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Clinical Indicators of Impaired Gas Exchange in Cardiac Postoperative Patients Vanessa Emille Carvalho Sousa, RN, MSN, Lívia Maia Pascoal, RN, MSN, Talita Ferreira Oliveira de Matos, RN, Ranielly Vidal do Nascimento, RN, Daniel Bruno Resende Chaves, RN, MSN, Nirla Gomes Guedes, RN, PhD, and Marcos Venícios de Oliveira Lopes, RN, PhD Vanessa Emille Carvalho Sousa, RN, MSN, is a CAPES Scholarship PhD Student—Process BEX 14504/13-8 at the CAPES Foundation, Ministry of Education of Brazil, Brasília, Brazil, Lívia Maia Pascoal, RN, MSN, is a Nursing Professor at the Federal University of Maranhão in Imperatriz, Brazil, Talita Ferreira Oliveira de Matos, RN, and Ranielly Vidal do Nascimento, RN, are Members of the Nursing Diagnosis, Interventions, and Outcomes Study Group at the Federal University of Ceará in Fortaleza, Ceará, Brazil, Daniel Bruno Resende Chaves, RN, MSN, is a Nursing Professor at the Faculdade de Ensino Superior do Ceará in Fortaleza, Ceará, Brazil, Nirla Gomes Guedes, RN, PhD, and Marcos Venícios de Oliveira Lopes, RN, PhD, are Nursing Professors at the Federal University of Ceará in Fortaleza, Ceará, Brazil.
Search terms: Diagnostic test, nursing diagnosis, postoperative care Author contact: [email protected], with a copy to the Editor: [email protected]
PURPOSE: To analyze the accuracy of the defining characteristics of impaired gas exchange (IGE). METHODS: Cross-sectional study carried out in 93 cardiac postoperative adult patients. The 19 NANDA-I defining characteristics related to IGE were evaluated. FINDINGS: Ten defining characteristics were found in the sample. The best accuracy values, including specificity, diagnostic odds ratio, and likelihood ratios, inter alia, were found to decreased carbon dioxide, abnormal arterial blood gases, abnormal arterial pH, and abnormal breathing. CONCLUSION: Some clinical indicators predict the presence/absence of IGE more accurately than others. IMPLICATIONS FOR NURSING PRACTICE: Knowledge of the accuracy of defining characteristics allows an early identification of particular nursing diagnoses, which can prevent complications. PROPÓSITO: Analizar a acurácia das características definidoras de Troca de Gases Prejudicada (TGP). MÉTHODO: Estudo transversal desenvolvido com 93 pacientes adultos no pósoperatório cardíaco. Dezenove características definidoras da NANDA-I associadas a TGP foram avaliadas. RESULTADOS: Dez características definidoras foram encontradas na amostra. Os melhores valores de acurácia, incluindo especificidade, odds ratio diagnóstica e razões de verossimilhança, entre outras, foram encontrados para as características definidoras dióxido de carbono diminuído, gases sanguíneos arteriais anormais, pH arterial anormal e respiração anormal. CONCLUSÃO: Alguns indicadores clínicos predizem a presença/ausência de TGP mais precisamente que outros. IMPLICAÇÕES PARA A PRÁTICA DE ENFERMAGEM: O conhecimento acerca da acurácia das características definidoras contribui para uma identificação precoce de diagnósticos de enfermagem específicos, o que pode prevenir complicações.
A nursing diagnosis results from a series of clinical evaluations that can be complex, depending on the condition of the patient. Postoperative patients commonly present numerous signs and symptoms simultaneously, which can confound nurses during the nursing process, especially on the diagnostic step (Sarna et al., 2008). © 2014 NANDA International, Inc. International Journal of Nursing Knowledge Volume 26, No. 3, July 2015
Besides the range of different clinical clues, nurses usually face defining characteristics that apply to more than one nursing diagnosis, which occurs for example with respiratory clinical indicators, leading to variability of nurses’ assessments. Establishing good clinical indicators minimizes that variability, producing diagnoses that 141
Clinical Indicators of Impaired Gas Exchange accurately represent the patient’s state (Lopes, Silva, & Araujo, 2012). The nursing diagnoses of impaired gas exchange (IGE) (00030), ineffective airway clearance (00031), and ineffective breathing pattern (00032) have been identified in the literature as those that occur more frequently in different situations and age groups (Pivoto, Lunardi Filho, Santos, Almeida, & Silveira, 2010; Zeitoun, Barros, Michel, & Bettencourt, 2007). Their high prevalence is expected because people with various medical conditions and surgical problems are likely to present such physiological responses (Carlson-Catalano & Lunney, 1995). IGE, a nursing diagnosis approved by the NANDA-I in 1980 and reviewed in 1996 and 1998, is defined as “excess or deficit in oxygenation and/or carbon dioxide elimination at the alveolar-capillary membrane.” The defining characteristics of IGE are abnormal arterial blood gases, abnormal arterial pH, abnormal breathing, abnormal skin color, confusion, cyanosis, decreased carbon dioxide, diaphoresis, dyspnea, headache upon awakening, hypercapnia, hypoxemia, hypoxia, irritability, nasal flaring, restlessness, somnolence, tachycardia, and visual disturbances (Herdman, 2012). The incidence of respiratory dysfunctions in postoperative cardiac patients is about 25%. Even patients who do not present any severe cardiac dysfunction are reported to have a significant respiratory impairment for at least one week after an open heart surgery (Apostolakis, Filos, Koletsis, & Dougenis, 2010). In this context, one study found that lung function and oxygenation were compromised in 90% of a sample of patients undergoing cardiac surgery (Ranieri et al., 1999). The nursing diagnosis of IGE has been established in studies of patients in the cardiac perioperative period and is a frequent response in adult patients undergoing such interventions. Multiple specific factors contribute to this nursing diagnosis, such as general anesthesia, sternotomy, tissue manipulation, mammary artery dissection, pleural opening, chest tube insertion, and pain (Sousa et al., 2013). In this sense, a study developed on the cardiac postoperative period associated the occurrence of IGE with the surgery itself. The defining characteristics of abnormal breathing, dyspnea, tachycardia, abnormal skin color, and somnolence were considered most relevant (Pivoto et al., 2010). Other researchers found a significant association between IGE and abnormal blood gases and hypoxemia in patients using mechanical ventilation (Zeitoun et al., 2007). Furthermore, others demonstrated that body position is directly related to gas exchange disturbances, so nurses must be aware of how body positioning can affect the patient’s oxygenation (Hough, Johnston, Brauer, Woodgate, & Schibler, 2013). Despite a variety of studies showing the prevalence of respiratory nursing diagnoses and their defining characteristics, the production of studies that measure the accuracy of these clinical indicators is still scarce. 142
V. E. C. Sousa et al. Decision making in nursing should be based on clinical skills and scientific evidence (Santana & de Oliveira Lopes, in press). Thus, there is a need for studies to validate nursing diagnoses and to show relations of nursing diagnoses and other phenomena, such as which nursing diagnoses or what components are important for populations with specific medical diagnoses or conditions, as postoperative patients (Lunney, 2008). Early and accurate detection of IGE is important because respiratory complications are associated with increased morbidity and mortality, and with high postoperative financial costs, especially on the cardiac setting (Wynne & Botti, 2004). In this context, the following research question emerged: Which clinical indicators of IGE best predict the presence or absence of this nursing diagnosis in surgical cardiac patients? The purpose of this study is to analyze the accuracy of the defining characteristics of IGE in cardiac postoperative patients based on measures of sensitivity (Se), specificity (Sp), predictive values (PV), likelihood ratios (LR), diagnostic odds ratio (DOR), efficiency (E), and area under the receiver operating characteristic curve (ROC curve). Methods Design and Sample This is a cross-sectional study performed in the postoperative intensive care unit (ICU) of a referral center for cardiopulmonary diseases. The sample comprised 98 patients over 18 years of age, within 48 hr of cardiac surgery, and having a level of consciousness that would enable data collection. Individuals with clinical findings that prevented data collection, had undergone heart transplant, and/or supported by mechanical ventilation were excluded from the sample. To estimate the sample size, we used the formula for single test accuracy studies proposed by Zhou, Obuchowski, and McClish (2011), with the following parameters: a confidence level of 95%, desired width of one half of 95%, confidence interval of 10%, and a conjectured sensitivity of the clinical indicators equal to 85%, leading to a total of 98 patients. Patients were selected by consecutive sampling and evaluated at a single moment during hospitalization in the ICU, on the first 48 hr after surgery. Data were independently collected by three nurses who shared the assessments. The nurses underwent prior training and a written test about respiratory nursing diagnoses and physical examination of chest and lungs, and were considered able to make patient assessments correctly. The data collection involved a respiratory examination of patients and the use of a formulary, created specifically for the study, with items related to the IGE clinical indicators. Furthermore, operational definitions were given to support the data collection in order to determine IGE char-
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acteristics. As an example, abnormal breathing was defined as the change in the number of breaths per minute or change on the rhythmicity of breathing through inspection, the rate of 16–20 breaths per minute being considered a parameter for normal respiration. This and other definitions were obtained and adapted from a study in which operational definitions for respiratory clinical indicators were developed and validated (Silva et al., 2011). The clinical judgment regarding both the presence of the defining characteristics and the nursing diagnosis of IGE was conducted by a specialist in nursing diagnoses and per seeking consensus among members of a research group on nursing diagnoses, interventions, and outcomes. This specialist has previous experience in the use of the NANDA-I taxonomy, shown in publications involving the establishment of respiratory diagnoses and participation in the cited research group. The appropriate regional ethics committee approved the study. All the subjects gave informed consent and patients’ anonymity was preserved.
Table 1. Clinical Indicators of Impaired Gas Exchange Identified in the Sample Variables Defining characteristics Abnormal arterial blood gases Abnormal arterial pH Abnormal breathing Decreased carbon dioxide Dyspnea Hypercapnia Hypoxemia Hypoxia Restlessness Tachycardia Related factor Ventilation perfusion imbalance Impaired gas exchange
Presence
%
Absence
%
44
44.9
54
55.1
31 40 36
31.6 40.8 36.7
67 58 62
68.4 59.2 63.3
27 6 31 31 6 39
27.6 6.1 31.6 31.6 6.1 39.8
71 92 67 67 92 59
72.4 93.9 68.4 68.4 93.9 60.2
29
29.6
69
70.4
30
30.6
68
69.4
Statistical Analysis After the diagnostic inferences, measures such as Se, Sp, positive and negative PVs, LRs, E, DORs, and the area under the ROC curve were calculated for each defining characteristic of IGE. A brief description of each of these measures can be found on a scientific paper that describes the methodological stages in determining the accuracy of clinical indicators in identifying nursing diagnoses (Lopes et al., 2012). All analyses were performed with the support of R software version 2.15.1 (R Development Core Team, Vienna, Austria). The statistical significance was tested based on the analysis of confidence intervals. Statistical significance was attributed when the 95% confidence interval did not include the null value of 1. Results Patients’ mean age was 55.9 years (SD 13.4), 55.1% were male, 70.4% lived with a partner, and 51.1% had more than 8 years of schooling. The per capita income had an asymmetrical distribution (p < .05), and the median indicates that half of the sample earned up to $250.00 monthly. It is noteworthy that 54.1% of participants were currently or had been smokers at some point in their lives. The most prevalent medical diagnoses were angina/ coronary artery disease (37.8%), followed by valvular heart disease (36.7%), and the most frequent types of surgeries were coronary artery bypass graft (59.1%) and valve replacement (37.8%). The nursing diagnosis of IGE was present in 30.6% of the patients. Regarding its clinical indicators, the following defining characteristics prevailed: abnormal arterial blood gases (44.9%), abnormal breathing (40.8%), and tachycardia (39.8%). The related factor ventilation-perfusion (V-Q) imbalance was present in 29.6% of the sample (Table 1).
Some defining characteristics of IGE were absent on the sample. They are abnormal skin color, confusion, cyanosis, diaphoresis, headache upon awakening, irritability, nasal flaring, somnolence, and visual disturbances. Regarding accuracy measures, Table 2 shows that abnormal arterial blood gases was the only defining characteristic that achieved an Se above 80%. Restlessness and hypercapnia, however, had specificities of up to 90%. The indicators that showed statistical significance (95% confidence intervals not containing 1) were abnormal arterial blood gases, abnormal arterial pH, abnormal breathing, and decreased carbon dioxide. Among these clinical indicators, none showed a high PV+; however, decreased carbon dioxide and abnormal breathing showed a PV− of up to 80%. Beyond that, the LRs and the area under the ROC curve indicated that decreased carbon dioxide, abnormal arterial blood gases, abnormal arterial pH, and abnormal breathing are best isolated predictors of IGE. In summary, the defining characteristics of decreased carbon dioxide, abnormal arterial blood gases, abnormal arterial pH, and abnormal breathing were the most accurate. These clinical indicators presented moderate Sp, high values for DOR, significant LRs, and E above 70%, which show that these indicators are effective predictors of the correct identification of IGE. Figure 1 shows a performance comparison between the six indicators that showed the highest values for the area under the ROC curve. This figure combines the true positive rate and the false positive rate, which are two important measures to diagnostic accuracy analyses. It can be seen that abnormal arterial blood gases and abnormal breathing are located in the upper left quadrant, which means that these defining characteristics had a high true positive rate and a low false positive rate. In other words, they should be considered the clinical indicators that led to the most acutare identification of IGE in this study. 143
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Table 2. Sensitivity (Se), Specificity (Sp), Predictive Value (PV), Likelihood Ratio (LR), Diagnostic Odds Ratio (DOR), Efficiency (E), and Area Under ROC Curve (ROC) of Clinical Indicators for Impaired Gas Exchange Defining Characteristics
Se
Sp
PV+
PV−
Abnormal arterial blood gases Abnormal arterial pH Abnormal breathing Decreased carbon dioxide Dyspnea Hypercapnia Hypoxemia Restlessness Tachycardia
93.33
76.47
63.64
96.30
53.33 70.00 60.00 43.33 13.33 43.33 16.67 40.00
77.94 72.06 73.53 79.41 97.06 73.53 98.53 60.29
51.61 52.50 50.00 48.15 66.67 41.94 83.33 30.77
79.10 84.48 80.65 76.06 71.74 74.63 72.83 69.49
E
LR+
LR−
DOR
ROC
81.63
3.97 [2.55–6.17]
0.09 [0.02–0.33]
40.81 [10.63–295.16]
0.849
70.41 71.43 69.39 68.37 71.43 64.29 73.47 54.08
2.42 [1.43–4.09] 2.51 [1.60–3.91] 2.27 [1.41–3.65] 2.10 [1.21–3.67] 4.53 [1.11–18.51] 1.64 [0.99–2.71] 11.33 [1.58–81.52] 1.01 [0.63–1.60]
0.60 [0.40–0.90] 0.42 [0.24–0.73] 0.54 [0.34–0.86] 0.71 [0.51–1.00] 0.89 [0.77–1.03] 0.77 [0.55–1.09] 0.85 [0.72–1.00] 1.00 [0.70–1.41]
3.96 [1.58–10.21] 5.84 [2.32–15.83] 4.08 [1.66–10.46] 2.91 [1.14–7.54] 4.82 [0.83–40.89] 2.11 [0.84–5.26] 11.78 [1.70–322.74] 1.01 [0.41–2.45]
0.656 0.710 0.667 0.613 0.552 0.584 0.576 0.501
Figure 1. Performance Comparison of Clinical Indicators With the Largest Area Under the Receiver Operating Characteristic Curve Values
Regarding the related factor assessment, since V-Q imaging was not available to all patients in the study setting, we chose to evaluate V-Q mismatch by indirect methods, as the physical evaluation of chest and lungs and through laboratory tests. Ventilation was considered inappropriate when the patient showed signs of impairment on the passage of air through the lungs, observed by 144
respiration assessment (quality, rate, pattern, depth, sounds, and breathing effort) or in the presence of retained respiratory secretions or difficulty in expelling it, changes in the vocal fremitus or chest percussion, and/or asymmetric thoracic excursions. The perfusion was considered inadequate by the identification of signs of deficit in blood oxygenation, through changes in arterial blood gases, oxygen
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saturation, and/or when the patient had hypoxemia. These parameters and the techniques used were obtained by consulting semiology textbooks (Seidel et al., 2011; Swartz, 2010).
Discussion The nursing diagnosis of IGE was present in 30.6% of the sample; a similar result of 36.4% was found in a survey of adults in the cardiac postoperative period (Rocha, Maia, & Silva, 2006). Regarding the defining characteristics, the statistics showed that the best accuracy was attributed to abnormal arterial blood gases, present in 44.9% of patients, with high Se (over 80%) and significant values for LR, DOR, E, and the area under the ROC curve, which indicates that this clinical indicator may facilitate IGE detection by nurses. The arterial blood gases is a complementary measure of the need for oxygenation and respiration, allowing the nurse to assess whether the lung is correctly performing the gas exchange (White, 2012). This clinical indicator is closely associated with an IGE diagnosis and was considered a critical defining characteristic in a validation study of clinical indicators with patients using invasive ventilation, being present in 100% of the evaluations (Zeitoun et al., 2007). The defining characteristic of restlessness indicated a good positive predictive value (PV+ = 0.83) and a high specificity (Sp = 98.53), indicating that when IGE was present, restlessness was present as well. Likewise, in the absence of restlessness, the IGE diagnosis was rejected in most evaluations. Nevertheless, this was not considered a good isolated indicator for identifying the diagnosis. Restlessness is defined as episodic motor or verbal behavior that interferes with patient care or clearly requires physical or chemical restraints to prevent harm (Wolffbrandt, Poulsen, Engberg, & Hornnes, 2013). The fact that the patients in the study setting were receiving treatment with sedatives may have compromised the evaluation of this defining characteristic. Other defining characteristics considered to be good clinical indicators of IGE were decreased carbon dioxide, abnormal arterial pH, and abnormal breathing. Additionally, abnormal arterial blood gases and abnormal arterial pH presented high true positive rates and low false positive rates, indicating that they are good clinical indicators to predict IGE. The literature and healthcare practices show that there is a strong tendency for health professionals to evaluate the adequacy of the patient’s metabolic demands based on these clinical indicators (Lopes, Altino, & Silva, 2010). This is favorable because, in addition to being minimally invasive parameters, such indicators also allow the assessment of tissue perfusion and were accurate diagnosis indicators in this study. On the other hand, a detailed evaluation of oxygen delivery and utilization requires different sources (Kipnis et al., 2012).
Considering that the study was conducted with patients in the postoperative period, it is possible that some characteristics inherent to anesthesia are related to the findings. It is known that anesthesia produces muscle relaxation and reduces lung volume, which affect gas exchange and contribute to ventilator-induced lung injury (Dresse, Joris, & Hans, 2012). The simultaneous occurrence of IGE and other respiratory nursing diagnoses should also be considered. Authors state that if a nurse identifies IGE, a risk for ineffective airway clearance certainly exists (Zeitoun et al., 2007). The possibility of the occurrence of bias represents a limitation of diagnostic test studies. In the present study, two biases may be considered: the imperfect gold standard bias and the spectrum bias. The first bias is related to the inference of the presence/absence of a nursing diagnosis based on the evaluation of a person. This is considered a bias that can never be totally eliminated on nursing diagnosis researches, as the majority of the defining characteristcs cannot be solely measured by instruments or devices (Zhou et al., 2011). We attempted to minimize this effect by using the consensus of research group members. The spectrum bias is related to the exclusion of patients with several clinical conditions, but the evaluation of some clinical indicators of IGE can be extremely difficult, or even impossible, in critically ill patients. We also found a lack of studies addressing the accuracy of the defining characteristics of respiratory nursing diagnoses, which limited the discussion. Conclusions In conclusion, decreased carbon dioxide, abnormal arterial blood gases, abnormal arterial pH, and abnormal breathing were the best clinical indicators to predict IGE in cardiac postoperative patients. These findings can be helpful for nurses working in postsurgical recovery units. Accuracy measures are useful in assisting patients in specific situations, as on the cardiac postoperative period. The knowledge about the accuracy measures for the defining characteristics of IGE can contribute to an early identification of this response, which can prevent or minimize complications (such as respiratory infections). Postoperative complications are known for increasing the patient’s length of stay and rising the costs of treatment. Thus, we suggest further research with a similar approach involving patients in other clinical situations. Acknowledgment. Financial support for this research was provided by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ-Process 472257/2009-9). References Apostolakis, E., Filos, K. S., Koletsis, E., & Dougenis, D. (2010). Lung dysfunction following cardiopulmonary bypass. Journal of Cardiac Surgery, 25(1), 47–55.
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Clinical Indicators of Impaired Gas Exchange Carlson-Catalano, J., & Lunney, M. (1995). Quantitative methods for clinical validation of nursing diagnoses. Clinical Nurse Specialist, 9(6), 306–311. Dresse, C., Joris, J. L., & Hans, G. (2012). Mechanical ventilation during anesthesia: Pathophysiology and clinical implications. Trends in Anaesthesia and Critical Care, 2(2), 71–75. Herdman, T. H. (Ed.). (2012). NANDA International nursing diagnoses: Definitions and classification, 2012–2014. Oxford: Wiley-Blackwell. Hough, J. L., Johnston, L., Brauer, S., Woodgate, P., & Schibler, A. (2013). Effect of body position on ventilation distribution in ventilated preterm infants. Pediatric Critical Care Medicine, 14(2), 171–177. Kipnis, E., Ramsingh, D., Bhargava, M., Dincer, E., Cannesson, M., Broccard, A., . . . Thibault, R. (2012). Monitoring in the intensive care. Critical Care Research and Practice, 2(1), 473–507. Lopes, J. L., Altino, D., & Silva, R. C. G. (2010). Content validation of current and new defining characteristics of the nursing diagnosis: Decreased cardiac output. Acta Paulista de Enfermagem, 23(6), 764–768. Lopes, M. V. O., Silva, V. M., & Araujo, T. L. (2012). Methods for establishing the accuracy of clinical indicators in predicting nursing diagnoses. International Journal of Nursing Knowledge, 23(3), 134–139. Lunney, M. (2008). The need for international nursing diagnosis research and a theoretical framework. International Journal of Nursing Terminologies and Classifications, 19(1), 28–34. Pivoto, F. L., Lunardi Filho, W. D., Santos, S. S. C., Almeida, M. A., & Silveira, R. S. (2010). Nursing diagnoses in patients in the postoperative period of cardiac surgery. Acta Paulista de Enfermagem, 23(5), 665–670. Ranieri, V. M., Vitale, N., Grasso, S., Puntillo, F., Mascia, L., Paparella, D., . . . Fiore, T. (1999). Time course of impairment of respiratory mechanics after cardiac surgery and cardiopulmonary bypass. Critical Care Medicine, 27(8), 1454–1460. Rocha, L. A., Maia, T. F., & Silva, L. F. (2006). Nursing diagnoses in patients outgoing cardiac surgery. Revista Brasileira de Enfermagem, 59(3), 321– 326. Santana, R. F., & de Oliveira Lopes, M. V. (in press). Measures of clinical accuracy and indicators of the nursing diagnosis of delayed surgical recovery. Collegian. doi:10.1016/j.colegn.2014.02.001
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V. E. C. Sousa et al. Sarna, L., Cooley, M. E., Brown, J. K., Chernecky, C., Elashoff, D., & Kotlerman, J. (2008). Symptom severity 1 to 4 months after thoracotomy for lung cancer. American Journal of Critical Care, 17(5), 455–467. Seidel, H. M., Ball, J. W., Dains, J. E., Flynn, J. A., Solomon, B. S., & Stewart, R. W. (2011). Mosby’s physical examination handbook. St. Louis, MI: Elsevier. Silva, V. M., Lopes, M. V. O., Araujo, T. L., Beltrão, B. A., Monteiro, F. P., Cavalcante, T. F., . . . Santos, F. A. (2011). Operational definitions of outcome indicators related to ineffective breathing pattern in children with congenital heart disease. Heart and Lung: The Journal of Critical Care, 40(3), 70–77. Sousa, V. E. C., Lopes, M. V. O., Araujo, T. L., Rolim, I. L. T. P., Nascimento, R. V., & Oliveira, T. F. (2013). Clinical indicators of ineffective airway clearance for patients in the cardiac postoperative period. European Journal of Cardiovascular Nursing, 12(2), 193–200. Swartz, M. H. (2010). Textbook of physical diagnosis: History and examination. Philadelphia: Elsevier. White, G. C. (2012). Basic clinical lab competencies for respiratory care: An integrated approach. New York: Thompson, Delmar Learning. Wolffbrandt, M. M., Poulsen, I., Engberg, A. W., & Hornnes, N. (2013). Occurrence and severity of agitated behavior after severe traumatic brain injury. Rehabilitation Nursing, 38(3), 133–141. Wynne, R., & Botti, M. (2004). Postoperative pulmonary dysfunction in adults after cardiac surgery with cardiopulmonary bypass: Clinical significance and implications for practice. American Journal of Critical Care, 13(5), 384–393. Zeitoun, S. S., Barros, A. L. B. L., Michel, J. L. M., & Bettencourt, A. R. C. (2007). Clinical validation of the signs and symptoms and the nature of the respiratory nursing diagnoses in patients under invasive mechanical ventilation. Journal of Clinical Nursing, 16(8), 1417–1426. Zhou, X., Obuchowski, N. A., & McClish, D. K. (2011). Statistical methods in diagnostic medicine. New York: Wiley Interscience.
Clinical Indicators of Impaired Gas Exchange in Cardiac Postoperative Patients Vanessa Emille Carvalho Sousa, RN, MSN, Lívia Maia Pascoal, RN, MSN, Talita Ferreira Oliveira de Matos, RN, Ranielly Vidal do Nascimento, RN, Daniel Bruno Resende Chaves, RN, MSN, Nirla Gomes Guedes, RN, PhD, and Marcos Venícios de Oliveira Lopes, RN, PhD Vanessa Emille Carvalho Sousa, RN, MSN, is a CAPES Scholarship PhD Student—Process BEX 14504/13-8 at the CAPES Foundation, Ministry of Education of Brazil, Brasília, Brazil, Lívia Maia Pascoal, RN, MSN, is a Nursing Professor at the Federal University of Maranhão in Imperatriz, Brazil, Talita Ferreira Oliveira de Matos, RN, and Ranielly Vidal do Nascimento, RN, are Members of the Nursing Diagnosis, Interventions, and Outcomes Study Group at the Federal University of Ceará in Fortaleza, Ceará, Brazil, Daniel Bruno Resende Chaves, RN, MSN, is a Nursing Professor at the Faculdade de Ensino Superior do Ceará in Fortaleza, Ceará, Brazil, Nirla Gomes Guedes, RN, PhD, and Marcos Venícios de Oliveira Lopes, RN, PhD, are Nursing Professors at the Federal University of Ceará in Fortaleza, Ceará, Brazil.
Search terms: Diagnostic test, nursing diagnosis, postoperative care Author contact: [email protected], with a copy to the Editor: [email protected]
PURPOSE: To analyze the accuracy of the defining characteristics of impaired gas exchange (IGE). METHODS: Cross-sectional study carried out in 93 cardiac postoperative adult patients. The 19 NANDA-I defining characteristics related to IGE were evaluated. FINDINGS: Ten defining characteristics were found in the sample. The best accuracy values, including specificity, diagnostic odds ratio, and likelihood ratios, inter alia, were found to decreased carbon dioxide, abnormal arterial blood gases, abnormal arterial pH, and abnormal breathing. CONCLUSION: Some clinical indicators predict the presence/absence of IGE more accurately than others. IMPLICATIONS FOR NURSING PRACTICE: Knowledge of the accuracy of defining characteristics allows an early identification of particular nursing diagnoses, which can prevent complications. PROPÓSITO: Analizar a acurácia das características definidoras de Troca de Gases Prejudicada (TGP). MÉTHODO: Estudo transversal desenvolvido com 93 pacientes adultos no pósoperatório cardíaco. Dezenove características definidoras da NANDA-I associadas a TGP foram avaliadas. RESULTADOS: Dez características definidoras foram encontradas na amostra. Os melhores valores de acurácia, incluindo especificidade, odds ratio diagnóstica e razões de verossimilhança, entre outras, foram encontrados para as características definidoras dióxido de carbono diminuído, gases sanguíneos arteriais anormais, pH arterial anormal e respiração anormal. CONCLUSÃO: Alguns indicadores clínicos predizem a presença/ausência de TGP mais precisamente que outros. IMPLICAÇÕES PARA A PRÁTICA DE ENFERMAGEM: O conhecimento acerca da acurácia das características definidoras contribui para uma identificação precoce de diagnósticos de enfermagem específicos, o que pode prevenir complicações.
A nursing diagnosis results from a series of clinical evaluations that can be complex, depending on the condition of the patient. Postoperative patients commonly present numerous signs and symptoms simultaneously, which can confound nurses during the nursing process, especially on the diagnostic step (Sarna et al., 2008). © 2014 NANDA International, Inc. International Journal of Nursing Knowledge Volume 26, No. 3, July 2015
Besides the range of different clinical clues, nurses usually face defining characteristics that apply to more than one nursing diagnosis, which occurs for example with respiratory clinical indicators, leading to variability of nurses’ assessments. Establishing good clinical indicators minimizes that variability, producing diagnoses that 141
Clinical Indicators of Impaired Gas Exchange accurately represent the patient’s state (Lopes, Silva, & Araujo, 2012). The nursing diagnoses of impaired gas exchange (IGE) (00030), ineffective airway clearance (00031), and ineffective breathing pattern (00032) have been identified in the literature as those that occur more frequently in different situations and age groups (Pivoto, Lunardi Filho, Santos, Almeida, & Silveira, 2010; Zeitoun, Barros, Michel, & Bettencourt, 2007). Their high prevalence is expected because people with various medical conditions and surgical problems are likely to present such physiological responses (Carlson-Catalano & Lunney, 1995). IGE, a nursing diagnosis approved by the NANDA-I in 1980 and reviewed in 1996 and 1998, is defined as “excess or deficit in oxygenation and/or carbon dioxide elimination at the alveolar-capillary membrane.” The defining characteristics of IGE are abnormal arterial blood gases, abnormal arterial pH, abnormal breathing, abnormal skin color, confusion, cyanosis, decreased carbon dioxide, diaphoresis, dyspnea, headache upon awakening, hypercapnia, hypoxemia, hypoxia, irritability, nasal flaring, restlessness, somnolence, tachycardia, and visual disturbances (Herdman, 2012). The incidence of respiratory dysfunctions in postoperative cardiac patients is about 25%. Even patients who do not present any severe cardiac dysfunction are reported to have a significant respiratory impairment for at least one week after an open heart surgery (Apostolakis, Filos, Koletsis, & Dougenis, 2010). In this context, one study found that lung function and oxygenation were compromised in 90% of a sample of patients undergoing cardiac surgery (Ranieri et al., 1999). The nursing diagnosis of IGE has been established in studies of patients in the cardiac perioperative period and is a frequent response in adult patients undergoing such interventions. Multiple specific factors contribute to this nursing diagnosis, such as general anesthesia, sternotomy, tissue manipulation, mammary artery dissection, pleural opening, chest tube insertion, and pain (Sousa et al., 2013). In this sense, a study developed on the cardiac postoperative period associated the occurrence of IGE with the surgery itself. The defining characteristics of abnormal breathing, dyspnea, tachycardia, abnormal skin color, and somnolence were considered most relevant (Pivoto et al., 2010). Other researchers found a significant association between IGE and abnormal blood gases and hypoxemia in patients using mechanical ventilation (Zeitoun et al., 2007). Furthermore, others demonstrated that body position is directly related to gas exchange disturbances, so nurses must be aware of how body positioning can affect the patient’s oxygenation (Hough, Johnston, Brauer, Woodgate, & Schibler, 2013). Despite a variety of studies showing the prevalence of respiratory nursing diagnoses and their defining characteristics, the production of studies that measure the accuracy of these clinical indicators is still scarce. 142
V. E. C. Sousa et al. Decision making in nursing should be based on clinical skills and scientific evidence (Santana & de Oliveira Lopes, in press). Thus, there is a need for studies to validate nursing diagnoses and to show relations of nursing diagnoses and other phenomena, such as which nursing diagnoses or what components are important for populations with specific medical diagnoses or conditions, as postoperative patients (Lunney, 2008). Early and accurate detection of IGE is important because respiratory complications are associated with increased morbidity and mortality, and with high postoperative financial costs, especially on the cardiac setting (Wynne & Botti, 2004). In this context, the following research question emerged: Which clinical indicators of IGE best predict the presence or absence of this nursing diagnosis in surgical cardiac patients? The purpose of this study is to analyze the accuracy of the defining characteristics of IGE in cardiac postoperative patients based on measures of sensitivity (Se), specificity (Sp), predictive values (PV), likelihood ratios (LR), diagnostic odds ratio (DOR), efficiency (E), and area under the receiver operating characteristic curve (ROC curve). Methods Design and Sample This is a cross-sectional study performed in the postoperative intensive care unit (ICU) of a referral center for cardiopulmonary diseases. The sample comprised 98 patients over 18 years of age, within 48 hr of cardiac surgery, and having a level of consciousness that would enable data collection. Individuals with clinical findings that prevented data collection, had undergone heart transplant, and/or supported by mechanical ventilation were excluded from the sample. To estimate the sample size, we used the formula for single test accuracy studies proposed by Zhou, Obuchowski, and McClish (2011), with the following parameters: a confidence level of 95%, desired width of one half of 95%, confidence interval of 10%, and a conjectured sensitivity of the clinical indicators equal to 85%, leading to a total of 98 patients. Patients were selected by consecutive sampling and evaluated at a single moment during hospitalization in the ICU, on the first 48 hr after surgery. Data were independently collected by three nurses who shared the assessments. The nurses underwent prior training and a written test about respiratory nursing diagnoses and physical examination of chest and lungs, and were considered able to make patient assessments correctly. The data collection involved a respiratory examination of patients and the use of a formulary, created specifically for the study, with items related to the IGE clinical indicators. Furthermore, operational definitions were given to support the data collection in order to determine IGE char-
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acteristics. As an example, abnormal breathing was defined as the change in the number of breaths per minute or change on the rhythmicity of breathing through inspection, the rate of 16–20 breaths per minute being considered a parameter for normal respiration. This and other definitions were obtained and adapted from a study in which operational definitions for respiratory clinical indicators were developed and validated (Silva et al., 2011). The clinical judgment regarding both the presence of the defining characteristics and the nursing diagnosis of IGE was conducted by a specialist in nursing diagnoses and per seeking consensus among members of a research group on nursing diagnoses, interventions, and outcomes. This specialist has previous experience in the use of the NANDA-I taxonomy, shown in publications involving the establishment of respiratory diagnoses and participation in the cited research group. The appropriate regional ethics committee approved the study. All the subjects gave informed consent and patients’ anonymity was preserved.
Table 1. Clinical Indicators of Impaired Gas Exchange Identified in the Sample Variables Defining characteristics Abnormal arterial blood gases Abnormal arterial pH Abnormal breathing Decreased carbon dioxide Dyspnea Hypercapnia Hypoxemia Hypoxia Restlessness Tachycardia Related factor Ventilation perfusion imbalance Impaired gas exchange
Presence
%
Absence
%
44
44.9
54
55.1
31 40 36
31.6 40.8 36.7
67 58 62
68.4 59.2 63.3
27 6 31 31 6 39
27.6 6.1 31.6 31.6 6.1 39.8
71 92 67 67 92 59
72.4 93.9 68.4 68.4 93.9 60.2
29
29.6
69
70.4
30
30.6
68
69.4
Statistical Analysis After the diagnostic inferences, measures such as Se, Sp, positive and negative PVs, LRs, E, DORs, and the area under the ROC curve were calculated for each defining characteristic of IGE. A brief description of each of these measures can be found on a scientific paper that describes the methodological stages in determining the accuracy of clinical indicators in identifying nursing diagnoses (Lopes et al., 2012). All analyses were performed with the support of R software version 2.15.1 (R Development Core Team, Vienna, Austria). The statistical significance was tested based on the analysis of confidence intervals. Statistical significance was attributed when the 95% confidence interval did not include the null value of 1. Results Patients’ mean age was 55.9 years (SD 13.4), 55.1% were male, 70.4% lived with a partner, and 51.1% had more than 8 years of schooling. The per capita income had an asymmetrical distribution (p < .05), and the median indicates that half of the sample earned up to $250.00 monthly. It is noteworthy that 54.1% of participants were currently or had been smokers at some point in their lives. The most prevalent medical diagnoses were angina/ coronary artery disease (37.8%), followed by valvular heart disease (36.7%), and the most frequent types of surgeries were coronary artery bypass graft (59.1%) and valve replacement (37.8%). The nursing diagnosis of IGE was present in 30.6% of the patients. Regarding its clinical indicators, the following defining characteristics prevailed: abnormal arterial blood gases (44.9%), abnormal breathing (40.8%), and tachycardia (39.8%). The related factor ventilation-perfusion (V-Q) imbalance was present in 29.6% of the sample (Table 1).
Some defining characteristics of IGE were absent on the sample. They are abnormal skin color, confusion, cyanosis, diaphoresis, headache upon awakening, irritability, nasal flaring, somnolence, and visual disturbances. Regarding accuracy measures, Table 2 shows that abnormal arterial blood gases was the only defining characteristic that achieved an Se above 80%. Restlessness and hypercapnia, however, had specificities of up to 90%. The indicators that showed statistical significance (95% confidence intervals not containing 1) were abnormal arterial blood gases, abnormal arterial pH, abnormal breathing, and decreased carbon dioxide. Among these clinical indicators, none showed a high PV+; however, decreased carbon dioxide and abnormal breathing showed a PV− of up to 80%. Beyond that, the LRs and the area under the ROC curve indicated that decreased carbon dioxide, abnormal arterial blood gases, abnormal arterial pH, and abnormal breathing are best isolated predictors of IGE. In summary, the defining characteristics of decreased carbon dioxide, abnormal arterial blood gases, abnormal arterial pH, and abnormal breathing were the most accurate. These clinical indicators presented moderate Sp, high values for DOR, significant LRs, and E above 70%, which show that these indicators are effective predictors of the correct identification of IGE. Figure 1 shows a performance comparison between the six indicators that showed the highest values for the area under the ROC curve. This figure combines the true positive rate and the false positive rate, which are two important measures to diagnostic accuracy analyses. It can be seen that abnormal arterial blood gases and abnormal breathing are located in the upper left quadrant, which means that these defining characteristics had a high true positive rate and a low false positive rate. In other words, they should be considered the clinical indicators that led to the most acutare identification of IGE in this study. 143
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Table 2. Sensitivity (Se), Specificity (Sp), Predictive Value (PV), Likelihood Ratio (LR), Diagnostic Odds Ratio (DOR), Efficiency (E), and Area Under ROC Curve (ROC) of Clinical Indicators for Impaired Gas Exchange Defining Characteristics
Se
Sp
PV+
PV−
Abnormal arterial blood gases Abnormal arterial pH Abnormal breathing Decreased carbon dioxide Dyspnea Hypercapnia Hypoxemia Restlessness Tachycardia
93.33
76.47
63.64
96.30
53.33 70.00 60.00 43.33 13.33 43.33 16.67 40.00
77.94 72.06 73.53 79.41 97.06 73.53 98.53 60.29
51.61 52.50 50.00 48.15 66.67 41.94 83.33 30.77
79.10 84.48 80.65 76.06 71.74 74.63 72.83 69.49
E
LR+
LR−
DOR
ROC
81.63
3.97 [2.55–6.17]
0.09 [0.02–0.33]
40.81 [10.63–295.16]
0.849
70.41 71.43 69.39 68.37 71.43 64.29 73.47 54.08
2.42 [1.43–4.09] 2.51 [1.60–3.91] 2.27 [1.41–3.65] 2.10 [1.21–3.67] 4.53 [1.11–18.51] 1.64 [0.99–2.71] 11.33 [1.58–81.52] 1.01 [0.63–1.60]
0.60 [0.40–0.90] 0.42 [0.24–0.73] 0.54 [0.34–0.86] 0.71 [0.51–1.00] 0.89 [0.77–1.03] 0.77 [0.55–1.09] 0.85 [0.72–1.00] 1.00 [0.70–1.41]
3.96 [1.58–10.21] 5.84 [2.32–15.83] 4.08 [1.66–10.46] 2.91 [1.14–7.54] 4.82 [0.83–40.89] 2.11 [0.84–5.26] 11.78 [1.70–322.74] 1.01 [0.41–2.45]
0.656 0.710 0.667 0.613 0.552 0.584 0.576 0.501
Figure 1. Performance Comparison of Clinical Indicators With the Largest Area Under the Receiver Operating Characteristic Curve Values
Regarding the related factor assessment, since V-Q imaging was not available to all patients in the study setting, we chose to evaluate V-Q mismatch by indirect methods, as the physical evaluation of chest and lungs and through laboratory tests. Ventilation was considered inappropriate when the patient showed signs of impairment on the passage of air through the lungs, observed by 144
respiration assessment (quality, rate, pattern, depth, sounds, and breathing effort) or in the presence of retained respiratory secretions or difficulty in expelling it, changes in the vocal fremitus or chest percussion, and/or asymmetric thoracic excursions. The perfusion was considered inadequate by the identification of signs of deficit in blood oxygenation, through changes in arterial blood gases, oxygen
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saturation, and/or when the patient had hypoxemia. These parameters and the techniques used were obtained by consulting semiology textbooks (Seidel et al., 2011; Swartz, 2010).
Discussion The nursing diagnosis of IGE was present in 30.6% of the sample; a similar result of 36.4% was found in a survey of adults in the cardiac postoperative period (Rocha, Maia, & Silva, 2006). Regarding the defining characteristics, the statistics showed that the best accuracy was attributed to abnormal arterial blood gases, present in 44.9% of patients, with high Se (over 80%) and significant values for LR, DOR, E, and the area under the ROC curve, which indicates that this clinical indicator may facilitate IGE detection by nurses. The arterial blood gases is a complementary measure of the need for oxygenation and respiration, allowing the nurse to assess whether the lung is correctly performing the gas exchange (White, 2012). This clinical indicator is closely associated with an IGE diagnosis and was considered a critical defining characteristic in a validation study of clinical indicators with patients using invasive ventilation, being present in 100% of the evaluations (Zeitoun et al., 2007). The defining characteristic of restlessness indicated a good positive predictive value (PV+ = 0.83) and a high specificity (Sp = 98.53), indicating that when IGE was present, restlessness was present as well. Likewise, in the absence of restlessness, the IGE diagnosis was rejected in most evaluations. Nevertheless, this was not considered a good isolated indicator for identifying the diagnosis. Restlessness is defined as episodic motor or verbal behavior that interferes with patient care or clearly requires physical or chemical restraints to prevent harm (Wolffbrandt, Poulsen, Engberg, & Hornnes, 2013). The fact that the patients in the study setting were receiving treatment with sedatives may have compromised the evaluation of this defining characteristic. Other defining characteristics considered to be good clinical indicators of IGE were decreased carbon dioxide, abnormal arterial pH, and abnormal breathing. Additionally, abnormal arterial blood gases and abnormal arterial pH presented high true positive rates and low false positive rates, indicating that they are good clinical indicators to predict IGE. The literature and healthcare practices show that there is a strong tendency for health professionals to evaluate the adequacy of the patient’s metabolic demands based on these clinical indicators (Lopes, Altino, & Silva, 2010). This is favorable because, in addition to being minimally invasive parameters, such indicators also allow the assessment of tissue perfusion and were accurate diagnosis indicators in this study. On the other hand, a detailed evaluation of oxygen delivery and utilization requires different sources (Kipnis et al., 2012).
Considering that the study was conducted with patients in the postoperative period, it is possible that some characteristics inherent to anesthesia are related to the findings. It is known that anesthesia produces muscle relaxation and reduces lung volume, which affect gas exchange and contribute to ventilator-induced lung injury (Dresse, Joris, & Hans, 2012). The simultaneous occurrence of IGE and other respiratory nursing diagnoses should also be considered. Authors state that if a nurse identifies IGE, a risk for ineffective airway clearance certainly exists (Zeitoun et al., 2007). The possibility of the occurrence of bias represents a limitation of diagnostic test studies. In the present study, two biases may be considered: the imperfect gold standard bias and the spectrum bias. The first bias is related to the inference of the presence/absence of a nursing diagnosis based on the evaluation of a person. This is considered a bias that can never be totally eliminated on nursing diagnosis researches, as the majority of the defining characteristcs cannot be solely measured by instruments or devices (Zhou et al., 2011). We attempted to minimize this effect by using the consensus of research group members. The spectrum bias is related to the exclusion of patients with several clinical conditions, but the evaluation of some clinical indicators of IGE can be extremely difficult, or even impossible, in critically ill patients. We also found a lack of studies addressing the accuracy of the defining characteristics of respiratory nursing diagnoses, which limited the discussion. Conclusions In conclusion, decreased carbon dioxide, abnormal arterial blood gases, abnormal arterial pH, and abnormal breathing were the best clinical indicators to predict IGE in cardiac postoperative patients. These findings can be helpful for nurses working in postsurgical recovery units. Accuracy measures are useful in assisting patients in specific situations, as on the cardiac postoperative period. The knowledge about the accuracy measures for the defining characteristics of IGE can contribute to an early identification of this response, which can prevent or minimize complications (such as respiratory infections). Postoperative complications are known for increasing the patient’s length of stay and rising the costs of treatment. Thus, we suggest further research with a similar approach involving patients in other clinical situations. Acknowledgment. Financial support for this research was provided by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ-Process 472257/2009-9). References Apostolakis, E., Filos, K. S., Koletsis, E., & Dougenis, D. (2010). Lung dysfunction following cardiopulmonary bypass. Journal of Cardiac Surgery, 25(1), 47–55.
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