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Summary

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Competing interests
  7. Acknowledgements
  8. References

We studied whether reported physical activity and measurements of fitness (hand, leg and inspiration) were associated with postoperative in-hospital mortality, length of stay and discharge destination in 169 patients after major oncological abdominal surgery. In multivariate analysis, adequate activity level (OR 5.5, 95% CI 1.4–21.9) and inspiratory muscle endurance (OR 5.2, 95% CI 1.4–19.1) were independently associated with short-term mortality, whereas conventional factors, such as age and heart disease, were not. Adequate activity level (OR 6.7, 95% CI 1.4–3.0) was also independently associated with discharge destination. The factors that were independently associated with a shorter length of hospital stay were as follows: absence of chronic obstructive pulmonary disease (HR 0.6, 95% CI 0.3–1.1); adequate activity level (HR 0.6, 95% CI 0.4–0.8); and inspiratory muscle strength (HR 0.6, 95% CI 0.5–0.9). For all postoperative outcomes physical activity and fitness significantly improved the predictive value compared with known risk factors, such as age and comorbidities. We conclude that pre-operative questionnaires of physical activity and measurements of fitness contribute to the prediction of postoperative outcomes.

The pre-operative identification of high-risk patients may reduce postoperative complications [1]. Risk factors associated with complications after major abdominal and thoracic surgery include age and smoking, as well as comorbidities, such as diabetes, chronic obstructive pulmonary disease (COPD) and heart disease [2-7]. Risk evaluation may also include functional status [8-10], which predicts postoperative outcome in older patients [11-13].

A poor physical condition and functional status reduces the ability of a person to cope, mentally and physically, with hospitalisation and surgery [14] and may compromise postoperative functional recovery, potentially leading to postoperative complications, death, and protracted and sometimes permanent loss of mobility [2, 9, 11, 15, 16]. Some authors recommend pre-operative evaluation of functional status [8, 17, 18], but inexact definitions may preclude its use in research [19]. Functional status has physical, psychological and social elements, but it is often evaluated crudely, for example with ASA physical status [20] and activities of daily living [21]. Functional status is usually based upon the WHO International Classification of Functioning, Disability and Health [22]. This classification distinguishes between activity, reported by questionnaire, and observed physical ability, the combination of which may refine prognostic information [23].

We believe that this is the first prospective cohort study to investigate the association of this combination of questions and physical tests with the following short-term outcomes after scheduled major abdominal surgery in patients older than 59 years: mortality; length of hospital stay; and discharge destination.

Methods

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Competing interests
  7. Acknowledgements
  8. References

We prospectively recorded data for patients older than 59 years scheduled for oncological colorectal surgery between June 2006 and June 2009, at Gelderse Vallei Hospital in Ede, the Netherlands. Patients assessed as unable to do the fitness tests, based on the results of a physical activity readiness questionnaire, were not studied [24]. The study protocol was approved by the local Medical Ethics Committee of the Gelderse Vallei Hospital.

All patients were referred to the physical therapy outpatient department, as part of the multidisciplinary work-up, between 1 and 3 weeks before surgery. The physical therapist offered some patients physical training if their surgery was scheduled more than 2 weeks in advance. We recorded: age; diagnoses of diabetes, COPD, coronary heart disease, heart failure, metastatic cancer; and histories of smoking or productive cough.

We asked patients the frequency, duration and intensity of various activities over the previous 14 days, such as cycling, gardening and walking, using the LASA physical activity questionnaire (LAPAQ) [25].

An experienced physical therapist tested physical fitness by mobility and muscle function (ICF domains d4 and b7, respectively) [22]. Mobility was measured as the time taken to rise from an armchair, walk 3 m, turn, walk back and sit down again (known as ‘timed up-and-go’, TUG).

Function was measured in the hand, leg and inspiratory muscles. Handgrip strength was recorded as the highest measurement in the dominant hand, using a digital device three times, separated by intervals of 30 s (Mechatronics Instruments BV, Hoorn, the Netherlands) [28]. Leg power and endurance were measured as the time taken to rise from a treatment couch 10 times with the arms folded across the chest [26-28]. Maximal inspiratory muscle strength and respiratory cumulative energy were measured with the MicroRPM and MicroRMA, respectively (Micro Medical Ltd., Rochester, England) [29].

All patients in this study received usual care. The questionnaires and physical tests were not masked from the healthcare providers.

All assessments were made at the same appointment, always in the following sequence: mobility; leg strength; inspiratory strength; hand strength; questionnaire. We recorded: in-hospital mortality; discharge destination (to the home environment or to a nursing home); and length of hospital stay (in days). We used discharge destination as an outcome for three reasons: patients usually want to return home; it reflects functional recovery; and it is an important item for health insurers because of the high costs of admission to a nursing home.

Data were analysed with the software package IBM SPSS Statistics 19.0 (SPSS Inc., Chicago, IL, USA). We dichotomised the results of the questionnaire and each fitness test for two outcomes – mortality and discharge destination – with the best discriminatory point on a receiver operating characteristic (ROC) curve, as long as the area under curve was more than 0.6. We performed univariate logistic regression for the association of each factor with mortality and discharge destination, and univariate Cox regression for associations with length of stay. We entered significant factors (p < 0.1) in a multivariate regression analysis for all three outcomes. To obtain a valid assessment of the added value of the physical activity and fitness factors, the significant conventional risk factors were forced into the model. Statistical significance of models was determined by −2 log likelihood values for the regression models and a p value < 0.05 for the chi-squared tests. The predictive value for mortality and discharge destination was estimated by the C-index and positive and negative predictive values. The goodness of fit was assessed by the Hosmer and Lemeshow test.

Results

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Competing interests
  7. Acknowledgements
  8. References

We analysed data from 175 patients referred to the department of physical therapy between June 2006 and June 2009. The operation was cancelled in six patients because of deterioration of their medical condition or at the patient's request. Nine patients were not referred for screening because bowel pathology was not initially recognised as the cause for their symptoms. Table 1 lists patient's and surgical characteristics.

Table 1. Pre-operative characteristics including conventional risk factors in 169 subjects. Values are number, mean (SD) or number (proportion)
  1. BMI, body mass index; COPD, chronic obstructive pulmonary disease.

Male/female99/70
BMI; kg.m−226 (5)
Surgery
Colon resection (open)61 (36%)
Colon resection (endoscopic)14 (8%)
Rectosigmoid resection (open)72 (42%)
Rectosigmoid resection (endoscopic)22 (13%)
Open surgery duration; min110 (44)
Endoscopic surgery duration; min170 (53)
Re-operation31 (18%)
Pre-operative radiotherapy41 (24%)
Conventional risk factors
60–69 years61 (36%)
70–79 years82 (49%)
> 80 years26 (15%)
Metastatic cancer15 (9%)
Diabetes30 (18%)
COPD15 (9%)
Heart disease19 (11%)
Smoking24 (14%)
Productive cough23 (14%)

Thirteen patients (8%) died in hospital, a median (IQR [range]) of 19 (10–28 [8-39]) days after surgery. No patient died after discharge within this period. We compared mortality rates between two groups of patients, above and below a value for each factor, identified through ROC curve analyses: mobility, 11 s; stand-sit repeats, 27 s; inspiratory pressure, 90 cm H2O; respiratory cumulative energy, 41 J; hand strength, 233 N; questionnaire 416 kcal. Table 2 lists the univariate relative risks and subsequent multivariate odds ratios for each factor. Both the questionnaire score and cumulative respiratory energy were associated with in-hospital mortality. A model containing only conventional factors was not associated with mortality (p = 0.12), but the model including physical fitness and activity factors was associated with in-hospital mortality (p = 0.0005), confirmed by a significant difference in the log likelihood values of the two models (85.33 vs 66.62, p = 0.001). The C-index for the model including the physical fitness and activity factors was 0.82 vs 0.67 for the model with conventional factors. The frequencies of patients correctly and incorrectly predicted to die were 18 and 72 per 1000 for the conventional model and 30 and 28 for the model also including physical activity and fitness, respectively. The frequencies of patients correctly and incorrectly predicted to survive were 851 and 59 per 1000 patients for the conventional model and 895 and 47 for the model also including physical activity and fitness, respectively. The goodness of fit hypothesis of all models was not rejected by the Hosmer and Lemeshow test.

Table 2. Univariate and multivariate logistic regression analyses for associations between factors and postoperative mortality. The inclusion of questionnaire results (LAPAQ) and the respiratory cumulative energy (RCE) increased the statistical association with postoperative mortality. Values are number (proportion of total) or number (95% CI)
   Univariate analysesMultivariate analyses
Relative riskp valueForceda odds ratioNot forced odds ratio
  1. LAPAQ, LASA physical activity questionnaire; TUG, timed up-and-go; CRT, chair rise time; MIP, maximal inspiratory pressure; RCE, respiratory cumulative energy; HGS, hand grip strength.

  2. a

    Inclusion of physical activity and fitness increased the chi-squared from 5.85 to 20.12 and the p value from 0.12 to 0.0004: their inclusion also increased the C-index (95% CI) from 0.67 (0.51–0.84) to 0.82 (0.69–0.95).

Conventional factors
Age60–70 years61 (36%)1   
70–80 years82 (49%)2.6 (0.6–12.1)0.20  
> 80 years26 (15%)4.7 (0.9–24.0)0.04  
Metastatic cancer 15 (9%)3.1 (0.9–10.0)0.063.0 (0.6–14.7) 
Diabetes 30 (18%)1.4 (0.4–4.7)0.60  
Heart disease 19 (11%)1.4 (0.3–6.0)0.62  
COPD 15 (9%)0.9 (0.1–6.1)0.88  
Smoking 24 (14%)0.5 (0.1–3.7)0.48  
Productive cough 23 (12%)2.8 (0.9–8.2)0.073.2 (0.7–14.2)2.9 (0.7–12.5)
Physical activity and fitness
LAPAQ; kcal< 41660 (37%)5.8 (1.7–20.2)0.00025.6 (1.4–22.6)5.5 (1.4–21.9)
TUG; s> 1124 (15%)4.8 (1.8–13.0)0.001  
CRT; s> 2762 (40%)2.2 (0.7–7.6)0.18  
MIP; cm H2O< 90104 (65%)3.0 (0.7–13.1)0.12  
RCE; J< 4148 (32%)4.7 (1.5–14.5)0.0035.0 (1.4–18.6)5.2 (1.4–19.1)
HGS; N< 23337 (24%)2.7 (1.0–7.6)0.05  

Sixteen survivors were discharged to a nursing home and 140 to home. The ROC curve analyses generated different pre-operative discriminatory values for discharge destination: mobility, 8 s; stand-sit repeats, 26 s; inspiratory pressure, 90 cm H2O; respiratory cumulative energy, 60 J; hand strength, 270 N; questionnaire 530 kcal. Both the questionnaire score and maximal inspiratory pressure were associated with discharge destination as determined by multivariate analysis. A model containing only conventional factors was not associated with discharge destination (p = 0.51), but the model including physical fitness and activity factors was associated with discharge destination (p = 0.004), confirmed by a significant difference in the log likelihood values of the two models (97.23 vs 68.61; p = 0.003). The C-index for the model including the physical fitness and activity factors was 0.80 vs 0.66 for the model with conventional factors. The frequencies of patients correctly and incorrectly predicted to discharge to a nursing home were 26 and 116 per 1000 for the conventional model and 59 and 137 for the model also including physical activity and fitness, respectively. The frequencies of patients correctly and incorrectly predicted to discharge to home were 782 and 77 per 1000 patients for the conventional model and 760 and 44 for the model also including physical activity and fitness, respectively. The goodness of fit hypothesis of all models was not rejected by the Hosmer and Lemeshow test.

The median (IQR [range]) length of stay was 12 (9–21 [4–130]) days. Discriminatory values for the association with physical activity and fitness were as follows: mobilisation, 8 s; stand-sit repeats, 25 s; inspiratory pressure, 90 cmH2O; respiratory cumulative energy, 375 J; handgrip strength, 240 N; questionnaire 530 kcal. Table 3 shows the association of various factors with length of stay as determined by univariate Cox regression analysis. Diabetes, COPD, activity level and maximal inspiratory pressure were associated with increased length of stay on multivariate regression. Addition of physical activity and fitness factors to the model with only conventional factors confirmed a significant difference in the log likelihood values (p = 0.002). The chi-squared value improved from 9.85 (p = 0.007) to 16.47 (p = 0.00002).

Table 3. Univariate and multivariate Cox regression analyses for associations between factors and postoperative length of stay. The inclusion of questionnaire results (LAPAQ) and the maximal inspiratory pressure (MIP) increased the statistical association with postoperative length of stay. Values are number (proportion of total) or number (95% CI)
   Univariate analysesMultivariate analyses
Hazard ratiop valueForceda Hazard ratioNot forced Hazard ratio
  1. LAPAQ, LASA physical activity questionnaire; TUG, timed up-and-go; CRT, chair rise time; MIP, maximal inspiratory pressure; RCE, respiratory cumulative energy; HGS, hand grip strength.

  2. a

    Inclusion of physical activity and fitness increased the chi-squared from 9.85 to 16.47 and the p value from 0.007 to < 0.0001: their inclusion decreased the −2 log likelihood from 1303.49 to 1176.78 (p = 0.002).

Conventional factors
Age60–70 years61 (36%)1   
70–80 years82 (49%)0.9 (0.6–1.3)0.59  
> 80 years26 (15%)0.9 (0.6–1.5)0.75  
Metastatic cancer 15 (9%)0.8 (0.5–1.5)0.52  
Diabetes 30 (18%)0.6 (0.4–0.9)0.020.7 (0.5–1.2) 
Heart disease 19 (11%)0.9 (0.5–1.4)0.58  
COPD 15 (9%)0.5 (0.3–0.9)0.030.6 (0.3–1.2)0.6 (0.3–1.1)
Smoking 24 (14%)0.9 (0.6–1.3)0.52  
Productive cough 23 (12%)0.8 (0.5–1.3)0.37  
Physical activity and fitness
LAPAQ; kcal< 53079 (48%)0.6 (0.4–0.8)0.00040.6 (0.4–0.8)0.6 (0.4–0.8)
TUG; s> 866 (42%)0.8 (0.6–1.1)0.15  
CRT; s> 2581 (52%)0.8 (0.5–1.1)0.10  
MIP; cm H2O< 90104 (65%)0.6 (0.4–0.9)0.0030.6 (0.5–0.9)0.6 (0.5–0.9)
RCE; J< 375122 (82%)0.7 (0.4–1.1)0.10  
HGS; N< 23340 (26%)0.9 (0.6–1.3)0.44  

Discussion

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Competing interests
  7. Acknowledgements
  8. References

This study revealed that pre-operative physical activity and physical fitness are statistically significant and independent predictors of postoperative recovery in addition to conventional predictors. Individual determinants of physical activity and physical fitness were significantly correlated with one or more of the postoperative outcome measures. The same was true for three conventional risk factors; age; diabetes; and COPD. The performance of the prediction models for mortality, discharge destination and length of stay were all significantly improved by addition of the physical activity and fitness factors. In the multivariate regression analyses, physical activity, measured with a questionnaire, was the only factor that was significantly correlated with all outcome measures after correction for mutual correlation in logistic regression analyses. Thus, LAPAQ was the most robust predictor of postoperative recovery identified in this study.

Compared with the conventional factors, the physical activity and physical fitness factors predominated in explaining the variance in the postoperative measurements. Age was the most important conventional predictor, but was statistically overruled by the physical activity and physical fitness factors in regression analyses. This suggests that physiological age, i.e. the way that age affects physical functioning, is a better predictor than chronological age. Our results hold even when age was forced into the model. This seems to justify the addition of the physical activity and fitness factors to pre-operative evaluation. The major role of physical activity as a pre-operative predictor of postoperative outcome is consistent with recent research showing that the activity level, measured with an accelerometer, is correlated with postoperative complications [30]. It is also consistent with the general recommendation that older people should adopt and/or maintain a physically active lifestyle [31]. Robinson et al. also included comorbidities and functional measures in the prediction of postoperative mortality and found functional measures to be of added value [12]. These authors made a plea towards a pre-operative assessment using geriatric-specific markers. Independent functioning in terms of self-reported activities of daily living (ADL) is a frequently mentioned predictor of the postoperative course [32-35]: our findings with the ‘timed up-and-go’ test, a capacity-based measure of ADL, confirmed its importance. In the univariate analysis, handgrip strength was significantly correlated with mortality and discharge destination, which corroborates the results of three studies evaluating handgrip strength in isolation [36-38]. The handgrip strength cut-off for mortality was very similar to that reported by Chen et al. for patients with oesophageal cancer [38]. The predictive role of respiratory function is not widely recognised, but is probably due to the additional deterioration in inspiratory muscle function after major abdominal and thoracic surgery [39].

Although we included patients scheduled for elective colon surgery, the results could probably be extrapolated to other abdominal or thoracic surgery, because physical activity and fitness reflect the general capacity of the body to withstand the physiological and functional effects of major surgery [40]. A potential limitation of this study was our assessment of physical fitness. We did not use cardiorespiratory function as a marker of frailty. A bicycle ergometry test or stair-climb test could complement the assessments used here to predict the postoperative course [5, 41]. The study was also limited by the number of participants, which limited the statistical power of the study, especially because the mortality rate was low. This study focused on the short-term functional recovery: we suspect that the factors we identified would also associate with long-term survival, which is recommended as follow-up research.

The results of this study emphasise the role of physical activity and physical fitness in the pre-operative evaluation of elderly patients and the need to include these factors in prediction models of postoperative recovery after major surgery. An accurate pre-operative evaluation enables a timely start to be made to appropriate interventions to prevent postoperative complications and provides the patient with information to enable him/her to give truly informed consent.

Competing interests

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Competing interests
  7. Acknowledgements
  8. References

This study was performed in the Gelderse Vallei Hospital as part of thesis research within the Body@Work Research, TNO-VU University Medical Center. It was not externally funded and there are no conflicts of interest.

Acknowledgements

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Competing interests
  7. Acknowledgements
  8. References

The authors were grateful to Dr John Carlisle for his helpful comments on earlier drafts of this paper.

References

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Competing interests
  7. Acknowledgements
  8. References
  • 1
    Yen C, Tsai M, Macario A. Preoperative evaluation clinics. Current Opinion in Anesthesiology 2010; 23: 16772.
  • 2
    Boyd CM, Landefeld CS, Counsell SR, et al. Recovery of activities of daily living in older adults after hospitalization for acute medical illness. Journal of the American Geriatrics Society 2008; 56: 21719.
  • 3
    Smetana GW, Lawrence VA, Cornell JE. Preoperative pulmonary risk stratification for noncardiothoracic surgery: systematic review for the American College of Physicians. Annals of Internal Medicine 2006; 144: 58195.
  • 4
    Leung AM, Gibbons RL, Vu HN. Predictors of length of stay following colorectal resection for neoplasms in 183 Veterans Affairs patients. World Journal of Surgery 2009; 33: 21838.
  • 5
    Wilson RJ, Davies S, Yates D, Redman J, Stone M. Impaired functional capacity is associated with all-cause mortality after major elective intra-abdominal surgery. British Journal of Anaesthesia 2010; 105: 297303.
  • 6
    Gustafsson UO, Thorell A, Soop M, Ljungqvist O, Nygren J. Haemoglobin A1c as a predictor of postoperative hyperglycaemia and complications after major colorectal surgery. British Journal of Surgery 2009; 96: 135864.
  • 7
    Kennedy GD, Rajamanickam V, O'Connor ES, et al. Optimizing surgical care of colon cancer in the older adult population. Annals of Surgery 2011; 253: 50814.
  • 8
    Malani PN. Functional status assessment in the preoperative evaluation of older adults. Journal of the American Medical Association 2009; 302: 15823.
  • 9
    Covinsky KE, Pierluissi E, Johnston CB. Hospitalization-associated disability: “She was probably able to ambulate, but I'm not sure”. Journal of the American Medical Association 2011; 306: 178293.
  • 10
    Ettinger WH. Can hospitalization-associated disability be prevented? Journal of the American Medical Association 2011; 306: 18001.
  • 11
    Saxton A, Velanovich V. Preoperative frailty and quality of life as predictors of postoperative complications. Annals of Surgery 2011; 253: 12239.
  • 12
    Robinson TN, Eiseman B, Wallace JI, et al. Redefining geriatric preoperative assessment using frailty, disability and co-morbidity. Annals of Surgery 2009; 250: 44955.
  • 13
    Kothari A, Phillips S, Bretl T, Block K, Weigel T. Components of geriatric assessments predict thoracic surgery outcomes. Journal of Surgical Research 2010; 166: 513.
  • 14
    Lipsitz LA. Dynamics of stability: the physiologic basis of functional health and frailty. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 2002; 57: B11525.
  • 15
    Lawrence VA, Hazuda HP, Cornell JE, et al. Functional independence after major abdominal surgery in the elderly. Journal of the American College of Surgeons 2004; 199: 76272.
  • 16
    Gill TM, Allore HG, Gahbauer EA, Murphy TE. Change in disability after hospitalization or restricted activity in older persons. Journal of the American Medical Association 2010; 304: 191928.
  • 17
    Barnett S, Moonesinghe SR. Clinical risk scores to guide perioperative management. Postgraduate Medical Journal 2011; 87: 53541.
  • 18
    De Saint-Hubert M, Schoevaerdts D, Cornette P, D'Hoore W, Boland B, Swine C. Predicting functional adverse outcomes in hospitalized older patients: a systematic review of screening tools. The Journal of Nutrition, Health and Aging 2010; 14: 3949.
  • 19
    Wang TJ. Concept analysis of functional status. International Journal of Nursing Studies 2004; 41: 45762.
  • 20
    Agostini P, Cieslik H, Rathinam S, et al. Postoperative pulmonary complications following thoracic surgery: are there any modifiable risk factors? Thorax 2010; 65: 8158.
  • 21
    Arozullah AM, Khuri SF, Henderson WG, Daley J. Development and validation of a multifactorial risk index for predicting postoperative pneumonia after major noncardiac surgery. Annals of Internal Medicine 2001; 135: 84757.
  • 22
    World Health Organization International Classification of Functioning, Disability and Health: Geneva: WHO, 2001.
  • 23
    Reuben DB, Seeman TE, Keeler E, et al. Refining the categorization of physical functional status: the added value of combining self-reported and performance-based measures. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 2004; 59: 105661.
  • 24
    Thomas S, Reading J, Shephard RJ. Revision of the Physical Activity Readiness Questionnaire (PAR-Q). Canadian Journal of Sport Sciences 1992; 17: 33845.
  • 25
    Stel VS, Smit JH, Pluijm SM, Visser M, Deeg DJ, Lips P. Comparison of the LASA Physical Activity Questionnaire with a 7-day diary and pedometer. Journal of Clinical Epidemiology 2004; 57: 2528.
  • 26
    Bassey EJ, Fiatarone MA, O'Neill EF, Kelly M, Evans WJ, Lipsitz LA. Leg extensor power and functional performance in very old men and women. Clinical Science (London, England 1979) 1992; 82: 3217.
  • 27
    Csuka M, McCarty DJ. Simple method for measurement of lower extremity muscle strength. American Journal of Medicine 1985; 78: 7781.
  • 28
    Curb JD, Ceria-Ulep CD, Rodriguez BL, et al. Performance-based measures of physical function for high-function populations. Journal of the American Geriatrics Society 2006; 54: 73742.
  • 29
    American Thoracic Society/European Respiratory Society. ATS/ERS Statement on respiratory muscle testing. American Journal of Respiratory and Critical Care Medicine 2002; 166: 518624.
  • 30
    Feeney C, Reynolds JV, Hussey J. Preoperative physical activity levels and postoperative pulmonary complications post-esophagectomy. Diseases of the Esophagus 2011; 24: 48994.
  • 31
    Nelson ME, Rejeski WJ, Blair SN, et al. Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Medicine and Science in Sports and Exercise 2007; 39: 143545.
  • 32
    Arozullah AM, Daley J, Henderson WG, Khuri SF. Multifactorial risk index for predicting postoperative respiratory failure in men after major noncardiac surgery. The National Veterans Administration Surgical Quality Improvement Program. Annals of Surgery 2000; 232: 24253.
  • 33
    Audisio RA, Pope D, Ramesh HS, et al. Shall we operate? Preoperative assessment in elderly cancer patients (PACE) can help. A SIOG surgical task force prospective study. Critical Reviews in Oncology/Hematology 2008; 65: 15663.
  • 34
    Fukuse T, Satoda N, Hijiya K, Fujinaga T. Importance of a comprehensive geriatric assessment in prediction of complications following thoracic surgery in elderly patients. Chest 2005; 127: 88691.
  • 35
    Gupta H, Gupta PK, Fang X, et al. Development and validation of a risk calculator predicting postoperative respiratory failure. Chest 2011; 140: 120715.
  • 36
    Kerr A, Syddall HE, Cooper C, Turner GF, Briggs RS, Sayer AA. Does admission grip strength predict length of stay in hospitalised older patients? Age and Ageing 2006; 35: 824.
  • 37
    Mahalakshmi VN, Ananthakrishnan N, Kate V, Sahai A, Trakroo M. Handgrip strength and endurance as a predictor of postoperative morbidity in surgical patients: can it serve as a simple bedside test? International Surgery 2004; 89: 11521.
  • 38
    Chen CH, Chang H, Huang YZ, Hung TT. Hand-grip strength is a simple and effective outcome predictor in esophageal cancer following esophagectomy with reconstruction: a prospective study. Journal of Cardiothoracic Surgery 2011; 6: 98.
  • 39
    Rock P, Rich PB. Postoperative pulmonary complications. Current Opinion in Anaesthesiology 2003; 16: 12331.
  • 40
    Desborough JP. The stress response to trauma and surgery. British Journal of Anaesthesia 2000; 85: 10917.
  • 41
    Scholes RL, Browning L, Sztendur EM, Denehy L. Duration of anaesthesia, type of surgery, respiratory co-morbidity, predicted VO2max and smoking predict postoperative pulmonary complications after upper abdominal surgery: an observational study. Australian Journal of Physiotherapy 2009; 55: 1918.