Printer Friendly

Severe Acidemia, Leukocytosis and Low Hematocrit Levels at Admission as Mortality Predictors of Elderly Intensive Care Unit Patients/Yasli Hastalarda Mortalite Prediktoru Olarak Yogun Bakim Yatisinda Saptanan Ciddi Asidemi, Lokositoz ve Dusuk Hemoglobin Degerleri.


The prognosis of critically ill patients is generally attempted to be predicted by using illness severity scoring systems, especially the Acute Physiology and Chronic Health Evaluation score (APACHE) that generate prognostic information for patients within 24 hours of admission to the intensive care unit (ICU) (1,2). However, the use of these scoring systems, that combines the functional and physiological characteristics of patients as mortality predictors, is still limited since they don't give information till 24 hours of admission. Although various studies have been done investigating the association between patient parameters and mortality, they mostly aim proposing new scoring systems (3,4). Rapid prediction of prognosis using the tests done routinely on admission, as they are unbiased by clinical evaluation and easily accessible, might be helpful in reflecting disease severity and patient mortality. This is mandatory especially in critically ill elderly patients who need rapid prediction of prognosis because of high mortality risk (3). This study aimed to investigate the effects of admission laboratory results and medical histories on the prediction of prognosis in elderly patients who were admitted to the ICU.

Material and Methods

This retrospective study was conducted in a tertiary medical ICU between January 2011 and December 2013. The study included patients aged [greater than or equal to] 65 years old. All patients' demographic characteristics, ICU admission diagnosis, length of ICU stay (LOS), laboratory data on admission including arterial blood gas, complete blood count, creatinine, electrolytes, glucose, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), C-reactive protein (CRP), bilirubin, lactate dehydrogenase (LDH), troponin and international normalized ratio (INR), and comorbidities enlisted in the medical files were recorded. Admission diagnoses were classified accordingly; a diagnosis of infection included primarily sepsis-related admissions which were lung, gastrointestinal, urinary tract, central nervous system, soft tissue and catheter-related infections. A cardiac diagnosis encompassed cardiogenic shock, HF, acute myocardial infarction, rhythm problems and cardiac arrest not precipitated by an underlying disease such as sepsis or respiratory failure. A hepatic diagnosis included admission with chronic/active hepatic failure. A diagnosis of hemorrhage described any kind of bleeding. A diagnosis of neurologic diseases included motor neuron and neuromuscular diseases, status epilepticus and cerebrovascular accident (CVA). A diagnosis of respiratory diseases included chronic obstructive pulmonary disease (COPD) and asthma exacerbation, pulmonary embolism (PE), decompensation of chronic respiratory diseases except COPD and pneumothorax. Surgery encompassed any planned or unplanned surgery. In the event of multiple admissions, the first admission was accepted. Exclusion criterion was being <65 years old. Discharge from the ICU was recorded as survivors and nonsurvivors. Since baseline cognitive or social data were not routinely documented in the medical files, these parameters were not enrolled into the study. Premorbid functional state was only recorded as bedridden or not in the medical files and included into the study. The study protocol was approved by the ethical review board of the university (Number:2015/76).

Categorical variables were expressed as proportions and compared with the Chi-square or Fisher's exact tests. Normally distributed variables were analyzed by the Student's T test and results were expressed as means with standard deviations. Variables without normal distribution (AST, ALT, LDH, LOS and troponin) were reported as medians with interquartile ranges and compared using the Mann-Whitney U test. A forward stepwise-logistic-regression model was performed to investigate the impact of the measured variables on the mortality. The variables yielding p < 0.05 by the univariate analysis were entered into the multivariate logistic regression analysis. The model was first performed without parameters reflecting the admission diagnoses and intubation, following which these parameters were added to the analysis. Hosmer-Lemeshow goodness-of-fit test was performed, and odds ratios with 95% confidence interval were computed. Collinearity between variables was excluded before modeling by computing the Spearmen correlation, with an r >0.5 regarded as significant. A p <0.05 was considered as significant. All calculations were carried out using the Statistical Package for the Social Sciences version 21 (IBM Corp., Armonk, NY, USA).


A total of 748 patients were admitted to the ICU among whom 60% were aged [greater than or equal to] 65 years and included into the study. The mean age, APACHE II score and percentage of male patients were 78.9 [+ or -] 7.2 years, 26 [+ or -] 8.4 and 51.4%, respectively. The main comorbidities were hypertension (61%), hearth failure (45.2%), CVA (33%), diabetes mellitus (29.2%), COPD (24.1%), ischemic heart disease (21.4%), chronic kidney disease (CKD stage 2-5D, 20.3%), malignancy (13.4%) and dementia (12.7%). Among the patients, 9.1% received cardiopulmonary resuscitation (CPR) within 24 hours before the ICU admission, 20.7% were in a bedridden state, 3.1% had metastatic cancers and 56% were accepted to the ICU from the wards.

The ICU mortality rate was 47.4%. There wasn't any statistically significant difference between survivors and nonsurvivors according to sex, age groups, admission sites and comorbidities, except CKD (Table 1). With regard to laboratory parameters, nonsurvivors had lower pH, HC[O.sub.3], hematocrit and albumin levels, and platelet count than survivors (Table 2). In contrast, creatinine, bilirubin, AST, ALT, LDH, phosphorus, magnesium and CRP levels, and white blood cell (WBC) count were notably elevated in nonsurvivors, together with high rates of being in a bedridden state and having CPR. Since pH and albumin levels were significantly low in nonsurviors, we categorized pH and albumin levels into three categories where pH <7.20 and albumin [less than or equal to] 2 gr/dL were encountered more in nonsurvivors than in survivors. The patients with admission diagnoses of surgery and respiratory disease had higher survival rates than nonsurvivors. On the other hand, diagnosis of infection was the main cause of death in nonsurvivors. Endotracheal intubation was done in 299 patients and intubation rate was high in nonsurvivors (42.8% vs. 93%, p <0.001). Survivors had shorter LOS than nonsurvivors (4 (2-7) vs. 4 (2-18), p=0.026).

Since APACHE II is calculated from the data gathered within 24 hours and includes the variables of hematocrit, WBC, HC[O.sub.3] and pH, we excluded APACHE II from the multivariate analysis.

The first model showed that pH <7.20, albumin [less than or equal to] 2 gr/dL, high WBC count, high CRP level, low hematocrit level, having CPR and being in a bedridden state were mortality predictors. After the inclusion of admission diagnoses of infection, respiratory disease and surgery, and intubation into the model, pH <7.20, albumin [less than or equal to] 2 gr/dL, hematocrit level and WBC count remained as mortality predictors along with respiratory disease, surgical operation and intubation, while high CRP level, having CPR and being in a bedridden state lost their significance (Table 3).


The number of elderly patients admitted to the ICU increases every day. A study done by Blot et al. revealed that the number of patients aged [greater than or equal to] 75 years increased by 33% between 1992-1996 and 2002-2006 (5). An annual rise of 5.6% in critically ill patients aged [greater than or equal to] 80 years was reported by Bagshaw et al (6). This increased need results from high mortality risk since elderly patients have various comorbidities (6). A study done in the US in 2000 and examining the use of ICU in patients who were [greater than or equal to] 65 years showed that 27% of all hospitalized elderly patients used ICU and these patients had a mortality rate of 17% (7). Another study reported an ICU mortality rate of 37% in medical patients, 34% in unplanned surgery and 10.6% in planned surgery (8). In our study, 60% of the patients admitted to the ICU were aged [greater than or equal to] 65 years and we had a higher mortality rate than other studies. This is most probably due to demographic properties of our city harboring high number of state nursing care facilities and mostly sheltering retired people who live in their villages. Since it is not legal to limit therapy in our country, all patients who need ICU support can admit to an ICU, irrespective of the end-stage diseases or poor cognitive and functional states. Therefore, the physicians must legally give medical support to all patients whether they can benefit from the ICU or not. In this study population, 20.7% were in a bedridden state due to chronic diseases and 3.1% had metastatic cancers. While the rate of bedridden state in our population was high compared to the literature, the rate of metastatic cancer was similar to the literature (9-10). Metastatic cancer patients who have theoretically indication for chemotherapy were accepted into the ICU in these studies (10). In contrast, 60% of metastatic cancer patients in the present study had the decision of chemotherapy withdrawal. Old patients with end-stage disease and/or bedridden state more frequently lose their lives in the ICU as happened in our patients where 79% of metastatic cancer and 43% of bedridden patients died in the ICU (11,12). Other reason for high mortality rate was the inclusion of a considerable number of the patients with CPR (n=41 [9.1%]) before ICU admission and 82% of these patient were died. It is well known that survival rate of cardiac arrest in older patient decreases significantly with age (13).

In this study, nonsurvivors had lower pH levels than survivors and a plasma pH <7.20 was found to be a mortality predictor. Acidemia is a potentially life-threatening condition frequently seen in critically ill patients and it reflects the seriousness of underlying disease (14). Severe acidemia is described as plasma pH <7.20 (15). This term is important because severe acidemia itself causes organ dysfunction as decrease in cardiac output, arrhythmia, arterial dilatation with hypotension, worse neurologic outcome and respiratory muscle dysfunction (15-17). These patients have high rates of vasopressor treatment, mechanical ventilation (MV) and disease severity indexes (17,18). Patients with severe acidosis were reported to have high mortality rates as demonstrated in our study (14,17,18). WBC count was significantly associated with mortality in this study. Leukocytosis was disclosed to be a mortality predictor in many disease states including acute coronary syndrome, trauma, acute kidney injury and burn (19-21). In addition, in-hospital mortality was reported to be high in patients whose leukocyte count was elevated at emergency admission (4). Leukocytosis is a hallmark of systemic inflammation. It is also a component of systemic inflammatory response syndrome (SIRS) characterized by excessive immune-inflammatory cascade activation (22). Patients with [greater than or equal to] 2 SIRS criteria have high mortality rates (20,23). Thus, our result is compatible with the literature that elevated WBC is an important aspect of disease severity and mortality. Low hematocrit level, an additional mortality predictor in this study, should result from inflammation-related decrease in production and/or decreased erythrocyte half-life reported in critical care patients (24). The decrease in hemoglobin shows the severity of the illness, and a negative correlation between hemoglobin and APACHE II was demonstrated by Nguyen et al (25). The study done by Corwin et al showed that two thirds of general ICU patients had hemoglobin levels <12 gr/dL on day one, decrements in hemoglobin levels over time were demonstrated, irrespective of the admitting hemoglobin levels, and baseline hemoglobin levels of [less than or equal to] 10 gr/dL were associated with higher disease severity scores and mortality rates (26). Albumin, another mortality predictor in the present study, is a negative acute phase reactant which decreases in case of inflammation. In a study done by Reinhardt et al., increases in morbidity and mortality were shown with hypoalbuminemia where the serum albumin concentration of 3.4 g/dL or less was associated with a 30-day mortality rate of 24.6% and increased to 62% if the serum albumin concentration was <2 g/dL (27). In this study, even after the inclusions of admission disease diagnoses, intubation and CPR into the multivariate analysis, pH, hematocrit, WBC and albumin stayed as mortality predictors which reinforce the importance these laboratory parameters as alarming indications for mortality.

Our data showed that patients with admission diagnosis of respiratory failure secondary to COPD, asthma, PE and pneumothorax have high survival rates. In a European survey, mortality rates of the patients with acute respiratory failure plus other organ dysfunction and with only respiratory failure were 31% and 7% (28). A study done by Flaatten et al. also showed that hospital mortality rate was 14.7% among the patients with only acute respiratory failure whereas it was 40.5% in the patients with additional organ failures (29). We also included patients with only respiratory failure in the group, not the ones with other organ dysfunction, and mortality rate was 13.5%. Of these patients, 36 patients had COPD exacerbation and they had a mortality rate of 13.8%, similar to the literature. General in-hospital mortality rate in COPD patients were reported to be around 10%, and it increased to 14% among severe COPD patients not requiring MV and to 24% among very severe COPD patients requiring MV in the ICU (30,31). In our cohort, 22 COPD patients had invasive MV and 22.7% of them were died. We included 8 patients with PE and 3 of them (37.5%) died. Our mortality rate secondary to PE was in accordance with the mortality rates gained in studies done on patients admitted with massive PE to the ICU (32). Rapid intervention causes higher survival rates in patients with pneumothorax as seen in our two patients. In this study, surgical patients had a high ICU survival rate. Mortality rate in elderly surgical ICU patient is lower than in elderly medical ICU patients. Even when patients are classified as planned and unplanned surgical admissions, mortality rate is still lower in emergency surgical patients than in medical patients (6,8).

Multiple studies trying to find new scoring systems investigated the relationship between laboratory data and mortality. Asadollahi et al. showed that laboratory data routinely collected on admission can predict mortality (3). Another study done by Froom et al. in order to predict hospital mortality rates found that a logistic regression model including age and admission laboratory data could predict mortality (33). Similar to these studies, admission pH, WBC count, and albumin hematocrit levels were shown to be significantly associated with mortality in the present study. The important difference of this study is to displace these laboratory parameters as important predictors even after the inclusion of admission diagnoses, CPR before ICU admission and endotracheal intubation into the regression model which are well known determinants of outcome in severely ill elderly patients (6,8,11). Therefore, our results underline the importance of severe acidemia and inflammation on the ICU outcome.

The study has some limitations. First, this was a single centered retrospective study with limited number of patients. Socioeconomic biases may be present so results may not represent the general population. Second, only the laboratory data, medical history and admission diagnoses obtained at ICU admission were taken into account. The complications developed during ICU stay that might be effective on mortality, especially in the patients followed for longer duration were disregarded. Third, we did not take organ support therapies such as invasive or noninvasive mechanical ventilation, renal replacement therapy, plasmapheresis and vasoactive drug use into account which are important severity indicators and mortality predictors among critically ill patients.


The risk of death in critically ill older patients can be predicted by routinely retrieved laboratory parameters on admission to the ICU along with medical history and admission diagnosis. These easily accessible data may help clinicians rapidly foresee the risk of mortality, and decide on an efficient treatment protocol for the patients. Apart from leading their treatment, clinicians can inform patients' family accordingly.


(1.) Rocker G, Cook D, Sjokvist P, et al. Clinician predictions of intensive care unit mortality. Crit Care Med 2004; 32: 1149-1154. [CrossRef]

(2.) Knaus WA, Draper EA, Wagner DP, et al. APACHE II: a severity of disease classification system. Crit Care Med 1985; 13: 818-829. [CrossRef]

(3.) Asadollahi K, Hastings IM, Gill GV, et al. Prediction of hospital mortality from admission laboratory data and patient age: a simple model. Emerg Med Australas. 2011; 23: 354-363. [CrossRef]

(4.) Mohammed MA, Rudge G, Watson D, et al. Index blood tests and national early warning scores within 24 hours of emergency admission can predict the risk of in-hospital mortality: a model development and validation study. PLoS One 2013; 8: e64340. [CrossRef]

(5.) Blot S, Cankurtaran M, Petrovic M, et al. Epidemiology and outcome of nosocomial bloodstream infection in elderly critically ill patients: a comparison between middle-aged, old, and very old patients. Crit Care Med 2009; 37: 1634-1641. [CrossRef]

(6.) Bagshaw SM, Webb SA, Delaney A, et al. Very old patients admitted to intensive care in Australia and New Zealand: a multi-centre cohort analysis. Crit Care 2009; 13: R45. [CrossRef]

(7.) Yu W, Ash AS, Levinsky NG, et al. Intensive care unit use and mortality in the elderly. Gen Intern Med 2000; 15: 97-102. [CrossRef] https://

(8.) De Rooij SE, Govers A, Korevaar JC, et al. Short-term and long long-term mortality in very elderly patients admitted to an intensive care unit. Intensive Care Med 2006; 32: 1039-1044. [CrossRef]

(9.) M. S. Nielsson MS, Christiansen CF, Johansen MB, et al. Mortality in elderly ICU patients: a cohort study. Acta Anaesthesiol Scand 2014; 58: 19-26. [CrossRef]

(10.) Auclin E, Charles-Nelson A, 5, Baptiste Abbar B, et al. Outcomes in elderly patients admitted to the intensive care unit with solid tumors. Ann Intensive Care 2017; 7: 26. [CrossRef]

(11.) Mehlen P, Puisieux A. Metastasis: a question of life or death. Nat Rev Cancer 2006; 6: 449-458. [CrossRef]

(12.) Kim YJ, Kim MJ, Cho YJ, et al. Who should be admitted to the intensive care unit? The outcome of intensive care unit admission in stage IIIB-IV lung cancer patients. Med Oncol 2014; 31: 847. [CrossRef]

(13.) Segal N, di Pompeo C, Escutnaire J, et al. Evolution of survival in cardiac arrest with age in elderly patients: Is resuscitation a dead end? J Emerg Med 2017; 54: 295-301. [CrossRef]

(14.) Gunnerson KJ, Saul M, He S, et al. Lactate versus non-lactate metabolic acidosis: a retrospective outcome evaluation of critically ill patients. Crit Care 2006; 10: R22. [CrossRef]

(15.) Ganga HV, Kallur KR, Patel NB, et al. The impact of severe acidemia on neurologic outcome of cardiac arrest survivors undergoing therapeutic hypothermia. Resuscitation 2013; 84: 1723-1727. [CrossRef]

(16.) Kellum JA. Disorders of acid-base balance. Crit Care Med 2007; 35: 2630-2636. [CrossRef]

(17.) Jung B, Rimmele T, Le Goff C, et al. Severe metabolic or mixed acidemia on intensive care unit admission: incidence, prognosis and administration buffer therapy. A prospective, multiple-center study. Crit Care 2011; 15: R238. [CrossRef]

(18.) Noritomi DT, Soriano FG, Kellum JA, et al. Metabolic acidosis in patients with severe sepsis and septic shock: a longitudinal quantitative study. Crit Care Med 2009; 37: 2733-2739. [CrossRef]

(19.) Lloyd-Jones DM, Camargo CA Jr, Giugliano RP, et al. Effect of leukocytosis at initial examination on prognosis in patients with primary unstable angina. Am Heart J 2000; 139: 867-873. [CrossRef]

(20.) Malone DL, Kuhls D, Napolitano LM, et al. Back to basics: validation of the admission systemic inflammatory response syndrome score in predicting outcome in trauma. J Trauma 2001; 51: 458-463. [CrossRef]

(21.) Han SS, Ahn SY, Ryu J, et al. U-shape relationship of white blood cells with acute kidney injury and mortality in critically ill patients. Tohoku J Exp Med 2014; 23: 177-185. [CrossRef]

(22.) Bellingan G. Leukocytes: friend or foe. Intensive Care Med 2000; 26(Suppl 1): S111-118. [CrossRef]

(23.) Brun-Buisson C. The epidemiology of the systemic inflammatory response. Intensive Care Med 2000; 26(Suppl 1): S64-74. [CrossRef]

(24.) Astin R, Puthucheary Z. Anaemia secondary to critical illness: an unexplained phenomenon. Extrem Physiol Med 2014; 3: 4. [CrossRef]

(25.) Nguyen BV, Bota DP, Melot C, et al. Time course of hemoglobin concentrations in nonbleeding intensive care unit patients. Crit Care Med 2003; 31: 406-410. [CrossRef]

(26.) Corwin HL, Gettinger A, Pearl RG, et al. The CRIT Study: anemia and blood transfusion in the critically ill-current clinical practice in the United States. Crit Care Med 2004; 32: 39-52. [CrossRef]

(27.) Reinhardt GF, Myscofski JW, Wilkens DB, et al. Incidence and mortality of hypoalbuminemic patients in hospitalised veterans. J Parent Enter Nutr 1980; 4: 357-359. [CrossRef]

(28.) Vincent JL, Akca S, De Mendonca A, et al. The epidemiology of acute respiratory failure in critically ill patients. CHEST 2002; 121: 1602-1609. [CrossRef]

(29.) Flaatten H, Gjerde S, Guttormsen AB, et al. Outcome after acute respiratory failure is more dependent on dysfunction in other vital organs than the severity of the respiratory failure. Crit Care 2003; 7: R72-R77. [CrossRef]

(30.) Molinari N, Briand C, Vachier I, et al. Hospitalizations for COPD exacerbations: trends and determinants of death. COPD 2015; 12: 621-627.

(31.) Soo Hoo GW, Hakimian N, Santiago SM. Hypercapnic respiratory failure in COPD patients. CHEST 2000; 117: 169-177. [CrossRef]

(32.) Ergan B, Ergun R, Caliskan T, et al. Mortality related risk factors in high-risk pulmonary embolism in the ICU. Can Respir J 2016; 249: 2432808. [CrossRef]

(33.) Froom P, Shimoni Z. Prediction of hospital mortality rates by admission laboratory tests. Clin Chem 2006; 52: 325-328. [CrossRef]

Turkay AKBAS [1] [ID], Elif SENOCAK TASCI [2] [ID], Hafize TITIZ YILMAZTEPE [3] [ID], Feruze TURAN SONMEZ [4] [ID], Durdu Mehmet KOS5, Birgul ONEC [6] [ID]

[1] Duzce University School of Medicine, Department of Internal Medicine, Duzce, Turkey

[2] Semdinli State Hospital, Hakkari, Turkey

[3] Sultan Murat 1. State Hospital, Internal Medicine, Edirne, Turkey

[4] Duzce University School of Medicine, Department of Emergency Medicine, Duzce, Turkey

[5] Occupational Diseases Hospital, Clinic of Internal Medicine, Ankara, Turkey

[6] Duzce University School of Medicine, Hematology Department, Duzce, Turkey

Corresponding Author / Sorumlu Yazar: Turkay Akbas E mail:

Received/Gelis: 09.05.2019

Accepted/Kabul: 15.05.2019

Available online/

Cevrimici yayin: 10.06.2019


Concept: TA,EST, BO; Design: TA, EST, FTS, BO; Supervision: TA, DMK, BO; Resources: TA, BO, DMK, FTS; Materials: TA, BO, DMK; Data Collection and/or Processing: TA, EST, HYT, FTS; Analysis and/or Interpretation: TA, EST, DMK, BO; Literature Search: TA, EST, HTY, FTS; Writing Manuscript: TA, EST, HTY, BO; Critical Review: TA, BO, FTS, DMK.


Fikir: TA, EST, BO; Tasarim: TA, EST, FTS, BO; Denetleme: TA, DMK, BO; BB; Kaynaklar: TA, BO, DMK, FTS; Malzemeler: TA, BO, DMK; Veri Toplanmasi ve/ veya Islemesi: TA, EST, HYT, FTS; Analiz ve/veya Yorum: TA, EST, DMK, BO; Literatur Taramasi: TA, EST, HTY, BO; Yaziyi Yazan: xx; Elestirel Inceleme: TA, BO, FTS, DMK.

Ethics Committee Approval: Ethics committee approval was received for this study from the ethics committee of Duzce University (no:2015/76).

Informed Consent: Retrospective study

Peer-review: Externally peer-reviewed.

Conflict of Interest: Authors have no conflicts of interest to declare.

Financial Disclosure: The authors declared that this study has received no financial support.

Part of this study was presented at the 12th Congress of The Turkish Society of Medical and Surgical Intensive Care Medicine - 4th Euro-Asian Critical Care Meeting, November 4-7, Ankara, Turkey, 2015.

Etik Komite Onayi: Bu calisma icin etik kurul onayi Duzce Universitesi etik kurulundan alinmistir (no:2015/76).

Hasta Onami: Retrospektif calisma

Hakem Degerlendirmesi: Dis bagimsiz.

Cikar Catismasi: Yazarlar cikar catismasi bildirmemislerdir.

Finansal Destek: Yazarlar bu calisma icin finansal destek almadiklarini beyan etmislerdir.

Bu calismanin bir kismi, 4-7 Kasim 2015, Ankara, Turkiye'de, 12. Turkiye Tibbi ve Cerrahi Yogun Bakim Tibbi Dernegi, 4. Avrupa-Asya Yogun Bakim Toplantisinda yapildi.
Table 1. Comparison of demographic and clinical variables between
survivors and nonsurvivors

                                 Survivors         Nonsurvivors
Variables                        (n: 236)            (n: 213)

Age (years)                  79.2 [+ or -] 7.2   78.6 [+ or -] 7.1
APACHE II                     21 [+ or -] 6.1     33 [+ or -] 6.8
Age groups, n (%)
  65-74                          70 (29.7)           62 (29.1)
  75-84                         109 (46.2)          102 (47.9)
  [greater than or               57 (24.2)            49 (23)
  equal to] 85
Male, n (%)                     113 (47.9)          118 (55.4)
Comorbidity, n (%)
  HT                            145 (61.9)          128 (60.1)
  HF                            106 (44.9)           97 (45.5)
  IHD                            48 (20.3)           48 (22.5)
  DM                             72 (30.5)           59 (27.7)
  CKD                            39 (16.5)           52 (24.4)
  Dementia                       24 (10.2)           33 (15.5)
  COPD                           64 (27.1)           44 (20.7)
  Malignancy                     25 (10.6)           35 (16.4)
  CVA                            59 (25)             64 (30)
  CLD                             5 (2.1)            11 (5.2)
Bedridden state, n (%)           40 (16.9)           53 (24.9)
CPR, n (%)                        8 (3.4)            33 (15.5)
pH, n (%)a
  < 7.20                         14 (6)              57 (26.9)
  7.20-7.39                      92 (39.1)           84 (39.6)
  [greater than or equal        129 (54.9)           71 (33.5)
  to] 7.35
Albumin, n (%) (a)
  < 2 mg/dL                      11 (4.7)            29 (13.8)
  2.01-3.49 mg/dL               162 (96.2)          147 (70)
  [greater than or equal         61 (26.1)           34 (16.2)
  to] 3.5 mg/dL
Admission site, n (%)
  Emergency service             110 (46.6)           85 (39.9)
  Ward                          126 (53.4)          128 (60.1)
Admission diagnosis, n (%)
  Infection                      92 (39)            150 (70.4)
  Cardiac disease                20 (8.5)            15 (7)
  Respiratory disease            41 (17.4)           10 (4.7)
  Neurologic disease             20 (8.5)            18 (8.5)
  Surgery                        38 (16.1)            1 (0.5)
  Hepatic failure                 4 (1.7)             7 (3.3)
  Bleeding                        9 (3.8)             7 (3.3)
  Others (b)                     13 (6.7)             5 (2.3)

Variables                    p value

Age (years)                   0.385
APACHE II                    <0.001
Age groups, n (%)             0.931
  [greater than or
  equal to] 85
Male, n (%)                   0.310
Comorbidity, n (%)
  HT                          0.599
  HF                          0.894
  IHD                         0.571
  DM                          0.513
  CKD                         0.038
  Dementia                    0.098
  COPD                        0.122
  Malignancy                  0.073
  CVA                         0.231
  CLD                         0.082
Bedridden state, n (%)        0.030
CPR, n (%)                   <0.001
pH, n (%)a
  < 7.20                     <0.001
  7.20-7.39                   0.634
  [greater than or equal     <0.001
  to] 7.35
Albumin, n (%) (a)
  < 2 mg/dL                   0.002
  2.01-3.49 mg/dL             0.785
  [greater than or equal      0.011
  to] 3.5 mg/dL
Admission site, n (%)         0.154
  Emergency service
Admission diagnosis, n (%)
  Infection                  <0.001
  Cardiac disease             0.610
  Respiratory disease        <0.001
  Neurologic disease          0.869
  Surgery                    <0.001
  Hepatic failure             0.158
  Bleeding                    0.520
  Others (b)                  0.097

(a) Data of pH and albumin were missing in 2 and 5 patients,

(b) Others include electrolyte disorders (n:7), hypoglycemia (n:4),
acute pancreatitis (n:3), Addison's disease (n:1), intoxication (n:2)
and uremia (n:1).

Abbreviations: APACHE: Acute Physiology and Chronic Health Evaluation;
CKD: chronic kidney disease (Stage 2-5D); CLD: chronic liver disease;
COPD: chronic obstructive pulmonary disease; CVA: cerebrovascular
accident; DM: diabetes mellitus; HF: heart failure; HT: hypertension;
IHD: ischemic heart diseases; CPR: cardiopulmonary resuscitation.

Table 2. Comparison of laboratory parameters between survivors
and nonsurvivors

Variables                     Survivors           Nonsurvivors
                               (n: 236)             (n: 213)

pH                        7.36 [+ or -] 0.10   7.30 [+ or -] 0.37
HC[O.sub.3], mmol/L       23.4 [+ or -] 6.9    20.3 [+ or -] 7.8
PaC[O.sub.2], mm Hg       39.5 [+ or -] 18.2   40.1 [+ or -] 18.3
Pa[O.sub.2], mm Hg          70 [+ or -] 40.7   74.1 [+ or -] 40.8
WBC, x[10.sup.3]/L        10.8 [+ or -] 5.6      13 [+ or -] 10
Hematocrit, (%)           34.9 [+ or -] 6.9    30.2 [+ or -] 6.7
Platelet, x[10.sup.3]/L    217 [+ or -] 128     199 [+ or -]119
Creatinine, mg/dL         1.17 [+ or -] 1.4     1.8 [+ or -] 1.8
Total bilirubin, mg/dL    0.63 [+ or -] 1.24   0.78 [+ or -] 2.52
AST, U/L (a)                28 (19-48)           40 (22-99)
ALT, U/L (a)                17 (11-33)           24 (12-67)
LDH, U/L (a)               306 (224-417)        382 (269-559)
Albumin, gr/dL             3.0 [+ or -] 0.6     2.8 [+ or -] 0.6
INR                       1.19 [+ or -] 1.44   1.37 [+ or -] 3.1
Phosphorus, ng/mL          3.6 [+ or -] 1.7     4.3 [+ or -] 2.5
Magnesium, mg/dL           1.9 [+ or -] 0.4     2.1 [+ or -] 0.4
Calcium, mg/dL             8.3 [+ or -] 0.9     8.1 [+ or -] 5.2
Sodium, mEq/L              138 [+ or -] 7.2     138 [+ or -] 8.8
Potassium, mEq/L          4.46 [+ or -] 0.9    4.38 [+ or -] 1.1
Troponin T, ng/mL (a)      0.2 (0.04-0.28)     0.16 (0.84-0.21)
Glucose, mg/dL             135 [+ or -] 91      135 [+ or -] 74
Uric acid, mg/dL           6.1 [+ or -] 3.5     7.2 [+ or -] 3.9
CRP, mg/dL                 5.8 [+ or -] 9.6     9.2 [+ or -] 12

Variables                 p value

pH                        <0.001
HC[O.sub.3], mmol/L        0.015
PaC[O.sub.2], mm Hg        0.539
Pa[O.sub.2], mm Hg         0.447
WBC, x[10.sup.3]/L         0.001
Hematocrit, (%)           <0.001
Platelet, x[10.sup.3]/L    0.018
Creatinine, mg/dL         <0.001
Total bilirubin, mg/dL     0.001
AST, U/L (a)              <0.001
ALT, U/L (a)               0.001
LDH, U/L (a)               0.010
Albumin, gr/dL            <0.001
INR                        0.002
Phosphorus, ng/mL         <0.001
Magnesium, mg/dL           0.001
Calcium, mg/dL             0.626
Sodium, mEq/L              0.546
Potassium, mEq/L           0.147
Troponin T, ng/mL (a)      0.332
Glucose, mg/dL             0.340
Uric acid, mg/dL           0.288
CRP, mg/dL                <0.001

(a) Nonparametric tests were used since they were not normally
distributed. Abbreviations: ALT: alanine transaminase; AST: aspartate
aminotransferase; CRP: C-reactive protein; INR: international
normalized ratio, LDH: lactate dehydrogenase; WBC: white blood cell.

Table 3. Mortality predictors in the multivariate logistic regression
(Model 1 & Model 2)

Variables                   Model 1 (a)

                         B     Wald      p         OR (95% CI)

pH <7.20                1.65   14.94   <0.001   5.19 (2.25-11.97)
Albumin [less than      1.10    4.66    0.031   3.00 (1.11-8.14)
or equal to] 2 gr/dL
WBC                     0.05    8.94    0.003   1.06 (1.02-1.09)
Hematocrit             -0.04    4.35    0.037   0.96 (0.93-0.99)
CRP                     0.03    5.21    0.022   1.03 (1.01-1.05)
CPR                     1.32    8.03    0.005   3.73 (1.50-9.27)
Bedridden state         0.60    4.34    0.037   1.83 (1.04-3.23)
Respiratory disease

Variables                   Model 2 (b)

                         B     Wald      P         OR (95% CI)

pH <7.20                1.46    8.23    0.004    4.31 (1.59-11.70)
Albumin [less than      1.28    3.83    0.050    3.61 (0.99-13.03)
or equal to] 2 gr/dL
WBC                     0.06    6.40    0.011    1.06 (1.01-1.11)
Hematocrit             -0.05    5.52    0.019    0.94 (0.91-0.99)
CRP                     0.03    3.33    0.068    1.03 (0.99-1.06)
CPR                     0.42    0.70    0.402    1.52 (0.57-3.99)
Bedridden state         0.43    1.34    0.247    1.52 (0.74-3.18)
Intubation              2.94   58.71   <0.001   18.85 (8.89-39.96)
Respiratory disease    -1.63    8.52    0.004    0.20 (0.07-0.59)
Surgery                -3.91   10.53    0.001    0.02 (0.002-0.21)

(a) Hosmer-Lemeshow, [X.sup.2]: 7.9, df:8, p=0.448.bHosmer-Lemeshow,
[X.sup.2]: 6.5, df:8, p=0.595.

Abbreviations: CPR: cardiopulmonary resuscitation; CRP: C-reactive
protein; CI: confidence interval; OR: odds ratio; WBC: white blood
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Akbas, Turkay; Tasci, Elif Senocak; Yilmaztepe, Hafize Titiz; Sonmez, Feruze Turan; Kos, Durdu Mehme
Publication:Dahili ve Cerrahi Bilimler Yogun Bakim Dergisi (Journal of Medical and Surgical Intensive Care Medic
Date:Aug 1, 2019
Previous Article:Low Cortisol Levels as a Cause of Hypotension During Extended ICU Stay/Uzamis Yogun Bakim Yatisi Sirasinda Gelisen Hipotansiyon Sebebi Olarak Dusuk...
Next Article:Evaluation of Physical Restraint Practices and Their Neurovascular Effect on Intensive Care Unit Patients/Yogun Bakim Hastalarinda Kisitlama...

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |