Devices for Identifying Heart Failure Risk
Devices for Identifying Heart Failure Risk
The development set included data available from the OFISSER (n = 269), Italian ClinicalService Project (n = 174), and CONNECT (n = 478) studies. The validation set included data available from the PARTNERS-HF (n = 650), FAST (n = 134), PRECEDE-HF (n = 52), and SENSE-HF (n = 474) studies. Patient data were included in the data analysis cohorts if the patient had >90 days of device diagnostic data that includes intra-thoracic impedance monitoring. Details for each study and additional data inclusion criteria for this analysis are detailed in the Appendix. The studies were divided into development and validation data sets based on the chronological order in which data from the studies were made accessible for this investigation. The method for computing diagnostic information is the same in all the devices included for the data analysis. HFHs were used as the endpoint in the data analysis. Each cardiovascular hospitalization was carefully adjudicated for signs and symptoms of HF which included the administration of i.v. or oral diuretic during the hospitalization. Since a dynamic risk score for HFH was the focus of this study, death was not used as an endpoint in the data analysis.
Implanted medical devices monitor several clinical diagnostic parameters that may include IMP, AF burden, ventricular rate during atrial fibrillation (VRAF), ventricular tachycardia (VT) episodes, patient activity (ACT), day and night heart rate (NHR), and heart rate variability (HRV) (Figure 1). These parameters are monitored continuously and the device stores sample data points for each parameter daily. IMP is a surrogate measure for blood volume or pulmonary capillary wedge pressure, with an increase in fluid volume leading to a reduction in IMP. HRV is the standard deviation of 5 min median of atrial intervals during a 24 h period, with reducing HRV implying increases in sympathetic tone. NHR is the average heart rate between midnight and 4 am and is a measure for resting heart rate. ACT is the number of minutes in a 24 h period the patient is active and is a surrogate of functional capacity. AF burden is measured as total duration of fast atrial rate during a 24 h period, with atrio-ventricular conduction ratio ≥2:1. VRAF is the average ventricular rate during AF over a 24 h period. The device also records the % of CRT pacing delivered in a day, number of VT episodes and whether the patient received a defibrillation shock.
(Enlarge Image)
Figure 1.
The schematic for computation of the combined risk score using the different HF-related diagnostic variables in the Medtronic CRT-D system.
Features were extracted from the diagnostics parameters to ascertain an evidence level for each diagnostic parameter on a daily basis ( Appendix 1 and Appendix 2 ). A higher value of OptiVol fluid index implied a higher level of evidence for HF. Low or decreasing trend in ACT or HRV and high or increasing trend in NHR were considered as evidence for HF. If any two of the five arrhythmia/therapy related criteria were met it identified a higher evidence level for worsening HF. Absolute measurement thresholds used for the different diagnostic parameters were determined in earlier studies. The thresholds for the trend indexes which look for sustained increases or decreases in the measurements of NHR, ACT, and HRV were determined in the development set data.
A BBN framework was used to combine the evidence from each diagnostic parameter (Figure 1). On any day a certain set of diagnostic criteria is met which is categorized to different evidence levels as shown in Appendix. The evidence level for each diagnostic parameter is then used to generate the HF risk score for the day using a lookup table defined by the BBN model using data from the development set.
Monthly evaluations were simulated every 30 days, similar to the evaluation used in the PARTNERS-HF study, beginning on the 60th day from start of available diagnostic data. Each monthly evaluation included: (i) a retrospective look at maximum value of the diagnostic risk score in the last 30 days to ascertain the patient status into the diagnostic evaluation groups, and (ii) a prospective assessment for the first HFH in the next 30 days. A monthly evaluation was included only if there was >30 days of device data and clinical follow-up following the diagnostic evaluation, thus excluding deaths from the analysis. The risk score was categorized into three diagnostic evaluation groups: high, medium, and low. The first natural break after the top 10% of the risk score in the development set was chosen as the threshold for the high group. The rest of the risk scores were divided into two similar sized groups at a natural breakpoint with the HFH event rate <0.5% in the low group in the development set. The high and medium monthly diagnostic evaluation groups were compared with the low group for time to first HFH in the next 30 days using the Anderson–Gill model, an extension of the Cox proportional hazards model that accounts for multiple evaluations in patients. The model was adjusted for baseline variables (age, gender, NYHA, history of coronary artery disease, MI, AF, diabetes, and hypertension) and baseline medications (ACE-I/ARB, diuretics, β-blockers, and anti-arrhythmic drugs) in the validation data set.
A sensitivity and specificity analysis was performed for the combined diagnostic score using the same monthly evaluation scheme. Sensitivity (and specificity) is defined as the number of evaluations with score ≥ (or <) threshold and HFH (or no HFH) event in next 30 days divided by the total number of evaluations with HFH (without HFH) in next 30 days. The sensitivity and specificity computations are adjusted for multiple evaluations in patients using generalized estimating equation (GEE) with an exchangeable correlation structure. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Inc., Cary, NC, USA).
Methods
Data Set and Event Definitions
The development set included data available from the OFISSER (n = 269), Italian ClinicalService Project (n = 174), and CONNECT (n = 478) studies. The validation set included data available from the PARTNERS-HF (n = 650), FAST (n = 134), PRECEDE-HF (n = 52), and SENSE-HF (n = 474) studies. Patient data were included in the data analysis cohorts if the patient had >90 days of device diagnostic data that includes intra-thoracic impedance monitoring. Details for each study and additional data inclusion criteria for this analysis are detailed in the Appendix. The studies were divided into development and validation data sets based on the chronological order in which data from the studies were made accessible for this investigation. The method for computing diagnostic information is the same in all the devices included for the data analysis. HFHs were used as the endpoint in the data analysis. Each cardiovascular hospitalization was carefully adjudicated for signs and symptoms of HF which included the administration of i.v. or oral diuretic during the hospitalization. Since a dynamic risk score for HFH was the focus of this study, death was not used as an endpoint in the data analysis.
Diagnostic Parameters
Implanted medical devices monitor several clinical diagnostic parameters that may include IMP, AF burden, ventricular rate during atrial fibrillation (VRAF), ventricular tachycardia (VT) episodes, patient activity (ACT), day and night heart rate (NHR), and heart rate variability (HRV) (Figure 1). These parameters are monitored continuously and the device stores sample data points for each parameter daily. IMP is a surrogate measure for blood volume or pulmonary capillary wedge pressure, with an increase in fluid volume leading to a reduction in IMP. HRV is the standard deviation of 5 min median of atrial intervals during a 24 h period, with reducing HRV implying increases in sympathetic tone. NHR is the average heart rate between midnight and 4 am and is a measure for resting heart rate. ACT is the number of minutes in a 24 h period the patient is active and is a surrogate of functional capacity. AF burden is measured as total duration of fast atrial rate during a 24 h period, with atrio-ventricular conduction ratio ≥2:1. VRAF is the average ventricular rate during AF over a 24 h period. The device also records the % of CRT pacing delivered in a day, number of VT episodes and whether the patient received a defibrillation shock.
(Enlarge Image)
Figure 1.
The schematic for computation of the combined risk score using the different HF-related diagnostic variables in the Medtronic CRT-D system.
Combined Diagnostics
Features were extracted from the diagnostics parameters to ascertain an evidence level for each diagnostic parameter on a daily basis ( Appendix 1 and Appendix 2 ). A higher value of OptiVol fluid index implied a higher level of evidence for HF. Low or decreasing trend in ACT or HRV and high or increasing trend in NHR were considered as evidence for HF. If any two of the five arrhythmia/therapy related criteria were met it identified a higher evidence level for worsening HF. Absolute measurement thresholds used for the different diagnostic parameters were determined in earlier studies. The thresholds for the trend indexes which look for sustained increases or decreases in the measurements of NHR, ACT, and HRV were determined in the development set data.
A BBN framework was used to combine the evidence from each diagnostic parameter (Figure 1). On any day a certain set of diagnostic criteria is met which is categorized to different evidence levels as shown in Appendix. The evidence level for each diagnostic parameter is then used to generate the HF risk score for the day using a lookup table defined by the BBN model using data from the development set.
Statistical Analysis
Monthly evaluations were simulated every 30 days, similar to the evaluation used in the PARTNERS-HF study, beginning on the 60th day from start of available diagnostic data. Each monthly evaluation included: (i) a retrospective look at maximum value of the diagnostic risk score in the last 30 days to ascertain the patient status into the diagnostic evaluation groups, and (ii) a prospective assessment for the first HFH in the next 30 days. A monthly evaluation was included only if there was >30 days of device data and clinical follow-up following the diagnostic evaluation, thus excluding deaths from the analysis. The risk score was categorized into three diagnostic evaluation groups: high, medium, and low. The first natural break after the top 10% of the risk score in the development set was chosen as the threshold for the high group. The rest of the risk scores were divided into two similar sized groups at a natural breakpoint with the HFH event rate <0.5% in the low group in the development set. The high and medium monthly diagnostic evaluation groups were compared with the low group for time to first HFH in the next 30 days using the Anderson–Gill model, an extension of the Cox proportional hazards model that accounts for multiple evaluations in patients. The model was adjusted for baseline variables (age, gender, NYHA, history of coronary artery disease, MI, AF, diabetes, and hypertension) and baseline medications (ACE-I/ARB, diuretics, β-blockers, and anti-arrhythmic drugs) in the validation data set.
A sensitivity and specificity analysis was performed for the combined diagnostic score using the same monthly evaluation scheme. Sensitivity (and specificity) is defined as the number of evaluations with score ≥ (or <) threshold and HFH (or no HFH) event in next 30 days divided by the total number of evaluations with HFH (without HFH) in next 30 days. The sensitivity and specificity computations are adjusted for multiple evaluations in patients using generalized estimating equation (GEE) with an exchangeable correlation structure. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Inc., Cary, NC, USA).
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