed gender-specific differences in mortality and the use of invasive critical care therapies, including tracheostomy and extracorporeal life supporting a large intensive care unit (ICU) cohort.

METHODS

Study Design

This observational, retrospective single-center study was approved by the Ethics Committee of the University of Regensburg (original approval: February 2018; file number 17-850-104) and registered at the local Center for Clinical Studies (Z-2018-1015-7. Registered April 26, 2018). Based on the study design, the requirement for written informed consent was waived by the institutional review board.

Sample

F1
Figure.:
Consort statement: flow chart of ICU patients throughout the study. ICU indicates intensive care unit.

The Figure gives an overview of the sample size of this study. The study preliminarily included 42,250 ICU patients who had been treated at 1 of the 6 ICUs of the University Medical Center Regensburg between January 2010 and December 2017. The reasons for study exclusion were patient age below 18 years (n = 2711), multiple admissions to the ICU during the same hospital stay (n = 2315), and ICU treatment for <24 hours (n = 10,513) because this short time span would not allow assessment of the Simplified Acute Physiology Score II (SAPS II). A total of 26,711 patients meeting the inclusion criteria were analyzed.

Data Collection and Measuring Instruments

The German Institute for Medical Documentation and Information has published official medical classifications such as the Tenth Revision of the International Classification of Diseases and Related Health Problems (ICD-10-GM) and the German Procedure Classification (OPS). Statements of accounts of German hospitals use diagnoses and treatments encoded by means of the ICD-10-GM and OPS.12 These classifications contain codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases and their treatment. Illness severity of ICU patients had been assessed within 24 hours after ICU admission by means of the SAPS II score. Three physicians using the ICD-10-GM independently evaluated the reasons for ICU admission. Each physician preselected ICU patients by means of the admission diagnosis and defined the reason for ICU admission. In the case of unclear admission diagnosis, the documented ICD-10-GM and OPS were discussed by all physicians to find a consensus. All cases that could not be assigned to one of the defined groups of ICU admission after discussion were declared as “other.” The intensity of ICU treatment was assessed according to the application of invasive monitoring (PAC) and treatment (CVC, endotracheal intubation, and tracheostomy), as well as the duration of mechanical ventilation and organ replacement therapy (dialysis and ECMO). All diagnoses and treatments were analyzed by means of ICD-10-GM and OPS. In this study, all disease characteristics and treatments, as well as information on mortality or discharge from the ICU and hospital, were analyzed using the electronic data capture system SAP (SAP SE, Walldorf, Germany).

Primary and Secondary End Points

Our primary study end point was the gender-specific frequency of tracheostomy and ECMO implementation. Secondary end points were the differences in other types of invasive monitoring and treatment (endotracheal intubation rate, duration of mechanical ventilation, PAC, CVC, and dialysis) and the reason for ICU admission, illness severity, and ICU and hospital mortality. Additionally, we analyzed gender-specific differences in ICU treatment for ICU nonsurvivors.

Statistical Analysis

Continuous data are shown as mean ± standard deviation (SD) or median (Md) and interquartile range (IQR) depending on the underlying distribution (normal or nonnormal). Categorical variables are presented in absolute and relative frequencies. Normality was assessed using descriptive values (mean versus Md, skewness, and kurtosis) and graphically by means of histograms and Q–Q Plots (data not shown). Groups (men versus women) were compared using the Student t test for normal distributed variables, the Mann–Whitney U test for nonnormal distributed variables, and the χ2 test of independence for categorical variables. To assess differences between men and women regarding primary and secondary end points, univariable and multivariable logistic and negative binomial regression models were calculated. Multivariable models were adjusted by age, SAPS II, and reason for hospitalization. Odds ratios (OR) for the logistic regression models and incidence rate ratios (IRR) for the negative binomial regression models were calculated as effect estimates together with the corresponding 95% confidence intervals (95% CI). A P value <.05 was considered significant. For type I error, a Bonferroni–Holm adjustment was used. All secondary end points were exploratory, and no adjustments for multiple testing were performed. All analyses were conducted using the software R (version 3.5.1; R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org).

No a-priori sample size calculation was performed for this study. Furthermore, there were no a-priori assumptions about potential differences between men and women because of the exploratory nature of the trial to assess differences in our clinical setting.

RESULTS

The study included 26,711 patients admitted to 1 of the 6 ICUs at the University Medical Center Regensburg. Characteristics of ICU admission, patient demographics, and total outcomes differentiated between men and women are presented in Table 1. A total of 64.8% of the patients were men. Women were significantly older at ICU admission. The most common reasons for ICU admission were cardiac, gastrointestinal, neurologic, or respiratory disease. The Md ICU length of stay (LOS) was 3 days (IQR: 1–7), and 21.8% of ICU patients (n = 5811) had been mechanically ventilated. According to the SAPS II, men and women did not differ in illness severity at ICU admission, ICU and hospital LOS, or in ICU and hospital mortality.

Table 1. - Patient Demographics, Admission Characteristics, and Outcomes of the ICU Study Cohort
All Admissions Men Women P
(n = 26,711) (n = 17,326) (n = 9385)
Age, y (mean [SD]) 61.8 (15.8) 61.3 (15.1) 62.6 (17.0) <.001a
SAPS II (mean [SD]) 32.4 (15.8) 32.3 (15.7) 32.6 (15.9) .205
Reason for ICU admission (n [%]) <.001a
 Neurologic 2530 (9.5) 1340 (7.7) 1190 (12.7)
 Cardiac 9363 (35.1) 6596 (38.1) 2767 (29.5)
 Respiratory 2030 (7.6) 1322 (7.6) 708 (7.5)
 Gastrointestinal 3850 (14.4) 2364 (13.6) 1486 (15.8)
 Renal 369 (1.4) 225 (1.3) 144 (1.5)
 Sepsis or infection 1155 (4.3) 735 (4.2) 420 (4.5)
 Metabolic or endocrine 288 (1.1) 151 (0.9) 137 (1.5)
 Intoxication 328 (1.2) 156 (0.9) 172 (1.8)
 Trauma 1366 (5.1) 943 (5.4) 423 (4.5)
 Hematologic 1914 (7.2) 1178 (6.8) 736 (7.8)
 Vascular 1723 (6.5) 1165 (6.7) 558 (5.9)
 Other 1795 (6.7) 1151 (6.6) 644 (6.9)
ICU LOS, h (median [IQR]) 69 (32–173) 68 (32–170) 70 (33–181) .096
Hospital LOS, d (median [IQR]) 14 (9–23) 14 (9–23) 14 (9–24) .439
ICU mortality (n [%]) 2342 (8.8) 1529 (8.8) 813 (8.7) .671
Hospital mortality (n [%]) 2571 (9.6) 1674 (9.7) 897 (9.6) .8
Abbreviations: ICU, intensive care unit; IQR, interquartile range; LOS, length of stay; SAPS II, Simplified Acute Physiology Score II; SD, standard deviation.
aP < .05.

Univariable and multivariable analyses of primary end points found significantly higher rates of tracheostomy (OR = 1.39 [1.26–1.54]) and ECMO (OR = 1.37 [1.02–1.83]) in men (Table 2). Patients treated with tracheostomy, ECMO, or both, were younger (59.7 vs 62.0 years; P < .001) and had higher SAPS II scores (38.6 vs 31.8; P < .001) and ICU mortality rates (20.9% vs 7.6%; P < .001).

Table 2. - Multivariable Logistic Regression Analyses for Treatment Differences Between Men and Women Regarding Tracheostomy and ECMO (Primary End Points)
Men (n = 17,326) Women (n = 9385) ORa (95% CI) P Adjustedb ORa (95% CI) P Level of Significancec
Tracheostomy (n [%]) 1531 (8.8) 661 (7.0) 1.28 (1.16–1.41) <.001d 1.39 (1.26–1.54) <.001d 0.025
ECMO (n [%]) 185 (1.1) 69 (0.7) 1.46 (1.10–1.92) .008d 1.37 (1.02–1.83) .035d 0.05
Abbreviations: CI, confidence interval; ECMO, extracorporeal membrane oxygenation; OR, odds ratio; SAPS II, Simplified Acute Physiology Score II.
aMen compared to women (reference value).
bAdjusted for age, SAPS II, and reason for hospitalization.
cLevel of significance defined according to Bonferroni-Holm.
dP < .05.

Multivariable analysis of secondary end points found that male ICU patients had higher rates of dialysis (OR = 1.29 [1.18–1.41]) and PAC (OR = 1.81 [1.40–2.33]) and 7% longer duration of mechanical ventilation (IRR = 1.07 [1.02–1.12]) than that of women (Table 3). The frequency of endotracheal intubation (OR = 1.04 [0.98–1.11]) and placement of CVC (OR = 1.05 [0.98–1.11]) did not differ by gender.

Table 3. - Treatment Differences Between Men and Women Analyzed by Using Multivariable Logistic Regression Models Regarding Pulmonary Artery Catheterization, Dialysis, Central Venous Catheterization, Endotracheal Intubation Rate, and Multiple Negative Binomial Regression Model for Duration of Mechanical Ventilation (Secondary End Points)
Men (n = 17,326) Women (n = 9385) ORa (95% CI) P Adjustedb ORa (95% CI) P
Pulmonary artery catheter (n [%]) 324 (1.9) 78 (0.8) 2.27 (1.77–2.92) <.001c 1.81 (1.40–2.33) <.001c
Dialysis (n [%]) 2103 (12.1) 918 (9.8) 1.27 (1.17–1.38) <.001c 1.29 (1.18–1.41) <.001c
Central venous catheter (n [%]) 4576 (26.4) 2562 (27.3) 0.96 (0.90–1.01) .118 1.05 (0.98–1.11) .157
Endotracheal intubation (n = 5811) (n [%]) 3776 (21.8) 2035 (21.7) 1.01 (0.95–1.07) .835 1.04 (0.98–1.11) .189
Duration of Mechanical Ventilation (n = 5811) Men (n = 17,326) Women (n = 9385) IRRa P Adjustedb IRRa P
Days (median [IQR]) 3.62 [1.29, 10.21] 3.67 [1.33, 9.67] 1.04 [0.99–1.09] .108 1.07 [1.02–1.12] .008c
Abbreviations: CI, confidence interval; IQR, interquartile range; IRR, incidence rate ratio; OR, odds ratio; SAPS II, Simplified Acute Physiology Score II.
aMen compared to women (reference value).
bAdjusted for age, SAPS II, and reason for hospitalization.
cP < .05.

Table 4. - Characteristics of ICU Nonsurvivors (n = 2342)
Men Women P
(n = 1529) (n = 813)
Age, y (mean [SD]) 64.9 (13.74) 66.3 (15.2) .028a
SAPS II (mean [SD]) 46.1 (18.1) 44.3 (18.0) .019a
ICU LOS, h (median [IQR]) 160 [66–356] 145.00 [63–321] .122
Tracheostomy (n [%]) 308 (20.1) 124 (15.3) .004a
ECMO (n [%]) 63 (4.1) 22 (2.7) .104
Endotracheal intubation (n [%]) 902 (59.0) 466 (57.3) .46
Duration of ventilation (n = 1368), d (median [IQR]) 6.33 [2.29–14.10] 5.38 [2.17–12.12] .015a
Central venous catheter (n [%]) 897 (58.7) 418 (51.4) .001a
Pulmonary artery catheter (n [%]) 99 (6.5) 37 (4.6) .072
Dialysis (n [%]) 826 (54.0) 377 (46.4) <.001a
Reason for ICU admission (n [%]) .008a
 Neurologic 151 (9.9) 125 (15.4)
 Cardiac 416 (27.2) 190 (23.4)
 Respiratory 217 (14.2) 87 (10.7)
 Gastrointestinal 246 (16.1) 137 (16.9)
 Renal 11 (0.7) 5 (0.6)
 Sepsis or infection 157 (10.3) 93 (11.4)
 Metabolic or endocrine 1 (0.1) 2 (0.2)
 Intoxication 4 (0.3) 2 (0.2)
 Trauma 60 (3.9) 27 (3.3)
 Hematologic 96 (6.3) 47 (5.8)
 Vascular 104 (6.8) 56 (6.9)
 Other 66 (4.3) 42 (5.2)
Abbreviations: ECMO, extracorporeal membrane oxygenation; ICU, intensive care unit; IQR, interquartile range; LOS, length of stay; SAPS II, Simplified Acute Physiology Score II; SD, standard deviation.
aP < .05.

During ICU treatment, 8.8% of patients had died. Patient characteristics and treatment of ICU nonsurvivors are shown in Table 4. Among nonsurvivors, women were older (P = .028) and had lower SAPS II scores (P = .019) at ICU admission. Male patients who died during ICU treatment had longer mechanical ventilation times than women (Md: 6.33 vs 5.38 days: P = .015) and higher implementation rates of tracheostomy (20.1% vs 15.3%; P = .004), CVC (58.7% vs 51.4%; P < .001), and dialysis (54.0% vs 46.4%; P < .001).

DISCUSSION

In this study, we analyzed a large ICU cohort of 26,711 patients with regard to gender-related differences in ICU treatment and mortality rates. After multivariable analysis, we found that male sex was an independent factor for tracheostomy or ECMO or both. Secondary findings were that (1) men also were more often to undergo dialysis and PAC and had longer durations of mechanical ventilation; (2) men and women did not differ in illness severity, ICU LOS, and mortality; and (3) in ICU nonsurvivors, men had been ventilated longer and had received more often tracheostomy, dialysis, and CVC than women.

Our results are consistent with previous studies. Our ICU and hospital mortality rates of 8.8% and 9.6% were consistent with mortality rates between 8.2% and 12.3% for comparable ICU cohorts, and as with previous trials, we found no gender effect on mortality.3,13,14 One 2003 study found higher mortality rates for women than in our study but also older age at ICU admission and higher SAPS II scores.1 SAPS II is a validated and sufficient score that provides an estimate of the risk of death at ICU admission.15 Depending on the intensive care treatment provided, however, the SAPS II score decreases with increasing ICU LOS, making it unsuitable for analyzing the course of ICU treatment.16 In addition, because the SAPS II score does not include the factor gender as a variable, this tool may not be useful to accurately predict illness severity differentiated between men and women.

Large observational ICU studies have found that 55.2%–60.1% of ICU cohorts consist of men.3,14 In the present study, nearly two-thirds of ICU patients had been men (64.8%). Our results suggest that men had a significantly higher probability of receiving several invasive procedures. In other European studies, men also more often received invasive treatment during ICU stays and used more ICU resources per admission.13,17 A 2016 study of prehospital transport also found that female trauma patients were less likely to receive the highest prehospital transport priority such as direct transportation to a trauma center.18

The mechanisms underlying our results are unclear. In our hospital, the majority of nursing staff are women, and the percentage of women in the medical profession is steadily increasing. This trend may play a role. Older studies of nurse perception suggested, for example, that male patients tended to be better liked, were shown a more positive attitude, and were treated differently than female patients.19,20 A 1991 study in the United States found that nurses less often encouraged female patients to be active, administered fewer analgesics, and had less time for psychological support.21 A 2001 study suggested that physicians may prescribe more pain medicine to patients of similar gender.22 Current data do not suggest a gender effect on the decision to admit a patient to the ICU.23 More multicenter epidemiological studies and investigations into the gender-specific attitudes of nurses and physicians are needed to better understand gender differences in critical care.

LIMITATIONS

Our study has limitations. First, as a retrospective single-center study, our results may not be reproducible in other centers. Two-thirds of admissions to our preliminary ICU cohort lasted less than 24 hours, which may have distorted any gender-related differences present in patients receiving critical care for a longer period. Because of incomplete data, we could not evaluate for all potential confounders. SAPS II is a valid score for measuring illness severity at ICU admission but only allows a limited analysis of comorbidities (metastatic cancer, hematologic malignancy, and acquired immunodeficiency syndrome [AIDS]). Finally, differences in the invasive care of ICU patients may have been caused by wishes of the patient or family members that could not be addressed in this analysis.

CONCLUSIONS

ICU treatment differs between men and women, independent of outcome. Men received invasive treatment in the form of tracheostomy and ECMO more frequently than women. Further studies are needed to address decision-making in intensive care with respect to potential gender-related bias.

ACKNOWLEDGMENTS

We thank the Department of Medical Data Management and Computing of the University Medical Center Regensburg for data acquisition and generation. Furthermore, we are grateful to Monika Schoell for the linguistic revision of the manuscript.

DISCLOSURES

Name: Sebastian Blecha, MD.

Contribution: This author had the idea for the study, applied for the ethics approval, contributed to the conception and design of the study, helped with the provision of the study materials, conducted the data analysis and interpretation, drafted the manuscript, reviewed the manuscript for important intellectual content, and approved the final manuscript.

Conflicts of Interest: None.

Name: Florian Zeman, MSc.

Contribution: This author contributed to the conception and design of the study, helped with the provision of the study materials, conducted the data analysis and interpretation, reviewed the manuscript for important intellectual content, and approved the final manuscript.

Conflicts of Interest: None.

Name: Simon Specht, MD.

Contribution: This author contributed to the conception and design of the study, helped with the provision of the study materials, conducted the data analysis and interpretation, reviewed the manuscript for important intellectual content, and approved the final manuscript.

Conflicts of Interest: None.

Name: Anna Lydia Pfefferle, MD.

Contribution: This author contributed to the conception and design of the study, helped with the provision of the study materials, conducted the data analysis and interpretation, reviewed the manuscript for important intellectual content, and approved the final manuscript.

Conflicts of Interest: None.

Name: Sabine Placek, MD.

Contribution: This author contributed to the conception and design of the study, helped with the provision of the study materials, conducted the data analysis and interpretation, reviewed the manuscript for important intellectual content, and approved the final manuscript.

Conflicts of Interest: None.

Name: Christian Karagiannidis, PhD.

Contribution: This author contributed to the conception and design of the study, helped with the provision of the study materials, conducted the data analysis and interpretation, reviewed the manuscript for important intellectual content, and approved the final manuscript.

Conflicts of Interest: C. Karagiannidis received travel grants and lecture fees from Maquet, Rastatt, Germany.

Name: Thomas Bein, PhD.

Contribution: This author had the idea for the study, contributed to the conception and design of the study, helped with the provision of the study materials, conducted the data analysis and interpretation, reviewed the manuscript for important intellectual content, and approved the final manuscript.

Conflicts of Interest: T. Bein is a member of the German ARDS Network and received honoraria for lectures and activities as a member of the advisory board of Novalung, Heilbronn, Germany.

This manuscript was handled by: Avery Tung, MD, FCCM.

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