bs_bs_banner
Journal of Evaluation in Clinical Practice ISSN 1365-2753
Networking and data sharing reduces hospitalization cost of heart failure: the experience of GISC study Franco Pisanò MD,1 Giulia Lorenzoni RN MA,6 Stefano S. Sabato Eng PhD,10 Nicola Soriani PhD,7 Ottavio Narraci MD,2 Michele Accogli MD,3 Carlo Rosato Eng,4 Paola de Paolis MD,5 Franco Folino MD PhD,11 Gianfranco Buja MD,12 Francesco Tona MD,13 Ileana Baldi PhD,8 Sabino Iliceto MD,14 Dario Gregori MA PhD9 and the GISC Study Group* 1 Adjunct Professor, 2Medical Director, 3Head of Cardiology Unit, 4Head of Department of Epidemiology and Statistics, 5Head of Cardiology Ambulatory, AUSL/Lecce, Lecce, Italy 6 Research Associate, 7Post-doc Fellow, 8Assistant Professor of Biostatistics, 9Associate Professor of Biostatistics, Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiology, Thoracic and Vascular Sciences, University of Padova, Padova, Italy 10 Engineer, MediaSoft Ltd, Galatina, Italy 11 Cardiologist, 12Senior Teacher of Cardiology, 13Assistant Professor of Cardiology, 14Full Professor of Cardiology, Cardiology Unit, Department of Cardiology, Thoracic and Vascular Sciences, University of Padova, Padova, Italy
Keywords evaluation, health services research, public health Correspondence Associate Professor Dario Gregori Unit of Biostatistics Epidemiology and Public Health Department of Cardiology, Thoracic and Vascular Sciences University of Padova Via Loredan 18 35121 Padova (PD) Italy E-mail: [email protected] Accepted for publication: 18 August 2014 doi:10.1111/jep.12255
Abstract Rationale, aims and objectives Heart failure (HF) is a concerning public health burden in Western society because, despite the improvement of medical treatments, it is still associated with adverse outcomes (high morbidity and mortality), resulting in one of the most expensive chronic disease in Western countries. Hospital admission particularly is the most expensive cost driver among the several resources involved in the management of HF. The aim of our study was to investigate the cost of hospitalization before and after the enrolment to a new strategy (GISC) in the management of patients with HF. Methods We enrolled a cohort of 90 patients. Patients were eligible to the study if they were hospitalized with a new diagnosis of HF or a diagnosis of decompensated HF. The enrolment to the study corresponded to the enrolment to the GISC intervention. We calculated the cost for every hospital admission at 6 and 12 months before and after the enrolment using the tariff paid for the diagnosis-related group. Results Comparing per-patient cumulative cost before and after the enrolment, we showed that patient’s hospitalization was less expensive after the enrolment to the GISC intervention. The strategy resulted in an average cumulative estimated saving of €439 322.00 (95% CI €413 890.70; €464 753.40) at 6 months and of €832 276.80 (95% CI €786 863.70; €877 690.00) at 12 months after the enrolment. Conclusions We found out that the intervention was a cost-saving strategy for follow-up of the patients suffering from HF at 6 and 12 months after the enrolment compared with hospitalizations’ cost before the recruitment.
* The GISC (Gestione Integrata Scompenso Cardiaco) Study group All members are General Practitioners of SIMG (Società Italiana di Medicina Generale) of Health Care Districts in Gagliano (director Giuseppe Guida) and Poggiardo (director Virna Rizzelli) (Lecce, Italy). Accogli Addolorata, Alba Mauro, Baglivo Carmine, Barbieri Giovanna, Bello Cosimo, Bisanti Antonio, Bitonti Leopoldo, Buffelli Fernando, Calzolaro Mariano, Carluccio Gaetano, Cazzato Daniela, Cazzato Maria Elisa, Colopi Giovanni, Corlianò Renato, Cremis Maurizio, De Francesco Luigi, Del Giudice Greca Antonio, Ecclesia Luigi, Fachechi Carlo, Ferraro Oliviero, Fersini Luigi, Gabellone Michele, Girasole Anna Maria, Giuri Sebastiano, Giuseppe Quaranta, Leomanni Renato, Lezzi Nicola, Luzio Domenico, Maisto Antonio, Malvindi Cosimo, Mariano Raffaele, Martella Franco, Martella Luce, Mastria Rocco, Mele Francesco, Mengoli Pietro, Milo Giovanni, Minonne Vittorio, Monaco Giuseppe, Morciano Luigi, Muscatello Antonio, Muscatello Filomena, Nicolardi Maria Elena, Paiano Gabriella, Pede Salvatore, Pedone Anchora Alberto, Petracca Adalgisa, Petracca Francesco, Renna Francesco, Retucci Luciano, Riso Vincenzo Rocco, Rizzo Luigi, Santo Domenico, Santoro Antonio Luigi, Sirignano Giovanna, Tosi Filiberto, Vadruccio Marina, Vincenti Cesarea, Zappatore Luigi Antonio, Zocco Maria Lucia.
Journal of Evaluation in Clinical Practice 21 (2015) 103–108 © 2014 John Wiley & Sons, Ltd.
103
Networking impact management of heart failure
Introduction Heart failure (HF) is a concerning public health burden in Western society [1]. The prevalence of the disease is increasing because of the increasing of population age; the Rotterdam study shows that HF prevalence rise with age: 0.9% (55–64 years of age), 4.0% (65–74 years of age), 9.7% (75–84 years of age), 17.4% (85 years of age and over), compared with the prevalence of HF determined in the previous study’s year (6.4% in the entire cohort) [2]. Despite the improvement of medical treatments, HF is still associated with adverse outcomes (high morbidity and mortality), resulting in one of the most expensive chronic disease in Western countries. Hospital admission, particularly, is the most expensive cost driver among the several resources involved in the clinical pathway (it includes about two-thirds of HF expenditure) [3]. In the EuroHeart Failure survey, 2046 (24.2%) of 8463 patients suffering from HF were re-hospitalized within 12 weeks [4]. Several preventable factors seem to be associated to the risk of hospital re-admission: non-compliance with medications and diet indications, delays in providing medical attention and poor social support [5]. Thus, there is a need for new strategies in the management of patients with HF. In the last years, several studies [6–8] have investigated the relationship between these strategies and patients outcome. Telemonitoring alone and low-cost nurse-led education intervention integrated with follow-up visits seem not to improve patients’ outcomes [9,10], while strategies provided from multidisciplinary team of health care providers appear to be more effective in reducing re hospitalization and mortality rates [11]. These multidisciplinary programs seem also to be cost-saving [12]. However, given the different features of these interventions, it’s difficult to provide useful evidence and further studies are needed in order to demonstrate what type of intervention is better in improving patients’ outcome and reducing costs. A retrospective study [13] shows that also in Italy HF is the leading cause of hospitalization: re-hospitalization rate was70% for one re-admission and 30% for two or more re-admissions in the time period considered from the study. Regarding costs, HF hospitalizations represent about 2% of the health care expenditure. In order to improve clinical outcomes and cost of assistance, several Italian regions provide different strategies that share involvement in follow-up by hospital and community care providers in the management of patients suffering from HF. The aim of our study was to investigate the costs related to hospitalization before and after the introduction of a new strategy (GISC) in management of patients with HF in Puglia region (Italy). The strategy has been devised by composing a specific medical protocol and a custom expert software system that implements data warehouse techniques.
Materials and methods Patient population Patients were eligible to the study if they were hospitalized between June 2011 and September 2012 with a new diagnosis of HF or a diagnosis of decompensated HF. The enrolment to the study corresponded to the enrolment to the GISC intervention, patients were recruited from cardiology clinic of health care districts in Gagliano and Poggiardo (Lecce, Italy) Data collection at 104
F. Pisanò et al.
baseline regarded demographical characteristics, aetiology of HF and its co-morbidities, (New York Heart Association) NYHA class, results of laboratory tests and data reporting heart function (e.g. ejection fraction). Patients have been followed for 1 year after the enrolment: at 1, 3, 6 and 12 months. At each follow-up, laboratory tests and electrocardiogram were performed. At 6 and 12 months also an echocardiogram was performed.
GISC intervention The GISC strategy represents an integrated approach that links hospital and community care in the management of patients suffering from HF. It has been developed on the basis of Chronic Care Model (CCM), which aims to improve ambulatory care pathway in the management of patients suffering from chronic diseases. It has been demonstrated that this approach is useful in improving clinical outcomes of patients with chronic illness, thanks to key elements of the project structure: a multidisciplinary team care, information and decision sharing between health care professionals, sharing of adequate information tools [14]. Consistently with CCM, the GISC intervention aims to provide continuity of care in patients with HF after hospital’s discharge. In order to do that, a network between general practitioners and cardiologists work in hospitals and in ambulatories is created thanks to electronic health record. The electronic health record collects general and clinical information of every patient and general practitioners and cardiologists update it at each visit. The expert system, which represents the core of the GISC software platform, provides two different statistic subsystems: one focused on each specific patient and a second one more general that takes data from all the observation of more than 7000 cases of the overall system. Such system implements also multichannel data delivery by providing users both tablet and web interfaces, thus helping doctors in their everyday work thought the pre-emptive analysis it is able to perform. The intervention includes also a group of volunteers who deliver home care to patients with III or IV NYHA class. In the study, cardiologists enrolled patients eligible for the intervention and cardiologists worked in ambulatories provided follow-up visits. General practitioners prescribed laboratory tests and reported to cardiologists every worsening of HF’s symptoms in order to provide a promptly intervention and avoid patients’ re-admission.
Cost analysis The aim of our study was to compare hospitalizations’ costs before and after the enrolment to the GISC intervention. We collected information for every patient about hospital admissions performed in the year before and in the year after the enrolment to the GISC intervention. The cost of every hospital admission was calculated on the basis of the tariff paid for the diagnosis-related group (DRG) associated to hospitalization (a list of most frequent DRG before and after the enrolment is provided in Table 1). We calculated cumulative cost for hospital admissions before and after the enrolment and then we compared them at 6 and 12 months before and after the enrolment.
Statistical analysis Basic descriptive statistics have been produced using median (I–III quartile) for continuous variables and percentage (absolute
© 2014 John Wiley & Sons, Ltd.
F. Pisanò et al.
Networking impact management of heart failure
Table 1 Most frequent DRG before and after the enrolment to the GISC intervention Before the enrolment DRG Heart failure and shock Pulmonary edema and respiratory failure Circulatory disorders except AMI, with cardiac catheterization and complex diagnosis Renal failure
Patients enrolled (n = 90) 41.98 (68) 4.94 (8) 4.32 (7) 4.32 (7)
After the enrolment DRG Heart failure and shock Renal failure Pulmonary oedema and respiratory failure Cardiac defibrillator implant without cardiac catheterization
Patients enrolled (n = 90) 20.00 (14) 8.57 (6) 7.14 (5) 7.14 (5)
Data are % (absolute number). AMI, acute myocardial infarction; DRG, diagnosis-related group.
number) for categorical variables. Multivariable analyses were made considering, respectively, the cumulative hospitalization costs of the subjects in the 6 and 12 months before and after the subject enrolment. Variables were entered in the model on the basis of clinical judgment and no formal selection of variables has been performed. The multivariable analyses were performed by generalized linear models using a gamma distribution and log link [15]. In order to consider the correlation in the data because of the cumulative costs for the same subject, the models were estimated by generalized estimated equations assuming for the data an autoregressive (AR1) dependence structure. A sensitivity analysis has been performed to evaluate the impact of the assumed correlation structure on effect estimates. Goodness of fit has been evaluated using the QIC statistics [16]. Bootstrap methods were used in order to estimate the median cumulative hospitalization cost of the subjects at 6 and 12 months before and after the enrolment. Bootstrap method were also used to estimate the average saving deriving from GISC programme adoption. All the analyses were performed using the rms [17] and geepack packages [18] of the statistical software R [17].
Results Ninety patients were eligible to the study. Six patients died after the enrolment, four of them died 6 months after the recruitment. Demographical and clinical characteristics at enrolment are summarized in Table 2. Table 3 reports hospitalizations’ median cumulative cost per patients before the enrolment (at 6 and 12 months), stratifying respect demographical characteristics, NYHA class, co-morbidities and aetiology. Cumulative cost for female sex, age under 80 years, III and IV NYHA classes where higher than for the other conditions. Women presented major co-morbidities than men: more women suffered from hypertension, dyslipidemia, diabetes and chronic kidney disease, so the cost was higher for female sex. Regarding
© 2014 John Wiley & Sons, Ltd.
NYHA classes, probably the cost was higher than the other class because patients experienced worsen symptoms, thus the need for more medical interventions. Cost for diabetes and ischaemic cardiomyopathy was the highest among the co-morbidities and the aetiology, respectively. Diabetic patients suffered more often than the others from chronic kidney disease (a consequence of diabetes) and patients who developed HF because of ischaemic cardiomyopathy were more often implanted with pacemakers and implantable cardioverter defibrillators (ICDs): device’s implantation, particularly of ICDs, represented one of the most expensive tariffs paid for DRG in our patient population. Impact of GISC intervention has been presented in Table 4, which shows both the estimated median cumulative cost of the per-subject hospitalizations at 6 and 12 months before and after the enrolment and the estimate overall saving for the entire cohort coming out from the GISC within the first 6 and the 12 months after study enrolment (it reports estimates and their corresponding 95% confidence interval). Patient’s clinical and demographic characteristics impacting costs have been presented in Table 5, where also the estimated coefficients, the standard errors and the P-values of the variable in the multivariable analyses are shown. The only statistically significant variable was the ejection fraction (P-value 0.040 at 6 months and 0.032 at 12 months), it resulted in an inverse relationship with cost: patients with better ejection fraction had less expensive hospitalizations. The estimated trajectories of the global cumulative hospitalization costs considering with and without the introduction of the GISC programme are presented in Fig. 1, showing a statistically significant and highly relevant saving obtained by GISC introduction, most likely attributable to a decrease in the number of hospitalizations for both ‘Heart failure and shock’ and ‘Pulmonary oedema and respiratory failure’ after the enrolment to the GISC strategy (Table 1).
Table 2 Patient population characteristics Patients enrolled (n = 90) Male, % (n) Age (years) NYHA class, % (n) II III IV Co-morbidities, % (n) Diabetes COPD Hypertension Chronic kidney disease Dyslipidemia Aetiology, % (n) Dilated cardiomyopathy Ischaemic cardiomyopathy Valvular disease Myocardial infarction Smokers, % (n)
49% (44) 81.00 [77.00;86.00] 10% (8) 69% (58) 21% (18) 38% (34) 49% (44) 51% (46) 34% (29) 33% (30) 12% (11) 24% (22) 23% (21) 12% (11) 3% (3)
Data are median [I; III quartile] for continuous variables and % (absolute numbers) for categorical variables
105
Networking impact management of heart failure
F. Pisanò et al.
Table 3 Cumulative costs (€) per patient at 6 and 12 months before the enrolment
Sex Male Female Age (years) 80 NYHA class II III IV Co-morbidities Diabetes Presence Absence Chronic kidney disease Presence Absence Dyslipidemia Presence Absence COPD Presence Absence Hypertension Presence Absence Aetiology Myocardial infarction Presence Absence Ischaemic cardiomyopathy Presence Absence Valvular disease Presence Absence Dilated cardiomyopathy Presence Absence Smoking Smokers No smokers
6 months
12 months
3507.300 [3261.540; 6794.698] 6523.080 [3299.410; 7871.285]
5155.900 [3261.540; 9707.335] 6854.530 [4183.450; 10706.530]
4611.160 [3261.540; 6994.160] 4187.260 [3261.540; 9455.535]
6598.820 [3261.540; 9630.050] 6533.165 [3356.602; 10926.605]
5074.025 [3164.548; 7014.283] 4187.260 [3261.540; 6976.710] 6523.080 [3261.540; 9615.860]
6946.445 [4032.920; 7608.890] 6543.250 [3261.540; 10072.310] 6523.080 [3261.540; 9784.620]
6523.080 [3261.540; 10875.925] 4002.015 [3261.540; 6893.283]
8787.490 [3787.775;14057.350] 6259.875 [3261.540; 8895.110]
3507.300 [3106.970; 7098.890] 6523.080 [3261.540; 7608.890]
6259.875 [3261.540;10448.083] 6630.970 [3322.980; 9745.977]
3422.290 [3222.897; 6523.080] 6523.080 [3261.540; 9449.925]
4187.26 [3261.54; 9540.83] 6704.80 [3585.62; 11359.13]
4183.450 [3261.540; 7082.120] 6523.080 [3261.540; 9784.620]
6164.630 [3261.540; 9373.260] 6778.625 [3477.548; 13672.060]
4183.450 [3261.540; 6906.200] 6440.660 [3261.540; 9784.620]
5155.900 [3261.540; 7090.505] 7799.130 [3585.620; 12938.667]
4187.260 [3322.980; 6045.100] 6397.890 [3261.540; 8826.075]
4611.160 [2816.680; 8904.615] 6630.970 [3261.540; 10000.388]
9373.260 [5112.160; 11679.540] 3507.300 [3261.540; 6778.625]
9707.335 [6989.948; 15821.347] 6164.630 [3261.540; 7784.480]
4611.160 [3261.540; 8222.005] 4187.260 [3261.540; 7441.685]
6906.200 [5387.895; 12985.030] 6439.100 [3261.540; 10000.388]
3663.940 [3261.540; 4186.307] 6481.870 [3261.540; 8826.075]
4183.450 [3663.940; 5949.615] 6668.840 [3261.540; 10709.388]
6746.480 [5004.010; 17359.140] 4187.260 [3261.540; 7441.685]
6746.480 [5004.010; 17359.140] 6533.165 [3261.540; 10000.388]
Data are median [I; III quartile].
Per-subject hospitalization cost
Before the enrolment After the enrolment
6 months 4990.397 (4914.905; 5065.889) 1088.077 (1005.841; 1170.314)
12 months 6459.965 (6427.808; 6492.123) 1504.535 (1425.020; 1584.051)
Overall saving
Average saving
106
6 months 439 322.00 (413 890.70;464 753.40)
Table 4 Bootstrap estimates (95% CI) for the median cumulative per-subject hospitalization cost (€) at 6 and 12 months before and after the enrolment and for the overall average saving (€) at 6 and 12 months after the enrolment
12 months 832 276.80 (786 863.70; 877 690.00)
© 2014 John Wiley & Sons, Ltd.
F. Pisanò et al.
Table 5 Estimates, standard error (SE) and P-value of the multivariable model (AR-1 correlation structure) for costs (€) at 6 and 12 months before and after the enrolment
Networking impact management of heart failure
6 months
Male sex Age Diabetes Chronic kidney disease Dyslipidemia COPD Hypertension Myocardial infarction Ejection fraction
12 months
Estimate
SE
P-value
Estimate
SE
P-value
−0.79598 −0.01762 −0.45251 0.38062 −0.07273 −0.17174 0.29061 0.22425 −0.02292
0.45686 0.02005 0.54037 0.45224 0.38488 0.37025 0.37193 0.28333 0.01117
0.081 0.379 0.402 0.400 0.850 0.643 0.435 0.429 0.040
−0.136871 −0.002500 0.038360 0.082607 −0.008759 −0.240210 −0.170344 −0.098754 −0.015217
0.128324 0.009347 0.128125 0.142864 0.142941 0.136619 0.131745 0.176379 0.007102
0.286 0.789 0.765 0.563 0.951 0.079 0.196 0.576 0.032
QIC of the AR-1 model 4944, QIC for exchangeable correlation model 4879.
Figure 1 Estimated trajectories of the cumulative hospitalization costs at 6 months and 12 months before and after the enrolment. The greens are the estimates with GISC programme, the reds are assuming no GISC programme. The time zero is the enrolment of the subject. See Table 1 for a detailed presentation of savings.
Discussion Our patient population was octogenarians. Studies on elderly patients suffering from HF showed that the management of these subjects did not meet international guidelines, with consequently multiple co-morbidities, high mortality rate, low use of HF drugs (ACE-inhibitors and beta-blockers) [19]. Therefore, management of very elderly patients resulted to be more difficult than the younger patients. However, we shown that the intervention, thanks to an integrated approach that linked hospital and community care in the management of patients suffering from HF, was not only a cost-saving strategy but it also improved patients’ clinical outcomes, even if the patient population was octogenarians. In fact, comparing DRG before and after the intervention, we showed a reduction of hospitalizations due to ‘Heart failure and shock’ and ‘Pulmonary oedema and respiratory failure’, that is because an
© 2014 John Wiley & Sons, Ltd.
integrated approach in the management of HF provide a prompt reaction when symptoms worsen, reducing hospital re-admission and related costs also in the elderly. The intervention reflected the most recent evidence in managing patients discharged from the hospital and suffering from chronic diseases (especially regarding the key features of CCM): effective interventions are characterized by health care electronic tools that allow the access to clinical information and the clinical data and decision sharing between health care professionals working in the hospital and in the community [20], as suggested from the CCM [14]. Another Italian study [21] evaluated a similar intervention in the management of patients suffering from HF: the strategy consisted on a web-based clinical report form, and patients were managed from both specialists and nurses, the monitoring was coordinated by general practitioners. Their data demonstrated that an integrated approach between hospital and community care 107
Networking impact management of heart failure
providers resulted in a reduction of hospital admissions, even if, differently from our study, they did not compute hospitalizations’ cost before and after the enrolment to the programme. Regarding the international experience [22–24], several studies investigated the relationship between cost of care and patients’ clinical outcome and different types of interventions in the management of patients with HF. We could find similar characteristics in the design of these different types of strategies, which consist of nurse-led telephone-based monitoring, educational programme and scheduled follow-up. Despite the fact that these strategies appear to improve key patient outcomes such as hospitalization or mortality, these interventions show a lack of interaction between hospital and primary care, which is demonstrated to be useful in the management of patients after hospital’s discharge [20], especially in the context of chronic diseases. Conversely, GISC strategy, consistent with the principles of the CCM, proposes an expert system based on statistical and data warehouse analysis that allows a continuous and shared data observation, thus filling the gap previously mentioned by the active involvement of all the stakeholders. The sharing of clinical data between general practitioners and cardiologists working in the hospital and in the ambulatories, thanks to the software platform, allows an early detection and a consequently prompt reaction (in terms of providing appropriate medical intervention when clinical parameters change) to the worsening of HF’s symptoms, resulting in a reduction of hospitalizations’ cost, also among elderly patients.
References 1. Zannad, F., Agrinier, N. & Alla, F. (2009) Heart failure burden and therapy. Europace: European Pacing, Arrhythmias, and Cardiac Electrophysiology: Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology, 11 (Suppl. 5), v1–v9. 2. Bleumink, G. S., Knetsch, A. M., Sturkenboom, M. C., Straus, S. M., Hofman, A., Deckers, J. W., Witteman, J. C. & Stricker, B. H. (2004) Quantifying the heart failure epidemic: prevalence, incidence rate, lifetime risk and prognosis of heart failure The Rotterdam Study. European Heart Journal, 25 (18), 1614–1619. 3. Liao, L., Allen, L. A. & Whellan, D. J. (2008) Economic burden of heart failure in the elderly. Pharmacoeconomics, 26 (6), 447–462. 4. Cleland, J. G., Swedberg, K., Follath, F., et al. (2003) The EuroHeart Failure survey programme – a survey on the quality of care among patients with heart failure in Europe. Part 1: patient characteristics and diagnosis. European Heart Journal, 24 (5), 442–463. 5. Annema, C., Luttik, M. L. & Jaarsma, T. (2009) Reasons for readmission in heart failure: perspectives of patients, caregivers, cardiologists, and heart failure nurses. Heart & Lung : The Journal of Critical Care, 38 (5), 427–434. 6. Krumholz, H. M., Amatruda, J., Smith, G. L., Mattera, J. A., Roumanis, S. A., Radford, M. J., Crombie, P. & Vaccarino, V. (2002) Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. Journal of the American College of Cardiology, 39 (1), 83–89. 7. Kasper, E. K., Gerstenblith, G., Hefter, G., Van Anden, E., Brinker, J. A., Thiemann, D. R., Terrin, M., Forman, S. & Gottlieb, S. H. (2002) A randomized trial of the efficacy of multidisciplinary care in heart failure outpatients at high risk of hospital readmission. Journal of the American College of Cardiology, 39 (3), 471–480.
108
F. Pisanò et al.
8. Chaudhry, S. I., Barton, B., Mattera, J., Spertus, J. & Krumholz, H. M. (2007) Randomized trial of telemonitoring to improve heart failure outcomes (Tele-HF): study design. Journal of Cardiac Failure, 13 (9), 709–714. 9. Nucifora, G., Albanese, M. C., De Biaggio, P., et al. (2006) Lack of improvement of clinical outcomes by a low-cost, hospital-based heart failure management programme. Journal of Cardiovascular Medicine, 7 (8), 614–622. 10. Desai, A. S. (2012) Home monitoring heart failure care does not improve patient outcomes: looking beyond telephone-based disease management. Circulation, 125 (6), 828–836. 11. McAlister, F. A., Stewart, S., Ferrua, S. & McMurray, J. J. (2004) Multidisciplinary strategies for the management of heart failure patients at high risk for admission: a systematic review of randomized trials. Journal of the American College of Cardiology, 44 (4), 810– 819. 12. McAlister, F. A., Lawson, F. M., Teo, K. K. & Armstrong, P. W. (2001) A systematic review of randomized trials of disease management programs in heart failure. The American Journal of Medicine, 110 (5), 378–384. 13. Gigli, G., Lispi, L., Donati, C., Orlandi, S., Vallebona, A., Gigli, L. & Reggiardo, G. (2009) Trends in hospitalization for heart failure in Italy 2001–2003. Journal of Cardiovascular Medicine, 10 (5), 367–371. 14. Coleman, K., Austin, B. T., Brach, C. & Wagner, E. H. (2009) Evidence on the Chronic Care Model in the new millennium. Health Affairs, 28 (1), 75–85. 15. Gregori, D., Petrinco, M., Bo, S., Desideri, A., Merletti, F. & Pagano, E. (2011) Regression models for analyzing costs and their determinants in health care: an introductory review. International Journal for Quality in Health Care: Journal of the International Society for Quality in Health Care/ISQua, 23 (3), 331–341. 16. Pan, W. (2001) Akaike’s information criterion in generalized estimating equations. Biometrics, 57 (1), 120–125. 17. R. C. Team (2013) R: A Language and Environment for Statistical Computing [Internet]. Vienna: R Foundation for Statistical Computing. 18. Halekoh, U., Højsgaard, S. & Yan, J. (2006) The R package geepack for generalized estimating equations. Journal of Statistical Software, 15 (2), 1–11. 19. Komajda, M., Hanon, O., Hochadel, M., Follath, F., Swedberg, K., Gitt, A. & Cleland, J. G. (2007) Management of octogenarians hospitalized for heart failure in Euro Heart Failure Survey I. European Heart Journal, 28 (11), 1310–1318. 20. Hesselink, G., Schoonhoven, L., Barach, P., Spijker, A., Gademan, P., Kalkman, C., Liefers, J., Vernooij-Dassen, M. & Wollersheim, H. (2012) Improving patient handovers from hospital to primary care: a systematic review. Annals of Internal Medicine, 157 (6), 417–428. 21. Mazzuoli, F., Castelli, G., Nozzoli, C., et al. (2012) [Continuity of care in heart failure: pilot study of the Tuscany region]. Giornale Italiano Di Cardiologia, 13 (9), 615–621. 22. Riegel, B., Carlson, B., Kopp, Z., LePetri, B., Glaser, D. & Unger, A. (2002) Effect of a standardized nurse case-management telephone intervention on resource use in patients with chronic heart failure. Archives of Internal Medicine, 162 (6), 705–712. 23. Ledwidge, M., Barry, M., Cahill, J., Ryan, E., Maurer, B., Ryder, M., Travers, B., Timmons, L. & McDonald, K. (2003) Is multidisciplinary care of heart failure cost-beneficial when combined with optimal medical care? European Journal of Heart Failure, 5 (3), 381–389. 24. Kimmelstiel, C., Levine, D., Perry, K., et al. (2004) Randomized, controlled evaluation of short- and long-term benefits of heart failure disease management within a diverse provider network: the SPANCHF trial. Circulation, 110 (11), 1450–1455.
© 2014 John Wiley & Sons, Ltd.
Copyright of Journal of Evaluation in Clinical Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Journal of Evaluation in Clinical Practice ISSN 1365-2753
Networking and data sharing reduces hospitalization cost of heart failure: the experience of GISC study Franco Pisanò MD,1 Giulia Lorenzoni RN MA,6 Stefano S. Sabato Eng PhD,10 Nicola Soriani PhD,7 Ottavio Narraci MD,2 Michele Accogli MD,3 Carlo Rosato Eng,4 Paola de Paolis MD,5 Franco Folino MD PhD,11 Gianfranco Buja MD,12 Francesco Tona MD,13 Ileana Baldi PhD,8 Sabino Iliceto MD,14 Dario Gregori MA PhD9 and the GISC Study Group* 1 Adjunct Professor, 2Medical Director, 3Head of Cardiology Unit, 4Head of Department of Epidemiology and Statistics, 5Head of Cardiology Ambulatory, AUSL/Lecce, Lecce, Italy 6 Research Associate, 7Post-doc Fellow, 8Assistant Professor of Biostatistics, 9Associate Professor of Biostatistics, Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiology, Thoracic and Vascular Sciences, University of Padova, Padova, Italy 10 Engineer, MediaSoft Ltd, Galatina, Italy 11 Cardiologist, 12Senior Teacher of Cardiology, 13Assistant Professor of Cardiology, 14Full Professor of Cardiology, Cardiology Unit, Department of Cardiology, Thoracic and Vascular Sciences, University of Padova, Padova, Italy
Keywords evaluation, health services research, public health Correspondence Associate Professor Dario Gregori Unit of Biostatistics Epidemiology and Public Health Department of Cardiology, Thoracic and Vascular Sciences University of Padova Via Loredan 18 35121 Padova (PD) Italy E-mail: [email protected] Accepted for publication: 18 August 2014 doi:10.1111/jep.12255
Abstract Rationale, aims and objectives Heart failure (HF) is a concerning public health burden in Western society because, despite the improvement of medical treatments, it is still associated with adverse outcomes (high morbidity and mortality), resulting in one of the most expensive chronic disease in Western countries. Hospital admission particularly is the most expensive cost driver among the several resources involved in the management of HF. The aim of our study was to investigate the cost of hospitalization before and after the enrolment to a new strategy (GISC) in the management of patients with HF. Methods We enrolled a cohort of 90 patients. Patients were eligible to the study if they were hospitalized with a new diagnosis of HF or a diagnosis of decompensated HF. The enrolment to the study corresponded to the enrolment to the GISC intervention. We calculated the cost for every hospital admission at 6 and 12 months before and after the enrolment using the tariff paid for the diagnosis-related group. Results Comparing per-patient cumulative cost before and after the enrolment, we showed that patient’s hospitalization was less expensive after the enrolment to the GISC intervention. The strategy resulted in an average cumulative estimated saving of €439 322.00 (95% CI €413 890.70; €464 753.40) at 6 months and of €832 276.80 (95% CI €786 863.70; €877 690.00) at 12 months after the enrolment. Conclusions We found out that the intervention was a cost-saving strategy for follow-up of the patients suffering from HF at 6 and 12 months after the enrolment compared with hospitalizations’ cost before the recruitment.
* The GISC (Gestione Integrata Scompenso Cardiaco) Study group All members are General Practitioners of SIMG (Società Italiana di Medicina Generale) of Health Care Districts in Gagliano (director Giuseppe Guida) and Poggiardo (director Virna Rizzelli) (Lecce, Italy). Accogli Addolorata, Alba Mauro, Baglivo Carmine, Barbieri Giovanna, Bello Cosimo, Bisanti Antonio, Bitonti Leopoldo, Buffelli Fernando, Calzolaro Mariano, Carluccio Gaetano, Cazzato Daniela, Cazzato Maria Elisa, Colopi Giovanni, Corlianò Renato, Cremis Maurizio, De Francesco Luigi, Del Giudice Greca Antonio, Ecclesia Luigi, Fachechi Carlo, Ferraro Oliviero, Fersini Luigi, Gabellone Michele, Girasole Anna Maria, Giuri Sebastiano, Giuseppe Quaranta, Leomanni Renato, Lezzi Nicola, Luzio Domenico, Maisto Antonio, Malvindi Cosimo, Mariano Raffaele, Martella Franco, Martella Luce, Mastria Rocco, Mele Francesco, Mengoli Pietro, Milo Giovanni, Minonne Vittorio, Monaco Giuseppe, Morciano Luigi, Muscatello Antonio, Muscatello Filomena, Nicolardi Maria Elena, Paiano Gabriella, Pede Salvatore, Pedone Anchora Alberto, Petracca Adalgisa, Petracca Francesco, Renna Francesco, Retucci Luciano, Riso Vincenzo Rocco, Rizzo Luigi, Santo Domenico, Santoro Antonio Luigi, Sirignano Giovanna, Tosi Filiberto, Vadruccio Marina, Vincenti Cesarea, Zappatore Luigi Antonio, Zocco Maria Lucia.
Journal of Evaluation in Clinical Practice 21 (2015) 103–108 © 2014 John Wiley & Sons, Ltd.
103
Networking impact management of heart failure
Introduction Heart failure (HF) is a concerning public health burden in Western society [1]. The prevalence of the disease is increasing because of the increasing of population age; the Rotterdam study shows that HF prevalence rise with age: 0.9% (55–64 years of age), 4.0% (65–74 years of age), 9.7% (75–84 years of age), 17.4% (85 years of age and over), compared with the prevalence of HF determined in the previous study’s year (6.4% in the entire cohort) [2]. Despite the improvement of medical treatments, HF is still associated with adverse outcomes (high morbidity and mortality), resulting in one of the most expensive chronic disease in Western countries. Hospital admission, particularly, is the most expensive cost driver among the several resources involved in the clinical pathway (it includes about two-thirds of HF expenditure) [3]. In the EuroHeart Failure survey, 2046 (24.2%) of 8463 patients suffering from HF were re-hospitalized within 12 weeks [4]. Several preventable factors seem to be associated to the risk of hospital re-admission: non-compliance with medications and diet indications, delays in providing medical attention and poor social support [5]. Thus, there is a need for new strategies in the management of patients with HF. In the last years, several studies [6–8] have investigated the relationship between these strategies and patients outcome. Telemonitoring alone and low-cost nurse-led education intervention integrated with follow-up visits seem not to improve patients’ outcomes [9,10], while strategies provided from multidisciplinary team of health care providers appear to be more effective in reducing re hospitalization and mortality rates [11]. These multidisciplinary programs seem also to be cost-saving [12]. However, given the different features of these interventions, it’s difficult to provide useful evidence and further studies are needed in order to demonstrate what type of intervention is better in improving patients’ outcome and reducing costs. A retrospective study [13] shows that also in Italy HF is the leading cause of hospitalization: re-hospitalization rate was70% for one re-admission and 30% for two or more re-admissions in the time period considered from the study. Regarding costs, HF hospitalizations represent about 2% of the health care expenditure. In order to improve clinical outcomes and cost of assistance, several Italian regions provide different strategies that share involvement in follow-up by hospital and community care providers in the management of patients suffering from HF. The aim of our study was to investigate the costs related to hospitalization before and after the introduction of a new strategy (GISC) in management of patients with HF in Puglia region (Italy). The strategy has been devised by composing a specific medical protocol and a custom expert software system that implements data warehouse techniques.
Materials and methods Patient population Patients were eligible to the study if they were hospitalized between June 2011 and September 2012 with a new diagnosis of HF or a diagnosis of decompensated HF. The enrolment to the study corresponded to the enrolment to the GISC intervention, patients were recruited from cardiology clinic of health care districts in Gagliano and Poggiardo (Lecce, Italy) Data collection at 104
F. Pisanò et al.
baseline regarded demographical characteristics, aetiology of HF and its co-morbidities, (New York Heart Association) NYHA class, results of laboratory tests and data reporting heart function (e.g. ejection fraction). Patients have been followed for 1 year after the enrolment: at 1, 3, 6 and 12 months. At each follow-up, laboratory tests and electrocardiogram were performed. At 6 and 12 months also an echocardiogram was performed.
GISC intervention The GISC strategy represents an integrated approach that links hospital and community care in the management of patients suffering from HF. It has been developed on the basis of Chronic Care Model (CCM), which aims to improve ambulatory care pathway in the management of patients suffering from chronic diseases. It has been demonstrated that this approach is useful in improving clinical outcomes of patients with chronic illness, thanks to key elements of the project structure: a multidisciplinary team care, information and decision sharing between health care professionals, sharing of adequate information tools [14]. Consistently with CCM, the GISC intervention aims to provide continuity of care in patients with HF after hospital’s discharge. In order to do that, a network between general practitioners and cardiologists work in hospitals and in ambulatories is created thanks to electronic health record. The electronic health record collects general and clinical information of every patient and general practitioners and cardiologists update it at each visit. The expert system, which represents the core of the GISC software platform, provides two different statistic subsystems: one focused on each specific patient and a second one more general that takes data from all the observation of more than 7000 cases of the overall system. Such system implements also multichannel data delivery by providing users both tablet and web interfaces, thus helping doctors in their everyday work thought the pre-emptive analysis it is able to perform. The intervention includes also a group of volunteers who deliver home care to patients with III or IV NYHA class. In the study, cardiologists enrolled patients eligible for the intervention and cardiologists worked in ambulatories provided follow-up visits. General practitioners prescribed laboratory tests and reported to cardiologists every worsening of HF’s symptoms in order to provide a promptly intervention and avoid patients’ re-admission.
Cost analysis The aim of our study was to compare hospitalizations’ costs before and after the enrolment to the GISC intervention. We collected information for every patient about hospital admissions performed in the year before and in the year after the enrolment to the GISC intervention. The cost of every hospital admission was calculated on the basis of the tariff paid for the diagnosis-related group (DRG) associated to hospitalization (a list of most frequent DRG before and after the enrolment is provided in Table 1). We calculated cumulative cost for hospital admissions before and after the enrolment and then we compared them at 6 and 12 months before and after the enrolment.
Statistical analysis Basic descriptive statistics have been produced using median (I–III quartile) for continuous variables and percentage (absolute
© 2014 John Wiley & Sons, Ltd.
F. Pisanò et al.
Networking impact management of heart failure
Table 1 Most frequent DRG before and after the enrolment to the GISC intervention Before the enrolment DRG Heart failure and shock Pulmonary edema and respiratory failure Circulatory disorders except AMI, with cardiac catheterization and complex diagnosis Renal failure
Patients enrolled (n = 90) 41.98 (68) 4.94 (8) 4.32 (7) 4.32 (7)
After the enrolment DRG Heart failure and shock Renal failure Pulmonary oedema and respiratory failure Cardiac defibrillator implant without cardiac catheterization
Patients enrolled (n = 90) 20.00 (14) 8.57 (6) 7.14 (5) 7.14 (5)
Data are % (absolute number). AMI, acute myocardial infarction; DRG, diagnosis-related group.
number) for categorical variables. Multivariable analyses were made considering, respectively, the cumulative hospitalization costs of the subjects in the 6 and 12 months before and after the subject enrolment. Variables were entered in the model on the basis of clinical judgment and no formal selection of variables has been performed. The multivariable analyses were performed by generalized linear models using a gamma distribution and log link [15]. In order to consider the correlation in the data because of the cumulative costs for the same subject, the models were estimated by generalized estimated equations assuming for the data an autoregressive (AR1) dependence structure. A sensitivity analysis has been performed to evaluate the impact of the assumed correlation structure on effect estimates. Goodness of fit has been evaluated using the QIC statistics [16]. Bootstrap methods were used in order to estimate the median cumulative hospitalization cost of the subjects at 6 and 12 months before and after the enrolment. Bootstrap method were also used to estimate the average saving deriving from GISC programme adoption. All the analyses were performed using the rms [17] and geepack packages [18] of the statistical software R [17].
Results Ninety patients were eligible to the study. Six patients died after the enrolment, four of them died 6 months after the recruitment. Demographical and clinical characteristics at enrolment are summarized in Table 2. Table 3 reports hospitalizations’ median cumulative cost per patients before the enrolment (at 6 and 12 months), stratifying respect demographical characteristics, NYHA class, co-morbidities and aetiology. Cumulative cost for female sex, age under 80 years, III and IV NYHA classes where higher than for the other conditions. Women presented major co-morbidities than men: more women suffered from hypertension, dyslipidemia, diabetes and chronic kidney disease, so the cost was higher for female sex. Regarding
© 2014 John Wiley & Sons, Ltd.
NYHA classes, probably the cost was higher than the other class because patients experienced worsen symptoms, thus the need for more medical interventions. Cost for diabetes and ischaemic cardiomyopathy was the highest among the co-morbidities and the aetiology, respectively. Diabetic patients suffered more often than the others from chronic kidney disease (a consequence of diabetes) and patients who developed HF because of ischaemic cardiomyopathy were more often implanted with pacemakers and implantable cardioverter defibrillators (ICDs): device’s implantation, particularly of ICDs, represented one of the most expensive tariffs paid for DRG in our patient population. Impact of GISC intervention has been presented in Table 4, which shows both the estimated median cumulative cost of the per-subject hospitalizations at 6 and 12 months before and after the enrolment and the estimate overall saving for the entire cohort coming out from the GISC within the first 6 and the 12 months after study enrolment (it reports estimates and their corresponding 95% confidence interval). Patient’s clinical and demographic characteristics impacting costs have been presented in Table 5, where also the estimated coefficients, the standard errors and the P-values of the variable in the multivariable analyses are shown. The only statistically significant variable was the ejection fraction (P-value 0.040 at 6 months and 0.032 at 12 months), it resulted in an inverse relationship with cost: patients with better ejection fraction had less expensive hospitalizations. The estimated trajectories of the global cumulative hospitalization costs considering with and without the introduction of the GISC programme are presented in Fig. 1, showing a statistically significant and highly relevant saving obtained by GISC introduction, most likely attributable to a decrease in the number of hospitalizations for both ‘Heart failure and shock’ and ‘Pulmonary oedema and respiratory failure’ after the enrolment to the GISC strategy (Table 1).
Table 2 Patient population characteristics Patients enrolled (n = 90) Male, % (n) Age (years) NYHA class, % (n) II III IV Co-morbidities, % (n) Diabetes COPD Hypertension Chronic kidney disease Dyslipidemia Aetiology, % (n) Dilated cardiomyopathy Ischaemic cardiomyopathy Valvular disease Myocardial infarction Smokers, % (n)
49% (44) 81.00 [77.00;86.00] 10% (8) 69% (58) 21% (18) 38% (34) 49% (44) 51% (46) 34% (29) 33% (30) 12% (11) 24% (22) 23% (21) 12% (11) 3% (3)
Data are median [I; III quartile] for continuous variables and % (absolute numbers) for categorical variables
105
Networking impact management of heart failure
F. Pisanò et al.
Table 3 Cumulative costs (€) per patient at 6 and 12 months before the enrolment
Sex Male Female Age (years) 80 NYHA class II III IV Co-morbidities Diabetes Presence Absence Chronic kidney disease Presence Absence Dyslipidemia Presence Absence COPD Presence Absence Hypertension Presence Absence Aetiology Myocardial infarction Presence Absence Ischaemic cardiomyopathy Presence Absence Valvular disease Presence Absence Dilated cardiomyopathy Presence Absence Smoking Smokers No smokers
6 months
12 months
3507.300 [3261.540; 6794.698] 6523.080 [3299.410; 7871.285]
5155.900 [3261.540; 9707.335] 6854.530 [4183.450; 10706.530]
4611.160 [3261.540; 6994.160] 4187.260 [3261.540; 9455.535]
6598.820 [3261.540; 9630.050] 6533.165 [3356.602; 10926.605]
5074.025 [3164.548; 7014.283] 4187.260 [3261.540; 6976.710] 6523.080 [3261.540; 9615.860]
6946.445 [4032.920; 7608.890] 6543.250 [3261.540; 10072.310] 6523.080 [3261.540; 9784.620]
6523.080 [3261.540; 10875.925] 4002.015 [3261.540; 6893.283]
8787.490 [3787.775;14057.350] 6259.875 [3261.540; 8895.110]
3507.300 [3106.970; 7098.890] 6523.080 [3261.540; 7608.890]
6259.875 [3261.540;10448.083] 6630.970 [3322.980; 9745.977]
3422.290 [3222.897; 6523.080] 6523.080 [3261.540; 9449.925]
4187.26 [3261.54; 9540.83] 6704.80 [3585.62; 11359.13]
4183.450 [3261.540; 7082.120] 6523.080 [3261.540; 9784.620]
6164.630 [3261.540; 9373.260] 6778.625 [3477.548; 13672.060]
4183.450 [3261.540; 6906.200] 6440.660 [3261.540; 9784.620]
5155.900 [3261.540; 7090.505] 7799.130 [3585.620; 12938.667]
4187.260 [3322.980; 6045.100] 6397.890 [3261.540; 8826.075]
4611.160 [2816.680; 8904.615] 6630.970 [3261.540; 10000.388]
9373.260 [5112.160; 11679.540] 3507.300 [3261.540; 6778.625]
9707.335 [6989.948; 15821.347] 6164.630 [3261.540; 7784.480]
4611.160 [3261.540; 8222.005] 4187.260 [3261.540; 7441.685]
6906.200 [5387.895; 12985.030] 6439.100 [3261.540; 10000.388]
3663.940 [3261.540; 4186.307] 6481.870 [3261.540; 8826.075]
4183.450 [3663.940; 5949.615] 6668.840 [3261.540; 10709.388]
6746.480 [5004.010; 17359.140] 4187.260 [3261.540; 7441.685]
6746.480 [5004.010; 17359.140] 6533.165 [3261.540; 10000.388]
Data are median [I; III quartile].
Per-subject hospitalization cost
Before the enrolment After the enrolment
6 months 4990.397 (4914.905; 5065.889) 1088.077 (1005.841; 1170.314)
12 months 6459.965 (6427.808; 6492.123) 1504.535 (1425.020; 1584.051)
Overall saving
Average saving
106
6 months 439 322.00 (413 890.70;464 753.40)
Table 4 Bootstrap estimates (95% CI) for the median cumulative per-subject hospitalization cost (€) at 6 and 12 months before and after the enrolment and for the overall average saving (€) at 6 and 12 months after the enrolment
12 months 832 276.80 (786 863.70; 877 690.00)
© 2014 John Wiley & Sons, Ltd.
F. Pisanò et al.
Table 5 Estimates, standard error (SE) and P-value of the multivariable model (AR-1 correlation structure) for costs (€) at 6 and 12 months before and after the enrolment
Networking impact management of heart failure
6 months
Male sex Age Diabetes Chronic kidney disease Dyslipidemia COPD Hypertension Myocardial infarction Ejection fraction
12 months
Estimate
SE
P-value
Estimate
SE
P-value
−0.79598 −0.01762 −0.45251 0.38062 −0.07273 −0.17174 0.29061 0.22425 −0.02292
0.45686 0.02005 0.54037 0.45224 0.38488 0.37025 0.37193 0.28333 0.01117
0.081 0.379 0.402 0.400 0.850 0.643 0.435 0.429 0.040
−0.136871 −0.002500 0.038360 0.082607 −0.008759 −0.240210 −0.170344 −0.098754 −0.015217
0.128324 0.009347 0.128125 0.142864 0.142941 0.136619 0.131745 0.176379 0.007102
0.286 0.789 0.765 0.563 0.951 0.079 0.196 0.576 0.032
QIC of the AR-1 model 4944, QIC for exchangeable correlation model 4879.
Figure 1 Estimated trajectories of the cumulative hospitalization costs at 6 months and 12 months before and after the enrolment. The greens are the estimates with GISC programme, the reds are assuming no GISC programme. The time zero is the enrolment of the subject. See Table 1 for a detailed presentation of savings.
Discussion Our patient population was octogenarians. Studies on elderly patients suffering from HF showed that the management of these subjects did not meet international guidelines, with consequently multiple co-morbidities, high mortality rate, low use of HF drugs (ACE-inhibitors and beta-blockers) [19]. Therefore, management of very elderly patients resulted to be more difficult than the younger patients. However, we shown that the intervention, thanks to an integrated approach that linked hospital and community care in the management of patients suffering from HF, was not only a cost-saving strategy but it also improved patients’ clinical outcomes, even if the patient population was octogenarians. In fact, comparing DRG before and after the intervention, we showed a reduction of hospitalizations due to ‘Heart failure and shock’ and ‘Pulmonary oedema and respiratory failure’, that is because an
© 2014 John Wiley & Sons, Ltd.
integrated approach in the management of HF provide a prompt reaction when symptoms worsen, reducing hospital re-admission and related costs also in the elderly. The intervention reflected the most recent evidence in managing patients discharged from the hospital and suffering from chronic diseases (especially regarding the key features of CCM): effective interventions are characterized by health care electronic tools that allow the access to clinical information and the clinical data and decision sharing between health care professionals working in the hospital and in the community [20], as suggested from the CCM [14]. Another Italian study [21] evaluated a similar intervention in the management of patients suffering from HF: the strategy consisted on a web-based clinical report form, and patients were managed from both specialists and nurses, the monitoring was coordinated by general practitioners. Their data demonstrated that an integrated approach between hospital and community care 107
Networking impact management of heart failure
providers resulted in a reduction of hospital admissions, even if, differently from our study, they did not compute hospitalizations’ cost before and after the enrolment to the programme. Regarding the international experience [22–24], several studies investigated the relationship between cost of care and patients’ clinical outcome and different types of interventions in the management of patients with HF. We could find similar characteristics in the design of these different types of strategies, which consist of nurse-led telephone-based monitoring, educational programme and scheduled follow-up. Despite the fact that these strategies appear to improve key patient outcomes such as hospitalization or mortality, these interventions show a lack of interaction between hospital and primary care, which is demonstrated to be useful in the management of patients after hospital’s discharge [20], especially in the context of chronic diseases. Conversely, GISC strategy, consistent with the principles of the CCM, proposes an expert system based on statistical and data warehouse analysis that allows a continuous and shared data observation, thus filling the gap previously mentioned by the active involvement of all the stakeholders. The sharing of clinical data between general practitioners and cardiologists working in the hospital and in the ambulatories, thanks to the software platform, allows an early detection and a consequently prompt reaction (in terms of providing appropriate medical intervention when clinical parameters change) to the worsening of HF’s symptoms, resulting in a reduction of hospitalizations’ cost, also among elderly patients.
References 1. Zannad, F., Agrinier, N. & Alla, F. (2009) Heart failure burden and therapy. Europace: European Pacing, Arrhythmias, and Cardiac Electrophysiology: Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology, 11 (Suppl. 5), v1–v9. 2. Bleumink, G. S., Knetsch, A. M., Sturkenboom, M. C., Straus, S. M., Hofman, A., Deckers, J. W., Witteman, J. C. & Stricker, B. H. (2004) Quantifying the heart failure epidemic: prevalence, incidence rate, lifetime risk and prognosis of heart failure The Rotterdam Study. European Heart Journal, 25 (18), 1614–1619. 3. Liao, L., Allen, L. A. & Whellan, D. J. (2008) Economic burden of heart failure in the elderly. Pharmacoeconomics, 26 (6), 447–462. 4. Cleland, J. G., Swedberg, K., Follath, F., et al. (2003) The EuroHeart Failure survey programme – a survey on the quality of care among patients with heart failure in Europe. Part 1: patient characteristics and diagnosis. European Heart Journal, 24 (5), 442–463. 5. Annema, C., Luttik, M. L. & Jaarsma, T. (2009) Reasons for readmission in heart failure: perspectives of patients, caregivers, cardiologists, and heart failure nurses. Heart & Lung : The Journal of Critical Care, 38 (5), 427–434. 6. Krumholz, H. M., Amatruda, J., Smith, G. L., Mattera, J. A., Roumanis, S. A., Radford, M. J., Crombie, P. & Vaccarino, V. (2002) Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. Journal of the American College of Cardiology, 39 (1), 83–89. 7. Kasper, E. K., Gerstenblith, G., Hefter, G., Van Anden, E., Brinker, J. A., Thiemann, D. R., Terrin, M., Forman, S. & Gottlieb, S. H. (2002) A randomized trial of the efficacy of multidisciplinary care in heart failure outpatients at high risk of hospital readmission. Journal of the American College of Cardiology, 39 (3), 471–480.
108
F. Pisanò et al.
8. Chaudhry, S. I., Barton, B., Mattera, J., Spertus, J. & Krumholz, H. M. (2007) Randomized trial of telemonitoring to improve heart failure outcomes (Tele-HF): study design. Journal of Cardiac Failure, 13 (9), 709–714. 9. Nucifora, G., Albanese, M. C., De Biaggio, P., et al. (2006) Lack of improvement of clinical outcomes by a low-cost, hospital-based heart failure management programme. Journal of Cardiovascular Medicine, 7 (8), 614–622. 10. Desai, A. S. (2012) Home monitoring heart failure care does not improve patient outcomes: looking beyond telephone-based disease management. Circulation, 125 (6), 828–836. 11. McAlister, F. A., Stewart, S., Ferrua, S. & McMurray, J. J. (2004) Multidisciplinary strategies for the management of heart failure patients at high risk for admission: a systematic review of randomized trials. Journal of the American College of Cardiology, 44 (4), 810– 819. 12. McAlister, F. A., Lawson, F. M., Teo, K. K. & Armstrong, P. W. (2001) A systematic review of randomized trials of disease management programs in heart failure. The American Journal of Medicine, 110 (5), 378–384. 13. Gigli, G., Lispi, L., Donati, C., Orlandi, S., Vallebona, A., Gigli, L. & Reggiardo, G. (2009) Trends in hospitalization for heart failure in Italy 2001–2003. Journal of Cardiovascular Medicine, 10 (5), 367–371. 14. Coleman, K., Austin, B. T., Brach, C. & Wagner, E. H. (2009) Evidence on the Chronic Care Model in the new millennium. Health Affairs, 28 (1), 75–85. 15. Gregori, D., Petrinco, M., Bo, S., Desideri, A., Merletti, F. & Pagano, E. (2011) Regression models for analyzing costs and their determinants in health care: an introductory review. International Journal for Quality in Health Care: Journal of the International Society for Quality in Health Care/ISQua, 23 (3), 331–341. 16. Pan, W. (2001) Akaike’s information criterion in generalized estimating equations. Biometrics, 57 (1), 120–125. 17. R. C. Team (2013) R: A Language and Environment for Statistical Computing [Internet]. Vienna: R Foundation for Statistical Computing. 18. Halekoh, U., Højsgaard, S. & Yan, J. (2006) The R package geepack for generalized estimating equations. Journal of Statistical Software, 15 (2), 1–11. 19. Komajda, M., Hanon, O., Hochadel, M., Follath, F., Swedberg, K., Gitt, A. & Cleland, J. G. (2007) Management of octogenarians hospitalized for heart failure in Euro Heart Failure Survey I. European Heart Journal, 28 (11), 1310–1318. 20. Hesselink, G., Schoonhoven, L., Barach, P., Spijker, A., Gademan, P., Kalkman, C., Liefers, J., Vernooij-Dassen, M. & Wollersheim, H. (2012) Improving patient handovers from hospital to primary care: a systematic review. Annals of Internal Medicine, 157 (6), 417–428. 21. Mazzuoli, F., Castelli, G., Nozzoli, C., et al. (2012) [Continuity of care in heart failure: pilot study of the Tuscany region]. Giornale Italiano Di Cardiologia, 13 (9), 615–621. 22. Riegel, B., Carlson, B., Kopp, Z., LePetri, B., Glaser, D. & Unger, A. (2002) Effect of a standardized nurse case-management telephone intervention on resource use in patients with chronic heart failure. Archives of Internal Medicine, 162 (6), 705–712. 23. Ledwidge, M., Barry, M., Cahill, J., Ryan, E., Maurer, B., Ryder, M., Travers, B., Timmons, L. & McDonald, K. (2003) Is multidisciplinary care of heart failure cost-beneficial when combined with optimal medical care? European Journal of Heart Failure, 5 (3), 381–389. 24. Kimmelstiel, C., Levine, D., Perry, K., et al. (2004) Randomized, controlled evaluation of short- and long-term benefits of heart failure disease management within a diverse provider network: the SPANCHF trial. Circulation, 110 (11), 1450–1455.
© 2014 John Wiley & Sons, Ltd.
Copyright of Journal of Evaluation in Clinical Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
