Abstract
Background and Aim
Psoas muscle area (PMA) can reflect the status of skeletal muscle in the whole body. It has been also reported that decreased PMA was associated with postoperative mortality or morbidity after several surgical procedures. In this study, we aimed to investigate the relation between PMA and mortality in all age groups in intensive care unit (UNIT).
Materials and Method
The study consists of 362 consecutive patients. The demographic characteristics of patients, indications for ICU hospitalization, laboratory parameters, and clinical parameters consist of mortality and length of stay, and surgery history was obtained from intensive care archive records.
Results
The mean age was 61.2 ± 18.2 years, and the percentage of female was 33.3%. The mean duration of stay was 10.3 ± 24.4 days. Exitus ratio, partial healing, and healing were 25%, 70%, and 5%, respectively. The mean right, left, and total PMA were 8.7 ± 3.6, 8.9 ± 3.4, and 17.6 ± 6.9, respectively. The left and total PMA averages of the nonoperation patients were statistically significantly lower (p = 0.021 p = 0.043). The mean PMA between the ex and recovered patients were statistically significantly lower (p = 0.001, p = 0.001, p < 0.001). Dyspnoea, renal insufficiency, COPD, transfusion rate, operation rate, ventilator needy, and mean duration of hospitalization were statistically significant higher in patients with exitus. There is a significant difference in operation types, anesthesia type, and clinic rates.
Conclusion
Our data suggest that sarcopenia can be used to risk stratification in ICU patients. Future studies may use this technique to individualize postoperative interventions that may reduce the risk for an adverse discharge disposition related to critical illness, such as early mobilization, optimized nutritional support, and reduction of sedation and opioid dose.
1. Introduction
Every year, millions of patients are followed up in the intensive care unit (ICU) in postoperative period or various diseases and some of these patients died. There are many parameters used to determine mortality in patients in the ICU: age, gender, chronic illness, acute physiological values (vital findings), and laboratory values such as serum creatinine level, troponin, lactate, and serum cystatin C [1]. None of these parameters directly correlated with mortality. Therefore, the parameters that can predict mortality are being investigated [2].
Sarcopenia, which means decreasing volume and function of muscle tissue as it ages, generally refers to the reduction of the physiological reserve in the body [3]. Previous studies have shown that sarcopenia is associated with chronic heart failure, postoperative status, after surgery, trauma, extended mechanical ventilation, longer hospital stays, and mortality [4–8].
Psoas muscle area (PMA) because it is a core muscle can reflect the status of skeletal muscle in the whole body [5, 8]. It has been also reported that decreased PMA, as a marker of sarcopenia, was associated with postoperative mortality or morbidity after several surgical procedures [5]. While there were relatively more data available about the prognostic value of sarcopenia in patients suffered surgery, trauma, or cancer, its importance for patients with mortality in whole ICU patients, not only the elderly, was little [5, 6]. So, in this study, we aimed to investigate the relation between PMA and mortality in all age groups with intensive care unit.
2. Methods
Our study has cross-sectional design and included patients in intensive care unit of our hospital between May 2012 and May 2017. The relationship between the incidence of in-hospital mortality and sarcopenia level was investigated. Three hundred sixty-two ICU patients were included in the study. CT scan images were used to determine the quantity of skeletal muscle. The skeletal muscle cross-sectional area (cm2) was manually measured at the caudal end of the third lumbar vertebra. Computed tomography images were used to determine the quantity of skeletal muscle. CT scans were retrieved to measure right and left psoas muscle area, to obtain the total psoas area. The PSA was measured by an observer who was blinded to the outcome and disease severity.
For each patient record, following data were collected including age, sex, smoking, number of comorbidities presenting, ASA score, and Glasgow Coma Scale Score. During the length of stay in ICU all laboratory measurements, the reasons of admission to ICU (urgent, surgical, or internal reasons), type of anesthesia, ventilator requirement in ICU, transfusion requirement, duration of stay, and final status were recorded. The relationship between each of these parameters and the psoas muscle area was evaluated separately.
Statistical analyses were performed by SPSS 15.0 for Windows. In addition to descriptive statistics, mean, standard deviation, and minimum and maximum are used for numeric variables, and number and percentage for categorical variables. The Kolmogorov-Smirnov test was used to assess whether the variables were normally distributed. Student's t-test or Mann–Whitney U test was used to compare the continuous variables between the groups according to whether it was normally distributed or not. Comparisons of ratios in groups were made with Chi Square Analysis. Binary logistic regression analysis (backward stepwise method) was performed to identify independently associated factors with mortality. Variables with a p value < 0.25 in univariate analysis were incorporated in the binary logistic regression analysis. Statistical significance level of alpha was accepted as p < 0,05.
3. Results
The general and operative characteristics of the study group are summarized in Table 1. The mean age was 61.2 ± 18.2 years, and the percentage of men was 33.3%. The mean duration of stay was 10.3 ± 24.4 days. Exitus ratio, partial healing, and healing were 25%, 71%, and 4%, respectively. The laboratory and PMA evaluations of the study group are summarized in Tables 2 and 3, respectively. The mean right, left, and total PMA were 8.7 ± 3.6, 8.9 ± 3.4, and 17.6 ± 6.9, respectively.
Table 1.
The general and operative characteristics of the study group.
| Age Mean ± SD (Min–Max) | 61,2 ± 18,2 (18–92) |
| Gender n (%) | |
| Male | 238 (65,7) |
| Female | 124 (34,3) |
| Smoking | 227 (62,7) |
| Comorbidity n (%) | |
| Diabetes | 116 (32,0) |
| Dyspnea | 226 (62,4) |
| Renal insufficiency | 122 (33,7) |
| Cancer | 156 (43,1) |
| KKY | 115 (31,8) |
| KOAH | 68 (18,8) |
| Transfusion n (%) | 220 (60,8) |
| Operation n (%) | |
| Urgent | 110 (30,4) |
| Elective | 123 (34,0) |
| None | 129 (35,6) |
| Type of anesthesia n (%) | |
| General | 219 (60,5) |
| Regional | 14 (3,9) |
| None | 129 (35,6) |
| Clinic n (%) | |
| Urgent | 62 (17,1) |
| Surgical | 247 (68,2) |
| Internal | 53 (14,6) |
| ASA Score n (%) | 2,5 ± 1,0 (1–5) |
| 1 | 44 (18,0) |
| 2 | 75 (30,7) |
| 3 | 90 (36,9) |
| 4 | 32 (13,1) |
| 5 | 3 (1,2) |
| GCS Mean ± SD (Min–Max) | 13,3 ± 3,2 (3–15) |
| 0–8 | 40 (11,1) |
| >9 | 319 (88,9) |
| Ventilator requirement (day) Mean ± SD (Min–Max) | 14,4 ± 31,7 (1–322) |
| Duration of stay Mean ± SD (Min–Max) | 10,3 ± 24,4 (1–307) |
| Final status n (%) | |
| Ex | 91 (25,1) |
| Partial recovery | 256 (70,7) |
| Recovery | 15 (4,1) |
Table 2.
Laboratory parameters of the study population.
| Mean ± SD | Min–Max | |
|---|---|---|
| Glucose | 152,3 ± 69,3 | 51–721 |
| Urea | 65,8 ± 59,3 | 3,3–432 |
| CRE | 1,67 ± 2,23 | 0,18–23,2 |
| AST | 126,3 ± 369,7 | 4,3–3511 |
| ALT | 77,0 ± 218,5 | 1–2499 |
| GGT | 74,0 ± 121,4 | 5–1000 |
| ALP | 106,2 ± 104,8 | 23,3–902 |
| T. Protein | 5,0 ± 1,0 | 2,9–7 |
| Albumin | 2,6 ± 0,6 | 0,9–5 |
| CK | 259,5 ± 498,4 | 7,8–4838 |
| Sodium | 136,4 ± 8,1 | 25–156 |
| Potassium | 4,4 ± 3,0 | 2,06–59 |
| Calcium | 7,7 ± 1,0 | 2,5–12 |
| CRP | 121,2 ± 108,1 | 0,2–564 |
| WBC | 14,7 ± 10,8 | 1,9–86,1 |
| RBC | 3,8 ± 0,8 | 1,4–6 |
| HG | 10,5 ± 2,2 | 2,3–17,7 |
| HCT | 32,3 ± 6,6 | 10,9–54,2 |
| PLT | 230036,1 ± 143536,0 | 11000–1233000 |
| Neutrophil | 12,5 ± 9,6 | 1–83,5 |
| Lymphocyte | 1,3 ± 1,3 | 0–11,9 |
CRE: Creatinine, GGT: Gama glutamyl transferase, ALP: Alkanine Phosphatase, CK: Cratinine kinase, AST: Aspartate dehydrogenase ALT: Alanine dehydrogenase, CRP. Cerum reactive proeiin, Wbc: White blood cells, RBC: Red blood cells, HG: Hemoglobin, HCT: Hematocrit, PLT: PlateletLDH: Lactate dehydrogenase.
Table 3.
Physical characteristics of study population, PSOAS muscle area.
| Mean ± SD | Min–Max | |
|---|---|---|
| Right PSOAS | 8,7 ± 3,6 | 1,55–28,35 |
| Left PSOAS | 8,9 ± 3,4 | 1,23–22,4 |
| Total PSOAS | 17,6 ± 6,9 | 2,78–45,94 |
The mean right, left, and total PMA in patients with dyspnoea, COPD, CHF, female gender, and nonsmokers were statistically significantly lower (Table 4). The left and total PSOAS muscle area averages of the nonoperation patients were statistically significantly lower (p = 0.021 p = 0.043) (Table 5). We have obtained statistically significant difference in left PMA averages in anesthesia groups (p = 0.045) (Table 5). Patients' left PMA averages, who had regional anesthesia, are lower than the others who has not taken anesthesia (p = 0.017) (Table 5). The mean PMA between the ex and recovered patients was statistically significantly lower (p = 0.001 p = 0.001 p < 0.001) (Table 6).
Table 4.
Association of the demographic parameters and the physical characteristics in study population.
| Right PSOAS | Left PSOAS | Total PSOAS | |
|---|---|---|---|
| Mean ± SD | Mean ± SD | Mean ± SD | |
| Gender | |||
| Male | 9,88 ± 3,55 | 10,14 ± 3,30 | 20,01 ± 6,65 |
| Female | 6,33 ± 2,32 | 6,53 ± 2,19 | 12,86 ± 4,32 |
| <0,001 | <0,001 | <0,001 | |
| Smoking | |||
| Yes | 9,26 ± 3,51 | 9,57 ± 3,26 | 18,83 ± 6,59 |
| No | 7,65 ± 3,54 | 7,78 ± 3,40 | 15,44 ± 6,79 |
| <0,001 | <0,001 | <0,001 | |
| Diabetes Mellitus | |||
| Yes | 8,26 ± 2,57 | 8,54 ± 2,55 | 16,80 ± 4,97 |
| No | 8,85 ± 3,98 | 9,07 ± 3,75 | 17,92 ± 7,56 |
| 0,488 | 0,497 | 0,473 | |
| Dyspnea | |||
| Yes | 8,02 ± 3,52 | 8,26 ± 3,31 | 16,28 ± 6,63 |
| No | 9,72 ± 3,49 | 9,97 ± 3,35 | 19,69 ± 6,71 |
| <0,001 | <0,001 | <0,001 | |
| Renal insufficiency | |||
| Yes | 8,28 ± 3,38 | 8,54 ± 2,96 | 16,83 ± 6,06 |
| No | 8,86 ± 3,70 | 9,09 ± 3,63 | 17,93 ± 7,21 |
| 0,212 | 0,303 | 0,259 | |
| Cancer | |||
| Yes | 8,39 ± 3,00 | 8,68 ± 2,88 | 17,07 ± 5,78 |
| No | 8,87 ± 3,99 | 9,07 ± 3,78 | 17,93 ± 7,56 |
| 0,701 | 0,790 | 0,710 | |
| CHF | |||
| Yes | 7,98 ± 3,41 | 8,30 ± 2,92 | 16,29 ± 6,08 |
| No | 8,98 ± 3,65 | 9,18 ± 3,60 | 18,16 ± 7,12 |
| 0,005 | 0,030 | 0,013 | |
| COPD | |||
| Yes | 7,76 ± 3,68 | 7,77 ± 2,75 | 15,53 ± 6,10 |
| No | 8,87 ± 3,55 | 9,16 ± 3,51 | 18,03 ± 6,94 |
| 0,004 | 0,004 | 0,003 |
CHF: congestive heart failure; COPD: chronic obstructive pulmonary disease.
Table 5.
Association between the clinical parameters and the physical characteristics in study population.
| Right PSOAS | Left PSOAS | Total PSOAS | |
|---|---|---|---|
| Mean ± SD | Mean ± SD | Mean ± SD | |
| Operation | |||
| Yes | 8,86 ± 3,57 | 9,19 ± 3,48 | 18,04 ± 6,93 |
| No | 8,31 ± 3,63 | 8,39 ± 3,27 | 16,69 ± 6,66 |
| 0,091 | 0,021 | 0,043 | |
| Operation | |||
| Urgent | 8,82 ± 3,64 | 9,17 ± 3,59 | 17,99 ± 7,06 |
| Elective | 8,90 ± 3,53 | 9,20 ± 3,39 | 18,10 ± 6,84 |
| None | 8,31 ± 3,63 | 8,39 ± 3,27 | 16,69 ± 6,66 |
| 0,228 | 0,067 | 0,124 | |
| Type of anesthesia | |||
| General | 8,92 ± 3,63 | 9,25 ± 3,53 | 18,17 ± 7,03 |
| Regional | 7,95 ± 2,45 | 8,13 ± 2,44 | 16,08 ± 4,82 |
| None | 8,31 ± 3,63 | 8,39 ± 3,27 | 16,69 ± 6,66 |
| 0,188 | 0,045 | 0,087 | |
| Clinic | |||
| Urgent | 8,60 ± 3,33 | 8,93 ± 3,42 | 17,50 ± 6,63 |
| Surgical | 8,72 ± 3,55 | 9,06 ± 3,47 | 17,78 ± 6,91 |
| Internal | 8,45 ± 4,13 | 8,16 ± 3,14 | 16,63 ± 6,88 |
| 0,644 | 0,181 | 0,402 | |
| Transfusion | |||
| Yes | 8,65 ± 3,94 | 8,87 ± 3,61 | 17,52 ± 7,35 |
| No | 8,68 ± 3,02 | 8,96 ± 3,12 | 17,63 ± 6,03 |
| 0,404 | 0,518 | 0,442 | |
| GCS | |||
| 0–8 | 8,62 ± 4,48 | 8,13 ± 3,13 | 16,71 ± 7,20 |
| >9 | 8,65 ± 3,46 | 8,99 ± 3,46 | 17,64 ± 6,80 |
| 0,562 | 0,116 | 0,256 | |
| Final status | |||
| Recovery | 8,95 ± 3,48 | 9,22 ± 3,44 | 18,17 ± 6,79 |
| Ex | 7,80 ± 3,83 | 7,96 ± 3,21 | 15,75 ± 6,77 |
| 0,010 | 0,009 | 0,006 | |
| Final status | |||
| Ex | 7,80 ± 3,83 | 7,96 ± 3,21 | 15,75 ± 6,77 |
| Partial recovery | 8,87 ± 3,43 | 9,13 ± 3,38 | 18,00 ± 6,67 |
| Recovery | 10,40 ± 4,10 | 10,73 ± 4,20 | 21,13 ± 8,19 |
| 0,001 | 0,001 | <0,001 |
GCS: Glasgow Coma Scale.
Table 6.
Association between the PMA and mortality in study population.
| Recovery | Exitus | p | |
|---|---|---|---|
| Median ± SD (median) | Ave. ± SD (median) | ||
| Right PSOAS | 8,95 ± 3,48 (9) | 7,79 ± 3,83 (7) | 0,001 |
| Left PSOAS | 9,22 ± 3,44 (9) | 7,95 ± 3,21 (7) | 0,001 |
| Total PSOAS | 18,2 ± 6,8 (18) | 15,7 ± 6,8 (14) | 0,001 |
There was a statistically significant positive correlation with glucose, ALT, CK, WBC, RBC, Hg, Hct and negative correlation with age, ASA Score, urea, ALP, CRP, and right, left, and total PSOAS muscle area (Table 7). Dyspnoea, renal insufficiency, COPD, transfusion rate, operation rate, ventilator needy, and mean duration of hospitalization were statistically significant higher in patients with exitus (Table 8). There is a significant difference in operation types, anesthesia type, and clinic rates (Table 9). In the multiple logistic regression analysis, COPD (OR: 0.307 (CI: 0.113–0.835), p = 0.021) and total PMA (OR: 0.812 (CI: 0.741–0.890), p < 0.000) were found to be independently associated with mortality (Table 10).
Table 7.
| Right PSOAS | Left PSOAS | Total PSOAS | ||||
|---|---|---|---|---|---|---|
| rho | p | rho | p | rho | p | |
| Age | −0,365 | <0,001 | −0,359 | <0,001 | −0,370 | <0,001 |
| ASA Score | −0,209 | 0,001 | −0,182 | 0,004 | −0,200 | 0,002 |
| GCS | 0,090 | 0,088 | 0,119 | 0,024 | 0,110 | 0,037 |
| Ventilator requirement (day) | −0,157 | 0,033 | −0,140 | 0,057 | −0,149 | 0,043 |
| Duration of stay mean ± SD | −0,089 | 0,091 | −0,075 | 0,155 | −0,084 | 0,109 |
| Glucose | 0,165 | 0,002 | 0,139 | 0,008 | 0,158 | 0,003 |
| Urea | −0,124 | 0,018 | −0,118 | 0,026 | −0,119 | 0,024 |
| CRE | 0,044 | 0,411 | 0,023 | 0,676 | 0,038 | 0,487 |
| AST | 0,045 | 0,415 | 0,006 | 0,910 | 0,028 | 0,616 |
| ALT | 0,197 | <0,001 | 0,165 | 0,003 | 0,189 | 0,001 |
| GGT | −0,036 | 0,549 | −0,049 | 0,423 | −0,040 | 0,514 |
| ALP | −0,180 | 0,004 | −0,191 | 0,002 | −0,189 | 0,003 |
| T. protein | −0,050 | 0,579 | −0,068 | 0,445 | −0,052 | 0,560 |
| Albumin | 0,100 | 0,068 | 0,071 | 0,195 | 0,090 | 0,100 |
| CK | 0,182 | 0,028 | 0,209 | 0,012 | 0,209 | 0,012 |
| NA | −0,018 | 0,737 | −0,021 | 0,693 | −0,019 | 0,717 |
| K | 0,165 | 0,002 | 0,181 | 0,001 | 0,178 | 0,001 |
| CA | 0,052 | 0,329 | 0,036 | 0,502 | 0,045 | 0,392 |
| CRP | −0,158 | 0,003 | −0,137 | 0,010 | −0,149 | 0,005 |
| WBC | 0,125 | 0,017 | 0,079 | 0,135 | 0,106 | 0,044 |
| RBC | 0,116 | 0,027 | 0,110 | 0,038 | 0,115 | 0,029 |
| HG | 0,154 | 0,003 | 0,159 | 0,002 | 0,157 | 0,003 |
| HCT | 0,132 | 0,012 | 0,144 | 0,006 | 0,139 | 0,008 |
| PLT | 0,001 | 0,996 | −0,028 | 0,602 | −0,010 | 0,850 |
| Neutrophil | 0,115 | 0,030 | 0,077 | 0,146 | 0,102 | 0,054 |
| Lymphocyte | 0,072 | 0,172 | 0,019 | 0,719 | 0,045 | 0,390 |
| PT | −0,051 | 0,339 | −0,036 | 0,502 | −0,047 | 0,372 |
| PTT | −0,074 | 0,164 | −0,035 | 0,506 | −0,058 | 0,271 |
| INR | −0,019 | 0,714 | 0,001 | 0,981 | −0,012 | 0,814 |
Table 8.
Clinical parameters of survivors and dead patients at follow-up period in ICU.
| Recovery | Exitus | p | |
|---|---|---|---|
| Gender Median ± SD (median) | 60,1 ± 18,7 (62) | 64,5 ± 16,4 (68) | 0,070 |
| Gender n (%) | |||
| Male | 182 (67,2) | 56 (61,5) | 0,328 |
| Female | 89 (32,8) | 35 (38,5) | |
| Cigarette | 170 (62,7) | 57 (62,6) | 1,000 |
| Additional diseases n (%) | |||
| Diabetes | 80 (29,5) | 36 (39,6) | 0,076 |
| Dyspnea | 142 (52,4) | 84 (92,3) | <0,001 |
| Renal insufficiency | 72 (26,6) | 50 (54,9) | <0,001 |
| Cancer | 123 (45,4) | 33 (36,3) | 0,128 |
| KKY | 81 (29,9) | 34 (37,4) | 0,185 |
| KOAH | 40 (14,8) | 28 (30,8) | 0,001 |
| Transfusion n (%) | 155 (57,2) | 65 (71,4) | 0,016 |
| Operation n (%) | 202 (74,5) | 31 (34,1) | <0,001 |
| Operation n (%) | |||
| Urgent | 87 (32,1) | 23 (25,3) | <0,001 |
| Elective | 115 (42,4) | 8 (8,8) | |
| None | 69 (25,5) | 60 (65,9) | |
| Type of Anesthesia n (%) | |||
| General | 189 (69,7) | 30 (33,0) | <0,001 |
| Regional | 13 (4,8) | 1 (1,1) | |
| None | 69 (25,5) | 60 (65,9) | |
| Clinic n (%) | |||
| Urgent | 36 (13,3) | 26 (28,6) | <0,001 |
| Surgical | 208 (76,8) | 39 (42,9) | |
| Internal | 27 (10,0) | 26 (28,6) | |
| ASA Score n (%) | 2,4 ± 0,9 (2) | 2,9 ± 1,1 (3) | 0,011 |
| 1 | 39 (18,9) | 5 (13,2) | 0,023 |
| 2 | 67 (32,5) | 8 (21,1) | |
| 3 | 77 (37,4) | 13 (34,2) | |
| 4 | 21 (10,2) | 11 (28,9) | |
| 5 | 2 (1,0) | 1 (2,6) | |
| GCS mean ± SD (Min–Max) | 13,9 ± 2,6 (15) | 11,5 ± 4,1 (13) | <0,001 |
| 0–8 | 20 (7,4) | 20 (22,2) | <0,001 |
| >9 | 249 (92,6) | 70 (77,8) | |
| Ventilator need (day) Ave. ± SD (Min–Max) | 10,6 ± 35,0 (3) | 18,4 ± 27,6 (7) | <0,001 |
| Duration of stay Ave. ± SD (Min–Max) | 7,2 ± 21,7 (2) | 19,5 ± 29,2 (8) | <0,001 |
Table 9.
Laboratory parameters of survivors and dead patients at follow-up period in ICU.
| Recovery | Exitus | ||
|---|---|---|---|
| Ave. ± SD (median) | Ave. ± SD (median) | p | |
| Glucose | 152,1 ± 58,5 (139) | 152,7 ± 95,6 (128,5) | 0,136 |
| Urea | 59,6 ± 61,0 (39) | 85,0 ± 49,0 (77,5) | <0,001 |
| Creatinine | 1,61 ± 2,42 (0,95) | 1,84 ± 1,53 (1,11) | 0,003 |
| AST | 105,5 ± 337,6 (31) | 189,6 ± 449,9 (40) | 0,017 |
| ALT | 71,4 ± 232,3 (20) | 93,9 ± 170,5 (27) | 0,126 |
| GGT | 67,2 ± 104,8 (31) | 94,7 ± 160,6 (50,5) | 0,005 |
| ALP | 102,2 ± 109,2 (69) | 118,2 ± 90,0 (80,6) | 0,002 |
| T. Protein | 5,0 ± 1,0 (5) | 5,0 ± 1,0 (5) | 0,762 |
| Albumin | 2,6 ± 0,6 (2,6) | 2,4 ± 0,6 (2,4) | 0,012 |
| CK | 294,9 ± 570,6 (137) | 172,6 ± 226,8 (83,5) | 0,086 |
| NA | 136,0 ± 8,3 (137) | 137,6 ± 7,3 (138) | 0,118 |
| K | 4,4 ± 3,4 (4,1) | 4,4 ± 1,0 (4,2) | 0,170 |
| CA | 7,8 ± 0,9 (7,85) | 7,7 ± 1,1 (7,7) | 0,106 |
| CRP | 114,8 ± 106,4 (88,5) | 140,9 ± 111,7 (111) | 0,050 |
| WBC | 13,7 ± 8,5 (12,5) | 17,8 ± 15,6 (12,64) | 0,165 |
| RBC | 3,9 ± 0,8 (4) | 3,6 ± 0,8 (3,55) | 0,001 |
| HG | 10,7 ± 2,1 (10,9) | 9,7 ± 2,3 (9,6) | <0,001 |
| HCT | 32,9 ± 6,4 (33,2) | 30,3 ± 7,0 (30) | 0,001 |
| PLT | 242239,9 ± 132297,6 (225000) | 192876,4 ± 168797,7 (158000) | <0,001 |
| Neutrophil | 11,5 ± 7,4 (10,25) | 15,3 ± 14,1 (10,9) | 0,157 |
| Lymphocyte | 1,26 ± 1,21 (1) | 1,42 ± 1,55 (1) | 0,700 |
Table 10.
Multivariate regression analysis of predictors for mortality.
| Variables | Odds ratio (95% confidence interval) | p |
|---|---|---|
| COPD | 0.307 (0.113–0.835) | 0.021 |
| Total PMA | 0.812 (0.741–0.890) | <0.000 |
4. Discussion
Fragility is explicitly undefined and is known as failure to maintain homeostasis due to insufficient response to some stressors associated with reduced reserve in the multiple organ system [5–13]. It has been reported that frailty was predictor of mortality and morbidity than chronological age [14–17]. Some parameters such as physical activity level, unintentional weight loss, slow walking speed, fatigue, loss of physical strength, comorbid medical conditions, loss of independence for activities of daily living, low albumin levels, and cognitive impairments have been described in evaluating and defining the fragility [13, 17–20]. Present study demonstrated that decreased skeletal muscle mass was a significant predictor of in-hospital mortality in the sample of patients admitted to a tertiary medical center ICU. Sarcopenia, a frailty risk factor of particular interest is age-related loss of muscle mass and/or strength and performance and has been closely related to reduced quality of life, geriatric syndromes, greater morbidity, and mortality [20–24]. PMA because it is a core muscle can reflect the status of skeletal muscle in the whole body [12]. It has been reported that decreased PMA, as a marker of sarcopenia, was associated with postoperative mortality or morbidity after abdominal aortic aneurysm repair, liver transplantation, pancreatic cancer resection, adrenocortical cancer resection, colorectal cancer resection, radical cystectomy, and surgical or percutaneous aortic valve replacement [6]. It was also reported that direct measurement of muscle mass can give the best information about the physiological reserve. In some studies, measurement of the psoas muscle at the level of the third lumbar vertebra (L3) with CT has been used to determine the physiological reserve prior to some operations, such as liver transplantation [5, 12]. The prognostic value of sarcopenia has been determined for patients after surgery and trauma or with cancer. Moreover, Mueller et al. found that sarcopenia assessed with ultrasound predicts adverse discharge disposition as well as duration of hospitalization [9]. However their study did not conclude any comments on hospital mortality associated with sarcopenia. This is the first study, to the best of our knowledge, to examine the implications of sarcopenia evaluated by cross-sectional PMA, on mortality in ICU patients.
Single-slice muscle area has been found to be associated with total body muscle mass and as a predictor for the postoperative outcomes after various surgical procedures [5]. Similar to our study, Weijs et al. demonstrated a relationship between the low skeletal muscle mass assessed by CT and extended mechanical ventilation, longer hospital stays, and mortality [7]. In line with our findings, Moisey et al. recently found that low muscle mass as assessed by CT scans was associated with mortality in 149 injured elderly ICU patients [8]. Besides that, in contrast to Moisey et al.'s population, our findings are made in an ICU representative age group, and not in an elderly and only traumatic population. It is difficult to make these measurements in order to estimate skeletal muscle mass. It is often required as part of the initial study in patients. Therefore, it is possible that, in this patient population, an early evaluation of muscle mass and muscle cross-sectional view provide an objective method that estimates lean muscle easily obtained. So, in this study, CT was used to estimate total muscle mass determined by cross-sectional area of the psoas muscle as a marker of sarcopenia.
Risk estimation, prediction, and the results achieved by bringing the perspective of resource allocation and assessment of quality of health services are important [25]. The individual approach to patient care in the intensive care unit following the treatment plan for each patient to identify the optimal risk-benefit ratio should be evaluated. Clinical characteristics that impact the mortality rate and the length of the hospital stay include multiple comorbidities such respiratory, cardiac, renal, and infectious problems. Several factors such as comorbidity can be evaluated in a variety of ways that help predict prognosis [26–32]. In spite of the apparent variability between observers, ASA classification has been widely accepted in the prediction of morbidity and mortality [28–33].
Regardless of anesthesia application, it is expected to increase mortality and morbidity in patients with systematic disease. [32, 34]. Therefore, patients in bad health condition are expected to have higher rates of admission to the ICU [32, 35–37]. Our study showed that mortality, length of stay in the ICU, and duration of mechanical ventilation increased as PMA decreased.
All these scoring systems can help in the prediction of patient program, although it should be noted that the prognosis for each patient may be different [38]. Frail patients may have a lower functional capacity and decreased ability to mobilize at baseline. Thus, they are vulnerable against severe physiologic stressors, predisposing them to functional dependence at discharge and death.
In the current study, we confirmed associations between decreasing muscle mass and increased mortality in ICU patients. According to the results of our study, there was a close relationship between PMA values and mortality in ICU patients independent of other variables. Thus, fragility was quantitatively calculated and the prevalence of ICU patients emerged.
Because skeletal muscle atrophy can cause physical decline such as impaired cytokine [39, 40] and insulin signaling [41–43] that may result in glucose intolerance, we speculate that stratification by muscle mass may reflect physical condition. Due to the design of the study, sarcopenia and the relationship between the mechanisms of poor prognosis cannot be determined with certainty. However, the results of the current study emphasize mass and function of skeletal muscle in ICU patients.
As a result, it is appropriate to consider that frailty may be important in the treatment options and follow-up of the patients. While PMA is quantitatively indicative of frailty, CT exposure to PE patients is already present, but exposure to radiation and contrast remains. For this reason, it is plausible to plan studies to understand whether clinical frailty scores, such as Fried scoring or simple “FRAIL” Questionnaire Screening, on the outcome of PMA can influence the prognosis of ICU patients and lead to treatment [16, 18]. Mortality during hospital stay or functional information about the risk of dependence makes informed decisions about the goals of care may help.
The limitations of the present study are the lack of outpatient and surveys. Moreover, this is a retrospective analysis that could not lead to the conclusion which might only represent the background for future perspective studies that will confirm the impact of sarcopenia in ICU. Moreover, due to its retrospective nature, we could not assess our nutritional status of patients. We can clearly show the relationship between sarcopenia and malnutrition status of patients with tests such as the Mini Nutritional Assessment in further studies. Consequently, our data suggest that sarcopenia can be used in risk stratification in ICU patients. CT is a valid and simple technique that could also be used for longitudinal assessment of treatment success. Future studies may use this technique to individualize postoperative interventions that may reduce the risk for an adverse discharge disposition related to critical illness, such as early mobilization, optimized nutritional support, and reduction of sedation and opioid dose.
Conflicts of Interest
The authors have declared that no conflicts of interest exist.
References
- 1.Le Maguet P., Roquilly A., Lasocki S., et al. Prevalence and impact of frailty on mortality in elderly ICU patients: A prospective, multicenter, observational study. Intensive Care Medicine. 2014;40(5):674–682. doi: 10.1007/s00134-014-3253-4. [DOI] [PubMed] [Google Scholar]
- 2.Vincent J.-L., Moreno R. Clinical review: scoring systems in the critically ill. Critical Care. 2010;14(2, article 207) doi: 10.1186/cc8204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ravn B., Prowle J. R., Mårtensson J., Martling C.-R., Bell M. Superiority of Serum Cystatin C Over Creatinine in Prediction of Long-Term Prognosis at Discharge From ICU. Critical Care Medicine. 2017 doi: 10.1097/CCM.0000000000002537. [DOI] [PubMed] [Google Scholar]
- 4.Docherty A. B., Sim M., Oliveira J., et al. Early troponin I in critical illness and its association with hospital mortality: A cohort study. Critical Care. 2017;21(1, article no. 216) doi: 10.1186/s13054-017-1800-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Zuckerman J., Ades M., Mullie L., et al. Psoas Muscle Area and Length of Stay in Older Adults Undergoing Cardiac Operations. The Annals of Thoracic Surgery. 2017;103(5):1498–1504. doi: 10.1016/j.athoracsur.2016.09.005. [DOI] [PubMed] [Google Scholar]
- 6.Fairchild B., Webb T. P., Xiang Q., Tarima S., Brasel K. J. Sarcopenia and frailty in elderly trauma patients. World Journal of Surgery. 2015;39(2):373–379. doi: 10.1007/s00268-014-2785-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Weijs P. J. M., Looijaard W. G. P. M., Dekker I. M., et al. Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients. Critical Care. 2014;18(1, article no. R12) doi: 10.1186/cc13189. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Moisey L. L., Mourtzakis M., Cotton B. A., et al. Skeletal muscle predicts ventilator-free days, ICU-free days, and mortality in elderly ICU patients. Critical Care. 2013;17(5, article no. R206) doi: 10.1186/cc12901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Mueller N., Murthy S., Tainter C. R., et al. Can sarcopenia quantified by ultrasound of the rectus femoris muscle predict adverse outcome of surgical intensive care unit patients as well as frailty? a prospective, observational cohort study. Annals of Surgery. 2016;264(6):1116–1124. doi: 10.1097/SLA.0000000000001546. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.von Haehling S., Anker M. S., Anker S. D. Prevalence and clinical impact of cachexia in chronic illness in Europe, USA, and Japan: facts and numbers update 2016. Journal of Cachexia, Sarcopenia and Muscle. 2016;7(5):507–509. doi: 10.1002/jcsm.12167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Marra A. M., Arcopinto M., Bossone E., Ehlken N., Cittadini A., Grünig E. Pulmonary arterial hypertension-related myopathy: An overview of current data and future perspectives. Nutrition, Metabolism & Cardiovascular Diseases. 2015;25(2):131–139. doi: 10.1016/j.numecd.2014.10.005. [DOI] [PubMed] [Google Scholar]
- 12.Sheetz K. H., Zhao L., Holcombe S. A., et al. Decreased core muscle size is associated with worse patient survival following esophagectomy for cancer. Diseases of the Esophagus. 2013;26(7):716–722. doi: 10.1111/dote.12020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Finn M., Green P. The Influence of Frailty on Outcomes in Cardiovascular Disease. Revista espanola de cardiologia (English ed.) 2015;68(8):653–656. doi: 10.1016/j.rec.2015.04.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Song X., Mitnitski A., Rockwood K. Prevalence and 10-Year outcomes of frailty in older adults in relation to deficit accumulation. Journal of the American Geriatrics Society. 2010;58(4):681–687. doi: 10.1111/j.1532-5415.2010.02764.x. [DOI] [PubMed] [Google Scholar]
- 15.Rockwood K., Howlett S. E., MacKnight C., et al. Prevalence, Attributes, and Outcomes of Fitness and Frailty in Community-Dwelling Older Adults: Report From the Canadian Study of Health and Aging. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences. 2004;59(12):1310–1317. doi: 10.1093/gerona/59.12.1310. [DOI] [PubMed] [Google Scholar]
- 16.Shamliyan T., Talley K. M. C., Ramakrishnan R., Kane R. L. Association of frailty with survival: A systematic literature review. Ageing Research Reviews. 2013;12(2):719–736. doi: 10.1016/j.arr.2012.03.001. [DOI] [PubMed] [Google Scholar]
- 17.Fried L. P., Ferrucci L., Darer J., Williamson J. D., Anderson G. Untangling the Concepts of Disability, Frailty, and Comorbidity: Implications for Improved Targeting and Care. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences. 2004;59(3):255–263. doi: 10.1093/gerona/59.3.M255. [DOI] [PubMed] [Google Scholar]
- 18.Kim H., Hirano H., Edahiro A., et al. Sarcopenia: Prevalence and associated factors based on different suggested definitions in community-dwelling older adults. Geriatrics & Gerontology International. 2016;16:110–122. doi: 10.1111/ggi.12723. [DOI] [PubMed] [Google Scholar]
- 19.Shah N., Abeysundara L., Dutta P., et al. The association of abdominal muscle with outcomes after scheduled abdominal aortic aneurysm repair. Anaesthesia. 2017;72(9):1107–1111. doi: 10.1111/anae.13980. [DOI] [PubMed] [Google Scholar]
- 20.Fielding R. A., Vellas B., Evans W. J., et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. Journal of the American Medical Directors Association. 2011;12(4):249–256. doi: 10.1016/j.jamda.2011.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Heymsfield S. B., Adamek M., Gonzalez M. C., Jia G., Thomas D. M. Assessing skeletal muscle mass: Historical overview and state of the art. Journal of Cachexia, Sarcopenia and Muscle. 2014;5(1):9–18. doi: 10.1007/s13539-014-0130-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Shen W., Punyanitya M., Wang Z., et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. Journal of Applied Physiology. 2004;97(6):2333–2338. doi: 10.1152/japplphysiol.00744.2004. [DOI] [PubMed] [Google Scholar]
- 23.Mourtzakis M., Prado C. M. M., Lieffers J. R., Reiman T., McCargar L. J., Baracos V. E. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Applied Physiology, Nutrition, and Metabolism. 2008;33(5):997–1006. doi: 10.1139/h08-075. [DOI] [PubMed] [Google Scholar]
- 24.Gomez-Perez S. L., Haus J. M., Sheean P., et al. Measuring abdominal circumference and skeletal muscle from a single cross-sectional computed tomography image: A step-by-step guide for clinicians using National Institutes of Health ImageJ. Journal of Parenteral and Enteral Nutrition. 2016;40(3):308–318. doi: 10.1177/0148607115604149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Mayberry R. M., Nicewander D. A., Qin H., Ballard D. J. Improving Quality and Reducing Inequities: A Challenge in Achieving Best Care. Baylor University Medical Center Proceedings. 2017;19(2):103–118. doi: 10.1080/08998280.2006.11928138. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Bickenbach J., Fries M., Rex S., et al. Outcome and mortality risk factors in long-term treated ICU patients: A retrospective analysis. Minerva Anestesiologica. 2011;77(4):427–438. [PubMed] [Google Scholar]
- 27.Hopkins T. J., Raghunathan K., Barbeito A., et al. Associations between ASA Physical Status and postoperative mortality at 48 h: a contemporary dataset analysis compared to a historical cohort. Perioperative Medicine. 2016;5(1) doi: 10.1186/s13741-016-0054-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Wolters U., Wolf T., Stützer H., Shcr T., Shcröder T. Associations between ASA Physical Status and postoperative mortality at 48 h: a contemporary dataset analysis compared to a historical cohort. British Journal of Anaesthesia. 1996;77:217–222. [Google Scholar]
- 29.Sankar A., Johnson S. R., Beattie W. S., Tait G., Wijeysundera D. N. Reliability of the American Society of Anesthesiologists physical status scale in clinical practice. British Journal of Anaesthesia. 2014;113(3):424–432. doi: 10.1093/bja/aeu100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Sobol J. B., Wunsch H. Triage of high-risk surgical patients for intensive care. Critical Care. 2011;15(2, article no. 217) doi: 10.1186/cc9999. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Uzman S., Yilmaz Y., Toptas M., et al. A retrospective analysis of postoperative patients admitted to the intensive care unit. Hippokratia. 2016;20(1):38–43. [PMC free article] [PubMed] [Google Scholar]
- 32.Abbas S., Kahokher A., Mahmoud M., Hill A. Physiologic Modification of the American Society of Anaesthesiology Score (ASA) for Prediction of Morbidity and Mortality after Emergency Laparotomy. The Internet Journal of Surgery. 2008;20(2):1–9. [Google Scholar]
- 33.Zali A. R., Seddighi A. S., Seddighi A., Ashrafi F. Comparison of the acute physiology and chronic health evaluation score (APACHE) II with GCS in predicting hospital mortality of neurosurgical intensive care unit patients. Global Journal of Health Science. 2012;4(3):179–184. doi: 10.5539/gjhs.v4n3p179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Harrison D. A., Lone N. I., Haddow C., et al. External validation of the Intensive Care National Audit Research Centre (ICNARC) risk prediction model incritical care units in Scotland. BMC Anesthesiol. 2014;14:p. 116. doi: 10.1186/1471-2253-14-116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Minto G., Biccard B. Assessment of the high-risk perioperative patient. Continuing Education in Anaesthesia, Critical Care and Pain. 2014;14(1):12–17. doi: 10.1093/bjaceaccp/mkt020.mkt020 [DOI] [Google Scholar]
- 36.Pearse R. M., Moreno R. P., Bauer P., et al. Mortality after surgery in Europe: a 7 day cohort study. 380: Lancet; 2012. European Surgical Outcomes Study (EuSOS) group for the Trials groups of the European Society of Intensive Care Medicine and the European Society of Anesthesiology; pp. 1059–1065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Higgins T. L. Quantifying risk and benchmarking performance in the adult intensive care unit. Journal of Intensive Care Medicine. 2007;22(3):141–156. doi: 10.1177/0885066607299520. [DOI] [PubMed] [Google Scholar]
- 38.Kalyani R. R., Corriere M., Ferrucci L. Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. The Lancet Diabetes & Endocrinology. 2014;2(10):819–829. doi: 10.1016/s2213-8587(14)70034-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Fan J., Kou X., Yang Y., Chen N. MicroRNA-regulated proinflammatory cytokines in sarcopenia. Mediators of Inflammation. 2016;2016 doi: 10.1155/2016/1438686.1438686 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Lutz C. T., Quinn L. S. Sarcopenia, obesity, and natural killer cell immune senescence in aging: altered cytokine levels as a common mechanism. AGING. 2012;4(8):535–546. doi: 10.18632/aging.100482. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Umegaki H. Sarcopenia and diabetes: Hyperglycemia is a risk factor for age-associated muscle mass and functional reduction. Journal of Diabetes Investigation. 2015;6(6):623–624. doi: 10.1111/jdi.12365. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Morley J. E., Malmstrom T. K., Rodriguez M., et al. Frailty is also higher in the prevalence in older with DM. Journal of the American Medical Directors Association. 2014;15:853–859. doi: 10.1016/j.jamda.2014.10.001. [DOI] [PubMed] [Google Scholar]
- 43.Kalyani R. R., Metter E. J., Egan J., Golden S. H., Ferrucci L. Hyperglycemia predicts persistently lower muscle strength with aging. Diabetes Care. 2015;38(1):82–90. doi: 10.2337/dc14-1166. [DOI] [PMC free article] [PubMed] [Google Scholar]