- Email: [email protected]
Statins and hypercholesterolaemia: UK Standing Medical Advisory Committee guidelines
THE LANCET
COMMENTARY
CORRESPONDENCE
Fall-induced injuries among elderly people SIR—Fall-induced injury in elderly people is a world-wide problem, and ageing populations are increasing the burden of these injuries on our health care systems.1,2 However, to predict the likely increases in elderly patients needing treatment we need to know whether the number of fall-induced injuries is increasing more rapidly than can be accounted for by demographic changes alone We have examined trends in the incidence of fall-induced injuries in Finland for 1970–95. We obtained data from the National Hospital Discharge Register (NHDR) for all patients aged 60 years or older who were admitted to Finnish hospitals in these years for primary treatment of a fall-induced injury. Our NHDR is the oldest established nationwide discharge register in the world, and provides reliable data, especially for injury registration.3,4 The number of elderly patients with fall-induced injury increased considerably during the study period— from 4019 in 1970 to 17 604 in 1995. The average increase was 13·5% per year. The age-adjusted incidence (per 100 000 60-year-old or older individuals) of injuries showed a clear increase from 1970 to 1995: 840 to 1911 in women, and 484 to 1167 in men (figure). In 1995, 65% of these injuries were bone fractures, 11% soft2000
Number per 100 000
1800
Women Men
1600 1400 1200 1000 800 600 400 200 0 1970 1975 1980 1985 1990 1995 Year
Age-adjusted incidence of fall-related injury in elderly Finnish people, 1970–95
1174
tissue contusions, 6% head injuries other than fractures, 5% joint distortions and dislocations, and 4% soft-tissue wounds and lacerations. For all NHDR-recorded injuries in Finland (ie, all age groups and all types of injury), the proportion of 60-yearold or older persons’ fall-induced injuries showed a steady increase— from 8% in 1970 to 21% in 1995. In the population aged 60 years and older, the proportion of fall-induced injuries (of all types of injury in this age group) rose from 44% to 59%. Of all individuals treated in Finnish hospitals for fall-induced injury the proportion of older adults increased steadily, from 33% in 1970 to 48% in 1995. If this trend continues at the same rate, the number of fall-induced injuries will be threefold higher in the year 2030 than in 1995. The exact reasons for the increase in such injuries are not known. A rise in the ageadjusted incidence of falls in frail elderly people, and of fracture also due to deterioration in bone density and strength, may partly explain these findings.5 Old people may be less healthy and functionally less capable now than in the past. We believe that the number of fallinduced injuries among elderly people is increasing more rapidly than can be accounted for by demographic changes alone, and therefore vigorous preventive measures, such as avoidance of falls and osteoporosis of the elderly and protection of the critical anatomic sites of the body when a fall takes place, should be urgently adopted to control the increasing burden of these agerelated injuries. *Pekka Kannus, Seppo Niemi, Mika Palvanen, Jari Parkkari Accident and Trauma Research Centre, UKK Institute for Health Promotion Research, FIN-33500 Tampere, Finland e-mail: [email protected] 1
van Weel C, Vermeulen H, van den Bosh W. Falls, a community perspective. Lancet 1995; 345: 1549–51.
2
3
4
5
Rubenstein LZ, Josephson KR, Robbins AS. Falls in the nursing home. Ann Intern Med 1994; 121: 442–51. Salmela R, Koistinen V. Yleissairaaloiden poistoilmoitusrekisterien kattavuus ja luotettavuus. Sairaala 1987; 49: 480–82. Keskimäki I, Aro S. Accuracy of data on diagnosis, procedures and accidents in the Finnish hospital discharge register. Int J Health Sci 1991; 2: 15–21. Thorngren K-G. Fractures in older persons. Disabil Rehab 1994; 16: 119–26.
Statins and hypercholesterolaemia: UK Standing Medical Advisory Committee guidelines SIR—Management protocols for hypercholesterolaemia are based on the absolute risk of coronary heart disease (CHD). The Department of Health, through a Standing Medical Advisory Committee, has recently endorsed the Sheffield guidelines for primary prevention of CHD,1 which advocates statins for those with a 3% annual risk of CHD. The Committee’s guidelines also recommend highest priority for statins in the secondary prevention of CHD, advice based on the 4S and CARE trials2,3 in which the annual risks in the placebo cohorts were 4·2% and 2·6%, respectively. They therefore advise treatment for some with CHD who actually have a lower risk than that which they deem necessary for primary prevention. The Sheffield group originally proposed a 4·5% annual risk as a threshold for therapy in primary prevention, a risk similar to that in 4S. This level was lowered, arbitrarily, to 3·0% after the WOSCOPS trial,4 a primary prevention study in which the placebo cohort had an annual risk of 1·58%. It is doubtless a coincidence that the 3% figure falls mid way between the 4S and WOSCOPS placebo risk rates. Relative risks on statins have been the same in primary and secondary prevention trials. The
Vol 350 • October 18, 1997
THE LANCET
Sheffield investigators attribute these benefits to reductions in cholesterol, which is a class effect of the statins. The choice of threshold is based on costeffectiveness, and the justification for the 3% level reasoned on the historical costs of the drugs used in 4S and WOSCOPS. In clinical practice less expensive statins, which produce similar effects on cholesterol to those seen in the clinical trials, are being prescribed and should alter the economic threshold. The Sheffield table1 is claimed to identify those with an annual CHD risk of 3%. The Framingham equation, on which the table is based, uses the ratio of total to high-density lipoprotein (HDL) cholesterol. The Sheffield group have had to use assumed values for HDL in their calculations, and their figures can be reproduced with values of 1·15 mmol/L for men and 1·40 mmol/L for women. These concentrations are inappropriate for certain patients such as those with diabetes mellitus. The table therefore incorrectly suggests that few male diabetics, and no females, achieve a 3% risk, whereas gender has no effect on CHD risk in diabetes, which on average for type II diabetes is 2% per year.5 Furthermore, variations in HDL within the normal range have a large effect on the calculated CHD risk. The cholesterol value cited in the table may, depending on the actual HDL, be too high or low by 22%, or indeed more if the HDL is abnormal. For primary prevention, it may be preferable to calculate, rather than guess, the risk of CHD by measuring both cholesterol and HDL. It is theoretically possible for the laboratory computer to report a calculated CHD risk from the lipids and clinical information, and there is an established example of such risk calculations in biochemical screening programmes for Down’s syndrome. The investment would be fairly small and allow more accurate targeting of drugs. A F Jones Department of Clinical Biochemistry, Birmingham Heartlands Hospital, Birmingham B9 5SS, UK 1
2
3
Ramsay LE, Haq IU, Jackson PR, et al. Targeting lipid-lowering drug therapy for primary prevention of coronary heart disease: an updated Sheffield table. Lancet 1996; 348: 387–88. Scandinavian Simvastatin Survival Study Group. Randomised trial of cholesterol lowering in 4444 patients with heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994; 344: 1383–89. Sacks FM, Pfeffer MA, Moye LA, et al. The effect of parvastatin on coronary events after myocardial infarction in patients with average cholesterol levels. N Engl J Med 1996; 335: 1001–09.
Vol 350 • October 18, 1997
4
5
Shepherd J, Cobbe SM, Ford I, et al. Prevention of coronary heart disease with parvastatin in men with hypercholesterolaemia. N Engl J Med 1995; 333: 1301–07. UK Prospective Diabetes Study Group. UK Prospective Diabetes Study 16. Overview of 6 years’ therapy of type II diabetes: a progressive disease. Diabetes 1995; 44: 1249–58.
2 3
4
Cox DR. Regression models and life tables. J R Stat Soc 1972; 34: 187–220. Faraggi D, Simon R. A neural network model for survival data. Stat Med 1989; 14: 73–82. Liestol K, Andersen PK, Andersen U. Survival analysis and neural nets. Stat Med 1989; 13: 1189–200.
Authors’ reply
Artificial neural networks SIR—The report on neural networks by Leonard Bottaci and co-workers (Aug 16, p 469)1 demands comment. One cannot share their enthusiasm for a method presented as being able to predict the outcome of individual patients. They claim that traditional statistical analyses fail to predict when the individual patient will die. But so will neural networks. Predictions at the individual level are not merely outside our present research, they are fundamentally impossible. Irrespective of the statistical models used, the unexplained variability of individual outcomes will remain large, a fact that should not preclude us from finding better ways of predicting average outcomes for well defined groups of patients. How do neural networks perform in this respect? Bottaci and colleagues do not provide a convincing answer, because they have not made the relevant comparison. Instead of debunking traditional statistical methods based on linear models, they should have presented the predictive value of these models (for example, based on Cox’s proportional hazard regression2), instead of that of physicians presented with “data in tabular form”. Perhaps their most remarkable observation is that physicians who had not seen the patients did not do so badly without recourse to any mathematical model at all. We wish it were true that with neural networks clinicians “may well find the answers that they seek”, but we doubt it. Other authors who have used neural networks have come to less extravagant conclusions.3,4 Whilst advanced statistical tools may be useful, new treatments and a better understanding of the biology of colorectal cancer remain, as ever, the cornerstone of future progress. Marc Buyse, *Pascal Piedbois Limburgs Universitair Centrum, Department of Biostatistics, Diepenbeek, Belgium; and *Hôpital Henri Mondor, Department of Oncology, 94010 Créteil, France 1
Bottaci L, Drew PJ, Hartley JE, et al. Artifical neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet 1997; 350: 469–72.
SIR—It is obvious that no measurement can be so precise as to permit the design of a model to predict the behaviour of any system with complete accuracy. Therefore, it is theoretically impossible to produce “individual” predictions. But our use of neural networks for the prediction of outcome is a more pragmatic approach. When one considers assumptions about the statistical distribution and independent nature of the predictors required for regression analysis, its mathematical handicaps become clear.1 Traditional statistics can often provide a reliable answer only for groups of patients defined by a small number of linearly separable rules.2 Artificial neural networks allow the addition of further datasets to allow a more personalised prediction than our methods. This means that, whilst in the purest sense the prediction remains for groups of patients, neural networks are able to provide an individual prediction, especially when compared with clinicopathological methods. We agree that the “cornerstone of future” involves the elucidation of the biology of colorectal cancer and much of the work in our own unit is directed towards this aim. However, this research has continued for at least 50 years and, though advances have been made, we are still unable to provide many of our patients with an accurate prediction of their chance of survival. Use of neural networks to analyse data that are already available may well provide further insights into the nature of colorectal cancer and other tumours, with the advantage of individualised prediction of outcome rather than crude general estimates taken from the patient’s particular peer group. There is already a move towards a more connectionist approach to modelling the behaviour of biological systems, including cancer, which reflects the recognition of non-linear causal relations within complex systems.3 The design of neural networks makes them ideally suited for the analysis of these models, many of which are beyond the reach of traditional statistical methods. It is a fallacy to suggest that the clinicians had no recourse to a mathematical model. Even the most junior trainee is aware of the important
1175
COMMENTARY
CORRESPONDENCE
Fall-induced injuries among elderly people SIR—Fall-induced injury in elderly people is a world-wide problem, and ageing populations are increasing the burden of these injuries on our health care systems.1,2 However, to predict the likely increases in elderly patients needing treatment we need to know whether the number of fall-induced injuries is increasing more rapidly than can be accounted for by demographic changes alone We have examined trends in the incidence of fall-induced injuries in Finland for 1970–95. We obtained data from the National Hospital Discharge Register (NHDR) for all patients aged 60 years or older who were admitted to Finnish hospitals in these years for primary treatment of a fall-induced injury. Our NHDR is the oldest established nationwide discharge register in the world, and provides reliable data, especially for injury registration.3,4 The number of elderly patients with fall-induced injury increased considerably during the study period— from 4019 in 1970 to 17 604 in 1995. The average increase was 13·5% per year. The age-adjusted incidence (per 100 000 60-year-old or older individuals) of injuries showed a clear increase from 1970 to 1995: 840 to 1911 in women, and 484 to 1167 in men (figure). In 1995, 65% of these injuries were bone fractures, 11% soft2000
Number per 100 000
1800
Women Men
1600 1400 1200 1000 800 600 400 200 0 1970 1975 1980 1985 1990 1995 Year
Age-adjusted incidence of fall-related injury in elderly Finnish people, 1970–95
1174
tissue contusions, 6% head injuries other than fractures, 5% joint distortions and dislocations, and 4% soft-tissue wounds and lacerations. For all NHDR-recorded injuries in Finland (ie, all age groups and all types of injury), the proportion of 60-yearold or older persons’ fall-induced injuries showed a steady increase— from 8% in 1970 to 21% in 1995. In the population aged 60 years and older, the proportion of fall-induced injuries (of all types of injury in this age group) rose from 44% to 59%. Of all individuals treated in Finnish hospitals for fall-induced injury the proportion of older adults increased steadily, from 33% in 1970 to 48% in 1995. If this trend continues at the same rate, the number of fall-induced injuries will be threefold higher in the year 2030 than in 1995. The exact reasons for the increase in such injuries are not known. A rise in the ageadjusted incidence of falls in frail elderly people, and of fracture also due to deterioration in bone density and strength, may partly explain these findings.5 Old people may be less healthy and functionally less capable now than in the past. We believe that the number of fallinduced injuries among elderly people is increasing more rapidly than can be accounted for by demographic changes alone, and therefore vigorous preventive measures, such as avoidance of falls and osteoporosis of the elderly and protection of the critical anatomic sites of the body when a fall takes place, should be urgently adopted to control the increasing burden of these agerelated injuries. *Pekka Kannus, Seppo Niemi, Mika Palvanen, Jari Parkkari Accident and Trauma Research Centre, UKK Institute for Health Promotion Research, FIN-33500 Tampere, Finland e-mail: [email protected] 1
van Weel C, Vermeulen H, van den Bosh W. Falls, a community perspective. Lancet 1995; 345: 1549–51.
2
3
4
5
Rubenstein LZ, Josephson KR, Robbins AS. Falls in the nursing home. Ann Intern Med 1994; 121: 442–51. Salmela R, Koistinen V. Yleissairaaloiden poistoilmoitusrekisterien kattavuus ja luotettavuus. Sairaala 1987; 49: 480–82. Keskimäki I, Aro S. Accuracy of data on diagnosis, procedures and accidents in the Finnish hospital discharge register. Int J Health Sci 1991; 2: 15–21. Thorngren K-G. Fractures in older persons. Disabil Rehab 1994; 16: 119–26.
Statins and hypercholesterolaemia: UK Standing Medical Advisory Committee guidelines SIR—Management protocols for hypercholesterolaemia are based on the absolute risk of coronary heart disease (CHD). The Department of Health, through a Standing Medical Advisory Committee, has recently endorsed the Sheffield guidelines for primary prevention of CHD,1 which advocates statins for those with a 3% annual risk of CHD. The Committee’s guidelines also recommend highest priority for statins in the secondary prevention of CHD, advice based on the 4S and CARE trials2,3 in which the annual risks in the placebo cohorts were 4·2% and 2·6%, respectively. They therefore advise treatment for some with CHD who actually have a lower risk than that which they deem necessary for primary prevention. The Sheffield group originally proposed a 4·5% annual risk as a threshold for therapy in primary prevention, a risk similar to that in 4S. This level was lowered, arbitrarily, to 3·0% after the WOSCOPS trial,4 a primary prevention study in which the placebo cohort had an annual risk of 1·58%. It is doubtless a coincidence that the 3% figure falls mid way between the 4S and WOSCOPS placebo risk rates. Relative risks on statins have been the same in primary and secondary prevention trials. The
Vol 350 • October 18, 1997
THE LANCET
Sheffield investigators attribute these benefits to reductions in cholesterol, which is a class effect of the statins. The choice of threshold is based on costeffectiveness, and the justification for the 3% level reasoned on the historical costs of the drugs used in 4S and WOSCOPS. In clinical practice less expensive statins, which produce similar effects on cholesterol to those seen in the clinical trials, are being prescribed and should alter the economic threshold. The Sheffield table1 is claimed to identify those with an annual CHD risk of 3%. The Framingham equation, on which the table is based, uses the ratio of total to high-density lipoprotein (HDL) cholesterol. The Sheffield group have had to use assumed values for HDL in their calculations, and their figures can be reproduced with values of 1·15 mmol/L for men and 1·40 mmol/L for women. These concentrations are inappropriate for certain patients such as those with diabetes mellitus. The table therefore incorrectly suggests that few male diabetics, and no females, achieve a 3% risk, whereas gender has no effect on CHD risk in diabetes, which on average for type II diabetes is 2% per year.5 Furthermore, variations in HDL within the normal range have a large effect on the calculated CHD risk. The cholesterol value cited in the table may, depending on the actual HDL, be too high or low by 22%, or indeed more if the HDL is abnormal. For primary prevention, it may be preferable to calculate, rather than guess, the risk of CHD by measuring both cholesterol and HDL. It is theoretically possible for the laboratory computer to report a calculated CHD risk from the lipids and clinical information, and there is an established example of such risk calculations in biochemical screening programmes for Down’s syndrome. The investment would be fairly small and allow more accurate targeting of drugs. A F Jones Department of Clinical Biochemistry, Birmingham Heartlands Hospital, Birmingham B9 5SS, UK 1
2
3
Ramsay LE, Haq IU, Jackson PR, et al. Targeting lipid-lowering drug therapy for primary prevention of coronary heart disease: an updated Sheffield table. Lancet 1996; 348: 387–88. Scandinavian Simvastatin Survival Study Group. Randomised trial of cholesterol lowering in 4444 patients with heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1994; 344: 1383–89. Sacks FM, Pfeffer MA, Moye LA, et al. The effect of parvastatin on coronary events after myocardial infarction in patients with average cholesterol levels. N Engl J Med 1996; 335: 1001–09.
Vol 350 • October 18, 1997
4
5
Shepherd J, Cobbe SM, Ford I, et al. Prevention of coronary heart disease with parvastatin in men with hypercholesterolaemia. N Engl J Med 1995; 333: 1301–07. UK Prospective Diabetes Study Group. UK Prospective Diabetes Study 16. Overview of 6 years’ therapy of type II diabetes: a progressive disease. Diabetes 1995; 44: 1249–58.
2 3
4
Cox DR. Regression models and life tables. J R Stat Soc 1972; 34: 187–220. Faraggi D, Simon R. A neural network model for survival data. Stat Med 1989; 14: 73–82. Liestol K, Andersen PK, Andersen U. Survival analysis and neural nets. Stat Med 1989; 13: 1189–200.
Authors’ reply
Artificial neural networks SIR—The report on neural networks by Leonard Bottaci and co-workers (Aug 16, p 469)1 demands comment. One cannot share their enthusiasm for a method presented as being able to predict the outcome of individual patients. They claim that traditional statistical analyses fail to predict when the individual patient will die. But so will neural networks. Predictions at the individual level are not merely outside our present research, they are fundamentally impossible. Irrespective of the statistical models used, the unexplained variability of individual outcomes will remain large, a fact that should not preclude us from finding better ways of predicting average outcomes for well defined groups of patients. How do neural networks perform in this respect? Bottaci and colleagues do not provide a convincing answer, because they have not made the relevant comparison. Instead of debunking traditional statistical methods based on linear models, they should have presented the predictive value of these models (for example, based on Cox’s proportional hazard regression2), instead of that of physicians presented with “data in tabular form”. Perhaps their most remarkable observation is that physicians who had not seen the patients did not do so badly without recourse to any mathematical model at all. We wish it were true that with neural networks clinicians “may well find the answers that they seek”, but we doubt it. Other authors who have used neural networks have come to less extravagant conclusions.3,4 Whilst advanced statistical tools may be useful, new treatments and a better understanding of the biology of colorectal cancer remain, as ever, the cornerstone of future progress. Marc Buyse, *Pascal Piedbois Limburgs Universitair Centrum, Department of Biostatistics, Diepenbeek, Belgium; and *Hôpital Henri Mondor, Department of Oncology, 94010 Créteil, France 1
Bottaci L, Drew PJ, Hartley JE, et al. Artifical neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet 1997; 350: 469–72.
SIR—It is obvious that no measurement can be so precise as to permit the design of a model to predict the behaviour of any system with complete accuracy. Therefore, it is theoretically impossible to produce “individual” predictions. But our use of neural networks for the prediction of outcome is a more pragmatic approach. When one considers assumptions about the statistical distribution and independent nature of the predictors required for regression analysis, its mathematical handicaps become clear.1 Traditional statistics can often provide a reliable answer only for groups of patients defined by a small number of linearly separable rules.2 Artificial neural networks allow the addition of further datasets to allow a more personalised prediction than our methods. This means that, whilst in the purest sense the prediction remains for groups of patients, neural networks are able to provide an individual prediction, especially when compared with clinicopathological methods. We agree that the “cornerstone of future” involves the elucidation of the biology of colorectal cancer and much of the work in our own unit is directed towards this aim. However, this research has continued for at least 50 years and, though advances have been made, we are still unable to provide many of our patients with an accurate prediction of their chance of survival. Use of neural networks to analyse data that are already available may well provide further insights into the nature of colorectal cancer and other tumours, with the advantage of individualised prediction of outcome rather than crude general estimates taken from the patient’s particular peer group. There is already a move towards a more connectionist approach to modelling the behaviour of biological systems, including cancer, which reflects the recognition of non-linear causal relations within complex systems.3 The design of neural networks makes them ideally suited for the analysis of these models, many of which are beyond the reach of traditional statistical methods. It is a fallacy to suggest that the clinicians had no recourse to a mathematical model. Even the most junior trainee is aware of the important
1175