Author + information
- Received November 7, 2012
- Revision received January 4, 2013
- Accepted January 7, 2013
- Published online April 23, 2013.
- Alan Maisel, MD⁎,†,⁎ (, )
- Denise Barnard, MD⁎,†,
- Brian Jaski, MD‡,
- Geir Frivold, MD§,
- John Marais, MD∥,
- Maged Azer, MD¶,
- Michael I. Miyamoto, MD#,
- Dawn Lombardo, DO, MS⁎⁎,
- Damon Kelsay, MD††,
- Kelly Borden, MD⁎,†,
- Navaid Iqbal, MD⁎,
- Pam R. Taub, MD⁎,†,
- Ken Kupfer, PhD‡‡,
- Paul Clopton, MD⁎ and
- Barry Greenberg, MD†
- ↵⁎Reprint requests and correspondence:
Dr. Alan Maisel, 111-A, Veterans Affairs Medical Center, 3350 La Jolla Village Drive, San Diego, California 92161
Objectives This study was a multicenter, single-arm, double-blinded observational prospective clinical trial designed to monitor daily concentrations of B-type natriuretic peptide (BNP) and to determine how these concentrations correlate with acute clinical heart failure decompensation (ADHF) and related adverse clinical outcomes in at-risk HF patients.
Background Although BNP at discharge is predictive of 30-day outcomes, outpatient serial testing may improve the risk of detecting early decompensation.
Methods A total of 163 patients with HF signs and symptoms of ADHF discharged from the hospital or in an outpatient setting measured their weight and BNP levels daily for 60 days with a finger-stick test. Patients and physicians were blinded to BNP levels. The composite outcome was ADHF events: cardiovascular death, admission for decompensated HF, or clinical HF decompensation requiring either parenteral HF therapy or changes in oral HF medications.
Results A total of 6,934 daily BNP values were recorded, with a median of 46 measures per patient over a monitoring period of 65 days. Forty patients had 56 events. Correlations between BNP measures weakened over time, and the dispersion between BNP measures grew. During 10,035 patient-days, there were 494 (4.9%) days of weight gain (≥5 lbs). The hazard ratio per unit increase of ln BNP was 1.84, and the hazard ratio on a day of weight gain was 3.63. These effects retained significance when controlling for symptoms. When the monitoring period for each subject was broken into intervals based on ADHF events, there were 39 (18.4%) intervals of upward trending BNP corresponding to a risk increase of 59.8% and 64 (30.2%) downward trending intervals corresponding to a risk decrease of 39.0%. There were 94 (44.3%) intervals with 1 or more days of weight gain corresponding to a risk increase of 26.1%.
Conclusions This pilot study demonstrates that home BNP testing is feasible and that trials using home monitoring for guiding therapy are justifiable in high-risk patients. Daily weight monitoring is complementary to BNP, but BNP changes correspond to larger changes in risk, both upward and downward. (Heart Failure [HF] Assessment with B-type Natriuretic Peptide [BNP] In the Home [HABIT]; NCT00946231)
Repeated hospitalizations for heart failure (HF) are common and place a tremendous burden on a patient's health status, morbidity, and mortality and significantly increase national healthcare expenditures (1–3). In the current era of healthcare reform, the quality of care provided by physicians and hospitals is under increasing scrutiny Thirty-day readmission for HF is now used as a metric for quality of care by Medicare and third party payers and will influence reimbursement (4–8). Thus, reducing early readmissions is now a major focus in HF management.
Although as many as two-thirds of rehospitalizations may be preventable (9–13), early detection of decompensation is the first step toward achieving that goal. Often this decompensation is manifested by increased extracellular volume, which can occur up to 10 days prior to clinical presentation (14). Reporting weight and symptoms on a daily basis using telemedicine programs does not always yield a reduction in 30-day readmission rates (15,16). Natriuretic peptide (NP) levels are quite useful for detecting new or acutely decompensated HF, even when physical signs and radiographic findings are not present (17–19). Thus, adding NP levels to a home monitoring regimen might add value in early detection of decompensation. We report the results of the first study to use B-type NP (BNP) testing at home on a daily basis, using a novel finger-stick technology.
The study was a multicenter, single-arm, double-blinded observational prospective clinical trial designed to monitor daily concentrations of BNP and to determine how these concentrations correlate with acute clinical HF decompensation (ADHF) and related adverse clinical outcomes in at-risk HF patients. The study enrolled subjects who were either admitted to the hospital with decompensated HF and had a BNP concentration >400 pg/ml or an N-terminal B-type natriuretic peptide (NT-proBNP) concentration >1,600 pg/ml during admission, or were seen in an outpatient setting (i.e., HF clinic, general practice, cardiology office, or urgent care unit) with signs or symptoms of worsening HF. Patients with systolic left ventricular dysfunction as well as HF patients with preserved ejection fraction (HF-PEF) were included. Subjects were excluded if they had end-stage renal disease or anticipated cardiac transplantation or left ventricular assist device placement within 3 months. Those patients with dementia, tremor, or blindness that would make it impossible to perform daily home BNP testing using the finger-stick method were excluded. Finally, patients were excluded if their residence was in a region where either transmission of HeartCheck data or a home visit on day 5 was not possible.
After written informed consent was obtained and study eligibility confirmed, potential subjects were trained to perform finger-stick BNP self-testing with the HeartCheck system (Alere Technologies Ltd., Stirling, Scotland). Eligible subjects who successfully completed this training were then enrolled. The enrollment and baseline assessments were conducted between 24 h prior to hospital/clinic discharge and 7 days after discharge. Subjects then performed daily home finger-stick BNP tests up until their day-60 office visit. Results were recorded and transmitted electronically to the study database, which was monitored to confirm successful testing occurred. Results were fully blinded to the subjects, their physicians, and the clinical study staff. BNP self-test results were not used for patient assessment or management. Subjects were provided with a bath scale to measure their weight daily. Subjects reported both weight and symptoms daily by entering these values directly into the HeartCheck monitor, which transmitted the data electronically to the database. Subjects were also asked to keep a journal of their daily weight measurements and data transmission success.
After each subject had performed the daily finger-stick BNP assessments for 5 ± 2 days, the subject's proficiency and accuracy in using the HeartCheck system were reassessed during a visit to the subject's home by an independent home health professional. In addition, on days 30 and 60, subjects underwent physical examinations, clinical assessments, and a review of their medical status in the outpatient clinic and again were asked to demonstrate their ability to correctly use the HeartCheck system. A chart review was performed and/or the patient was called by telephone at 75 ± 3 days to collect final outcome data.
The primary endpoint of the study was a composite of any of the following occurring up to 5 days post-testing: cardiovascular death, hospital admission for decompensated HF, or clinical HF decompensation without hospital admission (but requiring parenteral HF therapy or changes in oral HF medications).
The Alere HeartCheck System was specifically designed for home monitoring of BNP levels by HF patients. It uses an immunoassay that generates an electrochemical detection signal that is directly proportional to the level of BNP in a fresh finger-stick sample of capillary whole blood. Following insertion of the test strip into the monitor, a drop of finger-stick blood (12 μl) is applied to the test strip, and the monitor analyzes the sample and determines the BNP concentration, which is transmitted through a wireless connection mechanism to a target location. The range of the BNP assay is 5 to 5,000 pg/ml.
The assay is a direct, 1-step sandwich immunoassay for the detection of human BNP. The performance of the system was evaluated based on duplicate finger-stick measurements made by 236 donors (174 male and 62 female), 201 of whom were diagnosed with HF and 35 of whom were non-HF controls (20). These samples covered the range of the BNP assay, and method comparison to the Access II BNP reference system (Beckman Coulter, Brea, California) gave the following results: slope = 1.08 (95% confidence interval [CI]: 1.03 to 1.13), intercept = 3.0 pg/ml (95% CI: −1.7 to 8.5 pg/ml), and correlation coefficient (r) = 0.95 (95% CI: 0.94 to 0.96). The precision of the system was evaluated based on duplicate finger-stick measurements made by the same set of donors, yielding coefficients of variation ranging from 10.4% to 14.6% depending upon BNP concentration range.
The system also records additional patient information and transmits all data by wireless General Packet Radio Service (GPRS) capability to a web portal that could be used for observation by a treating physician.
In this study, the patient's self-reported weight and HF symptoms were collected. Errors in weight data could occur if patients omitted data entry (in which case the system defaults to the previous day's value) or entered the value incorrectly. However, as patients were also instructed to record a written journal of their daily weight, these logs were inspected to resolve anomalous results. Upon each BNP test, the patients also reported qualitative clinical HF signs and symptoms based on yes/no answers to the following 5 questions: 1) Are you coughing more today? 2) Are you more short of breath today? 3) Do you have more swelling today? 4) Did you use extra pillows last night? 5) Are you feeling dizzy today?
The testing period was defined as the first successful home BNP test until the last successful home BNP test. The monitoring period extended 5 days beyond the testing period but not past the date of loss-to-follow-up or study completion. In addition, ADHF without hospital admission within 3 days of a prior event did not count as a new event.
Poisson regression was used to relate ADHF events that occurred during the monitoring period to the time-varying predictor variables (BNP, weight gain, and self-reported symptoms). The predictors are time-varying, but the baseline hazard is assumed to be constant. The Poisson model also permits multiple events per patient. For hospitalization for ADHF, only the day of admission counted as an event, and the remaining period of hospitalization was treated as nonexposure. Days of hospital admission for other causes were treated as nonexposure. BNP was treated as a continuous variable (natural logarithm [ln] of the concentration), and weight gain was treated as a dichotomous variable (≥5 lbs within the previous 3 days). The weight gain threshold was prospectively chosen based on the literature (European Society of Cardiology guidelines  recommend greater than 2 kg in 3 days). Missing values for the predictors were linearly interpolated from the nearest values. The period after the last measured value of a predictor until the end of the monitoring period was extrapolated as the last value carried forward. If patients recorded multiple values on a single day, then only the first value on each day was considered evaluable.
The Poisson model has the form ln (λ) = β0 + β1ln (BNP) + β2 WG, where λ is the expected number of events per patient day, BNP is the daily concentration, WG is the dichotomous daily weight gain, and β values are the computed coefficients. The coefficients of the model are estimated by Poisson regression of the predictors (indexed by patient day) versus the number of events on any given patient day (0 or 1). The resulting function λ is equivalent to the hazard rate, and exp(β1) and exp(β2) are equivalent to hazard ratios. Coefficients are static, and time dependence is carried implicitly by the predictors. Once the coefficients are determined by fitting the population, the risk change for an individual patient is evaluated as a change in λ due to the variation of BNP and weight over the monitoring period.
Patients' self-reported symptoms were added as dichotomous time-dependent predictors to the Poisson model, where shortness of breath was defined as the yes/no answer to “Are you more short of breath today?” and swelling was defined as the yes/no answer to “Do you have more swelling today?”
The correlation of BNP measures over time (autocorrelation) was evaluated using the Spearman correlation coefficient. The intraindividual coefficient of variation was calculated from the formula: CVi = (0.5 D2 − CVa2)1/2 where CVa is the assay's analytical coefficient of variation (taken as 0.15), and D is the dispersion coefficient (D = [exp (σ2) − 1]1/2, where σ equals 1.483 times the median absolute deviation of ln BNP between measures).
The sample size of the study was determined to detect differences in the mean BNP level (at a single time point) between patients who had ADHF events during the monitoring period and those who did not. A sample size of approximately 200 patients with 150 in the nonevent group and 50 in the event group has approximately 95% power to detect a mean shift of 225 pg/ml between the two groups, assuming a common standard deviation of 400 pg/ml.
All calculations were made using MATLAB version 7.5 software (The MathWorks, Inc., Natick, Massachusetts), and generalized linear model regression was used to fit the Poisson model.
A total of 187 patients enrolled in the study. There were 24 unevaluable subjects (20 conducted fewer than 5 successful BNP tests, 4 did not record their weight). The 20 subjects who conducted fewer than 5 successful BNP tests were withdrawn from the study for a variety of reasons: 6 were self-withdrawn, 5 were withdrawn because of noncompliance with study procedures, 3 because they moved to another location, 2 because of illness, and 1 was withdrawn because of loss of visual acuity. Only 3 of the 187 enrolled subjects (1.6%) were withdrawn specifically because of inability to perform the study-related finger-stick testing.
Demographic data and baseline clinical parameters for the 163 evaluable study participants are shown in Table 1. A total of 6,934 daily BNP values were recorded, with a median of 46 (interquartile range [IQR]: 33 to 54) measures per patient over a median monitoring period of 65 (IQR: 59 to 69) days. A total of 8,084 daily weights were recorded during the monitoring period, with a median of 53 (IQR: 43 to 58) weight measures per patient. Forty patients (24.5%) had 56 ADHF events during the monitoring period: 22 of these events were hospitalizations, 33 were occurrences of clinical HF decompensation that did not require hospital readmission, and 1 was cardiovascular death. There were a total of 51 all-cause hospitalizations during the monitoring period.
Of the 33 ADHF events that did not require hospital readmission, 7 corresponded to administration of intravenous (IV) diuretics, 16 corresponded to intensification of oral diuretics, 6 corresponded to intensification of beta-blockers (carvedilol), 2 corresponded to reduction of carvedilol followed by ADHF, 1 corresponded to reduction of oral diuretic followed by ADHF, and 1 corresponded to a change in oral HF medication without further specification.
Correlation (autocorrelation) was measured as a function of the time between BNP measures. The correlation coefficient weakens as the time between hospital discharge or entry value from outpatient enrollment increases (Spearman correlation coefficients were 0.936, 0.915, 0.896, 0.865, and 0.791 for separations of 1, 2, 3, 14, and 42 days between measures, respectively). The decay of the correlation coefficient is rapid for short-time differences of 1 to 3 days. For time differences >3 days, the rate of decay is less rapid but steady. Decay of the correlation coefficient corresponds to an increase in the intraindividual coefficient of variation (20.7%, 24.6%, 28.5%, 35.6%, and 48.9% for separations of 1, 2, 3, 14, and 42 days between measures, respectively). Poisson regression was used to test the associations between daily BNP and daily weight and ADHF events within the monitoring period. Of 10,035 patient days, there were 494 (4.9%) days of weight gain (≥5 lbs within the previous 3 days) and 710 (7.1%) days of acute BNP rise (more than double over 3 days). The Poisson regression models are shown in Table 2. In a 2-predictor model with daily BNP and weight gain, the hazard ratio per unit increase of ln BNP was 1.84 (95% CI: 1.42 to 2.39), and the hazard ratio on a day of weight gain was 3.63 (95% CI: 1.83 to 7.20). We noted that the number of days of weight gain (494) was much smaller than the total number of patient-days (10,035), whereas daily BNP could change on any patient-day (and trend over time), making it a stronger indicator of HF condition and risk for an event. The hazard ratios for BNP and weight gain retained significance when controlling for self-reported daily symptoms in the multivariate model. Daily BNP remained significant when adjusted for baseline BNP in a two-predictor model. In a time-varying Cox model associating daily BNP with time to first event (40 ADHF events; total exposure: 8,584 patient-days), the hazard ratio for ln BNP was 1.79 (95% CI: 1.33 to 2.41), which also retained significance when adjusted for baseline BNP values. Acute BNP rises were not significant predictors of ADHF events in either a univariate or multivariate model.
Left ventricular ejection fraction (LVEF) at baseline was not a significant predictor of ADHF in either a univariate model (dichotomized at 40%) or in a multivariate model together with BNP, or BNP and weight gain.
Although the study population exhibited a range of home BNP test frequencies (median number of BNP tests per day was 0.746; IQR: 0.577 to 0.827), the test frequency was not associated with ADHF events in either a univariate or multivariate Poisson regression model.
HF medication usage at enrollment (categories are shown in Table 1) was also not a significant predictor of ADHF in any model. This includes diuretic usage at baseline in 107 of 123 (87.0%) patients without events, versus 39 of 40 (97.5%) in patients with events.
In a 2-predictor Poisson regression model limiting events to 22 ADHF hospitalizations and 1 cardiovascular death, the hazard ratio per unit increase of ln BNP was 1.62 (95% CI: 1.10 to 2.39), and the hazard ratio on a day of weight gain was 3.61 (95% CI: 1.22 to 10.63), similar to the hazard ratios obtained in predicting all 56 ADHF events.
To further understand the value of BNP testing at home over daily weight monitoring, the net reclassification improvement was calculated according to the method of Pencina et al. (22), resulting in a net reclassification improvement of 0.504 (95% CI: 0.255 to 0.753) for category-free reclassification based on the hazard rate of the 2-predictor model (weight gain and BNP) compared with the hazard rate of the single-predictor model (weight gain alone).
The daily risk model (Poisson model, Table 2) relating daily BNP and daily weight gain to risk of ADHF is demonstrated in Figure 1. The monitoring period for each subject was broken into intervals based on ADHF events, yielding 212 intervals, including 56 intervals that terminated in an event (patients are represented by multiple intervals if they resumed self-testing following an event). Figure 1A shows each interval as a circle represented by its initial BNP value (abscissa) and its time-averaged hazard rate (ordinate) from the Poisson model. The size of each circle is proportional to the length of the interval; intervals that terminate in an ADHF event are red, and those that terminate without event are blue. Also demonstrated is the daily hazard rate as a function of BNP and weight gain on days of no weight gain (Fig. 1A, solid black line) and on days with weight gain (Fig. 1A, dashed black line).
The net displacement up or down of each circle relative to the solid black line (Fig. 1A) represents the change in mean risk over the interval; circles below the solid line have improved prognosis, whereas circles above the solid line have worsened prognosis. Shorter intervals (typically red) tend to be at higher initial BNP values or have worsened prognosis (above the solid line), whereas longer intervals (typically blue) tend to be at lower initial BNP values or have improved prognosis (below the solid line). The two red circles whose initial BNP are below 100 pg/ml are atypical. One represents a 53-day interval that culminated in an outpatient ADHF event with a BNP rise from an initial 64 to 544 pg/ml in the 3 days prior to the event. The other represents a 6-day interval that culminated in ADHF hospitalization. The patient had HF-PEF, and this interval was part of a characteristic pattern of large, approximately 5- to 10-fold BNP excursions without weight gain over the course of approximately 4 to 6 days.
The sensitivity and specificity of the daily hazard model are shown as a receiving-operator characteristic (ROC) curve classifying each patient day (Fig. 1B). Sensitivity was computed on days of ADHF (n = 56), and specificity was computed on days without ADHF (n = 9,979). We noted that days of ADHF were defined by patient-initiated visits to the outpatient clinic or emergency department (ED), resulting in an assessment of ADHF and therapeutic intervention by the treating physician; however, the patterns of daily BNP observed here suggest that traditional events defined by these visits may underestimate all instances of ADHF and worsening conditions requiring therapeutic intervention.
The risk change during intervals of positive BNP slope (n = 39), negative BNP slope (n = 64), or weight gain (n = 94) is shown in Figure 1C.
To characterize the change in risk associated with BNP trends, the slope for each interval was calculated by ordinary linear regression of ln BNP versus time. Intervals with at least 5 BNP measures were classified as positive slope (slope greater than 1% per day), negative slope (slope <−1% per day), or no trend. There were 39 (18.4%) intervals of upward trending BNP and 64 (30.2%) intervals of downward trending BNP concentrations. The median length of upward trending intervals was 40 days during which the median risk increase was 59.8% based on the Poisson model, and the median length of downward trending intervals was 52 days corresponding to a median risk decrease of 39.0%. In a similar fashion, there were 94 (44.3%) intervals with 1 or more days of weight gain (median of 4 days of weight gain, median length of 55 days) corresponding to a median risk increase of 26.1%.
The time-series plots of daily BNP, daily weight, and self-reported symptoms are shown for 4 examples in Figure 2.
In Figure 2A, the patient was observed to have had a rising BNP level for 28 days followed by an ADHF event that was treated with parenteral HF therapy without hospitalization. The patient's BNP level was reduced over the subsequent 5 days but rose again, dramatically, at which point the patient was hospitalized for ADHF. Weight gain also preceded this hospitalization by 5 to 10 days.
In Figure 2B, the patient had extremely high and increasing BNP levels until day 14 when an ADHF event was noted and treated with parenteral HF therapy without hospitalization. The patient continued at roughly the same BNP level for the next 7 days, when a second ADHF event was recorded. This was treated with intensification of oral HF therapy without hospitalization. BNP levels then increased further over the next 10 days. This was followed by hospitalization with ADHF on day 31. The patient reported no weight gain during this entire period.
As shown in Figure 2C, the patient exhibited a pattern of large, approximately 5- to 10-fold, and periodic BNP excursions over the course of 4 to 6 days. These excursions occurred without corresponding weight changes and without self-reported symptoms or clinical events. Other patients showed a similar pattern of BNP excursions. The reason for these excursions and the clinical implications are unknown.
Figure 2D shows the patient manifested a consistent downward trend in BNP, corresponding to a slope of −3.6% per day over the monitoring period of 67 days. BNP decreased without significant changes in weight. The patient had no adverse clinical events.
In the United States, approximately 1 in 4 patients hospitalized with ADHF is rehospitalized within 30 days of discharge. Although readmission rates may vary widely by hospital, the total number of readmissions has risen continuously over the past 2 decades. Most of the cost associated with care of HF patients is attributable to this rehospitalization. Although it is the consensus that many hospitalizations are preventable, until recently, effective strategies were underused due to a lack of incentives. In fact, as many as one-half to two-thirds of hospital readmissions are thought to be preventable with attention to modifiable factors (23).
Most of these modifiable factors have to do with the prevention of recurrent pulmonary congestion. Although this may be manifested by shortness of breath, edema, and/or weight gain, up to one-third of patients have no overt manifestation of elevated left ventricular filling pressures. Pulmonary congestion may occur many days before symptoms are noted, perhaps too late to reverse course. Home monitoring (home nursing visits, telehealth, or telephone monitoring) has been used and has been somewhat effective in reducing ADHF recurrence, but because of the poor sensitivity of weight gain and symptoms, the overall results of these interventions have not been as robust as expected. Changes in NP levels, especially compared with a baseline “dry” or optivolemic state, are useful in predicting elevated filling pressures as well as risk of overt decompensation (24). A consensus panel concluded that NP levels, in addition to symptoms, signs and weight change, add more certainty to the prediction of whether HF decompensation is occurring (25). However, NP measurements have not had a role in home monitoring primarily because they required phlebotomy.
For the first time, we present serial data from patients at high risk for recurrent ADHF who performed self finger-stick BNP testing in the home daily for 60 days. Our results demonstrate that it is feasible and safe for HF patients to measure their BNP levels at home on a daily basis. In this double-blind observational study, patients tested daily without difficulty, and there were no safety issues related to finger-stick sampling and BNP testing by patients in their home, similar to the experience for patients self-testing of blood glucose and international normalized ratio (INR). Furthermore, the daily BNP patterns following treatment for ADHF are rich in information that is as diverse and heterogeneous as the patients and their heart disease. BNP trends can indicate either progressively worsening or steadily improving conditions. The widely fluctuating patterns may identify those patients who are not optimized and therefore require tight observation and management. The daily BNP patterns we observed in this study also appear to be characteristic of the individual patients and their condition, which may facilitate more individually tailored therapies. This possibility is especially intriguing for HF-PEF patients who, in many cases, illustrated distinctive daily BNP patterns that included frequent spikes in BNP level.
The results also demonstrate that BNP levels can sometimes fluctuate widely on a day-to-day basis, and correlations are significantly weakened within approximately 2 weeks. Because BNP levels are traditionally measured infrequently (e.g., when patients are doing either well or poorly), healthcare providers may miss important changes that take place between these measurements. In fact, the present analysis illustrates that daily levels of BNP are a better indicator of the patient's condition and prognosis than a fixed (baseline) BNP level. Patients with high BNP concentration are at risk of events, and many patients modulate from low BNP to high BNP or vice versa, changing their risk over time. Because discharge BNP concentration has prognostic value, serial testing after discharge, especially in high-risk patients, may be warranted.
Although the treatment of HF should not be predicated upon a single BNP value alone, the incremental data from serial home measurements following an episode of ADHF appear to provide a novel means with which to identify those patients who are at highest risk of recurrent decompensation. The pattern or trend in BNP values may be used to identify when to correlate with face-to-face clinical assessment or a therapeutic intervention, or to guide therapy with specific medication changes without additional office visits; this remains to be determined in interventional studies with formal outcome measures. Additionally, there were only 23 hard endpoints with which to make the above judgments, necessitating confirmation in a larger population. Nevertheless, the results reported here suggest that traditional events defined as hospital readmissions and outpatient clinic visits with therapeutic intervention may not truly represent all instances of acute decompensation and worsening conditions requiring therapeutic intervention. Rather, these conditions may be happening more frequently or at least at earlier times than when the patient initiates the clinic or ED visit. The possibility of detecting these conditions earlier based on BNP levels, weight, and clinical signs and symptoms, all reported on a daily basis, makes it possible to intervene in a personalized way to prevent hospital readmissions and improve clinical outcomes. The results of this pilot study suggest an interventional trial with home finger-stick BNP is feasible and warranted.
Dr. Maisel is a consultant for Alere, Inc. Dr. Kelsay's research clinic has received funds for pacemaker research from Medtronic, Boston Scientific, St. Jude Medical, and Biotronik. Dr. Kupfer is an employee of Alere, Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Gary S. Francis, MD, acted as Guest Editor for this paper.
- Abbreviations and Acronyms
- acute clinical heart failure decompensation
- B-type natriuretic peptide
- heart failure
- heart failure with preserved ejection fraction
- interquartile range
- left ventricular ejection fraction
- natriuretic peptide
- Received November 7, 2012.
- Revision received January 4, 2013.
- Accepted January 7, 2013.
- American College of Cardiology Foundation
- Lloyd-Jones D.,
- Adams R.J.,
- Brown T.M.,
- et al.
- Pang P.S.,
- Komajda M.,
- Gheorghiade M.
- Keenan P.S.,
- Normand S.L.,
- Lin Z.,
- et al.
- Ross J.S.,
- Chen J.,
- Lin Z.,
- et al.
- Bui A.L.,
- Fonarow G.C.
- Dendale P.,
- De Keulenaer G.,
- Troisfontaines P.,
- et al.
- McCullough P.A.,
- Nowak R.M.,
- McCord J.,
- et al.
- ↵HeartCheck BNP Test Strip package insert. 0017 SPEC-0363 Rev. 1 2010/09, Alere Technologies Ltd., Stirling, Scotland.
- Dickstein K.,
- Cohen-Solal A.,
- Filippatos G.,
- et al.