heart failure ( hf ) causes a significant economic burden , morbidity , and mortality .
the primary cause of hf hospitalization ( hfh ) is volume overload which is treated using diuretic therapy .
further , ace - inhibitors and -blockers are known to reduce mortality in hf patients .
implantable medical devices , such as pacemakers , implantable cardioverter defibrillator ( icd ) , and cardiac resynchronization therapy defibrillator ( crt - d ) , can provide daily measurements of several 
diagnostics such as intra - thoracic impedance ( imp ) , atrial fibrillation ( af ) burden and rate control information , and night heart rate , heart rate variability , and patient activity can identify when patients are risk for hf events and could potentially be used unilaterally or in a combined fashion for informed patient management . in the past decade multiple studies
have reported combining implantable device diagnostics to identify patients at risk of hf events and death .
the objective of this study was to develop and validate a single - hf risk score derived by combining information from multiple device diagnostic parameters in a bayesian belief network ( bbn ) framework to improve the ability to identify when patients are at risk for hfh .
multiple physiological processes interact in a complex manner during hf with a high degree of uncertainty in the severity of the manifestation of the disease that may or may not require hospitalization .
the bbn approach allows for uncertain reasoning to estimate the probability of an hfh under a set of given diagnostic evidence .
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 .
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 .
the device also records the % of crt pacing delivered in a day , number of vt episodes and whether the patient received a defibrillation shock . 

figure  1the schematic for computation of the combined risk score using the different hf - related diagnostic variables in the medtronic crt - d system .
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 ) . 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 .
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 , -blockers , and anti - arrhythmic drugs ) in the validation data set . a sensitivity and specificity analysis
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 .
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 .
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 .
the device also records the % of crt pacing delivered in a day , number of vt episodes and whether the patient received a defibrillation shock . 

figure  1the schematic for computation of the combined risk score using the different hf - related diagnostic variables in the medtronic crt - d system .
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 ) . 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 .
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 , -blockers , and anti - arrhythmic drugs ) in the validation data set . a sensitivity and specificity analysis
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 ) .
the development data set consisted of 921 patients with an average follow - up duration of 10.6  5.8 months with 28 deaths and 68 patients ( 7.4% ) with hfhs at a rate of 0.14 per patient year .
a total of 9790 patient - months of data was analysed of which there were 91 months with hfhs providing an event rate of 0.9% .
the validation data set consisted of 1310 patients with an average follow - up duration of 8.1  5.0 months with 33 deaths and 110 patients ( 8.4% ) with hfhs at a rate of 0.22 per patient year .
a total of 10 655 patient - months of data was analysed of which there were 163 months with hfhs providing an event rate of 1.5% .
the baseline characteristics of the patients in the study are shown in table  1 . 

table  1baseline demographics of patients in the development and validation setsdevelopment set ( n = 921)validation set ( n = 1310)mean age ( sd)68 ( 11)67 ( 11)male gender ( % ) 6974nyha ( % )  i24  ii1922  iii7670  iv34ischaemic ( % ) 6361myocardial infarction ( % ) 4348hypertension ( % ) 7062diabetes ( % ) 3738history of af ( % ) 2132lvef < 35% ( % ) 9692device type ( % )  icd04  crt - d10096baseline medications ( % ) ace / arb7084beta - blockers8788diuretics7787digoxin2933aldosterone antagonist2622aad1822anti - platelet or
anticoagulant8661  warfarin3325 baseline demographics of patients in the development and validation sets the event rates , expressed as a percentage of monthly evaluations that were followed by an hfh in the next 30 days , for the low , medium , and high evaluation groups in the development and the validation data sets are presented in table  2 .
the hazard ratios for the comparison of the event rates in the medium and high groups with respect to the low group are also shown in table  2 .
figure  2 shows the kaplan  meier plot for time to first hfh in the 30 days following monthly diagnostic evaluation in the validation data set . in the validation data set , a total of 163 monthly evaluations ( 1.5% ) were followed by an hfh in the next 30 days .
of the 1100 monthly evaluations when the risk score was in the high group , 75 ( 6.8% ) were followed by an hfh in the next 30 days .
the risk score was in the  high group in at least one monthly evaluation in 446 patients ( 34% ) .
monthly diagnostic evaluations with a risk score in the  high group were 10 times ( hr : 10.0 ; 95% ci : 6.415.7 , p <
0.001 ) more likely to have an hfh in the next 30 days compared with monthly evaluations with a risk score in the  low group .
results are similar if the model is adjusted for the presence of hfh in the last 30 days ( hr : 8.2 ; 95% ci : 5.113.1 , p < 0.001 ) . 
 table  2comparison of event rates between different evaluation groups within the development and validation setsdata setevaluation groupsevaluations ( % ) patientshf hospitalizations ( % of evaluations)hazard ratio ( 95% ci)p - valuedevelopment ( n = 921)low4525 ( 46)80215 ( 0.3)referencemedium4018 ( 41)83347 ( 1.2)3.7 ( 2.0 , 6.7)<0.001high1247 ( 13)40529 ( 2.3)6.2 ( 3.1 , 12.3)<0.001validation ( n = 1310)low4838 ( 45)108528 ( 0.6)referencemedium4717 ( 44)114260 ( 1.3)2.1 ( 1.3 , 3.4)0.001high1100 ( 10)44675 ( 6.8)10.0 ( 6.4 , 15.7)<0.001the high group consisted of risk scores > 20% and the low group consisted of risk scores 5% . 

figure  2kaplan  meier curves for time to first hf hospitalization after monthly diagnostic evaluation for the different risk score groups for the validation set .
comparison of event rates between different evaluation groups within the development and validation sets the high group consisted of risk scores > 20% and the low group consisted of risk scores 5% .
meier curves for time to first hf hospitalization after monthly diagnostic evaluation for the different risk score groups for the validation set .
figure  3 shows the event rates for individual diagnostic evidence levels described in appendix and the combined risk score for the validation set .
while each of the diagnostic element has the capability of stratifying patients at risk for hfhs , the combined risk score improves the ability to identify when patients are at a higher than normal risk and when patients are at lower than normal risk for hfhs . 

figure  3event rates for different levels of evidence for each diagnostic parameter and the combined risk score .
event rates for different levels of evidence for each diagnostic parameter and the combined risk score .
the number of times each of the individual diagnostic criteria was triggered as per cent of monthly evaluations with a risk score in the different risk score groups in the validation set is shown in figure  4a .
each diagnostic parameter exceeded threshold significant proportion of times when the risk score is in the  high group with reduced patient activity ( evidence level 2 in appendix ) and high optivol fluid index ( evidence level 3 and 4 in appendix ) exceeding threshold most often .
figure  4b shows the number of diagnostic parameter that were triggered at the same time when the risk score was in the  low , 
the evidence level criteria for the trigger of the diagnostic parameters for figure  4b were same as that used in figure  4a .
when the risk score was in the  low group , very often none of the diagnostic parameters triggered a high - evidence level , whereas three different diagnostic parameters triggered a high - evidence level most often when the risk score was in the  high group . 

figure  4(a ) per cent of monthly evaluations high - evidence level criteria was met for each diagnostic parameter in different risk score groups .
( b ) the distribution of number of diagnostic parameter that were triggered , based on evidence level criteria in ( a ) , when the risk score was in the  low ,  medium , and 
( a ) per cent of monthly evaluations high - evidence level criteria was met for each diagnostic parameter in different risk score groups .
( b ) the distribution of number of diagnostic parameter that were triggered , based on evidence level criteria in ( a ) , when the risk score was in the 
the 25-percentile , median , and 75-percentile of the risk score distribution in the validation set were 3.8 , 5.5 , and 11.5% , respectively .
the receiver operating characteristic ( roc ) curve , plotting the gee estimates of sensitivity and specificity , for the validation set is shown in figure  5 .
evaluations done every 30 days in the monthly evaluation scheme contribute one data point for the sensitivity and specificity calculations . in the validation
set , the threshold between the  low and  medium risk groups ( score of 5% ) had a sensitivity and specificity of 82.8 and 45.8% , respectively , and the threshold between the  medium- and 
high-risk groups ( score of 20% ) had a sensitivity of 46.0% , i.e. 46% of the months with hfhs were preceded by a 
high-risk score , and specificity of 90.2% , i.e. 10% of the months with no hfhs were also preceded by a 
figure  5receiver operating characteristics curve plotting the sensitivity vs. specificity in a 30-day evaluation framework for the validation set .
receiver operating characteristics curve plotting the sensitivity vs. specificity in a 30-day evaluation framework for the validation set . when monthly evaluations were in 
low-risk group , < 0.6% had hfhs in the next 30 days , i.e. a negative predictive value of 99.4% for the 
low risk can be used to triage for patients who are at lower than average risk for hf requiring hospitalization .
a very high - risk group , i.e. a group with high positive predictive value , can be formed for risk scores > 40% that occurs in 2% of all monthly evaluations with 14.2% having hfhs in next 30 days .
similarly , another very high - risk group would be evaluations with a risk score in 
high group for > 14 of 30 days , which happens in 3% of the evaluations with 11.9% having hfhs in the next 30 days .
high in the 30 days prior to months with hfh was 7  11 days .
the study presented the development and validation of a novel dynamic hf risk score derived from combining diagnostic parameters monitored in implantable devices .
patients who achieve a high - risk state on any day in the last 30 days are 10 times more likely to be hospitalized for hf in the next 30 days compared with patients who had a low risk on each of the last 30 days .
most of these risk scores identified a static risk at baseline or in an in - hospital setting , i.e. identify which patient is at risk for the development of hf or mortality .
the dynamic hf risk score developed in this study , can identify when a high - risk patient is at higher risk of an hfh in an ambulatory setting , thus providing incremental information beyond what is provided by a static risk score .
the dynamic hf risk score is time - varying and the same patient may be at high and low risk at different periods of time depending on the status of continuously monitored diagnostic parameters in the implanted device .
management based on a dynamic risk score is similar in approach to recently reported intra - cardiac pressure or intra - thoracic impedance measurements .
the key difference is incorporation of the multiple diagnostic parameters to form a combined diagnostic with the intention of improving the overall accuracy of the diagnostic .
long - term ambulatory monitoring using implantable devices featuring remote access and wireless alerting capabilities enables the dynamic assessment of the hf status , thus providing the opportunity to optimize treatment strategies for hf in a timely fashion . like any diagnostic ( weight , temperature , ecg , etc . ) , diagnostic information must be coupled with appropriate clinical actions in order to improve outcomes in hf patients . whether therapeutic interventions based on the dynamic hf risk score is safe and effective in improving outcomes in hf patients need prospective evaluation .
several randomized controlled studies for management of hf patients based on diagnostic information have yielded inconsistent results .
each of the diagnostic parameters in implantable devices correspond to one or more of the basic hf assessment metrics such as fluid status , functional capacity , resting tachycardia , autonomic balance , arrhythmia , and non - adherence .
combining multiple parameters into a single - risk score makes it a simple to use triaging scheme  indicating when a patient needs more attention in an ambulatory setting .
a transition to high - risk state can proactively initiate collection of more clinical and symptomatic information over the telephone or in person to facilitate a diagnostic decision .
further , having information regarding which device parameters caused transition to high - risk state may lead to more clarity on treatment options .
for example , a high - risk score caused by decrease in intra - thoracic impedance as well as new onset af with poor rate control at the same time may indicate a very specific treatment plan : acute control of the fluid status , if necessitated by additional evidence , followed by improved chronic management of heart rate , once euvolaemia is achieved , to prevent future occurrences .
a clinical action may not always need a medication adjustment ; it can also be counselling for non - adherence or deciding to monitor the patient more often in the clinic .
the chosen diagnostic criteria were optimized for simplicity of a quick visual review of each diagnostic variable .
the bbn approach also combines multiple diagnostic parameters , but it does so in a more rigorous decision - making framework that mimics clinical decision - making with elements of uncertain reasoning , causal relationships , and differential reasoning in the same framework .
the bbn approach generates a single - risk score that can be used for initial triage without having to go through each diagnostic variable , thus improving the simplicity .
the risk score is a continuous number allowing for the choice of flexible thresholds for obtaining optimal performance .
further , feature set definition for individual diagnostic variables can be improved without compromising the simplicity , e.g. relative change in activity , nhr , and hrv can be incorporated in addition to absolute thresholds .
finally , more diagnostic variables with orthogonal information , e.g. biomarkers , intra - cardiac pressures , can be easily added into the framework without needing data from all the variables in the same study to create a revised framework .
the absolute risk of an hfh in a 30-day period following a monthly evaluation with a high - risk state was only 6.8% .
thus , a high - risk state should not be used unilaterally to make treatment decisions as it may lead to over - reaction as it happened in the dot - hf study .
the absolute risk is low primarily because of the low rate of hfhs in this ambulatory monthly evaluation framework ( overall event rate of 1.5% ) , the denominator being all monthly evaluations in all patients . when the event rate is higher , for example , in evaluating readmissions for hf , the absolute risk is also higher
. the absolute risk for events with milder symptoms of hf not requiring hospitalization will be higher .
the hf risk score categorizes the baseline risk of 1.5% into three groups , one with a higher risk ( 6.8% ) and one with a lower risk ( 0.6% ) , with the middle group being similar to the overall risk of 1.5% .
although the absolute risk is low , the relative risk between the high- and low - risk groups is high .
thus , the risk score can be used as a tool to triage patients who may need more attention ( e.g. more frequent follow - up ) .
randomized control studies of intensive follow - up - based hf management have yielded varying results ; however , a risk score - based follow - up may improve the efficiency of disease management programmes by spending more resources on patients in a high - risk state and fewer resources on patients in a low - risk state .
the retrospective analysis was done by pooling data from multiple studies in order to increase the sample size for the development and validation sets . with the exception of the fast study ,
most of the data included in the study was from within the first year of the device life , thus the results may not reflect the performance of the risk score during the later years of the device life .
serial assessment of clinical diagnostic data related to hf , such as weight , blood - pressure , and bnp , was not performed in a consistent manner in all the studies .
thus , comparison of the dynamic risk score to previously described clinical risk scores could not be performed , and the adjustment for other clinical variables in the statistical analysis was limited to baseline history collected in the studies .
the incremental value of an hf risk score over clinical diagnostic measurements can not be established in this data set .
it is hypothesized that the dynamic assessment of an hf risk score provides an ambulatory triage mechanism to indicate when to gather additional clinical information to evaluate the patient status in a timely manner to improve the efficacy of disease management programmes .
the retrospective analysis was done by pooling data from multiple studies in order to increase the sample size for the development and validation sets . with the exception of the fast study ,
most of the data included in the study was from within the first year of the device life , thus the results may not reflect the performance of the risk score during the later years of the device life .
serial assessment of clinical diagnostic data related to hf , such as weight , blood - pressure , and bnp , was not performed in a consistent manner in all the studies .
thus , comparison of the dynamic risk score to previously described clinical risk scores could not be performed , and the adjustment for other clinical variables in the statistical analysis was limited to baseline history collected in the studies .
the incremental value of an hf risk score over clinical diagnostic measurements can not be established in this data set .
it is hypothesized that the dynamic assessment of an hf risk score provides an ambulatory triage mechanism to indicate when to gather additional clinical information to evaluate the patient status in a timely manner to improve the efficacy of disease management programmes .
we developed and validated a method for combining multiple device - derived diagnostic parameters into a single - dynamic hf risk score which may be evaluated in an ambulatory setting to triage patients at a higher risk for hf events in the next 30 days .
future studies are needed to prospectively evaluate whether timely clinical actions initiated by the stratification of hf patients using the hf risk score on a regular basis can reduce hfhs .
m.c . salary is supported by the national institute for health research biomedical research unit at the royal brompton hospital , london .
funding to pay the open access publication charges for this article was provided by medtronic inc .
conflict of interest : dr cowie : research grants from medtronic , and honoraria from medtronic , st jude medical and boston scientific .
dr crossley : consultant for medtronic , boston scientific , and cardiac control systems ; lecturing income from medtronic , boston scientific and sanofi ; research support from medtronic , boston scientific and st jude medical .
dr abraham : consulting fees from medtronic , st jude medical , biotronik , cardiomems , and novartis .
: consultant for st jude medical and employed for clevland clinic . w.h.w.t . : grants from national institutes of health and abbott laboratories . j.k . : patents ( planned , pending , or issued ) from medtronic .
g.c . : granted from medtronic and received payment for lectures including service on speakers bureaus from medtronic . m.c . :
consultant for medtronic and received payments for lectures including service on speakers bureaus from medtronic .
 backgroundwe developed and validated a heart failure ( hf ) risk score combining daily measurements of multiple device - derived parameters.methodsheart failure patients from clinical studies with implantable devices were used to form two separate data sets . 
 daily hf scores were estimated by combining changes in intra - thoracic impedance , atrial fibrillation ( af ) burden , rapid rate during af , % crt pacing , ventricular tachycardia , night heart rate , heart rate variability , and activity using a bayesian model . 
 simulated monthly follow - ups consisted of looking back at the maximum daily hf risk score in the preceding 30 days , categorizing the evaluation as high , medium , or low risk , and evaluating the occurrence of hf hospitalizations in the next 30 days . 
 we used an anderson  gill model to compare survival free from hf events in the next 30 days based on risk groups.resultsthe development data set consisted of 921 patients with 9790 patient - months of data and 91 months with hf hospitalizations . 
 the validation data set consisted of 1310 patients with 10 655 patient - months of data and 163 months with hf hospitalizations . in the validation data set , 10% of monthly evaluations in 34% of the patients were in the high - risk group . 
 monthly diagnostic evaluations in the high - risk group were 10 times ( adjusted hr : 10.0 ; 95% ci : 6.415.7 , p < 0.001 ) more likely to have an hf hospitalization ( event rate of 6.8% ) in the next 30 days compared with monthly evaluations in the low - risk group ( event rate of 0.6%).conclusionan hf score based on implantable device diagnostics can identify increased risk for hf hospitalization in the next 30 days . 
Background
Methods
Data set and event definitions
Diagnostic parameters
Combined diagnostics
Statistical analysis
Results
Discussion
Limitations
Conclusions
Funding