Diagnóstico de Comunicación interna con enfoque de género

Changes in weight, tness, anxiety and depression were calculated from the entry and exit values for each individual. Since height was not routinely recorded throughout, BMI was available for only 889 patients. Baseline weight was categorised into under 75kg, 75-90kg, and over 90kg and labelled A, B and C respectively for brevity. The index of multiple deprivation was ascertained from post code, occupation was coded under 9 headings (see Table 6.22), and age was categorised into under 50, 50-59, 60-69 and 70 and over. The Modied D'Hoore Co-morbidity Index is designed to as- sess non-coronary co-morbidity specically in the out-patient cardiac rehabilitation environment, rather than the acute setting (D'Hoore et al. [1996]), and was calcu- lated for each patient based on the recorded co-morbidities (see table 3.2 on page 58). Up to August 1995, tness was measured on a bicycle ergometer with ECG monitoring and measurement of estimated peak workload. After that date, exercise tests were performed on a treadmill, using either the Bruce protocol (Bruce et al.

[1973]) or, for frail or elderly patients, the modied Bruce protocol (Bruce [1973]). Peak exercise tolerance was expressed as the predicted oxygen uptake (V O2 max)

in ml/kg/minute, from the known oxygen cost of bicycling at dierent workloads (Astrand and Rodahl [1986]). The treadmill test used the same endpoints as for bicycle tests and V O2 max predicted on the assumption that each one minute of

the Bruce protocol uses one MET (metabolic equivalent - or 3.5ml O2/kg/min) and that the rst three stages of the modied Bruce protocol each use one MET. V O2

max<15ml/kg/min was categorised as low tness,V O2 max>22ml/kg/min as high

tness with the remainder as medium (established for cardiac rehabilitation by Ka- vanagh et al. [2002] Kavanagh et al. [2003]).

The researchers who collected the data report that the exercise test protocol for the treadmill tests was either the full Bruce or the modied Bruce protocol, with the latter used for frailer patients as it starts more gently. With the treadmill set at a constant 1.7 mph, the gradient is increased from 10% (full Bruce) or 0% (modied Bruce) every 3 minutes to a maximum gradient of 22%. The metabolic equivalents (METs) are calculated from the number of minutes until the patient had reached 85% of their predicted heart rate maximum for their age or developed symptoms which precluded the test's continuation using the standard conversion that 1 minute of the Bruce protocol used one MET, and the rst two stages of the modied Bruce protocol use 1 MET each. (Turner [2007]). Each MET is equivalent to 3.5ml/kg/min soV O2 max Max is estimated as 3.5(1+x), with x being the number of minutes and

the additional 3.5ml/kg/min representing the resting metabolic rate.

Treadmill testing using the Bruce protocol is used for both diagnosis and prognosis in patients at risk of coronary disease. Symptoms of Ischemia prompt a diagnosis of disease. Prognosis is estimated using exercise duration, exercise hypotension, ex- ercise hypertension chronotropic incompetence, heart rate recovery and ventricular ectopy. Exercise duration is a good measure of functional capacity and a longer du- ration indicates a lower probability of mortality from coronary disease or any other cause, including in healthy subjects and retains its prognostic value after adjusting for age and sex (Miller [2008]). Lower exercise duration can be an indicator of lower tness or more severe cardiovascular disease.

Depression and anxiety were categorised into none, borderline and depressed or anxious using a Hospital Anxiety and Depression Scale score below 8 to suggest no depression (anxiety), 8-10 to suggest borderline and a score over 10 to suggest clinical depression or clinical anxiety (Bjelland et al. [2002]).

In the main analysis, baseline categories of tness, depression and anxiety were used as predictors. Since the inuence of an improvement in tness, depression or anxiety categories on survival is of interest, and whether these depend on baseline categories, variables Fitness, Anxiety and Depression were dened so that a scale of 1 to 7 captured each starting category and improvement or deterioration (see Table 6.23 ). Very few patients started in the highest tness category and then deteriorated over the course of the programme (5 men) so these were combined with the `no change' category. There were no patients who deteriorated having started in the mid-tness category, so this category was omitted. Survival time was dened as the period between the date the participant joined the programme and the date of death.

Statistical analysis

In the survival analyses both all-cause mortality and cardiovascular mortality were considered, with non-cardiovascular deaths treated as censored in the latter anal- ysis. Cox proportional hazards models (Collett [2003], Cox [1972]) were used to model both all-cause and cardiovascular survival, beginning from the subset of vari- ables that were found to be signicant predictors (at 5% level) of all-cause mortality after preliminary univariate analyses as the baseline model. A backward stepwise selection algorithm was employed to nd the model with minimum AIC (Akaike [1974]), retaining age and sex as the minimum model. The high calibre practice at the rehabilitation programme gives us condence that there was no loss to follow-up. All analyses were carried out in R statistical software (R Development Core Team [2011]).

Missing data

The Cox proportional hazards model was used on the complete cases. There were 1,529 cases (56.3%) of the 2,714 available which were complete in all 36 variables which were signicant in the univariate analysis and became the starting point for the denitive analysis. There were 1,029 cases (38%) which were complete in these variables and also had observations of tness, depression and anxiety at the end of the programme. To address the issue of missing data in order to assess the credibility of a model built using only the complete cases (Little and Rubin [2002]), hazard ratios from the complete-case analysis were compared with those from an analysis of all data (2,714 cases) after replacement of missing values with imputed data. Multiple imputation was performed in the R package MICE (Multivariate Imputation using

Chained Equations, R Development Core Team [2011]), providing 20 completed data sets with values imputed where they were missing. The optimised model was then built with each of these 20 data sets, and pooled estimates of model coecients and variances calculated using the rules devised by Little and Rubin (Little and Rubin [2002]) for comparison. There were no known reasons to believe that the data were missing other than at random, except in the case of the end tness data. Reasons for end tness to be missing included referral for cardiac surgery and poor health, suggesting that time to death for these individuals might dier from those with end tness observations. A t-test showed that the mean time to death was not the same for those with and without an end tness measurement (P<0.001)

A total of 2,054 patients completed the programme (Turner [2007]); the main reasons for not completing were patient preference, referral for cardiac surgery, poor health or death. Those without complete end tness data included those who were taken to a dierent heart rate at the nal tness test, most of whom were on a beta blocker for which the dose had been changed or who were tested at a dierent time of day, and those who were tested using a dierent test protocol.

Patients with a baseline fitness measurement

N = 2714

Patients with complete data including end fitness N = 1029 (37.9%) Patients without complete data in variables of interest* N = 1185 (43.7%) Patients with complete

data except end fitness, anxiety and depression

N = 1529 (56.3%)

Figure 6.16: Patient recruitment and eligibility. *The number of patients with missing data in individual variables of interest varied, with tness after the programme missing in 48.5% of cases, and the remaining variables ranging between 0 and 29.4 % . Details of variables and numbers and percentage of missing observations are in Table 6.18 on page 146.

The mean follow-up for those with complete data was 11 years 4 months.

Detailed characteristics of patients are shown in Table 3.3. Women (13.7%) had a higher mean age. The largest age group, for both sexes, was 60-69 years. The most common reason for referral to the programme was acute myocardial infarction (51.1%). Nearly half the women had never smoked compared to just under one third of the men and close to a third of both sexes had recently given up smoking. Around half of all patients had a family history of coronary heart disease and over 70% were free from non-coronary co-morbidity, although a higher proportion of the women had diabetes. The men had a higher mean tness than the women and the percentage of men in the high-tness category at recruitment was 3 times that of the women; most of the women were in the low-tness category. At graduation, 67% of the men and 34% of the women were in the high-tness category, with one fth of the patients having improved from the mid-tness category. Median tness was improved by a similar amount in both sexes, and high tness was the largest group at graduation. There was little evidence of clinical depression in this cohort, and most of those whose scores suggested a borderline category improved by graduation, as did almost all of the few whose scores at recruitment suggested clinical depression. There was more anxiety, although the rates were not high. Again, the majority who began in the borderline category improved, as did a signicant proportion of those starting in the clinical anxiety category, but the proportion of women who remained in the clinical anxiety class was 3 times that of the men.

The maximum recorded co-morbidity score was 7 (very high) but only 2.6% of participants had a co-morbidity score of 3 or more (D'Hoore et al. [1996], see Table 3.2 on 58).

Survival

During the course of the study and follow-up of the 1529 participants, 385 died, and of those deaths 192 (49.9%) were from cardiovascular causes.

Two Cox proportional hazards models were constructed from these data, the primary model used baseline only measures of tness, depression and anxiety, for which there were 1,529 complete cases available. The secondary model used baseline and change tness, depression and anxiety as described in Table 6.23 for which 1,029 cases were available. Table 6.16 details all the signicant predictors in the primary propor- tional hazards model of all-cause mortality. In this primary model, age was the most

0 2 4 6 8 10 12 14 16 18 0.5 0.6 0.7 0.8 0.9 1.0

Time (years)

Proportion Surviving

All−cause mortality

Cardiovasular mortality

Figure 6.17: Kaplan-Meier survival curves for all cause and cardiovascular mortality for the primary model, the cohort with complete baseline data, 1529 cases. The plot is for the entire observation time and the dotted lines are the 95% condence intervals.

important predictor of all-cause mortality, with risk increasing with age. In this cohort, the next most important predictor was tness category at recruitment, with the ttest patients having lowest risk. Risk increased with co-morbidity, and both aspirin and statins prescriptions reduced risk. Myocardial infarction was the diag- nosis carrying the greatest risk, along with myocardial infarction with percutaneous coronary intervention, angina and other cardiac diagnoses. Coronary artery bypass graft and angina were much lower risk diagnoses. Female sex carried a lower risk of mortality, as did a lower resting heart rate. Systolic blood pressure was also a risk indicator. ACE inhibitor prescription was associated with higher risk, perhaps

because this prescription is given to the high risk cases. All-cause survival model

complete cases Imputed data

model model

Model Term Hazard Condence Pooled

Ratio Interval Hazard

lower .95 upper .95 Ratio

Age category under 50 1 - - 1

50-59 2.13 1.17 3.88 2.02 60-69 3.84 2.15 6.88 3.08 70+ 7.94 4.38 14.39 6.07 Fitness: High baseline 1 - - 1 Mid baseline 1.56 1.16 2.09 1.76 Low baseline 2.60 1.86 3.60 2.76 D'Hoore Co-morbidity score None 1 - - 1 1 (least) 1.23 0.91 1.66 1.17 2 1.42 1.08 1.86 1.48 3 1.35 0.68 2.66 1.87 4 (most) 4.50 2.13 9.50 2.67 Statins Yes 0.72 0.57 0.89 0.74 No 1 - - 1 Aspirin Yes 0.53 0.35 0.79 0.63 No 1 - - Diagnostic Category MI 1 - - 1 CABG 0.69 0.54 0.89 0.69 PCI 0.54 0.32 0.89 0.68 MI + PCI 0.85 0.44 1.67 0.82 Angina 0.87 0.54 1.39 0.79 Other cardiac 0.99 0.44 2.23 0.94 Sex Male 1 - - 1 Female 0.73 0.54 0.98 0.62 Systolic blood pressure before 0.995 0.991 0.999 0.998

Ace inhibitor Yes 1.26 1.01 1.57 1.19

No 1 - - 1

Resting heart rate 1.007 1.000 1.014 1.006

Table 6.16: All-cause survival model, ordered by importance of variables to the model, using baseline only tness, anxiety and depression categories (1,529 cases, 385 deaths). Pooled hazard ratios are from multiple imputation of missing data. MI is Myocardial Infarction, CABG is coronary artery bypass graft, PCI is percutaneous coronary intervention.

Cardiovascular survival model: baseline tness

complete cases Imputed data

model model

Model Term Hazard Condence Pooled

Ratio Interval Hazard

lower .95 upper .95 Ratio

Fitness: High baseline 1 - - 1

Mid baseline 1.69 1.11 2.60 2.16

Low baseline 4.00 2.54 6.30 4.12

Statin Yes 0.45 0.33 0.61

No 1 - -

Age category under 50 1 - - 1

50-59 1.59 0.76 3.32 1.53

60-69 2.68 1.32 5.43 2.42

70+ 4.10 1.98 8.48 3.83

Diagnostic

Category Myocardial Infarction (MI) 1 - - 1

Coronary Artery Bypass 0.60 0.42 0.85 0.62

Graft (CABG) Percutaneous Coronary 0.26 0.09 0.70 0.49 Intervention (PCI) MI + PCI 1.05 0.42 2.62 0.73 Angina 0.85 0.46 1.56 0.71 Other cardiac 0.72 0.23 2.29 1.01 Aspirin Yes 0.48 0.28 0.80 0.56 No 1 - - 1 Sex: MALE 1 - - 1 FEMALE 0.65 0.43 0.99 0.53

Ace inhibitor Yes 1.42 1.04 1.94 1.37

No 1 - - 1

Table 6.17: Optimised Cardiovascular survival model, ordered by importance of variables to the model, using only baseline values for tness, depression and anxiety (1,529 cases 192 cardiovascular deaths). Pooled hazard ratios are from multiple imputation of missing data.

The cardiovascular mortality model is detailed in Table 6.17. Fitness cate- gory at baseline was the strongest predictor of cardiovascular mortality, with higher tness associated with lower risk. A prescription for statins cut the risk of cardiovas- cular mortality by more than half in this cohort. Age was the next most signicant predictor of mortality, with risk increasing with age as expected. As with all-cause mortality, a diagnosis of myocardial infarction carried the highest associated risk of mortality, with MI+PCI, angina and other cardiac diagnoses equally high. Female sex was associated with lower risk, and a prescription for ACE inhibitor with higher risk.

In the cohort having complete data including end depression, anxiety and t- ness (the secondary model), age was still the strongest predictor of risk for all-cause mortality (Table 6.24). The next most important predictor was the combination of tness category at recruitment, and whether they improved or maintained that tness, with highest risk attributed to those who began in the low-tness category. There was no statistically signicant dierence (assessed at the 5% level) between those who began in the mid-tness category and improved to high tness and those who began in the high-tness category and maintained high tness. However, those who did not improve suciently to move up from the mid-tness category had sig- nicantly higher risk; improvement to a mid-tness from low-tness category did not signicantly reduce risk, although a signicant dierence in risk is evident between low and medium tness for the patients whose category did not change. Having a prescription for statins or having a prescription for aspirin was each associated with a lower risk of mortality. A prescription for ACE inhibitors was associated with a higher risk of mortality and females had lower all-cause mortality.

The cardiovascular mortality model for the cohort having complete data in- cluding end depression, anxiety and tness is detailed in Table 6.25. A prescription for statins was the most powerful predictor of cardiovascular mortality with those having a prescription having one third the risk of those without. Once again, tness was important, low baseline and failure to improve being powerful predictors of car- diovascular mortality. After tness, age was important with those over 70 years at higher risk of cardiovascular death. Aspirin was associated with lower risk, as was being female.

Imputed data

The hazard ratios derived from the pooled imputed data (shown in Tables 6.16 and 6.17), were very similar to those from the complete cases model, both in size and direction, and fall within the condence intervals given for the model estimates. This suggests that the analysis that used only the complete cases did not produce substantailly dierent results on account of removing the cases with incomplete data. There were 889 cases which were complete in baseline BMI as well as baseline tness, depression and anxiety as in the primary model, and both all-cause and cardiovas- cular mortality models were derived for these, to test the importance of BMI. In neither case did BMI remain in the optimised model.

Main ndings

Attaining a tness level ofV O2 max>22ml/kg/min (dened here as high tness) in

the early months following a cardiac event or procedure is associated with improved long-term survival in those who have experienced a coronary event or procedure. (A useful comparison is that a young, untrained male would typically have a tness level ofV O2 max 35-40 ml/kg/min and a cycling athleteV O2 max 80 ml/kg/min.)

High tness at recruitment to the rehabilitation programme was likely to reect high tness before a coronary event or procedure, but there was no statistically signicant dierence between patients who improved from moderate tness at recruitment to high tness at graduation and those who maintained high tness from recruitment, for those who completed the programme. Secondary prevention medications were also strongly associated with improved long-term survival in both all-cause and car- diovascular mortality. In particular, a prescription for statins or a prescription for aspirin were associated with lower risk of both all-cause and cardiovascular mortality. A prescription for ACE inhibitors was associated with higher mortality, but those on ACE inhibitors were the high risk cases. Patients having a CABG surgery and PCI have a signicantly higher long-term survival from cardiovascular mortality than do patients with a myocardial infarction or angina.

Fitness for life confers signicant potential benets for those who may go on to experience a coronary event or procedure. Promotion of tness after a coronary event or procedure, even for those already moderately t, had potential for improved life expectancy.

The key role of medications in the early weeks after a cardiac event or proce- dure on reducing long term mortality both from cardiovascular causes and all-causes in patients experiencing a cardiac event or procedure had been demonstrated previ- ously (Unal et al. [2004]) and in this model. We have no follow-up information on the adherence or changes to medication after graduation from the programme, so do not know how long patients continued with their medication. Given this, the strong eect of secondary preventative medication is striking.

The protective eect of female sex extends both to cardiovascular causes of death and to all-causes in cardiovascular patients, contrary to some other ndings (Dallongevillle et al. [2010]). Weight does not appear to aect all-cause or cardio- vascular mortality in cardiovascular patients, which is consistent with other studies (Romero-Corral et al. [2006], Shahian et al. [2012]). A prescription for ACE in-

hibitors at entry to the programme were closely related to a high risk level and were associated with increased risk of cardiovascular mortality. When tness is measured and included in the survival analysis, BMI ceases to be a predictor of all-cause or cardiovascular mortality.

Numbers and percentages of the individual variables that were missing in the 2714 cases which met the inclusion criterion of having a baseline tness measurement were shown in Table 6.18.

Variable description Missing %

id Unique identier 1 0.04

Illness date Date of the index event 0 0

Entry date Date entered the programme 0 0